Effects of Five Gasoline 

Properties on Emissions from 

Light-Duty Tier 2 Vehicles

Aron Butler, James Warila, Rafal Sobotowski, 

Cay Yanca, Paul Machiele 

(USEPA/OTAQ/ASD)

George Hoffman (ISI, Inc.)

CRC Real World Conference

April 10, 2013

1
Overview of Testing

(EPAct/V2/E-89 data collection co-sponsored by EPA, DOE/NREL, CRC)

2
Background

• Response to 2005 Energy Policy Act directive to develop an 

updated fuel effects model covering Tier 2 vehicles

• First comprehensive study to quantify impacts of fuel 

changes on emissions since the Auto/Oil AQIRP program of 

early 1990s

• Emission levels now an order of magnitude lower

• Studied effects of five fuel parameters individually and 

together as they interact in in-use fuels

• Aromatics, ethanol, RVP, T50, T90 (distillation behavior)

• (Separate study on sulfur)

3
Program Overview

• Optimized matrix of 27 test fuels (plus E85 on FFVs)

• Test fleet of 15 high-sales cars and light trucks from 2008 MY

• Testing performed at 75F over 3-bag LA92 cycle

• 2+ replicates per vehicle-fuel combination

• Measured gaseous pollutants and PM for all tests and bags

• Sec/sec data for THC, CH4, CO, CO2, NOxin all tests

• Alcohol, carbonyl, and HC speciation for a subset of tests/bags

• Vehicle handling and test procedures were highly specified to 

better isolate fuel effects from test-to-test variability and 

other artifacts that can erode statistical power

4
Fuel Matrix Overview

• Property ranges chosen to allow models to predict emissions for all 

in-use fuels found in U.S.

• Multiple levels of parameters to capture non-linear relationships 

between fuel properties and emissions

• Worked with a fuel study design expert to optimize fuel matrix to 

resolve interactions of interest while minimizing number of fuels 

(cost)

5

Fuel Parameter Number of Levels Target Values to Be Tested

Ethanol (vol%) 4 0, 10, 15, 20

T50 (°F) 5 150, 165 (E20 only), 190, 220, 240

T90 (°F) 3 300, 325, 340

Aromatics (vol%) 2 15, 35

RVP (psi) 2 7, 10
6

Fuel No. T50, 

o

F T90, 

o

F EtOH, % DVPE, psi Aro, %

1 150 300 10 10 15

2 240 340 0 10 15

3 220 300 10 7 15

4 220 340 10 10 15

5 240 300 0 7 35

6 190 340 10 7 15

7 190 300 0 7 15

8 220 300 0 10 15

9 190 340 0 10 35

10 220 340 10 7 35

11 190 300 10 10 35

12 150 340 10 10 35

13 220 340 0 7 35

14 190 340 0 7 15

15 190 300 0 10 35

16 220 300 10 7 35

20 165 300 20 7 15

21 165 300 20 7 35

22 165 300 20 10 15

23 165 340 20 7 15

24 165 340 20 10 15

25 165 340 20 10 35

26 165 340 15 10 35

27 220 340 15 7 15

28 220 300 15 7 35

30 150 325 10 10 35

31 165 325 20 7 35

Fuel Matrix Summary

T502

ETOH2

ETOH*ARO

ETOH*T50

ETOH*T90

ETOH*RVP

ETOH

ARO

T50

T90

RVP
Test Vehicle Fleet Overview

• Fleet was sized by statistical power analysis consistent with 

approach used in Auto/Oil

• Used test-to-test and vehicle-by-fuel uncertainties taken from other 

recent datasets

• After iteration considering budget, speciation needs, etc., 

arrived at 15 vehicles

• Chose high-sales engine families among MY2008 vehicles

• Cars and light trucks, target Bin 5 average with some Bin 4 and 8

• New vehicles were leased and dyno-aged to >4000 miles before start 

of program

7
Test Fleet Summary

8

Make Brand Model Engine Size Tier 2 Bin LEVII

Std

GM Chevrolet Cobalt 2.2L I4 5 NA

GM Chevrolet Impala FFV 3.5L V6 5 L2

GM Saturn Outlook 3.6L V6 5 L2

GM Chevrolet Silverado FFV 5.3L V8 5 NA

Toyota Toyota Corolla 1.8L I4 5 U2

Toyota Toyota Camry 2.4L I4 5 U2

Toyota Toyota Sienna 3.5L V6 5 U2

Ford Ford Focus 2.0L I4 4 U2

Ford Ford Explorer 4.0L V6 4 NA

Ford Ford F150 FFV 5.4L V8 8 NA

Chrysler Dodge Caliber 2.4L I4 5 NA

Chrysler Dodge Caravan FFV* 3.3L V6 8 NA

Chrysler Jeep Liberty 3.7L V6 5 NA

Honda Honda Civic 1.8L I4 5 U2

Honda Honda Odyssey 3.5L V6 5 U2

Nissan Nissan Altima 2.5L I4 5 L2

*Caravan FFV was used only for E85 testing
Resulting Dataset

• 15 vehicles x 27 test fuels x 2+ replicates = 956 tests for 

gaseous emissions, PM, and FE

• Sec/sec CAN/OBD data collected for 15 parameters during 

each test

• Lots of detailed speciation data

– Alcohol and carbonyl speciation in bag 1 for 926 tests, bags 2-3 for 

subset of vehicles (71 tests)

– Light hydrocarbons (C2-C12) speciation for a subset of fuels and 

vehicles (205 tests for bag 1)

– Rigorous LOQ and QA procedures were applied to ensure high quality 

data

• Fuel property data includes DHA for all fuels

9
Analysis & Results

(EPA’s work)

10
Analysis Approach

• Natural log transformation of emissions data

– Stabilize variance, obtain linearity between dependent variable and 

fixed and random effects, normalize distribution of residual

• “Standardize” fuel properties

– Subtract mean from each measurement, divide by standard dev

– Puts all five properties in “same space”

• Analyzed using mixed models by bag, pollutant

– Fuel properties were fixed effects, vehicles were random effects

etOH

etOH etOH

etOH

s

x x

Z





11
Modeling Process

• Round 1

– Identify outlier observations

– Result: A few PM points removed

• Round 2

– Identify influential vehicles

– Result: Two vehicles removed for subsets of NOx, NMOG 

data

• Round 3

– Fit final models 

• Reduced models based on original design term list

12
What do the results show?

• Fuel effects persist in new vehicles

• With this large dataset they are discernible even 

for hot running emissions

• Many trends similar to those in datasets for older 

vehicles

– E.g., effect of ethanol and aromatics on NOx, or 

aromatics and T90 on PM

• Some are different

– E.g., effect of RVP on THC

13
NOx

Model coefficients

14
15

... You may be 

tempted to 

take one of the 

coefficients

and run with it ...

... but DON’T...
16

... Because all the coefficients

need to work together as a team ...
An Example for Bag 1 NOx

Pulled from an AAMA Survey

Fuel Fuel*

EtOH 9.5 0

Aro 23.2 28.8

RVP 7.7 9.0

T50 213 212

T90 331 331

17

  Z Z  Z Z ...

NO

NO ln *

2

*

* 1

x

x



 0  0   1  1   2  2














Model-o-grams

PM

18
Model-o-grams

THC

19
Model-o-grams

CO

20
Final Thoughts

• Robust dataset for estimating emission impacts of current 

and future in-use fuels nationwide

• Developed a lot of best practices

• Lots of information here that hasn’t been fully utilized yet

• More analyses to come

21
The study reports and data will be available

soon via the OTAQ website:

http://www.epa.gov/otaq/fuelsmodel.htm

22

Further Reading