platform like CMAQ is based on adhering to consistent treatment of mass
conservation, by linking emission inputs with air concentrations and
concentrations to deposition. Inconsistencies from combining processes
from different analytical platforms increase the chance that mass (of
nitrogen or sulfur) would unintentionally be increased or decreased as
the internal checking that assures mass conservation is lost.
Transference ratios incorporate a broad suite of atmospheric processes
and consequently an analytical approach that instills consistency in
the linkage of these processes is preferable to an approach lacking
such inherent consistency. This contention does not mean that
observations alone, if available, could not be used, but suggests that
the inconsistencies in combining models and observations for the
purposes of developing transference ratios has the potential for
creating unintended artifacts.
    While there is a reasonable conceptual basis for the concept of an
aggregated deposition velocity referred to in the PA as a transference
ratio, there is very limited ability to compare observed and calculated
ratios. This is because the deposition velocity is dependent on
individual species, and the mass transfer processes of wet and dry
removal, while conceptually similar, are different. Consequently, there
does not exist a meaningful approach to measure such an aggregated or
lumped parameter. Therefore, at this time, the evaluation of
transference ratios is based on sensitivity studies, analysis of
variability, and comparisons with other models, as described in
Appendix F of the PA.
    As discussed in Appendix F, the interannual variability, as well as
the sensitivity to emission changes of roughly 50 percent, results in
changes of transference ratios of approximately 5 to 10 percent. Part
of the reason for this inherent stability is due to the co-dependence
of concentration and deposition. For example, as concentrations are
reduced as a result of emissions reductions, deposition in turn

is reduced since deposition is a direct linear function of
concentration leading to negligible impact on the deposition-to-
concentration ratio. Likewise, an overestimate of concentration likely
does not induce a bias in the transference ratio. While it is important
to continue to improve the model's ability to match ambient
concentrations in time and space, the bias of a modeled estimate of
concentration relative to observations does not necessarily result in a
bias in a calculated transference ratio. In effect, this consideration
of bias cancellation reduces the sensitivity of transference ratios to
model uncertainties and affords increased confidence in the stability
of these ratios. Based on the series of sensitivity and variability
analyses, the PA concludes that the transference ratios are relatively
stable and provide a sound metric for linking deposition and
concentration.
    As discussed in the PA, transference ratios are dependent on the
platform upon which they are constructed. Comparisons of transference
ratios constructed from different modeling platforms do exhibit
significant differences. While this divergence of results may be
explained by a variety of differences in process treatments, input
fields and incommensurabilities in species definitions and spatial
configurations, it does suggest two very important conclusions. First,
the idea of using multiple platforms for different parts of the country
may be problematic as there does not exist a reliable approach to judge
acceptance which is almost always based on comparisons to observations.
Second, since transference ratios are based on concentrations and
deposition, as the uncertainties in each of those components are
reduced, the relative uncertainty in the ratios also is reduced. This
means that basic improvements in the model's ability to reproduce
observed wet deposition and ambient concentration fields enhance the
relative confidence in the constructed transference ratios. Similarly,
as in-situ dry deposition flux measurements become available that
enable a more rigorous evaluation and diagnosis of modeled dry
deposition processes, the expected improved treatment of dry deposition
also would increase confidence in transference ratios. Finally,
deposition is directly related to ambient air concentrations. Models
like CMAQ rely on the concentration-to-deposition linkage to calculate
deposition, which is the foundation for broadly based and robust
assessments addressing atmospheric deposition. In principle, the use of
a modeled constructed transference ratio is based on the same premise
by which we use models to estimate deposition in the first place.
    The shortage of widely available ambient air observations and the
fact that estimates of dry deposition requires modeling, collectively
suggests that a unified modeling platform is the best approach for
constructing transference ratios. The PA (U.S. EPA, 2011, section 2)
considers CMAQ and other models, such as CAMx and Canada's AURAMS--A
Unified Regional Air-quality Modeling System (Smythe et al., 2008), and
concludes that CMAQ is the preferred modeling platform for constructing
transference ratios. This conclusion reflects the view that for the
purposes of defining transference ratios, a modeling platform should:
(1) Be a multiple pollutant model recognizing the myriad of connections
across pollutant categories that directly and indirectly impact
nitrogen and sulfur characterization, (2) include the most
comprehensive scientific treatments of atmospheric processes that
relate directly and indirectly to characterizing concentrations and
deposition, (3) have an infrastructure capability that accommodates the
inclusion of improved scientific treatments of relevant processes and
important input fields, and (4) undergo frequent reviews regarding the
adequacy of the underlying science as well as the appropriateness in
applications. The CMAQ platform exhibits all these characteristics. It
has been (and continues to be) extensively evaluated for several
pollutant categories, and is supported by a central infrastructure of
EPA scientists, whose mission is to improve and evaluate the CMAQ
platform. More directly, CMAQ, and its predecessor versions, has a long
track record going back to the NAPAP in the 1980s of specific
improvements in deposition processes, which are described in Appendix F
of the PA.
4. Aquatic Acidification Index
    Having established the various expressions that link atmospheric
deposition of nitrogen and sulfur to ANC and the transference ratios
that translate atmospheric concentrations to deposition of nitrogen and
sulfur, the PA derived the following expression of these linkages,
which separates reduced forms of nitrogen, NHX, from
oxidized forms:
[GRAPHIC] [TIFF OMITTED] TP01AU11.026

    Equation III-3 is the basic expression of the form of a standard
that translates the conceptual framework into an explicit expression
that defines ANC as a function of the ambient air indicators,
NOY and SOX reduced nitrogen deposition,\9\ and
the critical load necessary to achieve a target ANC level. This
equation calculates an expected ANC value based on ambient
concentrations of NOY and SOX. The calculated ANC
will differ from the target ANC (ANClim) depending on how much the
nitrogen and sulfur deposition associated with NOY,
SOX, and NHX differs from the critical load
associated with just achieving the target ANC.
---------------------------------------------------------------------------

    \9\ Because NHx is characterized directly as deposition, not as
an ambient concentration in this equation, no transference ratio is
needed for this term.
---------------------------------------------------------------------------

    Based on equation III-3, the PA defines an AAI that is more simply
stated using terms that highlight the ambient air indicators:
[GRAPHIC] [TIFF OMITTED] TP01AU11.027

where the AAI represents the long term (or steady state) ANC level
associated with ambient air concentrations of NOY and
SOX. The factors F1 through F4 convey three attributes: a
relative measure of the ecosystem's ability to neutralize acids (F1),
the acidifying potential of reduced nitrogen deposition (F2), and the
deposition-to-concentration translators for NOY (F3) and
SOX (F4).

    Specifically:

F1 = ANClim + CLr/Qr;

F2 = NHx/Qr = NHx deposition divided by Qr;
F3 = TNOy/ Qr; TNOy is the transference ratio
that converts ambient air concentrations of NOy to deposition of
NOy; and
F4 = TSOx/ Qr; TSOx is the transference ratio
that converts ambient air concentrations of SOX to
deposition of SOX.

All of these factors include representative Qr to maintain unit (and
mass) consistency between the AAI and the terms on the right side of
equation III-4.
    The F1 factor is the target ANC level plus the amount of deposition
(critical load) the ecosystem can receive and still achieve the target
level. It incorporates an ecosystem's ability to generate acid
neutralizing capacity through base cation supply ([BC]*0) and to
neutralize acidifying nitrogen deposition through Neco, both of which
are incorporated in the CL term. As noted above, because Neco can only
neutralize nitrogen deposition (oxidized or reduced) there may be rare
cases where Neco exceeds the combination of reduced and oxidized
nitrogen deposition. Consequently, to ensure that the AAI equation is
applicable in all cases that may occur, equation III-4 is conditional
on total nitrogen deposition, {NHX + F3[NOy]{time} , being
greater than Neco. In rare cases where Neco is greater than
{NHX + F3[NOy]{time} , F2, F3, and Neco would be set equal
to 0 in the AAI equation. The consequence of setting F2 and F3 to zero
is simply to constrain the AAI calculation just to SOx, as
nitrogen would have no bearing on acidifying contributions in this
case.
    The PA concludes that equation III-4 (U.S. EPA, 2011,equation 7-
12), which defines the AAI, is ecologically relevant and appropriate
for use as the form of a national standard designed to provide
protection for aquatic ecosystems from the effects of acidifying
deposition associated with concentrations of oxides of nitrogen and
sulfur in the ambient air. This AAI equation does not, however, in
itself, define the spatial areas over which the terms of the equation
would apply. To specify values for factors F1 through F4, it is
necessary to define spatial areas over which these factors are
determined. Thus, it is necessary to identify an approach for spatially
aggregating water bodies into ecologically meaningful regions across
the U.S., as discussed below.
5. Spatial Aggregation
    As discussed in the PA, one of the unique aspects of this form is
the need to consider the spatial areas over which values for the F
factors in the AAI equation are quantified. Ecosystems across the U.S.
exhibit a wide range of geological, hydrological and vegetation
characteristics that influence greatly the ecosystem parameters, Q,
BC0-* and Neco that are incorporated in the AAI. Variations
in ecosystem attributes naturally lead to wide variability in the
sensitivities of water bodies in the U.S. to acidification, as well as
in the responsiveness of water bodies to changes in acidifying
deposition. Consequently, variations in ecosystem sensitivity, and the
uncertainties inherent in characterizing these variations, must be
taken into account in developing a national standard. In developing a
secondary NAAQS to protect public welfare, the focus of the PA is on
protecting sensitive populations of water bodies, not on each
individual water body, which is consistent with the Agency's approach
to protecting public health through primary NAAQS that focus on
susceptible populations, not on each individual.
    The approach used for defining ecologically relevant regions across
the U.S. in the PA (U.S. EPA, 2011, section 7.2.5) is described below,
along with approaches to characterizing each region as acid sensitive
or relatively non-acid sensitive. This characterization facilitates a
more detailed analysis and focus on those regions that are relatively
more acid sensitive. This characterization is also used to avoid over-
protection in relatively non-acid sensitive regions, regions that would
receive limited benefit from reductions in the deposition of oxides of
nitrogen and sulfur with respect to aquatic acidification effects.
Approaches to developing representative values for each of the terms in
the AAI equation (factors F1 through F4) for each ecologically relevant
region for which sufficient data are available are then discussed.
These spatial aggregation approaches are generally applicable to the
contiguous U.S. The following discussion also addresses the development
of factors for data-limited regions and specifically for Hawaii, Alaska
and the U.S. territories.
    Stated more simply, this section discusses appropriate ways to
divide the country into ecologically relevant regions; to characterize
each region as either acid sensitive or relatively non-acid sensitive;
and to determine values of factors F1 through F4 for each region,
taking into consideration the acid sensitivity of each region and the
availability of relevant data. For each such region, the AAI would be
calculated based on the values of factors F1 through F4 specified for
that region.
    In considering approaches to spatial aggregation, the PA focuses on
methods that have been developed to define ecologically relevant
regions, referred to as ecoregions, which are meaningfully related to
the factors that are relevant to aquatic acidification. As noted above,
the PA did not focus on looking at each individual water body, nor did
it focus on aggregating over the entire nation, which would preclude
taking into account the inherent variability in atmospheric and
ecological factors that fundamentally modify the relationships that are
central to the development of an ecologically relevant AAI.
    Based on considering available classification schemes, the PA
concludes that Omernik's ecoregion classification (as described at
http://www.epa.gov/wed/pages/ecoregions) is the most appropriate method
to consider for the purposes of this review. This classification offers
several levels of spatial delineation, has undergone an extensive
scientific peer review process, and has explicitly been applied to
delineating acid sensitive areas within the U.S. Further, the PA
concludes that ecoregion level III (Figure III-1) resolution, with 84
defined ecoregions in the contiguous U.S.,\10\ is the most appropriate
level to consider for this purpose. The spatial resolution afforded by
level III strikes an appropriate balance relative to the reasoning that
supports conclusions on indicators, as discussed above. The PA
concludes that the most detailed level of resolution (level IV) is not
appropriate given the limited data availability to address nearly 1,000
subdivisons within that level and the currently evolving nature of
level IV regions. Further, level III ecoregions are preferred to level
II in that level III ecoregions, but not level II ecoregions, are
largely contiguous in space which allows for a more coherent
development of information to quantify the AAI factors and to
characterize the concentrations of NOy and SOx in the ambient air
within each ecoregion.
---------------------------------------------------------------------------

    \10\ We note that an 85th area within Omernik's Ecoregion Level
III is currently being developed for California.
---------------------------------------------------------------------------

    Appendix C of the PA includes a description of each level III
ecoregion. The PA notes that the use of ecoregions is an appropriate
spatial aggregation scheme for an AAI-based standard focused on
deposition-related aquatic acidification effects, while many of the
same ecoregion attributes may be applicable in subsequent NAAQS reviews
that may address other deposition-related aquatic and terrestrial
ecological effects. Because atmospheric deposition is modified by
ecosystem attributes, the types of vegetation, soils, bedrock geology,
and

topographic features that are the basis of this ecoregion
classification approach also will likely be key attributes for other
deposition-related effects (e.g., terrestrial acidification, nutrient
enrichment) that link atmospheric concentrations to an aquatic or
terrestrial ecological indicator.
[GRAPHIC] [TIFF OMITTED] TP01AU11.028

a. Ecoregion Sensitivity
    The PA used Omernik's original alkalinity data (U.S. EPA, 2011,
section 2) and more recent ANC data to delineate two broad groupings of
ecoregions: Acid-sensitive and relatively non-acid sensitive
ecoregions. This delineation was made to facilitate greater focus on
those ecoregions with water bodies that generally have greater acid
sensitivity and to avoid over-protection in regions with generally less
sensitive water bodies. The approach used to delineate acid-sensitive
and relatively non-acid sensitive regions included an initial
numerical-based sorting scheme using ANC data, which categorized
ecoregions with relatively high ANC values as being relatively non-acid
sensitive. This initial delineation resulted in 29 of the 84 Omernik
ecoregions being categorized as acid sensitive. Subsequently, land use
data were also considered to determine to what extent an ecoregion is
of a relatively pristine and rural nature by quantifying the degree to
which active management practices related to development and
agriculture occur in each ecoregion.
    The overall objective is to produce a logical and practical
grouping of ecoregions that experience adverse conditions with respect
to aquatic acidification and are likely to respond to changes in
concentrations of NOy and SOx in the ambient air
and to the related deposition levels. To achieve this goal, a two-step
process has been applied, first identifying acid sensitive ecoregions
based on water quality data alone, and second identifying among those
acid-sensitive ecoregions those with highly developed and managed
areas. These highly developed and managed ecoregions are placed in a
non-acid sensitive category to avoid over protection beyond what is
requisite to protect public welfare. More specifically, in determining
an ecoregion's acid sensitivity status in step 1, ANC data across the
84 ecoregions are sorted (U.S. EPA, 2011, section 7) to determine the
number of water bodies within a region with ANC values suggestive of
acid sensitivity, so as to screen out regions with an overabundance of
high ANC values. In reviewing the ANC data, the PA identified 29
ecoregions that meet two criteria: (1) Greater than 5 percent of water
bodies with data with ANC values less than 200 [micro]eq/L and (2)
greater than 1 percent of water bodies with ANC values less than 100
[micro]eq/L. In step 2, land use data were used to identify those acid
sensitive ecoregions with significant managed areas that would not be
considered as having a relatively pristine and rural character. The
percentage of the combination of developed (residential,
transportation, industrial and commercial) and agricultural (croplands,
pastures, orchards, vineyards) land use was used as an indicator of
managed land use area. Forest cover was used as an indicator of non-
managed land use more directly reflecting the pristine quality of a
region. Based on the 2006 National Land Cover Data base (NLCD, http://www.epa.gov/mrlc/nlcd-2006.html), acid sensitive ecoregions would meet
both of the following land use data

criteria: Percent of developed and agricultural area less than 20
percent combined with forested area greater than 50 percent. The
combination of steps 1 and 2 identify 22 relatively acid sensitive
areas (Table III-1 and Figure III-2).
    Consideration was also given to the use of naturally acidic
conditions in defining relatively non-acid sensitive areas. For
example, several of the ecoregions located in plains near the coast
exhibit elevated dissolved organic carbon (DOC) levels, which is
associated with naturally acidic conditions. The DOC in surface waters
is derived from a variety of weak organic acid compounds generated from
the natural availability and decomposition of organic matter from
biota. Consequently, high DOC is associated with ``natural'' acidity,
with the implication that a standard intended to protect against
atmospheric contributions to acidity is not an area of focus. The
evidence suggests that several of the more highly managed ecoregions in
coastal or near coastal transition zones are associated with relatively
high DOC values, typically exceeding on average 5 mg/l, compared to
other acid sensitive areas. Although there is sound logic to interpret
naturally acidic areas as relatively non-acid sensitive, natural
acidity indicators were not explicitly included in defining relatively
non-acid sensitive areas as there does not exist a consensus-based
quantifiable scientific definition of natural acidity. Approaches to
explicitly define natural acidity likely will be pursued in future
reviews of the standard.

              Table III-1--List of 22 Acid-Sensitive Areas
------------------------------------------------------------------------
                                                               Ecoregion
                       Ecoregion name                             No.
------------------------------------------------------------------------
Ridge and Valley............................................       8.4.1
Northern Appalachian Plateau and Uplands....................       8.1.3
Piedmont....................................................       8.3.4
Western Allegheny Plateau...................................       8.4.3
Southwestern Appalachians...................................       8.4.9
Boston Mountains............................................       8.4.6
Blue Ridge..................................................       8.4.4
Ouachita Mountains..........................................       8.4.8
Central Appalachians........................................       8.4.2
Northern Lakes and Forests..................................       5.2.1
Maine/New Brunswick Plains and Hills........................       8.1.8
North Central Appalachians..................................       5.3.3
Northern Appalachian and Atlantic Maritime Highlands........       5.3.1
Columbia Mountains/Northern Rockies.........................       6.2.3
Middle Rockies..............................................      6.2.10
Wasatch and Uinta Mountains.................................      6.2.13
North Cascades..............................................       6.2.5
Cascades....................................................       6.2.7
Southern Rockies............................................      6.2.14
Sierra Nevada...............................................      6.2.12
Idaho Batholith.............................................      6.2.15
Canadian Rockies............................................       6.2.4
------------------------------------------------------------------------

                                                              [GRAPHIC] [TIFF OMITTED] TP01AU11.029

b. Representative Ecoregion-Specific Factors
    Having concluded that the Omernik level III ecoregions are an
appropriate approach to spatial aggregation for the purpose of a
standard to address deposition-related aquatic acidification effects,
the PA uses those ecoregions to define each of the factors in the AAI
equation. As discussed below, factors F1 through F4 in equation III-4
are defined for each ecoregion by specifying ecoregion-specific values
for each factor based on monitored or modeled data that are
representative of each ecoregion.
i. Factor F1
    As discussed above, factor F1 reflects a relative measure of an
ecosystem's ability to neutralize acidifying deposition, and is defined
as: F1 = ANClim + CLr/Qr. The value of F1 for each ecoregion would be
based on a representative critical load for the ecoregion
(CLr) associated with a single national target ANC level
(ANClim, discussed below in section III.D), as well as on a
representative runoff rate (Qr). To specify ecoregion-
specific values for the term Qr, the PA used the median
value of the distribution of Q values that are available for water
bodies within each ecoregion. To specify ecoregion-specific
representative values for the term CLr in factor F1, a
distribution \11\ of calculated critical loads was created for the
water bodies in each ecoregion for which sufficient water quality and
hydrology data are available.\12\ The representative critical load was
then defined to be a specific percentile of the distribution of
critical loads in the ecoregion. Thus, for example, using the 90th
percentile means that within an ecoregion, 90 percent of the water
bodies would be expected to have higher calculated critical loads than
the representative critical load. That is, if the representative
critical load were to occur across the ecoregion, 90 percent of the
water bodies would be expected to achieve the national ANC target or
better.
---------------------------------------------------------------------------

    \11\ The distribution of critical loads was based on CL values
calculated with Neco at the lake level. Consideration could also be
given to using a distribution of CLs without Neco and adding the
ecoregion average Neco value to the nth percentile critical load.
This would avoid cases where the lake-level Neco value potentially
could be greater than total nitrogen deposition. The CL at the lake
level represents the CL for the lake to achieve the specified
national target ANC value.
    \12\ The PA judged the data to be sufficient for this purpose if
data are available from more than 10 water bodies in an ecoregion.
---------------------------------------------------------------------------

    The specific percentile selected as part of the definition of F1 is
an important parameter that directly impacts the representative
critical load specified for each ecoregion, and therefore the degree of
protectiveness of the standard. A higher percentile corresponds to a
lower critical load and, therefore, to lower allowable ambient air
concentrations of NOy and SOx and related
deposition to achieve a target AAI level. In conjunction with the other
terms in the AAI equation, alternative forms can be appropriately
characterized in part by identifying a range of alternative
percentiles. The choice of an appropriate range of percentiles to
consider for acid-sensitive and relatively non-acid sensitive
ecoregions, respectively, is discussed below.
a. Acid-Sensitive Ecoregions
    In identifying percentiles that are appropriate to consider for the
purpose of calculating factor F1 for ecoregions characterized as acid
sensitive, the PA concludes that it is appropriate to focus on the
lower (more sensitive) part of the distribution of critical loads, so
as to ensure that the ecoregion would be represented by relatively more
acid sensitive water bodies within the ecoregion. Specifying factor F1
in this way would help to define a standard that would be protective of
the population of acid sensitive water bodies within an ecoregion,
recognizing that even ecoregions characterized as acid sensitive may
contain a number of individual water bodies that are not acid
sensitive. The PA recognizes that there is no basis for independently
evaluating the degree of protectiveness afforded by any specific
percentile value, since it is the combination of form and level, in
conjunction with the indicator and averaging time, which determine the
degree of protectiveness of a standard. In light of this, the PA
concludes that it is appropriate to consider a range of percentiles,
from well above the 50th percentile, or median, of the distribution to
somewhat below the highest value (in terms of sensitivity; a high
degree of sensitivity corresponds to a low value for critical load).
More specifically, the PA concludes it is appropriate to consider
percentiles in the range of the 70th to the 90th percentile (of
sensitivity). This conclusion is based on the judgment that it would
not be appropriate to represent an ecoregion with the lowest or near
lowest critical load, so as to avoid potential extreme outliers that
can be seen to exist at the extreme end of the data distributions,
which would not be representative of the population of acid sensitive
water bodies within the ecoregion and could lead to an overly
protective standard. At the same time, in considering ecoregions that
are inherently acid sensitive, it is judged to be appropriate to limit
the lower end of the range for consideration to the 70th percentile, a
value well above the median of the distribution, so that a substantial
majority of acid-sensitive water bodies are protected.
    In considering this conclusion, the CASAC Panel noted that the data
bases for calculating critical loads within an ecoregion are not
necessarily representative of all water bodies within an ecoregion.
That is, in many ecoregions the lake sampling design used in studies
that generated the relevant data may have focused on the relatively
more sensitive water bodies within an ecoregion (Russell and Samet,
2011a). Consequently, a given percentile of the distribution of
calculated critical loads, based on sampled water bodies, may not be
representative of that percentile of all water bodies across an entire
ecoregion. To the extent that the sampling of water bodies within an
ecoregion was skewed toward the relatively more sensitive water bodies,
selecting a given percentile from the distribution of available
critical loads would result in a somewhat higher percentile of all
water bodies within that ecoregion having a higher calculated critical
load than the representative critical load value. Thus, the extent to
which study sampling designs have resulted in skewed distributions of
calculated critical loads is an uncertainty that is appropriate to
consider in selecting a percentile for the purpose of defining the
factor F1 in the AAI equation.
b. Non-Acid Sensitive Ecoregions
    With regard to identifying percentiles that are appropriate to
consider for the purpose of calculating factor F1 for ecoregions
characterized as relatively non-acid sensitive, the PA recognizes that
while such ecoregions are generally less sensitive to acidifying
deposition from oxides of nitrogen and sulfur, they may contain a
number of water bodies that are acid sensitive. This category includes
ecoregions that are well protected from acidification effects due to
natural production of base cations and high ANC levels, as well as
naturally acidic systems with limited base cation production and
consequently very low critical loads. Therefore, the use of a critical
load that would be associated with a highly sensitive water body in a
naturally acidic system would impose a high degree of relative
protection in terms of allowable ambient air concentrations of oxides
of nitrogen and sulfur and

related deposition, while potentially affording little or no public
welfare benefit from attempting to improve a naturally acidic system.
    Based on these considerations, the PA concludes it is appropriate
to consider the use of a range of percentiles that extends lower than
the range identified above for acid-sensitive ecoregions. Consideration
of a lower percentile would avoid representing a relatively non-acid
sensitive ecoregion by a critical load associated with relatively more
acid-sensitive water bodies. In particular, the PA concludes it is
appropriate to focus on the median or 50th percentile of the
distribution of critical loads so as to avoid over-protection in such
ecoregions. Recognizing that relatively non-acid sensitive ecoregions
generally are not sampled to the extent that acid-sensitive ecoregions
are, it also is appropriate to consider using the median critical load
of all relatively non-acid sensitive ecoregions for each such
ecoregion.
ii. Factor F2
    As discussed above, factor F2 is the amount of reduced nitrogen
deposition within an ecoregion, including the deposition of both
ammonia gas and ammonium ion, and is defined as: F2 = NHX/
Qr. The PA calculated the representative runoff rate, Qr,
using a similar approach as noted above for factor F1; i.e., the median
value of the distribution of Q values that are available for water
bodies within each ecoregion. In the PA, 2005 CMAQ model simulations
over 12-km grids are used to calculate an average value of
NHX for each ecoregion. The NHX term is based on
annual average model outputs for each grid cell, which are spatially
averaged across all the grid cells contained in each ecoregion to
calculate a representative annual average value for each ecoregion. The
PA concludes that this approach of using spatially averaged values is
appropriate for modeling, largely due to the relatively rapid mixing of
air masses that typically results in relatively homogeneous air quality
patterns for regionally dispersed pollutants. In addition, there is
greater confidence in using spatially averaged modeled atmospheric
fields than in using modeled point-specific fields.
    This averaging approach is also used for the air concentration and
deposition terms in factors F3 and F4, as discussed below. The PA notes
that modeled NHX deposition exhibits greater spatial
variability than the other modeled terms in factors F3 and F4.
Recognizing this greater variability, the PA concludes that it would be
appropriate to consider alternative approaches to specifying the value
of NHX. One such approach might involve the use of more
localized and/or contemporaneous modeling in areas where this term is
likely to be particularly variable and important.
iii. Factors F3 and F4
    As discussed above, factors F3 and F4 are the ratios that relate
ambient air concentrations of NOy and SOX to the
associated deposition, and are defined as follow: F3 = TNOy/
Qr and F4 = TSOx/ Qr. TNOy is the transference
ratio that converts ambient air concentrations of NOy to
deposition of NOy and TSOx is the transference
ratio that converts ambient air concentrations of SOX to
deposition of SOX. The representative runoff rate,
Qr, is calculated as for factors F1 and F2. The transference
ratios are based on the 2005 CMAQ simulations, using average values for
each ecoregion, as noted above for factor F2. More specifically, the
transference ratios are calculated as the annual deposition of
NOy or SOX spatially averaged across the
ecoregion and divided by the annual ambient air concentration of
NOy or SOX, respectively, spatially averaged
across the ecoregion.
c. Factors in Data-Limited Ecoregions
    As discussed above in section III.B.5.a, in the PA the initial
delineation of acid-sensitive and relatively non-acid sensitive
ecoregions was based on available ANC and alkalinity data. Areas not
meeting the ANC criteria described above are categorized as relatively
non-acid sensitive. The development of a reasonable distribution of
critical loads for water bodies within an ecoregion for the purpose of
identifying the representative critical load requires additional data,
including more specific water quality data for major cations and
anions. This means that the water bodies that can be used to develop a
distribution of critical loads is generally a subset of those water
bodies for which ANC data are available Consequently, there are certain
ecoregions with sparse data that are not suitable for developing a
distribution of critical loads.
    As noted above, the PA judges that it is not appropriate to develop
such distributions based on data from less than ten water bodies within
an ecoregion. Twelve such ecoregions, which included only relatively
non-acid sensitive ecoregions, were characterized as being data-
limited. For these ecoregions, the PA considered alternative approaches
to specifying values for the terms CLr and Qr for
the purpose of determining values for each of the factors in the AAI
equation. For these data-limited ecoregions, the PA judges that it is
appropriate to use the median values of CLr and
Qr from the distributions of these terms for all other
relatively non-acid sensitive ecoregions, rather than attempting to use
severely limited data to develop a value for these terms based solely
on data from such an ecoregion. Further, consideration could be given
to using a single national default value for all relatively non-acid
sensitive ecoregions. The PA notes that this data limitation is not a
concern in specifying values for the other terms in the AAI equation
for such ecoregions, since those terms are based on data from the 2005
CMAQ model simulation, which covers all ecoregions across the
contiguous U.S.
d. Application to Hawaii, Alaska, and the U.S. Territories
    The above methods for specifying ecoregion-specific values for the
factors in the AAI equation apply to those ecoregions within the
contiguous U.S. For areas outside the continental U.S., including
Hawaii, Alaska, and the U.S. Territories, there is currently a lack of
available data to characterize the sensitivity of such areas, as well
as a lack of water body-specific data and CMAQ-type modeling to specify
values for the F1 through F4 factors. Thus, the PA has considered
possible alternative approaches to specifying values for factors F1
through F4 in the AAI equation for these areas.
    One such approach could be to specify area-specific values for the
factors based on values derived for ecoregions with similar acid
sensitivities, to the extent that relevant information can be obtained
to determine such similarities. Such an approach would involve
conducting an analysis to characterize similarities in relevant
ecological attributes between ecoregions in the contiguous U.S. and
these areas outside the contiguous U.S. so as to determine the
appropriateness of utilizing ecoregion-specific values for the
CLr and Qr terms from one or more ecoregions
within the contiguous U.S. This approach would also involve conducting
additional air quality modeling for the areas that are outside the
geographical scope of the currently available CMAQ model simulations,
so as to develop the other information necessary to specify values for
factors F2 through F4 for these areas.
    A second approach could rely on future data collection efforts to
establish relevant ecological data within these areas that, together
with additional air quality modeling, could be used to specify area-
specific values for factors

F1 through F4. Until such time as relevant data become available, these
areas could be treated the same as data-limited ecoregions in the
contiguous U.S. that are relatively non-acid sensitive.
    The PA concludes that either approach would introduce substantial
uncertainties that arise from attempting to extrapolate values based on
similarity assumptions or arbitrarily assigning values for factors in
the AAI equation that would be applicable to these areas outside the
contiguous U.S. In light of such uncertainties, the PA concludes that
it would also be appropriate to consider relying on the existing
NO2 and SO2 secondary standards in these areas
for protection of any potential direct or deposition-related ecological
effects that may be associated with the presence of oxides of nitrogen
and sulfur in the ambient air. The PA concludes that relying on
existing secondary standards in these areas is preferable to using a
highly uncertain approach to allow for the application of a new
standard based on the AAI in the absence of relevant area-specific
data.
6. Summary of the AAI Form
    With regard to the form of a multi-pollutant air quality standard
to address deposition-related aquatic acidification effects, the PA
concludes that consideration should be given to an ecologically
relevant form that characterizes the relationships between the ambient
air indicators for oxides of nitrogen and sulfur, the related
deposition of nitrogen and sulfur, and the associated aquatic
acidification effects in terms of a relevant ecological indicator.
Based on the available information and assessments, consideration
should be given to using ANC as the most appropriate ecological
indicator for this purpose, in that it provides the most stable metric
that is highly associated with the water quality properties that are
directly responsible for the principal adverse effects associated with
aquatic acidification: Fish mortality and reduced aquatic species
diversity.
    The PA developed such a form, using a simple equation to calculate
an AAI value in terms of the ambient air indicators of oxides and
nitrogen and sulfur and the relevant ecological and atmospheric factors
that modify the relationships between the ambient air indicators and
ANC. Recognizing the spatial variability of such factors across the
U.S., the PA concludes it is appropriate to divide the country into
ecologically relevant regions, characterized as acid-sensitive or
relatively non-acid-sensitive, and specify the value of each of the
factors in the AAI equation for each such region. Omernik ecoregions,
level III, are identified as the appropriate set of regions over which
to define the AAI. There are 84 such ecoregions that cover the
continental U.S. This set of ecoregions is based on grouping a variety
of vegetation, geological, and hydrological attributes that are
directly relevant to aquatic acidification assessments and that allow
for a practical application of an aquatic acidification standard on a
national scale.
    The PA defines AAI by the following equation: AAI = F1 - F2 -
F3[NOy] - F4[SOX]. Factors F1 through F4 would be
defined for each ecoregion by specifying ecoregion-specific values for
each factor based on monitored or modeled data that are representative
of each ecoregion. The F1 factor is also defined by a target ANC value.
More specifically:
    (1) F1 reflects a relative measure of an ecosystem's ability to
neutralize acidifying deposition. The value of F1 for each ecoregion
would be based on a representative critical load for the ecoregion
associated with a single national target ANC level, as well as on a
representative runoff rate. The representative runoff rate, which is
also used in specifying values for the other factors, would be the
median value of the distributions of runoff rates within the ecoregion.
The representative critical load would be derived from a distribution
of critical loads calculated for each water body in the ecoregion for
which sufficient water quality and hydrology data are available. The
representative critical load would be defined by selecting a specific
percentile of the distribution.
    In identifying a range of percentiles that are appropriate to
consider for this purpose, regions categorized as acid sensitive were
considered separately from regions categorized as relatively non-acid
sensitive. For acid sensitive regions, the PA concludes that
consideration should be given to selecting a percentile from within the
range of the 70th to the 90th percentile. The lower end of this range
was selected to be appreciably above the median value so as to ensure
that the critical load would be representative of the population of
relatively more acid sensitive water bodies within the region, while
the upper end was selected to avoid the use of a critical load from the
extreme tail of the distribution which is subject to a high degree of
variability and potential outliers. For relatively non-acid sensitive
regions, the PA concludes that consideration should be given to
selecting the 50th percentile to best represent the distribution of
water bodies within such a region, or alternatively to using the median
critical load of all relatively non-acid sensitive areas, recognizing
that such areas are far less frequently evaluated than acid sensitive
areas. Using either of these approaches would avoid characterizing a
generally non-acid-sensitive region with a critical load that is
representative of relatively acid sensitive water bodies that may exist
within a generally non-acid sensitive region.
    (2) F2 reflects the deposition of reduced nitrogen. Consideration
should be given to specifying the value of F2 for each region based on
the averaged modeled value across the region, using national CMAQ
modeling that has been conducted by EPA. Consideration could also be
given to alternative approaches to specifying this value, such as the
use of more localized and/or contemporaneous modeling in areas where
this term is likely to be particularly variable and important.
    (3) F3 and F4 reflect transference ratios that convert ambient air
concentrations of NOy and SOX, respectively, into
related deposition of nitrogen and sulfur. Consideration should be
given to specifying the values for F3 and F4 for each region based on
CMAQ modeling results averaged across the region. We conclude that
specifying the values or the transference ratios based on CMAQ modeling
results alone is preferred to an alternative approach that combines
CMAQ model estimates with observational data.
    (4) The terms [NOy] and [SOX] reflect ambient
air concentrations measured at monitoring sites within each region.
    Using the equation, a value of AAI can be calculated for any
measured values of ambient NOy and SOX. For such
a NAAQS, the Administrator would set a single, national value for the
level of the AAI used to determine achievement of the NAAQS, as
discussed below in section III.D. The ecoregion-specific values for
factors F1 through F4 would be specified by EPA based on the most
recent data and CMAQ model simulations, and codified as part of such a
standard. These factors would be reviewed and updated as appropriate in
the context of each periodic review of the NAAQS.
    The PA developed specific F factors for each ecoregion based on the
approach discussed above, using alternative percentiles and alternative
national target ANC levels. The results of this analysis for ecoregions
characterized as acid sensitive are presented in Table 7-1a-d in the
PA.

C. Averaging Time

    As discussed in section 7.3 of the PA, aquatic acidification can
occur over both long- and short-term timescales. Long-term cumulative
deposition of nitrogen and sulfur is reflected in the chronic acid-base
balance of surface waters as indicated by measured annual ANC levels.
Similarly, the use of steady state critical load modeling, which
generates critical loads in terms of annual cumulative deposition of
nitrogen and sulfur, means that the focus of ecological effects studies
based on critical loads is on the long-term equilibrium status of water
quality in aquatic ecosystems. Much of the evidence of adverse
ecological effects associated with aquatic acidification, as discussed
above in section II.A, is associated with chronically low ANC levels.
Protection against a chronic ANC level that is too low is provided by
reducing overall annual average deposition levels for nitrogen and
sulfur.
    Reflecting this focus on long-term acidifying deposition, the PA
developed the AAI that links ambient air indicators to deposition-
related ecological effects, in terms of several factors, F1 through F4.
As discussed above, these factors are all calculated as annual average
values, whether based on water quality and hydrology data or on CMAQ
model simulations. In the context of a standard defined in terms of the
AAI, the PA concludes that it is appropriate to consider the same
annual averaging time for the ambient air indicators as is used for the
factors in the AAI equation.
    We also recognize that short-term (i.e., hours or days) episodic
changes in water chemistry, often due to changes in the hydrologic flow
paths, can have important biological effects in aquatic ecosystems.
Such short-term changes in water chemistry are termed ``episodic
acidification.'' Some streams may have chronic or base flow chemistry
that is generally healthy for aquatic biota, but may be subject to
occasional acidic episodes with potentially lethal consequences. Thus,
short-term episodic ecological effects can occur even in the absence of
long-term chronic acidification effects.
    Episodic declines in pH and ANC are nearly ubiquitous in drainage
waters throughout the eastern U.S. Episodic acidification can result
from several mechanisms related to changes in hydrologic flow paths.
For example, snow can store nitrogen deposited throughout the winter
and snowmelt can then release this stored nitrogen, together with
nitrogen derived from nitrification in the soil itself, in a pulse that
leads to episodic acidification in the absence of increased deposition
during the actual episodic acidification event. The PA notes that
inputs of nitrogen and sulfur from snowpack and atmospheric deposition
largely cycle through soil. As a result, short-term direct deposition
inputs are not necessarily important in episodic acidification. Thus,
as noted in chapter 3 of the ISA, protection against episodic acidity
events can be achieved by establishing a higher chronic ANC level.
    Taken together, the above considerations support the conclusion
that it is appropriate to consider the use of a long-term average for
the ambient air indicators NOy and SOX for an
aquatic acidification standard defined in terms of the AAI. The use of
an annual averaging time for NOy and SOX
concentrations would be appropriate to provide protection against low
chronic ANC levels, which in turn would protect against both long-term
acidification and acute acidic episodes.
    The PA has also considered interannual variability in both ambient
air quality and in precipitation, which is directly related to the
deposition of oxides of nitrogen and sulfur from the ambient air. While
ambient air concentrations show year-to-year variability, often the
year-to-year variability in precipitation is considerably greater,
given the highly stochastic nature of precipitation. The use of
multiple years over which annual averages are determined would dampen
the effects of interannual variability in both air quality and
precipitation. For the ambient air indicators, the use of multiple-year
averages would also add stability to calculations used to judge whether
an area meets a standard defined in terms of the AAI. Consequently, the
PA concludes that an annual averaging time based on the average of each
year over a consecutive 3- to 5-year period is appropriate to consider
for the ambient air indicators NOy and SOX. In
reaching this conclusion, the PA notes that in its comments on the
second draft PA, CASAC agreed that a 3- to 5-year averaging time was
appropriate to consider (Russell and Samet, 2010b).

D. Level

    As discussed above, the PA concludes that ANC is the ecological
indicator best suited to reflect the sensitivity of aquatic ecosystems
to acidifying deposition from oxides of nitrogen and sulfur in the
ambient air. The ANC is an indicator of the aquatic acidification
expected to occur given the natural buffering capacity of an ecosystem
and the loadings of nitrogen and sulfur resulting from atmospheric
deposition. Thus, the PA developed a new standard for aquatic
acidification that is based on the use of chronic ANC as the ecological
indicator as a component in the AAI.
    The level of the standard would be defined in terms of a single,
national value of the AAI. The standard would be met at a monitoring
site when the multi-year average of the calculated annual values of the
AAI was equal to or above the specified level of the standard.\13\ The
annual values of the AAI would be calculated based on the AAI equation
using the assigned ecoregion-specific values for factors F1 through F4
and monitored annual average NOy and SOX
concentrations. Since the AAI equation is based on chronic ANC as the
ecological indicator, the level chosen for the standard would reflect a
target chronic ANC value. As noted above, the assigned F factors for
each ecoregion would be determined by EPA in the rulemaking to set the
NAAQS, based on water quality and hydrology data, CMAQ modeling, the
selected percentile that is used to identify a representative critical
load within the ecoregion, and the selected level of the standard. The
combination of the form of the standard, discussed above in section
III.B, defined by the AAI equation and the assigned values of the F
factors in the equation, other elements of the standard including the
ambient air indicators (section III.A) and their averaging time
(section III.C), and the level of the standard determines the allowable
levels of NOy and SOX in the ambient air within
each ecoregion. All of the elements of the standard together determine
the degree of protection from adverse aquatic acidification effects
associated with oxides of nitrogen and sulfur in the ambient air. The
level of the standard plays a central role in determining the degree of
protection provided and is discussed below.
---------------------------------------------------------------------------

    \13\ Unlike other NAAQS, where the standard is met when the
relevant value is at or below the level of the standard since a
lower standard level is more protective, in this case a higher
standard level is more protective.
---------------------------------------------------------------------------

    The PA focuses primarily on information that relates degrees of
biological impairment associated with adverse ecological effects to
aquatic ecosystems to alternative levels of ANC in reaching conclusions
regarding the range of target ANC levels that is appropriate to
consider for the level of the standard. The PA develops the rationale
for identifying a range of target ANC levels that is appropriate to
consider by addressing questions related to the following areas: (1)
Associations between ANC and pH levels to provide an initial bounding
for the range of ANC

values to be considered; (2) evidence that allows for the delineation
of specific ANC ranges associated with varying degrees of severity of
biological impairment ecological effects; (3) the role of ANC in
affording protection against episodic acidity; (4) implications of the
time lag response of ANC to changes in deposition; (5) past and current
examples of target ANC values applied in environmental management
practices; and (6) data linking public welfare benefits and ANC.
1. Association Between pH Levels and Target ANC Levels
    As discussed above in section II.A and more fully in chapter 3 of
the PA, specific levels of ANC are associated with differing levels of
risk of biological impairment in aquatic ecosystems, with higher levels
of ANC resulting in lower risk of ecosystem impacts, and lower ANC
levels resulting in risk of both higher intensity of impacts and a
broader set of impacts. While ANC is not the causal agent determining
biological effects in aquatic ecosystem, it is a useful metric for
determining the level at which a water body is protected against risks
of acidification. There is a direct correlation between ANC and pH
levels which, along with dissolved aluminum, are more closely linked to
the biological causes of ecosystem response to acidification.
    Because there is a direct correlation between ANC and pH levels,
the selection of target ANC levels is informed in part through
information on effects of pH as well as direct studies of effects
related to ANC. Levels of pH are closely associated with ANC in the pH
range of approximately 4.5 to 7. Within this range, higher ANC levels
are associated with higher pH levels. At a pH level of approximately
4.5, further reductions in ANC generally do not correlate with pH, as
pH levels remain at approximately 4.5 while ANC values fall
substantially. Similarly, at a pH value of approximately 7, ANC values
can continue to increase with no corresponding increase in pH. As pH is
the primary causal indicator of effects related to aquatic
acidification, this suggests that ANC values below approximately -50
[mu]eq/L (the apparent point in the relationship between pH and ANC
where pH reaches a minimum) are not likely to result in further damage.
In addition, ANC values around and above approximately 100 [mu]eq/L
(the apparent region in the relationship where pH reaches a maximum)
are not likely to confer additional protection. As a result, the
initial focus in the PA was on target ANC values in the range of -50 to
100 [mu]eq/L.
2. ANC Levels Related to Effects on Aquatic Ecosystems
    As discussed above in section II.A, the number of fish species
present in a water body has been shown to be positively correlated with
the ANC level in the water, with higher values supporting a greater
richness and diversity of fish species. The diversity and distribution
of phyto-zooplankton communities also are positively correlated with
ANC.
    A summary of effects related to ANC ranges is shown above in Table
II-1. Within the ANC range from approximately -50 to 100 [mu]eq/L,
linear and sigmoidal relationships are observed between ANC and
ecosystem effects. On average, fish species richness is lower by one
fish species for every 21 [mu]eq/L decrease in ANC in Shenandoah
National Park streams (ISA, section <IP_ADDRESS>). As shown in Table II-1,
ANC levels have been grouped into five categories related to expected
ecological effects, including categories of acute concern (<0 [mu]eq/
L), severe concern (0-20 [mu]eq/L), elevated concern (20-50 [mu]eq/L),
moderate concern (50-100 [mu]eq/L), and low concern (>100 [mu]eq/L).
This categorization is supported by a large body of research completed
throughout the eastern U.S. (Sullivan et al., 2006).
    Water bodies with ANC values less than or equal to 0 [mu]eq/L at
based flow are chronically acidic. Such ANC levels can lead to complete
loss of species and major changes in the ability of water bodies to
support diverse biota, especially in water bodies that are highly
sensitive to episodic acidification. Based on the above considerations,
the PA has focused on target ANC levels no lower than 0 [mu]eq/L.
    As discussed in the PA, biota generally are not harmed when ANC
values are >100 [mu]eq/L, due to the low probability that pH levels
will be below 7. In the Adirondacks, the number of fish species also
peaks at ANC values >100 [mu]eq/L. This suggests that at ANC levels
greater than 100 [mu]eq/L, little risk from acidification exists in
many aquatic ecosystems. At ANC levels below 100 [mu]eq/L, overall
health of aquatic communities can be maintained, although fish fitness
and community diversity begin to decline. At ANC levels ranging from
100 down to 50 [mu]eq/L, there is increasing likelihood that the
fitness of sensitive species (e.g., brook trout, zooplankton) will
begin to decline. When ANC concentrations are below 50 [mu]eq/L, the
probability of acidification increases substantially, and negative
effects on aquatic biota are observed, including large reductions in
diversity of fish species and changes in the health of fish
populations, affecting reproductive ability and fitness, especially in
water bodies that are affected by episodic acidification. While there
is evidence that ANC levels above 50 can confer additional protection
from adverse ecological effects associated with aquatic acidification
in some sensitive ecosystems, the expectation that such incremental
protection from adverse effects will continue up to an ANC level of 100
is substantially reduced. The PA concludes that the above
considerations support a focus on target ANC levels up to a level
greater than 50 [mu]eq/L but below 100 [mu]eq/L, such as up to a level
of 75 [mu]eq/L.
    In considering the available scientific evidence, as summarized
here and discussed in more detail in the ISA and REA, in its review of
the second draft PA, CASAC expressed the following views about the
range of biological responses that corresponds to this range of ANC
levels (i.e., 0-100 [mu]eq/L):

    There will likely be biological effects of acidification at
higher ANC values within this range, and there are relatively
insensitive organisms that are not impacted at ANC values at the low
end of this range. Adverse effects of acidification on aquatic biota
are fairly certain at the low end of this range of ANC and
incremental benefits of shifting waters to higher ANC become more
uncertain at higher ANC levels. There is substantial confidence that
there are adverse effects at ANC levels below 20 [mu]eq/L, and
reasonable confidence that there are adverse effects below 50
[mu]eq/L. Levels of 50 [mu]eq/L and higher would provide additional
protection, but the Panel has less confidence in the significance of
the incremental benefits as the level increases above 50 [mu]eq/L.
(Russell and Samet, 2010b)

    The PA concludes that the above considerations, including the views
of CASAC, provide support for focusing on target ANC levels in the
range of 20 to 75 [mu]eq/L.
3. Consideration of Episodic Acidity
    As discussed in the PA, across the broad range of ANC values from 0
to 100 [mu]eq/L, ANC affords protection against the likelihood of
decreased pH (and associated increases in Al) during long or short
periods. In general, the higher the ANC within this range, the lower
the probability of reaching low pH levels where direct effects such as
increased fish mortality occur, as shown in Table 3-1 of the PA.
Accordingly, greater protection would be achieved by target chronic ANC
values set high enough to avoid pH depression to levels associated with
elevated risk.

    The specific relationship between ANC and the probability of
reaching pH levels of elevated risk varies by water body and fish
species. The ANC levels below 20 [mu]eq/L are generally associated with
high probability of low pH, leading to death or loss of fitness of
biota that are sensitive to acidification (US EPA, 2008, section
<IP_ADDRESS>; US EPA, 2009, section <IP_ADDRESS>). At these levels, during
episodes of high acidifying deposition, brook trout populations may
experience lethal effects. In addition, the diversity and distribution
of zooplankton communities decline sharply at ANC levels below 20
[mu]eq/L. Overall, there is little uncertainty that significant effects
on aquatic biota are occurring at ANC levels below 20 [mu]eq/L.
    It is clear that at ANC levels approaching 0 [mu]eq/L (Table II-1),
there is significant impairment of sensitive aquatic ecosystems with
almost complete loss of fish species. Avoiding ANC levels approaching 0
[mu]eq/L is particularly relevant to episodic spikes in acidity that
occur during periods of rapid snow melt and during and after major
precipitation events. Since the ANC range considered in the PA reflects
average, long-term base flow values, it is appropriate to consider
protecting against episodic drops in ANC values to a level as low as 0
[mu]eq/L. Staddard et al. (2003) noted on average a 30 [mu]eq/L
depression of ANC between spring and summer time values, indicating the
need to maintain higher base flow ANC levels to protect against ANC
levels below 0 [mu]eq/L. The above considerations do not provide
support for a target chronic ANC level as low as 0 [mu]eq/L for a
standard that would protect against significant harm to aquatic
ecosystems, including harm from episodic acidification. The PA
concludes that these considerations also support a lower end of the
range for consideration no lower than 20 [mu]eq/L.
    The CASAC agreed with this conclusion in its comments on the second
draft PA (Russell and Samet, 2010b). The CASAC noted that ``there are
clear and marked biological effects at ANC values near 0 [mu]eq/L, so
this is probably not an appropriate target value'' for the AAI. With
regard to the likelihood of impairment of aquatic ecosystems due to
episodic acidification, in terms of specific target levels for chronic
ANC, CASAC expressed the following view:

    Based on surface waters studied in the Northeast, decreases in
ANC associated with snowmelt [are] approximately 50 [mu]eq/L. Thus,
based on these studies, a long term ANC target level of 75 [mu]eq/L
would generally guard against effects from episodic acidification
down to a level of about 25 [mu]eq/L. (Russell and Samet, 2010b)

4. Consideration of Ecosystem Response Time
    The PA notes that when considering a standard level to protect
against aquatic acidification, it is appropriate to take into account
both the time period to recovery as well as the potential for recovery
in acid-sensitive ecoregions. Ecosystems become adversely impacted by
acidifying deposition over long periods of time and have variable time
frames and abilities to recover from such perturbations. Modeling
presented in the REA (U.S. EPA, 2009, section 4.2.4) shows the
estimated ANC values for Adirondack lakes and Shenandoah streams under
pre-acidification conditions and indicates that for a small percentage
of lakes and streams, natural ANC levels would have been below 50
[mu]eq/L. Therefore, for these water bodies, reductions in acidifying
deposition are not likely to achieve an ANC of 50 [mu]eq/L or greater.
Conversely, for some lakes and streams the level of perturbation from
long periods of acidifying deposition has resulted in very low ANC
values compared to estimated natural conditions. For such water bodies,
the time to recovery would be largely dependent on future inputs of
acidifying deposition.
    Setting a standard level in terms of a target chronic ANC level is
based on the long-term response of aquatic ecosystems. The time
required for a water body to achieve the target ANC level--given a
decrease in ambient air concentrations of NOy and
SOx and related acidifying deposition such that the critical
load for a target ANC is not exceeded--is often decades if not
centuries. In recognition of the potential public welfare benefits of
achieving the target ANC in a shorter time frame, the concept of target
loads had been developed. Target loads represent the depositional
loading that is expected to achieve a particular level of the
ecological indicator by a given time. For example, to achieve an ANC
level of 20 [mu]eq/L by 2030, it might be necessary to specify a higher
target ANC level of, for example, 50 [mu]eq/L, such that the
depositional loading would be reduced more quickly than would occur if
the depositional loading was based on achieving a target ANC level of
20 [mu]eq/L as a long-term equilibrium level. In this example, the
target ANC of 50 [mu]eq/L would ultimately be realized many years
later.
    The above considerations have implications for selecting an
appropriate standard level, in that the standard level affects not only
the ultimate degree of protection that would be afforded by the
standard, but also the time frame in which such protection would be
realized. However, the PA recognizes that there is a great deal of
heterogeneity in response times among water bodies and that there is
only very limited information from dynamic modeling that would help to
quantify recovery time frames in areas across the country. As a
consequence, quantification of a general relationship between critical
loads associated with a specific long-term target ANC level and target
loads associated with achieving the target ANC level within a specific
time frame is not currently possible. Thus, while the time frame for
recovery is an important consideration in selecting an appropriate
range of levels to consider, the PA concludes that it can only be
considered in a qualitative sense at this time.
5. Prior Examples of Target ANC Levels
    A number of regional organizations, states, and international
organizations have developed critical load frameworks to protect
against acidification of sensitive aquatic ecosystems. In considering
the appropriate range of target ANC levels for consideration in this
review, it is informative to evaluate the target ANC levels selected by
these different organizations, as well as the rationale provided in
support of the selected levels. Chapter 4 of the PA provides a detailed
discussion of how critical loads have been developed and used in other
contexts. Specific target values and their rationales are summarized
below.
    The UNECE has developed critical loads in support of international
emissions reduction agreements. As noted in chapter 4 of the PA,
critical loads were established to protect 95 percent of surface waters
in Europe from an ANC less than 20 [micro]eq/L based on protection of
brown trout. Individual countries have set alternative ANC targets; for
example, Norway targets an ANC of 30 [micro]eq/L based on protection of
Atlantic salmon. Several states have established target ANC or pH
values related to protection of lakes and streams from acidification.
While recognizing that some lakes in the Adirondacks will have a
naturally low pH, the state of New York has established a target pH
value of 6.5 for lakes that are not naturally below 6.5. As noted
above, this level is associated with an ANC value that is likely to be
between 20 and 50 [micro]eq/L or possibly higher. New Hampshire and
Vermont have set ANC targets of 60 [micro]eq/L and 50 [micro]eq/L,
respectively. Tennessee has established site-specific target ANC

values based on assessments of natural acidity, with a default value of
50 [micro]eq/L when specific data are not available.
    Taken together, these policy responses to concerns about ecological
effects associated with aquatic acidification indicate that target ANC
values between 20 and 60 [mu]eq/L have been selected by states and
other nations to provide protection of lakes and streams in some of the
more sensitive aquatic ecosystems.
6. Consideration of Public Welfare Benefits
    The point at which effects on public welfare become adverse is not
defined in the CAA. Characterizing a known or anticipated adverse
effect to public welfare is an important component of developing any
secondary NAAQS. According to the CAA, welfare effects include:

* * * effects on soils, water, crops, vegetation, manmade materials,
animals, wildlife, weather, visibility, and climate, damage to and
deterioration of property, and hazards to transportation, as well as
effect on economic values and on personal comfort and well-being,
whether caused by transformation, conversion, or combination with
other air pollutants. (CAA, section 302(h)).

    Consideration of adversity to public welfare in the context of the
secondary NAAQS for oxides of nitrogen and sulfur can be informed by
information about losses in ecosystem services associated with
acidifying deposition and the potential economic value of those losses,
as summarized above in section II.C and discussed more fully in chapter
4 of the PA.
    Ecosystem service losses at alternative ANC levels are difficult to
enumerate. However, in general there are categories of ecosystem
services, discussed in chapter 4 of the PA, that are related to the
specific ecosystem damages expected to occur at alternative ANC levels.
Losses in fish populations due to very low ANC (below 20 [mu]eq/L) are
likely associated with significant losses in value for recreational and
subsistence fishers. Many acid sensitive lakes are located in areas
with high levels of recreational fishing activity. For example, in the
northeastern U.S., where nearly 8 percent of lakes are considered
acidic, more than 9 percent of adults participate in freshwater
fishing, with an estimated value of approximately $5 billion in 2006.
This suggests that improvements in lake fish populations may be
associated with significant recreational fishing value.
    As discussed in the PA, inland surface waters also provide cultural
services such as aesthetic and existence value and educational
services. To the extent that piscivorous birds and other wildlife are
harmed by the absence of fish in these waters, hunting and birdwatching
activities are likely to be adversely affected. A case study of the
value to New York residents of improving the health of lakes in the
Adirondacks found significant willingness to pay for those
improvements. When scaled to evaluate the improvement in lake health
from achieving ANC values of either 20 or 50 [mu]eq/L, the study
implies benefits to the New York population roughly on the order of
$300-900 million per year (in constant 2007$). The survey administered
in this study recognized that participants were thinking about the full
range of services provided by the lakes in question--not just the
recreational fishing services. Therefore the estimates of willingness
to pay include resident's benefits for potential hunting and
birdwatching activities and other ancillary services. These results are
just for New York populations. The PA concludes that if similar
benefits exist for improvements in other acid sensitive lakes, the
economic value to U.S. populations could be very substantial,
suggesting that, at least by one measure of impact on public welfare,
impacts associated with ANC less than 50 [mu]eq/L may be adverse to
public welfare.
7. Summary of Alternative Levels
    Based on all the above considerations, the PA concludes that
consideration should be given to a range of standard levels from 20 to
75 [mu]eq/L. The available evidence indicates that target ANC levels
below 20 [mu]eq/L would be inadequate to protect against substantial
ecological effects and potential catastrophic loss of ecosystem
function in some sensitive aquatic ecosystems. While ecological effects
occur at ANC levels below 50 [mu]eq/L in some sensitive ecosystems, the
degree and nature of those effects are less significant than at levels
below 20 [mu]eq/L. Levels at and above 50 [mu]eq/L would be expected to
provide additional protection, although uncertainties regarding the
potential for additional protection from adverse ecological effects are
much larger for target ANC levels above about 75 [mu]eq/L, as effects
are generally appreciably less sensitive to changes in ANC at such
higher levels.
    In reaching this conclusion in the PA, consideration was given to
the extent to which a target ANC level within this range would protect
against episodic as well as long-term ecological effects. Levels in the
mid- to upper-part of this range would be expected to provide greater
protection against short-term, episodic peaks in aquatic acidification,
while lower levels within this range would give more weight to
protection from long-term rather than episodic acidification.
Similarly, levels in the mid- to upper-part of this range would be
expected to result in shorter time periods for recovery given the lag
in ecosystem response in some sensitive ecosystems relative to levels
in the lower part of this range. The PA also notes that this range
encompasses target ANC values that have been established by various
States and regional and international organizations to protect against
acidification of aquatic ecosystems.
    The PA recognizes that the level of the standard together with the
other elements of the standard, including the ambient air indicators,
averaging time, and form, determine the overall protectiveness of the
standard. Thus, consideration of a standard level should reflect the
strengths and limitations of the evidence and assessments as well as
the inherent uncertainties in the development of each of the elements
of the standard. The implications of considering alternative standards,
defined in terms of alternative combinations of levels and percentile
values that are a critical component of factor F1 in the form of the
standard, are discussed below in section III.E. Key uncertainties in
the various components of the standard are summarized and considered
below in section III.F.

E. Combined Alternative Levels and Forms

    To provide some perspective on the implications of various
alternative multi-pollutant, AAI-based standards, the PA presented the
number of acid-sensitive ecoregions that would likely not meet various
sets of alternative standards. The alternative standards considered
were based on combinations of alternative target ANC levels, within the
range of 20 to 75 [mu]eq/L, and alternative forms, characterized by
alternative representative percentiles within the range of the 70th to
90th percentile. These alternative standards are also defined in terms
of the other elements of the standard: ambient air indicators NOy and
SOx, discussed above in section III.A; other elements of the form of
the standard, including ecoregion-specific values for factors F1
through F4 in the AAI equation, discussed above in section III.B.5; and
an annual averaging time for NOy and SOx,
discussed above in section III.C. With regard to the averaging time,
the assessment did not consider multi-year averaging of the calculated
annual AAI

values due to data limitations, including, for example, the lack of
CMAQ modeling for multiple consecutive years. In this assessment, we
characterize an ecoregion as likely not meeting a given alternative
standard if the calculated AAI value is less than the target ANC level
of the standard, recognizing that higher AAI values are more protective
than lower values.
    The results of this assessment are presented in Table 7-1a-d in the
PA for a subset of ecoregions including those characterized as acid
sensitive. Calculated annual AAI values at the ecoregion level are
shown for each alternative standard considered. Based on these AAI
values, Table 7-2 in the PA summarizes the number of acid-sensitive
ecoregions that would likely not meet each of the alternative standards
considered.\14\ Calculated AAI values for all ecoregions categorized as
relatively non-acid sensitive are shown in Table D-5 in Appendix D of
the PA. In all cases, these relatively non-acid sensitive ecoregions
were estimated to meet all of the alternative standards considered in
this assessment.
---------------------------------------------------------------------------

    \14\ Tables 7-1a-d and 7-2 in the PA present assessment results
for 29 ecoregions that had been initially characterized as acid
sensitive. Subsequently, based on a broader set of criteria used to
characterize ecoregions as acid sensitive, as discussed above in
section III.B.5.a, the set of ecoregions characterized as acid
sensitive was narrowed to include 22 ecoregions.
---------------------------------------------------------------------------

    As described above, the AAI values presented in Table 7-1a-d of the
PA are based in part on data from 2005 CMAQ model simulations, which
was used to generate values for F2 through F4 in the AAI equation, as
well as to estimate annual average ambient air concentrations of
NOy and SOx that reflect recent air quality in
the absence of currently available monitored concentrations in
sensitive ecoregions across the country. Water quality and hydrology
data from water bodies within each ecoregion were also used in
calculating the AAI values. Such data were initially used to calculate
critical loads for each water body with sufficient data within an
ecoregion so as to identify the nth percentile critical load
representative of the ecoregion used in calculating the F1 factor for
the ecoregion. As expected, the number of ecoregions that likely would
not meet alternative standards increases with increasing percentile
values and target ANC levels (U.S. EPA, 2011, Table 7-2). Out of 22
acid-sensitive ecoregions, the number of ecoregions that would likely
not meet the alternative standards ranges from 22 for the most
protective alternative standard considered (75 [mu]eq/L, 90th
percentile) to 4 for the least protective alternative standard (20
[mu]eq/L, 70th percentile). It is apparent that both the percentile and
the level chosen have a strong influence, over the ranges considered,
in determining the number of areas that would likely not meet this set
of alternative standards.
    The PA observes that there is one grouping of these acid-sensitive
ecoregions that would likely not meet almost all combinations of level
and form under consideration (U.S. EPA, 2011, Table 7-2 and Appendix
D). This group is made up of southern Appalachian mountain areas,
including North Central Appalachians, 5.3.3; Ridge and Valley, 8.4.1;
Central Appalachians, 8.4.2; Blue Ridge, 8.4.4; and Southwestern
Appalachians, 8.4.9. In addition, these ecoregions exhibit the highest
amounts of exceedance relative to alternative standards.
    The Northern Appalachian and Atlantic Maritime Highlands (5.3.1),
which includes the Adirondacks, and the Northern Lakes and Forests
(5.2.1) of the upper midwest exhibit similar patterns with respect to
in the role of level and percentile in identifying regions not likely
to meet alternative standards, although there are considerably fewer
cases compared to the regions in the Appalachians.
    In the mountainous west, the Sierra Nevada (6.2.12), Idaho
Batholith (6.2.15) and the Cascades (6.2.7) ecoregions likely would not
meet alternative standards in fewer cases relative to eastern regions,
with the Sierra Nevada ecoregion exhibiting relatively greater
sensitivity compared to all western regions. Only in the upper part of
the ranges of level and percentile do regions in the northern and
central Rockies likely not meet alternative standards.
    In considering these findings, the PA observes that the standard as
defined by the AAI behaves in an intuitively logical manner. That is,
an increase in ecoregions likely not to meet the standard is associated
with higher alternative levels and percentiles, both of which
contribute to a lower regionally representative critical load.
Moreover, the areas of known adverse aquatic acidification effects are
identified, mostly in high elevation regions or in the northern
latitudes--the Adirondacks, Shenandoahs, northern midwest lakes and the
mountainous west. These results reflect the first application of a
nationwide model that integrates water quality and atmospheric
processes at a national scale and provides findings that are consistent
with our basic understanding of the extent of aquatic acidification
across the U.S. What is particularly noteworthy is that this model is
not initialized with a starting ANC based on water quality data, which
likely would result in a reproduction of water quality observations.
Rather, this standard reflects the potential of the changes in
atmospheric concentrations of NOy and SOx to
induce long-term sustained changes in surface water systems. The PA
notes that the fact that the patterns of adversity based on applying
this standard are commensurate with what is observed in surface water
systems provides confidence in the basic underlying formulation of the
standard.
    The PA notes that the Appalachian mountain regions merit further
inspection as they stand out as areas with the largest relative
exceedances from a national perspective. Water quality data from these
regions as well as an emissions sensitivity CMAQ simulation were
considered to better understand the simulated behavior of these
regions. The maps and tables in appendix D of the PA include paired
comparisons of the CMAQ 2005 and emissions sensitivity simulations. The
emissions sensitivity simulation reflects domain-wide reductions in
NOy and SOx emissions of 48 percent and 42
percent, respectively, relative to 2005 base year emissions. The PA
assumes that this emissions sensitivity simulation is indicative of
future conditions.
    The emissions sensitivity results project that many of the regions
that likely would not meet the alternative standards based on recent
air quality, especially at alternative levels of 20 and 35 [micro]eq/L,
would likely meet such standards in the future year scenario for the
Appalachian mountain regions. It is apparent that the AAI calculations
are especially sensitive to changes in SOx emissions as the
Appalachian regions have the highest SOx concentrations and
deposition rates (U.S. EPA, 2011,section 2), and the AAI equation
responds as expected to modeled reductions in SOx. The
emissions sensitivity scenario is a prospective application of the
standard, in the sense that rules derived from the air quality
management process result in reductions of NOy and
SOx emissions. Expected emission changes over the next two
decades should be far greater than the 42 percent and 48 percent,
respectively, SOx and NOy reductions used in this
analysis, with a consequent further reduction in areas that would
likely not meet alternative standards.
    The Appalachian mountain regions generally have low DOC levels,
average runoff rates, moderately low base cation supply and highly
elevated sulfate concentrations. Collectively, those attributes do not
suggest naturally acidic conditions as the availability of

anthropogenic contributions of mineral acids is likely responsible for
observed low ANC values in those regions.
    The PA notes the Sierra Nevada region as an interesting case study,
as it has some of the lowest critical load values nationally (U.S. EPA,
2011, Table D-3). Water quality data indicate extremely low sulfate, as
expected given the relatively low SO2 emissions in the
western U.S. Extremely low base cation supply and low Neco, which
mitigate the effect of nitrogen deposition, explain the low critical
load values. Low Neco values appear to associate well with high
elevation western U.S. regions, perhaps reflecting the more arid and
reduced vegetation density relative to eastern U.S. regions. The
proximity to high level nitrogen emissions combined with very low base
cation supply explains the cases where the Sierra region likely does
not meet alternative standards. Because Neco values are low in the
Sierras, the system responds effectively to reductions of
NOx emissions, as illustrated in the maps and tables of
Appendix D of the PA. Although Neco affords protection from the
acidifying effects of nitrogen deposition, the availability of
excessive nitrogen neutralization capacity also means that reductions
in nitrogen are not as effective as reductions in SOx in
reducing the calculated AAI.
    In reviewing these results, the PA observes that the analysis of
the alternative combinations of level and form presented provide
context for considering the impact of different standards. Since the
AAI equation has been newly developed in the PA, these examples of
estimated exceedances help to address the question of whether the AAI
equation responds in a reasonable manner with regard to identifying
areas of concern and to prospective changes in atmospheric conditions
likely to result from future emissions reduction strategies. The PA
concludes that the behavior of the AAI calculations is both reasonable
and explainable, which the PA concludes serves to increase confidence
in considering a standard defined in terms of the AAI.

F. Characterization of Uncertainties

    This section summarizes discussions of the results of analyses and
assessments, presented more fully in the PA (U.S. EPA, 2011, section
7.6 and Appendices F and G), intended to address the relative
confidence associated with the linked atmospheric-ecological effects
system described above. An overview of uncertainties is presented in
the context of the major structural components underlying the standard,
as well as with regard to areas of relatively high uncertainty. The
section closes with a discussion of data gaps and uncertainties
associated with the use of ecological and atmospheric modeling to
specify the factors in the AAI equation, which can be used to guide
future field programs and longer-term research efforts.
1. Overview of Uncertainty
    As discussed in the PA (U.S. EPA, 2011, Table 7-3), there is
relatively low uncertainty with regard to the conceptual formulation of
the overall structure of the AAI-based standard that incorporates the
major associations linking biological effects to air concentrations.
Based on the strength of the evidence that links species richness and
mortality to water quality, the associations are strongly causal and
without any obvious confounding influence. The strong association
between the ecosystem indicator (ANC) and the causative water chemistry
species (dissolved aluminum and hydrogen ion) reinforces the confidence
in the linkage between deposition of nitrogen and sulfur and effects.
This strong association between ANC and effects is supported by a sound
mechanistic foundation between deposition and ANC. The same mechanistic
strength holds true for the relationship between ambient air levels of
nitrogen and sulfur and deposition, which completes the linkage from
ambient air indicators through deposition to ecological effects.
    There are relatively higher uncertainties, however, in considering
specific elements within the structure of an AAI-based standard,
including the deposition of SOx, NOy, and
NHx as well as the critical load-related component, each of
which can vary within and across ecoregions. Overall system uncertainty
relates not just to the uncertainty in each such element, but also to
the combined uncertainties that result from linking these elements
together within the AAI-based structure. Some of these elements--
including, for example, dry deposition, pre-industrial base cation
production, and reduced nitrogen deposition--are estimated with less
confidence than other elements (U.S. EPA, 2011, Table 7.3). The
uncertainties associated with all of these elements, and the
combination of these elements through the AAI equation, are discussed
below and in the following sections related to measured data gaps and
modeled processes for both air quality and water quality.
    The lack of observed dry deposition data is constrained by
resources and the lack of efficient measurement technologies. Progress
in reducing uncertainties in dry deposition will depend on improved
atmospheric concentration data and direct deposition flux measurements
of the relevant suite of NOy and SOx species.
    Pre-industrial base cation productivity by definition is not
observable. Contemporary observations and inter-model comparisons are
useful tools that would help reduce the uncertainty in estimates of
preindustrial base cation productivity used in the AAI equation. In
characterizing contemporary base cation flux using basic water quality
measurements (i.e., major anion and cation species as defined in
equation 2.11 in the PA), it is reasonable to assume that a major
component of contemporary base cation flux is associated with pre-
industrial weathering rates. To the extent that multiple models
converge on similar solutions, greater confidence in estimating pre-
industrial base cation production would be achieved.
    Characterization of NHx deposition has been evolving
over the last decade. The relatively high uncertainty in characterizing
NHx deposition is due to both the lack of field measurements
and the inherent complexity of characterizing NHx with
respect to source emissions and dry deposition. Because ammonia
emissions are generated through a combination of man-made and
biological activities, and ammonia is semi-volatile, the ability to
characterize spatial and temporal distributions of NHx
concentrations and deposition patterns is challenging. While direct
measurement of NHx deposition is resource intensive because
of the diffuse nature of sources (i.e., area-wide and non-point
sources), there have been more frequent deposition flux studies,
relative to other nitrogen species, that enable the estimation of both
emissions and dry deposition. Also, while ammonia has a relatively high
deposition velocity and traditionally was thought to deposit close to
the emissions release areas, the semi-volatile nature of ammonia
results in re-entrainment back into the lower boundary layer resulting
in a more dispersed concentration pattern exhibiting transport type
characteristics similar to longer lived atmospheric species. These
inherent complexities in source characterization and ambient
concentration patterns raise the uncertainty level of NHx in
general. However, the PA notes that progress is being made in measuring
ammonia with cost efficient samplers and anticipates the gradual
evolution of a spatially robust ammonia sampling network that would
help support analyses to reduce underlying uncertainties in
NHx

deposition. Also, from an aquatic acidification perspective,
NHx is not as important a driver as NOy and
SOx in the mountainous areas in the eastern U.S. However,
the relative importance of NHx is likely to increase over
time, in light of air quality rules in place designed to reduce
emissions of NOy and SOx.
2. Uncertainties Associated With Data Gaps
    In summarizing uncertainties with respect to available measurement
data and the use of ecological and atmospheric models, the PA
indentified data gaps and model uncertainties in relative terms by
comparing, for example, the relative richness of data between
geographic areas or environmental media. With regard to relevant air
quality measurements, the PA notes that such measurements are
relatively sparse in the western U.S. While the spatial extent of
CASTNET coverage has gradually incorporated western U.S. locations with
support from the NPS, the relative density of monitoring sites is much
less than that in the eastern U.S. This relative disparity in spatial
density of monitors is exacerbated as air quality patterns in the
mountainous west generally exhibit greater spatial heterogeneity due to
dramatic elevation gradients that impact meteorology and air mass flow
patterns. Similarly, water quality data coverage is far more
comprehensive in the eastern U.S. relative to the west
    Measurements of NOy notably are lacking in both eastern
and western acid-sensitive ecoregions. This adds uncertainty to the use
of the AAI equation as the lack of NOy data limits efforts
to evaluate air quality modeling of NOy that is the basis
for quantifying factor F3 in the AAI equation. The lack of
NOy measurements also limits efforts to characterize the
variability and representativeness of modeled NOy
concentrations within and across ecoregions. Currently, the Agency's
ability to define the protection likely to be afforded by alternative
standards (in terms of alternative levels and percentiles) is
compromised by the lack of a full set of ambient air quality indicator
measurements, notably including NOy, throughout sensitive
ecoregions across the U.S.
    Further, obtaining measurement of the dominant species that
comprise NOy (HNO3, true NO2, NO, p-
NO3, and PAN) would be useful to evaluate performance of
NOy samplers. Beyond the more well known dominant components
of NOy, research efforts would be needed to characterize
total reactive nitrogen that may include significant amounts of
organically-bound nitrogen (beyond PAN) which is poorly understood with
regard to emission sources and concentration levels.
    Field measurements of NHx have been extremely limited,
but have begun to be enhanced through the NADP's passive ammonia
network (AMoN). The AMoN measures ammonia at over 50 sites, with more
than 35 at CASTNET locations. Enhanced spatial coverage of reduced
nitrogen measurements, particularly to understand within and across
ecoregion variability, and the inclusion of some continuous
observations would provide a better understanding of the uncertainty in
the F2 factor in the AAI equation and of the representativeness of
modeled NHx deposition within and across ecoregions.
    With regard to water quality data, the PA notes that such data are
typically limited relative to air quality data sets, and are also
relatively sparse in the western U.S. The TIME/LTM water quality
sampling program in the eastern U.S. (as described in chapter 2 of the
PA) is an appropriate complement to national air monitoring programs as
it affords consistency across water bodies in terms of sampling
frequency and analysis protocols. Consideration should be given to
extending the TIME/LTM design to all acid sensitive ecoregions, with
priority for areas in the western mountains that are data limited and
showing initial signs of adversity particularly with respect to aquatic
acidification. The lack of a regulatory requirement for TIME/LTM often
jeopardizes funding support of this resource that is especially
valuable and cost effective. While there are several state and local
agency water quality data bases, it is unclear the extent to which
differences in sampling, chemical analysis and reporting protocols
would impact the use of such data for the purpose of better
understanding the degree of protectiveness that would be afforded by an
AAI-based standard within sensitive ecoregions across the country. In
addition, our understanding of water quality in Alaska and Hawaii and
the acid sensitivity of their ecoregions is particularly limited.
    Water quality data and modeling support the standard setting
process. As more water bodies are sampled, the critical load data bases
would expand, enabling clearer delineation of ecoregion representative
critical loads in terms of the nth percentile. This would provide more
refined characterization of the degree of protection afforded by a
given standard. Longer term, the availability of water quality trend
data (annual to monthly sampled) would support accountability
assessments that examine if an ecoregion's response to air management
efforts is as predicted by earlier model forecasting. The most obvious
example is the long-term response of water quality ANC change to
changes in calculated AAI, deposition, ambient NOy and
SOx concentrations, and emissions. In addition, water
quality trends data provide a basis for evaluating and improving the
parameterizations of processes in critical load models applied at the
ecoregion scale related to nitrogen retention and base cation supply. A
better understanding of soil processes, especially in the southern
Appalachians, would enhance efforts to examine the variability within
ecoregions of the soil-based adsorption and exchange processes which
moderate the supply of major cations and anions to surface waters and
strongly influence the response of surface water ANC to changes in
deposition of nitrogen and sulfur.
3. Uncertainties in Modeled Processes
    As discussed in the PA, from an uncertainty perspective, gaps in
field measurement data are related to uncertainties in modeled
processes and in the specific application of such models. As noted
above, processes that are embodied in an AAI-based standard are modeled
using the CMAQ atmospheric model and steady state ecological models.
These models are characterized in the ISA as being well established and
they have undergone extensive peer review. Nonetheless, the application
of these models for purposes of specifying the factors in the AAI
equation, on an ecoregion scale, is a new application that introduces
uncertainties, as noted below, especially in areas with limited
observational data that can be used to evaluate this specific
application. Understanding uncertainties in relevant modeled process
thus involves consideration of the uncertainties associated with
applying each model as well as the combination of these uncertainties
as the models are applied in combination within the AAI framework.
    With regard to the application of CMAQ for purposes of use in an
AAI-based standard, the modeling of dry deposition has been identified
as having a relatively high degree of uncertainty. Due to a combination
of system complexity and resource constraints, there is no routine
observational basis for directly comparing modeled dry deposition and
measurements. Periodic dry deposition flux experiments covering a
variety of vegetation, surfaces and meteorology across seasons would

enable a more robust evaluation of modeled deposition of nitrogen and
sulfur. Given the difficulty in acquiring dry deposition observations,
it becomes especially important to evaluate the model's ability to
capture temporal and spatial ambient air patterns of individual
nitrogen and sulfur species which are used to drive dry deposition
calculations in models. For example, reducing a generally acknowledged
positive bias in model-predicted SO2 relative to
observations is especially relevant to the AAI-based standard, as
SO2 deposition is a dominant contributor to total acidifying
deposition in the eastern U.S. With respect to oxidized nitrogen,
observations of individual NOy species are important as air
quality models calculate the individual deposition of each species. The
modeled transference ratios, TNOy and TSOx used
in factors F3 and F4 rely on CMAQ's ability to characterize both
deposition and concentration. Consequently, a better understanding of
the variability of these factors within and across ecoregions could be
achieved by improved availability of measured ambient concentrations
and deposition observations.
    Steady state biogeochemical ecosystem modeling is used to develop
critical load estimates that are incorporated in the AAI equation
through factor F1. Consequently, the PA notes that an estimate of the
temporal response of surface water ANC to deposition and air
concentration changes is not directly available. Lacking a predicted
temporal response impairs the ability to conduct accountability
assessments down to the effects level. Accountability assessments would
examine the response of each step in the emissions source through air
concentration--deposition--surface water quality--biota continuum. The
steady state assumption at the ecosystem level does not impair
accountability assessments through the air concentration/deposition
range of that continuum. However, in using steady state ecosystem
modeling, several assumptions are made relative to the long-term
importance of processes related to soil adsorption of major ions and
ecosystem nitrogen dynamics. Because these models often were developed
and applied in glaciated areas with relatively thin and organically
rich soils, their applicability is relatively more uncertain in areas
such as those in the non-glaciated clay-based soil regions of the
central Appalachians. Consequently, it is desirable to develop the
information bases to drive simple dynamic ecosystem models that
incorporate more detailed treatment of subsurface processes, such as
adsorption and exchange processes and sulfate absorption.
4. Applying Knowledge of Uncertainties
    An understanding of the relative uncertainties in a system assists
in setting priorities for data collection efforts and research, with
the expectation that such efforts would reduce uncertainties over time
and afford greater confidence in applications of an AAI-based standard.
Because of the uniquely wide breadth of pollutants and environmental
media addressed by an AAI-based multi-pollutant standard, there are a
wide range of uncertainties that are important to consider relative to
single pollutant standards that typically address only direct effects
of ambient air exposures. For an AAI-based standard, a reduction of the
uncertainties across the various modeled processes at the ecoregion
scale would lead to greater confidence in the degree of protection
afforded by the standard.
    The PA notes that there is generally low uncertainty with regard to
the conceptual development and related major components of this
standard. In recognizing the scientific soundness of the basic
structure of this standard, the PA notes that future efforts would be
appropriately directed at expanding the availability of relevant data
for ecoregion-specific evaluation and application of the relevant
modeling of ecological and atmospheric processes, as identified above.
Such efforts would further support consideration of an AAI-based
standard and would guide field studies and analyses designed to improve
the longer-term confidence in such a standard.

G. CASAC Advice

    The CASAC has advised EPA concerning the ISA, the REA, and the PA.
The CASAC has endorsed EPA's interpretation of the science embodied in
the ISA and the assessment approaches and conclusions incorporated in
the REA.
    Most recently, CASAC has considered the information in the final PA
in providing its recommendations on the review of the new multi-
pollutant standard developed in that document and discussed above
(Russell and Samet, 2011a). In so doing, CASAC has expressed general
support for the conceptual framework of the standard based on the
underlying scientific information, as well as for the conclusions in
the PA with regard to indicators, form, averaging time, and level of
the standard that are appropriate for consideration by the Agency in
reaching decisions on the review of the secondary NAAQS for oxides of
nitrogen and sulfur:

    The final Policy Assessment clearly sets out the basis for the
recommended ranges for each of the four elements (indicator,
averaging time, level and form) of a potential NAAQS that uses
ambient air indicators to address the combined effects of oxides of
nitrogen and oxides of sulfur on aquatic ecosystems, primarily
streams and lakes. As requested in our previous letters, the Policy
Assessment also describes the implications of choosing specific
combinations of elements and provides numerous maps and tabular
estimates of the spatial extent and degree of severity of NAAQS
exceedances expected to result from possible combinations of the
elements of the standard.
    We believe this final PA is appropriate for use in determining a
secondary standard to help protect aquatic ecosystems from
acidifying deposition of oxides of sulfur and nitrogen. EPA staff
has done a commendable job developing the innovative Aquatic
Acidification Index (AAI), which provides a framework for a national
standard based on ambient concentrations that also takes into
account regional differences in sensitivities of ecosystems across
the country to effects of acidifying deposition. (Russell and Samet,
2011a)

    The CASAC also recommended that as EPA moves forward in the
regulatory process ``some attention should be given to our residual
concern that the available data may reflect the more sensitive water
bodies and thus, the selection of percentiles of waterbodies to be
protected could be conservatively biased'' (Russell and Samet, 2011a).
In addition, CASAC found some improvements could be made to the
uncertainty analysis, as noted below. With respect to indicators, CASAC
supports the use of SOx and NOy as ambient air
indicators (discussed above in section III.A) and ANC as the ecological
indicator (discussed above in section III.B.1):
    The use of NOy and SOx as atmospheric
indicators of oxides of nitrogen and sulfur atmospheric concentrations
is well justified. The use in the AAI of NOy and
SOx as atmospheric indicators of oxides of nitrogen and
sulfur concentrations is useful and corresponds with other efforts by
EPA. As we have stated previously, CASAC also agrees that ANC is the
most appropriate ecological indicator of aquatic ecosystem response and
resiliency to acidification (Russell and Samet, 2011a).
    With respect to the form of the standard (discussed above in
section III.B), CASAC stated the following:

    EPA has developed the AAI, an innovative ``form'' of the NAAQS
itself that incorporates

the multi-pollutant, multi-media, environmentally modified,
geographically variable nature of SOx/NOy
deposition-related aquatic acidification effects. With the caveats
noted below, CASAC believes that this form of the NAAQS as described
in the final Policy Assessment is consistent with and directly
reflective of current scientific understanding of effects of
acidifying deposition on aquatic ecosystems. (Russell and Samet,
2011a)
    CASAC agrees that the spatial components of the form in the
Policy Assessment are reasonable and that use of Omernick's
ecoregions (Level III) is appropriate for a secondary NAAQs intended
to protect the aquatic environment from acidification * * * (Russell
and Samet, 2011a)

    The ``caveats'' noted by CASAC include a recognition of the
importance of continuing to evaluate the performance of the CMAQ and
ecological models to account for model uncertainties and to make the
model-dependent factors in the AAI more transparent. In addition, CASAC
noted that the role of DOC and its effects on ANC would benefit from
further refinement and clarification (Russell and Samet, 2011a). While
CASAC expressed the view that the ``division of ecoregions into
`sensitive' and `non-sensitive' subsets, with a more protective
percentile applied to the sensitive areas, also seems reasonable''
(Russell and Samet, 2011a), CASAC also noted that there was the need
for greater clarity in specifying how appropriate screening criteria
would be applied in assigning ecoregions to these categories. Further,
CASAC identified potential biases in critical load calculations and in
the regional representativeness of available water chemistry data,
leading to the observation that a given percentile of the distribution
of estimated critical loads may be protective of a higher percentage of
surface waters in some regions (Russell and Samet, 2011a).
    With respect to averaging time (discussed above in section III.C),
CASAC stated the following:

    Considering the cumulative nature of the long-term adverse
ecological effects and the year-to-year variability of atmospheric
conditions (mainly in the amount of precipitation), CASAC concurs
with EPA that an averaging time of three to five years for the AAI
parameters is appropriate. A longer averaging time would mask
possible trends of AAI, while a shorter averaging time would make
the AAI being more influenced by the conditions of the particular
years selected. (Russell and Samet, 2011a)

    With respect to level as well as the combination of level and form
as they are presented as alternative standards (discussed above in
sections III.D-E), CASAC stated the following:

    CASAC agrees with EPA staff's recommendation that the ``level''
of the alternative AAI standards should be within the range of 20
and 75 [mu]eq/L. We also recognize that both the ``level'' and the
form of any AAI standard are so closely linked in their
effectiveness that these two elements should be considered together.
(Russell and Samet, 2011a)
    When considered in isolation, it is difficult to evaluate the
logic or implications of selecting from percentiles (70th to 90th)
of the distribution of estimated critical loads for lakes in
sensitive ecoregions to determine an acceptable amount of deposition
for a given ecoregion. However, when these percentile ranges are
combined with alternative levels within the staff-recommended ANC
range of 20 to 75 microequivalents per liter ([mu]eq/L), the results
using the AAI point to the ecoregions across the country that would
be expected to require additional protection from acidifying
deposition. Reasonable choices were made in developing the form. The
number of acid sensitive regions not likely to meet the standard
will be affected both by choice of ANC level and the percentile of
the distribution of critical loads for lakes to meet alternative ANC
levels in each region. These combined recommendations provide the
Administrator with a broad but reasonable range of minimally to
substantially protective options for the standard. (Russell and
Samet, 2011a)

    CASAC also commented on EPA's uncertainty analysis, and provided
advice on areas requiring further clarification in the proposed rule
and future research. The CASAC found it ``difficult to judge the
adequacy of the uncertainty analysis performed by EPA because of lack
of details on data inputs and the methodology used, and lack of clarity
in presentation'' (Russell and Samet, 2011a). In particular, CASAC
identified the need for more thorough model evaluations of critical
load and atmospheric modeling, recognizing the important role of models
as they are incorporated in the form of the standard. In light of the
innovative nature of the standard developed in the PA, CASAC identified
``a number of areas that should be the focus of further research''
(Russell and Samet, 2011a). While CASAC recognized that EPA staff was
able to address some of the issues in the PA, they also noted areas
``that would benefit from further study or consideration in potential
revisions or modifications to the form of the standard.'' Such research
areas include ``sulfur retention and mobilization in the soils,
aluminum availability, soil versus water acidification and ecosystem
recovery times.'' Further, CASAC encouraged future efforts to monitor
individual ambient nitrogen species, which would help inform further
CMAQ evaluations and the specification of model-derived elements in the
AAI equation (Russell and Samet, 2011a).

H. Administrator's Proposed Conclusions

    Having concluded that the existing NO2 and
SO2 secondary standards are neither sufficiently protective
nor appropriate to address deposition-related effects associated with
oxides of nitrogen and sulfur (section II.D above), the Administrator
has considered whether it is appropriate at this time to set a new
multi-pollutant standard for that purpose, with a structure that would
better reflect the available science regarding acid deposition. In
considering this, she recognizes that such an appropriate standard, for
purposes of section 109(b) and (d) of the CAA,\15\ must in her judgment
be requisite to protect public welfare, such that it would be neither
more nor less stringent that necessary for that purpose. In particular,
she has focused on the new standard developed in the PA and reviewed by
CASAC, as discussed above. In so doing, the Administrator first
considered the extent to which there is a scientific basis for
development of such a standard, specifically with regard to a standard
that would provide protection from deposition-related aquatic
acidification in sensitive aquatic ecosystems in areas across the
country. As discussed above, the Administrator notes that the ISA
concludes that the available scientific evidence is sufficient to infer
a causal relationship between acidifying deposition of nitrogen and
sulfur in aquatic ecosystems, and that the deposition of oxides of
nitrogen and sulfur both cause such acidification under current
conditions in the U.S. Further, the ISA concludes that there are well-
established water quality and biological indicators of aquatic
acidification as well as well-established models that address
deposition, water quality, and effects on ecosystem biota, and that
ecosystem sensitivity to acidification varies across the country
according to present and historic nitrogen and sulfur deposition as
well as geologic, soil, vegetative, and hydrologic factors. Based on
these considerations, the Administrator agrees with the conclusion in
the PA, and supported by CASAC, that there is a strong scientific basis
for development

of a standard with the general structure presented in the PA.
---------------------------------------------------------------------------

    \15\ Section 109(d)(1) requires that ``* * * the Administrator
shall complete a thorough review * * * and shall make such revisions
in such criteria and standards and promulgate such new standards as
may be appropriate under * * * subsection 109(b) of this section.''
[emphasis added]
---------------------------------------------------------------------------

    The Administrator also recognizes that the conceptual framework for
an ecologically relevant, multi-pollutant standard, which was initially
explored in the REA and further developed in the PA, builds on the
information in the ISA. She notes that the structure of the standard
addresses the combined effects of deposition from oxides of nitrogen
and sulfur by characterizing the linkages between ambient
concentrations, deposition, and aquatic acidification, and that the
structure of the standard takes into account relevant variations in
these linkages across the country. She recognizes that while the
standard is innovative and unique, the structure of the standard is
well grounded in the science underlying the relationships between
ambient concentrations of oxides of nitrogen and sulfur and the aquatic
acidification related to deposition of nitrogen and sulfur associated
with such ambient concentrations.
    While the Administrator recognizes the strong scientific foundation
for the structure of an AAI-based standard, she also recognizes that
the standard depends on atmospheric and ecological modeling, based on
appropriate data, to specify the terms of an equation that incorporates
the linkages between ambient concentrations, deposition, and aquatic
acidification. This equation, which defines an aquatic acidification
index (AAI), has the effect of translating spatially variable
ecological effects into a potential national standard. With respect to
establishing the specific terms of this equation, there are a number of
inherent uncertainties and complexities that are relevant to the
question of whether it is appropriate under section 109 to set a
specific AAI-based standard at this time, recognizing that such a
standard must be requisite to protect public welfare without being
either more or less stringent than necessary for this purpose. As
discussed above, these uncertainties and complexities generally relate
not to the structure of the standard, but to the quantification of the
various elements of the standard, such as the F factors discussed
earlier in this section and their representativeness at an ecoregion
scale. These uncertainties and complexities currently limit efforts to
characterize the degree of protectiveness that would be afforded by
such a standard, within the ranges of levels and forms identified in
the PA, and the representativeness of F factors in the AAI equation
described above and in the PA. These important uncertainties have been
generally categorized as limitations in available field data as well as
uncertainties that are related to reliance on the application of
ecological and atmospheric modeling at the ecoregion scale to specify
the various elements of the AAI.
    With regard to data limitations, the Administrator observes that
there are several important limitations in the available data upon
which elements of the AAI are based. For example, while ambient
measurements of NOy are made as part of a national
monitoring network, the monitors are not located in locations that are
representative of sensitive aquatic ecosystems. While air and water
quality data are generally available in areas in the eastern U.S.,
there is relatively sparse coverage in mountainous western areas where
a number of sensitive aquatic ecosystems are located. Further, even in
areas where relevant data are available, small sample sizes impede
efforts to characterize the representativeness of the available data,
which was noted by CASAC as being of particular concern. Also,
measurements of reduced forms of nitrogen are available from only a
small number of monitoring sites, and emission inventories for reduced
forms of nitrogen used in atmospheric modeling are subject to
considerable uncertainty.
    With regard to uncertainties related to the use of ecological and
atmospheric modeling, the Administrator notes in particular that model
results are difficult to evaluate due to a lack of relevant
observational data. For example, relatively large uncertainties are
introduced by a lack of data with regard to pre-industrial
environmental conditions and other parameters that are necessary inputs
to critical load models that are the basis for factor F1 in the AAI
equation. Also, observational data are not generally available to
evaluate the modeled relationships between nitrogen and sulfur in the
ambient air and associated deposition, which are the basis for the
other factors (i.e., F2, F3, and F4) in the AAI equation.
    In combination, these limitations and uncertainties result in a
considerable degree of uncertainty as to how well the quantified
elements of the AAI standard would predict the actual relationship
between varying ambient concentrations of oxides of nitrogen and sulfur
and steady state ANC levels across the distribution of water bodies
within the various ecoregions in the U.S. Because of this, there is
considerable uncertainty as to the actual degree of protectiveness that
such a standard would provide, especially for acid-sensitive
ecoregions. The Administrator recognizes that the AAI equation, with
factors quantified in the ranges discussed above and described more
fully in the PA, generally performs well in identifying areas of the
country that are sensitive to such acidifying deposition and indicates,
as expected, that lower ambient levels of oxides of nitrogen and sulfur
would lead to higher calculated AAI values. However, the uncertainties
discussed here are critical for determining the actual degree of
protection that would be afforded such areas by any specific target ANC
level and percentile of water bodies that would be chosen in setting a
new AAI-based standard, and thus for determining an appropriate AAI-
based standard that meets the requirements of section 109.
    In considering these uncertainties, the Administrator notes that
CASAC acknowledged that important uncertainties remain that would
benefit from further study and data collection efforts, which might
lead to potential revisions or modifications to the form of the
standard developed in the PA. She also notes that CASAC encouraged the
Agency to engage in future monitoring and model evaluation efforts to
help inform the specification of model-derived elements in the AAI
equation.
    Based on the above considerations, the Administrator has determined
that it is not appropriate under section 109 to set a new multi-
pollutant standard to address deposition-related effects of oxides of
nitrogen and sulfur on aquatic acidification at this time. Setting a
NAAQS generally involves consideration of the degree of uncertainties
in the science and other information, such as gaps in the relevant data
and, in this case, limitations in the evaluation of the application of
relevant ecological and atmospheric models at an ecoregion scale. As
noted above, the issue here is not a question of uncertainties about
the scientific soundness of the structure of the AAI, but instead
uncertainties in the quantification and representativeness of the
elements of the AAI as they vary in ecoregions across the country. At
present, these uncertainties prevent an understanding of the degree of
protectiveness that would be afforded to various ecoregions across the
country by a new standard defined in terms of a specific nationwide
target ANC level and a specific percentile of water bodies for acid-
sensitive ecoregions and thus prevent identification of an appropriate
standard.. The Administrator has considered whether these uncertainties
could be appropriately accounted for by choosing either a more or less
protective target ANC level and percentile of water bodies than would
otherwise be chosen if the uncertainties did not substantially limit
the confidence that can

appropriately be ascribed to the quantification of the AAI elements.
However, in the Administrator's judgment, the uncertainties are of such
nature and magnitude that there is no reasoned way to choose such a
specific nationwide target ANC level or percentile of water bodies that
would appropriately account for the uncertainties, since neither the
direction nor the magnitude of change from the target level and
percentile that would otherwise be chosen can reasonably be ascertained
at this time.
    Based on the above considerations, the Administrator judges that
the current limitations in relevant data and the uncertainties
associated with specifying the elements of the AAI based on modeled
factors are of such nature and degree as to prevent her from reaching a
reasoned decision such that she is adequately confident as to what
level and form (in terms of a selected percentile) of such a standard
would provide any particular intended degree of protection of public
welfare that the Administrator determined satisfied the requirements to
set an appropriate standard under section 109. While acknowledging that
CASAC supported moving forward to establish the standard developed in
the PA, the Administrator also observes that CASAC supported conducting
further field studies that would better inform the continued
development or modification of such a standard. Given the large
uncertainties and complexities inherent in quantifying the elements of
such a standard, largely deriving from the unprecedented nature of the
standard under consideration in this review, and having fully
considered CASAC's advice, the Administrator provisionally concludes
that it is premature to set a new, multi-pollutant secondary standard
for oxides of nitrogen and sulfur at this time, and as such she is
proposing not to set such a new secondary standard.
    While it is premature to set such a multi-pollutant standard at
this time, the Administrator determines that the Agency should
undertake a field pilot program to gather additional data, and that it
is appropriate that such a program be undertaken before, rather than
after, reaching a decision to set such a standard. As described below
in section IV, the purpose of the program is to collect and analyze
data so as to enhance our understanding of the degree of protectiveness
that would likely be afforded by a standard based on the AAI as
developed in the PA. This will provide additional information to aid
the Agency in considering an appropriate multi-pollutant standard,
specifically with respect to the acidifying effects of deposition of
oxides of nitrogen and sulfur. PA. Data generated by this field program
will also support development of an appropriate monitoring network that