United States
Environmental Protection
Agency
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
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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
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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.
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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
11
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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 diesel-
powered 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 conditions21,350 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 1-77 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-2.5PM with an aerodynamic
diameter equal to or less than 2.5 jim) 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.
in
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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
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3.3 Continuous PM-2.5 Monitoring 3-4
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 Poly cyclic Aromatic HydrocarbonsPhotoelectric 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 1-77 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
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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
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ChapterS: 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
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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.
Table 2-2.
Table 2-3.
Table 3-1.
Table 3-2.
Table 4-1.
Table 4-2.
Table 4-3.
Table 4-4.
Table 4-5.
Table 4-6.
Table 4-7.
Table 4-8.
Table 5-1.
Table 5-2.
Table 5-3.
Table 6-1.
General Specifications for the DEAL 2-2
Summary of DEAL Vehicle System and Environmental Measurements 2-4
Vehicle Parameters and Sensors 2-15
Analytical Plan for On-Road Truck Experiments 3-9
Fuel and Lube Oil Analysis Methodsa 3-10
Test Matrix for Fuel Type, Vehicle Speed, Speciation, and Vehicle Weight 4-2
Measurements Taken during the Tests on 1-77 4-4
DEAL Loaded and Unloaded Testing Weights 4-6
Vehicle Operating Parameters 4-6
Fuel and Oil Samples Collected and Analyzed 4-8
Temperature Controller Setpoints for CEM System 4-9
FM-200 Consumption Rates 4-10
Miscellaneous Operating Procedures 4-11
Analytical MOPs 5-1
Composites of Quartz Filter Samples 5-3
GC/MS Operating Conditions 5-6
Measurement Parameters Analyzed and their Source Files 6-2
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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 forNOx 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
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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
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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 OldUS-70 4-3
Figure 4-2. On-Road Diesel Emissions Test Route in the Mountains on 1-77 4-5
Figure 4-3. Chase Vehicle Used during Low Speed Tests 4-7
Figure 4-4. Tracer Gas Inj ection 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 Flighway 1-77 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
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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
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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
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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 Telemetries International
Bruell andKjaer
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
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Acronym
Definition
DP
DQI
DR
EC
EC/OC
EC/PM
EF
EFM
EFN
ELPI
EM
EPA
ER
ESP
FIA
FID
FM-200
FPCL
FTP
GC
GC/MS
GHV
GMD
GVW
H
H2O
HL
HP
HPLC
differential pressure
data quality indicator
dilution ratio
elemental carbon
elemental carbon/organic carbon
(weight) fraction of elemental carbon in particulate matter
emission factor
mass emission factor
number emission factor
total hydrocarbon emission factor
Electrical Low Pressure Impactor
distance-specific emission rate
Environmental Protection Agency
emission rate
emission rate for oxides of nitrogen
electrostatic precipitator
Flame lonization Analyzer
flame ionization detector
hydrofluorocarbon tracer gas (1,1,1,2,3,3-heptafluoropropane)
Fine Particle Characterization Laboratory
Federal Test Procedure
gas chromatography
gas chromatography/mass spectroscopy
gross heat value
geometric mean diameter
gross vehicle weight
hydrogen
water
heated sample line
Hewlett-Packard
high performance liquid chromatograph
xv
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Acronym
Definition
I/O
1C
kW
LPM
MFM
MnC>2
MOP
MPA
MS
MSD
MW C3H8
MWCO
MWNO
N/A
NC
NCDOT
ND
NDIR
NH4+
NIOSH
NO
NO/NOx
NO2
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 parti culate matter
xvi
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Acronym
Definition
PAH
PAS
PM
PM2.5
PSD
PTV
PUF
QAPP
QCM
QF
R&P
RH
RHC
RL
RPD
RPM
RSD
SAE
SD
SG
SMPS
SO2
SO42
ss
TCD
TD
TEOM
TF
THC
U.S.
polycyclic aromatic hydrocarbons
photoelectric aerosol sensor
particulate matter
particles with an aerodynamic diameter of 10 jim or less
particles with an aerodynamic diameter of 2.5 jim 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
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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
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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 jim)
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
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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 soy-
derived 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 1-77 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-to-
particle 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 PM-
2.5 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
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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: Steady-
State 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
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Figure 2-1. Kenworth T-800 Tractor and Great Dane Trailer, Locations of the Plume and
Background Sampling Probes
Table 2-1. General Specifications for the DEAL
Vehicle Parameter
SAE Vehicle Classification
Gross Vehicle Weight (GVW) classification
Service classification
Gross train weight or gross vehicle weight
Tractor wheelbase
Length of trailer
Tire size/type
Engine
Engine displacement
Engine power output
Engine emission limit (measured at WVU)a
Specification
3-S2
8
D
36,280 kg (80,000 Ibs)
6.1 m(20ft)
14m (45 ft)
Michelin 11R24.5 radial
2000 Detroit Diesel Series 60
12.7 liters
373 kW (500 hp) @ 2100 rpm
0.13g/kWXhr(0.1 g/bhpXhr)
a. See Kinsey et al. 2006a.
2-2
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Tractor Exhaust Stack
Outside Heated Line
Refrigeration Unit
\
PM10 Heads & "
PM2sCyclones
Oven and
Heated CEM Pump
Background Sampling Tunnel
9.8 cm inside diameter "~---.
stainless pipe, 1,9 m long
Concrete
Loading Blocks
y
)
truments
"'"x^-'^-'XVN
1|
1
CD /}
en |<
-------
Table 2-2. Summary of DEAL Vehicle System and Environmental Measurements
Experimental Parameter Sampling Location '
Measurement
Technique
Type of Sample
Collection
Instrument(s) /
Sampling Media
Serial Number(s)
Total PM-2.5 Mass
Concentration
Background/Splitter 1
Tapered element
microbalance
Background/Splitter 1 Gravimetric analysis
Continuous
Time-integrated
Plume/Splitter 1
Plume/Splitter 1
Plume/Splitter 1
Microbalance
Microbalance
Gravimetric analysis
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
(1105aTEOM)
SEMTECH Model RPM-
100 Quartz Crystal
Microbalance, (QCM)
47-mm Teflon filter in
FTP holder equipped with
double quartz back-up
filters for collection of
gasphase "blow-off'
(FTP1)C
1400AB2314900007
N/A'
1105A201359902
N/A
Total PM-2.5 Number
Concentration d
Background/Splitter 2
Plume/Splitter 2
Condensation nuclei
counter
Condensation nuclei
counter
Condensation nuclei
counter
Condensation nuclei
counter
TSI Model 3025a 1236
Condensation Particle
Counter (CPC)
TSI Model 3025a 1238
Condensation Particle
Counter (CPC)
2-4
-------
Experimental Parameter Sampling Location '
Measurement
Technique
Type of Sample
Collection
Instrument(s) /
Sampling Media
Serial Number(s)
Particle Size Distribution Background/Splitter 2
Background/Splitter 2
Plume/Splitter 2
Plume/Splitter 2
Low-pressure cascade
impactor (aerodynamic
diameter)
Electrical mobility
classifier/condensation
nuclei counter (electrical
mobility diameter)
Low-pressure cascade
impactor (aerodynamic
diameter)
Electrical mobility
classifier/ condensation
nuclei counter (electrical
mobility diameter)
Continuous/Time-
integrated e
Continuous
Continuous/time
integrated e
Continuous
Dekati Electrical Low
Pressure Impactor (ELPI)
TSI Model 3934
Scanning Mobility
Particle Sizer(SMPS):
Model 3071A Classifier
and Model 3010
Condensation Particle
Counter (CPC) (3934
SMPS
Dekati Electrical Low
Pressure Impactor (ELPI)
TSI Model 3936 (long)
Scanning Mobility
Particle Sizer (SMPS):
Model 3080 Classifier,
Model 3025a CPC, and
Model 3081 DMA'(3936
SMPS)
TSI Model 3936 (nano)
Scanning Mobility
Particle Sizer (SMPS):
Model 3080 Classifier,
Model 3025a CPC, and
Model 3085 DMA (Nano
SMPS)
24139
543 (Classifier)
2151 (CPC)
24167
8237 (Classifier)
1339 (CPC)
1042 (DMA)
8041 (Classifier)
1239 (CPC)
5125 (DMA)
2-5
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Experimental Parameter
Elemental/Organic Carbon
(EC/OC)
Semivolatile Organic
compounds h
PM volatile organic
compounds
Sampling Location a
Background/Splitter 1
Plume/Splitter 1
Plume/Splitter 3
Stack
Background/Splitter 1
(speciated tests)
Plume/Splitter
1 (speciated tests)
Plume/Splitter 3
Plume/ Splitter 3
Measurement
Technique
Thermo-optical analysis
(NIOSH Method 5040)
Thermo-optical analysis
(NIOSH Method 5040)
Optical attenuation/UV
absorption ("black" and
"blue" carbon) g
Thermo-optical analysis
(NIOSH Method 5040)
GC/MS '
GC/MS '
UV analyzer (surface
PAHs) k
Gravimetric/thermo-
optical analysis
Type of Sample
Collection
Time-integrated
Time-integrated
Continuous
Time-integrated
Time-integrated
Time-integrated
Continuous
Time-integrated
s^plSSL
Pre-fired 47-mm quartz
filter with double quartz
back-up filter (Quartz
Filter)
Pre-fired 47-mm quartz
filter with double quartz
back-up filter (FTP 2)
Magee (Andersen) Model
AE-2 Aethalometer
(Aethalometer)
Heated pre-fired 142 mm
quartz filter in special
holder
Pre-fired 47-mm quartz
filter equipped with (4)
PUF plugs for collection
of gas-phase "blow-off' cj
(quartz filter)
Pre-fired 47-mm quartz
filter equipped with (4)
PUF plugs for collection
of gas-phase "blow-off' cj
(FTP 2)
EcoChem Model PAS
2000 (PAS 2000)
Dekati Model EKA-1 1 1
thermal denuderwith
parallel Teflon and
double pre-fired quartz
filters (Thermal Denuder)
Serial Number(s) >
N/A
N/A
181
N/A
N/A
N/A
145
63157
2-6
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Experimental Parameter
Tracer Gas '
Carbon Monoxide (CO)
Carbon Dioxide (CO2)
Oxygen (02)
Nitrogen Oxides (NOx)
Total Hydrocarbons (THC)
Engine Speed
Vehicle Speed
Fuel Flow
Shaft Torque
Sampling Location a
Background
Plume
Background
Stack
Background
Stack
Background
Stack
Background
Stack
Background
Stack
Tractor engine
Tractor chassis
Tractor engine
Engine Drive Shaft
Measurement
Technique
Infrared absorption
Infrared absorption
Nondispersive infrared
Nondispersive infrared
Nondispersive infrared
Nondispersive infrared
Magneto
Magneto
Chemiluminescence
Chemiluminescence
Heated flame ionization
Heated flame ionization
Rotation sensor
Optical fifth wheel
Injector position
Strain gages
Type of Sample
Collection
Periodic continuous
Periodic continuous
Integrated bag m
Continuous
Integrated bag m
Continuous
Integrated bag m
Continuous
Integrated bag m
Continuous
Integrated bag m
Continuous
Continuous
Continuous
Continuous
Continuous
Instrument(s) /
Sampling Media
INNOVA Air Tech
Instruments A/S Model
1314APhotoacoustic
Analyzer (Gas Tracer)
INNOVA Air Tech
Instruments A/S Model
1314APhotoacoustic
Analyzer (Gas Tracer)
California Analytical
Model 300
California Analytical
Model 300 (low
Concentration)
California Analytical
Model 300
California Analytical
Model 300
Horiba Model MPA
Horiba Model MPA
California Analytical
Model 400 HCLD
California Analytical
Model 400 HCLD
Horiba Model FIA
Horiba Model FIA
DDEC60 "
Datron DLS-1 Speed
Sensor
DDEC60
ATI 2000 Series
Serial Number(s) b
032-010
032-010
1L09016
1L09016
1L09016
1L09016
570762112
570762112
7L07002
7L07002
8512710101
8512710101
N/A
03.0488
N/A
6-4-7481-1 (module); 750
(electronics)
2-7
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Experimental Parameter
Grade and Acceleration ฐ
Engine Temperature
Exhaust Flow Rate
Exhaust Gas Temperature
Sampling Location a
Trailer Chassis
Various
Exhaust Stack
Exhaust Stack
Measurement
Technique
Gyroscope
Thermocouple
Multi-orifice pitottube
with differential pressure
cell
Thermocouple
Type of Sample
Collection
Continuous
Continuous
Continuous
Continuous
Instrument(s) /
Sampling Media
Crossbow Model
VG600CA
Omega Engineering
Dieterich Annubar OCR
16SwithValidyneP55D
Pressure Transmitter
Omega Engineering
Serial Number(s) b
9913611
N/A
11 9798 (pressure
transducer)
N/A
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 particulate deposited on the
primary filter by the airflow passing through the medium. During the "speciated" runs, the double quartz back-up filters are replaced by a series of 4 polyurethane
foam (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 the data analysis.
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 carbon.
h. Used during "speciated" runs only, except for EcoChem 2000.
GC/MS = gas chromatography/mass spectroscopy.
This sampling train replaces the usual quartz/double quartz filter system used in the other tests conducted. The primary quartz filter will also be analyzed for EC/OC
byNIOSH5040.
k. PAHs = polycyclic aromatic hydrocarbons.
I. 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 determined using a
microbarometer.
2-8
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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 jim) 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
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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 cross-
contamination.
Figure 2-4. Photographs of the DEAL during Fabrication Showing Sampling Tunnel and
Plume Bench (left), and with Instruments Installed (right)
inlet Probe
(above traiter roof)
) X
Mass I!
low
Her
Thermal
Denuder
"
Splitter
=u==
Gas
Tracar
3
Aethalo
meter
PAS
2000
..
CPC
r r
Splittsr 2
ELPI
3936
SMPS
Nano-
SfiflPS
FTP1
(Tefli>n)
.
Splitter 1
n
FTP 2
(Quartz)
QCm }
/
06a I
=OM \
Pump
r~s^~~i
I^Qjjartz^l
Mass Flow r~^
Control iar L-yJt
and v
Solenoid X
Valve
ป
2
PUF
2
PUF
r^
Mass Flow
L_J Controller
I and
ฎ Solenoid
J Valve
. . 1 ฃ
f
Pump
Figure 2-5. Flow Schematic of Plume Sampling System for Speciated Runs
2-10
-------
Inlet Probe
(above trailer roof)
) IX
msssf
Cantrc
Her
Thermal
Denuder
r r
Splitter 3
=LP^
Gas
Tracer
Aetiialo
meter
PAS
2000
CPC
,L
ELP1
Splitter 2
.
J |
3336
SMPS
Nsno-
SMPS
FTP1
{Teflon}
_t
FTP 2
(Quartz)
Splitter 1
t-^
QQffl
11058
TEOM
Pump
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 non-
speciated 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
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Figure 2-7. Background Bench Before the Background Tunnel and Instruments were
Installed (left), and after Installation (right)
Blower
Splitter 2
CPC ELPI
3934 Bag
SMPS Sampler
Splitter 1
Teflon
Filter
L
Quartz
Filter
-1=
Not II 1400
Used 1 1 TEOKI
Pump
Figure 2-8. Flow Schematic of the Background Sampling System for Speciated Tests
2-12
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PM-10 Head and
PM-2.5 Cyclone
Blower
Splitter 2
Splitter 1
CPC
I U 1
ELPI
3934
SMPS
Bag
Sampler
Teflon
Filter
I U 1
Quartz
Filter
Not
Used
1400
TEOM
Pump
fT
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
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-Stack Thermocouple
innubar
Differential Pressure Transducer
DBEC SO
Engine Computer
"HLS-1 Speed Sensor
t-ossbow Gyroscope
TI Series EOOO
Drive Shaft Torque Meter
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 Telemetries 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
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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
Table 2-3. Vehicle Parameters and Sensors
Experimental
Parameter
Engine speed
Vehicle speed
Fuel flow
Shaft torque
Exhaust flow rate
Grade/ Acceleration3
Sampling
Location
Engine
Under Tractor
Engine
Drive Shaft
Stack
Trailer
Measurement
Technique
Rotation Sensor
Optical Sensor
Engine Computer
Strain Gage
Differential Pressure
3-axis gyroscope
Instrument(s)
DDEC 60
Datron DLS1 Speed Sensor
DDEC 60
ATI Strain Gage
Dieterich Standard Annubar +
Validyne Pressure Transducer
Crossbow Model VG600CA
Type of Data
Collected
Digital
0-5 volt analog signal
Digital
0-5 volt analog signal
Analog Transducer
Signal
Digital
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
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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
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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 ง86.309-79 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, FIL2, 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 (62), 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 shock-
resistant 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
-------
A A
A A,
A A'
A T ^ -
I,
ra
Figure 3-1. Schematic of the Continuous Emissions Monitoring (CEM) System in the DEAL.
3-2
-------
3.1.1 O 2 Analyzer
In the Horiba Instruments Model MPA 220 (Magneto-Pneumatic) C>2 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 C>2 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
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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
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3.3.1 PM Mass Measurements
Three instruments located in the DEAL are designed to measure the mass of the paniculate
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 |im.
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 |im 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
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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 3025 A 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 A eth alometer
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 HydrocarbonsPhotoelectric 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
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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, ง 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 pre-
fired quartz filters for determination of OC "blow off.
3-7
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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
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Table 3-1. Analytical Plan for On-Road Truck Experiments
Type of Analysis
PM mass
(PM-2.5)
Elemental/organic
carbon (EC/OC)
Semi-volatile
organ icsa
Water-soluble ions
Elemental
composition
Sampling Media
47-mm Teflon filters
(unspeciated runs)b
47-mm Teflon filters
(speciated runs)b
Al foil ELPI substrates
47-mm pre-fired quartz filters
(unspeciated runs)d
47-mm pre-fired quartz filters
(speciated runs)d
47-mm pre-fired quartz filters
(included in total above)
PUF plugs'
Al foil ELPI substrates
(included in total above)9
47-mm pre-fired quartz filters
47-mm Teflon filters
47-mm Teflon filters
Analytical
Method
Gravimetric
Gravimetric
Gravimetric
NIOSH 5040
NIOSH 5040
Multi-solvent
extraction + GC/MS8
Multi-solvent
extraction + GC/MS8
Thermal desorption
+ GC/MS8
Thermal desorption
+ GC/MS8
Ion chromatography
X-ray fluorescence
spectroscopy
No. of
Samples/Test
5
3
24
12
10
4
8
24
2
2
2
Total
No. of
Tests3
12
5
17
12
5
5
7
7
7
7
7
Analyses
Total
Samples
Planned Actual
60
15
408
144
50
20
56
168
14
14
14
963
28
18
336C
114
56
32
56
Og
14
12
12
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
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Figure 3-2. New Bern Staging Area
Table 3-2. Fuel and Lube Oil Analysis Methods a
Fluid
Fuel
Oil
Parameter
Boiling Point
Flash Point
Cloud Point
Cetane Number
Lubricity
Water
C, H2, N2
N2, Trace
Sulfur
Ash
O2 (diff)
Heating Value
Viscosity
Specific Gravity
Sulfur
Units
Degree Fahrenheit (ฐF)
ฐF
ฐF
Mm@ ฐ60
ppmwt
WT%
ppmwt
WT%
WT%
WT%
British Thermal Units (BTU)/lb
cSt@40 ฐC
@ 60/60 ฐF
WT%
(ml)
100
75
50
2000
4
10
10
5
200
5
50
3
30
Method
ASTM D-86
ASTM D-93
ASTM D-2500
ASTM D-613
ASTM D-6079
ASTM E-203
ASTM D-5291
ASTM D-4629
ASTM D-2622
ASTM D-482
Calculated Value
ASTM D-240
ASTM D-445
ASTM D-4052
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
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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 1-77 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
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Table 4-1. Test Matrix for Fuel Type, Vehicle Speed, Speciation, and Vehicle Weight
TestDate Fue,Type>
Speciation'
Gross Weight, RoadGrade,
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
M7
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
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 time-
integrated instruments along with continuous sampling of key vehicle operating parameters.
4-2
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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.
> River
Ber-d
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
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4.2.2 Mountainous 1-77 Tests
Tests to investigate the effects of road grade on emission rates were conducted on a segment of I-
77 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 RTF, NC to a rest area
in Virginia that is located on 1-77 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 1-77 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.
Table 4-2. Measurements Taken during the Tests on 1-77
Parameter Instrument
CO California Analytical Model 300
CO2 California Analytical Model 300
O2 HoribaMPA220
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
4-4
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Truck Run Exit and Turnaround
Exil Number 8 on 1-77
Just North of Virglnla/North Carolina
State line
Legend
All Other Roads
Blue Ridge Parkway
-77
State Hwy 148
County Hwy 620
County Hwy 691
County Hwy 775
US Hwy 52
Truck Run Start/Stop
Exit Number 1 on 1-77
Just North of me
Virginia/North Carolina
State line
Elevation
Meters
^B High: 1032.13
Figure 4-2. On-Road Diesel Emissions Test Route in the Mountains on 1-77
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 Ibs) 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
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Table 4-3. DEAL Loaded and Unloaded Testing Weights
Station on DEAL
Steer Axial
Drive Axial
Trailer Axial
Gross Weight
Unloaded Weight,
kg (Lbs)
4900(10,800)
9760(21,520)
6690(14,740)
21,350(47,060)
Loaded Weight,
kg (Lbs)
5010(11,040)
13,940(30,740)
14,940(32,940)
33,890 (74,720)
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 1-77 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 1-77
Exit 1 in VA to 1-77 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 1-77. The driver
continued on 1-77 and left the interstate at Exit 8, turned left over the overpass, and re-entered I-
77 onto the south-bound lanes. Upon returning to Exit 1, the driver would exit the interstate and
reenter 1-77 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.
Table 4-4. Vehicle Operating Parameters
Location Vehicle Speed
56 km/h (35 mi/h)
Old US-70
105 km/h (65 mi/h)
89 km/h (55 mi/h)
I-77
73 km/h (45 mi/h)
Engine Speed
Gear , . . .
(r/mm)
8th 1550
10th 1500
variable
variable
4-6
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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 steady-
state 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
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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
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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:
Controller Setpoint
Outside Heated Sample Line (HL1) 360 ฐF (182 ฐC)
Inside Heated Sample Lines (HL2, HL3, HL4) 360 ฐF (182 ฐC)
NOX Heated Sample Line (HL5) 266 ฐF (130 ฐC)
Oven 365ฐF(185ฐC)
Hot Box 160ฐF(71ฐC)
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.
V
Tractor Exhaust Plume >-x
Containing FM-200 }
A Tracer Gas "}
Plume Tunnel
Sample Drawn
During Test
Analog Signal to DAS
Background
Bag Sample
Drawn After
Test Is
Complete
External
Pump
J
nd
Die
ter
e
A
k
Calibration
Gas
Rotameter
Needle
Valve
Slipstream to
Analyzer Inlet
Bypass
Exhaust
Exhaust
Analog Signal to DAS
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
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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
Volumetric Flow, Measured
Mass Flow, Calculated
(Gas Density of 0.032 g/L)
56km/h
1.81 L/min
3.50 kg/h
105 km/h
1.36 L/min
2.63 kg/h
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
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Table 4-8. Miscellaneous Operating Procedures
General Category MOP Title MOP Number
DEAL Facility Setup: Staging Configuration 1400
Load Shifting for the DEAL 1401
Vehicle Operation Preparations for DEAL On-Road Testing 1402
Return from On-Road Testing for the DEAL 1403
DEAL Shutdown Procedures 1404
CEMS Startup 1405
CEMS Multi-Point Calibration 1406
CEMS 2-Point Calibration Check 1407
CEMS System Bias Check 1408
CEMS Response Time Check 1409
Gaseous Pollutant ~,-,,,o^ ^/ox .n. ป .1 ***n
Monitorjng CEMS Overnight/Standby Mode 1410
Operation of CAI Model 300 CO2 Analyzer 1420
Operation of Horiba MPA 220 O2 Analyzer 1421
Operation of CAI Model 400 NO/NOX Analyzer 1422
Operation of Horiba AIA 210 CO Analyzer 1423
Operation of Horiba FIA 236 THC Analyzer 1424
Operation of TSISMPS Model 3934 1411
Operation of TSISMPS Model 3936 1412
Basic Operation and Maintenance of Dekati ELPI 1413
PM Monitoring Operation of Series 11 OSaTEOM 1414
Operation of Series 1400a TEOM 1415
Operation of Magee (Andersen) AE-2 Aethalometer 1416
Operation of Eco-Chem PAS 2000 Real Time PAH Monitor 1417
Operation of B&K 1302 Gas Analyzer for Tracer Gas ..._
Tracer Gas Monitoring Measurements
Performing Zero Check of the B&K 1302 Analyzer 1419
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 Paniculate). The PUT 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 foil-
lined, plastic Petri dishes inside a laboratory freezer maintained at -50 ฐC. 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
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of approximately -20 ฐC. 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 en-
route.
4-12
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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 ฐC 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 jig. 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 ฐC 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
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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 (1C) for isocratic ion analysis including
potassium (K+), ammonia (NH4+), magnesium (Mg+2), calcium (Ca+2), nitrate (NCV1), sulfate
(SO4 2), nitrite (NC^1), and chloride (CP1). 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 (1C). 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 ฐC. The
evolved carbon is catalytically oxidized to CC>2 in a bed of granular manganese dioxide (MnC^),
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 ฐC. 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 jig/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 jig/cm2.
For this reason, the purchased quartz filters were pre-fired in a kiln at 550 ฐC 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
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also baked at 550 ฐC 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 jig 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 (|ig/cm2) by
13.45 cm2 to obtain a mass value in jig.
5.5 Analysis of Particle Phase Organic Compounds
After OC/EC analysis, the semi-volatile organic corn-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 an d 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
Composite3 QF ID
Q100704H
T5&6 Plume Front Q100103C
Total
Q100704E
Q100704G
T5&6 Plume Backup Q100103A
Q100103B
Total
Q100704C
Q100704D
T5&6 Thermal Denuder Backup Q1 001 03D
Q100103E
Total
OC (mg)
1.341
1.039
2.380
0.154
0.110
0.219
0.083
0.567
0.033
0.013
0.044
0.015
0.105
# of Punches
1
1
2
1
1
1
1
4
1
1
1
1
4
5-3
-------
Composite3
T5&6 BKG Front
T5&6 BKG Backup
T19&20 Plume Front
T19&20 Plume Backup
T19&20 Thermal Denuder Backup
T19&20 BKG Front
T19&20 BKG Backup
QFID
Q100704K
Q100103H
Total
Q100704A
Q100704B
Q100103F
Q100103G
Total
Q093004X
Q100103L
Total
Q093004V
Q093004W
Q100103J
Q100103K
Total
Q100103I
Q093004Y
Q100103M
Q100103N
Total
Q092904T
Q100103Y
Total
Q100604H
Q100604E
Q100103O
Q100103P
Total
OC (mg)
0.030
0.047
0.077
0.033
0.020
0.023
0.018
0.095
0.242
0.244
0.486
0.102
0.097
0.099
0.064
0.362
0.081
0.013
0.058
0.015
0.167
0.051
0.044
0.095
0.048
0.025
0.024
0.021
0.117
# of Punches
1
1
2
2
1
2
1
6
1
1
1
1
1
2
5
1
1
1
1
4
1
2
3
1
1
1
1
4
a. Front = first filter(s) in a series; BKup = backup filter(s); BKG = background
5.5.2 Sample Extraction an d Con centration
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 ฐC.
5-4
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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 ฐC 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
ฐC. After concentration, the extract was completely transferred to a clean amber vial and further
concentrated by nitrogen blow-down to approximately 250 uL. 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 uL of high
purity methanol and 100 uL 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
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Table 5-3. GC/MS Operating Conditions
Operating Parameters
Instrument Setting
Injector Temperature (ฐC) 300
GC/MS Interface Temperature (ฐC) 300
Initial Oven Temperature (ฐC) 65
Initial Oven Hold Time (min) 10
Oven Temperature Ramp Rate (ฐC/min) 10
Final Oven Temperature (ฐC) 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 (ฐC) 230
Quad Temperature (ฐC) 150
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
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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) 1-77
Road Grade Test (12/16/04), and (6) 1-77 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 CC>2, CO, C>2, NOx, and THC in
the raw exhaust gas were recorded every second and stored in the files titled "Analog mm-dd-yy
Fl". 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 mm-
dd-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 micro-
barometer 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 (Tl to T20) conducted on a level segment of US-70 near New Bern, NC in
October 2004. Tl 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 (Tl 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 1-77 Exits 1 and 8 that had a road grade of approximately 4%. During these tests, only
gaseous emissions were measured.
6-1
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Table 6-1. Measurement Parameters Analyzed and their Source Files
Measurement Parameter
Vehicle Speed
Anubar Reading
Stack Temperature
Trace Injection Rate
Plume Front TFC Flow Rate
Plume Front QFd Flow Rate
Background TF Flow Rate
Background QF Flow Rate
TF after Denuder Flow Rate
QF after Denuder Flow Rate
Exhaust CO Concentration
Exhaust CC>2 Concentration
Exhaust NOx Concentration
Exhaust C>2 Concentration
Exhaust THC Concentration
Fuel Feed Rate
Micro-Barometer Reading
Exhaust Black carbon
Exhaust PAH concentration
ELPI background PM concentration
ELPI plume PM concentration
SMPS background PM concentration
SMPS plume PM concentration
Nano-SMPS plume PM concentration
TEOM background PM concentration
TEOM plume PM concentration
QCM plume PM concentration
Unit
km/h
inH2O
ฐC
L/min
L/min
L/min
L/min
L/min
L/min
L/min
ppm
%
ppm
%
Ppm
gal/h
Psi
ng/m3
ng/m3
#/cm3
#/cm3
#/cm3
#/cm3
#/cm3
u/cm3
mg/cm3
mg/cm3
File Name
Analog mm-dd-yy
Analog mm-dd-yy
Analog mm-dd-yy
Analog mm-dd-yy
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F2
Analog mm-dd-yy F2
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F1
Analog mm-dd-yy F1
DD60 mm-dd-yy
MB mm-dd-yy
Analog mm-dd-yy F2
Analog mm-dd-yy F2
ELPI C Test # mm-dd-yy
ELPI D Test # mm-dd-yy
Old SMPS mm-dd-yy
Long mm-dd-yy
Nano mm-dd-yy
1 400 mm-dd-yy
RPyyyymmddxxxxxxR
QCM mm-dd-yy
Column Title
5TH_WHEEL [kmh]
ANUBAR ["H2O]
STACK [-C]
MFMa[LPM]b
FTP1 [LPM]
FTP2 [LPM]
Back Teflon [LPM]
Back Quartz [LPM]
TDe. QUARTZ [LPM]
TD. TEFf. [LPM]
CO Low [ppm]
CO2 [%]
NOX [ppm]
02 [%]
THC [ppm]
Fuel Rate(gal/hr)
CHANNEL1
MAGEE [ng/mA3]
PAH [ng/mA3]
30-minMC&1-hrMC
Mass Cone 5 & Mass Cone 8
Cone (mg/mA3)
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 CC>2, 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 dataemissions measurements and power
measurementswere 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
-------
(6-1)
where
tt;? = uncertainty in 7?,
R = a function of individual variables, R = R(XI, x2, XXX., xn\
MI, z/2, X X X, un = uncertainties in independent variables x\, X2, XXX, 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 test-
average was calculated from the uncertainties of all the passes in the test by
(6-2)
n
where
wt = uncertainty in test average,
Wj = uncertainty in pass-average of the 7th 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 = ATB (6-3)
6-4
-------
The matrices Y, A, and B are
Y =
A =
0
0
0
0
0
[CO ]
[CO]
[THC}V
[NOX]W
-1
1
0
2
0
0
-1
0
0
0
1
0
0
0
2
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
1
0
2
1
0
0
-[C02}d
\CO}d
0
0
1
1
0
1
0
0
0
-1
0
0
1
3
8
0
0
0
0
0
-1
0
1
0
0
1
1
0
0
0
0
-1
1
0
0
2
0
1
0
0
0
0
1
-(T + U)
-(2/7 + 4C7)
(2R + 2T + h)
-2S
0
0
0
0
0
0
B =
where
[CO2Jd = 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 CsHg, 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,
YI = total g-mol of exhaust produced per 100 g of fuel burned,
ฅ2 = 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
YIQ = g-mol of air for burning 100 g of fuel.
6-5
-------
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
EFCO = 10 Y6 -MWCO (g/kg fuel burned) (6-4)
For THC
EFTHC = 10 77 -MWC3HS (g/kg fuel burned) (6-5)
For NOX
(g/kg fuel burned) (6-6)
where
MWco = molecular weight of CO,
= molecular weight of CsHg, and
xo = molecular weight of NO.
In order to solve Eq. 6-3, the moisture content in ambient air on a dry basis, /z, must be known.
This was obtained from the weather reports provided by the State Climate Office. The reports
provided the data of air temperature (ฐF), 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.
DTT p
100 p. (6-7)
where
RH= relative moisture, %,
Pv = water vapor pressure at temperature (ra), Mbar, and
Pa = atmospheric pressure, mbar.
The ambient air moisture on dry basis, h, was calculated from the above moisture fraction by
h =
(1-AJ (6-8)
6-6
-------
6.3 Estimate of Exhaust Flow Rate
The YI obtained from solving the SAE matrix equation can be used to estimate the exhaust flow
rate by
QSAE=Yl- 0.024055- F^-^- (6-9)
oil
where
QSAE = exhaust gas flow rate at standard condition (20 ฐC and 1 atm), m3/min,
0.024055 = volume per mole at standard condition (20 ฐC and 1 atm), mVg-mol, and
Ffuei = 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
(6-10)
where
Vs = exhaust gas velocity (m/sec),
Kp = Pitot tube constant:
= 34 96 g/gmolex(mmHg)
sec"\| ฐKxmmH2O
Cp = manufacturer specified pitot tube coefficient = 0.6168,
AP = pressure drop reading from pressure transducer (mmH^O),
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 (M/) and the moisture fraction (Bws) by
(6_n)
6-7
-------
in the Eq. 6-11 is estimated from the exhaust gas composition obtained from the
monitoring instruments by
Ms = 0.44- C<92(%) + 0.32- 02 (%)
+ 0.28 [7V2 (%) + C<9(%)] 12
The 5Wi in the Eq. 6-11 is estimated from the exhaust gas composition and ambient air moisture
fraction (Bwa) as
Bws=l-{[l5%(l-Bwa)]/[o.79(l-Bwa)F +
2flwa(lOO- 02(%) - C0(%) - C02(ฐ/o
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
Q h =60-F .^.' (6-14)
ziannubar "" ' s \ ^ -r/'n
{ls-760j
where
Qannubar = exhaust flow rate at 293 ฐK 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 (ฐK), 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
-------
^-1000
C* (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 -W6-CN-fc-DR
cm +10000 + 10000 (particles/kg fuel) (6-16)
where
CN = particle count concentration in plume recorded by the PM instrument (I/cm3),
fc = mass fraction of carbon in diesel fuel (g/g),
DR = plume dilution ratio,
Cco2 = wet basis carbon dioxide concentration in exhaust (%),
Ceo = 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, EFu, was calculated from the particle mass
concentration recorded by the PM instrument, such as the TEOM or QCM, using
_ 200.3.Ci,./c./M ( /k fuel) (6.17)
M C 1-C
C^rv) . -> ^TH/"*
nr,
002 10000 10000
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 CC>2
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 CC>2
concentration on a dry basis, the wet basis CO2 concentration in this study was obtained from the
solution of YI and 72 in the Equation. 6-3 as
72xlOO
6.5 Conversion of Emission Measures
(6-18)
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
ER = EF-Ffuel
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
Ffuei = fuel feed rate (kg/h).
The emission rate was then used to calculate the distance-specific emission factor by
ER
EM = 5782.4
Ur (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
(6-21)
and
xlogD,.
(6-22)
GMD = 10
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, Paccei, as expressed by
6-11
-------
p - p
r-rRLaccel (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
where
RL = Road load (N)
KO = rolling resistance coefficient (dimensionless),
W= Gross vehicle weight (N),
9= grade angle determined from micro-barometer readings,
KI = first degree drag coefficient (N-s/m),
KI = second degree drag coefficient (N-s2/m2), and
Vy= vehicle speed (m/s).
In this study, the coefficients, KQ, K\, and KI, 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 (Vi, . . . , VN from 105
to 10 km/h), a system of linear equations (6-25) can be established based on finite differences
W (V -\-V \
W VV^ = K0W + K,V2 + K2V22 (6-25a)
o i 2 2 2
9.81 2
= KW + KV+KV1 (6-25b)
9.81 2 o i i 2 i
X
X
X
W (-VN-2 + VN) = K^w + Ky^ + K^2^ (6-25n-l)
6-12
-------
KlVN + KM (6-25n)
lN
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
2
ma= K0W+Kyn + K2n
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 KQ, K\, and K2 were estimated from the multipoint data equations 6-25a to 6-25n.
After the coefficients KO, K\, 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
(6-26)
pL
^ 3600
where
PRL = road load power demand (kW),
RL = road load (N), and
Uy = 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-flP
Grade = -
dL-Pa
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
-------
pa = density of air (1.2 kg/m3).
The road grade angle 9m Eq.6-24 was then given as
(Grade'] ((- ~Rx
9 = arctan (6-2o)
I 100 J
Eqs. 6-24 and 6-26 indicate that, under steady-state conditions, the three primary factors
vehicle speed, weight, and road gradeaffect the truck power demand.
6-14
-------
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.
120
Loaded Run 1
Loaded Run 2
Unloaded Run 1
Unloaded Run 2
100 200 300
Coast Down Run Time (sec)
400
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 coefficientsKO, K}, and K2which 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 + VyK} + Vy2K2 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
-------
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
Ko
K2
Loaded
Unloaded
1
2
1
2
Overall Average
-
0.00466
0.00426
0.00542
0.00348
0.00445
Nxs/m
21.919
55.532
3.793
57.000
34.561
NxS2/m2
3.421
2.566
3.945
2.129
3.015
0.945
0.962
0.956
0.971
0.979
6000
1000 2000 3000 4000
Measured ma (N)
5000
6000
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 9 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 OE. The 0% road grade determined
during T4 indicates that tests Tl 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
Pass
1
2
3
4
5
6
7
8
9
10
Average
SD
Speed
km/h
105.18
106.86
106.86
162.45
106.21
106.92
106.91
106.92
106.93
106.97
106.8
0.3
Road
Grade
%
0.01
-0.01
0.03
-0.03
-0.01
-0.03
0.03
0.00
0.02
-0.02
0.00
0.02
Grade
6
Degree
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
For the segment of 1-77 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 1-77 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. 1-77
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
-------
(a) 1-77 North Bound
0.45
0.40
0.05
0.00
3456789
Travel Distance (km)
10 11 12 13
98
92
(b) I-77 South Bound
456789
Travel Distance (km)
10 11 12 13
Figure 7-3. Road Grade of Highway 1-77 Measured by the Microbarometer (Mbar)
Northbound (a) and Southbound (b)
7-4
-------
Table 7-3. Dilution Ratios and Their Uncertainties for Individual Tests
Test No.
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Date
10/19/04
10/20/04
10/21/04
10/21/04
10/22/04
10/22/04
11/23/04
11/23/04
11/26/04
11/26/04
10/27/04
10/27/04
10/28/04
10/28/04
10/29/04
10/30/04
Average
69.81
57.68
73.00
72.36
99.35
92.43
75.21
75.21
88.76
88.76
78.31
82.28
101.66
70.98
68.70
79.86
Test
SDa
4.89
3.51
12.81
9.72
19.95
16.55
8.58
8.58
18.43
18.43
13.06
13.03
15.36
12.22
3.31
5.26
Condition
RSDb
7.0
6.1
17.6
13.4
20.1
17.9
11.4
11.4
20.8
20.8
16.7
15.8
15.1
17.2
4.8
6.6
Average
63.74
72.68
95.89
75.21
88.76
80.30
86.32
74.28
SD
3.01
8.04
12.96
8.58
18.43
9.22
9.82
3.11
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 Tl 1 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, CC>2 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 (Tl 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
Cl *
c
H
O
N
S
H/C (mol/mol)
Density (g/cm3)
Flash Point (EC)
Cetane Number
GHV (kcal/g)
in
80 :
C 70 :
o>
=ฃ eo
O
4*
n 50:
5 40 :
ฃ
"- 30
"o
= 20:
10 :
0"
Pur
npD
iesel
Base Fuel
wt% ,.;!lmore , wt% ;
100 g fuel 10
86.794
13.153
0
0.014
0
0394
7.23 86.238
13.05 13.760
0
0
^' Bio-Diesel (B20)
mole/ .0/ gซmole/
Ogfuel 1 00 g fuel
7.18 83.378 6.94
13.65 13.200 13.09
0 3.420 0.449
0.0010 0.0009 0.0001 0.0007 0.0001
0.0012 0.0015 0.0000 0.0012 0.0000
1.806
0.854
64.4
45.1
10.80
DBase Diesel
B20
DPump Diesel
1.901
0.857
64.4
50.2
10.93
1.886
0.846
67.8
51.1
10.61
T
21,350kg
33
,890
kg
56 km/h
rl
I
21,350kg
I i T i
1
Ir
33,890 kg
105 km/h
Level Grade
I
33,890 kg
73 km/h
T
f
1
21,350kg
97 km/h
4% Grade
Figure 7-4. Effects of Driving Condition and Fuel Type on Fuel Consumption
7-7
-------
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.
y = 0.0001X2 + 0.0216X + 8.2783
r2 = 0.9896
0 50 100 150 200 250 300 350 400
Power Demand (kW)
Figure 7-5. Relationship of Fuel Consumption with Truck Power Demand
F , = 6.0791 + 0.1311^+0.0002^
RL
(7-1)
where
Ffuei = 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
-------
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.
I" 35
a) OK
ซ 25;
* -n
5 20
o
^ 15
+j
3 10
(0
ฃ
UJ ฐ
D Base Diesel
B20
DPump Diesel
-f-
21,350kg
56k
-i-
33,890 kg
m/h
Level (
-s-
+1
21,350kg
105k
Srade
-i-
33,890 kg
;m/h
rih
33,890 kg
73 km/h
4%(
21,350kg
97 km/h
Brade
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
-------
30
O)
E
3
I/)
C
3
O
"3
25
20
15
= 10
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 + 0.0216^+ 0.000 LP^2 (7-2)
where
Q = exhaust flow rate, m /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
-------
-18
E
to
4-*
g 16 H
-------
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 fuel-
specific 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.
J-l
-------
Table 8-1. Test Average Emission Factors and Rates for NOX
Test No.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
T21
T22
Average'
Ave
21.99
21.48
12.30
10.26
13.97
13.56
22.60
22.96
21.72
21.74
13.57
10.71
10.27
10.29
21.67
22.47
22.54
23.98
EFa
(g/kg fuel)
SD
0.19
0.19
0.61
0.25
0.50
0.51
0.14
0.14
0.16
0.22
0.44
0.23
0.47
0.42
0.15
0.16
2.44
1.07
ERb
(g/s)
RSD (%)
0.87
0.90
4.92
2.45
3.59
3.75
0.63
0.62
0.73
1.01
3.27
2.16
4.61
4.04
0.68
0.73
10.81
4.47
2.79
Ave
0.189
0.186
0.044
0.034
0.045
0.043
0.183
0.182
0.166
0.042
0.033
0.037
0.035
0.186
0.201
0.435
0.514
SD
0.006
0.006
0.004
0.002
0.002
0.002
0.005
0.004
0.018
0.002
0.001
0.003
0.003
0.006
0.007
0.066
0.029
EMC
(g/km)d
Ave
6.35
6.23
2.72
2.13
2.77
2.66
6.13
6.11
5.92
2.57
1.98
2.22
2.17
6.31
6.78
16.24
25.45
SD
0.20
0.20
0.27
0.14
0.15
0.14
0.16
0.15
0.61
0.14
0.09
0.17
0.16
0.21
0.24
2.60
1.79
PRLe
kW
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
316.15
338.12
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
-------
Fuel-Specific Emission Factor (g/kg
o ซ, 3 S S S Si
DBase Diesel
B20
DPump Diesel
i-
'
21,350kg
i
33,890 kg
56 km/hr
21,350kg
33,890 kg
105 km/hr
Level Grade
33,890 kg
73 km/hr
n
21,350kg
97 km/hr
4% Grade
30
20
ซ
LU
U
JC
I
ซ
u
C
re
to
Q
'j= 15
10
5
DBase Diesel
B20
DPump Diesel
r* rm ffl HI
21,350kg 33,890kg 21,350kg 33,890kg 33,890kg
56 km/hr 105 km/hr 73 km/hr
Level Grade 4% C
'
21,350kg
97 km/hr
3rade
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
-------
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.
0.7
0.6
0.0
Level Road, Base Fuel
o Level Road, B20
A 4% Road Grade, Pump Fuel
0 50 100 150 200 250 300 350 400
Power Demand (kW)
Figure 8-2. Correlation between NOx Emission Rate and Power Demand
ERNOx =-0.0275 + 0.0015^
(8-1)
where
ERNQx= 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 Emiss
Source
Fuel
Speed
GVW
Fuel x Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
2.7781
417.1940
6.3892
0.9213
3.3058
1.0139
0.1443
6.7660
438.5126
df
1
1
1
1
1
1
1
8
15
MSC
2.778
417.194
6.389
0.921
3.306
1.014
0.144
0.846
Fd
3.285
493.284
7.554
1.089
3.909
1.199
0.171
Pr>Fe
0.1075
0.0000
0.0251
0.3271
0.0834
0.3054
0.6904
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/df.
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.
5-5
-------
Table 8-3. Test Average Emission Factors and Emission Rates for CO
Test No.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
T21
T22
Average'
EFa
(g/kg fuel)
Ave
1.54
1.39
6.62
7.35
8.11
8.92
2.32
2.25
2.05
1.94
7.27
8.06
6.28
6.50
1.70
1.34
4.52
2.13
SD
0.11
0.11
0.74
0.80
0.67
0.74
0.12
0.13
0.12
0.16
0.58
0.62
0.59
0.67
0.10
0.11
3.60
0.63
RSD (%)
7.1
7.7
11.3
10.9
8.3
8.3
5.3
5.6
5.7
8.1
7.9
7.7
9.5
10.3
5.9
8.5
79.7
29.5
13.2
ERb
(9/s)
Ave
0.013
0.012
0.024
0.025
0.026
0.028
0.019
0.018
0.014
0.022
0.024
0.022
0.022
0.015
0.012
0.082
0.046
SD
0.001
0.001
0.003
0.003
0.002
0.003
0.001
0.001
0.002
0.002
0.002
0.003
0.003
0.001
0.001
0.066
0.013
EMC
(g/km)d
Ave
0.44
0.40
1.46
1.52
1.60
1.75
0.63
0.60
0.46
1.37
1.49
1.36
1.37
0.49
0.40
0.77
2.27
SD
0.03
0.03
0.20
0.19
0.15
0.16
0.04
0.04
0.06
0.12
0.13
0.16
0.17
0.03
0.04
0.20
0.68
PRLe
kW
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
316.15
338.12
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.
-------
0.16
0.14
0.12
K 0.08
C
.2 0.06
U)
U)
'I 0.04
LJJ
0.02
0.00
D Base Diesel
B20
D Pump Diesel
21,350kg 33,890kg
56 km/hr
21,350kg 33,890kg
105 km/hr
Level Grade
33,890kg
73 km/hr
21,350kg
97 km/hr
4% Grade
u
re
u.
c
o
c 2
Distance-Spe
1 :
Figure 8-3. Effects of Experimental Conditions on CO: Fuel-Specific Emission Factor
(top); Emission Rate (center); and Distance-Specific Emission Factor (bottom)
3-7
-------
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.
ฃ
O)
=ฃ 10
3
0
ft 0
O 8
re
Li.
C
0 6
w 6
ijk
y = -0.0567x+ 10.109
r2 = 0.9771
Level Road Base Fuel
Level Road B20
A 4% Grade, Pump Fuel
\
y = -0.0522x H
r2 = 0.97
^
h 9.2211
99
V
s
L
\
I
T
50 100 150 200 250 300
Power Demand (kW)
350
400
Figure 8-4. Correlation between CO Fuel-Specific Emission Factor and Power Demand
-------
For base fuel:
EFCO = 10.109-0.0567PflL (8-3)
ForB20:
EFCO = 9.2211 - 0.0522Pa (8-4)
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
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
0.701332
124.21187
4.2331298
0.3708697
0.5666887
0.0920397
0.0017843
1.0185646
131.19628
df
1
1
1
1
1
1
1
8
15
MSC
0.701
124.212
4.233
0.371
0.567
0.092
0.002
0.127
Fd
5.508
975.584
33.248
2.913
4.451
0.723
0.014
Pr>Fe
0.0469
0.0000
0.0004
0.1263
0.0679
0.4199
0.9087
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/df.
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.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
T21
T22
Average'
EFa
(g/kg fuel)
Ave
1.11
1.13
5.20
5.65
5.56
6.53
1.35
1.42
1.13
1.15
4.18
5.77
3.74
4.99
1.08
1.00
1.01
0.87
SD
0.04
0.03
0.58
0.60
0.43
0.48
0.03
0.03
0.02
0.06
0.29
0.44
0.35
0.49
0.03
0.05
0.06
0.07
RSD (%)
3.20
3.05
11.17
10.59
7.78
7.32
2.08
2.18
2.15
5.12
7.04
7.63
9.32
9.80
2.65
4.98
6.31
7.65
6.11
ERb
(9/s)
Ave
0.0096
0.0098
0.0186
0.0189
0.0178
0.0207
0.0109
0.0113
0.0083
0.0128
0.0175
0.0133
0.0170
0.0093
0.0089
0.0201
0.0186
SD
0.0004
0.0004
0.0025
0.0023
0.0016
0.0017
0.0004
0.0004
0.0009
0.0010
0.0015
0.0015
0.0020
0.0004
0.0005
0.0033
0.0016
EMC
(g/km)d
Ave
0.32
0.33
1.15
1.17
1.10
1.28
0.37
0.38
0.28
0.79
1.06
0.81
1.05
0.32
0.30
0.74
0.92
SD
0.01
0.01
0.16
0.14
0.10
0.11
0.01
0.01
0.03
0.06
0.09
0.09
0.12
0.01
0.02
0.13
0.09
PRLe
kW
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
316.15
338.12
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
-------
01
3
21,350kg 33,890kg
56 km/hr
1,350 kg | 33,890 kg
105 km/hr
Level Grade
33,890 kg
73 km/hr
21,350kg
97 km/hr
4% Grade
0 0.020
ง
8. \
I
21,350kg
56 kr
|
*
D Base Dies
B20
DPump Die
-
33,890 kg
n/hr
Level
el
il
21,350kg
105k
3rade
33,890 kg
m/hr
H
33,890 kg
73 km/hr
4%C
21,350kg
97 km/hr
3rade
ฃ 12
2 :
_o
'to
to
o :
Q.
o ฐ-4
c
5
T
I
T
\
=
21,350kg
56 kr
33,890 kg
n/hr
Level
h
21,350kg
105k
Srade
"*"
33,890 kg
m/hr
DBase Diesel
B20
DPump Die
sel
33,890 kg
73 km/hr
4%<
T
T
21,350kg
97 km/hr
Srade
Figure 8-5. Effects of Test Conditions on THC: Fuel-specific Emission Factor (top);
Emission Rate (center); Distance-Specific Emission Factor (bottom)
8-11
-------
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.
t
D) I
U.
c
o
LU
U
HI
3
U.
4
Lev
l ev
A 4%
|li.
1 Nv v = -ฐ-0428x + 7.4873
\ \ r2 = 0.9807
r\\
*\\
el Road, Base Fuel
el Roari R5n
Grade, Pump Fuel
\\
y = -0.0351x + 6.1325
\
T
A A
100 200
Power Demand (kW)
300
400
Figure 8-6. Correlation between THC Fuel-Specific Emission Factor and Power Demand
For the base fuel:
EFTHC = 7.4873 - 0.0428P^
(8-5)
ForB20:
EFTHC = 6.1325- 0.035 LP
(8-6)
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
Fuel
Speed
GVW
Fuel x Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
1.516662
64.928542
0.629788
0.8111505
0.1835844
0.0077604
0.0057069
2.6243531
70.707547
df
1
1
1
1
1
1
1
8
15
MSC
1.517
64.929
0.630
0.811
0.184
0.008
0.006
0.328
Fd
4.623
197.926
1.920
2.473
0.560
0.024
0.017
Pr>Fe
0.0638
0.0000
0.2033
0.1545
0.4758
0.8816
0.8983
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/df.
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.
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total PM
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
EFa (mg/kg)
Volatile
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
Non-Vold
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
ERb (mg/s)
Total PM
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
Volatile
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
Non-Vold
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
EMC (mg/km)
Total PM
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
Volatile
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
Non-Vold
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.
56 km/h
21,350 kg 33,390 kg
105 km/h
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.
1400
40
60 80 100 120
Power Demand (kW)
140
160
180
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 Mi
Source
Fuel
Speed
GVW
Fuel x Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
1202530.1
606238.4
9135.4
65310.1
158727.6
148115.9
131226.9
237538.5
2558822.7
df
1
1
1
1
1
1
1
8
15
MSC
1202530.1
606238.4
9135.4
65310.1
158727.6
148115.9
131226.9
29692.3
Fd
40.500
20.417
0.308
2.200
5.346
4.988
4.420
Pr>Fe
0.0002
0.0020
0.5943
0.1763
0.0495
0.0560
0.0687
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/df.
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).
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.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
EFa
(mg/kg fuel)
Ave
55.83
45.59
263.31
190.06
246.27
271.82
80.54
68.87
84.33
206.62
347.82
197.89
229.61
43.99
39.14
SD
60.59
47.01
104.35
89.48
180.92
165.09
75.43
72.86
46.10
113.39
104.85
239.65
5474.31
1459.49
2299.39
ERb
(mg/s)
Ave
0.48
0.39
0.94
0.64
0.79
0.86
0.64
0.53
0.64
0.65
1.05
0.72
0.78
0.37
0.35
SD
0.52
0.40
0.38
0.30
0.57
0.52
0.60
0.57
0.36
0.35
0.32
0.87
18.77
12.69
19.22
EMC
(mg/km)d
Ave
16.10
13.14
58.03
39.11
48.58
53.23
21.59
17.83
21.64
39.64
64.28
43.50
48.17
12.62
11.73
SD
17.37
13.49
23.32
18.69
35.56
32.32
20.30
19.32
12.25
21.80
19.65
52.71
1159.36
431.13
647.62
PRLe
kW
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
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
-------
3 300
0) 250 +-
i! 200^
u
o
'
M
E 100
UJ
o
'o 50 4-
DBase Diesel
1 Bio-Diesel
21,350kg 33,390kg
56 km/h
21,350kg 33,390kg
105 km/h
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
Fuel
Speed
GVW
Fuel x Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
8.2
131547.9
6609.7
5.7
211.3
622.2
38.0
13613.4
152656.3
df
1
1
1
1
1
1
1
8
15
MSC
8.2
131547.9
6609.7
5.7
211.3
622.2
38.0
1701.7
Fd
0.005
77.305
3.884
0.003
0.124
0.366
0.022
Pr>Fe
0.9464
0.0000
0.0842
0.9554
0.7337
0.5622
0.8849
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/df.
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 steady-
state experimental conditions specified in this study by the Equations 9-1 and 9-2.
For base fuel:
EFM= 314.49-1.7322PRL
forB20:
EFM = 324.82-1.858^
(9-1)
(9-2)
where
= 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.
80 100 120
Power Demand (kW)
Figure 9-5.
Correlation between the TEOM Fuel-Specific PM Mass Emission Factor and
the Truck Power Demand
9-7
-------
1600
._. 1400
01
g 1200
1000
o
ra
800
c 600
in
in
ra
400
200
y=2.4978x +65.48
r2 = 0.3684
y = 0.6023x +84.128
r2 = 0.4457
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.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Average'
EFa
(count/kg fuel)
Ave.
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
SD
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
RSD (%)
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
20.0
ERb
(count/s)
Ave.
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
SD
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
EMC
(g/km)d
Ave
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
SD
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
PRLe
kW
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
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
Test No.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Average'
Average
PM Particle Number Results Obtained
EFa
(count/kg fuel)
Ave
5.82E+15
6.50E+15
8.03E+15
7.89E+15
1.00E+16
9.97E+15
3.16E+14
1.16E+14
4.26E+14
1.47E+16
1.36E+16
1.10E+16
1.03E+16
1.73E+14
8.03E+13
SD
8.67E+14
8.90E+14
2.33E+15
1.70E+15
2.72E+15
2.65E+15
6.31E+13
2.76E+13
1.69E+14
3.83E+15
2.58E+15
2.45E+15
2.22E+15
2.45E+13
9.85E+12
RSD (%)
14.9
13.7
29.0
21.6
27.1
26.6
19.9
23.8
39.7
26.1
19.0
22.2
21.5
14.1
12.3
22.1
ERb
(count/s)
Ave
5.02E+13
5.63E+13
2.87E+13
2.64E+13
3.23E+13
3.17E+13
2.58E+12
9.20E+11
3.29E+12
4.55E+13
4.12E+13
3.95E+13
3.51E+13
1.47E+12
7.16E+11
SD
7.60E+12
7.91E+12
8.60E+12
5.93E+12
8.84E+12
8.50E+12
5.20E+11
2.23E+11
1.39E+12
1.19E+13
7.96E+12
9.17E+12
7.80E+12
2.18E+11
9.23E+10
from the ELPI
EMC
(g/km)d
Ave
1.68E+15
1.89E+15
1.77E+15
1.64E+15
1.99E+15
1.96E+15
8.67E+13
3.08E+13
1.11E+14
2.79E+15
2.51E+15
2.39E+15
2.17E+15
5.00E+13
2.41E+13
SD
2.56E+14
2.66E+14
5.32E+14
3.68E+14
5.45E+14
5.27E+14
1.75E+13
7.49E+12
4.72E+13
7.28E+14
4.86E+14
5.57E+14
4.85E+14
7.41E+12
3.11E+12
PRLe
kW
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
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
-------
_ 4.5E+15
21,350kg 33,890kg
105 km/h
1.2E+13
ซ 1.0E+13--
8.0E+12 --
n
m
| 4.0E+12^
UJ
2.0E+12f
DBase Diesel
B20
I
I
21 ,350 kg
56
T
I
T
T
33,890 kg
km/h
,.
^B
I " 1 I 1 1
21,350kg 33,890kg
105 km/h
ฃ 9.0E+14
>ฃ
~m
ฃ 8.0E+14
3
O
H. 7.0E+14
'o
5
,g- 2.0E+14
0)
c 1.0E+14
I
Q O.OE+00
21,350 kg 33,890 kg
56 km/h
21,350kg 33,890kg
105 km/h
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
-------
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:
EFN =4xl02
-3.2003
(9-3)
ForB20:
(9-4)
5.0E+15
4.5E+15
4.0E+15
3.5E+15
3.0E+15
2.5E+15
2.0E+15
1.5E+15
1.0E+15
5.0E+14
O.OE+00
60 80 100 120
Power Demand (kW)
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
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
2.27E+29
2.28E+31
1.62E+30
3.52E+29
1.34E+30
1.18E+28
4.83E+28
7.74E+29
2.71 E+31
df
1
1
1
1
1
1
1
8
15
MSC
2.27E+29
2.28E+31
1.62E+30
3.52E+29
1.34E+30
1.18E+28
4.83E+28
9.67E+28
Fd
2.345
235.519
16.744
3.637
13.886
0.122
0.499
Pr>Fe
0.1642
0.0000
0.0035
0.0930
0.0058
0.7360
0.4999
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/df.
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. Three-way ANOVA Results for Fuel-specific PM Number Emission Factor
by the ELPI
Source
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuelx GVW
Fuel x Speed x GVW
Error
Total
ssa
2.48E+29
3.21 E+32
7.10E+27
4.00E+31
3.10E+31
1.46E+31
5.85E+30
1.13E+30
4.13E+32
df
1
1
1
1
1
1
1
8
15
MSC
2.48E+29
3.21 E+32
7.10E+27
4.00E+31
3.10E+31
1.46E+31
5.85E+30
1.41E+29
Fd
1.760
2273.139
0.050
283.720
219.581
103.859
41.511
Pr>Fe
0.2212
0.0000
0.8281
0.0000
0.0000
0.0000
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/df.
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+17
(fl
'E
m
O
E
o
V) 0)
-------
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
1000
Base-21350 kg
- B20--21350 kg
*Base-33890 kg
- 315 - - B20-33890 kg
Base-21,350 kg
- 620-21,350 kg
x Base-33,890 kg
- - * - - 620-33,890 kg
100
Particle Size (nm)
1000
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
- B20-21350 kg
Base-33890 kg
- * - - B20-33890 kg
3.0E+14
,3 2.5E+14
O)
2.0E+14
ง 1.5E+14
n
Q.
Q
01
ฃ
TJ
IL
LU
TJ
1.0E+14
5.0E+13
O.OE+00
Base-21350 kg
B20-21350 kg
Base-33890 kg
- B20-33890 kg
Particle Size (nm)
100
100
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.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Avea
44.4
37.9
25.7
25.1
25.3
26.4
23.9
46.2
21.8
38.6
26.6
26.9
22.4
24.5
26.6
47.6
SMPS
SDb
2.6
2.9
1.0
0.7
0.9
0.8
2.1
1.8
2.0
2.7
0.7
0.7
0.9
0.6
2.0
1.7
Average
a. Ave :
b. SD =
c. RSD
= average
standard
= relative
deviation
standard
nanoSMPS
RSDC
(%)
5.9
7.6
3.9
2.9
3.5
2.9
8.9
4.0
9.3
7.0
2.5
2.7
3.8
2.6
7.5
3.6
4.9
deviation
Ave
28.4
30.3
28.8
30.4
28.4
30.0
28.9
23.5
17.1
34.6
30.9
29.4
26.1
12.3
SD
10.9
7.0
6.5
4.1
12.2
11.0
1.3
1.2
5.4
2.6
1.1
3.0
1.0
RSD
(%)
38.5
23.0
21.5
14.4
40.8
38.2
5.5
6.7
15.6
8.5
3.9
11.5
8.4
18.2
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
-------
50
45
40
35
30
25
20
15
10
5
0
21,350 kg 33,890 kg
56 km/h
21,350 kg 33,890 kg
105 km/h
Figure 9-12. Particle Geometric Mean Diameters by SMPS under Various Test Conditions
50 100 150
Power Demand (kW)
200
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
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuelx GVW
Fuel x Speed x GVW
Error
Total
ssa
24.5
442.1
21.4
15.3
69.8
1.1
3.5
634.6
1212.2
df
1
1
1
1
1
1
1
8
15
MSC
24.5
442.1
21.4
15.3
69.8
1.1
3.5
79.3
Fd
0.309
5.574
0.270
0.192
0.880
0.014
0.044
Pr>Fe
0.5937
0.0459
0.6174
0.6725
0.3758
0.9091
0.8392
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/df.
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 CC>2 range because the Horiba Model 210 was replaced
with the CAI Model 300. In addition, the QAPP has a typographical error that lists a CC>2 range
of 0-2000 ppm; this 0-2000 ppm range should have been labeled CO^ indicating low range
carbon monoxide, instead of CC>2. 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 ฑ 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 ฑ
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
Precision
Accuracy Completeness
Detection Limit or
Range
Gas phase
measurements
(THC, NO/NOx, O2
CO2, CO)
CEMs
ฑ2%
ฑ2%
THC:0-100ppm
NOX: 0-3000 ppm
95% O2:0-25%
CO2: 0-20%
COL: 0-2000 ppm
1,1,1,2,3,3,3
heptafluoropropane
Temperature
Volumetric airflow
rate
Differential pressure
PM massd
Photoacoustic + ,-0,
analysis
Thermocouple 5%
Mass flow ,-.
controllers0 5/ฐ
Transducers 5%
Gravimetric analysis 3 uge
ฑ5%
ฑ5%
ฑ 10%
ฑ 10%
ฑ15|jg
95%
95%
95%
95%
90%
0-10 ppmv
K-type:
-200ฐCto1250ฐC
J-type: 0 ฐC to 750 ฐC
0-2 L/min
0-15 L/min
0-50 L/min
0-17.5 inches H20
1 M9
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
THC
NOX
02
CO2
CO
Calibration Gas Quantities
(% Full Scale)
0, 29, 59, 90
0, 30, 60, 91
0, 30, 60, 90
0, 12, 24, 36, 49, 62, 73
0, 15,31,45,60,75,89
Instrument
Range
300 ppm
3000 ppm
25%
20%
2000 ppm
Scaling Equation
y = 60.106 x- 0.2011
y = 300.66 x + 3.2978
y = 5.0163 x- 0.0756
y = 1. 9951 x- 0.078
y = 200.85 x- 5.7935
Date of
Calibration
11/21/2003
12/03/2003
11/18/2003
10/31/2003
12/03/2003
r2
1
0.9999
1
1
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, C>2, CC>2 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 02 gas measurements are listed in Table 10-5. All instances when 02
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
Test
Pretest
Accuracy
(% bias)
Precision
(% RSD)
Bias Calibration Check
Post Test Pretest Post Test
Accuracy
(% bias)
Precision Accuracy
(% RSD) (% bias)
Precision Accuracy Precision
(% RSD) (% bias) (% RSD)
Full Span Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
-0.21%
0.02%
-0.08%
-0.28%
0.03%
-0.21%
0.02%
0.41%
-0.25%
0.04%
-0.08%
0.04%
0.00%
-0.77%
0.32%
0.03%
0.02%
0.03%
0.03%
0.03%
0.03%
0.51%
0.04%
0.08%
0.03%
0.05%
0.06%
0.87%
-3.28%
-1.66%
-0.20%
0.83%
-0.35%
0.65%
1.13%
0.41%
2.03%
3.09%
1.60%
0.98%
3.09%
4.52%
0.04% -2.71%
0.04% 1.08%
0.04%
0.03%
0.03%
0.04%
0.03%
0.03%
0.03%
0.03% 0.63%
0.02% 0.47%
0.02%
0.03%
0.04%
0.04% -3.86% 0.05%
0.08% -5.47% 0.17%
-8.74% 0.04%
-6.30% 0.08%
-3.70% 0.11%
0.05%
0.08%
2.33% 0.04%
2.97% 0.07%
Midrange
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
0.14%
0.32%
0.10%
-0.23%
0.16%
0.34%
0.47%
0.23%
0.36%
0.40%
0.24%
0.72%
0.24%
0.62%
0.04%
0.05%
0.07%
0.03%
0.04%
0.02%
0.03%
0.03%
0.02%
0.09%
0.04%
0.07%
0.21%
0.05%
-1.53%
-0.10%
1.04%
0.31%
0.80%
1.62%
1.23%
2.95%
4.02%
2.30%
2.07%
3.11%
5.93%
0.05%
0.06%
0.04%
0.12%
0.03%
0.03%
0.05%
0.05% 0.40%
0.04%
0.04%
0.06%
0.04%
0.03%
1.32% 0.03%
0.04%
3.26% 0.04%
10-4
-------
Table 10-4. DQI Values for Oxides of Nitrogen Gas Measurements for All Tests
Direct Calibration Check
Test
Pretest
Accuracy
(% bias)
Precision
(% RSD)
Post Test
Accuracy
(% bias)
Precision
(% RSD)
Bias Calibration Check
Pretest Post Test
Accuracy Precision Accuracy Precision
(%bias) (%RSD) (% bias) (% RSD)
Full Span Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
-0.02%
0.02%
-0.86%
0.13%
0.05%
0.06%
-0.03%
0.00%
0.06%
0.01%
0.14%
-0.25%
-0.02%
0.00%
0.04%
0.05%
0.05%
0.03%
0.04%
0.02%
0.11%
0.15%
0.07%
0.13%
0.06%
0.71%
0.24%
0.05%
-1.45%
-3.84%
-1.00%
-1.13%
3.87%
-0.07%
-1.49%
-1.89%
-2.24%
-2.28%
-1.68%
-7.97%
-10.05%
-5.01%
0.02%
0.18%
0.03%
0.03%
0.19%
0.11%
0.09%
0.07%
0.06%
0.12%
0.05%
0.08%
0.11%
0.12%
-3.77% 10.04% -0.90% 0.09%
-3.55% 0.02% -8.53% 0.06%
-1.25% 0.11%
-2.49% 0.04%
4.85% 0.03%
-8.12% 0.11%
-1.91% 0.10%
-2.08% 0.22%
0.10% 0.41% -51.07% 31.69%
-0.87% 0.07% -7.84% 0.38%
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% 0.07%
-22.27% 0.26%
-6.22% 0.08%
2.12% 0.08% -0.11% 0.09%
-33.14% 0.09%
-1.07% 0.09%
4.16% 0.15%
10-5
-------
Table 10-5. DQI Values for Oxygen Gas Measurements for All Tests
Direct Calibration Check
Test
Pretest
Accuracy
(% bias)
Precision
(% RSD)
Post Test
Accuracy
(% bias)
Precision
(% RSD)
Bias Calibration Check
Pretest Post Test
Accuracy Precision Accuracy Precision
(%bias) (%RSD) (% bias) (% RSD)
Full Span Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
0.33%
-0.33%
0.01%
-0.22%
0.10%
0.03%
0.09%
0.29%
-0.08%
0.19%
0.09%
0.16%
0.30%
0.11%
0.15%
0.14%
0.13%
0.15%
0.18%
0.13%
0.15%
0.36%
0.17%
0.19%
0.15%
0.13%
0.14%
0.13%
0.34%
0.05%
0.00%
0.16%
0.24%
-0.59%
-0.26%
0.26%
-0.04%
0.42%
0.17%
0.41%
-0.42%
0.03%
0.12%
0.16%
0.13%
0.13%
0.14%
0.16%
0.14%
0.14%
0.17%
0.15%
0.15%
0.13%
0.18%
0.14%
0.13% 0.16%
-0.62% 0.16%
-0.57% 0.13%
-1.68% 0.57%
-0.01% 0.15% -0.05% 0.15%
-0.25% 0.19%
0.08% 0.13% 0.36% 0.14%
-0.05% 0.15%
-0.38% 0.33%
M/drange
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
2.10%
0.40%
1.26%
2.37%
1.89%
0.52%
1.03%
0.85%
1.15%
0.98%
0.59%
1.27%
1.59%
2.34%
0.21%
0.23%
0.16%
0.16%
0.14%
0.19%
0.18%
0.21%
0.17%
0.83%
0.32%
0.19%
0.18%
0.20%
2.53%
0.97%
1.64%
3.08%
2.24%
-0.83%
0.14%
1.17%
0.97%
2.37%
-0.26%
1.54%
0.78%
2.07%
0.14%
0.15%
0.19%
0.18%
0.30%
0.21%
0.16%
0.15%
0.13%
0.18%
0.18%
0.20%
0.14%
0.15%
0.07% 0.13%
0.45% 0.15%
0.39% 0.39%
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
Test
Pretest
Accuracy
(% bias)
Precision
(% RSD)
Bias Calibration Check
Post Test Pretest Post Test
Accuracy
(% bias)
Precision Accuracy
(% RSD) (% bias)
Precision Accuracy Precision
(% RSD) (% bias) (% RSD)
Full Span Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
0.27%
0.07%
0.18%
-0.05%
0.02%
0.14%
-0.14%
0.10%
-0.03%
-0.47%
0.18%
0.08%
0.48%
-0.18%
0.04%
0.04%
0.04%
0.05%
0.05%
0.04%
0.05%
0.22%
0.04%
0.27%
0.04%
0.06%
0.66%
0.04%
0.42%
1.60%
0.53%
0.62%
1.17%
-1.12%
0.29%
1.06%
-2.41%
-1.11%
-0.60%
1.92%
-1.77%
-0.36%
0.06% 1.96%
0.05% 0.21%
0.05% 1.10%
0.05%
0.04% 1.96%
0.04% -3.80%
0.06% -0.73%
0.04% 1.78%
0.04%
0.05% -1.20%
0.04%
0.04% -0.14%
0.05% 1.46%
0.04% 2.29%
0.08% -0.13% 0.09%
0.04%
0.09%
-0.14% 0.05%
0.10%
1.46%
-2.88% 0.10%
0.04%
-3.70% 0.05%
0.07%
0.12%
0.14%
Midrange
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
4.17%
3.96%
3.86%
3.37%
3.81%
4.14%
4.03%
4.19%
4.39%
4.28%
4.57%
3.98%
4.22%
3.75%
0.06%
0.07%
0.07%
0.08%
0.08%
0.07%
0.09%
0.06%
0.07%
0.07%
0.07%
0.09%
0.07%
0.06%
4.09%
5.17%
4.19%
4.15%
5.30%
2.62%
4.47%
5.63%
1.93%
3.36%
3.78%
6.07%
1.89%
3.83%
0.08%
0.13%
0.08%
0.07%
0.07%
0.06%
0.07%
0.07%
0.06% 5.82%
0.07%
0.07%
0.07%
0.07%
0.06%
5.74% 0.13%
-0.43% 0.20%
5.67% 0.06%
0.06%
1.81% 0.07%
2.75% 2.04%
4.71% 0.16%
10-7
-------
Table 10-7. DQI Values for Carbon Monoxide Gas Measurements for All Tests
Direct Calibration Check
Test -
Pretest
Accuracy
(% bias)
Precision
(% RSD)
Post Test
Accuracy
(% bias)
Precision
(% RSD)
Bias Calibration Check
Pretest Post Test
Accuracy
(% bias)
Precision Accuracy
(% RSD) (% bias)
Precision
(% RSD)
Full Span of the Low Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
0.13%
-0.42%
-0.58%
-0.26%
0.50%
-0.83%
-0.13%
0.57%
0.06%
-0.22%
0.48%
0.25%
1.06%
-0.03%
0.93%
0.91%
0.93%
0.93%
0.90%
0.90%
0.91%
0.94%
0.93%
0.89%
0.93%
0.92%
0.95%
0.89%
0.63%
1.07%
-0.28%
0.43%
1.25%
-1.29%
0.15%
1.44%
-1.26%
-0.64%
-0.02%
1.49%
-0.02%
0.80%
0.94%
1.11%
0.92%
0.93%
0.97%
0.90%
0.90%
0.96%
0.92%
0.91%
0.89%
0.96%
0.89%
0.90%
0.84%
-0.25%
-0.08%
1.07%
-3.35%
-0.42%
1.56%
-0.68%
0.11%
1.84%
1.14%
0.97% 0.72%
0.93%
0.88%
-0.10%
0.98%
1.86%
0.91%
1.00%
-1.79%
0.90%
-1.56%
0.89%
1.03%
0.97%
0.97%
0.92%
0.96%
0.92%
Middle of the Low Range
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
-1.75%
-1.83%
-2.05%
-2.50%
-1 .06%
-2.52%
-1.87%
-1.08%
-1.54%
-1.64%
-0.90%
-1.37%
-0.71%
-1.71%
1.28%
1.26%
1.26%
1.28%
1.29%
1.28%
1.29%
1.27%
1.27%
1.30%
1.25%
1.27%
1.29%
1.27%
-1.57%
-0.20%
-1.85%
-1.20%
0.29%
-2.89%
-1.48%
0.86%
-1.61%
-0.71%
-0.22%
0.63%
-1.79%
0.27%
1.32%
1.45%
1.27%
1.34%
1.28%
1.29%
1.29%
1.20%
1.25% -0.65%
1.21%
1.18%
1.22%
1.25%
1.23%
-0.63%
-3.68%
0.74%
1.36%
-1.13%
-2.32%
0.45%
1.39%
1.32%
1.24%
1.20%
2.60%
1.20%
10-8
-------
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:
I Average of PMCalibrationCheck -Avgerageof AMCalibrationCheck |
^ Gas Analyzer Full Scale j
(10-1)
Table 10-8. CEM Span & Zero Drift for the Pump Diesel and the Low Sulfur Diesel Fuel
Tests
Test
#1
9/2/2004
Test
#2
10/1/2004
Test
#3
10/13/2004
Test
#4
10/14/2004
Test
#5
10/19/2004
Test
#6
10/20/2004
Test*
7&8
10/21/2004
Test*
9&10
10/22/2004
Test*
11&12
10/23/2004
Span Drift
CO
CO2
02
NOX
THC
0.44%
0.11%
0.01%
-1.31%
-2.77%
1.32%
1.11%
0.34%
-3.53%
-1.51%
0.26%
0.25%
-0.01%
-0.13%
-0.11%
0.61%
0.48%
0.34%
-1.15%
1.00%
0.66%
0.84%
0.13%
3.48%
-0.34%
-0.41%
-0.91%
-0.56%
-0.12%
0.78%
0.24%
0.31%
-0.32%
-1.34%
1.00%
0.77%
0.70%
-0.02%
-1.73%
0.00%
-1.17%
-1.73%
0.04%
-2.10%
2.01%
Zero Drift
CO
CO2
02
NOX
THC
-0.14%
-0.08%
0.17%
0.01%
-0.55%
0.42%
0.03%
-0.32%
0.00%
19.03%
-0.06%
-0.07%
0.03%
0.01%
-0.03%
0.20%
-0.05%
0.46%
-0.01%
0.13%
0.29%
0.04%
0.20%
0.00%
0.04%
0.00%
-0.03%
0.00%
-0.01%
0.00%
0.12%
0.00%
-0.17%
0.00%
0.05%
1.31%
0.21%
-0.25%
0.00%
0.30%
0.72%
0.13%
-0.20%
0.00%
0.27%
10-9
-------
Table 10-9. CEM Span and Zero Drift for the Biodiesel Fuel Tests
Test # 13 & 14
10/26/2004
Test # 15 & 16
10/27/2004
Test # 17 & 18
10/28/2004
Test #19
10/29/2004
Test # 20
10/30/2004
Span Drift
CO
CO2
02
NOX
THC
-0.38%
-0.46%
0.21%
-2.08%
2.75%
-0.44%
-0.56%
0.08%
-1.66%
1.51%
1.10%
1.34%
0.23%
-7.04%
0.85%
-0.96%
-1.64%
-0.64%
-9.14%
2.79%
0.73%
-0.13%
-0.07%
-4.57%
4.78%
Zero Drift
CO
CO2
02
NOX
THC
1.24%
0.31%
0.93%
0.00%
0.35%
1.00%
0.20%
-0.28%
-0.01%
0.43%
0.58%
0.15%
-0.18%
-0.07%
0.37%
0.39%
0.09%
-0.20%
0.00%
1.14%
0.94%
0.20%
0.20%
-0.01%
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, FM-
200, 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
UA0971
UA0987
Water
Date(s) of Zero
Gas Name
Calibration
FM-200 10/7/2004
Propane 10/7/2004
Water 10/7/2004
Date(s) of Humidity Date(s) of Span
Interference Calibration Calibration
10/7/2004
10/7/2004
NA
10/11/2004, 10/19/2004
10/8/2004, 10/19/2004
10/8/2004
Table 10-11. DQI Values for FM-200 and Propane Gas Measurements for All Tests
FM-200
Test
1
2
3
4
5
6
7&8
9&10
11 &12
13&14
15&16
17&18
19
20
21
22
Pretest
Accuracy
(% bias)
1.00%
2.71%
0.10%
0.00%
0.69%
0.73%
1.00%
0.25%
1.29%
0.40%
0.00%
-1.50%
NA
NA
Precision
(% RPD)
0.70%
0.84%
0.22%
0.00%
0.26%
0.26%
0.00%
0.50%
0.80%
0.22%
0.00%
0.51%
NA
NA
Post Test
Accuracy
(% bias)
-0.44%
-0.83%
-1.90%
-1.25%
-1.75%
-1.68%
-1.63%
-2.56%
-2.06%
-1.71%
-1.75%
-1.64%
NA
NA
Precision
(% RPD)
0.68%
1.27%
0.23%
0.28%
0.28%
0.26%
0.24%
0.18%
0.17%
0.27%
0.43%
0.24%
NA
NA
Propane
Pretest
Accuracy
(% bias)
1.90%
-0.26%
0.68%
0.26%
0.00%
0.04%
-0.53%
-0.23%
-0.48%
-0.94%
-0.57%
0.23%
NA
NA
Precision
(% RPD)
1.11%
1.52%
0.00%
0.17%
0.22%
0.38%
0.27%
0.24%
0.29%
0.24%
0.28%
0.28%
NA
NA
Post Test
Accuracy
(% bias)
2.04%
1.90%
0.82%
0.53%
1.19%
1.25%
0.97%
1.19%
0.82%
0.94%
0.40%
0.49%
NA
NA
Precision
(% RPD)
0.58%
0.93%
0.30%
0.27%
0.25%
0.24%
0.13%
0.28%
0.30%
0.24%
0.26%
0.27%
NA
NA
10-11
-------
Table 10-12. Thermocouple Calibrations
Location
Background Tunnel
Ambient
Plume Tunnel
Stack
Exhaust
Water
Intake
Oil
Description
K-Type
K-Type
K-Type
K-Type
K-Type
K-Type
K-Type
K-Type
Met Lab
ID
02802
02803
02804
02796
02797
02798
02799
02800
Calibration
Range (ฐC)
20-100
20-100
20-120
25-265
25-265
25-120
25-120
25-120
Uncertainty calibration Date
( ^1
0.9
0.9
0.9
0.8
0.8
0.7
0.7
0.8
11//3/2003
11//3/2003
11//3/2003
11/12/2003
11/12/2003
11/12/2003
11/12/2003
11/12/2003
2/15/2005
2/16/2005
2/14/2005
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
V0.04 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
Location3
Plume FTP1
Plume FTP2
Background Teflon
Background Quartz
Background Bypass
Background Bag
Plume TDb Quartz
Plume TD Teflon
Plume Minor
Plume Major
Description
Mass Flow Controller
Mass Flow Controller
Mass Flow Controller
Mass Flow Controller
Mass Flow Controller
Mass Flow Controller
Mass Flow Meter
Mass Flow Meter
Mass Flow Meter
Mass Flow Meter
Met Lab ID
02805
02806
02807
02808
02809
02810
02212
02214
02812
02813
Calibration Range
0-30 SLPM
0-30 SLPM
2-1 5 SLPM
2-1 5 SLPM
2-1 5 SLPM
0-1. 4 SLPM
0-1 8 SLPM
0-1 8 SLPM
0-5 SCFM
0-38 SCFM
Uncertainty
0.33 SLPM
0.31 SLPM
0.07 SLPM
0.12 SLPM
0.08 SLPM
0.01 SLPM
0.16 SLPM
0.1 3 SLPM
1%
2.2%
Calibration Date
11/14/2003
04/08/2004
11/14/2003
11/05/2003
11/05/2003
11/05/2003
11/05/2003
05/17/2004
11/13/2003
11/13/2003
' Refer to Figures 2-5, 2-6, and 2-8 for schematics of the Plume and Background Sampling Systems.
'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 on-
road 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 jig), 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
-------
Table 10-14. Balance Variations from Standard Weights
Standard Weight
Date
12/7/2004
1/13/2005
4/6/2005
100.000 mg
99.994
99.997
99.998
Absolute
Differences, mg
0.006
0.003
0.002
200.000 mg
200.003
200.001
200.007
Absolute
Differences, mg
0.003
0.001
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
Test No.
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Sample ID
T020504W
T020504C
T092904A
T092904D
T020504H
T020504G
T092904G
T092904Y
T092904N
T092904L
T092904W
T092904U
T092904R
T092904T
T020504K
T020504O
PM mass (mg)
12/7/2004
2.088
1.662
0.196
0.871
0.890
0.560
0.448
0.366
0.352
0.299
0.427
0.442
0.290
0.310
0.261
0.309
2/18/2005
1.867
1.544
0.190
0.860
0.877
0.546
0.441
0.352
0.344
0.294
0.422
0.435
0.288
0.304
0.238
0.272
10-14
-------
2.5
ฃ. 2.0
>o
o
00
s
c 1.5
O
1.0
0.5
0.0
y = 0.9295 x
r2= 0.9959
0.0 0.5 1.0 1.5 2.0
PM mass weighed on 12/7/04 (mg)
2.5
Figure 10-1. PM Mass Affected by Sample Losses
The best OC/EC analyzer range of deposit concentration is 5-400 jig/cm2 for organic carbon and
1-15 jig/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 jig/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 jig/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 jig/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
-------
0 2 4 6 8 10
OC and EC on the First Punch (jig/cm2)
Figure 10-2. Repeatability of OC/EC Analysis
Table 10-16. Effect of Sample Source on Organic Compound Speciation
Sample Source
On-road diesel truck (this study)
Jet engine (APEX-2)
Residential oil boiler
Industrial oil-fired boiler (NC A&T2)
OC by Analyzer
(jig/filter)
2670
366.5
288
1020
OC by GC/MS
(Mg/filter)
29.5
6.0
51.8
97.5
Detected
(%)
1.1
1.6
18.0
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, ฑ 15%, set for the 1C 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
RSD (%)
Filter ID
K+ NH4+ 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
McCormicketal.,2002
Lin etal., 2006
Ropkinsetal.,2007
Durbin etal., 20079
Yang etal., 2007
Rosenblatt and
Rideout, 2007
Engine Type
1991 DD60b
20062.8-L4M40-
2AT1
1.8-L EURO I light
duty diesel6
6.5-L GM
1 993 5.9-L Cummins
1999 Caterpillar 312ฎ
Mitsubishi 4M40-
2AT1
Cummins ISX 435 ST
Operating
Cycle/Mode
HD-FTP Transient
HD-FTP Transient
(<300 hr)c>d
On-road urban driving
route (~0 to 70 km/h)
Light duty FTPh
AVL 8-mode
Light duty FTP transient
(0 km accumulated
operation))
Steady-state 50 km/h;
24,000 kg GVW
Steady-state 110 km/h;
24,000 kg GVW
Fuel Type
Fischer-Tropsch (F-T)
B20: F-T/soy
Petroleum
B20: Petroleum/ palm oil
Petroleum
B5: Petroleum/ rape seed
ULSD CARB (petroleum)1
B20: ULSD/soy
ULSD CARB (petroleum)1
B20: ULSD/soy
ULSD CARB (petroleum)1
B20: ULSD/soy
Low S petroleum
B20: Petroleum/ waste
cooking oil
Low S petroleum
B20: Petroleum/ canola oil
Low S petroleum
B20: Petroleum/ canola oil
B5: Petroleum/ canola oil
B20: Petroleum/ canola oil
B2: Petroleum/ tallow
B5: Petroleum/ tallow
Fuel Sulfur
Content
<10
14
30
<10
<50
Low not specified
Low not specified
Low not specified
Low not specified
Low not specified
Low not specified
Low not specified
22
21
<15 ppm
Not specified
<15 ppm
Not specified
Not specified
Not specified
Not specified
Not specified
Reported Emissions Factor/Rate3
g/bphxh
THC
0.007
0.005
0.280
0.250
0.122
0.118
CO
3.84
3.61
1.47
1.46
0.195
0.238
NOX
4.03
4.25
3.82
3.77
0.760
0.823
PM
0.17
0.146
0.125
0.117
N/Af
N/A
0.061
0.055
0.859
0.823
3.91
4.35
-0.06
-0.05
g/mi
THC
CO
NOX
PM
0.020
0.042
0.30
0.31
0.10
0.11
0.66
0.84
0.90
0.92
0.94
0.88
7.2
7.1
6.5
6.8
5.3
5.1
0.048
0.045
0.150
0.146
0.030
N/A
0.50
0.43
0.25
0.27
0.26
0.23
0.24
0.24
1.02
1.07
0.61
0.57
0.56
0.58
0.60
0.61
14.9
13.1
5.23
5.16
5.11
5.07
5.04
4.83
0.166
0.159
0.117
0.104
0.103
0.105
0.108
0.110
(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 LXTD 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; CARS = California Air Re-sources Board.
j. Emissions for B20 rose above base fuel after 20,000 km for THC, CO, and PM
11-3
-------
-50
-60
DCurrent Study (56 km/h; 21350 kg GVW)
Rosenblatt & Rideout, 2007 (50 km/h; 24000 kg GVW)
D Current Study (105 km/h; 21350 kg GVW)
D 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
-------
ซ 5
o
ป o
m -5
X
<
0- -10
_c
ง,-15-
e
(S
0 -20
ซ
in
ซ -25
**
S-30
ฃ
-------
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 steady-
state, 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 PM-
2.5 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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Trucks Operated on California In-Use Diesel, ARCO's EC-Diesel, and ARCO EC-Diesel with a
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Kinsey, J.S.; Mitchell, W.A.; Squier, W.C.; Wong, A.; Williams, C.D.; Logan, R.; and Kariher,
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D.E. (2003) Effect of Fuel Consumption on Combustion and Detailed Chemical/Physical
Characteristics of Diesel Exhaust, SAE Paper 2003-01-1899 Society of Automotive Engineers,
Warrendale, PA.
Lin, Y-C.; Lee, W-J.; Wu, T-S.; and Wang, C-T. (2006a) Comparison of PAH and Regulated
Harmful Matter Emissions from Biodiesel Blends and Paraffmic Fuel Blends on Engine
Accumulated Mileage Test. Fuel, 85(17/18):2516-2523.
McCormick, R.L.; Alvarez, J.R.; Graboski, M.S.; Tyson, K.S.; and Vertin, K. (2002) Fuel
Additive and Blending Approaches to Reducing NOx Emissions from Biodiesel. SAE Paper No.
2002-01-1658, Society of Automotive Engineers, Warrendale, PA.
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Ramamurthy, R., Clark, N.N., Atkinson, C.M., and Lyons, D.W. (1998) Models for Predicting
Transient Heavy-Duty Vehicle Emissions, SAE Paper 982652, Society of Automotive Engineers,
Warrendale, PA.
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Real-World Comparison of Probe Vehicle Emissions and Fuel Consumption Using Diesel and a
5% Biodiesel (B5) Blend. Sci. of the Tot. Environ. 376(l-3):267-284.
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ERMD Report 06-40, Environmental Science and Technology Centre, Environment Canada,
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Anemometry and Coast-Down Techniques, Surface Vehicle Recommended Practice, J2263,
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in the Atmosphere: Problems and Prospects, Atmos. Environ. 34(18): 2983-3013.
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Fluorescence (XRF) Spectroscopy, Method IO-3.3, in Compendium of Methods for the
Determination of Inorganic Compounds in Ambient Air, report No EPA/625/R-96/010a, Office
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Experiments, EPA Quality Assurance Project Plan, Category Ill/Applied Research, Revision 0,
(QTRAK #2051) Air Pollution Prevention and Control Division, January.
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Biodiesel on Emissions of Regulated Air Pollutants and Poly cyclic Aromatic Hydrocarbons
under Engine Durability Testing. Atmos. Environ. 41(34):7232-7240.
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Diesel Vehicles, Environ. Sci. & Tech. 34(5):729-740.
1O "
13-j
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Appendix A
Chemical Composition
A.I 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-l. 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-l 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-l
-------
Table A-l. Black Carbon and PM Surface PAH Emission Factors
EF
(mg/kg fuel)
Test No.
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Average
Avea
0.324
0.268
1.097
1.075
1.117
0.928
0.181
0.268
0.171
0.884
1.260
1.071
0.892
0.192
0.234
BC
SDb
0.126
0.098
0.348
0.281
0.566
0.462
0.126
0.150
0.142
0.370
0.333
0.384
0.290
0.086
0.090
RSDC
38.8
36.5
31.7
26.1
50.7
49.8
69.3
56.0
82.7
41.9
26.4
35.8
32.5
44.6
38.6
44.1
Ave
0.546
0.417
0.776
0.966
0.698
0.738
0.387
0.504
0.299
0.616
0.998
1.006
0.735
0.337
0.428
PAH
SD
0.058
0.039
0.247
0.239
0.265
0.289
0.050
0.059
0.055
0.202
0.207
0.296
0.208
0.030
0.051
RSD
10.6
9.4
31.8
24.8
37.9
39.2
12.8
11.6
18.5
32.8
20.7
29.4
28.3
9.0
12.0
21.9
a. Ave = average.
b. SD = standard deviation.
c. RSD = relative standard deviation.
A-2
-------
0.0
21,350kg 33,890kg
56 km/h
21,350kg 33,890kg
105 km/h
Figure A-l. Effects of Test Conditions on Fuel-Specific Black Carbon Emission Factor
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
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
0.0091
2.6525
0.0019
0.0016
0.0049
0.0083
0.0038
0.1112
2.7933
df
1
1
1
1
1
1
1
8
15
MSC
0.0091
2.6525
0.0019
0.0016
0.0049
0.0083
0.0038
0.0139
Fd
0.658
190.886
0.137
0.118
0.355
0.598
0.273
Pr>Fe
0.4407
0.0000
0.7212
0.7398
0.5676
0.4616
0.6157
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/df.
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
Fuel
Speed
GVW
Fuelx Speed
Speed x GVW
Fuel x GVW
Fuel x Speed x GVW
Error
Total
ssa
0.0091
2.6525
0.0019
0.0016
0.0049
0.0083
0.0038
0.1112
2.7933
df
1
1
1
1
1
1
1
8
15
MSC
0.0091
2.6525
0.0019
0.0016
0.0049
0.0083
0.0038
0.0139
Fd
0.658
190.886
0.137
0.118
0.355
0.598
0.273
Pr>Fe
0.4407
0.0000
0.7212
0.7398
0.5676
0.4616
0.6157
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/df.
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
-------
1.8
0) :
5 14 :
2 \
i
'S 08 :
u.
C OK
'in
"> 04 :
m 02^
n n
4
4
> <
Base Fuel
T T
r J
11 *
20 40 60 80 100 120
Power Demand (kW)
140
160
180
Figure A-3. Plot of Black Carbon Emission Factor against Truck Power Demand
1.4
a,
n
O)
1.0
0.8
o
ra 0.6
UJ 0.2
p
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
Test
Vehicle Speed (km/h)
GVW (kg)
PM EF (mg/kg fuel)
OC EF (mg/kg fuel)
EC EF (mg/kg fuel)
PM-OC-ECb (mg/kg fuel)
OC/PM (%)
EC/PM (%)
Base Fuel
T5&T6
105
33,890
872.2
462.1
NDa
410.1
52.3
ND
B20
T19&T20
105
33,890
78.1
23.0
ND
55.1
29.2
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
-------
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)
105 km/h and 33,890 kg
Base Fuel
B20
D Naphthalene
n-Tridecane(n-C-13)
n-Hexadecane (n-C16)
n-Octadecane (n-C18)
D Dodecylcyclohexane
2-Methylnonadecane
n-Eicosane (n-C20)
D Pentadecylcyclohexane
n n-Tricosane (n-C23)
D n-Pentacosane (n-C25)
DSqualane
anteiso-Hexacosane (C-27)
DPristane
Danteiso-Heptacosane (C-28)
iso-Octacosane (C-29)
n-Nonacosane (n-C29)
anteiso-Nonacosane (C-30)
n-Hentricontane (n-C31)
n-Tritriacontane (n-C33)
Pyrene
AAA-20S-C27-Cholestane
17B(H)-21A(H)-30-Norhopane
Caprylic/Octanoic Acid
Adipic/Hexanedioic Acid
Pimelic/Heptanedioic Acid
1,4-Benzenedicarboxylic Acid
D1,2-Benzenedicarboxylic Acid
DPentadecanoic Acid
D Hexandecanoic Acid
DLinoleicAcid
DEicosane Acid
Dn-Undecane(n-CH)
D n-Pentadecane (n-C15)
Dn-Heptadecane (n-C17)
Phytane
Dn-Nonadecane (n-C19)
3-Methylnonadecane
n-Heneicosane (n-C21)
Dn-Docosane (n-C22)
Dn-Tetracosane (n-C24)
Dn-Hexacosane (n-C26)
Diso-Hexacosane (C-27)
Dn-Heptacosane (n-C27)
D iso-Heptacosane (C-28)
Dn-Octacosane (n-C28)
Danteiso-Octacosane (C-29)
iso-Nonacosane (C-30)
n-Triacontane (n-C30)
n-Dotriacontane (n-C32)
n-Tetratriacontane (n-C34)
DABB-20R-C27-Cholestane
DABB-20R-C29-Ethylcholestane
D 17A(H)-21 B(H)-Hopane
DGIutaric/Pentanedioic Acid
Capric/Decanoic Acid
Azalaic/Nonanedioic Acid
1,3-Benzenedicarboxylic Acid
D Tetradecanoic Acid
DPalmitoleic/9-Hexadecenoic Acid
DHeptadecanoicAcid
DOIeicAcid
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
Emission Factor
(mg/kg fuel)
Organic Group No Backup
Correction
Alkanes
PAH
Acids
Base Fuel
3.69
0.14
1.15
B20
0.13
0.01
0.48
With Backup
Gas-Phase
on Primary
Filte
Organics
r Quartz
;r
Correction r/0'
Base Fuel
2.54
0.14
0.46
B20
0.13
0.01
0.15
Base Fuel
31.3
1.8
60.4
B20
4.4
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
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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 7
n n
Zn
Fe
Ca
Cl
s
36%
Al
105 km/h and 33,890 k
9
Fe
Ca
S
71%
DZn
Cu
Ni
DFe
Cr
DCa
K
DCI
DS
P
DAI
Base Fuel
B20
Figure A-6. Fuel-Specific Element Emission Factors by XRF (Si component removed)
The low sulfur emissions were verified by an 1C analysis of the Teflon filter samples. The
concentration in the PM samples from both fuels was so low that water-soluble sulfates
could not be detected by the 1C. 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 1C
results, the detected water soluble ions in the base fuel PM consisted of 44.5% ammonium
(NH4+), 24.6% nitrates (NCV1), and 30.8% nitrites (NO2~), whereas only NH4+ was detected in
the B20 PM.
?
O)
k.
o
W
P 50
LJJ
105 km/h and 33,890 kg
DNO2~
DNOg"1
nso4-2
Base Fuel
B20
Figure A-7. Fuel-specific Ion Emission Factors by 1C
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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 EC. 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
Emission Factor
(mg/kg fuel)
r^-,-.-----* Component
x>ompon6m
Soluble NH4+
Soluble NO3~1
Soluble NO2~
Elements
Si
Others
Total OC
Alkanes
PAH
Acid
Undefined
Total BC
Undetermined (ND)
Total PM
Instrument
1C
1C
1C
XRF
XRF
XRF
OC/EC analyzer
GC/MS
GC/MS
UV analyzer
GC/MS
OC/EC analyzer
OC/EC analyzer
Aethalometer
Gravimetric
Media
Teflon filters
Teflon filters
Teflon filters
Teflon filters
Teflon filters
Quartz filters
Quartz filters
Quartz filters
Quartz filters
Quartz filters
Quartz filters
Teflon filters a
Base
Component
97.4
53.8
67.4
35.4
462.1
0.3
155.8
872.2
Fuel
Component _
Constituent
34.5
0.9
2.54
0.14
0.5
0.46
458.6
B20
omponent *ฃ*ฃฃ*
60.7
4.6
3.9
0.7
23.0
0.13
0.01
0.4
0.15
22.3
0.2
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
-i
B20. The figure shows that the base fuel PM consists of 53% OC, 11% NH4 , 14% NO3 , 8%
A-10
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NC>2 , 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
Figure A-8. Percentage of Each Component in the PM for Base fuel (left) and B20 (right)
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