EPA/600/R-09/078 

August 2009 

Final Report 

Evaluation of the Emissions 

from Low-Sulfur and Biodiesel 

Fuel Used in a Heavy-Duty 

Diesel Truck during On-Road 

Operation 
Evaluation of the Emissions from Low-Sulfur and 

Biodiesel Fuel Used in a Heavy-Duty Diesel Truck 

during On-Road Operation 

Final Report 

John S. Kinsey 

Office of Research and Development 

National Risk Management Research Laboratory 

Research Triangle Park, NC 27711 

Yuanji Dong, Craig Williams, and Russell Logan
 

ARCADIS U.S., Inc. 

4915 Prospectus Drive 

Durham, NC 27713 

Contract No. EC-C-09-027 

Work Assignment No. 0-5 

EPA Project Officer: John Kinsey 

Air Pollution Prevention and Control Division 

National Risk Management Research Laboratory 

Research Triangle Park, NC 27711 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 

August 2009 
EPA Review Notice 

This report has been peer and administratively reviewed by the U.S. Environmental Protection 

Agency and approved for publication. Mention of trade names or commercial products does not 

constitute endorsement or recommendation for use. 

This document is available to the public through the National Technical Information Service, 

Springfield, Virginia 22161. 

i 
Foreword 

The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the 

Nation's land, air, and water resources. Under a mandate of national environmental laws, the 

Agency strives to formulate and implement actions leading to a compatible balance between 

human activities and the ability of natural systems to support and nurture life. To meet this 

mandate, EPA's research program is providing data and technical support for solving 

environmental problems today and building a science knowledge base necessary to manage our 

ecological resources wisely, understand how pollutants affect our health, and prevent or reduce 

environmental risks in the future. 

The National Risk Management Research Laboratory (NRMRL) is the agency's center for 

investigation of technological and management approaches for preventing and reducing risks 

from pollution that threaten human health and the environment. The focus of the laboratory's 

research program is on methods and their cost-effectiveness for prevention and control of 

pollution to air, land, water, and subsurface resources; protection of water quality in public water 

systems; remediation of contaminated sites, sediments and ground water; prevention and control 

of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and 

private sector partners to foster technologies that reduce the cost of compliance and to anticipate 

emerging problems. NRMRL's research provides solutions to environmental problems by: 

developing and promoting technologies that protect and improve the environment; advancing 

scientific and engineering information to support regulatory and policy decisions; and providing 

the technical support and information transfer to ensure implementation of environmental 

regulations and strategies at the national, state, and community levels. 

This publication has been produced as part of the laboratory's strategic long-term research plan. 

It is published and made available by EPA's Office of Research and Development to assist the 

user community and to link researchers with their clients. 

Sally Gutierrez, Director 

National Risk Management Research Laboratory 

ii 
Abstract 

In October of 2004, the Air Pollution Prevention and Control Division of the U.S. EPA’s 

National Risk Management Research Laboratory investigated the emissions from a dieselpowered tractor-trailer operating along a highway at near-zero grade. In place of a dynamometer 

and standard dilution tunnel, the Diesel Emissions Aerosol Laboratory (DEAL) was used to 

sample both the exhaust plume of the truck and the background environment, which eliminated 

the contributions of other vehicle emissions to the plume measurements. The primary thrust of 

the research was to compare the truck’s emissions when using low sulfur (15 ppm) diesel fuel 

(base fuel) with those when using a 20% soy-based biodiesel blend (B20). These comparisons 

were made for two speeds (56 and 105 km/h) and load conditions—21,350 and 33,850 kg gross 

vehicle weight (GVW). Each time the fuel was changed, the truck was returned to the dealer to 

have the filters replaced, the old fuel removed, and the new fuel added. The highway traversed 

during the bulk of the measurements was a level section of US-70 in eastern North Carolina near 

the town of New Bern. After 20 days of primary experiments near New Bern were completed, an 

additional two days were spent driving a section of Virginia’s I-77 between Exits 1 and 8, which 

is near the town of Fancy Gap, VA, to investigate the effect of road grade on diesel emissions. 

The truck used standard pump fuel during this phase of the research. The DEAL was 

instrumented to measure total hydrocarbons (THC), carbon monoxide (CO), carbon dioxide 

(CO2), oxides of nitrogen (NOX), fine particulate matter (PM-2.5—PM with an aerodynamic 

diameter equal to or less than 2.5 μm) and the chemical composition of selected gas- and 

particle-phase air pollutants. Using B20 in place of the base fuel reduced nearly all emissions 

under nearly all combinations of speed and GVW examined, but the greatest reduction was in 

PM emission factors. For example, at the higher GVW and 56 km/h, using B20 reduced 

emissions of NOX by 9%, CO by 8%, THC by 20%, and PM by 68% compared with the base 

fuel. At the lower GVW and 105 km/h, using B20 reduced emissions of NOX by 5%, CO by 

13%, THC by 18%, and PM by 19% compared with the base fuel. Changes in GVW at a given 

speed and fuel type had a smaller effect on emissions than changes in speed for a given load and 

fuel type. With regard to chemical composition, both black carbon (which approximates 

elemental carbon content) and particle-phase polycyclic aromatic hydrocarbons decreased at all 

speed and load conditions when using B20 in place of the base fuel. Also, B20 produced less 

C17-C31 alkanes when compared to the base fuel. 

iii 
Table of Contents 

EPA Review Notice ..................................................................................................................... i
 

Foreword..................................................................................................................................... ii
 

Abstract...................................................................................................................................... iii
 

List of Acronyms...................................................................................................................... xiv
 

Chapter 1: Introduction................................................................................................................ 1-1
 

1.1 Background.................................................................................................................. 1-1
 

1.2 Objectives .................................................................................................................... 1-2
 

1.3 Report Organization..................................................................................................... 1-2
 

Chapter 2: Diesel Emissions Aerosol Laboratory (DEAL) ......................................................... 2-1
 

2.1 General Description ..................................................................................................... 2-1
 

2.2 Plume Sample Extraction System................................................................................ 2-9
 

2.3 Background Sample Extraction System..................................................................... 2-11
 

2.4 Vehicle Operating Parameters ................................................................................... 2-13
 

2.5 Data Acquisition System............................................................................................ 2-15
 

Chapter 3: DEAL Instrumentation............................................................................................... 3-1
 

3.1 Continuous Exhaust Gas Monitoring........................................................................... 3-1
 

3.1.1 O2 Analyzer............................................................................................................ 3-3
 

3.1.2 NOX Analyzer........................................................................................................ 3-3
 

3.1.3 CO/CO2 Analyzer ..................................................................................................3-3
 

3.1.4 THC Analyzer........................................................................................................ 3-4
 

3.2 Tracer Gas Analyzer .................................................................................................... 3-4
 

iv 
3.3 Continuous PM-2.5 Monitoring................................................................................... 3-4
 

3.3.1 PM Mass Measurements........................................................................................ 3-5
 

3.3.1.1 Quartz Crystal Microbalance (QCM)............................................................. 3-5
 

3.3.1.2 Tapered Element Oscillating Microbalance (TEOM) .................................... 3-5
 

3.3.2 PM Count Measurements....................................................................................... 3-5
 

3.3.2.1 DEKATI Electrical Low Pressure Impactor (ELPI)....................................... 3-5
 

3.3.2.2 TSI Scanning Mobility Particle Sizer (SMPS)............................................... 3-6
 

3.3.2.3 Condensation Particle Counter (CPC)............................................................ 3-6
 

3.3.3	 PM Black Carbon Magee Aethalometer................................................................3-6
 

3.3.4	 PM Polycyclic Aromatic Hydrocarbons—Photoelectric Aerosol Sensor (PAS) 

2000 .......................................................................................................................3-6
 

3.4 Time-Integrated Sampling ........................................................................................... 3-7
 

3.5 Fuel .............................................................................................................................. 3-8
 

Chapter 4: Field Test Sites and Testing Procedures .................................................................... 4-1
 

4.1 Staging Area................................................................................................................. 4-1
 

4.2 General Experimental Procedures................................................................................ 4-1
 

4.2.1 New Bern Tests...................................................................................................... 4-1
 

4.2.2 Mountainous I-77 Tests ......................................................................................... 4-4
 

4.3 Tractor-Trailer Payload................................................................................................ 4-5
 

4.4 Vehicle Operation ........................................................................................................ 4-6
 

4.5 Coast-Down Testing .................................................................................................... 4-7
 

4.6 Test Fuel....................................................................................................................... 4-7 
 

4.7 CEM Operation............................................................................................................ 4-8
 

4.8 Tracer Gas Analysis..................................................................................................... 4-9
 

4.9 PM-2.5 Instrument Operation .................................................................................... 4-10
 

4.10 Time Integrated Sampling.......................................................................................... 4-11
 

v 
Chapter 5: Post-Test Laboratory Analysis................................................................................... 5-1
 

5.1 PM-2.5 Gravimetric Analysis ...................................................................................... 5-1
 

5.2 Elemental Analysis ...................................................................................................... 5-2
 

5.3 Analysis of Water-Soluble Inorganic Ions................................................................... 5-2
 

5.4 Analysis of Organic and Elemental Carbon................................................................. 5-2
 

5.5 Analysis of Particle Phase Organic Compounds ......................................................... 5-3
 

5.5.1 Sample Compositing and Spiking.......................................................................... 5-3
 

5.5.2 Sample Extraction and Concentration ................................................................... 5-4
 

5.5.3 Extract Methylation ............................................................................................... 5-5
 

5.5.4 GC/MS Analysis .................................................................................................... 5-5
 

Chapter 6: Experimental Data Analysis....................................................................................... 6-1
 

6.1 Raw Data Analysis....................................................................................................... 6-1
 

6.2 Emission Factors for Gaseous Pollutants..................................................................... 6-4
 

6.3 Estimate of Exhaust Flow Rate.................................................................................... 6-7
 

6.4 PM Emission Factors ................................................................................................... 6-8
 

6.5 Conversion of Emission Measures............................................................................. 6-10
 

6.6 Particle Size Distribution ........................................................................................... 6-11
 

6.7 Vehicle Power Demand ............................................................................................. 6-11
 

Chapter 7: Analysis of Vehicle Operating Parameters ................................................................ 7-1
 

7.1 Coast-Down Test Results............................................................................................. 7-1
 

7.2 Road Grade Determination .......................................................................................... 7-2
 

7.3 Plume Dilution Ratio ................................................................................................... 7-3
 

7.4 Truck Driving Conditions............................................................................................ 7-6
 

7.5 Fuel Types and Compositions...................................................................................... 7-6
 

7.6 Effects on Fuel Consumption and Exhaust Flow......................................................... 7-6
 

vi 
Chapter 8: Gaseous Emissions..................................................................................................... 8-1
 

8.1 NOX Emissions ............................................................................................................ 8-1
 

8.2 CO Emissions............................................................................................................... 8-5
 

8.3 THC Emissions .......................................................................................................... 8-10
 

Chapter 9: PM-2.5 Emissions ...................................................................................................... 9-1
 

9.1 PM Mass Emissions..................................................................................................... 9-1
 

9.2 PM Number Emissions ................................................................................................ 9-8
 

9.3 PM Particle Size Distribution .................................................................................... 9-15
 

Chapter 10: Quality Assurance and Quality Control ................................................................. 10-1
 

10.1 CEM Calibrations ...................................................................................................... 10-1
 

10.1.1 Multipoint CEM Calibration................................................................................ 10-1
 

10.1.2 Daily CEM Calibration Checks ........................................................................... 10-3
 

10.1.3 CEM Span Drift ................................................................................................... 10-9
 

10.2 Photoacoustic Multigas Analyzer ............................................................................ 10-10
 

10.3 Thermocouples......................................................................................................... 10-10
 

10.4 Mass Flow Controllers............................................................................................. 10-12
 

10.5 Pressure Transducer................................................................................................. 10-12
 

10.6 Post-Test Laboratory Analysis................................................................................. 10-13
 

Chapter 11: Comparison to Historical Data............................................................................... 11-1
 

Chapter 12: Research Findings .................................................................................................. 12-1
 

Chapter 13: References.............................................................................................................. 13-1
 

vii 
List of Appendices 

Appendix A: Chemical Composition 

Appendix B: Fuel Analysis Report 

Appendix C: INNOVA Analyzer Calibration Sheets 

Appendix D: Thermocouple Calibration Sheets 

Appendix E: Mass Flow Controllers Calibration Sheets 

Appendix F: Pressure Transducer Calibration Sheets 

List of Tables 

Table 2-1. General Specifications for the DEAL.......................................................................2-2
 

Table 2-2. Summary of DEAL Vehicle System and Environmental Measurements ...............2-4
 

Table 2-3. Vehicle Parameters and Sensors.............................................................................2-15
 

Table 3-1. Analytical Plan for On-Road Truck Experiments....................................................3-9
 

Table 3-2. Fuel and Lube Oil Analysis Methods a

...................................................................3-10
 

Table 4-1. Test Matrix for Fuel Type, Vehicle Speed, Speciation, and Vehicle Weight .........4-2
 

Table 4-2. Measurements Taken during the Tests on I-77 ........................................................4-4
 

Table 4-3. DEAL Loaded and Unloaded Testing Weights........................................................4-6
 

Table 4-4. Vehicle Operating Parameters ..................................................................................4-6
 

Table 4-5. Fuel and Oil Samples Collected and Analyzed........................................................4-8
 

Table 4-6. Temperature Controller Setpoints for CEM System................................................4-9
 

Table 4-7. FM-200 Consumption Rates...................................................................................4-10
 

Table 4-8. Miscellaneous Operating Procedures .....................................................................4-11
 

Table 5-1. Analytical MOPs.......................................................................................................5-1
 

Table 5-2. Composites of Quartz Filter Samples.......................................................................5-3
 

Table 5-3. GC/MS Operating Conditions ..................................................................................5-6
 

Table 6-1. Measurement Parameters Analyzed and their Source Files.....................................6-2
 

viii 
Table 7-1. Road Load Equation Coefficients Determined From the Coast-Down Tests .........7-2
 

Table 7-2. Road Grade Determined during Test 4 for the Segment of US-70..........................7-3
 

Table 7-3. Dilution Ratios and Their Uncertainties for Individual Tests..................................7-5
 

Table 7-4. Fuel Compositions and Properties ............................................................................7-7
 

Table 8-1. Test Average Emission Factors and Rates for NOX.................................................8-2
 

Table 8-2. Three-Way ANOVA Results for NOX Fuel-Specific Emission Factor...................8-5
 

Table 8-3. Test Average Emission Factors and Emission Rates for CO...................................8-6
 

Table 8-4. Three-way ANOVA Results for CO Fuel-specific Emission Factor ......................8-9
 

Table 8-5. Test Average Emission Factors and Emission Rates for THC ..............................8-10
 

Table 8-6. Three-way ANOVA Results for THC Fuel-specific Emission Factor..................8-13
 

Table 9-1. Results of PM Mass Emissions by Teflon Filters ....................................................9-2
 

Table 9-2. Three-Way ANOVA Results for Teflon Filter PM Mass Measurements...............9-4
 

Table 9-3. Results of PM Mass Emissions by TEOM...............................................................9-5
 

Table 9-4. Three-Way ANOVA Results for TEOM PM Mass Measurements........................9-6
 

Table 9-5. Test Average PM Particle Number Results Obtained from the SMPS ...................9-9
 

Table 9-6. Test Average PM Particle Number Results Obtained from the ELPI ...................9-10
 

Table 9-7. Three-way ANOVA Results for Fuel-specific PM Number Emission Factor 

by the SMPS............................................................................................................9-13
 

Table 9-8. Three-way ANOVA Results for Fuel-specific PM Number Emission Factor 

by the ELPI..............................................................................................................9-14
 

Table 9-9. GMD Results from the SMPS and Nano SMPS ....................................................9-18
 

Table 9-10. Three-Way ANOVA Results for GMD..................................................................9-20
 

Table 10-1. Data Quality Indicator Goals ..................................................................................10-2
 

Table 10-2. CEM Calibration Curves.........................................................................................10-2
 

Table 10-3. DQI Values for Total Hydrocarbon Gas Measurements for All Tests..................10-4
 

Table 10-4. DQI Values for Oxides of Nitrogen Gas Measurements for All Tests..................10-5
 

Table 10-5. DQI Values for Oxygen Gas Measurements for All Tests ....................................10-6
 

Table 10-6. DQI Values for Carbon Dioxide Gas Measurements for All Tests.......................10-7
 

ix 
Table 10-7. DQI Values for Carbon Monoxide Gas Measurements for All Tests ...................10-8
 

Table 10-8. CEM Span & Zero Drift for the Pump Diesel and the Low Sulfur Diesel 

Fuel Tests.................................................................................................................10-9
 

Table 10-9. CEM Span and Zero Drift for the Biodiesel Fuel Tests...................................... 10-10
 

Table 10-10. INNOVA 1314 Photoacoustic Multigas Analyzer Calibrations......................... 10-11
 

Table 10-11. DQI Values for FM-200 and Propane Gas Measurements for All Tests ........... 10-11
 

Table 10-12. Thermocouple Calibrations.................................................................................. 10-12
 

Table 10-13. Flow Calibrations ................................................................................................. 10-13
 

Table 10-14. Balance Variations from Standard Weights ........................................................ 10-14
 

Table 10-15. Comparison of the PM Mass Results Weighed On Two Different Days........... 10-14
 

Table 10-16. Effect of Sample Source on Organic Compound Speciation.............................. 10-16
 

Table 10-17. Relative Standard Deviation in Inorganic Ions Analysis .................................... 10-17
 

Table 11-1. Comparison of Emission Factors for Criteria Pollutants .......................................11-2
 

List of Figures 

Figure 2-1. Kenworth T-800 Tractor and Great Dane Trailer, Locations of the Plume 

and Background Sampling Probes............................................................................2-2
 

Figure 2-2. DEAL Trailer Layout................................................................................................2-3
 

Figure 2-3. Plume Sampling System Probe (left) and Virtual Impactor Connected to the
 

Sampling Tunnel (right)............................................................................................2-9
 

Figure 2-4. Photographs of the DEAL during Fabrication Showing Sampling Tunnel
 

and Plume Bench (left), and with Instruments Installed (right).............................2-10
 

Figure 2-5. Flow Schematic of Plume Sampling System for Speciated Runs .........................2-10
 

Figure 2-6. Flow Schematic of Plume Sampling System for Non-Speciated Runs.................2-11
 

Figure 2-7. Background Bench Before the Background Tunnel and Instruments were 

Installed (left), and after Installation (right) ...........................................................2-12
 

Figure 2-8. Flow Schematic of the Background Sampling System for Speciated Tests..........2-12
 

Figure 2-9. Flow Schematic for the Background Sampling System for Non-Speciated 

Tests.........................................................................................................................2-13
 

x 
Figure 2-10. Schematic of the DEAL Showing the Locations of Sensors that Monitor 

Various Vehicle Parameters....................................................................................2-14
 

Figure 2-11. The ATI Torque Meter Sensor (left) and the Datron Optical Speed Sensor
 

(right) .......................................................................................................................2-14
 

Figure 2-12. The Crossbow Gyroscope VG600CA ....................................................................2-15
 

Figure 3-1. Schematic of the Continuous Emissions Monitoring (CEM) System in the 

DEAL.........................................................................................................................3-2
 

Figure 3-2. New Bern Staging Area ..........................................................................................3-10
 

Figure 4-1. On-Road Diesel Emissions Test Route on Old US-70 ............................................4-3
 

Figure 4-2. On-Road Diesel Emissions Test Route in the Mountains on I-77...........................4-5
 

Figure 4-3. Chase Vehicle Used during Low Speed Tests .........................................................4-7
 

Figure 4-4. Tracer Gas Injection System and Tracer Gas Analyzer System..............................4-9
 

Figure 7-1. Truck Speed Recorded as a Function of Time in the Coast-Down Tests................7-1
 

Figure 7-2. Comparison between the Coast-Down Experimental Data and the 

Calculation Results from the Three Coefficients .....................................................7-2
 

Figure 7-3. Road Grade of Highway I-77 Measured by the Microbarometer (Mbar) 

Northbound (a) and Southbound (b).........................................................................7-4
 

Figure 7-4. Effects of Driving Condition and Fuel Type on Fuel Consumption .......................7-7
 

Figure 7-5. Relationship of Fuel Consumption with Truck Power Demand..............................7-8
 

Figure 7-6. Effects of Test Conditions on Exhaust Flow Rate ...................................................7-9
 

Figure 7-7. Correlation of Exhaust Flow Rate with Truck Power Demand.............................7-10
 

Figure 7-8. Comparison between Annubar Meter Measurement and SAE ARP 1533 

Calculation...............................................................................................................7-11
 

Figure 8-1. Effects of Experimental Conditions on NOX: Fuel-Specific Emission Factor 

(top); Emission Rate (center); and Distance-Specific Emission Factor 

(bottom) .....................................................................................................................8-3
 

Figure 8-2. Correlation between NOX Emission Rate and Power Demand ...............................8-4
 

Figure 8-3. Effects of Experimental Conditions on CO: Fuel-Specific Emission Factor 

(top); Emission Rate (center); and Distance-Specific Emission Factor 

(bottom) .....................................................................................................................8-7
 

Figure 8-4. Correlation between CO Fuel-Specific Emission Factor and Power Demand........8-8
 

xi 
Figure 8-5.	 Effects of Test Conditions on THC: Fuel-specific Emission Factor (top); 

Emission Rate (center); Distance-Specific Emission Factor (bottom)..................8-11
 

Figure 8-6.	 Correlation between THC Fuel-Specific Emission Factor and Power 

Demand....................................................................................................................8-12
 

Figure 9-1.	 Effects of Test Condition on Fuel-Specific Mass Emission Factor Results 

Based on Teflon Filter Gravimetric Analysis Data..................................................9-2
 

Figure 9-2.	 Plot of Fuel-Specific PM Mass Emission Factor Obtained by Teflon Filters 

as a Function of Power Demand...............................................................................9-3
 

Figure 9-3.	 Effects of Test Conditions on Volatile Fraction in PM Based on the Teflon 

Filter and Thermal Denuder Results.........................................................................9-4
 

Figure 9-4.	 Effects of Test Conditions on Fuel-Specific PM Mass Emission Factor 

Based on TEOM Measurements...............................................................................9-6
 

Figure 9-5.	 Correlation between the TEOM Fuel-Specific PM Mass Emission Factor 

and the Truck Power Demand ..................................................................................9-7
 

Figure 9-6.	 Comparison of PM Emission Factor Results Obtained by Different 

Instruments ................................................................................................................9-8
 

Figure 9-7. Effects of Test Conditions on Particle Number Emissions Measured by 

SMPS: Fuel-Specific Emission Factor (top); Emission Rate (center); and 

Distance-Specific Emission Factor (bottom)..........................................................9-11
 

Figure 9-8.	 Correlation between Fuel-Specific Particle Count Emission Factor 

Determined by SMPS and Truck Power Demand .................................................9-12
 

Figure 9-9.	 Comparison of Fuel-Specific Particle Number Emission Factors Between 

Instruments ..............................................................................................................9-14
 

Figure 9-10.	 Particle Size Distributions by SMPS Measurements under Various Test 

Conditions: 56 km/h (top); 105 km/h (bottom)......................................................9-16
 

Figure 9-11.	 Particle Size Distributions by Nano SMPS under Various Test Conditions: 

56 km/h (top); 105 km/h (bottom) ..........................................................................9-17
 

Figure 9-12.	 Particle Geometric Mean Diameters by SMPS under Various Test 

Conditions................................................................................................................9-19
 

Figure 9-13.	 Correlation between Particle GMD and Truck Power Demand under
 

Steady-state Driving Conditions.............................................................................9-19
 

Figure 10-1.	 PM Mass Affected by Sample Losses ................................................................. 10-15
 

Figure 10-2.	 Repeatability of OC/EC Analysis........................................................................ 10-16
 

xii 
Figure 11-1. Percent Change in Distance-Specific Emission Factor for B20 Relative to 

the Base Fuel ...........................................................................................................11-4 

Figure 11-2. Percent Change in Distance-Specific PAH Emission Factor when Using 

B20 Relative to the Emission Factor when Using Base Fuel (Data for 

Current Study taken from Continuous PAH Analyzer not Chemical 

Analysis of the Quartz Filters)................................................................................11-5 

xiii 
List of Acronyms 

Acronym Definition 

AC 

AIM 

ANOVA 

APEX 

APPCD 

ASAE 

ASTM 

ATI 

B&K 

BC 

BTU 

C 

C3H8

CAI 

CD 

CEM 

CEMS 

CFD 

CFR 

CO 

CO2

CPC 

DAS 

DC 

DD60 

DDEC60 

DEAL 

DLS 

DMA 

alternating current 

Aerosol Instrument Manager 

analysis of variance 

Aircraft Particulate Emissions Experiment 

Air Pollution Prevention and Control Division 

American Society of Agricultural Engineers 

American Society for Testing and Materials 

Advanced Telemetrics International 

Bruell and Kjaer 

black carbon 

British thermal units 

carbon 

 propane 

California Analytical Instruments, Inc 

compact disc 

continuous emissions monitors 

continuous emission monitoring systems 

computational fluid dynamics 

Code of Federal Regulations 

carbon monoxide 

 carbon dioxide 

Condensation Particle Counter 

Data Acquisition System 

direct current 

Detroit Diesel Series 60 

Detroit Diesel Series 60 Engine Computer 

Diesel Emissions Aerosol Laboratory (Previously named Mobile 

Diesel Laboratory or MDL) 

descriptive level of significance 

Differential Mobility Analyzer 

xiv 
Acronym Definition 

DP differential pressure 

DQI data quality indicator 

DR dilution ratio 

EC elemental carbon 

EC/OC elemental carbon/organic carbon 

EC/PM (weight) fraction of elemental carbon in particulate matter 

EF emission factor 

EFM mass emission factor 

EFN number emission factor 

EFTHC total hydrocarbon emission factor 

ELPI Electrical Low Pressure Impactor 

EM distance-specific emission rate 

EPA Environmental Protection Agency 

ER emission rate 

ERNOx emission rate for oxides of nitrogen 

ESP electrostatic precipitator 

FIA Flame Ionization Analyzer 

FID flame ionization detector 

FM-200 hydrofluorocarbon tracer gas (1,1,1,2,3,3-heptafluoropropane) 

FPCL Fine Particle Characterization Laboratory 

FTP Federal Test Procedure 

GC gas chromatography 

GC/MS gas chromatography/mass spectroscopy 

GHV gross heat value 

GMD geometric mean diameter 

GVW gross vehicle weight 

H hydrogen 

H2O water 

HL heated sample line 

HP Hewlett-Packard 

HPLC high performance liquid chromatograph 

xv 
Acronym Definition 

I/O 

IC 

kW

LPM 

MFM 

MnO2

MOP 

MPA 

MS 

MSD 

MW C3H8 

MWCO 

MWNO 

N/A 

NC 

NCDOT 

ND 

NDIR 

+ NH4

NIOSH 

NO 

NO/NOX 

NO2

– NO3

NOX 

NREL 

NS 

OAQPS 

OC 

OC/EC 

OC/PM 

input/output 

ion chromatography 

 kilowatt 

liters per minute 

mass flow meter 

 manganese dioxide 

miscellaneous operating procedure 

magneto pneumatic 

mass spectrometer 

mass-selective detection 

molecular weight of C3H8 

molecular weight of CO 

molecular weight of NO 

not applicable 

North Carolina 

North Carolina Department of Transportation 

not determined 

non dispersive infrared 

 ammonium ion 

National Institute for Occupational Safety and Health 

nitric oxide 

nitric oxide/oxides of nitrogen 

 nitrogen dioxide 

 nitrate ion 

oxides of nitrogen 

National Renewable Energy Laboratory 

non speciated 

Office of Air Quality Planning and Standards 

organic carbon 

organic carbon/elemental carbon 

(weight) fraction of organic carbon in particulate matter 

xvi 
Acronym Definition 

PAH 

PAS 

PM 

PM10 

PM2.5 

PSD 

PTV 

PUF 

QAPP 

QCM 

QF 

R&P 

RH 

RHC 

RL 

RPD 

RPM 

RSD 

SAE 

SD 

SG 

SMPS 

SO2

–2 SO4

SS 

TCD 

TD 

TEOM 

TF 

THC 

U.S. 

polycyclic aromatic hydrocarbons 

photoelectric aerosol sensor 

particulate matter 

particles with an aerodynamic diameter of 10 μm or less 

particles with an aerodynamic diameter of 2.5 μm or less 

particle size distribution 

programmable temperature vaporizing (inlet) 

polyurethane foam 

Quality Assurance Project Plan 

quartz crystal microbalance 

quartz filter 

Rupprecht & Patashnick 

relative humidity 

ratio of hydrogen to carbon 

road load 

relative percent difference 

revolutions per minute 

relative standard deviation 

Society of Automotive Engineers 

standard deviation 

span gas 

Scanning Mobility Particle Sizer 

 sulfur dioxide 

 sulfate ion 

sum of squares 

thermal conductivity detector 

thermal denuder 

tapered element oscillating microbalance 

Teflon 

total hydrocarbons 

United States 

xvii 
Acronym Definition 

U.S. EPA United States Environmental Protection Agency 

UPS uninterruptible power supplies 

UV ultraviolet 

VV gross vehicle weight variable used in equations 

WT weight 

WVU West Virginia University 

XRF X Ray fluorescence 

xviii 
 

Chapter 1 

Introduction 

1.1 Background 

The fine particulate matter (PM) from diesel trucks is of interest to both the U. S. Environmental 

Protection Agency’s (EPA’s) Office of Transportation and Air Quality in their regulatory 

program for on-road vehicles and the Office of Air Quality Planning and Standards for 

implementation of the PM-2.5 (PM with an aerodynamic diameter equal to or less than 2.5 μm) 

ambient air quality standard. In prior research conducted by EPA’s National Risk Management 

Research Laboratory, Air Pollution Prevention and Control Division (APPCD), stack dilution 

sampling was used to characterize the PM emissions from APPCD’s Kenworth T-800 tractor 

equipped with a high mileage Detroit Diesel Series 60 (DD60) engine and associated 14 m 

(45 ft) Great Dane trailer. However, when stack emissions data were compared to concurrent 

measurements conducted in the exhaust plume, it was determined that the stack dilution method 

produced substantially smaller particles than actually exist in the plume (Brown et al., 2000). 

Therefore, in order to collect data more representative of the “real world,” a decision was made 

to abandon stack dilution sampling in favor of plume measurements for all future on-road 

experiments. 

In order to design a suitable capture/collection system for plume sampling purposes, a significant 

effort was conducted consisting of a combination of computational fluid dynamics modeling 

(CFD), tracer gas analyses, and flow visualization experiments using smoke, colored streamers, 

and oil stain tests (Kinsey et al., 2006a). This effort was conducted in collaboration with an 

expert in vehicle aerodynamics at the Kenworth Truck Company in Kirkland, WA. The research 

resulted in a special probe and associated flow tunnel system that was installed in the rear of the 

Great Dane trailer. The probe position was selected from the results of the tracer gas 

measurements to incorporate a significant portion of the exhaust plume under varying wind 

conditions. 

In addition, due to the overall age and condition of the original DD60, a new 2000 model year 

replacement engine was purchased for the Kenworth tractor. Before its installation, however, a 

series of tests were performed in the engine test cell at West Virginia University (WVU) using a 

secondary dilution tunnel constructed for this purpose (Kinsey et al., 2006b). The objective of 

these experiments was to determine the “baseline” emissions from the new engine and, more 

importantly, to compare the data obtained by various aerosol analyzers, samplers, and sampling 

media and to assess their usefulness in future on-road plume sampling. The WVU experiments 

were conducted in two phases with the first phase (June 2001) devoted to evaluation of the new 

DD60 under different operating conditions and the second phase (January 2003) to the 

assessment of alternate analyzer operating protocols and sampling media using a smaller 

Navistar engine operated at steady state. The results of the WVU testing were used in the 

selection of the various samplers and analyzers described in Section 4. 

1-1 
This report contains a description of the Diesel Emissions Aerosol Laboratory (DEAL), followed 

by the procedures for and the results from conducting a series of on-road experiments in the fall 

of 2004 using a low-sulfur base fuel meeting 2005 specifications and a 20% mixture of soyderived biodiesel and base (B20) fuels. These experiments were conducted on a stretch of US-70 

at near zero grade in New Bern, North Carolina and on a stretch of I-77 at ~4% grade that 

crosses the North Carolina-Virginia border around Fancy Gap, Virginia. Partial funding for this 

project was provided by the U.S. Department of Energy’s (DOE’s) National Renewable Energy 

Laboratory in Golden, CO, through Interagency agreement No. DE-A104-2001AL67139. 

1.2 Objectives 

The objectives of the on-road experiments were to 

•	 Generate the data necessary to develop predictive emission factors that relate PM emissions 

to key vehicle operating parameters, 

•	 Develop chemical source profiles of the PM emissions from the APPCD research vehicle 

under “real world” conditions focusing on the effects of atmospheric dilution on gas-toparticle conversion, 

•	 Compare the emissions generated by the two fuel types, and 

•	 Begin development of a database from which a suitable dilution sampling methodology can 

be developed for a chassis dynamometer system that emulates the characteristics of the “real 

world” plume produced by heavy-duty diesel trucks. 

1.3 Report Organization 

This document reports the results of the on-road testing conducted during the experimental 

program. Sections 1 and 2 describe the DEAL and its associated instrumentation. Section 4 

provides the field test procedures, Section 5 provides the post-test laboratory analyses, and 

Section 6 provides the data analysis procedures used to produce the experimental results. 

Sections 7 through 9 present the test results for vehicle operation, gaseous emissions, and PM2.5 emissions, respectively. Quality Assurance and Quality Control is described in Section 10, 

and Section 11 compares the results to historical data. Finally, the research findings are provided 

in Section 12 and the references in Section 13. Due to the generally low reliability of the black 

carbon and particle surface polycyclic aromatic hydrocarbon instruments as well as the small 

time-integrated sample set collected, these data are provided in Appendix A. Also appended are 

the fuel analyses and instrument calibrations for the study. 

1-2 
 

Chapter 2 

Diesel Emissions Aerosol Laboratory (DEAL) 

2.1 General Description 

The DEAL consists of a Kenworth T-800 diesel-powered tractor and a 14-m (45-ft) Great Dane 

trailer shown in the photograph in Figure 2-1. The general specifications of the DEAL are 

outlined in Table 2-1. A unique capability of the DEAL is the ability to capture a sample from a 

target source using a plume sampling system while simultaneously measuring a combination of 

the ambient plus vehicular background using a separate system collecting a sample from the 

tractor-trailer gap (see Kinsey et al., 2006a) . In addition to capturing air samples from a target 

source and background, the DEAL monitors other vehicle system parameters such as drive shaft 

torque, exhaust flow, engine RPM, and acceleration. 

Figure 2-2 shows the DEAL layout and the locations of the two sampling systems. The sample 

measurements for the air pollutants and tracer gases measured in the study are summarized in 

Table 2-2. A detailed description of the construction and operation of the DEAL and the various 

instruments may be found in the Mobile Diesel Laboratory Fine PM Emissions Support: SteadyState Experiments, Quality Assurance Project Plan (QAPP), U.S. EPA, Research Triangle Park, 

NC, January 2003, which is included here by reference (U.S. EPA, 2004). 

Electric power is supplied to the trailer through two panel boxes from which individual circuits 

are run to various locations inside the trailer to support the power requirements of all the 

instruments, pumps, blowers, and other equipment. The panel boxes can receive power from a 

conventional power source or from two 12 kW diesel-powered generators mounted to the 

underside of the trailer. The exhaust from the generators are ducted into the flow field beneath 

the trailer so as not to contaminate the exhaust plume from the main propulsion engine. When 

the DEAL is in its staging configuration, it can accept external (i.e., utility) power and additional 

calibration gases. All instruments used are supplied conditioned power via uninterruptible power 

supplies (UPS). Pumps and other equipment that do not contain delicate electronics do not 

receive conditioned power. 

The facility includes removable weights (large blocks of concrete), which simulate the effects of 

truck payload on emissions. The weight is removable in 12 discrete increments, limited only by 

the necessity to distribute the load evenly within the cargo area. The presence or absence of 

weight, which is undetectable by the Data Acquisition System (DAS), is recorded in the project 

notebook. The gross vehicle weight of both the loaded and unloaded vehicle is determined prior 

to a sampling campaign by running the vehicle across a certified scale. Section 3.2.1 discusses 

further the procedure for transferring the weights, the actual number of weights used and the 

loaded testing weight for the campaign. 

2-1 
Table 2-1. General Specifications for the DEAL 

Vehicle Parameter Specification 

SAE Vehicle Classification 3-S2 

Gross Vehicle Weight (GVW) classification 8 

Service classification D 

Gross train weight or gross vehicle weight 36,280 kg (80,000 lbs) 

Tractor wheelbase 6.1 m (20 ft) 

Length of trailer 14 m (45 ft) 

Tire size/type Michelin 11R24.5 radial 

Engine 2000 Detroit Diesel Series 60 

Engine displacement 12.7 liters 

Engine power output 373 kW (500 hp) @ 2100 rpm 

Engine emission limit (measured at WVU)a 0.13 g/kWΧhr (0.1 g/bhpΧhr) 

Figure 2-1. Kenworth T-800 Tractor and Great Dane Trailer, Locations of the Plume and 

Background Sampling Probes 

a. See Kinsey et al. 2006a. 

2-2 
Figure 2-2. DEAL Trailer Layout 

2-3 
Table 2-2. Summary of DEAL Vehicle System and Environmental Measurements 

Experimental Parameter Sampling Location a	 Measurement Technique Type of Sample Collection Instrument(s) / Sampling Media 

Total PM-2.5 Mass Concentration 

Background/Splitter 1 

Background/Splitter 1 Plume/Splitter 1 Plume/Splitter 1 Plume/Splitter 1 

Tapered element 

microbalance

Gravimetric analysis 

Microbalance 

Microbalance 

Gravimetric analysis 

	 

Continuous 

Time-integrated 

Continuous 

Continuous 

Time-integrated 

Thermo Electron Series 

1400a Tapered Element Oscillating Microbalance (1400 TEOM) 

47-mm Teflon filter in 

stainless steel holder equipped with double quartz back-up filters for collection of gas-phase “blow-off” c (Teflon Filter) 

Thermo Electron Series 

1105a Tapered Element Oscillating Microbalance (1105a TEOM) 

SEMTECH Model RPM100 Quartz Crystal 

Microbalance, (QCM) 

47-mm Teflon filter in 

FTP holder equipped with double quartz back-up filters for collection of gasphase “blow-off” (FTP 1) c 

Total PM-2.5 Number 

d Concentration Background/Splitter 2 

Plume/Splitter 2 

Condensation nuclei 

counter 

Condensation nuclei 

counter 

Condensation nuclei counter Condensation nuclei counter 

TSI Model 3025a Condensation Particle Counter (CPC) TSI Model 3025a Condensation Particle Counter (CPC) 

2-4 
Experimental Parameter Sampling Location a Measurement Technique Type of Sample Collection Instrument(s) / Sampling Media 

Particle Size Distribution Background/Splitter 2 Low-pressure cascade 

impactor (aerodynamic diameter) 

Continuous/Timeintegrated e Dekati Electrical Low Pressure Impactor (ELPI) 

Background/Splitter 2 Electrical mobility 

classifier/condensation nuclei counter (electrical mobility diameter) 

Continuous TSI Model 3934 

Scanning Mobility Particle Sizer (SMPS): Model 3071A Classifier and Model 3010 Condensation Particle Counter (CPC) (3934 SMPS 

 Plume/Splitter 2 Low-pressure cascade 

impactor (aerodynamic diameter) 

Continuous/time integrated e 

Dekati Electrical Low Pressure Impactor (ELPI) 

 Plume/Splitter 2 Electrical mobility 

classifier/ condensation nuclei counter (electrical mobility diameter) 

Continuous TSI Model 3936 (long) 

Scanning Mobility Particle Sizer (SMPS): Model 3080 Classifier, Model 3025a CPC, and Model 3081 DMA f (3936 

SMPS) TSI Model 3936 (nano) Scanning Mobility Particle Sizer (SMPS): Model 3080 Classifier, Model 3025a CPC, and Model 3085 DMA (Nano SMPS) 

2-5 
Experimental Parameter Sampling Location a Measurement Technique Type of Sample Collection Instrument(s) / Sampling Media 

Elemental/Organic Carbon (EC/OC) 

Background/Splitter 1 Thermo-optical analysis 

(NIOSH Method 5040) 

Time-integrated Pre-fired 47-mm quartz 

filter with double quartz back-up filter (Quartz Filter) 

Plume/Splitter 1 Thermo-optical analysis 

(NIOSH Method 5040) 

Time-integrated Pre-fired 47-mm quartz 

filter with double quartz back-up filter (FTP 2) 

 Plume/Splitter 3 Optical attenuation/UV 

absorption (“black” and “blue” carbon) g 

Continuous Magee (Andersen) Model 

AE-2 Aethalometer (Aethalometer) 

 Stack Thermo-optical analysis 

(NIOSH Method 5040) 

Time-integrated Heated pre-fired 142 mm 

quartz filter in special holder 

Semivolatile Organic compounds h Background/Splitter 1 (speciated tests) GC/MS i Time-integrated Pre-fired 47-mm quartz filter equipped with (4) 

PUF plugs for collection of gas-phase “blow-off” c, j 

(quartz filter) 

 Plume/Splitter 

1(speciated tests) 

GC/MS i Time-integrated Pre-fired 47-mm quartz 

filter equipped with (4) PUF plugs for collection of gas-phase “blow-off” c, j 

(FTP 2) 

Plume/Splitter 3 UV analyzer (surface 

PAHs) k 

Continuous EcoChem Model PAS 

2000 (PAS 2000) 

PM volatile organic compounds 

Plume/ Splitter 3 Gravimetric/thermooptical analysis Time-integrated Dekati Model EKA-111 thermal denuder with 

parallel Teflon and double pre-fired quartz filters (Thermal Denuder) 

2-6 
Experimental Parameter Sampling Location a Measurement Technique Type of Sample Collection Instrument(s) / Sampling Media 

Tracer Gas l Background Infrared absorption Periodic continuous INNOVA Air Tech 

Instruments A/S Model 1314A Photoacoustic Analyzer (Gas Tracer) 

Plume Infrared absorption Periodic continuous INNOVA Air Tech 

Instruments A/S Model 1314A Photoacoustic Analyzer (Gas Tracer) 

Carbon Monoxide (CO) Background Nondispersive infrared Integrated bag m California Analytical 

Model 300 

 Stack Nondispersive infrared Continuous California Analytical 

Model 300 (low Concentration) 

Carbon Dioxide (CO2) Background Nondispersive infrared Integrated bag m California Analytical 

Model 300 

 Stack Nondispersive infrared Continuous California Analytical 

Model 300 

Oxygen (O2) Background Magneto Integrated bag m Horiba Model MPA 

 Stack Magneto Continuous Horiba Model MPA 

Nitrogen Oxides (NOX) Background Chemiluminescence Integrated bag m California Analytical 

Model 400 HCLD 

 Stack Chemiluminescence Continuous California Analytical 

Model 400 HCLD 

Total Hydrocarbons (THC) Background Heated flame ionization Integrated bag m Horiba Model FIA 

Stack Heated flame ionization Continuous Horiba Model FIA 

Engine Speed Tractor engine Rotation sensor Continuous DDEC60 n 

Vehicle Speed Tractor chassis Optical fifth wheel Continuous Datron DLS-1 Speed 

Sensor 

Fuel Flow Tractor engine Injector position Continuous DDEC60 Shaft Torque Engine Drive Shaft Strain gages Continuous ATI 2000 Series 

2-7 
Experimental Parameter Sampling Location a Measurement Technique Type of Sample Collection Instrument(s) / Sampling Media 

Grade and Acceleration o Trailer Chassis Gyroscope Continuous Crossbow Model 

VG600CA 

Engine Temperature Various Thermocouple Continuous Omega Engineering Exhaust Flow Rate Exhaust Stack Multi-orifice pitot tube 

with differential pressure cell 

Continuous Dieterich Annubar DCR 

16S with Validyne P55D Pressure Transmitter 

Exhaust Gas Temperature Exhaust Stack Thermocouple Continuous Omega Engineering 

a.	 Speciated refers to those tests for which PM chemical composition was determined. 

b.	 N/A = not available. 

c. FTP = Federal Test Procedure per 40 CFR, Part 86. “Blow off” are gas-phase semi-volatile species that have been released from the parti

primary filter by the airflow passing through the medium. During the “speciated” runs, the double quartz back-up filters are replaced by a sefoam (PUF) plugs. 

d.	 These are redundant measurements and were not used in the data analysis. 

e.	 Aluminum foil substrates from ELPI were also analyzed gravimetrically to determine particle size distribution by mass, but not included in t

f. DMA (differential mobility analyzer) is part of SMPS and classifies aerosols by electrical mobility. 

g.	 The Aethalometer measures “black” carbon, which approximates elemental carbon content, and “blue” carbon, which is similar to organic 

h.	 Used during “speciated” runs only, except for EcoChem 2000. 

I. GC/MS = gas chromatography/mass spectroscopy. 

j.	 This sampling train replaces the usual quartz/double quartz filter system used in the other tests conducted. The primary quartz filter will als

by NIOSH 5040. 

k. PAHs = polycyclic aromatic hydrocarbons. 

l.	 Tracer gas = 1,1,1,2,3,3,3 heptafluoropropane. Background determined pre-test from engine exhaust gas sampling. 

m. Post-test analysis of time integrated Tedlar bag sample collected over the entire test period. 

n.	 Detroit Diesel Series 60 engine computer. Signals will also be obtained for fuel flow and percent rate torque. 

o.	 This instrument was operated but did not provide any useful data. Acceleration was calculated from vehicle speed, and grade was determ

microbarometer. 

2-8 
 

 

2.2 Plume Sample Extraction System 

Two fine PM monitoring systems, each consisting of a similar array of particle instruments, are 

installed in the trailer shown in Figure 2-1. One of the two arrays of particle instruments receives a 

representative sample through an isokinetic probe positioned in the tractor exhaust plume. 

As stated previously, the probe design and location were selected from the tracer gas 

measurements. The plume sample flows through the isokinetic probe into a PM-2.5 “cut-point” 

(i.e., 50% collection efficiency for unit density spheres ≤ 2.5 μm) virtual impactor, and then into 

a 8.8 m, 15.2 cm diameter stainless steel sampling tunnel. (Note: the probe is maintained at 

isoknetic sampling conditions by manually adjusting the open area to the mean wind velocity 

based on vehicle speed.) Particle losses to the tunnel walls were not characterized but are 

expected to be minimal based on well-established aerosol theory. A photograph of the probe and 

the virtual impactor is shown in Figure 2-3. The right picture in Figure 2-3 was taken from the 

rear of the trailer with a view toward the front so that the background sampling bench is also 

visible in the far back between the gas cylinder storage racks. 

Figure 2-3. Plume Sampling System Probe (left) and Virtual Impactor Connected to the 

Sampling Tunnel (right) 

Inside the trailer, the tunnel is supported from the trailer floor by columns integrated into the 

plume instrument rack. Figure 2-4 shows photographs of the plume tunnel and plume bench. The 

pictures were taken from the side door looking toward the rear of the trailer. The left picture was 

taken during initial fabrication and before installing the continuous emission monitor (CEM) 

bench and any particle instruments or other sensors. Also visible is the plume instrument bench 

below the tunnel. The right picture was taken after installation of the instruments in the plume 

sampling bench. 

Positioned through ports installed in the tunnel are “buttonhook” probes traditionally used for 

stack sampling which are staggered in height. The probes extract a sample of the gas inside the 

tunnel, which is directed to the instruments through four-way splitters and either stainless steel or 

conductive silicon tubing (Vanguard Products). Figure 2-5 shows a schematic of the plume 

sampling system with complete organic speciation and Figure 2-6 shows the plume sampling 

system in the non-speciated configuration. The only difference between the speciated and the 

non-speciated configurations is that, in the speciated configuration, polyurethane foam (PUF) 

2-9 
plugs are substituted for the quartz back-up filters downstream of FTP 2 on splitter 1. For more 

information about how the two configurations change the analytical plan refer to Section 2.8. As 

can be seen in Figures 2-5 and 2-6, all instruments sample the same gas stream with no crosscontamination. 

Figure 2-4. Photographs of the DEAL during Fabrication Showing Sampling Tunnel and 

Plume Bench (left), and with Instruments Installed (right) 

Figure 2-5. Flow Schematic of Plume Sampling System for Speciated Runs 

2-10 
Figure 2-6. Flow Schematic of Plume Sampling System for Non-Speciated Runs 

2.3 Background Sample Extraction System 

The background PM instrument array receives a sample external to the tractor exhaust plume, 

which takes into account both ambient background and the emissions from other vehicles (see 

Kinsey et al., 2006a). Pictures of the background sampling bench are shown in Figure 2-7. The 

background sample flows from the probe into two parallel precollectors to remove particles with 

an aerodynamic diameter greater than PM-2.5 and then flows into a tunnel from which the 

instruments draw their sample through a series of staggered probes and splitters similar to the 

plume sampling tunnel. Figure 2-8 shows a schematic of the background sampling system for 

runs with complete organic speciation, and Figure 2-9 shows the background sampling system in 

the non-speciated configuration. The only difference between the speciated and the nonspeciated configurations is that, in the speciated configuration, PUF plugs are substituted for the 

quartz back-up filters downstream of the quartz filter on splitter 1. For more information about 

how the two different configurations change the analytical plan refer to Section 2.8. 

Vibration isolators are installed on all particle instruments in both the plume and background 

sampling systems. The bottoms of the isolators are mounted on aluminum channels cut to fit the 

depth of the instrument racks, which are fabricated of 10-cm angle iron. These channels with the 

instruments mounted on top are securely clamped to the instrument rack and the rack base is 

bolted to the trailer floor. 

2-11 
Figure 2-7. Background Bench Before the Background Tunnel and Instruments were 

Installed (left), and after Installation (right) 

Figure 2-8. Flow Schematic of the Background Sampling System for Speciated Tests 

2-12 
Figure 2-9. Flow Schematic for the Background Sampling System for Non-Speciated Tests 

2.4 Vehicle Operating Parameters 

Finally, other instrumentation is also installed in the DEAL to measure key vehicle operating 

parameters, including parameters that must be determined in order to report emissions in 

appropriate units and ancillary information that may effect emissions. Monitored vehicle 

parameters are shown in the schematic illustrated in Figure 2-10. 

The data acquisition system (DAS) receives input from sensors that indicate various vehicle 

operating parameters in real time. Except for the gyroscope and DDEC60 (Detroit Diesel Engine 

Computer, Series 60), all signals from the vehicle sensors are run through an analog to digital 

converter and further converted by DASYLAB software to the applicable units of measure. 

The DDEC60 engine speed sensor consists of a frequency-to-voltage converter connected to the 

truck's tachometer input. Other signals logged from the DDEC60 include fuel consumption rate 

and percent of rated engine torque. 

An optical “5th wheel” measurement device (Datron DLS1), attached to the underside of the 

tractor, measures vehicle speed. This device uses a combination of lenses, a rotating optical 

grating, photo diodes, and sophisticated analog and digital signal processing to measure the 

speed of “objects” moving across its illuminated field of vision. For this application, the sensor 

points straight down toward the pavement and uses irregularities in the roadway surface to detect 

movement. The raw output consists of a voltage proportional to velocity. A picture 

representative of this sensor is shown on the bottom of Figure 2-11. 

2-13 
Figure 2-10. Schematic of the DEAL Showing the Locations of Sensors that Monitor 

Various Vehicle Parameters 

Figure 2-11. The ATI Torque Meter Sensor (left) and the Datron Optical Speed Sensor 

(right) 

An Advanced Telemetrics International (ATI) Series 2000 torque meter measures drive shaft 

torque. The torque meter uses a Wheatstone bridge made up of four resistive strain gauges to 

measure the elastic deformation of the drive shaft as torque is applied by the drive train. The 

bridge is connected to an electronics module that occupies half of a balanced split-ring housing 

clamped around the drive shaft. The electronics module filters and amplifies the signal, which is 

transmitted away from the shaft by a frequency modulated transmitter housed in the other half of 

the ring. A small antenna mounted nearby picks up the signal for the receiving unit mounted in 

the truck cab. Pictures representative of the balanced ring and the receiver are shown in Figure 2­

11. 

2-14 
Table 2-3. Vehicle Parameters and Sensors 

Experimental 

Parameter 

Sampling 

Location 

Measurement 

Technique Instrument(s) Type of Data  Collected 

Engine speed Engine Rotation Sensor DDEC 60 Digital 

Vehicle speed Under Tractor Optical Sensor Datron DLS1 Speed Sensor 0-5 volt analog signal 

Fuel flow Engine Engine Computer DDEC 60 Digital 

Shaft torque Drive Shaft Strain Gage ATI Strain Gage 0-5 volt analog signal 

Exhaust flow rate Stack Differential Pressure Dieterich Standard Annubar + Analog Transducer 

Validyne Pressure Transducer Signal 

Grade/ Accelerationa Trailer 3-axis gyroscope Crossbow Model VG600CA Digital 

Exhaust flow is measured by an averaging pitot tube (Annubar), which generates a differential 

pressure (dP) signal proportional to exhaust gas velocity. A transducer converts the dP to a 

voltage, which is logged by the DAS. Another dP transducer, along with a thermocouple, 

provides the static pressure and temperature data necessary to calculate exhaust gas density. 

Finally, the Crossbow VG600CA measures vehicle grade and acceleration using three rate 

sensors oriented in the x, y, and z plane. The sensors consist of a vibrating ceramic plate that 

utilizes the Coriolis Effect to output the angular rate of change and acceleration. A picture 

representing the Crossbow gyroscope is shown in Figure 2-12. Table 2-3 lists the various sensors 

and signal outputs used for the monitoring of vehicle operation. 

Figure 2-12. The Crossbow Gyroscope VG600CA 

a. Paroscientific Model 745-16B microbarometer (altimeter) was actually used to determine grade as discussed in Section 

4.2.2. 

2.5 Data Acquisition System 

The DAS used in the DEAL consists of a multicomputer network containing five computers, a 

monitor, a keyboard, and a mouse installed in the CEM rack plus a sixth computer, two flat 

screen monitors, a keyboard, and a mouse installed in the tractor sleeper compartment. The 

computers in the trailer are networked via a wireless router to the computer in the sleeper to 

allow file access and transfers. A keyboard-video-monitor switch also allows the operator in the 

2-15 
sleeper to access and run instrument operating software on the computers in the trailer. The 

network is time synchronized by a clock card in the master computer, which is set daily to an 

atomic clock traceable to a National Institute of Standards and Technology standard. All 

instrument measurements are recorded on the DAS as digital or analog-to-digital data streams 

and stored in individual files, which are archived daily on compact disks. All calculated 

quantities are determined post test from raw data as described in Section 6. 

2-16 
Chapter 3 

DEAL Instrumentation 

3.1 Continuous Exhaust Gas Monitoring 

The continuous emissions monitors (CEM) system design and operation generally follows the 

requirements of 40 CFR (Code of Federal Regulations) 86 Subpart D §86.309-79 and is shown in 

Figure 3-1. A heated pump inside the trailer is used to extract the exhaust gas sample from the 

stack under negative pressure through HL1 (heated sample line 1) to the front of the trailer where 

it enters the trailer through the bulkhead. At this point, a second heated line, HL2, inside the 

trailer carries the sample flow to an oven that contains a filter and heated pump. The sample exits 

the heated pump into a third heated line and flows to a cross that is located inside a heated box 

(“hot box”) mounted in the CEM bench. A backpressure regulator inside the hot box is used to 

regulate the sample gas pressure in all lines downstream of the cross. One exit of the cross leads 

to a second filter inside the hot box and then flows through a tee into a Nafion dryer and finally 

to a manifold that supplies sample flow to the oxygen (O2), carbon dioxide (CO2), carbon 

monoxide (CO) (low range) and CO (high range) analyzers. The other exit from the cross 

connects to a fourth heated line that allows a portion of the sample flow to be extracted before 

further filtering and water removal and flows to the sample inlet of the total hydrocarbon (THC) 

analyzer. Downstream of the second filter, a portion of the flow is extracted from the branch of 

the tee before water removal and flows through a fifth heated line and into the nitrogen oxides 

(NOX) analyzer. All continuous gas analyzers are mounted in the trailer's vibration and shockresistant instrument rack. 

The calibration gas delivery system consists of several toggle valves, two manifolds and a 

pressure regulator. The cylinder regulators are connected directly to the toggle valves in the span 

gas (SG) manifold. The calibration gas flows from the SG manifold into a pressure-reducing 

regulator and into the calibration manifold. Through the calibration manifold, any one of the gas 

analyzers can be fed any of the calibration gases directly for instrument calibration, or the gas 

can be fed to a three-way valve at the exhaust stack so a bias check can be performed on the 

entire system. 

The analog signal outputs from the CEMs are connected to the DAS through an analog-to-digital 

converter. In addition to providing an instantaneous display of analyzer response, the DAS 

compiles, averages, and saves analyzer data at a user-set frequency. Descriptions of the analyzers 

used to measure the gaseous emissions from the heavy-duty diesel engine are given below. 

3-1 
Figure 3-1. Schematic of the Continuous Emissions Monitoring (CEM) System in the DEAL. 

3-2 
3.1.1 O2 Analyzer 

In the Horiba Instruments Model MPA 220 (Magneto-Pneumatic) O2 analyzer, an uneven 

magnetic field is applied to the sample gas. If oxygen, a paramagnetic gas, is present, the gas is 

drawn to the strongest part of the magnetic field, raising the pressure at that point. A gas that is 

not paramagnetic (such as nitrogen) can be used to take the pressure rise out of the magnetic 

field. Two electromagnets are excited alternately and the pressure changes are converted into 

electrical signals by a condenser microphone. Output is linear in proportion to the oxygen 

concentration. The O2 concentration range measured by this instrument is 0–25 percent. 

3.1.2 NOX Analyzer 

The California Analytical Instruments, Inc., (CAI) HCLD 400 heated chemiluminescence 

analyzer automatically and continuously determines the concentration of nitric oxide (NO) or 

NOX. Operated under the NOX mode, the analyzer directs the sample through a reaction chamber 

where nitrogen dioxide (NO2) completely dissociates into NO. The NO is completely converted 

to NO2 via gas phase oxidation by the molecular ozone generated by the analyzer. In this 

reaction, the NO2 molecules are energized to an electronically excited state. Immediate reversion 

of the excited NO2 to the non-excited ground state takes place and is accompanied by the 

emission of photons. The photons impinge on a photomultiplier detector and generate a low-level 

DC current. The DC current is amplified to drive a front panel meter and data recorder. The NOX 

concentration seen by the instrument includes the contributions of both the NO in the sample and 

the NO resulting from the dissociation of the NO2 in the sample. The NOX concentration range 

measured by this instrument is 0–3000 ppm. 

3.1.3 CO/CO2 Analyzer 

A CAI Model 300 Non Dispersive Infrared (NDIR) analyzer has three channels. Two were used 

in these experiments to continuously measure concentrations of CO (0–2000 ppm) and CO2 (0– 

20%) in the sample gas. 

The CAI Model 300 analyzer is based on the infrared absorption characteristics of gases. A 

single infrared light beam is modulated by a chopper system and passed through a sample cell of 

predetermined length containing the gas sample to be analyzed. As the beam passes through the 

cell, the sample gas absorbs some of its energy. The attenuated beam (transmittance) emerges 

from the cell and is introduced to the front chamber of a two-chamber infrared microflow 

detector. The detector is filled with the gas component of interest and, consequently, the beam 

experiences further energy absorption. This absorption process increases the pressure in both 

chambers. The differential pressure between the front and rear chambers of the detector causes a 

slight gas flow between the two chambers. This flow is detected by a mass-flow sensor and is 

converted into an alternate current (AC) signal. The AC signal is amplified and rectified into a 

direct current (DC) voltage signal and ultimately supplied to the output terminal and digital panel 

meter. The electrical output signal is directly proportional to the concentration of the sample gas. 

Note that the Quality Assurance Project Plan (QAPP) states that the Horiba Model AIA 210 

NDIR CO2 Analyzer would be used to measure the CO2 concentrations, but it was removed from 

the CEM bench to allow room for an additional computer before starting the testing campaign. 

3-3 
3.1.4 THC Analyzer 

The Horiba Instruments Model FIA-236 incorporates a flame ionization detector (FID), which 

adds hydrogen to the column effluent and passes the mixture through a jet where it is mixed with 

entrained air and burned. The ionized gas (charged particles and electrons produced during 

combustion) passes through a cylindrical electrode. A voltage applied across the jet and 

cylindrical electrode sets up a current in the ionized particles. An electrometer monitors this 

current to derive a measure of the component concentration. The THC concentration range 

measured by this instrument is 0–100 ppm assuming a one carbon gas is used. The upper range 

becomes a multiple of the number of carbon atoms in the gas. For example, this research used 

propane which resulted in an upper range of 300 ppm. 

3.2 Tracer Gas Analyzer 

Since PM samples are being extracted from the plume and not directly from the DEAL tractor 

exhaust, a method is needed to convert the average plume concentration to an equivalent stack 

concentration and, using the exhaust gas volumetric flow rate, to the appropriate PM-2.5 

emission rate. To accomplish this, a hydrofluorocarbon tracer gas (1,1,1,2,3,3- 

heptafluoropropane, or FM-200) was injected into the exhaust stack and measured in the plume 

sampling system (Figures 2-5 and 2-6) using an INNOVA Model 1314 photoacoustic gas 

analyzer. FM-200 was metered into the exhaust gas stream from a cylinder using a regulator and 

calibrated precision rotameter. Using the average flow of tracer gas and measured plume 

concentration, both the dilution ratio and equivalent PM-2.5 emission rate were calculated as 

outlined in Section 6.4. 

In the INNOVA Model 1314, the pump draws air from the sampling point through two air-filters 

to flush out the "old" sample in the measurement system and replaces it with a "new" air sample. 

The "new" sample is hermetically sealed in the analysis cell by closing the inlet and outlet 

valves. Light from an infrared source is reflected off a mirror and passes first through a 

mechanical chopper, which pulsates it, and then through one of the optical filters in the filter 

carousel. The light transmitted by the optical filter is selectively absorbed by the gas being 

monitored, causing the temperature of the gas to increase. Because the light is pulsating, the gas 

temperature increases and decreases, causing a corresponding increase and decrease in the 

pressure of the gas (an acoustic signal) in the closed cell. Two microphones mounted in the cell 

wall measure this acoustic signal, which is directly proportional to the concentration of the 

monitored gas present in the cell. 

The calibration of this instrument was verified before deployment with a calibration check using 

certified calibration gas. Checks spanning the instrument range were performed during field 

testing using the calibration gases specified in Section 10.1.2. Zero checks were also conducted 

in the field per miscellaneous operating procedure (MOP) 1419 (U.S.EPA, 2004). 

3.3 Continuous PM-2.5 Monitoring 

Reference the schematics of the plume and background sampling system in Figures 2-5, 2-6, 2-8, 

and 2-9 for the locations of the instruments described in the following sections. 

3-4 
3.3.1 PM Mass Measurements 

Three instruments located in the DEAL are designed to measure the mass of the particulate 

matter directly. Two of them receive a sample from the plume sampling system and one receives 

its sample from the background sampling system. 

3.3.1.1 Quartz Crystal Microbalance (QCM) 

An older instrument, which has recently been re-introduced to automotive engine testing, is the 

QCM. The harmonic oscillator principle used in the QCM is similar to the Tapered Element 

Oscillating Microbalance (see TEOM below) except that the collected PM is actually deposited 

onto the crystal element using an electrostatic precipitator (ESP). Due to its high frequency 

operation, the QCM exhibits far less instrumental noise than the TEOM but also can overload in 

a relatively short period. To offset this problem, a dilutor is supplied with the instrument to 

extend the useful life of the crystal element. 

3.3.1.2 Tapered Element Oscillating Microbalance (TEOM) 

The TEOM Series 1105 Diesel Particulate Monitor and Series 1400a Ambient Particulate 

Monitor incorporates a patented inertial balance that directly measures the mass collected on an 

exchangeable filter cartridge. It monitors the change in the natural oscillating frequency of a 

tapered element as additional mass collects on the filter. The sample flow passes through the 

filter, where PM collects and then continues through the hollow tapered element on its way to a 

dynamic flow control system and vacuum pump. 

The TEOM mass transducer does not normally require recalibration because it is specially 

designed and constructed from nonfatiguing materials. Its mass calibration may be verified 

however, using an optional mass calibration verification kit that contains a filter of known mass. 

A flow controller maintains the sample flow rate input by the user. TEOM Series 1105 interfaces 

with the multicomputer via an I/O (input/output) card, cable and software supplied by the 

manufacturer. The TEOM Series 1400a monitor uses the same technology as the 1105a but 

incorporates an internal microprocessor and data storage system. 

3.3.2 PM Count Measurements 

The DEAL contains a variety of particle counters that cover the range of particle sizes from 2 nm 

to 10 μm. 

3.3.2.1 DEKATI Electrical Low Pressure Impactor (ELPI) 

The DEKATI Electrical Low Pressure Impactor (ELPI) is a real-time particle size spectrometer 

designed for real time monitoring of aerosol particle size distribution. The ELPI measures 

airborne particle sizes in the range of 0.03 to 10 μm with 12 channels. The principle is based on 

charging, inertial classification, and electrical detection of the aerosol particles. The instrument 

consists primarily of a corona charger, low-pressure cascade impactor and multi-channel 

electrometer. It communicates with the DAS via a serial port using the ELPIVI software 

provided with the instrument. The software is used for setup and configuration and to view data. 

3-5 
3.3.2.2	 TSI Scanning Mobility Particle Sizer (SMPS) 

The TSI Model 3934 SMPS is a system that measures the size distribution of aerosols in the size 

range from 10 to 1000 nm. The particles are classified by their electrical mobility with the Model 

3071A Electrostatic Classifier, and their concentration is measured with the Model 3010 

Condensation Particle Counter (CPC). The system communicates with the DAS via a serial port. 

The Aerosol Instrument Manager (AIM) software is used for setup and configuration and to view 

data. 

The TSI Model 3936 Long SMPS is a system that measures the size distribution of aerosols in 

the size range from ~9 nm to 1000 nm. The particles are classified by their electrical mobility 

with the Model 3080 Electrostatic Classifier with a Model 3081 Long Differential Mobility 

Analyzer (DMA), and their concentration is then measured with the Model 3025A CPC. The 

Long DMA is the traditional length DMA used in the older Model 3071 Electrostatic Classifier. 

The system communicates with the multicomputer via a serial port. The AIM software package 

is used for setup and configuration and to view data. 

The TSI Model 3936 Nano SMPS is a system that measures the size distribution of aerosols in 

the size range from 2 nm to 150 nm. The particles are classified with the Model 3080 

Electrostatic Classifier together with a Model 3085 Nano DMA, and their concentration is then 

measured with the Model 3025A CPC. The Nano DMA is optimized for the size range below 20 

nm. The system communicates to the multicomputer via a serial port. The AIM software package 

is used for setup and configuration and to view data. 

3.3.2.3	 Condensation Particle Counter (CPC) 

The Model 3025A CPC detects and counts particles larger than 3 nm in diameter by an optical 

detector after a supersaturated vapor (n-butyl alcohol) condenses onto the particles, causing them 

to grow into larger droplets. The range of particle concentration extends from less than 0.01 

particle/cm3 to 9.99 x 104 particle/cm3. The system communicates with the multi-computer via a 

serial port. The CPC LOG software developed by APPCD is used to log the data. 

3.3.3	 PM Black Carbon Magee Aethalometer 

The Magee (Andersen) Model AE-2 Aethalometer measures real-time “black” (or elemental) 

carbon. It is designed for fully automatic and unattended operation. The sample is collected as a 

spot on a roll of quartz fiber tape. An optical method is then used to measure the attenuation of a 

beam of light transmitted through the sample. The optical attenuation is linearly proportional to 

the amount of black carbon collected on the quartz fiber tape. The aethalometer communicates 

with the DAS via an analog output signal with a voltage range of 0–5 volts. Operation of the 

instrument is checked using an optical test strip. 

3.3.4	 PM Polycyclic Aromatic Hydrocarbons—Photoelectric Aerosol Sensor (PAS) 

2000 

PAS 2000 (Photoelectric Aerosol Sensor) works on the principle of photoionization of particle 

surface-bound Polycyclic Aromatic Hydrocarbons (PAHs). Using an excimer lamp the aerosol 

flow is exposed to UV radiation. The excimer lamp offers a high intensity, narrow band source 

3-6 
of UV radiation. The wavelength of the light is chosen such that only the PAH coated aerosols 

are ionized, while gas molecules and noncarbon aerosols remain neutral. The aerosol particles 

that have PAH molecules adsorbed on the surface emit electrons, which are subsequently 

removed when an electric field is applied. The remaining positively charged particles are 

collected on a filter inside an electrometer, where the charge is measured. The resulting electric 

current establishes a signal that is proportional to the concentration of total particle-bound PAHs. 

3.4 Time-Integrated Sampling 

As discussed previously, the on-road study encompassed both “speciated” and “non-speciated” 

tests. The number and types of sampling media used in these tests are different as shown 

previously in Figures 2-5 and 2-6 for the plume sampling tunnel and Figures 2-8 and 2-9 for the 

background sampling system. 

The filter FTP filter holders and Teflon sampling media used in the time-integrated samplers for 

plume sampling (Figures 2-5 and 2-6 of Section 2) comply with 40 CFR, Part 86, § 86.1310­

2007 (January 18, 2001). Commercially available stainless steel, 47-mm filter holders were used 

in the remainder of the measurements. 

For the speciated runs (Figures 2-5 and 2-8), the specific media used in the program were as 

follows: 

•	 FTP Sampler 1: Teflon primary filter for total PM-2.5 mass plus two back-to-back pre-fired 

quartz filters for determination of organic carbon (OC) “blow off”. (“Blow-off” refers to the 

removal of volatiles by the sample gas passing through the collected PM on the filter.) 

•	 FTP Sampler 2: Pre-fired quartz primary filter for elemental/organic carbon (EC/OC) content 

plus a series of four PUF plugs for determination of gas-phase semi-volatile organic 

composition. 

•	 Thermal denuder (TD): Parallel filter holders with one containing a Teflon filter collecting 

non-volatile PM mass and the other incorporating two back-to-back pre-fired quartz filters 

for EC/OC composition. 

•	 ELPI: Aluminum foil substrates plus pre-fired quartz back-up filter for the determination of 

PM mass and semi-volatile organic composition. 

In the case of the non-speciated runs (Figures 2-6 and 2-9), the following media were used for 

the on-road testing: 

•	 FTP Sampler 1: Teflon primary filter for total PM-2.5 mass plus two back-to-back pre-fired 

quartz filters for determination of OC “blow off”. 

•	 FTP Sampler 2: Pre-fired quartz primary filter for EC/OC content plus two back-to-back prefired quartz filters for determination of OC “blow off”. 

3-7 
•	 TD: Parallel filter holders with one containing a Teflon filter representing non-volatile PM 

mass and the other incorporating two back-to-back pre-fired quartz filters for EC/OC 

composition. 

•	 ELPI: Aluminum foil substrates plus Teflon back-up filter for the determination of PM mass. 

Table 3-1 lists all time-integrated samples collected during the on-road truck experiments, the 

media and analytical technique used, and the total number of samples including the blank run. 

Details of the media preparation procedures and individual analyses performed are contained in 

the Quality Assurance Project Plan (QAPP) for the EPA’s Fine Particle Characterization 

Laboratory (FPCL), which is included in this document by reference. (U.S. EPA, 2005) 

3.5 Fuel 

Three different fuels were used in the testing campaign. The first was a locally available pump 

grade fuel, second was a low sulfur fuel (15 ppm) meeting EPA 2005 standards, and third was a 

20% blend of soy-based biodiesel and low sulfur diesel fuels (B20). Both the low sulfur fuel and 

the biodiesel blend were supplied by DOE. Once received, the fuel was stored in a temperature 

controlled fuel storage building to prevent its degradation. When time to begin the testing 

campaign approached, a trailer equipped with a temperature control unit was leased to store the 

fuel at the test site in New Bern, NC. The photograph in Figure 3-2 shows the temperature 

controlled trailer on left side of the photograph as positioned at the New Bern staging area. 

Diesel fuel samples and engine oil samples were collected during the testing campaign and 

analyzed using the methods in Table 3-2. The table lists the parameters that were determined for 

all the fuel and oil analyses, the method used for each analysis, and the minimum volume 

required for each method. The combined parameters in the shaded area constitute the Ultimate 

Analysis for the fuel. Fuel and oil analysis procedures were performed by CORE Laboratories in 

Houston, Texas 

3-8 
 

 

 

 

 

 

 

Table 3-1. Analytical Plan for On-Road Truck Experiments 

Type of Analysis Sampling Media Analytical 

Method 

No. of 

Samples/Test 

No. of 

Testsa 

Total Samples 

Planned Actual 

PM mass 47-mm Teflon filters Gravimetric 5 12 60 28 

(PM-2.5) (unspeciated runs)b 

47-mm Teflon filters Gravimetric 3 5 15 18 

(speciated runs)b 

Al foil ELPI substrates Gravimetric 24 17 408 336c 

Elemental/organic 

carbon (EC/OC) 

47-mm pre-fired quartz filters 

(unspeciated runs)d NIOSH 5040 12 12 144 114 

47-mm pre-fired quartz filters 

(speciated runs)d NIOSH 5040 10 5 50 56 

Semi-volatile 

organicsa 47-mm pre-fired quartz filters 

(included in total above) 

Multi-solvent 

extraction + GC/MSe 4 5 20 32 

PUF plugsf

 Multi-solvent 

extraction + GC/MSe 8 7 56 56 

Al foil ELPI substrates 

(included in total above)g 

Thermal desorption 

+ GC/MSe 24 7 168 0g 

47-mm pre-fired quartz filters Thermal desorption 

+ GC/MSe 2 7 14 14 

Water-soluble ions 47-mm Teflon filters Ion chromatography 2 7 14 12 

Elemental 47-mm Teflon filters X-ray fluorescence 2 7 14 12 

composition spectroscopy 

Total Analyses 963 846 

a. Includes shakedown tests and tunnel blank run. 

b. Includes all Teflon filters for total PM-2.5 mass, ELPI back-ups, and thermal denuder. 

c. Data not used. 

d. Totals include primary quartz filters, back-up quartz filters, and filters for thermal denuder train. 

e. GC/MS = gas chromatography/mass spectroscopy. 

f. Four plugs/test for the background and plume. 

g. Thermal desorption analysis of ELPI samples have not been done. 

3-9 
 

 

 

 

 

Figure 3-2. New Bern Staging Area 

Table 3-2. Fuel and Lube Oil Analysis Methods a 

Fluid Parameter Units (ml) Method 

Fuel Boiling Point Degree Fahrenheit (°F) 100 ASTM D-86 

Flash Point °F 75 ASTM D-93 

Cloud Point °F 50 ASTM D-2500 

Cetane Number 2000 ASTM D-613 

Lubricity Mm @ °60 4 ASTM D-6079 

Water ppm wt ASTM E-203 

C, H2, N2 WT % 10 ASTM D-5291 

N2, Trace ppm wt 10 ASTM D-4629 

Sulfur WT % 5 ASTM D-2622 

Ash WT % 200 ASTM D-482 

O2 (diff) WT % Calculated Value 

Heating Value British Thermal Units (BTU)/lb 5 ASTM D-240 

Viscosity cSt @ 40 °C 50 ASTM D-445 

Specific Gravity @ 60/60 °F 3 ASTM D-4052 

Oil Sulfur WT% 30 ASTM D-4294 

a. Note that results are generally provided in English units and then converted to SI for the purpose of this report. 

3-10 
Chapter 4 

Field Test Sites and Testing Procedures 

4.1 Staging Area 

The staging area for the New Bern, NC testing campaign was arranged with the cooperation of 

the North Carolina Department of Transportation (NCDOT), Division 2, District 2 located on 

231 S. Glenburnie Rd, New Bern, NC. They shared space inside their maintenance yard for 

setting up the staging area. A photograph of the staging area at the NCDOT was shown 

previously in Figure 3-2. The photograph shows the temperature controlled fuel storage trailer on 

the left, the trailer used to transfer the concrete blocks in and out of the DEAL in the center, and 

the DEAL on the right. Also located behind the DEAL is a 20 foot office trailer that was used for 

media storage and as a mobile lab to load and recover media before and after tests. Also located 

behind the DEAL is the electrical power drop that was used to supply the DEAL with electrical 

power. 

4.2 General Experimental Procedures 

The campaign was divided into two stages. From 2 September 2004 to 30 October 2004, 20 tests 

were conducted on a level stretch of old highway U.S-70 near New Bern, NC. These tests 

examined the influence of fuel type, vehicle speed, and vehicle load on diesel truck emissions. 

Two test were conducted during the second stage, one on 16 December 2004 and one on 21 

December 2004. These two tests were conducted on a mountainous stretch of I-77 between exits 

1 and 8 and investigated the influence road grade has on diesel truck emissions. This stage used 

only readily available pump fuel. 

4.2.1 New Bern Tests 

Twenty on-road experiments were performed over 14 days during the New Bern 2004 sampling 

campaign. On 8 of the 14 days, only 1 test was completed per day; on the remaining 6 days, 2 

tests were completed per day. Initially, a full day was required to perform one test, but as the 

field team established a routine and improved efficiency, two tests per day became feasible. 

Other factors that affected the number of tests the team was able to conduct were the logistics of 

recovering and re-loading media, fuel and oil changes for the tractor, changing the payload, etc. 

Duplicate tests were conducted per the QAPP (U.S. EPA, 2004). 

Originally, two sets of on-road experiments were planned. In the first set the DEAL was to be 

operated on a pump grade diesel fuel (~350 ppm sulfur content), and in the second set, a 20% 

biodiesel blend. Shortly before testing began, the U.S. DOE requested that 2005 specification 

low sulfur diesel fuel be used as the baseline fuel. Table 4-1 lists the matrix of tests that were 

actually completed during the New Bern campaign. 

4-1 
Table 4-1. Test Matrix for Fuel Type, Vehicle Speed, Speciation, and Vehicle Weight 

Test a Number Test Date b Fuel Type Vehicle Speed, 

km/h Speciation c Gross Weight, 

kg 

Road Grade, 

% 

T1

T2

T3

T4

T5 

T6 

T7

T8

T9 

T10 

T11 

T12 

T13 

T14 

T15 

T16 

T17 

T18 

T19 

T20 

T21 

T22 

 9/02/04 

 10/01/04 

 10/13/04 

 10/14/014 

10/19/04 

10/20/04 

 10/21/04 

 10/21/04 

10/22/04 

10/22/04 

10/23/04 

10/23/04 

10/26/04 

10/26/04 

10/27/04 

10/27/04 

10/28/04 

10/28/04 

10/29/04 

10/30/04 

12/16/04 

12/21/04 

Pump Diesel 

Pump Diesel 

Pump Diesel 

Pump Diesel 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Low Sulfur 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Bio Diesel 

Pump Diesel 

Pump Diesel 

105 

105 

105 

105 

105 

105 

56 

56 

56 

56 

105 

105 

105 

105 

56 

56 

56 

56 

105 

105 

89 

73 

Y 

Y 

Y 

N 

Y 

Y 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

Y 

Y 

N 

N 

33,890 

33,890 

33,890 

33,890 

33,890 

33,890 

33,890 

33,890 

21,350 

21,350 

21,350 

21,350 

21,350 

21,350 

21,350 

21,350 

33,890 

33,890 

33,890 

33,890 

21,350 

33,890 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

3.9 

3.9 

 

 

a.	 Duplicate test pairs indicated by shading every other pair beginning with test pair 5&6 

b.	 Low sulfur fuel has a sulfur content ~15 ppm (weight). B20 is a blend of 20% soy biodiesel and low sulfur diesel fuel 

meeting the requirements of ASTM D6751, ASTM (2007). 

c. “Speciation” indicates those tests where complete chemical characterization is performed. 

The test matrix lists the test numbers assigned to each test in the field. Duplicate tests were 

conducted back to back starting with Test 5 in Table 4-1. Every other duplicate test pair is 

colored for easier identification. In addition to the 16 tests specified in the QAPP, four tests were 

performed using a pump diesel fuel, referred to as test numbers 1–4 in this report. These four 

tests served as shakedown tests. 

Testing was performed at two vehicle speeds and load conditions. Also, at the conclusion of the 

on-road testing, a “tunnel blank” run was performed to determine if any correction to the data 

collected during the other tests was needed. During each experiment, concurrent plume, 

background, and exhaust gas sampling was performed using a combination of on-line and timeintegrated instruments along with continuous sampling of key vehicle operating parameters. 

4-2 
Details of the specific instruments used and sampling performed are provided in Section 2 of this 

report. 

Before each day’s testing, the DEAL was prepared by first activating all analyzers, pumps, 

sampling tunnels, and the DAS to allow the equipment to warm up. (Note that most instruments 

were left “hot” overnight to assure stable operation). During this period, the sampling media 

were installed in each of the time-integrated samplers and the samplers mounted on the 

respective sampling tunnel flow splitter. In addition, all appropriate quality control checks were 

performed of the instruments including leaks checks, zero/span/bias checks of the CEM bench, 

etc. The truck engine was started and allowed to idle. After a sufficient warm up period (~10 

min), the truck was driven to the road test section to be used for that day’s experiments. 

After arriving at the selected route, testing began immediately upon achieving a stable speed for 

the desired test. For each test, the road section was driven as many times as necessary to obtain 

adequate sample mass for chemical analysis. Eight passes were required when driving at 105 

km/h and four passes when driving at 56 km/h. During each pass (~20 min at 105 km/h or ~38 

min at 56 km/h), the time-integrated sampling equipment was activated or deactivated as 

necessary, with a time log of each test recorded by the system operator (EPA principal 

investigator) in a bound laboratory notebook. At the completion of the required number of 

passes, the truck returned to the staging area for sample and data recovery. A map of the test 

route is shown in Figure 4-1. The eastern end of the test route is defined by the intersection of 

US-70 and NC-43, and the West end of the route is defined by a break in the median (not shown) 

just east of Dover. One pass is one trip driving from the eastern end of the route to the western 

end of the route, or vice versa, a distance of about 35 km. 

2 mi 

5 km 

Figure 4-1. On-Road Diesel Emissions Test Route on Old US-70 

After arriving at the staging area at the completion of each experiment, the time-integrated 

samplers were removed from the DEAL and returned to the field laboratory for sample recovery 

and the electronic files recovered from the DAS. During sample recovery, all electronic data files 

saved on the DAS were organized into the proper hard drive directory, and an archive of each 

file set was copied to compact disk. At the end of each day’s testing, all electronic files were 

reviewed and any questionable data identified so the test(s) could be repeated if necessary. 

4-3 
Table 4-2. Measurements Taken during the Tests on I-77 

Parameter Instrument 

CO California Analytical Model 300 

CO2 California Analytical Model 300 

O2 Horiba MPA 220 

NOX California Analytical HCLD 400 

THC (as Propane) Horiba FIA 236 

Drive Shaft Torque ATI 2000 Series 

Engine Speed DDEC60 

Fuel Flow DDEC60 

Vehicle Speed Datron DLS-1 speed sensor 

Grade and Acceleration Crossbow Model VG600CA 

Engine Temperature Omega Engineering 

Exhaust Gas Temperature Omega Engineering 

Change in Elevation (i.e., grade) Paroscientific 745-16B 

4.2.2 Mountainous I-77 Tests 

Tests to investigate the effects of road grade on emission rates were conducted on a segment of I77 near Fancy Gap, VA, a short distance north of the North Carolina border. The route is shown 

in Figure 4-2. A total of two tests were completed on two different days. The DEAL was 

operated without a load on the first test day (see Table 4-1) and with a load on the second day 

Each test day began by driving about 150 miles from the EPA facility in RTP, NC to a rest area 

in Virginia that is located on I-77 immediately after crossing the border. Prior to departing from 

EPA, the DEAL’s generators were started, and the CEM analyzers were turned on to allow them 

time to warm-up in-route. The rest stop served as a convenient staging area close to the segment 

of road on which the emissions from the DEAL would be tested. Once at the rest stop, the DAS 

computers were started, the CEMs were calibrated, and other instruments used during the tests 

were activated. A new instrument, the Paroscientific 745-16B Microbarometer, was also added 

to the DEAL for the purpose of measuring road grade during these tests. All instruments and 

sensors used during tests in the mountains are listed in Table 4-2. 

After completing the pre-test procedures, the driver would leave the rest stop and drive directly 

onto I-77 North to begin the test, which consisted of four round trip passes between Exit 1 and 

Exit 8. After completing the test, the driver would return to the rest area for post test calibrations 

and shut down procedures. 

4-4 
Figure 4-2. On-Road Diesel Emissions Test Route in the Mountains on I-77 

4.3 Tractor-Trailer Payload 

As mentioned in Section 2.1, the facility uses weights to simulate different payloads. These 

payload weights positioned in the DEAL trailer are blocks of concrete measuring about 1 m3 and 

weighing about 1360 kg (3000 lbs) each. Table 4-3 lists the certified weights taken of the DEAL 

for the loaded and unloaded tests for the New Bern testing campaign. The unloaded weight 

includes the tractor-trailer and all the equipment present in the trailer during testing. The loaded 

weight is the same as the unloaded weight with the addition of nine of the blocks of concrete that 

simulate a payload. During the New Bern campaign, the test matrix was configured to minimize 

the number of times that the blocks of concrete would have to be moved into or out of the trailer. 

4-5 
Table 4-3. DEAL Loaded and Unloaded Testing Weights 

Unloaded Weight, Loaded Weight, Station on DEAL kg (Lbs) kg (Lbs) 

Steer Axial 4900 (10,800) 5010 (11,040) 

Drive Axial 9760 (21,520) 13,940 (30,740) 

Trailer Axial 6690 (14,740) 14,940 (32,940) 

Gross Weight 21,350 (47,060) 33,890 (74,720) 

Table 4-4. Vehicle Operating Parameters 

Location Vehicle Speed Gear Engine Speed 

(r/min) 

Old US-70 56 km/h (35 mi/h) 

105 km/h (65 mi/h) 

8th 

10th 

1550 

1500 

I-77 89 km/h (55 mi/h) 

73 km/h (45 mi/h) 

variable 

variable 

4.4 Vehicle Operation 

As mentioned previously, a test consisted of operating the tractor-trailer at a constant speed for a 

number of passes either on a 35 km stretch of old US-70 near New Bern, NC that has 

approximately zero grade or on a section of Virginia I-77 between exits 1 and 8, which is near 

Fancy Gap, VA. The two target speeds selected for the zero grade tests were 56 and 105 km/h. 

The driver maintained a constant vehicle speed during the pass while the actual speed used to 

calculate the emission factors was recorded on DAS by the optical fifth wheel (Section 2.4). The 

time when the vehicle reached the target speed at the beginning of each pass was recorded by 

hand, and the time was recorded again at the end of each pass before decelerating and making the 

turn for the return pass. 

Vehicle operation during the tests in the mountains consisted of driving the DEAL from I-77 

Exit 1 in VA to I-77 Exit 8, a total distance of about 12 km (7.5 mi). North-bound from Exit 1, 

the DEAL traveled up a road grade of approximately 3.9% for about 11 km (6.5 mi) before 

reaching the top of the grade where the Blue Ridge Parkway passes over I-77. The driver 

continued on I-77 and left the interstate at Exit 8, turned left over the overpass, and re-entered I77 onto the south-bound lanes. Upon returning to Exit 1, the driver would exit the interstate and 

reenter I-77 onto the northbound lanes for another round trip pass. One day’s test consisted of 

making four complete round trips. 

Table 4-4 lists the test speeds, the gear in which the tractor was driven, and the approximate 

engine speed. Table 4-1 lists the test numbers and the constant speed for each test along with the 

vehicle weight for all tests conducted. Precautions were taken for operating a slow moving 

vehicle on old US-70 by following the DEAL with a chase vehicle during tests at low speed. A 

photograph of the chase vehicle is shown in Figure 4-3. 

4-6 
 

Figure 4-3. Chase Vehicle Used during Low Speed Tests 

4.5 Coast-Down Testing 

A coast-down technique was employed to produce a model of road load force as a function of 

vehicle speed per SAE Recommended Practice J1236 and J2263 (SAE, 1996a; SAE 1996b). The 

road load force determined from the results of this model was then used to determine the power 

specific emission factors. As outlined in Section 6.6, the coast-down tests were performed on the 

same segment of old US-70 near New Bern, NC as the on-road tests but on a day when no other 

tests were planned. The coast down procedure required only a few of the DEAL instruments; 

therefore, the fine PM particle instruments and gas analyzers were not used. Instruments that 

were employed were the optical fifth wheel to record vehicle speed and the gyroscope. After 

starting the necessary instruments, the procedure required that the tractor accelerate to 

approximately 105 or 56 km/h (depending on the test conditions for which road load was 

required) then, with the transmission in neutral, allow the tractor-trailer to coast down to 8 km/h. 

Triplicate tests were performed with the trailer loaded and unloaded. 

4.6 Test Fuel 

In tests 1 through 4, sampling was conducted with the vehicle operated loaded and under steadystate conditions at near zero grade using a pump grade diesel fuel obtained near the staging area. 

Tests 5 through 12 used the low sulfur fuel, and tests 13 through 20 used the B20. Finally, tests 

21 and 22 also used pump grade fuel for the measurements on mountain road grades. 

Before switching from the pump grade fuel to the low sulfur fuel, the Kenworth tractor and the 

fuel storage trailer had to be driven from the New Bern, NC test site to a local truck dealership in 

Dunn, NC, to remove the pump grade fuel and to refill the tanks with the low sulfur fuel. In 

addition to changing the fuel and the fuel filter, the oil and oil filter were changed. The one-way 

trip of 170 km back to the staging area in New Bern was used as a run-in period to re-condition 

4-7 
 

 

 

the engine on the new fuel. The same procedure was followed to switch from the low sulfur fuel 

to B20. During the fuel changing process, sample containers were on hand to collect fuel and oil 

samples. Table 4-5 lists the fuel samples collected, and the fuel analysis reports are provided in 

Appendix B. 

Table 4-5. Fuel and Oil Samples Collected and Analyzed 

Sample Type Sample ID 

Pump Diesel Fuel Pump Diesel Fuel 10/15/04 

Low Sulfur Diesel Fuel Low Sulfur Diesel Fuel 10/15/04 

B20 Bio Diesel Fuel 10/25/04 

Engine Oil Pre-Low Sulfur Diesel Fuel Tests 10/15/04 

Engine Oil Post-Low Sulfur Diesel Fuel Tests 10/25/04 

Engine Oil Pre-Bio-Diesel Fuel Tests 10/25/04 

Engine Oil Post-Bio-Diesel Fuel Tests 10/30/04 

4.7 CEM Operation 

Neither of the two Horiba Instruments Model AIA 210 NDIR Analyzers mentioned in Section 4 

of the QAPP were used during the tests. The CAI Model NDIR 300 Analyzer, also mentioned in 

Section 4 of the QAPP, was determined to be sufficient to measure the expected concentrations 

of carbon monoxide and carbon dioxide. It was necessary to remove the Horiba analyzers to 

make room for additional computers. 

The pre- and post-test procedures for the operation of the CEMs in the New Bern campaign 

followed Section 4.3 of the QAPP. These procedures are briefly outlined below. 

Pre-test CEM procedures: 

•	 Filters were changed on a daily basis in the “Hot Box” and the “Oven”. 

•	 The heated sample lines were turned on first thing allowing them to warm-up before 

calibrations. 

•	 The instruments were allowed to remain on 24 hours a day during the campaign. 

•	 Bias checks were performed before every test to check for leaks. 

•	 The set points for the heated sample line controllers were checked daily. Table 4-6 lists the 

controller setpoints. 

•	 Gas analyzer flows were checked and monitored using the rotameters installed in the CEM 

bench. 

4-8 
•	 The zero and span gases were introduced under the same flow and pressure conditions that 

were used during the multipoint calibrations. 

Immediately upon returning to the staging area after completing the final test of the day, zero and 

span responses were checked as soon as practically possible. 

Table 4-6. Temperature Controller Setpoints for CEM System 

Component a Controller Setpoint b 

Outside Heated Sample Line (HL1) 360 °F (182 °C) 

Inside Heated Sample Lines (HL2, HL3, HL4) 360 °F (182 °C) 

NOX Heated Sample Line (HL5) 266 °F (130 °C) 

Oven 365 °F (185 °C) 

Hot Box 160 °F (71 °C) 

a. See CEM description in Section 3.1. 

b. Based on 40 CFR, Part 86 requirements 

4.8 Tracer Gas Analysis 

A general description of the tracer gas analyzer is provided in section 3.2 along with the general 

description of the DEAL. A more detailed schematic of the tracer gas injection system and the 

tracer gas analyzer system is shown in Figure 4-4. 

Figure 4-4. Tracer Gas Injection System and Tracer Gas Analyzer System 

After completion of the pretest multigas analyzer calibration checks, the analyzer was allowed to 

sample continuously from the plume sample tunnel until time to perform the post-test calibration 

checks. When the DEAL reached the desired constant operating test speed at the beginning of 

4-9 
 

 

 

each pass, the toggle valve was switched open to allow flow of the FM-200 gas to enter the 

tractor exhaust stack, and the time was recorded on a log sheet. A real-time reading of the 

concentration of the FM-200 gas was observed during on-road testing using the remote 

operator’s station located in the tractor sleeper. A concentration of approximately 2 ppm was 

expected based on previous shake-down runs and the position of the needle valve upstream of 

the flow meter. If a concentration close to that expected was observed, then no adjustments were 

made to the valve position. 

The analog signal from the flow meter recorded the real time flow of the tracer gas being 

injected into the stack while the multi-gas analyzer recorded the real time concentrations of the 

FM-200 gas in the exhaust plume sample. At the end of each pass, the toggle valve was closed to 

conserve FM-200 and the “Off” time recorded on a log sheet. The sample data readings from the 

multi-gas analyzer were stored in the instrument’s internal memory and were downloaded at the 

end of the test day via an RS-232 serial port. 

Table 4-7 lists the FM-200 consumption rate that was necessary to achieve a concentration of 

about 2 ppm of FM-200 in the plume sample at two different speeds. 

Table 4-7. FM-200 Consumption Rates 

FM-200 56 km/h 105 km/h 

Volumetric Flow, Measured 1.81 L/min 1.36 L/min 

Mass Flow, Calculated 3.50 kg/h 2.63 kg/h 

(Gas Density of 0.032 g/L) 

4.9 PM-2.5 Instrument Operation 

Since there are no standard techniques for on-road determination of diesel PM or monitoring of 

vehicle operating parameters, the various instrumentation methods used in the study were 

developed from the applicable operating manual and the experience gained during the 

experiments conducted at WVU described earlier (Kinsey et al., 2006b). Miscellaneous operating 

procedures (MOPs) were developed for each instrument type as provided in the approved QAPP 

(U.S. EPA, 2004). Table 4-8 lists the specific MOP applicable to each instrument previously 

described in Section 2. 

A situation worthy of mention relevant to the operation of the fine PM instruments is need for 

frequent cleaning of the QCM crystal. Early in the campaign, the instrument would go offline in 

the middle of a pass, invalidating that pass. Therefore, the procedures were modified in the field 

to include cleaning the QCM crystal at the end of every pass during a 56 km/h test and at the end 

of every other pass during a 105 km/h test. Also, a spare crystal holder was available in the event 

the crystal failed completely during a particular experiment. 

4-10 
General Category MOP Title MOP Number 

Vehicle Operation 

DEAL Facility Setup: Staging Configuration 

Load Shifting for the DEAL 

Preparations for DEAL On-Road Testing 

Return from On-Road Testing for the DEAL 

DEAL Shutdown Procedures 

1400 

1401 

1402 

1403 

1404 

CEMS Startup 1405 

CEMS Multi-Point Calibration 1406 

CEMS 2-Point Calibration Check 1407 

Gaseous Pollutant
 

Monitoring
 

CEMS System Bias Check 

CEMS Response Time Check 

CEMS Overnight/Standby Mode 

Operation of CAI Model 300 CO2 Analyzer 

Operation of Horiba MPA 220 O2 Analyzer 

Operation of CAI Model 400 NO/NOX Analyzer 

Operation of Horiba AIA 210 CO Analyzer 

Operation of Horiba FIA 236 THC Analyzer 

1408 

1409 

1410 

1420 

1421 

1422 

1423 

1424 

Operation of TSI SMPS Model 3934 1411 

PM Monitoring 

Operation of TSI SMPS Model 3936 

Basic Operation and Maintenance of Dekati ELPI 

Operation of Series 1105a TEOM 

Operation of Series 1400a TEOM 

Operation of Magee (Andersen) AE-2 Aethalometer 

1412 

1413 

1414 

1415 

1416 

Operation of Eco-Chem PAS 2000 Real Time PAH Monitor 1417 

Tracer Gas Monitoring 

Operation of B&K 1302 Gas Analyzer for Tracer Gas 

Measurements 

Performing Zero Check of the B&K 1302 Analyzer 

1418 

1419 

Table 4-8. Miscellaneous Operating Procedures 

4.10 Time Integrated Sampling 

The pre-fired quartz filters installed in both sampling arrays were prepared and analyzed 

according to NIOSH Method 5040, Elemental Carbon (Diesel Particulate). The PUF plugs used 

in the speciated tests were prepared and analyzed per the applicable MOPs outlined in the 

Facility Manual for the FPCL appended to the QAPP (U.S. EPA, 2004). 

All sampling media were prepared in the FPCL before leaving for the field. Prior to and after 

sampling, the quartz filters and ELPI substrates listed in Table 3-1 were stored in aluminum foillined, plastic Petri dishes inside a laboratory freezer maintained at –50 °C. The Teflon filters 

were also stored inside plastic Petri dishes in the –50E freezer. Finally, the PUF plugs were 

stored and transported in glass jars with Teflon twist caps. During transport and in the field 

laboratory, all sampling media were stored in a small portable freezer operated at a temperature 

4-11 
of approximately –20 °C. This portable freezer was also used as the primary shipping container 

for the sampling media to and from the sampling site and was operated on generator power enroute. 

4-12 
 

 

Chapter 5 

Post-Test Laboratory Analysis 

The samples of fine PM collected during testing by integrated sampling media were taken to the 

laboratory for chemical analysis. The samples were stored in a freezer during transportation to 

minimize sample losses. Upon arrival at the laboratory, the sampling information (such as the 

sampling location of individual media and the test date, ID, and conditions) was collected and 

recorded in the sample log system. The samples were stored in a freezer at temperatures below 

-20 °C until analysis. By keeping in a sealed container in a frozen state, samples could safely be 

stored without degradation for a long period of time. The instruments and procedures for the 

analyses conducted in the laboratory are described below, with the applicable MOPs listed in 

Table 5-1. 

Table 5-1. Analytical MOPs 

Procedure MOP 

Gravimetric analysis 2503 

Elemental analysis EPA Method IO-3.3 

Analysis of water-soluble inorganic ions 2512 & 2513 

OC/EC analysis 2511 

Analysis of organic PM: 

Compositing and spiking 504 

Sample extraction and concentration 2504

 Extract methylation 2505

 GC/MS analysis 2507 

Analysis of PUFs 2509 

5.1 PM-2.5 Gravimetric Analysis 

The PM-2.5 gravimetric analysis was performed by weighing the individual Teflon filters before 

and after sampling on a Sartorius microbalance having a detection limit of 1 μg. The filter 

weighing was done in accordance with the standard procedure for PM-2.5 samples (MOP 2503). 

The method requires that the filter samples be conditioned before weighing by equilibration for a 

minimum of 24 hours in an environmental chamber which is maintained at 20–23 °C and a 

relative humidity of 30% to 40%. To eliminate the possible electrical charge accumulating on the 

surface, both sides of each Teflon filter were exposed to Polonium strips for at least 20 seconds 

before placing the filter on the balance. Before sampling, the blank Teflon filters were tare 

weighed and placed in Analyslide dishes that were purchased from Pall Gelman and had 

5-1 
 

individual IDs. The weight change in the same filter after sampling was then used for PM mass 

emission calculation. 

5.2 Elemental Analysis 

After post-test weighing, the Teflon filters were analyzed by EPA’s National Exposure Research 

Laboratory using X-Ray Fluorescence (XRF) and EPA Method IO-3.3 to quantitatively 

determine elements in the PM collected on the filters (U.S. EPA, 1999). In the XRF analysis, a 

Kevek Model EDX-171 energy dispersive spectrometer with a 200 watt rhodium target tube as 

an excitation source was used. 

5.3 Analysis of Water-Soluble Inorganic Ions 

After non-destructive analyses (weighing and XRF), the Teflon membrane filter samples were 

analyzed using a Dionex DX-120 ion chromatograph (IC) for isocratic ion analysis including 

potassium (K+

), ammonia (NH4

+

), magnesium (Mg+2), calcium (Ca+2), nitrate (NO3

–1), sulfate 

(SO4

–2), nitrite (NO2–1), and chloride (Cl–1). During this analysis, PM on each Teflon filter was 

extracted by placing the filter in a vial with 10 mL HPLC (high performance liquid 

chromatograph) grade (low conductivity) water (H2O). The extract was then sonicated for 30 min 

and introduced at the head of an ion-exchange resin column of the ion chromatograph (IC). The 

ions in the sample were detected by the conductivity detector and quantified through the use of 

external standards. The concentrations of the ions found in the water solution were then 

converted to the mass on the filter by multiplying the concentrations with the volume of the 

extraction water, 10 mL. 

5.4 Analysis of Organic and Elemental Carbon 

The quartz fiber filter samples were analyzed by a thermal/optical carbon analyzer provided by 

Sunset laboratory Inc. for determination of EC/OC before undergoing solvent extraction. The OC 

and EC were determined based on the two-stage thermal-optical method outlined in NIOSH 

Method 5040 (NIOSH, 2003). The method proceeds in two stages. First, organic and carbonate 

carbon are evolved in a helium atmosphere as the temperature is stepped to about 850 °C. The 

evolved carbon is catalytically oxidized to CO2 in a bed of granular manganese dioxide (MnO2), 

and then reduced to methane in an N/firebrick methanator. Methane is subsequently quantified 

by FID. In the second stage, the oven temperature is reduced, an oxygen-helium mix is 

introduced, and the temperature is stepped to about 940 °C. As oxygen enters the oven, 

pyrolytically generated carbon is oxidized, and a concurrent increase in filter transmittance 

occurs. The point at which the filter transmittance reaches its initial value is defined as the split 

between OC and EC. Carbon evolved prior to the split is considered OC (including carbonate), 

and carbon volatilized after the split is considered EC. The instrument has a lower detection limit 

on the order of 0.2 μg/cm2

 filter for both OC and EC. 

It has been found that new quartz fiber filters usually have an OC background of 2 to 5 μg/cm2. 

For this reason, the purchased quartz filters were pre-fired in a kiln at 550 °C for 12 hours before 

use. The clean quartz filters were stored in petri dishes lined with cleaned aluminum foil. 

Aluminum foil liners were cut to cover the inside surfaces of the Petri dishes so that the filters 

did not directly touch the dish when placed inside the lined dishes. The aluminum liners were 

5-2 
 

 

 

 

 

 

 

 

 

 

also baked at 550 °C for 12 hours and then compressed into the Petri dishes using a cleaned 

Lucite plug machined to fit snugly into the dishes. The filters and liners were handled with 

Teflon forceps to avoid any contamination. 

Only a portion of quartz filter sample was used for OC and EC analysis, which was obtained 

using a punch tool specially provided by Sunset Lab. The 1.45 cm2 filter punch was then placed 

on the sample holder of the instrument for analysis. The analyzer reports the OC and EC contents 

in μg per cm2

. Since the actual exposure area of quartz filter was 13.45 cm2, the OC and EC 

mass on the filter were calculated by multiplying the reported OC or EC content (μg/cm2) by 

13.45 cm

2

 to obtain a mass value in μg. 

5.5 Analysis of Particle Phase Organic Compounds 

After OC/EC analysis, the semi-volatile organic com-pounds in the PM collected on quartz 

filters were solvent extracted and quantified with gas chromatography/mass spectroscopy 

(GC/MS). The PM speciation included (1) sample compositing; (2) solvent extraction and 

concentration; (3) extract methlation; and (4) GC/MS analysis as described in the following 

sections. 

5.5.1 Sample Compositing and Spiking 

For better GC/MS results, ideally a filter sample should contain approximately 1.0 mg of OC as 

measured by the OC/EC analyzer. However, the OC on each of the quartz filter collected from 

the on-road tests was much less. As a result, the filters at the same sampling positions for the 

same types of fuel but different tests were composited as described in Table 5-2. The composited 

filters were placed in a jar and were spiked with internal standard solution. The exact volume of 

spike solution is recorded since this value is used in the quantification calculations. 

Table 5-2. Composites of Quartz Filter Samples 

Compositea QF ID OC (mg) # of Punches 

Q100704H 1.341 1 

T5&6 Plume Front Q100103C 1.039 1 

Total 

Q100704E 0.154 1 

Q100704G 0.110 1 

T5&6 Plume Backup Q100103A 0.219 1 

Q100103B 0.083 1 

Total 

Q100704C 0.033 1 

Q100704D 0.013 1 

T5&6 Thermal Denuder Backup Q100103D 0.044 1 

Q100103E 0.015 1 

2.380 2 

0.567 4 

Total 0.105 4 

5-3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Compositea QF ID OC (mg) # of Punches 

Q100704K 0.030 1 

T5&6 BKG Front Q100103H 0.047 1 

Total 

Q100704A 0.033 2 

Q100704B 0.020 1 

T5&6 BKG Backup Q100103F 0.023 2 

Q100103G 0.018 1 

Total 

Q093004X 0.242 1 

T19&20 Plume Front Q100103L 0.244 1 

Total 

Q093004V 0.102 1 

Q093004W 0.097 1 

T19&20 Plume Backup Q100103J 0.099 1 

Q100103K 0.064 2 

Total 

Q100103I 0.081 1 

Q093004Y 0.013 1 

T19&20 Thermal Denuder Backup Q100103M 0.058 1 

Q100103N 0.015 1 

Total 

Q092904T 0.051 1 

T19&20 BKG Front Q100103Y 0.044 2 

Total 

0.077 2 

0.095 6 

0.486 2 

0.362 5 

0.167 4 

0.095 3 

Q100604H 0.048 1 

Q100604E 0.025 1 

T19&20 BKG Backup Q100103O 0.024 1 

Q100103P 0.021 1 

Total 0.117 4 

a. Front = first filter(s) in a series; BKup = backup filter(s); BKG = background 

5.5.2 Sample Extraction and Concentration 

Spiked filter composites were extracted with five successive 10-minute sonication steps. The 

first two extractions were performed with hexane and then were followed by three extractions 

with a 2:1 mixture of benzene and isopropyl alcohol. Filters were sonicated for 10 minutes at 

ambient temperature. The water temperature in the sonicator was monitored and maintained 

below 32 °C. 

5-4 
 

Following sonication, the extract was transferred to the flask of the in-line transfer and filtration 

apparatus. The transfer apparatus was thoroughly cleaned before extract transfer. The glass parts, 

including the quartz wool-packed Pasteur pipette, were solvent rinsed and then baked in 

aluminum foil at 550 °C for at least 12 hours. Teflon parts were cleaned by sonication with 

dichloromethane and then air dried. The assembled in-line transfer apparatus was rinsed by 

transferring high purity distilled benzene up through the Teflon transfer line and the quartz 

packed pipette into the flask by use of a vacuum system. The rinse benzene was discarded, and 

the flask was then re-rinsed and then reinstalled. The extract was transferred to the flask by 

connecting a vacuum of approximately 10 cm of mercury via corrugated Teflon tubing 

connected to the side arm. All five extracts were collected together in the same flask. 

The composited extract was then transferred and concentrated in the test tube of a Zymark Model 

TurboVap II Concentration Workstation. In the Zymark, the extract was concentrated by passing 

a gentle stream of pure nitrogen over the surface of the liquid to evaporate the solvent to a total 

liquid volume of 0.5–0.75 mL. The water bath temperature of the concentrator was kept at 35–40 

°C. After concentration, the extract was completely transferred to a clean amber vial and further 

concentrated by nitrogen blow-down to approximately 250 μL. Concentrated extract was stored 

in the vial with a Teflon-lined cap in a freezer until derivatization and analysis. 

5.5.3 Extract Methylation 

Each concentrated extract was split into two fractions: neutral and methylated. The sample was 

split by first measuring the total volume of the concentrated extract with a thoroughly cleaned 

gas-tight volumetric syringe. The total volume of sample was recorded. Half of it was returned to 

the original vial, and the other half was placed in a second cleaned vial and labeled for 

methylation. 

Methylation was performed to yield methyl esters of fatty acids that would otherwise not be 

eluted from the GC column. The sample was methylated by adding approximately 10 μL of high 

purity methanol and 100 μL of diazomethane solution to the methylation fraction of extract. 

After the reaction was complete, the methyled extract was reconcentrated by nitrogen blowdown 

to the original volume of aliquot before methylation. The methylated extract was stored in the 

freezer until analysis. 

5.5.4 GC/MS Analysis 

The extracts were analyzed with an HP 6890/5973 GC/MS equipped with thermal conductivity 

detector (TCD) model HP-G1530A, autoinjector model HP-G1513A, programmable 

Temperature Vaporizing (PTV) inlet, mass selective detection (MSD) interface, and FID model 

HP-G1526A. An HP-5ms GC column was used to separate the various organic compounds in the 

sample. Ultra pure helium was used as the carrier gas. The GC/MS operating conditions are 

summarized in Table 5-3. Positive identification of a compound via GC/MS was confirmed when 

the GC retention time and mass spectrum of the unknown compound match those of an authentic 

standard compound under identical instrumental conditions. 

5-5 
Operating Parameters Instrument Setting 

Injector Temperature (°C) 300 

GC/MS Interface Temperature (°C) 300 

Initial Oven Temperature (°C) 65 

Initial Oven Hold Time (min) 10 

Oven Temperature Ramp Rate (°C/min) 10 

Final Oven Temperature (°C) 300 

Final Oven Temperature Hold Time (min) 41.5 

Carrier Gas Helium 

Carrier Gas Flowrate (mL/min) 1.0 

Injection Splitless 

Purge Flow to Split Vent (mL/min) 30 

Gas Saver (mL/min) 20 

Mass Spectrometer Conditions: 

Solvent Delay (min) 3.5 

Data Collection Mode Scan 

Scan Range (amu) 50-500 

Source Temperature (°C) 230 

Quad Temperature (°C) 150 

Table 5-3. GC/MS Operating Conditions 

For quantification of the target marker compounds by GC/MS, known quantities of deuterated 

internal standards were included in each quantification standard and spiked onto each sample. 

Each compound that was quantified by GC/MS is referenced to one of more internal standards 

such that the response of each compound relative to the appropriate internal standard(s) is fixed 

with only minor variation in MS detector response, MS tune parameters, GC injection 

conditions, and GC column conditions. Detail operating procedure for GC/MS analysis can be 

found in MOP 2507 (U.S. EPA, 2005). 

5-6 
Chapter 6 

Experimental Data Analysis 

The post-test data analysis involved four primary steps: raw data gathering, emission factor 

calculation, power demand calculation, and data correlation. The methods and procedures used 

in each step of data reduction are discussed below. 

6.1 Raw Data Analysis 

All electronic data recorded in the test field were stored on six CDs: (1) New Bern Test (base 

fuel plus shakedown), (2) New Bern Test (B20 runs), (3) Roll Down, (4) Tunnel Blank, (5) I-77 

Road Grade Test (12/16/04), and (6) I-77 Road Grade Test (12/21/04). The first step of data 

analysis was to gather all useful data from these raw data CDs for emission and power demand 

calculation. The analog files recorded the vehicle operating parameters, flow rates, exhaust gas 

compositions, as well as fuel feed rates. The concentrations of CO2, CO, O2, NOX, and THC in 

the raw exhaust gas were recorded every second and stored in the files titled “Analog mm-dd-yy 

F1”. These files also stored the sampling flow rates for all the plume and background filters. 

However, the flow rates for the filters behind the TDs were recorded in the files “Analog mmdd-yy F2”. The vehicle speed was stored in the files “Analog mm-dd-yy”. The fuel feed rate was 

recorded in “DD60 mm-dd-yy”. The change in atmospheric pressure monitored by the microbarometer and used to determine road grade was stored in “MB mm-dd-yy”. Table 6-1 

summarizes these parameters and their raw data file sources. The electronic data recorded by 

various PM monitoring instruments were stored in the corresponding raw data files. 

In addition to the above electronic data, the information required for emission evaluation 

includes weather reports and fuel analytical reports. The field weather reports, which were 

obtained from State Climate Office of North Carolina, provided data needed for calculation of 

the ambient air moisture content as a function of test time. The fuel compositions (Appendix B) 

were used in emission factor calculation. 

There were 20 tests (T1 to T20) conducted on a level segment of US-70 near New Bern, NC in 

October 2004. T1 to T4 were shakedown tests using available pump grade diesel fuel. For tests 

T5 to T12, a low-sulfur diesel fuel, called “base fuel” in this study, was used. To investigate the 

effects of fuel type on emissions, B20 was used in tests T13 to T20. Since the data recorded from 

the shakedown tests (T1 to T4) were not adequate for comparing of pump grade diesel with the 

other two diesel fuels, only the results for base fuel and B20 are discussed in this report. The 

effects of road grade on emissions were investigated in two preliminary grade tests (T21 and 

T22). These additional tests were conducted again using available pump fuel in December 2004 

between I-77 Exits 1 and 8 that had a road grade of approximately 4%. During these tests, only 

gaseous emissions were measured. 

6-1 
 

 

 

 

 

 

 

 

 

 

 

 

Table 6-1. Measurement Parameters Analyzed and their Source Files 

Measurement Parameter Unit File Name Column Title 

Vehicle Speed km/h Analog mm-dd-yy 5TH_WHEEL [kmh] 

Anubar Reading in H2O Analog mm-dd-yy ANUBAR [“H2O] 

Stack Temperature o

C Analog mm-dd-yy STACK [ーC] 

Trace Injection Rate L/min Analog mm-dd-yy MFM a [LPM]b 

Plume Front TFc Flow Rate L/min Analog mm-dd-yy F1 FTP1 [LPM] 

Plume Front QFd Flow Rate L/min Analog mm-dd-yy F1 FTP2 [LPM] 

Background TF Flow Rate L/min Analog mm-dd-yy F1 Back Teflon [LPM] 

Background QF Flow Rate L/min Analog mm-dd-yy F1 Back Quartz [LPM] 

TF after Denuder Flow Rate L/min Analog mm-dd-yy F2 TD e. QUARTZ [LPM] 

QF after Denuder Flow Rate L/min Analog mm-dd-yy F2 TD. TEF f. [LPM] 

Exhaust CO Concentration ppm Analog mm-dd-yy F1 CO Low [ppm] 

Exhaust CO2 Concentration % Analog mm-dd-yy F1 CO2 [%] 

Exhaust NOX Concentration ppm Analog mm-dd-yy F1 NOX [ppm] 

Exhaust O2 Concentration % Analog mm-dd-yy F1 O2 [%] 

Exhaust THC Concentration Ppm Analog mm-dd-yy F1 THC [ppm] 

Fuel Feed Rate gal/h DD60 mm-dd-yy Fuel Rate(gal/hr) 

Micro-Barometer Reading Psi MB mm-dd-yy CHANNEL1 

Exhaust Black carbon ng/m3 Analog mm-dd-yy F2 MAGEE [ng/m^3] 

Exhaust PAH concentration ng/m3 Analog mm-dd-yy F2 PAH [ng/m^3] 

ELPI background PM concentration #/cm3 ELPI C Test # mm-dd-yy 

ELPI plume PM concentration #/cm3 ELPI D Test # mm-dd-yy 

SMPS background PM concentration #/cm3 Old SMPS mm-dd-yy 

SMPS plume PM concentration #/cm3

 Long mm-dd-yy 

Nano-SMPS plume PM concentration #/cm3

 Nano mm-dd-yy 

TEOM background PM concentration μ/cm3 1400 mm-dd-yy 30-min MC & 1-hr MC 

TEOM plume PM concentration mg/cm3 RPyyyymmddxxxxxxR Mass Conc 5 & Mass Conc 8 

QCM plume PM concentration mg/cm3

 QCM mm-dd-yy Conc (mg/m^3) 

a. MFM = mass flow meter 

b. LPM = liters per minute 

c. TF = Teflon filter 

d. QF = quartz filter 

e. TD = thermal denuder 

f. TEF = Teflon 

6-2 
In addition to fuel type and road grade, the effects of vehicle weight and vehicle speed on 

emissions were investigated. In these steady-state, constant speed tests, the tractor-trailer was 

operated either unloaded at a gross vehicle weight (GVW) of 21,350 kg or loaded with large 

blocks of concrete to a GVW of 33,890 kg. The GVW was measured by running the vehicle 

across a certified scale. The effect of vehicle speed on emissions was also investigated by 

operating the vehicle at either 105 or 56 km/h. It should be pointed out that, due to operating 

difficulty, the two speeds maintained in the 4% grade road tests in Virginia were different from 

those used in the level road tests. The test conditions were summarized previously in Table 4-1. 

The raw data recorded from the measurements of emissions and vehicle parameters were 

smoothed using the “medsmooth” function in MathCad, version 2001 Professional. The 

measurements that were smoothed in this study include CO2, CO, NOX, THC, vehicle speed, fuel 

feed rate, and microbarometer pressure. 

Because different instruments were used in monitoring vehicle emissions, it is important to 

synchronize them with clock time so that the emission and vehicle operating data for each 

individual pass can be extracted and analyzed. Many of the instruments used in this study 

recorded data every second. However, there were two notable exceptions. The atmospheric 

pressure was recorded every 1.6 s by the micro-barometer, while the weather data were reported 

every hour. These two raw data parameters had to be interpolated to obtain their values at each 

second. An Excel macro based on the Lagrange interpolation technique was developed for this 

analysis. 

Due to the inherent difference in response time of different instruments as well as the transport 

time required in the sample collection system, it was expected that, in the emission evaluation, 

the emissions measurement would always lag the power measurement. A cross-correlation 

technique was used to determine the measurement delay time for emissions of each gas species. 

In the cross-correlation, two sets of time-series data—emissions measurements and power 

measurements—were correlated and the correlation coefficient was calculated. The time delay 

was determined by adjusting how the times of the two data sets are aligned with respect to one 

another until the correlation coefficient between emissions and power measurements was 

maximized. The on-road steady-state tests were conducted by running the vehicle back and forth 

on the same segment of highway, and the data when the vehicle reached a specified constant 

speed were collected for emission calculation. 

Each individual test consists of a number of repeated passes with the identical vehicle driving 

condition. To characterize the steady-state emissions, the gathered data for each test were broken 

down to groups according to the time period of each individual pass in which the truck was 

operated under the same steady-state condition. The emission data in each pass recorded by an 

instrument were averaged to obtain an average for that pass and that measurement. The standard 

deviation in the recorded data was also estimated for each pass and used as a measure of 

uncertainty in the pass-average obtained. The pass-average obtained was then used to calculate 

the emission factor and power demand for that pass. The uncertainty in pass-average emission 

factor was estimated from the uncertainties in the primary measurements involved in the 

calculation and is given as 

6-3 
1/ 2 2 2 2 ⎡ ⎤ ⎛ ∂R ⎞ ⎛ ∂R ⎞ ⎛ ∂R ⎞

uR = ⎢⎜ u1 ⎟

⎟ + ⎜ u2 ⎟ + ⋅⋅⋅ + ⎜ un ⎟ ⎥ ⎜ ⎜ ⎟ ⎜ ⎟ ⎢ ∂x ∂x ∂x ⎝ 1 ⎠ ⎝ 2 ⎠ ⎝ n ⎠ ⎥ ⎣ ⎦

 

 

2

∑

n (wi )
 i=1

w t = n 

(6-1)

where 

uR = uncertainty in R, 

R = a function of individual variables, R = R(x1, x2, Χ Χ Χ, xn), 

u1, u2, Χ Χ Χ, un = uncertainties in independent variables x1, x2, Χ Χ Χ, un, and 

n = number of independent variables. 

The test-average is the average of all the pass averages in the same test. The uncertainty in a testaverage was calculated from the uncertainties of all the passes in the test by 

(6-2)

where 

wt = uncertainty in test average, 

wi = uncertainty in pass-average of the I

th pass, and 

n = number of passes in the test. 

6.2 Emission Factors for Gaseous Pollutants 

The gaseous emission factors were calculated using the method recommended by the Society of 

Automotive Engineers (SAE), ARP 1533 (SAE, 2004). The method calculates the emission 

factors by means of a comprehensive material balance between the inputs of fuel and combustion 

air and the outputs of exhaust gas compositions. The method provides advantages in emission 

factor calculation in two respects: first, it makes it possible to calculate instant emission factors 

from the continuous CEM measurements, which is particularly useful if unsteady-state test data 

are to be analyzed; second, it calculates the emission factor without the use of exhaust gas flow 

rate, which requires the measurement of local velocities in the stack. Since the exhaust gas 

velocity varies with time and measurement location in the stack, the SAE method eliminates the 

uncertainty attributed by the anubar meter measurement. This will be demonstrated later in 

Section 7.6. 

According to the SAE method, a matrix equation as described by Equation (6-3) was established 

from the material balances of C, H, O, N, and S over the combustion process: 

Y = AT B (6-3) 

6-4 
⎡Y1 ⎤ ⎢ ⎥ Y2 ⎢ ⎥

⎡ 0 

⎢ 0 ⎢

1

0

0

0

0 

0 

0 

2 

1

0

3

8

0

0

0
 

0

− (T +U
 ) ⎤

⎥ − (2h + 4U)
 ⎥

⎡m⎤ ⎢ ⎥ n ⎢ ⎥ ⎢Y ⎥ 3 ⎢ ⎥ ⎢Y4 ⎥

⎢

⎢

⎢

0 

0 

2

0

0

2

2 

0 

1 

0 

1

0

0

0

1

1

2

0

(2R
 + 2T
 +
 h)
 ⎥

⎥ − 2S
 ⎥

⎢ p ⎥ ⎢ ⎥

q ⎢ ⎥ ⎢Y ⎥ 5 Y =
 ⎢ ⎥ , ⎢Y6 ⎥

⎢ 0 

A =
 ⎢

[⎢ CO2 ]d

0

−1

0

0 

0 0 

0 −[CO2 ] d 

0

0

0

0

0

0

1 

0 

0 ⎥

⎥ , 0 ⎥

⎢r ⎥

B =
 ⎢ ⎥

0 ⎢ ⎥ ⎢ ⎥ Y7 ⎢ ⎥ ⎢Y8 ⎥ ⎢ ⎥ Y9 ⎢ ⎥

⎢ [CO]d ⎢

⎢[THC]w ⎢

[ NO
 ] X w ⎢

0

0

0

0

0

0

0 [CO]d 

0 0 

0 0 

−1

0

0

0

−1

0

0

0

−1

0 

0 

0 

⎥ 0 ⎥

0 ⎥

⎥ 0 ⎥

⎢ ⎥ 0
 ⎢ ⎥ ⎢ 0
 ⎥ ⎢ ⎥ 0
 ⎢ ⎥ ⎢⎣Y10 ⎥⎦ ⎢⎣ −1 1 1 1 1 1 1 1 1 0
 ⎥⎦ ⎢⎣ 0
 ⎥⎦

The matrices Y, A, and B are 

where 

[CO2]d = CO2 mole fraction concentration on dry basis measured by CEM, %, 

[CO]d = CO mole fraction concentration of dry basis measured by CEM, ppm, 

[THC]w = mole fraction concentration of total hydrocarbon, expressed as C3H8, on wet 

basis measured by CEM, ppm, 

[NOX]w = oxides of nitrogen mole fraction concentration, expressed as NO, on wet basis 

measured by CEM, ppm,
 

m = weight percentage of carbon in fuel,
 

n = weight percentage of hydrogen in fuel, 

p = weight percentage of oxygen in fuel,
 

q = weight percentage of nitrogen in fuel,
 

r = weight percentage of sulfur in fuel, 

T = mole fraction of CO2 in dry air = 0.00034, 

U = mole fraction of methane in dry air = 0, 

R = mole fraction of O2 in dry air = 0.20948, 

S = mole fraction of N2 and Ar in dry air = 0.79018, 

h = moisture content in ambient air, mole of moisture per mole of dry air, 

Y1 = total g-mol of exhaust produced per 100 g of fuel burned, 

Y2 = g-mol of CO2 produced per 100 g of fuel burned, 

Y3 = g-mol of N2 in exhaust gas per 100 g of fuel burned, 

Y4 = g-mol of O2 in exhaust gas per 100 g of fuel burned, 

Y5 = g-mol of moisture in exhaust gas per 100 g of fuel burned, 

Y6 = g-mol of CO produced per 100 g of fuel burned, 

Y7 = g-mol of THC produced per 100 g of fuel burned, 

Y8 = g-mol of NOX produced per 100 g of fuel burned, 

Y9 = g-mol of SO2 produced per 100 g of fuel burned, and 

Y10 = g-mol of air for burning 100 g of fuel. 

6-5 
RH Pv Bwa (mol / mol) = ⋅ 100 Pa 

Thus, the vector Y was solved at every second from the corresponding CEM measurement 

results and ambient moisture data. The fuel-specific emission factors of gaseous pollutants were 

then calculated by Eq. 6-4 through 6-6. 

For CO 

EF =10 ⋅Y ⋅ MW (g/kg fuel burned) CO 6 CO (6-4)

For THC 

EF =10 ⋅Y ⋅ MW (g/kg fuel burned) THC 7 C3H 8 (6-5)

For NOX

 (g/kg fuel burned) (6-6) 

where 

MWCO = molecular weight of CO, 

MWC3H8 = molecular weight of C3H8, and 

MWNO = molecular weight of NO. 

In order to solve Eq. 6-3, the moisture content in ambient air on a dry basis, h, must be known. 

This was obtained from the weather reports provided by the State Climate Office. The reports 

provided the data of air temperature (°F), relative humidity (percent), atmospheric pressure 

(Mbar), and wind speed (mi/h) monitored in every hour. In order to calculate the ambient 

moisture content as a function of test time, the reported temperature Ta, relative humidity RH, 

and pressure Pa were first interpolated to obtain the data in every second. The moisture fraction 

in ambient air, Bwa, was then calculated by Eq. 6-7. 

(6-7) 

where 

RH = relative moisture, %, 

Pv = water vapor pressure at temperature (Ta), Mbar, and 

Pa = atmospheric pressure, mbar. 

The ambient air moisture on dry basis, h, was calculated from the above moisture fraction by 

Bwa h = (1− Bwa ) (6-8) 

6-6 
⎡ m g/gmole×(mmHg)⎤

Kp = 34.96 ⎢ ⎥ ⎣sec °K ×mmH O2 ⎦

M s = Md (1− Bws ) +18Bws 

6.3 Estimate of Exhaust Flow Rate 

The Y1 obtained from solving the SAE matrix equation can be used to estimate the exhaust flow 

rate by 

1 Q = Y ⋅ 0.024055 ⋅ F ⋅ (6-9) SAE 1 fuel 60 

where 

QSAE = exhaust gas flow rate at standard condition (20 °C and 1 atm), m3/min, 

0.024055 = volume per mole at standard condition (20 °C and 1 atm), m3/g-mol, and 

Ffuel = fuel flow rate, 102 g/h. 

As mentioned previously, the exhaust gas flow rate in this study can also be estimated from the 

annubar meter measurement. The annubar meter records the pressure difference, which can be 

used to calculate the exhaust gas velocity in the stack by 

(T )s avg V = K C ( ΔP )avg P M s p p

s s
 (6-10) 

where 

Vs = exhaust gas velocity (m/sec), 

Kp = Pitot tube constant: 

Cp = manufacturer specified pitot tube coefficient = 0.6168, 

ΔP = pressure drop reading from pressure transducer (mmH2O), 

Ts = average exhaust gas temperature from thermocouple (K), 

Ps = absolute exhaust gas pressure from pressure transducer (mmHg), and 

Ms = molecular weight of wet exhaust gas (g/g-mole). 

The molecular weight of wet exhaust (Ms) in Eq. 6-10 is calculated from the dry exhaust 

molecular weight (Md) and the moisture fraction (Bws) by 

(6-11) 

6-7 
The Md in the Eq. 6-11 is estimated from the exhaust gas composition obtained from the 

monitoring instruments by 

s = 0 44 ⋅ CO ( ) + 0 32 .⋅ O2 M . (%) 2 % 

+ 0 28 . ⋅ N2 [ ( ) % + CO( ) % ] (6-12) 

The Bws in the Eq. 6-11 is estimated from the exhaust gas composition and ambient air moisture 

fraction (Bwa) as 

B = − 1 {[158 1 ( − B )] [0 79 1 . ( − B ) F + ws wa wa 

2 B wa (100 − O 2 ( ) % % − CO( ) − CO 2 ( ) % )]} (6-13) 

where 

F = 200 + RHC[CO(%) + CO2(%)] and the molar ratio of hydrogen to carbon, RHC, in the 

fuel is obtained from fuel analysis. 

Once the average velocity of exhaust gas in the stack is calculated by the Eq. 6-10 to 6-13, the 

exhaust flow rate under standard conditions can be determined using 

⎛ 293⋅ P ⎞ Qannubar = 60 ⋅Vs ⋅ A⋅⎜ ⎟ (6-14) ⎜ s ⎟ T ⋅760 ⎝ s ⎠

where 

Qannubar = exhaust flow rate at 293 °K and 760 mm Hg (m3/min), 

Vs = exhaust gas velocity from Eq. 6-10 (m/s), 

A = cross section area of exhaust stack (m2), 

Ts = exhaust gas temperature (°K), and 

Ps = absolute exhaust gas pressure (mmHg). 

6.4 PM Emission Factors 

Various instruments were used in this study to measure PM-2.5 concentrations, either in mass or 

as particle counts. These measurements were implemented in both plume and background 

sampling systems. Since the plume sampling was diluted with ambient air, the dilution ratio in 

the plume system must be determined before the emissions can be estimated. In this study, a 

known amount of trace FM-200 was injected into the exhaust stack, and its concentration in the 

plume system was then measured. The dilution ratio was consequently calculated by dividing the 

trace concentration in the stack with the concentration in the plume by 

6-8 
200.3⋅CM ⋅ fC ⋅ DR EF = (mg/kg fuel) M CCO 3⋅CTHC CCO2 + +

10000 10000 

Rtr ⋅1000

QSAE DR = Ctr (6-15) 

where 

DR = dilution ratio in the plume 

Rtr = injection rate of FM-200 tracer into the exhaust stack (L/min), 

QSAE = stack exhaust flow rate determined by the SAE method (m3/min), and 

Ctr = FM-200 tracer concentration measured in plume (ppmv). 

The fuel-specific emission factor for PM particle counts, EFN, was calculated from the particle 

number concentration in the plume recorded through a PM monitoring instrument, such as the 

ELPI, SMPS, or nano SMPS, using 

200.3⋅106 ⋅CN ⋅ fC ⋅ DR EFN = C CO 3⋅CTHC CCO2 + +

10000 10000 (particles/kg fuel) (6-16) 

where 

CN = particle count concentration in plume recorded by the PM instrument (1/cm3), 

fC = mass fraction of carbon in diesel fuel (g/g), 

DR = plume dilution ratio, 

CCO2 = wet basis carbon dioxide concentration in exhaust (%) , 

CCO = wet basis carbon monoxide concentration in exhaust (ppmv), and 

CTHC = wet basis hydrocarbon concentration in exhaust (ppmv). 

The fuel-specific emission factor for PM mass, EFM, was calculated from the particle mass 

concentration recorded by the PM instrument, such as the TEOM or QCM, using 

(6-17)

where 

CM = particle mass concentration in plume recorded by the PM instrument, mg/m3

. 

6-9 
Because the CO and THC concentration in the exhaust were usually negligible compared to CO2 

for diesel engine combustion, the second and third terms in the denominator of the Equations. 6­

16 and 6-17 were neglected in the calculation in this study. Since the CEM measured CO2 

concentration on a dry basis, the wet basis CO2 concentration in this study was obtained from the 

solution of Y1 and Y2 in the Equation. 6-3 as 

Y2 ×100 CCO 2 = Y1 (%) (6-18) 

6.5 Conversion of Emission Measures 

There are many ways to quantify emissions. As discussed previously, the fuel-specific emission 

factor is expressed based on one kg of fuel burned. The emissions can also be expressed as a 

distance-specific emission factor based on one kilometer of vehicle travel. In addition, the 

emission rate expressed in milligrams per second is also used as a measure of the emissions. The 

emission rate was calculated from the fuel-specific emission factor through 

Ffuel ER = EF ⋅

3600 (6-19) 

where 

ER = emission rate (particles/s or mg/s), 

EF = fuel-specific emission factor (particles/kg fuel or mg/kg fuel), and 

Ffuel = fuel feed rate (kg/h). 

The emission rate was then used to calculate the distance-specific emission factor by 

ER EM = 5782.4⋅
 

UV
 (6-20) 

where 

EM = distance-specific emission factor (particles/km or mg/km), 

ER = emission rate (particles/s or mg/s), and 

UV = vehicle speed (km/h). 

Use of fuel-specific emission factors for estimating light-duty vehicle emissions has been 

reported by Singer and Harley (1996). Recently, this approach has been utilized by Dreher and 

Harley (1998) for establishing emission inventory for heavy-duty diesel trucks. In this method, 

emission factors are normalized to fuel consumption, and vehicle activity is measured by the 

amount of diesel fuel consumed. Its advantages over the travel based approach are (1) the diesel 

fuel consumption data are readily available from the tax records, and (2) the fuel based emission 

6-10 
factors fluctuate much less than travel based emission factors as driving conditions change 

(Pierson et al., 1996). 

6.6 Particle Size Distribution 

The ELPI, SMPS, and nano SMPS were used in this study to determine the particle size 

distributions of truck PM emissions under various test conditions. Although the ELPI is a 

powerful monitoring tool for fast measurement of particle number concentration, it was operated 

primarily to collect size-classified samples for organic speciation. Therefore, in this report, only 

the particle size distributions monitored by SMPS and nano SMPS are discussed. To obtain an 

average particle size distribution for a specific test, the dN/dlogDp data recorded in all the run 

passes of the test for each size bin were averaged to generate the dN/dlogDp for that size bin. 

The dN/dlogDp data of all size bins were then smoothed against the particle size by using the 

supsmooth function in MathCad Version 2001 Professional. The test average particle size 

distribution was then obtained by multiplying the smoothed dN/dlogDp data by the plume 

dilution ratio. 

After the particle size distribution was determined, the total particle number concentration and 

particle geometric mean diameter were calculated for the test by 

dN d d )i 

i 1 

) ( ∑ i =

M

N
 =
 ( logD
 p × log DP

(6-21) 

and 

⎛ M

⎜

⎜

⎜

⎜

⎜

⎞

⎟

⎟

⎟

⎟

⎟

⎠

)

i dN d 

=1

⎝

( ) ×( ∑ i logD d logD logD pi p p ×

i 

N 

GMD =10 (6-22) 

where 

N = particle number concentration (particles/cm3

), 

DP = particle size (nm), 

M = total number of size bins, and 

GMD = geometric mean diameter, nm 

6.7 Vehicle Power Demand 

In this report, the gaseous and PM emissions were studied to investigate their correlations with 

vehicle power demand. In general, the total power demand for a vehicle consists of road load 

power, PRL, and acceleration power, Paccel, as expressed by 

6-11 
 W (−V +V ) 1 3 2 = K W + K V + K V 0 1 2 2 2 9.81 2
 

W (−3V + 4V −V ) 1 2 3 2 ⋅ = K W + K V + K V 0 1 1 2 1 9.81 2 

Χ
 

Χ
 

Χ
 

W (−V +V ) N −2 N 2 = K W + K V + K V 0 1 N −1 2 N −1 9.81 2 

P = P RL + Paccel (6-23) 

Under steady-state conditions, the total power demand is determined only by the road load 

power. Thus, the road load power is the power required for a vehicle to maintain a constant 

speed. The road load depends on rolling resistance of the tires with road, aerodynamic drag, and 

road grade resistance and is expressed as 

RL = K W cosθ + K V + K V 2 

0 1 V 2 V +W sinθ (6-24) 

where 

RL = Road load (N) 

K0 = rolling resistance coefficient (dimensionless), 

W = Gross vehicle weight (N), 

θ = grade angle determined from micro-barometer readings, 

K1 = first degree drag coefficient (N-s/m), 

K2 = second degree drag coefficient (N-s2

/m2), and 

VV = vehicle speed (m/s). 

In this study, the coefficients, K0, K1, and K2, in the above equation (6-24) were determined from 

the coast-down testing as described in Sec. 4.5. The coast-down tests were conducted at the 

segment of US-70 where the emissions tests were performed. During the coast down tests, the 

truck was initially brought to a speed of approximately 105 or 56 km/h, and then the 

transmission was switched to neutral and the variation in vehicle speed with time as the vehicle 

slowed down was recorded every second. From N readings of vehicle speed (V1, ..., VN from 105 

to 10 km/h), a system of linear equations (6-25) can be established based on finite differences 

(6-25a)

(6-25b)

(6-25n-1)

6-12 
W (−3V + 4V −V ) N N −1 N −2 2 = K0

W + K1VN + K2VN 9.81 2 

ma = K W + K V + K V 2 

0 1 n 2 n 

(6-25n)

where “9.81” is the acceleration due to gravity (m/s2

) and the gross vehicle weight (W) of the 

truck is known. These equations are of the form 

where 

m = vehicle mass = W/9.81, kg and 

a = (Vx...Vy)/2 

The term a is the finite difference expression of the truck deceleration during coast-down tests. 

The parameters K0, K1, and K2 were estimated from the multipoint data equations 6-25a to 6-25n. 

After the coefficients—K0, K1, and K2—were determined, the road load, RL, was then calculated 

by the Eq. 6-24, and then the road load power, PRL, was given by 

RL ×UV P = (6-26) RL 3600 

where 

PRL = road load power demand (kW), 

RL = road load (N), and 

UV = truck speed (km/h). 

The road grade in Eq. 6-24 was determined by the microbarometer, which monitored the 

pressure changes at every second caused by the change in road altitude as the truck was traveling 

along the road. The road grade was calculated from the measured pressure change and the 

corresponding truck travel distance by 

70310 ⋅ dP Grade = (6-27)

dL ⋅ ρ a 

where 

Grade = road grade (%), 

dP = pressure change measured by the micro-barometer as the truck moves for dL 

distance along the road (psi), 

dL = road distance (m), and 

6-13 
ρa = density of air (1.2 kg/m3

). 

The road grade angle θ in Eq.6-24 was then given as 

⎛ Grade ⎞ θ = arctan⎜ ⎟ (6-28) ⎝ 100 ⎠

Eqs. 6-24 and 6-26 indicate that, under steady-state conditions, the three primary factors— 

vehicle speed, weight, and road grade—affect the truck power demand. 

6-14 
0 100 200 300 400 

Coast Down Run Time (sec) 

0 

20 

40 

60 

80 

100 

120 

Vehicle Velocity (km/hr) 

Loaded Run 1 

Loaded Run 2 

Unloaded Run 1 

Unloaded Run 2 

Chapter 7 

Analysis of Vehicle Operating Parameters 

7.1 Coast-Down Test Results 

Coast-down testing was performed with the truck at both an unloaded GVW of 21,350 kg and a 

loaded GVW of 33,890 kg. Figure 7-1 presents the vehicle speed as a function of time recorded 

for each coast-down test. In the figure, there are two replicate tests for each truck weight 

condition. The curves for the loaded truck are on the top, which is believed to be due to its higher 

momentum. The figure also shows that the curves obtained from the replicate tests are almost 

identical one to another, indicating good repeatability. 

Figure 7-1. Truck Speed Recorded as a Function of Time in the Coast-Down Tests 

From the coast-down test results, the system of linear equations (6-25a to 6-25d) was used to 

calculate the three coefficients—K0, K1, and K2—which are shown in Table 7-1. The correlation 

coefficients (r2

) obtained from the data regression for individual tests range from 0.945 to 0.971. 

By averaging each of the three coefficients for all the four tests, the overall average coefficients 

were estimated, which are provided at the bottom of the table. 

To verify the calculated results, the above three average coefficients were substituted into the 

right side of Equations 6-25a to 6-25N to calculate the value of WK0 + VVK1 + VV

2

K2 as a 

function of the truck speed. The results were then compared to the values calculated from the left 

side of Equations 6-25a to 6-25N at the same truck speed. Figure 7-2 shows the comparison, in 

which the results calculated from both sides of Equations 6-25a to 6-25n agree well, having a 

correlation coefficient of r2

 = 0.979. As expected, the coefficients are independent of vehicle 

7-1 
 

 

 

 

 

6000 

Loaded Run 1 

Loaded Run 2 

5000 Unloaded Run 1 

Unloaded Run 2 

4000 

3000 

r

2

 = 0.979 

2000 

1000 

0 

0 1000 2000 3000 4000 5000 6000 

Measured ma (N) 

Calculated WK0 + VK1 + V2

K2 (N) 

weight and can be used in Equation 6-24 for road load power calculation under different truck 

conditions. 

Table 7-1. Road Load Equation Coefficients Determined From the Coast-Down Tests 

Test Run 

K0

-

K1

N×s/m 

K2 

N×s2

/m2 r

2 

1 0.00466 21.919 3.421 0.945 

Loaded 

2 0.00426 55.532 2.566 0.962 

1 0.00542 3.793 3.945 0.956 

Unloaded 

2 0.00348 57.000 2.129 0.971 

Overall Average 0.00445 34.561 3.015 0.979 

Figure 7-2. Comparison between the Coast-Down Experimental Data and the Calculation 

Results from the Three Coefficients 

7.2 Road Grade Determination 

The road grade in this study was determined from the microbarometer measurements. Table 7-2 

presents the results of the road grade and angle θ obtained from Equations 6-27 and 6-28 for 

each run pass of Test 4 (T4) during the New Bern phase of this study. The table also includes the 

average truck speed for each pass and the ensemble average of the ten passes for T4, which was 

106.8 km/h with a standard deviation (SD) of 0.3 km/h. The test average road grade was 0% with 

a SD of 0.02%, which indicates the road grade angle was 0Ε. The 0% road grade determined 

during T4 indicates that tests T1 to T20 conducted on the segment of US-70 near New Bern, NC 

were conducted at near-zero grade. 

7-2 
 

 

 

 

 

 

 

 

 

 

Table 7-2. Road Grade Determined during Test 4 for the Segment of US-70 

Road Grade 

Speed 

Pass Grade θ 

km/h % Degree 

1 105.18 0.01 0.00 

2 106.86 -0.01 0.00 

3 106.86 0.03 0.00 

4 162.45 -0.03 0.00 

5 106.21 -0.01 0.00 

6 106.92 -0.03 0.00 

7 106.91 0.03 0.00 

8 106.92 0.00 0.00 

9 106.93 0.02 0.00 

10 106.97 -0.02 0.00 

Average 106.8 0.00 0.00 

SD 0.3 0.02 0.00 

For the segment of I-77 near Fancy Gap, VA where 4% road grade tests T21 and T22 were 

conducted, the response of the microbarometer measurement with the variation in road grade 

variation was monitored. Figure 7-3(a) presents the recorded microbarometer readings as the 

truck traveled on I-77 north from Exit 1 to Exit 8, showing a 10 km constant uphill grade of 

approximately 3.93% from Exit 1 and then a downhill grade of 1.16% to Exit 8. The road grade 

test data in this study were mostly collected in the 10 km uphill section of 3.93% road grade. I-77 

southbound from Exit 8 to Exit 1, as shown in Figure 7-3(b), consists approximately of four 

sections that begin with 1.6 km of 0.98% uphill grade followed by another 1.6 km of 3.81% 

downhill grade. The downhill grade is then reduced to 1.4% for the next 1.2 km, and finally, the 

road goes downhill at a constant grade of 4.4% in the last section. These results demonstrate the 

great potential of the microbarometer in road grade measurement for on-road diesel truck 

emissions testing. 

7.3 Plume Dilution Ratio 

The plume dilution ratio and its uncertainty for each pass were determined from the tracer gas 

measurement data. From the pass-average plume dilution ratios and uncertainties, the test 

average dilution ratio and uncertainty were then calculated. Table 7-3 is a summary of the 

calculation results. 

7-3 
98 

(a) I-77 North Bound 

92 

93 

94 

95 

96 

97 

98 

Microbarometer Reading (kP) 

0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

0.35 

0.40 

0.45 

Altitude (km) 

MBar Reading 

Altitude 

3.93% 

-1.16% 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 

Travel Distance (km) 

(b) I-77 South Bound 

95 

94 

93 

92 

MBar Readings 

Altitude 

0.98% 

- 3.81% 

- 1.40% 

- 4.40% 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 

Travel Distance (km) 

97 

0.35 

96 0.30 

0.45 

0.40 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

Microbarometer Reading (kP) 

Altitude (km) 

Figure 7-3. Road Grade of Highway I-77 Measured by the Microbarometer (Mbar) 

Northbound (a) and Southbound (b) 

7-4 
 

 

 

 

 

 

 

 

 

 

 

 

Table 7-3. Dilution Ratios and Their Uncertainties for Individual Tests 

Test Condition 

Test No. Date 

Average SDa

 RSDb Average SD 

5 10/19/04 69.81 4.89 7.0 

63.74 3.01 

6 10/20/04 57.68 3.51 6.1 

7 10/21/04 73.00 12.81 17.6 

72.68 8.04 

8 10/21/04 72.36 9.72 13.4 

9 10/22/04 99.35 19.95 20.1 

95.89 12.96 

10 10/22/04 92.43 16.55 17.9 

11 11/23/04 75.21 8.58 11.4 

12 11/23/04 75.21 8.58 11.4 

75.21 8.58 

13 11/26/04 88.76 18.43 20.8 

14 11/26/04 88.76 18.43 20.8 

88.76 18.43 

15

16

10/27/04 

10/27/04 

78.31

82.28

13.06

 13.03 

16.7

15.8 

80.30 9.22

17

18

10/28/04

 10/28/04 

101.66

70.98

15.36

 12.22 

15.1

17.2 

86.32 9.82

19 10/29/04 68.70 3.31 4.8

74.28 3.11

20 10/30/04 79.86 5.26 6.6

a. SD = standard deviation. 

b. RSD = relative standard deviation. 

In this study, the tracer concentration was not successfully monitored under all circumstances. 

For those run passes that failed to properly collect the tracer concentration, the corresponding 

test-average dilution ratio and its uncertainty calculated from the correct run passes was used for 

the missing pass-average dilution ratios. For T12 and T14, the tracer measurements failed in all 

the run passes, and their dilution ratios were assumed to be equal to the test averages of T11 and 

T13, respectively (see the numbers in the shaded cells in the table). 

The relative standard deviations (RSD) in the table indicate that, because of the difficulty in 

tracer gas measurements, a relative error up to 20% in dilution ratio determination was observed 

for most of the tests in this study. The large uncertainty in the plume dilution ratio determination 

had a substantial inverse impact on the results of the PM emission factor and rate calculations. 

Therefore, CO2 in the plume should be measured directly in the future on-road studies. 

Dilution ratio control was reported to have great influence on the particle size distribution. In 

their diesel engine tests, Abdul-Khalek et al. (1998) found that, for dilution ratios below 60, the 

entire size distribution changed with dilution ratio. The dilution ratio had more influence on the 

particles smaller than 30 nm. Increasing the primary dilution ratio from 4 to 60 resulted in 

significant decrease in number concentrations. In our study, the dilution ratio was controlled to 

be greater than 60 for most of the tests. Only T6 had a slightly lower dilution ratio of 57.7. 

7-5 
Therefore, the plume dilution ratios used for this study are considered appropriate for PM 

emissions measurement. 

7.4 Truck Driving Conditions 

The effects of truck driving conditions on emissions were investigated in this study by varying 

truck GVW and driving speed as previously discussed. 

The actual average truck speed for each pass of a test was determined from the Datron optical 

speed sensor data recorded in the field as a function of time. The test-average speed was then 

calculated from the pass-average speeds of all the passes in the same test. Table 4-1 summarized 

the test-average truck speed and its uncertainty for each test. The same color bars represent the 

replicate tests under the same experimental conditions. The very small uncertainties for the level 

road tests, T5 to T20, indicate that the truck was operated at constant speeds and a steady-state 

condition was well established. However, the fluctuation in truck speed increased during the two 

uphill tests, T21 and T22, because it was difficult to maintain a constant speed on the steeper 

grade The average truck speeds for T21 and T22 were calculated from the data collected when 

the truck was maintained at nearly constant speed while going uphill with a constant road grade. 

7.5 Fuel Types and Compositions 

There were three types of diesel fuel used in this study. Pump diesel was obtained locally and 

was used in the shake-down tests (T1 to T4) and the 4% road grade tests (T21 and T22). The 

base fuel (a low-sulfur diesel) and a 20% soy-based biodiesel blend (B20) were used in the level 

road tests (T5 to T20) to investigate the effects of fuel type on truck emissions. Table 7-4 

provides the compositions and other important properties of the fuels used. The value of g-moles 

for each element was estimated based on 100 grams of fuel and is included in the table. The 

composition in the table shows that both base fuel and B20 have sulfur contents less than 15 ppm 

in weight as compared with the pump diesel (394 ppm). The three fuels have essentially the same 

hydrogen content. In comparison to the other two fuels, B20 contains about 3.4% oxygen and has 

the lowest carbon content, resulting in its slightly lower gross heat value (GHV). Among three 

fuels, the cetance number is highest for B20. The impact of fuel type on truck emissions will be 

discussed later in this report. 

7.6 Effects on Fuel Consumption and Exhaust Flow 

The fuel flow rate is known to closely relate to the truck driving conditions. The monitored fuel 

feed rates under the different experimental conditions were processed from engine computer data 

to obtain the pass-average and test average fuel flow rates. 

The effects of experimental conditions on fuel flow rate are illustrated as a bar chart by Figure 7­

4. The figure shows that fuel type had little impact on fuel consumption. The increase in truck 

GVW from 21,350 kg to 33,890 kg only increased the fuel flow rate by 10 percent and 15 

percent for the base fuel and B20, respectively, at zero grade. However, truck speed and road 

grade had strong effects on fuel consumption. In the level road tests, the fuel consumption 

increased by 2.5 times when the truck speed increased from 56 to 105 km/h. The results also 

show much greater fuel consumption for the truck driving upgrade than on the level road. If the 

7-6 
 

 

 

 

 

finding of fuel type having little impact on fuel consumption can also be applied to the pump 

fuel, approximately twice as much fuel was required for the unloaded truck driving on the 4% 

grade at about 99 km/h as compared to zero grade. The comparison of the results for the two 

road grade tests seem to indicate that, unlike what was seen in the level road tests, GVW had 

more influence than truck speed on the fuel feed rate when traveling uphill. 

Table 7-4. Fuel Compositions and Properties 

Element 

C 

Pump Diesel 

wt% g•mole/ 

100 g fuel 

86.794 7.23 

Base Fuel (LowSulfur Diesel 

wt% g•mole/ 

100 g fuel 

86.238 7.18

Bio-Diesel (B20) 

wt% g•mole/ 

100 g fuel 

 83.378 6.94 

H 13.153 13.05 13.760 13.65 13.200 13.09 

O 0 0 0 0 3.420 0.449 

N 0.014 0.0010 0.0009 0.0001 0.0007 0.0001 

S 0.0394 0.0012 0.0015 0.0000 0.0012 0.0000 

H/C (mol/mol) 

Density (g/cm3

)

1.806 

 0.854 

1.901 

0.857 

1.886 

0.846 

Flash Point (ΕC) 

Cetane Number 

64.4

45.1 

 64.4 

50.2 

67.8 

51.1 

GHV (kcal/g) 10.80 10.93 10.61 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/h 105 km/h 73 km/h 97 km/h 

Level Grade 4% Grade 

Fuel Flow Rate (kg/h) 

Base Diesel 

B20 

Pump Diesel 

Figure 7-4. Effects of Driving Condition and Fuel Type on Fuel Consumption 

7-7 
0 

5 

10 

15 

20 

25 

30 

35 

40 

Fuel Cunsumption (kg/h) 

Base Diesel 

B20 

Pump Diesel 

y = 0.0001x2 + 0.0216x + 8.2783 

r2 = 0.9896 

The correlation between fuel consumption and truck power demand was investigated as shown in 

Figure 7-5. When the fuel flow rates obtained from various experimental conditions were plotted 

against truck power demand, a second order polynomial equation (7-1) with a correlation 

coefficient greater than 0.998 was obtained. It demonstrated that the fuel feed rate was closely 

correlated to the truck power demand regardless of fuel type. 

0 50 100 150 200 250 300 350 400 

Power Demand (kW) 

Figure 7-5. Relationship of Fuel Consumption with Truck Power Demand 

Ffuel = 6.0791+ 0.1311PRL + 0.0002PRL 

2 (7-1) 

where 

Ffuel = fuel consumption, kg/h, and 

PRL = truck road load power, kW. 

The intercept of 6.0791 kg/h in the equation (7-1) might be attributable to the fuel consumption 

by truck idle and ancillary loads, such as the air conditioner, etc. (Brodrick et al. 2004) 

The close correlation between the observed fuel consumption and power demand explains why 

the truck speed had a substantial impact on fuel consumption. This is because road load power is 

dominated by truck speed as described previously in Equations 6-24 and 6-26. 

Similar effects were also observed for the exhaust flow rate. Figure 7-6 presents the exhaust flow 

rate data under different experimental conditions as calculated by Equation 6-9 from the results 

of the SAE equation (Equation 6-3). The figure again shows that, as expected, the exhaust flow 

rate was directly related to truck speed and road grade. It is expected that the experimental 

7-8 
conditions should also have a similar relationship to exhaust flow as was the case for fuel 

consumption because more fuel requires more volume of combustion air which, in turn, produces 

more volume of exhaust. 

0 

5 

10 

15 

20 

25 

30 

35 

40 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/h 105 km/h 73 km/h 97 km/h 

Level Grade 4% Grade 

Exhaust Flow Rate (m3 /min) 

Base Diesel 

B20 

Pump Diesel 

Figure 7-6. Effects of Test Conditions on Exhaust Flow Rate 

As with fuel consumption, the exhaust flow rate was found to be only slightly affected by fuel 

type. The B20 produced relatively less exhaust flow than the base fuel. For the unloaded truck, 

the exhaust flow rate from the base fuel was 10% to 15% higher than that of the B20. When the 

GVW increased to 33,890 kg, the impact of fuel type lessened, and the base fuel produced only 

about 5% to 10% more exhaust. The relatively lower exhaust flow rate from the use of B20 is 

probably caused by slight differences in fuel properties affecting engine operation. If the 

relatively small influence of fuel type is neglected, the exhaust flow rate can be plotted as a 

function of truck power demand as demonstrated in Figure 7-7. 

7-9 
0 

5 

10 

15 

20 

25 

30 

35 

40

Fuel Cunsumption (kg/h) 

Base Diesel 

B20 

Pump Diesel 

y = 0.0001x2 + 0.0216x + 8.2783 

r2 = 0.9896 

0 50 100 150 200 250 300 350 400 

Power Demand (kW) 

Figure 7-7. Correlation of Exhaust Flow Rate with Truck Power Demand 

This figure shows that the exhaust flow rate under the steady state experimental conditions can 

be approximated from the power demand by Equation 7-2. 

Q = 8.2783 + 0.0216P + 0.0001P 2 (7-2) RL RL 

where 

Q = exhaust flow rate, m3

/min, and 

PRL = road load power, kW. 

The exhaust flow rate data calculated from the annubar measurements were compared to the data 

obtained from the SAE calculation as shown in Figure 7-8. The plot of the results shows a 

correlation coefficient of r2

 = 0.931 between the results obtained by the two methods. The 

intercept of 2.4747 in the correlation equation probably represents a systematic error existing in 

the annubar measurement. It seems that the annubar overestimated the exhaust flow rate for all 

the tests. As discussed previously, the exhaust gas velocity fluctuated with run time. In addition, 

the radial distribution of gas velocity across the exhaust pipe makes it difficult to accurately 

determine the flow rate from the velocity measurement. Thus, the annubar measurement for 

exhaust flow rate has relatively greater uncertainty than the method recommended by SAE. 

7-10 
y = 1.0086x + 2.4747 

r2 = 0.9311 

8 

10 

12 

14 

16 

18 

8 10 12 14 16 18 

Exhaust Flow Rate by Annubar Measurement (m3 /min) 

Exhaust Flow Rate by SAE Calculation (m3/min) 

Figure 7-8. Comparison between Annubar Meter Measurement and SAE ARP 1533 

Calculation 

7-11 
Chapter 8 

Gaseous Emissions 

The gaseous emissions monitored in the truck exhaust include NOX, CO, and THC. To 

characterize the emission behavior under different experimental conditions, the fuel-specific 

emission factor for each pollutant was calculated every second from the gaseous pollutant 

concentrations using Equations 6-3 to 6-6 and the emission rate and distance-specific emission 

factor were calculated using Equations 6-19 and 6-20. The pass-averaged and test-averaged fuelspecific emission factor, distance-specific emission factor, and emission rate and their 

uncertainties were then calculated for individual passes and tests. The results are used in the 

following discussions of the effects of experimental conditions on these gaseous emissions. 

8.1 NOX Emissions 

The test-average NOX fuel-specific emission factors, emission rates, and distance-specific 

emission factors and their corresponding standard deviations (SDs) obtained in this study are 

summarized in Table 8-1. The relative standard deviation (RSD) of the fuel-specific emission 

factor for each test is also included as a measure of the data quality, indicating that the 

measurement of NOX emission data for this study are generally satisfied with an average RSD of 

2.79%. For the level road test results, the RSDs are all below 0.1%. The road grade tests has a 

RSD ranging from 1% to 2.5%. 

The effects of the experimental conditions on NOX fuel emission factor, emission rate, and 

distance-specific emission factor observed in this study are illustrated in Figure 8-1. The figure 

shows that all three measures of NOX emissions increase significantly with truck speed and road 

grade. However, the changes in truck gross weight and fuel type were found to have less impact 

on NOX emissions. 

Figure 8-1 shows that fuel type did not have a major effect on the NOX fuel-specific emission 

factor when the truck was loaded (33,890 kg) and ran at higher speed (105 km/h). However, 

using B20 reduced the fuel-specific emission factor by about 9% from the base fuel condition 

when the loaded truck was running at 56 km/h. For the unloaded truck (21,350 kg) at low speed 

(56 km/h), the switch from base fuel to B20 reduced the fuel-specific NOX emission factor by 

about 12%; whereas a 5% decrease in NOX was observed when the unloaded truck was traveling 

at 105 km/h. 

The NOX emissions were strongly affected by truck speed. For the unloaded truck (GVW = 

21,350 kg), the fuel-specific emission factor increased by 40% for the base fuel and by 44% for 

B20 when the truck speed was increased from 56 km/h to 105 km/h. For the loaded truck (GVW 

= 33,890 kg), the increase in fuel-specific emission factor at higher truck speeds was found to be 

about 48% for the base fuel and 53% for the B20 as the truck speed increased from 56 to 105 

km/h. 

8-1 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8-1. Test Average Emission Factors and Rates for NOX 

Test No. 

EFa 

(g/kg fuel) 

ERb 

(g/s) 

EMc 

(g/km)d PRL e 

Ave SD RSD (%) Ave SD Ave SD kW 

T5 21.99 0.19 0.87 0.189 0.006 6.35 0.20 153.50 

T6 21.48 0.19 0.90 0.186 0.006 6.23 0.20 153.37 

T7 12.30 0.61 4.92 0.044 0.004 2.72 0.27 45.76 

T8 10.26 0.25 2.45 0.034 0.002 2.13 0.14 45.54 

T9 13.97 0.50 3.59 0.045 0.002 2.77 0.15 36.95 

T10 13.56 0.51 3.75 0.043 0.002 2.66 0.14 36.65 

T11 22.60 0.14 0.63 0.183 0.005 6.13 0.16 137.46 

T12 22.96 0.14 0.62 0.182 0.004 6.11 0.15 137.59 

T13 21.72 0.16 0.73 0.166 0.018 5.92 0.61 134.42 

T14 21.74 0.22 1.01 135.49 

T15 13.57 0.44 3.27 0.042 0.002 2.57 0.14 37.27 

T16 10.71 0.23 2.16 0.033 0.001 1.98 0.09 37.73 

T17 10.27 0.47 4.61 0.037 0.003 2.22 0.17 47.04 

T18 10.29 0.42 4.04 0.035 0.003 2.17 0.16 45.99 

T19 21.67 0.15 0.68 0.186 0.006 6.31 0.21 150.13 

T20 22.47 0.16 0.73 0.201 0.007 6.78 0.24 152.55 

T21 22.54 2.44 10.81 0.435 0.066 16.24 2.60 316.15 

T22 23.98 1.07 4.47 0.514 0.029 25.45 1.79 338.12 

Averagef 2.79 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

f. arithmetic average. 

8-2 
Distance-Specific Emission Factor (g/km) Emission Rate (g/sec) Fuel-Specific Emission Factor (g/kg fuel) 30 

25 

20 

15 

10 

5 

0 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

30 

25 

20 

15 

10 

5 

0 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Base Diesel 

B20 

Pump Diesel 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Base Diesel 

B20 

Pump Diesel 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Base Diesel 

B20 

Pump Diesel 

Figure 8-1. Effects of Experimental Conditions on NOX: Fuel-Specific Emission Factor 

(top); Emission Rate (center); and Distance-Specific Emission Factor (bottom) 

8-3 
 

0 50 100 150 200 250 300 350 400 

Power Demand (kW) 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

Emission Rate (g/sec) 

Level Road, Base Fuel 

Level Road, B20 

4% Road Grade, Pump Fuel 

y = 0.0015x-0.0275 

r

2

 = 0.991 

The truck GVW had less effect on NOX emissions at the same speed. Compared to the unloaded 

condition, increasing GVW from 21,350 to 33,890 kg reduced the NOX fuel-specific emission 

factor at 56 km/h by 18% for the base fuel and by 15% for B20. At 105 km/h, the truck load had 

no significant impact on the emission factor. 

Determination of the road grade effect on NOX emissions in this study is difficult because tests 

were conducted with different fuels, on only two road grades, and the truck speeds on the steeper 

grade did not match those on the level road. However, as expected, a general trend shows that the 

truck always produces more NOX emissions when running on a road with a higher grade than on 

a level road. 

By comparing Figure 8-1 with Figures 7-5 and 7-7, quite similar trends were found on how the 

experimental conditions affected the NOX emission rate, fuel consumption, and exhaust flow 

rate. This implies that, just like fuel consumption and exhaust flow, a correlation between the 

NOX emission rate and truck power demand may exist regardless of the type of fuel used, as 

shown by Figure 8-2. Figure 8-2 is a plot of NOX emission rate with truck power demand, 

showing that, within the experimental conditions of this study, the NOX emission rate can be 

correlated (with a correlation coefficient of 0.991) to truck power demand by linear Equation 

8-1. 

Figure 8-2. Correlation between NOX Emission Rate and Power Demand 

ER NOx = −0.0275 + 0.0015PRL (8-1) 

where 

ERNOx = NOX emission rate (g/sec), and 

PRL = road load power, kW 

The linear correlation of NOX with power demand is consistent with that found by a number of 

previous investigations (Yanowitz et al., 2000; Ramamurthy et al., 1998; Brown et al., 2002). 

8-4 
 

 

 

 

 

 

 

 

 

 

 

A three-way analysis of variance (ANOVA) with two replicate tests for each factor was used to 

further analyze the effects of three parameters (fuel type, vehicle speed, and GVW) and their 

interactions in the level road tests on the NOX fuel-specific emission factor. Table 8-2 is a 

summary of the analysis results, indicating that, within the range of experimental conditions, 

truck speed is the parameter most affecting the NOX emission factor, with a descriptive level of 

significance (DLS) less than 0.0001. The second important factor is GVW with a DLS of 

0.0251, which is followed by the interaction between speed and GVW at DLS = 0.0834, and then 

fuel type at DLS of 0.1075. The other parameters have little impact on the NOX emission factor, 

and their impacts are considered to be within experimental error. 

Table 8-2. Three-Way ANOVA Results for NOX Fuel-Specific Emission Factor 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 2.7781 1 2.778 3.285 0.1075 

Speed 417.1940 1 417.194 493.284 0.0000 

GVW 6.3892 1 6.389 7.554 0.0251 

Fuel × Speed 0.9213 1 0.921 1.089 0.3271 

Speed × GVW 3.3058 1 3.306 3.909 0.0834 

Fuel × GVW 1.0139 1 1.014 1.199 0.3054 

Fuel × Speed × GVW 0.1443 1 0.144 0.171 0.6904 

Error 6.7660 8 0.846 

Total 438.5126 15 

a. SS = sum of squared measurement deviations from the overall mean.. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

8.2 CO Emissions 

The results of emission factors and emission rate obtained in this study for CO emissions are 

presented in Table 8-3. The table shows that the RSD for the test average CO fuel-specific 

emission factor was in the range of 5% to 11% for the level road tests and 30% to 80% for the 

tests at 4% road grade, indicating that the CO emission measurement had greater uncertainty 

than the NOX measurement. 

The effects of test conditions on the truck CO emissions are summarized in Figure 8-3. The error 

bars in the figure are the uncertainties in the results observed from the tests, indicating that the 

uncertainties in the CO emissions results are generally greater than in the NOX emissions 

measurements. 

8-5 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8-3. Test Average Emission Factors and Emission Rates for CO 

Test No. 

EFa 

(g/kg fuel) 

ERb 

(g/s) 

EMc 

(g/km)d PRL e 

Ave SD RSD (%) Ave SD Ave SD kW 

T5 1.54 0.11 7.1 0.013 0.001 0.44 0.03 153.50 

T6 1.39 0.11 7.7 0.012 0.001 0.40 0.03 153.37 

T7 6.62 0.74 11.3 0.024 0.003 1.46 0.20 45.76 

T8 7.35 0.80 10.9 0.025 0.003 1.52 0.19 45.54 

T9 8.11 0.67 8.3 0.026 0.002 1.60 0.15 36.95 

T10 8.92 0.74 8.3 0.028 0.003 1.75 0.16 36.65 

T11 2.32 0.12 5.3 0.019 0.001 0.63 0.04 137.46 

T12 2.25 0.13 5.6 0.018 0.001 0.60 0.04 137.59 

T13 2.05 0.12 5.7 0.014 0.002 0.46 0.06 134.42 

T14 1.94 0.16 8.1 135.49 

T15 7.27 0.58 7.9 0.022 0.002 1.37 0.12 37.27 

T16 8.06 0.62 7.7 0.024 0.002 1.49 0.13 37.73 

T17 6.28 0.59 9.5 0.022 0.003 1.36 0.16 47.04 

T18 6.50 0.67 10.3 0.022 0.003 1.37 0.17 45.99 

T19 1.70 0.10 5.9 0.015 0.001 0.49 0.03 150.13 

T20 1.34 0.11 8.5 0.012 0.001 0.40 0.04 152.55 

T21 4.52 3.60 79.7 0.082 0.066 0.77 0.20 316.15 

T22 2.13 0.63 29.5 0.046 0.013 2.27 0.68 338.12 

Averagef 13.2 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

f. arithmetic average. 

8-6 
0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Fuel-Specific Emission Factor (g/kg fuel)

Base Diesel 

B20 

Pump Diesel 

0.00 

0.02 

0.04 

0.06 

0.08 

0.10 

0.12 

0.14 

0.16 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Emission Rate (g/sec) 

Base Diesel 

B20 

Pump Diesel 

0 

1 

2 

3 

4 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Distance-Specific Emission Factor (g/km) 

Base Diesel 

B20 

Pump Diesel 

Figure 8-3. Effects of Experimental Conditions on CO: Fuel-Specific Emission Factor 

(top); Emission Rate (center); and Distance-Specific Emission Factor (bottom) 

8-7 
It can be seen from Figure 8-3 that the CO emissions were inversely related to truck speed. The 

CO fuel-specific emission factor decreased as the truck speed increased. For the unloaded truck, 

the CO fuel-specific emission factor was reduced by 73% for the base fuel and 74% for the B20 

when the truck speed was increased from 56 to 105 km/h. A similar trend in the CO emission 

factor was found for the loaded truck as well. The increase in the truck speed from 56 to 105 

km/h reduced the CO fuel-specific emission factor by 79% for the base fuel and by 76% for the 

B20. Since CO is formed by incomplete combustion, the reduction in CO emissions indicates 

that more compete fuel combustion was reached when the truck operated at the higher speed. 

In comparison to the truck speed, the GVW appeared to have relatively less, but still notable, 

influence on the CO emissions. At the lower truck speed (56 km/h), the CO fuel-specific 

emission factor was reduced by 17–18% for both fuels as the truck GVW increased from 21,350 

to 33,890 kg. When the DEAL was running at the higher speed (105 km/h), the CO reduction 

was higher for increased GVW: 36% reduction for the base fuel and 24% reduction for the B20. 

The effect of fuel type on the CO emission factor was small. About a 10% reduction in the CO 

emission factor was observed when using B20 and the truck was unloaded. For the loaded truck, 

the fuel type showed little effect on the CO emissions. 

When the CO fuel-specific emission factor is plotted against the power demand, as shown in 

Figure 8-4, it is seen that, unlike NOX emission rate vs. power in Figure 8-2, the emission factors 

for the 4% road grade tests can be more than twice those seen at level grade for the same power 

demand and thus cannot be included in the level road regression. The figure shows that, for the 

level road tests, the relationship between the CO fuel-specific emission factor and the truck 

power demand can be expressed by linear correlation Equations 8-3 and 8-4 for the base fuel and 

B20, respectively. 

y = -0.0567x + 10.109 

r

2

 = 0.9771 

y = -0.0522x + 9.2211 

r

2

 = 0.9799 

0 

2 

4 

6 

8 

10 

12 

0 50 100 150 200 250 300 350 400 

Fuel-Specific Emission Factor (g/kg fuel)

Level Road Base Fuel 

Level Road B20 

4% Grade, Pump Fuel 

Power Demand (kW) 

Figure 8-4. Correlation between CO Fuel-Specific Emission Factor and Power Demand 

8-8 
 

 

 

 

 

 

 

 

 

For base fuel: 

EF =10.109 − 0.0567P (8-3) CO RL

For B20: 

EF = 9.2211− 0.0522P (8-4) CO RL

where 

EFCO = CO Fuel specific emission factor, g/kg fuel, and 

PRL = road load power, kW. 

The correlation coefficients for the above two equations are 0.977 and 0.978, respectively. The 

comparison of the above two equations indicates that the truck will emit less CO if it switches 

from base fuel to B20 while the other test conditions remain unchanged. However, driving 

conditions and road grade will have greater impact on the CO emissions. It should be noted that 

Equations 8-3 and 8-4 are only valid within the experimental conditions of this study. Because of 

the limited number of test conditions, caution must be exercised when extrapolation of the results 

is needed. The data under the 4% road grade conditions are not correlated because of limited 

number of available data points. 

As with NOX, the level road test data for CO were analyzed by three-way ANOVA to investigate 

the importance of the test parameters on the CO fuel-specific emission factor. The results are 

summarized in Table 8-4. It can be seen from the table that both truck speed and GVW play very 

important roles in affecting the CO emissions. The DLS is less than 0.0001 for the truck speed 

and is 0.0004 for the GVW. The next important parameter is fuel type, which has a DLS of 

0.0469. The interaction between speed and GVW also affects the CO emissions with DLS = 

0.0679. The rest of factors in the table have DLS greater than 0.1 and, therefore, are considered 

that their contributions to the CO emissions are within the experimental errors. 

Table 8-4. Three-way ANOVA Results for CO Fuel-specific Emission Factor 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 0.701332 1 0.701 5.508 0.0469 

Speed 124.21187 1 124.212 975.584 0.0000 

GVW 4.2331298 1 4.233 33.248 0.0004 

Fuel x Speed 0.3708697 1 0.371 2.913 0.1263 

Speed x GVW 0.5666887 1 0.567 4.451 0.0679 

Fuel x GVW 0.0920397 1 0.092 0.723 0.4199 

Fuel x Speed x GVW 0.0017843 1 0.002 0.014 0.9087 

Error 1.0185646 8 0.127 

Total 131.19628 15 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

8-9 
 

 

 

8.3 THC Emissions 

All the test-average THC emission factors and emission rates determined in this study are 

summarized in Table 8-5. The RSD values in this table for all the test-average THC fuel-specific 

emission factors range from 2% to 11%, with an overall average of 6.1%, indicating that the 

THC measurements in this study were better than the CO measurements, though not as good as 

the NOX monitoring. 

Table 8-5. Test Average Emission Factors and Emission Rates for THC 

Test No. 

EFa 

(g/kg fuel) 

ERb 

(g/s) 

EMc 

(g/km)d PRL e 

Ave SD RSD (%) Ave SD Ave SD kW 

T5 1.11 0.04 3.20 0.0096 0.0004 0.32 0.01 153.50 

T6 1.13 0.03 3.05 0.0098 0.0004 0.33 0.01 153.37 

T7 5.20 0.58 11.17 0.0186 0.0025 1.15 0.16 45.76 

T8 5.65 0.60 10.59 0.0189 0.0023 1.17 0.14 45.54 

T9 5.56 0.43 7.78 0.0178 0.0016 1.10 0.10 36.95 

T10 6.53 0.48 7.32 0.0207 0.0017 1.28 0.11 36.65 

T11 1.35 0.03 2.08 0.0109 0.0004 0.37 0.01 137.46 

T12 1.42 0.03 2.18 0.0113 0.0004 0.38 0.01 137.59 

T13 1.13 0.02 2.15 0.0083 0.0009 0.28 0.03 134.42 

T14 1.15 0.06 5.12 135.49 

T15 4.18 0.29 7.04 0.0128 0.0010 0.79 0.06 37.27 

T16 5.77 0.44 7.63 0.0175 0.0015 1.06 0.09 37.73 

T17 3.74 0.35 9.32 0.0133 0.0015 0.81 0.09 47.04 

T18 4.99 0.49 9.80 0.0170 0.0020 1.05 0.12 45.99 

T19 1.08 0.03 2.65 0.0093 0.0004 0.32 0.01 150.13 

T20 1.00 0.05 4.98 0.0089 0.0005 0.30 0.02 152.55 

T21 1.01 0.06 6.31 0.0201 0.0033 0.74 0.13 316.15 

T22 0.87 0.07 7.65 0.0186 0.0016 0.92 0.09 338.12 

Averagef 6.11 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

f. arithmetic average. 

In this study, the test conditions showed similar effects on THC emissions, shown in Figure 8-5, 

as was the case for CO emissions, which can be seen by comparing Figure 8-5 with Figure 8-3. It 

was found that truck speed exhibited significant impact on THC emissions. As the truck speed 

increased from 56 to 105 km/h, the THC fuel-specific emission factor for the unloaded truck was 

reduced by 77% when using either base fuel or B20. 

8-10 
0 

1 

2 

3 

4 

5 

6 

7 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Fuel-Specific Emission Factor (g/kg fuel)

Base Diesel 

B20 

Pump Diesel 

0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Emission Rate (g/sec) 

Base Diesel 

B20 

Pump Diesel 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 33,890 kg 21,350 kg 

56 km/hr 105 km/hr 73 km/hr 97 km/hr 

Level Grade 4% Grade 

Distance-Specific Emission (g/km) 

Base Diesel 

B20 

Pump Diesel 

Figure 8-5. Effects of Test Conditions on THC: Fuel-specific Emission Factor (top); 

Emission Rate (center); Distance-Specific Emission Factor (bottom) 

8-11 
0 100 200 300 400 

Power Demand (kW) 

y = -0.0428x + 7.4873 

r

2

 = 0.9807 

y = -0.0351x + 6.1325 

r

2

 = 0.9168 

0 

1 

2 

3 

4 

5 

6 

7 

8 

Fuel-Specific Emission Factor (g/kg fuel)

Level Road, Base Fuel 

Level Road, B20 

4% Grade, Pump Fuel 

When the truck was loaded (GVW = 33,890 kg), the reduction in the THC emission factor by 

increasing truck speed was 79% for the base fuel and 76% for B20. 

Truck load was found to have less impact than speed on THC emissions. Under the base fuel 

condition, the THC fuel-specific emission factor was reduced by only 10% at a speed of 56 km/h 

when the truck GVW was increased from 21,350 to 33,890 kg. At 105 km/h, this reduction 

increased to 19%. For the B20, the reduction in THC emission factor by the GVW increase was 

12% at 56 km/h and 9% at 105 km/h. 

The benefit of using B20 as truck fuel in reducing THC emissions was also observed. A 

reduction of 18% in the THC fuel-specific emission factor was achieved by switching fuel from 

base diesel to B20 for the unloaded truck regardless the truck speed. For the loaded truck, the 

THC emission factor reduction by use of B20 was 20% at 56 km/h but dropped to a 7% decrease 

in THC emissions at 105 km/h. Figure 8-5 also indicates that the road grade has little influence 

on the THC fuel-specific emission factor. 

Figure 8-6 shows that, when the THC fuel-specific emission factor data obtained from the level 

road tests were plotted against the truck power demand, straight lines were obtained separately 

for base fuel and B20 as presented by the Equations 8-5 and 8-6. 

Figure 8-6. Correlation between THC Fuel-Specific Emission Factor and Power Demand 

For the base fuel: 

EF = 7.4873 − 0.0428P (8-5) THC RL 

For B20: 

EF = 6.1325 − 0.0351P (8-6) THC RL 

8-12 
 

 

 

 

 

 

 

 

 

 

where 

EFTHC = THC fuel specific emission factor, g/kg fuel, and 

PRL = road load power demand, kW. 

The above correlation equations indicate that THC emissions will reduce as the truck power 

demand is increased, and B20 produces about 20% less THC emissions in comparison to the 

base fuel. However, the effectiveness in reducing THC emissions by using B20 is gradually 

reduced as the truck power demand is increased. 

The three-way ANOVA was again used to investigate the importance of the test parameters on 

the THC fuel-specific emission factor based the level road test results. Table 8-6 presents the 

analysis results. The DLS for the truck speed in the table is below 0.0001, indicating that the 

truck speed has the greatest effect on the THC emission factor. The second important parameter 

that influences the THC emission factor is fuel type, which has a DLS value of 0.0638. All the 

other factors have the values of DLS greater than 0.1, indicating that their impacts on THC 

emissions are insignificant. 

Table 8-6. Three-way ANOVA Results for THC Fuel-specific Emission Factor 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 1.516662 1 1.517 4.623 0.0638 

Speed 64.928542 1 64.929 197.926 0.0000 

GVW 0.629788 1 0.630 1.920 0.2033 

Fuel x Speed 0.8111505 1 0.811 2.473 0.1545 

Speed x GVW 0.1835844 1 0.184 0.560 0.4758 

Fuel x GVW 0.0077604 1 0.008 0.024 0.8816 

Fuel x Speed x GVW 0.0057069 1 0.006 0.017 0.8983 

Error 2.6243531 8 0.328 

Total 70.707547 15 

a. SS = sum of squared measurement deviations from the overall mean.. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

8-13 
Chapter 9 

PM-2.5 Emissions 

9.1 PM Mass Emissions 

The PM mass emissions from the truck in this study were characterized based on the 

measurement results by Teflon filter sampling, TEOM, and QCM measurements. Teflon filters 

were installed to collect both plume and background PM, so the PM emission factors and 

emission rate calculated are background corrected. For the TEOM monitoring, both the plume 

and background were also measured so that the background correction was made in the estimates 

of their emission factors and emission rates. In contrast, only the plume was monitored by the 

QCM, and, therefore, the emission factors calculated from the QCM data were not background 

corrected. 

The Teflon filter gravimetric analysis data were used to calculate the fuel-specific PM mass 

emission factors under various test conditions. The results are presented in Table 9-1 and 

compared by different experimental conditions in Figure 9-1. It is interesting to see from the 

figure that the high emission factors obtained from Teflon filter sampling were also very high for 

T5 and T6, making these tests questionable. These emission factors are, however, consistent with 

the PM number measurement results from the ELPI discussed in the next section. Besides T5 and 

T6, a clear trend regarding how the test conditions affect PM mass emissions can be seen for the 

rest of tests. The figure shows that, by replacing the base fuel with the B20, about a 58% 

reduction in fuel-specific PM mass emission factor was achieved when the truck was unloaded 

and ran at 56 km/h. For the loaded truck, the reduction in PM emissions by switching from the 

base fuel to B20 was 68% at 56 km/h and 91% at 105 km/h. 

As will be seen for PM number emissions in the next section, the truck speed had a major impact 

on the PM mass emissions. For the unloaded truck, the fuel-specific PM mass emission factor 

was reduced by 78% for the base fuel and 58% for B20 when the truck speed increased from 56 

to 105 km/h. For the loaded truck using B20, as the truck speed increased the PM mass emission 

factor dropped by 76%. 

The truck GVW showed less influence than vehicle speed on PM mass emissions. At 56 km/h, 

the loaded truck (33,390 kg) emitted 12% less PM than the unloaded truck (21,350 kg) in terms 

of fuel-specific mass emission factor for the base fuel and 34% less for B20. 

9-1 
Table 9-1. Results of PM Mass Emissions by Teflon Filters 

Test 

No. 

EFa (mg/kg) ERb (mg/s) EMc (mg/km) 

Total PM Volatile Non-Vold

 Total PM Volatile Non-Vold Total PM Volatile Non-Vold 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

1036.5 

707.8 

 

1008.9 

1445.7 

852.4

283.3 

219.9 

220.7 

188.6 

460.3 

508.2 

365.1 

278.0 

74.3

81.9

990.4

628.5

861.0 

1191.0 

 615.4 

178.5 

117.5 

98.9 

70.7 

244.0 

276.1 

113.5 

95.4 

 6.3 

 38.1 

 46.1 

 79.3 

 

147.9 

254.7 

236.9 

104.8 

102.4 

121.9 

117.9 

216.3 

232.1 

251.6 

182.6 

68.1 

43.7

8.91 

6.11 

3.38 

4.65 

2.71 

2.29

1.74

1.68

 

1.42

1.54

1.30

0.95

0.64 

 0.73

8.52 

5.43 

2.89 

3.83 

1.95 

 1.44 

 0.93 

 0.75 

 0.75 

 0.84 

 0.40 

 0.33 

0.05 

 0.34 

0.40

0.68

 

0.50

0.82

0.75 

0.85

0.81

0.93

 

0.67

0.71

0.90

0.63

0.58 

0.39

 299.4 

 205.3 

 209.0 

 286.4 

167.4

 76.8

 58.5

 56.9

 87.0

 94.1

 78.9

 58.6

21.6

 24.7

286.0

182.3

178.4

235.9

 120.9 

 48.4 

 31.2 

 25.5 

 46.1 

 51.1 

 24.5 

 20.1 

 1.8 

 11.5 

 13.3 

 23.0 

 30.7 

 50.4 

46.5 

28.4 

27.2 

31.4 

40.9 

43.0 

54.4 

38.5 

19.8 

13.2 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor; 1 g/mi = 0.621 g/km. 

d. Non-Vol = nonvolatile PM determined downstream of thermal denuders. 

0 

200 

400 

600 

800 

1000 

1200 

1400 

21,350 kg 33,390 kg 21,350 kg 33,390 kg 

56 km/h 105 km/h 

Fuel Specific Emission Factor (mg/kg fuel) 

Base Diesel 

B20 

T5&T6 

Figure 9-1. Effects of Test Condition on Fuel-Specific Mass Emission Factor Results Based 

on Teflon Filter Gravimetric Analysis Data 

9-2 
 

The fuel-specific PM mass emission factor from the Teflon filter measurements are plotted as a 

function of truck power demand in Figure 9-2. The point identified by the red square in the 

figure represents the average emission factor for T5 and T6. Without accounting for T5 and T6, 

two straight lines were obtained from the data points of the rest of tests for base fuel and B20, 

respectively. Because of the unusually high Teflon emission factors obtained in T5 and T6, these 

tests should have been repeated but, unfortunately, this not possible due to time and resource 

constraints. 

The three-way ANOVA was used again to further analyze the Teflon filter data for the 

importance of the experiment parameters used on affecting the PM mass emissions. Table 9-2 is 

the ANOVA results. The factors fuel type, truck speed, the interaction between speed and GVW, 

the interaction between fuel and GVW, and the interaction of fuel, speed and GVW all had a 

DLS value less than 0.1, indicating that they have significant impact on the fuel-specific PM 

mass emission factor. 

Thermal denuders (TDs) were used in the tests to remove volatile materials in the PM emitted. 

As a result, the nonvolatile PM was collected on Teflon filters installed down-stream of the TDs. 

By comparing the gravimetric analysis results obtained from the Teflon filters with and without 

TDs in front, the fraction of volatile PM was calculated for individual tests. Figure 9-3 presents 

the percentage of volatile PM under different test conditions. It is seen that, for the base fuel, the 

PM emitted by the unloaded truck at 56 km/h contained 77% volatiles. As the unloaded truck 

speed increased to 105 km/h, the fraction of volatile PM dropped to 58%, a 25% reduction. At 56 

km/h, the unloaded truck burning B20 produced 53% volatile PM, whereas the volatile content 

dropped to 41% when the unloaded truck was operated at 105 km/h. Both fuels show a 25% 

decrease in the volatile PM when the unloaded-truck speed was increased from 56 to 105 km/h. 

For the loaded truck, the PM volatile content actually increased slightly when the base was used 

whereas a small decrease was observed for B20. 

y = -8.8206x + 1447.5 

r

2

 = 0.9959 

y = -2.6744x + 520.54 

r

2

 = 0.8527 

0 

200 

400 

600 

800 

1000 

1200 

1400 

Fuel-Specific Emission Factor (mg/kg fuel)

Base Fuel (T7-T12) 

Base Fuel (T5&T6) 

B20 

0 20 40 60 80 100 120 140 160 180
 

Power Demand (kW)
 

Figure 9-2. Plot of Fuel-Specific PM Mass Emission Factor Obtained by Teflon Filters as a 

Function of Power Demand 

9-3 
Table 9-2. Three-Way ANOVA Results for Teflon Filter PM Mass Measurements. 

 Fd Source SSa df

b

 MSc Pr > Fe 

Fuel 1202530.1 1 1202530.1 40.500 0.0002 

Speed 606238.4 1 606238.4 20.417 0.0020 

GVW 9135.4 1 9135.4 0.308 0.5943 

Fuel x Speed 65310.1 1 65310.1 2.200 0.1763 

Speed x GVW 158727.6 1 158727.6 5.346 0.0495 

Fuel x GVW 148115.9 1 148115.9 4.988 0.0560 

Fuel x Speed x GVW 131226.9 1 131226.9 4.420 0.0687 

Error 237538.5 8 29692.3 

Total 2558822.7 15 

 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

21,350 kg 33,390 kg 21,350 kg 33,390 kg 

56 km/h 105 km/h 

Volatile Fraction in PM (%) 

Base Diesel 

B20 

Figure 9-3. Effects of Test Conditions on Volatile Fraction in PM Based on the Teflon 

Filter and Thermal Denuder Results 

9-4 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

It was also observed that the B20 produced less volatile PM than the base fuel for all conditions. 

For the unloaded truck traveling at 56 km/h, the volatile PM produced by B20 was about 30% 

lower than that produced by the base fuel. At 105 km/h, this difference increased to more than 

60%. The effects of truck GVW on the fraction of volatile PM were mixed. As the GVW 

increased, the base fuel showed an increase in volatile PM percentage, but the volatile PM 

produced by B20 decreased. 

Table 9-3 summarizes the results obtained from the TEOM measurements, which, as can be 

seen, are much lower than the Teflon filter data presented earlier. RSD was not calculated 

because of the nature of fluctuation in the TEOM measurements as evidenced by the high SDs. 

The fuel-specific PM mass emission factors obtained from the TEOM measurement data under 

various test conditions are compared in Figure 9-4. The TEOM results show, as was seen from 

the Teflon filter data, the PM mass reduction when the truck speed was increased from 56 to 105 

km/h was in the 70–80% range. The PM mass emissions also dropped as the truck gross weight 

increased, though it had less effect than truck speed. It should be pointed out here that the TEOM 

results in the figure do not show significant emissions improvement by switching from the base 

fuel to B20. This conflicts with the filter measurements. Since the B20 fueled truck emitted 

smaller sized and fewer particles as will be discussed in Section 9-2, it can be expected that there 

should be less PM mass emissions than the base fuel. Therefore, the TEOM results on the effect 

of fuel type are questionable. 

Table 9-3. Results of PM Mass Emissions by TEOM 

Test No. 

EFa 

(mg/kg fuel) 

ERb 

(mg/s) 

EMc 

(mg/km)d PRL e 

Ave SD Ave SD Ave SD kW 

T5 55.83 60.59 0.48 0.52 16.10 17.37 153.50 

T6 45.59 47.01 0.39 0.40 13.14 13.49 153.37 

T7 263.31 104.35 0.94 0.38 58.03 23.32 45.76 

T8 190.06 89.48 0.64 0.30 39.11 18.69 45.54 

T9 246.27 180.92 0.79 0.57 48.58 35.56 36.95 

T10 271.82 165.09 0.86 0.52 53.23 32.32 36.65 

T11 80.54 75.43 0.64 0.60 21.59 20.30 137.46 

T12 68.87 72.86 0.53 0.57 17.83 19.32 137.59 

T13 84.33 46.10 0.64 0.36 21.64 12.25 134.42 

T14 135.49 

T15 206.62 113.39 0.65 0.35 39.64 21.80 37.27 

T16 347.82 104.85 1.05 0.32 64.28 19.65 37.73 

T17 197.89 239.65 0.72 0.87 43.50 52.71 47.04 

T18 229.61 5474.31 0.78 18.77 48.17 1159.36 45.99 

T19 43.99 1459.49 0.37 12.69 12.62 431.13 150.13 

T20 39.14 2299.39 0.35 19.22 11.73 647.62 152.55 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

9-5 
 

 

 

 

 

 

 

 

 

0 

50 

100 

150 

200 

250 

300 

21,350 kg 33,390 kg 21,350 kg 33,390 kg 

56 km/h 105 km/h 

Fuel-Specific Emission Factor (mg/kg fuel)

Base Diesel 

Bio-Diesel 

Figure 9-4. Effects of Test Conditions on Fuel-Specific PM Mass Emission Factor Based on 

TEOM Measurements 

The three-way ANOVA results of the TEOM data are presented in Table 9-4. The table shows 

that, among all the factors considered, truck speed and GVW had the DLS less than 0.1 and 

played important roles in determining the PM mass emissions. This is consistent with the above 

quantitative discussion. 

Table 9-4. Three-Way ANOVA Results for TEOM PM Mass Measurements 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 8.2 1 8.2 0.005 0.9464 

Speed 131547.9 1 131547.9 77.305 0.0000 

GVW 6609.7 1 6609.7 3.884 0.0842 

Fuel x Speed 5.7 1 5.7 0.003 0.9554 

Speed x GVW 211.3 1 211.3 0.124 0.7337 

Fuel x GVW 622.2 1 622.2 0.366 0.5622 

Fuel x Speed x GVW 38.0 1 38.0 0.022 0.8849 

Error 13613.4 8 1701.7 

Total 152656.3 15 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

The fuel-specific PM mass emission factors obtained by the TEOM measurement were plotted 

against the truck power demand as illustrated in Figure 9-5, showing the linear correlation of the 

PM mass emissions measured by the TEOM with the truck power demand. The fuel-specific 

9-6 
 

emission factor can be predicted approximately from the truck power demand under the steadystate experimental conditions specified in this study by the Equations 9-1 and 9-2. 

For base fuel: 

EFM = 314.49 −1.7322PRL (9-1)

for B20:

EFM = 324.82 −1.858PRL (9-2)

where 

EFM = fuel specific PM mass emission factor, mg/kg fuel, and 

PRL = road load power demand, kW. 

Great uncertainty in the QCM measurements was found in this study, though the observed trends 

of the test conditions effecting the PM mass emissions were generally similar. The PM mass 

emission data measured by the TEOM were compared with those obtained by Teflon filter 

sampling and QCM measurements as shown in Figure 9-6. Due to the large errors in the 

measurements, the fuel-specific PM mass emission factors obtained from the three different 

sampling instruments were poorly correlated to each other for no immediately apparent reason. 

This is in contrast to test cell measurements conducted by Kinsey et al. (2006b) at West Virginia 

University, which showed a correlation coefficient of 0.93 between the TEOM and the Teflon 

filter results. 

y = -1.7322x + 314.49 

r

2

 = 0.9505 

y = -1.858x + 324.82 

r

2

 = 0.8344 

0 

50 

100 

150 

200 

250 

300 

350 

400 

Fuel-Specific Emission Factor (mg/kg

fuel) 

Base Fuel 

B20 

0 20 40 60 80 100 120 140 160 180
 

Power Demand (kW)
 

Figure 9-5. Correlation between the TEOM Fuel-Specific PM Mass Emission Factor and 

the Truck Power Demand 

9-7 
y = 0.6023x + 84.128 

r

2

 = 0.4457 

y = 2.4978x + 65.48 

r

2

 = 0.3684 

0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

PM Mass Emission Factor (mg/kg fuel) 

TF (T7-T20) 

QCM 

TF (T5&T6) 

0 50 100 150 200 250 300 350 400 

PM Mass Emission Factor by TEOM (mg/kg fuel) 

Figure 9-6. Comparison of PM Emission Factor Results Obtained by Different Instruments 

9.2 PM Number Emissions 

The fuel-specific emission factors, emission rates, and distance-specific emission factors in terms 

of the number of particles were calculated from the SMPS, and ELPI measurement data under 

different experimental conditions. PM number emissions measured by the nano SMPS are not 

included here because of the limited particle size range of this instrument. Since both the plume 

and background were monitored by both the SMPS and ELPI, their fuel-specific emission factors 

were calculated with background correction. Tables 9-5 and 9-6 summarize the results for SMPS 

and ELPI, respectively. From the tables it can be seen that the average RSD is 20% for the SMPS 

and 22% for the ELPI. This indicates: (1) the uncertainty in the particle number measurements 

was generally greater than that for the gaseous emissions measurements, and (2) the SMPS 

appeared to have smallest uncertainty in the results among the two PM monitoring instruments. 

Therefore, the discussion of the effects of test condition on PM count emissions will begin with 

the SMPS results followed by its comparison with the other two instruments. 

The SMPS results are compared in Figure 9-7. Similar trends of the effect of experimental 

conditions are seen from these plots for all the three emission measures. A large emission 

reduction in PM number emissions was observed by increasing the truck speed regardless of 

whether the truck was loaded or unloaded. A reduction of about 98% in fuel-specific PM number 

emission factor was found for the base fuel and about 95% reduction for the B20 when the truck 

speed was increased from 56 to 105 km/h. 

9-8 
Table 9-5. Test Average PM Particle Number Results Obtained from the SMPS 

Test No. 

EFa 

(count/kg fuel) 

ERb 

(count/s) 

EMc 

(g/km)d 

e PRL 

Ave. SD RSD (%) Ave. SD Ave SD kW 

T5 

T6 

T7 

T8 

T9 

T10 

T11 

T12 

T13 

T14 

T15 

T16 

T17 

T18 

T19 

T20 

f Average 

5.44E+13 

4.11E+13 

2.38E+15 

1.70E+15 

3.55E+15 

3.29E+15 

7.56E+13 

2.25E+13 

1.63E+14 

3.21E+15 

2.23E+15 

1.77E+15 

1.57E+15 

6.47E+13 

3.64E+13 

8.98E+12 

6.47E+12 

6.25E+14 

2.67E+14 

7.89E+14 

6.83E+14 

1.46E+13 

3.81E+12 

3.35E+13 

 

5.78E+14 

3.41E+14 

4.65E+14 

3.08E+14 

1.90E+13 

6.21E+12 

16.5 

15.7 

26.3 

15.7 

22.2 

20.8 

19.3 

16.9 

20.6 

18.0 

15.3 

26.2 

19.7 

29.3 

17.1 

4.68E+11 

3.53E+11 

8.48E+12 

5.70E+12 

1.14E+13 

1.04E+13 

6.18E+11 

1.78E+11 

1.24E+12 

9.95E+12 

6.78E+12 

6.31E+12 

5.37E+12 

5.40E+11 

3.26E+11 

7.84E+10 

5.60E+10 

2.31E+12 

9.63E+11 

2.59E+12 

2.21E+12 

1.20E+11 

3.05E+10 

3.33E+11 

1.81E+12 

1.06E+12 

1.71E+12 

1.10E+12 

1.58E+11 

5.80E+10 

 

1.57E+13 

1.19E+13 

5.24E+14 

3.53E+14 

7.04E+14 

6.46E+14 

2.08E+13 

5.96E+12 

4.21E+13 

6.10E+14 

4.13E+14 

3.83E+14 

3.30E+14 

1.83E+13 

1.10E+13 

2.64E+12 

1.88E+12 

1.43E+14 

5.98E+13 

1.59E+14 

1.37E+14 

4.05E+12 

1.03E+12 

1.19E+13 

1.11E+14 

6.47E+13 

1.04E+14 

6.80E+13 

5.36E+12 

1.96E+12 

153.50 

153.37 

45.76 

45.54 

36.95 

36.65 

137.46 

137.59 

134.42 

135.49 

37.27 

37.73 

47.04 

45.99 

150.13 

152.55 

20.0 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

f. arithmetic average. 

9-9 
 

 

 

Table 9-6. Test Average PM Particle Number Results Obtained from the ELPI 

Test No. 

EFa 

(count/kg fuel) 

ERb 

(count/s) 

EMc 

(g/km)d PRL e 

Ave SD RSD (%) Ave SD Ave SD kW 

T5 5.82E+15 8.67E+14 14.9 5.02E+13 7.60E+12 1.68E+15 2.56E+14 153.50 

T6 6.50E+15 8.90E+14 13.7 5.63E+13 7.91E+12 1.89E+15 2.66E+14 153.37 

T7 8.03E+15 2.33E+15 29.0 2.87E+13 8.60E+12 1.77E+15 5.32E+14 45.76 

T8 7.89E+15 1.70E+15 21.6 2.64E+13 5.93E+12 1.64E+15 3.68E+14 45.54 

T9 1.00E+16 2.72E+15 27.1 3.23E+13 8.84E+12 1.99E+15 5.45E+14 36.95 

T10 9.97E+15 2.65E+15 26.6 3.17E+13 8.50E+12 1.96E+15 5.27E+14 36.65 

T11 3.16E+14 6.31E+13 19.9 2.58E+12 5.20E+11 8.67E+13 1.75E+13 137.46 

T12 1.16E+14 2.76E+13 23.8 9.20E+11 2.23E+11 3.08E+13 7.49E+12 137.59 

T13 4.26E+14 1.69E+14 39.7 3.29E+12 1.39E+12 1.11E+14 4.72E+13 134.42 

T14 135.49 

T15 1.47E+16 3.83E+15 26.1 4.55E+13 1.19E+13 2.79E+15 7.28E+14 37.27 

T16 1.36E+16 2.58E+15 19.0 4.12E+13 7.96E+12 2.51E+15 4.86E+14 37.73 

T17 1.10E+16 2.45E+15 22.2 3.95E+13 9.17E+12 2.39E+15 5.57E+14 47.04 

T18 1.03E+16 2.22E+15 21.5 3.51E+13 7.80E+12 2.17E+15 4.85E+14 45.99 

T19 1.73E+14 2.45E+13 14.1 1.47E+12 2.18E+11 5.00E+13 7.41E+12 150.13 

T20 8.03E+13 9.85E+12 12.3 7.16E+11 9.23E+10 2.41E+13 3.11E+12 152.55 

Averagef 22.1 

a. EF = fuel-specific emission factor. 

b. ER = emission rate. 

c. EM = distance-specific emission factor. 

d. 1 g/mi = 0.621 g/km. 

e. PRL = road load power. 

f. arithmetic average. 

9-10 
0.0E+00 

5.0E+14 

1.0E+15 

1.5E+15 

2.0E+15 

2.5E+15 

3.0E+15 

3.5E+15 

4.0E+15 

4.5E+15 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Fuel-Specific Emission Factor (counts/kg fuel)

Base Diesel 

B20 

0.0E+00 

2.0E+12 

4.0E+12 

6.0E+12 

8.0E+12 

1.0E+13 

1.2E+13 

1.4E+13 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Emission Rate (counts/sec) 

Base Diesel 

B20 

0.0E+00 

1.0E+14 

2.0E+14 

3.0E+14 

4.0E+14 

5.0E+14 

6.0E+14 

7.0E+14 

8.0E+14 

9.0E+14 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Distance-Specific Emission Factor (counts/km)

Base Diesel 

B20 

Figure 9-7. Effects of Test Conditions on Particle Number Emissions Measured by SMPS: 

Fuel-Specific Emission Factor (top); Emission Rate (center); and DistanceSpecific Emission Factor (bottom) 

9-11 
The GVW had relatively less effect on the number of particles produced. In comparison to the 

unloaded truck, the fuel-specific particle number emission factor for both fuels at 56 km/h was 

lower by about 40% for the loaded truck. At the higher truck speed (105 km/h), the emissions 

were low and, therefore, the load effect was not significant. 

The use of B20 showed an advantage in PM number emissions at the lower truck speed (56 

km/h). An approximate 20% reduction in PM number count emissions was achieved by using 

B20 in comparison to the base fuel. However, at the higher truck speed, there was no notable 

improvement in PM number reduction observed by using B20. 

The particle number emission factor results obtained from the SMPS data are plotted against 

truck power demand in Figure 9-8. The figure shows that the emission factors for base fuel and 

B20 can be correlated to the power demand separately by a power function as described in the 

Equations 9-3 and 9-4. 

For base fuel: 

−3.2003 EFN = 4×1020 ⋅ PRL (9-3) 

For B20: 

−2.7189 EFN = 5×1019 ⋅PRL (9-4) 

y = 4E+20x-3.2003 

r

2

 = 0.9679 

y = 5E+19x-2.7189 

r 2 = 0.9639 

0.0E+00 

5.0E+14 

1.0E+15 

1.5E+15 

2.0E+15 

2.5E+15 

3.0E+15 

3.5E+15 

4.0E+15 

4.5E+15 

5.0E+15 

Fuel-Specific Emission Factor (counts/kg fuel)

Base Fuel 

B20 

0 20 40 60 80 100 120 

Power Demand (kW) 

140 160 180 

Figure 9-8. Correlation between Fuel-Specific Particle Count Emission Factor Determined 

by SMPS and Truck Power Demand 

The curve for B20 in the figure is slightly lower than that for the base fuel when the power 

demand was below 50 kW, indicating the improvement in particle number emissions by B20 

under low power demand. Comparing the fuel compositions in Table 7-4, the B20 contains about 

9-12 
 

 

 

 

 

 

 

 

3% oxygen by weight. Adding oxygen into the fuel decreases the fuel/air equivalence ratio and 

promotes better combustion efficiency. 

Table 9-7 is the three-way ANOVA results for the fuel-specific PM number emission factor 

obtained from the SMPS measurement. As indicated by the values of DLS (Pr > F) in the table, 

the truck speed, GVW, and their interaction are factors that had significant impact on the PM 

number emissions. The DLS for the factor of fuel type is greater than 0.1 because of the 

interference of measurement errors at the lower truck speed. The ELPI ANOVA results 

presented in Table 9-8 show the same trend. The interactions of fuel type with truck speed and 

with GVW all much less than 0.0001. 

In order to evaluate the results from the two instruments used in this study, the test-average 

fuel-specific particle number emission factors obtained by the ELPI were compared with the 

SMPS data in Figure 9-9. The data in the figure show that the results by all instruments are 

basically proportional to one another except for the ELPI results for T5 and T6 (two points in 

the figure that are identified by red squares), which were conducted under the same test 

conditions: base fuel, GVW = 33,890 kg, and 105 km/h. Compared to the SMPS, the ELPI 

recorded much higher PM number concentrations for T5 and T6, which is consistent with the 

Teflon filter results. Because of the apparent anomaly between the T5 & T6 runs and the rest of 

the data set, T5 and T6 should have been repeated, which was not possible due to resource 

limitations. 

Table 9-7. Three-way ANOVA Results for Fuel-specific PM Number Emission Factor 

by the SMPS 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 2.27E+29 1 2.27E+29 2.345 0.1642 

Speed 2.28E+31 1 2.28E+31 235.519 0.0000 

GVW 1.62E+30 1 1.62E+30 16.744 0.0035 

Fuel x Speed 3.52E+29 1 3.52E+29 3.637 0.0930 

Speed x GVW 1.34E+30 1 1.34E+30 13.886 0.0058 

Fuel x GVW 1.18E+28 1 1.18E+28 0.122 0.7360 

Fuel x Speed x GVW 4.83E+28 1 4.83E+28 0.499 0.4999 

Error 7.74E+29 8 9.67E+28 

Total 2.71E+31 15 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

9-13 
Table 9-8. 

Source 

Fuel

Three-way ANOVA Results for Fuel-specific PM Number Emission Factor 

by the ELPI 

 Fd SSa df

b

 MSc Pr > Fe 

 2.48E+29 1 2.48E+29 1.760 0.2212 

Speed

GVW 

 3.21E+32 

7.10E+27 

1 

1 

3.21E+32 

7.10E+27 

2273.139 

0.050 

0.0000 

0.8281 

Fuel x Speed 

Speed x GVW 

Fuel x GVW 

4.00E+31 

3.10E+31 

1.46E+31 

1 

1 

1 

4.00E+31 

3.10E+31 

1.46E+31 

283.720 

219.581 

103.859 

0.0000 

0.0000 

0.0000 

Fuel x Speed x GVW 

Error 

Total 

5.85E+30 

1.13E+30 

4.13E+32 

1 

8 

15 

5.85E+30 

1.41E+29 

41.511 0.0002 

 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

1.0E+13 1.0E+14 1.0E+15 1.0E+16 1.0E+17
 

Particle Number Fuel-specific Emission Factor by SMPS 

(particles/kg fuel)
 

Figure 9-9. Comparison of Fuel-Specific Particle Number Emission Factors Between 

Instruments 

1.0E+13 

1.0E+14 

1.0E+15 

1.0E+16 

1.0E+17 

Particle Number Fuel Specific Emission Factor 

(particles/kg fuel) 

ELPI (T7-T20) 

ELPI (T5&T6) 

9-14 
 

9.3 PM Particle Size Distribution 

As specified earlier, the particle size distributions (PSDs) in this study were characterized based 

on the results obtained from the long DMA SMPS and nano SMPS. Also note that the nano 

SMPS results are of interest only for particle sizes less than 100 nm due to its effective operating 

range. 

In order to investigate the effects of fuel type, truck speed, and truck GVW, the PSDs under the 

different experimental conditions in this study were expressed and compared by plotting 

d(EF)/dlogDp vs. particle size. The values of d(EF)/dlogDp were the fuel-specific particle number 

emission factors for the individual bins, which were calculated from the particle number 

concentrations, dN/dlogDp, for the same bins recorded by the instrument. 

The average PSDs obtained by SMPS under vehicle speeds of 56 and 105 km/h of truck speed 

are plotted separately in Figure 9-10. In the figure, the dark blue lines represent the particle size 

distributions for the base fuel and the dashed red lines for the B20. By comparing the results, it 

was found that the PSD was greatly affected by the truck speed. When the truck was travelling at 

56 km/h, a unimodal size distribution was generally observed with the mean particle sizes in the 

range of 20–30 nm. When the truck was operated at 105 km/h, however, the particle size 

distribution became bimodal with a nuclei mode centered at 10 nm or less and an accumulation 

mode of 40 nm or larger. Similar trends were also observed both during prior testing of this 

particular engine as well as in the open literature (Kinsey et al., 2006b; Kittleson et al., 2006) 

Similar trends were observed by the nano-SMPS under the same test conditions as presented in 

Figure 9-11. Though the particle numbers measured by the nano SMPS were slightly different 

from those obtained by the SMPS under the same experimental conditions, the mean particle 

sizes determined for the nuclei and accumulation modes were essentially the same for both 

instruments. 

To reveal how the average particle size of diesel exhaust in this study was affected by the 

experimental conditions, the particle geometric mean diameter (GMD) and their standard 

deviations were calculated for each particle size distribution. The results from both SMPS and 

nano SMPS measurements are summarized in Table 9-9. It can be seen that the GMDs obtained 

from the nano SMPS were generally smaller than those obtained from the SMPS due to its 

limited size range. The overall average RSD for the SMPS measurement is 4.9%, which is better 

than the average RSD of 18.2% for the nano SMPS. 

9-15 
1.4E+16
 

Base--21350 kg
 

B20--21350 kg
 1.2E+16
 Base--33890 kg
 

B20--33890 kg
 

1.0E+16
 

8.0E+15 

6.0E+15 

4.0E+15 

2.0E+15 

0.0E+00 

10 100 1000 

1.0E+14 

Base--21,350 kg 

9.0E+13 B20--21,350 kg 

Base--33,890 kg
 

8.0E+13
 B20--33,890 kg 

7.0E+13 

6.0E+13 

5.0E+13 

4.0E+13 

3.0E+13 

2.0E+13 

1.0E+13 

0.0E+00 

10 100 1000 

Particle Size (nm) 

dEF/dlogDp at 105 km/h (1/kg fuel) dEF/dlogDp at 56 km/h (1/kg fuel) 

Figure 9-10. Particle Size Distributions by SMPS Measurements under Various Test 

Conditions: 56 km/h (top); 105 km/h (bottom) 

9-16 
1.6E+15
 

Base--21350 kg
 

1.4E+15
 B20--21350 kg
 

Base--33890 kg
 

B20--33890 kg
 1.2E+15 

1.0E+15 

8.0E+14 

6.0E+14 

4.0E+14 

2.0E+14 

0.0E+00 

1 10 100

3.0E+14 

Base-21350 kg 

B20--21350 kg 

2.5E+14 Base--33890 kg
 

B20--33890 kg
 

2.0E+14 

1.5E+14 

1.0E+14 

5.0E+13 

0.0E+00 

1 10 100

Particle Size (nm) 

dEF/dlogDp at 105 km/h (1/kg fuel) dEF/dlogDp at 56 km/h (1/kg fuel) 

Figure 9-11. Particle Size Distributions by Nano SMPS under Various Test Conditions: 

56 km/h (top); 105 km/h (bottom) 

9-17 
Table 9-9. GMD Results from the SMPS and Nano SMPS 

GMD 

(nm) 

Test No. SMPS nano SMPS 

RSDc RSD Avea

 SDb Ave SD (%) (%) 

T5 44.4 2.6 5.9 28.4 10.9 38.5
 

T6 37.9 2.9 7.6 30.3 7.0 23.0
 

T7 25.7 1.0 3.9 28.8
 

T8 25.1 0.7 2.9 30.4 6.5 21.5
 

T9 25.3 0.9 3.5 

T10 26.4 0.8 2.9 28.4 4.1 14.4
 

T11 23.9 2.1 8.9 30.0 12.2 40.8
 

T12 46.2 1.8 4.0 28.9 11.0 38.2
 

T13 21.8 2.0 9.3 23.5 1.3 5.5 

T14 38.6 2.7 7.0 17.1 1.2 6.7 

T15 26.6 0.7 2.5 34.6 5.4 15.6
 

T16 26.9 0.7 2.7 30.9 2.6 8.5 

T17 22.4 0.9 3.8 29.4 1.1 3.9 

T18 24.5 0.6 2.6 26.1 3.0 11.5
 

T19 26.6 2.0 7.5 12.3 1.0 8.4 

T20 47.6 1.7 3.6 

Average 4.9 18.2

a. Ave = average. 

b. SD = standard deviation. 

c. RSD = relative standard deviation. 

Figure 9-12 provides the comparison of the GMDs obtained under different experimental 

conditions. It shows that the GMD increased with truck speed. The particle GMD is around 25 

nm (unimodal) for 56 km/h and increased to 30–40 nm (bimodal) when the truck was operated at 

105 km/h. This is due to the fact that the PSD has both nuclei and accumulation modes under the 

higher truck speed. From the results in the figure, we found that both truck load and fuel type had 

some effect on the GMD. 

Figure 9-13 plots the diesel exhaust GMD against the truck power demand. It can be seen that, 

within the experimental range of this study, the average particle size of diesel exhaust increased 

with truck power demand for both fuels. However, if the two data sets are conjoined, the plot 

shows that the GMD varies linearly with the truck power with a correlation coefficient of 0.82. 

Table 9-10 is the results of the three-way ANOVA for GMD analysis. The DLS in the table for 

truck speed is 0.0459, indicating that the truck speed has significant impact on the GMD. The 

effects of the rest of factors and their interactions on the GMD are insignificant in comparison to 

the experimental errors. 

9-18 
0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Geometric Mean Diameter (nm)

Base Diesel 

B20 

Figure 9-12. Particle Geometric Mean Diameters by SMPS under Various Test Conditions 

0 

10 

20 

30 

40 

50 

Geometric Mean Diameter (nm) 

Base Fuel 

B20 

y = 0.105x + 20.9 

r

2

 = 0.817 

0 50 100 150 200
 

Power Demand (kW)
 

Figure 9-13. Correlation between Particle GMD and Truck Power Demand under 

Steady-state Driving Conditions 

9-19 
Table 9-10. Three-Way ANOVA Results for GMD 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 24.5 1 24.5 0.309 0.5937

Speed 442.1 1 442.1 5.574 0.0459 

GVW 21.4 1 21.4 0.270 0.6174 

Fuel x Speed 15.3 1 15.3 0.192 0.6725 

Speed x GVW 69.8 1 69.8 0.880 0.3758 

Fuel x GVW 1.1 1 1.1 0.014 0.9091 

Fuel x Speed x GVW 3.5 1 3.5 0.044 0.8392 

Error 

Total

634.6 8 79.3 

 1212.2 15 

 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

9-20 
Chapter 10
 

Quality Assurance and Quality Control 

The DQI (Data Quality Indicator) goals referenced in the QAPP are presented in Table 10-1. 

This table includes a correction to the CO2 range because the Horiba Model 210 was replaced 

with the CAI Model 300. In addition, the QAPP has a typographical error that lists a CO2 range 

of 0–2000 ppm; this 0–2000 ppm range should have been labeled COL, indicating low range 

carbon monoxide, instead of CO2. The CO range of 0–1% in the QAPP is deleted because all the 

CO data was within the 0–2000 ppm range. The THC analyzer has a range of 0–100 ppm 

assuming a one carbon gas is used; the upper range becomes a multiple of the number of carbon 

atoms in the gas. For example, this study used propane, which resulted in an upper range of 

300 ppm. 

The accuracy completeness goal based on an accuracy value of 2% was too stringent for these 

types of measurements. To determine most appropriate DQI goal for these analyzers, 

completeness was recalculated for several slightly elevated DQI goals. The pre-test direct 

calibration checks were all 100% complete with a DQI of ± 5% for all instruments. With one 

exception, this was the case for the post-test checks as well when the DQI goal was raised to ± 

8%, the NOX analyzer was 93% complete. The values for the bias accuracy completeness were 

also recalculated at slightly elevated values for the bias DQI accuracy goals since they too are 

believed to have been too stringent. The pre-test bias checks were 96% complete with a DQI 

goal of + 5% and the post-test bias checks were 94% complete with an accuracy DQI goal of 

+ 9%. 

The calculated DQI values for the gas phase CEM measurements are presented in Section 10.1. 

The DQIs for the photoacoustic analysis, thermocouple, mass flow controllers, transducers, and 

gravimetric analysis are discussed in Sections 10.2 through 10.6, respectively. 

10.1 CEM Calibrations 

10.1.1 Multipoint CEM Calibration 

Multipoint CEM calibrations were performed once before the sampling campaign started to 

establish appropriate calibration curves for the data acquisition system. Table 10-2 presents the 

multipoint CEM calibration curves for each of the gases. 

10-1 
 

 

 

 

 

 

 

 

 

 

 

 

Table 10-1. Data Quality Indicator Goals 

Experimental 

Parameter 

Measurement 

Method Precisiona

 Accuracyb Completeness Detection Limit or 

Range 

Gas phase 

measurements 

(THC, NO/NOX, O2 

CO2, CO) 

CEMs ± 2% ± 2% 95% 

THC: 0–100 ppm 

NOX: 0–3000 ppm 

O2: 0–25% 

CO2: 0–20% 

COL: 0–2000 ppm 

1,1,1,2,3,3,3 

heptafluoropropane 

Photoacoustic 

analysis ± 5% ± 5% 95% 0–10 ppmv 

Temperature Thermocouple 5% ± 5% 95% 

K-type: 

–200 °C to 1250°C 

J-type: 0 °C to 750 °C 

Volumetric air flow 

rate 

Mass flow 

controllersc 5% ± 10% 95% 

0–2 L/min 

0–15 L/min 

0–50 L/min 

Differential pressure Transducers 5% ± 10% 95% 0–17.5 inches H20 

PM massd Gravimetric analysis 3 μge ± 15 μg 90% 1 μg 

a. Calculated as the relative standard deviation of the reference measurements obtained at a constant instrument set 

point. 

b. Average variation between the reference measurements and instrument readings as determined over the entire 

operating range. 

c. Includes all on-line and time-integrated instruments as well as sampling tunnels. 

d. For time-integrated sampling only. 

e. Determined as the standard deviation of the results of multiple analyses of the same filter on the same microbalance. 

Table 10-2. CEM Calibration Curves 

Gas Calibration Gas Quantities 

(% Full Scale) 

Instrument 

Range Scaling Equation Date of Calibration r

2 

THC 0, 29, 59, 90 300 ppm y = 60.106 x – 0.2011 11/21/2003 1 

NOX 0, 30, 60, 91 3000 ppm y = 300.66 x + 3.2978 12/03/2003 0.9999 

O2 0, 30, 60, 90 25% y = 5.0163 x – 0.0756 11/18/2003 1 

CO2 0, 12, 24, 36, 49, 62, 73 20% y = 1.9951 x – 0.078 10/31/2003 1 

CO 0, 15, 31, 45, 60, 75, 89 2000 ppm y = 200.85 x – 5.7935 12/03/2003 1 

10-2 
10.1.2 Daily CEM Calibration Checks 

Quality control checks for gas phase measurements were performed before and after each test per 

the QAPP. The DQI values were calculated from these calibration checks using the formulas 

listed in Section 7.2.7 of the QAPP (U.S. EPA, 2004) and are presented here. Tables 10-3 

through 10-7 present the DQI values for THC, NO, O2, CO2 and CO, respectively for each day of 

testing and include the direct and bias calibration checks. 

The QAPP did not reference a separate DQI goal for the CEM system bias checks, which could 

have been made less stringent (higher) than the direct calibration checks. 

Accuracy and precision were calculated each time direct instrument calibration checks and bias 

system checks were performed, both of which were done twice per test day. The first check was 

performed in the morning before testing was begun, and the second was done after testing was 

completed for the day. Because any gas can be used for the bias check, it is redundant and a 

waste of time and resources to run every gas, so there are some blanks where the bias calibration 

check DQI values are presented in Tables 10-3 through 10-7. However, more than one gas was 

always run. 

Nearly all of the DQI values for THC measurements shown in Table 10-3 that exceeded the 

goals were, nonetheless, very close to those goals and occurred when conducting the post-test 

calibration checks. These errors are due to small drifts in the instrumentation. The bias results 

show that some values for accuracy were as high as 8.7% but the problem was not consistent 

through all tests. This may have been a result of “cold” spots in the heated sampling system. 

Table 10-4 lists the DQI values for NOX measurements. About half of the instances when 

measurements exceeded the goals occurred during the post test direct measurements and half 

occurred during the system bias measurements. In all but one instance the values were less than 

10% and are believed to be due to instrument drift. On two consecutive days (Tests 15-18), the 

bias values indicate a drop of 30–50% in the instrument readings that is not reflected in the direct 

DQI values, nor was such a large drop noted in the bias readings of the other CEMs. The 

problem was suspected to be isolated to the NOX sampling system rather than the instrument. 

The lines were cleaned in the field, and the problem readings improved. 

DQI values for O2 gas measurements are listed in Table 10-5. All instances when O2 

measurements violated goals occurred when conducting the direct midrange calibration checks. 

This instrument performed very well, and all DQI values exceeding the DQI goals were 

marginal. 

DQI values for CO2 gas measurements are shown in Table 10-6. Nearly all instances when CO2 

measurements violated goals occurred when conducting the direct midrange calibration checks. 

This instrument performed very well, and all DQI values exceeding the DQI goals were 

marginal. 

DQI values for carbon monoxide gas measurements are listed in Table 10-7. This multirange 

instrument was used only in its low range and performed very well All DQI values exceeding the 

DQI goals were marginal. 

10-3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 10-3. DQI Values for Total Hydrocarbon Gas Measurements for All Tests 

Direct Calibration Check Bias Calibration Check 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) 

Full Span Range 

1 –0.21% 0.32% –3.28% 0.04% –2.71% 0.04% –3.86% 0.05% 

2 0.02% 0.03% –1.66% 0.04% 1.08% 0.08% –5.47% 0.17% 

3 –0.08% 0.02% –0.20% 0.04% — — — — 

4 –0.28% 0.03% 0.83% 0.03% — — –8.74% 0.04% 

5 0.03% 0.03% –0.35% 0.03% — — –6.30% 0.08% 

6 –0.21% 0.03% 0.65% 0.04% — — –3.70% 0.11% 

7 & 8 0.02% 0.03% 1.13% 0.03% — — — — 

9 & 10 0.41% 0.51% 0.41% 0.03% — — — — 

11 & 12 –0.25% 0.04% 2.03% 0.03% — — — — 

13 & 14 0.04% 0.08% 3.09% 0.03% 0.63% 0.05% — — 

15 & 16 –0.08% 0.03% 1.60% 0.02% 0.47% 0.08% — — 

17 & 18 0.04% 0.05% 0.98% 0.02% — — — — 

19 0.00% 0.06% 3.09% 0.03% — — 2.33% 0.04% 

20 –0.77% 0.87% 4.52% 0.04% — — 2.97% 0.07% 

Midrange 

1 0.14% 0.04% — — — — — — 

2 0.32% 0.05% –1.53% 0.05% — — — — 

3 0.10% 0.07% –0.10% 0.06% — — — — 

4 –0.23% 0.03% 1.04% 0.04% — — — — 

5 0.16% 0.04% 0.31% 0.12% — — — — 

6 0.34% 0.02% 0.80% 0.03% — — — — 

7 & 8 0.47% 0.03% 1.62% 0.03% — — 1.32% 0.03% 

9 & 10 0.23% 0.03% 1.23% 0.05% — — — — 

11 & 12 0.36% 0.02% 2.95% 0.05% 0.40% 0.04% — — 

13 & 14 0.40% 0.09% 4.02% 0.04% — — 3.26% 0.04% 

15 & 16 0.24% 0.04% 2.30% 0.04% — — — — 

17 & 18 0.72% 0.07% 2.07% 0.06% — — — — 

19 0.24% 0.21% 3.11% 0.04% — — — — 

20 0.62% 0.05% 5.93% 0.03% — — — — 

10-4 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 10-4. DQI Values for Oxides of Nitrogen Gas Measurements for All Tests 

Direct Calibration Check Bias Calibration Check 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) 

Full Span Range 

1 –0.02% 0.04% –1.45% 0.02% –3.77% 10.04% –0.90% 0.09% 

2 0.02% 0.05% –3.84% 0.18% –3.55% 0.02% –8.53% 0.06% 

3 –0.86% 0.05% –1.00% 0.03% –1.25% 0.11% — — 

4 0.13% 0.03% –1.13% 0.03% — — –2.49% 0.04% 

5 0.05% 0.04% 3.87% 0.19% 4.85% 0.03% — — 

6 0.06% 0.02% –0.07% 0.11% –8.12% 0.11% — — 

7 & 8 –0.03% 0.11% –1.49% 0.09% –1.91% 0.10% — — 

9 & 10 0.00% 0.15% –1.89% 0.07% — — — — 

11 & 12 0.06% 0.07% –2.24% 0.06% — — –2.08% 0.22% 

13 & 14 0.01% 0.13% –2.28% 0.12% — — — — 

15 & 16 0.14% 0.06% –1.68% 0.05% 0.10% 0.41% –51.07% 31.69% 

17 & 18 –0.25% 0.71% –7.97% 0.08% — — — — 

19 –0.02% 0.24% –10.05% 0.11% –0.87% 0.07% –7.84% 0.38% 

20 0.00% 0.05% –5.01% 0.12% 3.35% 0.28% –2.91% 0.11% 

Midrange 

1 

2 

3 

4 

5 

6 

7 & 8 

9 & 10 

11 & 12 

13 & 14 

15 & 16 

17 & 18 

19 

20 

1.38%

2.63% 

–0.32% 

2.27%

–2.20% 

2.29%

1.44%

1.98%

1.73%

1.93% 

2.14%

3.46% 

2.57% 

4.31%

 0.05% 

0.05% 

0.03%

 0.45%

0.10%

 0.09% 

 0.11%

 0.10% 

 0.09%

0.10% 

 0.05%

0.14% 

0.16%

 0.17% 

0.37%

–1.10% 

 0.73%

 0.81%

 6.31%

1.96%

 1.14%

–0.15% 

 0.44%

–0.39% 

 1.18%

–5.57% 

 –7.10% 

–1.97% 

 0.06% 

0.08% 

 0.06% 

 0.09%

 0.06% 

 0.08% 

 0.07% 

0.06%

 0.07% 

0.07% 

 0.19% 

0.08% 

0.06%

0.12%

— 

— 

— 

 0.19%

— 

— 

— 

 2.12%

— 

— 

— 

— 

 –1.07% 

 4.16%

— 

— 

— 

 0.07% 

— 

— 

— 

 0.08% 

— 

— 

— 

— 

0.09% 

 0.15% 

— 

— 

— 

— 

–22.27% 

— 

–6.22% 

–0.11% 

— 

— 

— 

–33.14% 

— 

— 

— 

— 

— 

— 

0.26% 

— 

0.08% 

0.09% 

— 

— 

— 

0.09% 

— 

— 

10-5 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 10-5. DQI Values for Oxygen Gas Measurements for All Tests 

Direct Calibration Check Bias Calibration Check 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) 

Full Span Range 

1 0.33% 0.15% 0.34% 0.12% — — 0.13% 0.16% 

2 –0.33% 0.14% 0.05% 0.16% — — — — 

3 0.01% 0.13% 0.00% 0.13% –0.62% 0.16% — — 

4 –0.22% 0.15% 0.16% 0.13% –0.57% 0.13% — — 

5 0.10% 0.18% 0.24% 0.14% — — — — 

6 0.03% 0.13% –0.59% 0.16% — — –1.68% 0.57% 

7 & 8 0.09% 0.15% –0.26% 0.14% –0.01% 0.15% –0.05% 0.15% 

9 & 10 0.29% 0.36% 0.26% 0.14% –0.25% 0.19% — — 

11 & 12 –0.08% 0.17% –0.04% 0.17% — — — — 

13 & 14 0.19% 0.19% 0.42% 0.15% — — — — 

15 & 16 0.09% 0.15% 0.17% 0.15% 0.08% 0.13% 0.36% 0.14% 

17 & 18 0.16% 0.13% 0.41% 0.13% — — –0.05% 0.15% 

19 0.30% 0.14% –0.42% 0.18% –0.38% 0.33% — — 

20 0.11% 0.13% 0.03% 0.14% — — — — 

Midrange 

1 2.10% 0.21% 2.53% 0.14% — — — — 

2 0.40% 0.23% 0.97% 0.15% — — — — 

3 1.26% 0.16% 1.64% 0.19% — — — — 

4 2.37% 0.16% 3.08% 0.18% — — — — 

5 1.89% 0.14% 2.24% 0.30% — — — — 

6 0.52% 0.19% -0.83% 0.21% — — — — 

7 & 8 1.03% 0.18% 0.14% 0.16% — — — — 

9 & 10 0.85% 0.21% 1.17% 0.15% — — 0.07% 0.13% 

11 & 12 1.15% 0.17% 0.97% 0.13% — — — — 

13 & 14 0.98% 0.83% 2.37% 0.18% — — — — 

15 & 16 0.59% 0.32% -0.26% 0.18% — — — — 

17 & 18 1.27% 0.19% 1.54% 0.20% 0.45% 0.15% — — 

19 1.59% 0.18% 0.78% 0.14% — — 0.39% 0.39% 

20 2.34% 0.20% 2.07% 0.15% 1.22% 0.33% 1.17% 0.23% 

10-6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 10-6. DQI Values for Carbon Dioxide Gas Measurements for All Tests 

Direct Calibration Check Bias Calibration Check 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) 

Full Span Range 

1 0.27% 0.04% 0.42% 0.06% 1.96% 0.08% –0.13% 0.09% 

2 0.07% 0.04% 1.60% 0.05% 0.21% 0.04% — — 

3 0.18% 0.04% 0.53% 0.05% 1.10% 0.09% — — 

4 –0.05% 0.05% 0.62% 0.05% — — –0.14% 0.05% 

5 0.02% 0.05% 1.17% 0.04% 1.96% 0.10% — — 

6 0.14% 0.04% –1.12% 0.04% –3.80% 1.46% — — 

7 & 8 –0.14% 0.05% 0.29% 0.06% –0.73% — — — 

9 & 10 0.10% 0.22% 1.06% 0.04% 1.78% — — — 

11 & 12 –0.03% 0.04% –2.41% 0.04% — — –2.88% 0.10% 

13 & 14 –0.47% 0.27% –1.11% 0.05% –1.20% 0.04% — — 

15 & 16 0.18% 0.04% –0.60% 0.04% — — –3.70% 0.05% 

17 & 18 0.08% 0.06% 1.92% 0.04% –0.14% 0.07% — — 

19 0.48% 0.66% –1.77% 0.05% 1.46% 0.12% — — 

20 -0.18% 0.04% –0.36% 0.04% 2.29% 0.14% — — 

Midrange 

1 4.17% 0.06% 4.09% 0.08% — — — — 

2 3.96% 0.07% 5.17% 0.13% — — 5.74% 0.13% 

3 3.86% 0.07% 4.19% 0.08% — — — — 

4 3.37% 0.08% 4.15% 0.07% — — — — 

5 3.81% 0.08% 5.30% 0.07% — — –0.43% 0.20% 

6 4.14% 0.07% 2.62% 0.06% — — — — 

7 & 8 4.03% 0.09% 4.47% 0.07% — — — — 

9 & 10 4.19% 0.06% 5.63% 0.07% — — 5.67% 0.06% 

11 & 12 4.39% 0.07% 1.93% 0.06% 5.82% 0.06% — — 

13 & 14 4.28% 0.07% 3.36% 0.07% — — 1.81% 0.07% 

15 & 16 4.57% 0.07% 3.78% 0.07% — — — — 

17 & 18 3.98% 0.09% 6.07% 0.07% — — — — 

19 4.22% 0.07% 1.89% 0.07% — — 2.75% 2.04% 

20 3.75% 0.06% 3.83% 0.06% — — 4.71% 0.16% 

10-7 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 10-7. DQI Values for Carbon Monoxide Gas Measurements for All Tests 

Direct Calibration Check Bias Calibration Check 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) (% bias) (% RSD) 

Full Span of the Low Range 

1 0.13% 0.93% 0.63% 0.94% 0.84% 0.97% 0.72% 0.97% 

2 –0.42% 0.91% 1.07% 1.11% –0.25% 0.93% — — 

3 –0.58% 0.93% –0.28% 0.92% –0.08% 0.88% — — 

4 –0.26% 0.93% 0.43% 0.93% — — –0.10% 0.92% 

5 0.50% 0.90% 1.25% 0.97% 1.07% 0.98% — — 

6 –0.83% 0.90% –1.29% 0.90% –3.35% 1.86% — — 

7 & 8 –0.13% 0.91% 0.15% 0.90% –0.42% 0.91% — — 

9 & 10 0.57% 0.94% 1.44% 0.96% 1.56% 1.00% — — 

11 & 12 0.06% 0.93% –1.26% 0.92% — — –1.79% 0.96% 

13 & 14 –0.22% 0.89% –0.64% 0.91% –0.68% 0.90% — — 

15 & 16 0.48% 0.93% –0.02% 0.89% — — –1.56% 0.92% 

17 & 18 0.25% 0.92% 1.49% 0.96% 0.11% 0.89% — — 

19 1.06% 0.95% –0.02% 0.89% 1.84% 1.03% — — 

20 –0.03% 0.89% 0.80% 0.90% 1.14% 0.97% — — 

Middle of the Low Range 

1 –1.75% 1.28% –1.57% 1.32% — — — — 

2 –1.83% 1.26% –0.20% 1.45% — — –0.63% 1.39% 

3 –2.05% 1.26% –1.85% 1.27% — — — — 

4 –2.50% 1.28% –1.20% 1.34% — — — — 

5 –1.06% 1.29% 0.29% 1.28% — — –3.68% 1.32% 

6 –2.52% 1.28% –2.89% 1.29% — — — — 

7 & 8 –1.87% 1.29% –1.48% 1.29% — — — — 

9 & 10 –1.08% 1.27% 0.86% 1.20% — — 0.74% 1.24% 

11 & 12 –1.54% 1.27% –1.61% 1.25% –0.65% 1.36% — — 

13 & 14 –1.64% 1.30% –0.71% 1.21% — — –1.13% 1.20% 

15 & 16 –0.90% 1.25% –0.22% 1.18% — — — — 

17 & 18 –1.37% 1.27% 0.63% 1.22% — — — — 

19 –0.71% 1.29% –1.79% 1.25% — — –2.32% 2.60% 

20 –1.71% 1.27% 0.27% 1.23% — — 0.45% 1.20% 

10-8 
Test 

# 1 

Test 

# 2 

Test 

# 3 

Test 

# 4 

Test 

# 5 

Test 

# 6 

Test # 

7 & 8 

Test # 

9 & 10 

Test # 

11 & 12 

9/2/2004 10/1/2004 10/13/2004 10/14/2004 10/19/2004 10/20/2004 10/21/2004 10/22/2004 10/23/2004 

Span Drift 

CO 0.44% 1.32% 0.26% 0.61% 0.66% –0.41% 0.24% 0.77% –1.17% 

CO2 0.11% 1.11% 0.25% 0.48% 0.84% –0.91% 0.31% 0.70% –1.73% 

O2 0.01% 0.34% –0.01% 0.34% 0.13% –0.56% –0.32% –0.02% 0.04% 

NOX –1.31% –3.53% –0.13% –1.15% 3.48% –0.12% –1.34% –1.73% –2.10% 

THC –2.77% –1.51% –0.11% 1.00% –0.34% 0.78% 1.00% 0.00% 2.01% 

Zero Drift 

CO –0.14% 0.42% –0.06% 0.20% 0.29% 0.00% 0.12% 1.31% 0.72% 

CO2 –0.08% 0.03% –0.07% –0.05% 0.04% –0.03% 0.00% 0.21% 0.13% 

O2 0.17% –0.32% 0.03% 0.46% 0.20% 0.00% –0.17% –0.25% –0.20% 

NOX 0.01% 0.00% 0.01% –0.01% 0.00% –0.01% 0.00% 0.00% 0.00% 

THC –0.55% 19.03% –0.03% 0.13% 0.04% 0.00% 0.05% 0.30% 0.27% 

10.1.3 CEM Span Drift 

Table 10-8 (pump diesel and low sulfur diesel) and Table 10-9 (biodiesel) list values for the 

CEM gas analyzer’s span drift and zero drift calculated for each test day. The drift was 

calculated per CFR 86.315-79, which states that the drift shall be less than 2% of full-scale over 

a 1-hour period. All the percent drifts in the tables were measured over a much longer period 

than 1-hour. The period of time that the gas analyzers were operated between the morning and 

evening calibration checks was at least 8 hours or more every test day. Nearly all of the values 

for percent drift in the two tables below are less than 2% while most of the remaining values are 

between 2% and 4%. Percent drift was calculated using Equation 10-1: 

⎛ Averageof PM CalibrationCheck − Avgerageof AM CalibrationCheck ⎞ % Drift = ⎜

⎜	 ⎟⎟ (10-1)

⎝	 Gas Analyzer Full Scale ⎠

Table 10-8. CEM Span & Zero Drift for the Pump Diesel and the Low Sulfur Diesel Fuel 

Tests 

10-9 
Table 10-9. CEM Span and Zero Drift for the Biodiesel Fuel Tests 

Test # 13 & 14 Test # 15 & 16 Test # 17 & 18 Test # 19 Test # 20 

10/26/2004 10/27/2004 10/28/2004 10/29/2004 10/30/2004 

Span Drift 

CO –0.38% –0.44% 1.10% –0.96% 0.73% 

CO2 –0.46% –0.56% 1.34% –1.64% –0.13% 

O2 0.21% 0.08% 0.23% –0.64% –0.07%

NOX –2.08% –1.66% –7.04% –9.14% –4.57% 

THC 2.75% 1.51% 0.85% 2.79% 4.78% 

Zero Drift 

CO 1.24% 1.00% 0.58% 0.39% 0.94% 

CO2 0.31% 0.20% 0.15% 0.09% 0.20% 

O2 0.93% –0.28% –0.18% –0.20% 0.20% 

NOX 0.00% –0.01% –0.07% 0.00% –0.01% 

THC 0.35% 0.43% 0.37% 1.14% 0.55% 

10.2 Photoacoustic Multigas Analyzer 

Prior to beginning the testing campaign, a new INNOVA 1314 Photoacoustic Multigas Analyzer 

for sampling the FM-200 tracer gas in the exhaust plume was substituted for the Bruell and Kjaer 

(B&K) 1302 Multigas analyzer referenced in the QAPP. The main difference between the two 

devices is that the INNOVA model allows the operator to change the integration time to achieve 

a faster sampling rate. Table 10-10 lists the calibrations that were performed on the INNOVA 

analyzer, and the calibration data sheets are included in Appendix C. 

The analyzer uses two channels to measure concentrations of propane and the tracer gas, FM200, in the plume. Quality control checks for FM-200 and propane measured by the 

photoacoustic analyzer were performed before and after each test per the QAPP. The propane 

concentration used in the daily pretest and post test calibration checks was 29.4 ppm, and the 

concentration of FM-200 used in the two checks was 20.0 ppm. The DQI values for propane and 

FM-200 in Table 10-11 were calculated from these calibration checks using the formulas listed 

in Section 7.2.7 of the QAPP (U.S. EPA, 2004). Measurements for the photoacoustic analyzer 

were 100% complete. 

10.3 Thermocouples 

The Metrology Lab (met lab) calibrations of the DEAL thermocouples are listed in Table 10-12, 

and the calibration files are included in Appendix D. The thermocouple DQIs can be addressed 

using the information in the met lab reports. The reports include a “combined expanded 

uncertainty" value that is applicable over the calibration range of that thermocouple. As long as 

there were no observations of a thermocouple responding with unexpected values, it is assumed 

that the true value is +/- that uncertainty of the recorded value. No observations found 

unexpected values. Met lab experience has determined that thermocouple results are consistent 

10-10 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

and reliable within one year of the calibration date. Thermocouple measurements were 100% 

complete. 

Table 10-10. INNOVA 1314 Photoacoustic Multigas Analyzer Calibrations 

Optical 

Filter Gas Name Date(s) of Zero Calibration 

Date(s) of Humidity 

Interference Calibration 

Date(s) of Span 

Calibration 

UA0971 FM-200 10/7/2004 10/7/2004 10/11/2004, 10/19/2004 

UA0987 Propane 10/7/2004 10/7/2004 10/8/2004, 10/19/2004 

Water Water 10/7/2004 NA 10/8/2004 

Table 10-11. DQI Values for FM-200 and Propane Gas Measurements for All Tests 

FM-200 Propane 

Test Pretest Post Test Pretest Post Test 

Accuracy Precision Accuracy Precision Accuracy Precision Accuracy Precision 

(% bias) (% RPD) (% bias) (% RPD) (% bias) (% RPD) (% bias) (% RPD) 

1 — — — — — — — — 

2 — — — — — — — — 

3 1.00% 0.70% -0.44% 0.68% 1.90% 1.11% 2.04% 0.58% 

4 2.71% 0.84% -0.83% 1.27% -0.26% 1.52% 1.90% 0.93% 

5 0.10% 0.22% -1.90% 0.23% 0.68% 0.00% 0.82% 0.30% 

6 0.00% 0.00% -1.25% 0.28% 0.26% 0.17% 0.53% 0.27% 

7 & 8 0.69% 0.26% -1.75% 0.28% 0.00% 0.22% 1.19% 0.25% 

9 & 10 0.73% 0.26% -1.68% 0.26% 0.04% 0.38% 1.25% 0.24% 

11 & 12 1.00% 0.00% -1.63% 0.24% -0.53% 0.27% 0.97% 0.13% 

13 & 14 0.25% 0.50% -2.56% 0.18% -0.23% 0.24% 1.19% 0.28% 

15 & 16 1.29% 0.80% -2.06% 0.17% -0.48% 0.29% 0.82% 0.30% 

17 & 18 0.40% 0.22% -1.71% 0.27% -0.94% 0.24% 0.94% 0.24% 

19 0.00% 0.00% -1.75% 0.43% -0.57% 0.28% 0.40% 0.26% 

20 -1.50% 0.51% -1.64% 0.24% 0.23% 0.28% 0.49% 0.27% 

21 NA NA NA NA NA NA NA NA 

22 NA NA NA NA NA NA NA NA 

10-11 
Table 10-12. Thermocouple Calibrations 

Met Lab Calibration Uncertainty Location Description Calibration Date ID Range (°C) (°C) 

Background Tunnel K-Type 02802 20–100 0.9 11//3/2003 2/15/2005 

Ambient K-Type 02803 20–100 0.9 11//3/2003 2/16/2005 

Plume Tunnel K-Type 02804 20–120 0.9 11//3/2003 2/14/2005 

Stack K-Type 02796 25–265 0.8 11/12/2003 — 

Exhaust K-Type 02797 25–265 0.8 11/12/2003 — 

Water K-Type 02798 25–120 0.7 11/12/2003 — 

Intake K-Type 02799 25–120 0.7 11/12/2003 — 

Oil K-Type 02800 25–120 0.8 11/12/2003 — 

10.4 Mass Flow Controllers 

The met lab calibrations for the DEAL mass flow meters, and the DEAL mass flow controllers 

are listed in Table 10-13. The calibration files are included in Appendix E. The flow DQIs can be 

addressed using the information in the met lab reports. The reports include a "combined 

expanded uncertainty" value that is applicable over the calibration range of that flow device. As 

long as there were no observations of a flow device responding with unexpected values, it is 

assumed that the true value is +/- the uncertainty of the recorded value. No observations found 

unexpected values. Met lab experience has determined that the flow calibrations are consistent 

and reliable within one year of the calibration date. Mass flow measurements were 100% 

complete. 

10.5 Pressure Transducer 

A Dietrich Standard Annubar was used to sense a differential pressure (dP) signal in the tractor 

exhaust stack, and then the Validyne pressure transducer was used to convert the dP to an analog 

voltage that could be recorded in the DAS. After completing the study, it was decided that data 

from the pressure transducer would not be used to determine the exhaust flow, but the exhaust 

flow would be calculated from the exhaust gas composition instead. 

On 10/31/03, the met lab calibrated the DEAL pressure transducer as having an uncertainty of 

∀0.04 in. of water over its range of 0 to 17.5 in. of water (met lab ID 02801), and the calibration 

file is included in Appendix F. The DQIs can be addressed using the information in the met lab 

report. The report includes a "combined expanded uncertainty" value that is applicable over the 

calibration range of the transducer. As long as there were no observations of the device 

responding with unexpected values, it is assumed that the true value is +/- the uncertainty of the 

recorded value. Met lab experience has determined that the transducer calibrations are consistent 

and reliable within one year of the calibration date. The pressure measurements were 99.1% 

complete. 

10-12 
 

 

Table 10-13. Flow Calibrations 

Locationa Description Met Lab ID Calibration Range Uncertainty Calibration Date 

Plume FTP1 Mass Flow Controller 02805 0–30 SLPM 0.33 SLPM 11/14/2003 

Plume FTP2 Mass Flow Controller 02806 0–30 SLPM 0.31 SLPM 04/08/2004 

Background Teflon Mass Flow Controller 02807 2–15 SLPM 0.07 SLPM 11/14/2003 

Background Quartz Mass Flow Controller 02808 2–15 SLPM 0.12 SLPM 11/05/2003 

Background Bypass Mass Flow Controller 02809 2–15 SLPM 0.08 SLPM 11/05/2003 

Background Bag Mass Flow Controller 02810 0–1.4 SLPM 0.01 SLPM 11/05/2003 

Plume TDb

 Quartz Mass Flow Meter 02212 0–18 SLPM 0.16 SLPM 11/05/2003 

Plume TD Teflon Mass Flow Meter 02214 0–18 SLPM 0.13 SLPM 05/17/2004 

Plume Minor Mass Flow Meter 02812 0–5 SCFM 1% 11/13/2003 

Plume Major Mass Flow Meter 02813 0–38 SCFM 2.2% 11/13/2003 

a

 Refer to Figures 2-5, 2-6, and 2-8 for schematics of the Plume and Background Sampling Systems. b

 TD = thermal denuder 

10.6 Post-Test Laboratory Analysis 

The data quality objectives, measurement acceptance criteria, and quality assurance and control 

used for the post-test laboratory analysis have been provided in detail in the Fine PM 

Characterization Laboratory QAPP (U.S.EPA, 2005). The post-test laboratory analysis of the onroad samples was conducted carefully in accordance with the guidelines set in the QAPP. 

As described in MOP No. 2503 in the DEAL QAPP (U.S.EPA, 2004), the working standard 

weights, the lab control Teflon filter, and random re-weighing were used in this study to insure 

the quality of gravimetric analysis of the on-road Teflon filter samples. Although the results of 

individual weighing satisfy the data quality indicator goal (precision < 3 μg), it was found that 

the weights of the on-road samples were affected by the vaporization losses of volatiles in the 

samples. 

Several quality control checks were performed on the balance used to obtain filter weights. 

During each weigh session, standard weights of 100.000 mg and 200.000 mg were weighed to 

demonstrate balance accuracy. Acceptance criteria for accuracy was a difference between the 

obtained and the standard of less than 0.015 mg. Table 10-14 shows values obtained and the 

differences from the standard weights. Differences were all less than 0.015 mg in all cases. 

Teflon control filters were weighed repeatedly to demonstrate balance precision. The control 

filter was weighed a total of 13 times over 3 weigh sessions with a minimum value of 172.621 

mg and a maximum value of 172.629 mg. The average weight for the control filter was 172.626 

mg with a standard deviation of 0.003 mg, which meets the acceptance criteria for precision of 

0.003 mg. DQI goals were met for accuracy and precision making these measurements 100% 

complete. 

10-13 
 

Test No. Sample ID PM mass (mg) 

12/7/2004 2/18/2005 

5 T020504W 2.088 1.867

6 T020504C 1.662 1.544

7 T092904A 0.196 0.190

8 T092904D 0.871 0.860

9 T020504H 0.890 0.877

10 T020504G 0.560 0.546

11 T092904G 0.448 0.441

12 T092904Y 0.366 0.352

13 T092904N 0.352 0.344

14 T092904L 0.299 0.294

15 T092904W 0.427 0.422

16 T092904U 0.442 0.435

17 T092904R 0.290 0.288

18 T092904T 0.310 0.304

19 T020504K 0.261 0.238

20 T020504O 0.309 0.272

Table 10-14. Balance Variations from Standard Weights 

Standard Weight 

Date 

100.000 mg Absolute 

Differences, mg 200.000 mg Absolute 

Differences, mg 

12/7/2004 99.994 0.006 200.003 0.003 

1/13/2005 99.997 0.003 200.001 0.001 

4/6/2005 99.998 0.002 200.007 0.007 

Table 10-15 displays the PM mass results on the Teflon filters weighed on the two different 

days, which demonstrates the vaporization losses. It can be seen that the PM weights obtained on 

2/18/2005 are always lower than those obtained on 12/7/2004 for the same filters. The PM 

weight results of 2/18/2005 were found linearly correlated with the results of 12/7/2004 as shown 

in Figure 10-1, indicating a consistently 7% sample loss on all the filters. Since the on-road 

diesel PM samples contain 30–90% volatile (see Section 6.10), the sample losses occurred 

during the overnight equilibrium required for Teflon filter samples in the weighing room. 

Table 10-15. Comparison of the PM Mass Results Weighed On Two Different Days 

10-14 
y 0.9295x

R2 0.9959

PM mass reweighed on 2/18/05 (mg)

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

= 

= 

y = 0.9295 x 

r2

 = 0.9959 

0.0 0.5 1.0 1.5 2.0 2.5 

PM mass weighed on 12/7/04 (mg) 

 

Figure 10-1. PM Mass Affected by Sample Losses 

The best OC/EC analyzer range of deposit concentration is 5–400 μg/cm2 for organic carbon and 

1–15 μg/cm2

 for elemental carbon. However, quite a number of quartz filters collected in this 

study were found to have less OC and EC than the best OC/EC analyzer range. The 

concentrations in some of them, such as those behind the thermal denuder, were even below 

the lower detection limit (0.2 μg/cm2

). To evaluate the data quality of OC/EC results at low 

concentrations, a second punch (specimen from the filter sample) was taken from the selected 

quartz filters and analyzed. The OC and EC results from the two samples for the selected filters 

are plotted in Figure 10-2. It can be seen that, when the OC concentration on the filter is greater 

than 5 μg/cm2

, the results from the two samples are almost identical. It also shows that the EC 

results of two samples are close each other when the EC concentration is greater than 1 μg/cm2

. 

As the OC and EC concentrations on the filters decrease, the quality of OC results becomes less 

satisfactory. 

For the PM speciation, it was found that the percentage of organic compounds in the PM that 

could be detected by the GC/MS with the currently practiced solvent-extraction protocol 

depended on the type of PM being measured. Table 10-16 gives some of the results obtained by 

the laboratory for the samples of different sources. In the table, the mass of organic carbon on the 

quartz filter determined by the OC/EC analyzer is compared with the mass of organic 

compounds detected by the GC/MS on the same filter. It can be seen that the diesel truck 

samples had lowest percentage of organic compound detection. 

10-15 
 

y = 0.9766x + 0.0985 

r

2

 = 0.8721 

y = 0.9285x + 0.0514 

r

2

 = 0.8895 

0 

2 

4 

6 

8 

10 

OC and EC on the Second Punch (

μg/cm2 

)

OC 

EC 

0 2 4 6 8 10 
 

OC and EC on the First Punch (μg/cm2)
 

Figure 10-2. Repeatability of OC/EC Analysis 

Table 10-16. Effect of Sample Source on Organic Compound Speciation 

OC by Analyzer OC by GC/MS Detected 

Sample Source 

(μg/filter) (μg/filter) (%) 

On-road diesel truck (this study) 2670 29.5 1.1 

Jet engine (APEX-2) 366.5 6.0 1.6 

Residential oil boiler 288 51.8 18.0 

Industrial oil-fired boiler (NC A&T2) 1020 97.5 9.6 

The quality of the on-road inorganic ion analysis was evaluated by comparing the results of three 

replicate injections of the sample extracts. Table 10-17 provides the relative standard deviation 

for the filter samples analyzed. All the RSDs in the table are below the measurement acceptance 

criteria, ± 15%, set for the IC analysis. 

In the XRF analytical report, the concentrations of elements were reported together with their 

uncertainties. In order to insure the quality of emission data calculated accordingly, a criterion 

was set to discriminate the data reported. Only the element with the concentration three times 

greater than its uncertainty was considered acceptable for further emission factor estimation. 

10-16 
 

 

 

 

 

 

 

 

 

 

 

Table 10-17. Relative Standard Deviation in Inorganic Ions Analysis 

Filter ID 

K+ NH4 

+ 

RSD (%) 

SO4 

–2 NO3 –1 NO2 – 

T020504W 3.46 2.38 0.00 1.40 

T020504Y 1.30 

T020504X 3.77 1.10 

T020504C 3.77 1.33 

T020504B 

T020504A 3.01 

T020504K 4.28 4.45 

T020504J 

T020504L 1.90 

T020504O 2.20 0.81 

T020504N 6.19 0.46 

T020504M 1.27 3.67 

10-17 
Chapter 11
 

Comparison to Historical Data 

A fairly large number of studies on the use of biodiesel fuels have been conducted over the past 

10–15 years. Most of these involved dynamometer measurements employing standard test cycles 

such as those used for engine certification. Some of the more pertinent results using an ultra-low 

sulfur base fuel and a B20 blend are shown in Table 11-1 for criteria pollutants. As can be seen 

from this table, no other studies were found in the literature that duplicate the one described in 

this report. 

The results of this study were compared to those of Rosenblatt and Rideout (2007) at 

Environment Canada (EC) that most closely match the experimental conditions of the current 

work. The EC study used a chassis dynamometer to determine the emissions from a Cummins 

ISX 435 ST heavy duty engine installed in an International 9200i tractor (24,000 kg GVW) 

burning a B20 blend of canola oil and ultra-low sulfur diesel fuel while operating at steady 

speeds of 50 and 110 km/h. Figure 11-1 shows the percent difference in emissions from the base 

fuel calculated for all four criteria pollutants as compared to the DEAL at similar operating 

conditions. Also shown is a single value for the percent change in emissions for a variety of 

different base and biofuel blends as compiled by EPA in 2001. (U.S. EPA, 2002) 

As shown in the figure, all data sets indicate an emissions reduction of up to 20% for THC. In 

the case of CO, however, only the EC study for the 50 km/h operation found an increase in 

emissions. For NOX, the EPA historical database indicated a small increase in emissions whereas 

the other data indicated up to a 16% decrease. Finally, although significant decreases in PM were 

consistently reported from all three data sources, the amount of decrease determined in the 

current study during low speed operation was up to 15 times greater. It should be noted, 

however, that the Rosenblatt and Rideout data were collected under similar vehicle speeds but on 

a dynamometer instead of on-road and with a different type of engine, sampling system, and 

biodiesel fuel blend, which could help explain at least some of this variation. 

There are also very limited data available for the PSD generated from burning ultra-low sulfur 

and biodiesel blends. Only one study was found in the literature which provides a typical PSD 

for burning low sulfur (50–100 ppm) fuel while operating on-road under conditions generally 

similar to that of the current program. Kittelson et al. (2006) measured a bimodal PSD with 

modes at approximately 10 and 60 nm for a Caterpillar 3406E engine, which is comparable to 

that shown in Figure 9-10 for high speed on-road operation during this study. 

11-1 
Table 11-1. Comparison of Emission Factors for Criteria Pollutants 

Reference Engine Type Operating Cycle/Mode Fuel Type Fuel Sulfur Content 

Reported Emiss

g/bph×h 

THC CO NOX PM 

McCormick et al., 2002 1991 DD60b HD-FTP Transientc Fischer-Tropsch (F-T) <10 0.007 3.84 4.03 0.17 

B20: F-T/soy 14 0.005 3.61 4.25 0.14

Lin et al., 2006 2006 2.8-L 4M40- 2AT1 HD-FTP Transient (<300 hr)c,d Petroleum 30 0.280 1.47 3.82 0.12

B20: Petroleum/ palm oil <10 0.250 1.46 3.77 0.11

Ropkins et al., 2007 1.8-L EURO I light duty diesele On-road urban driving route (~0 to 70 km/h) Petroleum <50 0.122 0.195 0.760 N/A

B5: Petroleum/ rape seed Low not specified 0.118 0.238 0.823 N/A 

6.5-L GM ULSD CARB (petroleum)i Low not specified 

Light duty FTPh B20: ULSD/soy Low not specified 

Durbin et al., 2007g 1993 5.9-L Cummins 

AVL 8-mode 

ULSD CARB (petroleum)i Low not specified 

B20: ULSD/soy Low not specified 

j ULSD CARB (petroleum)i Low not specified 

1999 Caterpillar 3126 B20: ULSD/soy Low not specified 

Mitsubishi 4M40- Light duty FTP transient Low S petroleum 22 0.061 0.859 3.91 ~0.0

Yang et al., 2007 2AT1 (0 km accumulated operation)j B20: Petroleum/ waste cooking oil 21 0.055 0.823 4.35 ~0.0

Steady-state 50 km/h; Low S petroleum <15 ppm 

24,000 kg GVW B20: Petroleum/ canola oil Not specified 

Low S petroleum <15 ppm 

Rosenblatt and Cummins ISX 435 ST B20: Petroleum/ canola oil Not specified 

Rideout, 2007 Steady-state 110 km/h; 

24,000 kg GVW 

B5: Petroleum/ canola oil Not specified 

B20: Petroleum/ canola oil Not specified 

B2: Petroleum/ tallow Not specified B5: Petroleum/ tallow Not specified 

(Table notes on next page) 

11-2 
a. All data in most commonly used units and not converted to SI 

b. DD60 = Detroit Diesel Series 60 c. HD-FTP Transient = Heavy Duty-Federal Test Procedure transient cycle per 40 CFR Part 86, Subpart N. d. Tested after 300 hrs of engine operation. e. Light-duty Ford Mondeo LX TD automobile. f. N/A = not available g. All emissions factors taken from graphs presented in paper. Therefore, all emission values are approximate. h. Per 40 CFR Part 86, Subpart N. i. ULSD = ultra-low sulfur diesel fuel; CARB = California Air Re-sources Board. j. Emissions for B20 rose above base fuel after 20,000 km for THC, CO, and PM 

11-3 
NOx

 

-60 

-50 

-40 

-30 

-20 

-10 

0 

10 

Percent Mass Change in Emissions

Current Study (56 km/h; 21350 kg GVW) 

Rosenblatt & Rideout, 2007 (50 km/h; 24000 kg GVW) 

Current Study (105 km/h; 21350 kg GVW) 

Rosenblatt & Rideout, 2007 (110 km/h; 24000 kg GVW) 

EPA420-P-02-001 

THC CO NOX PM 

Pollutant Measured 

Figure 11-1. Percent Change in Distance-Specific Emission Factor for B20 Relative to the 

Base Fuel 

With regard to PM chemical composition, the greatest amount of information available is for 

total PAHs. Figure 11-2 provides a comparison of PAH analyzer results from this study to data 

obtained in three other studies that used filter sampling and subsequent GC/MS analysis. The 

PAH analyzer results from this study were used for comparison in the figure since they are more 

complete than those derived from the quartz filter analyses. As shown in Figure 11-2, the percent 

change in PAH emissions for a B20 blend were similar except for the two low speed conditions 

in the present study. In these cases, there was either no change or a small increase in PAH 

emissions. This trend is consistent with the gas phase THC emissions measured in the current 

study, which also tended to decrease with increasing speed as shown above. 

11-4 
Figure 11-2. Percent Change in Distance-Specific PAH Emission Factor when Using B20 

Relative to the Emission Factor when Using Base Fuel (data for current study 

taken from continuous PAH analyzer not chemical analysis of the quartz 

filters) 

11-5 
Chapter 12
 

Research Findings 

The following conclusions were reached as a result of the research conducted in the study: 

1.	 The emissions of NOX from the DD60 heavy duty diesel engine tested increases linearly with 

power demand. Only a relatively small difference in NOX was observed between the use of 

the low-sulfur base fuel and B20. 

2.	 Emissions of CO and THC decreased linearly with increasing power demand for the steadystate, near-zero grade tests. Some differences were seen for the two fuels, however, 

especially at higher power demand. 

3.	 With the exception of the high speed/high load conditions at near-zero grade, the total PM2.5 mass emissions also decrease linearly with increasing power demand. Substantial 

differences were observed, however, between the various instruments used to measure this 

parameter and for the two fuels during low speed (56 km/h) operation. 

4.	 PM-2.5 number emissions decrease exponentially with increasing power demand in the 

steady-state, near-zero grade tests for both fuels. This was accompanied by the development 

of an accumulation mode in the PSD with an associated increase in the GMD for the high 

speed (105 km/h) tests. 

5.	 At the high speed/high load condition, the amount of semi-volatile organic compounds in the 

PM decreased through the use of B20. These organics appear to be dominated by C17 to C31 

alkanes. 

6.	 Using B20 in place of the base fuel reduced nearly all emissions under nearly all operating 

conditions but the greatest reduction was in the PM-2.5 emission factors. 

Based on the study results, the following recommendations are offered for future research: 

1.	 Since the PM-2.5 emission factors determined from the Teflon filter samples for Tests 5 and 

6 did not decrease linearly with increasing power demand as was found in other tests, at least 

a portion of the test matrix should be repeated to verify these results. Also, additional 

investigation is needed to reconcile the results determined by the filter measurements as 

compared to the TEOM and QCM. 

2.	 In future on-road investigations, CO2 and not a tracer gas should be used in the plume 

measurements. The use of CO2 would allow a direct determination of fuel-specific emission 

factors without the use of dilution ratio thus substantially improving the reliability of the data 

collected. 

12-1 
3.	 The experimental results are limited to only one diesel engine burning two fuel types. 

Additional measurements are recommended to determine if the emission vs. power demand 

relationships developed in the current study also hold true for other engines and fuels. 

4.	 Since only criteria gas emissions were determined in the uphill grade tests, additional 

measurements should be made for PM-2.5 and its constituents. This work should also include 

multiple fuel types to provide emission factors with a wider range of application. 

12-2 
Chapter 13
 

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Brodrick, C-J.; Laca, E.A.; Burke, A.F.; Farshchi, M.; Li, L.; and Deaton, M. (2004) Effect of 

Vehicle Operation, Weight, and Accessory Use on Emissions from a Modern Heavy-Duty Diesel 

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Dreher, D.B. and Harley, R.A. (1998) A Fuel-Based Inventory for Heavy-Duty Diesel Truck 

Emissions, J. Air & Waste Manage. Assoc. 48:352–358. 

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Trucks Operated on California In-Use Diesel, ARCO’s EC-Diesel, and ARCO EC-Diesel with a 

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Durbin, T.D., Cocker, D.R.; Sawant, A.A.; Johnson, K.; Miller, J.W.; Holden, B.B.; Helgeson, 

N.L.; and Jack, J.A. (2007). Regulated Emissions from Biodiesel Fuels from On/Off-Road 

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Hays, M.D. and Lavrich, R.J. (2007) Developments in Direct Thermal Extraction Gas 

Chromatography-Mass Spectrometry of Fine Aerosols, Trends in Analy. Chem. 26(2):88–102. 

13-1 
Kinsey, J.S.; Mitchell, W.A.; Squier, W.C.; Wong, A.; Williams, C.D.; Logan, R.; and Kariher, 

P.H. (2006a) Development of a New Mobile Laboratory for Characterization of the Fine 

Particulate Emissions from Heavy-Duty Diesel Trucks, J. of Auto. Eng. D3, 220:335–345. 

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Thompson, G.J.; and Clark, N.N. (2006b) Evaluation of Methods for the Determination of 

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Warrendale, PA. 

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Real-World Comparison of Probe Vehicle Emissions and Fuel Consumption Using Diesel and a 

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13-2 
 

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13-3 
Appendix A 

Chemical Composition 

A.1 Black Carbon and PAH Emissions 

The recorded black carbon (BC) and particle surface PAH concentration data from the 

aethalometer and PAH analyzer were used to calculate the average emission factors to 

investigate their dependence on test conditions. It should be noted that because their emissions 

were only monitored in the plume, the emission factors obtained from these instruments could 

not be background corrected and, thus, are probably high. 

The test-average fuel-specific emission factors obtained for black carbon and PAH are 

summarized in Table A-1. The average RSD is 44.1% for BC and 21.9% for PAH, indicating 

that the quality of the PAH data measured is better than that of BC measurements. 

Figures A-1 and A-2 show comparisons of the fuel-specific emission factor results obtained 

under the different test conditions. The error bars in the figure represent the uncertainties in the 

emission factor determination. It can be seen that, despite large uncertainties, truck speed 

exhibits strong effects on these emissions. The increase in unloaded truck speed from 56 to 105 

km/h resulted in about a 78% reduction in the BC emission factor with the base fuel and about 

84% reduction when fueled with the B20. When the truck was loaded (GVW = 33,890 kg), the 

increase in truck speed resulted in a BC reduction of 73% for the base fuel and 78% for B20. The 

increase in truck speed also reduced the PAH emission factor. With the base fuel, a 38% 

reduction in the fuel-specific PAH emission factor was observed for the unloaded truck and 

about 45% for the loaded truck. When B20 was used, a greater PAH reduction was observed. 

The increase in truck speed resulted in a 63% reduction in PAH emission factor for the unloaded 

truck and a 56% reduction for the loaded truck. 

However, a reduction in black carbon and PAH emissions from a change in fuel type was only 

observed at the higher truck speed (105 km/h). For the unloaded truck, the use of B20 resulted in 

a reduction by 24% for BC and 33% for PAH. When the truck was loaded, the reduction from 

using B20 was 28% for BC and 21% for PAH. The truck GVW was found to have little impact 

on the BC and PAH emissions. 

A-1 
 

 

 

 

 

 

 

 

Table A-1. Black Carbon and PM Surface PAH Emission Factors 

EF 

(mg/kg fuel) 

Test No. BC PAH 

Avea

 SDb RSDc 

(%) 

Ave SD RSD 

(%) 

T5 0.324 0.126 38.8 0.546 0.058 10.6 

T6 0.268 0.098 36.5 0.417 0.039 9.4 

T7 1.097 0.348 31.7 0.776 0.247 31.8 

T8 1.075 0.281 26.1 0.966 0.239 24.8 

T9 1.117 0.566 50.7 0.698 0.265 37.9 

T10 0.928 0.462 49.8 0.738 0.289 39.2 

T11 0.181 0.126 69.3 0.387 0.050 12.8 

T12 0.268 0.150 56.0 0.504 0.059 11.6 

T13 0.171 0.142 82.7 0.299 0.055 18.5 

T14 

T15 0.884 0.370 41.9 0.616 0.202 32.8 

T16 1.260 0.333 26.4 0.998 0.207 20.7 

T17 1.071 0.384 35.8 1.006 0.296 29.4 

T18 0.892 0.290 32.5 0.735 0.208 28.3 

T19 0.192 0.086 44.6 0.337 0.030 9.0 

T20 0.234 0.090 38.6 0.428 0.051 12.0 

Average 44.1 21.9 

a. Ave = average. 

b. SD = standard deviation. 

c. RSD = relative standard deviation. 

A-2 
0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

1.6 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Emission Factor (mg/kg fuel) 

Base Diesel 

B20 

Figure A-1. Effects of Test Conditions on Fuel-Specific Black Carbon Emission Factor 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

21,350 kg 33,890 kg 21,350 kg 33,890 kg 

56 km/h 105 km/h 

Emission Factor (mg/kg fuel) 

Base Diesel 

B20 

Figure A-2. Effects of Test Conditions on Fuel-Specific PAH Emission Factor 

The three-way ANOVA results for the BC and PAH data are presented in Tables A-2 and A-3. In 

Table A-2, the only parameter that had significant impact on the BC emission factor was found 

to be truck speed with a DLS less than 0.0001. Table A-3 shows that the truck speed was also the 

only parameter affecting the PAH emission factor. The effects of fuel type on BC and PAH 

emissions were not shown by the ANOVA analysis because of their mixed influences at the 

higher truck speed. 

The effects of experimental conditions on the black carbon and PAH emission factors mentioned 

above can be explained by the truck power demand. In Figures A-3 and A-4, the fuel-specific 

emission factors of BC and PAH are plotted against truck power demand, showing that the 

emission factors are reduced as the power demand increases. This trend is consistent with the 

finding discussed in Section 9 of this report on PM mass and number emissions. 

A-3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table A-2. Three-Way ANOVA Results for Black Carbon 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 0.0091 1 0.0091 0.658 0.4407 

Speed 2.6525 1 2.6525 190.886 0.0000 

GVW 0.0019 1 0.0019 0.137 0.7212 

Fuel x Speed 0.0016 1 0.0016 0.118 0.7398 

Speed x GVW 0.0049 1 0.0049 0.355 0.5676 

Fuel x GVW 0.0083 1 0.0083 0.598 0.4616 

Fuel x Speed x GVW 0.0038 1 0.0038 0.273 0.6157 

Error 0.1112 8 0.0139 

Total 2.7933 15 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

Table A-3. Three-Way ANOVA Results for PAH 

Source SSa df

b

 MSc Fd Pr > Fe 

Fuel 0.0091 1 0.0091 0.658 0.4407 

Speed 2.6525 1 2.6525 190.886 0.0000 

GVW 0.0019 1 0.0019 0.137 0.7212 

Fuel x Speed 0.0016 1 0.0016 0.118 0.7398 

Speed x GVW 0.0049 1 0.0049 0.355 0.5676 

Fuel x GVW 0.0083 1 0.0083 0.598 0.4616 

Fuel x Speed x GVW 0.0038 1 0.0038 0.273 0.6157 

Error 0.1112 8 0.0139 

Total 2.7933 15 

a. SS = sum of squared measurement deviations from the overall mean. 

b. df = degrees of freedom (for each source, number of parameters considered –1). 

c. MS = SS/dt. 

d. F = ratio of MS of the source to MS of the error. 

e. Pr = probability of obtaining an F value equal to or greater than the calculated F (= DLS). 

A-4 
0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

1.6 

1.8 

Emission Factor (mg/kg fuel) 

Base Fuel 

B20 

0 20 40 60 80 100 120 140 160 180 

Power Demand (kW) 

Figure A-3. Plot of Black Carbon Emission Factor against Truck Power Demand 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

Emission Factor (mg/kg fuel) 

Base Fuel 

B20 

0 20 40 60 80 100 120 140 160 180 

Power Demand (kW) 

Figure A-4. Plot of PAH Emission Factor against Truck Power Demand 

A.2 PM Organic and Elemental Carbon 

The organic and elemental carbon collected on the quartz fiber filters during the tests were 

determined by the Sunset Laboratory OCEC carbon aerosol analyzer using National Institute for 

Occupational Safety and Health (NIOSH) Method 5040. (NIOSH, 2003) Because quartz fiber 

filters have a large specific surface area for adsorbing gases, the particle-phase organics 

measured on them is increased by gaseous and condensable organics. To minimize these artifacts 

in the quartz filter sampling, the approach developed by Turpin et al. (2000) was used in this 

study. According to Turpin’s approach, backup quartz filters were installed behind the Teflon 

filters in both plume and background sampling systems to correct for the gas adsorption artifact 

by the primary quartz filters. The concentration of PM organic carbon was determined by 

subtracting the concentration of gaseous organic carbon found on the backup quartz filter from 

the overall concentration of particulate and adsorbed gaseous organic carbon on the primary 

A-5 
 

 

 

quartz filter. The background-corrected emission factor for particulate OC was then calculated 

from the backup filter corrected plume and background OC concentrations. The elemental 

carbon is always considered as particulate; therefore, no backup filter correction was needed in 

the elemental carbon emission factor calculation. The fraction of non-volatile OC was 

determined from the analysis of the quartz filters installed behind the TD. 

The quartz fiber filters collected for T5, T6, T19, and T20 were analyzed for OC and EC 

contents. These four tests were conducted at the same truck driving conditions (105 km/h and 

GVW = 33,890 kg) but with the different diesel fuels. The base diesel was used in T5 and T6, 

while B20 was used for T19 and T20. Thus, by comparison of the OC and EC emission factors 

from these tests, the effects of fuel type on OC and EC emission factors could be obtained. Table 

A-4 presents the backup and background corrected fuel-specific particulate OC and EC emission 

factors for each type of fuel as well as their weight percentages in the PM. As shown, a large 

reduction in organic carbon emissions, from 462 mg/kg fuel to 23 mg/kg fuel, was achieved by 

switching to B20. The percentage of organic carbon in the PM was found to be 52% for the base 

fuel and 29% for B20, respectively. 

Table A-4. Effects of Fuel Type on OC and EC Emissions for Speciated Tests 

Parameter Base Fuel B20 

Test T5 & T6 T19 & T20 

Vehicle Speed (km/h) 105 105 

GVW (kg) 33,890 33,890 

PM EF (mg/kg fuel) 872.2 78.1 

OC EF (mg/kg fuel) 462.1 23.0 

EC EF (mg/kg fuel) NDa

 ND 

PM-OC-ECb (mg/kg fuel) 410.1 55.1 

OC/PM (%) 52.3 29.2 

EC/PM (%) ND ND 

a. ND = not detected. 

b. PM-OC-EC = The part of PM excluding OC and EC. 

As can be seen from Table A-4, the PM collected on the quartz filters for both fuels contains 

very little elemental carbon, which is unusual for a diesel engine. However, the elemental carbon 

emission factors determined by the aethalometer measurement for these two fuels are in the 

range of 0.2–0.3 mg/kg fuel. The discrepancy in EC results between the quartz filters and the 

aethalometer is probably due to the different techniques used. The black carbon was measured by 

the aethalometer based on light-absorption, and the elemental carbon was measure by the OC/EC 

analyzer based on thermal refraction. 

The results of very low fraction of EC in the PM observed in this study do not appear to agree 

with that reported by some of other investigators using low-sulfur fuel in dynamometer studies 

(Durbin and Norbeck, 2003; Kweon et al., 2003). 

A-6 
Emission Factor (mg/kg fuel) 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Base Fuel B20 

Naphthalene n-Undecane (n-C11) 

n-Tridecane (n-C-13) n-Pentadecane (n-C15) 

n-Hexadecane (n-C16) n-Heptadecane (n-C17) 

n-Octadecane (n-C18) Phytane 

Dodecylcyclohexane n-Nonadecane (n-C19) 

2-Methylnonadecane 3-Methylnonadecane 

n-Eicosane (n-C20) n-Heneicosane (n-C21) 

Pentadecylcyclohexane n-Docosane (n-C22) 

n-Tricosane (n-C23) n-Tetracosane (n-C24) 

n-Pentacosane (n-C25) n-Hexacosane (n-C26) 

Squalane iso-Hexacosane (C-27) 

anteiso-Hexacosane (C-27) n-Heptacosane (n-C27) 

Pristane iso-Heptacosane (C-28) 

anteiso-Heptacosane (C-28) n-Octacosane (n-C28) 

iso-Octacosane (C-29) anteiso-Octacosane (C-29) 

n-Nonacosane (n-C29) iso-Nonacosane (C-30) 

anteiso-Nonacosane (C-30) n-Triacontane (n-C30) 

n-Hentricontane (n-C31) n-Dotriacontane (n-C32) 

n-Tritriacontane (n-C33) n-Tetratriacontane (n-C34) 

Pyrene ABB-20R-C27-Cholestane 

AAA-20S-C27-Cholestane ABB-20R-C29-Ethylcholestane 

17B(H)-21A(H)-30-Norhopane 17A(H)-21B(H)-Hopane 

Caprylic/Octanoic Acid Glutaric/Pentanedioic Acid 

Adipic/Hexanedioic Acid Capric/Decanoic Acid 

Pimelic/Heptanedioic Acid Azalaic/Nonanedioic Acid 

1,4-Benzenedicarboxylic Acid 1,3-Benzenedicarboxylic Acid 

1,2-Benzenedicarboxylic Acid Tetradecanoic Acid 

Pentadecanoic Acid Palmitoleic/9-Hexadecenoic Acid 

Hexandecanoic Acid Heptadecanoic Acid 

Linoleic Acid Oleic Acid 

Eicosane Acid 

Hexadecanoic 

Acid 

n-C19 

n-C18 

n-C21 

n-C20 

n-C27 

105 km/h and 33,890 kg 

n-C26 

n-C28 

n-C29 

A.3 Semi-Volatile Organic Compounds in Particulate Emissions 

The material collected on quartz filters during the four speciated test in this study were 

composited as shown in Table 5-2 of the main report. The back-up and background correction 

discussed in Section A.2 for the OC emission factor calculation was also used in calculating the 

backup and background-corrected emission factors for individual organic compounds from the 

GC/MS analysis of the quartz filters. Figure A-5 shows the difference in the fuel specific PM 

organic emission factors obtained from the two fuels at high speed and load. As can be seen, the 

PM emitted from the truck contained many more organic compounds when using the base fuel 

(T5 and T6) than when using B20, which is consistent with the observations for organic carbon 

discussed in the previous section. Figure A-5 also shows that PM from the use of base fuel was 

dominated by C17–C31 alkanes. This agrees well with the observations by other investigators. 

(Canagaratna et al., 2004) 

Figure A-5. Effects of Fuel Type on Emission Factors of PM Organic Species 

In comparison to the base fuel, burning B20 produced less alkanes. Since both the diesel fuel and 

lubricating oil contain high concentrations of alkanes, this suggests that the B20 has higher 

combustion efficiency than the base fuel. 

In addition to alkanes, the PM from burning the base fuel also contained a small amount of PAHs 

and notable quantities of organic acids. For the B20, on the other hand, the PM contained 

approximately equal amounts of alkanes and acids. To further investigate the speciation results, 

the fuel-specific emission factors of the compound groups for the two fuels are compared in 

Table A-5. The table includes the results with and without the backup quartz filter correction. As 

discussed in Section A.2, the backup quartz filters installed behind the Teflon filters were used to 

A-7 
 

correct the sampling artifact caused by adsorption of gas-phase organics on the surface of quartz 

filters. Therefore, by comparing the results with and without backup filter correction, the effects 

of gas-phase organic compounds can be identified. For the base fuel, about 31% of the alkanes 

and 60% of the acids were gas-phase organics adsorbed on the primary quartz filters. For the 

B20, 4% of the alkanes and 69% of the acids were in the gas-phase. However, for both fuels, 

very little amount of gas-phase PAH was found in the PM collected by the primary quartz filters. 

Table A-5. Effects of Quartz Filter Sampling Artifact for 105 mkm/h and 33,890 kg 

GVW 

Organic Group 

Emission Factor 

(mg/kg fuel) 

No Backup 

Correction 

With Backup 

Correction 

Gas-Phase Organics 

on Primary Quartz 

Filter 

(%) 

Base Fuel B20 Base Fuel B20 Base Fuel B20 

Alkanes 3.69 0.13 2.54 0.13 31.3 4.4 

PAH 0.14 0.01 0.14 0.01 1.8 

Acids 1.15 0.48 0.46 0.15 60.4 68.9 

A.4 Elements and Ions in Diesel Truck Particulate Emissions 

Various trace elements in the PM are considered to originate from the presence of the elements 

in fuels, from the organometallic additives in lubricating oils, and from the wear and corrosion of 

engine and exhaust system. In this study, the background corrected fuel-specific emission factors 

for the elements found in PM were determined from the XRF (U.S.EPA, 1999) analytical results 

of the Teflon filters collected from both plume and background sampling systems in tests T5, T6, 

T19, and T20. Note again the unusually high PM-2.5 emission factors obtained for T5 and T6. 

The emission factor calculation found that the total fuel-specific emission factor for all the 

elements detected by the XRF was about 35.4 mg/kg of base fuel, which is almost eight times 

higher than the total elemental emission factor of 4.5 mg/kg of the B20. The higher elemental 

emissions for the base fuel is consistent with the higher PM mass emissions obtained from the 

Teflon filter gravimetric analysis and with the higher OC and semi-volatile organics obtained 

from quartz filter analyses for the same tests. It is also in agreement with the higher particle 

number emissions obtained by the ELPI for T5 and T6. 

The XRF analytical results show that about 98% of the detected element mass was silicon (Si) 

for T5 and T6 and about 85% for T19 and T20. Since soil dust from ambient air that gets mixed 

in with the plume is usually considered to account for all of the silicon found in the PM (Pieson 

and Brachaczek, 1983), the Si is excluded in the comparison of element emission factors for the 

two fuels as shown in Figure A-6. The figure shows that, sulfur (S) was the primary component 

existing in the PM, accounting for 36% of the total elements for the base fuel and 71% for the 

B20. It was also seen that there was notable amount of aluminum (Al) and chlorine (Cl) in the 

PM samples from the base fuel. Most Al found in the PM can be considered to originate from 

soil dust. The Cl in the PM may come from either soil or a fuel additive. However, it should be 

A-8 
NO2

NO3

SO4

NH4

pointed out that, except for Si, the emission factors for the rest of detected elements were 

insignificant in comparison to the total PM mass. This probably indicates that the Teflon filters 

in this study collected a lot of soil dust. The lower emission factor of sulfur (less than 0.5 mg/kg 

fuel) observed for both base fuel and B20 is consistent with the low sulfur content of the two 

fuels used in this study. 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.0 

Emission Factor (mg/kg fuel) 

Zn 

Cu 

Ni 

Fe 

Cr 

Ca 

K 

Cl 

S 

P 

Al 

S 

36% 

S 

71% 

Al 

Cl 

Ca 

Ca 

Fe 

Fe 

Zn 

105 km/h and 33,890 kg 

Base Fuel B20 

Figure A-6. Fuel-Specific Element Emission Factors by XRF (Si component removed) 

The low sulfur emissions were verified by an IC analysis of the Teflon filter samples. The 

concentration in the PM samples from both fuels was so low that water-soluble sulfates (SO4

–2) 

could not be detected by the IC. Figure A-7 compares the ion emission factors for the two diesel 

fuels, showing a higher ion content in emissions from use of the base fuel. According to the IC 

results, the detected water soluble ions in the base fuel PM consisted of 44.5% ammonium 

(NH4

+

), 24.6% nitrates (NO3

–1), and 30.8% nitrites (NO2–

), whereas only NH4

+

 was detected in 

the B20 PM. 

Figure A-7. Fuel-specific Ion Emission Factors by IC 

0 

50 

100 

150 

200 

250 

Base Fuel B20 

Emission Factor (mg/kg fuel) 

105 km/h and 33,890 kg NO2 

– 

SO4 

–2 

NH4 

+ 

NO3 

–1 

A-9 
 

 

 

 

 

 

 

 

 

 

 

 

 

A.5 Composition of PM Emissions 

The fuel-specific emission factor results obtained for PM mass, organic carbon, black carbon, 

semi-volatile organic compounds, inorganic ions, and elements from the combustion of each 

diesel fuel are summarized in Table A-6. The table shows that only a small fraction of the total 

organic carbon determined by the OC/EC analyzer could be ascertained by solvent extraction 

and GC/MS analysis. For the samples from the base fuel, for example, the fuel-specific OC 

emission factor determined by the OC/EC analyzer is 462 mg/kg fuel, but the organics detected 

by solvent extraction analysis could only account for 0.8% of this total. This difference occurs 

partly because not all organic compounds in the sample can be solvent extracted and resolved 

and not all those organic compounds extracted can evolve at the GC operating temperature of 

300 ΕC. Moreover, the difference is consistent with previous analyses by EPA and others and 

highlights the limitations of solvent extraction discussed in Hays and Lavrich (2007). 

Table A-6. Emission Factors of PM Components from Base Fuel and B20 for 105 km/h 

and 33,890 kg GVW 

PM Component 

Instrument Media 

Emission Factor 

(mg/kg fuel) 

Base Fuel B20 

Component Component 

Constituent Component Component Constituent Component Component Constituent 

Soluble NH4 

+ 

Soluble NO3 

–1 

IC 

IC 

Teflon filters 

Teflon filters 

97.4

53.8 

 60.7 

Soluble NO2 

– IC Teflon filters 67.4 

Elements XRF 35.4 4.6

 Si XRF Teflon filters 34.5 3.9 

 Others XRF Teflon filters 0.9 0.7 

Total OC 

Alkanes 

PAH 

OC/EC analyzer 

GC/MS 

GC/MS 

UV analyzer 

Quartz filters 

Quartz filters 

Quartz filters 

462.1 

2.54 

0.14 

0.5 

23.0 

0.13 

0.01 

0.4 

Total BC

Acid 

 Undefined 

GC/MS 

OC/EC analyzer 

 OC/EC analyzer 

 Aethalometer 

Quartz filters 

Quartz filters 

Quartz filters 

0.3 

0.46 

458.6 

0.2 

0.15 

22.3

Undetermined (ND) 

Total PM Gravimetric Teflon filters a 

155.8 

872.2 78.1 

a. Note that this emission factor fell outside of the trend observed for the other tests conducted. 

See Figure 9-2 of the main report. 

Figure A-8 illustrates the mass percentage of each component in the PM from the base fuel and 

B20. The figure shows that the base fuel PM consists of 53% OC, 11% NH4

+

, 14% NO3

–1, 8% 

A-10 
 

 

 

 

 

 

NO2

–

, and 4% Si. The undetermined compounds account for 18% of PM. For the B20, its PM 

contains approximately 26% OC, 69% NH4

+

, and 4% Si. 

Because very limited experimental conditions were studied, the results of emission factors 

obtained are valid only under these specified operating conditions and fuels. In order to establish 

a complete emission inventory, more studies are required with other types of engines under 

additional levels of vehicle speed and GVW, and different road grades. 

ND = not determined 

OC 26% 

NH4 Si 4% 69% 

OC 53% 

ND 18% NH4 11% 

NO2 8% 

NO3 6% 

Si 4% 

Figure A-8. Percentage of Each Component in the PM for Base fuel (left) and B20 (right) 

A-11