fuel effects on particulate matter emissions variability from a gasoline direct injection engine ·...

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Fuel Effects on Particulate Matter Emissions

Variability from a Gasoline Direct Injection Engine

Manuel J.M.G. Ramos and James S. Wallace

Version Post-print/accepted manuscript

Citation (published version)

MJMG Ramos and J.S. Wallace, “Fuel effects on particulate matter emissions variability from a gasoline direct injection engine,” SAE

Technical Paper No. 2018-01-0355, SAE World Congress 2018, Detroit, Michigan, April 10-12, 2018.

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7/20/2015

2018-01-0355

Fuel Effects on Particulate Matter Emissions Variability from a Gasoline Direct

Injection Engine

Author, co-author (Do NOT enter this information. It will be pulled from participant tab in

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Abstract

Particulate matter emissions from gasoline direct injection engines are

a concern due to the health effects associated with ultrafine particles.

This experimental study investigated sources of particulate matter

emissions variability observed in previous tests and also examined the

effect of ethanol content in gasoline on particle number (PN)

concentrations and particle mass (PM) emissions. FTIR measurements

of gas phase hydrocarbon emissions provided evidence that changes in

fuel composition were responsible for the variability. Exhaust

emissions of toluene and ethanol correlated positively with emitted PN

concentrations, while emissions of isobutylene correlated negatively.

Exhaust emissions of toluene and isobutylene were interpreted as

markers of gasoline aromatic content and gasoline volatility

respectively. Tests conducted with gasoline containing added toluene

(10%) supported this hypothesis and led to the overall conclusion that

the emissions variability observed in PN concentrations can be

attributed to changes in the composition of the pump gasoline being

used. Tests conducted with gasoline containing added ethanol (10% and

30%) found that increasing ethanol fuel content increased emitted PN

concentrations at steady-state operation.

Introduction

Particulate matter emissions from gasoline direct injection engines are

a concern due to the health effects associated with ultrafine particles.

Particulate matter emissions are characterized by measurements of

particle number (PN) concentrations, particle mass (PM) and

composition. Characteristics of both the fuel and fuel injectors in a GDI

engine play critical roles in the combustion process and subsequent

pollutant formation due to their influence on fuel spray development.

Since engine operating characteristics have significant influence on the

spray development in GDI engines, particulate matter emissions can

therefore be related to engine operating parameters such as engine

coolant temperature, spark timing, fuel injection timing and duration,

fuel-air equivalence ratio, engine load, and valve timing. Many studies

have directly investigated the sensitivity of altering these parameters on

particulate matter emissions. Other engine and sampling and

measurement conditions can also influence the measured particulate

matter emissions levels.

The work reported here grew out of a study that was originally intended

to investigate the effect of ethanol content in gasoline on particulate

matter emissions. However, variability in measured particulate matter

levels made fuel effects difficult to discern with statistical confidence

[1]. Both systemic and fuel composition factors were identified as

potential contributors to the measurement variability. Each contributor

was investigated in turn. The results from the investigation of systemic

factors have been reported [2], but are summarized here to facilitate

discussion of the fuel effects that are the focus of this paper. Systemic

effects that were identified to be relevant to the tests and investigated

included: PN concentration measurement system dilution air humidity,

engine fuel temperature, engine fuel injector deposit formation, intake

system deposits, and engine oil age and fuel dilution. Some equipment

and procedural changes were implemented as a result of this

investigation of systemic effects:

• The diluter system for PN measurements was modified to use dry

dilution air

• An oil cooler was added to stabilize the oil temperature at 99oC ±2oC

• A fuel cooler was added to maintain fuel temperature at 22oC ± 2oC

• A coalescing oil separator was added to the PCV system to eliminate

carryover of oil mist or oil vapor from the crankcase into the intake

system. The entire intake system was also cleaned of all deposits

that had accumulated prior to fitting the coalescing oil separator.

While the changes implemented did reduce variability in the measured

PN concentration levels, none of the systemic effects, either individually

or collectively, could account for the extent of variability observed.

Measurements of the exhaust emissions of various individual

hydrocarbon species by FTIR suggested that fuel composition changes

might be a contributor to the variability. Accordingly, a second phase

of the study, looking at fuel effects on particulate matter emissions

variability, was undertaken.

Fuel Effects and Particulate Matter Emissions

Fuel composition is critical to the formation of particulate matter in GDI

engines and many studies have explored the effect of fuel chemistry on

particulate matter emissions in conjunction with other factors. Studies

[3, 4, 5, 6, 8, 9, 12, 16] have directly assessed the effect of alcohol

content in gasoline fuel on particulate matter emissions, while others

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have also isolated for aromatic and paraffin hydrocarbon compounds

typically found in large quantities in commercial gasoline. Physical

properties such as volatility have also been researched [11]. The

following will discuss some of the results of those studies as they pertain

to particulate matter emissions Gasoline-alcohol blends are expressed

here in a percent volume of alcohol basis; i.e. a 10% by volume ethanol-

90% by volume gasoline blend is expressed as E10.

Price et al. [3] found that ethanol blends (E85 and E30) showed the

lowest PN concentration emissions, followed by methanol blends (M85

and M30) for lean fuel-air mixtures; however, pure certification

gasoline produced the lowest PN concentration emissions for rich

mixtures. Chen et al. [4] observed a reduction of PN concentration

emissions when running E10 over E0 (gasoline) with a cold engine

coolant temperature (ECT) of 20oC. The availability of oxygen atoms

in the fuel from the ethanol is believed by the authors to have aided the

oxidation of local fuel rich pockets in the charge. The converse was true

for a hot ECT of 90oC, owing to the higher enthalpy of vaporization of

ethanol giving a more inhomogeneous charge. Using a single cylinder

research variant of the same engine in [4], Chen et al. [5] observed an

opposing trend; increasing ethanol content resulted in increased number

and mass emissions, with a greater response shown by the cold engine

(ECT of 20oC) than the warm one (ECT of 90oC). As an example, E85

produced 15 times greater total PN concentrations and 11 times more

total mass emissions than gasoline for the cold condition [5]. This is a

direct contradiction to the results reported by [4]. Under cold conditions

(ECT of 0-40oC), Dimou et al. [6] note that a significant increase in PN

emissions below 15oC for E0 was not seen for E10, or higher blends for

that matter. The authors in this case believe that the effect of ethanol on

particle formation chemistry should be the main reason for this

difference, as the evaporative properties of the fuel should not have

changed significantly from gasoline to E10. Warey et al. [7] explored

the effect of molecular structure by comparing the PN emissions from

liquid fuel of two major constituents in gasoline; isooctane (a surrogate

for alkanes in gasoline) and toluene (a surrogate for the aromatics in

gasoline). Though they have similar boiling points, and thus similar

volatility, toluene yielded greater mass and larger mean size emissions.

Toluene's propensity to form soot-causing PAHs, due to its aromatic

structure, was given as the reason for this finding. Two paraffin fuels

were also tested, with n-pentane injection resulting in greater particle

size and mass loading as compared to the emissions from the n-

undecane fuel. Volatility differences were thought to be at the root of

this finding; however, the phenomena of film boiling—hindrance to

evaporation—was explained to cause n-undecane (lower volatility) to

evaporate more readily than n-pentane. Vehicle tests have also been

conducted. Chan et al. [8] found that particulate matter emissions levels

from a GDI vehicle under standard emissions drive cycles were similar

for either an E0 or an E10 fuel. He et al. [9] also saw very little in terms

of difference at the E0 and E10 levels, though at E20 reductions were

evident. Munoz et al. [10] found that increasing ethanol content in

gasoline from 0 to 10% and then 85% resulted in decreased PN and PAH

concentrations, with most of the change occurring from 0-10% and less

from 10-85%. The effect was most pronounced under transient

conditions.

Gasoline fuel volatility was studied by Khalek et al. [11] by running

gasoline fuel with varying grades of volatility in a GDI vehicle over

standard emissions cycle tests. The fuels included certification gasoline,

as well as a high volatility, cold climate gasoline and a low volatility,

hot climate gasoline; the certification fuel represented the middle

ground in terms of volatility. From the test results, it was found that

increasing the volatility of the fuel made a marked decrease in the PM

emissions. This is an expected result but still indicates some

discrepancies with above mentioned results from [7]. As evidenced by

this discussion, the effect of fuel on particle formation is not currently

well understood.

Several of the studies cited have presented conflicting results on the

effect of ethanol addition. Thus, the original purpose of this study was

to investigate the effect of E10 and E30 ethanol-gasoline blends on the

emissions from a wall-guided GDI engine at a steady-state operating

condition—representative of a highway cruise condition—and that

remained an objective of the work. A second objective was to make use

of gaseous emissions data from the FTIR to examine possible variability

in commercial gasoline and how that variability could impact particulate

matter emissions.

Apparatus

An overall view of the equipment and instrumentation used in the

experiments is provided in Figure 1.The engine used in this research is

a pre-production four-cylinder GDI engine used in the 2012 and newer

Ford Focus. It employs side mounted, wall-guided direct fuel injectors

and is naturally aspirated. Table 1 lists selected major specifications of

the engine. A GO-Power D-557 water brake dynamometer was used to

load the engine.

Table 1. Test Engine Specifications

Displacement 1999 cm3

Bore x Stroke 3.44 x 3.27 in.

Compression ratio 12.0:1

Maximum power output 160 hp @6500 rpm

Maximum torque 146 ft-lbf @ 4450 rpm

Fuel injection Gasoline direct injection

Valvetrain

Double overhead camshafts (DOHC)

Four valves per cylinder

Twin independent variable camshaft timing

(Ti-VCT)

Recommended fuel 87 octane

Emission Control Three-way catalyst (removed for these

tests)

Emissions Standards Tier 2 Bin 4/LEV II

An OEM Powertrain Control Module (PCM) was used. A custom dyno-

wiring harness interfaces the ERDL-made engine control panel with the

stock PCM and engine wiring harness. The PCM was supplied with a

non-production engine calibration used in durability testing campaigns.

The exact calibration of the PCM is not known and could not be

modified. In prior work [1], it was found to not be configured for proper

three-way catalyst (TWC) function. During subsequent engine tests,

both the PCM oxygen sensor feedback and an external wide-band

oxygen sensor readings were monitored. Representative values are

shown in Figure A-1 (Appendix A). The PCM oxygen sensor shows a

stoichiometric mixture while the wide band oxygen sensor showed a

fuel-air equivalence ratio of approximately 1.015, i.e. slightly rich, at

steady conditions. Since the wide band oxygen sensor requires a fuel

composition that had to be assumed, the OEM sensor is likely correct.

Without access to the PCM control software, no attempt was made to

optimize engine operation for different fuels. Figure A-2 shows that the

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Figure 1. Research engine and emissions sampling arrangement.

ignition timing and air flow rate were constant within 1% between fuels.

The exhaust oxygen concentration was typically 1%, which also points

to stoichiometric operation.

Given that the PCM was not configured to run with a TWC, the exhaust

manifold was modified to remove the stock exhaust after treatment.

Thus, all emissions reported are engine-out emissions without the TWC

in place. No specific measures were taken to artificially adjust the

engine backpressure to the value seen with the catalyst in place.

However, the exhaust backpressure was measured to be approximately

10 kPa at the singular test condition performed for this work.

Test Condition

The experimental results reported in this paper were all collected at a

single steady-state operating condition (42 ft-lbf / 57 N-m of torque at

2600 rpm) representative of a highway cruise condition. The engine was

started cold (overnight soak at room temperature), idled for two minutes,

taken up to the test condition by a programmed ramp lasting 2 minutes

and then allowed to stabilize. Oil temperature was the last parameter to

stabilize. The stabilized steady-state condition was reached

approximately 20 minutes after engine start. A typical test lasted for 80

to 100 minutes, which provided a minimum one hour period of

stabilized operation. Appendix B contains plots showing the

stabilization of various engine temperatures and operating conditions

with time during a typical test.

Emissions Measurements

Gaseous emissions - A standard emissions bench was used to measure

THC, NOx, CO, CO2 and O2 in the exhaust. Details are provided in

Table 2. An MKS 2030HS Fourier Transform Infared Spectroscope

(FTIR) was used to measure selected gaseous species in the exhaust in

real time. Spectra were captured and then subsequently analyzed using

a “recipe” of known spectra. Table 3 summarizes the species included

in the FTIR analysis. The FTIR hydrocarbon (HC) emissions data,

specifically the emissions of isobutylene and toluene, proved to be

extremely useful in helping to flag fuel composition changes, as will be

discussed in the Results section.

Table 2. Emissions Bench Instruments

Instrument Analyzer type Species

CAI 600 HFID Heated flame ionization detector THC-C3

CAI 500 HCLD Heated chemiluminescence detector NOx

CAI 601*P/602 NDIR Non-dispersive infrared and

Paramagnetic*

CO2/O2*,

CO

___________________________

*Oxygen measurements were corrected for interferences.

Table 3. Species Quantified by FTIR Exhaust Gas Analysis

Nitric oxide

(NO)

Carbon

monoxide (CO)

Carbon dioxide

(CO2) Water (H2O)

Nitrogen

dioxide (NO2) Methane (CH4) Ethane (C2H6) Pentane (C5H12)

Nitrous oxide

(N2O) Ethylene (C2H4)

Propylene

(C3H6)

Isobutylene

(C4H8)

Acetylene

(C2H2)

1,3-Butadiene

(C4H6) Benzene (C6H6) Toluene (C7H8)

Formaldehyde

(CH2O)

Acetaldehyde

(CH3CHO)

Methanol

(CH3OH)

Ethanol

(C2H5OH)

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Particulate matter sampling and measurements - Total particle number

(PN) concentrations and particle size distributions were measured using

a TSI 3090 Engine Exhaust Particle Sizer (EEPS). A TSI 379020A

Rotating Disk Thermodiluter supplied diluted exhaust gas to the EEPS.

While the diluter produced stable dilution ratios, actual dilution ratios

were consistently below the set dilution ratio. Thus, the true dilution

ratio was computed for each experiment using CO2 as a tracer gas. A

LI-COR LI-840A CO2/H2O analyzer was used to measure the CO2

concentration at the diluter outlet; the diluter inlet concentration was the

exhaust concentration measured by the emissions bench. The second

stage of the Thermodiluter includes a thermal conditioner that heats the

sample to 300oC prior to the secondary dilution. No thermodenuder was

used. Gravimetric measurements (PM) were obtained by collecting

exhaust samples on 47 mm Teflo™ membrane filters. A 20 minute

sampling period was typically used, but some samples were taken with

10 minute and 40 minute sampling periods. A Detaki FPS 4000 Fine

Particle Sampler was used to provide diluted exhaust to the filters.

Filter inlet CO2 concentrations were measured using the LI-COR and

true dilution ratios calculated. All gravimetric measurements were

conducted in a class 100 clean room maintained at a temperature of

22±1oC and relative humidity of 45±5% using a Sartorius SE-2F

microbalance. Samples for elemental carbon/organic carbon analysis

(EC/OC) were collected on 47 mm Tissuquartz filters and analyzed

using a Sunset Thermal-Optical Semi Continuous OC/EC analyzer. A

10 minute sampling period was used to collect the EC/OC samples. All

the particulate matter measurements (PN, mass (PM), or compositional)

reported in this paper are engine-out values calculated using their true

dilution ratios.

Fuels

Since the objective of the project was to investigate the effect of ethanol

in gasoline on PM and PN emissions, it was necessary to have an

ethanol-free gasoline as a basis for comparison. Fire safety limitations

on the amount of gasoline stored in the laboratory required regular

purchases of commercial gasoline (a “batch”) instead of working with

drums of reference gasoline. Due to renewable fuel mandates in

Ontario, the only ethanol-free gasoline commercially available was 91

octane (AKI) grade from two suppliers (Fuel A and Fuel B). Fuel A was

used initially as the base gasoline for all tests, but starting with the June

batch of Fuel A, it became evident that Fuel A was no longer ethanol

free. This was detected by reprocessing previous Fuel A FTIR spectra

to include ethanol in the recipe. Fuel A had previously (prior to the work

presented here) contained no ethanol. A simple test confirmed the

presence of approximately 8-9% alcohol. Accordingly, Fuel B was used

as the base gasoline for all subsequent tests, including all rests reported

in this paper, and found to be ethanol-free for the duration of the tests.

Ethanol containing blends were created by splash blending 10% and

30% (v/v) ethanol with the base gasoline to create E10 and E30 blends

respectively. In the course of the research program, blends with added

toluene were also used. Toluene containing blends were created by

splash blending 10% (v/v) toluene with the base gasoline to create a T10

blend and also a mix of 10% (v/v) toluene and 10% (v/v) ethanol with

the base gasoline to create a T10E10 blend. The toluene was of 99.5%

pure reagent grade, while the ethanol was 99.9% pure anhydrous grade.

Table 4 summarizes the fuel blends tested.

Fuel changeover was accomplished by draining and refilling the fuel

system and then running the engine at the test condition for

approximately 30-35 minutes. Neither the engine nor the exhaust

system exhibited any particle storage effects. Exploratory tests were

conducted switching between base gasoline and a low emitting fuel and

no storage effect was observed in fuel switches either from low-emitting

to high emitting or from high-emitting to low emitting fuels.

Table 4. Test Fuels Using Fuel B as a Base

Test group identifier Added fuel components (v/v)

E0 No ethanol

E10 10% ethanol

E30 30% ethanol

E0 return* No ethanol

T10 10% toluene

T10E10 10% toluene,10% ethanol

___________________________

*E0 return is a repeat test group with base gasoline.

RESULTS PREAMBLE

The first sets of tests were carried out to investigate the effect of ethanol

addition. Tests were conducted with a base gasoline (E0), followed by

tests with E10 and E30 blends respectively. Three to four tests were

performed with each test fuel. Following the ethanol blend tests, a

repeat test series (E0 Return) was performed to ensure that results with

the base gasoline were repeatable. Figure 2 compares the two base

gasoline test groups (E0 and E0 Return) and shows that there was a large

difference in PN concentrations between tests conducted with nominally

the same fuel, purchased less than a month apart. Comparing these two

results shows total particle counts of the E0 Return series to be on

average almost two times greater than the initial E0 data set. This

surprising result provided the first evidence that variability in

commercial gasoline composition or properties could account for the

previously observed variability in particle number measurements.

Figure 2. PN concentrations with two different batches of base Fuel B (ethanol

free) after the engine cleaning. Test condition 57 N-m @ 2600 rpm. Each data

point is the average of 4 tests. Shaded areas indicate standard error.

Table 5 summarizes the purchases of base gasoline showing the date of

purchase, which aids in understanding the contribution of seasonal

changes in fuel properties. All of the gasoline was purchased in late

spring, summer and early fall (June through September).

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Table 5. Base Gasoline (Fuel B) Purchase History

Fuel delivery date Tests utilizing each fuel batch

June 9th Preliminary tests (shown as Fuel B-June)

July 9th E0 – all three tests

July 24th E0 (test 4)

August 2nd E10 – all three tests

August 12th E30 – all three tests, E0 return – test 1

August 18th E0 return – tests 2 and 3, T10 – test 1

August 26th T10 – tests 2 and 3

September 2nd T10E10 – all three tests

While it was not possible to retroactively analyze the previous batches

of gasoline, the FTIR data provided a basis for identifying possible

composition changes. The concentrations of various hydrocarbon

species measured in the exhaust using the FTIR were different for the

two fuel batches (E0 and E0 return). Two species in particular showed

significant differences: Toluene, 75 ppm for E0 and 111 ppm for E0

return, and Isobutylene, 34 ppm for E0 and 27 ppm for E0 return.

These changed emissions levels suggested that fuel composition,

especially aromatic content, had likely changed. In order to further

investigate this, the original plan to study the effect of 10% and 30%

ethanol addition (E10, E30) was expanded to include investigating the

effect of the addition of toluene to the base gasoline. Two additional

blends were created. T10 and T10E10 by blending 10% toluene (v/v)

with the base gasoline and both 10% toluene and 10% ethanol (v/v) the

base gasoline respectively. Data presented in the following results

sections are grouped by the test fuel used.

Results

Emission measurements taken included particle number concentrations,

particle size distributions, particle mass emission concentrations, the

results of elemental carbon/organic carbon measurements, as well as

both regulated gaseous emissions from the emissions bench and

concentrations for specific gaseous species obtained by the FTIR.

Results from each will be presented in turn in the following sections.

Particle Number (PN) Concentration

Figure 3 presents the test group averaged PN concentration

measurements as a function of run time. Data points for each test fuel

represent an average taken over a two minute period at the respective

measurement time during the run and the shaded regions indicate the

standard error of these test group averaged data sets. The figure includes

the E0 and E0 return data from Figure 2 for comparison.

The range of variability between fuels is striking – nearly a factor of 4

in particle number concentrations. Compared to the E0 fuel, the E10

fuel caused an average increase in measured PN concentrations of

approximately 67%, with E30 causing a further increase of

approximately 33%. The toluene containing fuels would be expected

to emit higher particle number concentrations, given that toluene is

aromatic and known for its propensity to form soot. Compared to the

E0 return fuel, the T10 fuel emitted a slightly lower particle number

concentration while the T10E10 blend had the highest particle counts of

any of the fuels tested. The results for T10E10 suggest a synergetic

effect for the combination of ethanol and toluene. A similar conclusion

was reached by Capatano et al. [12], where ethanol addition was found

to create favorable conditions for the particle formation from the other

compounds with higher sooting propensity. He et al. [13] express a

similar view in that the slow vaporization of ethanol (high latent heat of

vaporization) slows the vaporization of the other components,

increasing the possibility of fuel rich regions leading to particle

formation.

Figure 3: Two-minute average PN concentrations as a function of test run time

for each fuel test group identified in Table 4. Test condition 57 N-m @ 2600 rpm.

Shaded areas indicate standard error.

Additional statistical information on these data sets is presented in

Figure 4, which shows for each test fuel, the averaged relative standard

deviation (RSD) values at each measurement time. Beginning with a

comparison of RSD values for the E0 and E0 return tests, RSD values

in Figure 4 for E0 return increased an average of 1.6 times over the

initial E0 datum, showing that repeatability also did not return to initial

values despite using gasoline purchased from the same commercial

supplier only weeks apart. The addition of ethanol resulted in very large

increase in RSD, on average a factor of 5.5 for E10 and 9 for E30,

compared to the E0 value. Both blends with added toluene also showed

significant increases in RSD, although not as large as for the E30 blend.

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Figure 4: Relative standard deviations in PN concentration with test run-time for

each fuel blend test group identified in Table 4. Test condition 57 N-m @ 2600

rpm.

Particle Size Distribution

The particle size distribution data shows some differences between the

different fuel blends. Figure 5 shows the PN size distributions averaged

for each fuel blend tested–the six test fuels are divided amongst Figures

5 (a) and (b). The data presented here represents a two minute average

distribution taken at the 90 minute point in the test where the engine is

fully stabilized. The error bars here indicate the standard error for the

fuel blend average and serve as a marker for the run-to-run variability

seen.

Comparing the different fuel blends shows that the distributions all

largely follow the same shape, with a dominant accumulation mode in

the 70-80 nm size range. Though difficult to appreciate on these plots,

the distributions also show a nucleation mode near the 10 nm size range,

which is markedly subdued in concentration magnitude when compared

to the accumulation mode. These distributions are similar to the ones

found by Mireault [1] in his previous work on this engine and in other

studies in literature [14]. The large increases in PN concentrations with

increasing ethanol or toluene, noted in the previous section's discussion,

were manifested as increases in the accumulation mode of the

distribution; the nucleation mode was affected here to a much lesser

extent. The large changes in magnitude in Figure 5 make it difficult to

compare the different distributions shown. Normalizing the

distributions removes this effect and permits a better comparison. Figure

6 plots the distributions in Figure 5 normalized to the total PN

concentration for each fuel blend, using the following formula:

��

��

where i denotes the size bin in question.

On a percentage basis it is evident that the E0 distribution is different

than the other fuel blends, including the E0 Return test fuel. When

compared to the E0 Return fuel data, a greater proportion of nucleation

mode particles and a lower proportion of accumulation mode particles

were emitted. This is quite unexpected because the E0 Return fuel was

nominally the same fuel as the E0 fuel, yet it produced a different

particle size distribution.

Calculated modal and median diameters for the different distributions

are contained in Table 6. Again, comparing the E0 and E0 Return groups

shows that there was indeed a shift towards larger particles for the E0

Return group. The ethanol fuel blends also showed a shift to larger

particles when compared to the E0 group, and a very slight decrease in

size when compared to the E0 Return group. Increasing the ethanol

content (from E10 to E30) appears to cause a slight shift towards larger

particles, which is masked even in the median and modal diameter

calculations. The influence of toluene is more pronounced, as a shift

towards larger particles was noted. This is consistent with the available

literature, which also showed size shifts to larger particles when using

toluene [7].

Table 6: Modal and median diameters (nm) for the particle size distributions

measured for the test fuels identified in Table 4.

Test Fuel Median Diameter* Median Diameter Size Bin+ Modal Diameter^

E0 51.1 52.3 69.8

E10 57.0 60.4 69.8

E30 57.7 60.4 69.8

E0 Return 59.2 60.4 80.6

T10 60.1 60.4 80.6

T10E10 64.7 69.8 80.6

Notes: *Median diameter defined as the size bin where 50% of the particles are

either smaller or larger in size. +Corresponding EEPS size bin for the median diameter. Value listed is

the midpoint of the size bin

^Most frequently occurring particle size bin in the distribution

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(a) (b)

Figure 5. Fuel based comparison of particle size distributions. Data is fuel test group, two minute average at the 90 minute point of a typical test. Test condition of 57 N-m @ 2600 rpm. Error bars indicate

standard error. (a) Fuels E0, E10, E30, (b) Fuels E0 return, T10, T10E10 (see Tables 4 & 5).

(a) (b)

Figure 6. Fuel based comparison of normalized particle size distributions. Data is fuel test group, two minute average at the 90 minute point of a typical test. Test condition 57 N-m @ 2600 rpm. Error

bars indicate standard error. (a) Fuels E0, E10, E30, (b) Fuels E0 return, T10, T10E10 (see Tables 4 & 5).

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Gravimetric Results

Gravimetric results from this investigation are presented in Figure 7,

where they are plotted with corresponding PN concentrations measured

by the EEPS during the filter collection. Filters were collected during

the back half of a typical 80-100 minute test to ensure that engine

conditions had fully stabilized (see Appendix B for details on the

stabilization of various temperatures over the course of a test). Filter

duplicates (two filters at the same time) were taken for some tests and

are identified in this figure by the data point pairs at constant PN

concentration. These duplicates show good agreement and were

generally within the acceptable replicate filter weight variance [15] with

the exception of one T10 run. The data in Figure 7 shows that PM

concentrations increase linearly with increasing PN concentrations,

confirming that changes in PN concentrations observed resulted in

corresponding changes in mass. As before, increasing the ethanol or the

toluene fraction increased the PM concentrations. However, comparing

the different fuel blends more closely shows that a division between

toluene and non-toluene blends occurred, with the latter emitting more

mass at fixed PN concentrations. Plotting regression lines show this

disparity more clearly, with the slopes of these lines giving the number

of particles emitted per unit mass. Both regressions, slopes 1.43 x 1012

#/mg for the blends without added toluene and 1.09 x 1012 #/mg for

those with added toluene, show good agreement (R2) with their

respective data.

Figure 7: Gravimetric filter results grouped by test fuel with overlaid particle

number to mass correlations. Red dashed line includes E0, E10, E30, and E0

Return data sets; blue-dashed line includes T10 and T10E10 test groups. Error

bars indicate acceptable replicate filter weight variance and one standard

deviation of PN concentrations for the x and y axes, respectively.

Maricq et al. [16] found the same correlation to be ∼ 2 x 1012 #/mg for

low ethanol blends and noted that this value is also typical for diesel

soot. They observed an increase in their correlation for E32 blended fuel

to ∼ 4 x 1012 #/mg and reasoned that this indicated that the higher

ethanol blends in their study produced more nuclei particles [16]. The

PN size distributions presented in the previous section show that the E30

blend in this study did not produce any measurable increase in nuclei

particles, so it should be expected that the correlation presented here

does not shift to more particles per mass. The same logic may be used

to explain the observed difference for the toluene blends with added

toluene in Figure 7. It was established in the PN size distribution

discussion that the toluene blended fuels (T10 and T10E10) showed

shifts to larger particle distributions. Since larger particles are generally

more massive than smaller ones, the observed decrease in the number-

to-mass correlation is logical in this regard. Therefore, the addition of

toluene has the additional effect of emitting disproportionately more PM

mass along with the observed increase in PN concentrations.

Elemental Carbon/Organic Carbon (EC/OC) Results

The EC/OC ratios in Figure 8 demonstrate an increasing trend–more

elemental carbon–with increasing ethanol content. Comparing the two

ethanol-free tests shows that a higher elemental fraction was emitted for

the E0 Return test than the initial E0 test, though this cannot be said with

confidence given the interval overlap shown. The composition does not

appear to have changed significantly when increasing the aromatic

fraction for the T10 blend. The same cannot be said, however, for

T10E10; the additional ethanol caused a statistically significant

(p<0.05) increase in the elemental fraction over both E0 test groups and

the T10 group.

Figure 8: Elemental-to-organic carbon ratio (EC/OC) for the different test fuels

broken down by individual test run and including an average for the fuel. Error

bars indicate 95% confidence intervals using pooled variances.

The fuel blend average EC/OC concentrations are presented in Figure

9. The influence of ethanol, shown in previous figures to result in an

increase in both PN concentration and PM concentration, appears to be

manifested largely through changes to the elemental concentration of

the PM. The organic concentration in this case stays largely constant

between 7-11 mg/m3, while the elemental concentration varied by 3-12

mg/m3 over the same range of ethanol addition (E0-E30). The same

effect is also observed when comparing the two toluene fuel blends,

where increasing the ethanol caused a 44% increase in the elemental

fraction and only a 13% increase in the organic portion. The conclusion

is that increasing the ethanol content provided a disproportionate

increase in the emitted elemental carbon.

One hypothesis is that the increase in ethanol led to an increase in piston

crown and cylinder wall impingement due to vaporization difficulties.

Ethanol has a high latent heat of vaporization when compared to

gasoline, so at fixed injection pressures spray penetration lengths ought

to increase with increasing ethanol. This can lead to wall impingement

giving rise to liquid fuel burning and entrainment of lubricating oil in

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the fuel charge—both are known sources of soot emissions. Fatouraie

et al. [17] have studied the effects of ethanol (E0, E50, E100) on spray

development and wall impingement in an optically accessible GDI

engine having a similar fuel injection configuration to the current test

engine. Wall wetting was observed and found to be dependent on

coolant temperature, fuel rail pressure and fuel type. Images showed

that the E0 fuel spray exhibited a wider plume angle compared to the

ethanol blend and the pure ethanol and wall impingement for E0 also

appears lower than the other fuels [17].

Figure 9: Fuel average elemental and organic carbon concentrations for each test

fuel, with gravimetric concentrations overlaid (red rectangle). Error bars indicate

95% confidence intervals using pooled variances.

Comparison of Gravimetric and EC/OC results

Figure 9 also provides useful information for comparing the measured

mass concentrations using either the gravimetric or EC/OC methods.

The elemental carbon concentration should in theory provide a very

similar value to the gravimetric one, as they are essentially measures of

the same thing. Inspecting the figure shows that the same general trend

is observed for both series and some of the test groups give good

agreement, while others—chiefly E10, E0 Return, and most apparent,

T10E10—do not agree as well. A possible source of error has to do with

filter processing; gravimetric filters are only weighed after 24 hours of

equilibration as per EPA prescribed procedures, while the EC/OC

analysis is performed soon after collection. It is possible that some of

the volatile organic species that evaporate from the gravimetric filters

are being pyrolized during the EC/OC analysis and end up as elemental

carbon; but this was compensated for in post-processing by adjustment

of the split point.

Gaseous Emissions

Gaseous emissions, including regulated and non-regulated compounds,

are broken down by test group (fuel blend) and presented accordingly.

A standard emissions bench measured regulated compounds, while the

FTIR provided hydrocarbon speciation (Table 3 lists the species) in

addition to the regulated compounds.

Regulated Emissions

The regulated compounds measured during this investigation are

presented in Figures 10 and 11 for the FTIR and emissions bench,

respectively. Note that the FTIR is incapable of measuring homonuclear

molecules, so O2 measurements are only shown from the emissions

bench. The general magnitudes of the standard gaseous emissions in

Figures 10 and 11 are typical for spark ignited engines at a

stoichiometric equivalence ratio operating point [18]. Though not

shown here, NOx emissions from this engine were found previously by

Mireault [1] to be nearly entirely composed of NO, with less than 0.1%

being NO2. Ethanol did not have a significant (p>0.05) influence on the

NOx emissions from this engine at this condition. This has been shown

previously in other studies [1, 9] and is expected given that NOx

production is highly thermally dependent (Zeldovich mechanism) and

engine out exhaust temperatures did not vary appreciably between fuel

blends. Other studies have shown marked reductions in NOx emissions

[19, 20] owing to the charge cooling effect of ethanol, but this is not

seen here. The emission of O2 was also found to not deviate from

expected values at stoichiometric conditions (∼1%) no matter the fuel

used; an expected result because these emissions are reported as dry,

removing the influence of water in the exhaust. A general trend upward

of THC emissions is seen in Figure 11, though considering the

confidence intervals shown, this cannot be said to be significant. These

emissions appear to be most affected by the base gasoline (i.e. E0 vs.

E0 Return) and not by the addition of ethanol or toluene.

Figure 10: Average standard emissions measured by the FTIR for the different

test fuels. CO2 and CO emissions are reported as dry, calculated using measured

H2O concentration. Error bars indicate 95% confidence intervals.

In contrast, ethanol did have a pronounced effect on CO concentrations.

Comparing the different fuel blends shows that both instruments

measured statistically significant (p<0.05) decreases in CO

concentrations with increasing ethanol content. This ethanol

dependence even shows up in comparing the T10 and T10E10 runs, with

the latter emitting less CO than the former. Several studies have also

found this CO reduction with increasing ethanol to varying degrees [8,

12, 19, 20, 21, 22]. Wallner and Frazee [19] argue that the presence of

oxygen in the ethanol molecule promotes the oxidation of CO to CO2

thereby mitigating its presence in the exhaust. This conclusion is

applicable here given the strong reductions in CO output observed.

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Figure 11: Average standard emissions measured by the emissions bench for the

different test fuels. Emissions are reported as measured wet for THC and NOx,

and dry for CO2, O2, and CO. H2O values calculated from stoichiometry. Error

bars indicate 95% confidence intervals.

FTIR Speciation

Using the FTIR to provide a hydrocarbon speciation of the gaseous

constituents in the exhaust stream provides a unique opportunity to

correlate the PM emissions behavior to certain gas phase compounds.

The FTIR results presented in Figures 12 and 13 are averaged for the

different fuel blends tested. The error bars again show 95% confidence

intervals and are used here to determine the statistical significance of

the results.

Aldehyde emissions have been linked to ethanol combustion in several

previous studies, where increasing the ethanol content led to increases

in measured aldehyde emissions [9, 19, 20, 23, 24, 25]. The results from

this engine are no different in this regard; both formaldehyde and

acetaldehyde emissions increased with increasing ethanol content.

Acetaldehyde is an intermediate species in the combustion of ethanol,

and is readily formed via hydrogen abstraction (H-abstraction) of the

ethanol molecule [26]; formaldehyde can also form from the breakage

of the C-C bond in the initial step of ethanol combustion, though this is

not observed as major intermediate species [26]. Inspection of Figure

12 demonstrates significant increases (p<0.05) in acetaldehyde

emissions for the E10 fuel blend, and further increases with the E30 fuel

when compared to the two gasoline data sets (E0 and E0 Return). This

observation again holds true for the toluene blended fuels, with T10E10

emitting more than T10. With regards to formaldehyde, the increase is

less certain; only the E30 test group showed statistically higher

emissions at the 95% confidence level when compared to the other fuels.

This follows the assertion that formaldehyde production is largely

independent of ethanol fraction, especially at these low levels, a

conclusion also reached by other studies [9, 20, 24].

In both figures presented here, the E30 blend resulted in a reduction of

measured gaseous species of three or more carbon atoms (i.e.

isobutylene, 1,3 butadiene, propylene, and pentane); this effect was not

generally seen with E10. This result is likely due to the displacement

effect that follows with splash blending ethanol with gasoline; the

higher order hydrocarbons are reduced in quantity so it follows that the

intermediate species from the combustion of these compounds are also

reduced. Other compounds that show trends with increased ethanol and

toluene fractions are exactly those species–ethanol and toluene. While

this may seem intuitive and expected, the implication of this is rather

important; the presence of these species in the exhaust can be correlated

back to the overall fuel composition. Indeed, Kar et al. [23] saw

increasing species emission with increasing species fuel mass fractions

for different constituents, which corroborates the above observation in

this work. Of course the production of intermediate species during

combustion is an important consideration here, especially in discussions

regarding particulate matter production, but the implication of this

concept is that measurements from the FTIR can be used to make

inferences on fuel composition retroactively. As further evidence to this,

comparing the two 10% ethanol blends (E10 and T10E10) shows that

even with the additional toluene, ethanol emission levels remained

constant and at levels seen previously on this engine for E10 [1].

Figure 12: Average hydrocarbon emissions measured by the FTIR for the

different test fuels. Error bars indicate 95% confidence intervals.

Returning to the question of particle formation: ethylene and acetylene

are fundamental for the discussion on partciulate matter due to their

ability to form unsaturated polyacetylenes from the H-abstraction

acetylene-addition (HACA) mechanism. These polyacetylenes are

considered a major precursor to soot production, along with PAHs [18].

Figure 13 demonstrates that ethylene showed only modest changes in

concentration depending on the fuel composition; acetylene production

appears to have been largely independent of fuel composition according

to Figure 12. The fact that these two compounds, which are important

for particle formation, did not vary in significant quantities implies that

the observed particulate matter response with varying fuel composition

did not come about from these pathways. Instead, aromatics that can

decompose to form PAHs from dehydrogenation and recombination

likely played a more influential role in the observed variability. Figure

12 shows that some changes in benzene concentrations occurred when

comparing the different fuel blends, though this cannot be said with

confidence at the 95% level. Turning to the other aromatic compound

measured here paints a different picture; measured toluene

concentrations in Figure 13 did exhibit statistically significant (p<0.05)

changes with changing fuel composition. Exhaust toluene levels were

taken to be a relative indicator of the aromatic content in the base

gasoline fuel. Not surprisingly, the purposely added toluene in the

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toluene blended fuels (T10 and T10E10) produced the highest

concentrations of toluene emissions, which again follows the conclusion

of Kar et al. [23]. However, comparing the two gasoline test groups (E0

and E0 Return) shows almost a 50% increase in measured toluene

concentrations. This is surprising because the fuel blend was nominally

the same in both situations; i.e. same fuel supplier and no ethanol or

toluene purposely added. The concern here is that a substantial change

in base fuel composition had occurred and as a result, was the main

driving force behind the observed changes in PN concentrations for

these two test groups in question. This has further implications for the

other fuel blends because changes to the base gasoline could have

propagated to their overall composition as well. This puts into question

whether fuel compositional changes at the base level were really behind

all the variability noted, especially when considering the error bars for

the toluene data in Figure 13 trend well with the standard error of the

PN averages in Figure 3. Considering the remaining compounds

presented in these two figures, only isobutylene demonstrates a

substantial and statistically significant difference in concentration for

the two gasoline fueled test groups, and notable variability for the other

fuel blends.

Figure 13. Average hydrocarbon emissions measured by the FTIR for the

different test fuels. Error bars indicate 95% confidence intervals.

PN vs. Gaseous Species

The preceding section established the presence of a pronounced fuel

dependence on particulate matter emission during this investigation.

Through analysis of gaseous emissions data, several gas phase

compounds that displayed large changes in exhaust concentration were

identified. Using the assumption that differences in exhaust

concentrations of specific species equates to differences in fuel

composition, it was postulated that perhaps these gaseous components

could be used as markers for the observed particulate matter emission

variability. In order to judge the validity of this hypothesis, the

measured PN concentrations were regressed with the selected gaseous

phase compounds independently; the results from that analysis work are

presented in this section. Three compounds were selected on the basis

of their observed behavior; ethanol, toluene, and isobutylene. All three

species appeared to visually trend with either changes in magnitude of

the PN concentrations or the observed emissions variability. Linear

least-squares (LLS) regressions were performed for both toluene and

isobutylene against PN concentrations on an individual basis; ethanol

was not directly regressed because measured concentrations behaved in

a step-wise fashion. Instead, regressions were performed at constant

ethanol levels by segregating the data based on the amount of ethanol in

the fuel blend. Note that data for Fuel A (from prior work [2]), which

contained approximately 8-9% alcohol, has been included in the Figures

14 and 15, where they are treated as an E10 equivalent. A few data

points with shaded markers also appear on the figures. These are from

an earlier batch of Fuel B used before the tests described in this paper,

but are included because they help show the variability of the base

gasoline. The same test procedure was used for those earlier tests. A

regression for 30% ethanol was not performed due to the limited number

of data points available (3); however, the data is presented on these plots

for visual comparison. PN concentrations were obtained at run-end (80-

100 minutes after start) to ensure full engine stabilization.

Toluene Regression

The correlation of measured toluene exhaust concentrations to PN

concentrations is presented in Figure 14. The presence of ethanol in

gasoline shifts both of the correlations upwards, e.g. higher PN

concentrations at a given exhaust toluene or isobutylene concentration.

While the presence of ethanol should lower sooting propensity, the

impact of ethanol on fuel vaporization is significant and can delay

vaporization and result in more spray/cylinder wall interaction, which

would account for the higher PN observed. In general, both regressions

show good agreement with their respective data, as shown by the

coefficients of determination. This is an indication that the perceived

influence of toluene on particulate matter emissions is in fact valid. In

both cases, PN concentrations generally climb as the toluene exhaust

concentrations increase, while increasing the ethanol content to 10%

also shifts PN concentrations upward, as was previously noted. The fact

that both regression lines show similar slopes implies that this effect is

applied quasi uniformly at varying concentrations of toluene.

Figure 14. Two-minute average PN vs exhaust toluene concentration, taken at

run end for the different test fuels. LLS regression lines shown (dashed lines).

However, there still appears to be some scattering of the data at constant

toluene concentrations, seen most evidently in the Fuel A data group.

Here toluene emissions did not vary by more than approximately 20%,

yet measured PN concentrations varied by almost 90%. Inspection of

ethanol emissions for this group shows negligible variance within

statistical significance, thereby implying that some other factor was at

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play. The three shaded triangles group (Fuel B – June) provide a similar

observation at the 0% ethanol level, where increases in PN

concentrations occurred at more or less constant toluene exhaust

concentrations. This group will be important for the identification of the

missing factor.

Isobutylene regression

Regressing the PN concentrations with isobutylene provides a pathway

for this missing factor. Figure 15 demonstrates the correlation of PN

concentrations with isobutylene; increasing concentrations of

isobutylene coincide with reductions in PN concentrations. Again, both

regression lines show acceptable agreement with the data, giving

mathematical credence to this co-dependence at both fixed ethanol

levels. Recall that the three shaded triangles test group in Figure 14

showed increases at fixed toluene concentrations. Here, the same group

of data points show that this coincides with changes in measured

isobutylene concentrations. Furthermore, the Fuel A data points are now

aligned with decreasing isobutylene levels. In this case, the observed

scatter serves as an indication for the variability in measured toluene

concentrations in Figure 14, and vice versa. As a final indication for this

observation, inspecting the E10 data series (open circles) shows in

Figure 15 that isobutylene concentrations did not vary substantially;

those changes in PN concentrations are therefore a result in changes to

the toluene fraction, as shown in Figure 14.

Figure 15. Two-minute average PN vs exhaust isobutylene concentration, taken

at run end for the different test fuels. LLS regression lines shown (dashed lines).

Multiple Linear Regression

The combination of these two regressions provides some insight to the

observed variability on three levels: (1) increasing the ethanol fraction

provides a step upwards in measured PN concentrations; (2) increasing

the toluene fraction of the fuel provides for increasing PN

concentrations; and (3) the observed scatter of PN concentrations at

fixed toluene and ethanol levels can be effectively explained by changes

in the isobutylene emissions. Note that observations (2) and (3) can be

reversed and are interchangeable in that regard; changes in toluene can

also explain the scatter of PN concentrations at fixed isobutylene

emissions levels. As a final point of analysis, the three gaseous

compounds were regressed with measured PN concentrations in a

multiple linear regression (MLR). This in effect creates a predictive

model of PN concentrations, though this does not serve a purpose in this

discussion here. Instead, this MLR also provides confirmation that the

three influences cover the observed fuel compositional dependence well

as a combined effect. When combined together, the resulting regression

provided an excellent fit of the data shown by an adjusted coefficient of

determination of 0.85. Further, all the coefficients given were

determined to be statistically significant (p<0.01), implying that they are

relevant for explaining the changes in measured PN concentrations.

�� #��

� 1.06 10! " 4.37 10&'(��) *" 1.195'-�.� ��* / 2.67 101'23�4)�5� *

where [ ] denotes measured species exhaust concentration in ppm.

Variations of Base Gasoline

The gaseous emissions results provided evidence that the composition

of the base gasoline varied significantly. Figures 16 and 17 present the

speciated hydrocarbon emissions for the two base gasolines used in

prior work [2] (Fuel A) and this investigation (Fuel B), except now

organized by the month in which the fuel batches were purchased and

used. This is done to highlight the seasonal variation of the fuel, which

is readily demonstrated in both figures. For example, in both figures

lighter olefins—such as ethylene, propylene, and isobutylene—are

shown to decrease in concentration with the progression from late

winter/early spring to the summer. These olefins are readily formed

during combustion of lighter alkanes (C4-C8) as intermediate species

through H-abstraction and β-scission mechanisms [26, 27]. Thus, the

presence of light olefins in the exhaust is an indication of the presence

of lighter alkanes, having relatively high vapor pressure, in the fuel. As

a result, these compounds are of interest when discussing changes in

fuel vapor pressure.

Figure 16: Average hydrocarbon emissions measured by the FTIR for the two

base gasolines (Fuel A and Fuel B). Data series with * represents a single run.

Error bars indicate 95% confidence intervals or one standard deviation*.

Comparing these three compounds, isobutylene underwent the largest

percentage change through the progression of seasons (as shown by the

change in bar heights for each compound in Figure 17); 32% vs. 20%

and 12% for propylene and ethylene, respectively. Additionally,

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7/20/2015

ethylene is likely confounded by the fact that it is readily formed by

many of the typical constituents in gasoline, including ethanol. Finally,

Figure 15 presented the correlation of PN concentration to exhaust

isobutylene concentration. The corresponding correlations for ethylene

and propylene are presented in Appendix C. Comparison of the three

figures shows that measured PN concentrations have the strongest

correlation to isobutylene of these three compounds (R2 = 0.82, 0.76 for

isobutylene, R2 = 0.57, 0.50 for propylene and R2 = 0.00, 0.08 for

ethylene, where the first value is for E0 and the second for E10). Thus,

the suitability of isobutylene as a marker for vapor pressure by extension

of lighter hydrocarbon combustion can therefore be considered both

theoretically valid and corroborated by emissions data.

Figure 17 Average hydrocarbon emissions measured by the FTIR for the two

base gasolines (Fuel A and Fuel B). Data series with * represents a single run.

Error bars indicate 95% confidence intervals or one standard deviation*.

Considering just Fuel B confirms that fuel compositional changes

occurred. In Figures 16 and 17, the Fuel B - July and Fuel B - August

data correspond with the E0 and E0 Return test groups, respectively.

Comparing these two groups shows once again that statistically

significant (p<0.05) increases in toluene and decreases in isobutylene

occurred.

PN Index: Explaining the Observed Variability

Figure 18 compares the measured particle number concentrations at run

end (final 2 minute average—corresponding to the data presented in

Figures 14 and 15) for each fuel and illustrates the magnitude of the PN

concentration variability observed. Two indices have been developed

to link PN concentrations to fuel properties. Aikawa et al. [28] and

Leach et al. [29] have each provided particulate matter emissions

indices based on two metrics of fuel composition; the vapor pressure

and the fuel structure. The PN Index from Leach et al. is defined by the

equation [29]:

��2 6 � ∑ 89:-� " 1;;<��=�9<�-8>��; 100>��

where: DBE = double bond equivalent

DVPE = Dry vapor pressure equivalent

The double bond equivalent value of a fuel component characterizes

the propensity of that fuel structure to soot and is given by:

9:-� �12 82� / ? / � " 2;

where: C, H, and N are the number of atoms of carbon, hydrogen and

nitrogen respectively in the fuel component.

Vapor pressure describes how readily the fuel will evaporate and mix

with the air. These two are combined into a single measure which is

proportional to the fuel’s DBE score and inversely proportional to its

vapor pressure.

Figure 18: Two-minute average PN concentration at run end, averaged for fuel

groupings shown. Test condition 57 N-m @ 2600 rpm.

It was not possible to calculate accurate PN Index values for the fuels

tested because the composition of the base gasolines were not known,

nor was the vapor pressure measured. However, in the present

experiments, the exhaust toluene concentration represents the fuel

structure component, while the exhaust isobutylene concentration

represents the fuel’s vapor pressure. Working from the FTIR exhaust

concentration measurements, a kind of surrogate PN index can be

defined by:

@)��A�� 6� 6.�)3��) �� 8BB�;6.�)3��3�4)�5� �� 8BB�;

In this surrogate PN index, the numerator represents the DBE term and

the denominator represents the DVPE term of the original index. Figure

19 shows PN concentration at run end plotted as a function of this

surrogate PN index calculated from the FTIR measurements (the data in

Figures 14 and 15) for each test run of all fuels tested. With one

exception, the surrogate PN index accounts for the main PN

concentration trends observed.

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7/20/2015

Figure 19. Two-minute average PN concentration vs the ratio of

toluene/isobutylene exhaust concentrations at run end for the different test fuels.

LLS regression lines shown (dashed lines).

The one exception is the Fuel A data set, which shows high variability

at more or less constant ratio of exhaust toluene concentration to exhaust

isobutylene concentration. However, the Fuel A data shown are the

results from an earlier study of systemic effects on PN concentration

variability [2] and those systemic effects account for the remaining

variability.

Summary/Conclusions

Fuel effects on observed variability in measurements of particle number

concentrations were explored. Inducing changes to the fuel composition

through the creation of different fuel blends generated a high degree of

emissions variability. Increasing the ethanol or toluene fraction of the

fuel caused increases in measured PN concentrations and PM, along

with seemingly greater emissions variability on a run-to-run basis.

Fundamental changes were observed to the particle size distributions

and the composition of the PM (as characterized by the EC/OC

analysis).

Analyzing gaseous emissions data identified possible markers for fuel

compositional changes at the base gasoline level. Indeed, PN

concentrations were shown to positively correlate with toluene and

ethanol exhaust concentrations; in contrast, a correlation with

isobutylene showed a negative response. The exhaust toluene

concentration is related to the fuel’s propensity to soot, while the

exhaust isobutylene concentration is related to the fuel components that

most relate to high vapor pressure. Using these parameters in the

context of the PN Index accounts qualitatively for the measured

particulate matter emissions variability.

While the results presented are engine out emissions, the increase in

particle number concentrations by nearly a factor of 2 between the E0

and E0 return tests with two different purchases of commercial gasoline

suggests that seasonal and other changes in commercial gasoline could

have a significant impact on on-road emissions. The changes observed

were for gasoline from the same supplier purchased only weeks apart.

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Contact Information

Professor James S. Wallace

Department of Mechanical & Industrial Engineering

University of Toronto

5 King’s College Road

Toronto, Ontario M5S 3G8

CANADA

[email protected]

Acknowledgments

This work was supported by the AUTO21 Network of Centres of

Excellence, project D506-DMP. The authors gratefully acknowledge

prior work carried out by Phillip Mireault. Naomi Zimmerman carried

out the EC/OC analysis for most of the samples and all of the EC/OC

post-processing work.

Definitions/Abbreviations

AKI Anti-Knock Index

DBE Double-bond Equivalent score

DVPE Dry Vapour Pressure Equivalent

ECT Engine Coolant Temperature

EC/OC Elemental Carbon to Orgranic Carbon ratio

EEPS Engine Exhaust Particle Sizer

FTIR Fourier Transform Infared spectroscope

GDI Gasoline Direct Injection

HACA H-abstraction acetylene-addition

HC Hydrocarbon

LLS Linear Least Squares

MLR Multiple Linear Regression

PAH Polycyclic Aromatic Hydrocarbons

PCV Positive Crankcase Ventilation

PCM Powertrain Control Module

PM Particulate Mass

PN Particle Number

RSD Relative Standard Deviation

THC Total Hydrocarbons

TWC Three-way Catalytic Converter

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Appendix A

Supplementary Data

Figure A-1. Comparison of equivalence ratio values from PCM feedback and

from the lambda meter.

Figure A-2. Test-to-test variability in engine load ignition timing and air mass

flowrate. The points are the average of the data for each individual test run for

the test fuels.

Appendix B

Engine Operating Conditions During a Test

Figure B-1. Operating parameter stabilization during a test.

Figure B-2. Temperature stabilization during a test. T/C 1 = exhaust gas at

manifold outlet, T/C 2 = catalyst housing outlet (catalyst removed), T/C 3 = PN

sampling inlet exhaust gas temperature, T/C 4 = emissions bench sampling inlet

exhaust gas temperature. See Figure 1.

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Appendix C

PN vs. Selected FTIR Gaseous Species

Figure C-1. Two-minute average PN vs isobutylene exhaust concentration at run

end for the different test fuels. LLS regression lines shown (dashed lines).

Figure C-2. Two-minute average PN vs ethylene exhaust concentration at run

end for the different test fuels. LLS regression lines shown (dashed lines).

fuel effects on particulate matter emissions variability from a gasoline direct injection engine ·...

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