1

Chapter 3: Impacts of the Program on Non-GHG Pollutants 

In addition to the GHG impacts laid out in Chapter 2, we project that the increased use of 

renewable fuels required by RFS2 will affect emissions of “criteria” pollutants (those pollutants 

for which a National Ambient Air Quality Standard has been established), criteria pollutant 

precursors, and air toxics. Changes in these emissions would derive from the direct effect of 

renewable fuels on the tailpipe and evaporative emissions of vehicles and off-road equipment; 

and increased renewable fuel production and distribution including the effect of decreases in the 

production and distribution of gasoline and diesel displaced by renewable fuel. For this analysis 

we have focused on estimating the change in mass emissions for these pollutants across the 

entire U.S. in 2022, when the program is fully implemented, and we have also conducted a fullscale air quality modeling and health impact assessment that accounts for geographic differences 

in impacts at the county level. This chapter presents national emission impacts for nitrogen 

oxides (NOx), volatile organic compounds (VOC), carbon monoxide (CO), particulate matter 10 

microns in diameter and less (PM10), particulate matter 2.5 microns in diameter and less 

(PM2.5), sulfur dioxide (SO2), ammonia (NH3), benzene, 1,3-butadiene, acrolein, formaldehyde, 

acetaldehyde, naphthalene, and ethanol, including the methodology for developing these 

estimates. Section 3.3 discusses the differences between the final rule emission inventories 

presented in Sections 3.1 and 3.2 compared to the inventories that were used for air quality 

modeling. Section 3.4 of this chapter presents the methodology and results of air quality 

modeling, and Sections 3.5 and 3.6 address health and environmental impacts of today's rule. 

3.1 Methodology for Calculating Non-GHG Emission Impacts 

 Our analysis focused on the projected impact of the renewable fuel volumes required in 

2022, the first year the RFS2 program is fully implemented. The emission impacts of the 2022 

RFS2 volumes are quantified in Section 3.2 for a range of renewable fuel scenarios relative to 

two reference cases discussed in detail above in Section 1.2.1. In order to allow assessment of 

total emission impacts of mandated renewable fuel volumes, the main reference case presented in 

this analysis was the RFS1 mandate volume of 7.5 billion gallons of renewable fuel (6.7 billion 

gallons ethanol). We are also presenting impacts relative to the 13.6 billion gallons of 

renewable fuels projected by the Department of Energy (DOE) Annual Energy Outlook (AEO) 

2007 to show the impact of the RFS2 renewable fuel volumes incremental to the projected 

renewable market pre-EISA. 

Our analysis of non-GHG emissions impacts was comprised of a) an analysis of direct 

impacts on motor vehicles, off-road equipment and other sources from burning (or evaporating) 

renewable fuels in place of petroleum-based fuels; and b) the emissions impacts from the 

production and distribution of renewable fuels. These analyses are discussed separately in 

Sections 3.1.1 and 3.1.2. 

3.1.1 Impact on Non-GHG Emissions from Motor Vehicles and Equipment 
 2

The volumes of renewable fuel called for in today's rule will directly affect emissions 

from most mobile source categories, and for this analysis we have quantified the effects on 

exhaust and evaporative emissions of gasoline-fueled vehicles and equipment including 

passenger cars, light trucks, heavy trucks, motorcycles and off-road sources such as lawn 

mowers, recreational boats and all-terrain vehicles. We have also estimated the impact of 

ethanol on emissions from portable fuel containers, and increased refueling emissions due to 

higher volatility of ethanol-blended fuel and increased refueling events due to lower energy 

content of biofuels. The emissions impacts of biodiesel were also estimated on heavy-duty 

diesel vehicles, assuming additional biodiesel would be burned by on-road sources only. 

 A considerable source of uncertainty in estimating the emission impacts of renewable 

fuels is the effect ethanol blends will have on emissions of cars and light trucks. Under today’s 

action every gasoline vehicle and piece of equipment would be fueled on at least E10. For the 

proposal, the uncertainty in the emission impacts of E10 was reflected by showing emission 

impacts under two cases representing different levels of sensitivity in the emissions of cars and 

light trucks to ethanol. In the final rule, we are reflecting preliminary results from work 

sponsored by EPA and DOE which suggests that emissions from Tier 2 vehicles show little 

sensitivity to E10.1

 In addition to E10, many flexible-fueled passenger vehicles will need to be 

operated on E85 to consume the increased volumes of renewable fuels. The amount of E85 

needed will be dependent in large part on the volume of ethanol as opposed to other renewable 

fuels utilized in the future. Data on E85 continues to be limited, and emission results have 

shown large variability of emission effects in some pollutants. As a result, for the final rule we 

have decided to assign no emission effect to the use of E85, except for the emissions of 

acetaldehyde and ethanol. 

For the analysis of all gasoline-fueled highway vehicles except motorcycles and all 

diesel-fueled highway vehicles, a draft version of MOVES (MOVES2009) was used to generate 

national inventories for the control and reference cases modeled for the RFS2 final rule. We 

decided to use a draft version of MOVES for this analysis to begin to reflect significant updates 

in emissions from MOBILE6. As the other mobile source categories in MOVES were still 

under development at the time of this analysis, onroad motorcycles and all off-road equipment 

emissions were calculated with the National Mobile Inventory Model (NMIM), a platform which 

generates emission inventories based on EPA’s MOBILE6 and NONROAD models. EPA has 

now released a final version of MOVES, but that was not available in time for the analyses 

conducted for this rule. 

The development of vehicle and equipment emission impact estimates for today’s rule 

required: a) developing fuel supply inputs at the county level for the 2005 base year and 2022 

reference and control cases which accounted for the projected change in fuel properties due to 

today’s action; b) developing individual vehicle fuel effects; and c) running MOVES and NMIM 

to produce raw inventory estimates and post-processing these results as needed to account for 

different baselines, to apply “off model” corrections, or to estimate impacts not accounted for in 

the models. Each of these steps are detailed in the following sections 

3.1.1.1 Fuel Inputs 
3

 As inputs to our emissions modeling, we developed a detailed profile of fuels for each 

modeling case. We prepared county-level databases of fuel properties and fuel market shares for 

the 2005 base case, the RFS1 reference case, the 2022 AEO reference case, and the 2022 control 

case. These county-level databases were applied in both NMIM and MOVES for consistency in 

fuel inputs across the different mobile source categories. 

The 2005 base case fuel properties were derived from 2005 historical data. These data 

included national summer and winter fuels surveys, studies that tracked the total amount of 

ethanol produced for use in gasoline each year, and Reformulated Gasoline (RFG) surveys. 

Additional data were available on the fuel properties of all gasoline produced and imported 

annually by refiners, and on the distribution of gasoline to and from Petroleum Administration 

for Defense Districts (PADDs). Where survey data was available, it was used to determine a 

county's fuel properties for summer and winter. Where survey data was not available, fuel 

properties were set to equal the average fuel properties in that PADD. Special adjustments were 

made to some counties to account for local gasoline volatility control programs and winter 

oxygenated gasoline programs. 

For the 2022 reference and control cases, the 2005 base case fuel properties were 

adjusted to account for implementation of other fuel regulations and to account for increased 

ethanol use. There is a greater percentage of ethanol in both the 2022 RFS1 and AEO 2007 

reference cases than in the 2005 base case because methyl tert-butyl ether (MTBE) has been 

replaced with ethanol and because of increased ethanol usage mandated by RFS1 (the RFS1 

reference case), and AEO-projected growth in ethanol production for 2022 (the AEO 2007 

reference case). For this analysis, ethanol was allocated to the state and county level based on 

the economics of distribution and blending, as well as other factors (refer to Section 1.7.1 of this 

document for details). The 2022 control cases model three different approaches to meeting the 

renewable fuel volume requirements of EISA. Even in the low ethanol control case (17.5 billion 

gallons of ethanol), there would be enough ethanol in the fuel supply to require use of at least 10 

percent ethanol (E10) in every county, while the choice of counties modeled with E85 was based 

on the economics and other factors. 

Future fuel properties in both the reference and control cases were adjusted to account for 

widespread increases in ethanol. This was done using two assumptions: 1) ethanol has 

historically been splash blended in conventional gasoline (CG), and 2) it will be match-blended 

by 2022 (i.e., the changes associated with ethanol addition will be accounted for by refiners 

when producing the base gasoline). We believe this is reasonable given that there will be a large 

(and thus more geographically predictable) volume of ethanol used in gasoline, and that certain 

property changes that take place when ethanol is blended (such as octane increase) could be 

economically beneficial to refiners if they can be assumed when producing the base gasoline. 

Thus, we adjusted aromatics, olefins, T50, and T90 fuel parameters by first backing out the 

effects of any existing oxygenate (by reverse dilution), and then re-adjusting the properties for 

ethanol blends based on refinery modeling done for the RFS1 rulemaking that projected how 

gasoline properties were likely to change given widespread use of ethanol. Table 3.1-1 shows 

the adjustment factors used per volume percent ethanol blended. Reid Vapor Pressure (RVP) 

was increased 1.0 psi wherever ethanol was present in conventional gasoline unless there was a 

local volatility control. 
 4

Table 3.1-1. 2022 Adjustments for Ethanol Added to Conventional Gasoline 

Additive change per vol% 

ethanol added 

Aromatics 

(vol%) 

Olefins 

(vol%) 

E200 

(vol%) 

E300 

(vol%) 

Summer -0.69 0.00 1.10 7.52 

Winter -0.68 0.00 0.78 7.21 

For example, the adjusted summer aromatics value would be calculated as follows: 

Current aromatics value – dilution effect of current ethanol level due to splash blending 

+ (new volume percent ethanol x -0.69 for match blend effect) 

For Reformulated Gasoline (RFG) areas, refiners already account for the blending of 

ethanol when producing the base gasoline, and therefore the properties are not predicted to 

change in the same ways as for conventional gasoline (CG). We used refinery modeling results 

for each PADD (produced using the same cases and renewable fuel volumes as described above 

for CG) to project the properties of fuel in RFG areas. RFG properties used in the reference and 

control cases in 2022 are shown here in Table 3.1-2. The 2022 reference and control cases also 

incorporate reductions in gasoline sulfur resulting from Tier 2 regulations. Fuel benzene levels 

presented in this table have been updated from what was used in the NPRM and air quality 

modeling to reflect the 2007 mobile source air toxics (MSAT) rule, which mandates a 0.62% fuel 

benzene standard.2

 

Table 3.1-2. 2022 Reformulated Gasoline Properties by PADD

PADD a RVP 

(psi) 

Aromatics 

(vol%) 

Benzene 

(vol%) 

Olefins (vol%) E200 E300 

Summer 

1 7.0 19.9 0.54 8.1 52 95 

2 7.0 18.8 0.60 6.8 52 95 

3 7.0 18.4 0.55 5.6 51 95 

5 6.8 21.5 0.62 5.7 54 86 

Winter 

1 13.2 19.9 0.54 14.1 58 95 

2 13.1 20.0 0.60 11.9 62 95 

3 11.8 19.8 0.55 13.0 55 95 

5 11.4 21.9 0.62 5.7 60 86 a

 There are no RFG areas in PADD 4. 

Unlike the proposal, for the final rule we did not model the effects of flexible-fueled 

vehicles running on E85 for any pollutants except acetaldehyde and ethanol. 

For each of the modeled scenarios, fuel information was input into an NMIM database 

and used for NMIM runs. For MOVES runs, the NMIM databases were converted into MOVES 

databases using a conversion program. To reduce time needed for MOVES runs, we reduced the 
5

size of the MOVES fuel database by processing the database with a "binner" program that 

grouped fuels with similar properties and assigned each group to a single fuel formulation 

identification number and a single set of fuel properties. A significant update to MOVES 

between the proposal and final rulemaking was the inclusion of direct calculation of fuel 

adjustments that allowed less aggregation in this binning approach, thus improving the resolution 

of fuel-based emission impacts. 

3.1.1.2 Effect of Fuels on Non-GHG Emissions from Vehicles & Equipment 

The average effect of renewable fuels on an individual vehicle/equipment basis, based on 

available research, is the foundation of the emission impact assessment. This section contains 

discussion of the effects used in the emission impact assessment for E10 on gasoline vehicles 

and equipment, for E85 on gasoline vehicles, and for biodiesel. 

3.1.1.2.1 On-road Gasoline Vehicle E10 Effects 

3.1.1.2.1.1 Exhaust Emissions 

Ethanol blends can affect exhaust emissions from vehicles and off-road equipment. A 

comprehensive analysis of E10 impacts on exhaust emissions was undertaken for the RFS1 rule, 

as documented in Chapter 3 of the RFS1 Regulatory Impact Analysis.3

 This analysis considered 

previous EPA work in coming up with a so-called “Predictive Model” to assess California’s 

request for an oxygenate waiver in 2000, as well as test data from several test programs 

conducted by the auto trade associations (AAM/AIAM), ExxonMobil, Toyota, and the Mexican 

Petroleum Institute. This assessment concluded that for Tier 1 and later vehicles (nominally 

model year 1996 and later, comprising the majority of the fleet in 2022) there was not enough 

consistency across these studies to confidently predict the impact of oxygenated fuel on exhaust 

HC and NOx emissions. As a result the RFS1 analysis carried forward two sets of fuel effects: a 

“primary” analysis assuming no effect of oxygen on non-methane hydrocarbon (NMHC) and 

NOx emissions from Tier 1 and later vehicles, and a “sensitivity” analysis which applied EPA’s 

Predictive Model effects to Tier 1 and later vehicles. For the RFS2 proposal we characterized 

ethanol effect scenarios: “less sensitive” based on the “primary” case used in RFS1, and “more 

sensitive” based on the RFS1 “sensitivity” case. 

We are now nearing completion of a large scale testing effort aimed at quantifying the 

effects on exhaust and evaporative emissions from Tier 2 vehicles of ethanol and several other 

fuel properties impacted by the blending of ethanol into gasoline.4

 Based on analysis of 

preliminary data from this test program, we are carrying forward effects that more closely reflect 

the "less sensitive" case, which does not apply any E10 effects to NOx or HC emissions for later 

model year vehicles, or E85 effects for most pollutants. While the effects of E10 on individual 

vehicles will vary depending on properties of the fuel (e.g., RVP, distillation, and aromatic 

content), Table 3.1-3 demonstrates the effects used for conventional and reformulated gasoline 

based on the fuel properties derived from Tables 3.1-1 and 3.1-2. For the “less sensitive” case 

for the proposal, the effects shown for NOx, HC and toxics were applied to only Tier 0 vehicles 

(mid 1990’s and older); in our analysis for this final rule, we extended these effects to Tier 1 and 

NLEV cars and light trucks (through the 2003 model year) based on a recently published study 
 6

from CRC.5 However, our preliminary analysis of the EPA/DOE test program did not justify 

attributing these effects to Tier 2 vehicles.

Table 3.1-3. Exhaust Effect of E10 Relative to E0 for Pre-Tier 2 Vehicles a

Pollutant Source CG RFG 

Exhaust HC (VOC) -7.4% -9.7% 

NOx 

EPA Predictive 

Models 7.7% 7.3% 

COb MOBILE6.2 -11% / -19% -36% 

Exhaust Benzene -24.9% -38.9% 

Formaldehyde 6.7% 2.3% 

Acetaldehyde 156.8% 173.7% 

1,3-Butadiene 

EPA Predictive 

and Complex 

Models 

-13.2% 6.1% a

Assumes summer (July) conditions 

b

The first figure shown applies to normal emitters; the second applies to high emitters. 

 

3.1.1.2.1.2 Evaporative Emissions 

While E10 affects evaporative emissions from gasoline vehicles due to the increased 

volatility of E10 blends, the increased permeation of fuel vapors through tanks and hoses, and 

the increased vapor emissions due to the lower molecular weight of E10, for cars and light trucks 

by far the largest of these effects is permeation. For the final rule, we estimated only the impact 

of permeation using updated estimates in the draft MOVES model, which separates permeation 

emissions from vapor venting emissions to allow better accounting for this effect. 

For the proposal, permeation effects were developed from Coordinating Research 

Council's (CRC) E-65 program6, which measured evaporative emissions from ten fuel systems 

that were removed from the vehicles on E0 and E5.7 fuels; fuel systems were removed to ensure 

that all evaporative emissions measured were from permeation of the fuel through the different 

components of the fuel system. For that analysis, we estimated the effect by calculating the 

percent increase in average emissions from all vehicles between E0 and E5.7 fuels over the 65 to 

105 degree Fahrenheit diurnal test. That value was 46 percent. In order to estimate the effect at 

E10 we simply multiplied this result by 1.75 (10/5.7), resulting in a 79 percent increase applied 

to cars and light trucks from all model years. That approach heavily weighted the emission 

contribution of older vehicles in the test program, and, in conjunction with lower emission rates 

for vehicles certified to Enhanced Evaporative and later standards in MOVES, served to 

underestimate the impact of E10 on permeation from newer vehicles. 

 The version of MOVES used for the FRM analysis significantly updates the permeation 

estimate used in the NPRM, particularly for newer technology vehicles, based on data collected 

by CRC as part of their E-77 series of evaporative emissions programs. This new data allowed 

us to make a distinction between the relative impact of E10 on vehicles certified to the enhanced 

evaporative and later standards, vs. older technology vehicles. The data showed a significant 

change in the relative impact of E10, from a 65 percent increase for pre-enhanced vehicles, to a 

213 percent increase for newer technology vehicles.7 

 

3.1.1.2.2 On-road Gasoline vehicle E85 effects 
7

In the proposal, the “more sensitive” case included impacts of E85 on several pollutants, 

based on analysis of limited data from EPA and Environment Canada. For the final rule we have 

decided not to apply these effects to the potential increase in E85 use, with the exception of 

acetaldehyde and ethanol. The rationale for this is the large range of uncertainty imposed by the 

limited nature of the dataset. EPA expects more data to become available to help assess this 

issue for future analyses, as CRC, EPA and DOE are all engaged in programs that will expand 

this dataset. We are including the discussion of E85 data considered in this RIA to provide 

documentation of available data; no new information is presented here relative to the proposal, as 

no new data has been generated in the interim. The only difference between proposal and final is 

that, of the effects determine in this analysis, only acetaldehyde and ethanol were included in the 

final rule inventories. 

For this analysis we identified three recent data sources that investigate the effects of E85 

on current technology (i.e. Tier 2 and similar) vehiclesA.

8, 9, 10 Two of these sources are test 

programs conducted by Southwest Research Institute and Environment Canada, and the third is 

EPA certification data. This section briefly describes each data source and highlights the key 

findings, and explains how these data were used to generate E85 effects. 

In 2006, Southwest Research Institute (SwRI) conducted a study for EPA on three model 

year 2005 Tier 2 FFVs (bins 5 and 8) operating on several gasoline and ethanol blends. This 

study was primarily focused on the impacts of fuel ethanol content and reduced ambient test 

temperature (tests were conducted at 75oF and 20oF) on VOC and PM emissions. Multiple fuel 

blends were evaluated in this program, although for this analysis we will focus only on E0 and 

E85 emissions at 75o F. At this test temperature, Tier 2 certification fuel was used as the nonoxygenated test fuel (E0) as well as the base gasoline for the splash-blended E10 and E85 fuels. 

Additionally, EPA certification “cold CO” wintertime gasoline was used for reduced ambient 

temperature (20 oF) testing – used alone (E0) and as the base fuel for wintertime E10 and E70 

blends. This base gasoline has a higher RVP than its summertime equivalent, which is necessary 

to ensure proper fuel vaporization at lower ambient temperatures. Repeat tests were conducted 

for the 20oF tests on the winter fuel blends, but no repeats were run for 75oF testing. In addition 

to the regulated pollutants, SwRI measured CO2, CH4, benzene, 1,3-butadiene, naphthalene, 

acetaldehyde, acrolein, and ethanol. This study saw reductions in PM 2.5, benzene, and 1,3-

butadiene of 55% - 70% with E85 relative to E0. HC emissions increased while NOx and CO 

decreased. Emissions of methane, formaldehyde, and acetaldehyde were found to increase 

significantly with E85 use. Table 3.1-6 summarizes the average percent change in emissions 

with E85 vs. E0. This table also compares the findings of this dataset with the other two 

programs described below. 

Environment Canada released a report in 2005 in which an NLEV and an interim nonTier 2 vehicle were tested on Tier 2 certification fuel and a commercially available E85 blend. 

Repeat tests were conducted in this study so that each vehicle was tested three times on each 

fuel. The pollutants measured include NMOG, NMHC, CO, NOx, CO2, CH4, N20, benzene, 

 

A EPA is aware of several test programs, either planned or underway, by CRC and others that may provide 

additional test data for future fuel effects modeling and rulemaking support. 
 8

1,3-butadiene, acetaldehyde, formaldehyde, acrolein, and ethanol, among others. The results, 

summarized in Table 3.1-6 showed statistically significant reductions in CO and NOx (-48% 

and -40%, respectively) when switching from E0 to E85. E85 caused non-methane organic gases 

(NMOG) emissions to increase in one vehicle and decrease in the other. Toxics reductions were 

of a similar order of magnitude as the vehicles tested in the SwRI study discussed above 

 EPA’s Certification and Fuel Economy Information System (CFEIS) database was 

accessed to identify data from five model year 2006 Tier 2 vehicles (bins 5, 8, and 9) tested on 

both E85 and Tier 2 certification gasoline. The E85 blend tested here was 85% denatured 

ethanol splash blended with 15% Tier 2 certification gasoline. Each vehicle was only tested once 

on each fuel. Weighted FTP results were reported for the regulated pollutants (except PM) as 

well as CO2, acetaldehyde, and ethanol (formaldehyde, acetaldehyde, and ethanol were only 

measured for tests where E85 was used; therefore these are expressed as fractions of NMOG 

here). This data indicates that E85 causes a slight increase in NMOG emissions, a slight 

decrease in NOx and CO2, and significant reductions in CO. The average percent change in 

each pollutant for these vehicles when operated on E85 is shown in Table 3.1-5, below. 

Table 3.1-5. 

Effect of E85 on LEV and Later Per-mile Exhaust Emissions Relative to 

Conventional Gasoline: Percent change separated by data source 

 EPA – CFEIS EPA - SwRI Env. Canada 

NMOG 10% 87% 5% 

CO -34% -15% -48% 

NOx -3% -42% -40% 

Benzene NA -61% -65% 

1,3 Butadiene NA -66% -74% 

Acetaldehyde 12% of NMOG 5600% 3121% 

Formaldehyde 2% of NMOG 116% 98% 

Acrolein 

(E85 mg/mile 

emissions) NA 0.023 0.010 

Unburned Ethanol 

(E85 mg/mile 

emissions)

28.3 (55% of 

NMOG) 

25.4 (33% of 

NMOG) 

34.6 (48% of 

NMOG) 

PM 2.5 NA -68% NA 

Viewed independently, each study provides only limited insight on the effects of E85 on 

emissions relative to E0. Table 3.1-5 shows that while changes in some pollutants compare 

reasonably well between studies, others can vary widely. This makes it difficult to determine 

quantitative trends in emissions, since calculating an average percent change in emissions across 

all three studies does nothing to address the variability of the test data. Without this assessment 

of variability there is no way to estimate the statistical significance of the reported values. Only 

the Environment Canada conducted the repeat tests necessary to assess the test-to-test variability 

of a given vehicle, and none of the studies tested enough vehicles to confidently state that their 
9

findings can be applied to the Tier 2 FFV fleet as a whole. This clearly illustrates the need for 

additional testing in this area. 

The fact that Environment Canada tested non-Tier 2 vehicles is noteworthy. The 2004 

Chrysler Sebring was an interim non-Tier 2 bin 8 vehicle. Despite its name, however, the 

standard is equivalent to the final Tier 2 bin 8 FTP standards in all areas but the full useful life 

(120K miles vs. 100K miles). In fact this vehicle was cleaner than required by the standard, with 

observed emissions on E0 at the level of a Tier 2 bin 7 vehicle. The second vehicle tested by 

Environment Canada was a 2002 Dodge Caravan certified to the NLEV LEV LDT level. The 

standards at this certification level are considerably more relaxed than Tier 2 levels for some 

pollutants but not others. While these vehicles share the same NMOG certification standard 

(0.100 g/mi), the CO standard is roughly 30% higher and the NOx standard nearly 4 times higher 

than the Tier 2 bin 8 level. As a result of this difference in standards, the Caravan emitted about 

20% more CO and 2.5 times more NOx than the Sebring. NMOG emissions were nearly the 

same for both vehicles with non-oxygenated gasoline. On a relative scale, both vehicles 

experienced similar percent changes in emissions between E0 and E85. The Sebring emitted 

more ethanol with E85 than did the Caravan, resulting in a higher E85 NMOG emissions factor 

for that vehicle. 

 The variability in the magnitude of these changes, however, is what weakens the 

analysis. Had additional observations been made, these results may have become more 

significant for more pollutants. For this final rule analysis we are only modeling emission effects 

with use of E85 in flex-fueled vehicles relative to E0 for two of the pollutants: ethanol and 

acetaldehyde, for which data suggests the effects are more certain. For the “more sensitive case” 

presented in the NPRM, and used in the air quality modeling, we had estimated changes to 

additional pollutants (including significant PM reductions) based on the limited data from the 

studies discussed above. However, until such time as additional data is collected to enhance this 

analysis we believe it is premature to use such assumptions. 

 The “more sensitive” case in the NPRM also included a 50 percent reduction in 

evaporative emissions with use of E85 based on results from just one vehicle from CRC’s E-65 

evaporative permeation program. Given the variability in not only vehicles, but also E85 

volatility in-use, we do not believe it appropriate to rely on just one data point, and as a result 

this reduction was also not applied in the final rule. 

Data from the analyses discussed above, and an additional dataset from a 1995 test 

program conducted by EPA’s Office of Research and Development, were used to develop inputs 

for MOVES, in order to model E85 impacts on air toxics inventories. Since MOBILE6 does not 

model air toxics for E85, ratios were developed to apply to E85 hydrocarbon or PM mass (Table 

3.1-6). The exhaust ratios for all pollutants except naphthalene were obtained from data on 

seven vehicles from the 1995 test program in EPA’s Office of Research and Development, along 

with the previously discussed 2007 test program at Southwest Research Institute,11 and the 2005 

test program at Environment Canada.12, 13 The data from the ORD test program is unpublished, 

but is available in the docket for this rule. Naphthalene inputs for E85 were derived from 

estimates from E10 values based on dilution of fuel with ethanol. The only data available on 
 10

evaporative emissions were results of hot soak tests from the Auto/Oil Air Quality Improvement 

Research Program.14 

Table 3.1-6. Toxic to THC/PM Ratios used for E85 Fuel in MOVES 

Pollutant Exhaust/Ratio Type Evaporative/Ratio Type 

Benzene 0.0036/THC 0.0054/THC 

1,3-Butadiene 0.0005/THC N.A. 

Acetaldehyde 0.0673/THC N.A. 

Formaldehyde 0.0093/THC N.A. 

Acrolein 0.0002/THC N.A. 

Ethanol 0.3316/THC 0.6123/THC 

Naphthalene 0.0126/PM 0.00006/THC 

3.1.1.2.3 Spark-Ignited Off-Road Engines 

Effects of E10 relative to E0 on exhaust as well as fuel tank and hose permeation 

emissions from gasoline-fueled off-road engines are contained in EPA’s NONROAD model, 

based on limited data. The effects on exhaust HC, NOx, and CO are shown in Table 3.1-7. 

Effects on tank and hose permeation emissions vary by equipment type and were recently 

updated to reflect new information on uncontrolled emissions and their control due to recently 

finalized new standards.15 For most small spark-ignition engines and recreational marine 

engines in 2022 E10 is estimated to double the tank and hose permeation emissions. There can 

also be increases in diurnal and refueling emissions with E10 if the fuel volatility of the blend is 

allowed to be greater than E0. These volatility effects are accounted for in the NMIM model that 

has the county-specific fuel properties that were used to generate the emission inventory impacts 

for this rule presented below in Section 3.2. 

 

Table 3.1-7. 

Exhaust Effect of Ethanol (E10) on Spark-Ignited Gasoline Emissions 

4 stroke 2 stroke 

HC exhaust -15.75% -2.1% 

NOx +40.25% +65.1%

CO -21.7% -22.75%

EPA and the California Air Resources Board (ARB) are in the midst of additional testing 

of off-road engines with gasoline and ethanol blends.16, 17 and DOE completed a report in early 

2009 which included small SI emission evaluation on a variety of ethanol blend fuels.18

Although preliminary results support the type of effects listed here, there are also upcoming 

allowances for manufacturers to start certifying small spark ignition engines on E10 fuel rather 

than the current E0 gasoline sometime in the 2011-2012 timeframe.19 If those plans proceed as 

expected, by 2022 most or all of the in-use small SI engines will have been certified on E10; 

thus we would expect none of the exhaust effects that we currently assume. Much of the in-use 
11

fleet of equipment will have turned over to new equipment certified on E10, and those that were 

originally certified on E0 are likely to be recalibrated. As a result, emissions are expected to 

result in roughly the same emissions on E10 as they currently achieve on E0. So the NOx 

inventory increase and HC and CO decreases associated with increasing E10 market share would 

probably go away by 2022, since most/all of the E0 certified engines will have been replaced by 

E10 certified engines by then. However, there will still likely be effects on the mix of 

hydrocarbons emitted, including increased proportions of ethanol and aldehydes in the exhaust 

HC. 

3.1.1.2.4 Biodiesel Effects on Diesel Emissions 

As discussed in Appendix A to this RIA, we investigated the emission impacts on NOx, 

PM, HC, and CO of 20 volume percent biodiesel fuels on emissions from heavy-duty diesel 

vehicles.20 Average NOx emissions were found to increase 2.2 percent, while PM, HC, and CO 

were found to decrease 15.6 percent, 13.8 percent, and 14.1 percent, respectively, for all test 

cycles run on 20 volume percent soybean-based biodiesel fuel (Table 3.1-8). These results are 

generally consistent with the exhaust emission impacts for heavy-duty, in-use diesel engines 

found in our previous work on this subject.21 The effects in Table 3.1-8 are for B20, while we 

assume biodiesel is mostly used in concentrations of 5% or less. In applying the emission 

impacts to the emission inventory we assumed that the effects were proportional to biodiesel 

concentration based on a resent investigation into the issue, so the inventory impacts are 

proportional to the overall biodiesel volume used. (Cite to Chien Sze et.al. SAE Paper). For our 

estimate of biodiesel impacts on toxics we applied the HC emission change from Table 3.1-8 to 

toxic emissions. 

Table 3.1-8. BD20 Emission Impacts 

 Percent change in emissions 

NOx 

PM 

HC 

CO 

+2.2% 

-15.6% 

-13.8% 

-14.1% 

3.1.1.3 Non-GHG Emission Impact Scenarios Analyzed 

For today’s rule we are estimating emission impacts of three different renewable fuel 

volume scenarios, as presented in Chapter 1.2, which are meant to bracket the range of likely 

combinations of renewable fuel volumes, and these are each analyzed relative to two different 

reference case ethanol volumes. To assess the impact of today’s rule relative to the current 

mandated volumes, we analyzed impacts relative to the RFS1 mandate of 7.5 billion gallons of 

renewable fuel use by 2012, which was estimated to include 6.7 billion gallons of ethanol. In 

order to assess the impact of the increased use of renewable fuels needed to meet the RFS2 

standards relative to a level of ethanol projected to already be in place by 2022, the AEO2007 
 12

projection of 13.2 billion gallons of ethanol (13.6 billion gallons of total renewable fuel) in 2022 

was analyzed. 

3.1.1.4 Non-GHG Emission Impact Calculation Methodology 

3.1.1.4.1 On-Road Gasoline 

Emissions from gasoline highway vehicles were generated with a specially modified 

version of EPA's Draft MOVES 2009 model. Although we applied a preliminary pre-draft 

version of the model and we anticipate changes between the version used for the NPRM analysis 

and the version to be released publicly (and the final rule version), our analysis does reflect 

significant updates in gasoline vehicle emissions from MOBILE6. Exhaust emission rates for 

HC, CO and NOx were developed based on an analysis of state inspection/maintenance and 

roadside remote sensing data from millions of vehicles.22 Emissions of particulate matter are 

based on EPA's recent Kansas City gasoline PM study.23,24 Evaporative emission rates have 

been updated based on extensive evaporative testing conducted by EPA and the Coordinating 

Research Council (CRC) since the release of MOBILE6, including investigations quantifying the 

effects of ethanol on permeation emissions.25 For this assessment of toxics, MOVES applies 

toxic ratios from the MOBILE6.2 model to updated MOVES HC estimates within the model.26 

Details of how MOVES was configured and run are documented in a report contained in 

the docket.27 To summarize, a separate MOVES run was configured for each year (2005 for the 

baseline and 2022 for the reference and control cases) and each fuel volume scenario: two 

reference cases (RFS1 mandate and AEO) and three control cases (low, mid, and high ethanol). 

Each of these runs required a unique “run specification” file and bundle of input databases to 

allow modeling of differences in analysis year and fuel supplies. For example, for the 2005 base 

year run, MOVES was configured to run with the estimates of fuel formulations and market 

shares by county in 2005, using the inputs developed as discussed in Section 3.1.1.1. Reference 

and control case runs in 2022 were run with estimates of fuel formulations and market shares by 

county as we project in 2022. VMT used to model onroad emissions was that developed for the 

Office of Air Quality Planning and Standards' 2002 Version 3 Modeling Platform28 and used in 

the Locomotive-Marine Rule published in 2008.29

 MOVES allows different levels of pre-aggregation depending on the level of resolution 

needed. For regional inventory applications, the finest level of aggregation the model can run is 

by county for each hour of the day, which maximizes the influence of inputs such as county-level 

fuel effects, hourly temperatures and activity patterns; however, since running the model at this 

level for the entire nation over multiple years and scenarios would be time prohibitive, the model 

was run at a higher level of aggregation to reduce run time. Exhaust emissions were run at the 

state/month aggregation for all cases, meaning that county-level inputs were aggregated to a 

single state average before being processed into MOVES, and hourly inputs were aggregated into 

an average monthly value. Likewise evaporative emissions were run at the state/hour level of 

aggregation, since evaporative emissions are required to be run at the hourly level. For further 

time savings runs were performed in January and July only and weighted to approximate an 

entire year – for most pollutants we simply used the number of days in each month to extrapolate 

to the full year (approximately 50/50), however due to the higher temperature sensitivity of PM, 
13

care was taken to develop weighting factors that better approximated a yearly total based on a 

month-by-month run (January/July weightings of approximately 30/70). While aggregation does 

lose some resolution in the overall emission results, test runs indicated that emissions differences 

are within a few percent of fully disaggregated runs and acceptable for estimating the emission 

impacts of the control programs. 

MOVES categorizes vehicles according to use type; for this analysis the majority of the 

focus was on light vehicles, which would include the MOVES use types of Passenger Car, 

Passenger Truck and Light Commercial Truck. To account for larger gasoline vehicles which 

will also have emissions affected by the change in gasoline formulations, we also ran additional 

MOVES use types: school buses, single-unit trucks, and motor homes. For the purposes of this 

rulemaking, the number of gasoline-powered refuse trucks, transit buses, intercity buses and 

combination trucks was determined to be negligible, and they were omitted from the MOVES 

runs to reduce run time. Because the PM10 calculation inputs for MOVES were incomplete, PM 

10 was calculated outside the model as PM2.5 times a correction factor of 1.086. 

 Because at the time of this final rule analysis the MOVES module for automating the 

calculation of E85 emissions from flexible fueled vehicles (FFVs) was not complete, we 

calculated the emission impact of E85 use (the impacts on just ethanol and acetaldehyde) by 

running MOVES for all E85 and for all E10 and then weighting the emissions in a postprocessing step. To run MOVES for "all E85" we created a special set of MOVES input files 

that essentially set all gasoline vehicles to run on E85. We created MOVES fuel supply and fuel 

adjustment tables that applied multiplicative E85 fuel adjustments from Table 3.1-6 to all 

gasoline vehicle emissions.B

 Because sulfate and vapor venting emissions are calculated using 

fuel properties (sulfur level and RVP) rather than fuel adjustments, we also created a specific 

MOVES table of E85 fuel properties as described in Section 3.1.1.1 In a post-processing step, 

we calculated a weighted average of the "all E85" results and the 2022 control case, sensitivity 

analysis results (called "all E10" results here). We chose to use the "sensitivity" results for 

consistency with its premise that modern vehicles are responsive to changes in fuel 

characteristics. The all E85 and all E10 results were weighted together by state, model year, and 

vehicle type using a weighting factor that was the product of the FFV fraction and the E85 

market share, where FFV fraction is the fraction of that vehicle type and model year that are 

projected to be E85 flexible-fueled vehicles, and the E85 marketshare is the state fraction of FFV 

energy use that we project will be provided by E85. These fractions were generated using the 

assumptions described in the sections in Chapter 1, Section 1.7.1 pertaining to Primary FFV 

Growth Assumptions and Projected Growth in E85 Access. We performed this calculation for 

passenger cars and trucks and light commercial trucks only since the number of heavy-duty 

vehicles using E85 is expected to be small. 

Toxic emissions are in development for MOVES; for this analysis some post-processing 

was required to generate complete inventory estimates. Specific toxic:hydrocarbon ratios by fuel 

formulation, vehicle class and model year were developed from a series of MOBILE6 runs and 

fed into MOVES, which applied these ratios to HC emissions to produce emissions of benzene, 

 

B

 The MOVES fuel adjustment table developed for this analysis contained all E85 fuel effects from Table 3.1-9, 

including the not statistically significant NOx and NMHC results; however, only results pollutants identified as 

statistically significant in Table 3.1-9 are reported in the sensitivity case inventory results 
 14

acetaldehyde, 1-3 butadiene, formaldehyde and acrolein for all of these cases analyzed. 

Naphthalene from heavy-duty vehicles was ratioed to PM 10 in MOVES. For light-duty 

vehicles, naphthalene emissions were calculated as the sum of PM 2.5 elemental carbon and PM 

2.5 organic carbon emissions times a ratio of 0.088. Aggregate ratios from the running 

emissions were also applied to start emissions to develop overall toxic emission inventories. E85 

emissions were calculated in MOVES using the factors in Table 3-1.6. 

3.1.1.4.2 Off-Road Gasoline 

Emissions from nonroad gasoline equipment were developed by running the National 

Mobile Inventory Model (NMIM), a consolidated emissions modeling system for EPA’s 

MOBILE6 and NONROAD models.30 The key feature of NMIM is a national county database 

(NCD), which includes county-level information on temperatures, fuel properties, equipment 

populations, etc. NMIM runs MOBILE6 and NONROAD based on information in the NCD. 

The NCD used to produce these inventories was updated as part of the 2005 National Emission 

Inventory (NEI) process.31 The NCD also included the 2005 and 2022 fuels described in Section 

3.1.1.2. The version of the NONROAD Model used included the effects of the 2008 Final Rule: 

Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines, Equipment, 

and Vessels.32 It is also capable of modeling the effects of gasoline blends containing 10 percent 

or less of ethanol. 

Emissions from onroad and nonroad diesel equipment were also developed by running 

NMIM (see above), using the same NCD and version of the NONROAD Model described above. 

The version of MOBILE was MOBILE6.2. Diesel fuels are less fully characterized than 

gasoline, since the only property used by MOBILE and NONROAD is fuel sulfur. 

Most toxic emissions for off-road equipment were taken directly from NMIM. The one 

exception was ethanol, which is not estimated by NMIM, so ethanol emissions were based on 

VOC speciation from light-duty gasoline vehicles. Ethanol inventories for the control case were 

developed by applying ratios of the aggregate MOVES ethanol exhaust, evaporative and 

refueling emissions for on-road gasoline for control versus reference cases, to the reference case 

ethanol emissions for off-road equipment. 

3.1.1.4.3 On-Road Diesel 

As discussed in section ?, we project the use of ?.?? billion gallons of biodiesel by 2022. 

As it is likely this will be consumed in a variety of blend levels (e.g. 20 percent, 5 percent, 2 

percent) by light-duty diesel vehicles and off-road diesel equipment as well as heavy-duty diesel 

vehicles, we assumed for this analysis that the effects of biodiesel on emissions are linear with 

biodiesel concentration as demonstrated by Sze, et al,33 and that impacts can be analyzed 

assuming all biodiesel is blended as BD20. We applied the BD20 effects discussed in Section 

3.1.1.2.4 to baseline heavy-duty emissions generated by NMIM, as MOVES heavy-duty diesel 

estimates were not available in time for this analysis. Biodiesel impacts were using the 

following formula: 
15

Biodiesel Impact P = Base HD Emissions P * Effect P * (Increase in BD20 Volume / Total Diesel Volume) 

Where: 

P = pollutant 

Effect = Percent change with BD20 blend from Section 3.1.1.3.3 

Increase in B20 Volume= Change in B20 volume from 2022 reference case to control case 

 in billion gallons of B20 blend (ie, change in gallons of biodiesel * 5) 

Total Volume = Total Highway Diesel Volume in 2022 in billion gallons 

Toxic effects were calculated using the HC effects from Table 3.1-8. 

3.1.1.4.4 Portable Fuel Containers 

 There are several sources of emissions associated with portable fuel containers (PFC) 

used for gasoline. These sources include vapor displacement and spillage while refueling the gas 

can at the pump, spillage during transport, permeation and evaporation from the gas can during 

transport and storage, and vapor displacement and spillage while refueling equipment. As the 

calculation of emissions for refueling non-road equipment includes spillage and some vapor 

displacement, these impacts are not included here. 

 As part of the 2007 regulation controlling emissions of hazardous pollutants from mobile 

sources (MSAT2 rule), EPA promulgated requirements to control VOC emissions from gas cans. 

The methodology used to develop emission inventories for gas cans is described in the regulatory 

impact analysis for the rule and in an accompanying technical support document.34, 35 

 Based on the MSAT work, we generated two sets of hypothetical nationwide annual 

estimates of PFC VOC emissions, for calendar years 2017 and 2030, based on all E0 and all E10. 

Interpolation can be used to estimate PFC VOC emissions for the reference cases. Proportions of 

national E0 and E10 fuel use were calculated for the 2022 reference and control cases. The 

reference case featured a mix of 89.1% E10 and 10.9% E0, while the policy case featured 100% 

E10. While E85 is used in flexible fueled highway vehicles, it is unlikely to be used in the near 

future in non-road equipment, and is therefore unlikely to be stored or dispensed from PFCs. 

 MSATs found in liquid gasoline will be present as a component of VOC emissions. 

These MSATs include benzene and naphthalene. Ethanol is present as well in VOC emissions 

from ethanol blends. Inventories for these pollutants were estimated by the application of toxic 

to VOC ratios. 

 For benzene emissions from all sources except permeation, the following formula was 

used to calculate toxic to VOC ratios: 

0.36

Re

Re

×⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×

=

LDGV

LDGV

fueling VOC

fueling Benzene PFC VOC Emissions

PFC Benzene Emissions
 16

where the ratio of refueling benzene to VOC was estimated using average nationwide fuel 

properties for zero and 10 percent ethanol gasoline from refinery modeling, done for RFS rule, 

and applied to EPA’s Complex Model for reformulated gasoline.36, 37 The 0.36 multiplier 

corrects for the difference in the percentage of gasoline in refueling emissions at 90o F, the 

temperature assumed for the algorithm in the Complex Model, versus a more typical lower fuel 

temperature of 60 o F for gas cans. The basis of this adjustment is discussed in more detail in the 

regulatory impact analysis for the Mobile Source Air Toxics Rule. An additional adjustment 

factor is applied to the ratio for permeation emissions, based on a recent study38 that suggests 

that the ratio of benzene from permeation to total VOC from permeation is about 1.77 times 

higher than the ratio associated with evaporation, according to the following formula: 

0.36 1.77

Re

Re

x

fueling VOC

fueling Benzene PFC VOC Emissions

PFC Benzene Emissions

LDGV

LDGV ×⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×

=

 

 The resulting ratios for 0% and 10% ethanol did not differ at the fifth decimal place, and 

were 0.0135 for all sources except for permeation, and 0.00239 for permeation. Thus, impacts of 

this rule on benzene emissions are due to the overall impact of RVP changes on total VOC 

emissions. 

 A naphthalene to VOC ratio was estimated using the following formula: 

× 0.0054 ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×

=

LDGV

LDGV

Evaporativ e VOC

Evaporativ e Naphthalen e PFC VOC Emissions

PFC Naphthalen e Emissions

 

 An evaporative naphthalene to VOC ratio for light-duty gasoline vehicles of 0.0004 was 

obtained from analyses done for the Mobile Source Air Toxics Rule, and did not vary by fuel 

type. The 0.0054 adjustment was based on a recent analysis of average nationwide percentage of 

naphthalene in gasoline vapor from gasoline distribution with an RVP of 10 psi at 60 degrees 

Fahrenheit.39, 40 The resulting ratio applied to PFC emissions was 0.0000022. 

 For E10 fuel, we assumed 16.74 percent of the evaporative emissions were ethanol 

(SPECIATE profile 1301)41 and 33.34 percent of permeation emissions were ethanol.42 

3.1.1.4.5 Refueling Emissions 

Refueling emissions were calculated by NMIM, based on MOBILE6 refueling module. 

Emissions are impacted by the increase in RVP due to ethanol, and also because the reduced 

energy density of ethanol would require more fillups. NMIM directly provides the emission 

increase due to increased RVP for the areas allowing the 1.0 psi waiver, so no additional 

processing was required to estimate RVP effects on refueling. 
17

In order to estimate the emission impact of the increase in refueling events, we developed 

ton per gallon refueling emission factors based on NMIM by dividing total refueling emissions 

from NMIM for each case by the number of gallons consumed in the AEO case. The ton per 

gallon emission factors were then applied to the total volume in gallons in each case. Fuel 

volumes for the RFS 1 mandate and AE0 reference cases compared to the NPRM and final rule 

control cases are listed in Table 3.1-9. Our estimates of total gallons were calculated from 

energy balance, reflecting the various numbers of gallons needed to consume the same energy. 

We assume the number of trips to the pump will increase in proportion to the increased gallons 

estimated for the rule. 

Table 3.1-9. Gasoline Volumes (Billion Gallons)

RFS 1 

Mandate 

AEO 

2007 

NPRM 

Control 

Case 

RFS1 

Mandate 

2022 

AEO 

2008 rev 

2022 

FRM 

RFS2 

Control 

Low 

EtOH 

FRM 

RFS2 

Control 

Primary 

FRM 

RFS2 

Control 

High 

EtOH 

E0 107.51 16.03 0 65.72 6.46 0.00 0.00 0.00

E10 36.40 131.00 124.6 70.46 131.82 134.25 128.79 115.82

E85 0.00 0.11 29.3 0.00 0.00 5.49 12.54 29.26

Total 

Gallons 143.91 147.14 153.9 136.18 138.28 139.74 141.32 145.08
 18

3.1.2 Impact on Non- GHG Emissions from Fuel Production and Distribution 

In addition to the effects of increased renewable fuel use on emissions from the vehicles 

and equipment that use the fuels, as discussed above, there are shifts in the fuel production and 

transport/distribution methods that can have substantial impacts on emissions. These "upstream" 

emissions are associated with all stages of biofuel production and distribution, including biomass 

production (agriculture, forestry), fertilizer and pesticide production and transport, biomass 

transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to 

blending/distribution terminals, and distribution of finished fuels to retail outlets. Additionally, 

changes in agricultural economics associated with increased biomass production can result in 

shifts in related agricultural production, such as livestock. 

This section describes the changes in upstream emission sources and related emission 

rates connected with the renewable fuel use. The emission inventory impacts resulting from 

these changes are described in Section 3.2. This section is divided into two major sub-sections, 

the first covering emissions of criteria pollutants, their precursors, and ammonia, and the second 

covering non-criteria air toxic emissions and ethanol. The specific air toxics covered are: 

benzene, acetaldehyde, formaldehyde, 1,3-butadiene, acrolein, and naphthalene. 

3.1.2.1 Upstream Criteria Pollutants 

3.1.2.1.1 Agricultural Sector 

Introduction 

In prior EPA estimates, such as the RFS1 rule, changes in agricultural emissions were 

based solely on the increases in bushels of corn (and soybeans for biodiesel), and the necessary 

acreage to produce those additional bushels. Given the greater pressure on farmland use likely in 

the 2022 timeframe for today’s rule (15 billion gallons of corn ethanol plus up to 16 billion 

gallons of cellulosic ethanol) compared to the 2012 assessment for RFS1 (6.7 or 9.6 billion 

gallons of ethanol depending on scenario), additional factors have been added to the agricultural 

analysis, such as likely shifts of acreage to corn from certain other crops as corn prices increase. 

The number of acres of cropland for corn, soy, and all other principle crops were 

estimated using the FASOM agriculture and forestry model, as described in Section 5.1 of this 

document. We are using the change in total acres of planted cropland to estimate changes in 

certain agricultural emissions, such as tillage dust, that are not directly calculated by FASOM. 

Another substantial source of agricultural emissions (especially ammonia and methane) is 

livestock. Changes in livestock-related emissions are estimated based on the change in head 

counts of cattle, swine, and poultry predicted by FASOM. 

The impacts relative to the RFS1 mandate reference case (6.7 billion gallons of ethanol) 

rely only on applying ethanol volume proportions to the modeling results of the AEO reference 

case (13.2 billion gallons). Due to the complex interactions involved in projections in the 

agricultural modeling, we did not attempt to adjust the agricultural inputs of the AEO reference 

case for the RFS1 reference case. So the fertilizer and pesticide quantities, livestock counts, and 
19

total agricultural acres were the same for both reference cases. The agricultural modeling that 

had been done for the RFS1 rule itself was much simpler and inconsistent with the new 

modeling, so it would be inappropriate to use those estimates. We had planned to conduct 

additional agricultural modeling specifically for the RFS1 mandate case prior to finalizing this 

rule, but there was not sufficient time and resources to accomplish that after all the other updates 

and sensitivities analyzed for the AEO case alone for the final rule. 

3.1.2.1.1.1 VOC/NOx/CO/SOx/PM2.5 

Criteria pollutants related to agricultural operations come from five major sources: farm 

equipment (mainly diesel engine emissions), fertilizer production and application, pesticide 

production and application, burning of crop residue, and fugitive dust from field tilling and 

related activities. 

Agricultural Equipment Emissions 

Changes in farm equipment emissions were estimated by multiplying an average fuelbased emission factor for diesel or gasoline farm equipment by the change in farm fuel 

consumption predicted by FASOM. The emission factors for each pollutant in units of grams 

emitted per million BTU of fuel burned were calculated from EPA NONROAD2005 nationwide 

modeling outputs for 2022 (pollutant tons emitted, gallons of fuel consumed) for each year of 

interest. The diesel emissions include all agricultural diesel equipment, which are dominated by 

agricultural tractors, while the gasoline emissions include only the limited number of larger 

agricultural gasoline-fueled equipment, such as tractors, combines, balers, swathers, and 

irrigation sets. The fuel energy contents (lower heating value) used for the unit conversions were 

115,000 BTU/gallon for gasoline and 130,000 BTU/gallon for diesel. For comparison, the 

corresponding 2020 emission factors from GREET are shown, where available. Most of the 

differences between NONROAD and GREET are small and are likely attributable to the 

difference between 2020 and 2022 values. And although the gasoline equipment emission 

factors for VOC and CO from NONROAD are much greater than those used in GREET, this 

does not have much impact on emission inventories due to the small number of gasoline-fueled 

equipment used in agriculture relative to diesel equipment. 
 20

Table 3.1-10. 

Agricultural Equipment Emission Factors 

(grams per mmBTU of fuel burned) 

Diesel Gasoline Pollutant NONROAD GREET NONROAD GREET 

NOx 306 298 204 208 

VOC 30.55 34.87 355.53 52.30 

PM10 21.12 22.67 7.49 9.07 

PM2.5 20.49 20.41 6.89 8.34 

CO 130 136 10,067 204 

Benzene 0.62 -- 11.90 -- 

Ethanol 0.00 -- 0.00 -- 

1,3-Butadiene 0.057 -- 1.90 -- 

Acetaldehyde 1.62 -- 1.63 -- 

Formaldehyde 3.61 -- 3.17 -- 

Naphthalene 0.027 -- 0.66 -- 

Acrolein 0.09 -- 0.14 -- 

SO2 0.44 -- 15.88 -- 

NH3 0.68 -- 1.01 -- 

Fertilizer and Pesticide Production 

The manufacturing processes for agricultural fertilizer and pesticides generate a variety 

of air pollutants. The agricultural inputs from GREET provide emission factors in grams of 

pollutant per ton of nutrient for various types of fertilizers, herbicides, and insecticide, as shown 

in Table 3.1-12. These air emission factors were multiplied by the changes in fertilizer and 

pesticide use predicted by FASOM, as shown in Table 3.1-11, to give projected changes in 

nationwide agricultural fertilizer and pesticide production emissions. 

Table 3.1-11. 

Changes in Agricultural Chemical Use for 2022 RFS2 Control Case Relative to AEO2007 

Reference Case 

Nitrogen 

(average)

Phosphate 

(P2O5)

Potash 

(K2O)

Limestone 

(CaCO3) Herbicides Pesticides

Annual Short 

Tons 750,629 357,069 662,157 260,304 -750 -381

Percentage 5.73% 12.72% 20.16% 2.55% -0.38% -0.86%
21

Table 3.1-12. 

Agricultural Chemical Production & Transport Air Emission Factors 

(grams per ton of nutrient) 

Pollutant Nitrogen 

(average)

Phosphate 

(P2O5)

Potash 

(K2O)

Limestone 

(CaCO3) Herbicides Pesticides

NOx 1,605 4,484 734 573 19,371 21,628

VOC 2,761 240 40.7 56.8 1,575 2,040

PM10 454 1,551 148 506 10,840 11,746

PM2.5 262 1,018 74.5 167 4,869 5,479

CO 2,595 790 129 186 5,417 6,872

Benzene 0.00 0.00 0.00 0.00 3.21 4.16

Ethanol 0.00 0.00 0.00 0.00 0.00 0.00

1,3-Butadiene 0.000 0.000 0.000 0.000 0.576 0.745

Acetaldehyde 0.018 0.001 0.000 0.000 0.082 0.106

Formaldehyde 20.75 1.55 0.19 0.41 18.11 23.44

Naphthalene 0.033 0.117 0.010 0.039 114.4 124.0

Acrolein 0.009 0.001 0.000 0.000 0.024 0.031

SO2 703 53,299 321 701 11,300 12,895

NH3 0.00 0.00 0.00 0.00 0.00 0.00

Until the 1990s it was reasonable to assume that all fertilizers and pesticides used on 

domestic agriculture were produced within the U.S. This has been less true in recent years as 

more agricultural chemicals, especially fertilizers, are being imported from countries with a 

greater availability of natural gas at lower costs. For greenhouse gases the location of these 

emissions is of less importance, but for criteria pollutants and toxics it is important to reduce the 

estimated impacts by the percentage of production and transportation occurring outside of the 

U.S. Using data from USDA43,44 the percentages applied from domestic sources are shown in 

Table 3.1-13. After applying these percentages to the production and initial transportation 

portions of the GREET emission factors, the unadjusted final (domestic) transportation portion 

of the GREET emission factors was added back in. Since the relative emissions from production 

versus transportation vary by pollutant, the net adjustments to the GREET emission factors also 

vary by pollutant, as shown in the second Section of Table 3.1-13. To calculate an overall factor 

for nitrogen fertilizers, the proportions from GREET were used: 70.7% ammonia, 21.1% urea, 

and 8.2% ammonium nitrate. The pesticide adjustment does not vary by pollutant because 

virtually all of the pesticide emissions come from actual production rather than 

transportation/distribution. 
 22

Table 3.1-13. 

 Domestic Fractions of Fertilizer and Pesticide Production Applied to Crops 

 Nitrogen 

Fertilizers 

Potash Phosphate Pesticides 

Domestic Fraction of 

Production 

50% 20% 94% 76% 

 

Net Adjustment to Production, 

Transportation & Distribution 

Emission Factor from GREET 

 

VOC 50.63% 94.85% 53.62% 76% 

CO 52.47% 94.92% 54.37% 76% 

NOx 73.34% 94.92% 65.55% 76% 

PM10 52.64% 94.12% 24.06% 76% 

PM2.5 53.67% 94.14% 28.31% 76% 

SOx 60.48% 94.02% 33.66% 76% 

Fertilizer and Pesticide Application 

In addition to the agricultural equipment emissions mentioned above, the application of 

fertilizer and pesticides (herbicides, insecticides, fungicides, etc.) to agricultural fields causes the 

release of certain types of pollutants into the air. For nitrogen fertilizers the only pollutant 

considered to be significant is ammonia (NH3), the estimation of which is covered in Section 

3.1.3.1.1.2. Pesticide application emissions are mainly VOC and various individual organic 

compounds, most notably benzene and acrolein. A discussion of the toxic pollutant emissions as 

a fraction of VOC is presented in Section 3.1.3.2.2, but the resulting emission factors and 

inventory impacts are shown here in Table 3.1-14. There are also potential toxicity concerns 

with volatilization of the pesticide active ingredients, and this is discussed in Section 3.4 of this 

document. 

The basis of the pesticide application emissions for this analysis was the 2002 NEI areasource inventory. The ton per year emissions data from the NEI was used with USDA pesticide 

application data for 2002 (or the nearest year for which data were collected) to generate an 

overall average estimate of the pesticide application emissions per ton of pesticide applied. This 

ratio of pollutant tons (for VOC, benzene, and acrolein) per ton of pesticide applied was then 

multiplied by the change in total pesticide tons used (including herbicides) as projected by 

FASOM and shown in Table 3.1-11 to give the projected change in nationwide agricultural 

pesticide application emissions in Table 3.1-14. 
23

Table 3.1-14. 

Herbicide and Pesticide Application Air Emission Factors and Impacts for 2022 RFS2 

Control Case Relative to AEO2007 Reference Case 

Emission Factor Air Emission 

Pollutant Impact 

(tons per ton applied) (annual short tons) 

VOC 0.543 -614 

Benzene 0.142 -161 

Acrolein 0.0036 -4.06 

Agricultural Residue Burning Emissions 

One source of air pollution related to crop farming is the burning of crop residues. This 

practice is one of the methods that is used to clear fields between crop cycles so that the old crop 

residue does not build up and clog or otherwise hinder the tilling of the fields in preparation for 

new crop planting. This practice is mainly used for grassy crops like wheat, rye, and barley, but 

in some areas it is also used for corn and other crops. 

Crop residue burning produces substantial emissions of CO2, VOC, CO, NOx, as well as 

ammonia and toxic pollutants such as benzene, formaldehyde, 1,3-butadiene, and acrolein. 

The use of crop residue burning is quite variable from area to area and among individual 

farmers, since there are alternative methods to deal with crop residue, including use of 

conservation tillage methods and equipment that allows planting through the residue. In some 

locations and time periods crop residue burning has been prohibited by law, due to the possible 

health effects in nearby residential areas. Another aspect of uncertainty in estimating crop 

burning emissions is that the NEI does not currently cover all states where crop residue burning 

occurs. Despite these data limitations, the NPRM used the available data to generate a rough 

overall estimate of the average crop burning emissions per acre of planted crops, and then 

multiplied that emission rate to the change in total crop acres predicted by FASOM to generate 

an estimated emission inventory impact. 

For this final rule analysis we have reconsidered the inclusion of any crop residue 

burning impact and decided not to include it. This reconsideration was driven by the facts that 

(a) the crops most likely to be impacted by this rule do not tend to be ones for which residue 

burning is used, and (b) even for those crops affected by this rule that might otherwise have their 

residue burned, for this analysis they would much more likely have that residue harvested and 

used as cellulosic feedstock in a biofuel plant. Given the uncertainty in projecting these emission 

impacts, we did not want any rough assumptions made to unduly influence the emission impact 

assessment. 

Agricultural Dust Emissions 
 24

Soil and related dust particles (e.g., fertilizer, pesticide, manure) become airborne as a 

result of field tillage and animal grazing/foraging, especially in drier areas of the country. Some 

of this dust is in a size range that is a concern for human health and welfare. The NEI includes 

estimates of these particulate emissions by county. 

The agricultural dust data from the 2002 NEI was used to generate an estimate of the 

average fugitive dust emissions per acre of planted crops for the crop related dust, and per head 

of cattle for the dust related to cattle. This was done using 2002 nationwide crop acreage and 

livestock inventory data from USDA/NASS. The calculated pollutant mass (tons of PM) per 

total acre farmed was then multiplied by the change in total planted acres projected by FASOM 

to give a projected change in nationwide crop related dust emissions. And the calculated PM 

tons per head of cattle was multiplied by the change in cattle inventory projected by FASOM to 

give a projected change in nationwide livestock related dust emissions. 

The emission factors and inventory impacts of fugitive dust from crop related activities 

and livestock are shown in Table 3.1-15 and 3.1-16. The ton per year impacts for the croprelated emissions are based on a modeled increase of 8.1 million farmed acres (2.65 percent) in 

2022 relative to the AEO2007 reference case. The changes in fugitive dust from livestock 

operations are based on the head count changes shown in Table 3.1-18. 

Table 3.1-15. 

2022 Crop-related Dust Emission Impacts for the 2022 RFS2 Control Case 

Relative to AEO2007 Reference Case 

Emission Factors Inventory 

Impacts Pollutant 

(Tons per thousand 

acres farmed) (annual short tons) 

PM10 6.807 55,182 

PM2.5 1.021 8,277 

Table 3.1-16. 

2022 Livestock-related Dust Emission Impacts for the 2022 RFS2 Control Case 

Relative to the AEO2007 Reference Case 

Beef Cattle Dust Emissions Dairy Cattle Dust Emissions Pollutant (kg/head/year) (annual short tons) (kg/head/year) (annual short tons) 

PM10 0.888 -139.68 0.172 -8.34

PM2.5 0.089 -14.00 0.017 -0.82

3.1.2.1.1.2 Ammonia (NH3) 

The two primary sources of ammonia emissions into the air on farms are fertilizer 

application and livestock waste. Fertilizer application emissions were estimated using an 
25

average emission factor of 57,428.71 grams per ton of fertilizer nitrogen applied for all forms of 

nitrogen, which is a weighted average of the standard EPA emission factors that are used to 

generate the NEI. The weightings for each type of fertilizer come from USDA Economic 

Research Service data for 2006. The individual emission factors, weightings, and resulting 

average emission factor are shown in Table 3.1-17. This average emission factor was multiplied 

by the nitrogen application quantities generated by the FASOM model for each scenario. 

Table 3.1-17. Fertilizer Ammonia Emission Factors

Fertilizer Type SCC Emission Factor 

(lbs NH3/Ton Nitrogen) 

USDA 2006 all 

crops Weighting 

Anhydrous Ammonia 2801700001 24 15.46% 

Aqua Ammonia 2801700002 24 1.61% 

Nitrogen Solutions 2801700003 61 40.88% 

Urea 2801700004 364 21.73% 

Ammonium Nitrate 2801700005 49 3.9% 

Ammonium Sulfate 2801700006 194 4.93% 

Ammonium Thiosulfate 2801700007 64 

Other Straight Nitrogen 2801700008 61 11.49% 

Ammonium Phosphates 2801700009 97 

N-P-K 2801700010 97 

 

avg lbs/ton 126.61 

avg grams/ton 57428.71 

Changes in ammonia emissions from livestock waste were estimated using emission 

factors (kg/head/year) multiplied by the change in animal head counts predicted by FASOM. 

The ammonia emission factors and livestock head changes used in this analysis, along with 

resulting ammonia inventory impacts are shown in Table 3.1-18. This analysis was limited to 

these four types of livestock because they are the ones specifically modeled by FASOM. 

Table 3.1-18. Livestock Ammonia Emission Impacts for 2022 RFS2 Control Case Relative 

to the AEO2007 Reference Case 

Livestock 

Type 

kg NH3 per head 

per yeara

Head count change 

(million head) 

Percent 

change 

Change in NH3 emissions 

(annual short tons) 

Beef Cattle 9 -0.143 -0.23% -1,416 

Dairy Cattle 25 -0.044 -0.65% -1,212 

Swine 5 3.95 3.24% 21,711 

Poultry 0.22 -73.5 -0.98% -17,798 

a

 Source: EPA/600/R-02-017, "Review of Emission Factors and Methodologies to Estimate Ammonia Emissions From 

Animal Waste Handling," April 2002.

Although it is a minor source of ammonia compared to fertilizer and livestock emissions 

described above, changes in farm equipment ammonia emissions were estimated by multiplying 

an average fuel-based emission factor for diesel or gasoline farm equipment by the change in 

farm fuel consumption predicted by FASOM. The ammonia emission factors in units of grams 

emitted per million BTU of fuel burned were calculated from the default ammonia emission 
 26

factors used in the EPA NMIM model: 116 mg per gallon of gasoline burned; and 88.3 mg per 

gallon of diesel fuel burned. 

3.1.2.1.2 Biofuel Production 

 Emissions from the production of biofuels include the emissions from the production 

facility itself as well as the emissions from production and transport of the biomass and any other 

fuels used by the biofuel plant, such as natural gas, coal, and electricity. The biomass feedstock 

production emissions are discussed above in the section on agricultural emissions. The 

calculation of emissions from corn ethanol, cellulosic ethanol, and biodiesel plants, including 

feedstock transport, was done using the basic methodology of the GREET model. But some 

updates and enhancements were made to GREET, including updated feedstock energy 

requirements and estimates of excess electricity available for sale from new cellulosic ethanol 

plants, based on modeling by the National Renewable Energy Laboratory (NREL). Since 

certain biofuel production processes generate co-products that could also be used in the gasoline 

market, we have accounted for those by decreasing the refined gasoline volume on an equal fuel 

energy basis to the co-products. This was done in two cases -- co-product naphtha from the 

Fischer-Tropsch process and C3+ alcohols from the thermochemical ethanol from mixed 

alcohols process. 

 The facility emission factors used are shown in Table 3.1-19. These have been updated 

for this final rule based on new analyses of projected plant efficiency improvements, rather than 

using older analyses, such as dry mill corn plant emission data from plants existing in 2005 as 

was used for the NPRM and air quality modeling inventories. These new analyses, discussed in 

Section 1.5.1.3 for corn ethanol, Section 1.5.3 for cellulosic ethanol and diesel, and Section 1.5.4 

for biodiesel, provide projections of energy and feedstock requirements for biofuel production. 

These energy requirements are then multiplied by emission factors (grams per mmBTU of 

feedstock consumed) from the GREET model to yield the gram per gallon emission factors 

presented here. 
27

Table 3.1-19. 

Biofuel Production Plant Emission Factors in 2022 

(grams per gallon produced) 

Biofuel Plant Type 

VOC CO NOx PM10 PM2.5 SOx NH3 

Corn Ethanol, Dry Mill NG 2.29 0.58 0.94 0.94 0.23 0.01 0.00 

Corn Ethanol, Dry Mill NG (wet DGS) 2.27 0.37 0.60 0.91 0.20 0.00 0.00 

Corn Ethanol, Dry Mill Biogas 2.29 0.62 1.00 0.94 0.23 0.01 0.00 

Corn Ethanol, Dry Mill Biogas (wet DGS) 2.27 0.39 0.63 0.91 0.20 0.00 0.00 

Corn Ethanol, Dry Mill Coal 2.31 2.65 3.68 3.64 1.54 3.48 0.00 

Corn Ethanol, Dry Mill Coal (wet DGS) 2.28 1.68 2.34 2.62 1.03 2.21 0.00 

Corn Ethanol, Dry Mill Biomass 2.42 2.55 3.65 1.28 0.36 0.14 0.00 

Corn Ethanol, Dry Mill Biomass (wet 

DGS) 2.35 1.62 2.32 1.12 0.28 0.09 0.00 

Corn Ethanol, Wet Mill NG 2.33 1.04 1.68 1.00 0.29 0.01 0.00 

Corn Ethanol, Wet Mill Coal 2.33 3.50 4.86 4.53 1.98 4.60 0.00 

Cellulosic Ethanol (Enzymatic, 

switchgrass or corn stover) 1.45 4.68 6.71 1.63 0.53 0.25 0.00 

Cellulosic Ethanol (Enzymatic, forest 

waste) 1.46 4.93 7.06 1.67 0.55 0.26 0.00 

Cellulosic Ethanol (Thermochemical, 

switchgrass or corn stover) 0.49 6.99 10.03 1.16 0.58 0.37 0.00 

Cellulosic Ethanol (Thermochemical, 

forest waste) 0.49 6.99 10.03 1.16 0.58 0.37 0.00 

Biodiesel, Soybean oil 0.04 0.43 0.69 0.06 0.06 0.01 0.00 

Biodiesel, Yellow grease/tallow 0.04 0.50 0.80 0.07 0.07 0.01 0.00 

Biodiesel, Fuel grade corn oil 0.04 0.50 0.80 0.07 0.07 0.01 0.00 

Biodiesel, Algae 0.01 0.10 0.16 0.01 0.01 0.00 0.00 

Renewable Diesel*, Yellow grease 0.00042 0.00475 0.00767 0.00065 0.00065 0.00006 0.00 

Cellulosic Diesel (Thermochemical, 

Fischer-Tropsch forest waste) 0.91 13.39 20.22 2.39 1.20 1.80 0.00 

* The renewable diesel emission factors are based only on the energy needed for hydrotreating at an existing 

refinery, which is different from stand-alone facilities we project in Section 1 will be making renewable diesel (RD). 

An RD plant would have more feedstock handling, pumping, etc., as well as general plant energy overhead than for 

a marginal unit in a refinery. 

3.1.2.1.3 Crude Oil Production/Transport/Refining 

 The estimate of emissions associated with production of gasoline and diesel fuel from 

crude oil is based on emission factors in the GREET model. The actual calculation of the 

emission inventory impacts of the decreased gasoline and diesel production is done in EPA's 

spreadsheet model for upstream emission impacts. This model uses the decreased volumes of 

the crude based fuels and the various crude production and transport emission factors from 

GREET to estimate the net emissions impact, which is shown below in Section 3.2 (see the 

displaced gasoline row of Table 3.2-5). 

3.1.2.1.4 Finished Fuel Transport and Distribution 
 28

Transfer and Storage Evaporative Emissions from Gasoline, Gasoline/Ethanol Blends, 

and Ethanol -- VOC emissions are produced by transfer and storage activities associated with 

distribution of gasoline, gasoline/ethanol blends, and ethanol. These are referred to as Stage I 

emissions.45 Stage I distribution begins at the point the fuel leaves the production facility and 

ends when it is loaded into the storage tanks at dispensing facilities. 

There are five types of facilities that make up this distribution chain for gasoline. Bulk 

gasoline terminals are large storage facilities that receive gasoline directly from the refineries via 

pipelines, barges, or tankers (or are collocated at refineries). Gasoline from the bulk terminal 

storage tanks is loaded into cargo tanks (tank trucks or railcars) for distribution to smaller 

intermediate storage facilities (bulk plants), or directly to gasoline dispensing facilities (retail 

public service stations and private service stations). When ethanol is blended into gasoline it 

usually occurs in the pipes which supply the tank trucks. 

There are two types of pipeline facilities found at various intervals along gasoline 

distribution pipelines: pipeline breakout stations and pipeline pumping stations. Pipeline 

breakout stations receive gasoline via pipelines, store it in storage tanks, and re-inject it into 

pipelines as needed to meet the demand from downstream facilities. Pipeline pumping stations 

are located along the entire length of a pipeline at about 40 mile intervals. Their purpose is to 

provide the extra ‘‘push’’ needed to move the product through the pipeline. They do not 

normally have gasoline storage capability. 

Bulk plants are intermediate storage and distribution facilities that normally receive 

gasoline or gasoline/ethanol blends from bulk terminals via tank trucks or railcars. Gasoline and 

gasoline/ethanol blends from bulk plants are subsequently loaded into tank trucks for transport to 

local dispensing facilities. 

Gasoline and gasoline/ethanol blend dispensing facilities include both retail public outlets 

and private dispensing operations such as rental car agencies, fleet vehicle refueling centers, and 

various government motor pool facilities. Dispensing facilities receive gasoline and 

gasoline/ethanol blends via tank trucks from bulk terminals or bulk plants. Inventory estimates 

for this source category only include the delivery of gasoline at dispensing facilities and does not 

include the vehicle or equipment refueling activities. 

Emission factors (EFs) for gasoline were based on inventory estimates from the 2002 

NEI.46 We used these data to develop E0 gasoline emission factors even though the 2002 

emissions included the E10 that was in the fuel pool at that time. In 2002 this was still a 

relatively small proportion of gasoline consumption, so it should not substantially affect the 

national E0 estimates. Since ethanol is blended with gasoline at bulk terminals to produce E10 

and E85 at the point fuel is loaded into tank trucks, we assumed bulk terminal emissions were 

associated with unblended gasoline. We then divided emissions into a refinery to bulk terminal 

component and a bulk terminal to dispensing facility component. Total nationwide emissions for 

these two components were divided by the energy content of the total volume of gasoline 

distributed in 1999, to obtain the emission factor in g/mmBTU. Total volume of gasoline was 

based on gasoline sales as reported by the Energy Information Administration.47 These emission 

factors are provided in Table 3.1-20. 
29

We also developed emission factors for Stage 1 emissions of E10 and E85 subsequent to 

blending at bulk terminals. These emission factors were calculated by applying adjustment 

factors to the gasoline EF. The adjustment factors for E10 and E85 were based on an algorithm 

from the 1994 On-Board Refueling Vapor Recovery Rule48: 

EF (g/gal) = exp[-1.2798 - 0.0049(ΔT) + 0.0203(Td) + 0.1315(RVP)] (1) 

where delta T is the difference in temperature between the fuel in the tank and the fuel being 

dispensed, and Td is the temperature of the gasoline being dispensed. We assumed delta T is 

zero, temperature of the fuel being dispensed averages 60 degrees over the year, and that the 

RVP of conventional gasoline is 8.7 psi, 10% ethanol is 9.7, and 85% ethanol is 6.2. Using these 

assumptions, the adjustment factor is +14% for E10 and -30% for E85. Emission factors in 

grams per million BTU of fuel transferred are given in Table 3.1-20. 

In addition to these Stage I emissions for gasoline and gasoline/ethanol blends, transport 

of ethanol to bulk terminals also results in evaporative emissions of ethanol, a VOC. For the 

NPRM analysis these emissions were estimated using a very simplified approach based on an 

adjustment to the gasoline transport VOC emissions to account for the much lower vapor 

pressure and molecular weight of ethanol versus gasoline. Using that method the NPRM 

assumed an emission factor of 3.56 g/mmBTU ethanol, which greatly underestimated the ethanol 

vapor and VOC losses, since it did not attempt to account for differences between ethanol and 

gasoline transport modes, distances, or transfer methods in movement of the fuel from 

production facility to the bulk distribution terminal. 

For the air quality analysis and final rule analysis this method was replaced using data 

from an Oak Ridge National Laboratory (ORNL) analysis of projected ethanol transport modes, 

distances, and volumes transferred under various ethanol volume scenarios.71 The final results of 

that study yielded greatly increased EFs of 26.9 - 31.7 g/mmBTU (2.04 - 2.41 g/gal) depending 

on the scenario, due to the added fuel transfer losses compared to pipeline-based transport of 

gasoline. The EF shown in Table 3.1-20 (28.78 g/mmBTU) corresponds to the High Ethanol 

minus RFS1 reference case, and was used for calculation of VOC and ethanol vapor for all cases 

in this FRM analysis. The air quality analysis used preliminary results of the ORNL analysis, 

which yielded somewhat greater ethanol and VOC emission rates than used for this FRM 

analysis. Further discussion of these calculations can be found in Section 3.3 of this RIA 

chapter. 

Significant evaporative emissions are not expected from storage and transport of 

biodiesel fuel due to its low volatility. 
 30

Table 3.1-20. 

VOC Emission Factors for Gasoline and Gasoline/Ethanol 

Blend Storage and Transfer Emissions (Stage 1) 

Process Blend EF(g/mmBTU) 

Refinery to Bulk Terminal E0 14.94 

Refinery to Bulk Terminal E100 28.78a

Bulk Terminal to Pump E0 27.79 

Bulk Terminal to Pump E10 32.74 

Bulk Terminal to Pump E85 25.93 a

 E100 ethanol vapor EF ranges from 26.9 - 31.7 depending on scenario. 

EF shown corresponds to the High Ethanol minus RFS1 reference case, and 

was used for calculation of all cases in this FRM analysis. 

Combustion Emissions from Transport and Distribution of Fuels and Feedstocks -- Emissions 

are produced by the vehicles and engines used to transport feedstocks such as crude oil, corn, and 

cellulosic biomass to fuel production facilities, as well as transport/distribution of the finished 

fuels from the production plants to distribution terminals and retail outlets. For example, corn 

would be transported from farms and grain facilities to ethanol plants by truck and possibly rail. 

The finished ethanol would be transported from there to bulk distribution terminals by truck, rail, 

or barge, and distribution from terminal to retail outlet is by truck. The emission factors for the 

year 2022 in Table 3.1-21 are taken from the most recent rulemaking analyses, accounting for 

the mix of newer better controlled engines (including trucks meeting the standards for 2008 and 

later engines49 and engines meeting the 2008 locomotive/marine diesel engine rule50), as well as 

any remaining older engines subject to less stringent standards. The truck EFs are given in terms 

of grams per vehicle mile traveled, while the other EFs are in grams per million BTU of fuel 

burned by the engine. The ocean tanker emission factors are from the base case analysis of the 

Category 3 ocean-going vessel proposed rule.77

 To estimate the net emission rates for the assumed mix of transport modes for each fuel 

type, these emission factors were incorporated into a modified version of GREET51, since 

GREET 1.7 and 1.8 retained emission factors based only on earlier regulations. Thus, the miles 

traveled and quantities of fuel burned are those used by GREET for each transport mode and fuel 

being transported. For the final rule air quality analysis we will have a more detailed analysis of 

miles and fuel volumes transported by mode within each county. 
31

Table 3.1-21. 2022 Criteria Emissions from Fuel and Feedstock Transport/Distribution 

Transport Mode Year VOC CO NOx PM10 PM2.5 

Class 2B HD Diesel Trucks (g/mile) 2005 0.282 1.303 3.594 0.163 0.139 

 2022 (2020) 0.137 0.205 0.483 0.033 0.019 

Medium HD DIesel Trucks (g/mile) 2005 0.653 2.482 8.297 0.309 0.271 

 2022 (2020) 0.289 0.417 1.243 0.053 0.035 

Locomotive (g/mmBTU of fuel burned) 2005 84.733 212.861 1620.376 51.575 50.028 

 2022 (2020) 34.070 203.984 815.271 19.015 18.445 

Barge (avg of C1 & C2 vessels)52 2005 26.761 237.513 1276.901 47.923 46.485 

 (g/mmBTU of fuel burned) 2022 (2020) 15.527 188.994 676.097 22.017 21.356 

Ocean Tanker (C3 vessels) 2005 79.298 180.314 2176.240 179.982 165.408

 (g/mmBTU of fuel burned) 2022 (2020) 79.160 179.525 2038.314 179.645 165.273

3.1.2.2 Upstream Air Toxics 

3.1.2.2.1 Upstream Air Toxics Reference Case 

Air toxic emissions are associated with a variety of upstream processes. These processes 

include production of agricultural pesticides and fertilizers, as well as their application, operation 

of petroleum refineries, operation of ethanol and biodiesel production facilities, operation of 

electrical production facilities which supply power to these facilities, and distribution of 

agricultural pesticides and fertilizers, feedstocks, gasoline, gasoline/ethanol and biodiesel blends. 

Although a large number of compounds which are considered air toxics could be 

impacted by this rule, we focused on those which were identified as national and regional-scale 

cancer and noncancer risk drivers in the 2002 NATA53 and were also likely to be significantly 

impacted by this rule. These compounds include benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde, and acrolein. Naphthalene impacts were included for petroleum refineries, since it 

is a significant emission product for those facilities. Ethanol impacts were also included in our 

analyses because of health concerns (Section 3.4.5) and its role as an acetaldehyde precursor. 

2002 air toxic emissions for stationary sources, other than for fires, were obtained from 

the 2002 National Emissions Inventory (NEI), version 3. Future year emissions of benzene, 1,3-

butadiene, formaldehyde, acetaldehyde, and acrolein were estimated for sectors only, rather than 

individual sources. These sectors included non-EGU (electric generating unit) point sources, 

EGU point sources, the nonpoint storage and transfer subsector, and other nonpoint sources. 

Emissions were estimated by applying the 2002 air toxics to VOC ratio to the future year VOC 

emission estimates. Air toxics from fires were estimated by applying toxics-to-VOC ratios to the 

VOC emissions from a fire inventory developed for air quality modeling. 2002 and future year 

ethanol emissions were estimated by speciating the VOC estimates. This was done using the 

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system, version 2.3. More details 

on the methods and data used to develop these inventories are found in a memo included in the 

docket for this rule.54
 32

Air toxic emission estimates for agricultural equipment (mainly diesel agricultural 

tractors) were obtained from the EPA NMIM model, as described for criteria pollutants in 

Section 3.1.2.1.1.1. 

3.1.2.2.2 Upstream Air Toxics Control Cases 

As described below, we developed emission factors for several air toxics using the most 

recent available data. These emission factors were used with estimates of changes in fuel 

volumes and associated energy outputs to estimate inventory changes associated with the RFS2 

volumes. In general, emission factors are expressed as grams per million BTU (g/mmBTU) of 

energy produced or distributed as part of the process. Underlying data are available in the docket 

for the rule. 

Agricultural Pesticides and Fertilizers – The estimation of air toxic emissions from production 

and application of pesticides and fertilizers was done using toxic fractions of the corresponding 

VOC emissions described in Section 3.1.3.1.1.1. Table 3.1-22 shows the toxic fractions, which 

were calculated from the 2002 NEI inventories for VOC and each of the listed toxic pollutants. 

All the pollutants except acrolein from pesticide application are based on nationwide inventories. 

California was the only state that reported acrolein emissions associated with pesticide 

application, so the 0.66% value shown in the table represents the sum of acrolein emissions 

divided by the sum of VOC emissions from pesticide application for all counties in California in 

2002. The fertilizer and pesticide application data come from queries of the NEI area source 

inventories for SCCs like "28017*" (for fertilizer application) and SCCs like "246180*" or like 

"246185*" (for pesticide application). 

The production and blending data for fertilizer and pesticides come from queries of the 

NEI point source data that were submitted by 40 states and Puerto Rico for the following MACT 

codes 

 0911 - Pesticide Active Ingredient Production 

 0960 - Agricultural Chemicals and Pesticides Manufacturing 

 1410 - Phosphate Fertilizers Production 

The data for these codes was compiled for the following four categories: Fertilizer production 

(F), Fertilizer mixing blending (FMB), Pesticide production (P), and Pesticide mixing blending 

(PMB). 
33

Table 3.1-22. Air Toxic Fractions of VOC for Fertilizers and Pesticides

 Fertilizer 

Production & 

Blending 

Pesticide 

Production & 

Blending 

Fertilizer 

Application 

Pesticide 

Application 

1,3-Butadiene -- 0.0003653 -- --

Acetaldehyde 6.530 E-06 5.198 E-05 -- --

Acrolein 3.320 E-06 1.513 E-05 -- 0.0066

Benzene -- 0.002038 -- 0.2615

Ethanol -- -- -- --

Formaldehyde 0.007517 0.011494 -- --

Petroleum Refineries – Total nationwide emissions of air toxics for 153 U. S. petroleum 

refineries in 2002 were obtained from data collected as part of a risk and technology review 

(RTR) for EPA’s proposed rule, “National Emission Standards for Hazardous Air Pollutants 

From Petroleum Refineries.”55 These emissions were divided by BTUs of energy produced by 

those refineries in 2002 to obtain emission factors in g/mmBTU. Thus the resultant emission 

factors represent 2002 technology and emission standards. Energy output estimates included all 

refinery products, such as conventional and reformulated gasoline, aviation gasoline, jet fuel, 

kerosene, distillate fuel oil, residual fuel oil, petrochemical feedstocks, naphthas, lubricants, and 

other miscellaneous products. Energy output was estimated by multiplying volume of each 

product supplied56 by its heating value in BTUs per gallon. 

Resultant emission factors are provided below in Table 3.1-24, along with those for 

ethanol and electricity production. 

Ethanol Production Facilities – There are a number of processes at ethanol production facilities 

that result in emissions of air toxics. These processes include fermentation, distillation of the 

resultant mash, and drying of spent wet grain to produce animal feed. Emissions of air toxics 

vary tremendously from facility to facility due to a variety of factors, and it is difficult to 

determine how differences in the production processes individually impact emissions. Numerous 

production facilities have commenced operation in the last few years. To develop emission 

factors we used the most recent available inventory for benzene, formaldehyde, acetaldehyde, 

and acrolein, from calendar year 2005. These data were obtained from two sources: 

1) 2005 NEI State submittals for SCCs associated with ethanol production facilities 

2) the 2005 Toxics Release Inventory (TRI) 

2005 NEI data submittals were obtained from EPA’s Office of Air Quality Planning and 

Standards. These data are included in the docket for the rule. Additional data for facilities not 

included in these submittals were obtained from the 2005 TRI 

(http://www.epa.gov/triexplorer/list-chemical-hap.htm). Where emissions data were not 

available for a facility, the facility was excluded from subsequent calculations. 

Only a few facilities reported very low emissions of 1,3-butadiene, and the rest reported 

no emissions, so emissions of this pollutant from ethanol production facilities were assumed to 
 34

be insignificant. Almost all of the data were from wet mill plants running on natural gas, so it 

was not possible to develop separate emission factors for wet and dry mill plants, or those 

running on coal or natural gas. 

Energy output for each facility was estimated by multiplying production capacity by the 

heating value for ethanol. Since data on actual production by facility were not available, all 

plants were assumed to operate at capacity. Estimates of production capacity were obtained 

from data collected by the Renewable Fuels Association 

(http://www.ethanolrfa.org/industry/locations/). For some major ethanol producers production 

capacity was not available for specific facilities. 

Data for facilities where both emissions and production capacity were available were 

used to estimate nationwide emission rates in g/mmBTU. Table 3.1-23 lists the number of 

ethanol production facilities with emissions data for various air toxics, as well as production 

capacity estimates. 

Table 3.1-23. 

Number of Facilities with Emission Inventory Data 

by Pollutant and Production Capacity Estimates

Pollutant No. of Facilities with Emissions Data and 

Production Capacity Estimates. 

Benzene 30 

Formaldehyde 35 

Acetaldehyde 50 

Acrolein 22 

An emission factor for ethanol was estimated using data collected in Minnesota from 16 

facilities, all of which were dry mill plants.57 Since most ethanol emissions occur during 

fermentation, and new production of ethanol is likely to occur at dry mill facilities, these data are 

likely to provide representative estimates of future year increases in ethanol emissions under the 

control scenarios modeled. The resultant emission factors for ethanol production facilities are 

provided in Table 3.1-24. 

Distillers’ grains with solubles (DGS) is a co-product of dry mill corn ethanol production 

that can be used as animal feed. Corn oil remaining in the DGS can be extracted and sold for 

commercial uses, such as biodiesel production, at a relatively high value compared to the DGS 

itself. The oil can be extracted by gravimetric methods or by extraction with n-hexane, which is 

a potentially important toxic emission associated with increased ethanol production. Capital 

costs for solvent extraction are higher, but so are yields. 

Corn oil for food grade use is produced by a process wherein corn is separated into 

component parts, prior to fermentation, with the starch heavy dehulled-degermed corn portion 

fed to the ethanol plant and the corn germ fed to a hexane-based corn oil extraction facility. This 

process is capital intensive and must be designed into the plant. We expect the food grade 

extraction process to be less widespread than commercial grade processes for these reasons. 
35

VeraSun recently submitted an application to the Iowa Department of Natural Resources 

(IDNR) to add a facility for solvent extraction of corn oil to an ethanol plant in Fort Dodge 

Iowa.58 In this application, Verasun proposed to control particulate matter emissions from the 

process using a baghouse, and to minimize VOC emissions through good design and operating 

processes. Verasun estimated that this plant, with an annual DDGS capacity of 455,000 tons of 

DDGS per year, would produce 305 tons of VOCs per year, with n-hexane emissions of 295 tons 

per year. PM10 emissions would be about 13 tons. 

EPA used the Verasun application data to develop an estimate of potential nationwide nhexane emissions from ethanol plants nationwide. EPA estimates that about 40% of ethanol 

production will have corn oil extraction by 2022; thus, we assumed that about half of this would 

be from solvent extraction and 20% of dry mill plants would employ this process. It is likely a 

number of plants will use gravimetric recovery, since it can be easily retrofitted to any size plant 

at modest capital cost. First, we developed emission rates per ton of DDGS production. Then 

we developed an estimate of DDGS produced nationwide, using industry characterization 

estimates of 13.67 billion gallons of dry mill ethanol production in 2022, and 0.00334 tons 

DDGS per gallon of ethanol produced by dry mills.59 Multiplying the emission rate from the 

Verasun application by total production of DDGS, EPA estimates these facilities could emit 

about 9,000 tons of n-hexane nationwide. However, given the very limited data on emissions 

from such facilities and the nascent nature of this process at ethanol production facilities, such 

estimates should be regarded as highly uncertain. 

Biodiesel Production Facilities -- To estimate emission factors for biodiesel production facilities, 

we identified air toxic emission data for individual facilities developed for the 2005 NEI. 

Unfortunately, only toxics data for two existing biodiesel facilities could be found. These data 

were used to develop toxic to VOC ratios, then applied to VOC emission factors for biodiesel 

plants obtained from GREET, with modifications to add energy used in crushing soybeans. 

VOC emission rates vary by feedstock. Toxic to VOC ratios, VOC emission rates, and resultant 

toxic emission rates in grams per gallon are given in Table 3.1-25. 

Transportation and Distribution of Gasoline, Ethanol, Gasoline/Ethanol Blends and Biodiesel -- 

Air toxic emissions associated with distributing fuel and fuel blends come from two sources. 

The first source is evaporative, spillage and permeation emissions from storage and transfer 

activities, and the second source is emissions from vehicles and pipeline pumps used to transport 

the fuels. Since a pipeline system does not exist for ethanol, increased ethanol use is likely to 

increase toxic emissions from vehicles used to transport it, while a corresponding decrease in 

gasoline distribution would decrease any emissions related to pipeline pumping. 

Storage and transfer activities result in evaporative emissions of benzene and ethanol 

from gasoline, ethanol, and gasoline/ethanol blends. Evaporative emissions from biodiesel fuel 

are not expected to be significant. Emissions of ethanol occur both during transport of ethanol 

from production facilities to bulk terminals, and after blending, at bulk terminals. In addition, 

emission factors for benzene must be estimated separately for fuel before and after blending. As 

previously discussed, we assumed bulk terminal emissions were associated with unblended 

gasoline. We then divided emissions into a refinery to bulk terminal component and a bulk 

terminal to dispensing facility component. Benzene emission factors for gasoline transport from 
 36

refinery to bulk terminals were weighted by the fraction of 2002 VOC emissions for this part of 

the process, whereas emission factors for E0 gasoline, E10 gasoline, and E85 were weighted by 

the fraction of 2002 VOC from the bulk terminal to the pump. Benzene emission rates from 

these activities also vary with the year being modeled, since phase-in of the recently finalized 

Mobile Source Air Toxics Rule will substantially reduce the amount of benzene in gasoline 

beginning in 2011.60 Thus, one set of emission factors were developed for 2002, and a separate 

set of emission factors for the reference case in that year. The reference case also includes 

impacts of the 2007 renewable fuels standard.61 Thus, the reference case already reflects ethanol 

volumes mandated by RFS1. 

The emission factors used for 2002 were derived from the estimated gasoline distribution 

inventory for benzene in 1999, estimated for the Mobile Source Air Toxics Rule.62 Total 

nationwide emissions were divided by the energy content of the total volume of gasoline 

distributed in 1999, to obtain the emission factor in g/mmBTU. Total volume of gasoline was 

based on gasoline sales as reported by the Energy Information Administration.63 To estimate the 

energy content, sales of fuel types (conventional, Federal reformulated, California reformulated) 

were multiplied by their respective heating values. 

The emission factors used for the reference case in 2022 were derived from an estimated 

gasoline distribution inventory for that year. This inventory estimate was calculated by linear 

interpolation of 2020 and 2030 inventories from the Mobile Source Air Toxics Rule. Total 

nationwide emissions were divided by the energy content of the total volume of gasoline 

projected for 2022 by the Energy Information Administration.64 To estimate the energy content, 

the projected gasoline volume was multiplied by the heating value for low-sulfur gasoline 

(115,000 BTU/gallon). 

We assumed that in order to attain the fuel benzene standard for gasoline promulgated in 

the Mobile Source Air Toxics Rule, E10 would have the same fuel benzene content per gallon as 

E0 . However, for E10 the E0 emission factor was adjusted to account for the lower energy 

content of E10 relative to E0. For E85, the E0 emission factor was adjusted to account for 66% 

lower benzene emissions per gallon, as well as the lower energy content of E85. 

The emission factors for benzene are provided in Table 3.1-26. 

To estimate ethanol emissions associated with the distribution of E10 and E85, ethanol to 

benzene emission ratios were applied to benzene estimates. The ratios were 14.8 for E10 and 

112.8 for E85. The ratio for E10 was obtained from the profile for composite evaporative 

emissions from U. S. EPA’s SPECIATE database, profile 1301.65 The ratio for E85 was 

obtained from analyses of evaporative emissions from three vehicles tested as part of the 

Auto/Oil program in the early 1990’s.66 These emission factors are reported in Table 3.1-26. 
37

Table 3.1-24. Air Toxic Emission Factors for Petroleum Refineries, Ethanol 

Refineries, and Electricity Production (g/mmBTU of fuel or electricity produced) 

Pollutant 

Petroleum 

Refinery 

Ethanol 

refinery 

Electricity 

Production 

1,3-butadiene 0.0014 N. A. 0.0001 

Acetaldehyde 0.0002 3.0585 0.0297 

Acrolein 0.0001 0.1323 0.0115 

Benzene 0.0264 0.0998 0.0443 

Ethanol 0.0000 21.6858 

Formaldehyde 0.0042 0.5263 0.0629 

Naphthalene 0.0029 

Table 3.1-25. Air Toxic Emission Factors for Biodiesel 

Production Facilities (g/gallon produced) 

Pollutant Toxic/VOC 

Ratio 

Biodiesel 

Soybean Oil EF 

(g/gal) 

Biodiesel Yellow 

Grease/tallow 

(g/gal) 

Renewable 

Biodiesel Soybean 

Oil (g/gal) 

VOC 0.040 0.042 0.029

Benzene 7.4x10-7 3.0x10-8 3.1x10-8 2.1x10-8

1,3-Butadiene 0 0 0 0

Formaldehyde 3.5x10-5 1.4x10-6 1.5x10-6 1.0x10-6

Acetaldehyde 5.6x10-6 2.3x10-7 2.4x10-7 1.6x10-7

Acrolein 4.8x10-6 1.9x10-7 2.0x10-7 1.4x10-7

Ethanol 0 0 0 0

Naphthalene 6.3x10-7 2.5x10-8 2.6x10-8 1.8x10-8

 
 38

Table 3.1-26. Air Toxic Evaporative Emission Factors for Gasoline, Ethanol, 

and Blend Transport and Distribution (g/mmBTU of fuel transported) 

Pollutant Process Year Fuel EF (g/mmBTU) 

Benzene Refinery to 

Bulk Terminal 2002 E0 0.0488 

 Refinery to 

Bulk Terminal 2022 E0 0.0270 

 Bulk Terminal 

to Pump 2002 E0 0.0908 

 Bulk Terminal 

to Pump 2022 E0 0.0502 

 Bulk Terminal 

to Pump 2022 E10 0.0519 

 Bulk Terminal 

to Pump 2022 E85 0.0228 

Ethanol Bulk Terminal 

to Pump 2022 E10 1.8591 

 Bulk Terminal 

to Pump 2022 E85 7.1432 

As mentioned previously, ethanol vapor emissions during transport from the ethanol plant 

to the bulk terminal are based on an adjustment to the gasoline transport VOC emissions to 

account for the much lower vapor pressure and molecular weight. 

 There are also toxic emissions associated with combustion of fuels used in transport and 

distribution of feedstocks and fuels. The emission factors for these are shown in Table 3.1-27 as 

fractions of exhaust VOC, or PM10 for exhaust naphthalene. The VOC and PM10 emission 

factors that these fractions are applied to are presented above in Table 3.1-21. The locomotive, 

marine distillate, and residual boiler estimates come from a 2005 EPA report.67 The heavy-duty 

diesel truck emission fractions come from a 2002 report documenting the toxics module of 

EPA's MOBILE6.2 model,68 and the pipeline values come from the EPA AP-42 document.69 
39

Table 3.1-27. 

Toxic Fractions of Exhaust VOC (or fraction of PM10 for exhaust naphthalene) 

(grams toxics per gram of VOC or PM10) 

Mode Source 

1,3-

Butadiene Acetaldehyde Acrolein Benzene Formaldehyde Naphthalene

Rail 

Diesel 

Locomotive 0.003246519 0.018786 0.0031238 0.002587511 0.04328653 0.0018716 

Barge 

Marine 

Diesel – 

Distillate 0.00061 0.074298 0.0035 0.020344 0.1496 0.0018716 

Ocean 

Tanker 

Residual 

Boiler 0 0.003858 0 0.000165354 0.02645669 0.0025885 

Truck 

HD Diesel 

Trucks 0.00061 0.0288 0.0035 0.0105 0.0782 0.00128892 

Pipeline 

Natural Gas 

Turbines 0 0.019048 0.0030476 0.005714286 0.33809524 

Gasoline 

Farm Equip 

HD Gasoline 

Trucks 0.088005387 

3.2 Non-GHG Emission Impact Results 

3.2.1 U.S. Total Reference Case Inventories for All Sectors (AEO 2007 only) 

The reference case emission inventories used for this final rule analysis are based on 

different sources depending on sector, and for most sectors they match what was used for the 

proposed rule and air quality analysis. 

 For stationary/area sources and aircraft we used the 2002 National Emissions Inventory 

(NEI), Version 3, including the NEI projections for 2020. The development of these inventories 

is documented in the November 27, 2007, memo titled, “Approach for Developing 2002 and 

Future Year National Emission Summaries,” from Madeleine Strum to Docket EPA-HQ-OAR2007-0491. That memo summarizes the methodologies and additional reference documents for 

criteria air pollutants (CAP) and mobile source air toxics (MSATs). 

For onroad mobile sources we used a special version of the MOVES model that estimates 

emissions from light-duty and heavy-duty gasoline vehicles, except for motorcycles. For other 

onroad vehicles including diesel vehicles and motorcycles, we relied on the MOBILE6.2 model 

as run using the NMIM platform with county specific fuel properties and temperatures. Most 

nonroad equipment was modeled with NONROAD2005d using NMIM, which is a version of the 

NONROAD that includes the benefits of the two nonroad regulations published in 2008 (the 

locomotive and marine diesel rule and the small spark-ignition and recreational marine engine 

rule). 

Inventories for locomotives and commercial marine vessels are not covered by the NONROAD 

model, and they have been updated since the 2002 NEI was published. Thus we used the more 

recent inventories published in the regulatory impact analyses of their respective recent 

rulemakings. Locomotives and C1/C2 commercial marine vessel inventories come from the 
 40

spring 2008 final rule, and the C3 commercial marine emission inventory is from the Notice of 

Proposed Rulemaking (NPRM) published in August 2009.77 

Table 3.2-1 shows the total 2022 mobile and non-mobile source inventory projections 

that were used as the basis for the impact percentages shown above in Table 3.2-1 through 3.2-4. 

The mobile source values in this table use the inventory values of the AEO 2007 reference case. 

Table 3.2-1. 2022 AEO 2007 Reference Case Emissions by Sector 

VOC CO NOx PM10 PM2.5 SO2 NH3 

Onroad 

Gasoline 981,432 26,547,169 2,001,543 46,284 42,619 34,031 390,486 

Onroad Diesel 140,854 243,820 1,307,150 62,253 37,357 4,352 11,426 

Nonroad 

Gasoline 1,440,414 14,924,581 269,443 56,660 52,305 1,836 1,112 

Other Nonroada 270,707 1,402,948 3,353,753 230,305 209,516 1,026,510 3,034 

Stationary/Area 8,740,057 11,049,239 5,773,927 3,194,610 3,047,714 7,864,681 3,839,925 

Total 11,573,464 54,167,758 12,705,817 3,590,112 3,389,512 8,931,411 4,245,983 

Table 3.2-1 

continued Benzene Ethanol 1,3-

Butadiene

AcetaldehydeFormaldehyde Naphthalene Acrolein

Onroad 

Gasoline 33,607 15,985 4,487 6,455 10,681 3,787 513 

Onroad Diesel 1,749 0 958 3,857 10,589 20 513 

Nonroad 

Gasoline 26,193 66,150 4,935 4,033 7,245 713 436 

Other Nonroada 3,815 5,294 939 9,550 22,355 24 1,021 

Stationary/Area 111,337 462,566 1,847 13,118 23,846 9,404 3,412 

Total 176,701 549,995 13,166 37,013 74,716 13,949 5,895 

a

 Nonroad diesel, LPG, CNG engines and all locomotive, aircraft, and commercial marine

3.2.2 2022 RFS2 Total Non-GHG Emission Inventory Impacts 

Our projected overall emission impacts for each of the analyzed RFS2 renewable fuel 

scenarios are shown in Table 3.2-2 and Table 3.2-3 for 2022, showing the expected emission 

changes for the U.S. relative to each of the reference cases. The percent contribution of these 

impacts relative to the total U.S. inventory across all sectors is also shown, using the AEO 2007 

reference case totals from Table 3.2-1. . 
41

Table 3.2-2. 

RFS2 Emission Impacts in 2022 Relative to the AEO2007 Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 208,316 1.64% 184,820 1.45% 131,124 1.03% 

HC 20,123 0.17% 24,523 0.21% 35,342 0.31% 

PM10 71,779 2.00% 63,323 1.76% 44,099 1.23% 

PM2.5 17,355 0.51% 14,393 0.42% 7,678 0.23% 

CO -364,400 -0.67% -376,419 -0.69% -404,199 -0.75% 

Benzene -979 -0.55% -1,004 -0.57% -1,056 -0.60% 

Ethanol 33,749 6.14% 54,137 9.84% 102,359 18.61% 

1,3-Butadiene 59 0.45% 59 0.45% 59 0.45% 

Acetaldehyde 1,978 5.34% 3,108 8.40% 5,757 15.56% 

Formaldehyde 113 0.15% 130 0.17% 170 0.23% 

Naphthalene -4 -0.03% -4 -0.03% -4 -0.03% 

Acrolein 16 0.28% 21 0.35% 31 0.53% 

SO2 20,456 0.23% 5,065 0.06% -30,058 -0.34% 

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15% 

Table 3.2-3. 

RFS2 Emission Impacts in 2022 Relative to the RFS1 Mandate Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 271,100 2.13% 247,604 1.95% 193,907 1.53% 

HC 96,362 0.83% 100,762 0.87% 111,581 0.96% 

PM10 77,469 2.16% 69,013 1.92% 49,791 1.39% 

PM2.5 18,511 0.55% 15,549 0.46% 8,834 0.26% 

CO -2,857,823 -5.28% -2,869,842 -5.30% -2,897,622 -5.35% 

Benzene -4,240 -2.40% -4,264 -2.41% -4,316 -2.44% 

Ethanol 79,736 14.50% 100,123 18.20% 148,345 26.97% 

1,3-Butadiene 224 1.70% 224 1.70% 224 1.70% 

Acetaldehyde 4,718 12.75% 5,848 15.80% 8,497 22.96% 

Formaldehyde 338 0.45% 355 0.48% 395 0.53% 

Naphthalene -1 0.00% -1 -0.01% -1 0.00% 

Acrolein 18 0.31% 22 0.38% 33 0.56% 

SO2 18,678 0.21% 3,286 0.04% -31,836 -0.36% 

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15% 
 42

Fuel production and distribution emission impacts of the RFS2 program were estimated 

in conjunction with the development of life cycle GHG emission impacts, and the GHG emission 

inventories discussed in Chapter 2. These emissions are calculated according to the breakdowns 

of agriculture, feedstock transport, fuel production, and fuel distribution; the basic calculation is 

a function of fuel volumes in the analysis year and the emission factors associated with each 

process or subprocess. Additionally, the emission impact of displaced petroleum is estimated, 

using the same domestic/import shares discussed in chapter 2. 

In general the basis for this life cycle evaluation was the analysis conducted as part of the 

Renewable Fuel Standard (RFS1) rulemaking, but enhanced significantly. While our approach 

for the RFS1 was to rely heavily on the “Greenhouse Gases, Regulated Emissions, and Energy 

Use in Transportation” (GREET) model, developed by the Department of Energy’s Argonne 

National Laboratory (ANL), we are now able to take advantage of additional information and 

models to significantly strengthen and expand our analysis for this rule. In particular, the 

modeling of the agriculture sector was greatly expanded beyond the RFS1 rule analysis, 

employing economic and agriculture models to consider factors such as land-use impact, 

agricultural burning, fertilizer, pesticide use, livestock, crop allocation, and crop exports. 

Other updates and enhancements to the GREET model assumptions include updated 

emission factors for NOx, CO, and SO2 from new cellulosic ethanol plant modeling by the 

National Renewable Energy Laboratory (NREL), and updated fuel and feedstock transport 

emission factors that account for recent EPA emission standards and modeling, such as the Tier 4 

diesel truck standards published in 2004 and the locomotive and commercial marine standards 

finalized in 2008. Emission factors for new corn ethanol plants continue to use the values 

developed for the RFS1 rule, which were based on data submitted by states for dry mill plants. 

There are no new standards planned at this time that would offer any additional control of 

emissions from corn or cellulosic ethanol plants. In addition, GREET does not include air toxics 

or ethanol. Thus emission factors for ethanol and the following air toxics were added: benzene, 

1,3-butadiene, formaldehyde, acetaldehyde, acrolein and naphthalene. 

Results of these calculations relative to each of the reference cases for 2022 are shown in 

Table 3.2-4 and Table 3.2-5 for the criteria pollutants, ammonia, ethanol and individual air toxic 

pollutants. It should be noted that the impacts relative to the RFS1 reference case use the same 

agricultural impacts as for the AEO 2007 reference case, since there was no agricultural 

modeling done for the RFS1 case. Due to the complex interactions involved in projections in 

agricultural modeling, it was not considered reasonable to attempt any sort of proportional 

adjustments to the AEO 2007 agricultural projections to approximate the RFS1 case. 

The fuel production and distribution impacts of today's rule on VOC are mainly due to 

increases in emissions connected with biofuel production, countered by decreases in emissions 

associated with gasoline production and distribution as ethanol displaces some of the gasoline. 

Increases in PM2.5, SOx and especially NOx are driven by stationary combustion emissions 

from the substantial increase in corn and cellulosic ethanol production. Biofuel plants (corn and 

cellulosic) tend to have greater combustion emissions relative to petroleum refineries on a perBTU of fuel produced basis. Increases in SOx emissions are also due to increases in agricultural 

chemical production and transport, while substantial PM increases are also associated with 
43

fugitive dust from agricultural operations. Ammonia emissions are expected to increase 

substantially due to increased ammonia from fertilizer use. 

Ethanol vapor and most air toxic emissions associated with fuel production and 

distribution are projected to increase. Relative to the US total reference case emissions with 

RFS1 mandate ethanol volumes, the primary RFS2 control case is estimated to yield increases of 

4-13 percent for acetaldehyde and ethanol vapor, driven directly by the increased ethanol 

production and distribution. Formaldehyde and acrolein increases are smaller, on the order of 

0.4-1 percent. There are also very small decreases in benzene, 1,3-butadiene and naphthalene 

relative to the US total emissions. 

Table 3.2-4. Fuel Production and Distribution Impacts for 2022 RFS2 Control Cases 

Relative to the AEO 2007 Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 187,666 1.48% 164,170 1.29% 110,473 0.87% 

HC 16,604 0.14% 19,737 0.17% 27,547 0.24% 

PM10 72,348 2.02% 63,892 1.78% 44,669 1.24% 

PM2.5 17,670 0.52% 14,707 0.43% 7,993 0.24% 

CO 142,191 0.26% 130,172 0.24% 102,392 0.19% 

Benzene -208 -0.12% -236 -0.13% -298 -0.17% 

Ethanol 20,291 3.69% 35,865 6.52% 72,815 13.24% 

1,3-Butadiene 0 0.00% 0 0.00% 0 0.00% 

Acetaldehyde 823 2.22% 933 2.52% 1,193 3.22% 

Formaldehyde 170 0.23% 187 0.25% 227 0.30% 

Naphthalene -5 -0.04% -6 -0.04% -7 -0.05% 

Acrolein 33 0.56% 37 0.63% 48 0.81% 

SO2 20,435 0.23% 5,044 0.06% -30,078 -0.34% 

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15% 
 44

Table 3.2-5. Fuel Production and Distribution Impacts for 2022 RFS2 Control Cases 

Relative to the RFS1 Mandate Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 193,161 1.52% 169,665 1.34% 115,969 0.91% 

HC 73,881 0.64% 77,014 0.67% 84,825 0.73% 

PM10 78,039 2.17% 69,583 1.94% 50,360 1.40% 

PM2.5 18,826 0.56% 15,864 0.47% 9,149 0.27% 

CO 147,677 0.27% 135,658 0.25% 107,878 0.20% 

Benzene -203 -0.12% -231 -0.13% -294 -0.17% 

Ethanol 53,871 9.79% 69,445 12.63% 106,395 19.34% 

1,3-Butadiene -1 -0.01% -1 -0.01% -1 -0.01% 

Acetaldehyde 1,507 4.07% 1,617 4.37% 1,877 5.07% 

Formaldehyde 276 0.37% 293 0.39% 333 0.45% 

Naphthalene -7 -0.05% -8 -0.06% -9 -0.06% 

Acrolein 62 1.06% 67 1.13% 77 1.31% 

SO2 18,657 0.21% 3,266 0.04% -31,857 -0.36% 

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15% 

A breakout of these upstream emissions by where they occur in the 

production/distribution chain is shown in Table 3.2-6. The displaced gasoline line of this table 

refers to the impacts of decreasing the petroleum based gasoline and diesel fuel production as 

some of the needed energy is replaced with ethanol and biodiesel fuels. 
45

Table 3.2-6. Emission Inventory Impacts by Fuel Production/Distribution Segment for the 

Primary RFS2 Control Case Relative to the AEO 2007 Reference Case (annual short tons)

VOC CO NOx PM10 PM2.5 SO2 NH3 

Agriculture 2,398 11,831 6,597 56,512 9,169 22,157 48,709

Biofuel Feedstock 

Transport 355 508 1,440 239 106 584 0

Biofuel Production 18,867 129,586 193,040 22,621 11,241 8,932 2

Biofuel Transport 

& Distribution 18,041 3,316 7,599 323 219 962 0

Displaced gasoline -19,925 -15,069 -44,506 -15,803 -6,028 -27,591 0

Total Upstream 19,737 130,172 164,170 63,892 14,707 5,044 48,711

Table 3.2-6 

continued Benzene Ethanol 1,3-Butadiene

AcetaldehydeFormaldehyde Naphthaleneb Acrolein 

Agriculture -145.8 0 1.99 13.51 47.89 0.73 -3.24

Biofuel 

Feedstock 

Transport 

3.73 0 0.22 10.24 27.80 0.31 1.24

Biofuel 

Production 28.86 6,435 0.01 906.9 154.9 0.02 39.01

Biofuel 

Transport & 

Distribution 

-30.84 29,430 1.18 11.01 25.67 0.57 1.46

Displaced 

gasoline -91.80 0 -3.84 -8.72 -69.50 -7.55 -1.27

Total Upstream -235.8 35,865 -0.44 933.0 186.8 -5.92 37.21

 Tables 3.2-7 and 3.2-8 summarize the vehicle and equipment emission impacts in 2022, 

including the biodiesel impacts. Table 3.2-9 shows that the biodiesel contribution to these 

impacts is quite small. While the three fuel effect scenarios were only modeled for passenger 

cars and trucks, these totals reflect the net emissions from all mobile sources, including 

passenger cars and trucks, heavy duty trucks, off-road sources and portable fuel containers, using 

the same emissions in all three cases for the non-passenger car/truck categories. A full 

description of the basis of these vehicle and equipment emission impacts is given in Section 

3.1.1 of this document. 

Carbon monoxide, benzene, and acrolein are projected to decrease in 2022 under today’s 

rule, while NOx, HC and the other air toxics, especially ethanol and acetaldehyde, are projected 

to increase due to the impacts of E10. 
 46

Table 3.2-7. 

2022 Vehicle and Equipment Emission Impacts for the RFS2 Control Cases 

Relative to the AEO 2007 Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 20,650 0.16% 20,650 0.16% 20,650 0.16% 

HC 3,519 0.03% 4,786 0.04% 7,795 0.07% 

PM10 -569 -0.02% -569 -0.02% -569 -0.02% 

PM2.5 -315 -0.01% -315 -0.01% -315 -0.01% 

CO -506,591 -0.94% -506,591 -0.94% -506,591 -0.94% 

Benzene -771 -0.44% -768 -0.43% -758 -0.43% 

Ethanol 13,459 2.45% 18,272 3.32% 29,544 5.37% 

1,3-Butadiene 59 0.45% 59 0.45% 59 0.45% 

Acetaldehyde 1,155 3.12% 2,175 5.88% 4,564 12.33% 

Formaldehyde -57 -0.08% -57 -0.08% -57 -0.08% 

Naphthalene 2 0.01% 2 0.01% 3 0.02% 

Acrolein -16 -0.28% -16 -0.28% -16 -0.28% 

SO2 21 0.00% 21 0.00% 21 0.00% 

NH3 0 0.00% 0 0.00% 0 0.00% 
47

Table 3.2-8. 

2022 Vehicle and Equipment Emission Impacts for the RFS2 Control Cases 

Relative to RFS1 Mandate Reference Case

Low Ethanol 

Scenario 

Mid (Primary) 

Ethanol Scenario 

High Ethanol 

Scenario 

Pollutant 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

Annual 

Short Tons 

% of Total 

US 

Inventory 

NOx 77,939 0.61% 77,939 0.61% 77,939 0.61% 

HC 22,480 0.19% 23,748 0.21% 26,756 0.23% 

PM10 -569 -0.02% -569 -0.02% -569 -0.02% 

PM2.5 -315 -0.01% -315 -0.01% -315 -0.01% 

CO -3,005,500 -5.55% -3,005,500 -5.55% -3,005,500 -5.55% 

Benzene -4,036 -2.28% -4,033 -2.28% -4,022 -2.28% 

Ethanol 25,864 4.70% 30,678 5.58% 41,950 7.63% 

1,3-Butadiene 225 1.71% 225 1.71% 225 1.71% 

Acetaldehyde 3,210 8.67% 4,231 11.43% 6,620 17.89% 

Formaldehyde 62 0.08% 62 0.08% 62 0.08% 

Naphthalene 7 0.05% 7 0.05% 8 0.06% 

Acrolein -44 -0.75% -44 -0.75% -44 -0.75% 

SO2 21 0.00% 21 0.00% 21 0.00% 

NH3 0 0.00% 0 0.00% 0 0.00% 
 48

Table 3.2-9. 

2022 Biodiesel Emission Impacts for All RFS2 Control Cases 

Relative to Reference Cases 

(these impacts are included in Tables 3.2-7 and 3.2-8)

Biodiesel 

Impacts Pollutant 

Annual Short 

Tons 

NOx 1,346 

HC -2,422 

PM10 -569 

PM2.5 -315 

CO -4,104 

Benzene -30.08 

Ethanol 0.00 

1,3-Butadiene -16.48 

Acetaldehyde -66.34 

Formaldehyde -182.09 

Naphthalene -0.38 

Acrolein -8.82 

SO2 0 

NH3 0 

 Table 3.2-10 shows a breakout of the relative impacts of the RFS2 volumes on the 

various types of vehicle and equipment emissions for the primary (mid-ethanol) case relative to 

the AEO 2007 reference case. The gasoline vehicle exhaust emission values were generated by 

MOVES, while the NMIM model was used to generate the other vehicle and equipment emission 

impacts. The impacts on portable fuel container emissions were estimated using an analysis of 

available data, adjusted for the ethanol and gasoline fuel volumes in this rule. The methods used 

are described above in Section 3.1.1. The substantial CO reductions and NOx and ethanol 

increases from light-duty vehicles and nonroad gasoline equipment are due to the effects of 

increased E10 marketshare with no E0 remaining in the market. Evaporative and refueling vapor 

emissions only include VOC, ethanol, benzene, and naphthalene. 
49

Table 3.2-10. Vehicle and Equipment Emission Inventory Impacts by Source Type 

for the Primary RFS2 Control (mid-ethanol) Case 

Relative to the AEO 2007 Reference Case (annual short tons)

VOCa CO NOx PM10 PM2.5 SO2 NH3 

Light-duty gasoline 

vehicle exhaust -1,437 -72,872 10,034 0.0 0.0 0.0 0.0

Light-duty gasoline 

vehicle evap 3,447 n/a b n/a n/a n/a n/a n/a

Light-duty gasoline 

vehicle refueling 2,015 n/a n/a n/a n/a n/a n/a

Heavy-duty gasoline 

vehicle exhaust 2,168 -21,163 58 0.0 0.0 0.0 0.0

Heavy-duty gasoline 

vehicle evap -750 n/a n/a n/a n/a n/a n/a

Heavy-duty gasoline 

vehicle refueling 440 n/a n/a n/a n/a n/a n/a

Nonroad gasoline 

equipment exhaust -6,413 -408,453 9,212 0.0 0.0 20.7 0.0

Nonroad gasoline 

equipment evap 6,702 n/a n/a n/a n/a n/a n/a

Nonroad gasoline 

equipment refueling 563 n/a n/a n/a n/a n/a n/a

Portable fuel 

containers 1,037 n/a n/a n/a n/a n/a n/a

Onroad diesel 

vehicles -2,422 -4,104 1,346 -569 -315 0.0 0.0
 50

Table 3.2-10 

continued Benzene Ethanol 1,3-Butadiene

AcetaldehydeFormaldehydeNaphthalene Acrolein 

Light-duty gasoline 

vehicle exhaust -287 8,773 21.5 2,034 73 0.00 0.65

Light-duty gasoline 

vehicle evap 6.65 500 n/a n/a n/a 1.28 n/a

Light-duty gasoline 

vehicle refueling 6.63 770 n/a n/a n/a 0.82 n/a

Heavy-duty 

gasoline vehicle 

exhaust 

-47 57 0.00 19 -2.11 0.00 0.00

Heavy-duty 

gasoline vehicle 

evap 

-1.31 315 n/a n/a n/a 0.09 n/a

Heavy-duty 

gasoline vehicle 

refueling 

1.25 157 n/a n/a n/a 0.05 n/a

Nonroad gasoline 

equipment exhaust -737 2,497 57.4 189 54 0.00 -7.95

Nonroad gasoline 

equipment evap 106 4,556 n/a n/a n/a 0.00 n/a

Nonroad gasoline 

equipment refueling 106 972 n/a n/a n/a 0.00 n/a

Portable fuel 

containers -0.30 646 n/a n/a n/a 0.13 n/a

Onroad diesel 

vehicles -30.08 0.00 -16.48 -66.34 -182.09 -0.38 -8.82

a

 "VOC" values shown are actually THC for onroad gasoline exhaust and evaporative emissions. 

b

 n/a = Not applicable

 Table 3.2-11 shows the relative impacts of various types of renewable fuels on the basis 

of tons per million BTUs of renewable fuel consumed. These values include all 

vehicle/equipment as well as upstream fuel production/distribution impacts. 
51

Table 3.2-11. Emission Inventory Impacts by Type of Renewable Fuel 

for the Primary RFS2 Control Case Relative to the AEO 2007 Reference Case 

(tons per mmBTU)

VOC CO NOx PM10 PM2.5 SO2 NH3 

Ethanol from 

domestic corn 

(except coal) 

10,301 4,013 3,128 12,175 1,991 3,403 8,420

Ethanol from 

domestic corn 

(coal) 

439 317 320 1,330 351 427 349

Ethanol from 

domestic 

cellulosic 

10,121 29,888 32,440 13,168 2,350 -8,377 15,107

Ethanol from 

imported 

sugarcane 

1,717 -830 -2,621 -1,116 -418 -1,916 44

Biodiesel -1,049 913 -290 4,268 632 1,580 4,171

Renewable Diesel -1,602 23 -26.3 -102 -27 -169 0.0

Cellulosic Diesel -190 95,847 131,218 34,169 9,831 10,096 21,085

Table 3.2-11 

continued Benzene Ethanol 1,3-

Butadiene

AcetaldehydeFormaldehydeNaphthalene Acrolein

Ethanol from 

domestic corn 

(except coal) 

30.1 10,870 0.097 335 67.6 -0.617 141

Ethanol from 

domestic corn 

(coal) 

1.2 451 0.004 13.8 2.8 -0.026 5.8

Ethanol from 

domestic 

cellulosic 

39.3 17,024 0.078 245 47.1 -1.223 237

Ethanol from 

imported 

sugarcane 

26.4 5,220 -0.185 0.25 -3.0 -0.500 77.5

Biodiesel 10.4 0 -0.188 1.64 0.77 -0.658 62.6

Renewable Diesel 65.6 0 0.009 0.45 0.51 -0.061 0.06

Cellulosic Diesel -0.9 2,299 -0.26 337 71.0 -2.84 331
 52

3.3 Emission Inventories Used in the Air Quality Modeling 

3.3.1 Overview of Inventory Differences 

 Section 3.2 above describes our latest emission inventory impacts projected to result from 

the increased use of renewable fuels as required by the RFS2 standards. However, the air quality 

modeling had to be started long before these latest emission inventory impacts could be 

determined. The air quality modeling presented in Section 3.4 utilized inventory impact 

estimates based in large part on the analysis conducted for the NPRM, but with a few 

enhancements. Below is an overview of the differences between these inventory impact 

estimates. Details of the differences between these inventories are presented in Section 3.3.2 of 

this RIA. 

 To put the differences in context, Table 3.3-1 shows the different renewable fuel volumes 

considered for the three analyses. This shows that the volumes used for the NPRM analysis were 

also the basis of the inventories used for the air quality modeling. The RFS1 reference case 

listed here is the RFS1 mandate case. The primary (mid-ethanol) case considered for this final 

rule includes much less cellulosic ethanol than in the prior analyses, but makes up for that with 

diesel fuel produced from cellulosic feedstocks. The final rule case that is most comparable to 

the RFS2 control case considered in the NPRM and air quality analyses is the high ethanol case 

shown in the last row of the table. 

Table 3.3-1. 

Renewable Fuel Volumes Used in Each Analysis 

(Bgal/year in 2022) 

Analysis Ethanol Scenario Corn Cellulosic Imported Total Biodiesel Renewable 

Diesel 

Cellulosic 

Diesel 

RFS1 Ref 5.81 0.25 0.64 6.70 0.38 0.0 0.0 

AEO Ref 12.29 0.25 0.64 13.18 0.38 0.0 0.0 

NPRM 

& AQ 

RFS2 15.0 16.0 3.14 34.14 0.81 0.38 0.0 

RFS1 Ref 7.046 0.0 0.0 7.046 0.303 0.0 0.0 

AEO Ref 12.29 0.25 0.64 13.18 0.38 0.0 0.0 

Low 

Ethanol 15.0 0.25 2.24 17.49 1.67 0.15 9.26 

MidEthanol 

(Primary) 

15.0 4.92 2.24 22.16 1.67 0.15 6.52 

FRM 

High 

Ethanol 15.0 16.0 2.24 33.24 1.67 0.15 0.0 

Tables 3.3-2 and 3.3-3 summarize the differences between the US total sum of the 

county-level impacts used for the air quality modeling and the final rule nationwide impacts 

relative to the RFS1 mandate and AEO 2007 reference cases. 
53

Table 3.3-2. 

Comparison of Air Quality Inventory Impacts to FRM Impacts 

 in 2022 Relative to the RFS1 Reference Case

Air Quality 

Inventory 

Impacts 

FRM Mid 

(Primary) Ethanol 

Impacts 

FRM High 

Ethanol Impacts Pollutant 

Annual Short 

Tons 

Annual Short 

Tons 

% 

Change 

vs AQ 

Annual 

Short Tons 

% 

Change 

vs AQ 

NOx 365,968 247,604 -32% 193,907 -47%

HC 119,873 100,762 -16% 111,581 -7%

PM10 68,646 69,013 1% 49,791 -27%

PM2.5 18,199 15,549 -15% 8,834 -51%

CO -4,619,904 -2,869,842 38% -2,897,622 37%

Benzene -9,662 -4,264 56% -4,316 55%

Ethanol N/Ab 100,123 -- 148,345 --

1,3-Butadiene -194 224 216% 224 216%

Acetaldehyde 7,317 5,848 -20% 8,497 16%

Formaldehyde 173 355 105% 395 128%

Acrolein 79 22 -71% 33 -58%

SO2 57,380 3,286 -94% -31,836 -155%

NH3 141 48,711 34352% 48,709 34351% a

 Ethanol emissions for air quality modeling were generated by application of VOC 

speciation profiles in SMOKE, the emissions pre-processor for air quality modeling, so 

they were not one of the air quality inventory inputs.. 
 54

Table 3.3-3. 

Comparison of Air Quality Inventory Impacts vs FRM Impacts 

 in 2022 Relative to the AEO 2007 Reference Case

Air Quality 

Inventory 

Impacts 

FRM Mid 

(Primary) Ethanol 

Impacts 

FRM High 

Ethanol Impacts Pollutant 

Annual Short 

Tons 

Annual 

Short Tons 

% 

Change 

vs AQ 

Annual 

Short Tons 

% 

Change 

vs AQ 

NOx 258,357 184,820 -28% 131,124 -49%

HC 38,186 24,523 -36% 35,342 -7%

PM10 55,877 63,323 13% 44,099 -21%

PM2.5 17,277 14,393 -17% 7,678 -56%

CO -1,743,352 -376,419 78% -404,199 77%

Benzene -4,094 -1,004 75% -1,056 74%

Ethanol N/Ab 54,137 -- 102,359 --

1,3-Butadiene -291 59 120% 59 120%

Acetaldehyde 4,727 3,108 -34% 5,757 22%

Formaldehyde -127 130 202% 170 234%

Acrolein 47 21 -55% 31 -33%

SO2 15,311 5,065 -67% -30,058 -296%

NH3 210 48,711 23065% 48,709 23064% a

 Ethanol emissions for air quality modeling were generated by application of VOC 

speciation profiles in SMOKE, the emissions pre-processor for air quality modeling, 

so they were not one of the air quality inventory inputs.. 

Table 3.3-4 shows the US total emission inventories used for each of the air quality 

modeling cases along with the percent change from each reference case to the control case. 
55

Table 3.3-4. 

Air Quality Modeling Inventories and Percent Impacts in 2022 

US Total 

RFS1 

US Total 

AEO 

US Total 

RFS2 

RFS2 vs 

RFS1 

RFS2 vs 

AEO 

Pollutant 

Annual 

Short Tons 

Annual 

Short Tons 

Annual 

Short Tons 

Percent 

Change 

Percent 

Change 

NOx 11,415,147 11,522,759 11,781,115 3.21% 2.24% 

HC 10,292,785 10,374,472 10,412,658 1.16% 0.37% 

PM10 11,999,983 12,012,752 12,068,629 0.57% 0.47% 

PM2.5 3,371,024 3,371,946 3,389,223 0.54% 0.51% 

CO 51,631,075 48,754,523 47,011,171 -8.95% -3.58% 

Benzene 226,683 221,115 217,021 -4.26% -1.85% 

Ethanola -- -- -- -- -- 

1,3-Butadiene 14,458 14,554 14,264 -1.34% -2.00% 

Acetaldehyde 58,405 60,995 65,722 12.53% 7.75% 

Formaldehyde 140,156 140,456 140,330 0.12% -0.09% 

Acrolein 6,399 6,431 6,477 1.23% 0.73% 

SO2 8,878,706 8,920,775 8,936,086 0.65% 0.17% 

NH3 4,213,048 4,212,979 4,213,189 0.00% 0.00% a

 Ethanol emissions were generated by application of VOC speciation profiles in 

SMOKE, the emissions pre-processor for air quality modeling, so they were not one of 

the air quality inventory inputs..

3.3.1.1 Major Differences Between Air Quality Modeling Inventory and Nationwide NPRM 

and FRM Inventories 

In attempting to compare the inventory used for air quality modeling with the nationwide 

inventories presented in either the draft RIA of the proposed rule or this final RIA it is important 

to keep in mind that (a) the air quality inventories are actual estimates of total ton per year 

emissions for all sectors, whereas the emission inventory impacts presented in the RIA are only 

ton per year changes (increases or decreases) for the sectors that we consider to be affected by 

the rule, and (b) as described in Section 3.3.2, very different methods are used calculate the 

incremental upstream changes at a national level for the RIA versus the adjusted total countylevel upstream inventories used for air quality modeling. 

Differences Between NPRM and Air Quality Modeling Inventories 

• The renewable fuel volumes that were considered for the NPRM and air quality modeling 

were the same, as shown in Table 3.3-1, but there were substantial changes in some 

portions of the emission inventories. 

• The air quality modeling inventory used a greatly increased estimate of ethanol transport 

VOC and ethanol vapor losses, based on preliminary results of the ORNL analysis of 
 56

ethanol transport modes and distances.71 This is discussed in section 3.3.2.1.1 of this 

RIA. 

• The method used to calculate the upstream portion of the air quality modeling inventory 

differs from the method used to calculate the estimated nationwide impacts of the rule. 

The main difference was for certain non-mobile source sectors where adjustment factors 

were applied to existing air quality modeling inventories, rather than attempting to 

add/subtract the absolute tons of impact within each county for each source (SCC). This 

difference applies mainly to agriculture, crude oil production and transport, and gasoline 

refining and distribution. A more detailed description of these methodological 

differences is provided in section 3.3.2 of this RIA. 

• The downstream portion of the air quality modeling inventory was produced by running 

an updated version of the MOVES model at the state-month level and the NMIM model 

at the county-month level to generate the downstream impacts (vehicle and equipment 

emissions). More details are presented in Section 3.3.2.2 below. 

Differences Between Air Quality Modeling and FRM Inventories 

• The FRM inventory uses the final results of the ORNL analysis of ethanol transport 

modes and distances rather than the preliminary results used for the air quality modeling 

inventory analysis. Relative to the version used for the air quality modeling this included 

slightly increased truck and water transport, slightly less rail transport, and 16 percent 

less ethanol volume loaded into transport or storage tanks in the RFS2 control case (34 

bgal ethanol), due to use of fewer total mode transfers. 

• The FRM inventory also includes a substantial reduction of cellulosic biofuel plant 

energy requirements to account for the portion of the biomass feedstock that is not 

combusted for process heat. 

• The FRM downstream inventory incorporates a revision of E85 effects to remove all but 

ethanol and acetaldehyde emission effects, due to lack of sufficient data to justify any 

effects on other pollutants. 

• The FRM downstream inventory uses a hybrid approach, applying “more sensitive” 

impacts for E10 and pre-Tier 2 light duty vehicles, and applying the “less sensitive” E10 

effects for Tier 2 light duty cars and trucks (meaning no impact for NOx or NMHC). 

• The FRM downstream inventory uses updated estimates of evaporative permeation 

impacts of E10 based on recent studies. 

3.3.2 Detailed Explanation of Inventory Differences 

 This section describes how the county-level emission inventories were prepared for use in 

air quality modeling, and how they differ from the NPRM nationwide inventories and the final 

rule nationwide inventories. Air quality modeling requires much more detail and in some cases a 
57

very different method than estimation of nationwide totals. The information provided here only 

addresses the first step of inventory preparation for air quality modeling. The final steps involve 

processes like hourly allocation and certain types of temperature adjustments. Those steps, as 

well as application of adjustments related to the affected stationary (point and non-point) source 

categories, are explained in greater detail in a separate technical support document.70 

3.3.2.1 Differences in Upstream Impacts between Inventories 

3.3.2.1.1 Calculation of Vapor Losses During Ethanol Transport 

For “upstream” emissions associated with fuel production and distribution, the largest 

change from the NPRM to the air quality modeling analysis was the improved estimate of VOC 

and ethanol vapor emissions during ethanol transport, made possible by a detailed analysis of 

costs and transport modes conducted by Oak Ridge National Laboratory (ORNL).

71 This change 

substantially increased the ethanol and VOC emissions associated with this rule. 

For the NPRM analysis these emissions were estimated using a very simplified approach 

based on an adjustment to the gasoline transport VOC emissions to account for the much lower 

vapor pressure (approximately 3 psi at 100F for denatured ethanol versus 9 psi for gasoline) and 

molecular weight (48.7 for denatured ethanol versus approximately 72 for gasoline vapor). The 

net factor is 0.23 x gasoline evap VOC. Using the gasoline VOC EF of 14.94 g/mmBTU from 

Table 3.1-21 yields an EF of 5.20 g/mmBTU as shown in the following calculation. However, 

an oversight in the NPRM upstream impacts spreadsheet model resulted in use of an earlier 

estimate of the gasoline VOC EF of 10.2137 g/mmBTU for this ethanol calculation, which meant 

that the reported ethanol EF used in the NPRM was actually 3.56 g/mmBTU. 

5.20 g/mmBTU of ethanol 

 = 0.23 x gasoline VOC per-gallon EF / ethanol energy content 

 = 0.23 x (14.94 g/mmBTU x 115000 BTU/gal) / 76000 BTU/gal 

 

As mentioned in the NPRM, the main shortcoming of this methodology was that it did 

not account for differences between ethanol and gasoline transport modes, distances, or transfer 

methods in movement of the fuel from production facility to the bulk distribution terminal. For 

the air quality modeling analysis and final rule analysis this method was replaced using data 

from the ORNL analysis of projected ethanol transport modes, distances, and volumes 

transferred under various ethanol volume scenarios. That newer method yielded greatly 

increased ethanol vapor and VOC emissions. The air quality modeling analysis used preliminary 

results of the ORNL study, which yielded average ethanol EFs of 34.09, 36.06, and 37.94 

g/mmBTU for the RFS1 reference, AEO reference, and RFS2 control cases, respectively, when 

averaged across all the types of tank loading.72 For air quality modeling the detailed emission 

factors for each type of tank loading, shown in Table 3.3-7, were multiplied by the preliminary 

ORNL ktons loaded by type of tank for each county.73,74,75 

For the FRM analysis the use of the final ORNL results yielded EFs of 26.9 - 31.7 

g/mmBTU of ethanol (2.04 - 2.41 g/gal) depending on scenario, due to the added fuel transfer 

losses compared to pipeline-based transport of gasoline. The EF shown earlier in Table 3.1-21 
 58

(28.78 g/mmBTU) corresponds to the High Ethanol minus RFS1 reference case, and was used 

for calculation of VOC and ethanol vapor for all cases in this FRM analysis. 

 Table 3.3-5 summarizes the ethanol transport mode and volume analysis conducted by 

ORNL. It shows the ethanol quantities loaded into the tanks of each transport mode, which is 

used to calculate ethanol vapor losses during tank filling. The first three rows show the 

preliminary set of model results that was used to generate emission impacts for the air quality 

analysis. The next three rows show corrected results from the final ORNL report, which was too 

late to be included in the air quality modeling. These corrected results were then used as the 

basis for the ethanol transport emission estimates in this final rule, shown in the final three rows. 

Because the final rule ethanol volumes differ from the proposed rule and air quality analysis 

volumes, especially for the primary (mid) and low ethanol cases, we have estimated transport 

volumes and distances for the final rule by interpolation from the final corrected ORNL values. 

Table 3.3-5. Ethanol Transport Tank Loading Volumes in 2022 

Kilotons Ethanol Loaded or Transferred into Each 

Modea Source Case Bgal 

Ethanol Truck Rail Water Local Truck 

AQ modeled values RFS1 6.69 3,434 18,565 2,679 20,952

AQ modeled values AEO 13.18 6,005 35,555 3,860 42,915

AQ modeled values RFS2 34.14 17,012 76,053 11,959 133,907

 

Corrected FRM 

Basis RFS1 6.69 3,131 18,565 2,816 18,431

Corrected FRM 

Basis AEO 13.18 5,597 35,553 4,178 36,736

Corrected FRM 

Basis RFS2 34.14 17,151 76,023 11,619 82,460

 

FRM Control Low 17.49 7,973 43,875 5,708 46,138

FRM Control Mid 22.16 10,547 52,892 7,366 56,326

FRM Control High 33.24 16,654 74,285 11,299 80,496

a

 Includes original loading at ethanol production or import facility plus loading during transfer from 

another mode. 

 The VOC EFs shown in Table 3.3-7 are from AP-4276 10 psi gasoline emission rates 

adjusted for ethanol vapor pressure and molecular weight (net factor = 0.20 = 3 psi / 10 psi x 

48.7 MW / 72 MW). In calculating the vapor losses associated with the local truck ethanol 

volumes, a factor of two was applied to account for the losses during both loading of the truck 

and loading of the retail underground storage tank from the truck. 
59

Table 3.3-6. SCC Assignments Used for Ethanol Tank Loading

SCC SCC Description Segments of ORNL 

Analysis Applied to SCC 

30205031 Denatured Ethanol Storage 

Working Loss All other tank loading 

30205052 Ethanol Loadout to Truck All truck loading 

30205053 Ethanol Loadout to Railcar All railcar loading 

Table 3.3-7 Ethanol Tank Loading Vapor Emission Factors

ORNL Ethanol 

Transport Category 

Description Applied to 

SCC 

EF (tons ethanol 

vapor per thousand 

tons ethanol) 

H_Ld_Kt Initial transport truck loading 30205052 0.243 

R-H_Trf Transfer from rail to truck 30205052 0.243 

W-H_Trf Transfer from barge to truck 30205052 0.243 

LocTrkKt Local distribution truck 

loading 

30205052 0.243 

R_Ld_Kt Initial Railcar Loading 30205053 0.243 

H-R_Trf Transfer from truck to rail 30205053 0.243 

W-R_Trf Transfer from barge to rail 30205053 0.243 

W_Ld_Kt Initial barge loading 30205031 0.103 

R-W_Trf Transfer from railcar to barge 30205031 0.103 

H_ULd_Kt Unloading from transport 

truck to terminal tank 

30205031 0.222 

R_ULd_Kt Unloading from rail to 

terminal tank 

30205031 0.222 

W_ULd_Kt Unloading from barge to 

terminal tank 

30205031 0.222 

LocTrkKt Unloading from local truck to 

retail tank 

30205031 0.222a

a

 This local truck loading EF was also applied to loading the retail underground tank from 

the truck, using the same ethanol volume. 

3.3.2.1.2 Calculation of Combustion Emissions From Ethanol Transport 

 Table 3.3-8 summarizes the kiloton-miles transported by mode from the ORNL analysis, 

which is used for combustion (vehicle exhaust) emission impacts for tanker trucks, locomotives, 

and water (barge) transport. 
 60

Table 3.3-8. Ethanol Transport by Mode in 2022 

Kiloton-miles Ethanol Transport by Mode 

Source Case Bgal 

Ethanol Truck Rail Water Local 

Truck 

AQ modeled values RFS1 6.69 228,831 18,436,891 1,565,254 138,811

AQ modeled values AEO 13.18 436,498 30,543,455 2,415,480 268,368

AQ modeled values RFS2 34.14 1,053,071 49,422,639 3,628,079 695,386

 

Corrected FRM 

Basis RFS1 6.69 290,156 18,630,606 1,498,611 131,712

Corrected FRM 

Basis AEO 13.18 491,458 30,650,028 2,538,867 253,239

Corrected FRM 

Basis RFS2 34.14 1,164,335 47,822,752 3,905,640 534,322

 

FRM Control Low 17.49 629,822 34,181,251 2,819,916 311,038

FRM Control Mid 22.16 779,742 38,007,426 3,124,441 373,665

FRM Control High 33.24 1,135,442 47,085,374 3,846,952 522,253

3.3.2.1.2.1 Combustion Emissions from Rail Transport of Ethanol 

 The emission impacts of projected increases in rail transport of ethanol resulting from 

this rule were calculated by multiplying locomotive emission factors by the added ton-miles of 

ethanol transport. For the air quality modeling analysis and this FRM analysis we were able to 

make use of the ORNL projected ton-miles of rail transport of ethanol by county for each of the 

three cases of the air quality analysis, as summarized in Table 3.3-8. These ton-miles by county 

were then multiplied by the g/mmBTU EFs shown in Tables 3.1-22 and 3.1-28 along with a fuel 

consumption of 2.38 gallons per thousand ton-miles3

 and 130,000 BTU/gallon, to determine the 

additional emissions by county. 

 For the air quality modeling these were then added to the base case (NEI 2020 projection) 

emissions of Class I locomotive emissions (SCC 2285002006) to obtain the county-specific 

emissions for the RFS1 mandate reference case, AEO reference case, and RFS2 control cases. 

The 2020 NEI projection values were used as the base case because they were the closest year 

with data readily available, and the difference between 2020 and 2022 was not considered to be 

important for this analysis. As described elsewhere, it was later discovered that an error had 

been introduced during the data handling 

 For the FRM analysis we used the final set of projections from ORNL, which included a 

3.2 percent lesser estimate of rail transport of ethanol for the RFS2 control case compared to the 

values used for the air quality modeling. When put into terms of average one-way miles per trip 

for the nationwide upstream impacts spreadsheet, the final ORNL values yield a rail transport 

distance of 629 miles, compared to the 800 miles from GREET that was used for the NPRM 

 

3

 Per 2006 American Association of Railroads, “Railroad Facts” 2007 edition, in 2006 4,214,459 gallons of diesel 

fuel were consumed transporting 4,214,459 million ton-miles of goods, which equates to 2.38 gallons per thousand 

ton-miles. 
61

analysis. This value was calculated by dividing the 47,822,752 kton-miles by 76,023 kilotons 

loaded into rail tank cars in the RFS2 control (EISA) case. 

3.3.2.1.2.2 Combustion Emissions from Water Transport of Ethanol 

 Air quality modeling inventories for marine vessels using Category 3 (C3) propulsion 

engines (i.e., ocean-going vessels such as container ships), for calendar year 2022 were 

generated for a reference case and the RFS2 control case. Since ethanol imports were assumed 

to be zero under both RFS1 and AEO reference cases, the 2022 base case gridded inventory was 

used for both reference cases. The 2022 base case inventory accounts for growth and the current 

Tier 1 NOX controls for C3 engines and was developed using the methodology outlined in the 

C3 NPRM.77 

 For the RFS2 control case, the port portion of the 2022 base case inventory was adjusted 

to account for projected imported ethanol volumes. Gram per freight ton emission factors (EFs) 

by port were developed by dividing the emissions for each port by the corresponding commodity 

tonnage.78 The projected imported ethanol volumes by port were then converted to tons and 

multiplied by the gram per freight ton EFs to determine the additional emissions by port due to 

imported ethanol.79 These were then added to the 2022 base case port emissions to obtain the 

port-specific 2022 emissions for the RFS2 control case. The 2022 gridded inventory for the 

RFS2 control case air quality model run was then developed by incorporating the adjusted port 

inventories. 

 

 For vessels using Category 1 (C1) and Category 2 (C2) propulsion engines (i.e., harbor 

craft), calendar year 2022 emissions by county were calculated for the two reference cases and 

the RFS2 control case. The starting point was calendar year 2020 C1/C2 base case emissions by 

county. Given the low growth estimated for this source category and the absence of a countylevel 2022 inventory, the 2020 inventory was used for this analysis. A ton per ton-mile EF for 

each pollutant was then developed. For the numerator, the national level tons values for 2020 

were taken from the 2008 locomotive/marine FRM.50 For the denominator, projected ton-miles 

for U.S. domestic shipping for 2020 was used.80 ORNL supplied EPA with ton-miles of barge 

traffic by county for each of the three cases, as summarized in Table 3.3-8. For the air quality 

modeling the ton-miles by county were then multiplied by the tons per ton-mile EFs to determine 

the additional emissions by county for each case. These were then added to the base case 

emissions to obtain the county-specific emissions for the two reference cases and the RFS2 

control case. 

 For the FRM analysis we used the final set of projections from ORNL, which included a 

7.6 percent greater estimate of water transport of ethanol for the RFS2 control case compared to 

the values used for the air quality modeling. When put into terms of average one-way miles per 

trip for the nationwide upstream impacts spreadsheet, the final ORNL values yield a barge 

transport distance of 336 miles, compared to the 520 miles from GREET that was used for the 

NPRM analysis. This value was calculated by dividing the 3,905,640 kton-miles by 11,619 

kilotons loaded into barges in the RFS2 control case. 

3.3.2.1.2.3 Combustion Emissions from Truck Transport of Ethanol 
 62

 For the NPRM analysis we relied on the nationwide average truck transport distances 

assumed in GREET for transport to distribution/blending terminals (80 miles) and for local 

trucks distributing ethanol-gasoline blends from the terminal to the retail station (30 miles). For 

the air quality modeling and FRM analyses we were able to use the ORNL study described 

above, which supplied kton-miles of tank truck ethanol transport by county. VMT was 

calculated based on an average tank truck load of 52,720 lbs of ethanol. We doubled the VMT to 

account for return trips. Non-GHG emissions from heavy-duty diesel trucks were adjusted in the 

affected counties in proportion to their VMT increase. Excel versions of the ORNL data files 

plus calculations are available in the docket.81

3.3.2.1.3 Calculation of Biofuel Plant Emissions 

For the county-level air quality modeling emission inventories we treated the corn 

ethanol plants as point sources wherever possible, since most of them are either existing plants or 

under construction or planned with a specific location. The choice of corn/starch ethanol plant 

locations and capacities for the 2005 baseline air quality modeling run and each of the three 2022 

cases is described in Section 1.8.1.1 of this RIA. The emissions attributed to each plant were 

calculated using the emission rates presented in the NPRM DRIA Table 3.1-20 multiplied by the 

reported or planned capacities of each plant for each of the ethanol volume scenarios.82,83,84,85

For the county-level air quality modeling emission inventories we treated the cellulosic 

biofuel plants as area sources spread across the entire area of whatever county they were 

considered to be located in. The choice of plant locations and capacities is described in Section 

1.8.1.3 of this RIA. They were not treated as point sources because of the substantial uncertainty 

about where they might actually be built, and if their emissions were treated as a point source 

their human exposures and health impacts would have been highly dependent on proximity to 

urban areas. Cellulosic plant emissions were only included in the RFS2 control (EISA) case, 

since the production capacities of existing and planned corn ethanol plants was sufficient to meet 

the RFS1 reference case and AEO reference case ethanol volumes. The emission rates used for 

the cellulosic plants in the air quality modeling were presented in the NPRM DRIA Table 3.1-20. 

Those emission rates were multiplied by the assumed cellulosic ethanol plant capacities ranging 

from 14 - 300 million gallons per year (MGY) from forest waste, 63 - 129 MGY from corn 

stover, and 91 - 149 MGY from switchgrass.86 

Biodiesel plant emissions were also treated as area sources in the air quality modeling. 

All three modeled cases used the same set of 35 biodiesel plants, but used different plant 

"capacities" (actually just different plant operation factors applied to the same total capacity). 

The choice of plant locations and capacities is described in Section 1.8.1.4 of this RIA. The 

assumed capacities for the RFS1 reference case ranged from 50,000 to 34 million gallons per 

year with a combined capacity of 303 MGY. The AEO reference case capacities ranged from 

63,000 to 42 million gallons per year with a total capacity of 380 MGY, and the RFS2 control 

(EISA) case capacities ranged from 135,000 to 90 million gallons per year with a total capacity 

of 810 MGY. Projected emissions for each plant were calculated using the emission factors for 

soybean oil based biodiesel plants given in DRIA Table 3.1-20.87,88,89
63

Significant updates have been made to emissions from cellulosic plants, in part to reflect 

the assumed shift in volumes from cellulosic ethanol to diesel between the proposed and final 

rules. In addition, after the air quality modeling was done, we discovered that for cellulosic 

ethanol plants the calculation of emissions had been overestimated by a factor of about two due 

to failing to account for the portion of biomass that is not used for process energy. This change 

decreases the estimated NOx and CO impacts on the order of 50 percent, and shifts the PM 

impact from an increase to a small decrease. However, these changes are counterbalanced to 

varying degrees by shifting some of the cellulosic volume from ethanol to diesel, which requires 

nearly twice the biomass to produce one gallon of fuel. While the net effect of the changes in 

cellulosic plant emissions is a significant decrease in NOx and CO emissions, the shift to 

cellulosic diesel under the primary scenario results in a larger increase in “upstream” PM 

emissions than reported in the NPRM or used in the air quality modeling analysis. 

3.3.2.1.4 Calculation of Agricultural Emissions 

The county-level agriculture-related emission inventories for air quality modeling were 

based on the 2002 NEI, since the NEI does not include any changes in its projections to 2022. 

That inventory was used for both the RFS1 and AEO reference cases. For the RFS2 control 

(EISA) case that inventory was modified to account for the changes in domestic agricultural 

activity predicted by the FASOM model, as described in Section 5.1 of the DRIA. Later 

modifications to the FASOM modeling that were done for the final rule analysis were not 

available in time for the air quality modeling. Since FASOM was only run for the AEO 

reference case and the RFS2 control (EISA) case, the air quality modeling did not attempt to 

account for any differences between the RFS1 and AEO reference cases. 

The RFS2 control case agricultural emissions were estimated by applying adjustment 

factors shown in Table 3.3-9 to the NEI inventories for the affected source categories. The 

pollutants affected by these adjustments depend on the source; for example, NEI includes 

livestock dust for beef and dairy cattle, but not for swine or poultry. These adjustments were 

applied equally to all counties having any of the affected sources. This is one area of uncertainty 

in the inventories, since there would likely be variation from one county to another depending on 

how much of the predicted agricultural changes occurred in which counties. By using percent 

change adjustments rather than attempting to calculate absolute ton changes in each county we 

have attempted to minimize the inventory distortions that could occur if the calculated change for 

a given county was out of proportion to the reference case emissions for that county. For 

instance, a different approach could estimate reductions that were larger than the reference case 

NEI emissions, since there was no linkage between the NEI inventories and the FASOM 

modeling. The specific sources (SCCs) and affected pollutants that these adjustments were 

applied to are listed in a docket reference.90
 64

Table 3.3-9. Adjustments to Agricultural Emissions for RFS2 control Case 

Source Description FASOM Parameters Used 

(change from AEO to RFS2) 

Adjustment of 

Air Quality 

inputs 

Corresponding 

Changes in Final 

Ag Modeling 

Nitrogen Fertilizer 

Application Nitrogen fertilizer use +2.42% +5.73% 

Pesticide Application Pesticide + herbicide use -4.56% -0.46% 

Pesticide Production & 

Transport Pesticide + herbicide use -4.56% -0.46% 

Livestock Waste Beef+dairy cattle, swine, poultry 

head counts -0.99% -0.90% 

Livestock Dust Beef+dairy cattle head counts -1.32% -0.27% 

Tilling/Harvesting Dust Total acres in crop production +0.79% +2.65% 

Crop Residue Burning Total acres in crop production +0.79% --a

a

 Crop residue burning emissions are not included as impacts in the final rule analysis. 

Updates to agricultural modeling assumptions that have been made since the proposal and 

air quality modeling have had a significant impact on ammonia (NH3) emissions. Final 

modeling reflects an increase in fertilizer use with the primary control case, which results in an 

increase in NH3 emissions, a change from the modest decrease projected for the proposal and air 

quality analyses. 

3.3.2.1.5 Calculation of Petroleum Production Emissions 

Petroleum production includes crude oil extraction and transport to refineries. For the 

RFS2 air quality modeling these impacts were not considered large enough relative to the other 

upstream impacts to attempt to model them. In our nationwide emissions analysis we assumed 

that (a) 75% of the change in gasoline supply was projected to come from domestic refineries, 

and (b) 33.1% of the change in crude being used by domestic refineries would be domestic 

crude. Thus, using our assumption that 1.0 gallon less of gasoline equates to approximately 1.0 

gallon less crude throughput, the reduction in crude extraction and transport would equal about 

25% of the change in gasoline volume. Table 3.3-10 shows what the domestic crude adjustments 

would have been in the air quality modeling if they had been accounted for. 
65

Table 3.3-10. 

Domestic Crude Oil Volume Reductions Associated with RFS2 in 2022 

Scenario Gasoline Volume 

Reduction (Bgal) 

Domestic Crude 

Reduction (Bgal)

AQ Modeling RFS2 vs RFS1 18.5 4.6 

RFS2 vs AEO 14.2 3.5 

Final Rule: High RFS2 vs RFS1 17.3 4.3 

Ethanol Case RFS2 vs AEO 13.3 3.3 

Final Rule: Mid RFS2 vs RFS1 10.0 2.5 

Ethanol Case RFS2 vs AEO 5.9 1.5 

Final Rule: Low RFS2 vs RFS1 6.9 1.7 

Ethanol Case RFS2 vs AEO 2.8 0.7 

3.3.2.1.6 Calculation of Refinery Emissions (combustion and vapor) 

For the air quality modeling of refinery emissions, adjustment factors were applied to 

existing NEI inventory projections for all SCCs related to refineries. These adjustments were 

based on ratios of crude throughput estimates from refinery modeling for each case, which varied 

by PADD. Different adjustment factors were applied for the AEO reference case and for the 

RFS2 control (EISA) case.91 The RFS1 reference case was assumed to be the existing NEI 

projected inventory with no adjustments applied. Table 3.3-11 summarizes the adjustments that 

were used. 

Table 3.3-11. 

Refinery Emission Adjustments for RFS2 Air Quality Modeling 

Scenario PADD 

1 

PADD 

2 

PADD 

3 

PADD 

4 & 5 

PADD 

5 (CA) US Total 

AEO Reference 0.0% -2.5% -1.8% -0.4% -0.7% -1.5%

RFS2 (EISA) 0.0% -9.2% -6.7% -1.6% -2.5% -5.7%

Note that this method used for estimation of county level refinery emissions is not 

directly comparable with the method used for nationwide impacts in the NPRM and FRM 

analyses, for which we used the GREET-based upstream impacts spreadsheet model to calculate 

the absolute change in tons for each stage of the upstream inventory. 

3.3.2.1.7 Calculation of Gasoline Transport, Storage and Distribution emissions: (vapor) 

With the displacement of some of the gasoline pool to E10 and E85 as predicted in this 

analysis there would be changes in the quantity of vapor losses during the transport and 

distribution of gasoline and gasoline-ethanol blends. The analysis of these impacts was 

separated into two segments: refinery to bulk terminal (RBT) and bulk terminal to pump (BTP). 

The reference cases analyzed would include some amount of E0 in the BTP segment, but the 

ethanol volumes considered as policy options in this rule are all beyond the volume that would 

require E10 blends for all gasoline-fueled engines (onroad and nonroad). Thus the transport of 
 66

E0 gasoline would only occur between refineries and blending terminals in the policy cases, i.e., 

the RBT segment. The BTP segment would include both E10 and E85. 

E0 – Refinery to Bulk Terminal (RBT) 

E0 – Bulk Terminal to Pump (BTP, used for reference cases only) 

E10 – Bulk Terminal to Pump (BTP) 

E85 – Bulk Terminal to Pump (BTP) 

For each of the above fuel type and transport stage combinations, nationwide VOC 

impacts (ton deltas) (and benzene and ethanol vapor) were calculated using EPA’s upstream 

impacts spreadsheet model for each control scenario (RFS2 control vs RFS1 mandate and RFS2 

control vs AEO). For air quality modeling the three BTP values were combined into a total BTP 

impact for each scenario. These impact values were renormalized to be ton deltas relative to the 

RFS1 mandate reference case, which was treated as corresponding to the NEI. Then all the 

SCCs in the NEI related to gasoline transport, storage, and distribution (TS&D) were categorized 

as either RBT or BTP, and the NEI VOC emissions were summed for each category. The 

nationwide VOC percent change for the AEO case relative to the RFS1 mandate reference case 

for RBT was calculated as the AEO case RBT delta tons (versus RFS1) divided by the NEI RBT 

tons. Similarly, the nationwide VOC percent change for the AEO case for BTP was calculated 

as the AEO case BTP delta tons (versus RFS1) divided by the NEI BTP tons. The same 

calculations were done for the RFS2 control case to get RBT and BTP percent changes in VOC 

from the RFS1 mandate case.92 

The county level air quality inventories for the AEO and RFS2 control cases were then 

calculated by applying these percent changes in VOC to the corresponding sets of SCCs (point 

and non-point sources) for every county. The same adjustment factors were applied to benzene, 

which is reasonable for the VOC decrease in the refinery to bulk terminal segment. But in the 

terminal to pump segment benzene would be expected to decrease while VOC increases, since 

the VOC increaase is due to addition of ethanol to the fuel, rather than any increase in gasoline 

itself. 

3.3.2.2 Differences in Downstream Impacts between Inventories 

3.3.2.2.1 On-Road Gasoline 

In the proposal we provided two different analyses based on two different assumptions 

regarding the effects of E10 and E85 versus E0 on exhaust emissions from cars and trucks. 

Those were referred to as "less sensitive" and "more sensitive" cases. Based on analysis of 

recent emissions test data conducted since publication of the NPRM, we are modeling a single 

case. As detailed above in Section 3.2, the case modeled for the final rule is a hybrid approach, 

applying “more sensitive” impacts for E10 and pre-Tier 2 light duty vehicles, and applying the 

“less sensitive” E10 effects for Tier 2 light duty cars and trucks (meaning no impact for NOx or 

NMHC). We have also updated our estimates of evaporative permeation impacts of E10 based 

on recent studies. Finally, for the final rule inventories we are only claiming emission effects 

with use of E85 in flex-fueled vehicles relative to E0 for two pollutants: ethanol and 

acetaldehyde, for which data suggests the effects are more certain. For the “more sensitive 
67

case” presented in the NPRM, and used in the air quality modeling, we had estimated changes to 

additional pollutants (including significant PM reductions) based on some very limited data. 

Until such time as additional data is collected to enhance this analysis it is premature to use such 

assumptions. 

For the air quality inventory, EPA executed a preliminary version of MOVES dated 

9/9/2009 using default database MOVESDB20080828 plus several modifications to the code and 

to the database. The MOVES runs produced emission factors at the state-month level for all 

onroad gasoline vehicles except motorcycles. Onroad gasoline inventories were generated by 

multiplying MOVES emission factors by VMT developed for the Office of Air Quality Planning 

and Standards's 2002 Version 3 Modeling Platform93 and used in the recently published 

Locomotive-Marine Rule.94 The MOBILE6 Model (M6203ChcOxFixNMIM, a special version 

to include cold-start VOC) and the NONROAD Model (NR05d-Bond-Final) were run using 

NMIM (NMIM20071009) with database NCD20080724. NMIM supplied emissions from the 

NONROAD Model and from onroad sources not produced from MOVES. Onroad emissions 

generated at the state-month level from MOVES were distributed to the county-month level 

using the results from MOBILE6 as run by NMIM. Details of how MOVES and NMIM were 

configured and run are documented in a report contained in the docket.95

 Differences between the air quality modeling inventories and the NPRM and FRM 

inventories are due to differences in MOVES code and database versions, run granularity 

(national annual vs. state-month), and running of the “more sensitive” case for the air quality 

modeling. One specific change from the air quality version to the FRM version was an update of 

reformulated gasoline properties to reflect the lower benzene levels called for by the MSAT rule, 

as described above in Section 3.1.1.1. 

3.3.2.2.2 Off-Road Gasoline 

For the NPRM, emissions from nonroad gasoline equipment were developed by running 

the National Mobile Inventory Model (NMIM) for January and July. We limited the runs to 

these months to speed the analysis while also capturing the temperature extremes that can affect 

emissions. The NMIM County Database (NCD) used by NMIM to produce those inventories 

was updated as part of the 2005 National Emission Inventory (NEI) process. The NCD also 

included the 2005 and 2022 fuels described in Section 3.1.1.2 of the DRIA. The version of the 

NONROAD Model used included the effects of the 2008 Final Rule: Control of Emissions of Air 

Pollution from New Nonroad Spark-Ignition Engines, Equipment, and Vessels.96 It is also 

capable of modeling the effects of gasoline blends containing 10 percent or less of ethanol. That 

version of NONROAD was later released as NONROAD2008. 

For air quality modeling and the final rule analysis, NMIM was also used, but all twelve 

months were run.

For the NPRM, emissions from onroad and nonroad diesel equipment were also 

developed by running NMIM, using the same NCD and version of the NONROAD Model 

described above. The version of MOBILE was MOBILE6.2. Diesel fuels are less fully 
 68

characterized than gasoline, since the only property used by MOBILE and NONROAD is fuel 

sulfur. 

For the NPRM, toxic emissions for off-road reference cases were taken directly from 

NMIM. Inventories for the control case were developed by applying ratios of the aggregate 

MOVES toxic exhaust, evaporative and refueling emissions for on-road gasoline for control 

versus reference case, to reference case toxic emissions for off-road from NMIM. 

For the air quality modeling and FRM analysis of nonroad gasoline engine emissions we 

used the same version of NMIM. Most toxic emissions for off-road equipment were taken 

directly from NMIM. The one exception was ethanol, which is not estimated by NMIM, so 

ethanol emissions were based on VOC speciation from light-duty gasoline vehicles. Ethanol 

inventories for the control case were developed by applying ratios of the aggregate MOVES 

ethanol exhaust, evaporative and refueling emissions for on-road gasoline for control versus 

reference cases, to the reference case ethanol emissions for off-road equipment. 

3.3.2.2.3 On-Road Diesel 

For the NPRM the proposed increase in biodiesel to 0.81 billion gallons by 2022 was 

modeled assuming that the emission effects of biodiesel are linear with biodiesel concentration 

as demonstrated by Sze, et al,97 and that impacts can be analyzed assuming all biodiesel is 

blended as BD20. We applied the BD20 effects discussed in Section 3.1.1.2.4 to baseline heavyduty emissions generated by NMIM, as MOVES heavy-duty diesel estimates were not available 

in time for the NPRM analysis. 

For the air quality and FRM analysis diesel emission inventories were generated using 

the same method as the NPRM. 

3.3.2.2.4 Portable Fuel Containers 

 The NPRM and air quality analysis used the same projected inventories for VOC, 

benzene, and ethanol vapor as the FRM analysis described above in Section 3.1.1.4.4. 

3.4 Air Quality Impacts 

This section presents the methodology and results of our air quality modeling to 

determine the projected impact of the renewable fuel volumes required by this rule on ambient 

concentrations of criteria and air toxic pollutants. The air quality modeling results presented 

here reflect the impact of increased renewable fuels use required by RFS2 compared with two 

different reference cases that include the use of renewable fuels: a 2022 baseline projection based 

on the RFS1-mandated volume of 7.5 billion gallons of renewable fuels, and a 2022 baseline 

projection based on the AEO 2007 volume of roughly 13.6 billion gallons of renewable fuels. 

Thus, the results represent the impact of an incremental increase in ethanol and other renewable 

fuels. We note that the air quality modeling results presented in this final rule do not constitute 
69

the “anti-backsliding” analysis required by Clean Air Act section 211(v). EPA will be analyzing 

air quality impacts of increased renewable fuel use through that study and will promulgate 

appropriate mitigation measures under section 211(v), separate from this final action. 

Following the discussion of modeling results in Section 3.4, Sections 3.5 and 3.6 describe the 

health and environmental effects associated with the criteria and air toxic pollutants that are 

impacted by the required renewable fuel volumes. In addition, Section 5.4 describes the 

methodology for calculating monetized benefits due to reductions in adverse health effects 

associated with PM2.5 and ozone. 

3.4.1 Air Quality Modeling Methodology 

Air quality models use mathematical and numerical techniques to simulate the physical 

and chemical processes that affect air pollutants as they disperse and react in the atmosphere. 

Based on inputs of meteorological data and source information, these models are designed to 

characterize primary pollutants that are emitted directly into the atmosphere and secondary 

pollutants that are formed as a result of complex chemical reactions within the atmosphere. 

Photochemical air quality models have become widely recognized and routinely utilized tools for 

regulatory analysis by assessing the effectiveness of control strategies. These models are applied 

at multiple spatial scales - local, regional, national, and global. This section provides detailed 

information on the photochemical model used for our air quality analysis (the Community Multiscale Air Quality (CMAQ) model), atmospheric reactions and the role of chemical mechanisms 

in modeling, and model uncertainties and limitations. Further discussion of the modeling 

methodology is included in the Air Quality Modeling Technical Support Document (AQM TSD) 

found in the docket for this rule. Results of the air quality modeling are presented in Section 

3.4.2. 

3.4.1.1 Modeling Methodology 

A national-scale air quality modeling analysis was performed to estimate future year 

annual PM2.5 concentrations, 24-hour PM2.5 concentrations, 8-hour ozone concentrations, air 

toxics concentrations, and nitrogen and sulfur deposition levels for future years. The 2005-based 

CMAQ modeling platform was used as the basis for the air quality modeling of the two future 

reference cases and the RFS2 future control scenario for this final rule. This platform represents 

a structured system of connected modeling-related tools and data that provide a consistent and 

transparent basis for assessing the air quality response to projected changes in emissions. The 

base year of data used to construct this platform includes emissions and meteorology for 2005. 

The platform was developed by the U.S. EPA’s Office of Air Quality Planning and Standards in 

collaboration with the Office of Research and Development and is intended to support a variety 

of regulatory and research model applications and analyses. 

The CMAQ modeling system is a non-proprietary, publicly available, peer-reviewed, 

state-of-the-science, three-dimensional, grid-based Eulerian air quality grid model designed to 

estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations, 

acid deposition, and air toxics, over regional and urban spatial scales for given input sets of 

meteorological conditions and emissions.98,99,100 The CMAQ model version 4.7 was most 

recently peer-reviewed in February of 2009 for the U.S. EPA.101 The CMAQ model is a well-
 70

known and well-respected tool and has been used in numerous national and international 

applications.102,103,104 This 2005 multi-pollutant modeling platform used the latest publiclyreleased CMAQ version 4.74

 with a minor internal change made by the U.S. EPA CMAQ model 

developers intended to speed model runtimes when only a small subset of toxics species are of 

interest. 

CMAQ includes many science modules that simulate the emission, production, decay, 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the 

atmosphere. We used the most recent CMAQ version (v4.7) which was officially released by 

EPA’s Office of Research and Development (ORD) in December 2008, and reflects updates to 

earlier versions in a number of areas to improve the underlying science. These include (1) 

enhanced secondary organic aerosol (SOA) mechanism to include chemistry of isoprene, 

sesquiterpene, and aged in-cloud biogenic SOA in addition to terpene; (2) improved vertical 

convective mixing; (3) improved heterogeneous reaction involving nitrate formation; and (4) an 

updated gas-phase chemistry mechanism, Carbon Bond 05 (CB05), with extensions to model 

explicit concentrations of air toxic species as well as chlorine and mercury. This mechanism, 

CB05-toxics, also computes concentrations of species that are involved in aqueous chemistry and 

that are precursors to aerosols. Section 3.4.1.2.2 of this RIA discusses the chemical mechanism, 

SOA formation and details about the improvements made to the SOA mechanism within this 

recent release of CMAQ. 

3.4.1.1.1 Model Domain and Configuration 

The CMAQ modeling domain encompasses all of the lower 48 States and portions of 

Canada and Mexico. The modeling domain is made up of a large continental U.S. 36 kilometer 

(km) grid and two 12 km grids (an Eastern US and a Western US domain), as shown in Figure 

3.4-1. The modeling domain contains 14 vertical layers with the top of the modeling domain at 

about 16,200 meters, or 100 millibars (mb). 

 

4

 CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and Analysis 

System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org. 
71

Figure 3.4-1. Map of the CMAQ Modeling Domain 

3.4.1.1.2 Model Inputs 

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic 

sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological 

input files were derived from simulations of the Pennsylvania State University/National Center 

for Atmospheric Research Mesoscale Model105 for the entire year of 2005 over model domains 

that are slightly larger than those shown in Figure 3.4-1. This model, commonly referred to as 

MM5, is a limited-area, nonhydrostatic, terrain-following system that solves for the full set of 

physical and thermodynamic equations which govern atmospheric motions.106 The meteorology 

for the national 36 km grid and the two 12 km grids were developed by EPA and are described in 

more detail within the AQM TSD. The meteorological outputs from MM5 were processed to 

create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor 

(MCIP) version 3.4, for example: horizontal wind components (i.e., speed and direction), 

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical 

layer.107

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.108 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitudelongitude) and 20 vertical layers. The predictions were used to provide one-way dynamic 
 72

boundary conditions at three-hour intervals and an initial concentration field for the 36 km 

CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used 

as the initial/boundary state for all subsequent 12 km finer grid modeling. 

The emissions inputs used for the 2005 base year and each of the future year base cases 

and control scenarios analyzed for this rule are summarized in Section 3.3 of this RIA. 

3.4.1.1.3 CMAQ Evaluation 

An operational model performance evaluation for ozone, PM2.5 and its related speciated 

An operational model performance evaluation for ozone, PM2.5 and its related speciated 

components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate and sulfate 

deposition, and specific air toxics (formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and 

acrolein) was conducted using 2005 state/local monitoring data in order to estimate the ability of 

the CMAQ modeling system to replicate base year concentrations. Model performance statistics 

were calculated for observed/predicted pairs of daily/monthly/seasonal/annual concentrations. 

Statistics were generated for the following geographic groupings: domain wide, Eastern vs. 

Western (divided along the 100th meridian), and each Regional Planning Organization (RPO) 

region.5

 The “acceptability” of model performance was judged by comparing our results to those 

found in recent regional PM2.5 model applications for other, non-EPA studies.6

 Overall, the 

performance for the 2005 modeling platform is within the range or close to that of these other 

applications. The performance of the CMAQ modeling was evaluated over a 2005 base case. 

The model was able to reproduce historical concentrations of ozone and PM2.5 over the land with 

low amounts of bias and error. Model predictions of annual formaldehyde, acetaldehyde and 

benzene showed relatively small bias and error percentages when compared to observations. The 

model yielded larger bias and error results for 1,3-butadiene and acrolein based on limited 

monitoring sites. A more detailed summary of the 2005 CMAQ model performance evaluation 

is available within the AQM TSD found in the docket of this rule. 

3.4.1.1.4 Model Simulation Scenarios 

As part of our analysis for this rulemaking, the CMAQ modeling system was used to 

calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual and 

seasonal air toxics concentrations, and nitrogen and sulfur deposition total levels for each of the 

following emissions scenarios: 

- 2005 base year 

- 2022 reference case projection (RFS1 Mandate) 

 

5

 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU), Midwest 

Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO), Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership 

(CENRAP), and Western Regional Air Partnership (WRAP). 

6 These other modeling studies represent a wide range of modeling analyses which cover various models, model 

configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. 
73

- 2022 reference case projection (AEO 2007) 

- 2022 control case projection (RFS2; also referred to as EISA for the purpose of 

reporting some air quality results, e.g., maps of ambient concentrations) 

It should be noted that the emission inventories used in the air quality and benefits 

modeling were somewhat enhanced compared to what was described in the proposal, but due to 

the timing of the analysis did not include some of the later enhancements and corrections of the 

final emission inventories presented in this FRM. The emissions modeling TSD, found in the 

docket for this rule (EPA-HQ-OAR-2005-0161), contains a detailed discussion of the emissions 

inputs used in our air quality modeling. Section 3.3 of this RIA describes the changes in the 

inputs and resulting emission inventories between the preliminary assumptions used for the air 

quality modeling and the final regulatory scenario. These refinements, along with other 

inventory issues, have implications for modeling results. These implications are discussed in 

Sections 3.4.1.3 and 3.4.2. 

We use the predictions from the model in a relative sense by combining the 2005 baseyear predictions with predictions from each future-year scenario and applying these modeled 

ratios to ambient air quality observations to estimate daily and annual PM2.5 concentrations, and 

8-hour ozone concentrations for each of the 2022 scenarios. The ambient air quality 

observations are average conditions, on a site-by-site basis, for a period centered around the 

model base year (i.e., 2003-2007). 

The projected daily and annual PM2.5 design values were calculated using the Speciated 

Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference Method 

(FRM) mass construction methodology that results in reduced nitrates (relative to the amount 

measured by routine speciation networks), higher mass associated with sulfates (reflecting water 

included in FRM measurements), and a measure of organic carbonaceous mass that is derived 

from the difference between measured PM2.5 and its non-carbon components. This 

characterization of PM2.5 mass also reflects crustal material and other minor constituents. The 

resulting characterization provides a complete mass balance. It does not have any unknown 

mass that is sometimes presented as the difference between measured PM2.5 mass and the 

characterized chemical components derived from routine speciation measurements. However, 

the assumption that all mass difference is organic carbon has not been validated in many areas of 

the U.S. The SMAT methodology uses the following PM2.5 species components: sulfates, 

nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a 

fixed value of 0.5 μg/m3

). More complete details of the SMAT procedures can be found in the 

report "Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application 

of the (Revised) Speciated Modeled Attainment Test (SMAT)".109 For this latest analysis, 

several datasets and techniques were updated. These changes are fully described within the 

technical support document for the Small SI Engine Rule modeling AQM TSD.110 The projected 

8-hour ozone design values were calculated using the approach identified in EPA's guidance on 

air quality modeling attainment demonstrations.111

Additionally, we conducted an analysis to compare the annual and seasonal, absolute and 

percent differences between the 2022 control case and the two 2022 reference cases for nitrate 
 74

and sulfate deposition, ethanol, and five air toxics of interest (formaldehyde, acetaldehyde, 

benzene, 1,3-butadiene, and acrolein). These data were not compared in a relative sense due to 

the limited observational data available. 

3.4.1.2 Chemical Mechanisms in Modeling 

The RFS2 rule presents inventories for NOX, VOC, CO, PM2.5, SO2, NH3, ethanol and 

five air toxics: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol and 

the five air toxics are explicit model species in the CMAQv4.7 model with carbon bond 5 

(CB05) mechanisms.112 Emissions of all the pollutants included in the rule inventories, except 

ethanol, were generated using the Motor Vehicle Emissions Simulator (MOVES) hydrocarbon 

(HC) emissions and toxic-to-HC ratios calculated using MOBILE 6 (see Section 3.1.1.4.1 of the 

draft RIA).113 Ethanol emissions for air quality modeling were based on speciation of VOC 

using different ethanol profiles (E0, E10, and E85). In addition to direct emissions, 

photochemical processes mechanisms are responsible for formation of some of these compounds 

in the atmosphere from precursor emissions. For formaldehyde and acetaldehyde, many 

photochemical processes are involved. CMAQ therefore also requires inventories for a large 

number of other air toxics and precursor pollutants. Inventories for toxic pollutants not 

estimated using MOVES and MOBILE6 ratios were developed by running the National Mobile 

Inventory Model (NMIM). Emissions of other precursor pollutants were estimated by 

application of speciation profiles to VOC. 

 

 In the CB05 mechanism, the chemistry of thousands of different VOCs in the 

atmosphere are represented by a much smaller number of model species which characterize the 

general behavior of a subset of chemical bond types; this condensation is necessary to allow the 

use of complex photochemistry in a fully 3-D air quality model.114

Complete combustion of ethanol in fuel produces carbon dioxide (CO2) and water (H2O). 

Incomplete combustion results in the production of other air pollutants, such as acetaldehyde and 

other aldehydes, and the release of unburned ethanol. Ethanol is also present in evaporative 

emissions. In the atmosphere, ethanol from unburned fuel and evaporative emissions can 

undergo photodegradation to form aldehydes (acetaldehyde and formaldehyde) and peroxyacetyl 

nitrate (PAN), and also plays a role in ground-level ozone formation. Mechanisms for these 

reactions are included in CMAQ. Additionally, other aromatic hydrocarbons (AHC) and 

hydrocarbons are considered because any increase in acetyl peroxy radicals due to ethanol 

increases might be counterbalanced by a decrease in radicals resulting from decreases in AHC 

and other hydrocarbons. 

CMAQ includes 63 inorganic reactions to account for the cycling of all relevant oxidized 

nitrogen species and cycling of radicals, including the termination of NO2 and formation of nitric 

acid (HNO3) without PAN formation.7

NO2 + ·OH + M → HNO3 + M k = 1.19 x 10-11 cm3

molecule-1s

-1 115

 

7

 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
75

The CB05 mechanism also includes more than 90 organic reactions that include alternate 

pathways for the formation of acetyl peroxy radical, such as by reaction of methylglyoxal, which 

is also formed from reactions of AHC. Alternate reactions of acetyl peroxy radical, such as 

oxidation of NO to form NO2, which again leads to ozone formation, are also included. 

Atmospheric reactions and chemical mechanisms involving several key formation pathways are 

discussed in more detail in the following sections. 

3.4.1.2.1 Acetaldehyde 

Acetaldehyde is the main photodegradation product of ethanol, as well as other precursor 

hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the atmosphere, 

acetaldehyde can react with the OH radical and O2 to form the acetyl peroxy radical 

[CH3C(O)OO·].8

 This radical species can then further react with nitric oxide (NO), to produce 

formaldehyde (HCHO), or with nitrogen dioxide (NO2), to produce PAN [CH3C(O)OONO2]. 

An overview of these reactions and the corresponding reaction rates are provided below. 9

CH3CHO + ·OH → CH3C·O + H2O k = 1.5 x 10-11 cm3molecule-1s

-1 116

CH3C·O + O2 + M → CH3C(O)OO· + M 

CH3C(O)OO· + NO → CH3C(O)O· + NO2 k = 2.0 x 10-11 cm3molecule-1s

-1 117

CH3C(O)O· → ·CH3 + CO2 

·CH3 + O2 + M → CH3OO· + M 

CH3OO· + NO → CH3O· + NO2

CH3O· + O2 → HCHO + HO2

CH3C(O)OO· + NO2 + M → CH3C(O)OONO2 + M k = 1.0 x 10-11 cm3molecule-1s

-1 118

Acetaldehyde can also photolyze (hν), which predominantly produces ·CH3 and HCO: 

CH3CHO + hν → ·CH3 + HCO λ = 240-380 nm 119

As mentioned above, ·CH3 is oxidized in the atmosphere to produce formaldehyde 

(HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, 

formaldehyde undergoes photolysis and reaction with the OH radical, NO3 radical, and ozone, 

and the resulting lifetimes are ~4 hours, 1.2 days, 83 days, and >4.5 years, respectively.10 

 

8

 Acetaldehyde is not the only source of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl 

compounds (methylglyoxal, biacetyl, and others) also form acetyl radicals, which can further react to form 

peroxyacetyl nitrate (PAN). 

9

 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 

10 Lifetime calculated using the following: for photolysis, with overhead sun (at noontime during the summer); for 

OH radical reactions, a 12-hour daytime average of 2.0 x 106

 molecule cm-3; for NO3 radical reactions, a 12-hour 

nighttime average of 5 x 108

 molecule cm-3; and for ozone, a 24-hour average of 7 x 1011 molecule cm-3. 
 76

Formaldehyde is removed mainly by photolysis whereas the higher aldehydes, those with two or 

more carbons such as acetaldehyde, react predominantly with OH radicals. The photolysis of 

formaldehyde is a source of additional radicals, and as shown above, these radicals can react 

with NO2 to form PAN in the atmosphere. 

HCHO + hν → H + HCO λ = 240-360 nm 120

CB05 mechanisms for acetaldehyde formation warrant a detailed discussion given the 

increase in vehicle and engine exhaust emissions for this pollutant and ethanol, which can form 

acetaldehyde in the air. Acetaldehyde is represented explicitly in the CB05 chemical 

mechanism121,122 by the ALD2 model species, which can be both formed from other VOCs and 

can decay via reactions with oxidants and radicals. The reaction rates for acetaldehyde, as well 

as for the inorganic reactions that produce and cycle radicals, and the representative reactions of 

other VOCs have all been updated to be consistent with recommendations in the literature.123

The decay reactions of acetaldehyde are fewer in number and can be characterized well because 

they are explicit representations. Acetaldehyde can photolyze in the presence of sunlight or react 

with molecular oxygen (O3

(P)), hydroxyl radical (OH), or nitrate radicals. Of these reactions, 

both photolysis and reaction with OH are the most important reactions determining loss of 

acetaldehyde. The reaction rates are based on expert recommendations,124 and the photolysis 

rate is from IUPAC recommendations. 

In CMAQ v4.7, the acetaldehyde that is formed from photochemical reactions is tracked 

separately from that which is due to direct emission and transport of direct emissions. In CB05, 

there are 25 different reactions that form acetaldehyde in molar yields ranging from 0.02 (ozone 

reacting with lumped products from isoprene oxidation) to 2.0 (cross reaction of acylperoxy 

radicals, CXO3). The specific parent VOCs that contribute the most to acetaldehyde 

concentrations vary spatially and temporally depending on characteristics of the ambient air, but 

alkenes in particular are found to play a large role. The IOLE model species, which represents 

internal carbon-carbon double bonds, has high emissions and relatively high yields of 

acetaldehyde. The OLE model species, representing terminal carbon double bonds, also plays a 

role because it has high emissions although lower acetaldehyde yields. Production from 

peroxyproprional nitrate and other peroxyacylnitrates (PANX) and aldehydes with 3 or more 

carbon atoms also play an important role. Thus, the amount of acetaldehyde (and formaldehyde 

as well) formed in the ambient air as well as emitted in the exhaust (the latter being accounted 

for in emission inventories) is affected by changes in these precursor compounds due to the 

addition of ethanol to fuels (e.g., decreases in alkenes would cause some decrease of 

acetaldehyde, and to a larger extent, formaldehyde). 

The reaction of ethanol (CH3CH2OH) with OH is slower than some other important 

reactions but can be an important source of acetaldehyde if the emissions are large. Based on 

kinetic data for molecular reactions, the only important chemical loss process for ethanol (and 

other alcohols) is reaction with the hydroxyl radical (·OH).125 This reaction produces 

acetaldehyde (CH3CHO) with a 90% yield.126 The lifetime of ethanol in the atmosphere can be 

calculated from the rate coefficient, k, and due to reaction with the OH radical, occurs on the 

order of a day in polluted urban areas or several days in unpolluted areas. 11 

 

11 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
77

CH3CH2OH + ·OH → CH3C·HOH + H2O k = 3.2 x 10-12 cm3molecule-1s

-1 127

CH3C·HOH + O2 → CH3CHO + HO2

In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. It 

assumes the majority of the reaction occurs through H-atom abstraction of the more weaklybonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy 

radical (HO2), and the remainder of the reaction occurs at the –CH3 and -OH groups, creating 

formaldehyde (HCHO), oxidizing NO to NO2 (represented by model species XO2) and creating 

glycoaldehyde, which is represented as ALDX: 

CH3CHOH + OH → HO2 + 0.90 CH3CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO2 

3.4.1.2.2 Secondary Organic Aerosols (SOA) 

Secondary organic aerosol (SOA) chemistry research described below has led to implementation 

of new pathways for secondary organic aerosol (SOA) in CMAQ 4.7, based on recommendations 

of Edney et al. and the recent work of Carlton et al.128, 129 In previous versions of the CMAQ 

model, all SOA was treated as semi-volatile, whereas in CMAQ v4.7, non-volatile SOA are 

simulated as well, including SOA originating from aromatic oxidation under low-NOx

conditions. 

3.4.1.2.2.1 SOA Research 

SOA results when products of atmospheric transformation or photooxidation of a volatile 

organic compound (VOC) form or partition to the particle phase. Current research suggests SOA 

contributes significantly to ambient organic aerosol (OA) concentrations, and in Southeast and 

Midwest States may make up more than 50% (although the contribution varies from area to area) 

of the organic fraction of PM2.5 during the summer (but less in the winter).130,131 A wide range of 

laboratory studies conducted over the past twenty years show that anthropogenic aromatic 

hydrocarbons and long-chained alkanes, along with biogenic isoprene, monoterpenes, and 

sesquiterpenes, contribute to SOA formation.132,133,134,135,136 Anthropogenic SOA is a small 

portion of all SOA; most is biogenic and varies with season. Based on these laboratory results, 

SOA chemical mechanisms have been developed and integrated into air quality models such as 

the CMAQ model and have been used to predict OA concentrations.137 

Over the past 10 years, ambient OA concentrations have been routinely measured in the 

U.S. and some of these data have been used to determine, by employing source/receptor 

methods, the contributions of the major OA sources, including biomass burning and vehicular 

gasoline and diesel exhaust. Since mobile sources are a significant source of VOC emissions, 

currently accounting for approximately 50% of anthropogenic VOC,138 mobile sources are also 

an important source of SOA. 

Toluene is an important contributor to anthropogenic SOA. Other aromatic compounds 

contribute as well, but the extent of their contribution has not yet been quantified. Mobile 
 78

sources are the most significant contributor to ambient toluene concentrations as shown by 

analyses done for the 2002 National Air Toxics Assessment (NATA)139 and the Mobile Source 

Air Toxics (MSAT) Rule.140 2002 NATA indicates that onroad and nonroad mobile sources 

accounted for 70% (2.24 μg/m3

) of the total average nationwide ambient concentration of 

toluene (3.24 μg/m3

), when the contribution of the estimated “background” is apportioned 

among source sectors. 

The amount of toluene in gasoline influences the amount of toluene emitted in vehicle 

exhaust and evaporative emissions, although, like benzene, some toluene is formed in the 

combustion process. In turn, levels of toluene and other aromatics in gasoline are potentially 

influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of 

ethanol, it greatly reduces the need for and levels of other high-octane components such as 

aromatics including toluene (which is the major aromatic compound in gasoline). Since toluene 

contributes to SOA and the toluene level of gasoline is decreasing, it is important to assess the 

effect of these reductions on ambient PM. 

It is unlikely that ethanol would directly form SOA or affect SOA formation indirectly 

through changes in the radical populations from increasing ethanol exhausts. Nevertheless, 

scientists at the U.S. EPA’s Office of Research and Development’s National Exposure Research 

Laboratory recently directed experiments to investigate ethanol’s SOA forming potential.141 The 

experiments were conducted under conditions where peroxy radical reactions would predominate 

(irradiations performed in the absence of NOx and OH produced from the photolysis of hydrogen 

peroxide). This was the most likely scenario under which SOA formation could occur, since a 

highly oxygenated C4 organic would be potentially made. As expected, no SOA was produced. 

From these experiments, the upper limit for the aerosol yield would have been less than 0.01% 

based on scanning mobility particle sizer (SMPS) data. Given the expected negative result based 

on these initial smog chamber experiments, these data were not published. 

In general, a review of the literature shows limited data on SOA concentrations, largely 

due to the lack of analytical methods for identifying and determining the concentrations of the 

highly polar organic compounds that make up SOA. The most widely applied method of 

estimating total ambient SOA concentrations is the EC tracer method using ambient data which 

estimates of the OC/EC ratio in primary source emissions.142,143 SOA concentrations have also 

been estimated using OM (organic mass) to OC (organic carbon) ratios, which can indicate that 

SOA formation has occurred, or by subtracting the source/receptor-based total primary organic 

aerosol (POA) from the measured OC concentration.144 Such methods, however, may not be 

quantitatively accurate and provide no information on the contribution of individual biogenic and 

anthropogenic SOA sources, which is critical information needed to assess the impact of specific 

sources and the associated health risk. These methods assume that OM containing additional 

mass from oxidation of OC comes about largely (or solely) from SOA formation. In particular, 

the contributions of anthropogenic SOA sources, including those of aromatic precursors, are 

required to determine exposures and risks associated with replacing fossil fuels with biofuels. 

Upon release into the atmosphere, numerous VOC compounds can react with free 

radicals in the atmosphere to form SOA. While this has been investigated in the laboratory, there 

is relatively little information available on the specific chemical composition of SOA compounds 
79

themselves from specific VOC precursors. This absence of compositional data from the 

precursors has largely prevented the identification of aromatically-derived SOA in ambient 

samples which, in turn, has prevented observation-based measurements of the aromatic and other 

SOA contributions to ambient PM levels. 

As a first step in determining the ambient SOA concentrations, EPA has developed a 

tracer-based method to estimate such concentrations.145,146 The method is based on using mass 

fractions of SOA tracer compounds, measured in smog chamber-generated SOA samples, to 

convert ambient concentrations of SOA tracer compounds to ambient SOA concentrations. This 

method consists of irradiating the SOA precursor of interest in a smog chamber in the presence 

of NOx, collecting the SOA produced on filters, and then analyzing the samples for highly polar 

compounds using advanced analytical chemistry methods. Employing this method, candidate 

tracers have been identified for several VOC compounds which are emitted in significant 

quantities and known to produce SOA in the atmosphere. Some of these SOA-forming 

compounds include toluene, a variety of monoterpenes, isoprene, and β-caryophyllene, the latter 

three of which are emitted by vegetation and are more significant sources of SOA than toluene. 

Smog chamber work can also be used to investigate SOA chemical formation 

mechanisms.147,148,149,150

Although these concentrations are only estimates, due to the assumption that the mass 

fractions of the smog chamber SOA samples using these tracers are equal to those in the ambient 

atmosphere, there are presently no other means available for estimating the SOA concentrations 

originating from individual SOA precursors. Among the tracer compounds observed in ambient 

PM2.5 samples are two tracer compounds that have been identified in smog chamber aromatic 

SOA samples.151 To date, these aromatic tracer compounds have been identified, in the 

laboratory, for toluene and m-xylene SOA. Additional work is underway by the EPA to 

determine whether these tracers are also formed by benzene and other alkylbenzenes (including 

o-xylene, p-xylene, 1,2,4-trimethylbenzene, and ethylbenzene). 

One caveat regarding this work is that a large number of VOCs emitted into the 

atmosphere, which have the potential to form SOA, have not yet been studied in this way. It is 

possible that these unstudied compounds produce SOA species which are being used as tracers 

for other VOCs. This means that the present work could overestimate the amount of SOA 

formed in the atmosphere by the VOCs studied to date. This approach may also estimate entire 

hydrocarbon classes (e.g., all methylsubstituted-monoaromatics or all monoterpenes) and not 

individual precursor hydrocarbons. Thus the tracers could be broadly representative and not 

indicative of individual precursors. This is still unknown. Also, anthropogenic precursors play a 

role in formation of atmospheric radicals and aerosol acidity, and these factors influence SOA 

formation from biogenic hydrocarbons. This anthropogenic and biogenic interaction, important 

to EPA and others, needs further study. The issue of SOA formation from aromatic precursors is 

an important one to which EPA and others are paying significant attention. For benzene, smog 

chamber studies show that benzene forms SOA possibly through reactions with NOx. Early 

smog chamber work suggests benzene might be relatively inert in forming SOA, although this 

study may not be conclusive.152 However, more recent work shows that benzene does form SOA 

in smog chambers.153,154 This new smog chamber work shows that benzene can be oxidized in 

the presence of NOx to form SOA with maximum mass of SOA being 8-25% of the mass of 
 80

benzene. As mentioned above, work is needed to determine if a tracer compound can be found 

for benzene SOA which might indicate how much of ambient SOA comes from benzene. 

The aromatic tracer compounds and their mass fractions have also been used to estimate 

monthly ambient aromatic SOA concentrations from March 2004 to February 2005 in five U.S. 

Midwestern cities.155 The annual tracer-based SOA concentration estimates were 0.15, 0.18, 

0.13, 0.15, and 0.19 μg carbon/m3

 for Bondville, IL, East St. Louis, IL, Northbrook, IL, 

Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations occurring in the 

summer. On average, the aromatic SOA concentrations made up 17 % of the total SOA 

concentration. Thus, this work suggests that we are finding ambient PM levels on an annual 

basis of about 0.15 μg/m3

 associated with present toluene levels in the ambient air in these 

Midwest cities. Based on preliminary analysis of recent laboratory experiments, it appears the 

toluene tracer could also be formed during photooxidation of some of the xylenes.156

Over the past decade a variety of modeling studies have been conducted to predict 

ambient SOA levels, with most studies focusing on the contributions of biogenic monoterpenes 

and anthropogenic aromatic hydrocarbons. More recently, modelers have begun to include the 

contribution of the isoprene SOA to ambient OC concentrations.157 In general, the studies have 

been limited to comparing the sum of the POA and SOA concentrations with ambient OC 

concentrations. The general consensus in the atmospheric chemistry community appears to be 

that monoterpene contributions, which are clearly significant, and the somewhat smaller 

aromatic contributions, are insufficient to account for observed ambient SOA levels. 158 Part of 

this gap has been filled recently by SOA predictions for isoprene. Furthermore, the identification 

in ambient SOA of a tracer compound for the sesquiterpene β-caryophyllene,159 coupled with the 

high sesquiterpene SOA yields measured in the laboratory,160 suggests this class of hydrocarbons 

should be included in SOA chemical mechanisms. In addition, recent data on SOA formation 

from aromatic hydrocarbons suggest their contributions, while much smaller than biogenic 

hydrocarbons, could be larger than previously thought.161,162

3.4.1.2.3 Ozone 

As mentioned above, the addition of ethanol to fuels has been shown to contribute to 

PAN formation and this is one way for it to contribute therefore to ground-level ozone formation. 

PAN is a reservoir and carrier of NOx and is the product of acetyl radicals reacting with NO2 in 

the atmosphere. One source of PAN is the photooxidation of acetaldehyde (Section 3.4.1.2.1), 

but any hydrocarbon having a methyl group has the potential for forming acetyl radicals and 

therefore PAN.12 PAN can undergo thermal decomposition with a lifetime of approximately 1 

hour at 298K or 148 days at 250K. 13

CH3C(O)OONO2 + M → CH3C(O)OO· + NO2 + M k = 3.3 x 10-4 s

-1 163

 

12 Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, p-xylene, 

and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal and biacetyl, which are also strong generators of acetyl 

radicals (Smith, D.F., T.E. Kleindienst, C.D. McIver (1999) Primary product distribution from the reaction of OH 

with m-, p-xylene and 1,2,4- and 1,3,5-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.). 

13 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air. 
81

The reaction above shows how NO2 is released in the thermal decomposition of PAN. 

NO2 can also be formed in photodegradation reactions where NO is converted to NO2 (see OH 

radical reaction of acetaldehyde in Section 3.4.1.2.1). In both cases, NO2 further photolyzes to 

produce ozone (O3). 

NO2 + hν → NO + O(3

P) λ = 300-800 nm 164

O(3

P) + O2 + M → O3 + M 

The temperature sensitivity of PAN allows it to be stable enough at low temperatures to 

be transported long distances before decomposing to release NO2. NO2 can then participate in 

ozone formation in regions remote from the original NOx source.165 A discussion of CB05 

mechanisms for ozone formation can be found in Yarwood et al. (2005).166

3.4.1.3 Modeling Uncertainties and Limitations 

All the results presented below must be interpreted with the understanding that there are 

considerable uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of 

the modeling process. While it is beyond the scope of this Regulatory Impact Analysis to 

include a comprehensive discussion of all limitations and uncertainties associated with air quality 

modeling, the key ones which could significantly impact analyses for this rule are addressed. 

3.4.1.3.1 Emission Inventory Limitations 

A key limitation of the analysis is that it employed interim emission inventories, which 

were enhanced compared to what was described in the proposal, but did not include some of the 

later enhancements and corrections of the final emission inventories presented in this FRM 

(Section 3.3). Most significantly, our modeling of the air quality impacts of the renewable fuel 

volumes required by RFS2 relied upon interim inventories that assumed that ethanol will make 

up 34 of the 36 billion gallon renewable fuel mandate, that approximately 20 billion gallons of 

this ethanol will be in the form of E85, and that the use of E85 results in fewer emissions of 

direct PM2.5 from vehicles. The emission impacts and air quality results would be different if, 

instead of E85, more non-ethanol biofuels are used or mid-level ethanol blends are approved. 

In fact, as explained in Chapter 1 of the RIA, our more recent analyses indicate that 

ethanol and E85 volumes are likely to be significantly lower than what we assumed in the 

interim inventories. Furthermore, the final emission inventories do not include vehicle-related 

PM reductions associated with E85 use, as discussed in Section 3.1 and 3.3 above. Moreover, as 

discussed in Section 3.3, differences in estimates of renewable fuel transport volumes and 

distances between the air quality modeling and final rule inventories also have an impact on 

results. There are additional, important limitations and uncertainties associated with the interim 

inventories that must be kept in mind when considering the results: 
 82

• Error in PM2.5 emissions from locomotive engines 

After the air quality modeling was completed, we discovered an error in the way that 

PM2.5 emissions from locomotive engines were allocated to counties in the inventory. 

Locomotive emissions between the two reference cases and the control case vary due 

to differences in activity for this sector due to transported volumes of ethanol. To 

account for these differences, adjustments were to be applied to a common base 

inventory developed for a 2022 projection of the 2005 air quality modeling platform 

(http://www.epa.gov/ttn/chief/emch/index.html). The result should have been 

inventories which reflected county emissions given the RFS1, AEO 2007 and RFS2 

fuel volumes. However, in processing the data, errors were introduced which led to 

inconsistencies in the common base inventory used to develop the PM inventories for 

the three modeling cases. These errors were random, resulting in PM emission 

changes that were too high in some counties and too low in other counties. This error 

had very little impact on national-level PM2.5 emissions. The error in locomotive 

PM2.5 inventory impacts for the RFS2 control case versus the RFS1 mandate 

reference case was 111 tons, out of a total PM2.5 inventory impact of about 18,000 

tons. The error in the impact of the RFS2 control case versus the AEO 2007 

reference case was 1377 tons, out of a total PM2.5 inventory impact of about 16,000 

tons. It is important to note that the total nationwide PM2.5 inventory is projected to 

be over 3.3 million tons in 2022. However, an analysis of the error indicated local 

impacts in both cases were quite large, and in a number of locations, dominated PM2.5 

impacts. These impacts are summarized in a memorandum to the docket.14 As a 

result of the error, we do not present the modeling results for specific localized PM2.5

impacts. However, we have concluded that PM2.5 modeling results are still 

informative for national-level benefits assessment, as described in Section 5.4 of the 

RIA. 

• Sensitivity of light-duty vehicle exhaust emissions to ethanol blends

As discussed above in Sections 3.1 and 3.3, the interim emission inventories used for 

the air quality modeling analysis are the “more sensitive” case described in the 

proposal. As a result, the interim inventories used for air quality modeling assume 

that vehicles operating on E10 have higher NOx emissions and lower VOC, CO and 

PM exhaust emissions compared to the FRM inventories. 

• Cellulosic plant emissions

The interim emission inventories used in air quality modeling generally assumed 

higher emissions from cellulosic plants than the FRM inventories, which used revised 

estimates based on updates to the fraction of biomass burned at these plants. 

However, as noted in Section 3.1 and 3.3, the shift of some cellulosic volume from 

ethanol to diesel results in higher PM emissions from cellulosic plants in the final rule 

inventories than used in the air quality modeling inventories. 

 

14 Memorandum from Rich Cook to Docket ID No. EPA-HQ-OAR-2005-0161, “Impact of an Error in the 

Locomotive Particulate Matter (PM2.5) Inventory on RFS2 Modeling Results.” 
83

• Ethanol volume

As mentioned above, the interim emission inventories used in our air quality 

modeling reflect the use of ethanol in about 34 of the mandated 36 billion gallons and 

do not include any cellulosic diesel. As shown in Table VI.A-1 of the preamble, the 

FRM inventories assume 22 billion gallons of ethanol in the primary case and 6.5 

billion gallons of cellulosic diesel. The inventories used for air quality modeling 

assume ethanol volumes are more consistent with the FRM's high-ethanol case 

inventory, which reflects the use of 33 billion gallons of ethanol and no cellulosic 

diesel. 

There are also some important uncertainties associated with the emissions inventories, 

apart from the differences between the interim inventories and the FRM inventories. For 

example, E85 exhaust and evaporative emissions data are limited, as are data on E10 exhaust and 

evaporative emissions for nonroad spark ignition engines. There is also considerable uncertainty 

in how increased use of ethanol will impact other fuel properties which can affect emission 

inventories and air quality. There are also limited data on activity and emission rates for key 

upstream sources (especially future technology corn ethanol plants and cellulosic ethanol and 

diesel plants). There are uncertainties in the surrogates used to allocate emissions spatially and 

temporally; this is particularly significant in projecting the location of new ethanol plants, 

especially future cellulosic biofuel plants and the location of these emissions. These plants can 

have large impacts on local emissions. While most increased production of corn ethanol can 

reasonably be assumed to occur at existing or planned facilities, there is no way to know with 

certainty where cellulosic biofuel production will occur. Future cellulosic biofuel plant siting 

was based on the types of feedstocks that would be most economical, and we assumed refineries 

would be located in close proximity to feedstocks, as discussed in Section 1.8 of the RIA. While 

corn ethanol plants were treated as point sources, cellulosic biofuel plants were modeled as 

county-wide area sources, as described in Section 1.8. Finally, there are numerous assumptions 

about land use changes that impact inventories for upstream sources and consequently can 

impact air quality modeling results. 

3.4.1.3.2 Uncertainties in Hydrocarbon Speciation Profiles 

Another source of uncertainty involves the hydrocarbon speciation profiles, which are 

used in the air quality modeling emission pre-processor, SMOKE, to break total hydrocarbons 

down into individual constituent compounds. Given the complexity of the atmospheric 

chemistry, the hydrocarbon speciation has an important influence on the air quality modeling 

results. For example, we found that adjusting the speciation profile for gasoline headspace 

emissions changed the ambient concentration of acetaldehyde. SMOKE uses gasoline headspace 

profiles for E0 and E10 from EPA’s SPECIATE database to speciate emissions from gasoline 

storage, gasoline distribution, and gas cans. These are key sources of upstream emissions 

affected by increased use of E10. The EPA profiles initially used in the reference case scenarios 

for gasoline headspace emissions (i.e., emissions from gas cans and tanker truck distribution – 

profiles 8736 and 8737 for E10 and E0) in EPA’s SPECIATE4.2 database show much greater 

differences in alkene (olefin) compounds than one would expect between E0 and E10. Alkenes 
 84

react in the atmosphere to form secondary acetaldehyde, and can also form ozone. E0 has 13% 

of the VOC (volatile organic compounds) as alkenes while the E10 profile has only 4% alkenes. 

By contrast, the profiles for exhaust from Tier 2 vehicles (8756 and 8757 for E0 and E10 

respectively) show similar levels of alkenes for E0 and E10 (about 20%).167 The evaporative 

emissions profiles (profiles 8753 and 8754 in EPA’s SPECIATE4.2 database168) show lower 

olefin contents of 3% and 6% respectively. 

One expects the headspace from E10 blends to have similar olefin content to that from E0 

blends. Available data indicate that ethanol forms an azeotrope with various hydrocarbon 

compounds such as olefins.169,170 That azeotrope for olefins would result in the partial vapor 

pressure of the olefins in the E10 blends being about the same or somewhat higher than in an E0 

blends. The difference between the E0 and E10 profile is likely because the limited fuel samples 

taken for headspace analysis were taken in different locations and time periods. 

Recent measurements of speciated gasoline headspace vapors were collected by EPA’s Office of 

Research and Development (ORD) to compare differences between an E0 fuel and a splashblended E10.171 The addition of 10% ethanol to the base E0 fuel only slightly decreased the 

olefin content from 7.6% to 6.3% of total VOC observed in the headspace vapors. While there is 

some uncertainty in representativeness of the splash-blended fuel, a follow-up analysis of 

speciated headspace vapors from in-use E10 gasolines showed significant variation in olefin 

composition from one fuel to another, illustrating the need for speciation profiles collected under 

controlled conditions.172 

Because the E0 and E10 headspace profiles initially used in the reference case scenarios 

have an uncharacteristic difference in relative alkene levels, EPA reran the control case using an 

adjusted E0 gasoline headspace profile.15 However, due to time constraints, EPA did not rerun 

the two reference control cases with the adjusted E0 profile, resulting in an inconsistency 

between the control case and the reference cases. Implications of this inconsistency are 

discussed in Section 3.4.2. EPA believes that it is important to correct the gasoline headspace 

profile, although we recognize that using an adjusted profile introduces inconsistencies between 

the reference and control cases. It should be noted that this is but one example of potential 

weaknesses in the emission speciation data. Profiles for a number of key sources are based on 

data with significant limitations. 

3.4.1.3.3 Uncertainties Associated with Chemical Mechanisms 

Another key source of uncertainty is the photochemical mechanisms in CMAQ 4.7. 

Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3-butadiene can be 

formed secondarily through atmospheric chemical processes. Since secondarily formed 

pollutants can result from many different reaction pathways, there are uncertainties associated 

with each pathway. Simplifications of chemistry must be made in order to handle reactions of 

thousands of chemicals in the atmosphere. Mechanisms for formation of ozone, PM, 

acetaldehyde and peroxyacetyl nitrate (PAN) are particularly relevant for this rule, and are 

discussed in Section 3.4.1.2. 

 

15 Use of the adjusted profile in the control case rerun is discussed in the emissions modeling TSD, found in the 

docket for this rule (EPA-HQ-OAR-2005-0161). 
85

For PM, there are a number of uncertainties associated with SOA formation that should 

be addressed explicitly. As mentioned in Section 3.4.1.2.2, a large number of VOCs emitted 

into the atmosphere, which have the potential to form SOA, have not yet been studied in detail. 

In addition, the amount of ambient SOA that comes from benzene is uncertain. Simplifications 

to the SOA treatment in CMAQ have also been made in order to preserve computational 

efficiency. These simplifications are described in release notes for CMAQ 4.7 on the 

Community Modeling and Analysis System (CMAS) website.173 

 

3.4.2 Air Quality Modeling Results 

As described above, we performed a series of air quality modeling simulations for the 

continental U.S in order to assess the impacts of the renewable fuel volumes required by RFS2. 

The results presented here are based on inventory projections for RFS2 compared against the 

AEO 2007 and RFS1 mandate reference cases, both of which include some usage of ethanol 

fuels. These results are important for understanding the potential differences between RFS2 

volumes of ethanol and AEO 2007 or RFS1 mandate reference cases; however, these results do 

not constitute the “anti-backsliding” analysis required by Clean Air Act section 211(v). EPA 

will be analyzing air quality impacts of increased renewable fuel use through that study and will 

promulgate appropriate mitigation measures under section 211(v), separate from this final action. 

Notably, the anti-backsliding exercise will be able to include inventory improvements based on 

additional results from the EPAct test program which we could not include in this analysis due to 

time restraints.174 The following results are based on the interim inventories detailed in Section 

3.3 and subject to the uncertainties and limitation detailed in Section 3.4.1.3. 

3.4.2.1 Current and Projected Ambient Levels of Pollutants 

Although the purpose of this final rule is to implement the renewable fuel requirements 

established by the Energy Independence and Security Act (EISA) of 2007, the renewable fuel 

volumes required by this rule would also impact emissions of criteria and air toxic pollutants and 

their resultant ambient concentrations. The fuels changes detailed in Section 3.1 of the RIA will 

influence emissions of VOCs, PM, NOX, SOX, CO and air toxics. Air quality modeling 

performed for this final rule illustrates the changes in ambient concentrations of PM2.5 and ozone 

as well as changes in ambient concentrations of ethanol and the following air toxics: 

acetaldehyde, acrolein, benzene, 1,3-butadiene, and formaldehyde. These changes are expected 

to occur with emissions changes from the renewable fuel volumes required by RFS2. The air 

quality modeling results also include changes in deposition of nitrogen and sulfur which are 

expected to occur with emissions changes from the renewable fuel volumes required by RFS2. 

This section describes current ambient levels of ozone, PM, air toxics, and nitrogen and 

sulfur deposition and presents the projected ambient levels resulting from the increased use of 

renewable fuels. Note that the projected results for PM are impacted by the error in the PM2.5

locomotive inventory (Section 3.4.1.3) and therefore we do not present the modeling results for 

specific localized PM2.5 impacts. 
 86

3.4.2.1.1 Particulate Matter (PM2.5 and PM10) 

As described in Section 3.5, PM causes adverse health effects, and the EPA has set 

national ambient air quality standards (NAAQS) to protect against those health effects. In this 

section we present information on current and model-projected future PM levels. 

3.4.2.1.1.1 Current Levels of PM

Figures 3.4-2 and 3.4-3 show a snapshot of annual and 24-hour PM2.5 concentrations in 

2007. There are two U.S. National Ambient Air Quality Standards (NAAQS) for PM2.5: an 

annual standard (15 μg/m3

) and a 24-hour standard (35 μg/m3). In 2007, the highest annual 

average PM2.5 concentrations were in California, Arizona, Alabama, and Pennsylvania and the 

highest 24-hour PM2.5 concentrations were in California, Idaho, and Utah. 

Figure 3.4-2. Annual Average PM2.5 Concentrations in μg/m3 for 2007 (from 2008 Air 

Trends Report)
87

Figure 3.4-3. 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in 

μg/m3

 for 2007 (from 2008 Air Trends Report)

The most recent revisions to the PM standards were in 1997 and 2006. In 2005, the U.S. 

EPA designated nonattainment areas for the 1997 PM2.5 NAAQS (70 FR 19844, April 14, 

2005).16 As of November 13, 2009, approximately 88 million people live in the 39 areas that are 

designated as nonattainment for the 1997 PM2.5 National Ambient Air Quality Standard 

(NAAQS). These PM2.5 nonattainment areas are comprised of 208 full or partial counties. 

Nonattainment areas for the 1997 PM2.5 NAAQS are pictured in Figure 3.4-4. On October 8, 

2009, the EPA issued final nonattainment area designations for the 2006 24-hour PM2.5 NAAQS 

(74 FR 58688, November 13, 2009). These designations include 31 areas composed of 120 full 

or partial counties with a population of over 70 million. Nonattainment areas for the 2006 PM2.5

NAAQS are pictured in Figure 3.4-5. In total, there are 54 PM2.5 nonattainment areas composed 

of 245 counties with a population of 101 million people. 

 

16 A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is 

contributing to a nearby area that is violating the standard. 
 88

Figure 3.4-4. 1997 PM2.5 Nonattainment Areas 
89

Figure 3.4-5. 2006 PM2.5 Nonattainment Areas 
 90

As of November 13, 2009, approximately 26 million people live in the 47 areas that are 

designated as nonattainment for the PM10 NAAQS. There are 40 full or partial counties that 

make up the PM10 nonattainment areas. Nonattainment areas for the PM10 NAAQS are pictured 

in Figure 3.4-6. 

Figure 3.4-6. PM10 Nonattainment Areas

3.4.2.1.1.2 Projected Levels of PM2.5

Generally, our modeling indicates that the required renewable fuel volumes will reduce 

PM2.5 concentrations in some areas of the country and increase PM2.5 concentrations in other 

areas. In the following sections we describe projected PM2.5 levels in the future, with and 

without the required renewable fuel volumes. Information on the air quality modeling 

methodology is contained in Section 3.4.1. Additional detail can be found in the air quality 

modeling technical support document (AQM TSD) in the docket for this rule. 

3.4.2.1.1.2.1 Projected PM2.5 Levels without RFS2 Volumes 

EPA has already adopted many mobile source emission control programs that are 

expected to reduce ambient PM levels. These control programs include the New Marine 
91

Compression-Ignition Engines at or Above 30 Liters per Cylinder rule (XX FR XXXX, DATE), 

the Marine Spark-Ignition and Small Spark-Ignition Engine rule (73 FR 59034, October 8, 

2008), the Locomotive and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6, 

2008), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), the Heavy Duty Engine 

and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 

18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control 

Requirements (65 FR 6698, Feb. 10, 2000). As a result of these programs, the number of areas 

that fail to meet the PM2.5 NAAQS in the future is expected to decrease. However, even with the 

implementation of all current state and federal regulations, there are projected to be U.S. counties 

violating the PM2.5 NAAQS well into the future. 

Recent air quality modeling for the “Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder” rule projects that in 2020, at 

least 10 counties with a population of almost 25 million may not attain the 1997 annual PM2.5

standard of 15 μg/m3

 and 47 counties with a population of over 53 million may not attain the 

2006 24-hour PM2.5 standard of 35 μg/m3

.

17 Since the emission changes from the volumes of 

renewable fuel required by RFS2 will go into effect during the period when some areas are still 

working to attain the PM2.5 NAAQS, the projected emission changes will impact state and local 

agencies in their effort to attain and maintain the PM2.5 standard. 

3.4.2.1.1.2.2 Projected PM2.5 Levels with RFS2 Volumes 

 This section includes a summary of the results of our modeling of PM2.5 air quality 

impacts in the future due to the required renewable fuel volumes. We compare the RFS1 

mandate reference case and AEO 2007 reference case scenarios to the RFS2 control scenario. 

When discussing the projected changes in PM2.5 it is important to remember that there are 

uncertainties and limitations related to the air quality modeling (see Section 3.4.1.3), in large part 

due to uncertainties in projecting the future types of renewable fuels, the location of their 

production, and their method of use. Section 3.3 discusses the differences in the air quality 

modeling inventories and the final rule inventories in more detail. 

Changes in ambient PM2.5 concentrations due to required renewable fuel volumes are a 

result of changes to upstream and downstream emission sources, complex chemical reactions 

(direct emissions and secondary formation), transport and meteorology. As is detailed in Section 

3.2, the required renewable fuel volumes impact upstream and tailpipe emissions of primary 

PM2.5 and PM2.5 precursors such as NOx and VOCs. Primary PM is emitted directly into the 

atmosphere and, on a mass basis, is largely carbonaceous in nature. Various studies have shown 

that mobile sources are a major source of primary PM in urban areas over many portions of the 

United States.175,176,177,178,179,180,181 Primary PM that is carbonaceous is also referred to as 

primary organic aerosol (POA). Secondary PM is formed in the atmosphere from chemical 

transformations of gases. The forms of secondary PM most impacted by the renewable fuel 

volumes are nitrates and organics or secondary organic aerosol (SOA). 

 

17 US EPA (2009). Final Rule “Control of Emissions from New Marine Compression-Ignition Engines at or Above 

30 Liters per Cylinder”. (XX FR XXXXX, DATE). 
 92

Due to the error in the PM inventory for locomotives we only present design value 

changes averaged over all 577 modeled counties, and do not present local impacts. The 

modeled counties are located across the country and have monitors that allow the calculation of a 

PM2.5 design value. A large majority of the modeled counties will see relatively minor annual 

average PM2.5 design value changes of between -0.05 μg/m3

 and +0.05 μg/m3. On a populationweighted basis, the average modeled future-year annual PM2.5 design values are projected to 

decrease by 0.002 μg/m3

 when compared with the RFS1 mandate or AEO reference case.18 We 

also looked at changes in daily PM2.5 design values. A majority of the modeled counties will see 

daily PM2.5 design value changes of between -0.25 μg/m3

 and +0.25 μg/m3. On a populationweighted basis, the average modeled future-year daily PM2.5 design value is projected to 

decrease by 0.06 μg/m3

 when compared with the RFS1 mandate reference case or 0.05 μg/m3

when compared with the AEO reference case. 

The changes in ambient PM2.5 described above are likely due to both increased emissions 

at biofuel production plants and from biofuel transport, and reductions in SOA formation and 

reduced emissions from gasoline refineries. In addition, decreases in ambient PM are predicted 

because our modeling inventory assumed large volumes of E85 use and also that E85 usage 

reduces PM tailpipe emissions. As mentioned previously, these direct PM emission reductions 

would not occur with final rule inventory assumptions. 

3.4.2.1.2 Ozone 

As described in Section 3.5, ozone causes adverse health effects, and the EPA has set 

national ambient air quality standards (NAAQS) to protect against those health effects. In this 

section, we present information on current and model-projected future ozone levels. 

3.4.2.1.2.1 Current Levels of Ozone 

Figure 3.4-7 shows a snapshot of ozone concentrations in 2007. The highest ozone 

concentrations were located in California, Connecticut, Georgia, Massachusetts, North Carolina, 

and Pennsylvania. Fifty-seven percent of the sites were above 0.075 ppm, the level of the 2008 

standard. 

 

18 Note that the change in annual average PM2.5 for design values differs from the change in national populationweighted annual average PM2.5 discussed in Sections I and VIII of the preamble and Chapter 5 of the RIA. National 

population-weighted annual average PM2.5 with respect to health impacts is based on modeling data across all 

populated grid cells rather than just those counties with monitors. We find that there is a small increase in national 

population-weighted annual average PM2.5 across all populated grid cells in the air quality modeling domain. 
93

Figure 3.4-7. Ozone Concentrations (fourth highest daily maximum 8-hour concentration) 

in ppm for 2007 (from 2008 Air Trends Report)
 94

Figure 3.4-8. 1997 Ozone Nonattainment Areas 

The primary and secondary national ambient air quality standards (NAAQS) for ozone 

are 8-hour standards set at 0.075 ppm. The most recent revision to these standards was in 2008; 

the previous 8-hour ozone standards, set in 1997, had been set at 0.08 ppm. In 2004, the U.S. 

EPA designated nonattainment areas for the 1997 8-hour ozone NAAQS (69 FR 23858, April 30, 

2004). As of November 13, 2009, there are 52 8-hour ozone nonattainment areas for the 1997 

ozone NAAQS composed of 268 full or partial counties with a total population of over 122 

million. Figure 3.4-8 presents the 1997 NAAQS ozone nonattainment areas. On September 16, 

2009, the Administrator announced that the EPA is reconsidering the 2008 ozone standards to 

determine whether they adequately protect public health and the environment. She also 

announced that the Agency will propose to temporarily stay the 2008 standards for the purpose 

of attainment and nonattainment area designations. Under the stay, all activities to designate 

areas for the 2008 ozone standards would be suspended for the duration of the reconsideration 

period. EPA intends to complete the reconsideration by August 31, 2010. If, as a result of the 

reconsideration, EPA determines that the 2008 ozone standards are not supported by the 

scientific record and promulgates different ozone standards, the new 2010 ozone standards 

would replace the 2008 ozone standards and the requirement to designate areas for the 2008 

standards would no longer apply. If EPA promulgates new ozone standards in 2010, EPA 

intends to accelerate the designations process so that the designations would be effective in 

August 2011. 

Table 3.4-1 provides an estimate, based on 2005-07 air quality data, of the counties with 

design values greater than the 2008 ozone NAAQS. 
95

Table 3.4-1. 

Counties with Design Values Greater Than the 2008 Ozone NAAQS 

Based on 2005-2007 Air Quality Data 

 NUMBER OF 

COUNTIES 

POPULATIONa

1997 Ozone Standard: counties within the 54 

areas currently designated as nonattainment (as 

of 11/13/09) 

268 122,344,087 

2008 Ozone Standard: additional counties that 

would not meet the 2008 NAAQSb

227 41,285,262 

Total 495 163,629,349 

Notes: 

a

 Population numbers are from 2000 census data. 

b

 Attainment designations for the 2008 ozone NAAQS have not yet been made. Nonattainment for the 2008 Ozone 

NAAQS will be based on three years of air quality data from later years. Also, the county numbers in the table 

include only the counties with monitors violating the 2008 Ozone NAAQS. The numbers in this table may be an 

underestimate of the number of counties and populations that will eventually be included in areas with multiple 

counties designated nonattainment.

3.4.2.1.2.2 Projected Levels of Ozone 

Achieving the required renewable fuel volumes by 2022 is projected to adversely impact 

ozone air quality over much of the U.S. However, ozone air quality improvements are projected 

in a few highly-populated areas which currently have poor air quality. In the following sections 

we describe projected ozone levels in the future resulting from the increased use of renewable 

fuels. Information on the air quality modeling methodology is contained in Section 3.4.1. 

Additional detail can be found in the air quality modeling technical support document (AQM 

TSD) in the docket for this rule. 

3.4.2.1.2.2.1 Projected Ozone Levels without RFS2 Volumes 

EPA has already adopted many emission control programs that are expected to reduce 

ambient ozone levels. These control programs include the New Marine Compression-Ignition 

Engines at or Above 30 Liters per Cylinder rule (XX FR XXXX, DATE), the Marine SparkIgnition and Small Spark-Ignition Engine rule (73 FR 59034, October 8, 2008), the Locomotive 

and Marine Rule (73 FR 25098, May 6, 2008), the Clean Air Interstate Rule (70 FR 25162, May 

12, 2005), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), and the Heavy Duty 

Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 

5002, Jan. 18, 2001). As a result of these programs, 8-hour ozone levels are expected to improve 

in the future. 

The baseline air quality modeling projects that in 2022, with all current controls in effect 

but excluding the emissions changes expected to occur as a result of the required renewable fuel 

volumes, at least 7 counties, with a projected population of over 22 million people, may not 

attain the 1997 8-hour ozone standard of 0.08 ppm and at least 25 counties, with a projected 

population of nearly 41 million people, may not attain the 2008 8-hour ozone standard of 75 ppb.

 

This modeling supports the conclusion that there are a number of counties across the U.S. 
 96

projected to experience ozone concentrations at or above the ozone NAAQS into the future. 

Since the emission changes from the required renewable fuel volumes go into effect during the 

period when some areas are still working to attain the ozone NAAQS, the projected emission 

changes will impact state and local agencies in their effort to attain and maintain the ozone 

standard. In the following section we discuss projected nonattainment areas and how they 

compare to the areas which are projected to experience either ozone reductions or ozone 

increases from the required renewable fuel volumes. 

3.4.2.1.2.2.2 Projected Ozone Levels with RFS2 Volumes 

This section summarizes the results of our modeling of ozone air quality impacts in the 

future due to required renewable fuel volumes. Specifically, we compare the RFS1 mandate and 

AEO 2007 reference case scenarios to the RFS2 control scenario.19 Our modeling indicates that 

the required renewable fuel volumes will increase ozone design value concentrations in many 

areas of the country and decrease ozone design value concentrations in a small number of areas. 

Figures 3.4-9 and 3.4-10 present the changes in 8-hour ozone design value concentration in 2022 

when the RFS2 control scenario is compared to the RFS1 mandate reference case and the AEO 

2007 reference case respectively. 

 

19 We used a different speciation profile for E10 gasoline headspace emissions in the EISA control case than was 

used for the RFS1 and AEO 2007 reference cases. This inconsistency is described in Section 3.4.1.3. 
97

Figure 3.4-9. Projected Change in 2022 8-hour Ozone Design Values Between the RFS2 

Control Scenario and RFS1 Mandate Reference Case Scenario 
 98

Figure 3.4-10. Projected Change in 2022 8-hour Ozone Design Values Between the RFS2 

Control Scenario and AEO 2007 Reference Case Scenario 

As can be seen in Figure 3.4-9 and 3.4-10 most counties with modeled data, especially 

those in the Midwest, see increases in their ozone design values. The majority of these design 

value increases are less than 0.5 ppb. However, there are some counties that will see 8-hour 

ozone design value increases above 0.5 ppb; these counties are mainly in the eastern U.S. The 

maximum projected increase in an 8-hour ozone design value is in Morgan County, Alabama, 

1.56 ppb and 1.27 ppb when compared with the RFS1 mandate and AEO 2007 reference cases 

respectively. There are also some counties that are projected to see 8-hour ozone design value 

decreases. The counties with ozone design value decreases greater than 0.5 ppb are in Southern 

California. The maximum decrease projected in an 8-hour ozone design value is in Riverside, 

CA, 0.66 ppb and 0.60 ppb when compared with the RFS1 mandate and AEO 2007 reference 

cases respectively. 

There are 26 counties, mainly in California, that are projected to have 8-hour ozone 

design values above the 2008 NAAQS in 2022 with the required renewable fuel volumes in 

place. Table 3.4-2 below presents the changes in design values for these counties when 

comparing the RFS2 control scenario with the RFS1 mandate and AEO 2007 reference case 

scenarios. 
99

Table 3.4-2. 

Change in Ozone Design Values (ppb) for Counties Projected 

to be Above the 2008 Ozone NAAQS in 2022 

 RFS2 control -

RFS1 mandate 

RFS2 control –

AEO 2007 

San Bernardino County, California -0.58 -0.53 

Riverside County, California -0.66 -0.60 

Los Angeles County, California -0.16 -0.16 

Kern County, California 0.02 -0.02 

Tulare County, California 0.34 0.07 

Harris County, Texas 0.12 0.05 

Fresno County, California 0.11 0.08 

Brazoria County, Texas 0.18 0.09 

Suffolk County, New York -0.09 -0.05 

East Baton Rouge County, Louisiana 0.39 0.27 

Sacramento County, California 0.04 0.04 

Orange County, California -0.57 -0.52 

Calaveras County, California 0.15 0.14 

Nevada County, California 0.07 0.06 

El Dorado County, California 0.05 0.04 

Harford County, Maryland 0.23 0.03 

Ventura County, California -0.01 -0.03 

Fairfield County, Connecticut -0.08 -0.08 

Placer County, California 0.05 0.04 

San Diego County, California 0.25 0.19 

Merced County, California -0.10 -0.09 

Westchester County, New York 0.35 0.23 

Kenosha County, Wisconsin -0.11 -0.11 

Philadelphia County, Pennsylvania 0.19 0.12 

New Haven County, Connecticut 0.92 0.68 

Table 3.4-3 shows the average change in 2022 8-hour ozone design values for: (1) all 

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that 

exceeded the 2008 ozone standard, (3) counties with 2005 baseline design values that did not 

exceed the 2008 standard, but were within 10% of it, (4) counties with 2022 design values that 

exceeded the 2008 ozone standard, and (5) counties with 2022 design values that did not exceed 

the standard, but were within 10% of it. Counties within 10% of the standard are intended to 

reflect counties that meet the standard, but will likely benefit from help in maintaining that status 

in the face of growth. Many of these statistics show an increase in ozone design values in 2022, 

more often when compared with the RFS1 case, but the magnitude of the increase varies and 

there are some statistics which show a decrease in 8-hour ozone design values. On a populationweighted basis, the average modeled future-year 8-hour ozone design values are projected to 

increase by 0.28 ppb in 2022 when compared with the RFS1 mandate reference case and increase 

by 0.16 ppb when compared with the AEO 2007 reference case. On a population-weighted basis 
 100

those counties that are projected to be above the 2008 ozone standard in 2022 will see decreases 

of 0.14 when compared with the RFS1 mandate reference and 0.15 ppb when compared with the 

AEO 2007 reference case scenario. 

Table 3.4-3. 

Average Change in Projected Future Year 8-hour Ozone Design Value 

as a Result of the Required Renewable Fuel Volumes 

AVERAGEa NUMBER 

OF US 

COUNTIES 

2020 

POPULATIONb

CHANGE IN 

2022 DESIGN 

VALUE (PPB) 

RFS2-RFS1 

CHANGE IN 

2022 DESIGN 

VALUE (PPB) 

RFS2-AEO 

2007 

All 678 238,378,342 0.46 0.30 

All, population-weighted 678 238,378,342 0.28 0.16 

Counties whose 2005 base year is 

violating the 2008 8-hour ozone 

standard 389 174,967,297 0.44 0.28 

Counties whose 2005 base year is 

violating the 2008 8-hour ozone 

standard, population-weighted 389 174,967,297 0.26 0.14 

Counties whose 2005 base year is 

within 10 percent of the 2008 8-hour 

ozone standard 208 43,172,228 0.52 0.36 

Counties whose 2005 base year is 

within 10 percent of the 2008 8-hour 

ozone standard, population-weighted 215 45,008,435 0.35 0.22 

Counties whose 2022 RFS2 control case 

is violating the 2008 8-hour ozone 

standard 26 41,017,324 0.04 0.00 

Counties whose 2022 RFS2 control case 

is violating the 2008 8-hour ozone 

standard, population-weighted 26 41,017,324 -0.14 -0.15 

Counties whose 2022 RFS2 control case 

is within 10% of the 2008 8-hour ozone 

standard 110 61,618,519 0.34 0.22 

Counties whose 2022 RFS2 control case 

is within 10% of the 2008 8-hour ozone 

standard, population-weighted 110 61,618,519 0.31 0.19 

Notes: 

a Averages are over counties with 2005 modeled design values 

b Population numbers based on 2000 census data 

Ground-level ozone pollution is formed by the reaction of VOCs and NOx in the 

atmosphere in the presence of heat and sunlight. The science of ozone formation, transport, and 

accumulation is complex.182 The projected ozone increases in some areas and decreases in other 

areas which are seen in the air quality modeling for this final rule are likely a result of the 
101

emissions changes due to the increased volumes of renewable fuels combined with the 

photochemistry involved, the different background concentrations of VOCs and NOx in different 

areas of the country, and the different meteorological conditions in different areas of the country. 

When VOC levels are relatively high, relatively small amounts of NOx enable ozone to form 

rapidly. Under these conditions VOC reductions have little effect on ozone and while NOx 

reductions are highly effective in reducing ozone, NOx increases lead to increases in ozone. 

Such conditions are called “NOx-limited.” Because the contribution of VOC emissions from 

biogenic (natural) sources to local ambient ozone concentrations can be significant, even some 

areas where man-made VOC emissions are relatively low can be NOx-limited. Rural areas are 

usually NOx-limited, due to the relatively large amounts of biogenic VOC emissions in such 

areas. The ozone increases seen in the southeastern U.S. and many of the other rural areas are 

likely due to the fact that those areas are NOx-limited and this final rule is projected to increase 

NOx and decrease VOCs. A recent review article looking at ethanol in gasoline indicates that 

increasing usage of E10 fuels, when compared with E0 fuels, can increase NOx emissions and 

thereby increase ozone concentrations (see Section 3.4.3.3).183

When NOx levels are relatively high and VOC levels relatively low, NOx forms 

inorganic nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOClimited.” Under these conditions, VOC reductions are effective in reducing ozone, but NOx 

reductions can actually increase local ozone under certain circumstances. In the air quality 

modeling done for this final rule, the ozone decreases seen in southern California and some of 

the other urban areas, like Cleveland and Miami, are likely due to the fact that those areas are 

VOC-limited areas and they are projected to see decreases in VOCs and increases in NOx due to 

this final rule. 

As mentioned in Section 3.3, the inventories used for the air quality modeling differ from 

those being presented in this final rule, and as mentioned in Section 3.4.1.3, there are 

uncertainties and limitations related to the air quality modeling. When looking at the changes in 

projected ozone the most important uncertainty has to do with the fact that the modeled 

inventory assumes increases in NOx for vehicles using E10 fuel. These NOx increases 

contribute to the ozone increases in NOx-limited areas and the ozone decreases in VOC-limited 

areas. 

3.4.2.1.3 Air Toxics 

3.4.2.1.3.1 Current Levels of Air Toxics 

The majority of Americans continue to be exposed to ambient concentrations of air toxics 

at levels which have the potential to cause adverse health effects.184 The levels of air toxics to 

which people are exposed vary depending on where people live and work and the kinds of 

activities in which they engage, as discussed in detail in U.S. EPA’s recent Mobile Source Air 

Toxics Rule.185 In order to identify and prioritize air toxics, emission source types and locations 

which are of greatest potential concern, U. S. EPA conducts the National-Scale Air Toxics 

Assessment (NATA). The most recent NATA was conducted for calendar year 2002, and was 

released in June 2009.186 NATA for 2002 includes four steps: 
 102

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources 

2) Estimating ambient concentrations of air toxics across the United States 

3) Estimating population exposures across the United States 

4) Characterizing potential public health risk due to inhalation of air toxics including both 

cancer and noncancer effects 

Figures 3-4.11 and 3-4.12 depict estimated county-level carcinogenic risk and noncancer 

respiratory hazard from the assessment. The respiratory hazard is dominated by a single 

pollutant, acrolein. 

According to NATA for 2002, mobile sources were responsible for 47 percent of outdoor 

toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the noncancer hazard.

187,20 Benzene is the largest contributor to cancer risk of all 124 pollutants quantitatively 

assessed in the 2002 NATA, and mobile sources were responsible for 59 percent of benzene 

emissions in 2002. Over the years, EPA has implemented a number of mobile source and fuel 

controls which have resulted in VOC reductions, which also reduced benzene and other air toxic 

emissions. 

Figure 3-4.11. County Level Average Carcinogenic Risk, 2002 NATA. 

 

20 NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to 

estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this rule were modeled 

with CMAQ 4.7, which has only recently been updated to include air toxics. 
103

Figure 3-4.12. County Level Average Noncancer Hazard Index, 2002 NATA. 

3.4.2.1.3.2 Projected Levels of Air Toxics 

In the following sections, we describe results of our modeling of air toxics levels in the 

future with the renewable fuel volumes required by this action. Although a large number of 

compounds which are considered air toxics could be impacted by increases in renewable fuel 

volumes, we focused on those which were identified as national and regional-scale cancer and 

noncancer risk drivers in the 2002 NATA188 and were also likely to be significantly impacted by 

the renewable fuel volumes required by RFS2. These compounds include benzene, 1,3-

butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol impacts were also included in our 

analyses because of health concerns (Section 3.4.5) and its role as an acetaldehyde precursor. 

Information on the air quality modeling methodology is contained in Section 3.4.1.1. Additional 

detail can be found in the air quality modeling technical support document (AQM TSD) in the 

docket for this rule. 

It should be noted that EPA has adopted many mobile source emission control programs 

that are expected to reduce ambient air toxics levels. These control programs include the Heavyduty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine 

Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098, 

May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air 

Nonroad Diesel Rule (69 FR 38957, June 29, 2004), Heavy Duty Engine and Vehicle Standards 

and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier 

2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, 

Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the 
 104

future is expected to decrease. The reference case and control case scenarios include these 

controls. 

This section summarizes the results of our modeling of ambient air toxics impacts in the 

future from the renewable fuel volumes required by RFS2. Specifically, we compare the RFS1 

mandate and AEO 2007 reference scenarios to the RFS2 control scenario for 2022 (see Section 

3.3 for more information on the scenarios).21 Our modeling indicates that, while there are some 

localized impacts, the renewable fuel volumes required by RFS2 have relatively little impact on 

national average ambient concentrations of the modeled air toxics. An exception is increased 

ambient concentrations of ethanol. Because overall impacts are small, we concluded that 

assessing exposure to ambient concentrations and conducting a quantitative risk assessment of 

air toxic impacts was not warranted. However, we did develop population metrics, including the 

population living in areas with increases or decreases in concentrations of various magnitudes. 

We also estimated aggregated populations above and below reference concentrations for 

noncancer effects. 

Our discussion of the air quality modeling for air toxics primarily focuses on impacts of 

the renewable fuel volumes required by RFS2 in reference to the RFS1 mandate for 2022; this 

comparison has a greater difference in projected ethanol volumes between the reference and the 

control case than a comparison using the AEO 2007 reference case. Except where specifically 

discussed below, air quality modeling results of the RFS2 control case in comparison with the 

AEO 2007 reference case are presented in Appendix 3.A of this RIA. 

Acetaldehyde 

Our air quality modeling does not show substantial overall nationwide impacts on 

ambient concentrations of acetaldehyde as a result of the renewable fuel volumes required by this 

rule. Annual percent changes in ambient concentrations of acetaldehyde are less than 1% for 

most of the country (Figure 3.4-13). Several urban areas show decreases in ambient acetaldehyde 

concentrations ranging from 1 to 10%, and some rural areas associated with new ethanol plants 

show increases in ambient acetaldehyde concentrations ranging from 1 to 10% with RFS2. 

Annual absolute changes in ambient concentrations of acetaldehyde are generally less than 0.1 

μg/m3 (Figure 3.4-14). However, as discussed below, there are considerable limitations and 

uncertainties in our assessment of impacts of the renewable fuel volumes required by this rule on 

ambient concentrations of acetaldehyde. 

 

21 We used a different speciation profile for E10 gasoline headspace emissions in the RFS2 control case than was 

used for the RFS1 and AEO reference cases. This inconsistency is described in Section 3.4.1.3. 
105

Figure 3.4-13. Acetaldehyde Annual Percent Change in Concentration Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022 
 106

Figure 3.4-14. Acetaldehyde Annual Absolute Changes in Ambient Concentrations 

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3) 

As noted above, the results show that the largest increases in ambient acetaldehyde 

concentrations with RFS2 volumes occur in areas associated with new ethanol plants. This 

result is due to an increase in emissions of primary acetaldehyde and precursor emissions from 

ethanol plants not included in the RFS1 baseline scenario. Locations for projected corn ethanol 

plants emissions were based on existing or planned plants, whereas cellulosic ethanol plants were 

projected based on available feedstocks. Details on how this was done are described in Section 

1.8 of the RIA.189 As discussed in Section 3.4.1.3, the location of these localized increases is 

limited by uncertainties in the placement of the new plants. 

Significant increases in ambient acetaldehyde might be expected based on the significant 

increases in primary acetaldehyde and ethanol emissions (18% and 16% for the primary case 

relative to RFS1, nationally, as described in Section 3.2). However, the chemical formation of 

acetaldehyde is complex; most ambient acetaldehyde is formed from secondary photochemical 

reactions of numerous precursor compounds, and many photochemical mechanisms are 

responsible for this process (see Section 3.4.1.2 and 3.4.1.3). As discussed in more detail in 

Section 3.4.3.1, some previous U.S. monitoring studies have suggested an insignificant or small 
107

impact of increased use of ethanol in fuel on ambient acetaldehyde.190,191,192 These studies 

suggest that increases in direct emissions of acetaldehyde are offset by decreases in the 

secondary formation of acetaldehyde. Other past studies have shown increases in ambient 

acetaldehyde with increased use of ethanol in fuel, although factors such as differences in vehicle 

fleet, lack of RVP control, exclusion of upstream impacts, and differences in the levels of other 

compounds in the ambient air may limit the ability of these studies to inform expected impacts 

on ambient air quality (Section 3.4.3.1). Given the conflicting results among past studies and the 

limitations of our analysis as discussed in the following paragraphs, considerable additional work 

is needed to address the impacts of the renewable fuel volumes required by this rule on ambient 

concentrations of acetaldehyde. 

The comparison of the RFS1 mandate reference case with the RFS2 control case for 

summer and winter shows decreases in ambient acetaldehyde concentrations in urban areas 

(Figures 3.4-13 and 3.4-14). Decreases are less pronounced in winter when there is less 

secondary formation of acetaldehyde (Figures 3.4-15 and 3.4-16). A key reason for the decrease 

in urban areas is reductions in certain acetaldehyde precursors, primarily alkenes (olefins). 

These reductions are due to differences in the E0 gasoline headspace speciation profiles used for 

the control case and the reference cases, as discussed in Section 3.4.1.3. Headspace profiles are 

used to speciate hydrocarbon emissions from gasoline storage, gasoline distribution, and gas 

cans. The differences between cases arose when EPA noticed that the headspace profiles used in 

the reference case scenarios exhibited a reduction in alkene levels going from E0 to E10 that was 

not consistent with what one would expect as a result of increased ethanol use. In these cases, 

the E0 gasoline headspace profile has 13% of the VOC as alkenes and the E10 profile has an 

alkene content of 4%. 

To address this issue, EPA adjusted the E0 headspace profile based on the assumption 

that the emissions have an alkene content of 4%, consistent with the percent alkene content of 

the E10 headspace profile because a 13% alkene content is much higher than typically seen in 

fuel surveys and one expects the headspace from E0 and E10 to be similar.193 However, due to 

time constraints, we were not able to make this improvement for the reference cases. Thus, 

alkene levels associated with E0 use are lower in the control case than the reference cases, 

leading to a reduction in secondarily formed acetaldehyde. 

To determine the potential impact of this inconsistency, EPA conducted a sensitivity 

analysis of the RFS1 mandate reference case for the Eastern U.S. modeling domain.22 This 

sensitivity analysis was conducted for a single day in January, April, July and October, and 

compared results with the control case for the following two cases: 

1) RFS1 case with no change in alkene levels between headspace profiles for E0 and 

E10 (i.e., adjusted E0 profile) 

2) RFS1 case with higher alkene levels for E0 headspace profile 

 

22 Details of the sensitivity run are discussed in the emissions modeling TSD, found in the docket for this rule (EPAHQ-OAR-2005-0161). 
 108

The results of the sensitivity analysis showed that acetaldehyde levels were significantly 

higher for the comparison between Case 1 and the control case than for the comparison between 

Case 2 and the control case. The sensitivity analysis thus confirmed that the decrease in these 

acetaldehyde precursors between the reference cases and the control case E0 headspace profile is 

driving the decrease in ambient concentrations of acetaldehyde in urban areas. Thus, while the 

air quality modeling results presented in this RIA suggest impacts of increased renewable fuel 

use on ambient acetaldehyde are not substantial and there may be decreases in urban areas, there 

is considerable uncertainty associated with these results. In fact, if the reference cases were rerun 

with revised E0 headspace profiles, some of the observed decreases could become increases. 

Additional research is underway to address these uncertainties, e.g., measurement of 

representative fuels to create better headspace speciation profiles (Section 3.4.1.3) and 

improvements in other speciation profiles based on additional results from the EPAct emissions 

test program.194

It should also be noted (see Section 3.3 above) that we modeled the “more sensitive” 

emission inventory case similar to that presented in the NPRM which assumed that use of E10 

would lead to increases in NOx emissions for later model year vehicles. Increases in NOx may 

result in more acetyl peroxy radical forming PAN rather than acetaldehyde. Recent EPA testing 

results, which have been included in the FRM scenarios, do not show these increases in NOx for 

later model year vehicles.195 Our air quality modeling results may therefore underestimate the 

impacts of the renewable fuel volumes required by RFS2 on ambient concentrations of 

acetaldehyde. 

Figure 3.4-15. Summer Changes in Acetaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes 

and (b) Absolute Changes (μg/m3) 

a b 
109

Figure 3.4-16. Winter Changes in Acetaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes 

and (b) Absolute Changes (μg/m3) 

Formaldehyde 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of formaldehyde from the renewable fuel volumes required by this rule. As shown 

in Figure 3.4-17, most of the U.S. experiences a 1% or less change in ambient formaldehyde 

concentrations. Decreases in ambient formaldehyde concentrations range between 1 and 5% in a 

few urban areas. Increases range between 1 and 2.5% in some rural areas associated with new 

ethanol plants; this result is due to increases in emissions of primary formaldehyde and 

formaldehyde precursors from the new ethanol plants. As discussed above, uncertainties in the 

placement of new ethanol plants limit the model’s projected location of associated emission 

increases (Section 3.4.1.3). Figure 3.4-18 shows that absolute changes in ambient concentrations 

of formaldehyde are generally less than 0.1 μg/m3. 

b a 
 110

Figure 3.4-17. Formaldehyde Annual Percent Change in Concentration Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022 
111

Figure 3.4-18. Formaldehyde Annual Percent Changes in Ambient Concentrations 

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3) 

Ethanol 

Our modeling projects that the renewable fuel volumes required by this rule will lead to 

significant nationwide increases in ambient ethanol concentrations. Increases ranging between 

10 to 50% are seen across most of the country (Figure 3.4-19). The largest increases (more than 

100%) occur in urban areas with high amounts of onroad emissions and in rural areas associated 

with new ethanol plants. Absolute increases in ambient ethanol concentrations are above 1.0 ppb 

in some urban areas (Figure 3.4-20). The location of these localized increases is limited by 

uncertainties in the placement of the new plants, as discussed in Section 3.4.1.3. 

It should be noted here that these increases are overestimated because the speciated 

profile combination used for modeling nonroad emissions was misapplied. While sensitivity 

analyses suggest that the impact of this error was negligible for other pollutants, it resulted in 

overestimates of ethanol impacts by more than 10% across much of the modeling domain. For a 

detailed discussion, please refer to the emissions modeling TSD, found in the docket for this rule 

(EPA-HQ-OAR-2005-0161). 
 112

Figure 3.4-19. Ethanol Annual Percent Changes Change in Concentration Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 
113

Figure 3.4-20. Ethanol Annual Absolute Changes in Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (ppb) 

Benzene 

Our modeling projects that the renewable fuel volumes required by this rule will lead to 

small nationwide decreases in ambient benzene concentrations. As shown in Figure 3.4-21, 

decreases in ambient benzene concentrations range between 1 and 10% across most of the 

country and can be higher in a few urban areas. Absolute changes in ambient concentrations of 

benzene show reductions up to 0.2 μg/m3 (Figure 3.4-22). 
 114

Figure 3.4-21. Benzene Annual Percent Change in Concentration Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022 
115

Figure 3.4-22. Benzene Annual Absolute Changes in Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3) 

1,3-Butadiene 

The results of our air quality modeling show small increases and decreases in ambient 

concentrations of 1,3-butadiene in parts of the U.S. as a result of the renewable fuel volumes 

required this rule. Generally, decreases occur in some southern areas of the country and 

increases occur in some northern areas and areas with high altitudes (Figure 3.4-23). Percent 

changes in 1,3-butadiene concentrations are over 50% in several areas; but the changes in 

absolute concentrations of ambient 1,3-butadiene are generally less than 0.005 μg/m3 (Figure 

3.4-24). Annual increases in ambient concentrations of 1,3-butadiene are driven by wintertime 

rather than summertime changes (Figures 3.4-25 and 3.4-26). These increases appear in rural 

areas with cold winters and low ambient levels but high contributions of emissions from 

snowmobiles, and a major reason for this modeled increase may be deficiencies in available 

emissions test data used to estimate snowmobile 1,3-butadiene emission inventories. These data 

were based on tests using only three engines, which showed significantly higher 1,3-butadiene 
 116

emissions with 10% ethanol. However, they may not have been representative of real-world 

response of snowmobile engines to ethanol. 

Figure 3.4-23. 1,3-Butadiene Annual Percent Change in Concentration Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022 
117

Figure 3.4-24. 1,3-Butadiene Annual Absolute Changes in Ambient Concentrations 

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3) 
 118

Figure 3.4-25. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes 

and (b) Absolute Changes (μg/m3) 

Figure 3.4-26. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes 

and (b) Absolute Changes (μg/m3) 

a b 

a b 
119

Acrolein 

Our air quality modeling shows small regional increases and decreases in ambient 

concentrations of acrolein as a result of the renewable fuel volumes required by this rule. As 

shown in Figure 3.4-27, decreases in acrolein concentrations occur in some eastern and southern 

parts of the U.S. and increases occur in some northern areas and areas associated with new 

ethanol plants. Figure 3.4-28 indicates that changes in absolute ambient concentrations of 

acrolein are between ± 0.001 μg/m3 with the exception of the increases associated with new 

ethanol plants. These increases can be up to and above 0.005 μg/m3 with percent changes above 

50% and are due to increases in emissions of acrolein from the new plants. As discussed in 

Section 3.4.1.3, uncertainties in the placement of new ethanol plants limit the model’s projected 

location of associated emission increases. Ambient acrolein increases in upper Michigan, 

Canada, the Northeast, and the Rocky Mountain region are driven by wintertime changes 

(Figures 3.4-29 and 3.4-30), and occur in the same areas of the country that have wintertime 

rather than summertime increases in ambient 1,3-butadiene. 1,3-butadiene is a precursor to 

acrolein, and these increases are likely associated with the same emission inventory issues in 

areas of high snowmobile usage seen for 1,3-butadiene, as described above. 

Figure 3.4-27. Acrolein Annual Percent Changes Change in Concentration Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 
 120

Figure 3.4-28. Acrolein Annual Absolute Changes in Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3) 
121

Figure 3.4-29. Summer Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) 

Absolute Changes (μg/m3) 

Figure 3.4-30. Winter Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) 

Absolute Changes (μg/m3) 

a b 

a b 
 122

Population Metrics 

 To assess the impact of projected changes in air quality with increased renewable fuel 

use, we developed population metrics that show population experiencing increases and decreases 

in annual ambient concentrations across the modeled air toxics. Figure 3.4-31 below illustrates 

the number of people impacted by changes of various magnitudes in annual ambient 

concentrations with the renewable fuel volumes required by RFS2 in 2022, as compared to the 

RFS1 mandate reference case. For ambient concentrations of ethanol, over 98% of the 

population (334,730,202 people) experiences an increase greater than or equal to 10%. For the 

other modeled air toxics, more than 90% of the population (greater than 305,658,000 people) 

will experience a change in ambient concentration of ±1%. For acrolein, 9.9% of the population 

(33,354,866 people) will live in areas with an increase in ambient concentrations ranging from 1 

to 50%; 0.13% (439,535 people) of the population experiences an increase greater than 50%. 

For 1,3-butadiene, 5.9% of the population (20,171,533 people) experiences a 1 to 50% increase 

in ambient concentrations, and 0.33% (1,135,806 people) of the population experiences an 

increase greater than 50%. The percentage of the population living in areas with increases in 

ambient concentrations of acetaldehyde and formaldehyde are as follows: 0.30% of the 

population (1,007,009 people) experiences acetaldehyde increases between 1 and 10%, and 

0.48% of the population (1,642,944 people) experiences formaldehyde increases between 1 and 

2.5%. 
123

136,287

95,491 108,636

51 28 258,420 65,570 13,969 1,615

122,378 620 46,391 11,699 9,936 6,782 5,118 269 171

4,697 43,717 65,804 5,211 3,846 3,822 7,293 935 201

46,024 165,604 43,867 4,002 80,156

2,568 85,793 177,428 45,123 27,734 811 144 52

737 4,169 214,965 2,610 117,155

EthanolAcetaldehyde

FormaldehydeBenzene1,3-Butadiene

Acrolein

≤ -100 >-100 to

≤-50

>-50 to 

≤-10

>-10 to 

≤-5

>-5 to 

≤-2.5

>-2.5 to 

≤-1

>-1 to 

<+1

≥+1 to 

<+2.5

≥+2.5 to 

<+5

≥+5 to 

<+10

≥+10 to 

<+50

≥+50 to 

<+100

≥ +100

Percent Change in Ambient Concentration from RFS1 Mandate to RFS2

Population (in Thousands) Impacted by Changes in Annual Ambient 

Concentrations of Toxic Pollutants with RFS2

.

11

.

 1

.

 4

.

 3

Figure 3.4-31. Number of People Impacted by Changes in Annual Ambient Concentrations or Toxic Pollutants by 

Percent Change Brackets, RFS 1 Mandate Reference Case Compared to the RFS2 Control Case 
 124

The population exposed to average ambient concentrations of air toxics above and below 

reference concentrations for noncancer health effects in 2022 is presented for the two reference 

cases in Table 3.4-4 below. Reference concentration (RfC) values presented in this table are the 

same as those used in the 2002 NATA.196 At present, no RfC exists for ethanol; EPA is 

conducting an IRIS assessment for this air toxic. The RfC is an estimate, with uncertainty 

spanning perhaps an order of magnitude, of an inhalation exposure to the human population 

(including sensitive subgroups) that is likely to be without appreciable risks of deleterious effects 

during a lifetime. Exposures to levels above the RfC do not necessarily suggest a likelihood of 

adverse health effects, because many RfCs incorporate protective assumptions in the face of 

uncertainty. Exposures above the RfC can best be interpreted as indicating that a potential exists 

for adverse health effects. In addition, average population exposures could be lower or higher 

than the modeled ambient concentrations. 

Table 3.4-4 shows that population-weighted nationwide annual average concentrations 

for the modeled air toxics are below the RfC values for both the RFS1 reference case and the 

AEO 2007 reference case. However, the population-weighted nationwide annual average for 

acrolein is very close to the RfC. Table 3.4-4 also shows the national population that is exposed 

to ambient concentrations above and below the RfC for the modeled air toxics. In both reference 

cases, over 94 million people are exposed to ambient concentrations above the RfC for acrolein 

but the national population is exposed to ambient concentrations below the RfC for acetaldehyde, 

benzene, 1,3-butadiene, and formaldehyde. 

Table 3.4-4. Populations Exposed to Ambient Concentrations of Air Toxics above and 

below Reference Concentrations for Noncancer Health Effects in 2022 without RFS2 

Population-weighted 

Concentration 

(Nationwide Annual 

Average in μg/m3) 

National Population 

above RfC (Annual 

Average) 

National Population below 

RfC (Annual Average) CAS No. 

RFS1 

Mandate AEO 2007 

RfC 

(μg/m3) 

RFS1 

Mandate AEO 2007 

RFS1 

Mandate AEO 2007 

Acetaldehyde 75070 1.618 1.613 9 0 0 339,652,451 339,652,451 

Acrolein 107028 0.018 0.017 0.02 95,059,422 94,087,145 244,593,029 245,565,306 

Benzene 71432 0.535 0.527 30 0 0 339,652,451 339,652,451 

1,3-Butadiene 106990 0.023 0.023 2 0 0 339,652,451 339,652,451 

Ethanola 64175 1.039 1.112 - - - - - 

Formaldehyde 50000 1.558 1.555 9.8 0 0 339,652,451 339,652,451 

Table 3.4-5 shows changes in the population exposed to average ambient concentrations 

of air toxics above and below reference concentrations for noncancer health effects in 2022 that 

are projected to occur with increased renewable fuel use as required by RFS2. Differences in 

population-weighted annual average concentrations between the RFS2 control case and the RFS1 

mandate reference case are small, and ethanol is the only compound shown to increase with 

RFS2 fuel volumes. Table 3.4-5 also shows that the renewable fuel volumes required by RFS2 

do not result in any increases in the number of people exposed to ambient concentrations above 
125

the RfC values. The results indicate there may be a reduction in the number of people exposed 

to ambient concentrations of acrolein with RFS2 fuel volumes. 

Table 3.4-5. Populations Exposed to Ambient Concentrations of Air Toxics above and 

below Reference Concentrations for Noncancer Health Effects in 2022 with RFS2 

Population-weighted 

Concentration 

(Annual Average in μg/m3) 

National Population above RfC 

(Annual Average) CAS No. 

RFS2 

RFS1 

Mandate Diff. RFS2 

RFS1 

Mandate Diff. 

Acetaldehyde 75070 1.590 1.618 -0.028 0 0 0 

Acrolein 107028 0.017 0.018 -0.001 92,452,143 95,059,422 -2,607,279 

Benzene 71432 0.520 0.535 -0.015 0 0 0 

1,3-Butadiene 106990 0.022 0.023 -0.001 0 0 0 

Ethanol 64175 1.521 1.039 0.482 - - - 

Formaldehyde 50000 1.549 1.558 -0.009 0 0 0 

3.4.2.2 Deposition of Nitrogen and Sulfur 

3.4.2.2.1 Current Levels of Nitrogen and Sulfur Deposition 

Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen 

and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show 

that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years 

although many areas continue to be negatively impacted by deposition. Deposition of inorganic 

nitrogen and sulfur species routinely measured in the U.S. between 2004 and 2006 were as high 

as 9.6 kilograms of nitrogen per hectare per year (kg N/ha/yr) and 21.3 kilograms of sulfur per 

hectare per year (kg S/ha/yr). Figures 3.4-32 and 3.4-33 show that annual total deposition (the 

sum of wet and dry deposition) decreased between 1989-1999 and 2004-2006 due to sulfur and 

NOX controls on power plants, motor vehicles and fuels in the U.S. The data show that 

reductions were more substantial for sulfur compounds than for nitrogen compounds. These 

numbers are generated by the U.S. national monitoring network and they likely underestimate 

nitrogen deposition because neither ammonia nor organic nitrogen is measured. In the eastern 

U.S., where data are most abundant, total sulfur deposition decreased by about 36 % between 

1990 and 2005, while total nitrogen deposition decreased by 19% over the same time frame.197 
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Figure 3.4-32. Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2004 -2006 
127

Figure 3.4-33. Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2004-2006 
 128

3.4.2.2.2 Projected Levels of Nitrogen and Sulfur Deposition 

Our air quality modeling does not show substantial overall nationwide impacts on the 

annual total sulfur and nitrogen deposition occurring across the U.S. as a result of increased 

renewable fuel volumes required by this rule. Figure 3.4-34 shows that when compared to the 

RFS1 mandate reference case, the RFS2 renewable fuel volumes will result in nearly the entire 

eastern half of the United States seeing nitrogen deposition increases ranging from 0.5% to more 

than 2%. The largest increases will occur in the states of Illinois, Michigan, Indiana, Wisconsin, 

and Missouri, with large portions of each of these states seeing nitrogen deposition increases of 

more than 2%. The Pacific Northwest will also experience increases in nitrogen of 0.5% to more 

than 2%. Figure 3.4-35 shows that when compared to the AEO 2007 reference case, the changes 

in nitrogen deposition as a result of the RFS2 renewable fuel volumes are more limited. The 

eastern half of the United States will still see nitrogen deposition increases ranging from 0.5% to 

more than 2%; however, the size of the area with these changes will be smaller. Increases of 

more than 2% will primarily occur only in Illinois, Indiana, Michigan, and Missouri. Fewer 

areas in the Pacific Northwest will have increases in nitrogen deposition when compared to the 

AEO 2007 reference case. In both the RFS1 mandate and AEO 2007 reference cases the 

Mountain West and Southwest will see only minimal changes in nitrogen deposition, ranging 

from decreases of less than 0.5% to increases of less than 0.5%. A few areas in Minnesota and 

western Kansas would experience reductions of nitrogen up to 2%. 
129

Figure 3.4-34. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain 

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 
 130

Figure 3.4-35. Percent Change in Annual Total Nitrogen over the U.S. Modeling Domain 

Between the AEO Reference Case and the RFS2 Control Case in 2022 

For sulfur deposition, Figure 3.4-36 shows that when compared to the RFS1 mandate 

reference case, the RFS2 renewable fuel volumes will result in annual percent increases in the 

Midwest ranging from 1% to more than 4%. Some rural areas in the west, likely associated with 

new ethanol plants, will also have increases in sulfur deposition ranging from 1% to more than 

4% as a result of the RFS2 renewable fuel volumes. Figure 3.4-37 shows that when compared to 

the AEO 2007 reference case, the changes are more limited. The Midwest will still have sulfur 

deposition increases ranging from 1% to more than 4%, but the size of the area with these 

changes will be smaller. The Pacific Northwest has minimal areas with increases in sulfur 

deposition when compared to the AEO 2007 reference case. When compared to both the RFS1 

mandate and 2007 reference cases, areas along the Gulf Coast in Louisiana and Texas will 

experience decreases in sulfur deposition of 2% to more than 4%. The remainder of the country 

will see only minimal changes in sulfur deposition, ranging from decreases of less than 1% to 

increases of less than 1%. 
131

Figure 3.4-36. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain 

Between the RFS1 Mandate Reference Case and the RFS2 Case in 2022 
 132

Figure 3.4-37. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain 

Between the AEO Reference Case and the RFS2 Case in 2022

3.4.3 Ambient Monitoring and Modeling Studies of Ethanol’s Impacts on Air Quality 

A number of ambient monitoring and modeling studies in the U.S. and abroad have 

quantified the relative concentrations of ethanol emissions and the potential air quality impacts 

of using ethanol in fuels. This section summarizes the main results of these studies and is not 

meant to be a comprehensive examination of this work. 

3.4.3.1 U.S. Studies 

In 1986, Colorado adopted the first state-wide regulation in the country that required the 

use of oxygenated fuels in winter time months to reduce carbon monoxide (CO) emissions. 

From the time the regulation was first adopted, the fuels used quickly shifted from methyl 

tertiary butyl ether (MTBE) and gasoline to largely ethanol-blended fuels. By the winter of 

1996-1997, nearly all of the fuel was blended with ethanol at 10% by volume. The effect of 

using oxygenated fuels on formaldehyde and acetaldehyde concentrations was monitored by 

ambient air quality measurements from the winter of 1987-1988 (95% of fuel blended with 
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MTBE) through the winter of 1996-1997 (nearly all fuel blended with ethanol). Analysis of the 

data by Anderson et al. showed no clear effect on ambient concentrations of formaldehyde and 

acetaldehyde through ten winters of oxygenated fuels use.198,199 Furthermore, Anderson et al. 

reported that the concentrations of formaldehyde and acetaldehyde during the winter of 1995-

1996 when nearly all of the fuel was blended with ethanol were not significantly different from 

those measured during the winter of 1988-1989 when 95% of the fuel was blended with MTBE. 

It was hypothesized that the photochemical production and destruction of these compounds 

suppressed the emissions effect. They concluded that mobile source emissions are the major 

photochemical sources and sinks for both compounds.200 Anderson et al. supported this 

conclusion by citing the work of Altshuller, which showed that most acetaldehyde production 

comes from alkenes.201, 202, 203 

Albuquerque, NM is another location that mandated the use of oxygenates in the 

wintertime to reduce CO emissions. A field study was conducted in Albuquerque to determine 

the atmospheric impacts of the use of ethanol fuels.204,205 Atmospheric concentrations of ozone, 

NOx, CO, PAN, aldehydes, and organic acids were measured in the summer of 1993, before the 

use of ethanol fuels, and in the winters of 1994 and 1995, during the use of 10% ethanol fuel 

(>99%). There were no data for pre-ethanol winter conditions. Results showed increased levels 

of PAN and an increase in acetaldehyde in one winter, but a decrease in the other. Seasonal 

differences were not considered. The authors noted that the daytime temperatures were fairly 

comparable for the summer and winter study periods so it appeared that the significantly higher 

winter values, despite the much lower photochemical reactivity in winter, were primarily due to 

local production of PAN. For acetaldehyde, winter values were about twice as high as the 

summer values. These acetaldehyde levels anti-correlated with PAN levels, indicating a primary 

source of aldehydes in the winter. 

Grosjean et al (2002) conducted monitoring studies in various California cities and 

measured daily maximum PAN concentrations ranging from 0.2 to 6.9 ppb.206 Peroxypropionyl 

nitrate (PPN, a compound similar to PAN) concentrations were measured at lower levels and 

ranged from 0.33-1.04 ppb. This study concluded that aromatics and alkene compounds are 

responsible for significant PAN formation with ethanol and acetaldehyde having a minor role. 

A modeling analysis using the Urban Airshed Model (UAM), was performed by the California 

Air Resources Board in 1999.207 Acetaldehyde and ethanol concentrations in 2003, relative to an 

MTBE baseline, were estimated to increase for 3.5% by weight ethanol-blended gasoline by 4% 

and 72%, respectively. There was no significant impact on PAN formation. Benzene increased 

1%, formaldehyde increased 2 to 4%, butadiene decreased about 2%, and NO2 (0 to 1%) and 

peroxypropionyl nitrate (PPN) were essentially unchanged. It should be noted that the chemical 

mechanism used in this modeling is a previous version of the mechanism used in the modeling 

for this rule, so comparability of results are limited. 

Another air quality modeling study by Jacobson et al. investigated the projected impacts 

of widespread usage of E85 in Los Angeles and the US in 2020.208 Overall results showed 

increases in acetaldehyde and formaldehyde and decreases in 1,3-butadiene and benzene in Los 

Angeles and the U.S. Sources of acetaldehyde included direct emissions and to a larger degree 

photooxidation of unburned ethanol. Results of this modeling study also showed increases in 

unburned ethanol, PAN, and ozone for a future E85 scenario. The results of Jacobson et al. 
 134

study differ from the results of our air quality modeling analysis for a number of reasons. First, 

the scenario modeled in Jacobson et al. study would result in much larger volumes of ethanol in 

the fuel supply than mandated under EISA (and much greater than could feasibly be produced). 

This study also did not include upstream impacts from fuel distribution. As discussed elsewhere 

(Section 3.4.1.3), VOC speciation data used for gasoline storage and distribution and gas cans 

result in reduction of some acetaldehyde precursor emissions. Finally, the modeled scenario 

includes large reductions in NOx emissions. In contrast, we modeled the “more sensitive” 

emission inventory case where NOx emissions increased with greater use of E10 fuel. Increases 

in NOX, may result in more acetyl peroxy radical forming PAN rather than acetaldehyde. 

The U.S. monitoring studies discussed here are largely winter studies and the lack of summer 

studies makes it difficult to quantify the magnitude of air quality impacts of ethanol fuel usage 

over the entire year. 

3.4.3.2 Brazilian Studies 

The following studies investigate changes in ambient concentrations of several air 

pollutants that result from the use of ethanol fuels in Brazil. These studies are not directly 

relevant to the U.S. due to differences such as vehicles (including less stringent emission 

standards), fuels, and climate. However, these studies do provide useful information on potential 

directional changes in pollutant levels with widespread ethanol use. 

Brazil is the first country in the world where a nationwide, large-scale alcohol fuel 

program has been implemented. In 1997, approximately 4 million automobiles ran on neat 

ethanol and approximately nine million automobiles ran on a 22% ethanol-blended gasoline 

mixture.209 It should be noted that Brazilian ethanol blended gasoline does not have RVP 

controls like U. S. blends. 

In Salvador, Bahia, Brazil, ambient levels of formaldehyde and acetaldehyde and their 

relationship with vehicular fleet composition were evaluated.210 The measured concentrations 

for formaldehyde and acetaldehyde ranged from 0.20 to 88 parts per billion by volume (ppbv) 

and from 0.40 to 93 ppbv, respectively. The ratio of formaldehyde to acetaldehyde revealed the 

relationship of vehicular fleet composition to ambient levels. In locations where ethanol-fueled 

vehicular emissions dominated, the ratio decreased, versus locations where diesel-fueled vehicles 

dominated. Sampling in rural areas showed no relationship between formaldehyde and 

acetaldehyde. 

Acetaldehyde and formaldehyde concentrations were measured in the winter of 1999 in 

Sao Paulo, Brazil.211 Ambient levels of these carbonyls were similar. Higher average mixing 

ratios of acetaldehyde and formaldehyde were found in the morning (18.9 and 17.2 ppbv) than 

midday (9.5 and 11.8 ppbv) and evening (7.2 and 10.2 ppbv). In the morning, direct emission 

from vehicles seemed to be the main primary source, whereas at midday and evening these 

compounds appeared to result mainly from photochemistry. 

A survey of volatile organic compounds in areas impacted by heavy traffic, including a 

tunnel, was obtained for Sao Paulo.212 Researchers found the ambient air was dominated by 

ethanol (414 ppbv) with elevated methanol and 1- and 2-propanol. These levels were well above 
135

those measurements available for U.S. cities, particularly Los Angeles, CA. The overall data 

trend also showed levels of C4-C9 n-aldehydes to be approximately 10 times higher than in Los 

Angeles. They conclude that the use of alcohol-based fuels is the primary source for these 

differences since alcohol comprises about 40% of the mobile fuel by volume compared to 3% in 

Los Angeles. Also, the single-ring aromatic hydrocarbons (2.6 ppbv benzene, 9.0 ppbv toluene, 

4.6 ppbv m,p-xylene) and the C4-C11 n-alkanes were similar or slightly elevated in concentration 

compared to Los Angeles. 

A study in Rio de Janeiro, Brazil some years ago measured and modeled ambient PAN 

concentrations.213 The measurements were as high as 5 ppb over a 200 day period, but typically 

below 1 ppb, at one site; at another site, as high as 3 ppb, but again generally below 1 ppb. 

Modeling estimates were as high as 3 ppb for PAN and 1 ppb for PPN. This study concluded 

that with increased use of ethanol in fuels there would be increases in ambient PAN. 

More recent monitoring studies in Brazil measured daily maximum PAN concentrations ranging 

from 0.19 to 6.67 ppb.214 Also, PPN was measured at lower levels of 0.06 to 0.72 ppb. During 

the 41 days of these measurements, PAN levels accounted for a large fraction of the ambient 

NOx. This study concluded that aromatics and alkene compounds are responsible for significant 

PAN formation with ethanol and acetaldehyde having a minor role. 

Speciated ambient carbonyls have also been measured in Rio de Janeiro.215 The most 

abundant carbonyls were formaldehyde (9.3 ppb) and acetaldehyde (9.0 ppb). The researchers 

also examined the ambient acetaldehyde to formaldehyde concentration ratio in Brazilian cities 

since mid-1980 in the context of changes in Brazil’s reliance on ethanol as a vehicle fuel. They 

showed that this ratio has begun to decrease in recent years due to fleet turnover and decrease in 

ethanol-fueled vehicles. Ethanol-fueled vehicles are being replaced by lower-emitting newer 

models that run on a gasoline-ethanol blend. 

Using an empirical kinetic modeling approach (EKMA), researchers simulated ozone, 

formaldehyde, and acetaldehyde concentrations for the urban downtown area of Rio de 

Janeiro.216 The simulated ozone peak was in good agreement with monitoring results. Modeling 

results also showed that acetaldehyde and formaldehyde concentrations were highest in early 

morning, reaching a maximum which coincided with peak vehicular traffic. Additionally, they 

confirmed monitoring evidence that the high acetaldehyde to formaldehyde ratios were due to 

the use of alcohol-based fuels. 

These studies modeled and measured ambient concentrations of several compounds that 

result from the use of ethanol fuels. However, the direct impacts of ethanol fuel usage on air 

quality in Brazil could not be evaluated since there were no ambient data available prior to the 

use of ethanol fuel. Notably, these studies did not include ambient measurements of 

acetaldehyde prior to the use of ethanol fuel, and measured concentrations were much higher 

than those found in the United States. However, gasolines in Brazil lacked RVP control, 

resulting in higher evaporative ethanol emissions than would be likely in the U. S., fuel ethanol 

levels were much higher (neat ethanol or 22% ethanol by volume), and there were significant 

differences in the vehicle fleet and meteorology. Also, vehicles in Brazil do not meet the same 

stringent emissions exhaust and evaporative emission standards as vehicles do in the U.S. These 
 136

factors would all contribute to larger acetaldehyde impacts with ethanol use than expected in the 

U.S. under the control case evaluated for this rule. 

3.4.3.3 Other Studies 

A review was conducted on studies that looked at the environmental impacts of E10 and 

E85 compared to E0.217 The review article focused on five environmental outcomes including 

the impact of increased usage of E10 and/or E85 on air pollutant emissions. The review article 

focuses on studies that are relevant to Australia but includes work done in the US and elsewhere 

and the results are characterized as being “broad and applicable to most industrialized countries 

in moderate temperate climates.” The author concludes that using E10 fuel instead of E0 fuel 

provides minimal improvements in air pollutant emissions, specifically E10 causes lower tailpipe 

CO and particulate emissions but higher acetaldehyde, ethanol and NOx emissions and that there 

is some evidence of a connection between E10 and higher ground-level ozone concentrations. 

Since this is a review article it is difficult to compare the modeling for this final rule to the 

article, however some of the conclusions from the article are useful when interpreting the air 

quality modeling results. 

Some smog chamber studies were recently conducted at EPA218 with two fuels. These 

studies were done on headspace vapors which are the hydrocarbon compounds formed by 

vaporization of hydrocarbon components from gasoline stored in closed (or semi-closed) 

containers such as fuel storage tanks or tanker trucks. These emissions are different from 

exhaust emissions which include products of combustion. They are also somewhat different 

from vehicle evaporative emissions in that the charcoal canister tends to adsorb the higher 

molecular weight hydrocarbons resulting in emissions of the more volatile fuel components. 

The first fuel used in these smog chamber studies was a base gasoline and the second was 

this gasoline with 10% ethanol added to it (a “splash blend” without any control of fuel 

volatility). The smog chamber runs were conducted for 24 hours with a target HC/NOx ratio of 

10-to-1. The chamber runs with the base gasoline had a higher initial concentration of 

isopentane compared to the runs with the base gasoline with 10% ethanol. The runs showed that 

the photoxidation processes of ethanol results in higher levels of acetaldehyde. This 

photoxidation should also increase acetone levels with the base gasoline which is also observed. 

The significance of these results is that ethanol does result in increased acetaldehyde formation 

in smog chamber work. It is important to be able to translate these smog chamber results to 

actual atmospheric conditions by using such studies to better improve the chemical mechanisms 

in air quality models to simulate what happens in the atmosphere with other emission 

components (exhaust, evaporative, other emissions) in actual atmospheric condtions (with 

meteorology, mixing conditions, temperature, concentrations, and mixing being what they are in 

the atmosphere). 

3.5 Health Effects 

In this section we discuss the health and environmental effects associated with particulate 

matter, ozone, NOX, SOX, carbon monoxide and air toxics. The renewable fuel requirements 
137

established by the Energy Independence and Security Act (EISA) of 2007 will impact emissions 

of criteria and air toxic pollutants. 

3.5.1 Particulate Matter 

3.5.1.1 Background 

 Particulate matter (PM) is a generic term for a broad class of chemically and physically 

diverse substances. It can be principally characterized as discrete particles that exist in the 

condensed (liquid or solid) phase spanning several orders of magnitude in size. Since 1987, EPA 

has delineated that subset of inhalable particles small enough to penetrate to the thoracic region 

(including the tracheobronchial and alveolar regions) of the respiratory tract (referred to as 

thoracic particles). Current national ambient air quality standards (NAAQS) use PM2.5 as the 

indicator for fine particles (with PM2.5 referring to particles with a nominal mean aerodynamic 

diameter less than or equal to 2.5 μm), and use PM10 as the indicator for purposes of regulating 

the coarse fraction of PM10 (referred to as thoracic coarse particles or coarse-fraction particles; 

generally including particles with a nominal mean aerodynamic diameter greater than 2.5 μm 

and less than or equal to 10 μm, or PM10-2.5). Ultrafine particles are a subset of fine particles, 

generally less than 100 nanometers (0.1 μm) in aerodynamic diameter. 

Particles span many sizes and shapes and consist of hundreds of different chemicals. 

Particles originate from sources and are also formed through atmospheric chemical reactions; the 

former are often referred to as “primary” particles, and the latter as “secondary” particles. In 

addition, there are also physical, non-chemical reaction mechanisms that contribute to secondary 

particles. Particle pollution also varies by time of year and location and is affected by several 

weather-related factors, such as temperature, clouds, humidity, and wind. A further layer of 

complexity comes from a particle’s ability to shift between solid/liquid and gaseous phases, 

which is influenced by concentration, meteorology, and temperature. 

Fine particles are produced primarily by combustion processes and by transformations of 

gaseous emissions (e.g., SOX, NOX and VOCs) in the atmosphere. The chemical and physical 

properties of PM2.5 may vary greatly with time, region, meteorology and source category. Thus, 

PM2.5 may include a complex mixture of different pollutants including sulfates, nitrates, organic 

compounds, elemental carbon and metal compounds. These particles can remain in the 

atmosphere for days to weeks and travel through the atmosphere hundreds to thousands of 

kilometers.219 

3.5.1.2 Health Effects of PM 

This section provides a summary of the health effects associated with exposure to 

ambient concentrations of PM.23 The information in this section is based on the data and 

conclusions in the PM Air Quality Criteria Document (PM AQCD) and PM Staff Paper prepared 

 

23 Personal exposure includes contributions from many different types of particles, from many sources, and in many 

different environments. Total personal exposure to PM includes both ambient and nonambient components; and 

both components may contribute to adverse health effects. 
 138

by the U.S. Environmental Protection Agency (EPA).24,220,221 We also present additional recent 

studies published after the cut-off date for the PM AQCD.222,25 Taken together this information 

supports the conclusion that exposure to ambient concentrations of PM are associated with 

adverse health effects. 

3.5.1.2.1 Short-term Exposure Mortality and Morbidity Studies 

As discussed in the PM AQCD, short-term exposure to PM2.5 is associated with 

premature mortality from cardiopulmonary diseases,223 hospitalization and emergency 

department visits for cardiopulmonary diseases,224 increased respiratory symptoms,225 decreased 

lung function226 and physiological changes or biomarkers for cardiac changes.227,

228 In addition, 

the PM AQCD described a limited body of new evidence from epidemiologic studies for 

potential relationships between short term exposure to PM and health endpoints such as low birth 

weight, preterm birth, and neonatal and infant mortality.229 

Among the studies of effects associated with short-term exposure to PM2.5, several 

specifically address the contribution of mobile sources to short-term PM2.5-related effects on 

premature mortality. The results from these studies generally indicated that several combustionrelated fine particle source-types are likely associated with mortality, including motor vehicle 

emissions as well as other sources.230 The analyses incorporate source apportionment tools into 

short-term exposure studies and are briefly mentioned here. Analyses incorporating source 

apportionment by factor analysis with daily time-series studies of daily death rates indicated a 

relationship between mobile source PM2.5 and mortality.231,232,233,234 Another recent study in 14 

U.S. cities examined the effect of PM10 exposures on daily hospital admissions for 

cardiovascular disease. This study found that the effect of PM10 was significantly greater in 

areas with a larger proportion of PM10 coming from motor vehicles, indicating that PM10 from 

these sources may have a greater effect on the toxicity of ambient PM10 when compared with 

other sources.235 These studies provide evidence that PM-related emissions, specifically from 

mobile sources, are associated with adverse health effects. 

 

24 The PM NAAQS is currently under review and the EPA is considering all available science on PM health effects, 

including information which has been published since 2004, in the development of the upcoming PM Integrated 

Science Assessment Document (ISA). A second draft of the PM ISA was completed in July 2009 and was 

submitted for review by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board. 

Comments from the general public have also been requested. For more information, see 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=210586. 

25 These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of 

Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe) 

undergo the extensive critical review by CASAC and the public, as did the PM AQCD. The provisional assessment 

found that the “new” studies expand the scientific information and provide important insights on the relationship 

between PM exposure and health effects of PM. The provisional assessment also found that “new” studies generally 

strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to thoracic coarse 

particles are associated with health effects. Further, the provisional science assessment found that the results 

reported in the studies did not dramatically diverge from previous findings, and taken in context with the findings of 

the AQCD, the new information and findings did not materially change any of the broad scientific conclusions 

regarding the health effects of PM exposure made in the AQCD. However, it is important to note that this 

assessment was limited to screening, surveying, and preparing a provisional assessment of these studies. For 

reasons outlined in Section I.C of the preamble for the final PM NAAQS rulemaking in 2006 (see 71 FR 61148-49, 

October 17, 2006), EPA based its NAAQS decision on the science presented in the 2004 AQCD. 
139

3.5.1.2.2 Long-term Exposure Mortality and Morbidity Studies 

Long-term exposure to ambient PM2.5 is associated with premature mortality from 

cardiopulmonary diseases and lung cancer,236 and effects on the respiratory system such as 

decreased lung function or the development of chronic respiratory disease.237 Of specific 

importance, the PM AQCD also noted that the PM components of gasoline and diesel engine 

exhaust represent one class of hypothesized likely important contributors to the observed 

ambient PM-related increases in lung cancer incidence and mortality.238

The PM AQCD and PM Staff Paper emphasized the results of two long-term 

epidemiologic studies, the Six Cities and American Cancer Society (ACS) prospective cohort 

studies, based on several factors – the large air quality data set for PM in the Six Cities Study, 

the fact that the study populations were similar to the general population, and the fact that these 

studies have undergone extensive reanalysis.239,240,241,242243,244 These studies indicate that there 

are positive associations for all-cause, cardiopulmonary, and lung cancer mortality with longterm exposure to PM2.5. One analysis of a subset of the ACS cohort data, which was published 

after the PM AQCD was finalized but in time for the 2006 Provisional Assessment, found a 

larger association than had previously been reported between long-term PM2.5 exposure and 

mortality from all causes and cardiopulmonary diseases in the Los Angeles area using a new 

exposure estimation method that accounted for variations in concentration within the city.245

As discussed in the PM AQCD, the morbidity studies that combine the features of crosssectional and cohort studies provide the best evidence for chronic exposure effects. Long-term 

studies evaluating the effect of ambient PM on children’s development have shown some 

evidence indicating effects of PM2.5 and/or PM10 on reduced lung function growth.246 In another 

recent publication included in the 2006 Provisional Assessment, investigators in southern 

California reported the results of a cross-sectional study of outdoor PM2.5 and a measure of 

atherosclerosis development in the Los Angeles basin.247 The study found positive associations 

between ambient residential PM2.5 and carotid intima-media thickness (CIMT), an indicator of 

subclinical atherosclerosis that is an underlying factor in cardiovascular disease. 

3.5.1.2.3 Roadway-Related PM Exposure and Health Studies 

 A recent body of studies examines traffic-related PM exposures and adverse health 

effects. However, note that the near-road environment is influenced by both gasoline sparkignition (SI) and diesel vehicles, as well as re-entrained road dust and brake and tire wear. One 

study was done in North Carolina looking at concentrations of PM2.5 inside highway patrol cars 

and corresponding physiological changes in state troopers driving the cars. The authors report 

significant elevations in markers of cardiovascular effects (i.e., inflammation, coagulation, and 

cardiac rhythm) associated with concentrations of PM2.5 inside highway patrol cars on North 

Carolina state highways.248 Other studies have found associations between traffic-generated 

particle concentrations at residences and adverse effects, including all-cause mortality, infant 

respiratory symptoms, and reduced cognitive functional development.249,250,251,252 There are 

other pollutants present in the near-roadway environment, including air toxics which are 

discussed in Section 3.4.5, and it is important to note that current studies do not identify a single 
 140

pollutant that is most associated with adverse health effects. Additional information on nearroadway health effects can be found in the recent Mobile Source Air Toxics rule (72 FR 8428, 

February 26, 2007). 

3.5.2 Ozone 

3.5.2.1 Background 

Ground-level ozone pollution is formed by the reaction of VOCs and NOX in the 

atmosphere in the presence of heat and sunlight. These pollutants, often referred to as ozone 

precursors, are emitted by many types of pollution sources such as highway vehicles and 

nonroad engines, power plants, chemical plants, refineries, makers of consumer and commercial 

products, industrial facilities, and smaller area sources. 

The science of ozone formation, transport, and accumulation is complex. Ground-level 

ozone is produced and destroyed in a cyclical set of chemical reactions, many of which are 

sensitive to temperature and sunlight. When ambient temperatures and sunlight levels remain 

high for several days and the air is relatively stagnant, ozone and its precursors can build up and 

result in more ozone than typically would occur on a single high-temperature day. Ozone can be 

transported hundreds of miles downwind of precursor emissions, resulting in elevated ozone 

levels even in areas with low VOC or NOX emissions. 

As mentioned above in Section 3.4.2.1.2, the highest levels of ozone are produced when 

both VOC and NOX emissions are present in significant quantities on clear summer days. 

Relatively small amounts of NOX enable ozone to form rapidly when VOC levels are relatively 

high, but ozone production is quickly limited by removal of the NOX. Under these conditions 

NOX reductions are highly effective in reducing ozone while VOC reductions have little effect. 

Such conditions are called “NOX-limited.” Because the contribution of VOC emissions from 

biogenic (natural) sources to local ambient ozone concentrations can be significant, even some 

areas where man-made VOC emissions are relatively low can be NOX-limited. 

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO) 

with ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle continues, 

the NO2 forms additional ozone. The importance of this reaction depends, in part, on the relative 

concentrations of NOX, VOC, and ozone, all of which change with time and location. When 

NOX levels are relatively high and VOC levels relatively low, NOX forms inorganic nitrates (i.e., 

particles) but relatively little ozone. Such conditions are called “VOC-limited”. Under these 

conditions, VOC reductions are effective in reducing ozone, but NOX reductions can actually 

increase local ozone under certain circumstances. Even in VOC-limited urban areas, NOX

reductions are not expected to increase ozone levels if the NOX reductions are sufficiently large. 

Rural areas are usually NOX-limited, due to the relatively large amounts of biogenic VOC 

emissions in such areas. Urban areas can be either VOC- or NOX-limited, or a mixture of both, 

in which ozone levels exhibit moderate sensitivity to changes in either pollutant. 

3.5.2.2 Health Effects of Ozone 
141

Exposure to ambient ozone contributes to a wide range of adverse health effects.26 These 

health effects are well documented and are critically assessed in the EPA ozone air quality 

criteria document (ozone AQCD) and EPA staff paper.253,254 We are relying on the data and 

conclusions in the ozone AQCD and staff paper, regarding the health effects associated with 

ozone exposure. 

Ozone-related health effects include lung function decrements, respiratory symptoms, 

aggravation of asthma, increased hospital and emergency room visits, increased asthma 

medication usage, and a variety of other respiratory effects. Cellular-level effects, such as 

inflammation of lungs, have been documented as well. In addition, there is suggestive evidence 

of a contribution of ozone to cardiovascular-related morbidity and highly suggestive evidence 

that short-term ozone exposure directly or indirectly contributes to non-accidental and 

cardiopulmonary-related mortality, but additional research is needed to clarify the underlying 

mechanisms causing these effects. In a recent report on the estimation of ozone-related 

premature mortality published by the National Research Council (NRC), a panel of experts and 

reviewers concluded that short-term exposure to ambient ozone is likely to contribute to 

premature deaths and that ozone-related mortality should be included in estimates of the health 

benefits of reducing ozone exposure.255 People who appear to be more susceptible to effects 

associated with exposure to ozone include children, asthmatics and the elderly. Those with 

greater exposures to ozone, for instance due to time spent outdoors (e.g., children and outdoor 

workers), are also of concern. 

Based on a large number of scientific studies, EPA has identified several key health 

effects associated with exposure to levels of ozone found today in many areas of the country. 

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to ambient ozone 

concentrations have been linked to lung function decrements, respiratory symptoms, increased 

hospital admissions and emergency room visits for respiratory problems.256, 257, 258, 259, 260, 261 

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung 

inflammation and can aggravate preexisting respiratory diseases, such as asthma.262, 263, 264, 265, 266

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the lung, 

impairment of lung defense mechanisms, and possibly irreversible changes in lung structure, 

which over time could affect premature aging of the lungs and/or the development of chronic 

respiratory illnesses, such as emphysema and chronic bronchitis.267, 268, 269, 270 

Children and adults who are outdoors and active during the summer months, such as 

construction workers, are among those most at risk of elevated ozone exposures.271 Children and 

outdoor workers tend to have higher ozone exposure because they typically are active outside, 

working, playing and exercising, during times of day and seasons (e.g., the summer) when ozone 

levels are highest.272 For example, summer camp studies in the Eastern United States and 

Southeastern Canada have reported statistically significant reductions in lung function in 

children who are active outdoors.273, 274, 275, 276, 277, 278, 279, 280 Further, children are more at risk of 

experiencing health effects from ozone exposure than adults because their respiratory systems 

are still developing. These individuals (as well as people with respiratory illnesses, such as 

 

26 Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people 

move between locations which have notable different ozone concentrations. Also, the amount of ozone delivered to 

the lung is not only influenced by the ambient concentrations but also by the individuals breathing route and rate. 
 142

asthma, especially asthmatic children) can experience reduced lung function and increased 

respiratory symptoms, such as chest pain and cough, when exposed to relatively low ozone levels 

during prolonged periods of moderate exertion.281, 282, 283, 284

3.5.3 Nitrogen Oxides and Sulfur Oxides 

3.5.3.1 Background 

Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed from 

burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting 

metals from ore. Nitrogen dioxide (NO2) is a member of the nitrogen oxide (NOX) family of 

gases. Most NO2 is formed in the air through the oxidation of nitric oxide (NO) emitted when 

fuel is burned at a high temperature. SO2 andNO2 can dissolve in water vapor and further 

oxidize to form sulfuric and nitric acid which react with ammonia to form sulfates and nitrates, 

both of which are important components of ambient PM. The health effects of ambient PM are 

discussed in Section 3.5.1.2. NOX along with non-methane hydrocarbons (NMHC) are the two 

major precursors of ozone. The health effects of ozone are covered in Section 3.5.2.2. 

3.5.3.2 Health Effects of Sulfur Oxides 

Information on the health effects of SO2 can be found in the U.S. Environmental 

Protection Agency Integrated Science Assessment for Sulfur Oxides.27 SO2 has long been 

known to cause adverse respiratory health effects, particularly among individuals with asthma. 

Other potentially sensitive groups include children and the elderly. During periods of elevated 

ventilation, asthmatics may experience symptomatic bronchoconstriction within minutes of 

exposure. Following an extensive evaluation of health evidence from epidemiologic and 

laboratory studies, the EPA has concluded that there is a causal relationship between respiratory 

health effects and short-term exposure to SO2. Separately, based on an evaluation of the 

epidemiologic evidence of associations between short-term exposure to SO2 and mortality, the 

EPA has concluded that the overall evidence is suggestive of a causal relationship between shortterm exposure to SO2 and mortality. 

3.5.3.3 Health Effects of Nitrogen Oxides 

Information on the health effects of NO2 can be found in the U.S. Environmental 

Protection Agency Integrated Science Assessment (ISA) for Nitrogen Oxides.28 The U.S. EPA 

has concluded that the findings of epidemiologic, controlled human exposure, and animal 

toxicological studies provide evidence that is sufficient to infer a likely causal relationship 

between respiratory effects and short-term NO2 exposure. The ISA concludes that the strongest 

evidence for such a relationship comes from epidemiologic studies of respiratory effects 

 

27 U.S. EPA. (2008). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria (Final Report). 

EPA/600/R-08/047F. Washington, DC: U.S. Environmental Protection Agency. Retrieved on March 18, 2009 from 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843 

28 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria (Final Report).

EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 from 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. 
143

including symptoms, emergency department visits, and hospital admissions. The ISA also draws 

two broad conclusions regarding airway responsiveness following NO2 exposure. First, the ISA 

concludes that NO2 exposure may enhance the sensitivity to allergen-induced decrements in lung 

function and increase the allergen-induced airway inflammatory response following 30-minute 

exposures of asthmatics to NO2 concentrations as low as 0.26 ppm. In addition, small but 

significant increases in non-specific airway hyperresponsiveness were reported following 1-hour 

exposures of asthmatics to 0.1 ppm NO2. Second, exposure to NO2 has been found to enhance 

the inherent responsiveness of the airway to subsequent nonspecific challenges in controlled 

human exposure studies of asthmatic subjects. Enhanced airway responsiveness could have 

important clinical implications for asthmatics since transient increases in airway responsiveness 

following NO2 exposure have the potential to increase symptoms and worsen asthma control. 

Together, the epidemiologic and experimental data sets form a plausible, consistent, and 

coherent description of a relationship between NO2 exposures and an array of adverse health 

effects that range from the onset of respiratory symptoms to hospital admission. 

Although the weight of evidence supporting a causal relationship is somewhat less certain 

than that associated with respiratory morbidity, NO2 has also been linked to other health 

endpoints. These include all-cause (nonaccidental) mortality, hospital admissions or emergency 

department visits for cardiovascular disease, and decrements in lung function growth associated 

with chronic exposure. 

3.5.4 Carbon Monoxide 

 This section summarizes the data and conclusions in the EPA Air Quality Criteria 

Document for CO (CO Criteria Document), which was published in 2000, regarding the health 

effects associated with CO exposure.29,285 Carbon monoxide enters the bloodstream through the 

lungs and forms carboxyhemoglobin (COHb), a compound that inhibits the blood’s capacity to 

carry oxygen to organs and tissues.286,287 Carbon monoxide has long been known to have 

substantial adverse effects on human health, including toxic effects on blood and tissues, and 

effects on organ functions. Although there are effective compensatory increases in blood flow to 

the brain, at some concentrations of COHb somewhere above 20 percent, these compensations 

fail to maintain sufficient oxygen delivery, and metabolism declines.288 The subsequent hypoxia 

in brain tissue then produces behavioral effects, including decrements in continuous performance 

and reaction time.289

Carbon monoxide has been linked to increased risk for people with heart disease, reduced 

visual perception, cognitive functions and aerobic capacity, and possible fetal effects.290 Persons 

with heart disease are especially sensitive to CO poisoning and may experience chest pain if they 

breathe the gas while exercising.291 Infants, elderly persons, and individuals with respiratory 

diseases are also particularly sensitive. Carbon monoxide can affect healthy individuals, 

 

29 The CO NAAQS is currently under review and the EPA is considering all available science on CO health effects, 

including information which has been published since 2000, in the development of the upcoming CO Integrated 

Science Assessment Document (ISA). A second draft of the CO ISA was completed in September 2009 and was 

submitted for review by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board. 

For more information, see http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=213229. 
 144

impairing exercise capacity, visual perception, manual dexterity, learning functions, and ability 

to perform complex tasks.292

Several epidemiological studies have shown a link between CO and premature morbidity 

(including angina, congestive heart failure, and other cardiovascular diseases). Several studies in 

the United States and Canada have also reported an association between ambient CO exposures 

and frequency of cardiovascular hospital admissions, especially for congestive heart failure 

(CHF). An association between ambient CO exposure and mortality has also been reported in 

epidemiological studies, though not as consistently or specifically as with CHF admissions. EPA 

reviewed these studies as part of the CO Criteria Document review process and noted the 

possibility that the average ambient CO levels used as exposure indices in the epidemiology 

studies may be surrogates for ambient air mixes impacted by combustion sources and/or other 

constituent toxic components of such mixes. More research will be needed to better clarify CO’s 

role.293 

3.5.5 Health Effects of Air Toxics 

 

Motor vehicle emissions contribute to ambient levels of air toxics known or suspected as 

human or animal carcinogens, or that have noncancer health effects. The population experiences 

an elevated risk of cancer and other noncancer health effects from exposure to air toxics.294 

These compounds include, but are not limited to, benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. These compounds, 

except acetaldehyde, were identified as national or regional risk drivers in the 2002 Nationalscale Air Toxics Assessment (NATA) and have significant inventory contributions from mobile 

sources. 

According to NATA for 2002, mobile sources were responsible for 47 percent of outdoor 

toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the noncancer hazard. 

Benzene is the largest contributor to cancer risk of all 124 pollutants quantitatively assessed in 

the 2002 NATA and mobile sources were responsible for 59 percent of benzene emissions in 

2002. In 2007, EPA finalized vehicle and fuel controls that address this public health risk; it will 

reduce total emissions of mobile source air toxics by 330,000 tons in 2030, including 61,000 tons 

of benzene. 295

Noncancer health effects can result from chronic,30 subchronic,31 or acute32 inhalation 

exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory 

effects as well as effects on the immune and reproductive systems. According to the 2002 

NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics 

that has the potential for adverse noncancer respiratory health effects. This will continue to be 

the case in 2030, even though toxics concentrations will be lower. Mobile sources were 

 

30 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 

10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal 

species). 

31 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 

32 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 
145

responsible for over 80 percent of the noncancer (respiratory) risk from outdoor air toxics in 

2002. The majority of this risk was from exposure to acrolein. The confidence in the RfC for 

acrolein is medium and confidence in NATA estimates of population noncancer hazard from 

ambient exposure to this pollutant is low.296,297

The NATA modeling framework has a number of limitations which prevent its use as the 

sole basis for setting regulatory standards. These limitations and uncertainties are discussed on 

the 2002 NATA website.298 Even so, this modeling framework is very useful in identifying air 

toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the 

decision making process. 

3.5.5.1 Benzene 

 

The EPA’s IRIS database lists benzene as a known human carcinogen (causing leukemia) 

by all routes of exposure, and concludes that exposure is associated with additional health 

effects, including genetic changes in both humans and animals and increased proliferation of 

bone marrow cells in mice.299,300,301 EPA states in its IRIS database that data indicate a causal 

relationship between benzene exposure and acute lymphocytic leukemia and suggest a 

relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic 

lymphocytic leukemia. The International Agency for Research on Carcinogens (IARC) has 

determined that benzene is a human carcinogen and the U.S. Department of Health and Human 

Services (DHHS) has characterized benzene as a known human carcinogen.302,303

A number of adverse noncancer health effects including blood disorders, such as 

preleukemia and aplastic anemia, have also been associated with long-term exposure to 

benzene.304,305 The most sensitive noncancer effect observed in humans, based on current data, 

is the depression of the absolute lymphocyte count in blood.306,307 In addition, recent work, 

including studies sponsored by the Health Effects Institute (HEI), provides evidence that 

biochemical responses are occurring at lower levels of benzene exposure than previously 

known.308,309,310,311 EPA’s IRIS program has not yet evaluated these new data. 

3.5.5.2 1,3-Butadiene 

EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.312,313 The 

IARC has determined that 1,3-butadiene is a human carcinogen and the U.S. DHHS has 

characterized 1,3-butadiene as a known human carcinogen.314,315 There are numerous studies 

consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by 

experimental animals and humans. The specific mechanisms of 1,3-butadiene-induced 

carcinogenesis are unknown; however, the scientific evidence strongly suggests that the 

carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females 

may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure; 

there are insufficient data in humans from which to draw conclusions about sensitive 

subpopulations. 1,3-butadiene also causes a variety of reproductive and developmental effects in 

mice; no human data on these effects are available. The most sensitive effect was ovarian 

atrophy observed in a lifetime bioassay of female mice.316
 146

3.5.5.3 Ethanol

EPA is conducting an assessment of the cancer and noncancer effects of exposure to 

ethanol, a compound which is not currently listed in EPA’s IRIS. A description of these effects 

to the extent that information is available will be presented, as required by Section 1505 of 

EPAct, in a Report to Congress on public health, air quality and water resource impacts of fuel 

additives. We expect to release that report in 2010. 

Extensive data are available regarding adverse health effects associated with the ingestion 

of ethanol while data on inhalation exposure effects are sparse. As part of the IRIS assessment, 

pharmacokinetic models are being evaluated as a means of extrapolating across species (animal 

to human) and across exposure routes (oral to inhalation) to better characterize the health hazards 

and dose-response relationships for low levels of ethanol exposure in the environment. 

The IARC has classified “alcoholic beverages” as carcinogenic to humans based on 

sufficient evidence that malignant tumors of the mouth, pharynx, larynx, esophagus, and liver are 

causally related to the consumption of alcoholic beverages.317 The U.S. DHHS in the 11th Report 

on Carcinogens also identified “alcoholic beverages” as a known human carcinogen (they have 

not evaluated the cancer risks specifically from exposure to ethanol), with evidence for cancer of 

the mouth, pharynx, larynx, esophagus, liver and breast.318 There are no studies reporting 

carcinogenic effects from inhalation of ethanol. EPA is currently evaluating the available human 

and animal cancer data to identify which cancer type(s) are the most relevant to an assessment of 

risk to humans from a low-level oral and inhalation exposure to ethanol. 

Noncancer health effects data are available from animal studies as well as epidemiologic 

studies. The epidemiologic data are obtained from studies of alcoholic beverage consumption. 

Effects include neurological impairment, developmental effects, cardiovascular effects, immune 

system depression, and effects on the liver, pancreas and reproductive system.319 There is 

evidence that children prenatally exposed via mothers’ ingestion of alcoholic beverages during 

pregnancy are at increased risk of hyperactivity and attention deficits, impaired motor 

coordination, a lack of regulation of social behavior or poor psychosocial functioning, and 

deficits in cognition, mathematical ability, verbal fluency, and spatial 

memory.320,321,322,323,324,325,326,327 In some people, genetic factors influencing the metabolism of 

ethanol can lead to differences in internal levels of ethanol and may render some subpopulations 

more susceptible to risks from the effects of ethanol. 

3.5.5.4 Formaldehyde 

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.328 EPA is currently reviewing 

recently published epidemiological data. For instance, research conducted by the National 

Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and 

lymphohematopoietic malignancies such as leukemia among workers exposed to 

formaldehyde.329,330 In an analysis of the lymphohematopoietic cancer mortality from an 

extended follow-up of these workers, NCI confirmed an association between 

lymphohematopoietic cancer risk and peak exposures.331 A recent National Institute of 
147

Occupational Safety and Health (NIOSH) study of garment workers also found increased risk of 

death due to leukemia among workers exposed to formaldehyde.332 Extended follow-up of a 

cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or 

lymphohematopoietic cancers, but a continuing statistically significant excess in lung cancers 

was reported.333

 In the past 15 years there has been substantial research on the inhalation dosimetry for 

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement of 

the quantitative cancer dose-response assessment.334,335,336 CIIT’s risk assessment of 

formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. However, 

it should be noted that recent research published by EPA indicates that when two-stage modeling 

assumptions are varied, resulting dose-response estimates can vary by several orders of 

magnitude.337,338,339,340 These findings are not supportive of interpreting the CIIT model results 

as providing a conservative (health protective) estimate of human risk. EPA research also 

examined the contribution of the two-stage modeling for formaldehyde towards characterizing 

the relative weights of key events in the mode-of-action of a carcinogen. For example, the 

model-based inference in the published CIIT study that formaldehyde’s direct mutagenic action 

is not relevant to the compound’s tumorigenicity was found not to hold under variations of 

modeling assumptions. 

Based on the developments of the last decade, in 2004, the working group of the IARC 

concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient 

evidence in humans and sufficient evidence in experimental animals - a higher classification than 

previous IARC evaluations. After reviewing the currently available epidemiological evidence, 

the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as 

“sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on 

leukemia was characterized as “strong.”341 EPA is reviewing the recent work cited above from 

the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other 

studies, as part of a reassessment of the human hazard and dose-response associated with 

formaldehyde. 

Formaldehyde exposure also causes a range of noncancer health effects, including 

irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated 

exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial 

lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also 

cause airway inflammation – including eosinophil infiltration into the airways. There are several 

studies that suggest that formaldehyde may increase the risk of asthma – particularly in the 

young.342,343

3.5.5.5 Acetaldehyde 

Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen, 

based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous 

routes.344 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS 

in the 11th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 
 148

2B) by the IARC.345,346 EPA is currently conducting a reassessment of cancer risk from 

inhalation exposure to acetaldehyde. 

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of 

the eyes, skin, and respiratory tract.347 In short-term (4 week) rat studies, degeneration of 

olfactory epithelium was observed at various concentration levels of acetaldehyde 

exposure.348,349 Data from these studies were used by EPA to develop an inhalation reference 

concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements 

in functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde 

inhalation.350 The agency is currently conducting a reassessment of the health hazards from 

inhalation exposure to acetaldehyde. 

3.5.5.6 Acrolein

EPA determined in 2003 that the human carcinogenic potential of acrolein could not be 

determined because the available data were inadequate. No information was available on the 

carcinogenic effects of acrolein in humans and the animal data provided inadequate evidence of 

carcinogenicity.351 The IARC determined in 1995 that acrolein was not classifiable as to its 

carcinogenicity in humans.352

Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure 

resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. Levels 

considerably lower than 1 ppm (2.3 mg/m3

) elicit subjective complaints of eye and nasal 

irritation and a decrease in the respiratory rate.353,354 Lesions to the lungs and upper respiratory 

tract of rats, rabbits, and hamsters have been observed after subchronic exposure to acrolein. 

Based on animal data, individuals with compromised respiratory function (e.g., emphysema, 

asthma) are expected to be at increased risk of developing adverse responses to strong respiratory 

irritants such as acrolein. This was demonstrated in mice with allergic airway-disease by 

comparison to non-diseased mice in a study of the acute respiratory irritant effects of acrolein.355

EPA is currently in the process of conducting an assessment of acute exposure effects for 

acrolein. The intense irritancy of this carbonyl has been demonstrated during controlled tests in 

human subjects, who suffer intolerable eye and nasal mucosal sensory reactions within minutes 

of exposure.356

3.5.6.7 Peroxyacetyl nitrate (PAN) 

PAN has not been evaluated by EPA’s IRIS program. Information regarding the 

potential carcinogenicity of PAN is limited. As noted in the EPA air quality criteria document 

for ozone and related photochemical oxidants, cytogenetic studies indicate that PAN is not a 

potent mutagen, clastogen (a compound that can cause breaks in chromosomes), or DNAdamaging agent in mammalian cells either in vivo or in vitro. Some studies suggest that PAN 

may be a weak bacterial mutagen at high concentrations much higher than exist in present urban 

atmospheres.357

Effects of ground-level smog causing intense eye irritation have been attributed to 

photochemical oxidants, including PAN.358 Animal toxicological information on the inhalation 
149

effects of the non-ozone oxidants has been limited to a few studies on PAN. Acute exposure to 

levels of PAN can cause changes in lung morphology, behavioral modifications, weight loss, and 

susceptibility to pulmonary infections. Human exposure studies indicate minor pulmonary 

function effects at high PAN concentrations, but large inter-individual variability precludes 

definitive conclusions.359

3.5.6.8 Naphthalene

Naphthalene is found in small quantities in gasoline and diesel fuels. Naphthalene 

emissions have been measured in larger quantities in both gasoline and diesel exhaust compared 

with evaporative emissions from mobile sources, indicating it is primarily a product of 

combustion. EPA released an external review draft of a reassessment of the inhalation 

carcinogenicity of naphthalene based on a number of recent animal carcinogenicity studies.360 

The draft reassessment completed external peer review.361 Based on external peer review 

comments received, additional analyses are being undertaken. This external review draft does 

not represent official agency opinion and was released solely for the purposes of external peer 

review and public comment. The National Toxicology Program listed naphthalene as 

"reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting 

clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice.362 

California EPA has released a new risk assessment for naphthalene, and the IARC has 

reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans.363 

Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal 

cell changes and growth in respiratory and nasal tissues.364

3.5.6.9 N-Hexane 

N-Hexane is associated with polyneuropathy in humans. Effects observed in rodents 

include nasal lesions as well as neurotoxic effects. EPA has developed a reference concentration 

of 700 μg/m3

 from a study of peripheral neuropathy.365 There is inadequate data to assess its 

carcinogenic potential. 

3.5.6.10 Pesticides 

There are potential toxicity concerns with volatilization of pesticide active ingredients,366

in addition to concerns with contamination of foods and drinking water. Furthermore, raising 

acreage under corn production may increase the quantity of pesticide products in use. As the 

domestic corn supply grows between the years of 2005 and 2022, the percentage of corn used for 

ethanol production in the US is expected to increase, though the agricultural impacts of this 

shifting of crop production domestically are anticipated to be small. Whether there is the 

potential for adverse human health effects from any increase in pesticide use associated with 

increased corn production domestically warrants further assessment. Additional information on 

pesticides and health effects is included in Section 6.1 of this RIA. 

3.5.6.11 Other Air Toxics 
 150

In addition to the compounds described above, other compounds in gaseous hydrocarbon 

and PM emissions from vehicles will be affected by today’s proposed action. Mobile source air 

toxic compounds that will potentially be impacted include ethylbenzene, polycyclic organic 

matter, propionaldehyde, toluene, and xylene. Information regarding the health effects of these 

compounds can be found in EPA’s IRIS database.367

3.6 Environmental Effects 

In this section we discuss some of the environmental effects of PM and its precursors, 

such as visibility impairment, atmospheric deposition, and materials damage and soiling. We 

also discuss environmental effects associated with the presence of ozone in the ambient air, such 

as impacts on plants, including trees, agronomic crops and urban ornamentals. 

3.6.1 Visibility Degradation 

Emissions from LD vehicles contribute to poor visibility in the U.S. through their 

primary PM2.5 and secondary PM2.5 precursor emissions. These airborne particles degrade 

visibility by scattering and absorbing light. Good visibility increases the quality of life where 

individuals live and work, and where they engage in recreational activities. 

The U.S. Government places special emphasis on protecting visibility in national parks 

and wilderness areas. Section 169 of the Clean Air Act requires the U.S. Government to address 

existing visibility impairment and future visibility impairment in the national parks exceeding 

6,000 acres, and wilderness areas exceeding 5,000 acres, which are categorized as mandatory 

class I federal areas (62 FR 38680, July 18, 1997).33 Figure 3.6-1 shows the location of the 156 

mandatory class I federal areas. 

3.6.1.1 Visibility Monitoring 

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal 

land managers, and State organizations in the U.S., the U.S. EPA has supported visibility 

monitoring in national parks and wilderness areas since 1988. The monitoring network was 

originally established at 20 sites, but it has now been expanded to 110 sites that represent all but 

one of the 156 mandatory class I federal areas across the country (see Figure3.6-1). This longterm visibility monitoring network is known as IMPROVE (Interagency Monitoring of Protected 

Visual Environments).

IMPROVE provides direct measurement of fine particles that contribute to visibility 

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical and 

scene measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5 

mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon, 

soil dust, and several other elements. Measurements for specific aerosol constituents are used to 

 

33 These areas are defined in section 162 of the Act as those national parks exceeding 6,000 acres, wilderness areas 

and memorial parks exceeding 5,000 acres, and all international parks which were in existence on August 7, 1977. 
151

calculate "reconstructed" aerosol light extinction by multiplying the mass for each constituent by 

its empirically-derived scattering and/or absorption efficiency, with adjustment for the relative 

humidity. Knowledge of the main constituents of a site's light extinction "budget" is critical for 

source apportionment and control strategy development. Optical measurements are used to 

directly measure light extinction or its components. Such measurements are taken principally 

with either a transmissometer, which measures total light extinction, or a nephelometer, which 

measures particle scattering (the largest human-caused component of total extinction). Scene 

characteristics are typically recorded three times daily with 35 millimeter photography and are 

used to determine the quality of visibility conditions (such as effects on color and contrast) 

associated with specific levels of light extinction as measured under both direct and aerosolrelated methods. Directly measured light extinction is used under the IMPROVE protocol to 

cross check that the aerosol-derived light extinction levels are reasonable in establishing current 

visibility conditions. Aerosol-derived light extinction is used to document spatial and temporal 

trends and to determine how proposed changes in atmospheric constituents would affect future 

visibility conditions. 

Annual average visibility conditions (reflecting light extinction due to both anthropogenic 

and non-anthropogenic sources) vary regionally across the U.S. The rural East generally has 

higher levels of impairment than remote sites in the West, with the exception of urban-influenced 

sites such as San Gorgonio Wilderness (CA) and Point Reyes National Seashore (CA), which 

have annual average levels comparable to certain sites in the Northeast. Regional differences are 

illustrated by Figures 4-39a and 4-39b in the Air Quality Criteria Document for Particulate 

Matter, which show that, for Class I areas, visibility levels on the 20% haziest days in the West 

are about equal to levels on the 20% best days in the East.368

Higher visibility impairment levels in the East are due to generally higher concentrations 

of anthropogenic fine particles, particularly sulfates, and higher average relative humidity levels. 

In fact, sulfates account for 60-86% of the haziness in eastern sites.369 Aerosol light extinction 

due to sulfate on the 20% haziest days is significantly larger in eastern Class I areas as compared 

to western areas (Figures 4-40a and 4-40b in the Air Quality Criteria Document for Particulate 

Matter).370 With the exception of remote sites in the northwestern U.S., visibility is typically 

worse in the summer months. This is particularly true in the Appalachian region, where average 

light extinction in the summer exceeds the annual average by 40%.371 

3.6.1.2 Addressing Visibility in the U.S. 

The U.S. EPA is pursuing a two-part strategy to address visibility. First, to address the 

welfare effects of PM on visibility, EPA set secondary PM2.5 standards which act in conjunction 

with the establishment of a regional haze program. In setting this secondary standard, EPA has 

concluded that PM2.5 causes adverse effects on visibility in various locations, depending on PM 

concentrations and factors such as chemical composition and average relative humidity. Second, 

section 169 of the Clean Air Act provides additional authority to address existing visibility 

impairment and prevent future visibility impairment in the 156 mandatory Class I federal areas 

(62 FR 38680-81, July 18, 1997). In July 1999, the regional haze rule (64 FR 35714) was put in 
 152

place to protect the visibility in mandatory Class I federal areas. Visibility can be said to be 

impaired in both PM2.5 nonattainment areas and mandatory Class I federal areas.34

 

 

Figure 3.6-1: Mandatory Class I Areas in the U.S.

3.6.2 Particulate Matter Deposition 

Particulate matter contributes to adverse effects on vegetation and ecosystems, and to 

soiling and materials damage. These welfare effects result predominately from exposure to 

excess amounts of specific chemical species, regardless of their source or predominant form 

(particle, gas or liquid). Reflecting this fact, the PM AQCD concludes that regardless of size 

fractions, particles containing nitrates and sulfates have the greatest potential for widespread 

environmental significance, while effects are also related to other chemical constituents found in 

ambient PM, such as trace metals and organics. The following characterizations of the nature of 

these welfare effects are based on the information contained in the PM AQCD and PM Staff 

Paper.372,373

 

34 As mentioned above, the EPA recently amended the PM NAAQS, making the secondary NAAQS equal, in all 

respects, to the primary standards for both PM2.5 and PM10, (71 FR 61144, Oct. 17, 2006). In February 2009, the 

D.C. Circuit Court remanded the secondary standards for fine particles, based on EPA’s failure to adequately 

explain why setting the secondary PM2.5 NAAQS equivalent to the primary standards provided the required 

protection for public welfare including protection from visibility impairment. 
153

3.6.2.1 Deposition of Nitrogen and Sulfur 

Nitrogen and sulfur interactions in the environment are highly complex. Both are 

essential, and sometimes limiting, nutrients needed for growth and productivity. Excesses of 

nitrogen or sulfur can lead to acidification, nutrient enrichment, and eutrophication.374 

The process of acidification affects both freshwater aquatic and terrestrial ecosystems. 

Acid deposition causes acidification of sensitive surface waters. The effects of acid deposition on 

aquatic systems depend largely upon the ability of the ecosystem to neutralize the additional 

acid. As acidity increases, aluminum leached from soils and sediments, flows into lakes and 

streams and can be toxic to both terrestrial and aquatic biota. The lower pH concentrations and 

higher aluminum levels resulting from acidification make it difficult for some fish and other 

aquatic organisms to survive, grow, and reproduce. Research on effects of acid deposition on 

forest ecosystems has come to focus increasingly on the biogeochemical processes that affect 

uptake, retention, and cycling of nutrients within these ecosystems. Decreases in available base 

cations from soils are at least partly attributable to acid deposition. Base cation depletion is a 

cause for concern because of the role these ions play in acid neutralization and, because calcium, 

magnesium and potassium are essential nutrients for plant growth and physiology. Changes in 

the relative proportions of these nutrients, especially in comparison with aluminum 

concentrations, have been associated with declining forest health. 

At current ambient levels, risks to vegetation from short-term exposures to dry deposited 

particulate nitrate or sulfate are low. However, when found in acid or acidifying deposition, such 

particles do have the potential to cause direct leaf injury. Specifically, the responses of forest 

trees to acid precipitation (rain, snow) include accelerated weathering of leaf cuticular surfaces, 

increased permeability of leaf surfaces to toxic materials, water, and disease agents; increased 

leaching of nutrients from foliage; and altered reproductive processes—all which serve to 

weaken trees so that they are more susceptible to other stresses (e.g., extreme weather, pests, 

pathogens). Acid deposition with levels of acidity associated with the leaf effects described 

above are currently found in some locations in the eastern U.S.375 Even higher concentrations of 

acidity can be present in occult depositions (e.g., fog, mist or clouds) which more frequently 

impacts higher elevations. Thus, the risk of leaf injury occurring from acid deposition in some 

areas of the eastern U.S. is high. Nitrogen deposition has also been shown to impact ecosystems 

in the western U.S. A study conducted in the Columbia River Gorge National Scenic Area 

(CRGNSA), located along a portion of the Oregon/Washington border, indicates that lichen 

communities in the CRGNSA have shifted to a higher proportion of nitrophilous species and the 

nitrogen content of lichen tissue is elevated.376 Lichens are sensitive indicators of nitrogen 

deposition effects to terrestrial ecosystems and the lichen studies in the Columbia River Gorge 

clearly show that ecological effects from air pollution are occurring. 

Some of the most significant detrimental effects associated with excess nitrogen 

deposition are those associated with a syndrome known as nitrogen saturation. These effects 

include: (1) decreased productivity, increased mortality, and/or shifts in plant community 

composition, often leading to decreased biodiversity in many natural habitats wherever 

atmospheric reactive nitrogen deposition increases significantly above background and critical 
 154

thresholds are exceeded; (2) leaching of excess nitrate and associated base cations from soils into 

streams, lakes, and rivers, and mobilization of soil aluminum; and (3) fluctuation of ecosystem 

processes such as nutrient and energy cycles through changes in the functioning and species 

composition of beneficial soil organisms.377

In the U.S. numerous forests now show severe symptoms of nitrogen saturation. These 

forests include: the northern hardwoods and mixed conifer forests in the Adirondack and 

Catskill Mountains of New York; the red spruce forests at Whitetop Mountain, Virginia, and 

Great Smoky Mountains National Park, North Carolina; mixed hardwood watersheds at Fernow 

Experimental Forest in West Virginia; American beech forests in Great Smoky Mountains 

National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern California 

and the southwestern Sierra Nevada in Central California; the alpine tundra/subalpine conifer 

forests of the Colorado Front Range; and red alder forests in the Cascade Mountains in 

Washington. 

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or 

oceans) either from direct atmospheric deposition, surface runoff, or leaching from nitrogen 

saturated soils into ground or surface waters can contribute to conditions of severe water oxygen 

depletion; eutrophication and algae blooms; altered fish distributions, catches, and physiological 

states; loss of biodiversity; habitat degradation; and increases in the incidence of disease. 

Atmospheric deposition of nitrogen is a significant source of total nitrogen to many 

estuaries in the United States. The amount of nitrogen entering estuaries that is ultimately 

attributable to atmospheric deposition is not well-defined. On an annual basis, atmospheric 

nitrogen deposition may contribute significantly to the total nitrogen load, depending on the size 

and location of the watershed. In addition, episodic nitrogen inputs, which may be ecologically 

important, may play a more important role than indicated by the annual average concentrations. 

Estuaries in the U.S. that suffer from nitrogen enrichment often experience a condition known as 

eutrophication. Symptoms of eutrophication include changes in the dominant species of 

phytoplankton, low levels of oxygen in the water column, fish and shellfish kills, outbreaks of 

toxic alga, and other population changes which can cascade throughout the food web. In 

addition, increased phytoplankton growth in the water column and on surfaces can attenuate light 

causing declines in submerged aquatic vegetation, which serves as an important habitat for many 

estuarine fish and shellfish species.

Severe and persistent eutrophication often directly impacts human activities. For 

example, losses in the nation’s fishery resources may be directly caused by fish kills associated 

with low dissolved oxygen and toxic blooms. Declines in tourism occur when low dissolved 

oxygen causes noxious smells and floating mats of algal blooms create unfavorable aesthetic 

conditions. Risks to human health increase when the toxins from algal blooms accumulate in 

edible fish and shellfish, and when toxins become airborne, causing respiratory problems due to 

inhalation. According to a NOAA report, more than half of the nation’s estuaries have moderate 

to high expressions of at least one of these symptoms – an indication that eutrophication is well 

developed in more than half of U.S. estuaries.378
155

3.6.2.2 Materials Damage and Soiling

The effects of the deposition of atmospheric pollution, including ambient PM, on 

materials are related to both physical damage and impaired aesthetic qualities. The deposition of 

PM (especially sulfates and nitrates) can physically affect materials, adding to the effects of 

natural weathering processes, by potentially promoting or accelerating the corrosion of metals, 

by degrading paints, and by deteriorating building materials such as concrete and limestone. 

Only chemically active fine particles or hygroscopic coarse particles contribute to these physical 

effects. In addition, the deposition of ambient PM can reduce the aesthetic appeal of buildings 

and culturally important articles through soiling. Particles consisting primarily of carbonaceous 

compounds cause soiling of commonly used building materials and culturally important items 

such as statues and works of art. 

3.6.3 Impacts of Ozone on Vegetation 

 The Air Quality Criteria Document for Ozone and related Photochemical Oxidants notes 

that “ozone affects vegetation throughout the United States, impairing crops, native vegetation, 

and ecosystems more than any other air pollutant”.379 Like carbon dioxide (CO2) and other 

gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in leaves in 

a process called “uptake.”380 Once sufficient levels of ozone (a highly reactive substance), or its 

reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular 

components and functions, including enzyme activities, lipids, and cellular membranes, 

disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns.381,382 This 

damage is commonly manifested as visible foliar injury, such as chlorotic or necrotic spots, 

increased leaf senescence (accelerated leaf aging) and/or reduced photosynthesis. All these 

effects reduce a plant’s capacity to form carbohydrates, which are the primary form of energy 

used by plants.383 If enough tissue becomes damaged from these effects, a plant’s capacity to fix 

carbon to form carbohydrates, which are the primary form of energy used by plants, is 

reduced,384 while plant respiration increases. With fewer resources available, the plant 

reallocates existing resources away from root growth and storage, above ground growth or yield, 

and reproductive processes, toward leaf repair and maintenance, leading to reduced growth 

and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general 

loss of vigor, which can lead to secondary impacts that modify plants' responses to other 

environmental factors. Specifically, plants may become more sensitive to other air pollutants, 

more susceptible to disease, insect attack, harsh weather (e.g., drought, frost) and other 

environmental stresses. Furthermore, there is evidence that ozone can interfere with the 

formation of mycorrhiza, essential symbiotic fungi associated with the roots of most terrestrial 

plants, by reducing the amount of carbon available for transfer from the host to the 

symbiont.385,386 

 This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage 

described above. When visible injury is present, it is commonly manifested as chlorotic or 

necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Because ozone damage 

can consist of visible injury to leaves, it can also reduce the aesthetic value of ornamental 
 156

vegetation and trees in urban landscapes, and negatively affects scenic vistas in protected natural 

areas. 

 Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over 

the growing season of the plant, so that even lower concentrations experienced for a longer 

duration have the potential to create chronic stress on sensitive vegetation. Not all plants, 

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual 

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via 

leaf stomata (e.g., avoidance of ozone uptake through closure of stomata).387,388,389 Other 

resistance mechanisms may involve the intercellular production of detoxifying substances. 

Several biochemical substances capable of detoxifying ozone have been reported to occur in 

plants, including the antioxidants ascorbate and glutathione. After injuries have occurred, plants 

may be capable of repairing the damage to a limited extent.390

 

 Because of the differing sensitivities among plants to ozone, ozone pollution can also 

exert a selective pressure that leads to changes in plant community composition. Given the range 

of plant sensitivities and the fact that numerous other environmental factors modify plant uptake 

and response to ozone, it is not possible to identify threshold values above which ozone is 

consistently toxic for all plants. The next few paragraphs present additional information on 

ozone damage to trees, ecosystems, agronomic crops and urban ornamentals. 

 Ozone also has been conclusively shown to cause discernible injury to forest trees.391,392 

In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the 

greatest potential for regional-scale forest impacts. Studies have demonstrated repeatedly that 

ozone concentrations commonly observed in polluted areas can have substantial impacts on plant 

function.393,394 

 Because plants are at the base of the food web in many ecosystems, changes to the plant 

community can affect associated organisms and ecosystems (including the suitability of habitats 

that support threatened or endangered species and below ground organisms living in the root 

zone). Ozone impacts at the community and ecosystem level vary widely depending upon 

numerous factors, including concentration and temporal variation of tropospheric ozone, species 

composition, soil properties and climatic factors.395 In most instances, responses to chronic or 

recurrent exposure in forested ecosystems are subtle and not observable for many years. These 

injuries can cause stand-level forest decline in sensitive ecosystems.396,397,398 It is not yet 

possible to predict ecosystem responses to ozone with much certainty; however, considerable 

knowledge of potential ecosystem responses has been acquired through long-term observations 

in highly damaged forests in the United States. 

 Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). The most extensive field experiments, conducted under the National Crop Loss 

Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN 

results show that “several economically important crop species are sensitive to ozone levels 

typical of those found in the Unites States.”399 In addition, economic studies have shown 
157

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.400,401,402 

 Urban ornamentals represent an additional vegetation category likely to experience some 

degree of negative effects associated with exposure to ambient ozone levels. It is estimated that 

more than $20 billion (1990 dollars) are spent annually on landscaping using ornamentals, both 

by private property owners/tenants and by governmental units responsible for public areas.403 

This is therefore a potentially costly environmental effect. However, in the absence of adequate 

exposure-response functions and economic damage functions for the potential range of effects 

relevant to these types of vegetation, no direct quantitative analysis has been conducted. 

Air pollution can have noteworthy cumulative impacts on forested ecosystems by 

affecting regeneration, productivity, and species composition.404 In the U.S., ozone in the lower 

atmosphere is one of the pollutants of primary concern. Ozone injury to forest plants can be 

diagnosed by examination of plant leaves. Foliar injury is usually the first visible sign of injury 

to plants from ozone exposure and indicates impaired physiological processes in the leaves.405 

In the U.S. this indicator is based on data from the U.S. Department of Agriculture 

(USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 

program, formerly known as Forest Health Monitoring, FIA examines ozone injury to ozonesensitive plant species at ground monitoring sites in forest land across the country. For this 

indicator, forest land does not include woodlots and urban trees. Sites are selected using a 

systematic sampling grid, based on a global sampling design.406,407 At each site that has at least 

30 individual plants of at least three ozone-sensitive species and enough open space to ensure 

that sensitive plants are not protected from ozone exposure by the forest canopy, FIA looks for 

damage on the foliage of ozone-sensitive forest plant species. Monitoring of ozone injury to 

plants by the USDA Forest Service has expanded over the last 10 years from monitoring sites in 

10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002. 

3.6.3.1 Recent Ozone Data for the U.S. 

 

There is considerable regional variation in ozone-related visible foliar injury to sensitive 

plants in the U.S. The U.S. EPA has developed an environmental indicator based on data from 

the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) 

program which examines ozone injury to ozone-sensitive plant species at ground monitoring 

sites in forest land across the country (This indicator does not include woodlots and urban trees). 

Sites are selected using a systematic sampling grid, based on a global sampling design.408, 409 

Because ozone injury is cumulative over the course of the growing season, examinations are 

conducted in July and August, when ozone injury is typically highest. The data underlying the 

indictor in Figure 3.6-2 are based on averages of all observations collected in 2002, the latest 

year for which data are publicly available at the time the study was conducted, and are broken 

down by U.S. EPA Region. Ozone damage to forest plants is classified using a subjective fivecategory biosite index based on expert opinion, but designed to be equivalent from site to site. 

Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar injury 

to highly sensitive or moderately sensitive plants, respectively), and high or severe foliar injury, 

which would be expected to result in tree-level or ecosystem-level responses, respectively.410, 411
 158

The highest percentages of observed high and severe foliar injury, those which are most 

likely to be associated with tree or ecosystem-level responses, are primarily found in the MidAtlantic and Southeast regions. In EPA Region 3 (which comprises the States of Pennsylvania, 

West Virginia, Virginia, Delaware, Maryland and Washington D.C.), 12% of ozone-sensitive 

plants showed signs of high or severe foliar damage, and in Regions 2 (States of New York, New 

Jersey), and 4 (States of North Carolina, South Carolina, Kentucky, Tennessee, Georgia, Florida, 

Alabama, and Mississippi) the values were 10% and 7%, respectively. The sum of high and 

severe ozone injury ranged from 2% to 4% in EPA Region 1 (the six New England States), 

Region 7 (States of Missouri, Iowa, Nebraska and Kansas), and Region 9 (States of California, 

Nevada, Hawaii and Arizona). The percentage of sites showing some ozone damage was about 

45% in each of these EPA Regions. 
159

Figure 3.6-2: Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab 

3.6.3.1.1 Indicator Limitations 

Field and laboratory studies were reviewed to identify the forest plant species in each 

region that are highly sensitive to ozone air pollution. Other forest plant species, or even 

genetic variants of the same species, may not be harmed at ozone levels that cause effects on 

the selected ozone-sensitive species. 
 160

Because species distributions vary regionally, different ozone-sensitive plant species 

were examined in different parts of the country. These target species could vary with respect to 

ozone sensitivity, which might account for some of the apparent differences in ozone injury 

among regions of the U.S. 

 

Ozone damage to foliage is considerably reduced under conditions of low soil moisture, 

but most of the variability in the index (70%) was explained by ozone concentration.412 Ozone 

may have other adverse impacts on plants (e.g., reduced productivity) that do not show signs of 

visible foliar injury.413

 

Though FIA has extensive spatial coverage based on a robust sample design, not all 

forested areas in the U.S. are monitored for ozone injury. Even though the biosite data have been 

collected over multiple years, most biosites were not monitored over the entire period, so these 

data cannot provide more than a baseline for future trends. 

3.6.4 Impacts of Ozone on Forest Health 

Air pollution can impact the environment and affect ecological systems, leading to 

changes in the biological community (both in the diversity of species and the health and vigor of 

individual species). As an example, many studies have shown that ground-level ozone reduces 

the health of plants including many commercial and ecologically important forest tree species 

throughout the United States.414 

 When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Since photosynthesis occurs in cells within leaves, the ability of the 

plant to produce energy by photosynthesis can be compromised if enough damage occurs to 

these cells. If enough tissue becomes damaged it can reduce carbon fixation and increase plant 

respiration, leading to reduced growth and/or reproduction in young and mature trees. Ozone 

stress also increases the susceptibility of plants to disease, insects, fungus, and other 

environmental stressors (e.g., harsh weather). Because ozone damage can consist of visible 

injury to leaves, it also reduces the aesthetic value of ornamental vegetation and trees in urban 

landscapes, and negatively affect scenic vistas in protected natural areas. 

Assessing the impact of ground-level ozone on forests in the eastern United States 

involves understanding the risks to sensitive tree species from ambient ozone concentrations and 

accounting for the prevalence of those species within the forest. As a way to quantify the risks to 

particular plants from ground-level ozone, scientists have developed ozone-exposure/treeresponse functions by exposing tree seedlings to different ozone levels and measuring reductions 

in growth as “biomass loss.” Typically, seedlings are used because they are easy to manipulate 

and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone 

within the leaves of seedlings and mature trees are identical, though the magnitude of the effect 

may be higher or lower depending on the tree species. 415 

 Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), eastern white pine (Pinus 

strobus). Ozone-exposure/tree-response functions have been developed for each of these tree 

species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus ponderosa). 
161

Other common tree species, such as oak (Quercus spp.) and hickory (Carya spp.), are not nearly 

as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive species and 

the level of ozone at particular locations, it is possible to estimate a “biomass loss” for each 

species across their range. 

3.6.5 Environmental Effects of Air Toxics 

Fuel combustion emissions contribute to ambient levels of pollutants that contribute to 

adverse effects on vegetation. PAN is a well-established phytotoxicant causing visible injury to 

leaves that can appear as metallic glazing on the lower surface of leaves with some leafy 

vegetables exhibiting particular sensitivity (e.g., spinach, lettuce, chard).416,417,418 PAN has been 

demonstrated to inhibit photosynthetic and non-photosynthetic processes in plants and retard the 

growth of young navel orange trees.419,420 In addition to its oxidizing capability, PAN contributes 

nitrogen to forests and other vegetation via uptake as well as dry and wet deposition to surfaces. 

As noted above in Section 3.6.2.1, nitrogen deposition can lead to saturation of terrestrial 

ecosystems and research is needed to understand the impacts of excess nitrogen deposition 

experienced in some areas of the country on water quality and ecosystems.421

Volatile organic compounds (VOCs), some of which are considered air toxics, have long 

been suspected to play a role in vegetation damage.422 In laboratory experiments, a wide range 

of tolerance to VOCs has been observed.423 Decreases in harvested seed pod weight have been 

reported for the more sensitive plants, and some studies have reported effects on seed 

germination, flowering and fruit ripening. Effects of individual VOCs or their role in 

conjunction with other stressors (e.g., acidification, drought, temperature extremes) have not 

been well studied. In a recent study of a mixture of VOCs including ethanol and toluene on 

herbaceous plants, significant effects on seed production, leaf water content and photosynthetic 

efficiency were reported for some plant species.424

Research suggests an adverse impact of vehicle exhaust on plants, which has in some 

cases been attributed to aromatic compounds and in other cases to nitrogen oxides.425,426,427 The 

impacts of VOCs on plant reproduction may have long-term implications for biodiversity and 

survival of native species near major roadways. Most of the studies of the impacts of VOCs on 

vegetation have focused on short-term exposure and few studies have focused on long-term 

effects of VOCs on vegetation and the potential for metabolites of these compounds to affect 

herbivores or insects. 
 162

Appendix Chapter 3A: Additional Air Toxics Modeling Results 

3A.1 Annual Change Ambient Concentration Maps for Air Toxics using the 

AEO Reference Case 

The following section presents maps of annual changes in ambient concentrations of 

modeled air toxics in 2022 using the AEO 2007 reference case compared to the RFS2 control 

case. 

3A.1.1 Acetaldehyde 

Figure 3A-1. Acetaldehyde Annual Percent Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 
163

Figure 3A-2. Acetaldehyde Annual Absolute Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 

3A.1.2 Formaldehyde 

Figure 3A-3. Formaldehyde Annual Percent Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 
 164

Figure 3A-4. Formaldehyde Annual Absolute Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 

3A.1.3 Ethanol 

Figure 3A-5. Ethanol Annual Percent Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 
165

Figure 3A-6. Ethanol Annual Absolute Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 

3A.1.4 Benzene 

Figure 3A-7. Benzene Annual Percent Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 
 166

Figure 3A-8. Benzene Annual Absolute Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 

3A.1.4 1,3-Butadiene 

Figure 3A-9. 1,3-Butadiene Annual Percent Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 
167

Figure 3A-10. 1,3-Butadiene Annual Absolute Change in Concentration Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022 

3A.1.4 Acrolein 

Figure 3A-11. Acrolein Annual Percent Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 
 168

Figure 3A-12. Acrolein Annual Absolute Change in Concentration Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022 

3A.2 Seasonal Change Ambient Concentration Maps for Air Toxics using the 

RFS1 Reference Case 

The following section presents maps of seasonal changes in ambient concentrations of 

modeled air toxics in 2022 using the RFS1 reference case compared to the RFS2 control case. 
169

3A.2.1 Acetaldehyde 

Figure 3A-13. Spring Changes in Acetaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 

Figure 3A-14. Summer Changes in Acetaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
 170

Figure 3A-15. Fall Changes in Acetaldehyde Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-16. Winter Changes in Acetaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
171

3A.2.2 Formaldehyde 

Figure 3A-17. Spring Changes in Formaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 

Figure 3A-18. Summer Changes in Formaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
 172

Figure 3A-19. Fall Changes in Formaldehyde Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-20. Winter Changes in Formaldehyde Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
173

3A.2.3 Ethanol 

Figure 3A-21. Spring Changes in Ethanol Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-22. Summer Changes in Ethanol Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 174

Figure 3A-23. Fall Changes in Ethanol Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-24. Winter Changes in Ethanol Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
175

3A.2.4 Benzene 

Figure 3A-25. Spring Changes in Benzene Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-26. Summer Changes in Benzene Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 176

Figure 3A-27. Fall Changes in Benzene Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-28. Winter Changes in Benzene Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
177

3A.2.5 1,3-Butadiene 

Figure 3A-29. Spring Changes in 1,3-Butadiene Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 

Figure 3A-30. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
 178

Figure 3A-31. Fall Changes in 1,3-Butadiene Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-32. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the 

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes 

and (right) Absolute Changes (μg/m3) 
179

3A.2.6 Acrolein 

Figure 3A-33. Spring Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-34. Summer Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 180

Figure 3A-35. Fall Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-36. Winter Changes in Acrolein Ambient Concentrations Between the RFS1 

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

3A.3 Seasonal Change Ambient Concentration Maps for Air Toxics using the 

AEO Reference Case 

The following section presents maps of seasonal changes in ambient concentrations of 

modeled air toxics in 2022 using the AEO 2007 reference case compared to the RFS2 control 

case. 
181

3A.3.1 Acetaldehyde 

Figure 3A-37. Spring Changes in Acetaldehyde Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-38. Summer Changes in Acetaldehyde Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 182

Figure 3A-39. Fall Changes in Acetaldehyde Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-40. Winter Changes in Acetaldehyde Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
183

3A.3.2 Formaldehyde 

Figure 3A-41. Spring Changes in Formaldehyde Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-42. Summer Changes in Formaldehyde Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 184

Figure 3A-43. Fall Changes in Formaldehyde Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-44. Winter Changes in Formaldehyde Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
185

3A.3.3 Ethanol 

Figure 3A-45. Spring Changes in Ethanol Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 

Figure 3A-46. Summer Changes in Ethanol Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 186

Figure 3A-47. Fall Changes in Ethanol Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 

Figure 3A-48. Winter Changes in Ethanol Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 
187

3A.3.4 Benzene 

Figure 3A-49. Spring Changes in Benzene Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 

Figure 3A-50. Summer Changes in Benzene Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 188

Figure 3A-51. Fall Changes in Benzene Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 

Figure 3A-52. Winter Changes in Benzene Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
189

3A.3.5 1,3-Butadiene 

Figure 3A-53. Spring Changes in 1,3-Butadiene Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-54. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 190

Figure 3A-55. Fall Changes in 1,3-Butadiene Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-56. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the 

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
191

3A.3.6 Acrolein 

Figure 3A-57. Spring Changes in Acrolein Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 

Figure 3A-58. Summer Changes in Acrolein Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
 192

Figure 3A-59. Fall Changes in Acrolein Ambient Concentrations Between the AEO 2007 

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) 

Absolute Changes (μg/m3) 

Figure 3A-60. Winter Changes in Acrolein Ambient Concentrations Between the AEO 

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and 

(right) Absolute Changes (μg/m3) 
193

3A.4 Air Toxics Population Metrics using the AEO Reference Case 

The following section presents population metrics for the modeled air toxics in 2022 using 

the AEO 2007 reference case compared to the RFS2 control case including the estimated 

aggregated populations above and below reference concentrations for noncancer effects, and 

population living in areas with increases or decreases in concentrations of various magnitudes. 

Table 3A-1. 

Populations Exposed to Ambient Concentrations of Air Toxics above and below 

Reference Concentrations for Noncancer Health Effects in 2022 with RFS2 

Population-weighted 

Concentration 

(Annual Average in μg/m3) 

National Population above RfC 

(Annual Average) CAS No. 

RFS2 

AEO 

2007 Diff. RFS2 

AEO 

2007 Diff. 

Acetaldehyde 75070 1.590 1.613 -0.023 0 0 0 

Acrolein 107028 0.017 0.017 -0.0001 92,452,143 94,087,145 -1,635,002 

Benzene 71432 0.520 0.527 -0.007 0 0 0 

1,3-Butadiene 106990 0.022 0.023 -0.208 0 0 0 

Ethanol 64175 1.521 1.112 0.409 - - - 

Formaldehyde 50000 1.549 1.555 -0.006 0 0 0 
 194

130,779

118,204

50 302,871 28,490 7,065 1,164

185,190 81 7,978 6,952 5,037 1,934 1,606 54 39

25,775 355 105,740 82,829 1,413 1,682 1,340 2,206 99

109,167 21,317 1,732 207,436

1,196 88,035 189,607 41,589 18,599 562 59

86 1,786 12,329 267,974 2,837 54,635

Acrolein

1,3-ButadieneBenzeneFormaldehydeAcetaldehydeEthanol

≤ -100 >-100 to

≤-50

>-50 to 

≤-10

>-10 to 

≤-5 ≤

>-5 to 

-2.5

>-2.5 to 

≤-1

>-1 to 

<+1

≥+1 to 

<+2.5

≥+2.5 to 

<+5

≥

<+10

+5 to ≥

<+50

+10 to ≥

<+100

+50 to ≥ +100

Percent Change in Ambient Concentration from AEO 2007 to RFS2

Population (in Thousands) Impacted by Changes in Annual Ambient 

Concentrations of Toxic Pollutants with RFS2

.

 1

.

 4

.

 3

.

 6

.

 12

.

 10

Figure 3A-61. Number of People Impacted by Changes in Annual Ambient Concentrations or Toxic 

Pollutants by Percent Change Brackets, AEO 2007 Reference Case Compared to the RFS2 Control Case 
195

 

1

 “Summary of recent findings for fuel effects of a 10% ethanol blend on light duty exhaust emissions”, Memo from Aron Butler to

Docket EPA-HQ-OAR-2005-0161 

2

 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants from Mobile Sources; EPA420-R-07-002;

Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm 3

 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program; EPA420-R-07-004; Office of Transportation 

and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/renewablefuels/index.htm 4

 Overview Presentation to CRC on EPACT Testing (September 2008) 

5

 CRC E-74b report 

6

 Haskew, Harold M., Thomas F. Liberty and Dennis McClement, “Fuel Permeation from Automotive Systems,” Final Report, for the 

Coordinating Research Council and the California Air Resources Board, CRC Project E-65, September 2004. Available in Docket 

EPA-HQ-OAR-2005-0161. 

7

 “Analysis of ethanol evaporative permeation effects from CRC E-77 and E-65 programs”, Memo from David Hawkins to Docket 

EPA-HQ-OAR-2005-0161 

8

Southwest Research Institute. 2007. Flex Fuel Vehicles (FFVs) VOC/PM Cold Temperature Characterization When Operating on 

Ethanol (E10, E70, E85). Prepared for U. S. Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161. 

9

 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel Vehicles Operating on Gasoline and 

E85 Fuels. ERM Report No. 05-039, Emissions Research Division. Available in Docket EPA-HQ-OAR-2005-0161. 

10 EPA Certification and Fuel Economy Information System. http://www.epa.gov/otaq/cfeis.htm 

11 Southwest Research Institute. 2007. Flex Fuel Vehicles (FFVs) VOC/PM Cold Temperature Characterization When Operating on 

Ethanol (E10, E70, E85). Prepared for U. S. Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161. 

12 Graham, L. A.; Belisle, S. L. and C. Baas. 2008. Emissions from light duty gasoline vehicles operating on low blend ethanol 

gasoline and E85. Atmos. Environ. 42: 4498-4516. 

13 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel Vehicles Operating on Gasoline and 

E85 Fuels. ERM Report No. 05-039, Emissions Research Division. Available in Docket EPA-HQ-OAR-2005-0161. 

14 Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data. Prepared by Systems Applications 

International, Inc. 

15 U.S. EPA, 2007. Modeling of Ethanol Blend Effects on Nonroad Fuel Hose and Tank Permeation. Memorandum from Craig 

Harvey, U. S. EPA, March 26, 2007. 

16 U.S. EPA. 2008. Work Plan for Work Assignment 1-07, Amendment 1, “Ethanol Evaluation on Non-road Engines,” Contract EPC-07-028, October 3, 2008. 

17 U.S. EPA. 2008. Work Plan for Work Assignment 1-07, Amendment 2, “Ethanol Evaluation on Nonroad Engines,” Contract EPC-07-028, October 22, 2008. 

18 “Effects of Intermediate Ethanol Blends on Legacy Vehicles and Small Non-Road Engines, Report 1 – Updated,” National 

Renewable Energy Laboratory, NREL/TP-540-43543, ORNL/TM-2008/117, February 2009. 

19 U.S. EPA, 2008. Control of Emissions From Nonroad Spark-Ignition Engines and Equipment; Final Rule, 73 FR 59034, Oct. 8, 

2008. 

20 U.S. EPA, 2008. Biodiesel Effects on Heavy-Duty Highway Engines and Vehicles; Office of Transportation and Air Quality, Ann 

Arbor, MI. 

21 U.S. EPA 2002 A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions: Draft Technical Report (EPA420-P-02-

001) 

22 U.S. EPA, 2008. Development of MOVES Rates for HC, CO and NOx Exhaust Emissions from Light-duty Vehicles for use in the 

RFS2 NRPM, Memorandum from James Warila, U. S. EPA, November 2008. 

23 U.S. EPA. 2008. Kansas City Characterization Study: Final Report (EPA420-R-08-009) 

http://www.epa.gov/otaq/emission-factors-research/420r08009.pdf

24 U.S. EPA. 2008. Analysis of Particulate Matter Emissions from Light-Duty Vehicles in Kansas City (EPA420-R-08-010) 

http://www.epa.gov/otaq/emission-factors-research/420r08010.pdf

25 U.S. EPA, 2008. Modeling Evaporative Emissions in MOVES for the RFS2 NPRM. Memorandum from Prashanth Gururaja, U.S. 

EPA, November 2008. 

26 U.S. EPA. 2002. Technical Description of the Toxics Module for MOBILE6.2 and Guidance on Its Use for Emission Inventory 

Preparation. (EPA420-R-02-029) http://www.epa.gov/otaq/models/mobile6/r02029.pdf 27 U.S. EPA, 2008. Using MOVES to Generate Inventories for the RFS2 NPRM, Memorandum from Amanda Valente and Megan 

Beardsley, November 2008. 

28 See http://www.epa.gov/ttn/chief/emch/index.html. 

29 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters

per Cylinder (published May 6, 2008 and republished June 30, 2008). For details, see 

http://www.epa.gov/OMS/locomotv.htm#2008final 

30 Details about NMIM are available at http://www.epa.gov/otaq/nmim.htm. 
196

 31 Information about the 2005 NEI may be found at http://www.epa.gov/ttn/chief/net/2005inventory.html. 

32 Details concerning the Final Rule: Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines, Equipment, 

and Vessels may be found at http://www.epa.gov/otaq/equip-ld.htm#. 

33 Sze, C., Whinihan, J., Olson, B., Schenk, C., Sobotowski, R. Impact of Test Cycle and Biodiesel Concentration of Emissions, SAE 

report 2007-01-4040 (2007). 

34 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants from Mobile Sources; EPA420-R-07-

002; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm 35 Landman, L. C. (2007) Estimating Emissions Associated with Portable Fuel Containers (PFCs). U. S. EPA, Assessment and 

Standards Division, National Vehicle and Fuel Emissions Laboratory, Ann Arbor, MI, Report No. EPA420-R-07-001. 

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

36 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program; EPA420-R-07-004; Office of Transportation 

and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/renewablefuels/index.htm 37 U. S. EPA. 1993. Final Regulatory Impact Analysis for Reformulated Gasoline. December 13, 1993. 

http://www.epa.gov/otaq/regs/fuels/rfg/

38 Haskew, H. M.; Liberty, T. F.; McClement, D. 2004. Fuel Permeation from Automotive Systems. Prepared for the Coordinating 

Research Council by Harold Haskew and Associates and Automotive Testing Laboratories, Inc. September 2004. CRC Project No. 

E-65. http://www.crcao.com. This document is available in Docket EPA-HQ-OAR-2005-0036. 39 Hester, Charles. 2006. Review of Data on HAP Content in Gasoline. Memorandum from MACTEC to Steve Shedd, U. S. EPA, 

March 23, 2006. This document is available in Docket EPA-HQ-OAR-2003-0053. 

40 U. S. EPA. 2006. Determination that Gasoline Distribution Stage 1 Area Source (GD AS) Category Does Not Need to Be 

Regulated Under Section 112(c)6. Memoradum from Stephen Shedd to Kent Hustvedt, May 9, 2006. This document is available in 

Docket EPA-HQ-OAR-2005-0036. 

41 U. S. EPA. SPECIATE Version 4.0. http://www.epa.gov/ttn/chief/software/speciate/index.html 42 Haskew, H. M., Liberty, T. F., McClement, D. 2004. Fuel Permeation from Automotive Systems. Report No. CRC E-65, Prepared 

for California Air Resources Board and Coordinating Research Council. http://www.crcao.org/ 43 United States Department of Agriculture, Amber Waves, February 2004, page 9, U.S. Increasingly Imports Nitrogen and Potash 

Fertilizer. http://www.ers.usda.gov/amberwaves/February04/ 

44 Wen Huang, United States Department of Agriculture, Economic Research Service, personal communication to Craig Harvey, US 

EPA, November 9, 2007. 

45 40 CFR Part 63 National Emission Standards for Hazardous Air Pollutants for Source Categories: Gasoline Distribution Bulk 

Terminals, Bulk Plants, Pipeline Facilities, and Gasoline Dispensing Facilities; Proposed Rule, Thursday, November 9, 2006, 66064-

66092. 

46 E. H. Pechan. Documentation For The Final 2002 Nonpoint Sector (Feb 06 Version) National Emission Inventory For Criteria And 

Hazardous Air Pollutants, Appendix A, pp. A-89 to A-105. Prepared for Emissions Inventory and Analysis Group (C339-02) Air 

Quality Assessment Division Office of Air Quality Planning and Standards U.S. Environmental Protection Agency Research Triangle

Park, NC 27711, Report No. 05.10.001/9014.401 Jul-06. Available at 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/nonpoint/2002nei_final_nonpoint_documentation0206version.pdf. 

47 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting System. 

http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm

48 EPA. 1994. Final Regulatory Impact Analysis: Refueling Emission Regulations for Light Duty Vehicles and Trucks and Heavy 

Duty Vehicles. Office of Mobile Sources, Ann Arbor, MI. Available at http://www.epa.gov/otaq/orvr.htm. 49 U. S. EPA. 2001. Final Rule: Control of Air Pollution From New Motor Vehicles: Heavy-Duty Engine and Vehicle Standards and 

Highway Diesel Fuel Sulfur Control Requirements, January 18, 2001. 

50 U. S. EPA. 2008. Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines

Less than 30 Liters per Cylinder, Regulatory Impact Analysis, June 30, 2008. 

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

51 U.S. EPA. 2008. RFS2 Modified version of GREET1.7 Upstream Emissions Spreadsheet, October 31, 2008. 

52 Emission Inventories for Ocean-Going Vessels Using Category 3 Propulsion Engines In or Near the United States, Draft Technical 

Support Document, EPA420-D-07-007, December 2007. This is available in Docket OAR-2007-0121at http://www.regulations.gov/ . 

53 U. S. EPA. 2009. National-Scale Air Toxics Assessment for 2002. http://www.epa.gov/ttn/atw/nata2002. 

54 U. S. EPA. 2007. Approach for Developing 2002 and Future Emission Summaries. Memorandum from Madeleine Strum to 

Docket, November 26, 2007. This document is available in Docket EPA-HQ-OAR-2007-0491. 

55 Federal Register. 2007. National Emission Standards for Hazardous Air Pollutants From Petroleum Refineries. 72(170): 50715-

50742. http://www.epa.gov/EPA-AIR/2007/September/Day-04/a17009.htm. 56 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting System. 

http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm

57 Brady, D. and Pratt, G. C. 2007. Volatile Organic Compound Emissions from Dry Mill Fuel Ethanol Production. J. Air & Waste 

Manage. Assoc. 57: 1091-1102. 

58 Prevention of Significant Deterioration Construction Permit Application. Prepared for Verasun Fort Dodge, LLC, Fort Dodge Iowa, 

by Barr, Minneapolis, MN, December, 2007. 

59 See Chapter 1.5 of the Draft Regulatory Impact Assessment of this rulemaking. 

60U. S. EPA. 2007. Control of Hazardous Air Pollutants from Mobile Sources; Final Rule. Federal Register, 72: 8428-8570. 
197

 61 U. S. EPA. 2007. Regulation of Fuels and Fuel Additives: Renewable Fuel Standard Program; Final Rule. Federal Register, 72:

23900-24014. 

62 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants from Mobile Sources; EPA420-R-07-

002; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm 63 Energy Information Adminstration. 1999. Petroleum Marketing Annual. Report No. DOE/EIA-0487(99). 

http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/petroleum_marketing_annual/historical/1999/pma_1999.html

64 Energy Information Administration. 2007. Annual Energy Outlook 2007 with Projections to 2030. U.S. Department of Energy, 

Report # DOE/EIA-0383. http://www.eia.doe.gov/oiaf/aeo/index.html 65 U. S. EPA. SPECIATE Version 4.0. http://www.epa.gov/ttn/chief/software/speciate/index.html 66 66 Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data. Prepared by Systems Applications 

International, Inc. 

67 Diesel Locomotives, Marine Distillate, Residual Boiler -- E. H. Pechan. 2005. Documentation For Aircraft, Commercial Marine

Vessel, Locomotive, And Other Nonroad Components Of The National Emissions Inventory, Volume I - Methodology. 

Prepared for U. S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

http://www.epa.gov/ttn/chief/net/2002inventory.html 

68 HDD Trucks -- R. Cook, E. Glover. 2002. Technical Description of the Toxics Module for MOBILE6.2 and Guidance on Its Use 

for Emission Inventory Preparation. U. S. Environmental Protection Agency, Office of Transportation and Air Quality. Report No. 

EPA420-R-02-011. http://www.epa.gov/otaq/m6.htm 

69 Natural Gas Turbines -- U. S. EPA. 2000. AP-42, 5th Edition, Volume 1. Chapter 3: Stationary Internal Combustion Sources. 

http://www.epa.gov/ttn/chief/ap42/ch03/index.html 

70 U.S. EPA. 2010. “RFS2 Emissions Inventory for Air Quality Modeling Technical Support Document,” January 2010. [ XX Fix 

as needed when Marc Houyoux’s TSD is finalized & docketed ] 71 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints,” Oak Ridge National Laboratory, U.S.

Department of Energy, March 2009. 

72 U.S. EPA. 2009. spreadsheet tool, “2022 Ethanol Vapor Losses during Transport” 12/4/2009. 

73 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for RFS2 2022 AQ Modeling: RFS1 Case,” 

12/15/2008. 

74 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for RFS2 2022 AQ Modeling: AEO Case,” 

12/11/2008. 

75 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for RFS2 2022 AQ Modeling: EISA (RFS2) 

Case,” 12/15/2008. 

76 U. S. EPA. 2008. AP-42, 5th Edition, Volume 1. Chapter 5.2: Transportation And Marketing Of Petroleum Liquids, July 2008. 77 U.S. EPA. 2009. “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel 

Engines (Chapter 3: Emission Inventory),” EPA-420-D-09-002, June 2009. 

78 U.S. Army Corps of Engineers Principal Ports file for 2006 (PPORTS06.xls) 

79 U.S. EPA. 2008. Spreadsheet file: “RFS2 Ethanol Usage & Constraint Analysis Inputs.6.23.08.xls” 

80 “Annual Energy Outlook 2008,” Table 7 (Transportation Sector Key Indicators and Delivered Energy Consumption), DOE/EIA0383(2008), June 2008. 

81 The three cases and their corresponding Excel files are as follows: 

RFS1: County_To_CountyRefNew_RFS1_1116.xls 

AEO: County_To_CountyRefOld_1113R2.xls 

EISA: County_To_County2022_Controlled_1114.xls 

82 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2005 AQ model run,” Spreadsheet file: 

“Corn_EtOH_Plant_Inv_2005c.xls” 

83 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 RFS1 reference case AQ model run,” Spreadsheet file: 

“Corn_EtOH_Plant_Inv_2022-rfs1-fixC.xls” 

84 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 AEO reference case AQ model run,” Spreadsheet file: 

“Corn_EtOH_Plant_Inv_2022-aeo-fixC.xls” 

85 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 EISA reference case AQ model run,” Spreadsheet file: 

“Corn_EtOH_Plant_Inv_2022-eisa-fixC.xls” 

86 U.S. EPA. 2009. Spreadsheet file: Cellulosic Ethanol Plant inventory inputs for RFS2 2022 EISA control case AQ model run,”

2/25/2009. 

87 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 RFS1 reference AQ model run,” Spreadsheet file: 

“Biodsl_Plant_Inv_2022-rfs1-fixC.xls,” 2/25/2009. 

88 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 AEO reference AQ model run,” Spreadsheet file: 

Biodsl_Plant_Inv_2022-aeo-fixC.xls,” 2/25/2009. 

89 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 EISA control case AQ model run,” Spreadsheet file: 

Biodsl_Plant_Inv_2022-eisa-fixC.xls,” 2/25/2009. 
198

 90 U.S. EPA. 2008. “Agricultural SCCs and Emissions Affected by RFS2 in Air Quality Modeling,” spreadsheet file: 

“otaq_upstream_2020cc_2020ce_2005ag_tox-docket.xls” 11/5/2008. 

91 U.S. EPA. 2008. “Refinery Emission Adjustments for RFS2 Air Quality Modeling,” spreadsheet file: “RFS2_Refinery_Adjustdocket.xls,” 11/25/2008. 

92 U.S. EPA. 2008. “Adjustments to 2022 Platform Gasoline transp & distrib emissions for RFS2 AQ,” spreadsheet file: 

“2005ai_tox_SCC_50state_CAPHAP-20081216.xls” 12/16/2008. 

93 See http://www.epa.gov/ttn/chief/emch/index.html. 

94 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters

per Cylinder (published May 6, 2008 and republished June 30, 2008). For details, see 

http://www.epa.gov/OMS/locomotv.htm#2008final 

95 U.S. EPA, 2009. How Downstream Inventories Were Generated for RFS2 Air Quality Modeling, Memorandum from Harvey 

Michaels and Megan Beardsley, December 2009. 

96 Details concerning the Final Rule: Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines, Equipment, 

and Vessels may be found at http://www.epa.gov/otaq/equip-ld.htm#. 

97 Sze, C., Whinihan, J., Olson, B., Schenk, C., Sobotowski, R. Impact of Test Cycle and Biodiesel Concentration of Emissions, SAE 

report 2007-01-4040 (2007). 

98 Byun, D.W., Ching, J. K.S. (1999). Science algorithms of the EPA models-3 community multiscale air quality 

(CMAQ) modeling system. Washington, DC: U.S. Environmental Protection Agency, Office of Research and 

Development. 

99 Byun, D.W., Schere, K.L. (2006). Review of the Governing Equations, Computational Algorithms, and Other 

Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Journal of 

Applied Mechanics Reviews, 59(2), 51-77. 

100 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A. (1996). The next 

generation of integrated air quality modeling: EPA’s Models-3, Atmospheric Environment, 30, 1925-1938. 

101 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on 

the Peer Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research 

Triangle Park, NC 

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Environ Sci Technol 36: 1389-1395. 

216 EM Martins, G Arbilla (2003) Computer modeling study of ethanol and aldehyde reactivities in Rio de 

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217 Niven, R. (2005) Ethanol in Gasoline: environmental impacts and sustainability review article. Renewable 

& Sustainable Energy Reviews 9: 535-555. 

218 Kleindienst, T. E., W.A. Lonnman (2009) Chamber irradiations to measure acetaldehyde concentrations 

from fuels with and without 10% ethanol, EPA memo. 

219 U.S. EPA. (2005). Review of the National Ambient Air Quality Standard for Particulate Matter: Policy 

Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R-05-005a. Retrieved 

March 19, 2009 from http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221.pdf. Section 

2.2. 

220 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at 

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221 U.S. EPA. (2005). Review of the National Ambient Air Quality Standard for Particulate Matter: Policy 

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222 U.S. EPA. (2006). Provisional Assessment of Recent Studies on Health Effects of Particulate Matter 

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223 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at 

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224 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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225 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at 

http://www.regulations.gov/. Section 8.3.3.1. 

226 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-0190 at 

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227 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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205

 228 U.S. EPA. (2006). National Ambient Air Quality Standards for Particulate Matter. 71 FR 61144, October 

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229 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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230 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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231 Laden, F., Neas, L.M., Dockery D.W., et al. (2000). Association of fine particulate matter from different 

sources with daily mortality in six U.S. cities. Environ Health Perspectives, 108(10), 941-947. 

232 Schwartz, J., Laden, F. Zanobetti, A. (2002). The concentration-response relation between PM(2.5) and daily 

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233 Mar, T.F., Ito, K., Koenig, J.Q., Larson, T.V., Eatough, D.J., Henry, R.C., Kim, E., Laden, F., Lall, R., Neas, 

L., Stölzel, M., Paatero, P., Hopke, P.K., Thurston, G.D. (2006). PM source apportionment and health effects. 3. 

Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and 

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234 Ito, K., Christensen, W.F., Eatough, D.J., Henry, R.C., Kim, E., Laden, F., Lall, R., Larson, T.V., Neas, L., 

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235 Janssen N.A., Schwartz J., Zanobetti A., et al. (2002). Air conditioning and source-specific particles as 

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236 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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237 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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238 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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239 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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240 U.S. EPA. (2005). Review of the National Ambient Air Quality Standard for Particulate Matter: Policy 

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244 Krewski, D., Burnett, R.T., Goldberg, M.S., et al. (2000). Reanalysis of the Harvard Six Cities study and the 

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 245 Jerrett, M., Burnett, R.T., Ma, R., et al. (2005). Spatial Analysis of Air Pollution and Mortality in Los 

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246 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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249 Maynard, D.; Coull, B.A.; Gryparis, A.; Schwartz, J. (2007) Mortality risk associated with short-term 

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250 Ryan, P.H.; LeMasters, G.K.; Biswas, P.; Levin, L.; Hu, S.; Lindsey, M.; Bernstein, D.I.; Lockey, J.; 

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251 Morgenstern, V.; Zutavern, A.; Cyrys, J.; Brockow, I.; Gehring, U.; Koletzko, S.; Bauer, C.P.; Reinhart, D; 

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252 Franco Suglia, S.; Gryparis, A.; Wright, R.O.; Schwartz, J.; Wright, R.J. (2007) Association of black carbon 

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253 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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254 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of 

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255 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

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257 Thurston, G.D., Ito, K., Kinney, P.L., Lippmann, M. (1992). A multi-year study of air pollution and 

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261 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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262 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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271 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of 

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272 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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273 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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281 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003-

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286 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. 

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290 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. 

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291 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. 

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292 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. 

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293 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. 

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295 U.S. EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources. 72 FR 8428; February 26, 2007. 

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297 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. This material is available electronically 

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298 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. http://www.epa.gov/ttn/atw/nata2002. 

299 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is available 

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300 International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risk of 

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302 International Agency for Research on Cancer (IARC). 1987. Monographs on the evaluation of carcinogenic 

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307 U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects). Environmental Protection Agency, 

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308 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.; 

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311 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to human exposure 

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337 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde 

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338 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of 

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344 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, 

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345 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

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348 U.S. EPA. 2003. Integrated Risk Information System File of Acrolein. Research and Development, National 

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349 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. 

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351 Integrated Risk Information System File of Acrolein. Research and Development, National Center for 

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358 U.S. EPA Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Environmental 

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359 U.S. EPA Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Environmental 

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in Docket EPA-HQ-OAR-2005-0161. This document may be accessed electronically at: 

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360 U. S. EPA. 2004. Toxicological Review of Naphthalene (Reassessment of the Inhalation Cancer Risk), 

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361 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS Reassessment of 

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363 International Agency for Research on Cancer (IARC). (2002). Monographs on the Evaluation of the 

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364 U. S. EPA. 1998. Toxicological Review of Naphthalene, Environmental Protection Agency, Integrated Risk 

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DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm 

365 EPA. 2005. Full IRIS Summary for n-Hexane (CASRN 11054-3). Integrated Risk Information System 

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366 Inhalation health effect concerns for volatilized a.i. compounds are discussed for individual products, e.g., 

oxydimeton-methyl, carbaryl (and carbamates,generally), diazinon, at 

http://www.epa.gov/pesticides/reregistration/status.htm . A.I. vapor pressure and a discussion of product 

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367 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 

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369 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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370 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF and Volume II 

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372 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I Document No. EPA600/P99/002aF and Volume II Document No. EPA600/P-99/002bF. This document is available in Docket EPA-HQOAR-2005-0161. 

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