1 

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

In addition to the GHG impacts laid out in Chap ter 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 Standa rd has been established), criteria pollutant precursors, and air toxics. Changes in these e missions 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 produc tion and distribution including th e effect of decreases in the production and distribution of gasoline and diesel di splaced 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 as sessment 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 mic rons in diameter and less (PM2.5), sulfur dioxide (SO2), ammonia (NH3), benzene, 1,3-butad iene, acrolein, formaldehyde, acetaldehyde, naphthalene, and ethanol, includi ng 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 presen ts the methodology and results of air quality modeling, and Sections 3.5 and 3.6 address health and environmenta l 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 full y 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 presentin g 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 Equipment2 The volumes of renewable fuel called for in today's rule will directly affect emissions from most mobile source categories, and for th is analysis we have quantified the effects on exhaust and evaporative emissi ons of gasoline-fueled vehicles and equipment including passenger cars, light trucks, heavy trucks, moto rcycles 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 increase d 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 equipm ent would be fueled on at least E10. For the proposal, the uncertainty in the emission impact s 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 oppos ed 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 e mission effect to the use of E 85, 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 (MOVE S2009) was used to generate
national inventories for the cont rol 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 M obile Inventory Model (NMIM), a platform which generates emission inventorie s 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 th e projected change in fuel properties due to today’s action; b) developing i ndividual 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” correc tions, 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 Inputs3 As inputs to our emissions modeling, we de veloped a detailed prof ile of fuels for each modeling case. We prepared county- level databases of fuel propertie s and fuel market shares for the 2005 base case, the RFS1 reference case, th e 2022 AEO reference case, and the 2022 control case. These county-level databa ses were applied in both NMIM and MOVES for consistency in fuel inputs across the differen t mobile source categories.

The 2005 base case fuel properties were derive d from 2005 historical data. These data included national summer and winter fuels survey s, 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 pr operties of all gasolin e 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 prop erties in that PADD. Special adjustments were made to some counties to account for local ga soline volatility control programs and winter oxygenated gasoline programs.

For the 2022 reference and control cases, th e 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 becau se 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 mode l three different approaches to meeting the renewable fuel volume requirements of EISA. Ev en 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 th e choice of counties mode led with E85 was based
on the economics and other factors.

Future fuel properties in both the reference a nd control cases were adjusted to account for widespread increases in ethanol. This wa s done using two assump tions: 1) ethanol has historically been splash blende d in conventional gasoline (CG), and 2) it will be match-blended by 2022 (i.e., the changes associated with etha nol 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 refine rs 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 d ilution), 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 pres ent in conventional gaso line unless there was a local volatility control.4 Table 3.1-1. 2022 Adjustments for Ethano l 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, refi ners 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 gaso line (CG). We used refinery modeling results for each PADD (produced using the same cases a nd 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 fro m what was used in the NPRM and air quality modeling to reflect the 2007 mobile source air to xics (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 di d not model the effects of flexible-fueled vehicles running on E85 for any polluta nts except acetaldehyde and ethanol.

For each of the modeled scenarios, fuel in formation was input into an NMIM database and used for NMIM runs. For MOVES runs, th e NMIM databases were converted into MOVES databases using a convers ion program. To reduce time needed for MOVES runs, we reduced the5 size of the MOVES fuel database by processing the database with a "binner" program that grouped fuels with similar prope rties 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 Emi ssions from Vehicles & Equipment

The average effect of renewable fuels on an individual vehicle/equipment basis, based on available research, is the founda tion of the emission impact assessm ent. This section contains discussion of the effects used in the emission impact assessment for E10 on gasoline vehicles and equipment, for E85 on gasolin e 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 exhaus t 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-ca lled “Predictive Model” to assess California’s request for an oxygenate waiver in 2000, as we ll as test data from se veral 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 major ity of the fleet in 2022) there was not enough consistency across these studies to confidently pr edict the impact of oxygenated fuel on exhaust HC and NOx emissions. As a result the RFS1 analys is 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 et hanol and several other fuel properties impacted by the bl ending 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 po llutants. 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 demonstrat es 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 fi nal rule, we extended these effects to Tier 1 and NLEV cars and light trucks (through the 2003 m odel year) based on a recently published study6 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%

CO b 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 fro m 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 mol ecular 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 dr aft MOVES model, whic h separates permeation emissions from vapor venting emi ssions to allow better accounting for this effect.

For the proposal, permeation effects were developed from Coordinating Research Council's (CRC) E-65 program 6 , which measured evaporative e missions 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 fro m permeation of the fuel through the different components of the fuel system. For that analys is, we estimated the effect by calculating the percent increase in average emissions from all ve hicles 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 progr am, and, in conjunction w ith lower emission rates
for vehicles certified to Enhan ced Evaporative and later standards in MOVES, served to underestimate the impact of E10 on pe rmeation from newer vehicles.

The version of MOVES used for the FRM an alysis significantly updates the permeation estimate used in the NPRM, particularly for ne wer 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-en hanced vehicles, to a 213 percent increase for newer technology vehicles. 7

3.1.1.2.2 On-road Gasoline vehicle E85 effects7 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) vehicles A

. 8, 9, 10 Two of these sources are test programs conducted by Southwest Research Institu te and Environment Cana da, and the third is EPA certification data. This section briefly describes each data source and highlights the key findings, and explains how these data we re 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 et hanol blends. This study was primarily focused on the impacts of fu el 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, althoug h for this analysis we will focus only on E0 and E85 emissions at 75o F. At this test temperatur e, Tier 2 certification fuel was used as the nonoxygenated test fuel (E0) as well as the base ga soline for the splash-blended E10 and E85 fuels. Additionally, EPA certification “cold CO” wint ertime gasoline was used for reduced ambient temperature (20 oF) testing – used alone (E0) a nd as the base fuel for wintertime E10 and E70 blends. This base gasoline has a higher RVP th an its summertime equivale nt, which is necessary to ensure proper fuel vaporization at lower ambi ent 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 st udy saw reductions in PM 2.5, benzene, and 1,3-
butadiene of 55% - 70% with E 85 relative to E0. HC emissions increased while NOx and CO decreased. Emissions of methane, formaldehy de, and acetaldehyde were found to increase significantly with E85 use. Table 3.1-6 summari zes the average percent change in emissions with E85 vs. E0. This table also compares th e 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 certifica tion fuel and a commerciall y 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 NM OG, 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, acrole in, 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 d ecrease 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 Fu el Economy Information System (CFEIS) database was accessed to identify data from five model year 2006 Tier 2 vehicl es (bins 5, 8, and 9) tested on both E85 and Tier 2 certification gasoline. The E85 blend tested he re 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 (formald ehyde, acetaldehyde, and ethanol were only measured for tests where E85 was used; theref ore 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 reduc tions in CO. The average percent change in each pollutant for these vehicles when operat ed on E85 is shown in Table 3.1-5, below.

Table 3.1-5.
Effect of E85 on LEV and Later Per-m ile 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 th at 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 variab ility 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 c onducted the repeat tests necessary to assess the test-t o-test variability of a given vehicle, and none of the studies tested enough vehicles to confidently state that their9 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 inter im non-Tier 2 bin 8 vehicle. Despite its name, however, the standard is equivalent to the fi nal 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 seco nd vehicle tested by Environment Canada was a 2002 Dodge Caravan cer tified 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% high er 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 ga soline. On a relative scale, both vehicles experienced similar percent changes in emissi ons between E0 and E 85. The Sebring emitted more ethanol with E85 than did the Caravan, re sulting 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 relativ e 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, a nd 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 pr ogram 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 on10 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 Rati os 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 engi nes are contained in EPA’s NONROAD model,
based on limited data. The eff ects on exhaust HC, NOx, and CO are shown in Table 3.1-7. Effects on tank and hose permeation emissions va ry 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 ef fects are accounted for in the NMIM model that has the county-specific fuel prope rties that were used to genera te 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 (A RB) 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 evalua tion 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 sma ll 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-use11 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 asso ciated 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 in crease 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 biodi esel 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 concentrati ons of 5% or less. In applying the emission impacts to the emission inventory we assumed th at 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. (C ite to Chien Sze et.al. SAE Paper). For our estimate of biodiesel impacts on toxics we app lied 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 Impa ct Scenarios Analyzed

For today’s rule we are estimating emission impacts of three different renewable fuel volume scenarios, as presented in Chapter 1.2, wh ich are meant to bracket the range of likely combinations of renewable fuel volumes, and thes e 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 AEO200712 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 we re 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 emissi ons from MOBILE6. Exhaust emission rates for HC, CO and NOx were developed based on an an alysis of state insp ection/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 e mission 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 upda ted MOVES HC estimates within the model. 26

Details of how MOVES was conf igured and run are documented in a report contained in
the docket. 27 To summarize, a separate MOVES run wa s configured for each year (2005 for the
baseline and 2022 for the reference and control ca ses) and each fuel volume scenario: two reference cases (RFS1 mandate and AEO) and thr ee control cases (low, mid, and high ethanol). Each of these runs required a unique “run specifi cation” file and bundle of input databases to allow modeling of differences in analysis year an d fuel supplies. For example, for the 2005 base year run, MOVES was configured to run with th e estimates of fuel formulations and market shares by county in 2005, using the inputs deve loped as discussed in Section 3.1.1.1. Reference
and control case runs in 2022 were run with estimates of fuel formul ations and market shares by county as we project in 2022. VMT used to m odel onroad emissions was that developed for the Office of Air Quality Planning and Sta ndards' 2002 Version 3 Modeling Platform 28 and used in the Locomotive-Marine Rule published in 2008. 29

MOVES allows different levels of pre-ag gregation depending on th e level of resolution needed. For regional inventory applications, the finest level of aggrega tion 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 pa tterns; 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 th at 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 evaporativ e 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 ye arly 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 th at 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 ty pe; for this analysis the majority of the focus was on light vehicles, which would incl ude the MOVES use types of Passenger Car, Passenger Truck and Light Commerc ial Truck. To account for larg er 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 truc ks, and motor homes. For the purposes of this rulemaking, the number of gasoline-powered refu se 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 calc ulation 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 fuel ed 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 se t 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 ventin g emissions are calculated using fuel properties (sulfur level and RV P) rather than fuel adjustments, we also created a specific MOVES table of E85 fuel properties as describe d in Section 3.1.1.1 In a post-processing step, we calculated a weighted average of the "all E 85" 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 ve hicles are responsive to changes in fuel
characteristics. The all E85 and all E10 results we re 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-fuel ed 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 Chap ter 1, Section 1.7.1 pertaining to Primary FFV Growth Assumptions and Projected Growth in E8 5 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 m odel 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 results14 acetaldehyde, 1-3 butadiene, formaldehyde and acrolein for all of these cases analyzed. Naphthalene from heavy-duty vehicles was ratio ed 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 we re developed by running the National Mobile Inventory Model (NMIM), a consolid ated 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 informati on 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 a nd 2022 fuels described in Section
3.1.1.2. The version of the NONROAD Model used in cluded 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 eff ects of gasoline blends containing 10 percent
or less of ethanol.

Emissions from onroad and nonroad diesel equi pment were also developed by running NMIM (see above), using the same NCD and ve rsion 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 contro l 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 variet y 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 e missions are linear with
biodiesel concentration as de monstrated 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 us ing the HC effects from Table 3.1-8.

3.1.1.4.4 Portable Fuel Containers

There are several sources of emissions asso ciated with portable fu el 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 an d 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 e missions of hazardous pollutants from mobile sources (MSAT2 rule), EPA promulgated requireme nts to control VOC emissions from gas cans. The methodology used to develop e mission 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 tw o sets of hypothetical nationwide annual estimates of PFC VOC emissions, for calendar ye ars 2017 and 2030, based on a ll E0 and all E10. Interpolation can be used to estimate PFC VOC e missions 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 a nd 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 un likely to be stored or dispensed from PFCs.

MSATs found in liquid gasoline will be pres ent as a component of VOC emissions. These MSATs include benzene and naphthalene. Et hanol 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:

36

. 0 Re Re
× ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× =

LDGV LDGV VOC fueling Benzene fueling Emissions VOC PFC Emissions Benzene PFC16 where the ratio of refueling benzene to VOC was estimated using average nationwide fuel properties for zero and 10 percen t ethanol gasoline from refinery modeling, done for RFS rule, and applied to EPA’s Complex M odel 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 perm eation emissions, based on a recent study 38 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:

77

. 1 36

. 0 Re Re

x VOC fueling Benzene fueling Emissions VOC PFC Emissions Benzene PFC

LDGV LDGV × ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× =

The resulting ratios for 0% and 10% ethanol di d not differ at the fi fth 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 th e overall impact of RVP changes on total VOC
emissions.

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

0054

. 0 × ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× =

LDGV LDGV VOC e Evaporativ e Naphthalen e Evaporativ Emissions VOC PFC Emissions e Naphthalen PFC

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 dist ribution 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 permea tion 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 dividi ng 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 143.91 147.14 153.9 136.18 138.28 139.74 141.32 145.0818 3.1.2 Impact on Non- GHG Emissions from Fuel Production and Distribution

In addition to the effects of increased rene wable fuel use on emissi ons from the vehicles and equipment that use the fuels, as discussed ab ove, there are shifts in the fuel production and transport/distribution methods that can have subs tantial impacts on emissions. These "upstream" emissions are associated with all stages of bi ofuel production and distribu tion, including biomass production (agriculture, forestry), fertilizer a nd pesticide production and transport, biomass transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to blending/distribution terminals, a nd distribution of finished fuels to retail outlets. Additionally, changes in agricultural economics associated w ith increased biomass production can result in shifts in related agricultural production, such as livestock.

This section describes the changes in ups tream 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 pollutant s, their precursors, and ammonia, and the second covering non-criteria air toxic em issions and ethanol. The specifi c 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), additi onal 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 adju st the agricultural inputs of the AEO reference case for the RFS1 reference case. So the fertilizer and pesticide quantities, livestock counts, and19 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 inappr opriate 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 resour ces 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) us ed for the unit conversions were 115,000 BTU/gallon for gasoline and 130,000 BTU/ga llon for diesel. For comparison, the corresponding 2020 emission factors from GREET are shown, where available. Most of the
differences between NONROAD and GREET are sm all 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 inventorie s 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 fro m 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 Agricultur al Chemical Use for 2022 RFS2 Co ntrol 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 toxi cs it is important to reduce the estimated impacts by the percentage of productio n and transportation occurring outside of the U.S. Using data from USDA 43,44 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 una djusted 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 ne t adjustments to the GR EET 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 ad justment 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 em issions 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 da ta from the NEI was used with USDA pesticide application data for 2002 (or the nearest year fo r 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 ac rolein) 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 emi ssions in Table 3.1-14.23 Table 3.1-14. Herbicide and Pesticide App lication Air Emission Factors and Impacts for 2022 RFS2
Control Case Relative to AEO2007 Reference Case

Emission Factor Air Emission Impact Pollutant (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 farm ing is the burning of crop residues. This practice is one of the methods that is used to cl ear 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 vari able 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 ha s 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 current ly 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 em issions 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 rec onsidered the inclusion of any crop residue burning impact and decided not to include it. Th is reconsideration was driven by the facts that (a) the crops most likely to be impacted by th is rule do not tend to be ones for which residue burning is used, and (b) even for those crops affect ed by this rule that might otherwise have their residue burned, for this analysis they would mu ch 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 Emissions24 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 particulat e emissions by county.

The agricultural dust data from the 2002 NEI wa s 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. Th e 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 nationwid e 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 Impa cts 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 Im pacts for the 2022 RFS2 Control Case
Relative to the AE O2007 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 emissi ons into the air on farms are fertilizer application and livestock waste. Fertilizer application emissions were estimated using an25 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 genera ted 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 Im pacts for 2022 RFS2 Control Case Relative
to the AEO2007 Re ference Case

Livestock Type kg NH3 per head

per year a 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 comp ared 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 predicte d by FASOM. The ammonia emission factors in units of grams emitted per million BTU of fuel burned were calculated from the default ammonia emission26 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 discu ssed above in the section on agricultural emissions. The calculation of emissions from corn ethanol, cellu losic ethanol, and biodies el plants, including feedstock transport, was done using the basi c 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 Rene wable Energy Laboratory (NREL). Since certain biofuel production pr ocesses generate co-products that c ould also be used in the gasoline market, we have accounted for those by decreas ing 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 fro m 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 i nventories. 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 requi rements 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 Produc tion/Transport/Refining

The estimate of emissions a ssociated 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 gaso line 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 emissi ons 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 Distribution28 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 r eceive gasoline directly fr om 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 stati ons). 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 stati ons and pipeline pumping stations. Pipeline breakout stations receive gasolin e via pipelines, store it in stor age 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 th e 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 term inals via tank trucks or railcars. Gasoline and gasoline/ethanol blends from bulk pl ants 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 ga soline 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 et hanol is blended with gasoline at bulk terminals to produce E10 and E85 at the point fuel is loaded into tank trucks, we assumed bulk te rminal emissions were associated with unblended gasoline. We then divi ded emissions into a refinery to bulk terminal component and a bulk terminal to dispensing fac ility component. Total nationwide emissions for these two components were divided by the ener gy content of the total volume of gasoline distributed in 1999, to obtain the emission fact or 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 Rule 48 :

EF (g/gal) = exp[-1.2798 - 0.0049( Δ T) + 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 ga soline 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 ga soline is 8.7 psi, 10% ethanol is 9.7, and 85% ethanol is 6.2. Using these assumptions, the adjustment factor is +14% fo r 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 ga soline and gasoline/ethanol blends, transport
of ethanol to bulk terminals also results in ev aporative 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 em issions 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 et hanol, which greatly underestimated the ethanol vapor and VOC losses, since it di d not attempt to account for di fferences between ethanol and gasoline transport modes, distances, or transf er methods in movement of the fuel from production facility to the bulk distribution terminal.

For the air quality analysis and final rule an alysis this method was replaced using data from an Oak Ridge National Laboratory (ORNL) anal ysis of projected ethanol transport modes, distances, and volumes transferred unde r 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 cal culation 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 calculati ons 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 Transf er Emissions (Stage 1) Process Blend EF(g/mmBTU)
Refinery to Bulk Terminal E0 14.94

Refinery to Bulk Terminal E100 28.78 a

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 th ere to bulk distribution terminals by truck, rail, or barge, and distribution from term inal 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 controlle d engines (including trucks me eting the standards for 2008 and later engines 49 and engines meeting the 2008 locomotive/marine diesel engine rule 50 ), 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 incorpor ated into a modified version of GREET 51 , since
GREET 1.7 and 1.8 retained emission factors base d 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 transporte d by mode within each county.31 Table 3.1-21. 2022 Criteria Emissions from Fu el and Feedstock Tr ansport/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, opera tion of ethanol and biodiesel pr oduction 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 whic h were identified as national and regional-scale
cancer and noncancer risk drivers in the 2002 NATA 53 and were also likely to be significantly impacted by this rule. These compounds in clude benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Naphthalene impacts we re 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 sta tionary 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 sect ors included non-EGU (electric generating unit) point sources, EGU point sources, the nonpoint st orage 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 invent ory developed for air quality m odeling. 2002 and future year ethanol emissions were estimated by speciatin g 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. 5432 Air toxic emission estimates for agricultural equipment (mainly diesel agricultural tractors) were obtained from the EPA NMIM mo del, 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 devel oped emission factors for severa l 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 wa s done using toxic fractions of the corresponding
VOC emissions described in Sec tion 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 ta ble represents the sum of acrolein emissions divided by the sum of VOC emissions from pesticid e 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), Pesticid e 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 collect ed as part of a risk and technology review (RTR) for EPA’s proposed rule, “National Emi ssion Standards for Hazardous Air Pollutants
From Petroleum Refineries.” 55 These emissions were divided by BTUs of energy produced by
those refineries in 2002 to obtai n emission factors in g/mmBTU. Thus the resultant emission factors represent 2002 technology and emission sta ndards. Energy output estimates included all refinery products, such as conve ntional and reformulated gasolin e, 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 supplied 56 by its heating value in BTUs per gallon.

Resultant emission factors ar e 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 sp ent 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 proce sses 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. Th ese 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 to34 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 pr oduction 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 futu re 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 ex traction with n-hexane, which is a potentially important toxic emi ssion 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 mu st 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 pe r year, with n-hexane emissions of 295 tons per year. PM 10 emissions would be about 13 tons.

EPA used the Verasun application data to develop an estimate of potential nationwide nhexane emissions from ethanol pl ants 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 tota l production of DDGS, EPA estimates these facilities could emit about 9,000 tons of n-hexane nationwide. Howe ver, given the very limited data on emissions
from such facilities and the nascent nature of th is 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 emissi on 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, Etha nol, Gasoline/Ethanol Ble nds 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 vehi cles 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 assu med bulk terminal emissions we re 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 from36 refinery to bulk terminals were weighted by th e fraction of 2002 VOC emissions for this part of the process, whereas emission factors for E0 gaso line, 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 m odeled, 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 we re 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 derive d from the estimated gasoline distribution inventory for benzene in 1999, estimated for the Mobile Source Air Toxics Rule. 62 Total
nationwide emissions were divi ded by the energy content of the total volume of gasoline distributed in 1999, to obtain the emission fact or 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 reform ulated, Californi a 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. Th is inventory estimate was calculated by linear interpolation of 2020 and 2030 inve ntories from the Mobile Source Air Toxics Rule. Total
nationwide emissions were divi ded 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 adju sted 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 app lied to benzene estimates. The ratios were 14.8 for E10 and
112.8 for E85. The ratio for E10 was obtaine d 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 Factor s 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 Emissi on 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 -838 Table 3.1-26. Air Toxic Evaporative Emissi on 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 emi ssions 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 fu els. The emission factors for these are shown in Table 3.1-27 as fractions of exhaust VOC, or PM10 for exha ust naphthalene. The VOC and PM10 emission factors that these fractions are applied to are pr esented 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. 6939 Table 3.1-27. Toxic Fractions of Exhaust VOC (or frac tion 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 Inventor ies 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 mo st 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 andFuture 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 spec ific fuel properties a nd temperatures. Most nonroad equipment was modeled with NONROAD2005d using NMIM, which is a version of the
NONROAD that includes the benefits of the tw o nonroad regulations published in 2008 (the
locomotive and marine diesel rule and the sma ll spark-ignition and recr eational 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 commerc ial marine vessel inve ntories come from the40 spring 2008 final rule, and the C3 commercial ma rine emission inventory is from the Notice of Proposed Rulemaking (NPRM) published in August 2009. 77

Table 3.2-1 shows the total 2022 mobile a nd non-mobile source inventory projections that were used as the basis for the impact percen tages 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 Nonroad a 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 3,590,1 12 3,389,512 8,931,411 4,245,983

Table 3.2-1
continued Benzene Ethanol 1,3-
Butadiene Acetaldehyde Formaldehyde 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 Nonroad a 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 acro ss 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% -2,869,8 42 -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 distributi on 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 emi ssions 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 emissi on impact of displaced petroleum is estimated, using the same domestic/import shares discussed in chapter 2.

In general the basis for this life cycle evalua tion was the analysis conducted as part of the Renewable Fuel Standard (RFS1) rulemaking, but enhanced signif icantly. While our approach for the RFS1 was to rely heavily on the “Gree nhouse Gases, Regulated Emissions, and Energy Use in Transportation” (GREET) model, develo ped 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 great ly 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 ne w 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 th e 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 tim e 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 factor s for ethanol and the following ai r toxics were added: benzene, 1,3-butadiene, formaldehyde, acetal dehyde, 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 polluta nts, 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 refere nce case, since there was no agricultural modeling done for the RFS1 case. Due to the co mplex interactions invol ved in projections in agricultural modeling, it was not considered re asonable to attempt an y sort of proportional adjustments to the AEO 2007 agricultural projecti ons to approximate the RFS1 case.

The fuel production and distri bution impacts of today's rule on VOC are mainly due to increases in emissions connected with biofuel pr oduction, 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 producti on. Biofuel plants (corn and cellulosic) tend to have greater combustion emi ssions 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 substa ntial PM increases are also associated with43 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 emissi ons 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 ac rolein increases are smaller, on the order of 0.4-1 percent. There are also very small decr eases in benzene, 1,3-butadiene and naphthalene
relative to the US total emissions.

Table 3.2-4. Fuel Production and Distri bution 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 Distri bution 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 emissi ons by where they occur in the production/distribution chain is show n in Table 3.2-6. The displaced gasoline line of this table refers to the impacts of decreasing the petrol eum 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 Fu el 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 Acetaldehyde Formaldehyde Naphthalene b 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 vehi cle 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-r oad 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 ar e 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 Im pacts 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 Im pacts 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% -3,005,5 00 -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 vehicl e exhaust emission values were generated by MOVES, while the NMIM model was used to gene rate 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 et hanol 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 ga soline 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, etha nol, benzene, and naphthalene.49 Table 3.2-10. Vehicle and Equipment Emission Inventory Impacts by Source Type for the Primary RFS2 Cont rol (mid-ethanol) Case Relative to the AEO 2007 Reference Case (annual short tons)

VOC a 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 0.050 Table 3.2-10
continued Benzene Ethanol 1,3-
Butadiene Acetaldehyde Formaldehyde Naphthalene 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 va rious 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 fu el production/distribution impacts.51 Table 3.2-11. Emission Inventory Impa cts by Type of Renewable Fuel for the Primary RFS2 Control Case Re lative 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 Acetaldehyde Formaldehyde Naphthalene 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 33152 

### 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 im pacts 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 thes e 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 analys is 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 th at 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 cellulosi c feedstocks. The final rule cas e 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 Us ed in Each Analysis (Bgal/year in 2022)

Ethanol Analysis 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 diffe rences 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/A b 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/A b 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 inve ntories 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%

Ethanol a -- -- -- -- -- 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 th e 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 of56 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 por tion of the air quality modeling inventory
differs from the method used to calculate th e 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, cr ude oil production and tr ansport, and gasoline refining and distribution. A more deta iled description of these methodological differences is provided in section 3.3.2 of this RIA.

• The downstream portion of the air quality m odeling 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 pres ented 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 substan tial reduction of cellulosic biofuel plant energy requirements to account for the por tion 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 emissi on 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 tr ucks (meaning no impact for NOx or NMHC).

• The FRM downstream inventory uses update d estimates of evaporative permeation impacts of E10 based on recent studies.

3.3.2 Detailed Explanation of Inventory Differences

This section describes how th e county-level emission inventorie s were prepared for use in air quality modeling, and how they differ from th e NPRM nationwide inve ntories and the final
rule nationwide inventories. Ai r quality modeling requires much more detail and in some cases a57 very different method than estim ation of nationwide totals. Th e 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 adjustment s related to the affected stati onary (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 Lo sses During Ethanol Transport

For “upstream” emissions associated with fu el 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 transp ort, 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 de natured ethanol versus 9 psi for gasoline) and molecular weight (48.7 for denatured ethanol ve rsus 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/mmBT U 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 shortc oming of this methodology was that it did not account for differences between ethanol and ga soline 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 av erage 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 3.1-2158 (28.78 g/mmBTU) corresponds to the High Ethano l minus RFS1 reference case, and was used for calculation of VOC and ethanol vapor fo r all cases in this FRM analysis.

Table 3.3-5 summarizes the ethanol trans port 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

Mode a 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-42 76 10 psi gasoline emission rates adjusted for ethanol vapor pressure and molecula r 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 0.222 a

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 tr ansported 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 summari zed 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-miles 3 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 differen ce between 2020 and 2022 was not considered to be important for this analysis. As described elsewh ere, it was later discovered that an error had been introduced during the data handling

For the FRM analysis we used the final se t 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 m iles 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-mile s of goods, which equates to 2.38 gallons per thousand
ton-miles.61 analysis. This value was calculated by divi ding 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 ca se inventory accounts for growth and the current Tier 1 NOX controls for C3 engines and was de veloped 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 freig ht ton emission factors (EFs) by port were developed by dividing the emissi ons for each port by the corresponding commodity
tonnage. 78 The projected imported etha nol 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 contro l 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 Cate gory 2 (C2) propulsion engines (i.e., harbor craft), calendar year 2022 emissions by county we re 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 th is source category and the absence of a countylevel 2022 inventory, the 2020 inventory was used fo r this analysis. A ton per ton-mile EF for each pollutant was then developed. For the num erator, the national leve l 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 emissi ons for the two reference cases and the RFS2
control case.

For the FRM analysis we used the final se t 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 Ethanol62 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-mile s of tank truck ethanol tran sport 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 h eavy-duty diesel trucks were adjusted in the affected counties in proportion to their VMT incr ease. 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 em ission 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 locat ion. 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 emi ssion inventories we tr eated 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 planne d 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. 87,88,8963 Significant updates have been ma de to emissions from cellulosic plants, in part to reflect
the assumed shift in volumes from cellulosic etha nol to diesel between the proposed and final rules. In addition, after the air quality mode ling 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 cellulo sic 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 resu lts 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 incl ude any changes in its projections to 2022. That inventory was used for both the RFS1 a nd 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 de scribed 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 inventor ies 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 ca lculate absolute ton changes in each county we have attempted to minimize the i nventory distortions that could o ccur if the calculated change for a given county was out of proportion to the refe rence 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 be tween 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. 9064 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 modelin g 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 mode st decrease projected fo r the proposal and air quality analyses.

3.3.2.1.5 Calculation of Pe troleum 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 wa s projected to come from domestic refineries, and (b) 33.1% of the change in crude being us ed by domestic refineries would be domestic crude. Thus, using our assumption that 1.0 gall on less of gasoline equate s to approximately 1.0 gallon less crude throughput, the reduction in cr ude extraction and trans port 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 mode ling if they had been accounted for.65 Table 3.3-10.
Domestic Crude Oil Volume Reduction s Associated with RFS2 in 2022

Scenario Gasoline Volume Reduction (Bgal) Domestic Crude Reduction (Bgal)

RFS2 vs RFS1 18.5 4.6 AQ Modeling

RFS2 vs AEO 14.2 3.5

RFS2 vs RFS1 17.3 4.3 Final Rule: High
Ethanol Case RFS2 vs AEO 13.3 3.3

RFS2 vs RFS1 10.0 2.5 Final Rule: Mid
Ethanol Case RFS2 vs AEO 5.9 1.5

RFS2 vs RFS1 6.9 1.7 Final Rule: Low
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 emi ssions, 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 adju stments 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 upst ream impacts spreadsheet model to calculate the absolute change in tons for each stage of the upstream inventory.

3.3.2.1.7 Calculation of Gaso line Transport, Storage and Di stribution 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 quan tity of vapor losses dur ing the transport and distribution of gasoline and gasoline-ethanol bl ends. The analysis of these impacts was separated into two segments: refinery to bulk te rminal (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 engine s (onroad and nonroad). Thus the transport of66 E0 gasoline would only occur between refineries a nd 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 tr ansport stage combinations, nationwide VOC impacts (ton deltas) (and benzene and ethanol vapor) were calculated using EPA’s upstream impacts spreadsheet model for each control scen ario (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, st orage, 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 cas e 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 an alyses 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 conduc ted 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 re lative to E0 for two pollutants: ethanol and acetaldehyde, for which data suggests the effects are more certain. For the “more sensitive67 case” presented in the NPRM, and used in the air quality modeling, we had estimated changes to additional pollutants (including significant PM reductions) base d 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 pl us several modificati ons to the code and to the database. The MOVES runs produced emi ssion factors at the state-month level for all onroad gasoline vehicles except motorcycles. On road gasoline inventorie s were generated by multiplying MOVES emission factors by VMT developed for the Office of Air Quality Planning and Standards's 2002 Version 3 Modeling Plat form93 and used in the recently published
Locomotive-Marine Rule. 94 The MOBILE6 Model (M6203ChcOxFixNMIM, a special version to include cold-start VOC) and the NONROA D Model (NR05d-Bond-Final) were run using NMIM (NMIM20071009) with database NCD20 080724. NMIM supplied emissions from the NONROAD Model and from onroad sources not pr oduced 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 NM IM. 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 mode ling inventories and the NPRM and FRM
inventories are due to differences in MOVES code and database vers ions, 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 qua lity 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 gasolin e equipment were developed by running the National Mobile Inventory Model (NMIM) fo r 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 Secti on 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-Igniti on 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 sa me NCD and version of the NONROAD Model
described above. The version of MOBILE wa s MOBILE6.2. Diesel fuels are less fully68 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 de veloped by applying ratios of the aggregate MOVES toxic exhaust, evapora tive 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 speciati on from light-duty gasoline vehicles. Ethanol inventories for the control case were develope d by applying ratios of the aggregate MOVES ethanol exhaust, evaporative and refueling emi ssions 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 di scussed 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 we re 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 analys is 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 in clude the use of renewable fuel s: 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 roughl y 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 constitute69 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 fr om 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 volum es. In addition, Section 5.4 describes the methodology for calculating monetized benefits du e to reductions in adverse health effects
associated with PM 2.5 and ozone.

3.4.1 Air Quality Modeling Methodology

Air quality models use mathematical and numer ical 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 wide ly 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 detailedinformation 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 limita tions. Further discussion of the modeling methodology is included in the Air Quality Mo deling 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 PM 2.5 concentrations, 24-hour PM 2.5 concentrations, 8-hour oz one concentrations, air toxics concentrations, and nitrogen and sulfur deposition levels fo r 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-relate d tools and data that pr ovide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to c onstruct this platform includes em issions and meteorology for 2005. The platform was developed by the U.S. EPA’s O ffice of Air Quality Planning and Standards in
collaboration with the Office of Research and De velopment and is intended to support a variety of regulatory and research model applications and analyses.

The CMAQ modeling system is a non-proprieta ry, 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 precurs ors, primary and secondary PM concentrations, acid deposition, and air toxics, over regional and ur ban 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 us ed in numerous nationa l and international applications. 102,103,104 This 2005 multi-pollutant modeling platform used the latest publiclyreleased CMAQ version 4.7 4 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 si mulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase and particle-p hase 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) m echanism to include chemistry of isoprene, sesquiterpene, and aged in-cloud biogenic SOA in addition to terpene; (2) improved vertical convective mixing; (3) improved he terogeneous reaction involving n itrate formation; and (4) an updated gas-phase chemistry mechanism, Carbon Bond 05 (CB05), with extensions to model explicit concentrations of air toxic species as we ll as chlorine and mercury. This mechanism, CB05-toxics, also computes concentrations of sp ecies 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-r eview 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 si mulations of the Pennsylvania St ate University/National Center for Atmospheric Research Mesoscale Model 105 for the entire year of 2005 over model domains that are slightly larger than those shown in Figu re 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 grid s 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: horizonta l 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 chem istry model, the GEOS-CHEM model. 108 The global GEOS-CHEM model simulates atmospheric chem ical and physical processes driven by
assimilated meteor ological 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 predic tions were used to provide one-way dynamic72 boundary conditions at three-hour intervals and an initial concentration field for the 36 km CMAQ simulations. The future base conditions fr om 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 performan ce evaluation for ozone, PM 2.5 and its related speciated An operational model performan ce evaluation for ozone, PM 2.5 and its related speciated components (e.g., sulfate, nitrate, elemental carbo n, 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/pred icted pairs of daily/monthly/seas onal/annual concentrations. Statistics were generated for the following ge ographic groupings: dom ain 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 PM 2.5 model applications fo r other, non-EPA studies. 6 Overall, the performance for the 2005 modeling plat form 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 histor ical concentrations of ozone and PM 2.5 over the land with low amounts of bias and error. Model predic tions 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 rulema king, the CMAQ modeling system was used to calculate daily and annual PM 2.5 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 South east (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 re ferred 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 invent ories 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 presen ted 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 observati ons to estimate daily and annual PM 2.5 concentrations, and 8-hour ozone concentrations for each of the 2022 scenarios. The ambient air quality observations are average conditions, on a site-b y-site basis, for a period centered around the model base year (i.e., 2003-2007).

The projected daily and annual PM 2.5 design values were calculated using the Speciated
Modeled Attainment Te st (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 PM 2.5 and its non-carbon components. This
characterization of PM 2.5 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 PM 2.5 mass and the characterized chemical compone nts derived from routine specia tion 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 PM 2.5 species components: sulfates, nitrates, ammonium, organic car bon mass, elemental carbon, crusta l, water, and blank mass (a
fixed value of 0.5 μ g/m 3 ). More complete details of the SMAT procedures can be found in the report "Procedures for Estimating Future PM 2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)". 109 For this latest analysis,
several datasets and te chniques were updated. These changes are fully described within the technical support document for the Sma ll 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 attain ment demonstrations. 111

Additionally, we conducted an analysis to co mpare the annual and seasonal, absolute and percent differences between the 2022 control cas e and the two 2022 reference cases for nitrate74 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 NO X , VOC, CO, PM 2.5 , SO 2 , NH 3 , ethanol and five air toxics: benzene, 1,3-but adiene, formaldehyde, acetaldehy de, and acrolein. Ethanol and the five air toxics are explicit model sp ecies in the CMAQv4.7 model with carbon bond 5 (CB05) mechanisms. 112 Emissions of all the pollutants incl uded in the rule inventories, except ethanol, were generated using the Motor Vehi cle 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 mo deling were based on speciation of VOC using different ethanol profiles (E0, E10, a nd 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 ther efore also requires inventories for a large number of other air toxics and precursor pollu tants. Inventories for toxic pollutants not estimated using MOVES and MOBILE6 ratios were developed by running the National Mobile Inventory Model (NMIM). Emissions of othe r 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 type s; 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 (CO 2 ) and water (H 2 O). Incomplete combustion results in the production of other air pollutants, such as acetaldehyde and other aldehydes, and the release of unburned etha nol. Ethanol is also present in evaporative emissions. In the atmosphere, ethanol from unburned fuel and evaporative emissions can undergo photodegradation to form aldehydes (aceta ldehyde and formaldehyde) and peroxyacetyl nitrate (PAN), and also plays a role in ground-le vel ozone formation. Mechanisms for these reactions are included in CMAQ. Additionall y, other aromatic hydrocarbons (AHC) and hydrocarbons are considered because any increa se 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 NO 2 and formation of nitric acid (HNO 3 ) without PAN formation. 7

NO 2 +

· OH + M → HNO 3 + M k = 1.19 x 10 -11 cm 3 molecule -1 s -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 NO 2 , which again leads to ozone fo rmation, are also included. Atmospheric reactions and chemi cal mechanisms involving severa l key formation pathways are discussed in more detail in the following sections.

3.4.1.2.1 Acetaldehyde

Acetaldehyde is the main phot odegradation 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 O 2 to form the acetyl peroxy radical [CH 3 C(O)OO

· ]. 8 This radical species can then further react with nitric oxide (NO), to produce formaldehyde (HCHO), or with nitrogen dioxide (NO 2 ), to produce PAN [CH 3 C(O)OONO 2 ].
An overview of these reactions and the corr esponding reaction rates are provided below. 9

CH 3 CHO +

· OH → CH 3 C

· O + H 2 O k = 1.5 x 10 -11 cm 3 molecule -1 s -1 116

CH 3 C

· O + O 2 + M → CH 3 C(O)OO
· + M

CH 3 C(O)OO
· + NO → CH 3 C(O)O

· + NO 2 k = 2.0 x 10 -11 cm 3 molecule -1 s -1 117

CH 3 C(O)O

· → · CH 3 + CO 2

· CH 3 + O 2 + M → CH 3 OO

· + M

CH 3 OO

· + NO → CH 3 O

· + NO 2

CH 3 O

· + O 2 → HCHO + HO 2

CH 3 C(O)OO
· + NO 2 + M → CH 3 C(O)OONO 2 + M k = 1.0 x 10 -11 cm 3 molecule -1 s -1 118

Acetaldehyde can also photolyze (h ν ), which predominantly produces

· CH 3 and HCO:

CH 3 CHO + h ν → · CH 3 + HCO λ = 240-380 nm 119

As mentioned above,

· CH 3 is oxidized in the atmosphere to produce formaldehyde (HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, formaldehyde undergoes photolysis a nd reaction with the OH radical, NO 3 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 10 6 molecule cm -3 ; for NO 3 radical reactions, a 12-hour nighttime average of 5 x 10 8 molecule cm -3 ; and for ozone, a 24-hour average of 7 x 10 11 molecule cm -3

.76 Formaldehyde is removed mainly by photolysis wh ereas the higher aldehydes, those with two or more carbons such as acetaldehyde, react pre dominantly with OH radicals. The photolysis of formaldehyde is a source of additional radica ls, and as shown above, these radicals can react
with NO 2 to form PAN in the atmosphere.

HCHO + h ν → H + HCO λ = 240-360 nm 120

CB05 mechanisms for acetaldehyde formati on 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
mechanism 121,122 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 ca n photolyze in the presence of sunlight or react with molecular oxygen (O 3 (P)), hydroxyl radical (OH), or nitrate radicals. Of these reactions, both photolysis and reaction with OH are the mo st 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 fo rmed from photochemical reactions is tracked separately from that which is due to direct emi ssion 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 depe nding on characteristics of the ambient air, but alkenes in particular are found to play a large ro le. The IOLE model species, which represents internal carbon-carbon double bonds, has high emissions and relatively high yields of acetaldehyde. The OLE model species, represen ting terminal carbon double bonds, also plays a role because it has high emissions although lower acetaldehyde yields. Production from peroxyproprional nitrate and other peroxyacyln itrates (PANX) and aldehydes with 3 or more carbon atoms also play an important role. Thus , the amount of acetalde hyde (and formaldehyde as well) formed in the ambient air as well as em itted 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 (CH 3 CH 2 OH) with OH is slower th an some other important reactions but can be an important source of acet aldehyde if the emissions are large. Based on kinetic data for molecular reactions, the only im portant chemical loss process for ethanol (and other alcohols) is reaction with the hydroxyl radical (

· OH). 125 This reaction produces acetaldehyde (CH 3 CHO) 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 CH 3 CH 2 OH +

· OH → CH 3 C

· HOH + H 2 O k = 3.2 x 10 -12 cm 3 molecule -1 s -1 127

CH 3 C

· HOH + O 2 → CH 3 CHO + HO 2

In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. Itassumes the majority of the reaction occurs through H-atom abstraction of the more weaklybonded methylene group, which reacts with oxyg en to form acetaldehyde and hydroperoxy radical (HO2), and the remainder of the reacti on occurs at the –CH3 and -OH groups, creating formaldehyde (HCHO), oxidizing NO to NO2 (re presented 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 resear ch described below has led to implementation of new pathways for secondary organic aeroso l (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 origina ting from aromatic oxidation under low-NO

x 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 pa rticle phase. Current research suggests SOA contributes significantly to ambient organic aero sol (OA) concentrations, and in Southeast and Midwest States may make up more than 50% (alth ough the contribution varies from area to area) of the organic fraction of PM 2.5 during the summer (but less in the winter). 130,131 A wide range of laboratory studies conducted over the past twen ty years show that anthropogenic aromatic hydrocarbons and long-chained alkanes, along with biogenic isoprene, monoterpenes, and sesquiterpenes, contri bute 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 sour ces, 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 an thropogenic SOA. Other aromatic compounds contribute as well, but the extent of their contribution has not yet been quantified. Mobile78 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 onroa d and nonroad mobile sources accounted for 70% (2.24 μ g/m 3 ) of the total average nationwide ambient concentration of toluene (3.24

 μ g/m 3 ), when the contribution of the es timated “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 toluen e 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 leve ls of other high-octane components such as aromatics including toluene (which is the majo r 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 fo rm 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 Resear ch and Development’s National Exposure Research Laboratory recently directed experiments to investigate ethanol’s SOA forming potential. 141 The experiments were conducted under conditions wher e 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 poten tially made. As expected, no SOA was produced. From these experiments, the upper limit for the ae rosol 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 identif ying 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 (o r solely) from SOA formation. In particular, the contributions of anthropogenic SOA sources, including those of ar omatic precursors, are required to determine exposures and risks associat ed with replacing fossil fuels with biofuels.

Upon release into the atmosphere, numer ous 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 compounds79 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 ambien t 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 NO

x , collecting the SOA produced on filters, and th en 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 variet y 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 estim ates, 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

PM 2.5 samples are two tracer compounds that have been identified in smog chamber aromatic SOA samples. 151 To date, these aromatic tracer co mpounds 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, ha ve not yet been studied in this way. It is possible that these unstudied compounds produce SOA species whic h are being used as tracers for other VOCs. This means that the presen t 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 tr acers 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 anth ropogenic and biogenic in teraction, important to EPA and others, needs further study. The issue of SOA forma tion from aromatic precursors is an important one to which EPA and others are payi ng significant attention. For benzene, smog
chamber studies show that benzene form s SOA possibly through reactions with NO

x . 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 NO

x to form SOA with maximum mass of SOA being 8-25% of the mass of80 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 fr actions 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 concen tration estimates were 0.15, 0.18, 0.13, 0.15, and 0.19 μ g carbon/m 3 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 c oncentrations 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 μ g/m 3 associated with present toluene levels in the ambient air in these Midwest cities. Based on preliminary analysis of recent laboratory expe riments, it appears the
toluene tracer could also be formed dur ing photooxidation of some of the xylenes. 156

Over the past decade a variety of modelin g 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 cont ributions, 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 cont ribute therefore to ground-level ozone formation.
PAN is a reservoir and carrier of NO

x and is the product of acetyl radicals reacting with NO 2 in the atmosphere. One source of PAN is the phot ooxidation 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

CH 3 C(O)OONO 2 + M → CH 3 C(O)OO

· + NO 2 + 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 NO 2 is released in the thermal decomposition of PAN.

NO 2 can also be formed in photodegradation reactions where NO is converted to NO 2 (see OH radical reaction of acetaldehyde in Section 3.4.1.2.1). In both cases, NO 2 further photolyzes to produce ozone (O 3 ).

NO 2 + h ν → NO + O( 3 P) λ = 300-800 nm 164

O( 3 P) + O 2 + M → O 3 + M

The temperature sensitivity of PAN allows it to be stable enough at low temperatures to be transported long distances be fore decomposing to release NO 2 . NO 2 can then participate in ozone formation in regions remote from the original NO

x 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 th e understanding that there are considerable uncertainties in inventories, atmos pheric processes in CMAQ, and other aspects of the modeling process. While it is beyond the sc ope 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 fina l 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 PM 2.5 from vehicles. The emission impacts and air quality results would be different if, instead of E85, more non-ethanol biofuels are us ed 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 signif icantly lower than what we assumed in the
interim inventories. Furthermor e, the final emission inventorie s do not include vehicle-related PM reductions associated with E85 use, as discu ssed in Section 3.1 and 3. 3 above. Moreover, as discussed in Section 3.3, differences in estim ates of renewable fuel transport volumes and distances between the air quality modeling and fina l 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 PM 2.5 emissions from locomotive engines

After the air quality modeling was completed, we discovered an error in the way that

PM 2.5 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.ht ml). 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 deve lop 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 PM 2.5 emissions. The error in locomotive

PM 2.5 inventory impacts for the RFS2 control case versus the RFS1 mandate reference case was 111 tons, out of a total PM 2.5 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 PM 2.5 inventory impact of about 16,000 tons. It is important to note that the total nationwide PM 2.5 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, a nd in a number of locations, dominated PM 2.5 impacts. These impacts are summari zed in a memorandum to the docket. 14 As a result of the error, we do not present the modeling results for specific localized PM 2.5 impacts. However, we have concluded that PM 2.5 modeling results are still
informative for national-level benefits assessm ent, as described in Section 5.4 of the

RIA.

• Sensitivity of light-duty vehicle exhaust emissions to ethanol blends

As discussed above in Secti ons 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 invent ories used for air quality modeling assume that vehicles operating on E10 have high er 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 ai r 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 s hown 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 consiste nt 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 FR M inventories. For example, E85 exhaust and evaporative emissions da ta 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 co rn 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 mo st 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 o ccur. 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 bi ofuel 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 inventorie s for upstream sources and consequently can impact air quality modeling results.

3.4.1.3.2 Uncertainties in Hy drocarbon Speciation Profiles

Another source of uncertainty involves th e hydrocarbon speciation profiles, which are used in the air quality modeling emission pre-pr ocessor, 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 adjustin g 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 da tabase 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. Alkenes84 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 Ti er 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 database 168 ) 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 indicat e that ethanol forms an azeotrope with various hydrocarbon compounds such as olefins. 169,170 That azeotrope for olefins w ould 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 ofResearch and Development (ORD) to compare differences between an E0 fuel and a splashblended E10. 171 The addition of 10% ethanol to the ba se E0 fuel only slightly decreased the
olefin content from 7.6% to 6.3% of total VOC obs erved 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 cons traints, 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 ad justed profile introduces inconsistencies between
the reference and control cases. It should be not ed that this is but one example of potential weaknesses in the emission speciation data. Pr ofiles 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, form aldehyde, acrolein, and 1,3-butadiene can be formed secondarily through atmospheric chemi cal 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 th e 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 a ssociated 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 fo rm SOA, have not yet been studied in detail. In addition, the amount of ambient SOA that co mes 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 inventor y 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 unders tanding the potential differences between RFS2
volumes of ethanol and AEO 2007 or RFS1 mand ate 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 lim itation detailed in S ection 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 Secu rity 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, NO X , SO X , CO and air toxics. Air quality modeling performed for this final rule illustrates the changes in ambient concentrations of PM 2.5 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 renewa ble 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 PM 2.5 locomotive inventory (Section 3.4.1.3) and theref ore we do not present the modeling results for specific localized PM 2.5 impacts.86 3.4.2.1.1 Particulate Matter (PM 2.5 and PM 10 )

As described in Section 3.5, PM causes a dverse 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 sn apshot of annual and 24-hour PM 2.5 concentrations in
2007. There are two U.S. National Ambien t Air Quality Standards (NAAQS) for PM 2.5 : an annual standard (15 μ g/m 3 ) and a 24-hour standard (35 μ g/m 3 ). In 2007, the highest annual average PM 2.5 concentrations were in California, Ar izona, Alabama, and Pennsylvania and the highest 24-hour PM 2.5 concentrations were in Ca lifornia, Idaho, and Utah.

Figure 3.4-2. Annual Average PM 2.5 Concentrations in μ g/m 3 for 2007 (from 2008 Air Trends Report)87 Figure 3.4-3. 24-hour (98 th percentile 24- hour concentrations) PM 2.5 Concentrations in

μ g/m 3 for 2007 (from 2008 Air Trends Report)

The most recent revisions to the PM standa rds were in 1997 and 2006. In 2005, the U.S. EPA designated nonattainment areas for the 1997 PM 2.5 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 PM 2.5 National Ambient Air Quality Standard (NAAQS). These PM 2.5 nonattainment areas are comprised of 208 full or par tial counties.
Nonattainment areas for the 1997 PM 2.5 NAAQS are pictured in Figure 3.4-4. On October 8, 2009, the EPA issued final nonattainment ar ea designations for the 2006 24-hour PM 2.5 NAAQS (74 FR 58688, November 13, 2009). These designa tions include 31 areas composed of 120 full or partial counties with a population of over 70 million. Nonattainment areas for the 2006 PM 2.5 NAAQS are pictured in Figure 3.4-5. In total, there are 54 PM 2.5 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 PM 2.5 Nonattainment Areas89 Figure 3.4-5. 2006 PM 2.5 Nonattainment Areas90 As of November 13, 2009, approximately 26 million people live in the 47 areas that are designated as nonattainment for the PM 10 NAAQS. There are 40 full or partial counties that make up the PM 10 nonattainment areas. Nona ttainment areas for the PM 10 NAAQS are pictured in Figure 3.4-6.

Figure 3.4-6. PM 10 Nonattainment Areas

3.4.2.1.1.2 Projected Levels of PM 2.5

Generally, our modeling indicates that the required renewable fuel volumes will reduce

PM 2.5 concentrations in some areas of the country and increase PM 2.5 concentrations in other areas. In the following secti ons we describe projected PM 2.5 levels in the future, with and without the required renewabl e 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 PM 2.5 Levels without RFS2 Volumes

EPA has already adopted many mobile s ource emission control programs that are expected to reduce ambient PM levels. These control programs include the New Marine91 Compression-Ignition Engines at or Above 30 L iters per Cylinder rule (XX FR XXXX, DATE), the Marine Spark-Ignition and Small Spark-I gnition 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 Highwa y Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier 2 Motor Vehicle Emissi ons 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 PM 2.5 NAAQS in the future is expected to decrease. However, even with the implementation of all current state and federal regul ations, there are projecte d to be U.S. counties violating the PM 2.5 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 PM 2.5 standard of 15 μ g/m 3 and 47 counties with a population of over 53 million may not attain the
2006 24-hour PM 2.5 standard of 35 μ g/m 3

. 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 PM 2.5 NAAQS, the projected em ission changes will impact state and local agencies in their effort to attain and maintain the PM 2.5 standard.

3.4.2.1.1.2.2 Projected PM 2.5 Levels with RFS2 Volumes

This section includes a summary of the results of our modeling of PM 2.5 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 PM 2.5 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 PM 2.5 concentrations due to require d renewable fuel volumes are a result of changes to upstream and downstream emission sources, complex chemical reactions (direct emissions and secondary formation), transpor t and meteorology. As is detailed in Section 3.2, the required renewable fuel volumes impact upstream and tailpipe emissions of primary

PM 2.5 and PM 2.5 precursors such as NOx and VOCs. Primary PM is emitted directly into the atmosphere and, on a mass basis, is largely carbon aceous in nature. Various studies have shown that mobile sources are a major source of primar y 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 seconda ry 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

PM 2.5 design value. A large majority of the modeled counties will see relatively minor annual average PM 2.5 design value changes of between -0.05 μ g/m 3 and +0.05 μ g/m 3 . On a populationweighted basis, the average modeled future-year annual PM 2.5 design values are projected to decrease by 0.002 μ g/m 3 when compared with the RFS1 mandate or AEO reference case. 18 We also looked at changes in daily PM 2.5 design values. A majority of the modeled counties will see daily PM 2.5 design value changes of between -0.25 μ g/m 3 and +0.25 μ g/m 3 . On a populationweighted basis, the average modeled future-year daily PM 2.5 design value is projected to decrease by 0.06 μ g/m 3 when compared with the RFS1 mandate reference case or 0.05 μ g/m 3
when compared with the AEO reference case.

The changes in ambient PM 2.5 described above are likely du e to both increased emissions at biofuel production plants and from biofuel transport, and redu ctions in SOA formation and reduced emissions from gasoline refineries. In ad dition, 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 pr eviously, 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, Conn ecticut, 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 PM 2.5 for design values differs from the change in national populationweighted annual average PM 2.5 discussed in Sections I and VIII of the preamble and Chapter 5 of the RIA. National population-weighted annual average PM 2.5 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 PM 2.5 across all populated grid cells in the air quality modeling domain.93 Figure 3.4-7. Ozone Concentrat ions (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, th ere 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 pub lic 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 susp ended 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 POPULATION a

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 NAAQS b 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. Howeve r, ozone air quality improvements are projected in a few highly-populated areas which currently ha ve 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 me thodology is contained in Section 3.4.1. Additional detail can be found in the air qua lity 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 cont rol 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-Igniti on Engine rule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule (73 FR 25098, May 6, 2008), th e 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 Dies el 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 th ere are a number of counties across the U.S.96 projected to experience ozone c oncentrations 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 disc uss 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 m odeling 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 va lue 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 Scenario98 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 de sign value is in Morgan County, Alabama, 1.56 ppb and 1.27 ppb when compared with the RF S1 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 oz one design value decreases greater than 0.5 ppb are in Southern
California. The maximum decrease projected in an 8-hour ozone de sign 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 change s 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 Desi gn 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 wi th 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 sh ow 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 s how a decrease in 8-hour ozone design values. On a populationweighted basis, the average modeled future-yea r 8-hour ozone design values are projected to increase by 0.28 ppb in 2022 when compared with th e RFS1 mandate reference case and increase by 0.16 ppb when compared with the AEO 2007 reference case. On a population-weighted basis100 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 Project ed Future Year 8-hour Ozone Design Value as a Result of the Required Renewable Fuel Volumes

AVERAGE a NUMBER
OF US

COUNTIES 2020

POPULATION b 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 the101 emissions changes due to the increased volumes of renewable fuels combined with the photochemistry involved, the diffe rent 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, relativel y 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.” Be cause 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 relativ ely 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 s outhern 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, th e 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 wh ere 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 Nati onal-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 m obile 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 re sulted 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 to xics 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 require d 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 identif ied as national and regional-scale cancer and
noncancer risk drivers in the 2002 NATA 188 and were also likely to be significantly impacted by the renewable fuel volumes required by RF S2. 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 ma ny mobile source emission control programsthat are expected to reduce ambient air toxics levels. These control programs include the Heavyduty Onboard Diagnostic Rule (74 FR 8310, Februa ry 24, 2009), Small SI a nd Marine SI Engine Rule (73 FR 59034, October 8, 2008), Locomotiv e 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 Gaso line Sulfur Control Requirements (65 FR 6698, Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the104 future is expected to decrease. The refere nce 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 th e 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 decrea ses in concentrations of various magnitudes. We also estimated aggregated populations a bove and below reference concentrations for
noncancer effects.

Our discussion of the air quality modeling for ai r 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 s ubstantial 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 concen trations of acetaldehyde are less than 1% for most of the country (Figure 3.4-13). Several urba n 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 concentrati ons of acetaldehyde are generally less than 0.1

μ g/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 Ch ange in Concentration Between the RFS1
Mandate Reference Case and the RFS2 Control Case in 2022106 Figure 3.4-14. Acetaldehyde Annual Absolu te Changes in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 ( μ g/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 planne d plants, whereas cellulosic ethanol plants were projected based on available feedstocks. Details on how this was done ar e 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 Sect ion 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 discu ssed in more detail in Section 3.4.3.1, some previous U.S. monitoring studies have suggested an insignificant or small107 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 pa st 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 th e conflicting results amon g past studies and the limitations of our analysis as discussed in the fo llowing paragraphs, considerable additional work is needed to address the impacts of the renewabl e 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 acetalde hyde 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 disc ussed in Section 3.4.1.3. Headspace profiles are used to speciate hydrocarbon emissions from gaso line 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 woul d expect as a result of increased ethanol use. In these cases, the E0 gasoline headspace profile has 13% of th e 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 pe rcent 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 th is 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 seconda rily 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 da y 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 presente d in the NPRM which assumed that use of E10
would lead to increases in NO

x 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 NO

x 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 Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3) 

### a b109 Figure 3.4-16. Winter Changes in Acetaldehyde Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3)

Formaldehyde

Our air quality modeling results do not show substantial impacts on ambient concentrations of formaldehyde from the renewabl e 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 formaldehy de 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 disc ussed 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 μ g/m3. 

### b a110 Figure 3.4-17. Formaldehyde Annual Percent Change in Concentration Between the RFS1
Mandate Reference Case and the RFS2 Control Case in 2022111 Figure 3.4-18. Formaldehyde Annual Percent Changes in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 ( μ g/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 plan ts, as discussed in Section 3.4.1.3.

It should be noted here that these increas es 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 Change s Change in Concentration Between the
RFS1 Mandate Reference Case and the RFS2 Control Case in 2022113 Figure 3.4-20. Ethanol Annual Absolute Change s 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 co ncentrations. 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 μ g/m3 (Figure 3.4-22).114 Figure 3.4-21. Benzene Annual Percent Chan ge in Concentration Between the RFS1
Mandate Reference Case and the RFS2 Control Case in 2022115 Figure 3.4-22. Benzene Annual Absolute Change s in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 ( μ g/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 resu lt of the renewable fuel volumes required this rule. Generally, decreases occu r 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 ov er 50% in several areas; but the changes in absolute concentrations of ambient 1,3- butadiene are generally less than 0.005 μ g/m3 (Figure 3.4-24). Annual increases in ambient concentrati ons of 1,3-butadiene are driven by wintertime rather than summertime changes (Figures 3.4-25 a nd 3.4-26). These increases appear in rural
areas with cold winters and low ambient leve ls 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 inve ntories. These data were based on tests using only three engines, wh ich showed significantly higher 1,3-butadiene116 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 Chan ge in Concentratio n Between the RFS1
Mandate Reference Case and the RFS2 Control Case in 2022117 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 ( μ g/m3)118 Figure 3.4-25. Summer Changes in 1,3-Butadiene Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3)

Figure 3.4-26. Winter Changes in 1,3-Butadiene Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3) 

### a b a b119 Acrolein

Our air quality modeling shows small region al increases and decreases in ambient
concentrations of acrolein as a result of the re newable fuel volumes required by this rule. As shown in Figure 3.4-27, decreases in acrolein co ncentrations 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 μ g/m3 with the exception of the increases associated with new ethanol plants. These increases can be up to and above 0.005 μ g/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 Mountai n region are driven by wintertime changes (Figures 3.4-29 and 3.4-30), and occur in the sa me 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 Chan ges Change in Concentration Between the
RFS1 Mandate Reference Case and the RFS2 Control Case in 2022120 Figure 3.4-28. Acrolein Annual Absolute Chan ges in Ambient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 ( μ g/m3)121 Figure 3.4-29. Summer Changes in Acrolein Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3)

Figure 3.4-30. Winter Changes in Acrolein Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b) Absolute Changes ( μ g/m3) 

### a b a b122 Population Metrics

To assess the impact of projected changes in air quality with increased renewable fuel use, we developed population metr ics that show population experien cing 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 et hanol, over 98% of the population (334,730,202 people) experi ences 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) expe riences acetaldehyde increases between 1 and 10%, and 0.48% of the population (1,642,944 people) experi ences formaldehyde increases between 1 and
2.5%.123136,28795,491108,6365128258,42065,57013,9691,615122,37862046,391 11,699 9,9366,7825,1182691714,697 43,717 65,8045,211 3,8463,8227,293 93520146,024165,60443,8674,00280,1562,568 85,793177,42845,12327,734 811 14452737 4,169214,9652,610117,155EthanolAcetaldehydeFormaldehydeBenzene1,3-ButadieneAcrolein≤-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≥+100Percent Change in Ambient Concentration from RFS1 Mandate to RFS2Population (in Thousands) Impacted by Changes in Annual AmbientConcentrations of Toxic Pollutants with RFS2.11.1.4.3Figure 3.4-31. Number of People Impacted by Changes inAnnual Ambient Concentrations or Toxic Pollutants byPercent Change Brackets, RFS 1 Mandate Reference Case Compared to the RFS2 Control Case124 The population exposed to average ambient conc entrations of air toxics above and below
reference concentrations for noncancer health e ffects 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 n ecessarily 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, averag e 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 populat ion-weighted nationwide annual average for acrolein is very close to the Rf C. Table 3.4-4 also shows the na tional population that is exposed
to ambient concentrations above and below the Rf C 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 Ai r Toxics above and
below Reference Concentrations for Noncancer Health Effects in 2022 without RFS2

Population-weighted
Concentration (Nationwide Annual Average in μ g/m3) National Population above RfC (Annual Average) National Population below RfC (Annual Average) CAS No.

RFS1
Mandate AEO 2007 RfC ( μ g/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

Ethanol a 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 ex posed to average ambient concentrations
of air toxics above and below reference concentr ations 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 above125 the RfC values. The results indicate there may be a reduction in the number of people exposed
to ambient concentrations of acr olein with RFS2 fuel volumes.

Table 3.4-5. Populations Exposed to Ambient Concentrations of Ai r Toxics above and
below Reference Concentrations for Noncancer Health Effects in 2022 with RFS2

Population-weighted
Concentration (Annual Average in μ g/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 Ni trogen and Sulfur

3.4.2.2.1 Current Levels of Nitrogen and Sulfur Deposition

Over the past two decades, the EPA has undert aken 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 co mpounds 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

NO X controls on power plants, motor vehicles an d fuels in the U.S. The data show that
reductions were more substan tial 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 or ganic 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. 197126 Figure 3.4-32. Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2004 -2006127 Figure 3.4-33. Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2004-2006128 3.4.2.2.2 Projected Levels of Nitrogen and Sulfur Deposition

Our air quality modeling does not show subs tantial overall nationwide impacts on the annual total sulfur and nitrogen deposition occurr ing 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 thes e 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 re ductions of nitrogen up to 2%.129 Figure 3.4-34. Percent Change in Annual Total Sulfur ov er the U.S. Modeling Domain
Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022130 Figure 3.4-35. Percent Change in Annual Total Nitrogen ove r 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 th at 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 referen ce 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 ov er the U.S. Modeling Domain
Between the RFS1 Mandate Reference Case and the RFS2 Case in 2022132 Figure 3.4-37. Percent Change in Annual Total Sulfur ov er the U.S. Modeling Domain
Between the AEO Reference Case and the RFS2 Case in 2022

3.4.3 Ambient Monitoring and Modeling Studie s of Ethanol’s Impacts on Air Quality

A number of ambient monitoring and modelin g 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 summari zes 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 mont hs 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 larg ely 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 wint er of 1987-1988 (95% of fuel blended with133 MTBE) through the winter of 1996-1997 (nearly all fu el 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 acetalde hyde 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 wh en 95% of the fuel was blended with MTBE. It was hypothesized that the photochemical pr oduction and destruction of these compounds suppressed the emissions effect. They conclude d 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 emissi ons. A field study was conducted in Albuquerque to determine the atmospheric impacts of the use of ethanol fuels. 204,205 Atmospheric concentrations of ozone,

NO

x , 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 wint er 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 a cetaldehyde, 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) concen trations were measured at lower levels and ranged from 0.33-1.04 ppb. This study conc luded 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 Mo del (UAM), was performed by the California
Air Resources Board in 1999. 207 Acetaldehyde and ethanol concen trations 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 NO 2 (0 to 1%) and peroxypropionyl nitrate (PPN) were essentially unch anged. 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 stud y also showed increases in unburned ethanol, PAN, and ozone for a future E8 5 scenario. The results of Jacobson et al.134 study differ from the results of our air quality mo deling analysis for a number of reasons. First, the scenario modeled in Jacobson et al. study woul d 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 fr om 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 NO

x emissions. In contrast, we modeled the “more sensitive” emission inventory case where NO

x emissions increased with greater use of E10 fuel. Increases
in NO X , 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, thes e studies do provide useful information on potential directional changes in pollutant leve ls with widespread ethanol use.

Brazil is the first country in the world wh ere 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 fl eet 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 co mposition 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 a nd 10.2 ppbv). In the morning, direct emission from vehicles seemed to be the main primar y 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 above135 those measurements available for U.S. cities, particularly Los Angeles, CA. The overall data
trend also showed levels of C 4 -C 9 n -aldehydes to be approximately 10 times higher than in Los Angeles. They conclude that the use of alc ohol-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 C 4 -C 11 n -alkanes were similar or slightly elevated in concentration compared to Los Angeles.

A study in Rio de Janeiro, Brazil some y ears 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 anot her 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

NO

x . This study concluded that aromatics and al kene 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 acetal dehyde to formaldehyde concen tration 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 approa ch (EKMA), researchers simulated ozone, formaldehyde, and acetaldehyde concentrati ons for the urban downtown area of Rio de
Janeiro. 216 The simulated ozone peak was in good ag reement 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 meas ured 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 ther e 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. Howeve r, 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 ev aporative emission standards as vehicles do in the U.S. These136 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 studi es 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 improveme nts 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 oz one 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 EPA 218 with two fuels. These studies were done on headspace vapors which are the hydrocarbon compounds formed by vaporization of hydrocarbon components from gaso line stored in closed (or semi-closed) containers such as fuel storage tanks or ta nker trucks. These emi ssions are different from exhaust emissions which include products of comb ustion. 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 ga soline with 10% ethanol. The runs showed that the photoxidation processes of ethanol results in higher levels of acetaldehyde. This photoxidation should also increase ac etone levels with the base gaso line 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 studi es to better improve the chemical mechanisms in air quality models to simulate what happe ns in the atmosphere with other emission components (exhaust, evaporative, other emi ssions) in actual atmospheric condtions (with meteorology, mixing conditions, temperature, con centrations, 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, NO X , SO X , carbon monoxide and air toxics. The renewable fuel requirements137 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 ma gnitude in size. Since 1987, EPA
has delineated that subset of i nhalable particles sma ll enough to penetrate to the thoracic region (including the tracheobronchial and alveolar regi ons) of the respiratory tract (referred to as thoracic particles). Current national ambient air quality standards (NAAQS) use PM 2.5 as the indicator for fine particles (with PM 2.5 referring to particles with a nominal mean aerodynamic diameter less than or equal to 2.5 μ m), and use PM 10 as the indicator for purposes of regulating
the coarse fraction of PM 10 (referred to as thoracic coarse particles or coarse-fraction particles; generally including particles with a nominal mean aerodynamic diameter greater than 2.5 μ m and less than or equal to 10 μ m, or PM 10-2.5 ). Ultrafine particles are a subset of fine particles, generally less than 100 nanometers (0.1 μ m) in aerodynamic diameter.

Particles span many sizes a nd shapes and consist of hundreds of different chemicals. Particles originate from sources and are also fo rmed 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 re action 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 concentra tion, meteorology, and temperature.

Fine particles are produced primarily by com bustion processes and by transformations of gaseous emissions (e.g., SO X , NO X and VOCs) in the atmosphere. The chemical and physical properties of PM 2.5 may vary greatly with time, region, meteorology and source category. Thus,

PM 2.5 may include a complex mixture of different po llutants including sulfates, nitrates, organic compounds, elemental carbon and metal compounds . These particles can remain in the atmosphere for days to weeks and travel thro ugh 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 Docu ment (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 PM 2.5 is associated with premature mortality from cardiopulmonary diseases, 223 hospitalization and emergency department visits for ca rdiopulmonary diseases, 224 increased respiratory symptoms, 225 decreased lung function 226 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 shor t term exposure to PM and health endpoints such as low birth weight, preterm birth, and ne onatal and infant mortality. 229

Among the studies of effects associated with short-term exposure to PM 2.5 , several specifically address the contribution of mobile sources to short-term PM 2.5 -related effects on premature mortality. The results from these studi es 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 me ntioned here. Analyses incorporating source apportionment by factor analysis with daily time- series studies of daily death rates indicated a relationship between mobile source PM 2.5 and mortality. 231,232,233,234 Another recent study in 14
U.S. cities examined the effect of PM 10 exposures on daily hospital admissions for
cardiovascular disease. This st udy found that the effect of PM 10 was significantly greater in areas with a larger proportion of PM 10 coming from motor vehicles, indicating that PM 10 from these sources may have a greater effect on the toxicity of ambient PM 10 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 PM 2.5 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 devel opment 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 qua lity data set for PM in the Six Cities Study, the fact that the study populations were similar to the general popul ation, and the fact that these studies have undergone extensive reanalysis. 239,240,241,242243,244 These studies indicate that thereare positive associations for all-cause, cardiopulmonary, and lung cancer mortality with longterm exposure to PM 2.5 . One analysis of a subset of th e ACS cohort data, which was published after the PM AQCD was finalized but in tim e for the 2006 Provisional Assessment, found a larger association than had previous ly been reported between long-term PM 2.5 exposure and mortality from all causes and cardiopulmonary di seases in the Los Angeles area using a new exposure estimation method that accounted for variations in c oncentration within the city. 245

As discussed in the PM AQCD, the morbidity studies that combine the features of crosssectional and cohort studies provi de 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 PM 2.5 and/or PM 10 on reduced lung function growth. 246 In another recent publication included in the 2006 Provisiona l Assessment, investigators in southern California reported the results of a cross-sectional study of outdoor PM 2.5 and a measure of atherosclerosis development in the Los Angeles basin. 247 The study found positive associations
between ambient residential PM 2.5 and carotid intima-media thickness (CIMT), an indicator of
subclinical atherosclerosis that is an unde rlying factor in cardiovascular disease.

3.5.1.2.3 Roadway-Related PM Exposure and Health Studies

A recent body of studies examines traffi c-related PM exposures and adverse healtheffects. 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 PM 2.5 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 PM 2.5 inside highway patrol cars on North Carolina state highways. 248 Other studies have found associ ations 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 e nvironment, including air toxics which are discussed in Section 3.4.5, and it is important to note that current studies do not identify a single140pollutant that is most associated with adverse health effects. Additional information on nearroadway health effects can be found in the recen t 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 NO X in the atmosphere in the presence of he at and sunlight. These pollutant s, 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 f acilities, and smaller area sources.

The science of ozone formation, transport, and accumulation is co mplex. 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 precu rsor emissions, resulting in elevated ozone
levels even in areas with low VOC or NO X emissions.

As mentioned above in Section 3.4.2.1.2, th e highest levels of ozone are produced when
both VOC and NO X emissions are present in significant quantities on clear summer days. Relatively small amounts of NO X enable ozone to form rapidly when VOC levels are relatively high, but ozone production is quick ly limited by removal of the NO X . Under these conditions

NO X reductions are highly effective in reducing ozone while VOC reductions have little effect.
Such conditions are called “NO X -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 NO X -limited.

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO) with ozone, forming nitrogen dioxide (NO 2 ); as the air moves downwi nd and the cycle continues,
the NO 2 forms additional ozone. The importance of th is reaction depends, in part, on the relative
concentrations of NO X , VOC, and ozone, all of which change with time and location. When

NO X levels are relatively high and VOC levels relatively low, NO X forms inorganic nitrates (i.e., particles) but relatively little ozone. Such c onditions are called “VOC-limited”. Under these conditions, VOC reductions are effe ctive in reducing ozone, but NO X reductions can actually increase local ozone under certain circumstances. Even in VOC-limited urban areas, NO X reductions are not expected to increase ozone levels if the NO X reductions are sufficiently large. Rural areas are usually NO X -limited, due to the relatively large amounts of biogenic VOC
emissions in such areas. Urban areas can be either VOC- or NO X -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 Ozone141 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 a nd 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-rela ted 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 Re search 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-re lated mortality should be include d 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 sc ientific 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 ex posures (6 to 8 hours) to ambient ozone concentrations have been linked to lung func tion 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 possi bly 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 ac tive 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 expos ure 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 repor ted 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 ch anges 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 (SO 2 ), a member of the sulfur oxide (SO X ) 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 (NO 2 ) is a member of the nitrogen oxide (NO X ) family of gases. Most NO 2 is formed in the air through the oxidation of nitric oxide (NO) emitted when fuel is burned at a high temperature. SO 2 and NO 2 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 ambien t PM. The health effects of ambient PM are
discussed in Section 3.5.1.2. NO X along with non-methane hydrocarbons (NMHC) are the two major precursors of ozone. The health effect s 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 SO 2 can be found in the U.S. Environmental Protection Agency Integrated Science Assessment for Sulfur Oxides. 27 SO 2 has long been known to cause adverse respiratory health effects, particularly among indi viduals 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 th ere is a causal relationship between respiratory health effects and short-term exposure to SO 2 . Separately, based on an evaluation of the epidemiologic evidence of associations between short-term exposure to SO 2 and mortality, the
EPA has concluded that the overall evidence is su ggestive of a causal relationship between shortterm exposure to SO 2 and mortality.

3.5.3.3 Health Effects of Nitrogen Oxides

Information on the health effects of NO 2 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 epidemiolo gic, controlled human exposure, and animal toxicological studies provide evidence that is sufficient to infer a likely causal relationship between respiratory effects and short-term NO 2 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 NO 2 exposure. First, the ISA
concludes that NO 2 exposure may enhance the sensitivity to allergen-induced decrements in lung function and increase the allergen-induced airw ay inflammatory response following 30-minute exposures of asthmatics to NO 2 concentrations as low as 0.26 ppm. In addition, small but significant increases in non-speci fic airway hyperresponsiveness were reported following 1-hour exposures of asthmatics to 0.1 ppm NO 2 . Second, exposure to NO 2 has been found to enhance the inherent responsiveness of the airway to subsequent nonspecific ch allenges in controlled human exposure studies of asthma tic subjects. Enhanced airw ay responsiveness could have important clinical implications for asthmatics since transient increases in airway responsiveness following NO 2 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 NO 2 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 cau sal relationship is so mewhat less certain than that associated with respiratory morbidity, NO 2 has also been linked to other health endpoints. These include all-cause (nonaccidenta l) mortality, hospital admissions or emergency department visits for cardiovascul ar 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), whic h 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, in cluding toxic effects on blood and tissues, and effects on organ functions. Although there are eff ective 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 beha vioral effects, including decrem ents in continuous performance
and reaction time. 289

Carbon monoxide has been linked to increased ri sk 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, a nd 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 othe r cardiovascular diseases ). Several studies in the United States and Canada have also reported an association between ambient CO exposures and frequency of cardiovascular hospital admissi ons, 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 noncan cer health effects. Th e population experiences
an elevated risk of cancer and other noncance r 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 ma tter (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 ri sk, and over 80 percent of the noncancer hazard. Benzene is the largest contributor to cancer risk of all 124 pollutants qua ntitatively assessed in the 2002 NATA and mobile sources were responsib le for 59 percent of benzene emissions in 2002. In 2007, EPA finalized vehicl e and fuel controls that address this public health risk; it will
reduce total emissions of mobile source air toxi cs by 330,000 tons in 2030, including 61,000 tons
of benzene. 295

Noncancer health effects can result from chronic, 30 subchronic, 31 or acute 32 inhalation exposures to air toxics, and include neurologi cal, 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, de rmal, 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 exposur e 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 human s 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 acu te lymphocytic leukemia and suggest a relationship between benzene exposure and ch ronic non-lymphocytic leukemia and chronic lymphocytic leukemia. The International Agen cy 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 be nzene as a known human carcinogen. 302,303

A number of adverse noncancer health eff ects 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 absolu te 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 occurr ing 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 scientif ic 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. 316146 3.5.5.3 Ethanol

EPA is conducting an assessment of the can cer 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 inha lation) to better characterize the health hazards and dose-response relationships for low levels of ethanol exposure in the environment.

The IARC has classified “alcoholic bevera ges” as carcinogenic to humans based on sufficient evidence that malignant tumors of the mouth, pharynx, larynx, esophagus, and liver are causally related to the consum ption of alcoholic beverages. 317 The U.S. DHHS in the 11 th Report on Carcinogens also identified “alcoholic beverages” as a kno wn 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 inhalati on 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 impa irment, developmental effects, ca rdiovascular effects, immune system depression, and effects on the liver, pancreas and reproductive system. 319 There is evidence that children prenatally exposed via mothers’ ingesti on 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 psychos ocial 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 le vels 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 malignanc ies 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 of147 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 sta tistically 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 dos imetric 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 stud y that formaldehyde’s direct mutagenic action is not relevant to the compound’s tumorigenici ty 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 evid ence for formaldehyde carcinogenicity as “sufficient,” based upon the data on nasopharyn geal cancers; the epidemiologic evidence on leukemia was characterized as “strong.” 341 EPA is reviewing the rece nt 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 hum an 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 th e 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 in filtration 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, a nd 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 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group148 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 te st) 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 carcino genic 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 th e animal data provided inadequate evidence of carcinogenicity. 351 The IARC determined in 1995 that acrol ein 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/m 3 ) 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 hamste rs have been observed after subchronic exposure to acrolein. Based on animal data, individuals with compro mised 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, cy togenetic studies indicate that PAN is not apotent 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 be en attributed to photochemical oxidants, including PAN. 358 Animal toxicological information on the inhalation149 effects of the non-ozone oxidants has been limite d to a few studies on PAN. Acute exposure to levels of PAN can cause changes in lung morphol ogy, 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 sour ces, 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 numbe r 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 opi nion 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 carcinog en" 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 a ssessment for naphthale ne, 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 re spiratory and nasal tissues. 364

3.5.6.9 N-Hexane

N-Hexane is associated with polyneuropath y in humans. Effects observed in rodents
include nasal lesions as well as neurotoxic effects. EPA has de veloped a reference concentration
of 700 μ g/m 3 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 c ontamination 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 in crease, though the agricultural impacts of this shifting of crop production domestically are anticip ated 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. A dditional information on pesticides and health effects is incl uded in Section 6.1 of this RIA.

3.5.6.11 Other Air Toxics150 In addition to the compounds described a bove, 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 impact ed 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 so me of the environmental effect s 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 PM 2.5 and secondary PM 2.5 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 emphasi s on protecting visibility in national parks
and wilderness areas. Section 169 of the Clean Ai r Act requires the U.S. Government to address existing visibility impairment and future visi bility impairment in th e 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. Na tional 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 ar eas since 1988. The monitoring network was originally established at 20 site s, but it has now been expanded to 110 sites that represent all but one of the 156 mandatory class I federal areas acro ss 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 aeroso l measurements at all sites, and optical and
scene measurements at some of the sites. Aerosol measurements are taken for PM 10 and PM 2.5 mass, and for key constituents of PM 2.5 , 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 component s. 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 to tal extinction). Scene characteristics are typically recorded three times daily with 35 millimeter photography and are used to determine the quality of visibility c onditions (such as effect s on color and contrast)associated with specific levels of light extinction as measured under both direct and aerosolrelated methods. Directly measured light ex tinction 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 ac ross 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 th e 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 th e 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 PM 2.5 standards which act in conjunction with the establishment of a regional haze program. In setting this secondary standard, EPA has
concluded that PM 2.5 causes adverse effects on visibility in various locations, depending on PM
concentrations and factors such as chemical co mposition and average relati ve 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, th e regional haze rule (64 FR 35714) was put in152 place to protect the visibility in mandatory Class I federal areas. Visibility can be said to be impaired in both PM 2.5 nonattainment areas and manda tory 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 ef fects 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 n itrates 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 PM 2.5 and PM 10 , (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 Ni trogen 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 acidifica tion, nutrient enrichme nt, and eutrophication. 374

The process of acidification affects both fresh water 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 repr oduce. 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 attributab le 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 esse ntial nutrients for plant growth and physiology. Changes in the relative proportions of these nutrients, especially in comparison with aluminum concentrations, have been associat ed with declining forest health.

At current ambient levels, risks to vegetati on 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 cau se direct leaf injury. Specif ically, 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 alte red 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 acidi ty 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 l eaf 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 Oreg on/Washington border, indicates that lichen communities in the CRGNSA have shifted to a high er 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 mo rtality, and/or shifts in plant community composition, often leading to decreased biodive rsity in many natural habitats wherever atmospheric reactive nitrogen deposition increases significantly above background and critical154 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 Caro lina; mixed hardwood wa tersheds at Fernow Experimental Forest in West Virginia; American beech fore sts in Great Smoky Mountains National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern California
and the southwestern Sierra Neva da 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 depositi on, surface runoff, or leaching from nitrogen saturated soils into ground or surface waters can contribute to conditions of severe water oxygen depletion; eutrophication and alg ae blooms; altered fish distribu tions, catches, and physiological states; loss of biodiversity; hab itat degradation; and increases in the incidence of disease.

Atmospheric deposition of nitrogen is a signif icant 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 we ll-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, ep isodic 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 in clude 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 wh ich 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 beco me 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 sy mptoms – an indication that eutrophication is well developed in more than half of U.S. estuaries. 378155 3.6.2.2 Materials Damage and Soiling

The effects of the deposition of atmosphe ric 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 dete riorating 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 Unit ed States, impairing crops, native vegetation, and ecosystems more than any other air pollutant”. 379 Like carbon dioxide (CO 2 ) 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 ag ing) 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 prim ary 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 gr owth and storage, above ground growth or yield, and reproductive processes, towa rd 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 im pacts 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 ac companied 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 presen t, 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 ornamental156 vegetation and trees in ur ban landscapes, and negatively affects s cenic 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 interce llular 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 id entify threshold values above which ozone is consistently toxic for all plants. The next few paragraphs present additional information on ozone damage to trees, ecosystems, ag ronomic 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 di versity, ozone may be the pollutant with the greatest potential for regional-scale forest impacts. Studies have demonstrated repeatedly that ozone concentrations commonly observed in pollute d 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 specie s 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 te mporal variation of tropospheric ozone, species composition, soil propertie s 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 ec osystem 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 extensiv e field experiments, conducte d 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 shown157 reduced economic benefits as a result of predic ted 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 gove rnmental 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 cumula tive 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 pollutant s 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 im paired 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 Anal ysis (FIA) program. As part of its Phase 3program, 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, base d on a global sampling design. 406,407 At each site that has at least 30 individual plants of at leas t 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 develope d an environmental indi cator based on data from the U.S. Department of Agriculture (USDA) Fore st 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 indicat or does not include woodlo ts 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 forestplants is classified using a subjective fivecategory biosite index based on expe rt 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-le vel or ecosystem-level responses, respectively. 410, 411158 The highest percentages of observed high and severe foliar injury, those which are most likely to be associated with tree or ecosyste m-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 da mage, 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 fro m 2% to 4% in EPA Region 1 (the six New England States), Region 7 (States of Missouri, Io wa, Nebraska and Kansas), and Region 9 (States of California, Nevada, Hawaii and Arizona). The percentage of sites showing some oz one 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, 2002 ab

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 samp le 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 exam ple, 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 o ccurs 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 damage d 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, scientis ts have developed ozone-exposure/treeresponse functions by exposing tree seedlings to different ozone levels a nd 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 Eff ects of Air Toxics

Fuel combustion emissions cont ribute to ambient levels of pollutants that contribute to adverse effects on vegetation. PAN is a well-esta blished 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-pho tosynthetic 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. Ef fects of individual VOCs or their role in conjunction with other stressors (e.g., acidification, drought, te mperature 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 pr oduction, leaf water c ontent 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 shor t-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 Concentrat ion Maps for Air Toxics using the
AEO Reference Case

The following section presents maps of annu al changes in ambient concentrations of modeled air toxics in 2022 using the AEO 2007 re ference 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 2022163 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 Ch ange in Concentrat ion Between the AEO
2007 Reference Case and the RFS2 Control Case in 2022164 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 Chan ge in Concentration Between the AEO 2007
Reference Case and the RFS2 Control Case in 2022165 Figure 3A-6. Ethanol Annual Absolute Chan ge 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 Chan ge in Concentration Between the AEO 2007
Reference Case and the RFS2 Control Case in 2022166 Figure 3A-8. Benzene Annual Absolute Chan ge 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 Ch ange in Concentration Between the AEO
2007 Reference Case and the RFS2 Control Case in 2022167 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 Chan ge in Concentration Between the AEO 2007
Reference Case and the RFS2 Control Case in 2022168 Figure 3A-12. Acrolein Annual Absolute Ch ange 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 seas onal changes in ambien t 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 Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-14. Summer Changes in Acetaldehyde Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/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 ( μ g/m3)

Figure 3A-16. Winter Changes in Acetaldehyde Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)171 3A.2.2 Formaldehyde

Figure 3A-17. Spring Changes in Formaldehy de Ambient Concentr ations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-18. Summer Changes in Formaldehy de Ambient Concentr ations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/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 ( μ g/m3)

Figure 3A-20. Winter Changes in Formaldehy de Ambient Concentr ations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)173 3A.2.3 Ethanol

Figure 3A-21. Spring Changes in Ethanol Am bient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-22. Summer Changes in Ethanol Am bient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)174 Figure 3A-23. Fall Changes in Ethanol Am bient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-24. Winter Changes in Ethanol Am bient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)175 3A.2.4 Benzene

Figure 3A-25. Spring Changes in Benzene Ambien t Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-26. Summer Changes in Benzene Ambien t Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)176 Figure 3A-27. Fall Changes in Benzene Ambient Concentr ations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-28. Winter Changes in Benzene Ambien t Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)177 3A.2.5 1,3-Butadiene

Figure 3A-29. Spring Changes in 1,3-Butadiene Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-30. Summer Changes in 1,3-Butadiene Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)178 Figure 3A-31. Fall Changes in 1,3-Butadiene Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-32. Winter Changes in 1,3-Butadiene Ambient Co ncentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)179 3A.2.6 Acrolein

Figure 3A-33. Spring Changes in Acrolein Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-34. Summer Changes in Acrolein Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)180 Figure 3A-35. Fall Changes in Acrolein Am bient Concentrations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-36. Winter Changes in Acrolein Ambient Concen trations Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)



### 3A.3 Seasonal Change Ambient Concentration Maps for Air Toxics using the
AEO Reference Case

The following section presents maps of seas onal changes in ambien t concentrations of modeled air toxics in 2022 using the AEO 2007 re ference case compared to the RFS2 control

case.181 3A.3.1 Acetaldehyde

Figure 3A-37. Spring Changes in Acetaldehyde Ambient Con centrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-38. Summer Changes in Acetaldehyde Ambient Co ncentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)182 Figure 3A-39. Fall Changes in Acetaldehyde Ambient Concentratio ns Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-40. Winter Changes in Acetaldehyde Ambient Co ncentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)183 3A.3.2 Formaldehyde

Figure 3A-41. Spring Changes in Formaldehy de Ambient Concentr ations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-42. Summer Changes in Formaldehy de Ambient Concentr ations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)184 Figure 3A-43. Fall Changes in Formaldehyde Ambient Concentratio ns Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-44. Winter Changes in Formaldehy de Ambient Concentr ations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)185 3A.3.3 Ethanol

Figure 3A-45. Spring Changes in Ethanol Am bient Concentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-46. Summer Changes in Ethanol Ambient Concentratio ns Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)186 Figure 3A-47. Fall Changes in Ethanol Am bient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-48. Winter Changes in Ethanol Am bient Concentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)187 3A.3.4 Benzene

Figure 3A-49. Spring Changes in Benzene Ambien t Concentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-50. Summer Changes in Benzene Ambient Concen trations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/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 ( μ g/m3)

Figure 3A-52. Winter Changes in Benzene Ambient Concen trations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)189 3A.3.5 1,3-Butadiene

Figure 3A-53. Spring Changes in 1,3-Butadiene Ambient Co ncentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-54. Summer Changes in 1,3-Butadiene Ambient Co ncentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)190 Figure 3A-55. Fall Changes in 1,3-Butadiene Ambient Concen trations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-56. Winter Changes in 1,3-Butadiene Ambient Co ncentrations Between the AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)191 3A.3.6 Acrolein

Figure 3A-57. Spring Changes in Acrolein Ambient Concen trations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-58. Summer Changes in Acrolein Ambient Concen trations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)192 Figure 3A-59. Fall Changes in Acrolein Am bient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)

Figure 3A-60. Winter Changes in Acrolein Ambient Concen trations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right) Absolute Changes ( μ g/m3)193 

### 3A.4 Air Toxics Popul ation Metrics using the AEO Reference Case

The following section presents population metr ics 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 decrease s in concentrations of various magnitudes.

Table 3A-1. Populations Exposed to Ambient Concentrat ions of Air Toxics above and below
Reference Concentrations for Noncancer Health Effects in 2022 with RFS2

Population-weighted
Concentration (Annual Average in μ g/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 0194130,779118,20450302,87128,4907,0651,164185,19081 7,978 6,952 5,0371,9341,606543925,775355105,74082,8291,413 1,6821,3402,20699109,16721,3171,732207,4361,196 88,035189,60741,58918,599 562 5986 1,786 12,329267,9742,83754,635EthanolAcetaldehydeFormaldehydeBenzene1,3-ButadieneAcrolein≤-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≥+100Percent Change in Ambient Concentration from AEO 2007 to RFS2Population (in Thousands) Impacted by Changes in Annual AmbientConcentrations of Toxic Pollutants with RFS2.1.4.3.6.12.10Figure 3A-61. Number of People Impacted by Changes in Annual Ambient Concentrations or ToxicPollutants by Percent Change Brackets, AEO 2007 Reference Case Compared to the RFS2 Control Case195 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 (F FVs) VOC/PM Cold Temperature Ch aracterization 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, SA E 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, 660 64-

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 St andards 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 Technica l 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 Iow a, by Barr, Minneapolis, MN, December, 2007.

59 See Chapter 1.5 of the Draft Regulatory Impact Assessment of this rulemaking.

60 U. 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 Ai r 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 Descriptio n 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, SA E 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 Pr otection Agency, Office of Research and Development.

99 Byun, D.W., Schere, K.L. (2006). Re view of the Governing Equations, Co mputational 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 qua lity modeling: EPA’s Models-3, Atmospheric Environment, 30, 1925-1938.

101 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (F ebruary 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

102 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al . (2004). Simulating regionalscale ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17), 2627-2638.

103 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long-range transport of acidifying substances in East Asia-Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939-5955.

104 United States Environmental Protection Agency. (2008). Technical support document for the final locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Qua lity Assessment Division.

105 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR. , 138 pp, National Center for At mospheric Research, Boulder CO

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106 Gr ell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR. , 138 pp, National Center for Atmo spheric Research, Boulder CO.

107 U.S. Environmental Protection Agency, By un, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Ai r Quality (CMAQ) modeling system , EPA/600/R-99/030, Office of Research and Development). Please al so see: http://www.cmascenter.org/

108 Yantosca, B. (2004). GEOS-CHEMv7-01-02 User’s Guide , Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.

109 U.S. EPA, (2004), Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modeled A ttainment Test (SMAT)- Updated 11/8/04.

110 U.S. EPA, (2008), Control of Emissions from Nonroad Spark-I gnition Engines and Equipment, Technical Support Document

111 EPA's guidance on air quality modeling attainment demonstrations

112 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05. Final Report to the US EPA, RT-0400675, December 8, 2005. http://www.camx.com/publ/pdf s/CB05_Final_Report_120805.pdf.199 113 74 FR 24904. Regulation of fuels and Fuel Additives: Changes to Renewable Fuel Standard Program. Notice of Proposed Rulemaking.

114 Dodge, M.C., 2000. Chemical oxidant mechanisms for air quality modeling: critical review. Atmospheric Environment 34, 2103-2130.

115 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N ., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and P hotochemical Data for Atmospheric Chemistry -
IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Ch emistry. July 2005 web version. http://www.iupac-kinetic.ch.cam.ac.uk/index.html.

116 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N ., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and P hotochemical Data for Atmospheric Chemistry -
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117 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N ., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and P hotochemical Data for Atmospheric Chemistry -
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118 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N ., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and P hotochemical Data for Atmospheric Chemistry -
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119 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo , M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical Kinetics and
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120 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo , M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical Kinetics and
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121 Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond Mechanism: CB05. Final Report to the US EPA, RT-0400675. Yocke and Company, Novato, CA.

122 Luecken, D.J., Phillips, S., Sarwar, G., Jang, C., 2008b. Effects of using the CB05 vs. SAPRC99 vs. CB4 chemical mechanism on model predictions: Ozone and gas-phase photochemical precursor concentrations. Atmospheric Environment 42, 5805-5820.

123 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo , M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical Kinetics and
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124 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo , M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical Kinetics and
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127 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N ., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and P hotochemical Data for Atmospheric Chemistry -
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132 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of
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134 H Takekawa, H Minoura, S Yamazaki (2003) Temper ature dependence of secondary organic aerosol formation by photo-oxidation of hy drocarbons. Atmos Environ 37: 3413–3424.

135 M J. Kleeman, Q Ying, Jin Lu, M J. Mysliwiec, R J. Griffin, Jianjun Chen , Simon Clegg (2007) Source apportionment of secondary organic aerosol during a se vere photochemical smog episode. Atmos Environ 41:

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136 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pierce, J. R.; Pa ndis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging. Science 315: 1259-1262.

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138 U. S. EPA (2002) National Air Quality and Emissi ons Trends Report, 2001. EPA 454/K-02-001, September 2002. http://www.epa.gov/air/ai rtrends/aqtrnd01/summary.pdf

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166 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05. Final Report to the US EPA, RT-0400675, December 8, 2005. http://www.camx.com/publ/pdf s/CB05_Final_Report_120805.pdf.

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169 Ewell, .H., Harrison J.M., and Berg, L., (2002), Azeotropic distillation, Industrial and Engineering Chemistry, 40, 3, 871.

170 Skolnik, H., (2002), Correlation of azeotropic data, Industrial and Engineering Chemistry, 40, 3, 442.

171 U.S. EPA. 2009. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. EPAXX-X-XX-XXX, December 2009.

172 U.S. EPA. 2009. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. EPAXX-X-XX-XXX, December 2009.

173 http://www.cmascenter.org/help/model _docs/cmaq/4.7/RELEASE_NOTES.txt

174 “Summary of recent findings for fuel effects of a 10% ethanol blend on light dut y exhaust emissions”, Memo from Aron Butler to Docket EPA-HQ-OAR-2005-0161

175 Hannigan, Michael P., William F. Busby, Jr., Glen R. Cass (2005) Source Contributions to the Mutagenicity
of Urban Particulate Air Polluti on. J Air Waste Mgmt 55: 399-410.

176 Zheng M, GR Cass, JJ Schauer, ES E dgerton (2002) Source Apportionment of PM 2.5 in the Southeastern United States Using Solvent-Extractable Organic Co mpounds as Tracers. Environ Sci Technol 36: 2631-2371.

177 U.S. EPA (2002) National Air Quality and Emissi on Trends Report, 2001. EP A 454/K-02-001, September 2002. http://www.epa.gov/air/ai rtrends/aqtrnd01/summary.pdf

178 Schauer JJ, GR Cass (2000) Sour ce Apportionment of Wintertime Ga s-Phase and Particle-Phase Air Pollutants Using Organic Compounds as Trace rs, Environ Sci Technol 34(9): 1821-1832. http://pubs.acs.org/cgi-bin/article.c gi/esthag/2000/34/i09/pdf/es981312t.pdf

179 Kleeman MJ, GR Cass (1999) Identifying the Effect of Individual Emission S ources on Particulate Air Quality Within a Photochemical Aerosol Processe s Trajectory Model. Atmos Environ 33: 4597-4613.

180 Watson J, Fujita E, Chow J, Zielinska B, Richards L, Neff W, Dietrich D (1998) Northern Front Range Air Quality Study Final Report: Volume 1. June 30, 1998. For Colorado State University, Cooperative Institute for Research in the Atmosphere, by Desert Research Institute, Reno, NV. This document is available in EPA Docket EPA-HQ-OAR-2005-0161.

181 Schauer JJ, WF Rogge, LM Hildemann, MA Mazure k, GR Cass, B Simoneit ( 1996) Source Apportionment of Airborne Particulate Matter Using Organic Compounds as Tracers. Atmos Environ 30: 3837-3855.

182 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is contained in Do cket Identification EPAHQ-OAR-2004-0008-0455 to 0457.

183 Niven, R. (2005) Ethanol in Gasoline: environmental impacts and sustainability review article. Renewable & Sustainable Energy Reviews 9: 535-555.

184 U. S. Environmental Protection Agency (2007). Cont rol of Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007.

185 U. S. Environmental Protection Agency (2007). Cont rol of Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007.

186 U. S. EPA. (2009) 2002 National-S cale Air Toxics Assessment. h ttp://www.epa.gov/ttn/atw/nata2002/

187 U. S. EPA. (2009) 2002 National- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/risksum.html

188 U. S. EPA. (2009) 2002 National-S cale Air Toxics Assessment. h ttp://www.epa.gov/ttn/atw/nata2002/203 189 U. S. Environmental Protection Agency (2009). Draf t Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program. Assessmen t and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA-420-D-09-001. h ttp://www.epa.gov/otaq/renewablefuels/420d09001.pdf

190 Anderson LG, Lanning JA, Wilkes E, Wolfe P, Jone s RH (1997) Effects of Using Oxygenated Fuels on Carbon Monoxide, Formaldehyde and Acetaldehyde Concentrations in Denver. Paper 97-RP139.05, Air & Waste Management Association 90 th Annual Meeting & Exhibition, June 8-13, 1997, Toronto, Ontario, Canada.

191 Jeffrey S. Gaffney, Nancy A. Marley, Randal S. Martin , Roy W. Dixon, Luis G. Reyes, and Carl J. Popp (1997) Potential Air Quality Effects of Using Ethanol-G asoline Fuel Blends: A Field Study in Albuquerque, New Mexico. Environ Sci Technol 31(11): 3053 - 3061; DOI: 10.1021/es9610388.

192 Allen P, R Bradley, BE Croes, J DaMassa, R Effa, M Fuentes, A Hebert, D Luo, R Vincent, L Woodhouse, E Yang (1999) Analysis of the Air Quality Impacts of th e Use of Ethanol in Gasolin e. California Air Resources Board, November 18, 1999.

193 U.S. EPA. 2009. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. EPAXX-X-XX-XXX, December 2009.

194 U.S. EPA. 2009. EPAct Phase I and II Testing: Co mprehensive Gasoline Light-Duty Exhaust Fuel Effects Test Program to Cover Multiple Fuel Properties and Two Ambient Test Temperatures. EPA Contract: EPC-07- 028EPA. Southwest Research Institute, San Antonio, TX

195 “Summary of recent findings for fuel effects of a 10% ethanol blend on light dut y exhaust emissions”, Memo from Aron Butler to Docket EPA-HQ-OAR-2005-0161

196 U. S. EPA. (2009) 2002 National- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/02pdfs/health_effects.pdf

197 EPA 2008 Report on the Environment

198 Anderson LG, Lanning JA, Wilkes E, Wolfe P, Jone s RH (1997) Effects of Using Oxygenated Fuels on Carbon Monoxide, Formaldehyde and Acetaldehyde Concentrations in Denver. Paper 97-RP139.05, Air & Waste Management Association 90 th Annual Meeting & Exhibition, June 8-13, 1997, Toronto, Ontario, Canada.

199 Anderson, L.G., P. Wolfe, R.A. Barrell, and J.A. La nning (1994) “The Effects of Oxygenated Fuels on the Atmospheric Concentrations of Carbon Monoxide and Al dehydes in Colorado,” In Alternative Fuels and the Environment, Lewis Publishers, Boca Raton, Florida, pp. 75 103.

200 Anderson, Larry G., Lanning, John A., Barrell, R., Mi yagishima, Joyce, and Jones, Richard (1996) Sources and sinks of formaldehyde and acetaldhde: an analysis of Denver’s ambient concen tration data, Atmospheric Environment, 12, 2113-2133.

201 Altshuller, A. P. 1993. Producti on of Aldehydes as Primary Emissi ons and from Secondary Atmospheric
Reactions of Alkenes and Alkanes Du ring the Night and Early Morning Hours. Office of Research and Development, Research Tria ngle Park, North Carolina. Report No. EPA--600/J-93/467.

202 Altshuller A. P. (1991a) Chemical re actions and transport of alkanes and their products in the troposphere. J. Atmos. Chem. 12, 19-61.

203 Altshuller A. P. (1991b) Estimatin g product yields of carbon-containi ng products from the atmospheric pliotooxidation of ambient air alkenes. J. Atmos. Chem. 13, 131-154.

204 Jeffrey S. Gaffney, Nancy A. Marley, Randal S. Martin , Roy W. Dixon, Luis G. Reyes, and Carl J. Popp (1997) Potential Air Quality Effects of Using Ethanol-G asoline Fuel Blends: A Field Study in Albuquerque, New Mexico. Environ Sci Technol 31(11): 3053 - 3061; DOI: 10.1021/es9610388.

205 Gaffney, J.S., N.A. Marley, R.S. Martin, R.W. Dixon, L.G. Reyes, and C.J. Popp (1998) Response to Comment on ‘Potential Air Quality Effects of Using Ethanol-Gasoline Fuel Blends: A Field Study in Albuquerque, New Mexico. Environ. Sci.Technol., 32 : 3842-3833.

206 Grosjean D (1999) Ambient levels of peroxyacetyl n itrate in southern Californi a, Report from DGA, Inc., Ventura, CA to California Air Re sources Board, Sacramento, CA, Agreement 99-703, Draft, August 31.

207 Allen P, R Bradley, BE Croes, J DaMassa, R Effa, M Fuentes, A Hebert, D Luo, R Vincent, L Woodhouse, E Yang (1999) Analysis of the Air Quality Impacts of th e Use of Ethanol in Gasolin e. California Air Resources Board, November 18, 1999.

208 Jacobson M (2007) Effects of ethanol (E85) versus ga soline vehicles on cancer and mortality in the United States, Environ Sci Technol 41: 4150-4157.204 209 Grosjean D (1997) Atmospheric Chemistry of Alcohols. J Braz Chem Soc 8(4): 433-442.

210 Andrade JB de, Andrade MV, Pinheir o HLC (1998) Atmospheric levels of formaldehyde and acetaldehyde and their relationship with the vehicu lar fleet composition in Salvador, Bahia, Brazil. J Braz Chem Soc 9(3):

219-223.

211 Montero L, Vasconcellos PC, Souza SR, Pires MAF, Sanchez-Ccoyllo OR, Andrade MF, Carvalho LRF (2001) Measurements of Atmospheric Carboxylic Acid s and Carbonyl Compounds in Sao Paulo City, Brazil.
Environ Sci Technol 35: 3071-3081.

212 Colon M, Pleil JD, Hartlage TA, Guardani ML, Martins MH (2001) Survey of volatile organic compounds
associated with automotive emissions in the urban ai rshed of Sao Paulo, Brazil. Atmos Environ 35: 4017–4031.

213 Tanner R, Miguel A, deAndrade J, Gaffney J, St reit G (1988) Atmospheric Chemistry of Aldehydes, Enhanced Peroxyacetyl Nitrate Formation from Et hanol-Fueled Vehicles. E nvion Sci Technol 22(9): 1026-

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214 Grosjean E, Grosjean D, Woodhouse L, Yang Y, (2002) Peroxyacetyl nitrate and peroxypropionyl nitrate in Porto Alegre, Brazil, Atmos Environ 36: 2405-2419.

215 Grosjean D, Grosjean E, Moreira LFR (2002) Specia ted Ambient Carbonyls in Rio de Janeiro, Brazil.
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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 i rradiations to measure acet aldehyde concentrations
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219 U.S. EPA. (2005). Review of the National Ambient Air Qua lity 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/s tandards/pm/data/pmsta ffpaper_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 http://www.regulations.gov/.

221 U.S. EPA. (2005). Review of the National Ambient Air Qua lity 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.

222 U.S. EPA. (2006). Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. EPA/600/R-06/063. Retrieved on March 19, 2009 from http://www.epa.gov/air/particle pollution/pdfs/ord_report_20060720.pdf.

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 http://www.regulations.gov/. p. 8-305.

224 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/. p. 9-93.

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 http://www.regulations.gov/. Table 8-34.

227 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 8.3.1.3.4.205 228 U.S. EPA. (2006). National Ambient Air Quality Standards for Particulate Matter . 71 FR 61144, October 17, 2006.

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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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 daily mortality in Phoenix, AZ. J. Exposure Anal. Environ. Epidemiol, 16, 311-320.

234 Ito, K., Christensen, W.F., Eatough, D.J., Henry, R.C ., Kim, E., Laden, F., Lall, R., Larson, T.V., Neas, L., Hopke, P.K., Thurston, G.D. (2006). PM source apportionment and health e ffects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. J. Exposure Anal. Environ. Epidemiol., 16, 300-310.

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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 Qua lity 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/naa qs/standards/pm/data/pmstaffpaper_20051221.pdf. p.3-18.

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280 Spektor, D. M., Lippman, M., Lioy, P. J., Thurston, G. D ., Citak, K., James, D. J., Bock, N., Speizer, F. E., Hayes, C. (1988). Effects of ambient ozone on re spiratory function in ac tive, normal children. Am. Rev. Respir. Dis., 137 , 313-320.

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. Retrieve d on March 19, 2009 from Docket EPA-HQ-OAR-2003- 0190 at http://www.regulations.gov/.208 282 Hazucha, M. J., Folinsbee, L. J., Seal, E., Jr. (1992). E ffects of steady-state and va riable ozone concentration profiles on pulmonary function. Am. Rev. Respir. Dis., 146, 1487-1493.

283 Horstman, D.H., Ball, B.A., Folinsbee, L.J., Brown, J., Gerrity, T. (1995) Comparison of pulmonary responses of asthmatic and nonasthma tic subjects performing light exercise while exposed to a low level of

ozone. Toxicol. Ind. Health., 11(4), 369-85.

284 Horstman, D.H.,; Folinsbee, L.J., Ives, P.J., Abdul-Salaam, S., McDonne ll, W.F. (1990). Ozone concentration and pulmonary respons e relationships for 6.6-hour exposur es with five hours of moderate exercise to 0.08, 0.10, and 0.12 ppm. Am. Rev. Respir. Dis., 142 , 1158-1163.

285 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

286 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

287 Coburn, R.F. (1979) Mechanisms of carbon monoxide toxicity. Prev. Med. 8:310-322.

288 Helfaer, M.A., and Traystman, R. J. (1996) Cerebrovascular effect s of carbon monoxide. In: Carbon Monoxide (Penney, D.G., ed). Boca Raton, CRC Press, 69-86.

289 Benignus, V.A. (1994) Behavioral effects of carbon m onoxide: meta analyses and extrapolations. J. Appl. Physiol. 76:1310-1316.

290 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

291 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

292 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

293 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is available online at http://www.epa.gov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.

294 U. S. EPA. (2009) 2002 National- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/risksum.html

295 U.S. EPA (2007) Control of Hazardous Air Pollutant s from Mobile Sources. 72 FR 8428; February 26, 2007.

296 U.S. EPA (2003) Integrated Risk Information System File of Acrolein. Nationa l Center for Environmental Assessment, Office of Research a nd Development, Washington, D.C. 2003. This material is available electronically at http://www.epa.gov/iris/subst/0364.htm.

297 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. This ma terial is availa ble electronically at http://www.epa.gov/ttn/atw/nata2002/risksum.html.

298 U.S. EPA (2009) National-Scale Air Toxics Asse ssment 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 electronically at: http://www.epa.gov/iris/subst/0276.htm.

300 International Agency for Research on Cancer, IARC m onographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemi cals and dyestuffs, International Agency for Research on Cancer, World Health Organization, Lyon, France, p. 345-389, 1982.209 301 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony-stimulating factor in vitro, Proc. Na tl. Acad. Sci. 89:3691-3695.

302 International Agency for Research on Cancer (IARC) . 1987. Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Supplement 7, Some industrial chemicals and dyestuffs, World Health Organization, Lyon, France.

303 U.S. Department of Health and Human Services National Toxicology Progra m 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183.

304 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197.

305 Goldstein, B.D. (1988). Benzene toxicity. Occupati onal medicine. State of th e Art Reviews. 3: 541-554.

306 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246.

307 U.S. EPA 2002 Toxicological Review of Benzene (Non cancer Effects). Environmental Protection Agency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington DC. This material is available electronically at http://www.epa.gov/iris/subst/0276.htm.

308 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validati on & Evaluation of Biomarkers in Workers Exposed to
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309 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohe n, et al. (2002). Hematological changes among Chinese workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275-285.

310 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al . (2004). Hematotoxically in Workers Exposed to Low
Levels of Benzene. Science 306: 1774-1776.

311 Turtletaub, K.W. and Mani, C. ( 2003). Benzene metabolism in rodents at doses relevant to human exposure from Urban Air. Research Reports Health Effect Inst. Report No.113.

312 U.S. EPA. 2002. Health Assessment of 1,3-Butadien e. Office of Research and Development, National Center for Environmental Assessment, Washington O ffice, Washington, DC. Report No. EPA600-P-98-001F. This document is available electronically at http://www.epa.gov/iris/supdocs/buta-sup.pdf.

313 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butad iene (CASRN 106-99-0)” E nvironmental Protection Agency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington, DC http://www.epa.gov/iris/subst/0139.htm.

314 International Agency for Research on Cancer (IARC) (1999) Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 71, Re-evaluation of some organic chemicals, hydrazine and hydrogen peroxide and Volume 97 (in preparation), World Health Organization, Lyon, France.

315 U.S. Department of Health and Human Services National Toxicology Progra m 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183.

316 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subc hronic toxicity of 4-vinylcyclohexene in rats and mice by inhalation. Fundam. Appl. Toxicol. 32:1-10.

317 International Agency for Research on Cancer (IA RC). 1988. Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 44, Alcohol Drinking, World Health Organization, Lyon, France.

318 U.S. Department of Health and Human Services . 2005. National Toxicology Program 11th Report on Carcinogens available at: ntp.niehs.nih.gov/i ndex.cfm?objectid=32BA9724-F1 F6-975E-7FCE50709CB4C932.

319 U.S. Department of Health and Hu man Services. 2000. 10th Special Repor t to the U.S. Congress on Alcohol
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320 Goodlett CR, KH Horn, F Zhou. 2005. Alcohol teratogeni esis: mechanisms of damage and strategies for intervention. Exp. Biol. Med. 230:394-406.

321 Riley EP, CL McGee. 2005. Fetal alcohol spectrum di sorders: an overview with emphasis on changes in brain and behavior. Exp. Biol. Med. 230:357-365.210 322 Zhang X, JH Sliwowska, J Weinberg. 2005. Prenatal alcohol exposure and fetal programming: effects on
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323 Riley EP, CL McGee, ER Sowell. 2004. Teratogenic eff ects of alcohol: a decade of brain imaging. Am. J.
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324 Gunzerath L, V Faden, S Zakhari, K Warren. 2004. National Institute on Alcohol Abuse and Alcoholism report on moderate drinking. Alc ohol. Clin. Exp. Res. 28:829-847.

325 World Health Organization (WHO) . 2004. Global status report on al cohol 2004. Geneva , Switzerland: Department of Mental Health a nd Substance Abuse. Available: http://www.who.int/substance_abuse/publicati ons/global_status_report_2004_overview.pdf

326 Chen W-JA, SE Maier, SE Parnell, FR West. 2003. Alcohol and the developing brain: neuroanatomical
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327 Driscoll CD, AP Streissguth, EP Riley. 1990. Prenatal alcohol exposure comparability of effects in humans
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328 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Cert ain Home Residents from Exposure to Formaldehyde, Office of Pes ticides and Toxic Substances, April 1987.

329 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic malignancies among workers in formal dehyde industries. Journa l of the National Cancer
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330 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130.

331 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewar t, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from lymphohematopoie tic malignancies among workers in formaldehyde industries: The
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332 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

333 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of British chemical workers exposed to formaldehyde. J National Cancer Inst. 95:1608-1615.

334 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically motivated computational modeling of formaldehyde carcinogenicity in th e F344 rat. Tox Sci 75: 432-447.

335 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory tract cancer risks of inhaled formaldehy de: Dose-response predictions derived from biologicallymotivated computational modeling of a combined rodent and human datase t. Tox Sci 82: 279-296.

336 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and doseresponse assessment for carci nogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC.

337 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde with Relevance to Other Biologically-Based Dose Re sponse (BBDR) Models. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-08/103, 2008

338 Subramaniam, R; Chen, C; Crump, K; .et .al. ( 2008) Uncertainties in biologically-based modeling of formaldehyde-induced cancer risk: identifica tion of key issues. Risk Anal 28(4):907-923.

339 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237

340 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehyde-induced respiratory cancer in humans. Ann Occup Hyg 52:481-495.

341 International Agency for Research on Cancer (2 006) Formaldehyde, 2-Butoxyethanol and 1-tertButoxypropan-2-ol. Monographs Volume 88. World Health Organization, Lyon, France.

342 Agency for Toxic Substances and Disease Regi stry (ATSDR). 1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S. De partment of Health a nd Human Services, Public Health Service. http://www.atsdr.cdc.gov/toxprofiles/tp111.html

343 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations Environm ent Programme, the International Labour Organization,211 and the World Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound Management of Chemicals. Geneva.

344 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm.

345 U.S. Department of Health and Human Services National Toxicology Progra m 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183.

346 International Agency for Research on Cancer (IA RC). 1999. Re-evaluation of some organic chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.

347 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm.

348 U.S. EPA. 2003. Integrated Risk Information System F ile of Acrolein. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0364.htm.

349 Appleman, L.M., R.A. Woutersen, and V.J. Feron. ( 1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23: 293-297.

350 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Ma tsuda, T. (1993) Aerosolized acetaldehyde induces
histamine-mediated bronchoconstr iction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943.

351 Integrated Risk Information System File of Acrole in. Research and Developm ent, National Center for Environmental Assessment, Washington, DC. This material is available at http://www.epa.gov/iris/subst/0364.htm

352 International Agency for Research on Cancer (IA RC). 1995. Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 63, Dry cleani ng, some chlorinated solv ents and other industrial chemicals, World Health Organization, Lyon, France.

353 Weber-Tschopp, A; Fischer, T; Gier er, R; et al. (1977) Experimentelle reizwirkungen von Acrolein auf den Menschen. Int Arch Occup Environ Hlth 40(2):117-130. In German

354 Sim, VM; Pattle, RE. (1957) Effect of possible smog irritants on human subjects. J Am Med Assoc 165(15):1908-1913.

355 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Imme diate sensory nerve-mediat ed respiratory responses to irritants in healthy and allergic airway-diseased mice. J Appl Physiol 94(4):1563-1571.

356 Sim VM, Pattle RE. Effect of possible smog irritants on human subjects JAMA165: 1980-2010, 1957.

357 U.S. EPA. 2006. Air quality criteria for ozone and rela ted photochemical oxidants (Ozone CD). Research Triangle Park, NC: Natio nal Center for Environm ental Assessment; report no. EPA/600/R-05/004aF-cF.3v. page 5-78 Available at http://cfpub.epa.gov/ncea/.

358 U.S. EPA Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF-cF , 2006. page 5-63. This document is available in Docket EPA-HQ-OAR-2005-0161. This doc ument may be accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html

359 U.S. EPA Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF- cF, 2006. page 5-78. This document is available in Docket EPA-HQ-OAR-2005-0161. This doc ument may be accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html

360 U. S. EPA. 2004. Toxicological Review of Naphth alene (Reassessment of the Inhalation Cancer Risk), Environmental Protection Agency, Integrated Risk Info rmation System, Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm.

361 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=84403212 362 National Toxicology Program (NTP). (2004). 11th Repor t on Carcinogens. Public Health Service, U.S.Department of Health and Human Services, Research Triangle Park, NC. Available from: http://ntpserver.niehs.nih.gov.

363 International Agency for Research on Cancer (IA RC). (2002). Monographs on the Evaluation of the Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France.

364 U. S. EPA. 1998. Toxicological Review of Naphthalene , Environmental Protection Agency, Integrated Risk Information System, Research and De velopment, National Center for Envi ronmental Assessment, Washington,
DC. This material is ava ilable electronically at http ://www.epa.gov/iris/subst/0436.htm

365 EPA. 2005. Full IRIS Summary for n-Hexane (CAS RN 11054-3). Integrated Risk Information System (IRIS), Office of Research and Development, National Ce nter for Environmental Assessment, Washington, DC. Available at www.epa.gov/iris/subst/0486.htm.

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 volatilization can be found at http://www.epa. gov/ttn/chief/ap42/ch09/ final/c9s02-2.pdf

367 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris

368 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/. p. 4-179.

369 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/. p. 4-236.

370 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/. p. 4-182.

371 Sisler, J.F. (1996) Spatial and seasonal patterns and long term variability of the composition of the haze in the United States: an analysis of data from the IMPROVE network . CIRA Report, ISSN 0737-5352-32, Colorado State University.

372 U.S. EPA (2004) Air Quality Criteria for Particulat e 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.

373 U.S. EPA (2005) Review of the National Ambient Ai r Quality Standard for Pa rticulate Matter: Policy
Assessment of Scientif ic and Technical Informati on, OAQPS Staff Paper. EPA- 452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0161.

374 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulf ur- Ecological Criteria (Final). U.S. EPA, Wash ington D.C., EPA/600/R-08/082F.

375 Environmental Protection Agency (2003). Response Of Surface Water Chemistry to the Clean Air Act
Amendments of 1990. National Health a nd Environmental Effects Research La boratory, Office of Research and Development, U.S. Environmental Protection Agen cy. Research Triangle Park, NC. EPA 620/R–03/001.

376 Fenn, M.E. and Blubaugh, T.J. (2005) Winter Depositi on of Nitrogen and Sulfur in the Eastern Columbia River Gorge National Scenic Area, USDA Forest Service.

377 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitr ogen and the world: 200 years of change. Ambio 31:

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378 Bricker, Suzanne B., et al., National Estuarine Eutrophica tion Assessment, Effects of Nutrient Enrichment in the Nation’s Estuaries, National Ocean Service, Nationa l Oceanic and Atmospheric Ad ministration, September,

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379 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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380 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences for growth.” Trends in Ecology and Evolution 1:15-18.213 381 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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382 Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual model of physiological events.” In: Effects of Gaseous Air Pollution in Agriculture and Horticulture (Unsworth, M.H., Omrod, D.P., eds.) London, UK: Butterworth Scie ntific, pp.113-138.

383 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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384 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is contained in Do cket Identification EPAHQ-OAR-2004-0008-0455 to 0457.

385 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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386 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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387 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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388 Ollinger, S.V., J.D. Aber and P.B. Reich. 1997. “Simulating ozone effects on forest productivity: interactions between leaf canopy and sta nd level processes.” Ecolog ical Applications 7:1237-1251.

389 Winner, W.E., 1994. “Mechanistic analysis of plant re sponses to air pollution.” Ecological Applications, 4(4):651-661.

390 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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392 Fox, S., and R. A. Mickler, eds. 1996. Impact of Ai r Pollutants on Southern Pine Forests. Springer-Verlag, NY, Ecol. Studies, Vol. 118, 513 pp.

393 De Steiguer, J., J. Pye, C. Love. 1990. “Air Polluti on Damage to U.S. Forests.” Journal of Forestry, Vol 88 (8) pp. 17-22.

394 Pye, J.M. 1988. “Impact of ozone on the growth and yi eld of trees: A review.” Journal of Environmental Quality 17 pp.347-360.

395 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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396 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This doc ument is contained in Docket EPA-HQ-OAR-2005-

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397 McBride, J.R., P.R. Miller, and R.D. Laven. 1985. “E ffects of oxidant air pollutants on forest succession in the mixed conifer forest type of southern California. ” In: Air Pollutants Effects On Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.

398 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory (EPA600-D-82-276).214 399 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-

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