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1

2 III. Rationale for Proposed Decision on Alternative Multi-pollutant Approach to 

3 Secondary Standards for Aquatic Acidification

4 Having reached the conclusion that the current NO2 and SO2 secondary standards are not 

5 adequate to provide appropriate protection against deposition-related effects associated with 

6 oxides of nitrogen and sulfur, the Administrator then considered what new multi-pollutant 

7 standard might be appropriate, at this time, to address such effects on public welfare. The 

8 Administrator recognizes that the inherently complex and variable linkages between ambient 

9 concentrations of nitrogen and sulfur oxides, the related deposited forms of nitrogen and sulfur, 

10 and the ecological responses that are associated with public welfare effects call for consideration 

11 of an ecologically relevant design of a standard that reflects these linkages. The Administrator 

12 also recognizes that characterization of such complex and variable linkages will necessarily 

13 require consideration of information and analyses that have important limitations and 

14 uncertainties.

15 Despite its complexity, an ecologically relevant multi-pollutant standard to address 

16 deposition-related effects could still appropriately be defined in terms of the same basic elements 

17 that are used to define any NAAQS – indicator, form, averaging time, and level. The form 

18 would incorporate additional structural elements that reflect relevant multi-pollutant and 

19 multimedia attributes. These structural elements include the use of an ecological indicator, tied 

20 to the ecological effect we are focused on, and other elements that account for ecologically

21 relevant factors other than ambient air concentrations. All of these elements would be needed to 

22 enable a linkage from ambient air indicators to the ecological indicator to define an ecologically 

23 relevant standard. As a result, such a standard would necessarily be more complex than the 
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1 NAAQS that have been set historically to address effects associated with ambient concentrations 

2 of a single pollutant.

3 More specifically, the Administrator considered an ecologically relevant multi-pollutant

4 standard to address effects associated with acidifying deposition related to ambient 

5 concentrations of oxides of nitrogen and sulfur in sensitive aquatic ecosystems. This focus is 

6 consistent with the information presented in the ISA, REA, and Policy Assessment which 

7 highlighted the sufficiency of the quantity and quality of the available evidence and assessments 

8 associated with aquatic acidification relative to the information and assessments available for 

9 other deposition-related effects, including terrestrial acidification and aquatic and terrestrial 

10 nutrient enrichment. Based on its review of these documents, CASAC agreed that aquatic 

11 acidification should be the focus for developing a new multi-pollutant standard in this review. In 

12 reaching conclusions about an air quality standard designed to address deposition-related aquatic 

13 acidification effects, the Administrator also recognizes that such a standard may also provide 

14 some degree of protection against other deposition-related effects.

15 As discussed in chapter 7 of the Policy Assessment, the development of a new multi16 pollutant standard to address deposition-related aquatic acidification effects recognizes the need 

17 for consideration of a nationally applicable standard for protection against adverse effects of 

18 aquatic acidification to public welfare, while recognizing the complex and heterogeneous 

19 interactions between ambient air concentrations of nitrogen and sulfur oxides, the related 

20 deposition of nitrogen and sulfur, and associated ecological responses. The development of such 

21 a standard also needs to take into account the limitations and uncertainties in the available 

22 information and analyses upon which characterization of such interactions are based. The 

23 approach used in the Policy Assessment also recognizes that while such a standard would be 
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1 national in scope and coverage, the effects to public welfare from aquatic acidification will not 

2 occur to the same extent in all locations in the U.S., given the inherent variability of the 

3 responses of aquatic systems to the effects of acidifying deposition.

4 As discussed above in section II, many locations in the U.S. are naturally protected 

5 against acid deposition due to underlying geological conditions. Likewise, some locations in the 

6 U.S., including lands managed for commercial agriculture and forestry, are not likely to be 

7 negatively impacted by current levels of nitrogen and sulfur deposition. As a result, while a new 

8 ecologically relevant secondary standard would apply everywhere, it would be structured to 

9 account for differences in the sensitivity of ecosystems across the country. This would allow for 

10 appropriate protection of sensitive aquatic ecosystems, which are relatively pristine and wild and 

11 generally in rural areas, and the services provided by such sensitive ecosystems, without 

12 requiring more protection than is needed elsewhere. 

13 As discussed below, the multi-pollutant standard developed in the Policy Assessment 

14 would employ (1) NOy and SOx as the atmospheric ambient air indicators; (2) a form that takes 

15 into account variable factors, such as atmospheric and ecosystem conditions that modify the 

16 amounts of deposited nitrogen and sulfur; the distinction between oxidized and reduced forms of 

17 nitrogen; effects of deposited nitrogen and sulfur on aquatic ecosystems in terms of the 

18 ecological indicator ANC; and the representativeness of water bodies within a defined spatial 

19 area; (3) a multi-year averaging time, and (4) a standard level defined in terms of a single, 

20 national target ANC value that, in the context of the above form, identifies the levels of 

21 concentrations of NOy and SOx in the ambient air that would meet the standard. The form of 

22 such a standard has been defined by an index, termed an aquatic acidification index (AAI), 

23 which reflects the relationship between ambient concentrations of NOy and SOx and aquatic 
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1 acidification effects that result from nitrogen and sulfur deposition related to these ambient 

2 concentrations.

3 In presenting the considerations associated with such an air quality standard to address 

4 deposition-related aquatic acidification effects, the following sections focus on each element of 

5 the standard, including indicator (section III.A), form (section III.B), averaging time (section 

6 III.C), and level (section III.D). Alternative combinations of levels and forms are discussed in 

7 section III.E. Considerations related to important uncertainties inherent in such an approach are 

8 discussed in section III.F. Advice from CASAC on such a new standard is presented in section 

9 III.G. The Administrator’s proposed decisions on such a new standard are presented in section 

10 III.H.

11 A. Ambient Air Indicators

12 In considering alternative ambient air indicators, the Policy Assessment primarily focuses 

13 on the important attribute of association. Association in a broad sense refers to how well an 

14 ambient air indicator relates to the ecological effects of interest by virtue of both the framework 

15 that links the ambient indicator and effects and the empirical evidence that quantifies the 

16 linkages. The Policy Assessment also considers how measurable or quantifiable an indicator is 

17 to enable its use as an effective indicator of relevant ambient air concentrations.

18 As discussed above in section II.C, the Policy Assessment concludes that indicators other 

19 than NO2 and SO2 should be considered as the appropriate indicators of oxides of nitrogen and 

20 sulfur in the ambient air for protection against the acidification effects associated with deposition 

21 of the associated nitrogen and sulfur. This conclusion is based on the recognition that all forms 

22 of nitrogen and sulfur in the ambient air contribute to deposition and resulting acidification, and 

23 as such, NO2 and SO2 are incomplete indicators. In principle, the ambient indicators should 
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1 represent the species that are associated with oxides of nitrogen and sulfur in the ambient air and 

2 can contribute acidifying deposition. This includes both the species of oxides of nitrogen and 

3 sulfur that are directly emitted as well as species transformed in the atmosphere from oxides of 

4 nitrogen and sulfur that retain the nitrogen and sulfur atoms from directly emitted oxides of 

5 nitrogen and sulfur. All of these compounds are associated with oxides of nitrogen and sulfur in 

6 the ambient air and can contribute to acidifying deposition. 

7 The Policy Assessment focuses in particular on the various compounds with nitrogen or 

8 sulfur atoms that are associated with oxides of nitrogen and sulfur, because the acidifying 

9 potential is specific to nitrogen and sulfur, and not other atoms (e.g., H, C, O) whether derived 

10 from the original source of oxides of nitrogen and sulfur emissions or from atmospheric 

11 transformations. For example, the acidifying potential of each molecule of NO2, NO, HNO3 or 

12 PAN is identical, as is the potential for each molecule of SO2 or ion of particulate sulfate, p-SO4. 

13 Each atom of sulfur affords twice the acidifying potential of each atom of nitrogen.

14 1. Oxides of Sulfur

15 As discussed in the Policy Assessment (US EPA, 2011, section 7.1.1), oxides of sulfur 

16 include the gases sulfur monoxide (SO), sulfur dioxide (SO2), sulfur trioxide (SO3), disulfur 

17 monoxide (S2O), and particulate-phase sulfur compounds [referred to as SO4] that result from 

18 gas-phase sulfur oxides interacting with particles. However, the sum of SO2 and SO4 does 

19 represent virtually the entire ambient air mass of sulfur that contributes to acidification. In 

20 addition to accounting for virtually all the potential for acidification from oxidized sulfur in the 

21 ambient air, there are reliable methods to monitor the concentrations of SO2 and particulate SO4. 

22 In addition, much of the data used to develop the technical basis for the standard developed in 
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the Policy Assessment is based on monitoring or modeling of these species.11 The Policy

2 Assessment concludes that the sum of SO2 and SO4, referred to as SOx, are appropriate ambient 

3 air indicators of oxides of sulfur because they represent virtually all of the acidification potential 

4 of ambient air oxides of sulfur and there are reliable methods suitable for measuring SO2 and 

5 SO4.

6 2. Oxides of Nitrogen

7 As discussed in the Policy Assessment (US EPA, 2011,section 7.1.2), total reactive 

8 oxidized nitrogen, NOy, as defined in chapter 2 of the Policy Assessment, incorporates basically 

9 all of the oxidized nitrogen species that have acidifying potential and as such, NOy should be 

10 considered as an appropriate indicator for oxides of nitrogen. NOy is an aggregate measure of 

11 NO and NO2 and all of the reactive oxidized products of NO and NO2. That is, NOy is a group 

12 of nitrogen compounds in which all of the compounds are either an oxide of nitrogen or 

13 compounds in which the nitrogen atoms came from oxides of nitrogen. NOy is especially 

14 relevant as an ambient indicator for acidification in that it both relates to the oxides of nitrogen in 

15 the ambient air and also represents the acidification potential of all oxidized nitrogen species in 

16 the ambient air, whether an oxide of nitrogen or derived from oxides of nitrogen. 

17 There are currently available reliable methods of measuring aggregate NOy. The term 

18 “aggregate” measure means that the NOY, as measured, is not based on measuring each 

19 individual species of NOy and calculating an NOy value by summing the individual species. 

20 Rather, as described in chapter 2 of the Policy Assessment, current measurement techniques 

21 process all of the individual NOy species to produce a single aggregate measure of all of the 

 

1 As discussed in chapter 2 of the Policy Assessment, SO2 and particulate SO4 are routinely measured in ambient air 

monitoring networks, although only CASTNET filter packs do not intentionally exclude particle size fractions. The 

CMAQ treatment of SOx is the simple addition of both species, which are treated explicitly in the model

formulation. All particle size fractions are included in the CMAQ SOx estimates.
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1 nitrogen atoms associated with any NOy species. Consequently, the NOy measurement 

2 effectively provides the sum of all individual species, but the identity of the individual species is 

3 lost. As discussed above, the accounting for the individual nitrogen atoms is an accounting of 

4 the ambient air acidification potential of oxides of nitrogen and their transformation products and 

5 therefore the most relevant ambient indicator for aquatic acidification effects associated with 

6 oxides of nitrogen. 

7 This loss of the information on individual species motivated consideration of alternative 

8 or more narrowly defined indicators for oxides of nitrogen in the Policy Assessment. 

9 Consideration of a subset of NOy species was based on the following reasoning. First, the actual 

10 dry deposition of nitrogen is determined on an individual species basis by multiplying the species 

11 concentration times a species-specific deposition velocity and then summed to develop an 

12 estimate of total dry deposition. Consequently, the use of individual ambient species has the 

13 potential to be more consistent with the underlying science of deposition and, therefore, has the 

14 potential to allow for a more rigorous evaluation of dry deposition with specialized field studies. 

15 In addition, there has been a suggestion of focusing only on the most quickly depositing NOy 

16 species, such as HNO3, as contributions from other NOy species such as NO2 may be negligible. 

17 These alternative indicators are discussed below.

18 The Policy Assessment considers the relative merits of using each individual NOy

19 species as part of a group of indicators. In so doing, it was first noted that dry deposition of NOy

20 is treated as the sum of the deposition of each individual species in advanced process-based air 

21 quality models like CMAQ, as described in chapter 2 of the Policy Assessment. Conceptually

22 one could extend this process-based approach by using all NOY species individually as separate 

23 indicators for oxides of nitrogen and requiring, for example, measurements of each of the 
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1 species, including the dominant species of HNO3, particulate nitrate (p-NO3), true NO2, NO, and 

2 PAN. The potential attraction of using individual species would be the reliance on actual 

3 deposition velocities. This could have more physical meaning in comparison to a constructed 

4 model of aggregate deposition of NOy, which is difficult to evaluate with observations because 

5 of the assimilation of many species with disparate deposition behavior. The Policy Assessment 

6 notes that the major drawback of using individual NOy species as the indicators is the lack of 

7 reliable measurement techniques, especially for PAN and NO2 in rural locations, which renders 

8 the use of virtually any individual NOy species, except for NO and perhaps p-NO3, as 

9 functionally inadequate from a measurement perspective.

10 The Policy Assessment next considered the relative merits of using a subset of NOy

11 species as the indicators for oxides of nitrogen, as was discussed above for oxides of sulfur. To 

12 the extent that certain species provide relatively minor contributions to total NOy deposition, it 

13 may be appropriate to consider excluding them as part of the indicator. As discussed in chapter 2 

14 of the Policy Assessment, each nitrogen species within the array of NOy species has species15 specific dry deposition velocities. For example, the deposition velocity of HNO3 is much 

16 greater than the velocity for nitrogen dioxide and, consequently, for a similar ambient air 

17 concentration, HNO3 contributes more deposition of acidifying nitrogen relative to nitrogen 

18 dioxide. In transitioning from source-oriented urban locations to rural environments, the ratio of 

19 the concentrations of HNO3 and PAN to NO2 increases.

20 Based on the reasoning that a larger fraction of the deposited NOY is accounted for by

21 total nitrate (the sum of HNO3 and p-NO3), a surrogate for the more rapidly depositing fraction 

22 of NOy, combined with the availability of reliable total nitrate measurements through the Clean 

23 Air Status and Trends Network (CASTNET), the Policy Assessment considered using total 
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1 nitrate as the indicator for oxides of nitrogen (US EPA, 2011, appendix E). Nitrate would be 

2 expected to correlate well with total reactive oxidized nitrogen deposition relative to NOY (US 

3 EPA, 2011,chapter 2), despite the inherent noise associated with variable contributions of low 

4 deposition velocity species (e.g., NO2) that may have relatively high ambient concentrations. 

5 However, modeling simulations suggest that NOy may be a more robust indicator, relative to 

6 HNO3, in terms of relating absolute changes in ambient air concentrations to changes in nitrogen 

7 deposition driven by changes in ambient concentrations of oxides of nitrogen (US EPA, 

8 2011,Figure 2-32).

9 Based on the above considerations, the Policy Assessment concludes that total reactive 

10 oxidized nitrogen, NOy, should be considered as the appropriate ambient indicator for oxides of 

11 nitrogen based on its direct relationship to oxides of nitrogen in the ambient air and its direct 

12 relationship to deposition associated with aquatic acidification. Because NOy represents all of 

13 the potentially acidifying oxidized nitrogen species in the ambient air, it is appropriately 

14 associated with the deposition of potentially acidifying compounds associated with oxides of 

15 nitrogen in the ambient air. In addition, there are reliable methods available to measure NOy. 

16 Measurement of each individual species of NOy, or the measurement of only a subset of species 

17 of NOy, is less appropriate because there are not reliable measurements methods available to 

18 measure all of the individual species of NOy and a subset of species would fail to account for 

significant portions of the oxidized reactive nitrogen that relates to acidification.219

20 B. Form

21 Based on the evidence of the aquatic acidification effects caused by the deposition of 

22 NOy and SOx, the Policy Assessment (US EPA, 2011,section 7.2) presents the development of a 

 

2 The Policy Assessment also notes that NOY is a useful measurement for model evaluation purposes, which is 

especially important, recognizing the unique role that CMAQ plays in the development of this standard, as described 

below in section III.B.
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1 new form that is ecologically relevant for addressing such effects. The conceptual design for the 

2 form of such a standard includes three main components: an ecological indicator, deposition 

3 metrics that relate to the ecological indicator, and a function that relates ambient air indicators to 

4 deposition metrics. Collectively, these three components link the ecological indicator to ambient 

5 air indicators, as illustrated above in Fig II-1.

6 The simplified flow diagram in Figure II-1 compresses the various atmospheric, 

7 biological, and geochemical processes associated with acidifying deposition to aquatic 

8 ecosystems into a simplified conceptual picture. The ecological indicator (left box) is related to 

9 atmospheric deposition through biogeochemical ecosystem models (middle box), which 

10 associate a target deposition load to a target ecological indicator. Once a target deposition is 

11 established, associated allowable air concentrations are determined (right box) through the 

12 relationships between concentration and deposition that are embodied in air quality models such 

13 as CMAQ. The following discussion describes the development and rationale for each of these 

14 components, as well as the integration of these components into the full expression of the form 

15 of the standard using the concept of a national AAI that represents a target ANC level as a 

16 function of ambient air concentrations. Spatial aggregation issues associated with defining each 

17 of the terms of this index are also addressed below.

18 The AAI is designed to be an ecologically relevant form of the standard that determines 

19 the levels of NOy and SOx in the ambient air that would achieve a target ANC limit for the U.S. 

20 The intent of the AAI is to weight atmospheric concentrations of oxides of nitrogen and sulfur by 

21 their propensity to contribute to acidification through deposition, given the fundamental 

22 acidifying potential of each pollutant, and to take into account the ecological factors that govern 

23 acid sensitivity in different ecosystems. The index also accounts for the contribution of reduced 
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1 nitrogen to acidification. Thus, the AAI encompasses those attributes of specific relevance to 

2 protecting ecosystems from the acidifying potential of ambient air concentrations of NOy and 

3 SOx.

4 1. Ecological Indicator

5 In considering alternative ecological indicators, the Policy Assessment again primarily

6 focuses on the attribute of association. In the case of an ecological indicator for aquatic 

7 acidification, association refers to the relationship between the indicator and adverse effects as 

8 discussed in section II. Because of the conceptual structure of the form of this standard (Figure 

9 III-1), this particular ecological indicator must also link up in a meaningful and quantifiable 

10 manner with acidifying atmospheric deposition. In effect, the ecological indicator for aquatic 

11 acidification is the bridge between biological impairment and deposition of NOy and SOx. 

12 This section presents the rationale in the Policy Assessment for selecting acid 

13 neutralizing capacity (ANC) as the appropriate ecological indicator for consideration. 

14 Recognizing that ANC is not itself the causative or toxic agent for adverse aquatic acidification 

15 effects, the rationale for using ANC as the relevant ecological indicator is based on the 

16 following:

17 (1) ANC is directly associated with the causative agents, pH and dissolved aluminum, both 

18 through empirical evidence and mechanistic relationships;

19 (2) Empirical evidence shows very clear and strong relationships between adverse effects 

20 and ANC;

21 (3) ANC is a more reliable indicator from a modeling perspective, allowing use of a body of 

22 studies and technical analyses related to ANC and acidification to inform the 

23 development of the standard; and
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1 (4) ANC literally embodies the concept of acidification as posed by the basic principles of 

2 acid base chemistry and the measurement method used to estimate ANC and, therefore, 

3 serves as a direct index to protect against acidification. 

4

5 Ecological indicators of acidification in aquatic ecosystems can be chemical or biological 

6 components of the ecosystem that are altered by the acidifying effects of nitrogen and sulfur 

7 deposition. A desirable ecological indicator for aquatic acidification is one that is measurable or 

8 estimable, linked causally to deposition of nitrogen and sulfur, and linked causally, either 

9 directly or indirectly to ecological effects known or anticipated to adversely affect public 

10 welfare.

11 As summarized in chapter 2 of the Policy Assessment, atmospheric deposition of NOy

and SOx causes aquatic acidification through the input of strong acid anions (e.g., NO3

-

12 and 

SO4

2-

13 ) that ultimately shifts the water chemistry equilibrium toward increased hydrogen ion 

14 levels (or decreased pH). The anions are deposited either directly to the aquatic ecosystem or 

15 indirectly via transformation through soil nitrification processes and subsequent drainage from 

16 terrestrial ecosystems. In other words, when these anions are mobilized in the terrestrial soil, 

17 they can leach into adjacent water bodies. Aquatic acidification is indicated by changes in the 

18 surface water chemistry of ecosystems. In turn, the alteration of surface water chemistry has been 

19 linked to negative effects on the biotic integrity of freshwater ecosystems. There is a suite of 

20 chemical indicators that could be used to assess the effects of acidifying deposition on lake or 

21 stream acid-base chemistry. These indicators include acid neutralizing capacity (ANC); 

22 alkalinity (ALK); base neutralizing capacity, commonly referred to as acidity (ACY); surface 
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water pH; concentrations of trivalent aluminum, Al+3; and concentrations of major anions (SO4

2-

1 , 

NO3

-

), cations (Ca2+, Mg+2, K+2 ), or sums of cations or anions.

3 ANC and ALK are very similar quantities and are used interchangeably in the literature 

4 and for some of the analyses presented in this document. Both ANC and ALK are defined as the 

5 amount of strong acid required to reach a specified equivalence point. For acid-base solutions, 

6 an equivalence point can be thought of as the point at which the addition of strong acids (i.e., 

7 titration) is no longer neutralized by the solution. This explains the term acid neutralizing 

8 capacity, or ANC, as ANC relates directly to the capacity of a system to neutralize acids. The 

9 differences between ANC and ALK are based on operational definitions and subject to various 

10 interpretations. ANC is preferred over ALK as the body of scientific evidence has focused on 

11 ANC and effects relationships. ALK is more widely associated with more general 

12 characterizations of water quality such as the relative hardness of water associated with 

13 carbonates.

14 Indictors such as the concentrations of specific anions, cations, or their groupings, while 

15 relevant to acidification processes, are not robust acidification indicators as it is the relative 

16 balance of cations and anions that is more directly associated with acidification. That balance is 

17 captured by ANC and ALK. Acidity, ACY, is the coverse of ANC and indicates how much 

18 strong base it takes to reach an equivalence point. Because ACY is not used in most ecosystem 

19 assessments, the body of information relating ACY to effects is too limited to serve as a basis for 

20 an appropriate ecological indicator. Aluminum and other metals are causative toxic agents that 

21 directly impair biological functions. However, aluminum, or metals in general, have high 

22 variability in concentrations that can be linked to effects, often at extremely low levels which in 

23 some cases approach detectability limits, exhibit rapid transient responses, and are often 
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1 confounded by the presence of other toxic metals. These concerns limit the use of metals as 

reliable and measurable ecological indicators. Hydrogen ion (H+2 ) concentrations, using their 

3 negative logarithmic values, or pH, are well correlated with adverse effects, as discussed above 

4 in section II.A, and determine the solubility of metals such as aluminum. However, pH is not a 

5 preferred acidification indicator due to its highly transient nature and other concerns, as 

6 discussed below. 

7 Having reasoned that ANC is a preferred indicator to ALK, ACY, individual metals or 

8 groupings of ions, the Policy Assessment considers the relative merits of ANC compared to pH, 

9 which is a well recognized indicator of acidity and a more direct causative agent with regard to 

10 adverse effects. First, the linkage between ANC and pH is considered in recognition of the 

11 causative association between pH and effects.

12 ANC is not the direct causative toxic agent impacting aquatic species diversity. The 

13 scientific literature generally emphasizes the links between pH and adverse effects as described 

14 above in section II.A. It is important, therefore, to consider the extent to which ANC and pH are 

15 well related from a mechanistic perspective as well as through empirical evidence. ANC and 

16 pH are co-dependent on each other based on the requirement that all solutions are electrically

17 neutral, meaning that any solution must satisfy the condition that all negatively charged species 

18 must be balanced by all positively charged species. ANC is defined as the difference between 

19 strong anions and cations (US EPA, 2011, equation 7-13).

20 While the chemistry can be complex, the co-dependency between ANC and pH is 

explained by recognizing that positively charged hydrogen, H+21 , is incorporated in the charge 

22 balance relationships related to the overall solution chemistry which also defines ANC. The 

23 positive, directional co-dependency (i.e., ANC and pH increase together) is further explained in 
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1 concept as ANC reflects how much strong acid (i.e., how much hydrogen ion) it takes to titrate to 

2 an equivalence point. Strong observed correlations between pH and ANC as described in the 

3 Policy Assessment support these mechanistic relationships. 

4 As discussed above in section II.A, there are well established examples of ANC 

5 correlating strongly with a variety of ecological effects which are summarized in the Policy

6 Assessment (US EPA, 2011, Table 3-1). Because pH and ANC are well correlated and linearly 

7 dependent over the pH ranges (4.5-6) where adverse ecological effects are observed, evidence of 

8 clear associations exist between ANC and adverse ecological effects as described in the Policy 

9 Assessment. In large measure, this dependence between pH and ANC and the relationship of 

10 both pH and ANC to effects, speak directly to the appropriateness of ANC with respect to its use 

11 as an ecological indicator.

12 Thus, there is a clear association between ANC and ecological effects, although there is a 

13 more direct causal relationship between pH and effects. Nonetheless, ANC is preferred as an 

14 ecological indicator based on its superior ability to provide a linkage with deposition in a 

15 meaningful and quantifiable manner, a role that is served far more effectively by ANC than by 

16 pH. While both ANC and pH are clearly associated with the effects of concern, ANC is superior 

17 in linking these effects to deposition. 

18 The Policy Assessment notes that the basis for this conclusion is that acidifying

19 atmospheric deposition of nitrogen and sulfur is a direct input of potential acidity (ACY), or, in 

20 terms of ANC, such deposition is relevant to the major anions that reduce the capacity of a water 

21 body to neutralize acidity. Consequently, there is a well defined linear relationship between 

22 potential acidifying deposition and ANC. This ANC-deposition relationship facilitates the 

23 linkage between ecosystem models that calculate an ecological indicator and the atmospheric 
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1 deposition of NOy and SOx. On the other hand, there is no direct linear relationship between 

2 deposition and pH. While acid inputs from deposition lower pH, the relationship can be 

3 extremely nonlinear and there is no direct connection from a modeling or mass balance 

4 perspective between the amount of deposition entering a system and pH. The term “mass 

5 balance” underlies the basic formulation of any physical modeling construct, for atmospheric or 

6 aquatic systems, and refers to the accounting of the flow of mass into a system, the 

7 transformation to other forms, and the loss due to flow out of a system and other removal 

8 processes. ANC is a conserved property. This means that ANC in a water body can be 

9 accounted for by knowledge of how much ANC initially exists, how much flows in and is 

10 deposited, and how much flows out. In contrast, hydrogen ion concentration in the water, the 

11 basis for pH, is not a conserved property as its concentration is affected by several factors such 

12 as temperature, atmospheric pressure, mixing conditions of a water body, and the levels of 

13 several other chemical species in the system. The disadvantage of pH lacking conservative 

14 properties is that there is a very complex connection between changes in ambient air 

15 concentrations of NOy and SOx and pH. 

16 The discussion of basic water chemistry of natural systems in chapter 2 of the Policy

17 Assessment provides further details on why pH is not a conserved quantity and is subject to rapid 

18 transient response behavior that makes it difficult to use as a reliable and functional ecological 

19 indicator. The observed pH-to-ANC relationship (US EPA, 2011, figure 7-2) partially explains 

20 the concern with pH responding too abruptly. In the region where pH ranges roughly from 4.5 

21 to 6 and is of greatest relevance to effects (US EPA, 2011, figure 7-4), there clearly is more 

22 sensitivity of pH to changes in ANC in the ANC range from approximately 0 to 50 μeq/L. A 

23 focus on this part of the ANC-to-pH relationship shows that ANC associates well with pH in a 
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1 fairly linear manner. However, the pH range from 4.5 to 6 also includes one of the very steepest 

2 parts of the slope relating pH as a function of ANC, where ANC ranges down below 0 μeq/L, 

3 which is subject to very rapid change in ANC, or deposition inputs. This part of the relationship 

4 coincides with reduced levels of ANC and hence with reduced ability to neutralize acids and 

5 moderate pH fluctuations. This response behavior can be extended to considering how pH 

6 would change in response to deposition, or ambient concentrations, of NOy and SOx, which can 

7 be viewed as “ANC-like” inputs. 

8 In summary, because ANC clearly links both to biological effects of aquatic acidification 

9 as well as to acidifying inputs of NOy and SOx deposition, the Policy Assessment concludes that 

10 ANC is an appropriate ecological indicator for relating adverse aquatic ecosystem effects to 

11 acidifying atmospheric deposition of SOx and NOy, and is preferred to other potential indicators. 

12 In reaching this conclusion, the Policy Assessment notes that in its review of the first draft Policy

13 Assessment, CASAC concluded that “information on levels of ANC protective to fish and other 

14 aquatic biota has been well developed and presents probably the lowest level of uncertainty in 

15 the entire methodology” (Russell and Samet, 2010a). In its more recent review of the second 

16 draft Policy Assessment, CASAC agreed “that acid neutralizing capacity is an appropriate 

17 ecological measure for reflecting the effects of aquatic acidification” (Russell and Samet, 2010b; 

18 p. 4). 

19 2. Linking ANC to Deposition

20 There is evidence to support a quantified relationship between deposition of nitrogen and 

21 sulfur and ANC. This relationship was analyzed in the REA for two case study areas, the 

22 Adirondack and Shenandoah Mountains, based on time-series modeling and observed trends. 
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1 In the REA analysis, long-term trends in surface water nitrate, sulfate and ANC were modeled 

2 using Model of Acidification of Groundwater in Catchment (MAGIC) for the two case study

3 areas. These data were used to compare recent surface water conditions in 2006 with 

4 preindustrial conditions (i.e. preacidification 1860). The results showed a marked increase in the 

5 number of acid impacted lakes, characterized as a decrease in ANC levels, since the onset of 

6 anthropogenic nitrogen and sulfur deposition, as discussed in chapter 2 of the Policy Assessment.

7 In the REA, more recent trends in ANC, over the period from 1990 to 2006, were assessed using 

8 monitoring data collected at the two case study areas. In both case study areas, nitrate and 

9 sulfate deposition decreased over this time period. In the Adirondack Mountains, this 

10 corresponded to a decreased concentration of nitrate and sulfate in the surface waters and an 

11 increase in ANC (US EPA, 2009, section 4.2.4.2). In the Shenandoah Mountains, there was a 

12 slight decrease in nitrate and sulfate concentration in surface waters corresponding to modest 

13 increase in ANC from 50 ueq/L in 1990 to 67 ueq/L in 2006 (US EPA, 2009, section 

14 4.2.4.3,Appendix 4, and section 3.4).

15 In the REA, the quantified relationship between deposition and ANC was investigated 

16 using ecosystem acidification models, also referred to as acid balance models or critical loads 

17 models (US EPA, 2011, section 2 and US EPA, 2009, section 4 and Appendix 4). These models 

18 quantify the relationship between deposition of nitrogen and sulfur and the resulting ANC in 

19 surface waters based on an ecosystem’s inherent generation of ANC and ability to neutralize 

20 nitrogen deposition through biological and physical processes. A critical load is defined as the 

21 amount of acidifying atmospheric deposition of nitrogen and sulfur beyond which a target ANC 

22 is not reached. Relatively high critical load values imply that an ecosystem can accommodate 
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1 greater deposition levels than lower critical loads for a specific target ANC level. Ecosystem 

2 models that calculate critical loads form the basis for linking deposition to ANC. 

3 As discussed in chapter 2 of the Policy Assessment, both dynamic and steady state 

4 models calculate ANC as a function of ecosystem attributes and atmospheric nitrogen and sulfur 

5 deposition, and can be used to calculate critical loads. Steady state models are time invariant 

6 and reflect the long term consequences associated with an ecosystem reaching equilibrium under 

7 a constant level of atmospheric deposition. Dynamic models are time variant and take into 

8 account the time dependencies inherent in ecosystem hydrology, soil and biological processes. 

9 Dynamic models like MAGIC can provide the time series response of ANC to deposition 

10 whereas steady state models provide a single ANC relationship to any fixed deposition level. 

11 Dynamic models naturally are more complex than steady state models as they attempt to capture 

12 as much of the fundamental biogeochemical processes as practicable, whereas steady state 

13 models depend on far greater parameterization and generalization of processes that is afforded, 

14 somewhat, by not having to accounting for temporal variability. 

15 The Policy Assessment notes that steady state models are capable of addressing the 

16 question of what does it take to reach and sustain a specific level of ANC. Dynamic models are 

17 also capable of addressing that question, but can also address the question of how long it takes to 

18 achieve that result. Dynamic models afford the ability for more comprehensive treatment of a 

19 variety of processes throughout the surface, soil and bedrock layers within an ecosystem. For 

20 example, steady state models treat sulfate as a mobile anion throughout the system, meaning that 

21 the sulfate that is deposited to a watershed enters the water column and is not influenced by soil 

22 adsorption or cation exchange. Dynamic models can incorporate these time variant processes. 

23 The use of a steady state model treating sulfate as totally mobile does not necessarily conflict 
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1 with the possibility of sulfate acting as a less than mobile ion at certain times. The steady state 

2 assumption is premised on the long term behavior of sulfate which can undergo periods of net 

3 adsorption followed by periods of net desorption which can balance out over time. The Policy 

4 Assessment recognizes that as the richness of the available data increases, in terms of parameters 

5 and spatial resolution, the incorporation of dynamic modeling approaches in the standard setting 

6 process should become more feasible. In determining an appropriate modeling approach for the 

7 development of a NAAQS in this review, the Policy Assessment considers both the relevance of 

8 the question addressed as well as the ability to perform modeling that provides relevant 

9 information for geographic areas across the country. 

10 Dynamic models require a large amount of catchment level-specific data relative to 

11 steady state models. Because of the time invariant nature of steady state models, the data 

12 requirements that integrate across a broad spectrum of ecosystem processes is achievable and 

13 available now at the national level. Water quality data to support steady state models currently 

14 exist for developing a national data base for modeling nearly 10,000 catchments in the 

15 contiguous U.S. In contrast, the data needs to support dynamic models for national-scale 

16 analyses simply are not available at this time. Further, the information provided by steady state 

17 modeling would be sufficient to develop and analyze alternative NAAQS and the kind of 

18 protection they would afford. While it would be of interest to also obtain information about how 

19 much time it would take for a target ANC level to be achieved, the absence of such information 

20 does not preclude developing and evaluating alternative NAAQS using the AAI structure. Based 

21 on the above considerations, the Policy Assessment concludes that at this time steady state 

22 critical load modeling is an appropriate tool for linking long-term ANC levels to atmospheric 

23 deposition of nitrogen and sulfur for development of an AAI that has national applicability.
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1 A steady state model is used to define the critical load, which is the amount of 

2 atmospheric deposition of nitrogen (N) and sulfur (S) beyond which a target ANC is not 

achieved and sustained.33 It is expressed as:

4

CLANClim(N + S) = ([BC]0

*

5 - [ANClim])Q + Neco (III-1)

6

7 Where:

8 CLANClim(N + S) is the critical load of deposition, with units of equivalent charge/(area-time);

[BC]0

*

9 is the natural contribution of base cations from weathering, soil processes and 

10 preindustrial deposition, with units of equivalent charge/volume;

11 [ANClim] is the target ANC value, with units of equivalent charge/volume; 

12 Q is the catchment level runoff rate governed by water mass balance and dominated by

13 precipitation, with units of distance/time; and

14 Neco is the amount of nitrogen deposition that is effectively neutralized by a variety of biological 

15 (e.g., nutrient uptake) and physical processes, with units of equivalent charge/ (area-time).

16

17 Equation III- 1 is a modified expression that adopts the basic formulation of the steady

18 state models that are described in chapter 2 of the Policy Assessment. More detailed discussion 

19 of the rationale, assumptions and derivation of equation III- 1, as well as all of the equations in 

20 this section, are included in Appendix B of the Policy Assessment. The equation simply reflects 

21 the amount of deposition of nitrogen and sulfur from the atmosphere, CLANClim(N + S), that is 

22 associated with a sustainable long-term ANC target, [ANClim], given the capacity of the natural 

 

3

 This section discusses the linkages between deposition of nitrogen and sulfur and ANC. Section III.B.3 then 

discusses the linkages between atmospheric concentrations of NOy and SOx and deposition of nitrogen and sulfur.
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system to generate ANC, [BC]0

*

1 , and the capacity of the natural system to neutralize nitrogen 

2 deposition, Neco. This expression of critical load is valid when nitrogen deposition is greater 

than Neco.43 The runoff rate, Q, allows for balancing mass in the two environmental mediums –

4 atmosphere and catchment. This critical load expression can be focused on a single water system 

5 or more broadly. To extend applicability of the critical load expression (equation III-1) from the 

6 catchment level to broader spatial areas, the terms Qr and CLr, are used, which are the runoff rate 

7 and critical load, respectively, of the region over which all the atmospheric terms in the equation 

8 are defined. 

9 In considering the contributions of SOx or NOy species to acidification, it is useful to 

10 think of every depositing nitrogen atom as supplying one equivalent charge unit and every sulfur 

11 atom as depositing two charge units. The Policy Assessment uses equivalent charge per volume 

12 as a normalizing tool in place of the more familiar metrics such as mass or moles per volume. 

13 This allows for a clearer explanation of many of the relationships between atmospheric and 

14 ecosystem processes that incorporate mass and volume unit conventions somewhat specific to 

the environmental media of concern (e.g., m315 for air and liter for liquid water). Equivalent 

16 charge reflects the chemistry equilibrium fundamentals that assume electroneutrality, or 

17 balancing charge where the sum of cations always equals the sum of anions. 

18 As presented above, the terms S and N in the CLANClim (N + S) term broadly represent all 

19 species of sulfur or nitrogen that can contribute to acidifying deposition. This follows 

20 conventions used in the scientific literature that addresses critical loads, and it reflects all 

21 possible acidifying contributions from any sulfur or nitrogen species. For all practical purposes, 

22 S reflects SOx as described above, the sum of sulfur dioxide gas and particulate sulfate. 

 

4 Because Neco is only relevant to nitrogen deposition, in rare cases where Neco is greater than the total nitrogen 

deposition, the critical load would be defined only in terms of acidifying deposition of sulfur and the Neco term in 

equation III-1 would be set to zero.
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1 However, N in equation III-1 includes both oxidized forms, consistent with the ambient 

2 indicator, NOy, in addition to the reduced nitrogen species, ammonia and ammonium ion, 

3 referred to as NHx. NHx is included in the critical load formulation because it contributes to 

4 potentially acidifying nitrogen deposition. Consequently, from a mass balance or modeling 

5 perspective, the form of the standard needs to account for NHx, as described below.

6 3. Linking Deposition to Ambient Air Indicators

7 The last major component of the form illustrated in Figure III-1 addresses the linkage 

8 between deposition of nitrogen and sulfur and concentrations of the ambient air indicators, NOy

9 and SOx. To link ambient air concentrations with deposition, the Policy Assessment defines a 

10 transference ratio, T, as the ratio of total wet and dry deposition to ambient concentration, 

11 consistent with the area and time period over which the standard is defined. To express 

12 deposition of NOy and SOx in terms of NOy and SOx ambient concentrations, two transference 

13 ratios were defined, where TSOx equals the ratio of the combined dry and wet deposition of SOx 

14 to the ambient air concentration of SOx, and TNOy equals the ratio of the combined dry and wet 

15 deposition of NOy to the ambient air concentration of NOy.

16 As described in Chapter 7 of the Policy Assessment, reduced forms of nitrogen (NHX) are 

17 included in total nitrogen in the critical load equation, III-1. Reduced forms of nitrogen are 

18 treated separately, as are NOy and SOx, and the transference ratios are applied. This results in 

19 the following critical load expression that is defined explicitly in terms of the indicators NOy and 

20 SOx:

21

CLANClim(N + S) = ([BC]0

*

22 - [ANClim])Q + Neco = [NOy]TNOy + [SOx]TSOx + NHx (III-2)

23
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1 This is the same equation as III-1, with the deposition associated with the critical load translated 

2 to deposition from ambient air concentrations via transference ratios. In addition, deposition of 

3 reduced nitrogen, oxidize nitrogen and oxidized sulfur are treated separately. 

4 Transference ratios are a modeled construct, and therefore cannot be compared directly to 

5 measurable quantities. There is an analogy to deposition velocity, as a transference ratio is 

6 basically an aggregated weighted average of the deposition velocities of all contributing species 

7 across dry and wet deposition, and transference ratio units are expressed as distance/time. 

8 However, wet deposition commonly is not interpreted as the product of a concentration times a 

9 velocity. Direct wet deposition observations are available which integrate all of the processes, 

10 regardless of how well they may be understood, related to wet deposition into a measurable 

11 quantity. There are reasonable analogies between the processes governing dry and wet 

12 deposition, from a fundamental mass transfer perspective. In both cases there is a transfer of 

13 mass between the dry ambient phase and another medium, either a surface or vegetation in the 

14 case of dry deposition, or a rain droplet or cloud in the case of wet precipitation. The specific 

15 thermodynamic properties and chemical/biological reactions that govern the transfer of dry mass 

16 to plants or aqueous droplets differ, but either process can be based on conceptualizing the 

17 product of a concentration, or concentration difference, times a mass transfer coefficient which is 

18 analogous to the basic dry deposition model: dry deposition = concentration x velocity (US 

19 EPA, 2011, Appendix F). 

20 Transference ratios require estimates of wet deposition of NOy and SOx, dry deposition 

21 of NOy and SOx, and ambient air concentrations of NOy and SOx. Possible sources of 

22 information include model estimates or a combination of model estimates and observations, 

23 recognizing that dry deposition is a modeled quantity that can use observed or modeled estimates 
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1 of concentration. The limited amount of NOy measurements in acid-sensitive areas as well as 

2 the combination of representative NOy, SO2 and SO4 observations generally preclude the use of 

3 observations for development of a standard that is applicable nationally.

4 The Policy Assessment considers a blending of observations and models to take 

5 advantage of their relative strengths; e.g., combining the NADP wet deposition observations, 

6 modeled dry deposition, and a mix of modeled and observed concentrations, using the model for 

7 those species not measured or measured with very sparse spatial coverage. A potential 

8 disadvantage of mixing and matching observations and model estimates is to lose consistency

9 afforded by using just modeling alone. A modeling platform like CMAQ is based on adhering to 

10 consistent treatment of mass conservation, by linking emission inputs with air concentrations and 

11 concentrations to deposition. Inconsistencies from combining processes from different analytical 

12 platforms increase the chance that mass (of nitrogen or sulfur) would unintentionally be 

13 increased or decreased as the internal checking that assures mass conservation is lost. 

14 Transference ratios incorporate a broad suite of atmospheric processes and consequently 

15 an analytical approach that instills consistency in the linkage of these processes is preferable to 

16 an approach lacking such inherent consistency. This contention does not mean that observations 

17 alone, if available, could not be used, but suggests that the inconsistencies in combining models 

18 and observations for the purposes of developing transference ratios has the potential for creating 

19 unintended artifacts. 

20 While there is a reasonable conceptual basis for the concept of an aggregated deposition 

21 velocity referred to in the Policy Assessment as a transference ratio, there is very limited ability

22 to compare observed and calculated ratios. This is because the deposition velocity is dependent 

23 on individual species, and the mass transfer processes of wet and dry removal, while 
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1 conceptually similar, are different. Consequently, there does not exist a meaningful approach to 

2 measure such an aggregated or lumped parameter. Therefore, at this time, the evaluation of 

3 transference ratios is based on sensitivity studies, analysis of variability, and comparisons with 

4 other models, as described in Appendix F of the Policy Assessment. 

5 As discussed in Appendix F, the interannual variability, as well as the sensitivity to 

6 emission changes of roughly 50%, results in changes of transference ratios of approximately 5 -

7 10%. Part of the reason for this inherent stability is due to the co-dependence of concentration 

8 and deposition. For example, as concentrations are reduced as a result of emissions reductions, 

9 deposition in turn is reduced since deposition is a direct linear function of concentration leading 

10 to negligible impact on the deposition-to-concentration ratio. Likewise, an overestimate of 

11 concentration likely does not induce a bias in the transference ratio. While it is important to 

12 continue to improve the model’s ability to match ambient concentrations in time and space, the 

13 bias of a modeled estimate of concentration relative to observations does not necessarily result in 

14 a bias in a calculated transference ratio. In effect, this consideration of bias cancellation reduces 

15 the sensitivity of transference ratios to model uncertainties and affords increased confidence in 

16 the stability of these ratios. Based on the series of sensitivity and variability analyses, the Policy

17 Assessment concludes that the transference ratios are relatively stable and provide a sound 

18 metric for linking deposition and concentration in the form of the standard.

19 As discussed in the policy Assessment, transference ratios are dependent on the platform 

20 upon which they are constructed. Comparisons of transference ratios constructed from different 

21 modeling platforms do exhibit significant differences. While this divergence of results may be 

22 explained by a variety of differences in process treatments, input fields and incommensurabilities 

23 in species definitions and spatial configurations, it does suggest two very important conclusions. 
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1 First, the idea of using multiple platforms for different parts of the country may be problematic 

2 as there does not exist a reliable approach to judge acceptance which is almost always based on 

3 comparisons to observations. Second, since transference ratios are based on concentrations and 

4 deposition, as the uncertainties in each of those components are reduced, the relative uncertainty 

5 in the ratios also is reduced. This means that basic improvements in the model’s ability to 

6 reproduce observed wet deposition and ambient concentration fields enhance the relative 

7 confidence in the constructed transference ratios. Similarly, as in-situ dry deposition flux 

8 measurements become available that enable a more rigorous evaluation and diagnosis of 

9 modeled dry deposition processes, the expected improved treatment of dry deposition also would 

10 increase confidence in transference ratios. Finally, deposition is directly related to ambient air 

11 concentrations. Models like CMAQ rely on the concentration-to-deposition linkage to calculate 

12 deposition, which is the foundation for broadly based and robust assessments addressing 

13 atmospheric deposition. In principle, the use of a modeled constructed transference ratio is based 

14 on the same premise by which we use models to estimate deposition in the first place.

15 The shortage of widely available ambient air observations and the fact that estimates of 

16 dry deposition requires modeling, collectively suggests that a unified modeling platform is the 

17 best approach for constructing transference ratios. The Policy Assessment (US EPA, 

18 2011,section 2) considers CMAQ and other models, such as CAMx and the Canada’s AURAMS 

19 - A Unified Regional Air-quality Modeling System (Smythe et al., 2008), and concludes that 

20 CMAQ is the preferred modeling platform for constructing transference ratios for purposes of 

21 developing a new secondary standard for consideration. This conclusion reflects the view that 

22 for the purposes of defining transference ratios, a modeling platform should (1) be a multiple 

23 pollutant model recognizing the myriad of connections across pollutant categories that directly
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1 and indirectly impact nitrogen and sulfur characterization, (2) include the most comprehensive 

2 scientific treatments of atmospheric processes that relate directly and indirectly to characterizing 

3 concentrations and deposition, (3) have an infrastructure capability that accommodates the 

4 inclusion of improved scientific treatments of relevant processes and important input fields, and 

5 (4) undergo frequent reviews regarding the adequacy of the underlying science as well as the 

6 appropriateness in applications. The CMAQ platform exhibits all these characteristics. It has 

7 been (and continues to be) extensively evaluated for several pollutant categories, is supported by

8 a central infrastructure of EPA scientists, with considerable interfacing with the scientific 

9 research communities in academia and industry, whose mission is to improve and evaluate the 

10 CMAQ platform. More directly, CMAQ, and its predecessor versions, has a long track record 

11 going back to the NAPAP in the 1980’s of specific improvements in deposition processes, which 

12 are described in Appendix F of the Policy Assessment.

13 4. Aquatic Acidification Index

14 Having established the various expressions that link atmospheric deposition of nitrogen 

15 and sulfur to ANC and the transference ratios that translate atmospheric concentrations to 

16 deposition of nitrogen and sulfur, the Policy Assessment derives the following expression of 

17 these linkages, which separates reduced forms of nitrogen, NHx, from oxidized forms:

18

19 ANCcalc = {ANClim + CLr/Qr } – NHx/Qr - TNOy [NOy]/Qr - TSOx[SOx]/Qr (III-3)

20

21 Equation III- 3 is the basic expression of the form of a standard that translates the 

22 conceptual framework into an explicit expression that defines ANC as a function of the ambient 

23 air indicators, NOy and SOx. This equation calculates an expected ANC value based on ambient 
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1 concentrations of NOy and SOx. The calculated ANC will differ from the target ANC (ANClim) 

2 depending on how much the nitrogen and sulfur deposition associated with NOy, SOx, and NHx 

3 differs from the critical load associated with just achieving the target ANC. Based on equation 

4 III- 3, the Policy Assessment defines an aquatic acidification index (AAI) that is more simply

5 stated using terms that highlight the ambient air indicators:

6

7 AAI = F1 – F2 – F3[NOy] – F4[SOx] (III- 4)

8

9 where the AAI represents the long term (or steady state) ANC level associated with ambient air 

10 concentrations of NOy and SOx. The factors F1 through F4 convey three attributes: a relative 

11 measure of the ecosystem’s ability to neutralize acids (F1), the acidifying potential of reduced 

12 nitrogen deposition (F2), and the deposition-to-concentration translators for NOy (F3) and SOx 

13 (F4). 

14 Specifically:

15 F1 = ANClim + CLr/Qr ;

16 F2 = NHx/Qr = NHx deposition divided by Qr;

17 F3 = TNOy/ Qr ; TNOy is the transference ratio that converts ambient air concentrations of NOy to 

18 deposition of NOy; and

19 F4 = TSOx/ Qr ; TSOx is the transference ratio that converts ambient air concentrations of SOx to 

20 deposition of SOx.

21 All of these factors include representative Qr to maintain unit (and mass) consistency between 

22 the AAI and the terms on the right side of equation III-4.
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1 The F1 factor is the target ANC level plus the amount of deposition (critical load) the 

2 ecosystem can receive and still achieve the target level. It incorporates an ecosystem’s ability to 

3 generate acid neutralizing capacity through base cation supply ([BC]*0) and to neutralize 

4 acidifying nitrogen deposition through Neco, both of which are incorporated in the CL term. As 

5 noted above, because Neco can only neutralize nitrogen deposition (oxidized or reduced) there 

6 may be rare cases where Neco exceeds the combination of reduced and oxidized nitrogen 

7 deposition. Consequently, to ensure that the AAI equation is applicable in all cases that may

8 occur, equation III-4 is conditional on total nitrogen deposition, {NHx + F3[NOy]}, being 

9 greater than Neco. In rare cases where Neco is greater than {NHx + F3[NOy]}, F2, F3, and 

10 Neco would be set equal to 0 in the AAI equation. The consequence of setting F2 and F3 to zero 

11 is simply to constrain the AAI calculation just to SOx, as nitrogen would have no bearing on 

12 acidifying contributions in this case.

13 The Policy Assessment concludes that equation III- 4 (US EPA, 2011,equation 7-12),

14 which defines the AAI, is ecologically relevant and appropriate for use as the form of a national 

15 standard designed to provide protection for aquatic ecosystems from the effects association with 

16 acidifying deposition associated with concentrations of oxides of nitrogen and sulfur in the 

17 ambient air. This AAI equation does not, however, in itself, define the spatial areas over which 

18 the terms of the equation would apply. To specify values for factors F1 through F4, it is 

19 necessary to define spatial areas over which these factors are determined. Thus, it is necessary to 

20 identify an approach for spatially aggregating water bodies into ecologically meaningful regions 

21 across the U.S., as discussed below.

22 5. Spatial Aggregation
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1 As discussed in the Policy Assessment, one of the unique aspects of this form is the need 

2 to consider the spatial areas over which values for the F factors in the AAI equation are 

3 quantified. Ecosystems across the U.S. exhibit a wide range of geological, hydrological and 

vegetation characteristics that influence greatly the ecosystem parameters, Q, BC0

*

4 and Neco that 

5 are incorporated in the AAI. Variations in ecosystem attributes naturally lead to wide variability 

6 in the sensitivities of water bodies in the U.S. to acidification, as well as in the responsiveness of 

7 water bodies to changes in acidifying deposition. Consequently, variations in ecosystem 

8 sensitivity, and the uncertainties inherent in characterizing these variations, must be taken into 

9 account in developing a national standard. In developing a secondary NAAQS to protect public 

10 welfare, the focus of the Policy Assessment is on protecting sensitive populations of water 

11 bodies, not on each individual water body, which is consistent with the Agency’s approach to 

12 protecting public health through primary NAAQS that focus on susceptible populations, not on 

13 each individual.

14 The approach used for defining ecologically relevant regions across the U.S. in the Policy 

15 Assessment (US EPA, 2011,section 7.2.5) is described below, along with approaches to 

16 characterizing each region as acid sensitive or relatively non-acid sensitive. This 

17 characterization facilitates a more detailed analysis and focus on those regions that are relatively 

18 more acid sensitive. This characterization is also used to avoid over-protection in relatively non19 acid sensitive regions, regions that would receive limited benefit from reductions in the 

20 deposition of oxides of nitrogen and sulfur with respect to aquatic acidification effects. 

21 Approaches to developing representative values for each of the terms in the AAI equation 

22 (factors F1 through F4) for each ecologically relevant region for which sufficient data are 

23 available are then discussed. These spatial aggregation approaches are generally applicable to 
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1 the contiguous United States. The following discussion also addresses the development of 

2 factors for data-limited regions and specifically for Hawaii, Alaska and the U.S. territories.

3 Stated more simply, this section discusses appropriate ways to divide the country into 

4 ecologically relevant regions; to characterize each region as either acid sensitive or relatively 

5 non-acid sensitive; and to determine values of factors F1 through F4 for each region, taking into 

6 consideration the acid sensitivity of each region and the availability of relevant data. For each 

7 such region, the AAI would be calculated based on the values of factors F1 through F4 specified 

8 for that region.

9 In considering approaches to spatial aggregation, the Policy Assessment focuses on 

10 methods that have been developed to define ecologically relevant regions, referred to as 

11 ecoregions, which are meaningfully related to the factors that are relevant to aquatic 

12 acidification. As noted above, the Policy Assessment did not focus on looking at each individual 

13 water body, nor did it focus on aggregating over the entire nation, which would preclude taking 

14 into account the inherent variability in atmospheric and ecological factors that fundamentally 

15 modify the relationships that are central to the development of an ecologically relevant AAI. 

16 Based on considering available classification schemes, the Policy Assessment concludes 

17 that Omernik’s ecoregion classification is the most appropriate method to consider for the 

18 purposes of this review. This classification offers several levels of spatial delineation, has 

19 undergone an extensive scientific peer review process, and has explicitly been applied to 

20 delineating acid sensitive areas within the U.S. Further, the Policy Assessment concludes that 

ecoregion level III (Figure III-1) resolution, with 84 defined ecoregions in the contiguous U.S.,521

22 is the most appropriate level to consider for this purpose. The spatial resolution afforded by 

23 level III strikes an appropriate balance relative to the reasoning that supports conclusions on 

 

5 We note that an 85th area within Omernik’s Ecoregion Level III is currently being developed for California.
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1 indicators, as discussed above. The Policy Assessment concludes that the most detailed level of 

2 resolution (level IV) is not appropriate given the limited data availability to address nearly 1000 

3 subdivisons within that level and the currently evolving nature of level IV regions. Further, level 

4 III ecoregions are preferred to level II in that level III ecoregions, but not level II ecoregions, are 

5 largely contiguous in space which allows for a more coherent development of information to 

6 quantify the AAI factors and to characterize the concentrations of NOy and SOx in the ambient 

7 air within each ecoregion.

8 Appendix C of the Policy Assessment includes a description of each level III ecoregion. 

9 The Policy Assessment notes that the use of ecoregions is an appropriate spatial aggregation 

10 scheme for this standard focused on deposition-related aquatic acidification effects, while many 

11 of the same ecoregion attributes may be applicable in subsequent NAAQS reviews that may

12 address other deposition-related aquatic and terrestrial ecological effects. Because atmospheric 

13 deposition is modified by ecosystem attributes, the types of vegetation, soils, bedrock geology, 

14 and topographic features that are the basis of this ecoregion classification approach also will 

15 likely be key attributes for other deposition-related effects (e.g., terrestrial acidification, nutrient 

16 enrichment) that link atmospheric concentrations to an aquatic or terrestrial ecological indicator.
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1 Figure III-1. Omernik Ecoregion III areas (http://www.epa.gov/wed/pages/ecoregions).

2

3 a. Ecoregion Sensitivity

4 The Policy Assessment used Omernik’s original alkalinity data (US EPA, 2011,as section 

5 2) and more recent ANC data to delineate two broad groupings of ecoregions: acid-sensitive and 

6 relatively non-acid sensitive ecoregions. This delineation was made to facilitate greater focus on 

7 those ecoregions with water bodies that generally have greater acid sensitivity and to avoid over8 protection in regions with generally less sensitive water bodies. The approach used to delineate 

9 acid-sensitive and relatively non-acid sensitive regions included an initial numerical-based 

10 sorting scheme using ANC data, which categorized ecoregions with relatively high ANC values 
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1 as being relatively non-acid sensitive. This initial delineation resulted in 29 of the 84 Omernik 

2 ecoregions being categorized as acid sensitive. Subsequently, land use data were also considered 

3 to determine to what extent an ecoregion is of a relatively pristine and rural nature by quantifying 

4 the degree to which active management practices related to development and agriculture occur in 

5 each ecoregion. 

6 The overall objective is to produce a logical and practical grouping of ecoregions that 

7 experience adverse conditions with respect to aquatic acidification and are likely to respond to 

8 changes in concentrations of NOy and SOx in the ambient air and to the related deposition 

9 levels. To achieve this goal, a two-step process has been applied, first identifying acid sensitive 

10 ecoregions based on water quality data alone, and second identifying among those acid-sensitive 

11 ecoregions those with highly developed and managed areas. These highly developed and 

12 managed ecoregions are placed in a non-acid sensitive category to avoid over protection beyond 

13 what is requisite to protect public welfare. More specifically, in determining an ecoregion’s acid 

14 sensitivity status in step 1, ANC data across the 84 ecoregions are sorted (US EPA, 2011,section

15 7 ) to determine the number of water bodies within a region with ANC values suggestive of acid 

16 sensitivity, so as to screen out regions with an overabundance of high ANC values. In reviewing 

17 the ANC data, the Policy Assessment identified 29 ecoregions that meet two criteria: (1) greater 

18 than 5% of water bodies with data with ANC values less than 200 μeq/L and (2) greater than 1% 

19 of water bodies with ANC values less than 100 μeq/L. In step 2, land use data were used to 

20 identify those acid sensitive ecoregions with significant managed areas that would not be 

21 considered as having a relatively pristine and rural character. The percentage of the 

22 combination of developed (residential, transportation, industrial and commercial) and 

23 agricultural (croplands, pastures, orchards, vineyards) land use was used as an indicator of 
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1 managed land use area. Forest cover was used as an indicator of non-managed land use more 

2 directly reflecting the pristine quality of a region. Based on the 2006 National Land Cover Data 

3 base (NLCD, http://www.epa.gov/mrlc/nlcd-2006.html), acid sensitive ecoregions would meet 

4 both of the following land use data criteria: percent of developed and agricultural area less than 

5 20% combined with forested area greater than 50%. The combination of steps 1 and 2 identify

6 22 relatively acid sensitive areas (Table III-1 and Figure III-2). 

7 Consideration was also given to the use of naturally acidic conditions in defining 

8 relatively non-acid sensitive areas. For example, several of the ecoregions located in plains near 

9 the coast exhibit elevated dissolved organic carbon (DOC) levels, which is associated with 

10 naturally acidic conditions. DOC in surface waters is derived from a variety of weak organic 

11 acid compounds generated from the natural availability and decomposition of organic matter 

12 from biota. Consequently, high DOC is associated with “natural” acidity, with the implication 

13 that a standard intended to protect against atmospheric contributions to acidity is not an area of 

14 focus. The evidence suggests that several of the more highly managed ecoregions in coastal or 

15 near coastal transition zones are associated with relatively high DOC values, typically exceeding 

16 on average 5 mg/l, compared to other acid sensitive areas. Although there is sound logic to 

17 interpret naturally acidic areas as relatively non-acid sensitive, natural acidity indicators were not 

18 explicitly included in defining relatively non-acid sensitive areas as there does not exist a 

19 consensus-based quantifiable scientific definition of natural acidity. Approaches to explicitly

20 define natural acidity likely will be pursued in future reviews of the standard.
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Table III-1. List of 22 Acid-Sensitive Areas

Ecoregion name Ecoregion number

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

1
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1

2 Figure III-2. Acid-sensitive ecoregions identified in grey fill (22 out of 84 ecoregions).

3

4 b. Representative Ecoregion-specific Factors

5 Having concluded that the Omernik level III ecoregions are an appropriate approach to 

6 spatial aggregation for the purpose of a standard to address deposition-related aquatic 

7 acidification effects, the Policy Assessment uses those ecoregions to define each of the factors in 

8 the AAI equation. As discussed below, factors F1 through F4 in equation III-4 are defined for 

9 each ecoregion by specifying ecoregion-specific values for each factor based on monitored or 

10 modeled data that are representative of each ecoregion.
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1 i. Factor F1

2 As discussed above, factor F1 reflects a relative measure of an ecosystem’s ability to 

3 neutralize acidifying deposition, and is defined as: F1 = ANClim + CLr/Qr. The value of F1 for 

4 each ecoregion would be based on a representative critical load for the ecoregion (CLr) 

5 associated with a single national target ANC level (ANClim, discussed below in section III.D), 

6 as well as on a representative runoff rate (Qr). To specify ecoregion-specific values for the term 

Qr 7 , the Policy Assessment used the median value of the distribution of Q values that are available 

8 for water bodies within each ecoregion. To specify ecoregion-specific representative values for 

the term CLr in factor F1, a distribution69 of calculated critical loads was created for the water 

bodies in each ecoregion for which sufficient water quality and hydrology data are available.710 

11 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 12 percentile means that within an 

13 ecoregion, 90 percent of the water bodies would be expected to have higher calculated critical 

14 loads than the representative critical load. That is, if the representative critical load were to 

15 occur across the ecoregion, 90 percent of the water bodies would be expected to achieve the 

16 national ANC target or better.

17 The specific percentile selected as part of the definition of F1 is an important parameter 

18 that directly impacts the representative critical load specified for each ecoregion, and therefore 

19 the degree of protectiveness of the standard. A higher percentile corresponds to a lower critical 

20 load and, therefore, to lower allowable ambient air concentrations of NOy and SOx and related 

 

6 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.

7 The Policy Assessment judged the data to be sufficient for this purpose if data are available from more than 10 

water bodies in an ecoregion.
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1 deposition to achieve a target AAI level. In conjunction with the other terms in the AAI

2 equation, alternative forms can be appropriately characterized in part by identifying a range of 

3 alternative percentiles. The choice of an appropriate range of percentiles to consider for acid4 sensitive and relatively non-acid sensitive ecoregions, respectively, is discussed below. 

5 (a) Acid-sensitive Ecoregions

6 In identifying percentiles that are appropriate to consider for the purpose of calculating 

7 factor F1 for ecoregions characterized as acid sensitive, the Policy Assessment concludes that it 

8 is appropriate to focus on the lower (more sensitive) part of the distribution of critical loads, so 

9 as to ensure that the ecoregion would be represented by relatively more acid sensitive water 

10 bodies within the ecoregion. Specifying factor F1 in this way would help to define a standard 

11 that would be protective of the population of acid sensitive water bodies within an ecoregion, 

12 recognizing that even ecoregions characterized as acid sensitive may contain a number of 

13 individual water bodies that are not acid sensitive. The Policy Assessment recognizes that there 

14 is no basis for independently evaluating the degree of protectiveness afforded by any specific 

15 percentile value, since it is the combination of form and level, in conjunction with the indicator 

16 and averaging time, which determine the degree of protectiveness of a standard. In light of this, 

17 the Policy Assessment concludes that it is appropriate to consider a range of percentiles, from 

well above the 50th 18 percentile, or median, of the distribution to somewhat below the highest 

19 value (in terms of sensitivity; a high degree of sensitivity corresponds to a low value for critical 

20 load). More specifically, the Policy Assessment concludes it is appropriate to consider 

percentiles in the range of the 70th to the 90th 21 percentile (of sensitivity). This conclusion is based 

22 on the judgment that it would not be appropriate to represent an ecoregion with the lowest or 

23 near lowest critical load, so as to avoid potential extreme outliers that can be seen to exist at the 
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1 extreme end of the data distributions, which would not be representative of the population of acid 

2 sensitive water bodies within the ecoregion and could lead to an overly protective standard. At 

3 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 4 percentile, a value 

5 well above the median of the distribution, so that a substantial majority of acid-sensitive water 

6 bodies are protected. 

7 In considering this conclusion, the CASAC Panel noted that the data bases for calculating 

8 critical loads within an ecoregion are not necessarily representative of all water bodies within an 

9 ecoregion. That is, in many ecoregions the lake sampling design used in studies that generated 

10 the relevant data may have focused on the relatively more sensitive water bodies within an 

11 ecoregion (Russell and Samet, 2011a). Consequently, a given percentile of the distribution of 

12 calculated critical loads, based on sampled water bodies, may not be representative of that 

13 percentile of all water bodies across an entire ecoregion. To the extent that the sampling of water 

14 bodies within an ecoregion was skewed toward the relatively more sensitive water bodies, 

15 selecting a given percentile from the distribution of available critical loads would result in a 

16 somewhat higher percentile of all water bodies within that ecoregion having a higher calculated 

17 critical load than the representative critical load value. Thus, the extent to which study sampling 

18 designs have resulted in skewed distributions of calculated critical loads is an uncertainty that is 

19 appropriate to consider in selecting a percentile for the purpose of defining the factor F1 in the 

20 AAI equation. 

21 (b) Non-acid sensitive Ecoregions

22 With regard to identifying percentiles that are appropriate to consider for the purpose of 

23 calculating factor F1 for ecoregions characterized as relatively non-acid sensitive, the Policy
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1 Assessment recognizes that while such ecoregions are generally less sensitive to acidifying 

2 deposition from oxides of nitrogen and sulfur, they may contain a number of water bodies that 

3 are acid sensitive This category includes ecoregions that are well protected from acidification 

4 effects due to natural production of base cations and high ANC levels, as well as naturally acidic 

5 systems with limited base cation production and consequently very low critical loads. Therefore, 

6 the use of a critical load that would be associated with a highly sensitive water body in a 

7 naturally acidic system would impose a high degree of relative protection in terms of allowable 

8 ambient air concentrations of oxides of nitrogen and sulfur and related deposition, while 

9 potentially affording little or no public welfare benefit from attempting to improve a naturally

10 acidic system.

11 Based on these considerations, the Policy Assessment concludes it is appropriate to 

12 consider the use of a range of percentiles that extends lower than the range identified above for 

13 acid-sensitive ecoregions. Consideration of a lower percentile would avoid representing a 

14 relatively non-acid sensitive ecoregion by a critical load associated with relatively more acid15 sensitive water bodies. In particular, the Policy Assessment concludes it is appropriate to focus 

on the median or 50th 16 percentile of the distribution of critical loads so as to avoid over-protection 

17 in such ecoregions. Recognizing that relatively non-acid sensitive ecoregions generally are not 

18 sampled to the extent that acid-sensitive ecoregions are, it also is appropriate to consider using 

19 the median critical load of all relatively non-acid sensitive ecoregions for each such ecoregion.

20 ii. Factor F2

21 As discussed above, factor F2 is the amount of reduced nitrogen deposition within an 

22 ecoregion, including the deposition of both ammonia gas and ammonium ion, and is defined as: 

F2 = NHx/Qr. The Policy Assessment calculated the representative runoff rate, Qr 23 , using a 
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1 similar approach as noted above for factor F1; i.e., the median value of the distribution of Q 

2 values that are available for water bodies within each ecoregion. In the Policy Assessment, 2005 

3 CMAQ model simulations over 12-km grids are used to calculate an average value of NHx for 

4 each ecoregion. The NHx term is based on annual average model outputs for each grid cell, 

5 which are spatially averaged across all the grid cells contained in each ecoregion to calculate a 

6 representative annual average value for each ecoregion. The Policy Assessment concludes that 

7 this approach of using spatially averaged values is appropriate for modeling, largely due to the 

8 relatively rapid mixing of air masses that typically results in relatively homogeneous air quality 

9 patterns for regionally dispersed pollutants. In addition, there is greater confidence in using 

10 spatially averaged modeled atmospheric fields than in using modeled point-specific fields.

11 This averaging approach is also used for the air concentration and deposition terms in 

12 factors F3 and F4, as discussed below. The Policy Assessment notes that modeled NHx 

13 deposition exhibits greater spatial variability than the other modeled terms in factors F3 and F4. 

14 Recognizing this greater variability, the Policy Assessment concludes that it would be 

15 appropriate to consider alternative approaches to specifying the value of NHx. One such 

16 approach might involve the use of more localized and/or contemporaneous modeling in areas 

17 where this term is likely to be particularly variable and important.

18 iii. Factors F3 and F4

19 As discussed above, factors F3 and F4 are the ratios that relate ambient air concentrations 

20 of NOy and SOx to the associated deposition, and are defined as follow: F3 = TNOy/ Qr and F4 

21 = TSOx/ Qr. TNOy is the transference ratio that converts ambient air concentrations of NOy to 

22 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 23 , is calculated as for factors F1 and 
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1 F2. The transference ratios are based on the 2005 CMAQ simulations, using average values for 

2 each ecoregion, as noted above for factor F2. More specifically, the transference ratios are 

3 calculated as the annual deposition of NOy or SOx spatially averaged across the ecoregion and 

4 divided by the annual ambient air concentration of NOy or SOx, respectively, spatially averaged 

5 across the ecoregion.

6 c. Factors in Data-limited Ecoregions 

7 As discussed above in section III.B.5.a, in the Policy Assessment the initial delineation of 

8 acid-sensitive and relatively non-acid sensitive ecoregions was based on available ANC and 

9 alkalinity data. Areas not meeting the ANC criteria described above are categorized as 

10 relatively non-acid sensitive. The development of a reasonable distribution of critical loads for 

11 water bodies within an ecoregion for the purpose of identifying the representative critical load 

12 requires additional data, including more specific water quality data for major cations and anions. 

13 This means that the water bodies that can be used to develop a distribution of critical loads is 

14 generally a subset of those water bodies for which ANC data are available Consequently, there 

15 are certain ecoregions with sparse data that are not suitable for developing a distribution of 

16 critical loads.

17 As noted above, the Policy Assessment judges that it is not appropriate to develop such 

18 distributions based on data from less than ten water bodies within an ecoregion. Twelve such 

19 ecoregions, which included only relatively non-acid sensitive ecoregions, were characterized as 

20 being data-limited. For these ecoregions, the Policy Assessment considered alternative 

21 approaches to specifying values for the terms CLr and Qr for the purpose of determining values 

22 for each of the factors in the AAI equation. For these data-limited ecoregions, the Policy

23 Assessment judges that it is appropriate to use the median values of CLr and Qr from the 
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1 distributions of these terms for all other relatively non-acid sensitive ecoregions, rather than 

2 attempting to use severely limited data to develop a value for these terms based solely on data 

3 from such an ecoregion. Further, consideration could be given to using a single national default 

4 value for all relatively non-acid sensitive ecoregions. The Policy Assessment notes that this data 

5 limitation is not a concern in specifying values for the other terms in the AAI equation for such 

6 ecoregions, since those terms are based on data from the 2005 CMAQ model simulation, which 

7 covers all ecoregions across the contiguous U.S.

8 d. Application to Hawaii, Alaska, and the U.S. Territories

9 The above methods for specifying ecoregion-specific values for the factors in the AAI 

10 equation apply to those ecoregions within the contiguous U.S. For areas outside the continental 

11 U.S., including Hawaii, Alaska, and the U.S. Territories, there is currently a lack of available 

12 data to characterize the sensitivity of such areas, as well as a lack of water body-specific data and 

13 CMAQ-type modeling to specify values for the F1 through F4 factors. Thus, the Policy

14 Assessment has considered possible alternative approaches to specifying values for factors F1 

15 through F4 in the AAI equation for these areas.

16 One such approach could be to specify area-specific values for the factors based on 

17 values derived for ecoregions with similar acid sensitivities, to the extent that relevant 

18 information can be obtained to determine such similarities. Such an approach would involve 

19 conducting an analysis to characterize similarities in relevant ecological attributes between 

20 ecoregions in the contiguous U.S. and these areas outside the contiguous U.S. so as to determine 

21 the appropriateness of utilizing ecoregion-specific values for the CLr and Qr terms from one or 

22 more ecoregions within the contiguous U.S. This approach would also involve conducting 

23 additional air quality modeling for these area that are outside the geographical scope of the 
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1 currently available CMAQ model simulations, so as to develop the other information necessary

2 to specify values for factors F2 through F4 for these areas.

3 A second approach could rely on future data collection efforts to establish relevant 

4 ecological data within these areas that, together with additional air quality modeling, could be 

5 used to specify area-specific values for factors F1 through F4. Until such time as relevant data 

6 become available, these areas could be treated the same as data-limited ecoregions in the 

7 contiguous U.S. that are relatively non-acid sensitive.

8 The Policy Assessment concludes that either approach would introduce substantial 

9 uncertainties that arise from attempting to extrapolate values based on similarity assumptions or 

10 arbitrarily assigning values for factors in the AAI equation that would be applicable to these 

11 areas outside the contiguous U.S. In light of such uncertainties, the Policy Assessment 

12 concludes that it would also be appropriate to consider relying on the existing NO2 and SO2

13 secondary standards in these areas for protection of any potential direct or deposition-related 

14 ecological effects that may be associated with the presence of oxides of nitrogen and sulfur in the 

15 ambient air. The Policy Assessment concludes that relying on existing secondary standards in 

16 these areas is preferable to using a highly uncertain approach to allow for the application of a 

17 new standard based on the AAI in the absence of relevant area-specific data.

18 6. Summary of the AAI Form

19 With regard to the form of a multi-pollutant air quality standard to address deposition20 related aquatic acidification effects, the Policy Assessment concludes that consideration should 

21 be given to an ecologically relevant form that characterizes the relationships between the 

22 ambient air indicators for oxides of nitrogen and sulfur, the related deposition of nitrogen and 

23 sulfur, and the associated aquatic acidification effects in terms of a relevant ecological indicator. 
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1 Based on the available information and assessments, consideration should be given to using acid 

2 neutralizing capacity (ANC) as the most appropriate ecological indicator for this purpose, in that 

3 it provides the most stable metric that is highly associated with the water quality properties that 

4 are directly responsible for the principal adverse effects associated with aquatic acidification: 

5 fish mortality and reduced aquatic species diversity.

6 The Policy Assessment developed such a form, termed an aquatic acidification index 

7 (AAI), using a simple equation to calculate an AAI value in terms of the ambient air indicators of 

8 oxides and nitrogen and sulfur and the relevant ecological and atmospheric factors that modify

9 the relationships between the ambient air indicators and ANC. Recognizing the spatial 

10 variability of such factors across the U.S., the Policy Assessment concludes it is appropriate to 

11 divide the country into ecologically relevant regions, characterized as acid-sensitive or relatively 

12 non-acid-sensitive, and specify the value of each of the factors in the AAI equation for each such 

13 region. Omernik ecoregions, level III, are identified as the appropriate set of regions over which 

14 to define the AAI. There are 84 such ecoregions that cover the continental U.S. This set of 

15 ecoregions is based on grouping a variety of vegetation, geological, and hydrological attributes 

16 that are directly relevant to aquatic acidification assessments and that allow for a practical 

17 application of an aquatic acidification standard on a national scale.

18 The Policy Assessment defines AAI by the following equation: AAI = F1 – F2 –

19 F3[NOy] – F4[SOx]. Factors F1 through F4 would be defined for each ecoregion by specifying 

20 ecoregion-specific values for each factor based on monitored or modeled data that are 

21 representative of each ecoregion. The F1 factor is also defined by a target ANC value. More 

22 specifically:
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1 (1) F1 reflects a relative measure of an ecosystem’s ability to neutralize acidifying 

2 deposition. The value of F1 for each ecoregion would be based on a representative 

3 critical load for the ecoregion associated with a single national target ANC level, as well 

4 as on a representative runoff rate. The representative runoff rate, which is also used in 

5 specifying values for the other factors, would be the median value of the distributions of 

6 runoff rates within the ecoregion. The representative critical load would be derived from 

7 a distribution of critical loads calculated for each water body in the ecoregion for which 

8 sufficient water quality and hydrology data are available. The representative critical load 

9 would be defined by selecting a specific percentile of the distribution.

10 In identifying a range of percentiles that are appropriate to consider for this 

11 purpose, regions categorized as acid sensitive were considered separately from regions 

12 categorized as relatively non-acid sensitive. For acid sensitive regions, the Policy

13 Assessment concludes that consideration should be given to selecting a percentile from 

within the range of the 70th to the 90th 14 percentile. The lower end of this range was 

15 selected to be appreciably above the median value so as to ensure that the critical load 

16 would be representative of the population of relatively more acid sensitive water bodies 

17 within the region, while the upper end was selected to avoid the use of a critical load 

18 from the extreme tail of the distribution which is subject to a high degree of variability

19 and potential outliers. For relatively non-acid sensitive regions, the Policy Assessment 

concludes that consideration should be given to selecting the 50th 20 percentile to best 

21 represent the distribution of water bodies within such a region, or alternatively to using 

22 the median critical load of all relatively non-acid sensitive areas, recognizing that such 

23 areas are far less frequently evaluated than acid sensitive areas. Using either of these 
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1 approaches would avoid characterizing a generally non-acid-sensitive region with a 

2 critical load that is representative of relatively acid sensitive water bodies that may exist 

3 within a generally non-acid sensitive region.

4 (2) F2 reflects the deposition of reduced nitrogen. Consideration should be given to 

5 specifying the value of F2 for each region based on the averaged modeled value across 

6 the region, using national CMAQ modeling that has been conducted by EPA. 

7 Consideration could also be given to alternative approaches to specifying this value, such 

8 as the use of more localized and/or contemporaneous modeling in areas where this term is 

9 likely to be particularly variable and important. 

10 (3) F3 and F4 reflect transference ratios that convert ambient air concentrations of NOy

11 and SOx, respectively, into related deposition of nitrogen and sulfur. Consideration 

12 should be given to specifying the values for F3 and F4 for each region based on CMAQ 

13 modeling results averaged across the region. We conclude that specifying the values or 

14 the transference rations based on CMAQ modeling results alone is preferred to an 

15 alternative approach that combines CMAQ model estimates with observational data.

16 (4) The terms [NOy] and [SOx] reflect ambient air concentrations measured at 

17 monitoring sites within each region.

18 Using the equation, a value of AAI can be calculated for any measured values of ambient 

19 NOy and SOx. For such a NAAQS, the Administrator would set a single, national value for the 

20 level of the AAI used to determine achievement of the NAAQS, as discussed below in section 

21 III.D. The ecoregion-specific values for factors F1 through F4 would be specified by EPA based 

22 on the most recent data and CMAQ model simulations, and codified as part of such a standard. 
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1 These factors would be reviewed and updated as appropriate in the context of each periodic 

2 review of the NAAQS. 

3 The Policy Assessment developed specific F factors for each ecoregion based on the 

4 approach discussed above, using alternative percentiles and alternative national target ANC 

5 levels. The results of this analysis for ecoregions characterized as acid sensitive are presented in 

6 Table 7-1a-d in the Policy Assessment. 

7 C. Averaging Time

8 As discussed in section 7.3 of the Policy Assessment, aquatic acidification can occur over 

9 both long- and short-term timescales. Long-term cumulative deposition of nitrogen and sulfur is 

10 reflected in the chronic acid-base balance of surface waters as indicated by measured annual 

11 ANC levels. Similarly, the use of steady state critical load modeling, which generates critical 

12 loads in terms of annual cumulative deposition of nitrogen and sulfur, means that the focus of 

13 ecological effects studies based on critical loads is on the long-term equilibrium status of water 

14 quality in aquatic ecosystems. Much of the evidence of adverse ecological effects associated 

15 with aquatic acidification, as discussed above in section II.A, is associated with chronically low 

16 ANC levels. Protection against a chronic ANC level that is too low is provided by reducing 

17 overall annual average deposition levels for nitrogen and sulfur.

18 Reflecting this focus on long-term acidifying deposition, the Policy Assessment 

19 developed the AAI that links ambient air indicators to deposition-related ecological effects, in 

20 terms of several factors, F1 through F4. As discussed above, these factors are all calculated as 

21 annual average values, whether based on water quality and hydrology data or on CMAQ model 

22 simulations. In the context of a standard defined in terms of the AAI, the Policy Assessment 
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1 concludes that it is appropriate to consider the same annual averaging time for the ambient air 

2 indicators as is used for the factors in the AAI equation.

3 We also recognize that short-term (i.e., hours or days) episodic changes in water 

4 chemistry, often due to changes in the hydrologic flow paths, can have important biological 

5 effects in aquatic ecosystems. Such short-term changes in water chemistry are termed “episodic 

6 acidification.” Some streams may have chronic or base flow chemistry that is generally healthy 

7 for aquatic biota, but may be subject to occasional acidic episodes with potentially lethal 

8 consequences. Thus, short-term episodic ecological effects can occur even in the absence of 

9 long-term chronic acidification effects.

10 Episodic declines in pH and ANC are nearly ubiquitous in drainage waters throughout the 

11 eastern U.S. Episodic acidification can result from several mechanisms related to changes in 

12 hydrologic flow paths. For example, snow can store nitrogen deposited throughout the winter 

13 and snowmelt can then release this stored nitrogen, together with nitrogen derived from 

14 nitrification in the soil itself, in a pulse that leads to episodic acidification in the absence of 

15 increased deposition during the actual episodic acidification event. The Policy Assessment notes 

16 that inputs of nitrogen and sulfur from snowpack and atmospheric deposition largely cycle 

17 through soil. As a result, short-term direct deposition inputs are not necessarily important in 

18 episodic acidification. Thus, as noted in chapter 3 of the ISA, protection against episodic acidity

19 events can be achieved by establishing a higher chronic ANC level.

20 Taken together, the above considerations support the conclusion that it is appropriate to 

21 consider the use of a long-term average for the ambient air indicators NOy and SOx for an

22 aquatic acidification standard defined in terms of the AAI. The use of an annual averaging time 

23 for NOy and SOx concentrations would be appropriate to provide protection against low chronic 
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1 ANC levels, which in turn would protect against both long-term acidification and acute acidic 

2 episodes.

3 The Policy Assessment has also considered interannual variability in both ambient air 

4 quality and in precipitation, which is directly related to the deposition of oxides of nitrogen and 

5 sulfur from the ambient air. While ambient air concentrations show year-to-year variability, 

6 often the year-to-year variability in precipitation is considerably greater, given the highly 

7 stochastic nature of precipitation. The use of multiple years over which annual averages are 

8 determined would dampen the effects of interannual variability in both air quality and 

9 precipitation. For the ambient air indicators, the use of multiple-year averages would also add 

10 stability to calculations used to judge whether an area meets a standard defined in terms of the 

11 AAI. Consequently, the Policy Assessment concludes that an annual averaging time based on 

12 the average of each year over a consecutive 3 to 5 year period is appropriate to consider for the 

13 ambient air indicators NOy and SOx. In reaching this conclusion, the Policy Assessment notes 

14 that in its comments on the second draft Policy Assessment, CASAC agreed that a 3 to 5 year 

15 averaging time was appropriate to consider (Russell and Samet, 2010b).

16 D. Level

17 As discussed above, the Policy Assessment concludes that ANC is the ecological 

18 indicator best suited to reflect the sensitivity of aquatic ecosystems to acidifying deposition from 

19 oxides of nitrogen and sulfur in the ambient air. ANC is an indicator of the aquatic acidification 

20 expected to occur given the natural buffering capacity of an ecosystem and the loadings of 

21 nitrogen and sulfur resulting from atmospheric deposition. Thus, the Policy Assessment 

22 developed a new standard for aquatic acidification that is based on the use of chronic ANC as the 

23 ecological indicator as a component in the AAI.
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1 The level of the standard would be defined in terms of a single, national value of the 

2 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.83 

4 The annual values of the AAI would be calculated based on the AAI equation using the assigned 

5 ecoregion-specific values for factors F1 through F4 and monitored annual average NOy and SOx 

6 concentrations. Since the AAI equation is based on chronic ANC as the ecological indicator, the 

7 level chosen for the standard would reflect a target chronic ANC value. As noted above, the 

8 assigned F factors for each ecoregion would be determined by EPA in the rulemaking to set the 

9 NAAQS, based on water quality and hydrology data, CMAQ modeling, the selected percentile 

10 that is used to identify a representative critical load within the ecoregion, and the selected level 

11 of the standard. The combination of the form of the standard, discussed above in section III.B, 

12 defined by the AAI equation and the assigned values of the F factors in the equation, other 

13 elements of the standard including the ambient air indicators (section III.A) and their averaging

14 time (section III.C), and the level of the standard determines the allowable levels of NOy and 

15 SOx in the ambient air within each ecoregion. All of the elements of the standard together 

16 determine the degree of protection from adverse aquatic acidification effects associated with 

17 oxides of nitrogen and sulfur in the ambient air. The level of the standard plays a central role in 

18 determining the degree of protection provided and is discussed below.

19 The Policy Assessment focuses primarily on information that relates degrees of 

20 biological impairment associated with adverse ecological effects to aquatic ecosystems to 

21 alternative levels of ANC in reaching conclusions regarding the range of target ANC levels that 

22 is appropriate to consider for the level of the standard. The Policy Assessment develops the 

 

8 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.
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1 rationale for identifying a range of target ANC levels that is appropriate to consider by

2 addressing questions related to the following areas: (1) associations between ANC and pH levels 

3 to provide an initial bounding for the range of ANC values to be considered; (2) evidence that 

4 allows for the delineation of specific ANC ranges associated with varying degrees of severity of 

5 biological impairment ecological effects; (3) the role of ANC in affording protection against 

6 episodic acidity; (4) implications of the time lag response of ANC to changes in deposition; (5) 

7 past and current examples of target ANC values applied in environmental management 

8 practices; and (6) data linking public welfare benefits and ANC.

9 1. Association Between pH Levels and Target ANC Levels 

10 As discussed above in section II.A and more fully in chapter 3 of the Policy Assessment, 

11 specific levels of ANC are associated with differing levels of risk of biological impairment in 

12 aquatic ecosystems, with higher levels of ANC resulting in lower risk of ecosystem impacts, and 

13 lower ANC levels resulting in risk of both higher intensity of impacts and a broader set of 

14 impacts. While ANC is not the causal agent determining biological effects in aquatic ecosystem, 

15 it is a useful metric for determining the level at which a water body is protected against risks of 

16 acidification. There is a direct correlation between ANC and pH levels which, along with 

17 dissolved aluminum, are more closely linked to the biological causes of ecosystem response to 

18 acidification. 

19 Because there is a direct correlation between ANC and pH levels, the selection of target 

20 ANC levels is informed in part through information on effects of pH as well as direct studies of 

21 effects related to ANC. Levels of pH are closely associated with ANC in the pH range of 

22 approximately 4.5 to 7. Within this range, higher ANC levels are associated with higher pH 

23 levels. At a pH level of approximately 4.5, further reductions in ANC generally do not correlate 
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1 with pH, as pH levels remain at approximately 4.5 while ANC values fall substantially. 

2 Similarly, at a pH value of approximately 7, ANC values can continue to increase with no 

3 corresponding increase in pH. As pH is the primary causal indicator of effects related to aquatic 

4 acidification, this suggests that ANC values below approximately -50 μeq/L (the apparent point 

5 in the relationship between pH and ANC where pH reaches a minimum) are not likely to result in 

6 further damage. In addition, ANC values around and above approximately 100 μeq/L (the 

7 apparent region in the relationship where pH reaches a maximum) are not likely to confer 

8 additional protection. As a result, the initial focus in the Policy Assessment was on target ANC 

9 values in the range of -50 to 100 μeq/L.

10 2. ANC Levels Related to Effects on Aquatic Ecosystems 

11 As discussed above in section II.A, the number of fish species present in a water body has 

12 been shown to be positively correlated with the ANC level in the water, with higher values 

13 supporting a greater richness and diversity of fish species. The diversity and distribution of 

14 phyto-zooplankton communities also are positively correlated with ANC. 

15 A summary of effects related to ANC ranges is shown above in Table II-1. Within the 

16 ANC range from approximately -50 to 100 μeq/L, linear and sigmoidal relationships are 

17 observed between ANC and ecosystem effects. On average, fish species richness is lower by one 

18 fish species for every 21 μeq/L decrease in ANC in Shenandoah National Park streams (ISA, 

19 section 3.2.3.4). As shown in Table II-1, ANC levels have been grouped into five categories 

20 related to expected ecological effects, including categories of acute concern ( <0 μeq/L), severe 

21 concern (0-20 μeq/L), elevated concern (20-50 μeq/L), moderate concern (50-100 μeq/L), and 

22 low concern (>100 μeq/L). This categorization is supported by a large body of research 

23 completed throughout the eastern U.S. (Sullivan et al., 2006).
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1 Water bodies with ANC values less than or equal to 0 μeq/L at based flow are chronically 

2 acidic. Such ANC levels can lead to complete loss of species and major changes in the ability of 

3 water bodies to support diverse biota, especially in water bodies that are highly sensitive to 

4 episodic acidification. Based on the above considerations, the Policy Assessment has focused on 

5 target ANC levels no lower than 0 μeq/L.

6 As discussed in the Policy Assessment, biota generally are not harmed when ANC values 

7 are >100 μeq/L, due to the low probability that pH levels will be below 7. In the Adirondacks, 

8 the number of fish species also peaks at ANC values >100 μeq/L. This suggests that at ANC 

9 levels greater than 100 μeq/L, little risk from acidification exists in many aquatic ecosystems. At 

10 ANC levels below 100 μeq/L, overall health of aquatic communities can be maintained, although 

11 fish fitness and community diversity begin to decline. At ANC levels ranging from 100 down to 

12 50 μeq/L, there is increasing likelihood that the fitness of sensitive species (e.g., brook trout, 

13 zooplankton) will begin to decline. When ANC concentrations are below 50 μeq/L, the 

14 probability of acidification increases substantially, and negative effects on aquatic biota are 

15 observed, including large reductions in diversity of fish species and changes in the health of fish 

16 populations, affecting reproductive ability and fitness, especially in water bodies that are affected 

17 by episodic acidification. While there is evidence that ANC levels above 50 can confer 

18 additional protection from adverse ecological effects associated with aquatic acidification in 

19 some sensitive ecosystems, the expectation that such incremental protection from adverse effects 

20 will continue up to an ANC level of 100 is substantially reduced. The Policy Assessment 

21 concludes that the above considerations support a focus on target ANC levels up to a level 

22 greater than 50 μeq/L but below 100 μeq/L, such as up to a level of 75 μeq/L.
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1 In considering the available scientific evidence, as summarized here and discussed in 

2 more detail in the ISA and REA, in its review of the second draft Policy Assessment, CASAC 

3 expressed the following views about the range of biological responses that corresponds to this 

4 range of ANC levels (i.e., 0-100 μeq/L):

5 There will likely be biological effects of acidification at higher ANC values 

6 within this range, and there are relatively insensitive organisms that are not 

7 impacted at ANC values at the low end of this range. Adverse effects of 

8 acidification on aquatic biota are fairly certain at the low end of this range of 

9 ANC and incremental benefits of shifting waters to higher ANC become 

10 more uncertain at higher ANC levels. There is substantial confidence that 

11 there are adverse effects at ANC levels below 20 μeq/L, and reasonable 

12 confidence that there are adverse effects below 50 μeq/L. Levels of 50 

13 μeq/L and higher would provide additional protection, but the Panel has less 

14 confidence in the significance of the incremental benefits as the level 

15 increases above 50 μeq/L. (Russell and Samet, 2010b)

16

17 The Policy Assessment concludes that the above considerations, including the views of 

18 CASAC, provide support for focusing on target ANC levels in the range of 20 to 75 μeq/L.

19 3. Consideration of Episodic Acidity 

20 As discussed in the Policy Assessment, across the broad range of ANC values from 0 to 

21 100 μeq/L, ANC affords protection against the likelihood of decreased pH (and associated 

22 increases in Al) during long or short periods. In general, the higher the ANC within this range, 

23 the lower the probability of reaching low pH levels where direct effects such as increased fish 

24 mortality occur, as shown in Table 3-1 of the Policy Assessment. Accordingly, greater 

25 protection would be achieved by target chronic ANC values set high enough to avoid pH 

26 depression to levels associated with elevated risk. 

27 The specific relationship between ANC and the probability of reaching pH levels of 

28 elevated risk varies by water body and fish species. ANC levels below 20 μeq/L are generally 

29 associated with high probability of low pH, leading to death or loss of fitness of biota that are 
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1 sensitive to acidification (US EPA, 2008, section 5.2.2.1; US EPA, 2009, section 5.2.1.2). At 

2 these levels, during episodes of high acidifying deposition, brook trout populations may

3 experience lethal effects. In addition, the diversity and distribution of zooplankton communities 

4 decline sharply at ANC levels below 20 μeq/L. Overall, there is little uncertainty that significant 

5 effects on aquatic biota are occurring at ANC levels below 20 μeq/L. 

6 It is clear that at ANC levels approaching 0 μeq/L (Table II-1), there is significant 

7 impairment of sensitive aquatic ecosystems with almost complete loss of fish species. Avoiding 

8 ANC levels approaching 0 μeq/L is particularly relevant to episodic spikes in acidity that occur 

9 during periods of rapid snow melt and during and after major precipitation events. Since the 

10 ANC range considered in the Policy Assessment reflects average, long-term base flow values, it 

11 is appropriate to consider protecting against episodic drops in ANC values to a level as low as 0 

12 μeq/L. Staddard et al. (2003) noted on average a 30 μeq/L depression of ANC between spring 

13 and summer time values, indicating the need to maintain higher base flow ANC levels to protect 

14 against ANC levels below 0 μeq/L. The above considerations do not provide support for a target 

15 chronic ANC level as low as 0 μeq/L for a standard that would protect against significant harm to 

16 aquatic ecosystems, including harm from episodic acidification. The Policy Assessment 

17 concludes that these considerations also support a lower end of the range for consideration no 

18 lower than 20 μeq/L.

19 The CASAC agreed with this conclusion in its comments on the second draft Policy

20 Assessment (Russell and Samet, 2010b). CASAC noted that “there are clear and marked 

21 biological effects at ANC values near 0 μeq/L, so this is probably not an appropriate target 

22 value” for the AAI. With regard to the likelihood of impairment of aquatic ecosystems due to 
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1 episodic acidification, in terms of specific target levels for chronic ANC, CASAC expressed the 

2 following view:

3 Based on surface waters studied in the Northeast, decreases in ANC 

4 associated with snowmelt [are] approximately 50 μeq/L. Thus, based on these 

5 studies, a long term ANC target level of 75 μeq/L would generally guard against 

6 effects from episodic acidification down to a level of about 25 μeq/L. (Russell and 

7 Samet, 2010b)

8

9

10

11 4. Consideration of Ecosystem Response Time 

12 The Policy Assessment notes that when considering a standard level to protect against 

13 aquatic acidification, it is appropriate to take into account both the time period to recovery as 

14 well as the potential for recovery in acid-sensitive ecoregions. Ecosystems become adversely 

15 impacted by acidifying deposition over long periods of time and have variable time frames and 

16 abilities to recover from such perturbations. Modeling presented in the REA (US EPA, 2009,

17 section 4.2.4) shows the estimated ANC values for Adirondack lakes and Shenandoah streams 

18 under pre-acidification conditions and indicates that for a small percentage of lakes and streams, 

19 natural ANC levels would have been below 50 μeq/L. Therefore, for these water bodies, 

20 reductions in acidifying deposition are not likely to achieve an ANC of 50 μeq/L or greater. 

21 Conversely, for some lakes and streams the level of perturbation from long periods of acidifying 

22 deposition has resulted in very low ANC values compared to estimated natural conditions. For 

23 such water bodies, the time to recovery would be largely dependent on future inputs of acidifying 

24 deposition.

25 Setting a standard level in terms of a target chronic ANC level is based on the long-term 

26 response of aquatic ecosystems. The time required for a water body to achieve the target ANC 

27 level -- given a decrease in ambient air concentrations of NOy and SOx and related acidifying 
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1 deposition such that the critical load for a target ANC is not exceeded – is often decades if not 

2 centuries. In recognition of the potential public welfare benefits of achieving the target ANC in 

3 a shorter time frame, the concept of target loads had been developed. Target loads represent the 

4 depositional loading that is expected to achieve a particular level of the ecological indicator by a 

5 given time. For example, to achieve an ANC level of 20 μeq/L by 2030, it might be necessary to 

6 specify a higher target ANC level of, for example, 50 μeq/L, such that the depositional loading 

7 would be reduced more quickly than would occur if the depositional loading was based on 

8 achieving a target ANC level of 20 μeq/L as a long-term equilibrium level. In this example, the 

9 target ANC of 50 μeq/L would ultimately be realized many years later.

10 The above considerations have implications for selecting an appropriate standard level, in 

11 that the standard level affects not only the ultimate degree of protection that would be afforded 

12 by the standard, but also the time frame in which such protection would be realized. However, 

13 the Policy Assessment recognizes that there is a great deal of heterogeneity in response times 

14 among water bodies and that there is only very limited information from dynamic modeling that 

15 would help to quantify recovery time frames in areas across the country. As a consequence, 

16 quantification of a general relationship between critical loads associated with a specific long17 term target ANC level and target loads associated with achieving the target ANC level within a 

18 specific time frame is not currently possible. Thus, while the time frame for recovery is an 

19 important consideration in selecting an appropriate range of levels to consider, the Policy

20 Assessment concludes that it can only be considered in a qualitative sense at this time.

21 5. Prior Examples of Target ANC Levels 

22 A number of regional organizations, states, and international organizations have 

23 developed critical load frameworks to protect against acidification of sensitive aquatic 
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1 ecosystems. In considering the appropriate range of target ANC levels for consideration in this 

2 review, it is informative to evaluate the target ANC levels selected by these different 

3 organizations, as well as the rationale provided in support of the selected levels. Chapter 4 of the 

4 Policy Assessment provides a detailed discussion of how critical loads have been developed and 

5 used in other contexts. Specific target values and their rationales are summarized below.

6 The UNECE has developed critical loads in support of international emissions reduction 

7 agreements. As noted in chapter 4 of the Policy Assessment, critical loads were established to 

8 protect 95 percent of surface waters in Europe from an ANC less than 20 μeq/L based on 

9 protection of brown trout. Individual countries have set alternative ANC targets; for example, 

10 Norway targets an ANC of 30 μeq/L based on protection of Atlantic salmon.

11 Several states have established target ANC or pH values related to protection of lakes and 

12 streams from acidification. While recognizing that some lakes in the Adirondacks will have a 

13 naturally low pH, the state of New York has established a target pH value of 6.5 for lakes that 

14 are not naturally below 6.5. As noted above, this level is associated with an ANC value that is 

15 likely to be between 20 and 50 μeq/L or possibly higher. New Hampshire and Vermont have set 

16 ANC targets of 60 μeq/L and 50 μeq/L, respectively. Tennessee has established site-specific 

17 target ANC values based on assessments of natural acidity, with a default value of 50 μeq/L

18 when specific data are not available.

19 Taken together, these policy responses to concerns about ecological effects associated 

20 with aquatic acidification indicate that target ANC values between 20 and 60 μeq/L have been 

21 selected by states and other nations to provide protection of lakes and streams in some of the 

22 more sensitive aquatic ecosystems.

23 6. Consideration of Public Welfare Benefits
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1 The point at which effects on public welfare become adverse is not defined in the CAA. 

2 Characterizing a known or anticipated adverse effect to public welfare is an important 

3 component of developing any secondary NAAQS. According to the CAA, welfare effects 

4 include:

5 ...effects on soils, water, crops, vegetation, manmade materials, animals, 

6 wildlife, weather, visibility, and climate, damage to and deterioration of 

7 property, and hazards to transportation, as well as effect on economic values and 

8 on personal comfort and well-being, whether caused by transformation, 

9 conversion, or combination with other air pollutants. (CAA, section 302(h)).

10

11 Consideration of adversity to public welfare in the context of the secondary NAAQS for oxides 

12 of nitrogen and sulfur can be informed by information about losses in ecosystem services 

13 associated with acidifying deposition and the potential economic value of those losses, as 

14 summarized above in section II.C and discussed more fully in chapter 4 of the Policy 

15 Assessment. 

16 Ecosystem service losses at alternative ANC levels are difficult to enumerate. However, 

17 in general there are categories of ecosystem services, discussed in chapter 4 of the Policy

18 Assessment, that are related to the specific ecosystem damages expected to occur at alternative 

19 ANC levels. Losses in fish populations due to very low ANC (below 20 μeq/L) are likely 

20 associated with significant losses in value for recreational and subsistence fishers. Many acid 

21 sensitive lakes are located in areas with high levels of recreational fishing activity. For example, 

22 in the northeastern U.S., where nearly 8 percent of lakes are considered acidic, more than 9 

23 percent of adults participate in freshwater fishing, with an estimated value of approximately $5 

24 billion in 2006. This suggests that improvements in lake fish populations may be associated with 

25 significant recreational fishing value.
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1 As discussed in the Policy Assessment, inland surface waters also provide cultural 

2 services such as aesthetic and existence value and educational services. To the extent that 

3 piscivorous birds and other wildlife are harmed by the absence of fish in these waters, hunting 

4 and birdwatching activities are likely to be adversely affected. A case study of the value to New 

5 York residents of improving the health of lakes in the Adirondacks found significant willingness 

6 to pay for those improvements. When scaled to evaluate the improvement in lake health from 

7 achieving ANC values of either 20 or 50 μeq/L, the study implies benefits to the New York 

8 population roughly on the order of $300 - 900 million per year (in constant 2007$). The survey

9 administered in this study recognized that participants were thinking about the full range of 

10 services provided by the lakes in question – not just the recreational fishing services. Therefore 

11 the estimates of willingness to pay include resident’s benefits for potential hunting and 

12 birdwatching activities and other ancillary services. These results are just for New York 

13 populations. The Policy Assessment concludes that if similar benefits exist for improvements in 

14 other acid sensitive lakes, the economic value to U.S. populations could be very substantial, 

15 suggesting that, at least by one measure of impact on public welfare, impacts associated with 

16 ANC less than 50 μeq/L may be adverse to public welfare.

17 7. Summary of Alternative Levels

18 Based on all the above considerations, the Policy Assessment concludes that 

19 consideration should be given to a range of standard levels from 20 to 75 μeq/L. The available 

20 evidence indicates that target ANC levels below 20 μeq/L would be inadequate to protect against 

21 substantial ecological effects and potential catastrophic loss of ecosystem function in some 

22 sensitive aquatic ecosystems. While ecological effects occur at ANC levels below 50 μeq/L in 

23 some sensitive ecosystems, the degree and nature of those effects are less significant than at 
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1 levels below 20 μeq/L. Levels at and above 50 μeq/L would be expected to provide additional 

2 protection, although uncertainties regarding the potential for additional protection from adverse 

3 ecological effects are much larger for target ANC levels above about 75 μeq/L, as effects are 

4 generally appreciably less sensitive to changes in ANC at such higher levels.

5 In reaching this conclusion in the Policy Assessment, consideration was given to the 

6 extent to which a target ANC level within this range would protect against episodic as well as 

7 long-term ecological effects. Levels in the mid- to upper part of this range would be expected to 

8 provide greater protection against short-term, episodic peaks in aquatic acidification, while lower 

9 levels within this range would give more weight to protection from long-term rather than 

10 episodic acidification. Similarly, levels in the mid- to upper part of this range would be expected 

11 to result in shorter time periods for recovery given the lag in ecosystem response in some 

12 sensitive ecosystems relative to levels in the lower part of this range. The Policy Assessment 

13 also note that this range encompasses target ANC values that have been established by various 

14 States and regional and international organizations to protect against acidification of aquatic 

15 ecosystems.

16 The Policy Assessment recognizes that the level of the standard together with the other 

17 elements of the standard, including the ambient air indicators, averaging time, and form, 

18 determine the overall protectiveness of the standard. Thus, consideration of a standard level 

19 should reflect the strengths and limitations of the evidence and assessments as well as the 

20 inherent uncertainties in the development of each of the elements of the standard. The 

21 implications of considering alternative standards, defined in terms of alternative combinations of 

22 levels and percentile values that are a critical component of factor F1 in the form of the standard, 
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1 are discussed below in section III.E. Key uncertainties in the various components of the standard 

2 are summarized and considered below in section III.F. 

3 E. Combined Alternative Levels and Forms

4 To provide some perspective on the implications of various alternative multi-pollutant, 

5 AAI-based standards, the Policy Assessment presented the number of acid-sensitive ecoregions 

6 that would likely not meet various sets of alternative standards. The alternative standards 

7 considered were based on combinations of alternative target ANC levels, within the range of 20 

8 to 75 μeq/L, and alternative forms, characterized by alternative representative percentiles within 

the range of the 70th to 90th 9 percentile. These alternative standards are also defined in terms of 

10 the other elements of the standard: ambient air indicators NOy and SOx, discussed above in 

11 section III.A; other elements of the form of the standard, including ecoregion-specific values for 

12 factors F1 through F4 in the AAI equation, discussed above in section III.B.5; and an annual 

13 averaging time for NOy and SOx, discussed above in section III.C. With regard to the averaging 

14 time, the assessment did not consider multi-year averaging of the calculated annual AAI values 

15 due to data limitations, including, for example, the lack of CMAQ modeling for multiple 

16 consecutive years. In this assessment, we characterize an ecoregion as likely not meeting a given 

17 alternative standard if the calculated AAI value is less than the target ANC level of the standard, 

18 recognizing that higher AAI values are more protective than lower values.

19 The results of this assessment are presented in Table 7-1a-d in the Policy Assessment for 

20 a subset of ecoregions including those characterized as acid sensitive. Calculated annual AAI 

21 values at the ecoregion level are shown for each alternative standard considered. Based on these 

22 AAI values, Table 7-2 in the Policy Assessment summarizes the number of acid-sensitive 
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ecoregions that would likely not meet each of the alternative standards considered. 91 Calculated 

2 AAI values for all ecoregions categorized as relatively non-acid sensitive are shown in Table D-5 

3 in Appendix D of the Policy Assessment. In all cases, these relatively non-acid sensitive 

4 ecoregions were estimated to meet all of the alternative standards considered in this assessment.

5 As described above, the AAI values presented in Table 7-1a-d of the Policy Assessment 

6 are based in part on data from 2005 CMAQ model simulations, which was used to generate 

7 values for F2 through F4 in the AAI equation, as well as to estimate annual average ambient air 

8 concentrations of NOy and SOx that reflect recent air quality in the absence of currently 

9 available monitored concentrations in sensitive ecoregions across the country. Water quality and 

10 hydrology data from water bodies within each ecoregion were also used in calculating the AAI 

11 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 12 percentile critical load representative 

13 of the ecoregion used in calculating the F1 factor for the ecoregion. As expected, the number of 

14 ecoregions that likely would not meet alternative standards increases with increasing percentile 

15 values and target ANC levels (US EPA, 2011, Table 7-2). Out of 22 acid-sensitive ecoregions, 

16 the number of ecoregions that would likely not meet the alternative standards ranges from 22 for 

the most protective alternative standard considered (75 μeq/L, 90th 17 percentile) to 4 for the least 

protective alternative standard (20 μeq/L, 70th 18 percentile). It is apparent that both the percentile 

19 and the level chosen have a strong influence, over the ranges considered, in determining the 

20 number of areas that would likely not meet this set of alternative standards. 

 

9 Tables 7-1a-d and 7-2 in the Policy Assessment 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.
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1 The Policy Assessment observes that there is one grouping of these acid-sensitive 

2 ecoregions that would likely not meet almost all combinations of level and form under 

3 consideration (US EPA, 2011, Table 7-2 and Appendix D). This group is made up of southern 

4 Appalachian mountain areas, including North Central Appalachians, 5.3.3; Ridge and Valley, 

5 8.4.1; Central Appalachians, 8.4.2; Blue Ridge, 8.4.4; and Southwestern Appalachians, 8.4.9. In 

6 addition, these ecoregions exhibit the highest amounts of exceedance relative to alternative 

7 standards.

8 The Northern Appalachian and Atlantic Maritime Highlands (5.3.1), which includes the 

9 Adirondacks, and the Northern Lakes and Forests (5.2.1) of the upper midwest exhibit similar 

10 patterns with respect to in the role of level and percentile in identifying regions not likely to meet 

11 alternative standards, although there are considerably fewer cases compared to the regions in the 

12 Appalachians.

13 In the mountainous west, the Sierra Nevada (6.2.12), Idaho Batholith (6.2.15) and the 

14 Cascades (6.2.7) ecoregions likely would not meet alternative standards in fewer cases relative to 

15 eastern regions, with the Sierra Nevada ecoregion exhibiting relatively greater sensitivity

16 compared to all western regions. Only in the upper part of the ranges of level and percentile do 

17 regions in the northern and central Rockies likely not meet alternative standards.

18 In considering these findings, the Policy Assessment observes that the standard as 

19 defined by the AAI behaves in an intuitively logical manner. That is, an increase in ecoregions 

20 likely not to meet the standard is associated with higher alternative levels and percentiles, both of 

21 which contribute to a lower regionally representative critical load. Moreover, the areas of known 

22 adverse aquatic acidification effects are identified, mostly in high elevation regions or in the 

23 northern latitudes -- the Adirondacks, Shenandoahs, northern midwest lakes and the mountainous 
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1 west. These results reflect the first application of a nationwide model that integrates water 

2 quality and atmospheric processes at a national scale and provides findings that are consistent 

3 with our basic understanding of the extent of aquatic acidification across the U.S. What is 

4 particularly noteworthy is that this model is not initialized with a starting ANC based on water 

5 quality data, which likely would result in a reproduction of water quality observations. Rather, 

6 this standard reflects the potential of the changes in atmospheric concentrations of NOy and SOx 

7 to induce long-term sustained changes in surface water systems. The Policy Assessment notes 

8 that the fact that the patterns of adversity based on applying this standard are commensurate with 

9 what is observed in surface water systems provides confidence in the basic underlying 

10 formulation of the standard.

11 The Policy Assessment notes that the Appalachian mountain regions merit further

12 inspection as they stand out as areas with the largest relative exceedances from a national 

13 perspective. Water quality data from these regions as well as an emissions sensitivity CMAQ 

14 simulation were considered to better understand the simulated behavior of these regions. The 

15 maps and tables in appendix D of the Policy Assessment include paired comparisons of the 

16 CMAQ 2005 and emissions sensitivity simulations. The emissions sensitivity simulation reflects 

17 domain-wide reductions in NOy and SOx emissions of 48% and 42%, respectively, relative to 

18 2005 base year emissions. The Policy Assessment assumes that this emissions sensitivity

19 simulation is indicative of future conditions. 

20 The emissions sensitivity results project that many of the regions that likely would not 

21 meet the alternative standards based on recent air quality, especially at alternative levels of 20 

22 and 35 μeq/L, would likely meet such standards in the future year scenario for the Appalachian 

23 mountain regions. It is apparent that the AAI calculations are especially sensitive to changes in 
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1 SOx emissions as the Appalachian regions have the highest SOx concentrations and deposition 

2 rates (US EPA, 2011,section 2), and the AAI equation responds as expected to modeled 

3 reductions in SOx. The emissions sensitivity scenario is a prospective application of the 

4 standard, in the sense that rules derived from the air quality management process result in 

5 reductions of NOy and SOx emissions. Expected emission changes over the next two decades 

6 should be far greater than the 42 and 48% SOx and NOy reductions used in this analysis, with a 

7 consequent further reduction in areas that would likely not meet alternative standards.

8 The Appalachian mountain regions generally have low DOC levels, average runoff rates, 

9 moderately low base cation supply and highly elevated sulfate concentrations. Collectively, 

10 those attributes do not suggest naturally acidic conditions as the availability of anthropogenic 

11 contributions of mineral acids is likely responsible for observed low ANC values in those 

12 regions.

13 The Policy Assessment notes the Sierra Nevada region as an interesting case study, as it 

14 has some of the lowest critical load values nationally (US EPA, 2011, Table D-3). Water quality

15 data indicate extremely low sulfate, as expected given the relatively low SO2 emissions in the 

16 western U.S. Extremely low base cation supply and low Neco, which mitigate the effect of 

17 nitrogen deposition, explain the low critical load values. Low Neco values appear to associate 

18 well with high elevation western U.S. regions, perhaps reflecting the more arid and reduced 

19 vegetation density relative to eastern U.S. regions. The proximity to high level nitrogen 

20 emissions combined with very low base cation supply explains the cases where the Sierra region 

21 likely does not meet alternative standards. Because Neco values are low in the Sierras, the 

22 system responds effectively to reductions of NOx emissions, as illustrated in the maps and tables 

23 of Appendix D of the Policy Assessment. Although Neco affords protection from the acidifying 
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1 effects of nitrogen deposition, the availability of excessive nitrogen neutralization capacity also 

2 means that reductions in nitrogen are not as effective as reductions in SOx in reducing the 

3 calculated AAI.

4 In reviewing these results, the Policy Assessment observes that the analysis of the 

5 alternative combinations of level and form presented provide context for considering the impact 

6 of different standards. Since the AAI equation has been newly developed in the Policy

7 Assessment, these examples of estimated exceedances help to address the question of whether 

8 the AAI equation responds in a reasonable manner with regard to identifying areas of concern 

9 and to prospective changes in atmospheric conditions likely to result from future emissions 

10 reduction strategies. The Policy Assessment concludes that the behavior of the AAI calculations 

11 is both reasonable and explainable, which the Policy Assessment concludes serves to increase 

12 confidence in considering a standard defined in terms of the AAI.

13 F. Characterization of Uncertainties

14 This section summarizes discussions of the results of analyses and assessments, presented 

15 more fully in the Policy Assessment (US EPA, 2011, section 7.6 and Appendices F and G), 

16 intended to address the relative confidence associated with the linked atmospheric-ecological 

17 effects system described above. An overview of uncertainties is presented in the context of the 

18 major structural components underlying the standard, as well as with regard to areas of relatively 

19 high uncertainty. The section closes with a discussion of data gaps and uncertainties associated 

20 with the use of ecological and atmospheric modeling to specify the factors in the AAI equation, 

21 which can be used to guide future field programs and longer-term research efforts. 

22 1. Overview of Uncertainty
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1 As discussed in the Policy Assessment (US EPA, 2011,Table 7-3), there is relatively low 

2 uncertainty with regard to the conceptual formulation of the overall structure of the AAI-based 

3 standard that incorporates the major associations linking biological effects to air concentrations. 

4 Based on the strength of the evidence that links species richness and mortality to water quality, 

5 the associations are strongly causal and without any obvious confounding influence. The strong 

6 association between the ecosystem indicator (ANC) and the causative water chemistry species 

7 (dissolved aluminum and hydrogen ion) reinforces the confidence in the linkage between 

8 deposition of nitrogen and sulfur and effects. This strong association between ANC and effects 

9 is supported by a sound mechanistic foundation between deposition and ANC. The same 

10 mechanistic strength holds true for the relationship between ambient air levels of nitrogen and 

11 sulfur and deposition, which completes the linkage from ambient air indicators through 

12 deposition to ecological effects. 

13 There are relatively higher uncertainties, however, in considering specific elements

14 within the structure of an AAI-based standard, including the deposition of SOx, NOy, and NHx 

15 as well as the critical load-related component, each of which can vary within and across 

16 ecoregions. Overall system uncertainty relates not just to the uncertainty in each such element, 

17 but also to the combined uncertainties that result from linking these elements together within the 

18 AAI-based structure. Some of these elements – including, for example, dry deposition, pre19 industrial base cation production, and reduced nitrogen deposition – are estimated with less 

20 confidence than other elements (US EPA, 2011, Table 7.3). The uncertainties associated with all 

21 of these elements, and the combination of these elements through the AAI equation, are 

22 discussed below and in the following sections related to measured data gaps and modeled 

23 processes for both air quality and water quality.
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1 The lack of observed dry deposition data is constrained by resources and the lack of 

2 efficient measurement technologies. Progress in reducing uncertainties in dry deposition will 

3 depend on improved atmospheric concentration data and direct deposition flux measurements of 

4 the relevant suite of NOy and SOx species. 

5 Pre-industrial base cation productivity by definition is not observable. Contemporary 

6 observations and inter-model comparisons are useful tools that would help reduce the uncertainty

7 in estimates of preindustrial base cation productivity used in the AAI equation. In characterizing 

8 contemporary base cation flux using basic water quality measurements (i.e., major anioni and 

9 cation species as defined in equation 2.11 in the Policy Assessment), it is reasonable to assume 

10 that a major component of contemporary base cation flux is associated with pre-industrial 

11 weathering rates. To the extent that multiple models converge on similar solutions, greater 

12 confidence in estimating pre-industrial base cation production would be achieved.

13 Characterization of reduced nitrogen (NHx) deposition has been evolving over the last 

14 decade. The relatively high uncertainty in characterizing NHx deposition is due to both the lack 

15 of field measurements and the inherent complexity of characterizing NHx with respect to source 

16 emissions and dry deposition. Because ammonia emissions are generated through a combination 

17 of man-made and biological activities, and ammonia is semi-volatile, the ability to characterize 

18 spatial and temporal distributions of NHx concentrations and deposition patterns is challenging. 

19 While direct measurement of NHx deposition is resource intensive because of the diffuse nature 

20 of sources (i.e., area-wide and not point sources), there have been more frequent deposition flux 

21 studies, relative to other nitrogen species, that enable the estimation of both emissions and dry

22 deposition. Also, while ammonia has a relatively high deposition velocity and traditionally was 

23 thought to deposit close to the emissions release areas, the semi-volatile nature of ammonia 
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1 results in re-entrainment back into the lower boundary layer resulting in a more dispersed 

2 concentration pattern exhibiting transport type characteristics similar to longer lived atmospheric 

3 species. These inherent complexities in source characterization and ambient concentration 

4 patterns raise the uncertainty level of NHx in general. However, the Policy Assessment notes 

5 that progress is being made in measuring ammonia with cost efficient samplers and anticipates 

6 the gradual evolution of a spatially robust ammonia sampling network that would help support 

7 analyses to reduce underlying uncertainties in NHx deposition. Also, from an aquatic 

8 acidification perspective, NHx is not as important a driver as NOy and SOx in the mountainous 

9 areas in the eastern U.S. However, the relative importance of NHx is likely to increase over 

10 time, in light of air quality rules in place designed to reduce emissions of NOy and SOx.

11 2. Uncertainties Associated with Data Gaps

12 In summarizing uncertainties with respect to available measurement data and the use of 

13 ecological and atmospheric models, the Policy Assessment indentified data gaps and model 

14 uncertainties in relative terms by comparing, for example, the relative richness of data between 

15 geographic areas or environmental media. With regard to relevant air quality measurements, the 

16 Policy Assessment notes that such measurements are relatively sparse in the western U.S. While 

17 the spatial extent of CASTNET coverage has gradually incorporated western U.S. locations with 

18 support from the National Parks Service (NPS), the relative density of monitoring sites is much 

19 less than that in the eastern U.S. This relative disparity in spatial density of monitors is 

20 exacerbated as air quality patterns in the mountainous west generally exhibit greater spatial 

21 heterogeneity due to dramatic elevation gradients that impact meteorology and air mass flow 

22 patterns. Similarly, water quality data coverage is far more comprehensive in the eastern U.S. 

23 relative to the west
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1 Measurements of NOy notably are lacking in both eastern and western acid-sensitive 

2 ecoregions. This adds uncertainty to the use of the AAI equation as the lack of NOy data limits 

3 efforts to evaluate air quality modeling of NOy that is the basis for quantifying factor F3 in the 

4 AAI equation. The lack of NOy measurements also limits efforts to characterize the variability

5 and representativeness of modeled NOy concentrations within and across ecoregions. Currently, 

6 the Agency’s ability to define the protection likely to be afforded by alternative standards (in 

7 terms of alternative levels and percentiles) is compromised by the lack of a full set of ambient air 

8 quality indicator measurements, notably including NOy, throughout sensitive ecoregions across 

9 the U.S. 

10 Further, obtaining measurement of the dominant species that comprise NOy (HNO3, true 

11 NO2, NO, p–NO3, and PAN) would be useful to evaluate performance of NOy samplers. 

12 Beyond the more well known dominant components of NOy, research efforts would be needed to 

13 characterize total reactive nitrogen that may include significant amounts of organically-bound 

14 nitrogen (beyond PAN) which is poorly understood with regard to emission sources and 

15 concentration levels.

16 Field measurements of NHx have been extremely limited, but have begun to be enhanced 

17 through the National Atmospheric Deposition Program’s (NADP) passive ammonia network 

18 (AMoN). AMoN measures ammonia at over 50 sites, with more than 35 at CASTNET locations. 

19 Enhanced spatial coverage of reduced nitrogen measurements, particularly to understand within 

20 and across ecoregion variability, and the inclusion of some continuous observations would 

21 provide a better understanding of the uncertainty in the F2 factor in the AAI equation and of the 

22 representativeness of modeled NHx deposition within and across ecoregions.
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1 With regard to water quality data, the Policy Assessment notes that such data are 

2 typically limited relative to air quality data sets, and are also relatively sparse in the western U.S. 

3 The TIME/LTM water quality sampling program in the eastern U.S. (as described in chapter 2 

4 of the Policy Assessment) is an appropriate complement to national air monitoring programs as it 

5 affords consistency across water bodies in terms of sampling frequency and analysis protocols. 

6 Consideration should be given to extending the TIME/LTM design to all acid sensitive 

7 ecoregions, with priority for areas in the western mountains that are data limited and showing 

8 initial signs of adversity particularly with respect to aquatic acidification. The lack of a 

9 regulatory requirement for TIME/LTM often jeopardizes funding support of this resource that is 

10 especially valuable and cost effective. While there are several state and local agency water 

11 quality data bases, it is unclear the extent to which differences in sampling, chemical analysis 

12 and reporting protocols would impact the use of such data for the purpose of better 

13 understanding the degree of protectiveness that would be afforded by an AAI-based standard 

14 within sensitive ecoregions across the country. In addition, our understanding of water quality in 

15 Alaska and Hawaii and the acid sensitivity of their ecoregions is particularly limited. 

16 Water quality data and modeling support the standard setting process. As more water 

17 bodies are sampled, the critical load data bases would expand, enabling clearer delineation of 

ecoregion representative critical loads in terms of the nth 18 percentile. This would provide more 

19 refined characterization of the degree of protection afforded by a given standard. Longer term, 

20 the availability of water quality trend data (annual to monthly sampled) would support 

21 accountability assessments that examine if an ecoregion’s response to air management efforts is 

22 as predicted by earlier model forecasting. The most obvious example is the long-term response

23 of water quality ANC change to changes in calculated AAI, deposition, ambient NOy and SOx 
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1 concentrations, and emissions. In addition, water quality trends data provide a basis for 

2 evaluating and improving the parameterizations of processes in critical load models applied at 

3 the ecoregion scale related to nitrogen retention and base cation supply. A better understanding

4 of soil processes, especially in the southern Appalachians, would enhance efforts to examine the 

5 variability within ecoregions of the soil-based adsorption and exchange processes which 

6 moderate the supply of major cations and anions to surface waters and strongly influence the 

7 response of surface water ANC to changes in deposition of nitrogen and sulfur.

8 3. Uncertainties in Modeled Processes

9 As discussed in the Policy Assessment, from an uncertainty perspective, gaps in field 

10 measurement data are related to uncertainties in modeled processes and in the specific 

11 application of such models. As noted above, processes that are embodied in an AAI-based 

12 standard are modeled using the CMAQ atmospheric model and steady state ecological models. 

13 These models are characterized in the Integrated Science Assessment as being well established 

14 and they have undergone extensive peer review. Nonetheless, the application of these models for 

15 purposes of specifying the factors in the AAI equation, on an ecoregion scale, is a new 

16 application that introduces uncertainties, as noted below, especially in areas with limited 

17 observational data that can be used to evaluate this specific application. Understanding 

18 uncertainties in relevant modeled process thus involves consideration of the uncertainties 

19 associated with applying each model as well as the combination of these uncertainties as the

20 models are applied in combination within the AAI framework.

21 With regard to the application of CMAQ for purposes of use in an AAI-based standard, 

22 the modeling of dry deposition has been identified as having a relatively high degree of 

23 uncertainty. Due to a combination of system complexity and resource constraints, there is no 
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1 routine observational basis for directly comparing modeled dry deposition and measurements. 

2 Periodic dry deposition flux experiments covering a variety of vegetation, surfaces and 

3 meteorology across seasons would enable a more robust evaluation of modeled deposition of 

4 nitrogen and sulfur. Given the difficulty in acquiring dry deposition observations, it becomes 

5 especially important to evaluate the model’s ability to capture temporal and spatial ambient air

6 patterns of individual nitrogen and sulfur species which are used to drive dry deposition 

7 calculations in models. For example, reducing a generally acknowledged positive bias in model8 predicted SO2 relative to observations is especially relevant to the AAI-based standard, as SO2

9 deposition is a dominant contributor to total acidifying deposition in the eastern U.S. With 

10 respect to oxidized nitrogen, observations of individual NOy species are important as air quality 

11 models calculate the individual deposition of each species. The modeled transference ratios, 

12 TNOy and TSOx used in factors F3 and F4 rely on CMAQ’s ability to characterize both deposition 

13 and concentration. Consequently, a better understanding of the variability of these factors 

14 within and across ecoregions could be achieved by improved availability of measured ambient 

15 concentrations and deposition observations.

16 Steady state biogeochemical ecosystem modeling is used to develop critical load 

17 estimates that are incorporated in the AAI equation through factor F1. Consequently, the Policy

18 Assessment notes that an estimate of the temporal response of surface water ANC to deposition 

19 and air concentration changes is not directly available. Lacking a predicted temporal response 

20 impairs the ability to conduct accountability assessments down to the effects level. 

21 Accountability assessments would examine the response of each step in the emissions source 

22 through air concentration – deposition -- surface water quality – biota continuum. The steady

23 state assumption at the ecosystem level does not impair accountability assessments through the 
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1 air concentration/deposition range of that continuum. However, in using steady state ecosystem 

2 modeling, several assumptions are made relative to the long-term importance of processes 

3 related to soil adsorption of major ions and ecosystem nitrogen dynamics. Because these models 

4 often were developed and applied in glaciated areas with relatively thin and organically rich 

5 soils, their applicability is relatively more uncertain in areas such as those in the non-glaciated 

6 clay-based soil regions of the central Appalachians. Consequently, it is desirable to develop the 

7 information bases to drive simple dynamic ecosystem models that incorporate more detailed 

8 treatment of subsurface processes, such as adsorption and exchange processes and sulfate 

9 absorption.

10 4. Applying Knowledge of Uncertainties

11 An understanding of the relative uncertainties in a system assists in setting priorities for 

12 data collection efforts and research, with the expectation that such efforts would reduce 

13 uncertainties over time and afford greater confidence in applications of an AAI-based standard. 

14 Because of the uniquely wide breadth of pollutants and environmental media addressed by an

15 AAI-based multi-pollutant standard, there are a wide range of uncertainties that are important to 

16 consider relative to single pollutant standards that typically address only direct effects of ambient 

17 air exposures. For an AAI-based standard, a reduction of the uncertainties across the various 

18 modeled processes at the ecoregion scale would lead to greater confidence in the degree of 

19 protection afforded by the standard. 

20 The Policy Assessment notes that there is generally low uncertainty with regard to the 

21 conceptual development and related major components of this standard. In recognizing the 

22 scientific soundness of the basic structure of this standard, the Policy Assessment notes that 

23 future efforts would be appropriately directed at expanding the availability of relevant data for 
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1 ecoregion-specific evaluation and application of the relevant modeling of ecological and 

2 atmospheric processes, as identified above. Such efforts would further support consideration of 

3 an AAI-based standard and would guide field studies and analyses designed to improve the 

4 longer-term confidence in such a standard.

5 G. CASAC Advice

6 The CASAC has advised EPA concerning the Integrated Science Assessment, the Risk 

7 and Exposure Assessment, and the Policy Assessment. CASAC has endorsed EPA’s 

8 interpretation of the science embodied in the Integrated Science Assessment and the assessment 

9 approaches and conclusions incorporated in the Risk and Exposure Assessment.

10 Most recently, CASAC has considered the information in the final Policy Assessment in 

11 providing its recommendations on the review of the new multi-pollutant standard developed in 

12 that document and discussed above (Russell and Samet, 2011a). In so doing, CASAC has 

13 expressed general support for the conceptual framework of the standard based on the underlying 

14 scientific information, as well as for the conclusions in the Policy Assessment with regard to 

15 indicators, form, averaging time, and level of the standard that are appropriate for consideration 

16 by the Agency in reaching decisions on the review of the secondary NAAQS for oxides of 

17 nitrogen and sulfur:

18 The final Policy Assessment clearly sets out the basis for the recommended ranges 

19 for each of the four elements (indicator, averaging time, level and form) of a 

20 potential NAAQS that uses ambient air indicators to address the combined effects 

21 of oxides of nitrogen and oxides of sulfur on aquatic ecosystems, primarily 

22 streams and lakes. As requested in our previous letters, the Policy Assessment also 

23 describes the implications of choosing specific combinations of elements and 

24 provides numerous maps and tabular estimates of the spatial extent and degree of 

25 severity of NAAQS exceedances expected to result from possible combinations of 

26 the elements of the standard.

27

28 We believe this final PA is appropriate for use in determining a secondary

29 standard to help protect aquatic ecosystems from acidifying deposition of oxides 
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1 of sulfur and nitrogen. EPA staff has done a commendable job developing the 

2 innovative Aquatic Acidification Index (AAI), which provides a framework for a 

3 national standard based on ambient concentrations that also takes into account 

4 regional differences in sensitivities of ecosystems across the country to effects of 

5 acidifying deposition. (Russell and Samet, 2011a)

6

7 CASAC also recommended that as EPA moves forward in the regulatory process “some 

8 attention should be given to our residual concern that the available data may reflect the more 

9 sensitive water bodies and thus, the selection of percentiles of waterbodies to be protected could 

10 be conservatively biased” (Russell and Samet, 2011a). In additional, CASAC found some 

11 improvements could be made to the uncertainty analysis, as noted below.

12 With respect to indicators, CASAC supports the use of SOx and NOy as ambient air indicators 

13 (discussed above in section III.A) and ANC as the ecological indicator (discussed above in 

14 section III.B.1):

15 The use of total reactive oxidized nitrogen (NOy) and sulfur oxides (SOx) as atmospheric 

16 indicators of oxidized nitrogen (N) and sulfur (S) atmospheric concentrations is well justified. 

17 The use in the Aquatic Acidification Index (AAI) of NOy and SOx as atmospheric indicators of 

18 N and S concentrations is useful and corresponds with other efforts by EPA. As we have stated 

19 previously, CASAC also agrees that acid neutralizing capacity (ANC) is the most appropriate 

20 ecological indicator of aquatic ecosystem response and resiliency to acidification. (Russell and 

21 Samet, 2011a)

22 With respect to the form of the standard (discussed above in section III.B), CASAC 

23 stated the following:

24 EPA has developed the AAI, an innovative “form” of the NAAQS itself that 

25 incorporates the multi-pollutant, multi-media, environmentally modified, 

26 geographically variable nature of SOx/NOy deposition-related aquatic 

27 acidification effects. With the caveats noted below, CASAC believes that this 

28 form of the NAAQS as described in the final Policy Assessment is consistent with 
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1 and directly reflective of current scientific understanding of effects of acidifying 

2 deposition on aquatic ecosystems. (Russell and Samet, 2011a)

3

4 CASAC agrees that the spatial components of the form in the Policy Assessment 

5 are reasonable and that use of Omernick’s ecoregions (Level III) is appropriate for 

6 a secondary NAAQs intended to protect the aquatic environment from 

7 acidification . . . (Russell and Samet, 2011a)

8

9 The “caveats” noted by CASAC include a recognition of the importance of continuing to 

10 evaluate the performance of the CMAQ and ecological models to account for model 

11 uncertainties and to make the model-dependent factors in the AAI more transparent. In addition, 

12 CASAC noted that the role of DOC and its effects on ANC would benefit from further 

13 refinement and clarification (Russell and Samet, 2011a). While CASAC expressed the view that 

14 the “division of ecoregions into ‘sensitive’ and ‘non-sensitive’ subsets, with a more protective 

15 percentile applied to the sensitive areas, also seems reasonable” (Russell and Samet, 2011a),

16 CASAC also noted that there was the need for greater clarity in specifying how appropriate 

17 screening criteria would be applied in assigning ecoregions to these categories. Further, CASAC 

18 identified potential biases in critical load calculations and in the regional representativeness of 

19 available water chemistry data, leading to the observation that a given percentile of the 

20 distribution of estimated critical loads may be protective of a higher percentage of surface waters 

21 in some regions (Russell and Samet, 2011a).

22 With respect to averaging time (discussed above in section III.C), CASAC stated the 

23 following:

24 Considering the cumulative nature of the long-term adverse ecological effects and 

25 the year-to-year variability of atmospheric conditions (mainly in the amount of 

26 precipitation), CASAC concurs with EPA that an averaging time of three to five 

27 years for the AAI parameters is appropriate. A longer averaging time would mask 

28 possible trends of AAI, while a shorter averaging time would make the AAI being 
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1 more influenced by the conditions of the particular years selected. (Russell and 

2 Samet, 2011a)

3

4 With respect to level as well as the combination of level and form as they are presented 

5 as alternative standards (discussed above in sections III.D-E), CASAC stated the following:

6 CASAC agrees with EPA staff’s recommendation that the “level” of the 

7 alternative AAI standards should be within the range of 20 and 75 μeq/L. We also 

8 recognize that both the “level” and the form of any AAI standard are so closely

9 linked in their effectiveness that these two elements should be considered 

10 together. (Russell and Samet, 2011a)

11

12 When considered in isolation, it is difficult to evaluate the logic or implications 

13 of selecting from percentiles (70th to 90th) of the distribution of estimated critical 

14 loads for lakes in sensitive ecoregions to determine an acceptable amount of 

15 deposition for a given ecoregion. However, when these percentile ranges are 

16 combined with alternative levels within the staff-recommended ANC range of 20 

17 to 75 microequivalents per liter (μeq/L), the results using the AAI point to the 

18 ecoregions across the country that would be expected to require additional 

19 protection from acidifying deposition. Reasonable choices were made in 

20 developing the form. The number of acid sensitive regions not likely to meet the 

21 standard will be affected both by choice of ANC level and the percentile of the 

22 distribution of critical loads for lakes to meet alternative ANC levels in each 

23 region. These combined recommendations provide the Administrator with a 

24 broad but reasonable range of minimally to substantially protective options for the 

25 standard. (Russell and Samet, 2011a)

26

27 CASAC also commented on EPA’s uncertainty analysis, and provided advice on areas 

28 requiring further clarification in the proposed rule and future research. CASAC found it 

29 “difficult to judge the adequacy of the uncertainty analysis performed by EPA because of lack of 

30 details on data inputs and the methodology used, and lack of clarity in presentation” (Russell and 

31 Samet, 2011a). In particular, CASAC identified the need for more thorough model evaluations 

32 of critical load and atmospheric modeling, recognizing the important role of models as they are 

33 incorporated in the form of the standard. In light of the innovative nature of the standard 

34 developed in the Policy Assessment, CASAC identified “a number of areas that should be the 
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1 focus of further research” (Russell and Samet, 2011a). While CASAC recognized that EPA staff 

2 was able to address some of the issues in the Policy Assessment, they also noted areas “that 

3 would benefit from further study or consideration in potential revisions or modifications to the 

4 form of the standard.” Such research areas include “sulfur retention and mobilization in the 

5 soils, aluminum availability, soil versus water acidification and ecosystem recovery times.” 

6 Further, CASAC encouraged future efforts to monitor individual ambient nitrogen species, 

7 which would help inform further CMAQ evaluations and the specification of model-derived 

8 elements in the AAI equation (Russell and Samet, 2011a).

9 H. Administrator’s Proposed Conclusions

10 Having concluded that the existing NO2 and SO2 secondary standards are neither 

11 sufficiently protective nor appropriate to address deposition-related effects associated with 

12 oxides of nitrogen and sulfur (section II.D above), the Administrator has considered whether it is 

13 appropriate at this time to set a new multi-pollutant standard for that purpose, with a structure 

14 that would better reflect the available science regarding acid deposition. In considering this, she 

15 recognizes that such an appropriate standard, for purposes of section 109(b) and (d) of the 

CAA,

11 16 must in her judgment be requisite to protect public welfare, such that it would be neither 

17 more nor less stringent that necessary for that purpose. In particular, she has focused on the new 

18 standard developed in the Policy Assessment and reviewed by CASAC, as discussed above. In 

19 so doing, the Administrator first considered the extent to which there is a scientific basis for 

20 development of such a standard, specifically with regard to a standard that would provide 

21 protection from deposition-related aquatic acidification in sensitive aquatic ecosystems in areas 

22 across the country. As discussed above, the Administrator notes that the Integrated Science 

 

11 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]
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1 Assessment concludes that the available scientific evidence is sufficient to infer a causal 

2 relationship between acidifying deposition of nitrogen and sulfur in aquatic ecosystems, and that 

3 the deposition of oxides of nitrogen and sulfur both cause such acidification under current 

4 conditions in the U.S. Further, the Integrated Science Assessment concludes that there are well5 established water quality and biological indicators of aquatic acidification as well as well6 established models that address deposition, water quality, and effects on ecosystem biota, and 

7 that ecosystem sensitivity to acidification varies across the country according to present and 

8 historic nitrogen and sulfur deposition as well as geologic, soil, vegetative, and hydrologic 

9 factors. Based on these considerations, the Administrator agrees with the conclusion in the 

10 Policy Assessment, and supported by CASAC, that there is a strong scientific basis for 

11 development of a standard with the general structure presented in the Policy Assessment.

12 The Administrator also recognizes that the conceptual framework for an ecologically

13 relevant, multi-pollutant standard, which was initially explored in the Risk and Exposure 

14 Assessment and further developed in the Policy Assessment, builds on the information in the 

15 Integrated Science Assessment. She notes that the structure of the standard addresses the 

16 combined effects of deposition from oxides of nitrogen and sulfur by characterizing the linkages 

17 between ambient concentrations, deposition, and aquatic acidification, and that the structure of 

18 the standard takes into account relevant variations in these linkages across the country. She 

19 recognizes that while the standard is innovative and unique, the structure of the standard is well 

20 grounded in the science underlying the relationships between ambient concentrations of oxides 

21 of nitrogen and sulfur and the aquatic acidification related to deposition of nitrogen and sulfur 

22 associated with such ambient concentrations.
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1 While the Administrator recognizes the strong scientific foundation for the structure of an

2 AAI-based standard, she also recognizes that the standard depends on atmospheric and 

3 ecological modeling, based on appropriate data, to specify the terms of an equation that 

4 incorporates the linkages between ambient concentrations, deposition, and aquatic acidification. 

5 This equation, which defines an aquatic acidification index (AAI), has the effect of translating 

6 spatially variable ecological effects into a potential national standard. With respect to 

7 establishing the specific terms of this equation, there are a number of inherent uncertainties and 

8 complexities that are relevant to the question of whether it is appropriate under section 109 to set 

9 a specific AAI-based standard at this time, recognizing that such a standard must be requisite to 

10 protect public welfare without being either more or less stringent than necessary for this purpose. 

11 As discussed above, these uncertainties and complexities generally relate not to the structure of 

12 the standard, but to the quantification of the various elements of the standard, such as the F 

13 factors discussed earlier in this section and their representativeness at an ecoregion scale. These 

14 uncertainties and complexities currently limit efforts to characterize the degree of protectiveness 

15 that would be afforded by such a standard, within the ranges of levels and forms identified in the 

16 Policy Assessment, and the representativeness of F factors in the AAI equation described above 

17 and in the Policy Assessment. These important uncertainties have been generally categorized as 

18 limitations in available field data as well as uncertainties that are related to reliance on the 

19 application of ecological and atmospheric modeling at the ecoregion scale to specify the various 

20 elements of the AAI.

21 With regard to data limitations, the Administrator observes that there are several 

22 important limitations in the available data upon which elements of the AAI are based. For 

23 example, while ambient measurements of NOy are made as part of a national monitoring 
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1 network, the monitors are not located in locations that are representative of sensitive aquatic 

2 ecosystems. While air and water quality data are generally available in areas in the eastern U.S., 

3 there is relatively sparse coverage in mountainous western areas where a number of sensitive 

4 aquatic ecosystems are located. Further, even in areas where relevant data are available, small 

5 sample sizes impede efforts to characterize the representativeness of the available data, which 

6 was noted by CASAC as being of particular concern. Also, measurements of reduced forms of 

7 nitrogen are available from only a small number of monitoring sites, and emission inventories for 

8 reduced forms of nitrogen used in atmospheric modeling are subject to considerable uncertainty.

9 With regard to uncertainties related to the use of ecological and atmospheric modeling, 

10 the Administrator notes in particular that model results are difficult to evaluate due to a lack of 

11 relevant observational data. For example, relatively large uncertainties are introduced by a lack

12 of data with regard to pre-industrial environmental conditions and other parameters that are 

13 necessary inputs to critical load models that are the basis for factor F1 in the AAI equation. 

14 Also, observational data are not generally available to evaluate the modeled relationships 

15 between nitrogen and sulfur in the ambient air and associated deposition, which are the basis for 

16 the other factors (i.e., F2, F3, and F4) in the AAI equation.

17 In combination, these limitations and uncertainties result in a considerable degree of 

18 uncertainty as to how well the quantified elements of the AAI standard would predict the actual 

19 relationship between varying ambient concentrations of oxides of nitrogen and sulfur and steady 

20 state ANC levels across the distribution of water bodies within the various ecoregions in the U.S. 

21 Because of this, there is considerable uncertainty as to the actual degree of protectiveness that 

22 such a standard would provide, especially for acid-sensitive ecoregions. The Administrator 

23 recognizes that the AAI equation, with factors quantified in the ranges discussed above and 
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1 described more fully in the Policy Assessment, generally performs well in identifying areas of 

2 the country that are sensitive to such acidifying deposition and indicates, as expected, that lower 

3 ambient levels of oxides of nitrogen and sulfur would lead to higher calculated AAI values. 

4 However, the uncertainties discussed here are critical for determining the actual degree of 

5 protection that would be afforded such areas by any specific target ANC level and percentile of 

6 water bodies that would be chosen in setting a new AAI-based standard, and thus for determining 

7 an appropriate AAI-based standard that meets the requirements of section 109.

8 In considering these uncertainties, the Administrator notes that CASAC acknowledged 

9 that important uncertainties remain that would benefit from further study and data collection 

10 efforts, which might lead to potential revisions or modifications to the form of the standard 

11 developed in the Policy Assessment. She also notes that CASAC encouraged the Agency to 

12 engage in future monitoring and model evaluation efforts to help inform the specification of 

13 model-derived elements in the AAI equation.

14 Based on the above considerations, the Administrator has determined that it is not 

15 appropriate under section 109 to set a new multi-pollutant standard to address deposition-related 

16 effects of oxides of nitrogen and sulfur on aquatic acidification at this time. Setting a NAAQS 

17 generally involves consideration of the degree of uncertainties in the science and other 

18 information, such as gaps in the relevant data and, in this case, limitations in the evaluation of the 

19 application of relevant ecological and atmospheric models at an ecoregion scale. As noted 

20 above, the issue here is not a question of uncertainties about the scientific soundness of the 

21 structure of the AAI, but instead uncertainties in the quantification and representativeness of the 

22 elements of the AAI as they vary in ecoregions across the country. At present, these 

23 uncertainties prevent an understanding of the degree of protectiveness that would be afforded to
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1 various ecoregions across the country by a new standard defined in terms of a specific 

2 nationwide target ANC level and a specific percentile of water bodies for acid-sensitive 

3 ecoregions, and thus prevent identification of an appropriate standard. The Administrator has 

4 considered whether these uncertainties could be appropriately accounted for by choosing either a 

5 more or less protective target ANC level and percentile of water bodies than would otherwise be 

6 chosen if the uncertainties did not substantially limit the confidence that can appropriately be 

7 ascribed to the quantification of the AAI elements. However, in the Administrator’s judgment, 

8 the uncertainties are of such nature and magnitude that there is no reasoned way to choose such a 

9 specific nationwide target ANC level or percentile of water bodies that would appropriately 

10 account for the uncertainties, since neither the direction nor the magnitude of change from the 

11 target level and percentile that would otherwise be chosen can reasonably be ascertained at this 

12 time. 

13 Based on the above considerations, the Administrator judges that the current limitations 

14 in relevant data and the uncertainties associated with specifying the elements of the AAI based 

15 on modeled factors are of such nature and degree as to prevent her from reaching a reasoned 

16 decision such that she is adequately confident as to what level and form (in terms of a selected 

17 percentile) of such a standard would provide any particular intended degree of protection of 

18 public welfare that the Administrator determined satisfied the requirements to set an appropriate 

19 standard under section 109. While acknowledging that CASAC supported moving forward to 

20 establish the standard developed in the Policy Assessment, the Administrator also observes that 

21 CASAC supported conducting further field studies that would better inform the continued 

22 development or modification of such a standard. Given the large uncertainties and complexities 

23 inherent in quantifying the elements of such a standard, largely deriving from the unprecedented 
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1 nature of the standard under consideration in this review, and having fully considered CASAC’s 

2 advice, the Administrator provisionally concludes that it is premature to set a new, multi3 pollutant secondary standard for oxides of nitrogen and sulfur at this time, and as such she is 

4 proposing not to set such a new secondary standard.

5 While it is premature to set such a multi-pollutant standard at this time, the Administrator 

6 determines that the Agency should undertake a field pilot program to gather additional data, and 

7 that it is appropriate that such a program be undertaken before, rather than after, reaching a 

8 decision to set such a standard. As described below in section IV, the purpose of the program is

9 to collect and analyze data so as to enhance our understanding of the degree of protectiveness 

10 that would likely be afforded by a standard based on the AAI as developed in the Policy

11 Assessment. This will provide additional information to aid the Agency in considering an

12 appropriate multi-pollutant standard, specifically with respect to the acidifying effects of 

13 deposition of oxides of nitrogen and sulfur. Data generated by this field program will also 

14 support development of an appropriate monitoring network that would work in concert with such 

15 a standard to result in the intended degree of protection. The data and analyses generated as a 

16 result of this program will serve to inform the next review of the NAAQS for oxides of nitrogen 

17 and sulfur. The information generated during the field program can also be used to help state 

18 agencies and EPA better understand how an AAI-based standard would work in terms of the 

19 implementation of such a standard.

20 Based on the above considerations, the Administrator is proposing not to set a new multi21 pollutant AAI-based secondary standard for oxides of nitrogen and sulfur in this review. In 

22 reaching this decision, the Administrator recognizes that the new NO2 and SO2 primary 1-hour 

23 standards set in 2010, while not ecologically relevant for a secondary standard, will nonetheless 
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1 result in reductions in oxides of nitrogen and sulfur that will directionally benefit the 

2 environment by reducing NOy and SOx deposition to sensitive ecosystems. EPA is proposing to 

3 revise the secondary standards by adding secondary standards identical to the NO2 and SO2

4 primary 1-hour standards set in 2010. While this will not add secondary standards of an

5 ecologically relevant form to address deposition-related effects, it will directionally provide 

6 some degree of additional protection. This is consistent with the view that the current secondary

7 standards are neither sufficiently protective nor appropriate in form, but that it is not appropriate 

8 to propose to set a new, ecologically relevant multi-pollutant secondary standard at this time, for 

9 all of the reasons discussed above.

10 While not a basis for this decision, the Administrator also recognizes that a new, 

11 innovative AAI-based standard would raise significant implementation issues that would need to 

12 be addressed consistent with the CAA requirements for implementation-related actions following 

13 the setting of a new NAAQS. It will take time to address these issues, during which the Agency

14 will be conducting a field pilot program to gather relevant data and the environment will benefit

15 from reductions in oxides of nitrogen and sulfur resulting from the new NO2 and SO2 primary

16 standards, as noted above, as well as reductions expected to be achieved from EPA’s proposed 

17 Transport Rule and Mercury and Air Toxics standards. These implementation-related issues are 

18 discussed in more detail below in section IV.A.5.

19 The Administrator solicits comment on all aspects of this proposed decision, including 

20 the framework and elements of a multi-pollutant standard for oxides of nitrogen and sulfur to 

21 address deposition-related effects on sensitive ecosystems, with a focus on aquatic acidification, 

22 and the uncertainties and complexities associated with the development of such a standard at this 
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1 time. The Administrator also solicits comment on the field pilot program and related monitoring 

2 methods as discussed below in section IV.

3 IV. Field Pilot Program and Ambient Monitoring 

4 This section describes EPA’s plans for a field pilot program and the evaluation of 

5 monitoring methods for ambient air indicators of oxides of nitrogen (NOy) and oxides of sulfur 

6 (SOx) to implement the Administrator’s decision to undertake such a field monitoring program 

7 in conjunction with her decision to propose not to set a new multi-pollutant secondary standard 

8 in this review, as discussed above in section III.H. As noted above and discussed below in 

9 section IV.A, the field pilot program is intended to collect and analyze data so as to enhance our 

10 understanding of the degree of protectiveness that would likely be afforded by a standard based on 

11 the AAI as developed in the Policy Assessment. Data generated by this field program would also 

12 support development of an appropriate monitoring network that would work in concert with such a 

13 standard to result in the intended degree of protection. As discussed below in section IV.B, the 

14 evaluation of monitoring methods focuses on the development of Federal Reference 

15 Methods/Federal Equivalent Methods (FRM/FEM) for NOy and SOx. EPA notes that the 

16 monitoring program described here is intended to be coordinated with EPA’s CASTNET as a 

17 supplement to existing monitoring programs and is beyond the scope of the current CASTNET 

18 program. 

19 A. Field Pilot Program

20 This section presents the objectives of a field pilot program (section IV.A.1) that would 

21 gather relevant field data over a five-year period in a sample of 3 to 5 sensitive ecoregions across 

22 the country. An overview of the scope and structure of the field program, with a focus on 

23 measurements of ambient air indicators of oxides of nitrogen and sulfur, is presented in section 

24 IV.A.2. Section IV.A.3 explains the role of additional complementary measurements beyond the 
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1 ambient air indicators that would be included in the program, and section IV.A.4 discusses a 

2 parallel longer-term research agenda, both of which are guided by the uncertainties discussed 

3 above in section III. Section IV.A.5 identifies implementation challenges presented by an AAI4 based standard that could be addressed in parallel with a field pilot program. Section IV.A.6 

5 discusses engagement with stakeholder groups as part of the planned pilot program.

6 1. Objectives

7 Consideration of a new multi-pollutant standard to address deposition-related effects on 

8 sensitive aquatic ecoregions raises unique challenges relative to those typically raised in reviews 

9 of existing NAAQS for which an established network of FRM/FEM monitors, designed to 

10 measure the indicator pollutant, is generally available. The primary goal of this field pilot 

11 program, and the related monitoring program discussed in section IV.B, is to enhance our 

12 understanding of the degree of protectiveness that would likely be afforded by a standard based 

13 on the AAI, as described above in section III, so as to aid the Agency in considering an 

14 appropriate multi-pollutant standard that would be requisite to protect public welfare consistent 

15 with section 109 of the CAA, through the following objectives:

16 (1) evaluate measurement methods for the ambient air indicators of NOy and SOx and 

17 consider designation of such methods as FRMs;

18 (2) examine the variability and improve characterization of concentration and 

19 deposition patterns of oxides of nitrogen and sulfur, as well as reduced forms of 

20 nitrogen, within and across a number of sensitive ecoregions across the country;

21 (3) develop updated ecoregion-specific factors (i.e., F1 through F4) for the AAI 

22 equation based in part on new observed air quality data within the sample ecoregions 

23 as well as on updated nationwide air quality model results and expanded critical load 
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1 data bases, and explore alternative approaches for developing such representative 

2 factors;

3 (4) calculate ecoregion-specific AAI values using observed NOy and SOx data and 

4 updated ecoregion-specific factors to examine the extent to which the sample 

5 ecoregions would meet a set of alternative AAI-based standards;

6 (5) develop air monitoring network design criteria for an AAI-based standard;

7 (6) assess the use of total nitrate measurements as a potential alternative indicator for 

8 oxides of nitrogen;

9 (7) support related longer-term research efforts, including enhancements to and 

10 evaluation of modeled dry deposition algorithms; and

11 (8) facilitate stakeholder engagement in addressing implementation issues associated 

12 with possible future adoption of an AAI-based standard.

13 2. Overview of Field Pilot Program

14 The CASTNET program (Figure IV-1) affords an available infrastructure relevant to an 

15 AAI-based standard, given the location of sites in some acid-sensitive ecoregions and various 

16 measurements of sulfur and nitrogen species. The EPA plans to use CASTNET sites in selected 

17 acid-sensitive ecoregions to serve as the platform for this pilot program, potentially starting in 

18 late 2012 and extending through 2018. CASTNET sites in three to five ecoregions in acid19 sensitive areas would collect NOy and SOx (i.e., SO2 and p-SO4) measurements over a five-year 

20 period. The initial step in developing a data base of observed ambient air indicators for oxides of 

21 nitrogen and sulfur requires the addition of NOy samplers at the pilot study sites so that a full 

22 complement of indicator measurements are available to calculate AAI values. These CASTNET 
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1 sites would also be used to make supplemental observations useful for evaluation of CMAQ’s 

2 characterization of factors F2 –F4 in the AAI equation.

3 The selected ecoregions would account for geographic variability by including regions 

4 from across the U.S., including the east, upper midwest and west. Each selected region would 

5 have at least two existing CASTNET sites. Each of the pilot CASTNET sites would be used to 

6 evaluate the performance of the established methods, data retrieval and reporting procedures

7 used in the AAI equation.

8

9

10 Figure IV-1. Location of CASTNET sites in relation to acid sensitive ecoregions. 

11 
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1 Over the course of this five-year pilot program, the most current national air quality

2 modeling, based on the most current national emissions inventory, would be used to develop an 

3 updated set of F2 – F4 factors. A parallel multi-agency national critical load data base

4 development effort would be used as the basis for calculating updated F1 factors. As discussed 

5 above in section III.B, these factors would be based on average parameter values across an 

6 ecoregion. Using this new set of F factors, observations of NOy and SOx derived from the pilot 

7 program, averaged across each ecoregion, would be used to calculate AAI values in the sample 

8 ecoregions. The data from the pilot program would also be used to examine alternative 

9 approaches to generating representative air quality values, such as examining the appropriateness 

10 of spatial averaging in areas of high spatial variability.

11 3 . Complementary Measurements

12 Complementary measurements may be performed at some sites in the pilot network to 

13 reduce uncertainties in the recommended methods and better characterize model performance 

14 and application to the AAI. The CASAC Air Monitoring and Methods Subcommittee (AMMS) 

15 advised EPA that such supplemental measurements were of critical importance in a field 

16 measurement program related to an AAI-based standard (Russell and Samet, 2011b).

17 Candidate complementary measurements to address sulfur, in addition to those provided 

18 by the CASTNET filter pack (CFP), include trace gas continuous SO2 and speciated PM2.5 

19 measurements. The co-located deployment of a continuous SO2 analyzer with the CFP for SO2

20 will provide test data for determining suitability of continuous SO2 measurements as an FEM, as 

21 well as producing valuable time series data for model evaluation purposes. The weekly 

22 averaging time provided by the CFP adequately addresses the annual-average basis of an AAI23 based secondary standard, but would not be applicable to short-term (i.e., 1-hour) averages
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1 associated with the primary SO2 standard. Conversely, because of the low concentrations 

2 associated with many acid-sensitive ecoregions, existing SO2 FRMs designated for use in 

3 determining compliance with the primary standard would not necessarily be appropriate for use 

4 in conjunction with an AAI-based secondary standard. 

5 Co-locating the PM2.5 sampler used in the EPA Chemical Speciation Network and the 

6 IMPROVE network at pilot network sites would allow for characterizing the relationship 

7 between the CFP-derived p-SO4 and the speciation samplers used throughout the state and local 

8 air quality networks. Note that CASTNET already has several co-located IMPROVE chemical 

9 speciation samplers. Because the AAI equation is based on concentration of p-SO4, the original 

10 motivation for capturing all particle size fractions is not as important relative to simply capturing 

11 the concentration of total p-SO4. 

12 Candidate measurements to complement oxidized nitrogen measurements, in addition to 

13 the CFP, include a mix of continuous and periodic sampling for the dominant NOy species, 

14 namely NO, true NO2, PAN, HNO3, and p-NO3. While there are several approaches to acquiring 

15 these measurements, perhaps the most efficient strategy would take advantage of the available 

16 CFP for total nitrate, and add a three-channel chemiluminescence instrument that will cycle 

17 between NOy, true NO2 and NO by adding photolytic detection for true NO2. Other options for 

18 measuring true NO2 would include adding either a stand-alone photolytic or cavity ring-down 

19 spectroscopy instrument. Measurements of PAN may be acquired either on a periodic basis 

20 through canister sampling and subsequent laboratory analysis or through emerging in-situ 

21 sampling and analysis methods. Although the CFP yields a reliable measurement of total nitrate, 

22 the t-NO3 (i.e., the sum of HNO3 and p-NH4) value, strong consideration may be given to direct 

23 measurement of HNO3, which has the highest deposition velocity of all the dominant NOy
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1 species. Similar to the use of continuous SO2 data, these speciated NOy data serve two purposes:

2 evaluating total NOy instrument behavior and evaluating air quality models. The measurement 

3 of individual NOy species can be used to generate site-specific NOy values for comparison to 

4 modeled NOy, and will likely provide insight into and improvement of modeled dry deposition.

5 The CASAC AMMS (Russell and Samet, 2011b) recommended that EPA consider the 

6 use of t-NO3 obtained from CASTNET sampling as an indicator for NOy, reasoning that t-NO3 is 

7 typically a significant fraction of deposited oxidized nitrogen in rural environments and 

8 CASTNET measurements are widely available. Collection of this data would support further

9 consideration of using the CFP for t-NO3 as the indicator of oxides of nitrogen for use in an AAI10 based secondary standard. 

11 The CASAC AMMS also recommended that total NHx (NH3 and p-NH4) be considered 

12 as a proxy for reduced nitrogen species, reasoning that the subsequent partitioning to NH3 and p13 NH4 may be estimated using equilibrium chemistry calculations. Reduced nitrogen 

14 measurements are used to evaluate air quality modeling which is used in generating factor F2. 

15 Additional studies are needed to determine the applicability of NHx measurements and 

16 calculated values of NH3 and NH4 to the AAI. 

17 The additional supplemental measurements of speciated NOy, continuous SO2 and NHx 

18 will be used in future air quality modeling evaluation efforts. Because there often is significant 

19 lag in the availability of contemporary emissions data to drive air quality modeling, the complete 

20 use of these data sets will extend beyond the five-year collection period of the pilot program. 

21 Consequently, the immediate application of those data will address instrument performance 

22 comparisons that explore the feasibility of using continuous SO2 instruments in rural 

23 environments, and using the speciated NOy data to assess NOy instrument performance. 
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1 Although contemporary air quality modeling will lag behind measurement data availability, the 

2 observations can be used in deposition models to compare observed transference ratios with the 

3 previously calculated transference ratios to test temporal stability of the ratios. 

4 An extended water quality sampling effort should parallel the air quality measurement 

5 program to address some of the uncertainties related to factor F1 and the representativeness of 

the nth 6 percentile critical load as discussed in section III.B.5.b.i. The objective of the water 

7 quality sampling would be to develop a larger data base of critical loads in each of the pilot 

ecoregions such that the nth 8 percentile can adequately be characterized in terms of representing 

9 all water bodies. Opportunities to leverage and perhaps enhance existing ecosystem modeling 

10 efforts enabling more advanced critical load modeling and improved methods to estimate base 

11 cation production would be pursued. For example, areas with ongoing research studies 

12 producing data for dynamic critical load modeling would be considered when selecting the pilot 

13 ecoregions.

14 4. Complementary areas of research 

15 The EPA recognizes that a source of uncertainty in an AAI-based secondary standard that 

16 would not be directly addressed in the pilot program stems from the uncertainty in the model 

17 used to link atmospheric concentrations to dry deposition fluxes. Currently, there are no ongoing

18 direct dry deposition measurement studies at CASTNET sites that can be used to evaluate 

19 modeled results. It was strongly recommended by CASAC AMMS that a comprehensive 

20 sampling-intensive study be conducted in at least one, preferably two sites in different 

21 ecoregions to assess characterization of dry deposition of sulfur and nitrogen. These sites would 

22 be the same as those for the complementary measurements described above, but they would

23 afford an opportunity to also complement dry deposition process research that benefits from the 
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1 ambient air measurements collected in the pilot program. The concerns regarding uncertainties 

2 underlying an AAI-based secondary standard suggest that research that includes dry deposition 

3 measurements and evaluation of dry deposition models should be a high priority.

4 Similar leveraging should be pursued with respect to ecosystem research activities. For 

5 example, studies that capture a suite of soil, vegetation, hydrological, and water quality 

6 properties that can help evaluate more advanced critical load models would complement the 

7 atmospheric-based pilot program. In concept, such studies could provide the infrastructure for 

8 true multi-pollutant, multi-media “super” sites assuming the planning, coordination, and resource 

9 facets can be aligned. While this discussion emphasizes the opportunity of leveraging ongoing 

10 research efforts, consideration could be given to explicitly including related research components 

11 directly in the pilot program.

12 5. Implementation challenges

13 The CAA requires that once a NAAQS is established, designation and implementation 

14 must move forward. With a standard as innovative as the AAI-based standard considered in this 

15 review, the Administrator believes that its success will be greatly improved if, while additional 

16 data are being collected to reduce the uncertainties discussed above, the implementing agencies 

17 and other stakeholders have an opportunity to discuss and thoroughly understand how such a 

18 standard would work. And since, as noted above, emissions reductions that are directionally

19 correct to reduce aquatic acidification will be occurring as a result of other CAA programs, the 

20 Administrator believes that this period of further discussion will not delay progress but will 

21 ensure that once implementation is triggered, agencies will be prepared to implement it 

22 successfully.

23 Consideration of an AAI-based secondary standard for oxides of nitrogen and sulfur 
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1 would present significant implementation challenges because it involves multiple, regionally2 dispersed pollutants and relatively complex compliance determinations. The anticipated 

3 implementation challenges fall into three main categories: monitoring and compliance 

4 determinations for area designations, pre-construction permit application analyses of individual 

5 source impacts, and State Implementation Plan (SIP) development. Several overarching 

6 implementation questions that we anticipate will be addressed in parallel with the field pilot 

7 program’s five-year data collection period include: 

8 (1) What are the appropriate monitoring network density and siting requirements to 

9 support a compliance system based on ecoregions? 

10 (2) Given the unique spatial nature of the secondary standard (e.g., ecoregions), what are 

11 the appropriate parameters for establishing nonattainment areas?

12 (3) How can new or modified major sources of oxides of nitrogen and oxides of sulfur 

13 emissions assess their ambient impacts on the standard and demonstrate that they are not 

14 causing or contributing to a violation of the NAAQS for preconstruction permitting? To 

15 what extent does the fact that a single source may be impacting multiple areas complicate 

16 this assessment and how can these additional complexities best be addressed?

17 (4) What additional tools, information, and planning structures are needed to assist states 

18 with SIP development, including the assessment of interstate pollutant transport and 

19 deposition?

20 (5) Would transportation conformity apply in nonattainment and maintenance areas for 

21 this secondary standard, and, if it does, would satisfying requirements that apply for 

22 related primary standards (e.g., ozone, PM2.5, and NO2) be demonstrated to satisfy

23 requirements for this secondary standard?
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1 6. Final monitoring plan development and stakeholder participation

2 The existing CASTNET sampling site infrastructure provides an effective means of 

3 quickly and efficiently deploying a monitoring program to support potential implementation of 

4 an AAI-based secondary standard, and also provides an additional opportunity for federally 

5 managed networks to collaborate and support the states, local agencies and Tribes (SLT) in 

6 determining compliance with a secondary standard. A collaborative effort would help to 

7 optimize limited federal and SLT monitoring funds and would be beneficial to all involved. 

8 CASTNET is already a stakeholder-based program with over 20 participants and contributors, 

9 including federal, state and tribal partners.

10 The CASAC AMMS generally endorsed the technical approaches used in CASTNET, but 

11 concerns were raised by individual representatives of State agencies concerning the perception of 

12 EPA-controlled management aspects of CASTNET and data ownership. Potential approaches to 

13 resolve these issues will be developed and evaluated in existing NACAA/EPA ambient air 

14 monitoring workgroups. The EPA Office of Air and Radiation (which includes the Office of Air 

15 Quality Planning Standards, OAQPS; and the Office of Atmospheric Program’s Clean Air 

16 Markets Division, OAP-CAMD), and their partners on the NACAA monitor steering committee 

17 will develop a prioritized specific plan that identifies the 3-5 ecoregions and the instrumentation 

18 to be deployed. EPA anticipates that a cost estimate of the plan with priorities and options will 

19 be developed by January, 2012. Although this pilot program is focused on data collection, the 

20 plan will include details of the data analysis approaches as well as a vehicle that incorporates 

21 engagement from those within EPA and SLTs to foster progress on the implementation questions 

22 noted above in section IV.A.5.
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1 If an AAI-based secondary standard were to be set in the future, deployment of a full 

2 national network would follow the pilot monitoring program. The number of sites deployed in 

3 the network will lead to increased confidence in capturing spatial patterns of air quality. 

4 Recognizing that this section presents the general elements of the field pilot programs, EPA 

5 intends to develop a more detailed field pilot program plan through a process that will engage the 

6 air quality management and research (atmospheric and ecosystem) communities, as well as other 

7 federal agencies, state and local agencies, and non-government based centers of expertise. The 

8 EPA is seeking comment and input on all aspects of this field pilot program.

9 .B. Evaluation of Monitoring Methods

10 The EPA generally relies on monitoring methods that have been designated as FRMs or 

11 FEMs for the purpose of determining the attainment status of areas with regard to existing 

12 NAAQS. Such FRMs or FEMs are generally required to measure the air quality indicators that 

13 are compared to the level of a standard to assess compliance with a NAAQS. Prior to their 

14 designation by EPA as FRM/FEMs through a rulemaking process, these methods must be 

15 determined to be applicable for routine field use and need to have been experimentally validated 

16 by meeting or exceeding specific accuracy, reproducibility, and reliability criteria established by 

17 EPA for this purpose. As discussed above in section III.A, the ambient air indicators being 

18 considered for use in an AAI-based standard include SO2, particulate sulfate (p-SO4), and total 

19 reactive oxides of nitrogen (NOy).

20 The CASTNET provides a well established infrastructure that would meet the basic 

21 location and measurement requirements of an AAI-based secondary standard given the rural 

22 placement of sites in acid sensitive areas. In addition, CFPs currently provide very economical 
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1 weekly, integrated average concentration measurements of SO2, p-SO4, ammonium ion (NH4) 

2 and t-NO3, the sum of HNO3 and p-NO3. 

3 While routinely operated instruments that measure SO2, p-SO4, NOy and/or t-NO3 exist, 

4 instruments that measure p-SO4, NOy, t-NO3, or the CFP for SO2 have not been designated by 

5 EPA as FRMs or FEMs The EPA’s ORD has initiated work that will support future FRM 

6 designations by EPA for SO2 and p-SO4 measurements based on the CFP. Such a designation by

7 EPA could be done for the purpose of facilitating consistent research related to an AAI-based 

8 standard and/or in conjunction with setting and supporting an AAI-based secondary standard. 

9 Based on extensive review of literature and available data, the EPA has identified 

10 potential methods that appear suitable for measuring each of the three components of the 

11 indicators. These three methods are being considered as new FRMs to be used for measuring the 

12 ambient concentrations of the three components that would be needed to determine compliance 

13 with an AAI-based secondary standard.

14 For the SO2 and p-SO4 measurements, EPA is considering the CFP method, which 

15 provides weekly average concentration measurements for SO2 and p-SO4. This method has been 

16 used in the EPA’s CASTNET monitoring network for 15 years, and strongly indicates that it will 

17 meet the requirements for use as an FRM for the SO2 and p-SO4 concentrations for an AAI-based 

18 secondary standard. 

19 Although the CFP method would provide measurements of both the SO2 and p-SO4 

20 components in a unified sampling and analysis procedure, individual FRMs will be considered 

21 for each. The EPA recognizes that an existing FRM to measure SO2 concentrations using ultra22 violet fluorescence (UVF) exists (40 CFR Part 50, Appendix A-1) for the purpose of monitoring 

23 compliance for the primary SO2 NAAQS. However, several factors suggest that the CFP method 
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1 would be superior to that UVF FRM for monitoring compliance with an AAI-based secondary

2 standard and will be discussed in more detail below. 

3 For monitoring the NOy component, a continuous analyzer for measuring NOy is 

4 commercially available and is considered to be suitable for use as an FRM. This method is 

5 similar in design to the existing NO2 FRM (described in 40 CFR Part 50, Appendix F), which is 

6 based on the ozone chemiluminescence measurement technique. The method is adapted to and 

7 further optimized to measure all oxides of nitrogen (NOy). However, this NOy method requires 

8 further evaluation before it can be fully confirmed as a suitable FRM. The EPA is currently 

9 completing a full scientific assessment of the NOy method to determine whether it would be 

10 appropriate to consider for designation by EPA as an FRM. Specific details on these three 

11 methods are given below. 

12 On February 16, 2011, EPA presented this set of potential FRMs to the CASAC AMMS 

13 for their consideration and comment. In response, the CASAC AMMS stated that, overall, it 

14 believes that EPA’s planned evaluation of methods for measuring NOy, SO2 and p-SO4 as 

15 ambient air indicators are suitable approaches in concept. On supporting the CFP method as a 

16 potential FRM for SO2, CASAC stated that they felt that the CFP is adequate for measuring 

17 long-term average SO2 gas concentrations in rural areas with low levels (less than 5 ppbv) and is 

18 therefore suitable for consideration as an FRM. For p-SO4, CASAC generally supports the use 

19 of the CFP as a potential FRM for measuring p-SO4 for an AAI-based secondary standard. The 

20 method has been relatively well-characterized and evaluated, and it has a documented, long-term 

21 track record of successful use in a field network designed to assess spatial patterns and long-term 

22 trends.
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1 On supporting the photometric NOy method as a potential FRM, CASAC concluded that 

2 the existing NOy method is generally an appropriate approach for the indicator. However, 

3 CASAC agrees that additional characterization and research is needed to fully understand the 

4 method in order to designate it as a FRM. The EPA is now soliciting public comment on these 

5 methods as to their adequacy, suitability, and relative merits as FRMs for purposes of monitoring 

6 to determine compliance with an AAI-based secondary standard.

7 1. Potential FRMs for SO2 and p-SO4

8 The CFP is a combined, integrated sampling and analysis method based on the well9 established measurement technology that has been used extensively in EPA’s CASTNET 

10 monitoring network (see http://www.epa.gov/castnet). This method is in current use at over 80 

11 monitoring sites and has been in use at not less than 40 sites for over 15 years. This method 

12 employs a relatively simple and inexpensive sampler and uses four 47-mm filters placed in an 

13 open-faced filter pack to simultaneously collect integrated filter samples for the SO2 and p-SO4

14 components. In addition, the CFP is also capable of the collection of t-NO3, the sum of HNO3

15 and p-NO3.

The first stage of the filter pack assembly contains a Teflon® filter that collects p-SO4

2-

16

and p-NO3, the second stage contains a nylon filter that collects SO2 (as SO4

2-

17 ) and HNO3, and 

18 the third stage contains two cellulose fiber filters impregnated with potassium carbonate (K2CO3) 

that collect any remaining SO2 (as SO4

2-

19 ). The sampler collects one-week integrated samples at a 

20 very low, controlled flow rate (1.5 or 3 L/min) in an attempt simulate actual deposition. Weekly

21 averaged SO2 and p-SO4 concentrations could then be averaged over a 1-year period to calculate 

22 annual average values.
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1 Upon sample completion, the species-specific filters are extracted, with subsequent 

2 analysis by the well-established and documented ion chromatographic (IC) analytical technique. 

3 During the IC analysis, an aliquot of a filter extract is injected into a stream of eluent (ion 

4 chromatography mobile phase, generally a millimolar-strength solution of carbonate5 bicarbonate) and passed through a series of ion exchangers. The anions of interest are separated 

6 on the basis of their relative affinities for a low capacity and the strongly basic anion exchanger 

7 (guard and separator column). The separated anions are directed onto a cation exchanger 

8 (suppressor column) where they are converted to their highly conductive acid form, and the 

9 eluent is converted to a weakly conductive form. The now-separated anions, each in their acid 

10 form, are measured by conductivity. They are identified on the basis of retention time compared 

11 to that of standards and quantified by measurement of peak area compared to the peak areas of 

12 calibration standards. 

13 Calibration and quality assurance for the method are applied to the sample filters, the 

14 analytical processes, and the flow rate measurement and control aspects of the sampler. Overall 

15 method performance is typically assessed with collocated samplers. These quality assurance 

16 techniques are routinely used and have proved adequate for other types of FRMs and equivalent 

17 methods in air monitoring network service.

18 The measurement and analytical procedures and past performance data associated with 

19 the CFP method are well documented and available through Quality Assurance Performance 

20 Plans (QAPPs), Standard Operating Procedures (SOPs) and annual reports (US EPA, 2010a and 

21 2010b). The accumulated database on the CFP method is substantial and indicates that the 

22 method is sound, stable and has good reliability in routine, field operation. Data quality 

23 assessment results show the method to have good reproducibility, with collocated and analytical 
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1 precision values in the range of 2% – 10% (excluding very low concentration measurements near 

2 the method detection limits; US EPA 2010b).

3 Data quality objectives (DQOs) for a new FRM would be based upon current DQOs 

4 being used for this method by EPA’s OAP/CAMD and the National Park Service (NPS), the 

5 federal managers of CASTNET (US EPA, 2010a). In its current state, the CFP method is 

6 expected to meet or exceed (as past CASTNET data have indicated; US EPA, 2010b) the 

7 expected FRM DQOs, even when deployed in new monitoring networks outside of CASTNET. 

8 In addition, CASTNET samples have agreed favorably with other measures of SO2 and p-SO4 in 

9 comparison studies. For example, in direct comparison with an annular denuder sampler (ADS) 

10 method, CASTNET/ADS ratios for SO2 and p-SO4 were generally on the order of 0.9-1.1 

11 (Lavery et al, 2009; Sickles et al, 1999; Sickles et al, 2008), thus illustrating the accuracy of the 

12 CFP method in the determination of long-term average SO2 and p-SO4 concentrations. The EPA 

13 believes that the CFP method would be fully adequate as an FRM in determining yearly average 

14 SO2 and p-SO4 concentrations for compliance determination purposes.

15 The EPA recognizes that an existing FRM for SO2 has proven adequate for the purposes 

16 of monitoring compliance for the primary SO2 NAAQS, specifically the newly-promulgated 1-

17 hour standard. However, this FRM is better suited to the shorter-term, higher concentration 

18 primary and secondary SO2 NAAQS, and there is substantial uncertainty as to the adequacy of 

19 this SO2 FRM for monitoring the lower concentrations relevant to determining compliance with 

20 an AAI-based secondary standard. The performance specifications for SO2 FRM analyzers (40 

21 CFR Part 53, Table B-1) require a lower detectable limit (LDL) of 0.002 ppm for the standard 

22 measurement range and 0.001 ppm for the lower measurement range. These requirements 

correspond to mass per unit volume concentrations of 5.24 and 2.62 μg/m323 , respectively. 
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1 Analysis of 2009 CASTNET data shows that of the 84 CASTNET sampling sites, 63 measured 

2 annual average SO2 concentrations below even the lower of these LDL requirements of 2.62 

μg/m33 for the lower range SO2 FRM (US EPA, 2010a). In addition, 11 of the 84 sites measured 

4 annual (2009) average SO2 concentrations very near or below the manufacturers’ reported 

5 detection limits for trace level UVF SO2 monitors. Further, it is likely that the number of sites 

6 with annual average SO2 concentration below both the SO2 FRM LDL and the manufacturers 

7 reported detection limits will increase due to expected declines in mean SO2 concentrations (US

8 EPA, 2010b). For these reasons, EPA is considering the CFP method for use as the FRM for 

9 monitoring the SO2 component of an ambient air indicator for oxides of sulfur, with a 

10 recommendation for additional study and data collection to further evaluate the possible 

11 applicability of the continuous UVF SO2 FRM for this purpose.

12 2. Potential FRM for NOy

13 Atmospheric concentrations of NOy are measured continuously by an analyzer that 

14 photometrically measures the light intensity, at wavelengths greater than 600 nanometers (nm), 

15 resulting from the chemiluminescent reaction of ozone (O3) with NO in sampled air. This 

16 method is very similar to the chemiluminescence NO/NO2 analyzers widely used to collect NO2

17 monitoring data for determining compliance with the NO2 NAAQS. The various oxides of 

18 nitrogen species, excluding NO, are first quantitatively reduced to NO by means of a catalytic 

19 converter. These species include NO2, HNO2, PANs, HNO3 and p-NO3. The NO, which 

20 commonly exists in ambient air, passes through the converter unchanged, and, when combined 

21 with the NO resulting from the catalytic conversion of the other oxides of nitrogen, a 

22 measurement of the total NOy concentration results. To maximize the conversion of the more 

23 chemically active oxides of nitrogen species, the converter is located externally, at or near the air 
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1 sample inlet probe. This location minimizes losses of these active species that could otherwise 

2 occur from chemical reactions and wall losses in the sample inlet line.

3 The NOy analyzer is a suitable, commercially produced continuous chemiluminescence 

4 analyzer that includes an ozone generator, a reaction cell, a photometric detector, wavelength 

5 filters as necessary to reduce sensitivity to wavelengths below 600 nm, a pump and flow control 

6 system to draw atmospheric air through the converter and into the reaction cell, a suitable 

7 converter, a system to control the operation of the analyzer, and appropriate electronics to 

8 process and quantitatively scale the photometric signals. The converter contains a catalyst such 

9 as molybdenum and is heated to an optimum temperature designed to optimize the conversion of 

10 the various oxides of nitrogen to NO. It is connected to the analyzer via suitable lengths of 

11 Teflon® tubing. Hourly NOy measurements obtained by the analyzer would be averaged over 

12 the same 7-day period used by the CFP method to measure the SO2 and p-SO4 components, with 

13 further averaging over a 1-year period.

14 Commercial NOy analyzers are currently available, and the analyzers have been used for 

15 a variety of monitoring applications. During the 2006 TexAQS Radical and Aerosol 

16 Measurement Project (TRAMP), Luke et. al., 2010, compared measured NOy concentrations 

17 obtained with an NOy instrument based upon the above mentioned methodology with the sum of 

18 measured individual NOy species (i.e., NOyi = NO+NO2+HNO3+PANs+HNO2+p-NO3). This 

19 comparison yielded excellent overall agreement during both day ([NOy](ppb) = [NOyi](ppb) × 

1.03 – 0.42; r2= 0.9933) and night time ([NOy](ppb) = [NOyi](ppb) × 1.01 – 0.18; r220 = 0.9975) 

21 periods (Luke et al, 2010). The results of this study show that this NOy method is capable of the 

22 accurate determination of all the atmospherically relevant NOy components, resulting in an 

23 accurate determination of total NOy concentrations. NOy instruments have been routinely 
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1 operated in networks such as SEARCH, dating back several years. In addition, state monitoring 

2 agencies across the U.S. have begun, starting in 2009, the routine operation of commercially

3 available NOy instrumentation in anticipation of EPA’s NCore network transitioning to full 

4 operation in 2011. 

5 These initial assessments described above are promising and indicate that the photometric 

6 NOy method appears to be accurate, reliable, and capable of routine network operation. As a 

7 result, the method is most likely capable for use as an FRM for determining atmospheric NOy

8 concentrations as a component in determining compliance with an AAI-based secondary

9 standard. However, as described below, this continuous method for NOy requires additional 

10 time for further evaluation before it can be fully confirmed for adoption as a FRM. The EPA has 

11 identified measurement uncertainties and some remaining science questions associated with this 

12 method. Among these are (a) the ability of the method to capture all components of NOy

13 relevant to nitrogen deposition, (b) the efficiency of the molybdenum converter in converting all 

14 oxides of nitrogen to NO for detection (excluding NO2, as this conversion is already well 

15 documented), (c) appropriate inlet height specifications to minimize any bias associated with 

16 vertical concentration gradients of key NOy components, (d) identification and quantification of 

17 potential measurement interferences in the NOy determination, and (e) development and 

18 demonstration of effective calibration/challenge procedures to best represent the various 

19 mixtures of NOy components that are expected to be present in the different air sheds across the 

20 U.S.

21 To address these NOy method uncertainties and to fully assess this method for use as the 

22 NOy FRM, EPA has developed a detailed research plan (Russell and Samet, 2011b) which was 

23 presented to the CASAC AMMS on February 16, 2011. In response, CASAC recognized the 
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1 need for, and supported the general outline of EPA’s research plan to evaluate the NOy method 

2 for potential designation as an FRM (US EPA, 2011). In addition, the CASAC AMMS 

3 suggested additional areas of research associated with the photometric NOy method that warrant 

4 further assessment prior to final designation of the method as the NOy FRM. These include 

5 operation of the method during extremely low temperature conditions to investigate possible 

6 condensation in sample lines, method detection limits relative to low levels expected in remote 

7 areas, and ambient-based method evaluations in various air sheds across the U.S. In response to 

8 these CASAC AMMS suggestions, EPA is carrying out studies, in addition to the tasks outlined 

9 in the research plan, for the NOy method. The results of these studies will likely take a year or 

10 more to become available. As noted previously, EPA anticipates that these results will be 

11 favorable and will confirm the adequacy of the NOy method as a suitable FRM for determining 

12 compliance with an AAI-based secondary standard.

13 V. Statutory and Executive Order Reviews

14 A. Executive Order 12866: Regulatory Planning and Review and Review and Executive 

15 Order 13563: Improving Regulation and Regulatory Review

16 Under Executive Order 12866 (58 FR 51735, October 4, 1993), this action is a

17 “significant regulatory action.” Accordingly, EPA submitted this action to the Office of 

18 Management and Budget (OMB) for review under Executive Orders 12866 and 13563 (76 FR 

19 3821, January 21, 2011), and any changes made in response to OMB recommendations have 

20 been documented in the docket for this action.

21 B. Paperwork Reduction Act

22 This action does not impose an information collection burden under the provisions of the 

23 Paperwork Reduction Act, 44 U.S.C. 3501 et seq. Burden is defined at 5 CFR 1320.3(b). There 
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1 are no information collection requirements directly associated with the establishment of a 

2 NAAQS under section 109 of the CAA.

3 C. Regulatory Flexibility Act

4 For purposes of assessing the impacts of today’s rule on small entities, small entity is 

5 defined as: (1) a small business that is a small industrial entity as defined by the Small Business 

6 Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental jurisdiction 

7 that is a government of a city, county, town, school district or special district with a population of 

8 less than 50,000; and (3) a small organization that is any not-for-profit enterprise which is 

9 independently owned and operated and is not dominant in its field.

10 After considering the economic impacts of today's proposed rule on small entities, I

11 certify that this action will not have a significant economic impact on a substantial number of 

12 small entities. This proposed rule will not impose any requirements on small entities. Rather, 

13 this rule establishes national standards for allowable concentrations of oxides of nitrogen and 

14 sulfur in ambient air as required by section 109 of the CAA. See also American Trucking 

15 Associations v. EPA. 175 F. 3d at 1044-45 (NAAQS do not have significant impacts upon small 

16 entities because NAAQS themselves impose no regulations upon small entities). We continue to 

17 be interested in the potential impacts of the proposed rule on small entities and welcome 

18 comments on issues related to such impacts

19 D. Unfunded Mandates Reform Act

20 Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, 

21 establishes requirements for Federal agencies to assess the effects of their regulatory actions on 

22 State, local, and Tribal governments and the private sector. Under section 202 of the UMRA, 

23 EPA generally must prepare a written statement, including a cost-benefit analysis, for proposed 
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1 and final rules with “Federal mandates” that may result in expenditures to State, local, and Tribal 

2 governments, in the aggregate, or to the private sector, of $100 million or more in any 1 year. 

3 Before promulgating an EPA rule for which a written statement is needed, section 205 of the 

4 UMRA generally requires EPA to identify and consider a reasonable number of regulatory

5 alternatives and to adopt the least costly, most cost-effective or least burdensome alternative that 

6 achieves the objectives of the rule. The provisions of section 205 do not apply when they are 

7 inconsistent with applicable law. Moreover, section 205 allows EPA to adopt an alternative 

8 other than the least costly, most cost-effective or least burdensome alternative if the 

9 Administrator publishes with the final rule an explanation why that alternative was not adopted. 

10 Before EPA establishes any regulatory requirements that may significantly or uniquely affect 

11 small governments, including Tribal governments, it must have developed under section 203 of 

12 the UMRA a small government agency plan. The plan must provide for notifying potentially 

13 affected small governments, enabling officials of affected small governments to have meaningful 

14 and timely input in the development of EPA regulatory proposals with significant Federal 

15 intergovernmental mandates, and informing, educating, and advising small governments on 

16 compliance with the regulatory requirements. 

17 This action contains no Federal mandates under the provisions of Title II of the 

18 Unfundated Mandates Reform Act of 1995 (UMRA), 2 U.S.C. 1531-1538 for State, local, or 

19 tribal governments or the private sector. Therefore, this action is not subject to the requirements 

20 of sections 202 or 205. Furthermore, as indicated previously, in setting a NAAQS EPA cannot 

21 consider the economic or technological feasibility of attaining ambient air quality standards; 

22 although such factors may be considered to a degree in the development of State plans to 

23 implement the standards. See also American Trucking Associations v. EPA, 175 F. 3d at 1043 
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1 (noting that because EPA is precluded from considering costs of implementation in establishing 

2 NAAQS, preparation of a Regulatory Impact Analysis pursuant to the Unfunded Mandates 

3 Reform Act would not furnish any information which the court could consider in reviewing the 

4 NAAQS). Accordingly, EPA has determined that the provisions of sections 202, 203, and 205 of 

5 the UMRA do not apply to this proposed decision. The EPA acknowledges, however, that any 

6 corresponding revisions to associated SIP requirements and air quality surveillance requirements, 

7 40 CFR part 51 and 40 CFR part 58, respectively, might result in such effects. Accordingly, 

8 EPA will address, as appropriate, unfunded mandates if and when it proposes any revisions to 40 

9 CFR parts 51 or 58.

10 E. Executive Order 13132: Federalism

11 This proposed rule does not have federalism implications. It will not have substantial 

12 direct effects on the States, on the relationship between the national government and the States, 

13 or on the distribution of power and responsibilities among the various levels of government, as 

14 specified in Executive Order 13132 because it does not contain legally binding requirements. 

15 Thus, the requirements of Executive Order 13132 do not apply to this rule.

16 EPA believes, however, that this proposed rule may be of significant interest to State 

17 governments. As also noted in section E (above) on UMRA, EPA recognizes that States will 

18 have a substantial interest in this rule and any corresponding revisions to associated SIP 

19 requirements and air quality surveillance requirements, 40 CFR part 51 and 40 CFR part 58, 

20 respectively. Therefore, in the spirit of Executive Order 13132 and consistent with EPA policy

21 to promote communications between EPA and State and local governments, EPA specifically 

22 solicits comment on this proposed rule from State and local officials.

23 F. Executive Order 13175: Consultation and Coordination with Indian Tribal Governments
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1 Executive Order 13175, entitled “Consultation and Coordination with Indian Tribal 

2 Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an accountable 

3 process to ensure “meaningful and timely input by tribal officials in the development of 

4 regulatory policies that have tribal implications.” This rule concerns the establishment of 

5 national standards to address the public welfare effects of oxides of nitrogen and sulfur.

6 This action does not have Tribal implications, as specified in Executive Order 13175 (65 

7 FR 67249, November 9, 2000). It does not have a substantial direct effect on one or more Indian 

8 tribes, since tribes are not obligated to adopt or implement any NAAQS. Thus, Executive Order 

9 13175 does not apply to this rule.

10 G. Executive Order 13045: Protection of Children from Environmental Health & Safety 

11 Risks

12 This action is not subject to EO 13045 because it is not an economically significant rule 

13 as defined in EO 12866. 

14 H. Executive Order 13211: Actions that Significantly Affect Energy Supply, Distribution or 

15 Use

16 This action is not a “significant energy action” as defined in Executive Order 13211 (66 

17 FR 28355, May 22, 2001), because it is not likely to have a significant adverse effect on the 

18 supply, distribution, or use of energy. This action concerns the establishment of national 

19 standards to address the public welfare effects of oxides of nitrogen and sulfur. This action does 

20 not prescribe specific pollution control strategies by which these ambient standards will be met. 

21 Such strategies will be developed by States on a case-by-case basis, and EPA cannot predict 

22 whether the control options selected by States will include regulations on energy suppliers, 

23 distributors, or users.
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1 I. National Technology Transfer and Advancement Act

2 Section 12(d) of the National Technology Transfer and Advancement Act of 1995 

3 (NTTAA), Public Law No. 104-113, 12(d) (15 U.S.C. 272 note) directs EPA to use voluntary

4 consensus standards in its regulatory activities unless to do so would be inconsistent with 

5 applicable law or otherwise impractical. Voluntary consensus standards are technical standards 

6 (e.g., materials specifications, test methods, sampling procedures, and business practices) that are 

7 developed or adopted by voluntary consensus standards bodies. The NTTAA directs EPA to 

8 provide Congress, through OMB, explanations when the Agency decides not to use available and 

9 applicable voluntary consensus standards.

10 The EPA is not aware of any voluntary consensus standards that are relevant to the 

11 provisions of this proposed rule. The EPA welcomes any feedback on such standards that may

12 be applicable.

13 J. Executive Order 12898: Federal Actions to Address Environmental Justice in Minority 

14 Populations and Low-Income Populations

15 Executive Order 12898 (59 FR 7629 (Feb. 16, 1994)) establishes federal executive policy

16 on environmental justice. Its main provision directs federal agencies, to the greatest extent 

17 practicable and permitted by law, to make environmental justice part of their mission by

18 identifying and addressing, as appropriate, disproportionately high and adverse human health or 

19 environmental effects of their programs, policies, and activities on minority populations and low20 income populations in the United States. 

21 EPA has determined that this proposed rule will not have disproportionately high and 

22 adverse human health or environmental effects on minority or low-income populations because it 

23 retains the level of environmental protection for all affected populations without having any 
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1 disproportionately high and adverse human health or environmental effects on any population, 

2 including any minority or low-income population.

3

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8 US EPA, 2010b. CASTNET Annual Reports, 2004 - 2009, http://java.epa.gov/castnet/

9 US EPA 2011. Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for 

10 Oxides of Nitrogen and Oxides of Sulfur. U.S. Environmental Protection Agency, Washington, D.C., EPA11 452/R-11-005a.

12 US EPA, 2011b. Federal Reference Methods for NOy and p-SO4 for the New Combined NOx and SOx Secondary 

13 NAAQS Research Plan, EPA/600/1-11/002 January 20, 2011.

14 Wolff, G. T. 1993. CASAC closure letter for the 1993 Criteria Document for Oxides of Nitrogen addressed to U.S. 

15 EPA Administrator Carol M. Browner dated September 30, 1993.

16 Wolff, G. T. 1995. CASAC closure letter for the 1995 OAQPS Staff Paper addressed to U.S. EPA Administrator 

17 Carol M. Browner dated August 22, 1995.

18

19 List of Subjects in 40 CFR Part 50

20 Environmental protection, Air pollution control, Carbon monoxide, Lead, Nitrogen 

21 dioxide, Ozone, Particulater matter, Sulfur oxides.

22

23 ________________________________

24 Dated: July 12, 2011

25

26

27 Lisa P. Jackson,

28 Administrator

29
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1 For the reasons set forth in the preamble, part 50 of chapter 1 of title 40 of the code of 

2 Federal regulations is proposed to be amended as follows:

3

4 PART 50-NATIONAL PRIMARY AND SECONDARY AMBIENT AIR QUALITY 

5 STANDARDS 

6 1. The authority citation for part 50 continues to read as follows:

7 Authority: 42 U.S.C. 7401, et seq.

8

9 2. Section 50.5 is amended by revising paragraphs (b) and (c) and by adding paragraphs (d) and 

10 (e) to read as follows:

11 §50.5 National secondary ambient air quality standards for sulfur oxides (sulfur dioxide).

12 * * * * *

13 (b) The level of the national secondary 1-hour ambient air quality standard for oxides of sulfur is 

14 75 parts per billion (ppb, which is 1 part in 1,000,000,000), measured in the ambient air as sulfur 

15 dioxide (SO2).

16 (c) The levels of the standards shall be measured by a reference method based on Appendix A-1 

17 or A-2 of this part, or by a Federal Equivalent Method (FEM) designated in accordance with part 

18 53 of this chapter.

19 (d) To demonstrate attainment with the 3-hour secondary standard, the second-highest 3-hour 

20 average must be based upon hourly data that are at least 75 percent complete in each calendar 

21 quarter. A 3-hour block average shall be considered valid only if all three hourly averages for 
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1 the 3-hour period are available. If only one or two hourly averages are available, but the 3-hour 

2 average would exceed the level of the standard when zeros are substituted for the missing values, 

3 subject to the rounding rule of paragraph (a) of this section, then this shall be considered a valid 

4 3-hour average. In all cases, the 3-hour block average shall be computed as the sum of the 

5 hourly averages divided by 3.

6 (e) The 1-hour secondary standard is met at an ambient air quality monitoring site when the 

three-year average of the annual 99th 7 percentile of the daily maximum 1-hour average 

8 concentrations is less than or equal to 75 ppb, as determined in accordance with Appendix T of 

9 this part.

10

11 3. Section 50.11 is amended by deleting paragraphs (f) and (g) and revising paragraphs (a) 

12 through (e) to read as follows: 

13 §50.11 National primary and secondary ambient air quality standards for oxides of 

14 nitrogen (with nitrogen dioxide as the indicator).

15 (a) The level of the national primary and secondary annual ambient air quality standards for 

16 oxides of nitrogen is 53 parts per billion (ppb, which is 1 part in 1,000,000,000), annual average 

17 concentration, measured in the ambient air as nitrogen dioxide.

18 (b) The level of the national primary and secondary 1-hour ambient air quality standards for 

19 oxides of nitrogen is 100 ppb, 1-hour average concentration, measured in the ambient air as 

20 nitrogen dioxide. 

21 (c) The levels of the standards shall be measured by:
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1 (1) a reference method based on appendix F to this part; or 

2 (2) a Federal equivalent method (FEM) designated in accordance with part 53 of this chapter.

3 (d) The annual primary and secondary standards are met when the annual average concentration 

4 in a calendar year is less than or equal to 53 ppb, as determined in accordance with Appendix S

5 of this part for the annual standard.

6 (e) The 1-hour primary and secondary standards are met when the three-year average of the 

annual 98th 7 percentile of the daily maximum 1-hour average concentration is less than or equal to 

8 100 ppb, as determined in accordance with Appendix S of this part for the 1-hour standard.

9

10 4. Appendix S is amended as follows:

11 a. by revising paragraph 1.(a),

12 b. by revising the definition of “Design values” under paragraph 1.(c),

13 c. by revising paragraph 2.(b),

14 d. by revising paragraphs 3.1(a) through (d),

15 e. by revising paragraphs 3.2(a) through (e),

16 f. by revising paragraph 4.1(b),

17 g. by revising paragraph 4.2 (c),

18 h. by revising paragraph 5.1(b), and
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1 i. by revising paragraph 5.2(b) to read as follows:

2 Appendix S to Part 50—Interpretation of the Primary and Secondary National Ambient 

3 Air Quality Standards for Oxides of Nitrogen (Nitrogen Dioxide)

4 1. General.

5 (a) This appendix explains the data handling conventions and computations necessary for 

6 determining when the primary and secondary national ambient air quality standards for oxides of 

7 nitrogen as measured by nitrogen dioxide (“NO2 NAAQS”) specified in §50.11 are met. 

8 Nitrogen dioxide (NO2) is measured in the ambient air by a Federal reference method (FRM) 

9 based on appendix F to this part or by a Federal equivalent method (FEM) designated in 

10 accordance with part 53 of this chapter. Data handling and computation procedures to be used in 

11 making comparisons between reported NO2 concentrations and the levels of the NO2 NAAQS 

12 are specified in the following sections.

13 * * * * * 

14 (c) The terms used in this appendix are defined as follows: 

15 * * * * * 

16 Design values are the metrics (i.e., statistics) that are compared to the NAAQS levels to 

17 determine compliance, calculated as specified in section 5 of this appendix. The design values 

18 for the primary and secondary NAAQS are:

19 (1) The annual mean value for a monitoring site for one year (referred to as the “annual primary

20 or secondary standard design value”). 
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(2) The 3-year average of annual 98th 1 percentile daily maximum 1-hour values for a monitoring 

2 site (referred to as the “1-hour primary or secondary standard design value”).

3 * * * * * 

4 2. Requirements for Data Used for Comparisons with the NO2 NAAQS and Data

5 Reporting Considerations.

6 * * * * * 

7 (b) When two or more NO2 monitors are operated at a site, the state may in advance designate 

8 one of them as the primary monitor. If the state has not made this designation, the Administrator 

9 will make the designation, either in advance or retrospectively. Design values will be developed 

10 using only the data from the primary monitor, if this results in a valid design value. If data from 

11 the primary monitor do not allow the development of a valid design value, data solely from the 

12 other monitor(s) will be used in turn to develop a valid design value, if this results in a valid 

13 design value. If there are three or more monitors, the order for such comparison of the other 

14 monitors will be determined by the Administrator. The Administrator may combine data from 

15 different monitors in different years for the purpose of developing a valid 1-hour primary or 

16 secondary standard design value, if a valid design value cannot be developed solely with the data 

17 from a single monitor. However, data from two or more monitors in the same year at the same 

18 site will not be combined in an attempt to meet data completeness requirements, except if one 

19 monitor has physically replaced another instrument permanently, in which case the two 

20 instruments will be considered to be the same monitor, or if the state has switched the 

21 designation of the primary monitor from one instrument to another during the year.
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1 * * * * * 

2 3. Comparisons with the NO2 NAAQS.

3 3.1 The Annual Primary and Secondary NO2 NAAQS.

4 (a) The annual primary and secondary NO2 NAAQS are met at a site when the valid annual 

5 primary standard design value is less than or equal to 53 parts per billion (ppb).

6 (b) An annual primary or secondary standard design value is valid when at least 75 percent of the 

7 hours in the year are reported. 

8 (c) An annual primary or secondary standard design value based on data that do not meet the 

9 completeness criteria stated in section 3.1(b) may also be considered valid with the approval of, 

10 or at the initiative of, the Administrator, who may consider factors such as monitoring site 

11 closures/moves, monitoring diligence, the consistency and levels of the valid concentration 

12 measurements that are available, and nearby concentrations in determining whether to use such 

13 data.

14 (d) The procedures for calculating the annual primary and secondary standard design values are 

15 given in section 5.1 of this appendix.

16 3.2 The 1-hour Primary and Secondary NO2 NAAQS.

17 (a) The 1-hour primary or secondary NO2 NAAQS is met at a site when the valid 1-hour primary

18 or secondary standard design value is less than or equal to 100 parts per billion (ppb).

19 (b) An NO2 1-hour primary or secondary standard design value is valid if it encompasses three

20 consecutive calendar years of complete data. A year meets data completeness requirements 

21 when all 4 quarters are complete. A quarter is complete when at least 75 percent of the sampling 

22 days for each quarter have complete data. A sampling day has complete data if 75 percent of the 
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1 hourly concentration values, including state-flagged data affected by exceptional events which 

2 have been approved for exclusion by the Administrator, are reported.

3 (c) In the case of one, two, or three years that do not meet the completeness requirements of 

4 section 3.2(b) of this appendix and thus would normally not be useable for the calculation of a 

5 valid 3-year 1-hour primary or secondary standard design value, the 3-year 1-hour primary or 

6 secondary standard design value shall nevertheless be considered valid if one of the following 

7 conditions is true. 

8 (i) At least 75 percent of the days in each quarter of each of three consecutive years have at least 

9 one reported hourly value, and the design value calculated according to the procedures specified 

10 in section 5.2 is above the level of the primary or secondary 1-hour standard.

11 (ii) (A) A 1-hour primary or secondary standard design value that is below the level of the 

12 NAAQS can be validated if the substitution test in section 3.2(c)(ii)(B) results in a “test design 

13 value” that is below the level of the NAAQS. The test substitutes actual ‘‘high’’ reported daily 

14 maximum 1-hour values from the same site at about the same time of the year (specifically, in 

15 the same calendar quarter) for unknown values that were not successfully measured. Note that 

16 the test is merely diagnostic in nature, intended to confirm that there is a very high likelihood 

17 that the original design value (the one with less than 75 percent data capture of hours by day and 

18 of days by quarter) reflects the true under-NAAQS-level status for that 3-year period; the result 

19 of this data substitution test (the ‘‘test design value”, as defined in section 3.2(c)(ii)(B)) is not 

20 considered the actual design value. For this test, substitution is permitted only if there are at least 

21 200 days across the three matching quarters of the three years under consideration (which is 

22 about 75 percent of all possible daily values in those three quarters) for which 75 percent of the 

23 hours in the day, including state-flagged data affected by exceptional events which have been 
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1 approved for exclusion by the Administrator, have reported concentrations. However, maximum 

2 1-hour values from days with less than 75 percent of the hours reported shall also be considered 

3 in identifying the high value to be used for substitution.

4 (B) The substitution test is as follows: Data substitution will be performed in all quarter periods 

5 that have less than 75 percent data capture but at least 50 percent data capture, including state6 flagged data affected by exceptional events which have been approved for exclusion by the 

7 Administrator; if any quarter has less than 50 percent data capture then this substitution test

8 cannot be used. Identify for each quarter (e.g., January-March) the highest reported daily

9 maximum 1-hour value for that quarter, excluding state-flagged data affected by exceptional 

10 events which have been approved for exclusion by the Administrator, looking across those three 

11 months of all three years under consideration. All daily maximum 1-hour values from all days in 

12 the quarter period shall be considered when identifying this highest value, including days with 

13 less than 75 percent data capture. If after substituting the highest non-excluded reported daily 

14 maximum 1-hour value for a quarter for as much of the missing daily data in the matching 

15 deficient quarter(s) as is needed to make them 100 percent complete, the procedure in section 5.2

16 yields a recalculated 3-year 1-hour standard “test design value” below the level of the standard, 

17 then the 1-hour primary or secondary standard design value is deemed to have passed the 

18 diagnostic test and is valid, and the level of the standard is deemed to have been met in that 3-

19 year period. As noted in section 3.2(c)(i), in such a case, the 3-year design value based on the 

20 data actually reported, not the ‘‘test design value’’, shall be used as the valid design value.

21 (iii) (A) A 1-hour primary or secondary standard design value that is above the level of the 

22 NAAQS can be validated if the substitution test in section 3.2(c)(iii)(B) results in a “test design 

23 value” that is above the level of the NAAQS. The test substitutes actual ‘‘low” reported daily 
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1 maximum 1-hour values from the same site at about the same time of the year (specifically, in 

2 the same three months of the calendar) for unknown values that were not successfully measured. 

3 Note that the test is merely diagnostic in nature, intended to confirm that there is a very high 

4 likelihood that the original design value (the one with less than 75 percent data capture of hours 

5 by day and of days by quarter) reflects the true above-NAAQS-level status for that 3-year period; 

6 the result of this data substitution test (the ‘‘test design value”, as defined in section 

7 3.2(c)(iii)(B)) is not considered the actual design value. For this test, substitution is permitted 

8 only if there are a minimum number of available daily data points from which to identify the low

9 quarter-specific daily maximum 1-hour values, specifically if there are at least 200 days across 

10 the three matching quarters of the three years under consideration (which is about 75 percent of 

11 all possible daily values in those three quarters) for which 75 percent of the hours in the day have 

12 reported concentrations. Only days with at least 75 percent of the hours reported shall be 

13 considered in identifying the low value to be used for substitution.

14 (B) The substitution test is as follows: Data substitution will be performed in all quarter periods 

15 that have less than 75 percent data capture. Identify for each quarter (e.g., January-March) the 

16 lowest reported daily maximum 1-hour value for that quarter, looking across those three months 

17 of all three years under consideration. All daily maximum 1-hour values from all days with at 

18 least 75 percent capture in the quarter period shall be considered when identifying this lowest 

19 value. If after substituting the lowest reported daily maximum 1-hour value for a quarter for as 

20 much of the missing daily data in the matching deficient quarter(s) as is needed to make them 75

21 percent complete, the procedure in section 5.2 yields a recalculated 3-year 1-hour standard “test 

22 design value” above the level of the standard, then the 1-hour primary or secondary standard 

23 design value is deemed to have passed the diagnostic test and is valid, and the level of the 
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1 standard is deemed to have been exceeded in that 3-year period. As noted in section 3.2(c)(i), in 

2 such a case, the 3-year design value based on the data actually reported, not the ‘‘test design 

3 value’’, shall be used as the valid design value.

4 (d) A 1-hour primary or secondary standard design value based on data that do not meet the 

5 completeness criteria stated in 3.2(b) and also do not satisfy section 3.2(c), may also be 

6 considered valid with the approval of, or at the initiative of, the Administrator, who may consider 

7 factors such as monitoring site closures/moves, monitoring diligence, the consistency and levels 

8 of the valid concentration measurements that are available, and nearby concentrations in 

9 determining whether to use such data.

10 (e) The procedures for calculating the 1-hour primary and secondary standard design values are 

11 given in section 5.2 of this appendix.

12 4. Rounding Conventions. 

13 4.1 Rounding Conventions for the Annual Primary and Secondary NO2 NAAQS.

14 * * * * *

15 (b) The annual primary or secondary standard design value is calculated pursuant to section 5.1 

16 and then rounded to the nearest whole number or 1 ppb (decimals 0.5 and greater are rounded up 

17 to the nearest whole number, and any decimal lower than 0.5 is rounded down to the nearest 

18 whole number).

19 4.2 Rounding Conventions for the 1-hour Primary and Secondary NO2 NAAQS.

20 * * * * *

21 (c) The 1-hour primary or secondary standard design value is calculated pursuant to section 5.2 

22 and then rounded to the nearest whole number or 1 ppb (decimals 0.5 and greater are rounded up 
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1 to the nearest whole number, and any decimal lower than 0.5 is rounded down to the nearest 

2 whole number).

3 5. Calculation Procedures for the Primary and Secondary NO2 NAAQS.

4 5.1 Procedures for the Annual Primary and Secondary NO2 NAAQS

5 * * * * *

6 (b) The annual primary or secondary standard design value for a site is the valid annual mean 

7 rounded according to the conventions in section 4.1.

8 5.2 Calculation Procedures for the 1-hour Primary and Secondary NO2 NAAQS.

9 * * * * *

10 (b) The 1-hour primary or secondary standard design value for a site is the mean of the three 

annual 98th 11 percentile values, rounded according to the conventions in section 4.

12 * * * * *

13

14 5. Appendix T is amended as follows:

15 a. by revising paragraph 1.(a),

16 b. by revising the definition of “Design values” under paragraph 1.(c),

17 c. by revising paragraph 2.(b),

18 d. by revising paragraphs 3.(a) through (e),

19 e. by revising paragraph 4.(c), and

20 f. by revising paragraph 5.(b) to read as follows:
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1 Appendix T to Part 50—Interpretation of the Primary and Secondary National Ambient 

2 Air Quality Standards for Oxides of Sulfur (Sulfur Dioxide) 

3 1. General.

4 (a) This appendix explains the data handling conventions and computations necessary for 

5 determining when the primary and secondary national ambient air quality standards for Oxides 

6 of Sulfur as measured by Sulfur Dioxide (“SO2 NAAQS”) specified in § 50.17 and §50.5 (b), 

7 respectively, are met at an ambient air quality monitoring site. Sulfur Dioxide (SO2) is measured 

8 in the ambient air by a Federal reference method (FRM) based on appendix A-1 or A-2 to this 

9 part or by a Federal equivalent method (FEM) designated in accordance with part 53 of this 

10 chapter. Data handling and computation procedures to be used in making comparisons between 

11 reported SO2 concentrations and the levels of the SO2 NAAQS are specified in the following 

12 sections.

13 * * * * *

14 (c) The terms used in this appendix are defined as follows: 

15 * * * * *

16 Design values are the metrics (i.e., statistics) that are compared to the NAAQS levels to 

17 determine compliance, calculated as specified in section 5 of this appendix. The design value for 

the primary and secondary 1-hour NAAQS is the 3-year average of annual 99th 18 percentile daily 

19 maximum 1-hour values for a monitoring site (referred to as the “1-hour primary standard design 

20 value”).
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1 * * * * *

2 2. Requirements for Data Used for Comparisons with the SO2 NAAQS and Data

3 Reporting Considerations.

4 * * * * *

5 (b) Data from two or more monitors from the same year at the same site reported to EPA under 

6 distinct Pollutant Occurrence Codes shall not be combined in an attempt to meet data 

completeness requirements. The Administrator will combine annual 99th 7 percentile daily 

8 maximum concentration values from different monitors in different years, selected as described 

9 here, for the purpose of developing a valid 1-hour primary or secondary standard design value. 

10 If more than one of the monitors meets the completeness requirement for all four quarters of a 

11 year, the steps specified in section 5(a) of this appendix shall be applied to the data from the 

12 monitor with the highest average of the four quarterly completeness values to derive a valid 

annual 99th 13 percentile daily maximum concentration. If no monitor is complete for all four 

14 quarters in a year, the steps specified in section 3(c) and 5(a) of this appendix shall be applied to 

15 the data from the monitor with the highest average of the four quarterly completeness values in 

an attempt to derive a valid annual 99th 16 percentile daily maximum concentration. This paragraph 

17 does not prohibit a monitoring agency from making a local designation of one physical monitor 

18 as the primary monitor for a Pollutant Occurrence Code and substituting the 1-hour data from a 

19 second physical monitor whenever a valid concentration value is not obtained from the primary

20 monitor; if a monitoring agency substitutes data in this manner, each substituted value must be 

21 accompanied by an AQS qualifier code indicating that substitution with a value from a second 

22 physical monitor has taken place.
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1 * * * * *

2 3. Comparisons with the 1-hour Primary and Secondary SO2 NAAQS.

3 (a) The 1-hour primary or secondary SO2 NAAQS is met at an ambient air quality monitoring 

4 site when the valid 1-hour primary or secondary standard design value is less than or equal to 75

5 parts per billion (ppb).

6 (b) An SO2 1-hour primary or secondary standard design value is valid if it encompasses three

7 consecutive calendar years of complete data. A year meets data completeness requirements 

8 when all 4 quarters are complete. A quarter is complete when at least 75 percent of the sampling

9 days for each quarter have complete data. A sampling day has complete data if 75 percent of the 

10 hourly concentration values, including State-flagged data affected by exceptional events which 

11 have been approved for exclusion by the Administrator, are reported.

12 (c) In the case of one, two, or three years that do not meet the completeness requirements of 

13 section 3(b) of this appendix and thus would normally not be useable for the calculation of a 

14 valid 3-year 1-hour primary or secondary standard design value, the 3-year 1-hour primary or 

15 secondary standard design value shall nevertheless be considered valid if one of the following 

16 conditions is true. 

17 (i) At least 75 percent of the days in each quarter of each of three consecutive years have at least 

18 one reported hourly value, and the design value calculated according to the procedures specified 

19 in section 5 is above the level of the primary or secondary 1-hour standard.

20 (ii) (A) A 1-hour primary or secondary standard design value that is equal to or below the level 

21 of the NAAQS can be validated if the substitution test in section 3(c)(ii)(B) results in a “test 

22 design value” that is below the level of the NAAQS. The test substitutes actual ‘‘high’’ reported 

23 daily maximum 1-hour values from the same site at about the same time of the year (specifically, 
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1 in the same calendar quarter) for unknown values that were not successfully measured. Note that 

2 the test is merely diagnostic in nature, intended to confirm that there is a very high likelihood 

3 that the original design value (the one with less than 75 percent data capture of hours by day and 

4 of days by quarter) reflects the true under-NAAQS-level status for that 3-year period; the result 

5 of this data substitution test (the ‘‘test design value”, as defined in section 3(c)(ii)(B)) is not 

6 considered the actual design value. For this test, substitution is permitted only if there are at least 

7 200 days across the three matching quarters of the three years under consideration (which is 

8 about 75 percent of all possible daily values in those three quarters) for which 75 percent of the 

9 hours in the day, including State-flagged data affected by exceptional events which have been 

10 approved for exclusion by the Administrator, have reported concentrations. However, maximum 

11 1-hour values from days with less than 75 percent of the hours reported shall also be considered 

12 in identifying the high value to be used for substitution.

13 (B) The substitution test is as follows: Data substitution will be performed in all quarter periods 

14 that have less than 75 percent data capture but at least 50 percent data capture, including State15 flagged data affected by exceptional events which have been approved for exclusion by the 

16 Administrator; if any quarter has less than 50 percent data capture then this substitution test

17 cannot be used. Identify for each quarter (e.g., January-March) the highest reported daily

18 maximum 1-hour value for that quarter, excluding State-flagged data affected by exceptional 

19 events which have been approved for exclusion by the Administrator, looking across those three 

20 months of all three years under consideration. All daily maximum 1-hour values from all days in 

21 the quarter period shall be considered when identifying this highest value, including days with 

22 less than 75 percent data capture. If after substituting the highest reported daily maximum 1-hour 

23 value for a quarter for as much of the missing daily data in the matching deficient quarter(s) as is 
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1 needed to make them 100 percent complete, the procedure in section 5 yields a recalculated 3-

2 year 1-hour standard “test design value” less than or equal to the level of the standard, then the 1-

3 hour primary or secondary standard design value is deemed to have passed the diagnostic test 

4 and is valid, and the level of the standard is deemed to have been met in that 3-year period. As 

5 noted in section 3(c)(i), in such a case, the 3-year design value based on the data actually

6 reported, not the ‘‘test design value’’, shall be used as the valid design value.

7 (iii) (A) A 1-hour primary or secondary standard design value that is above the level of the 

8 NAAQS can be validated if the substitution test in section 3(c)(iii)(B) results in a “test design 

9 value” that is above the level of the NAAQS. The test substitutes actual ‘‘low” reported daily 

10 maximum 1-hour values from the same site at about the same time of the year (specifically, in 

11 the same three months of the calendar) for unknown hourly values that were not successfully

12 measured. Note that the test is merely diagnostic in nature, intended to confirm that there is a 

13 very high likelihood that the original design value (the one with less than 75 percent data capture

14 of hours by day and of days by quarter) reflects the true above-NAAQS-level status for that 3-

15 year period; the result of this data substitution test (the ‘‘test design value”, as defined in section 

16 3(c)(iii)(B)) is not considered the actual design value. For this test, substitution is permitted only

17 if there are a minimum number of available daily data points from which to identify the low

18 quarter-specific daily maximum 1-hour values, specifically if there are at least 200 days across 

19 the three matching quarters of the three years under consideration (which is about 75 percent of 

20 all possible daily values in those three quarters) for which 75 percent of the hours in the day have 

21 reported concentrations. Only days with at least 75 percent of the hours reported shall be 

22 considered in identifying the low value to be used for substitution.
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1 (B) The substitution test is as follows: Data substitution will be performed in all quarter periods 

2 that have less than 75 percent data capture. Identify for each quarter (e.g., January-March) the 

3 lowest reported daily maximum 1-hour value for that quarter, looking across those three months 

4 of all three years under consideration. All daily maximum 1-hour values from all days with at 

5 least 75 percent capture in the quarter period shall be considered when identifying this lowest 

6 value. If after substituting the lowest reported daily maximum 1-hour value for a quarter for as 

7 much of the missing daily data in the matching deficient quarter(s) as is needed to make them 75

8 percent complete, the procedure in section 5 yields a recalculated 3-year 1-hour standard “test 

9 design value” above the level of the standard, then the 1-hour primary or secondary standard 

10 design value is deemed to have passed the diagnostic test and is valid, and the level of the 

11 standard is deemed to have been exceeded in that 3-year period. As noted in section 3(c)(i), in 

12 such a case, the 3-year design value based on the data actually reported, not the ‘‘test design 

13 value’’, shall be used as the valid design value.

14 (d) A 1-hour primary or secondary standard design value based on data that do not meet the 

15 completeness criteria stated in 3(b) and also do not satisfy section 3(c), may also be considered 

16 valid with the approval of, or at the initiative of, the Administrator, who may consider factors 

17 such as monitoring site closures/moves, monitoring diligence, the consistency and levels of the 

18 valid concentration measurements that are available, and nearby concentrations in determining 

19 whether to use such data.

20 (e) The procedures for calculating the 1-hour primary or secondary standard design values are 

21 given in section 5 of this appendix.

22 4. Rounding Conventions for the 1-hour Primary and Secondary SO2 NAAQS. 
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1 * * * * * 

2 (c) The 1-hour primary or secondary standard design value is calculated pursuant to section 5 

3 and then rounded to the nearest whole number or 1 ppb (decimals 0.5 and greater are rounded up 

4 to the nearest whole number, and any decimal lower than 0.5 is rounded down to the nearest 

5 whole number).

6 5. Calculation Procedures for the 1-hour Primary and Secondary SO2 NAAQS.

7 * * * * *

8 (b) The 1-hour primary or secondary standard design value for an ambient air quality monitoring 

site is the mean of the three annual 99th 9 percentile values, rounded according to the conventions 

10 in section 4.