50% reduction. If the biofuel producer can demonstrate that it is able
to quantify the cellulosic portion of food wastes, fuel made from the
cellulosic portion can qualify as cellulosic biofuel. Since we have
determined that yard wastes are largely cellulosic, biofuel from yard
waste will qualify as cellulosic biofuel. The use of separated yard and
food wastes for biofuel production including the requirements for
demonstrating what portion of food waste is cellulosic feedstock is
discussed further in Section II.B.4.d. EPA believes that renewable fuel
produced from feedstocks consisting of wastes that would normally be
discarded or put to a secondary use, and which have not been
intentionally rendered unfit for productive use, should be assumed to
have little or no land use emissions of GHGs. The use of wastes that
would normally be discarded does not increase the demand for land. For
example, the use in biofuel production of food waste from a food
processing facility that would normally be placed in a landfill will
not increase the demand for land to grow the crops that were purchased
by the food processing facility. Similarly, wastes that would not
normally be discarded because there are alternative secondary uses for
them (for example contaminated vegetable oil might be burned in a
boiler) are not produced for the purpose of such secondary use and the
use of these feedstocks also does not increase demand for land. Since
these waste-derived feedstocks have little or no land use impact, the
lifecycle GHG emissions associated with their use for biofuel
production are largely the result of the energy required to collect and
process the feedstock prior to conversion, and the energy required to
convert that feedstock into a biofuel. This has led us to conclude it
is reasonable to include a restricted set of additional feedstocks in
pathways complying with the applicable threshold.
    The look-up table identifies a number of individual fuel
``pathways'' that allow for the use of waste feedstocks. These
feedstocks include (1) waste ethanol from beverage production, (2)
waste starches from food production and agricultural residues, (3)
waste oils/fats/greases, (4) waste sugar from food and beverage
production, and (5) food and beverage production wastes. For the
purpose of this rule only, EPA will consider these feedstocks to be
``wastes'' if they are used as feedstock to produce fuel, but would
otherwise normally be discarded or used for another secondary purpose
because they are no longer suitable for their original intended use.
They may be unsuitable for their original intended use either because
they are themselves waste from that original use (e.g., table scraps)
or because of contamination, spoilage or other unintentional acts. EPA
will not consider any material that has been intentionally rendered
unsuitable for its original use to be a ``waste.''
    As discussed in more detail in Section II.B.4.d, EPA has also
determined that the biogenic portion of post recycled MSW is eligible
to produce renewable fuel and will largely be made up of cellulosic
material. Therefore biofuel made from this waste-derived material will
qualify as cellulosic biofuel.
    EPA has also considered biofuels produced from annual cover crops
such as cover crops grown in the winter. These annual cover crops are
normally planted as a rotation between primary planted crops or between
trees and vines in orchards and vineyards, typically to protect soil
from erosion, improve the soil between periods of regular crops, or for
other conservation purposes. For annual cover crops grown on the same
land as the primary crops, we have determined that there is little or
no land use impact such that the GHG emissions associated with them
would largely result due to inputs required to grow the crop,
harvesting and transporting to the biofuel production facility, turning
that feedstock into a biofuel and transporting it to its end use. As
such, the biofuel from cellulosic biomass from annual cover crops are,
for example, determined to meet requirements of cellulosic biofuel, oil
from annual cover crops are determined to meet the requirements of
renewable diesel and starches from annual cover crops are determined to
meet the requirements of advanced biofuel.
    While we have not been able to model all possible feedstocks that
can and are being used for renewable fuel production, there are a
variety of feedstocks that should have similar enough characteristics
to those already modeled to allow them to be grouped in with already
modeled fuel pathways. In particular, as discussed below, there are
five categories of biofuel feedstock sources for which we are
confident, by virtue of their lack of any land-use change impact, in
qualifying them for particular renewable fuel standards (D-codes) on
the basis of our existing modeling.
    1. All crop residues which provide starch or cellulosic feedstock.
By virtue of the fact that they do not cause any land-use change
impacts, they should all have similar lifecycle GHG impacts. Thus,
modeling conducted for corn stover is being extended to other crop
residues such as wheat straw, rice straw, and citrus residue. These
residues are what remains after a primary crop is harvested, and can be
similarly collected, transported and used in biofuel production.
    2. Slash, forest thinnings, and forest residue providing cellulosic
feedstock. As excess material, these represent another form of residue
which should also result in no land-use change GHG impacts. Their GHG
emission impacts would only be associated with collection, transport,
and processing into biofuel. Consequently, modeling conducted for corn
stover is also being extended to these residues.
    3. Annual cover crops planted on existing crop land such as winter
cover crops and providing cellulosic material, starch or oil for
biofuel production. While different from crop residues, these secondary
crops also have no land use impact since they are planted on land
otherwise used for primary crop production. GHG emissions would only be
associated with growing, harvesting and transporting the secondary crop
and then processing into biofuel. In the case of secondary crops that
might be used for cellulosic biofuel production, they would also have
no land-use change impact, and consequently modeling conducted for corn
stover is also being extended to these crops. In the case of secondary
crops used for oil production, they would then have no land-use change
similar to waste fats, oils and greases. Consequently, modeling
conducted for biodiesel and renewable diesel from these waste oils is
also being extended to these annual cover crops.
    4. Separated food and yard wastes, including food and beverage
wastes

from food production and processing are another category of waste
product that would not have any land-use change impact. These waste
products can be used as feedstock for advanced biofuel production or
cellulosic biofuel production. Waste oils have already been modeled as
complying with the biomass-based diesel standard. Applying our
sugarcane results without the land-use change component to waste sugars
clearly demonstrates compliance with the advanced biofuel threshold.
Applying our corn results without the land-use component to waste
starches clearly demonstrates compliance with the renewable fuel
standard
    5. Perennial grasses including switchgrass and miscanthus. We
modeled switchgrass and miscanthus has higher yield per acre without
any significant (or perhaps less) inputs such as fertilizer per acre.
We believe other perennial grasses likely to compete as feedstock
sources will have similar land use and agricultural inputs are
therefore confident the results from switchgrass can be extended to
miscanthus and other perennial grasses. However, we note that the
energy crop industry is just starting to develop and therefore as
favored perennial grasses start to emerge, additional analyses may be
warranted.
    Applicable D-Codes for Fuel Pathways: Based on the above, corn
ethanol facilities using natural gas or biomass as the process energy
source will meet the applicable 20% GHG performance threshold if it
either also uses at least two of the technologies Table V.C-6 or one of
the technologies in Table V.C-6 but marketing at least 35% of its DGS
as wet. Alternatively, a facility using none of the advanced
technologies listed in Table V.C-6 will qualify as producing ethanol
meeting the 20% performance threshold if it sells at least 50% of its
DGS prior to drying.

               Table V.C-6--Modeled Advanced Technologies
------------------------------------------------------------------------

-------------------------------------------------------------------------
Corn oil fractionation
Corn oil extraction
Membrane separation
Raw starch hydrolysis
Combined heat and power
------------------------------------------------------------------------

    Following the criteria for D-Codes defined in Section II.A-1, the
following renewable fuel pathways have been found to comply with the
applicable lifecycle GHG thresholds and are therefore eligible for the
D-Codes specified in Table V.C-7.

                                        Table V.C-7--D-Code Designations
----------------------------------------------------------------------------------------------------------------
                                                             Production process
             Fuel type                    Feedstock             requirements                  D-Code
----------------------------------------------------------------------------------------------------------------
Ethanol...........................  Corn starch..........  All of the following:  6 (renewable fuel)
                                                           Drymill process,
                                                            using natural gas,
                                                            biomass or biogas
                                                            for process energy
                                                            and at least two
                                                            advanced
                                                            technologies from
                                                            Table V.C-6).
Ethanol...........................  Corn starch..........  All of the following:  6 (renewable fuel)
                                                           Dry mill process,
                                                            using natural gas,
                                                            biomass or biogas
                                                            for process energy
                                                            and one of the
                                                            advanced
                                                            technologies from
                                                            Table V.C-6 plus
                                                            drying no more than
                                                            65% of the DGS it
                                                            markets annually.
Ethanol...........................  Corn starch..........  All of the following:  6 (renewable fuel)
                                                           Dry mill process,
                                                            using natural gas,
                                                            biomass or biogas
                                                            for process energy
                                                            and drying no more
                                                            than 50% of the DGS
                                                            it markets annually.
Ethanol...........................  Corn starch..........  Wet mill process       6 (renewable fuel)
                                                            using biomass or
                                                            biogas for process
                                                            energy.
Ethanol...........................  Starches from          Fermentation using     6 (renewable fuel)
                                     agricultural           natural gas, biomass
                                     residues; starches     or biogas for
                                     from annual cover      process energy.
                                     crops.
Biodiesel, and renewable diesel...  Soy bean oil;          One of the following:  4 (biomass-based diesel)
                                    Oil from annual cover  Trans-Esterification.
                                     crops.
                                    Algal oil............  Hydrotreating........
                                    Biogenic waste oils/   Excluding processes
                                     fats/greases;          that coprocess
                                                            renewable biomass
                                                            and petroleum.
                                    Non-food grade corn
                                     oil.
Biodiesel, and renewable diesel...  Soy bean oil;          One of the following:  5 (Advanced)
                                    Oil from annual cover  Trans-Esterification.
                                     crops.
                                    Algal oil............  Hydrotreating........
                                    Biogenic waste oils/   Includes only
                                     fats/greases;          processes that
                                                            coprocess renewable
                                                            biomass and
                                                            petroleum.
                                    Non-food grade corn
                                     oil.
Ethanol...........................  Sugarcane............  Fermentation (Any)...  5 (Advanced)
Ethanol...........................  Cellulosic Biomass     Any..................  3 (Cellulosic Biofuel)
                                     from agricultural
                                     residues, slash,
                                     forest thinnings,
                                     forest product
                                     residues, annual
                                     cover crops,
                                     switchgrass and
                                     miscanthus;
                                     cellulosic
                                     components of
                                     separated yard
                                     wastes; cellulosic
                                     components of
                                     separated food
                                     wastes; and
                                     cellulosic
                                     components of
                                     separated MSW.

Cellulosic Diesel, Jet Fuel and     Cellulosic Biomass     Any..................  7 (Cellulosic Biofuel or
 Heating Oil.                        from agricultural                             Biomass-Based Diesel)
                                     residues, slash,
                                     forest thinnings,
                                     forest product
                                     residues, annual
                                     cover crops,
                                     switchgrass and
                                     miscanthus;
                                     cellulosic
                                     components of
                                     separated yard
                                     wastes, cellulosic
                                     components of
                                     separated food
                                     wastes, and
                                     cellulosic
                                     components of
                                     separated MSW.
 Butanol..........................  Corn starch..........  Fermentation; dry       6 (renewable fuel)
                                                            mill using natural
                                                            gas, biomass or
                                                            biogas for process
                                                            energy.
Cellulosic Naphtha................  Cellulosic Biomass     Fischer-Tropsch        3 (Cellulosic Biofuel)
                                     from agricultural      process.
                                     residues, slash,
                                     forest thinnings,
                                     forest product
                                     residues, annual
                                     cover crops,
                                     switchgrass and
                                     miscanthus;
                                     cellulosic
                                     components of
                                     separated yard
                                     wastes, cellulosic
                                     components of
                                     separated food
                                     wastes, and
                                     cellulosic
                                     components of
                                     separated MSW.
Ethanol, renewable diesel, jet      The non-cellulosic     Any..................  5 (Advanced)
 fuel, heating oil, and naphtha.     portions of
                                     separated food
                                     wastes.
Biogas............................  Landfills, sewage and  Any..................  5 (Advanced)
                                     waste treatment
                                     plants, manure
                                     digesters.
----------------------------------------------------------------------------------------------------------------

    Pathways for which we have not made a threshold compliance
decision: The pathways identified in the Table V.C-6 represent those
pathways we have analyzed and determined meet the applicable thresholds
as establish by EISA. We did not analyze all pathways that might be
feasible through 2022. In some cases, we did not have sufficient time
to complete the necessary lifecycle GHG impact assessment for this
final rule. In addition to the pathways identified in Table V.C-6, EPA
anticipates modeling grain sorghum ethanol, woody pulp ethanol, and
palm oil biodiesel after this final rule and including the
determinations in a rulemaking within 6 months. Based on current and
projected commercial trends and the status of current analysis at EPA,
biofuels from these three pathways are either currently being produced
or are planned production in the near-term. Our analyses project that
they will be used in meeting the RFS2 volume standard in the near-term.
During the course of the NPRM comment period, EPA received detailed
information on these pathways and is currently in the process of
analyzing these pathways. We have received comments on several
additional feedstock/fuel pathways, including rapeseed/canola,
camelina, sweet sorghum, wheat, and mustard seed, and we welcome
parties to utilize the petition process described below to request EPA
to examine additional pathways.
    In other cases, we have not modeled the lifecycle GHG performance
of pathways because we did not have sufficient information. For those
fuel pathways that are different than those pathways EPA has listed in
today's regulations, EPA is establishing a petition process whereby a
party can petition the Agency to consider new pathways for GHG
reduction threshold compliance. The petition process is meant for
parties with serious intention to moved forward with production via the
petitioned fuel pathway and who have moved sufficiently forward in the
business process to show feasibility of the fuel pathway's
implementation. The Agency will not consider frivolous petitions with
insufficient information and clarity for Agency analysis. In addition,
if the petition addresses a fuel pathway that already complies for one
or more types of renewable fuels under RFS (e.g., renewable fuel or
advanced biofuel), the pathway must have the potential to result in the
pathway qualifying for a new renewable fuel category for which it was
not previously qualified. Thus, for example, the Agency will not
undertake any additional review for a party wishing to get a modified
LCA value for a previously approved fuel pathway if the desired new
value would not change the overall pathway classification. EPA will
process these petitions as expeditiously as possible, taking into
consideration that some fuel pathways are closer to the commercial
production stage than others. In all events, parties are expected to
begin this process with ample lead time as compared to their commercial
start dates.
    In addition to the technical information described below and listed
in today's regulations (see Sec.  80.1416), a petition must include all
information required in the registration process except the engineering
review. The petition should demonstrate technical and commercial
feasibility. For example, a petition could include copies of
applications for air or construction permits, copies of blue prints of
the facility, or photographs of the facility or pilot plant. The
petition must include information necessary to allow EPA to effectively
determine the lifecycle green house gas emissions of the fuel. The
petitioner must describe the alternative production facility technology
applied and supply data establishing the energy savings that will
result from the use of the alternative technology. The information
required would include, at a minimum, a mass and energy balance for the
proposed fuel production process. This would include for example, mass
inputs of raw material feedstocks and consumables, mass outputs of fuel
product produced as well as co-products and waste materials production.
Energy inputs information should include fuels used by type, including
purchased electricity. If steam or hot water is purchased, the source
and fuel required for its generation would also be reported.

Energy output information should include energy content of the fuel
product produced (with heating value specified) as well as energy
content of any co-products. The petitioner should also report the
extent to which excess electricity is generated and distributed outside
the production facility. Information on co-products should include the
expected use of the co-products and their market value. All information
should be provided in a format such that it can be normalized on a fuel
output basis (for example, tons feedstock per gallon of fuel produced).
Other process descriptions necessary to understand the fuel production
process should be included (e.g., process modeling flowcharts). Any
other relevant information, including that pertaining to energy saving
technologies or other process improvements that document significant
differences between the fuel production processes outlined in this rule
and that used by the renewable fuel producer, should also be submitted
with the petition.
    For fuel pathways that utilize feedstocks that have not yet been
modeled for this rulemaking, the petition must also submit information
on the feedstock. Information would include, at a minimum, the
feedstock type and feedstock production source and data on the market
value of the feedstock and current uses of the feedstock, if any. The
petition should also include chemical input requirements (e.g.,
fertilizer, pesticides, etc.) and energy use in feedstock production
listed by type of energy. Yield information would also be required for
both the current yields of the feedstock as well as anticipated changes
in feedstock yields over time.
    EPA will use the data supplied in the petition and other data and
information available to the Agency to technically evaluate whether the
information is sufficient for EPA to make a determination of the RFS
standards for which the fuel pathway may qualify. If EPA determines
that the petition is insufficient for determination, the petitioner
will be so notified. If EPA determines it has been provided sufficient
data from the petitioner to evaluate the fuel pathway, we will then
proceed with any analyses required to make a technical determination of
compliance.
    EPA anticipates that for some petitioned fuel pathways with unique
modifications or enhancements to production technologies of pathways
otherwise modeled for the regulations listed today, EPA may be able to
evaluate the pathway as a reasonably straight-forward extension of our
current assessments. We expect such a determination would be pathway
specific, and would be based on a technical analysis that compared the
applicant fuel pathway to the fuel to pathway(s) that had already been
analyzed. In these cases, EPA would be able to make a determination
without proceeding through a full rulemaking process. For example,
petitions may submit unique biofuel production facility configurations,
operations, or co-product pathways that could result in greater
efficiencies than the pathways modeled for this rulemaking, but
otherwise do not differ greatly from the modeled fuel pathways. In such
cases, we would expect to make a decision for that specific pathway
without conducting a full rulemaking process. We would expect to
evaluate whether the pathway is consistent with the definitions of
renewable fuel types in the regulations, generally without going
through rulemaking, and issue an approval or disapproval that applies
to the petitioner. We anticipate that we will subsequently propose to
add the pathway to the regulations.
    If EPA determines that a petitioned fuel pathway requires
significant new analysis and/or modeling, EPA will need to give notice
and seek public comment. For example, we anticipate that pathways with
feedstocks or fuel types not yet modeled by EPA will require additional
modeling and public comment before a determination of compliance can be
made. In these cases, the determination would be incorporated into the
annual rulemaking process established in today's regulations.
    When EPA makes a technical determination is made that a petitioned
fuel pathway qualifies for a RFS volume standard, a D-code will be
assigned to the fuel pathway. We anticipate that renewable fuel
producers and importers will be able to generate RINs for the
additional pathway after the next available update of the EPA Moderated
Transaction System (EMTS) that follows a determination. EPA expects to
update the EMTS quarterly, as long as necessary. Renewable fuel
producers will be able to register the fuel pathway through the EPA
Fuels Programs Registration System two weeks after the date of
determination, but as described above, will not be able to generate
RINs until the quarterly EMTS update.
    In the proposal, we suggested a system of temporary D-codes for
biofuel pathways we had not analyzed. This was proposed as a means of
assuring no undue hardship for biofuel producers using feedstock
sources or processing technologies not analyzed by EPA. As proposed,
these producers could market their fuel on the basis of temporarily
assigned D-codes. While the objective was sound, EPA now believes it is
best to properly assure compliance with thresholds on the basis of
completed lifecycle GHG assessments. As noted above, the Agency commits
to expedited assessment and rulemaking for those pathways most likely
to generate biofuel in the immediate future, including ethanol produced
from grain sorghum, ethanol, woody pulp ethanol, and palm oil
biodiesel. We also plan to continue to model additional pathways we
expect will be commercially available in the U.S. as soon as sufficient
information is available to complete a quality lifecycle assessment.
For these reasons, EPA is not finalizing a provision for assigning
temporary D-codes.

D. Total GHG Reductions

    Similar to the analysis done in our proposal, our analysis of the
overall GHG emission impacts of increased volumes of renewable fuel was
performed in parallel with the lifecycle analysis performed to develop
the individual fuel thresholds described in previous sections. The same
sources of emissions apply such that this analysis includes the effects
of three main areas: (a) Emissions related to the production of
biofuels, including the growing of feedstock (corn, soybeans, etc.)
with associated domestic and international land use change impacts,
transport of feedstock to fuel production plants, fuel production, and
distribution of finished fuel; (b) emissions related to the extraction,
production and distribution of petroleum gasoline and diesel fuel that
is replaced by use of biofuels; and (c) difference in tailpipe
combustion of the renewable and petroleum based fuels.
    The main difference between the results of the proposal analysis
and the final rule analysis are higher domestic land use change
emissions in the final rule analysis. As was the case in the proposal,
simply adding up the individual lifecycle results determined in Section
V.C. multiplied by their respective volumes would yield a different
assessment of the overall impacts. The two analyses are separate in
that the overall impacts capture interactions between the different
fuels that can not be broken out into per fuels impacts, while the
threshold values represent impacts of specific fuels but do not account
for all the interactions.
    While individual fuel analysis generally had small domestic land
use change emission impacts, the overall impacts had larger domestic
land use change emissions. The primary reason

for the difference in domestic land use change between the individual
fuel scenarios and the combined fuel scenarios is that when looking at
individual fuels there is some interaction between different crops
(e.g., corn replacing soybeans), but with combined volume scenario when
all mandates need to be met there is less opportunity for crop
replacement (e.g., both corn and soybean acres needed) and therefore
more land is required.
    As discussed in previous sections on lifecycle GHG thresholds there
is an initial one time release from land conversion and smaller ongoing
releases, but there are also ongoing benefits of using renewable fuels
over time replacing petroleum fuel use. Based on the volume scenario
considered, the one time land use change impacts result in 313 million
metric tons of CO2-eq. emissions increase. There are,
however, based on the biofuel use replacing petroleum fuels, GHG
reductions in each year. Totaling the emissions impacts over 30 years
but assuming a 0% discount rate over this 30 year period would result
in an estimated total NPV reduction in GHG emissions of 4.15 billion
tons over 30 years.
    This total NPV reduction can be converted into annual average GHG
reductions, which can be used for the calculations of the monetized GHG
benefits as shown in Section VIII.C.3. This annualized value is based
on converting the lump sum present values described above into their
annualized equivalents. A comparable value assuming 30 years of GHG
emissions changes, but not applying a discount rate to those emissions
results in an estimated annualized average emission reduction of
approximately 138 million metrics tons of CO2-eq. emissions.
    We also considered the uncertainty in the international land use
change emission estimates for the overall impacts. Based on the range
of results for the international land use change emissions the overall
annualized average emission reductions of increased volumes of
renewable fuel could range from -136 to -140 million metrics tons of
CO2-eq. emissions.

E. Effects of GHG Emission Reductions and Changes in Global Temperature
and Sea Level

    The reductions in CO2 and other GHGs associated with
increased volumes of renewable fuel will affect climate change
projections. GHGs mix well in the atmosphere and have long atmospheric
lifetimes, so changes in GHG emissions will affect future climate for
decades to centuries. Two common indicators of climate change are
global mean surface temperature and global mean sea level rise. This
section estimates the response in global mean surface temperature and
global mean sea level rise projections to the estimated net global GHG
emissions reductions associated with increased volumes of renewable
fuel.
    EPA estimated changes in projected global mean surface temperatures
to 2050 using the MiniCAM (Mini Climate Assessment Model) integrated
assessment model \182\ coupled with the MAGICC (Model for the
Assessment of Greenhouse-Gas Induced Climate Change) simple climate
model.\183\ MiniCAM was used to create the globally and temporally
consistent set of climate relevant variables required for running
MAGICC. MAGICC was then used to estimate the change in the global mean
surface temperature over time. Given the magnitude of the estimated
emissions reductions associated with the increased volumes of renewable
fuel, a simple climate model such as MAGICC is reasonable for
estimating the climate response.
---------------------------------------------------------------------------

    \182\ MiniCAM is a long-term, global integrated assessment model
of energy, economy, agriculture and land use, that considers the
sources of emissions of a suite of greenhouse gases (GHGs), emitted
in 14 globally disaggregated global regions (i.e., U.S., Western
Europe, China), the fate of emissions to the atmosphere, and the
consequences of changing concentrations of greenhouse related gases
for climate change. MiniCAM begins with a representation of
demographic and economic developments in each region and combines
these with assumptions about technology development to describe an
internally consistent representation of energy, agriculture, land-
use, and economic developments that in turn shape global emissions.
Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model
Documentation for the MiniCAM. PNNL-14337, Pacific Northwest
National Laboratory, Richland, Washington. For a recent report and
detailed description and discussion of MiniCAM, see Clarke, L., J.
Edmonds, H. Jacoby, H. Pitcher, J. Reilly, R. Richels, 2007.
Scenarios of Greenhouse Gas Emissions and Atmospheric
Concentrations. Sub-report 2.1A of Synthesis and Assessment Product
2.1 by the U.S. Climate Change Science Program and the Subcommittee
on Global Change Research. Department of Energy, Office of
Biological & Environmental Research, Washington, DC., USA, 154 pp.
    \183\ MAGICC consists of a suite of coupled gas-cycle, climate
and ice-melt models integrated into a single framework. The
framework allows the user to determine changes in GHG
concentrations, global-mean surface air temperature and sea-level
resulting from anthropogenic emissions of carbon dioxide
(CO2), methane (CH4), nitrous oxide (N2O), reactive gases
(e.g., CO, NOX, VOCs), the halocarbons (e.g. HCFCs, HFCs,
PFCs) and sulfur dioxide (SO2). MAGICC emulates the global-mean
temperature responses of more sophisticated coupled Atmosphere/Ocean
General Circulation Models (AOGCMs) with high accuracy. Wigley,
T.M.L. and Raper, S.C.B. 1992. Implications for Climate and Sea-
Level of Revised IPCC Emissions Scenarios Nature 357, 293-300.
Raper, S.C.B., Wigley T.M.L. and Warrick R.A. 1996. In Sea-Level
Rise and Coastal Subsidence: Causes, Consequences and Strategies
J.D. Milliman, B.U. Haq, Eds., Kluwer Academic Publishers,
Dordrecht, The Netherlands, pp. 11-45. Wigley, T.M.L. and Raper,
S.C.B. 2002. Reasons for larger warming projections in the IPCC
Third Assessment Report J. Climate 15, 2945-2952.
---------------------------------------------------------------------------

    EPA applied the estimated annual GHG emissions changes for the
final rule to a MiniCAM baseline emissions scenario.\184\ Specifically,
the CO2, N2O, and CH4 annual emission
changes from 2022-2052 from Section V.D were applied as net reductions
to this baseline scenario for each GHG.
---------------------------------------------------------------------------

    \184\ The reference scenario is the MiniCAM reference (no
climate policy) scenario used as the basis for the Representative
Concentration Pathway RCP4.5 using historical emissions until 2005.
This scenario is used because it contains a comprehensive suite of
greenhouse and pollutant gas emissions including carbonaceous
aerosols. The four RCP scenarios will be used as common inputs into
a variety of Earth System Models for inter-model comparisons leading
to the IPCC AR5 (Moss et al. 2008). The MiniCAM RCP4.5 is based on
the scenarios presented in Clarke et al. (2007) with non-
CO2 and pollutant gas emissions implemented as described
in Smith and Wigley (2006). Base-year information has been updated
to the latest available data for the RCP process.
---------------------------------------------------------------------------

    Table V.E-1 provides our estimated reductions in projected global
mean surface temperatures and mean sea level rise associated with the
reductions in GHG emissions due to the increase in renewable fuels in
2022. To capture some of the uncertainty in the climate system, we
estimated the changes in projected temperatures and sea level across
the most current Intergovernmental Panel on Climate Change (IPCC) range
of climate sensitivities, 1.5 [deg]C to 6.0 [deg]C.\185\ To illustrate
the time profile of the estimated reductions in projected global mean
surface temperatures and mean sea level rise, we have also provided
Figures V.E-1 and V.E-2.
---------------------------------------------------------------------------

    \185\ In IPCC reports, equilibrium climate sensitivity refers to
the equilibrium change in the annual mean global surface temperature
following a doubling of the atmospheric equivalent carbon dioxide
concentration. The IPCC states that climate sensitivity is
``likely'' to be in the range of 2 [deg]C to 4.5 [deg]C and
described 3 [deg]C as a ``best estimate.'' The IPCC goes on to note
that climate sensitivity is ``very unlikely'' to be less than 1.5
[deg]C and ``values substantially higher than 4.5 [deg]C cannot be
excluded.'' IPCC WGI, 2007, Climate Change 2007--The Physical
Science Basis, Contribution of Working Group I to the Fourth
Assessment Report of the IPCC, http://www.ipcc.ch/.

  Table V.E-1--Estimated Reductions in Projected Global Mean Surface Temperature and Global Mean Sea Level Rise
                                           From Baseline in 2020-2050
----------------------------------------------------------------------------------------------------------------
                                               Climate sensitivity
-----------------------------------------------------------------------------------------------------------------
                                                   1.5         2         2.5         3         4.5         6
----------------------------------------------------------------------------------------------------------------
Year                                              Change in global mean surface temperatures (degrees Celsius)
----------------------------------------------------------------------------------------------------------------
2020..........................................      0.000      0.000      0.000      0.000      0.000      0.000
2025..........................................      0.000      0.000      0.000      0.000      0.000      0.000
2030..........................................      0.000      0.000      0.000      0.000      0.000      0.000
2035..........................................     -0.001     -0.001     -0.001     -0.001     -0.001     -0.001
2040..........................................     -0.001     -0.001     -0.001     -0.001     -0.001     -0.001
2045..........................................     -0.001     -0.001     -0.001     -0.001     -0.002     -0.002
2050..........................................     -0.001     -0.001     -0.002     -0.002     -0.002     -0.002
----------------------------------------------------------------------------------------------------------------
Year                                                   Change in global mean sea level rise (centimeters)
----------------------------------------------------------------------------------------------------------------
2020..........................................      0.000      0.000      0.000      0.000      0.000      0.000
2025..........................................      0.000      0.000      0.000      0.000      0.000      0.000
2030..........................................     -0.001     -0.001     -0.001     -0.001     -0.001     -0.001
2035..........................................     -0.002     -0.002     -0.002     -0.003     -0.003     -0.003
2040..........................................     -0.003     -0.004     -0.004     -0.005     -0.005     -0.006
2045..........................................     -0.005     -0.006     -0.006     -0.007     -0.008     -0.009
2050..........................................     -0.006     -0.008     -0.009     -0.009     -0.011     -0.012
----------------------------------------------------------------------------------------------------------------

    The results in Table V.E-1 and Figures V.E-1 and V.E-2 show small
reductions in the global mean surface temperature and sea level rise
projections across all climate sensitivities. Overall, the reductions
are small relative to the IPCC's ``best estimate'' temperature
increases by 2100 of 1.8 [deg]C to 4.0 [deg]C.\186\ Although IPCC does
not issue ``best estimate'' sea level rise projections, the model-based
range across SRES scenarios is 18 to 59 cm by 2099.\187\ While the
distribution of potential temperatures in any particular year is
shifting down, the shift is not uniform. The magnitude of the decrease
is larger for higher climate sensitivities. The same pattern appears in
the reductions in the sea level rise projections. Thus, we can conclude
that the impact of increased volumes of renewable fuel is to lower the
risk of climate change, as the probabilities of temperature increase
and sea level rise are reduced.
---------------------------------------------------------------------------

    \186\ IPCC WGI, 2007.
    \187\ ``Because understanding of some important effects driving
sea level rise is too limited, this report does not assess the
likelihood, nor provide a best estimate or an upper bound for sea
level rise.'' IPCC Synthesis Report, p. 45.
---------------------------------------------------------------------------

VI. How Would the Proposal Impact Criteria and Toxic Pollutant
Emissions and Their Associated Effects?

    This section presents our assessment of the changes in emissions
and air quality resulting from the increased renewable fuel volumes
needed to meet the RFS2 standards. Increases in emissions of
hydrocarbons, nitrogen oxides, particulate matter, and other pollutants
are projected to lead to increases in population-weighted annual
average ambient PM and ozone concentrations. The air quality impacts,
however, are highly variable from region to region. Ambient
PM2.5 is likely to increase in areas associated with biofuel
production and transport and decrease in other areas; for ozone, many
areas of the country will experience increases and a few areas will see
decreases. Ethanol concentrations will increase substantially; for the
other modeled air toxics there are some localized impacts, but
relatively little impact on national average concentrations.

A. Overview of Emissions Impacts

    Today's action will affect the emissions of ``criteria'' pollutants
(those pollutants for which EPA has established a National Ambient Air
Quality Standard has been established), criteria pollutant
precursors,\188\ and air toxics, which may affect overall air quality
and health. Emissions are affected by the processes required to produce
and distribute large volumes of biofuels required by today's action and
the direct effects of these fuels on vehicle and equipment emissions.
As detailed in Chapter 3 of the Regulatory Impact Analysis (RIA), we
have estimated emissions impacts of production and distribution-related
emissions using the life cycle analysis methodology described in
Section V with emission factors for criteria and toxic emissions for
each stage of the life cycle, including agriculture, feedstock
transportation, and the production and distribution of biofuel;
included in this analysis are the impacts of reduced gasoline and
diesel refining as these fuels are displaced by biofuels. Emission
impacts of tailpipe and evaporative emissions for on and off road
sources have been estimated by incorporating ``per vehicle'' fuel
effects from recent research into mobile source emission inventory
estimation methods.
---------------------------------------------------------------------------

    \188\ NOX and VOC are precursors to the criteria
pollutant ozone; we group them with criteria pollutants in this
chapter for ease of discussion.
---------------------------------------------------------------------------

    In the proposal we analyzed a single renewable fuel volume
scenario, largely dependent on ethanol, relative to three different
reference cases, including the RFS1 base case. For today's rule we are
presenting emission impacts for three fuel volume scenarios relative to
two reference cases (RFS1 mandate and AEO) to show a range of the
possible effects of biofuels depending on the relative quantities of
various biofuels that may be used to meet the overall renewable fuel
requirements. We have also updated our modeling for the RFS1 mandate
reference case to better reflect the emissions for this case. Table
VI.A-1 shows the fuel volumes for the two reference cases and all three
control scenarios. Further discussion of these fuel volumes and the
subcategories within each are available in Section IV.A. The emission
impacts of the primary control scenario (22.2 Bgal of ethanol) are
presented here relative to both reference cases. The corresponding
results for all three control cases are available in Chapter 3 of the
Regulatory Impact Analysis for this rule.

                                    Table VI.A-1--Renewable Fuel Volumes for Each Reference Case and Control Scenario
                                                                   [Bgal/year in 2022]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Ethanol
                       Scenario                        --------------------------------------------------------   Biodiesel     Renewable    Cellulosic
                                                            Corn       Cellulosic     Imported        Total                      diesel        diesel
--------------------------------------------------------------------------------------------------------------------------------------------------------
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
Mid Ethanol (Primary).................................         15.0          4.92          2.24         22.16          1.67          0.15          6.52
High Ethanol..........................................         15.0          16.0          2.24         33.24          1.67          0.15           0.0
--------------------------------------------------------------------------------------------------------------------------------------------------------

    There have been a number of other enhancements and corrections to
the non-GHG emission inventory estimates since the NPRM, some of which
were included in the air quality modeling inventories, while others
occurred later than that. The major changes are mentioned here, and all
the significant changes are explained in detail in Chapter 3 of the
RIA.
    One significant change relates to the ``downstream'' vehicle and
equipment emission impacts of using the increased proportions of
renewable fuels. In the proposal we provided two different analyses
based on two different assumptions regarding the effects of E10 and E85
versus E0 on exhaust emissions from cars and trucks. Those were
referred to as ``less sensitive'' and ``more sensitive'' cases. Based
on analysis of recent emissions test data conducted since publication
of the NPRM, we are modeling a single case. As detailed in Section
VI.C, 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, which means no impact for NOX or
non-methane hydrocarbons (NMHC). We have also updated our estimates of
evaporative permeation impacts of E10 based on recent studies. Finally,
for the final rule inventories we are only claiming emission effects
with use of E85 in flex-fueled vehicles relative to E0 for two
pollutants: ethanol and acetaldehyde, for which data suggests the
effects are more certain. For the ``more sensitive case'' presented in
the NPRM, and used in the air quality modeling, we had estimated
changes to additional pollutants (including significant PM reductions)
based on some very limited data. Until such time as additional data is
collected to enhance this analysis it is premature to use such
assumptions.
    For ``upstream'' emissions associated with fuel production and
distribution, the largest change that was included in the air quality
modeling was the improved estimate of VOC and ethanol vapor emissions
during ethanol transport, made possible by a detailed analysis of costs
and transport modes conducted by Oak Ridge National Laboratory
(ORNL).\189\ This change alone more than doubled the predicted overall
increase in ethanol emissions from the increased use of renewable
fuels, increasing the VOC enough to change the overall VOC impact from
a decrease to a substantial increase.
---------------------------------------------------------------------------

    \189\ ``Analysis of Fuel Ethanol Transportation Activity and
Potential Distribution Constraints,'' Oak Ridge National Laboratory,
U.S. Department of Energy, March 2009.
---------------------------------------------------------------------------

    Significant updates have also been made to emissions from
cellulosic biofuel plants, in part to reflect the assumed shift in
volumes from cellulosic ethanol to diesel between the proposed and
final rules. For cellulosic ethanol plants, after the air quality
modeling was done we discovered that the calculation of emissions from
these plants had been overestimated 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, and shifts the
PM impact of these plants from an increase to a small decrease.
However, these changes are counterbalanced by varying degrees by
shifting some of the cellulosic volume from ethanol to diesel, which
requires nearly twice the biomass as needed by ethanol to produce one
gallon. While the net effect of the changes in cellulosic plant
emissions is a decrease in NOX and CO emission impacts
relative to the proposal, the shift to cellulosic diesel under the
primary scenario results in a larger increase in ``upstream'' PM
emissions than reported in the NPRM or used in the air quality
analysis.
    Updates to agricultural modeling assumptions made between proposal
and final had a significant impact on ammonia (NH3) emissions. Final
modeling reflects an increase in fertilizer use with the primary
control case, which results in a 1.2 percent increase in NH3 emissions,
a change from the 0.5 percent decrease projected for the proposal and
negligible impact used in the air quality analyses.
    Analysis of criteria and toxic emission impacts was performed for
calendar year 2022, since this year reflects the full implementation of
today's rule. Our 2022 projections account for projected growth in
vehicle travel and the effects of applicable emission and fuel economy
standards, including Tier 2 and Mobile Source Air Toxics (MSAT) rules
for cars and light trucks and recently finalized controls on spark-
ignited off-road engines.
    The analysis presented here provides estimates of the change in
national emission totals that would result from the increased use of
renewable fuels to meet the statutory requirements of EISA. These
totals may not be a good indication of local or regional air quality
and health impacts. These results are aggregated across highly
localized sources, such as emissions from ethanol plants and
evaporative emissions from cars, and reflect offsets such as decreased
emissions from gasoline refineries. The location and composition of
emissions from these disparate sources may strongly influence the air
quality and health impacts of the increased use of renewable fuels, so
full-scale photochemical air quality modeling was also performed to
accurately assess this. These localized impacts are discussed in
Section VI.D.
    Our projected emission impacts for the primary renewable fuel
scenario relative to the two reference cases are shown in Table VI.A-2
for 2022. This shows the expected emission changes for the U.S. in that
year, and the percent contribution of this impact relative to the total
U.S. inventory. Overall we project that increases in the use of
renewable fuels will result in significant increases in ethanol and
acetaldehyde emissions--increasing the total U.S. inventories of these
pollutants by 16-18 percent in 2022 relative to the RFS1 mandate case.
We project more modest increases in NOX, HC, PM,
formaldehyde, 1,3-butadiene, acrolein, and ammonia (NH3) relative to
the RFS1 mandate case. We project a 5 percent

decrease in CO (due to impacts of ethanol on exhaust emissions from
vehicles and nonroad equipment), and a 2.4 percent decrease in benzene
(due to displacement of gasoline with ethanol in the fuel pool).
Impacts on SO2 and naphthalene are much smaller. Relative to
the AEO reference case the results are similar directionally, but
smaller in magnitude due to the less drastic differences in fuel
volumes.

 Table VI.A-2--Total Combined Upstream and Downstream Emission Impacts in 2022 for Primary Scenario Relative to
                                               Each Reference Case
----------------------------------------------------------------------------------------------------------------
                                                           RFS1 Mandate                         AEO
                                                 ---------------------------------------------------------------
                    Pollutant                      Annual short     % of total     Annual short     % of Total
                                                       tons       U.S. inventory       tons       U.S. inventory
----------------------------------------------------------------------------------------------------------------
NOX.............................................         247,604            1.95         184,820            1.45
HC..............................................         100,762            0.87          24,523            0.21
PM10............................................          69,013            1.92          63,323            1.76
PM2.5...........................................          15,549            0.46          14,393            0.42
CO..............................................      -2,869,842           -5.30        -376,419           -0.69
Benzene.........................................          -4,264           -2.41          -1,004           -0.57
Ethanol.........................................         100,123           18.20          54,137            9.84
1,3-Butadiene...................................             224            1.70              59            0.45
Acetaldehyde....................................           5,848           15.80           3,108            8.40
Formaldehyde....................................             355            0.48             130            0.17
Naphthalene.....................................              -1           -0.01              -4           -0.03
Acrolein........................................              22            0.38              21            0.35
SO2.............................................           3,286            0.04           5,065            0.06
NH3.............................................          48,711            1.15          48,711            1.15
----------------------------------------------------------------------------------------------------------------

    The breakdown of these results by the fuel production/distribution
(``well-to-pump'' emissions) and vehicle and equipment (``pump-to-
wheel'') emissions is discussed in the following sections.

B. Fuel Production & Distribution Impacts of the Proposed Program

    Fuel production and distribution emission impacts of the increased
use of renewable fuels were estimated in conjunction with the
development of life cycle GHG emission impacts and the GHG emission
inventories discussed in Section V. These emissions are calculated
according to the breakdowns of agriculture, feedstock transport, fuel
production, and fuel distribution; the basic calculation is a function
of fuel volumes in the analysis year and the emission factors
associated with each process or subprocess. Additionally, the emission
impact of displaced petroleum is estimated, using the same domestic/
import shares discussed in Section V above.
    In general the basis for this life cycle evaluation was the
analysis conducted as part of the Renewable Fuel Standard (RFS1)
rulemaking, but enhanced significantly. While our approach for the RFS1
was to rely heavily on the ``Greenhouse Gases, Regulated Emissions, and
Energy Use in Transportation'' (GREET) model, developed by the
Department of Energy's Argonne National Laboratory (ANL), we are now
able to take advantage of additional information and models to
significantly strengthen and expand our analysis for this rule. In
particular, the modeling of the agriculture sector was greatly expanded
beyond the RFS1 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 feedstock energy requirements and estimates of excess
electricity available for sale from new cellulosic ethanol plants,
based on modeling by the National Renewable Energy Laboratory (NREL).
Per-gallon emission factors for new corn ethanol plants were updated
based on EPA analysis of energy efficiency technologies currently
available (such as combined heat and power) and their expected market
penetrations. There are no new standards planned at this time that
would offer any additional control of emissions from corn or cellulosic
ethanol plants. EPA also updated the fuel and feedstock transport
emission factors to account for recent EPA emission standards and
modeling, such as the locomotive and commercial marine standards
finalized in 2008, and revised heavy-duty truck emission rates
contained in EPA's draft MOVES2009 model. EPA also modified the ethanol
transport distances based on a detailed analysis of costs versus
transport mode conducted by Oak Ridge National Laboratory. In addition,
GREET does not include air toxics or ethanol. Thus emission factors for
ethanol and the following air toxics were added: benzene, 1,3-
butadiene, formaldehyde, acetaldehyde, acrolein and naphthalene.
    Results of these calculations relative to each reference case in
2022 are shown in Table VI.B-1 for the criteria pollutants, ammonia,
ethanol and individual air toxic pollutants. Due to the complex
interactions involved in projections in the agricultural modeling, we
did not attempt to adjust the agricultural inputs of the AEO reference
case for the RFS1 mandate reference case. So the fertilizer and
pesticide quantities, livestock counts, and total agricultural acres
were the same for both reference cases. The agricultural modeling that
had been done for the RFS1 rule itself was much simpler and
inconsistent with the new modeling, so it would be inappropriate to use
those estimates.
    The fuel production and distribution impacts of the increased use
of renewable fuels on VOC are mainly due to increases in emissions
connected with biofuel production, countered by decreases in emissions
associated with gasoline production and distribution as ethanol
displaces some of the gasoline. Increases in PM2.5,
SOX and especially NOX are driven by stationary
combustion emissions from the substantial increase in corn and
cellulosic ethanol production. Biofuel plants (corn and cellulosic)
tend to have greater combustion emissions relative to petroleum
refineries on a per-BTU of fuel produced basis. Increases in
SOX emissions are also due to increases in agricultural
chemical production and transport, while substantial PM

increases are also associated with fugitive dust from agricultural
operations. Ammonia emissions are expected to increase substantially
due to increased ammonia from fertilizer use.
    Ethanol vapor and most air toxic emissions associated with fuel
production and distribution are projected to increase. Relative to the
US total reference case emissions with RFS1 mandate ethanol volumes,
increases of 4-13 percent for acetaldehyde and ethanol vapor are
especially significant because they are driven directly by the
increased ethanol production and distribution. Formaldehyde and
acrolein increases are smaller, on the order of 0.4-1 percent. There
are also very small decreases in benzene, 1,3-butadiene and naphthalene
relative to the US total emissions.

 Table VI.B-1--``Upstream'' Fuel Production and Distribution Impacts of the Primary Scenario in 2022 Relative to
                                               Each Reference Case
----------------------------------------------------------------------------------------------------------------
                                                           RFS1 mandate                         AEO
                                                 ---------------------------------------------------------------
                    Pollutant                      Annual short     % of Total     Annual short     % of Total
                                                       tons       U.S. inventory       tons       U.S. inventory
----------------------------------------------------------------------------------------------------------------
NOX.............................................         169,665            1.34         164,170            1.29
HC..............................................          77,014            0.67          19,737            0.17
PM10............................................          69,583            1.94          63,892            1.78
PM2.5...........................................          15,864            0.47          14,707            0.43
CO..............................................         135,658            0.25         130,172            0.24
Benzene.........................................            -231           -0.13            -236           -0.13
Ethanol.........................................          69,445           12.63          35,865            6.52
1,3-Butadiene...................................              -1           -0.01               0            0.00
Acetaldehyde....................................           1,617            4.37             933            2.52
Formaldehyde....................................             293            0.39             187            0.25
Naphthalene.....................................              -8           -0.06              -6           -0.04
Acrolein........................................              67            1.13              37            0.63
SO2.............................................           3,266            0.04           5,044            0.06
NH3.............................................          48,711            1.15          48,711            1.15
----------------------------------------------------------------------------------------------------------------

C. Vehicle and Equipment Emission Impacts of Fuel Program

    The effects of the increased use of renewable fuels on vehicle and
equipment emissions are a direct function of the effects of these fuels
on exhaust and evaporative emissions from vehicles and off-road
equipment, and evaporation of fuel from portable containers. To assess
these impacts we conducted separate analyses to quantify the emission
impacts of additional E10 due to the increased use of renewable fuels
on gasoline vehicles, nonroad spark-ignited engines and portable fuel
containers; E85 on cars and light trucks; biodiesel on diesel vehicles;
and increased refueling events due to lower energy density of
biofuels.\190\
---------------------------------------------------------------------------

    \190\ The impact of renewable diesel was not estimated for this
analysis; we expect little overall impact on criteria and toxic
emissions due to the relatively small volume change, and because
emission effects relative to conventional diesel are presumed to be
negligible.
---------------------------------------------------------------------------

    In the proposal we provided two different analyses based on two
different assumptions regarding the effects of E10 and E85 on exhaust
emissions from cars and trucks. Those were referred to as ``less
sensitive'' and ``more sensitive'' cases. Based on analysis of recent
studies, today's analysis is based on a hybrid of these two scenarios.
As detailed in the RIA, EPA and other parties have been gathering
additional data on the emission impacts of ethanol fuels on later model
vehicles. Data available in time for this analysis supports the
hypothesis of the ``less sensitive'' case that newer technology Tier 2
vehicles are generally able to control for changes to emissions
associated with low level ethanol blends; for this analysis we
therefore are not attributing any NOX or VOC impact to the
use of E10 on these vehicles. The data does show sensitivity for older
technology (pre-Tier 2) vehicles, so this analysis does attribute an
increase in NOX and decrease in NMHC to the use of E10 in
these vehicles. This analysis does not include any emission impacts
with use of E85 in flex-fueled vehicles, except for increases in
ethanol and acetaldehyde, as the limited data currently available is
insufficient to quantify the impact with any degree of certainty.
Overall the sensitivity of exhaust emissions to ethanol assumed for the
final rule analysis is closer to the ``less sensitive'' case presented
in the proposal; and is generally less sensitive than the case used for
the air quality modeling, as discussed in Section VI.D.
    We have also updated our estimates of E10 effects on permeation
emissions from light-duty vehicles based on testing recently completed
by the Coordinating Research Council (CRC), showing that the relative
increase in VOC emissions is higher for newer technology vehicles.
Nonroad spark ignition (SI) emission impacts of E10 were based on EPA's
NONROAD model and show trends similar to light duty vehicles. Biodiesel
effects for this analysis were unchanged from the proposal, and are
based on an analysis of recent biodiesel testing, detailed in the RIA,
showing a 2 percent increase in NOX with a 20 percent
biodiesel blend, a 16 percent decrease in PM, and a 14 percent decrease
in HC. These results essentially confirm the results of an earlier EPA
analysis. This analysis does not attribute any downstream emission
impact from the use of renewable diesel or cellulosic-based diesel
relative to conventional diesel due to their chemical similarity to
diesel fuel and limited test data.
    Summarized vehicle and equipment emission impacts in 2022, updated
as noted above, are shown in Table VI.C-1 relative to each reference
case. The totals shown below reflect the net impacts from all mobile
sources, including car and truck evaporative emissions, off road
emissions, and portable fuel containers. Additional breakdowns by
mobile source category can be found in Chapter 3 of the RIA.
    Carbon monoxide, PM, benzene, and acrolein are projected to
decrease in 2022 as a result of the increased use of renewable fuels,
while NOX, HC and the other air toxics, especially ethanol
and acetaldehyde, are projected to increase due to the impacts of E10.

 Table VI.C-1--``Downstream'' Vehicle and Equipment Emission Impacts of the Primary Scenario in 2022 Relative to
                                               Each Reference Case
----------------------------------------------------------------------------------------------------------------
                                                           RFS1 Mandate                         AEO
                                                 ---------------------------------------------------------------
                    Pollutant                      Annual short     % of Total     Annual short     % of Total
                                                       tons       U.S. inventory       tons       U.S. inventory
----------------------------------------------------------------------------------------------------------------
NOX.............................................          77,939            0.61          20,650            0.16
HC..............................................          23,748            0.21           4,786            0.04
PM10............................................            -569           -0.02            -569           -0.02
PM2.5...........................................            -315           -0.01            -315           -0.01
CO..............................................      -3,005,500           -5.55        -506,591           -0.94
Benzene.........................................          -4,033           -2.28            -768           -0.43
Ethanol.........................................          30,678            5.58          18,272            3.32
1,3-Butadiene...................................             225            1.71              59            0.45
Acetaldehyde....................................           4,231           11.43           2,175            5.88
Formaldehyde....................................              62            0.08             -57           -0.08
Naphthalene.....................................               7            0.05               2            0.01
Acrolein........................................             -44           -0.75             -16           -0.28
SO2.............................................              21            0.00              21            0.00
NH3.............................................               0            0.00               0            0.00
----------------------------------------------------------------------------------------------------------------

D. Air Quality Impacts

    Air quality modeling was performed to assess the projected impact
of the renewable fuel volumes required by RFS2 on emissions of criteria
and air toxic pollutants. Our air quality modeling reflects the impact
of increased renewable fuel use required by RFS2 compared with two
different reference cases that include the use of renewable fuels: A
2022 reference case projection based on the RFS1-mandated volume of 7.1
billion gallons of renewable fuels, and a 2022 reference case
projection based on the AEO 2007 volume of roughly 13.6 billion gallons
of renewable fuels. Thus, the results represent the impact of an
incremental increase in ethanol and other renewable fuels. We note that
the air quality modeling results presented in this final rule do not
constitute 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.
    It is critical to note that a key limitation of the analysis is
that it employed interim emission inventories, which were somewhat
enhanced compared to what was described in the proposal, but due to the
timing of the analysis did not include some of the later enhancements
and corrections of the final emission inventories presented in this FRM
(see Section VI.A through VI.C of this preamble). Most significantly,
our modeling of the air quality impacts of the renewable fuel volumes
required by RFS2 relied upon interim inventories that assumed that
ethanol will make up 34 of the 36 billion gallon renewable fuel
mandate, that approximately 20 billion gallons of this ethanol will be
in the form of E85, and that the use of E85 results in fewer emissions
of direct PM2.5 from vehicles. The emission impacts and air
quality results would be different if, instead of E85, more non-ethanol
biofuels are used or mid-level ethanol blends are approved.
    In fact, as explained in Section IV, our more recent analyses
indicate that ethanol and E85 volumes are likely to be significantly
lower than what we assumed in the interim inventories. Furthermore, the
final emission inventories do not include vehicle-related PM reductions
associated with E85 use, as discussed in Section VI.A and VI.C of this
preamble. There are additional, important limitations and uncertainties
associated with the interim inventories that must be kept in mind when
considering the results:
     Error in PM2.5 emissions from locomotive engines
    After the air quality modeling was completed, we discovered an
error in the way that PM2.5 emissions from locomotive
engines were allocated to counties in the inventory. Although there was
very little impact on national-level PM2.5 emissions,
PM2.5 emission changes were too high in some counties and
too low in others, by varying degrees. As a result, we do not present
the modeling results for specific localized PM2.5 impacts.
However, we have concluded that PM2.5 modeling results are
still informative for national-level benefits assessment, as discussed
at more length in Section VIII.D of this preamble and the RIA.
     Sensitivity of light-duty vehicle exhaust emissions to
ethanol blends
    As discussed above in Sections VI.A and VI.C of this preamble, the
interim emission inventories used for the air quality modeling analysis
are the ``more sensitive'' case described in the proposal. As a result,
the interim inventories used for air quality modeling assume that
vehicles operating on E10 have higher NOX emissions and
lower VOC, CO and PM exhaust emissions compared to the FRM inventories.
     Cellulosic plant emissions
    The interim emission inventories used in air quality modeling
generally assumed higher emissions from cellulosic plants than the FRM
inventories, which used revised estimates based on updates to the
fraction of biomass burned at these plants. However, as noted in
Section VI.A, the shift of some cellulosic volume from ethanol to
diesel results in higher PM emissions from cellulosic plants in the
final rule inventories than used in the air quality modeling
inventories.
     Ethanol volume
    As mentioned above, the interim emission inventories used in our
air quality modeling reflect the use of ethanol in about 34 of the
mandated 36 billion gallons and do not include any cellulosic diesel.
As shown in Table VI.A-1, the FRM inventories assume 22 billion gallons
of ethanol in the primary case and 6.5 billion gallons of cellulosic
diesel. The inventories used for air quality modeling assume ethanol
volumes are more consistent with the FRM's high-ethanol case inventory,
which reflects the use of 33 billion gallons of ethanol and no
cellulosic diesel.
     Renewable fuel transport emissions

    As discussed in Section 3.3, the estimates of renewable fuel
transport volumes and distances differ between the air quality modeling
and final rule inventories.
    In this section, we present information on current modeled levels
of pollution as well as projections for 2022, with respect to ambient
PM2.5, ozone, selected air toxics, and nitrogen and sulfur
deposition. The air quality modeling results indicate that ambient
PM2.5 is likely to increase in areas associated with biofuel
production and transport and decrease in other areas. The results of
the air quality modeling also indicate that many areas of the country
will experience increases in ambient ozone and a few areas will see
decreases in ambient ozone as a result of the renewable fuel volumes
required by RFS2. The modeling also shows that ethanol concentrations
increase substantially with increases in renewable fuel volumes. For
the other modeled air toxics, there are some localized impacts, but
relatively little impact on national average concentrations. Our air
quality modeling does not show substantial overall nationwide impacts
on the annual total sulfur and nitrogen deposition occurring across the
U.S. However, the air quality modeling results indicate that the entire
Eastern half of the U.S. along with the Pacific Northwest would see
increases in nitrogen deposition as a result of increased renewable
fuel use. The results of the modeling also show that sulfur deposition
will increase in the Midwest and in some rural areas of the west
associated with biofuel production. The results are discussed in more
detail below and in Section 3.4 of the RIA.
    We used the Community Multi-scale Air Quality (CMAQ) photochemical
model, version 4.7, for our analysis. This version of CMAQ includes a
number of improvements to previous versions of the model that are
important in assessing impacts of the increased use of renewable fuels,
including additional pathways for formation of soluble organic aerosols
(SOA). These improvements are discussed in Section 3.4 of the RIA.
    In addition to the limitations of the analysis that result from the
use of interim emission inventories rather than the FRM inventories,
there are uncertainties in the air quality analysis that should be
noted. First, there are uncertainties inherent in the modeling process.
Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and
1,3-butadiene can be formed secondarily through atmospheric chemical
processes. These processes can be very complex, and there are
uncertainties in emissions of precursor compounds and reaction
pathways. In addition, simplifications of chemistry must be made in
order to handle reactions of thousands of chemicals in the atmosphere.
Another source of uncertainty involves the hydrocarbon speciation
profiles, which are applied to the VOC inventories to break VOC down
into individual constituent compounds which react in the atmosphere.
Given the complexity of the atmospheric chemistry, the hydrocarbon
speciation has an important influence on the air quality modeling
results. Speciation profiles for a number of key sources are based on
data with significant limitations. Finally, 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. These
plants can have large impacts on local emissions. A more detailed
discussion of these and additional uncertainties and limitations
associated with our air quality modeling is presented in Section 3.4 of
the RIA.
1. Particulate Matter
a. Current Levels
    PM2.5 concentrations exceeding the level of the
PM2.5 NAAQS occur in many parts of the country. In 2005, EPA
designated 39 nonattainment areas for the 1997 PM2.5 NAAQS
(70 FR 943, January 5, 2005). These areas are composed of 208 full or
partial counties with a total population exceeding 88 million. The 1997
PM2.5 NAAQS was recently revised and the 2006 24-hour
PM2.5 NAAQS became effective on December 18, 2006. On
October 8, 2009, the EPA issued final nonattainment area designations
for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13,
2009). These designations include 31 areas composed of 120 full or
partial counties with a population of over 70 million. In total, there
are 54 PM2.5 nonattainment areas composed of 245 counties
with a population of 101 million people.
b. Projected Levels Without RFS2 Volumes
    States with PM2.5 nonattainment areas are required to
take action to bring those areas into compliance in the future. Areas
designated as not attaining the 1997 PM2.5 NAAQS will need
to attain the 1997 standards in the 2010 to 2015 time frame, and then
maintain them thereafter. The 2006 24-hour PM2.5
nonattainment areas will be required to attain the 2006 24-hour
PM2.5 NAAQS in the 2014 to 2019 time frame and then be
required to maintain the 2006 24-hour PM2.5 NAAQS
thereafter.
    EPA has already adopted many emission control programs that are
expected to reduce ambient PM2.5 levels and which will
assist in reducing the number of areas that fail to achieve the
PM2.5 NAAQS. Even so, recent air quality modeling for the
``Control of Emissions from New Marine Compression-Ignition Engines at
or Above 30 Liters per Cylinder'' rule projects that in 2020, at least
10 counties with a population of almost 25 million may not attain the
1997 annual PM2.5 standard of 15 [micro]g/m\3\ and 47
counties with a population of over 53 million may not attain the 2006
24-hour PM2.5 standard of 35 [micro]g/m\3\.\191\ These
numbers do not account for those areas that are close to (e.g., within
10 percent of) the PM2.5 standards. These areas, although
not violating the standards, will also benefit from any reductions in
PM2.5 ensuring long-term maintenance of the PM2.5
NAAQS.
---------------------------------------------------------------------------

    \191\ US EPA (2009). Final Rule ``Control of Emissions from New
Marine Compression-Ignition Engines at or Above 30 Liters per
Cylinder''. (This rule was signed on December 18, 2009 but has not
yet been published in the Federal Register. The signed version of
the rule is available at http://epa.gov/otaq/oceanvessels.htm).
---------------------------------------------------------------------------

c. Projected Levels With RFS2 Volumes
    We are not able to present air quality modeling results which
detail changes in PM2.5 design values for specific local
areas due to the error in the locomotive inventory mentioned in the
introduction to this section. However, we do know that ambient
PM2.5 increases in some areas of the country and decreases
in other areas of the country. Ambient PM2.5 is likely to
increase as a result of emissions at biofuel production plants and from
biofuel transport, both of which are more prevalent in the Midwest. PM
concentrations are likely to decrease in some areas due to reductions
in SOA formation and reduced emissions from gasoline refineries. In
addition, decreases in ambient PM are predicted because our modeling
inventory assumed that E85 usage reduces PM tailpipe emissions. The
decreases in ambient PM from reductions in SOA and tailpipe emissions
are likely to occur where there is a higher density of vehicles, such
as the Northeast. See Section VIII.D for a discussion of the changes in
national average population-weighted PM2.5 concentrations.

2. Ozone
a. Current Levels
    8-hour ozone concentrations exceeding the level of the ozone NAAQS
occur in many parts of the country. In 2008, the U.S. EPA amended the
ozone NAAQS (73 FR 16436, March 27, 2008). The final 2008 ozone NAAQS
rule set forth revisions to the previous 1997 NAAQS for ozone to
provide increased protection of public health and welfare. As of
January 6, 2010 there are 51 areas designated as nonattainment for the
1997 8-hour ozone NAAQS, comprising 266 full or partial counties with a
total population of over 122 million people. These numbers do not
include the people living in areas where there is a future risk of
failing to maintain or attain the 1997 8-hour ozone NAAQS. The numbers
above likely underestimate the number of counties that are not meeting
the ozone NAAQS because the nonattainment areas associated with the
more stringent 2008 8-hour ozone NAAQS have not yet been
designated.\192\ Table VI.D-1 provides an estimate, based on 2005-07
air quality data, of the counties with design values greater than the
2008 8-hour ozone NAAQS of 0.075 ppm.
---------------------------------------------------------------------------

    \192\ EPA recently proposed to reconsider the 2008 NAAQS.
Because of the uncertainty the reconsideration proposal creates
regarding the continued applicability of the 2008 ozone NAAQS, EPA
has used its authority to extend by 1 year the deadline for
promulgating designations for those NAAQS. The new deadline is March
2011. EPA intends to complete the reconsideration by August 31,
2010.

  Table VI.D-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             266    122,343, 799
 51 areas currently designated as
 nonattainment (as of 1/6/10)...........
2008 Ozone Standard: Additional counties             227      41,285,262
 that would not meet the 2008 NAAQS b...
                                         -------------------------------
    Total...............................             493     163,629,061
------------------------------------------------------------------------
Notes:
a Population numbers are from 2000 census data.
b Area designations for the 2008 ozone NAAQS have not yet been made.
  Nonattainment for the 2008 Ozone NAAQS would be based on three years
  of air quality data from later years. Also, the county numbers in this
  row 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.

b. Projected Levels Without RFS2 Volumes
    States with 8-hour ozone nonattainment areas are required to take
action to bring those areas into compliance in the future. Based on the
final rule designating and classifying 8-hour ozone nonattainment areas
for the 1997 standard (69 FR 23951, April 30, 2004), most 8-hour ozone
nonattainment areas will be required to attain the ozone NAAQS in the
2007 to 2013 time frame and then maintain the NAAQS thereafter. EPA has
recently proposed to reconsider the 2008 ozone NAAQS. If EPA
promulgates different ozone NAAQS in 2010 as a result of the
reconsideration, they would fully replace the 2008 ozone NAAQS and
there would no longer be a requirement to designate areas for the 2008
NAAQS. EPA would designate nonattainment areas for a potential new 2010
primary ozone NAAQS based on the reconsideration of the 2008 ozone
NAAQS in 2011. The attainment dates for areas designated nonattainment
for a potential new 2010 primary ozone NAAQS are likely to be in the
2014 to 2031 timeframe, depending on the severity of the problem.
    EPA has already adopted many emission control programs that are
expected to reduce ambient ozone levels and assist in reducing the
number of areas that fail to achieve the ozone NAAQS. Even so, our air
quality modeling projects that in 2022, with all current controls but
excluding the impacts of the renewable fuel volumes required by RFS2,
up to 7 counties with a population of over 22 million may not attain
the 2008 ozone standard of 0.075 ppm (75 ppb). These numbers do not
account for those areas that are close to (e.g., within 10 percent of)
the 2008 ozone standard. These areas, although not violating the
standards, will also benefit from any reductions in ozone ensuring
long-term maintenance of the ozone NAAQS.
c. Projected Levels With RFS2 Volumes
    Our modeling indicates that the required renewable fuel volumes
will cause increases in ozone design value concentrations in many areas
of the country and decreases in ozone design value concentrations in a
few areas. Air quality modeling of the expected impacts of the
renewable fuel volumes required by RFS2 shows that in 2022, most
counties with modeled data, especially those in the southeast U.S.,
will see increases in their ozone design values. These adverse impacts
are likely due to increased upstream emissions of NOX in
many areas that are NOX-limited (acting as a precursor to
ozone formation). The majority of these design value increases are less
than 0.5 ppb. The maximum projected increase in an 8-hour ozone design
value is in Morgan County, Alabama, 1.56 ppb and 1.27 ppb when compared
with the RFS1 mandate and AEO 2007 reference cases respectively. As
mentioned above there are some areas which see decreases in their ozone
design values. This is likely due to VOC emission reductions at the
tailpipe in urban areas that are VOC-limited (reducing VOC's role as a
precursor to ozone formation). The maximum decrease projected in an 8-
hour ozone design value is in Riverside, CA, 0.66 ppb and 0.6 ppb when
compared with the RFS1 mandate and AEO 2007 reference cases
respectively. On a population-weighted basis, the average modeled
future-year 8-hour ozone design values are projected to increase by
0.28 ppb in 2022 when compared with the RFS1 mandate reference case and
increase by 0.16 ppb when compared with the AEO 2007 reference case. On
a population-weighted basis the design values for those counties that
are projected to be above the 2008 ozone standard in 2022 will see
decreases of 0.14 ppb when compared with the RFS1 mandate reference
case and 0.15 ppb when compared with the AEO 2007 reference case.

3. Air Toxics
a. Current Levels
    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.\193\ The levels of air toxics to which
people are exposed vary depending on where people live and work and the
kinds of activities in which they engage, as discussed in detail in
U.S. EPA's recent Mobile Source Air Toxics Rule.\194\ According to the
National Air Toxic Assessment (NATA) for 2002,\195\ mobile sources were
responsible for 47 percent of outdoor toxic emissions, over 50 percent
of the cancer risk, and over 80 percent of the noncancer hazard.
Benzene is the largest contributor to cancer risk of all 124 pollutants
quantitatively assessed in the 2002 NATA and mobile sources were
responsible for 59 percent of benzene emissions in 2002. Over the
years, EPA has implemented a number of mobile source and fuel controls
resulting in VOC reductions, which also reduce benzene and other air
toxic emissions.
---------------------------------------------------------------------------

    \193\ U. S. EPA. (2009) 2002 National-Scale Air Toxics
Assessment. http://www.epa.gov/ttn/atw/nata2002/.
    \194\ U.S. Environmental Protection Agency (2007). Control of
Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR
8434, February 26, 2007.
    \195\ U.S. EPA. (2009) 2002 National-Scale Air Toxics
Assessment. http://www.epa.gov/ttn/atw/nata2002/.
---------------------------------------------------------------------------

b. Projected Levels
    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. For more information on the air toxics modeling results, see
Section 3.4 of the RIA for annual average results and Appendix 3A of
the RIA for seasonal average results. Our discussion of the air quality
modeling results focuses primarily on impacts of the renewable fuel
volumes required by RFS2 in reference to the RFS1 mandate for 2022.
Except where specifically discussed below, air quality modeling results
of increased renewable fuel use with RFS2 as compared to the AEO 2007
reference case are presented in Appendix 3A of this RIA.
i. Acetaldehyde
    Our air quality modeling does not show substantial overall
nationwide impacts on ambient concentrations of acetaldehyde as a
result of the renewable fuel volumes required by this rule, although
there is considerable uncertainty associated with the results. Annual
percent changes in ambient concentrations of acetaldehyde are less than
1% for most of the country, and annual absolute changes in ambient
concentrations of acetaldehyde are generally less than 0.1 [mu]g/
m3. Some urban areas show decreases in ambient acetaldehyde
concentrations ranging from 1 to 10%, and some rural areas associated
with new ethanol plants show increases in ambient acetaldehyde
concentrations ranging from 1 to 10% with RFS2 volumes. This increase
is due to an increase in emissions of primary acetaldehyde and
precursor emissions from ethanol plants. A key reason for the decrease
in urban areas is reductions in certain acetaldehyde precursors,
primarily alkenes (olefins). Most ambient acetaldehyde is formed from
secondary photochemical reactions of numerous precursor compounds, and
many photochemical mechanisms are responsible for this process.
    The uncertainty associated with these results is described in more
detail in Section 3.4 of the RIA. For example, some of the modeled
decreases would likely become increases using data recently collected
by EPA's Office of Research and Development on the composition of
hydrocarbon emissions from gasoline storage, gasoline distribution, and
gas cans. Furthermore, as noted in the introduction to Section VI.D,
the inventories used for air quality modeling may overestimate
NOX, because they assumed that use of E10 would lead to
increases in NOX emissions for later model year vehicles.
The emission inventories for the final rule no longer make this
assumption, based on recent EPA testing results.\196\ Because increases
in NOX may result in more acetyl peroxy radical forming PAN
rather than acetaldehyde, our air quality modeling results may
underestimate the ambient concentrations of acetaldehyde.
---------------------------------------------------------------------------

    \196\ ``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.
---------------------------------------------------------------------------

    Some previous U.S. monitoring studies have suggested an
insignificant or small impact of increased use of ethanol in fuel on
ambient acetaldehyde, as discussed in more detail in Section 3.4 of the
RIA. These studies suggest that increases in direct emissions of
acetaldehyde are offset by decreases in the secondary formation of
acetaldehyde. Other past studies have shown increases in ambient
acetaldehyde with increased use of ethanol in fuel, although factors
such as differences in vehicle fleet, lack of RVP control, and
exclusion of upstream impacts may limit the ability of these studies to
inform expected impacts on ambient air quality Given the conflicting
results among past studies and the limitations of our analysis,
considerable additional work is needed to address the impacts of the
renewable fuel volumes required by this rule on ambient concentrations
of acetaldehyde.
ii. Formaldehyde
    Our air quality modeling results do not show substantial impacts on
ambient concentrations of formaldehyde from the renewable fuel volumes
required by this rule. Most of the U.S. experiences a 1% or less change
in ambient formaldehyde concentrations. Decreases in ambient
formaldehyde concentrations range between 1 and 5% in a few urban
areas. Increases range between 1 and 2.5% in some rural areas
associated with new ethanol plants; this result is due to increases in
emissions of primary formaldehyde and formaldehyde precursors from the
new ethanol plants. Absolute changes in ambient concentrations of
formaldehyde are generally less than 0.1 [mu]g/m3.
iii. 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. The largest increases (more than 100%)
occur in urban areas with high amounts of on-road 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.
Analysis of a modeling error that impacted ethanol emissions suggests
that this error resulted in overestimates of ethanol impacts by more
than 10% across much of the country. For a detailed discussion of this
error, please refer to the emissions modeling TSD, found in the docket
for this rule (EPA-HQ-OAR-2005-0161).
iv. Benzene
    Our modeling projects that the renewable fuel volumes required by
this rule will lead to small nationwide decreases in ambient benzene
concentrations. 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 [mu]g/m3.

v. 1,3-Butadiene
    The results of our air quality modeling show small increases and
decreases in ambient concentrations of 1,3-butadiene in parts of the
U.S. as a result of increases in renewable fuel volumes required by
RFS2. Generally, decreases occur in some southern areas of the country
and increases occur in some northern areas and areas with high
altitudes. Percent changes in 1,3-butadiene concentrations are over 50%
in several areas; but the changes in absolute concentrations of ambient
1,3-butadiene are generally less than 0.005 [mu]g/m \3\. Annual
increases in ambient concentrations of 1,3-butadiene are driven by
wintertime changes. These increases appear in rural areas with cold
winters and low ambient levels but high contributions of emissions from
snowmobiles, and a major reason for this modeled increase may be
deficiencies in available emissions test data used to estimate
snowmobile 1,3-butadiene emission inventories.
vi. Acrolein
    Our air quality modeling shows small regional increases and
decreases in ambient concentrations of acrolein as a result of
increases in renewable fuel volumes required by this rule. Decreases in
acrolein concentrations occur in some eastern and southern parts of the
U.S. and increases occur in some northern areas and areas associated
with new ethanol plants. Changes in absolute ambient concentrations of
acrolein are between  0.001 [micro]g/m[sup3] with the
exception of the increases associated with new ethanol plants. These
increases can be up to and above 0.005 [micro]g/m[sup3] with percent
changes above 50% and are due to increases in emissions of acrolein
from the new plants. Ambient acrolein increases in northern regions are
driven by wintertime changes, and occur in the same areas of the
country that have wintertime 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.
vii. Population Metrics
    To assess the impact of projected changes in ambient air toxics as
a result of increases in renewable fuel volumes required by this rule,
we developed population metrics that show the population experiencing
increases and decreases in annual ambient concentrations of the modeled
air toxics. Table VI.D-2 below illustrates the percentage of the
population impacted by changes of various magnitudes in annual ambient
concentrations with the renewable fuel volumes required by RFS2, as
compared to the RFS1 mandate reference case.

    Table VI.D-2--Percent of Total Population Impacted by Changes in Annual Ambient Concentrations of Toxic Pollutants: RFS2 Compare to RFS1 Mandate
--------------------------------------------------------------------------------------------------------------------------------------------------------
      Percent change in annual ambient          Acetaldehyde        Acrolein           Benzene        1,3-Butadiene        Ethanol        Formaldehyde
                concentration                     (percent)         (percent)         (percent)         (percent)         (percent)         (percent)
--------------------------------------------------------------------------------------------------------------------------------------------------------
<=-100......................................  ................  ................  ................  ................  ................  ................
>-100 to <=-50..............................  ................  ................  ................  ................  ................  ................
>-50 to <=-10...............................              0.76  ................              1.18              1.38  ................  ................
>-10 to <=-5................................              8.17              0.18             12.92             28.11  ................  ................
>-5 to <=-2.5...............................             13.29             13.66             48.76             31.98  ................              4.11
>-2.5 to <=-1...............................             25.26             40.13             23.60             12.87  ................             19.30
>-1 to <1...................................             52.24             36.03             13.55             19.37  ................             76.08
>=1 to <2.5.................................              0.24              3.44  ................              1.53  ................              0.48
>=2.5 to <5.................................              0.04              2.93  ................              1.13              0.22              0.01
>=5 to <10..................................              0.02              2.00  ................              1.13              1.23  ................
>=10 to <50.................................  ................              1.51  ................              2.15             63.29  ................
>=50 to <100................................  ................              0.08  ................              0.28             34.49  ................
>=100.......................................  ................              0.05  ................              0.06              0.77  ................
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Table VI.D-3 shows changes in the population-weighted average
ambient concentrations of air toxics that are projected to occur in
2022 with increased renewable fuel use as required by this rule.

 Table VI.D-3--Population-Weighted Average Ambient Concentrations of Air Toxics in 2022 With RFS2 Renewable Fuel
                                                  Requirements
----------------------------------------------------------------------------------------------------------------
                                      Population-weighted concentration      Population-weighted concentration
                                       (Annual average in [mu]g/m \3\)        (Annual average in [mu]g/m \3\)
                                   -----------------------------------------------------------------------------
                                     RFS2 v. RFS1 mandate reference case      RFS2 v. AEO 2007 reference case
                                   -----------------------------------------------------------------------------
                                                     RFS1     Diff. RFS2-                            Diff. RFS2-
                                        RFS2       mandate        RFS1         RFS2       AEO 2007       AEO
----------------------------------------------------------------------------------------------------------------
Acetaldehyde......................        1.590        1.618       -0.028        1.590        1.613       -0.023
Acrolein..........................        0.017        0.018       -0.001        0.017        0.017      -0.0001
Benzene...........................        0.520        0.535       -0.015        0.520        0.527       -0.007
1,3-Butadiene.....................        0.022        0.023       -0.001        0.022        0.230       -0.208
Ethanol...........................        1.521        1.039        0.482        1.521        1.112        0.409
Formaldehyde......................        1.549        1.558       -0.009        1.549        0.004       -0.006
----------------------------------------------------------------------------------------------------------------

4. Nitrogen and Sulfur Deposition
a. Current Levels
    Over the past two decades, the EPA has undertaken numerous efforts
to reduce nitrogen and sulfur deposition across the U.S. Analyses of
long-term monitoring data for the U.S. show that deposition of both
nitrogen and sulfur compounds has decreased over the last 17 years
although many areas continue to be negatively impacted by deposition.
Deposition of inorganic nitrogen and sulfur species routinely measured
in the U.S. between 2004 and 2006 were as high as 9.6 kilograms of
nitrogen per hectare per year (kg N/ha/yr) and 21.3 kilograms of sulfur
per hectare per year (kg S/ha/yr). The data show that reductions were
more substantial for sulfur compounds than for nitrogen compounds.
These numbers are generated by the U.S. national monitoring network and
they likely underestimate nitrogen deposition because neither ammonia
nor organic nitrogen is measured. In the eastern U.S., where data are
most abundant, total sulfur deposition decreased by about 36% between
1990 and 2005, while total nitrogen deposition decreased by 19% over
the same time frame.\197\
---------------------------------------------------------------------------

    \197\ U.S. EPA. U.S. EPA's 2008 Report on the Environment (Final
Report). U.S. Environmental Protection Agency, Washington, DC, EPA/
600/R-07/045F (NTIS PB2008-112484).
---------------------------------------------------------------------------

b. Projected Levels
    Our air quality modeling does not show substantial overall
nationwide impacts on the annual total sulfur and nitrogen deposition
occurring across the U.S. as a result of increased renewable fuel
volumes required by this rule. For sulfur deposition, 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.
When compared to the AEO 2007 reference case, the changes are more
limited. The Midwest will still have sulfur deposition increases
ranging from 1% to more than 4%, but the size of the area with these
changes will be smaller. The Pacific Northwest has minimal areas with
increases in sulfur deposition when compared to the AEO 2007 reference
case. When compared to both the RFS1 mandate and AEO 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%. For a map of 2022 sulfur deposition impacts and additional
information on these impacts, see Section <IP_ADDRESS> of the RIA.
    Overall, nitrogen deposition impacts in 2022 resulting from the
renewable fuel volumes required by RFS2 are more widespread than the
sulfur deposition impacts. When compared to the RFS1 mandate 2007
reference case, nearly the entire eastern half of the United States
will see nitrogen deposition increases ranging from 0.5% to more than
2%. The largest increases will occur in the states of Illinois,
Michigan, Indiana, Wisconsin, and Missouri, with large portions of each
of these states seeing nitrogen deposition increases of more than 2%.
The Pacific Northwest will also experience increases in nitrogen of
0.5% to more than 2%. When compared to the AEO 2007 reference case, the
changes in nitrogen deposition are more limited. The eastern half of
the United States will still see nitrogen deposition increases ranging
from 0.5% to more than 2%; however, the size of the area with these
changes will be smaller. Increases of more than 2% will primarily occur
only in Illinois, Indiana, Michigan, and Missouri. Fewer areas in the
Pacific Northwest will have increases in nitrogen deposition when
compared to the AEO 2007 reference case. In both the RFS1 mandate and
AEO 2007 reference cases, the Mountain West and Southwest will see only
minimal changes in nitrogen deposition, ranging from decreases of less
than 0.5% to increases of less than 0.5%. A few areas in Minnesota and
western Kansas would experience reductions of nitrogen up to 2%. See
Section <IP_ADDRESS> of the RIA for a map and additional information on
nitrogen deposition impacts.

E. Health Effects of Criteria and Air Toxics Pollutants

1. Particulate Matter
a. Background
    Particulate matter is a generic term for a broad class of
chemically and physically diverse substances. It can be principally
characterized as discrete particles that exist in the condensed (liquid
or solid) phase spanning several orders of magnitude in size. Since
1987, EPA has delineated that subset of inhalable particles small
enough to penetrate to the thoracic region (including the
tracheobronchial and alveolar regions) of the respiratory tract
(referred to as thoracic particles). Current NAAQS use PM2.5
as the indicator for fine particles (with PM2.5 referring to
particles with a nominal mean aerodynamic diameter less than or equal
to 2.5 [mu]m), and use PM10 as the indicator for purposes of
regulating the coarse fraction of PM10 (referred to as
thoracic coarse particles or coarse-fraction particles; generally
including particles with a nominal mean aerodynamic diameter greater
than 2.5 [mu]m and less than or equal to 10 [mu]m, or
PM10-2.5). Ultrafine particles are a subset of fine
particles, generally less than 100 nanometers (0.1 [mu]m) in
aerodynamic diameter.
    Fine particles are produced primarily by combustion processes and
by transformations of gaseous emissions (e.g., SOX,
NOX and VOC) in the atmosphere. The chemical and physical
properties of PM2.5 may vary greatly with time, region,
meteorology, and source category. Thus, PM2.5 may include a
complex mixture of different pollutants including sulfates, nitrates,
organic compounds, elemental carbon and metal compounds. These
particles can remain in the atmosphere for days to weeks and travel
hundreds to thousands of kilometers.
b. Health Effects of PM
    Scientific studies show ambient PM is associated with a series of
adverse health effects. These health effects are discussed in detail in
EPA's 2004 Particulate Matter Air Quality Criteria Document (PM AQCD)
and the 2005 PM Staff Paper.198 199 200 Further discussion
of health effects associated with PM can also be found in the RIA for
this rule.
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    \198\ 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/.
    \199\ U.S. EPA. (2005). Review of the National Ambient Air
Quality Standard for Particulate Matter: Policy Assessment of
Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R-
05-005a. Retrieved March 19, 2009 from http://www.epa.gov/ttn/naaqs/
standards/pm/data/pmstaffpaper_20051221.pdf.
    \200\ 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.
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    Health effects associated with short-term exposures (hours to days)
to ambient PM include premature mortality, aggravation of
cardiovascular and lung disease (as indicated by

increased hospital admissions and emergency department visits),
increased respiratory symptoms including cough and difficulty
breathing, decrements in lung function, altered heart rate rhythm, and
other more subtle changes in blood markers related to cardiovascular
health.\201\ Long-term exposure to PM2.5 and sulfates has
also been associated with mortality from cardiopulmonary disease and
lung cancer, and effects on the respiratory system such as reduced lung
function growth or development of respiratory disease. A new analysis
shows an association between long-term PM2.5 exposure and a
subclinical measure of atherosclerosis.202 203
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    \201\ U.S. EPA. (2006). National Ambient Air Quality Standards
for Particulate Matte. 71 FR 61144, October 17, 2006.
    \202\ K[uuml]nzli, N., Jerrett, M., Mack, W.J., et al. (2004).
Ambient air pollution and atherosclerosis in Los Angeles. Environ
Health Perspect.,113, 201-206.
    \203\ This study is 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.
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    Studies examining populations exposed over the long term (one or
more years) to different levels of air pollution, including the Harvard
Six Cities Study and the American Cancer Society Study, show
associations between long-term exposure to ambient PM2.5 and
both all cause and cardiopulmonary premature
mortality.204 205 206 In addition, an extension of the
American Cancer Society Study shows an association between
PM2.5 and sulfate concentrations and lung cancer
mortality.\207\
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    \204\ Dockery, D.W., Pope, C.A. III, Xu, X, et al. (1993). An
association between air pollution and mortality in six U.S. cities.
N Engl J Med, 329, 1753-1759. Retrieved on March 19, 2009 from
http://content.nejm.org/cgi/content/full/329/24/1753.
    \205\ Pope, C.A., III, Thun, M.J., Namboodiri, M.M., Dockery,
D.W., Evans, J.S., Speizer, F.E., and Heath, C.W., Jr. (1995).
Particulate air pollution as a predictor of mortality in a
prospective study of U.S. adults. Am. J. Respir. Crit. Care Med,
151, 669-674.
    \206\ Krewski, D., Burnett, R.T., Goldberg, M.S., et al. (2000).
Reanalysis of the Harvard Six Cities study and the American Cancer
Society study of particulate air pollution and mortality. A special
report of the Institute's Particle Epidemiology Reanalysis Project.
Cambridge, MA: Health Effects Institute. Retrieved on March 19, 2009
from http://es.epa.gov/ncer/science/pm/hei/Rean-ExecSumm.pdf.
    \207\ Pope, C. A., III, Burnett, R.T., Thun, M. J., Calle, E.E.,
Krewski, D., Ito, K., Thurston, G.D., (2002). Lung cancer,
cardiopulmonary mortality, and long-term exposure to fine
particulate air pollution. J. Am. Med. Assoc., 287, 1132-1141.
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2. Ozone
a. Background
    Ground-level ozone pollution is typically formed by the reaction of
VOC and NOX in the lower atmosphere in the presence of heat
and sunlight. These pollutants, often referred to as ozone precursors,
are emitted by many types of pollution sources, such as highway and
nonroad motor vehicles and engines, power plants, chemical plants,
refineries, makers of consumer and commercial products, industrial
facilities, and smaller area sources.
    The science of ozone formation, transport, and accumulation is
complex.\208\ 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
occurs on a single high-temperature day. Ozone can be transported
hundreds of miles downwind from precursor emissions, resulting in
elevated ozone levels even in areas with low local VOC or
NOX emissions.
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    \208\ U.S. EPA. (2006). Air Quality Criteria for Ozone and
Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF.
Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket
EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/.
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b. Health Effects of Ozone
    The health and welfare effects of ozone are well documented and are
assessed in EPA's 2006 Air Quality Criteria Document (ozone AQCD) and
2007 Staff Paper.209 210 Ozone can irritate the respiratory
system, causing coughing, throat irritation, and/or uncomfortable
sensation in the chest. Ozone can reduce lung function and make it more
difficult to breathe deeply; breathing may also become more rapid and
shallow than normal, thereby limiting a person's activity. Ozone can
also aggravate asthma, leading to more asthma attacks that require
medical attention and/or the use of additional medication. In addition,
there is suggestive evidence of a contribution of ozone to
cardiovascular-related morbidity and highly suggestive evidence that
short-term ozone exposure directly or indirectly contributes to non-
accidental and cardiopulmonary-related mortality, but additional
research is needed to clarify the underlying mechanisms causing these
effects. In a recent report on the estimation of ozone-related
premature mortality published by the National Research Council (NRC), a
panel of experts and reviewers concluded that short-term exposure to
ambient ozone is likely to contribute to premature deaths and that
ozone-related mortality should be included in estimates of the health
benefits of reducing ozone exposure.\211\ Animal toxicological evidence
indicates that with repeated exposure, ozone can inflame and damage the
lining of the lungs, which may lead to permanent changes in lung tissue
and irreversible reductions in lung function. People who are more
susceptible to effects associated with exposure to ozone can include
children, the elderly, and individuals with respiratory disease such as
asthma. Those with greater exposures to ozone, for instance due to time
spent outdoors (e.g., children and outdoor workers), are of particular
concern.
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    \209\ U.S. EPA. (2006). Air Quality Criteria for Ozone and
Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF.
Washington, DC: U.S. EPA. Retrieved on March 19, 2009 from Docket
EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/.
    \210\ U.S. EPA. (2007). Review of the National Ambient Air
Quality Standards for Ozone: Policy Assessment of Scientific and
Technical Information, OAQPS Staff Paper. EPA-452/R-07-003.
Washington, DC, U.S. EPA. Retrieved on March 19, 2009 from Docket
EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/.
    \211\ National Research Council (NRC), 2008. Estimating
Mortality Risk Reduction and Economic Benefits from Controlling
Ozone Air Pollution. The National Academies Press: Washington, DC.
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    The 2006 ozone AQCD also examined relevant new scientific
information that has emerged in the past decade, including the impact
of ozone exposure on such health effects as changes in lung structure
and biochemistry, inflammation of the lungs, exacerbation and causation
of asthma, respiratory illness-related school absence, hospital
admissions and premature mortality. Animal toxicological studies have
suggested potential interactions between ozone and PM with increased
responses observed to mixtures of the two pollutants compared to either
ozone or PM alone. The respiratory morbidity observed in animal studies
along with

the evidence from epidemiologic studies supports a causal relationship
between acute ambient ozone exposures and increased respiratory-related
emergency room visits and hospitalizations in the warm season. In
addition, there is suggestive evidence of a contribution of ozone to
cardiovascular-related morbidity and non-accidental and cardiopulmonary
mortality.
3. NOX and SOX
a. Background
    Nitrogen dioxide (NO2) is a member of the NOX
family of gases. Most NO2 is formed in the air through the
oxidation of nitric oxide (NO) emitted when fuel is burned at a high
temperature. SO2, a member of the sulfur oxide
(SOX) family of gases, is formed from burning fuels
containing sulfur (e.g., coal or oil derived), extracting gasoline from
oil, or extracting metals from ore.
    SO2 and NO2 can dissolve in water
vapor and further oxidize to form sulfuric and nitric acid which react
with ammonia to form sulfates and nitrates, both of which are important
components of ambient PM. The health effects of ambient PM are
discussed in Section VI.D.1 of this preamble. NOX along with
non-methane hydrocarbon (NMHC) are the two major precursors of ozone.
The health effects of ozone are covered in Section VI.D.2.
b. Health Effects of NOX
    Information on the health effects of NO2 can be found in
the U.S. Environmental Protection Agency Integrated Science Assessment
(ISA) for Nitrogen Oxides.\212\ The U.S. EPA has concluded that the
findings of epidemiologic, controlled human exposure, and animal
toxicological studies provide evidence that is sufficient to infer a
likely causal relationship between respiratory effects and short-term
NO2 exposure. The ISA concludes that the strongest evidence
for such a relationship comes from epidemiologic studies of respiratory
effects including symptoms, emergency department visits, and hospital
admissions. The ISA also draws two broad conclusions regarding airway
responsiveness following NO2 exposure. First, the ISA
concludes that NO2 exposure may enhance the sensitivity to
allergen-induced decrements in lung function and increase the allergen-
induced airway inflammatory response following 30-minute exposures of
asthmatics to NO2 concentrations as low as 0.26 ppm. In
addition, small but significant increases in non-specific airway
hyperresponsiveness were reported following 1-hour exposures of
asthmatics to 0.1 ppm NO2. Second, exposure to
NO2 has been found to enhance the inherent responsiveness of
the airway to subsequent nonspecific challenges in controlled human
exposure studies of asthmatic subjects. Enhanced airway responsiveness