May 2003 • NREL/SR-540-33794 

Impact of Biodiesel Fuels on Air

Quality and Human Health: 

Task 1 Report 

Incorporate Biodiesel Data into Vehicle

Emissions Databases for Modeling 

C. Lindhjem and A. Pollack 

ENVIRON International Corporation 

Novato, California 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

NREL is a U.S. Department of Energy Laboratory

Operated by Midwest Research Institute • Battelle • Bechtel 

Contract No. DE-AC36-99-GO10337 
May 2003 • NREL/SR-540-33794 

Impact of Biodiesel Fuels on Air

Quality and Human Health: 

Task 1 Report 

Incorporate Biodiesel Data into Vehicle

Emissions Databases for Modeling 

C. Lindhjem and A. Pollack 

ENVIRON International Corporation 

Novato, California 

NREL Technical Monitor: K.S. Tyson and R.McCormick 

Prepared under Subcontract No. AXE-9-29079-01 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

NREL is a U.S. Department of Energy Laboratory

Operated by Midwest Research Institute • Battelle • Bechtel 

Contract No. DE-AC36-99-GO10337 
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EXECUTIVE SUMMARY 

BACKGROUND 

Biodiesel fuels have been investigated for a number of reasons, such as an extender for 

petroleum-based fuels derived from a domestic renewable energy source, but lately the primary 

interest is the potential for a more environmentally benign fuel. One potential benefit of 

biodiesel is that it can biologically degrade, making spills and leaks less of a concern. However, 

the potential for exhaust emission reductions and reductions in emissions toxicity are of the most 

interest. Several studies have shown that large reductions in hydrocarbon, particulate, and 

carbon monoxide emissions are expected from its use either as a neat fuel or as a blend with 

petroleum-derived fuels. 

There have been several studies regarding the effects of biodiesel fuels on exhaust emissions of 

NOx, VOC, CO, and particulate matter (PM). Almost all of these studies have examined 

emissions from heavy-duty diesel truck (HDDT) engines. However, the effects of biodiesel use 

on ambient air quality have not been quantified. Thus, the National Renewable Energy 

Laboratory (NREL) has retained ENVIRON International Corporation to estimate the air quality 

and resultant toxic impacts from the use of biodiesel fuels in several cities in the United States. 

Purpose 

This document is the Task 1 report for the NREL “Impact of Biodiesel Fuels on Air Quality and 

Human Health” study. This report provides a discussion and analysis of the available biodiesel 

test data, and makes recommendations for how biodiesel effects on pollutant mass emissions as 

well as chemical composition should be incorporated into emission inventories for use in air 

quality modeling. 

BIODIESEL EMISSION EFFECTS 

The effects on emissions from truck and bus engines using biodiesel or a blend of biodiesel and 

standard diesel have been studied to investigate the effect on total hydrocarbon (THC), nitrogen 

oxides (NOx), carbon monoxide (CO), and particulate matter (PM) emissions. Some studies 

have also investigated the chemical compositional effects such as toxic compounds. The effects 

of biodiesel fuel on emissions have been measured using both 2-stroke and 4-stroke engines for 

different model years that represent different levels of engine certification standards. These data 

were analyzed to obtain the mean effect of a 100% biodiesel fuel (B100) and a 20%/80% 

biodiesel/standard diesel fuel as shown in Table ES-1. The average of the biodiesel fuel effect 

was used to estimate the overall effect a B100 and B20 fuel would have on the Heavy Duty 

Diesel Vehicle (HDDV) fleet in the air quality modeling analysis. 

ES-1 
Table ES-1. Emission effects by technology type from engine dynamometer testing due to 

biodiesel fuels compared against a standard diesel. 

Engine Type/ THC Fuel Pair Engines NOx PM CO THC Model Year + PM

20% Biodiesel Emission Effects 

2-Stroke < 1991 D-2 / B-20 6 3.2% -1.8% -13.9% -20.9% -14.8% 

2-Stroke 1991+ D-2 / B-20 2 3.9% -17.8% -12.0% -17.5% -17.6% 

4-Stroke <1991 D-2 / B-20 3 2.9% -15.7% -13.6% -12.2% -13.5% 

4-Stroke 1991-3 D-2 / B-20 4 -0.9% -15.7% -12.0% -2.8% -12.0% 

4-Stroke 1994+ D-2 / B-20 5 2.8% -9.8% -15.2% -24.0% -19.2% 

Overall Average D-2 / B-20 20 2.5% -9.0% -13.3% -18.2% -15.1% 

Overall Average 

(w/o Sharp, 1998) D-2 / B-20 17 2.4% -8.9% -13.1% -17.9% -14.8% 

100% Biodiesel Emission Effects 

2-Stroke 1991+ D-2 / B-100 1 19.6% -33.0% -42.4% -72.7% -59.2% 

4-Stroke 1991-3 D-2 / B-100 2 13.3% -68.3% -41.8% -38.7% -58.8% 

4-Stroke 1994+ D-2 / B-100 5 9.9% -36.6% -41.5% -76.3% -62.8% 

Overall Average D-2 / B-100 8 11.8% -51.0% -42.0% -69.7% -61.5% 

Overall Average 

(w/o Sharp, 1998) D-2 / B-100 5 13.2% -55.3% -42.7% -63.2% -59.5% 

Diesel particulate matter includes several known toxic compounds that fall into a class known as 

polyaromatic hydrocarbons (PAH) and nitro-PAH compounds. Studies that measured the PAH 

and nitro-PAH compounds in standard diesel and a B20 and B100 biodiesel fuel were compared 

to obtain an estimate of the reduction in these toxic compounds in the biodiesel fuel as shown in 

Table ES-2. 

Table ES-2a. Relative fraction (x 106

) of selected PAH compounds to PM emissions from 

Sharp (1998) and Durbin (1999). 

Standard Diesel B20 B100 Compounds Sharp Durbin Sharp Durbin Sharp Durbin 

Benzo(a)anthracene 1.59 1.01 1.51 0.43 1.37 1.11 

Chrysene 2.21 1.01 1.32 0.65 1.04 0.89 

Benzo(b)fluoranthene 0.96 0.50 0.97 0.22 0.77 0.22 

Benzo(k)fluoranthene 1.01 * 0.92 * 0.73 * 

Benzo(a)pyrene 1.12 0.25 0.69 1.72 0.49 0.00 

Indeno(1,2,3-cd)pyrene 0.72 0.00 0.56 0.00 0.60 0.00 

Dibenz(a,h)anthracene 0.21 0.00 0.19 0.00 0.13 0.00 

Benzo(g,h,i)perylene 0.94 0.00 0.88 0.00 0.93 0.00 

Total 8.76 2.77 7.04 3.02 6.06 2.22 

* Included in Benzo(b)fluoranthene. 

ES-2 
Table ES-2b. Relative fraction (x 106

) of selected Nitro-PAH compounds to PM emissions. 

Compounds D2 B20 B100 

2-Nitrofluorene 0.14 0.11 0.09 

1-Nitropyrene 0.11 0.11 0.02 

7-Nitrobenz(a)anthracene 0.01 0.00 0.00 

6-Nitrochrysene 0.00 0.00 0.00 

6-Nitrobenz(a)pyrene 0.01 0.00 0.00 

Total 0.27 0.22 0.11 

Overall Effects of Biodiesel Fuel Use 

The overall effects of the use of biodiesel fuel on emissions in the HDDV fleet are assumed to be 

the average effect from the engine dynamometer test data provided in Table ES-1. Based on the 

data in Table ES-2 and the dilution effect of the biodiesel fuel, the toxicity of the PM emissions 

from a B100 and B20 fuels were assumed to be 20 percent and 5 percent less than a standard 

diesel fuel. 

ES-3 
TABLE OF CONTENTS 

Page 

EXECUTIVE SUMMARY .................................................................................................... ES-1 

Background..........................................................................................................................ES-1 

Biodiesel Emission Effects ..................................................................................................ES-1 

1. INTRODUCTION................................................................................................................ 1-1 

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

Purpose .............................................................................................................................. 1-2 

2. BIODIESEL EMISSION EFFECTS.................................................................................. 2-1 

Regulated Emission Effects ................................................................................................... 2-1 

Chemical Composition........................................................................................................... 2-4

Recommendations.................................................................................................................. 2-7 

3. EMISSION FACTOR MODELS ....................................................................................... 3-1 

MOBILE5 .............................................................................................................................. 3-1 

MOBILE6 .............................................................................................................................. 3-2 

EMFAC7G............................................................................................................................. 3-5 

EMFAC2000.......................................................................................................................... 3-5 

4. INCORPORATING BIODIESEL EMISSION EFFECTS

INTO MODELING DATA BASES ............................................................................... 4-1

Northeast Corridor and Chicago Ozone Modeling ................................................................ 4-1 

Las Vegas CO Modeling........................................................................................................ 4-2 

Los Angeles Ozone and PM Modeling.................................................................................. 4-2 

5. HEALTH EFFECTS IMPACT MODELING PLAN....................................................... 5-1 

Objective .............................................................................................................................. 5-1 

Background............................................................................................................................ 5-1 

Technical Approach............................................................................................................... 5-1 

Health Effects Impact Reporting ........................................................................................... 5-3 

REFERENCES.......................................................................................................................... R-1 
APPENDICES

Appendix A: Data References 

Appendix B: Bag Hydrocarbon Chemical Composition 

(values in red were not explicitly measured) 

TABLES 

Table ES-1. Emission effects by technology type from engine 

dynamometer testing due to biodiesel fuels compared 

against a standard diesel.....................................................................................ES-2 

Table ES-2a. Relative fraction (x 106

) of selected PAH compounds to 

PM emissions from Sharp (1998) and Durbin (1999). ......................................ES-2 

Table ES-2b. Relative fraction (x 106

) of selected Nitro-PAH 

compounds to PM emissions .............................................................................ES-3 

Table 2-1. Emission effects by technology type from changing

diesel to biodiesel fuels for engine dynamometer testing.................................... 2-2 

Table 2-2. Emission effects by technology type from changing diesel 

to biodiesel fuels for chassis dynamometer testing.............................................. 2-4 

Table 2-3. Percent unknown/unmeasured HC from two studies (100% - measured %)....... 2-6 

Table 2-4. Relative fraction (x 106

) of selected PAH compounds to 

PM emissions from Sharp (1998) and Durbin (1999). ........................................ 2-6 

Table 2-5. Relative fraction (x 106

) of selected Nitro-PAH compounds to PM emissions... 2-7 

Table 3-1. MOBILE6 heavy-duty vehicle classifications..................................................... 3-3 

Table 3-2a. Estimates of MOBILE6 zero-mile emission rates in g/mile (w/o defeat device

effects) for the class 8B trucks............................... ...................................3-4 

Table 3-2b Estimates of MOBILE6 zero-mile emission rates in g/mile (w/o defeat device

effects) for class 2B trucks.............................. ........................................3-4 

Table 3-2c. Estimates of MOBILE6 zero-mile emission rates in g/mile

(w/o defeat device effects) for urban buses ........................................................ .3-5 

Table 3-3. ARB heavy-duty trucks weight classes............................................................... .3-6 

Table 3-4. EPA heavy-duty diesel vehicle high altitude adjustment factors......................... 3-6 

Table 3-5. ARB estimated maximum percentage increase in emissions 

from engine deterioration (tampering and malmaintenance)............................... 3-8

Table 3-6. Emissions reduction due to lower sulfur and aromatic content .......................... .3-8 

Table 3-7a. EMFAC 2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi) – HHDDV............................................................................... 3-9 

Table 3-7b. EMFAC 2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi)– MHDDV ............................................................................... 3-9 

Table 3-7c. EMFAC 2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi)– HDDV................................................................................. 3-10 

Table 3-7d. EMFAC 2000 diesel urban bus emission factors (g/mile)................................. 3-10 

Table 5-1. PM benefits categories......................................................................................... 5-3 
1. INTRODUCTION 

BACKGROUND 

Biodiesel fuels have been investigated for a number of reasons, such as an extender for 

petroleum-based fuels derived from a domestic renewable energy source, but lately the primary 

interest is the potential for a more environmentally benign fuel. One potential benefit of 

biodiesel is that it can biologically degrade, making spills and leaks less of a concern. However, 

the potential for exhaust emission reductions and reductions in emissions toxicity are of the most 

interest. Several studies have shown that large reductions in hydrocarbon, particulate, and 

carbon monoxide emissions are expected from its use either as a neat fuel or as a blend with 

petroleum-derived fuels. 

Diesel particulate and, to a lesser extent, diesel hydrocarbon exhaust emissions, have garnered 

much scrutiny for their inherent toxicity. Diesel fuels are typically characterized by heavy 

hydrocarbons, which produce, through partial combustion, a variety of compounds commonly 

estimated to be toxic. The State of California has recently listed diesel particulates as a toxic 

substance. The recent Multiple Air Toxic Exposure Study in the South Coast Air Basin 

(MATES-II) estimated that 70 percent of the toxic risk in the South Coast (Los Angeles) Air 

Basin (SCAB) is due to diesel particulate emissions (SCAQMD, 1999). Therefore, the potential 

to reduce overall toxic emissions through fuel substitution is of great interest given the reduced 

toxicity of diesel particulates using biodiesel fuels. 

Biodiesel has emission advantages for use in diesel engines because it can provide improvements 

to a number of important fuel properties, most importantly cetane and oxygen content. 

Generally, these properties are thought to be responsible for the improvement in emissions by 

improving ignition and distribution of oxidant. The effect of fuel substitution may be unique by 

engine type; with the Engine Manufacturers Association (EMA) has theorized that newer engines 

may show less benefit because of the more precise engine management currently used (EMA, 

1995). Nonroad diesel engine lags highway engine technology, so emission benefits may be 

greatest for nonroad engines, which also have higher base emissions of HC and PM. 

Most of the potential problems typically cited for the introduction of biodiesel blends are the lack 

of a running history for their use. Unforeseen complications arising from materials 

compatibility, consistent fuel quality, gumming, low temperature effects, and various other long 

durability factors not specifically listed are feared with the use of biodiesel blends and may affect 

the penetration into the nonroad market. Most of these concerns do not affect the emission 

testing results in the laboratory; and the lack of cold temperature testing is typical of emission 

testing for diesel, gasoline, or other types and makes of vehicles and engines. 

The most consistent criticism of biodiesel is the low temperature and biologic stability and water 

sensitivity. The viscosity of biodiesel blends shows that the 20% blends show similar stability as 

more petroleum-based diesel fuels, while higher percentage blends of biodiesel show dramatic 

increases in low temperature kinematic viscosity (EMA, 1995). Cloud point, the cold 

temperature where a dramatic increase in the opacity of the fuel mixture occurs, is 

another variable thought to be a measure of the low temperature filter-plugging potential for a 

fuel. Likewise, biological and water stability is also of concern to ensure that the fuel does not 

produce gums and particles that could clog fuel filters. 

1-1 
Cost of an alternative fuel is always a concern, especially when the production facilities have 

neither been optimized nor can take advantage of economies of scale. The Department of 

Energy estimates that a fuel blend of 20% biodiesel and 80% diesel (known as B20) would cost 

approximately 30 to 40 cents a gallon more than regular diesel. This cost differential is 

significantly high to adversely affect the penetration of the market of biodiesel fuels. In most 

part, this price differential is due to the price of the base vegetable oil or tallow. In addition, 

there may be a small reduction in the fuel economy because biodiesel fuels have lower energy 

density than standard diesel fuels. 

There have been several studies regarding the effects of biodiesel fuels on exhaust emissions of 

NOx, VOC, CO, and particulate matter (PM). Almost all of these studies have examined 

emissions from heavy-duty diesel truck (HDDT) engines. However, the effect of biodiesel use 

on ambient air quality has not been quantified. Thus, the National Renewable Energy 

Laboratory (NREL) has retained ENVIRON International Corporation to estimate the air quality 

and resultant toxic impacts from the use of biodiesel fuels in several cities in the U.S. 

PURPOSE 

This document is the Task 1 report for the NREL “Impact of Biodiesel Fuels on Air Quality and 

Human Health” study. This report provides a discussion and analysis of the available biodiesel 

test data, and makes recommendations for how biodiesel effects on pollutant mass emissions as 

well as chemical composition should be incorporated into emission inventories for use in air 

quality modeling. This document also contains a description of the current and upcoming 

emission factor models from the U.S. EPA and the California Air Resources Board (ARB), and 

the basis of heavy-duty diesel vehicle (HDDV) emission factors in these models. Along with 

this report we are also transmitting via email an electronic database of the biodiesel effects data 

collected and analyzed. 

1-2 
2. BIODIESEL EMISSION EFFECTS 

The effect on emissions from truck and bus engines using biodiesel or a blend of biodiesel and 

typical highway diesel No. 2 has been investigated over the last several years. Many studies have 

investigated the effect on regulated emissions [total hydrocarbon (THC), nitrogen oxides (NOx), 

carbon monoxide (CO), and particulate matter (PM)], and some of these have investigated 

chemical compositional effects such as toxic PAH and other compounds as well. 

Starting with a review of the literature to date (Graboski and McCormick, 1998), and adding the 

most recent studies, a database of regulated emissions data was produced with data from the 

studies listed in Appendix A.. These studies investigated the engine emissions from a variety of 

diesel engines used in highway vehicles on the engine certification test for highway heavy-duty 

engines, and two studies investigated emissions on a chassis test cycle. 

Missing from this list of data sources and the analysis below are several studies by students and 

professors at the University of Minnesota (Bickel, 1999) and ongoing work at the University of 

Idaho. Because only summary results were available, these have not been incorporated into the 

estimates below. However, the summary results indicate that the University of Minnesota was 

finding similar effects with biodiesel replacement as those detailed below for engine 

dynamometer data for mining equipment and other over the road trucks. This indicates that 

results found here are likely applicable to nonroad applications as well as highway vehicles. 

REGULATED EMISSION EFFECTS 

Emission testing can be performed either with the engine only or with the entire vehicle in the 

chassis configuration. The engine testing proceeds with the engine removed from the vehicle and 

loaded through the flywheel of the engine, and nearly all engine testing, including those studies 

reviewed here, uses the certification test cycle. The chassis testing loads the engine through the 

wheels, and two testing cycles -- the light-duty federal certification and heavy-duty evaporative 

preparation test cycles -- were used in the studies reviewed here. 

The testing included repeat tests for each engine/vehicle/fuel combination. Repeat tests were 

averaged to produce a mass emissions estimate for each engine/vehicle/fuel combination. Overall 

mass emissions averages across engine/vehicle types were then produced for each biodiesel fuel 

blend and compared with those from the base fuel to produce the emission effects in percentage 

change. 

Engine Emission Testing 

The effect of biodiesel fuels either as blends or neat was first investigated for its effect on 

regulation emissions for a variety of possible engine types. The engine types investigated were 

defined as 2-stroke or 4-stroke engines of model years less-than-1991, 1991-1993, and 1994-andlater. These model years represent essentially precontrolled, NOx-only controlled, and NOx and 

PM-controlled engines based on the certification standards for those model years. Before 1994 

some engines were 2-stroke diesel engines produced for various (but a limited number of) 

2-1 
applications preferring lighter and smaller engines like urban buses, fire trucks, and other 

specialty vehicles. 

The most direct comparison of emission rates is to use fuel pair of standard highway diesel and 

20%/80% biodiesel/diesel blends (D-2 / B-20) or diesel and 100% biodiesel (D-2 / B-100) to 

determine the emission effects of biodiesel. Table 2-1 shows paired results by technology type 

from the available engine dynamometer test data. 

Table 2-1. Emission effects by technology type from changing diesel to biodiesel fuels for 

engine dynamometer testing. 

Engine Type/ THC Fuel Pair Engines NOx PM CO THC Model Year + PM

20% Biodiesel Emission Effects 

2-Stroke < 1991 D-2 / B-20 6 3.2% -1.8% -13.9% -20.9% -14.8% 

2-Stroke 1991+ D-2 / B-20 2 3.9% -17.8% -12.0% -17.5% -17.6% 

4-Stroke <1991 D-2 / B-20 3 2.9% -15.7% -13.6% -12.2% -13.5% 

4-Stroke 1991-3 D-2 / B-20 4 -0.9% -15.7% -12.0% -2.8% -12.0% 

4-Stroke 1994+ D-2 / B-20 5 2.8% -9.8% -15.2% -24.0% -19.2% 

Overall Average D-2 / B-20 20 2.5% -9.0% -13.3% -18.2% -15.1% 

Overall Average 

(w/o Sharp, 1998) D-2 / B-20 17 2.4% -8.9% -13.1% -17.9% -14.8% 

100% Biodiesel Emission Effects 

2-Stroke 1991+ D-2 / B-100 1 19.6% -33.0% -42.4% -72.7% -59.2% 

4-Stroke 1991-3 D-2 / B-100 2 13.3% -68.3% -41.8% -38.7% -58.8% 

4-Stroke 1994+ D-2 / B-100 5 9.9% -36.6% -41.5% -76.3% -62.8% 

Overall Average D-2 / B-100 8 11.8% -51.0% -42.0% -69.7% -61.5% 

Overall Average 

(w/o Sharp, 1998) D-2 / B-100 5 13.2% -55.3% -42.7% -63.2% -59.5% 

In order to determine overall effects, the data were averaged over all technology groups because 

the emission effects for the individual technology types were remarkably similar. For newer 

engines (4-strokes of model years 1994 and greater), the emission effects may seem to be 

different from the average effect for PM; however, the magnitude of the difference in relative or 

absolute terms was not great. For instance, with B20 replacement for the three newest technology 

engines, the emission effects for PM range from 0 to a 12 percent reduction, and for THC range 

from 17 to 27 percent reduction.. Though the THC effect for 4-stroke engine model years 1991-

1993 is lower than on average, this group of engines represents a limited fraction of the in-use 

fleet. 

Combining THC and PM reduces much of the variability between technology types and that 

might be explained by a variation in the gas/particulate partitioning of heavier unburned 

hydrocarbons. By combining THC and PM, the effect of heavier hydrocarbon condensation into 

the particulate phase that may occur preferentially with chassis testing (as demonstrated in the 

results below) is not apparent. 

2-2 
Because of the emissions measurement techniques1

, the total hydrocarbon and the particulate 

collection measurements may have measured some of the same compounds. The combined 

THC+PM measure may then have undercounted the benefit of biodiesel replacement of diesel 

fuel because the biodiesel fuels used were typically composed of heavier components than 

typical diesel fuel, so unburned or partially-burned fuel may be measured as both THC and 

particulate. Usually the PM measurement includes a secondary dilution tunnel, which may 

reduce condensation with that measure even though collection occurs at lower temperature. 

For reasons that are described in more detail under the Chemical Composition section below, the 

hydrocarbon results in the Sharp (1998) study are considered to result in too great a THC 

reduction estimate, especially with 100 percent biodiesel replacement. Therefore the average 

emissions effects used in the work and shown in Table 2-1 for both 20% and 100% biodiesel 

replacement was without the three engines that were tested in the Sharp (1998) testing program. 

However, as shown in Table 2-1, ignoring these data does not demonstrably affect the estimate 

of emission effects. 

Chassis Testing 

Data from chassis testing were available for Light Heavy-Duty Diesel Vehicles tested on various 

biodiesel, blends, and standard diesel fuels (Durbin, et al. (1999), Peterson et al. (1999), and 

Peterson and Haines (1999)). The effects found for these chassis tests were somewhat different 

than that for engine tests, as shown in Table 2-2. The two pre-1994 model year vehicles show 

quite dissimilar effects to other chassis data for later model year vehicles. The particulate matter 

is found to increase with biodiesel use in contrast with the results of the engine testing. And there 

is a slight decrease in NOx measured on chassis dynamometers as opposed to a slight increase 

measured on engine dynamometers. 

However, the combined THC+PM effects are more similar between the later model year vehicles 

tested on the chassis and engine dynamometers. The chassis testing likely had more heavy 

hydrocarbon condensation into the particulate phase because the chassis testing requires that the 

emissions traverse the entire length of the exhaust system, often use smaller diameter and/or 

high-surface area accordion tubing before entering the dilution tunnel, and have lower dilution 

ratios. Therefore chassis measurements may have resulted in additional cooling resulting in 

greater condensation of heavy hydrocarbons. This was observed as the organic (soluble) carbon 

represented the entire increase in PM emissions that is shown in Table 2-2, while the elemental 

(soot) carbon decreased with biodiesel use. Engine dynamometer testing generally has shorter 

exhaust lengths and higher dilution ratios, perhaps resulting in less heavy hydrocarbon 

condensation. 

The two pre-1994 vehicles show such dissimilar effects with biodiesel substitution that it does 

not seem appropriate to use these results. And of these two vehicles, only one engine was 

responsible for the majority of the percent change. 

1 Total hydrocarbons are measured with a heated flame-ionization detector and sampling systems that must be 

maintained at 191 ± 11o

C, while the particulate filters must be never exceed 51.7oC (Code of Federal Regulations 

86-1310-90). 

2-3 
Table 2-2. Emission effects by technology type from changing diesel to biodiesel fuels for 

chassis dynamometer testing. 

Engine Type/ Fuel Pair Engines NOx PM CO THC THC 

Model Year + PM 

20% Biodiesel Emission Effects 

<1994 D-2 / B-100 2 -0.9% 59.0% 0.6% 12.0% 26.2% 

1994 + D-2 / B-100 5 -4.0% 6.8% -20.5% -19.1% -15.7% 

Overall Average D-2 / B-20 7 - 3.4% 25.0% -18.0% -14.1% - 7.6% 

100% Biodiesel Emission Effects 

<1994 D-2 / B-100 2 9.8% 131.0% 0.0% -16.8% 27.8% 

1994 + D-2 / B-100 5 -6.4% 30.6% -36.4% -60.7% -48.5% 

Overall Average D-2 / B-100 7 -3.4% 65.7% -32.1% -53.7% -33.9% 

CHEMICAL COMPOSITION 

The chemical composition for emissions is important to determine the overall effect of diesel 

emissions on air quality. The chemical composition determined from the individual test results 

will be applied to the overall THC or the THC + PM emission estimates determined from 

modeling. The estimates derived from modeling will be modified to include the regulated 

pollutant emission effects detailed above. 

Two studies measured chemical composition from biodiesel and diesel fueled tests (Sharp, 1998 

and Durbin et al., 1999), and are compared here. There are two basic measurements taken for 

measuring chemical composition; a bag sample for lighter hydrocarbon, and two types of filters 

for heavier and particulate emissions. The lighter hydrocarbons are typically more difficult to 

determine because diesel fuel and therefore hydrocarbon emissions tend toward heavier 

compounds, which may adsorb on the bag surface and/or may be difficult to identify with a gas 

chromatograph. The two filters were the standard quartz-fiber filters followed by a polyurethane 

foam (PUF) resin-impregnated filter to trap semivolatile hydrocarbons. Both filters were then 

extracted, and measured for only polyaromatic hydrocarbons (PAH). Sharp (1998) combined the 

particulate and semivolatile filter extractions before analysis. 

Lighter Hydrocarbons 

In general, it is difficult to measure individual compounds for emissions from the bag samples, 

and the chemical species identified in these studies (Sharp, 1998 and Durbin et al., 1999) were 

much lower than the regulated hydrocarbon measurements. The measured emissions were 

typically quantified only up to carbon numbers of 12 (C12). Unburned diesel fuel (No. 2 fuel oil) 

is comprised of compounds predominately (>95 %) of C13 or greater (EPEFE, 1996). Biodiesel 

fuels are even less volatile where the T10, T50 and T90 points (temperature under which 10%, 

50% and 90% of the fuel is distilled) are at considerably higher temperatures than for typical 

diesel fuels. The unknown and unidentified compounds are therefore likely heavier compounds 

related to unburned fuel, so are not explicitly determined. The detection of these heavier 

compounds is much more difficult because of the higher boiling temperatures (volatility), 

number of isomers, and lack of available reference standards. 

The modeling for regulated pollutants relies on flame ionization detection (FID) measurements 

for THC emission estimates and is not completely compatible with the chemical compositional 

analysis. A review of the methods and data analysis is provided below. 

2-4 
The FID measures carbon content calibrated with propane, and this carbon content is converted 

to hydrocarbon mass by adding 1.85 atoms of hydrogen to each carbon atom (CFR, 1996) to 

determine the average molecular weight per carbon atom. The FID response varies only slightly 

per carbon atom for aromatic, olefin, and aliphatic carbon content, but is significantly less for 

alcohol carbons and practically unresponsive to carbonyl carbon atoms. So ketones, aldehydes 

and esters are undercounted generally, and in particular there is effectively no response for 

formaldehyde from the FID measurement. Therefore it is possible that the chemical speciation 

would estimate higher hydrocarbon emissions than the FID measurement. 

The chemical compositional analysis in g/mile or g/hp-hr units is compared with the FID 

measurement in order to determine the mass of hydrocarbon that was not measured or identified. 

Only the weight of the noncarbonyl fraction (modified) of the ketones and aldehydes was added 

to the bag measurement of chemical composition and compared with the weight of the THC 

from the FID measurement. As shown in the equation below, any difference was ascribed to 

compounds that were neither identified nor measured and assumed to be of higher molecular 

weight than were measured by the chromatograph. 

Unknown/Unmeasured HC = FID – (Bag HC) – (modified Aldehyde & Ketones) 

The fraction of individual chemicals was then calculated by dividing the chemical composition 

with the THC measurement by the FID. This will result in a chemical composition that in total 

will be slightly more than 100 percent of the THC estimate because the aldehydes and ketones 

are now estimated at their full molecular weight. This higher value of hydrocarbons is 

comparable with the total organic gas (TOG) estimate defined by the California Air Resources 

Board (ARB) as the THC plus the total moles of all aldehydes added by weight as formaldehyde. 

The ARB TOG estimate was not used because it was derived from an approximate conversion of 

THC measurements, and this study used the data more directly. 

Table 2-3 compares the measured chemical composition of hydrocarbons to the THC 

measurement by a heated flame ionization detector (FID). For most of the chemical composition 

measurements, it was possible to identify only 15 – 36 percent of the total hydrocarbons. In 

Sharp (1998), the total hydrocarbon measurement was extremely low for the 100 percent 

biodiesel runs, so it appears as if 100 percent of the hydrocarbons were determined. There are 

several reasons why this may have occurred, such as the exhaust composition tending toward the 

lighter side, or the FID was unable to measure unburned esters. But the likely explanation is that 

the THC measurement was too low, consistent with too low a sampling train temperature 

condensing the heavier unburned fuel components. This argues for a lower THC reduction 

estimate than that provided in Table 2-1. 

2-5 
Table 2-3. Percent unknown/unmeasured HC from two studies (100% - measured %). 

Study Diesel 20% Biodiesel 100% Biodiesel 

Sharp (1998) 84.8 63.8 -1.7 

Durbin (1999) 72.2 70.6 67.1 

For the Sharp (1998) study in particular, the chemical composition shows higher percentages of 

the measured compounds with increasing biodiesel use, which was an artifact of the lower THC 

emission results for these vehicles. The increase in the fraction of certain pollutants of interest, 

such as 1,3 Butadiene, Benzene, Formaldehyde, and Acetaldehyde, can be mostly or wholly 

explained by the low THC measurement, which likely was a flawed measurement. 

PAH Composition 

The composition of the particulate and semivolatile phase PAH is distinctly different between 

the work of Sharp (1998) and Durbin (1999). The Sharp work only measured certain PAH and 

nitro-PAH compounds, while the Durbin work measured many PAH but not nitro-PAH 

compounds. Table 2-4 compares the percentage of total mass PM for the compounds that both 

studies measured.. The Durbin work found a substantially lower quantity of these PAH 

compounds but with a similar trend in emission estimates. One odd measurement was the 

Benzo(a)pyrene quantity with the B20 run in the Durbin work, which was substantially higher 

than other estimates for similar PAH compounds. Otherwise the trend was for a lower fraction of 

PAH with increase use of biodiesel fuel. 

Table 2-4. Relative fraction (x 106

) of selected PAH compounds to PM emissions from Sharp 

(1998) and Durbin (1999). 

D2 B20 B100 Compounds Sharp Durbin Sharp Durbin Sharp Durbin 

Benzo(a)anthracene 1.59 1.01 1.51 0.43 1.37 1.11 

Chrysene 2.21 1.01 1.32 0.65 1.04 0.89 

Benzo(b)fluoranthene 0.96 0.50 0.97 0.22 0.77 0.22 

Benzo(k)fluoranthene 1.01 * 0.92 * 0.73 * 

Benzo(a)pyrene 1.12 0.25 0.69 1.72 0.49 0.00 

Indeno(1,2,3-cd)pyrene 0.72 0.00 0.56 0.00 0.60 0.00 

Dibenz(a,h)anthracene 0.21 0.00 0.19 0.00 0.13 0.00 

Benzo(g,h,i)perylene 0.94 0.00 0.88 0.00 0.93 0.00 

Total 8.76 2.77 7.04 3.02 6.06 2.22 

* Included in Benzo(b)fluoranthene. 

These compounds were the heavier compounds, so would be more likely to be associated with 

the particulate phase than the semivolatile phase. In the Durbin work, more of the lighter PAH 

compounds were measured, which were more associated with THC measurements. This was 

especially true for the lightest PAH, naphthalene, which was measured in greater quantity in the 

bag sample than from the filter samples. Also, in the Durbin work, Benzo(a)pyrene was found in 

relatively large quantities (oddly in the semivolatile sample when all other PAH components 

shown here were found in the particulate sample) with 20 percent biodiesel and was barely 

2-6 
detectable from testing of other fuels; otherwise this sample would have shown a lower fraction 

of PAH than the diesel fueled testing samples. 

Nitro-PAHs are compounds of some interest because of high specific toxicity. Sharp (1998) 

measured selected nitro-PAH with the results shown in Table 2-5. The table shows measurable 

reductions in the relative fraction of these compounds with increased biodiesel usage. 

Table 2-5. Relative fraction (x 106

) of selected Nitro-PAH compounds to PM emissions. 

Compounds D2 B20 B100 

2-Nitrofluorene 0.14 0.11 0.09 

1-Nitropyrene 0.11 0.11 0.02 

7-Nitrobenz(a)anthracene 0.01 0.00 0.00 

6-Nitrochrysene 0.00 0.00 0.00 

6-Nitrobenz(a)pyrene 0.01 0.00 0.00 

Total 0.27 0.22 0.11 

The relative fraction was lower for both PAH and nitro-PAH with increasing use of biodiesel. 

The lower fraction of PAH was found both for lower mass particulate (Sharp, 1998) and higher 

mass particulate (Durbin et al., 1999), which indicates that the fraction of PAH was not 

influenced by the mass emissions as the chemical composition was for the lighter hydrocarbons. 

This indicates that the toxicity of particulate matter would be reduced even without a change in 

the mass emissions of particulate. 

RECOMMENDATIONS 

We recommend that the average emission effects be derived from the engine dynamometer 

testing results. The primary reason for using engine data is that the sample sizes are larger. But 

the concern over whether the sampling method preferentially increases biodiesel particulate on 

the chassis tests also leads toward the engine test data, which typically use much shorter smooth 

exhaust pipes reducing the concern. 

The use of engine test results to determine fuel effects is consistent with both ARB and EPA 

modeling efforts. EPA currently models emissions from data collected on engine tests. While 

ARB now uses chassis test results to determine basic emission rates, ARB estimates the effect of 

using California 'Clean Diesel' from engine test data. 

From the data available, the chemical composition of lighter hydrocarbons (C12 and less) 

appears to be substantially unaffected by fuel used, so a base diesel chemical composition should 

be applied to the mass results shown here. The relative composition of the measured PAH and 

nitro-PAH compounds appears to be substantially decreased even as the particulate mass 

emissions are reduced. So inclusion of a lower relative fraction of PAH combined with overall 

PM reductions with biodiesel use is justified. 

For the most likely in-use scenario, 20 percent biodiesel fuel use, there is substantially more data 

available showing consistent relative effects across a range of studies and engine technology 

types. The THC results remain suspect and may overstate reductions, especially with 100 percent 

biodiesel replacement as found in the Sharp study. Other studies used to determine the effect of 

2-7 
biodiesel replacement did not have the chemical composition concurrently measured, so the 

regulated emission measurements may have had similar measurement problems. 

While the current data show higher particulate with biodiesel in chassis and lower particulate in 

engine testing results, this is the subject for further studies of the test methods. Because the 

combined THC+PM emission results are similar between engine and chassis testing, the likely 

explanation for the differences between chassis and engine testing results lie in the test methods 

with sampling line temperatures the most likely areas of study. For chassis testing, heavy 

hydrocarbon condensation is the likely basis for the increased particulate mass measurements for 

biodiesel replacement. Whether this is a real effect from the tailpipe or because of peculiarities of 

the test method deserves further study. 

The chemical composition appears to show an increase in the relative contribution of the lighter 

hydrocarbon constituents. However, the uncertainty of the THC measurement in the Sharp 

(1998) work questions if the chemical composition is actually affected. We recommend not 

changing the chemical composition between fuel types; the mass emission changes 

recommended thus best reflect the mass emissions of these components as measured by Sharp. 

For PAH compounds, the replacement of biodiesel is likely to result in emission reduction in 

addition to the particulate mass emissions reductions. The relative fraction of these PAH 

compounds measured in Sharp (1998) likely represent realistic fractions to apply to gross 

particulate emissions because the relative fraction decreased even as the mass particulate was 

reduced as well. 

2-8 
3. EMISSION FACTOR MODELS 

Two regulatory models exist that estimate emission factors for on-road heavy-duty and light-duty 

diesel trucks and vehicles. These are MOBILE5 and EMFAC7G, developed respectively by EPA 

and the California Air Resources Board (ARB). There are planned revisions due this year to 

both of these models to be called MOBILE6 and EMFAC2000. EMFAC2000 is expected to be 

released in Spring 2000, and MOBILE6 is expected to be released by the end of 2000. 

In this section we describe the current and future models and approaches to estimating HDDV 

g/mile emission factors. While this focus here is on emission factors, it should be noted that both 

ARB and EPA have also updated their heavy-duty vehicle fleet mileage accumulation rates and 

age distributions. 

MOBILE5 

For MOBILE5, the methodology entails determination of a gram per mile (g/mi) emission factor 

by multiplying a work-specific emission level (in units of grams per horsepower-hour (g/bhp-hr)) 

by a conversion factor which converts work units into mileage units (bhp-hr/mi): 

Emission Factor (g/mi) = Work-Specific Emission Level (g/bhp-hr) * 

Conversion Factor (bhp-hr/mi) 

The work-specific emission factors have historically been determined using the Federal Test 

Procedure (FTP) for heavy-duty engines. This test procedure runs the engine that has been 

removed from the vehicle through a loaded cycle. 

The conversion factor was determined from driver diaries of typical fuel economy (TIUS, 1988) 

coupled with manufacturers’ estimates of engine efficiency and an estimate of the fuel density. 

The engine efficiency is measured as the brake specific fuel consumption (BSFC) in units of 

pounds of fuel per horsepower-hour. The calculation is shown in the equation below. Each of 

these individual estimations adds an element of uncertainty to the emission factor estimate, and 

the overall estimate for the conversion factor is implicitly based on the average driving behavior 

of all trucks and buses. The average speed for all driving is considered to be 20 miles per hour, 

and an adjustment is made to account for other average speeds (described briefly below). 

Conversion Factor (bhp-hr/mi) = Fuel density (lb/gal.) / [BSFC (lb/hp-hr) * 

Fuel economy (mi/gal.)] 

In MOBILE5, vehicle class, as determined by gross vehicle weight (GVW), was not 

distinguished, so the estimates relied on one average set of basic emission rates for all heavyduty diesel vehicles. The vehicle class usually primarily affects the conversion factor estimate 

and especially the mile-per-gallon estimate because heavier vehicles require more fuel per mile 

while the fuel density is constant and the BSFC estimate changes only slightly with size of truck. 

Heavy-duty engine testing tends to be very costly. Due to the prohibitive costs involved in 

obtaining in-use emissions data on heavy-duty vehicles, very little recent test data existed at the 

time MOBILE5b was developed. Therefore, the heavy-duty emissions factors in MOBILE5b 

(1996) are the same ones that were developed for use in MOBILE4 (1989). The 1980 through 

3-1 
1990 model year emissions factors are based on data derived from a cooperative test program 

between EPA and engine manufacturers, involving 18 heavy-duty gasoline engines (model years 

1979 to 1982) and 22 heavy-duty diesel engines from model years 1979 to 1984. In MOBILE5b, 

emission rates from the cooperative program were used unless the certification rate was higher 

than that produced from the test program. In cases where the certification results were greater, 

that rate was used instead. 

MOBILE adjusts the basic emission rate to account for average speed and altitude. Average 

speed adjustments are intended to include vehicle stops, acceleration events, and idle time within 

a typical trip. Lower average speeds are characterized by more stop-and-go driving resulting in 

higher emissions per mile. Higher average speeds are characterized by less stop-and-go driving; 

however, added wind drag at very high speeds is modeled to affect higher fuel consumption and 

therefore higher emissions. Altitude adjustments are made to model the effect of lower 

barometric pressure ambient air, which reduces the amount of air available for combustion. 

In order to calculate the most recent estimates for emissions of heavy-duty diesel emissions, EPA 

(1999) has provided a conversion from MOBILE5 estimates to MOBILE6 estimates described in 

more detail below. This conversion accounts for more recent information about the certification 

emission rates and conversion factors and includes the effect of engines that employ defeat 

devices. EPA discovered that some engine manufacturers employed defeat devices that would 

determine if the engine was operating on the certification test cycle (or a similar driving pattern), 

and if not to adjust the engine controls to improve fuel economy with a resulting increase in the 

NOx emission rate. 

MOBILE6 

Based upon draft documentation, MOBILE6 is expected to use the same methodology as 

MOBILE5 for heavy-duty diesel vehicle emission estimates. Current public information about 

MOBILE6 indicates that they intend to revise the basic emission rates, conversion factors, and 

age and travel distributions however, so the numerical estimates may be different between 

MOBILE6 and MOBILE5. Also, the vehicle classes classified by GVW will be disaggregated 

into several individual classifications, shown in Table 3-1, in contrast to MOBILE5, which has 

only one set of emissions rates for all trucks. 

3-2 
Table 3-1. MOBILE6 heavy-duty vehicle classifications. 

Gross Vehicle 

Designation Description Weight (lbs.) 

Gasoline Vehicles 

HDGV (classes 2B-3) 

HDGV (classes 4-8) 

Diesel Vehicles 

HDDV (class 2B) 

HDDV (class 3) 

HDDV (classes 4-5) 

HDDV (classes 6-7) 

HDDV (class 8A) 

HDDV (class 8B) 

Urban Buses 

HDGB (school) 

HDGB (transit) 

HDDB (school) 

HDDB (transit) 

Heavy-duty gasoline vehicles 8,501-14,000 

Heavy-duty gasoline vehicles >14,000 

Light heavy-duty diesel trucks 8,501-10,000 

Light heavy-duty diesel trucks 10,001-14,000 

Light heavy-duty diesel trucks 14,001-19,500 

Medium heavy-duty diesel trucks 19,501-33,000 

Heavy heavy-duty diesel trucks 33,001-60,000 

Heavy heavy-duty diesel trucks >60,000 

Heavy-duty gasoline school buses All 

Heavy-duty gasoline transit buses All 

Heavy-duty diesel school buses All 

Heavy-duty diesel transit buses All 

The basic emission rates were determined for engines that have certification data including those 

engines produced after 1988. For engines before 1988, the 22 in-use engines, tested by EPA and 

used in MOBILE5, were used to estimate the average emission rates for the older engine types. 

Emission rates for newer engines were available only for the certification test. Emission rates 

generated on the engine dynamometer testing are available in units of grams per horsepowerhour need to be converted to grams per mile through energy conversion factors. The emission 

rates were only generally available by intended truck size; light, medium, heavy, or bus engine as 

shown in Table 3-1. 

The revised conversion factor was determined from estimates of the brake specific fuel 

consumption (BSFC in units of lb/hp-hr), fuel density, and typical fuel consumption (in units of 

gallons/mile) (Arcadis, 1998). The BSFC was determined from manufacturers’ estimates of fuel 

consumption by engine family and was sales weighted to produce an average BSFC value by 

model year and by truck and bus class. The fuel density was determined from in-use samples of 

diesel fuel. The typical fuel consumption was determined from driver/owner surveys of fuel 

consumption per mile and taken from the periodic Truck In-Use Survey (TIUS, 1993). 

However, because MOBILE6 is not public, for purposes of this modeling the conversion of 

MOBILE5 estimates to reflect the currently expected changes in MOBILE6 will be used. The 

conversion of hydrocarbon emission estimates from MOBILE5 to MOBILE6 is a simple 

multiplicative factor (i.e. 0.413 for the in-use year 2007) applied to the MOBILE5 emission 

estimates. The conversion of NOx emission estimates is more complicated because EPA 

estimates that defeat devices operation depends upon the average speed uniquely correlated to 

each roadway type (several subclasses of urban streets, arterial collectors, or limited access 

freeways). So for NOx emissions, the conversion is applied to the emissions projected on each 

roadway type within a given region. 

Deterioration rates in MOBILE6 are insignificant, so the zero-mile levels will determine the 

typical in-use emission rates except for the defeat device adjustment to in-use NOx emissions. 

Zero-mile levels calculated using the certification emission rates and applying the conversion 

3-3 
factor for various truck classes are shown in Table 3-2 for the largest trucks (Class 8B), smallest 

trucks (Class 2B), and Urban Buses. 

Table 3-2a. Estimates of MOBILE6 zero-mile emission rates in g/mile (w/o defeat device 

effects) for the class 8B trucks. 

Model HC CO NOx PM 

Year (g/mile) (g/mile) (g/mile) (g/mile) 

1988 1.53 4.37 20.49 1.44 

1989 1.52 4.33 20.29 1.42 

1990 1.66 5.79 15.52 1.22 

1991 0.95 5.77 14.46 0.67 

1992 0.94 5.72 14.32 0.66 

1993 0.93 5.67 14.20 0.65 

1994 0.52 3.30 14.22 0.25 

1995 0.52 3.27 14.10 0.24 

1996 0.52 3.24 13.97 0.24 

Table 3-2b. Estimates of MOBILE6 zero-mile emission rates in g/mile (w/o defeat device 

effects) for class 2B trucks. 

Model HC CO NOx PM 

Year (g/mile) (g/mile) (g/mile) (g/mile) 

1988 0.71 1.33 4.79 0.49 

1989 0.70 1.33 4.78 0.48 

1990 0.57 1.99 5.33 0.42 

1991 0.52 0.44 4.80 0.25 

1992 0.51 0.44 4.80 0.25 

1993 0.51 0.44 4.79 0.25 

1994 0.22 1.30 4.46 0.10 

1995 0.22 1.30 4.45 0.10 

1996 0.22 1.30 4.45 0.10 

3-4 
Table 3-2c. Estimates of MOBILE6 zero-mile emission rates in g/mile (w/o defeat device 

effects) for urban buses. 

Model HC CO NOx PM 

Year (g/mile) (g/mile) (g/mile) (g/mile) 

1988 2.16 6.17 28.90 2.02 

1989 2.17 6.18 28.94 2.03 

1990 2.40 8.36 22.39 1.75 

1991 2.87 12.49 21.04 2.13 

1992 2.87 12.51 21.09 2.13 

1993 1.39 13.47 19.79 1.07 

1994 0.37 4.93 22.72 0.28 

1995 0.37 4.95 22.77 0.28 

1996 0.37 4.96 22.83 0.19 

EMFAC7G 

EMFAC7G relied on the MOBILE5 heavy-duty diesel estimates for basic emission factors and 

average speed adjustments. EMFAC applied a deterioration estimate to account for tampering 

and malmaintenance for in-use vehicles and the effects of the inspection and maintenance 

program. This deterioration estimate was added as a percentage increase to the basic emission 

factors. California also added an adjustment to account for expected emission benefits of the 

reformulated diesel fuel program. 

EMFAC2000 

There has been considerable disagreement about how representative the converted engine testing 

is to in-use vehicle emissions. For that reason, ARB has revised the emission estimates for 

heavy-duty diesel vehicles by using the recently available emissions data collected with chassisloaded diesel vehicles (ARB, 2000a). 

Testing of heavy-duty vehicles to determine emissions may be performed in two ways. The first 

method involves removing the engine from the test vehicle’s chassis (frame), mounting it on a 

test stand, and operating the engine on a testing apparatus known as an engine dynamometer. 

Data derived from engine dynamometers was the basis for the current emission estimates in 

MOBILE5 and EMFAC7G. The second method involves testing the engine while it is still in the 

vehicle by operating the entire vehicle on what is known as a chassis dynamometer. The latter 

method is very similar to the approach used to test light-duty vehicle and light-duty truck 

emissions. Emission levels produced on the engine dynamometer are measured in grams per 

brake horsepower-hour (g/bhp-hr) or grams per kilowatt-hour (g/kW-hr) for a given test cycle, 

while emissions produced on a chassis dynamometer are measured in grams per mile (g/mi) or 

grams per kilometer (g/km). The results of these emissions tests are used to develop emission 

factors for heavy-duty vehicles that are then used in mobile source modeling and inventory 

development. 

The chassis-derived data available from several studies are now considered sufficient to estimate 

emissions. The emission factors were developed by ARB separately for three sets of diesel 

vehicles: (1) heavy heavy-duty and medium heavy-duty, (2) light heavy-duty vehicles, and (3) 

3-5 
urban buses, with the ARB weight classifications shown in Table 3-3. Each of these three 

groups of vehicles had emission factors estimates from totally separate testing programs with 

different test procedures, as described below. In EMFAC2000, diesel-powered trucks with a 

gross vehicle weight of 8,501 pounds or greater are classified in the following manner: 

Table 3-3. ARB heavy-duty trucks weight classes by Gross Vehicle Weight (GVW). 

GVW in lbs Vehicle Class 

8,501 to 14,000 Light-Heavy Duty Trucks (LHDT) 

14,001 to 33,000 Medium-Heavy Duty Trucks (MHDT) 

> 33,000 Heavy-Heavy Duty Trucks (HHDT) 

The chassis test cycles on which the emissions should be based is a point of debate. While 

several were considered, the urban dynamometer driving schedule (UDDS) (EPA, 1996) was 

used for medium-heavy and heavy-heavy-duty vehicles because it was developed based on the 

same data set used to produce certification engine test cycle and it was the only heavy-duty cycle 

intended to represent actual in-use driving behavior. The light-duty Federal Test Procedure 

(FTP) testing cycle was used for the light-heavy-duty vehicles because the cycle was considered 

appropriate for the lighter trucks represented by pickup trucks, sport-utility, and vans typically 

used in the role of more traditional light-duty applications. For urban buses, chassis data were 

only available on the central business testing cycle (CBD), which is a series of successive 

acceleration, cruise, braking, and idle typical of urban bus behavior. 

For heavy-heavy (HHDDV) and medium-heavy trucks (MHDDV), data from three sources were 

used to derive the chassis dynamometer based emission rates in EMFAC2000. The first data set, 

made available by U.S. EPA, was obtained from the New York State Department of 

Environmental Conservation and Energy (NYSDEC). Under sub-contract to Energy and 

Environmental Analysis, Inc. (EEA), U.S. EPA and NYSDEC, the West Virginia University 

(WVU) Department of Mechanical and Aerospace Engineering conducted chassis dynamometer 

based emissions tests on 35 heavy-heavy and medium-heavy diesel trucks on various chassis test 

cycles. The second data set was Graboski (1998), which tested 21 trucks and buses on various 

test procedures under hot and cold start conditions. Test data from a total of 11 heavy-heavy and 

medium-heavy diesel trucks tested on the UDDS cycle under hot start conditions were obtained 

from the database. The tests were conducted at high altitude; altitude correction factors were 

therefore applied before emissions test results were merged with other data for this analysis. The 

altitude correction factors were taken from EPA (1998) and are shown in Table 3-4. The third 

data set was obtained directly from WVU and included tests performed on four heavy-heavy 

diesel trucks on the UDDS cycle. 

Table 3-4. EPA heavy-duty diesel vehicle high altitude adjustment factors. 

HC CO NOX PM

2.05 2.46 1.02 1.47 

Two data sources were used to derive the emissions rates for light-heavy diesel trucks 

(LHDDV). The first data set was obtained from the U.S. EPA. The tests were conducted by the 

College of Engineering, Center for Environmental Research and Technology (CE-CERT) in 

Riverside under contract to the U.S. EPA with the objective to investigate the effect of payload 

on exhaust emissions. It included bag specific results from five trucks tested over the Federal 

Test Procedure and three different payloads. Staff used data obtained from testing the trucks at 

3-6 
the equivalent test weight (ETW). The ETW is the test weight equal to the empty weight of the 

vehicle plus 40 percent fuel fill in the tank. Vehicles in this data set were tested with California 

reformulated diesel fuel in the tank at the time the vehicle was received. The second data set was 

obtained from Norbeck (1998) and included bag specific FTP test results from 15 trucks tested at 

the equivalent test weight. Vehicles in this data set were tested with the Federal certification 

diesel fuel, Type 2-D. Fuel correction factors explained in more detail below were applied to the 

first data set before they were merged with the second data set. 

For HHDDV and MHDDV, regression equations were used to calculate the average emission 

rates for model years that were within the data points, i.e. model years 1981 to 1998 for HHDDV 

and 1985 to 1999 for MHDDV. Model years prior to 1981 or 1985 were assumed to have the 

same average emission rate as the 1981 or 1985 model year. For HHDDV of model years 1999 

and later, an average emission rate was calculated by multiplying the average emission rate of 

the 1991-93 model year group by the ratio of the standards of the 1999+ model year to the 1991-

93 model year groups. The 1991-93 model year group was considered as a basis for calculating 

the 1999+ model year average emissions because this group had the lowest NOx emissions and 

therefore was considered to be free of off-cycle (defeat device) NOx increases. For MHDDV of 

model years 2000 and later, average emission rates were calculated by taking the ratio of 

standards with respect to the 1998-99 model year estimates and multiplying by the 1998-99 

model year group average emission rate because no off-cycle NOx increases were observed for 

this vehicle class. 

For LHDDV, two average set of emission rates were calculated -- one for model years before 

1990 and a second for model years after 1990 -- because the data naturally fell into these two 

groups. These averages were applied for model years that are within the data set, i.e. 1982 to 

1996. Model years prior to 1982 were assumed to have the same average emission rate as the 

1982 model year. For model years after 1996, the average emission rates were calculated using 

the ratio of standards and the average emission rate of the 1991-93 model year group. 

For urban (transit) buses, a data set from West Virginia University under contract to the 

Department of Energy’s National Renewable Energy Laboratory was available to estimate 

emissions from 1988-1996 model year urban buses. Regression curves by model year were 

developed with buses of model years 1988 and earlier having the same emission rate. For 1999 

and later model years, the emission factors were determined from a ratio of emission standards 

from 1991-1993 model years to avoid including off-cycle emission effects. 

Deterioration was considered by ARB by employing an estimate of the frequency of occurrence 

and an emissions effect of failure of one or more given engine components. The type, frequency 

of occurrence, and emission effect are specific to each model year engine and are combined to 

produce a percentage increase in emissions. Because the available data showed no correlation of 

emissions with odometer, ARB does not estimate deterioration for urban buses. 

Table 3-5. ARB estimated maximum percentage increase in emissions from engine 

deterioration (tampering and malmaintenance). 

Vehicle Pollutant Pre88 88-90 

Model Year Group 

91-93 94-97 98-02 2002+ 

HHDDV NOx 3.4 5.5 9.8 7.6 5.6 5.8 

HC 226.9 343.7 332.1 525.8 512.4 240.9 

PM 125.1 107.3 138.3 200.2 169.8 100.6 

3-7 
MHDDV NOx 5.2 4.2 10.0 7.6 5.6 5.8 

HC 227.6 342.1 325.9 525.8 512.4 240.9 

PM 95.5 89.8 130.7 206.5 170.7 101.3 

LHDDV NOx 4.0 3.3 10.0 7.6 5.6 5.8 

HC 257.6 388.0 325.9 525.8 512.4 240.9 

PM 92.7 82.0 102.0 206.5 170.7 101.3 

Because the average emission rates determined from the data set included engines that had some 

deterioration, a method was devised where the average deterioration was estimated by averaging 

the emission effects of tampering (which can occur immediately upon purchase) with the 

emission effects of tampering and malmaintenance (due to wear just prior to rebuild). By 

applying this average deterioration to the emission estimates from the in-use data, a zero-mile 

level could be calculated. 

In addition, ARB accounts for the use of California diesel (so called “Clean Diesel”), which is a 

diesel fuel that restricts the aromatics content in addition to the lower sulfur requirement found 

with Federal (or 49 state) diesel. Emission effects for this fuel were taken from the testing of two 

heavy-duty diesel engines using fuels that varied by sulfur and aromatic content (ARB, 2000b), 

and the effects modeled are shown in Table 3-6. California diesel fuel was implemented 

statewide in October 1993, and diesel engines were certified on the lower (0.05%) sulfur Federal 

diesel fuel for the 1993 model year. So whether an in-use adjustments was made due to lower 

sulfur and aromatics or lower aromatics only depended upon the model year and the in-use year 

of interest. 

Table 3-6. Emissions reduction due to lower sulfur and aromatic content. 

Reduction Due to Reduction Due to Reduction Due to Combined Effect 

Low Sulfur (0.28 Low Aromatic Low Aromatic (30 of Lower Sulfur 

Model to 0.05 % by (30 to 10 % by to 10 % by and Aromatic 

Year weight) volume) volume) Contents 

PM PM NOx PM 

Pre 1991 3.86% 16.73% 5.57% 20.59% 

1991+ 22.70% 10.07% 12.4% 32.77% 

3-8 
The final emission factor for trucks and buses are shown in Table 3-7. These emission factors 

include the adjustments described above, and zero-mile levels can be compared with the 

estimates for MOBILE6 except for the off-cycle (defeat device) effects on NOx emissions. In 

general, the zero-mile emission rates are surprisingly comparable (given the differing data 

sources) with the MOBILE6 estimates. However there are some significant differences such as 

late model urban buses where EMFAC2000 estimates much higher HC and PM emissions than 

MOBILE6. And engine deterioration of HC and PM emissions make the EMFAC2000 estimates 

generally higher than the MOBILE6 estimates. 

Table 3-7a. EMFAC2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi) – HHDDV. 

MY HC CO NOx PM 

Group ZM DR ZM DR ZM DR ZM DR 

Pre 1975 1.60 0.018 8.36 0.095 28.52 0.012 1.98 0.016 

1975-76 1.45 0.018 7.81 0.098 27.17 0.013 1.85 0.016 

1977-79 1.45 0.019 7.81 0.101 27.17 0.013 1.85 0.017 

1980-83 1.45 0.020 7.81 0.108 27.17 0.014 1.85 0.018 

1984-86 0.74 0.011 4.87 0.074 20.18 0.011 1.18 0.012 

1987-90 0.34 0.009 2.48 0.065 16.79 0.015 0.84 0.008 

1991-93 0.28 0.009 1.74 0.056 15.97 0.030 0.51 0.009 

1994-97 0.19 0.016 0.84 0.068 19.06 0.042 0.32 0.010 

1998 0.18 0.014 0.63 0.049 23.01 0.037 0.26 0.007 

1999-02 0.18 0.009 0.63 0.031 13.36 0.013 0.21 0.003 

2003 0.14 0.003 1.01 0.023 6.68 0.007 0.26 0.003 

2004 0.14 0.003 1.01 0.023 6.68 0.007 0.26 0.003 

Table 3-7b. EMFAC2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi)– MHDDV. 

MY HC CO NOx PM 

Group ZM DR ZM DR ZM DR ZM DR 

Pre 1975 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1975-76 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1977-79 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1980-83 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1984-86 0.33 0.014 2.99 0.131 17.91 0.043 1.00 0.021 

1987-90 0.21 0.016 1.80 0.140 15.74 0.034 0.73 0.017 

1991-93 0.18 0.018 1.43 0.139 13.11 0.078 0.45 0.022 

1994-97 0.11 0.017 0.78 0.121 11.55 0.048 0.27 0.018 

1998 0.09 0.014 0.64 0.097 10.52 0.032 0.24 0.012 

1999-02 0.09 0.014 0.64 0.097 10.52 0.032 0.24 0.012 

2003 0.09 0.007 1.04 0.074 5.79 0.018 0.29 0.009 

2004+ 0.09 0.006 1.04 0.074 5.48 0.017 0.29 0.009 

3-9 
Table 3-7c. EMFAC2000 zero-mile emission (ZM) and deterioration (DR) rates 

(g/mi per 10k mi) – LHDDV. 

MY HC CO NOx PM 

Group ZM DR ZM DR ZM DR ZM DR 

Pre 1975 0.19 0.007 0.74 0.025 3.94 0.005 0.23 0.003 

1975-76 0.19 0.007 0.74 0.028 3.94 0.006 0.23 0.003 

1977-79 0.19 0.008 0.74 0.030 3.94 0.006 0.23 0.003 

1980-83 0.19 0.009 0.74 0.033 3.94 0.007 0.23 0.004 

1984-86 0.19 0.010 0.74 0.037 3.94 0.008 0.23 0.004 

1987-90 0.15 0.013 0.57 0.051 3.99 0.007 0.23 0.005 

1991-93 0.22 0.016 0.68 0.052 6.67 0.031 0.07 0.002 

1994 0.16 0.019 0.50 0.060 6.86 0.022 0.05 0.003 

1995 0.16 0.019 0.50 0.060 6.86 0.022 0.05 0.003 

1996-97 0.16 0.019 0.50 0.060 6.86 0.022 0.05 0.003 

1998-99 0.02 0.002 0.44 0.052 2.43 0.006 0.03 0.001 

2000-01 0.02 0.002 0.44 0.052 2.43 0.006 0.03 0.001 

2002-03 0.03 0.001 0.71 0.039 1.56 0.004 0.04 0.001 

2004+ 0.02 0.001 0.71 0.039 1.56 0.004 0.04 0.001 

Table 3-7d. EMFAC2000 diesel urban bus emission factors (g/mile) 

Model Year HC CO NOX PM 

Pre 1987 

1987-90 

1991-93 

1994-95 

1996-98 

1999-02 

2003 

2004-06 

2007 

2008 

2.06 18.19 46.18 1.29 

2.05 16.28 40.20 1.22 

2.02 9.71 25.49 1.16 

1.99 6.50 29.84 1.41 

1.98 5.10 39.17 1.69 

1.98 5.10 20.39 0.58 

0.84 4.05 10.20 0.12 

0.84 4.05 2.55 0.12 

0.84 4.05 1.02 0.12 

0.75 4.05 0.90 0.10 

3-10 
4. INCORPORATING BIODIESEL EMISSION EFFECTS 

INTO MODELING DATA BASES 

The effects of biodiesel on mass emissions in HDDV’s need to be incorporated into the emission 

inventory data bases that are used in air quality modeling. We describe here how this is done for 

the different modeling data bases to be used in the air quality modeling tasks. Information about 

other components of the air quality modeling to be performed can be found in the project Air 

Quality Modeling Plan. 

NORTHEAST CORRIDOR AND CHICAGO OZONE MODELING 

The Emissions Modeling System version 1995 (EMS95) is the emissions processing system used 

in the Northeast Ozone Transport Region (OTR) and Chicago area ozone modeling data bases. 

EMS95 is a large and complex modeling system that is written using the Statistical Analysis 

System (SAS7). In standard EMS95 emissions modeling, a composite fleet emission factor is 

calculated using MOBILE5. This composite emission factor is a function of speed and VMT by 

roadway type (e.g., rural interstate, urban arterial), vehicle class fleet mix, temperature, fuel 

parameters, and Inspection and Maintenance (I/M) program parameters input for each county in 

the modeling domain. Average speed and VMT inputs are across all vehicle categories. 

For the air quality modeling to be performed in this study, we have updated the EMS95 

modeling system to include adjustments that reflect what is expected in MOBILE6. Specifically, 

we will use the MOBILE6 adjustments that EPA developed for the modeling performed for the 

Tier 2 vehicle/sulfur standards final rulemaking (Koupal and Dolce, 1999). While these 

adjustments do not include all changes being made to MOBILE6, they do include many of the 

more significant effects. Adjustments are made to: 

• Light-duty vehicle (passenger cars and trucks) and heavy-duty gasoline vehicle emission 

factors for exhaust HC and NOX. The adjustments depend on fuel type (reformulated or 

conventional) and Inspection and Maintenance program type, and are made to each county 

using county-specific data. For the 2007 modeling year, these adjustments are all substantial, 

typically in the range of 20 to 70 percent reductions. Additional adjustments (additive) were 

made to account for increases in NOX emissions for air conditioning usage as modeled in 

MOBILE6 (dependent on county-specific ambient temperatures). 

• Evaporative emissions adjustments to reflect the benefits of Tier 2 vehicle controls only; 

these are small, only a few percent reduction. MOBILE6 will have significant revisions to 

all categories of evaporative HC emissions in light-duty vehicles, but the emissions modeling 

approach is so different from MOBILE5 that it was not possible to develop simple 

adjustment factors. 

• Heavy-duty diesel vehicle NOX emissions for the effects excess NOX emissions from the socalled “defeat device”. These adjustments differ by roadway type and speed, with the largest 

increases on interstates. 

• HDDV VOC and NOX emissions for other changes in MOBILE6 – new emissions standards, 

revised conversion factors (for converting engine dynamometer g/bhp-hr measurements to 

g/mile), and revised fleet characterization (age and mileage accumulation distributions). 

4-1
As the first step in the emissions processing, EMS95 generates a very large lookup table of 

MOBILE5 output emission factors, with the MOBILE6 adjustments described above, by vehicle 

class as a function of I/M program parameters, fuel parameters, speed (typically in 5 mph 

increments), and temperature (typically in 2°F increments). Emission factors by vehicle class are 

then interpolated using the county-specific I/M program parameters and temperature, and 

average speed inputs for each roadway type. To incorporate the biodiesel emission effects into 

the inventory, the MOBILE5/6 HDDV emission factors in this lookup table will be multiplied by 

the engine dynamometer biodiesel effects by pollutant as shown in Table 2-1 (using the overall 

average without the Sharp, 1998 data). EMS95 then calculates the on-road fleet average 

emissions by taking a weighted average of the vehicle class emission factors (weighted by fleet 

mix and vehicle class-specific VMT). 

LAS VEGAS CO MODELING 

The carbon monoxide emission inventory used in the Clark County (Las Vegas) SIP was 

developed using a combination of the Emissions Preprocessor System version 2 (EPS2.0) and the 

Direct Travel Impact Model version 2 (DTIM2). The DTIM2 system used in the Las Vegas 

modeling combines link specific traffic activity data from the TRANPLAN transportation model 

with emission factors from MOBILE5 to produce gridded hourly emissions estimates for on-road 

mobile sources. The EPS2.0 system then reads the inventory generated by the DTIM2 model 

and performs the final tasks needed in preparation for use in air quality modeling. 

For the CO modeling in the Las Vegas region, the biodiesel emission effects shown in Table 2-1 

will be multiplied by the MOBILE5 emission factors for HDDVs prior to running the 

DTIM2/EPS2 system. 

LOS ANGELES OZONE AND PM MODELING 

The emission processing system adopted by the South Coast Air Quality Management District 

(SCAQMD) to generate the inventories used in the Los Angeles 1987 Southern California Air 

Quality Study (SCAQS) domain uses the DTIM2 system to generate a gridded hourly on-road 

mobile source emission inventory. However, the emission factor model used was the EMFAC7 

model developed by the California Air Resources Board (CARB). Also, because of historic 

precedent, the SCAQMD uses an earlier version of the EPS2.0 system. This system, as used by 

SCAQMD, consists primarily of the program CENTEMS, which is responsible for chemical 

speciation and temporal allocation of emissions estimates, as well as application of any growth 

or controls. 

The methodology we will use in the Los Angeles area for ozone and PM modeling will be 

similar to that adopted for CO modeling in the Las Vegas region. The mobile source emission 

factor model that we will use will be EMFAC2000 version 2.0, which has not yet been released 

by ARB. The EMFAC2000 emission factors for HDDV will be adjusted using the biodiesel 

effects shown in Table 2-1. The adjusted emission factors will be used in the DTIM2 processing 

to generate on-road mobile emission estimates that will then be processed by the CENTEMS 

program to generate the mobile source emissions inventory estimates to be used in the ozone and 

PM air quality modeling. 

4-2
5. HEALTH EFFECTS IMPACT MODELING PLAN 

OBJECTIVE 

Introduction of biodiesel on a large-scale basis has the potential to provide public health benefits 

associated with decreased toxicity of diesel PM emissions. Benefits from decreases in the total 

mass of primary and possibly secondary PM emissions are also possible. A health impact 

modeling analysis will be performed as part of the project to quantify the potential health 

benefits. 

BACKGROUND 

Exhaust from conventional fueled diesel vehicles contains numerous known and suspected 

human carcinogens. Diesel exhaust was recently classified as a Toxic Air Contaminant by the 

California Air Resources Board (ARB, 1998); was the subject of an EPA health risk assessment 

(EPA, 1998), and a critical review by the Health Effects Institute (1995) which is sponsoring ongoing work in this area. One of the potential benefits of biodiesel fuel use is a decrease in the 

overall toxicity of PM emissions. This is a result of the elimination or reduction of certain toxic 

components of diesel exhaust which are present in conventional fuels but not in biodiesel. 

Additional benefits may be associated with potential reductions in the total mass of particulate 

matter in the exhaust. 

TECHNICAL APPROACH 

An assessment will be made of the potential health benefits impact of each biodiesel use scenario 

considered for the Los Angeles area PM modeling to be performed in PM10 modeling as 

described in the previous section. Results from a survey of available data on differences in 

toxicity between biodiesel and conventional diesel exhaust PM emissions will be used to develop 

appropriate toxicity adjustments factors to apply to the PM emission estimates. PM model 

results from the biodiesel PM10 impact modeling will be combined with an exposure model to 

estimate population exposures in Los Angeles to diesel exhaust under each fuel scenario. These 

results will take into account movement of people between various microenvironments including 

time spent indoors. Resulting exposure estimates will then be combined with toxicity factors to 

estimate differences in cancer incidence and morbidity/mortality rates between each fuel use 

scenario. Finally, costs associated with the computed morbidity/mortality rates will be estimated 

using cost factors for various outcomes developed for the PM NAAQS Regulatory Impact 

Analysis (EPA, 1997). 

Our analysis will focus on health effects associated with the toxicity of primary diesel particulate 

emissions although available dose/response data do not always distinguish between diesel 

particulate and whole exhaust. Effects associated specifically with primary vapor phase toxic 

emissions such as benzene and secondary pollutants (ozone, formaldehyde, secondary particulate 

such as secondary organic aerosols, etc.) will not be considered although this would be necessary 

5-1
for a complete health risk assessment. We also note that our emphasis will be on health effects 

associated with long-term, non-occupational exposures. Biodiesel use may also reduce the 

potential for adverse health consequences associated with both short-term (acute) exposures to 

“hot spot” concentrations and occupational exposures. 

Exposure Modeling 

EPA’s Office of Air Quality Planning and Standards (OAQPS) has recently enhanced the 

pNEM/CO (probabilistic NAAQS Exposure Model for Carbon Monoxide) and applied it to 

Denver and Los Angeles (Johnson et al., 1999). This new version of the pNEM/CO model 

represents the current state-of-the-art in estimating CO exposures. Application of pNEM/CO to

Los Angeles using 1995 data is currently undergoing some refinements to better account for the 

spatial variation in ambient CO; revised results along with a comprehensive report are expected 

to be available within the next month which will allow sufficient time for them to be used in the

biodiesel health impact analysis. EPA’s Clean Air Scientific Advisory Committee is scheduled 

to review the pNEM/CO analysis in Spring, 2000. 

The pNEM is usually run with indoor sources included to get a total exposure estimate. 

However the, results of a model run for Los Angeles with indoor sources turned off are available 

in this case. The output of pNEM/CO with indoor sources turned off will be adapted to provide 

seasonal CO exposure estimates for various age/gender/occupation population cohorts. These 

results will then be rescaled by the ratio of diesel PM emissions to total outdoor CO emissions to 

obtain diesel PM exposure estimates. This is the same methodology used in the EPA/OMS 

diesel PM exposure study (EPA, 1999). 

Health Impacts 

In addition to cancer, non-cancer health effects have been associated with chronic exposure to 

PM emissions in general and diesel exhaust in particular. Acute effects from short-term 

exposures have also been identified. Estimates of both an approximate “no effects” level for 

acute effects and a lifetime cancer unit risk factor have been published by EPA and ARB for 

diesel exhaust PM as a marker for whole diesel exhaust. Similar information is available for 

many known toxic components of diesel exhaust. However, quantitative exposure/response 

relationships for outcomes such mortality, respiratory illnesses, etc. have only been developed 

for urban ambient PM and not specifically for diesel exhaust. Nevertheless, results for urban 

ambient PM could be used to approximate diesel exhaust health effects. 

Annual average exposure estimates obtained from pNEM/CO as described above will be 

combined with published unit risk factors for conventional diesel and biodiesel to estimate 

cancer incidence rates attributable to diesel exhaust for the total population and sensitive 

subgroups for each of the fuel use scenarios used. Both a “best estimate” and a range of values 

will be generated based on the range of published unit risk factors. PM exposure/response 

relationships developed as part of the Regulatory Impact Analysis (RIA) for EPA’s 1997 PM 

NAAQS rule making will be used to estimate incidents of mortality associated with long-term 

exposures to diesel exhaust at ambient concentrations under each scenario. These relationships 

are summarized in Appendix I of the RIA (EPA, 1997). 

5-2
Cost Estimates for Biodiesel Health Impacts 

Costs associated with the morbidity and mortality estimates described above will be based on 

cost factors from EPA’s 1997 PM RIA (EPA, 1997). Table 5-1 lists health effects for which 

quantified benefit estimates are available from the PM RIA along with those for which 

quantitative estimates are not available. Given the high cost factors associated with mortality, it 

is reasonable to expect that changes in mortality will dominate the cost comparison between fuel 

use scenarios as it did in the PM NAAQS scenarios. We will therefore focus our cost 

comparison on changes in mortality. Our mortality benefits estimate will consider both value of 

a statistical life saved and the value of a statistical life-year extended as used in the PM RIA. 

Note that cost factors are not available in the RIA for cancer so we will not attempt to quantify 

costs for this outcome. 

Table 5-1. PM benefits categories. 

Unquantified Benefit Categories 

Quantified Benefit Categories 

(incidences reduced and/or dollars) 

Health 

Categories 

Changes in pulmonary function 

Morphological changes 

Altered host defense mechanisms 

Cancer 

Other chronic respiratory disease 

Infant Mortality 

Mercury Emission Reductions 

Mortality (acute and long-term) 

Hospital admissions for: 

all respiratory illnesses 

congestive heart failure 

ischemic heart disease 

Acute and chronic bronchitis 

Lower, upper, and acute respiratory symptoms 

Respiratory activity days 

Minor respiratory activity days 

Shortness of breath 

Moderate or worse asthma 

Work loss days 

HEALTH EFFECTS IMPACT REPORTING 

A comprehensive draft interim report will be prepared describing the data sources, methods, 

assumptions used in the health effects impact modeling along with the modeling results. 

Revisions as needed in response to reviewer comments will be incorporated into a document 

which will appear as a chapter integrated into the project final report. 

5-3
REFERENCES 

Adept Group (1995), “Emissions and Engine Performance of Soya and Canola Methyl Esters 

with ARB#2 Diesel Fuels,” Report to Saskatchewan Canola Development Commission, 

Saskatoon, Saskatchewan, July, 1995. 

ARB (2000a), “Heavy-Duty Trucks Emission Factors Development,” Section 10.0 EMFAC2000 

Documentation, Available Online at 

http://arbis.arb.ca.gov/msei/doctabletest/doctable_test.html, June 5, 2000. 

ARB (2000a), Public Meeting to Consider Approval of Revisions to the State's On-Road Motor 

Vehicle Emissions Inventory, Technical Support Document, Section 10, May, 2000. 

Arcadis (1998), “Update Heavy-Duty Engine Emission Conversion Factors For MOBILE6: 

Analysis Of BSFCS And Calculation Of Heavy-Duty Engine Emission Conversion 

Factors,” Prepared For EPA By Arcadis Geraghty & Miller, Inc., EPA420-P-98-015, 

M6.HDE.002 May, 1998. Also, “Update Heavy-Duty Engine Emission Conversion 

Factors For MOBILE6: Analysis Of Fuel Economy, Non-Engine Fuel Economy 

Improvements, And Fuel Densities,” EPA420-P-98-014, M6.HDE.002, May 1998 

Bickel, K.L. (1999), “Summary Of Biodiesel Research At The UMN Center For Diesel Research 

And U.S. Bureau Of Mines,” Prepared for the National Renewable Energy Laboratory, 

University of Minnesota, Center for Diesel Research, November 17, 1999. 

Durbin, T.D., Collins, J.R., Norbeck, J.M., and Smith, M.R., (1999) “Evaluation of the Effects of 

Alternative Diesel Fuel Formulations on Exhaust Emission Rates and Reactivity,” 

Prepared for the South Coast Air Quality Management District, Center for Environmental 

Research and Technology, University of California-Riverside, April, 1999. 

EPA (1999), “Development of On-Highway Inventory Adjustment Factors Used in the Tier 2 

Final Rule Air Quality Analysis,” Memorandum to Docket A-97-10 from John Koupal 

and Gary Dolce, EPA Office of Mobile Sources, October 19, 1999. Also, “Development 

and Use of Heavy-Duty NOx Defeat Device Emission Effects for MOBILE5 and 

MOBILE 6,” EPA420-P-99-030, or available as M6.HDE.003, October 1999. 

EPA (1998), “Update Heavy-Duty Engine Emission Conversion Factors for MOBILE6: Analysis 

of BSFCs and Calculation of Heavy-Duty Engine Emission Conversion Factors,” 

MOBILE6 Documentation, Report #M6HDE001, EPA 420-P-98-015, May, 1998. 

EPA (1996), Code of Federal Regulations (CFR), 40 CFR Part 86.1215-85. 

EPEFE (1996), “Annex 2 Analytical Sub-Group Report on Fuel Analysis & Speciation and 

Speciated Exhaust Gas Analysis,” ACEA/EUROPIA, January, 1996. 

Fosseen Manufacturing and Development (1995), “Optimize 20% Methylsoyate and Diesel 

Blend-1977 DDC 6V-71N Coach,” Report to National Biodiesel Board, Contract 219-2, 

January 10, 1995. 

R-1 
Fosseen Manufacturing and Development (1994a), “Emission Testing on Biodiesel and 

Biodiesel Blends,” Final Report No. NSDB4F15 to the National Soy Diesel Development 

Board, Jefferson, MO, July, 1994. 

Fosseen Manufacturing and Development (1994b), “DDC 6V-92 TA MUI Coach Upgrade 

Emission Optimization on 20%/80% Soy/Diesel Blend,” Report 260-2 and 241-1, 

September 30, 1994. 

Graboski, M.S., McCormick, R.L., Yanowitz, J., and Ryan, L. (1998), “Heavy-Duty Vehicle 

Testing for the Northern Front Range Air Quality Study.” Prepared for: The Office of the 

Vice President for Research and Information Technology, February, 24, 1998. 

Graboski, M.S. and McCormick, R.L. (1998), “Combustion of Fat and Vegetable Oil Derived 

Fuels in Diesel Engines,” Progress in Energy and Combustion Science, v.24, pp 125-164. 

Graboski, M.S., Ross, J.D., and McCormick, R.L. (1996), “Transient Emissions from No. 2 

Diesel and Biodiesel Blends in a DDC Series 60 Engine,” SAE-961166. 

Koupal, J. and G. Dolce. 1999. “Development of On-Highway Inventory Adjustment Factors 

Used in the Tier 2 Final Rule Air Quality Analysis,” Memorandum from John Koupal 

and Gary Dolce to Docket A-97-10, October 18, 1999. 

Liotta, Jr., R and Mantalvo, D. (1993), SAE-932734, 1993. 

Manicom, B. Green, C., and Goetz, W. (1993), “Methyl Soyate Evaluation of Various Diesel 

Blends in a DDC 6V-92TA Engine,” Ortech Int., Final Report No 93-E14-21 to Fossen 

Manufacturing and Development, April 12, 1993. 

McCormick, R.L., Graboski, M.S., and Alleman, T.L. (1999), “Health Related Emissions from 

Biodiesel,” Draft Final Report, Prepared for the National Renewable Energy Laboratory, 

October, 18, 1999. 

McCormick, R.L., Ross, J.D., and Graboski, M. (1997), Environmental Science and Technology, 

1997, 31, 1144. 

Ortech Corp. (1995), “Operation of Cummins N14 Diesel Engine on Biodiesel: Performance, 

Emissions, and Durability,” Final Report for Phase I to National Biodiesel Board, Report 

No. 95 E11-B004524, Mississauga, Ontario, January, 19, 1995. 

Ortech International (1993), “Evaluation of Methlysoyate/diesel Blend in a DDC 6V-92TA 

Engine: Optimization of NOx Emissions,” Report 93-E14-36 and 37, July 20, 1993. 

Peterson, C.L., Taberski, J.S., Thompson, J.C., Chase, C.L. (1999), “The Effect of Biodiesel 

Feedstock on Regulated Emissions in Chassis Dynamometer Tests of a Pickup Truck,” 

SAE-996135. 

Peterson, C.L., and Haines, H. (1999), “Truck-in-the-Park; Biodiesel Demonstration with 

Yellowstone National Park,” Final Technical Report, August, 1999. 

R-2 
Sharp, C.A. (1998), “Characterization of Biodiesel Exhaust Emissions for EPA 211 (b),” 

Prepared for the Biodiesel Board, Southwest Research Institute, April, 1998. 

Sharp, C. A. (1996), “Emissions and lubricity evaluation of rapeseed derived biodiesel fuels,” 

Final Report form Southwest Research Institute to Montana Department of 

Environmental Quality, November, 1996. 

Sharp, C. A. (1994), “Transient Emissions Testing of Biodiesel and Other Additives in a DDC 

Series 60 Engine,” Southwest Research Institute Report for National Biodiesel Board, 

December, 1994. 

Sharp, C.S., Mason, R., and Montavlco, D. (1990), “Study of Cetane Number and Aromatic 

Content Effects on Regulated Emissions from a Heavy-Duty Engine,” Southwest 

Research Institute Report No. 08-2940, CRC Contact VE-1, September, 1990. 

Smith, J.A., Endicott, D.L., and Graze, R.R. (1998), “Biodiesel Engine Performance and 

Emission Testing,” Caterpillar, Prepared for the Biodiesel Board, May, 1998. 

Starr, M.E. (1997), “Influence on Transient Emissions at Various Injection Timings, Using 

Cetane Improvers, Bio-Diesel, and Low Aromatic Fuels,” SAE-972904. 

Stolter, R. and Human, D. (1995), “Transient Emissions Evaluation of Biodiesel Fuel Blend in a 

1987 Cummins L-10 and DDC 6V-92-TA,” ETS report No. ETS-95-128 to National 

Biodiesel Board, November 30, 1995. 

TIUS (1988, 1993), “1992 Truck Inventory and Use Survey (TIUS) - Microdata File,” U.S. 

Department of Commerce, Economics and Statistics Administration, Bureau of the 

Census, 1988, 1993. 

Winsor, R.E. (1993), Fuels for Automotive and Industrial Diesel Engines, Institute for 

Mechanical Engineers Seminar, 1993, London, 1993. 

R-3 
Appendix A 

Data References 

A-1

Adept Group (1995), “Emissions and Engine Performance of Soya and Canola Methyl Esters 

with ARB#2 Diesel Fuels,” Report to Saskatchewan Canola Development Commission, 

Saskatoon, Saskatchewan, July, 1995. 

Durbin, T.D., Collins, J.R., Norbeck, J.M., and Smith, M.R., (1999) “Evaluation of the Effects of 

Alternative Diesel Fuel Formulations on Exhaust Emission Rates and Reactivity,” 

Prepared for the South Coast Air Quality Management District, Center for Environmental 

Research and Technology, University of California-Riverside, April, 1999. 

Fosseen Manufacturing and Development (1994a), “Emission Testing on Biodiesel and 

Biodiesel Blends,” Final Report No. NSDB4F15 to the National Soy Diesel Development 

Board, Jefferson, MO, July, 1994. 

Fosseen Manufacturing and Development (1994b), “DDC 6V-92 TA MUI Coach Upgrade 

Emission Optimization on 20%/80% Soy/Diesel Blend,” Report 260-2 and 241-1, 

September 30, 1994. 

Fosseen Manufacturing and Development (1995), “Optimize 20% Methylsoyate and Diesel 

Blend-1977 DDC 6V-71N Coach,” Report to National Biodiesel Board, Contract 219-2, 

January 10, 1995. 

Graboski, M.S., Ross, J.D., and McCormick, R.L. (1996), “Transient Emissions from No. 2 

Diesel and Biodiesel Blends in a DDC Series 60 Engine,” SAE-961166. 

Liotta, Jr., R and Mantalvo, D. (1993), SAE-932734, 1993. 

Manicom, B. Green, C., and Goetz, W. (1993), “Methyl Soyate Evaluation of Various Diesel 

Blends in a DDC 6V-92TA Engine,” Ortech Int., Final Report No 93-E14-21 to Fossen 

Manufacturing and Development, April 12, 1993. 

McCormick, R.L., Ross, J.D., and Graboski, M. (1997), Environmental Science and Technology, 

1997, 31, 1144. 

McCormick, R.L., Graboski, M.S., and Alleman, T.L. (1999), “Health Related Emissions from 

Biodiesel,” Draft Final Report, Prepared for the National Renewable Energy Laboratory, 

October, 18, 1999. 

Ortech International (1993), “Evaluation of Methlysoyate/diesel Blend in a DDC 6V-92TA 

Engine: Optimization of NOx Emissions,” Report 93-E14-36 and 37, July 20, 1993. 

Ortech Corp. (1995), “Operation of Cummins N14 Diesel Engine on Biodiesel: Performance, 

Emissions, and Durability,” Final Report for Phase I to National Biodiesel Board, Report 

No. 95 E11-B004524, Mississauga, Ontario, January, 19, 1995. 

Peterson, C.L., Taberski, J.S., Thompson, J.C., Chase, C.L., “The Effect of Biodiesel Feedstock 

on Regulated Emissions in Chassis Dynamometer Tests of a Pickup Truck,” SAE996135. 

A-2

Peterson, C.L., and Haines, H., "Truck-in-the-Park; Biodiesel Demonstration with Yellowstone 

National Park," Final Technical Report, August, 1999. 

Sharp, C.S., Mason, R., and Montavlco, D. (1990), “Study of Cetane Number and Aromatic 

Content Effects on Regulated Emissions from a Heavy-Duty Engine,” Southwest 

Research Institute Report No. 08-2940, CRC Contact VE-1, September, 1990. 

Sharp, C. A. (1994), “Transient Emissions Testing of Biodiesel and Other Additives in a DDC 

Series 60 Engine,” Southwest Research Institute Report for National Biodiesel Board, 

December, 1994. 

Sharp, C. A. (1996), “Emissions and lubricity evaluation of rapeseed derived biodiesel fuels,” 

Final Report form Southwest Research Institute to Montana Department of 

Environmental Quality, November, 1996. 

Sharp, C.A. (1998), “Characterization of Biodiesel Exhaust Emissions for EPA 211 (b),” 

Prepared for the Biodiesel Board, Southwest Research Institute, April, 1998. 

Smith, J.A., Endicott, D.L., and Graze, R.R. (1998), “Biodiesel Engine Performance and 

Emission Testing,” Caterpillar, Prepared for the Biodiesel Board, May, 1998. 

Starr, M.E. (1997), “Influence on Transient Emissions at Various Injection Timings, Using 

Cetane Improvers, Bio-Diesel, and Low Aromatic Fuels,” SAE-972904. 

Stolter, R. and Human, D. (1995), “Transient Emissions Evaluation of Biodiesel Fuel Blend in a 

1987 Cummins L-10 and DDC 6V-92-TA,” ETS report No. ETS-95-128 to National 

Biodiesel Board, November 30, 1995. 

Winsor, R.E. (1993), Fuels for Automotive and Industrial Diesel Engines, Institute for 

Mechanical Engineers Seminar, 1993, London, 1993. 

A-3

Appendix B 

Bag Hydrocarbon Chemical Composition 

(values in red were not explicitly measured) 

B-1

Sharp (1998) Durbin et al. (1999) 

Chemical Composition B100 2D B100 2D 

METHANE 2.3% 0.4% 0.4% 0.5% 1.3% 1.2% 

ETHANE 0.5% 0.1% 0.1% 0.2% 0.1% 0.1% 

ETHENE 37.3% 10.5% 9.4% 9.3% 6.0% 5.4% 

PROPANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

PROPENE 5.7% 2.4% 2.4% 2.3% 1.9% 1.9% 

ETHYNE 8.3% 2.2% 2.0% 1.9% 1.3% 1.3% 

PROPADIENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

BUTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-2-BUTENE 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 

1-BUTENE 2.3% 0.7% 0.7% 0.5% 0.2% 0.1% 

2-METHYLPROPENE 0.9% 0.6% 0.6% 0.4% 0.4% 0.4% 

2,2-DIMETHYLPROPANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

PROPYNE 0.3% 0.1% 0.2% 0.0% 0.0% 0.0% 

1,3-BUTADIENE 3.3% 1.0% 0.8% 0.9% 0.5% 0.5% 

2-METHYLPROPANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-BUTYNE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

METHANOL 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-2-BUTENE 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 

3-METHYL-1-BUTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

ETHANOL 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYLBUTANE 0.2% 0.1% 0.1% 0.0% 0.0% 0.0% 

2-BUTYNE 0.0% 0.0% 0.1% 0.2% 0.3% 0.1% 

1-PENTENE 0.7% 0.0% 0.3% 0.2% 0.2% 0.2% 

2-METHYL-1-BUTENE 0.3% 0.1% 0.1% 0.1% 0.0% 0.0% 

PENTANE 0.1% 0.2% 0.1% 0.0% 0.0% 0.0% 

UNIDENTIFIED C5 OLEFINS 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 

2-METHYL-1,3-BUTADIENE 0.2% 0.0% 0.1% 0.1% 0.1% 0.0% 

T-2-PENTENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 

3,3-DIMETHYL-1-BUTENE 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 

C-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYL-2-BUTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-BUTANOL 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

CYCLOPENTADIENE 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 

2,2-DIMETHYLBUTANE 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 

CYCLOPENTENE 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 

4-METHYL-1-PENTENE 0.0% 0.0% 0.1% 0.1% 0.1% 0.0% 

3-METHYL-1-PENTENE 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 

CYCLOPENTANE 0.0% 0.0% 0.0% 0.2% 0.1% 0.1% 

2,3-DIMETHYLBUTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Methyl-t-Butyl-Ether 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

4-METHYL-C-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYLPENTANE 0.1% 0.1% 0.2% 0.1% 0.0% 0.1% 

4-METHYL-T-2-PENTENE 0.0% 0.0% 0.0% 0.1% 0.2% 0.1% 

3-METHYLPENTANE 0.2% 0.1% 0.1% 0.0% 0.1% 0.1% 

2-METHYL-1-PENTENE 0.5% 0.2% 0.2% 0.0% 0.0% 0.0% 

1-HEXENE 0.5% 0.2% 0.2% 0.4% 0.1% 0.1% 

HEXANE 0.7% 0.1% 0.0% 0.1% 0.1% 0.1% 

UNIDENTIFIED C6 OLEFINS 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 

T-3-HEXENE 0.0% 0.0% 0.0% 0.6% 0.0% 0.0% 

B20 B20 

B-2

Sharp (1998) Durbin et al. (1999) 

Chemical Composition B100 2D B100 2D 

C-3-HEXENE 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 

DI-ISOPROPYL ETHER 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-2-HEXENE 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 

3-METHYL-T-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYL-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-METHYLCYCLOPENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-2-HEXENE 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 

Ethyl-t-Butyl-Ether 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-METHYL-C-2-PENTENE 0.0% 0.0% 0.0% 0.2% 0.1% 0.1% 

2,2-DIMETHYLPENTANE 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 

METHYLCYCLOPENTANE 0.3% 0.0% 0.0% 0.1% 0.0% 0.0% 

2,4-DIMETHYLPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2,3-TRIMETHYLBUTANE 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 

3,4-DIMETHYL-1-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-METHYLCYCLOPENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

BENZENE 5.2% 1.1% 0.9% 1.1% 0.4% 0.3% 

3-METHYL-1-HEXENE 0.0% 0.0% 0.0% 0.1% 0.4% 0.3% 

3,3-DIMETHYLPENTANE 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 

CYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,3-DIMETHYLPENTANE 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 

1,1-DIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

TERT-AMYL METHYL ETHER 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

CYCLOHEXENE 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 

3-METHYLHEXANE 0.0% 0.0% 0.0% 0.2% 0.1% 0.1% 

C-1,3-DIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-ETHYLPENTANE 0.6% 0.4% 0.0% 0.0% 0.0% 0.0% 

T-1,2-DIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

c-1,3-Dimethylcyclopentane 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 

1-HEPTENE 0.0% 0.0% 0.0% 0.2% 0.0% 0.0% 

2,2,4-TRIMETHYLPENTANE 0.6% 0.3% 0.2% 0.1% 0.2% 0.1% 

2-METHYL-1-HEXENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-3-HEPTENE 0.0% 0.0% 0.0% 0.3% 0.1% 0.0% 

HEPTANE 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 

C-3-HEPTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

UNIDENTIFIED C7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYL-2-HEXENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-METHYL-T-3-HEXENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-2-HEPTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-ETHYL-C-2-PENTENE 0.5% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4,4-TRIMETHYL-1-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,3-DIMETHYL-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-2-HEPTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

METHYLCYCLOHEXANE 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 

C-1,2-DIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2-DIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1,1,3-TRIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4,4-TRIMETHYL-2-PENTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2,3-TRIMETHYLPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,5-DIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

B20 B20 

B-3

Sharp (1998) Durbin et al. (1999) 

Chemical Composition B100 2D B100 2D 

ETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4-DIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-T-2-C-4-TRIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,3-DIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-T-2-C-3-TRIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,3,4-TRIMETHYLPENTANE 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 

2,3,3-TRIMETHYLPENTANE 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 

TOLUENE 2.1% 1.0% 0.5% 0.3% 0.3% 0.3% 

2,3-DIMETHYLHEXANE 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 

1,1,2-TRIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,4-DIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

4-METHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-METHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1C,2t,3-TRIMETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.3% 0.1% 0.0% 

C-1,3-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-1,4-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3-ETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2,5-TRIMETHYLHEXANE 0.4% 0.0% 0.1% 0.0% 0.0% 0.0% 

T-1-METHYL-3-ETHYLCYCLOPENTANE 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-1-METHYL-3-ETHYLCYCLOPENTANE 0.0% 0.2% 0.2% 0.0% 0.0% 0.0% 

1,1-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

TRANS-1-METHYL-2-ETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-METHYL-1-ETHYL-CYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4,4-TRIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2,4-TRIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

TRANS-1,2-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-OCTENE 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 

T-4-OCTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

OCTANE 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 

UNIDENTIFIED C8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-2-OCTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

T-1,3-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-2-OCTENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

ISOPROPYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,3,5-TRIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-1-METHYL-2-ETHYLCYCLOPENTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

4,4-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-1,2-DIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

ETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,6-DIMETHYLHEPTANE 0.0% 0.2% 0.2% 0.0% 0.0% 0.0% 

1,1,3-TRIMETHYLCYCLOHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,5-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,3-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,5-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

ETHYLBENZENE 0.1% 0.3% 0.3% 0.0% 0.0% 0.0% 

2,3,4-TRIMETHYLHEXANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,3-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

B20 B20 

B-4

Sharp (1998) Durbin et al. (1999) 

Chemical Composition B100 2D B100 2D 

m-& p-XYLENE 0.3% 0.3% 0.4% 0.0% 0.0% 0.0% 

4-METHYLOCTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,4-DIMETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

4-ETHYLHEPTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2-METHYLOCTANE 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 

3-METHYLOCTANE 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 

STYRENE 0.0% 0.0% 0.0% 0.2% 0.1% 0.1% o-XYLENE 0.0% 0.2% 0.2% 0.0% 0.1% 0.1% 

1-NONENE 0.0% 0.1% 0.0% 0.1% 0.1% 0.0% 

T-3-NONENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

C-3-NONENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

NONANE 0.0% 0.2% 0.2% 0.0% 0.0% 0.0% 

T-2-NONENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

I-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,2-DIMETHYLOCTANE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

2,4-DIMETHYLOCTANE 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% n-PROPYLBENZENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 

1-METHYL-3-ETHYLBENZENE 0.0% 0.0% 0.1% 0.1% 0.3% 0.2% 

1-METHYL-4-ETHYLBENZENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 

1,3,5-TRIMETHYLBENZENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 

1-METHYL-2-ETHYLBENZENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 

1,2,4-TRIMETHYLBENZENE 0.0% 0.1% 0.1% 0.1% 0.4% 0.4% 

T-BUTYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-DECENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

DECANE 0.0% 0.1% 0.1% 0.0% 0.1% 0.1% 

I-BUTYLBENZENE 0.0% 0.1% 0.1% 0.1% 1.3% 1.2% 

1,3,-DIMETHYL-5-ETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% s-BUTYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-METHYL-3-I-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 

1,2,3-TRIMETHYLBENZENE 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 

1-METHYL-4-I-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

INDAN 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-METHYL-2-I-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1,3-DIETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 

1,4-DIETHYLBENZENE 0.0% 0.1% 0.0% 0.0% 0.3% 0.3% 

1-METHYL-3-N-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1-METHYL-4-N-PROPYLBENZENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 

1,2-DIETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 

1-METHYL-2-N-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 

1,4-DIMETHYL-2-ETHYLBENZENE 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 

1,3-DIMETHYL-4-ETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1,2-DIMETHYL-4-ETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.3% 0.2% 

1,3-DIMETHYL-2-ETHYLBENZENE 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 

UNDECANE 0.0% 0.7% 0.6% 0.1% 0.1% 0.1% 

1,2-DIMETHYL-3-ETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 

1,2,4,5-TETRAMETHYLBENZENE 0.0% 0.0% 0.2% 0.8% 0.4% 0.5% 

2-METHYL-BUTYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

3,4 DIMETHYL-I-PROPYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1,2,3,5-TETRAMETHYLBENZENE 0.0% 0.4% 0.1% 0.0% 0.0% 0.0% 

T-1-BUTYL-2-METHYLBENZENE 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 

B20 B20 

B-5

Sharp (1998) Durbin et al. (1999) 

Chemical Composition B100 2D B100 2D 

1,2,3,4-TETRAMETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

N-PENT-BENZENE 0.0% 0.1% 0.2% 0.0% 0.0% 0.0% 

T-1-BUTYL-3,5-DIMETHYLBENZENE 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 

T-1-BUTYL-4-ETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

NAPHTHALENE 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 

DODECANE 0.0% 0.5% 0.4% 0.2% 0.1% 0.1% 

1,3,5-TRIETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

1,2,4-TRIETHYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

HEXYLBENZENE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

UNIDENTIFIED C9-C12+ 0.0% 0.9% 1.2% 0.0% 0.0% 0.0% 

Unknown (C1-C4) 0.0% 0.0% 0.0% 0.6% 0.8% 1.0% 

Unknown (C4-C12) 0.0% 0.0% 0.0% 3.9% 4.1% 4.6% 

FORMALDEHYDE 43.0% 13.8% 12.5% 9.6% 7.0% 7.0% 

ACETALDEHYDE 15.8% 5.6% 5.1% 3.8% 3.7% 2.8% 

ACROLEIN 8.7% 1.8% 2.0% 0.9% 1.5% 1.1% 

ACETONE 2.7% 1.5% 1.6% 0.5% 0.3% 0.2% 

METHACROLEIN 0.0% 0.0% 0.0% 0.2% 0.3% 0.2% 

PROPIONALDEHYDE 4.2% 2.0% 1.3% 0.8% 0.6% 0.5% 

CROTONALDEHYDE 3.8% 1.5% 1.4% 0.3% 0.2% 0.2% 

ISOBUTYRALDEHYDE, NOTE H 1.3% 0.4% 0.4% 0.0% 0.0% 0.0% 

METHYL ETHYL KETONE, NOTE H 1.3% 0.4% 0.4% 0.3% 0.1% 0.1% 

BENZALDEHYDE 1.6% 0.4% 0.5% 0.4% 0.6% 0.4% 

ISOVALERALDEHYDE 2.3% 0.4% 0.3% 0.0% 0.0% 0.0% 

VALERALDEHYDE 1.1% 0.4% 0.4% 0.4% 0.2% 0.3% 

O-TOLUALDEHYDE 1.4% 0.3% 0.2% 0.0% 0.0% 0.0% 

M/P-TOLUALDEHYDE 3.4% 1.1% 1.3% 0.1% 0.1% 0.2% 

HEXANALDEHYDE 1.8% 0.3% 0.2% 0.8% 0.3% 0.2% 

DIMETHYLBENZALDEHYDE 0.2% 0.3% 0.3% 0.0% 0.0% 0.0% 

B20 B20 

B-6

REPORT DOCUMENTATION PAGE Form Approved 

OMB NO. 0704-0188 

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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

May 2003 

3. REPORT TYPE AND DATES COVERED 

Subcontract Report 

September 1999—January 2003 

4. TITLE AND SUBTITLE 

Impact of Biodiesel Fuels on Air Quality and Human Health: Task 1 Report, Incorporate 

Biodiesel Data into Vehicle Emissions Databases for Modeling 

5. FUNDING NUMBERS 

AXE-9-29079-01 

6. AUTHOR(S) 

C. Lindhjem, A. Pollack 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

ENVIRON International Corporation 

101 Rowland Way 

Novato, CA 94945 

8. PERFORMING ORGANIZATION 

REPORT NUMBER 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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AGENCY REPORT NUMBER 

NREL/SR-540-33794 

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NREL Technical Monitor: K.S. Tyson and R. McCormick 

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13. ABSTRACT (Maximum 200 words) 

This document is the Task 1 report for the NREL “Impact of Biodiesel Fuels on Air Quality and Human Health” study. This 

report provides a discussion and analysis of the available biodiesel test data, and makes recommendations for how biodiesel 

effects on pollutant mass emissions as well as chemical composition should be incorporated into emission inventories for use in 

air quality modeling. 

14. SUBJECT TERMS 

biodiesel; air quality; modeling; emissions 

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