2 and details updates to the AERMOD model with an inte rnal post-processor option that supports such analyses.

2. Provides clarification on the use and accepta nce of Tier 2 and Tier 3 options for NO 2 , including updated model evaluation re sults for the OLM and PVMRM options incorporated in the AERMOD model.

3. Recommends that compliance dem onstrations for the 1-hour NO 2 NAAQS address emission scenarios that can logi cally be assumed to be rela tively continuous or which occur frequently enough to contribute significan tly to the annual distribution of daily

maximum 1-hour concentrations based on existing modeling guidelines, which provide sufficient discretion for reviewing authorities to not include intermittent emissions from emergency generators or startup/shutdow n operations from compliance demonstrations for the 1-hour NO 2 standard under appropriate

circumstances.

4. Provides additional clarification and a more detailed discussion of the factors to consider in determination of background c oncentrations as part of a cumulative impact assessment including identification of nearby sources to be explicitly

modeled.

5. Recommends an appropriate met hodology for incorporating background

concentrations in the cumulative impact assessment for the 1-hour NO 2 standard and details updates to the AERMOD model with an option to include temporally-varying background concentrations with in the modeling analysis.

PROCEDURES FOR DEMONSTRATIN G COMPLIANCE WITH 1-HOUR NO 2 NAAQS

Compliance with the 1-hour NO 2 NAAQS is based on the multiyear average of the 98 th

percentile of the annual distri bution of daily maximum 1-hour values not exceeding 100 ppb.

The 8 th -highest of the daily maximum 1-hour values across a year is an u nbiased surrogate for

the 98 th -percentile 1 . The AERMOD dispersion model, EP A’s preferred model for near-field applications under Appendix W, was recently modified (version dated 11059) to fully support

the form of the 1-hour NO 2 NAAQS, as well as other analyses that may be needed in order to

demonstrate that a source does not cause or co ntribute to a violation of the NAAQS based on the

interim SIL established in th e June 29, 2010, memorandum.

Application of Interim SIL to Project Impacts

Using the interim 1-hour NO 2 SIL, a permit applicant can determine: (1) whether, based on the proposed increase in NO

x emissions, a cumulative air qualit y analysis is required; (2) the area of impact within which a cumulative air quali ty analysis should focus; and (3) whether the proposed source’s NO

x emissions will contribute to any modeled violation of the 1-hour NO 2 NAAQS identified in the cumulative analysis.

To determine initially whether a proposed project’s emissions increase will have a significant impact (resulting in th e need for a cumulative impact assessment), the June 29, 2010,

memorandum recommended that the interim SIL shoul d be compared to either of the following:3 • The highest of the 5-year averages of the maximum modeled 1-hour NO 2 concentrations predicted each year at each receptor, based on 5 years of National Weather Service data; or

• The highest modeled 1-hour NO 2 concentration predicted across all receptors based on 1 year of site-specific meteorological data, or the highest of the multi-year averages of the maximum modeled 1-hour NO 2 concentrations predicted each year ateach receptor, based on 2 or more years, up to 5 complete years of available sitespecific meteorological data.

Since the form of the standard is based on the annual distribution of daily maximum 1-hour values, the maximum contribution that a project could make to the air quality impact at a receptor is the multiyear average of the highest 1- hour values at that rece ptor. If the multiyear average of the highest 1-hour values is below the SIL at all receptors, then the project could not contribute significantly to any mode led violations of the 1-hour NO 2 NAAQS, thus exempting that project from the cumulative impact assessment.

Application of Interim SIL to Cumulative Impact Assessment

If a project’s impacts exceed the SIL at any receptors based on this initial impact analysis, then a cumulative impact assessment should be completed to determine whether the project will cause or contribute to any modeled violati ons of the NAAQS. While not common practice in the past, given the more complex analys is procedures associated with the form of the

1-hour NO 2 NAAQS, we deem it appropriate and acceptable in most cases to limit the cumulative impact analysis to only those receptor s that have been shown to have significant impacts from a proposed new source based on the initial SIL analysis, assuming that the design of the original receptor grid was adequate to de termine all areas of ambient air where the source could contribute significan tly to modeled violations. This ma y especially be appropriate for the

1-hour NO 2 standard since the init ial modeling of the project emissions without other background emission sources may have a tendency to overestimate ambient NO 2 concentrations, even under Tier 3 applications , by understating the potential ozone limiting influence of the background NO

x emissions. If modeled violations of the NAAQS are found based on the cumulative impact assessment, then the project’s c ontribution to all modele d violations should be compared to the interim SIL to determine whether the project causes or contributes to any of the

modeled violations.

In past guidance (EPA, 1988), EPA has indi cated that the significant contribution analysis should be based on a source’s contribu tion to the modeled violation paired in time and space. The form of the 1-hour NO 2 NAAQS complicates this analysis since the modeled violation is based on a multiyea r average of the annual distribution of daily maximum 1-hour values, i.e., a particular modeled violation at a particular receptor represents an average based on specific hours on specific days from each of the five years of meteorologi cal data (for National Weather Service (NWS) data). It is important to point out here that the significant contribution analysis is not limited to analyzing the source’s contribution associated only with the modeled design value based on the 98 th -percentile cumulative air quality im pact at the receptor, but rather

must examine all cases where the cumulative impact exceeds the NAAQS at or below the 98 th

-4 percentile. In some cases a source’s contribution to the 98 th -percentile of the daily maximum 1- hour values from the cumulative impact (i.e., the cumulative impact value or modeled design value that is compared to the NAAQS) may be be low the SIL, while the source’s contribution to cumulative impacts below the 98 th -percentile but above the NAAQS could exceed the SIL. Therefore, the significant contribution analysis should examine every multiyear average of daily maximum 1-hour values, beginning with the 8 th -highest (98 th -percentile) 2 , continuing down the ranked distribution until the cumulative impact is below the NAAQS. Since the form of the

standard is based on the annual distribution of daily maximum 1-hour values, the significant contribution analysis should be limited to the distribution of daily maximum 1-hour values, i.e.,

the 2 nd , 3 rd , 4 th -highest 1-hour values during the day, a nd so on, are not considered in this analysis. In addition, for applications with mo re than one year of meteorological data, the significant contribution an alysis should only examine ranks paired across the years, i.e., the multiyear average of the N th -highest values across each of th e years processed. The recent update to the AERMOD model (dated 11059) includes an option (the MAXDCONT keyword) to automatically perform this cont ribution analysis (EPA, 2010b), ex amining the contribution from project emissions to the cumulative impacts at each receptor across a user-specified range of ranked values, paired in time and space, as an in ternal post-processor within the model. Other options are available in the recent AERMOD upda te that identify the specific data periods contributing to the cumulative modeled impacts at each receptor.

Applicability of Ambient Monitoring Requirements to Modeling Demonstrations

The June 29, 2010 memo addressed one aspect of the applicability of ambient monitoring requirements, set forth in Appendix S to 40 CFR Part 50 in relation to the 1-hour NO 2 standard 3

, to modeling applications to demonstrate complia nce with the NAAQS, namely the use of 3 years of ambient monitoring data as the basis for at tainment of the NAAQS using monitoring vs. the use of 5 years of meteorologica l data for modeling demonstra tions of compliance with the NAAQS. Specifically, the June 29, 2010 memo indicated that “ Although the monitored design value for the 1-hour NO 2 standard is defined in terms of the 3-year average, this definition does not preempt or alter the Appendix W requirement fo r use of 5 years of NWS meteorological data or at least 1 year of site sp ecific data. The 5-year average based on use of NWS data, or an average across one or more years of available s ite specific data, serves as an unbiased estimate of the 3-year average for purposes of modeling demonstrations of comp liance with the NAAQS. Modeling of ‘rolling 3-year averages,’ usi ng years 1 through 3, years 2 through 4, and years 3 through 5, is not required .”

We would also like to emphasize that ot her aspects of the ambient monitoring requirements for the 1-hour NO 2 standard should not be applied for modeling analyses to demonstrate compliance with the NAAQS. For example, Appendix S addresses the data completeness requirements for monitored NO 2 concentrations, procedures for handling missing data periods, and conventions for rounding of mo nitored values. Appendix S specifies that a sampling day is complete if at least 75 percent of the hourly values are valid and a quarter is complete if at least 75 percent of the sampli ng days have complete data, and establishes calculation procedures for identifying the monitored design value that should be compared to the

2 For the 1-hour SO 2 standard the analysis should begin with the 4 th -highest, or 99 th -percentile value.

3 Appendix T to 40 CFR Part 50 addresses ambient monitoring requirements for the 1-hour SO 2 standard.5 NAAQS. While the requirements of Appendix S are appropriate in the context of ambient monitoring, application of these requirements and procedures to a dispersion modeling analysis is not appropriate and may conflict with modeling guidance in many cases. Appendix W provides guidance on data completeness for meteorol ogical data which specifically addresses the needs of dispersion modeling, including procedures that are explicitly im plemented within the meteorological processor and dispersion model to account for missing data due to calm winds or other factors. Adjustments to the calculati on procedures for determining the modeled design value for comparison to the NAAQS based on A ppendix S data completeness criteria is not appropriate. The EPA Model Clearinghouse has also issued guidance in the past that modeled

concentrations should not be rounded before comparing the modeled design value to the NAAQS. The fundamental point to rec ognize here is that ambient monitoring requirements/procedures and dispersion modeling guidance/procedures addr ess different issues and needs relative to each aspect of air quality assessment, and are often motivated by different concerns and exigencies.

APPROVAL AND APPLICATION OF TIERING APPROACH FOR NO 2

Given the stringency of the 1-hour NO 2 standard relative to th e annual standard, many more permit applicants may find it necessary to use the less conservative Tier 2 or Tier 3 approaches in order to demonstrate compliance with the new NAAQS rather than relying on the Tier 1 assumption of full conversion. The June 29, 2010 memo highlighted some of the potential issues that may need to be addressed in the appl ication of these less cons ervative assumptions for estimating ambient NO 2 impacts, relative to the Tier 1 opti on of full conversion, and clarified the

status of the Tier 3 PVMRM a nd OLM approaches available as non-regulatory-default options

within the AERMOD model.

In order to ease the burden on permit applicants in addressi ng the need to demonstrate compliance with the 1-hour NO 2 NAAQS, as well as the burden on the permitting authority in reviewing such applications, we offer additiona l discussion and recomme ndations in relation to the use of Tier 2 and Tier 3 options. Specifically, we recommend the following:

• Use of 0.80 as a default ambient ratio for the 1-hour NO 2 standard under Tier 2 without additional justifi cation by applicants; and

• General acceptance of 0.50 as a default in-stack ratio of NO 2 /NO

x for input to the PVMRM and OLM options within AERMOD, in the absence of more appropriate source-specific information on in-stack ratios.

The following sections explain these recommendations in more detail and also discuss the relative merits of the PVMRM and OLM options, clarifying that we ha ve not indicated any preference of one option over the other. We also provide updated model evaluation results for the PVMRM and OLM options in AERMOD that lend fu rther credence to the use of these Tier 3 options for 1-hour NO 2 compliance demonstrations. We anti cipate that these recommendations and updated model evaluations will simplify and faci litate the process of gaining approval for use of these non-regulatory default options in AERMOD.6 Tier 2 Ambient Ratio Method (ARM) for NO-to-NO 2 Conversion

Regarding the Tier 2 option of applying an ambi ent ratio to the Tier 1 result, the June 29, 2010 memo cautioned against use of the 0.75 national default ratio recommended in Appendix W for the annual standard for estimating hourly NO 2 impacts, without some justification of the appropriateness of that assumption. We still do not consider 0.75 as an appropriate default ambient ratio for the 1-hour standard, but severa l references cite ambient ratios of about 0.80 for hourly NO 2 /NO

x (e.g., Wang, et al., 2011; Janssen, et al ., 1991), and we believe it would be appropriate to accept that as a defa ult ambient ratio for the 1-hour NO 2 standard. Consideration was given to adopting the default equilibrium ratio of 0.90 incorporated in the PVMRM option as an hourly ARM, but we do not consider that to be an appropriate choice since it is the maximum ratio applied on a source-by-source an d hourly basis, irrespective of the predicted hourly NO

x concentration, whereas the Tier 2 ARM of 0.80 would be applied to the maximum cumulative hourly NO

x concentration.

Tier 3 Options for NO-to-NO 2 Conversion

The June 29, 2010 memo clarified that th e OLM and PVMRM options in the AERMOD model should be considered as Tier 3 applic ations under Section 5.2.4 of Appendix W. Also,

since the OLM and PVMRM methods are current ly implemented as non-regulatory-default options within the AERMOD dispersion model (Cimorelli, et al. , 2004; EPA, 2004; EPA, 2010b), their use requires justif ication and approval by the Regional Office on a case-by-case basis, pursuant to Sections 3.1.2.c, 3.2.2.a, and A.1.a (2) of Appendix W. The June 29 memo also highlighted the importance of two key model in puts for both the OLM and PVMRM options in

the context of the 1-hour NO 2 standard, namely the in-stack ratios of NO 2 /NO

x emissions and background ozone concentrations. This secti on provides additional discussion of these key inputs for OLM and PVMRM and also clarifies th e similarities and differences between these

methods and discusses their relative merits for purposes of demonstrating compliance with the 1-

hour NO 2 standard.

As noted in the June 29, 2010 memo, limite d evaluations of PVMRM have been completed which show encouraging results, but the amount of data currently available is too limited to justify a designation of PVMRM as a refined method for NO 2 (Hanrahan, 1999; MACTEC, 2005). Furthermore, th e original evaluations focused on model performance for annual averages since the only NO 2 standard in effect at the time was annual. We have recently updated the evaluations to reflect the curre nt AERMOD modeling system components and extended them to examine model performance for hourly NO 2 concentrations. Preliminary

results from these recent evaluations are presented in Attachment A.

While the limited scope of the available field study data imposes limits on the ability to generalize conclusions regarding model performa nce, these preliminary results of hourly NO 2 predictions for Palaau and New Mexico show generally good performance for the PVMRM and OLM/OLMGROUP ALL options in AERMOD. We believe that these additional model

evaluation results lend further credence to the use of these Tier 3 options in AERMOD for estimating hourly NO 2 concentrations, and we recommend th at their use should be generally7 accepted provided some reasonable demonstration can be made of the appropriateness of the key inputs for these opti ons, the in-stack NO 2 /NO

x ratio and the background ozone concentrations. Although well-documented data on in-stack NO 2 /NO

x ratios is still limited for many source categories, we also feel that it would be appropriate in the abse nce of such source-specific instack data to adopt a default in-s tack ratio of 0.5 as being adequate ly conservative in most cases

and a better alternative to use of the Tier 1 full conversion or Tier 2 ambient ratio options. This value appears to represent a re asonable upper bound based on the av ailable in-stack data. We hope that over time the range of source categor ies for which in-stack ratio information is available increases and the quality of such information will improve.

These preliminary model evaluation result s also serve to highlight a point worth emphasizing, which is that the PVMRM option in AERMOD is not inherently superior to the OLM option for purposes of estimating cumulative ambient NO 2 concentrations. The June 29,

2010 memo indicated that both PV MRM and OLM should be considered as Tier 3 options, but did not indicate any preference between these two options. Both PVMRM and OLM simulate

the same basic chemical mechanism of ozone tit ration, the interaction of NO with ambient ozone (O 3 ) to form NO 2 and O 2 . The main distinction between PVMRM and OLM is the approach

taken to estimate the ambient concentrations of NO and O 3 for which the ozone titration mechanism should be applied. For isolated elevated point sources, the PVMRM option does represent a more refined trea tment of ozone titration sin ce it estimates the NO and O 3 available for conversion based on simulating the actual vol ume of the instantaneous plume as it is transported downwind. As a result, this me thod will generally provide a more realistic

simulation of the NO-to-NO 2 conversion rate along the path of the plume for a particular source, accounting for the influence of meteorologi cal conditions on the entrainment of O 3 associated with growth of the plume. However, the al gorithm incorporated in PVMRM for determining which plumes “compete” for available ozone for multi-source applications has not been thoroughly validated, and as shown in the mode l evaluation results for New Mexico, PVMRM may not always provide a “better” answer than the OLM option.

The PVMRM algorithm as currently implem ented may also have a tendency to

overestimate the conversion of NO to NO 2 for low-level plumes by overstating the amount of

ozone available for the conversion due to the ma nner in which the plume volume is calculated. The plume volume calculation in PVMRM does not acc ount for the fact that the vertical extent of the plume based on the vertical dispersion coefficient may extend below ground for low-level plumes. This overestimation of the volume of the plume could contribute to overestimating

conversion to NO 2 . The PVMRM option has further lim itations for area source applications, especially for elongated ar ea sources that may be used to simula te road segments. In these cases, the lateral extent of the plume used in calcu lating the plume volume depends on the projected width of the area source, even if only a portion of the area source actually impacts a nearby receptor. This again would tend to overestimate the volume of the plume for purposes of determining the amount of ozone av ailable for conversion of NO to NO 2 , and would likely

overestimate ambient NO 2 concentrations. In light of thes e issues, a series of volume sources rather than elongated area sources is recommended for simulating NO 2 impacts from roadway emissions with PVMRM, especially for receptor s located relatively close to the roadway. Furthermore, the OLM option with OLMGROUP ALL was used to estimate NO 2 concentrations

from mobile source emissions modeled as area sour ces for the Atlanta area as part of the EPA’s8 Risk and Exposure Assessment (REA) for the most recent NO 2 NAAQS review (EPA, 2008). Results of model-to-monitor comparisons from the REA show generally good performance, suggesting that use of OLM with OLMGROUP A LL is appropriate for modeling such emissions.

TREATMENT OF INTERMITTENT EMISSIONS

Modeling of intermittent emission units, such as emergency generators, and/or intermittent emission scenarios, such as startup/ shutdown operations, has proven to be one of the main challenges for permit applicants undertaki ng a demonstration of compliance with the 1-

hour NO 2 NAAQS. Prior to promul gation of the new 1-hour NO 2 standard, the only NAAQS applicable for NO 2 was the annual standard and these in termittent emissions typically did not factor significantly into the m odeled design value for the annual standard. Sources often take a 500 hour/year permit limit on operation of emergency generators for purposes of determining the potential to emit (PTE), but may actually operate far fewer hours than the permitted limit in many cases and generally have not been requir ed to assume continuous operation of these intermittent emissions for purposes of demons trating compliance with the annual NAAQS. Due in part to the relatively low release heights typi cally associated with emergency generators, an assumption of continuous operation for these inte rmittent emissions would in many cases result in them becoming the controlling emission scenar io for determining compliance with the 1-hour

standard.

EPA’s guidance in Table 8-2 of Appendix W i nvolves a degree of conservatism in the modeling assumptions for demonstrating compli ance with the NAAQS by recommending the use of maximum allowable emissions, which represents emission levels that the facility could, and might reasonably be expected to, achieve if a PS D permit is granted. However, the intermittent

nature of the actual emissions associated with emergency generators and startup/shutdown in many cases, when coupled with the probabilistic form of the standard, could result in modeled impacts being significantly higher th an actual impacts would realisti cally be expected to be for these emission scenarios. The potential overestimation in these cases results from the implicit assumption that worst-case emissions will coincide with worst-case meteorological conditions based on the specific hours on specific days of ea ch of the years associated with the modeled design value based on the form of the hourly sta ndard. In fact, the probabilistic form of the standard is explicitly intended to provide a more stable metric for characterizing ambient air quality levels by mitigating the impact that outliers in the distribution might have on the design value. The February 9, 2010, preamble to the rule promulgating the new 1-hour NO 2 standard stated that “it is desirable from a public health perspective to have a form that is reasonably stable and insulated from the impacts of extrem e meteorological events.” 75 FR 6492. Also, the Clean Air Science Advisory Committee (CASAC) “recommended a 98 th -percentile form averaged over 3 years for such a standard, give n the potential for instability in the higher percentile concentrations around major roadways.” 75 FR 6493.

To illustrate the importance of this point, consider the following example. Under a deterministic 1-hour standard, where the modeled design value would be based on the highest of the second-highest hourly impacts (allowing one exceedance per year), a single emission episode lasting 2 hours for an emergency generator or other intermittent emission scenario could9 determine the modeled design value if that epis ode coincided with worst-case meteorological conditions. While the probability of a partic ular 2-hour emission epis ode actually coinciding with the worst-case meteorological conditions is relatively low, there is nonetheless a clear linkage between a specific emission episode and the modeled design value. By contrast, under

the form of the 1-hour NO 2 NAAQS only one hour from that emission episode could contribute to the modeled design value, i.e., the daily maximum 1-hour value. However, by assuming continuous operation of intermittent emissions the modeled design value for the 1-hour NO 2 NAAQS effectively assumes that the intermittent emission scenario occurs on the specific hours of the specific days for each of the specific year s of meteorological data included in the analysis which factor into the multiyear average of the 98 th -percentile of the annua l distribution of daily maximum 1-hour values. The probability of th e controlling emission episode occurring on this particular temporal schedule to determine the de sign value under the prob abilistic standard is significantly smaller than the pr obability of occurrence under the deterministic standard; thereby increasing the likelihood that impact estimate s based on assuming continuous emissions would significantly overestimate actual impacts for these sources.

Given the implications of the pr obabilistic form of the 1-hour NO 2 NAAQS discussed above, we are concerned that assuming continuou s operations for intermittent emissions would effectively impose an additional level of stri ngency beyond that intended by the level of the standard itself. As a result, we feel that it would be inappr opriate to implement the 1-hour NO 2 standard in such a manner and recommend that compliance demonstrations for the 1-hour NO 2 NAAQS be based on emission scenarios that ca n logically be assumed to be relatively continuous or which occur frequently enough to contribute significan tly to the annual distribution of daily maximum 1-hour concentr ations. EPA believes that existing modeling guidelines provide sufficient disc retion for reviewing authorities to exclude certain types of intermittent emissions from compliance demonstrations for the 1-hour NO 2 standard under these

circumstances.

EPA’s Guideline on Air Quality Models provides recommendations regarding air quality modeling techniques that should be applied in preparation or revi ew of PSD permit applications and serves as a “common measure of acceptabl e technical analysis when supported by sound scientific judgment.” 40 C.F.R. Part 51, Appendix W, section 1.0.a. While the guidance establishes principles that may be controlling in certain circum stances, the guideline is not “a strict modeling ‘cookbook’” so that, as the guide line notes, “case-by-case analysis and judgment are frequently required.” Secti on 1.0.c. In particular, with re spect to emissions input data, section 8.0.a. of Appendix W estab lishes the general principle that “the most appropriate data available should always be selected for us e in modeling analyses,” and emphasizes the importance of “the exercise of professional judgement by the appr opriate reviewi ng authority” in determining which nearby sources should be incl uded in the model emission inventory. Section

8.2.3.b.

For the reasons discussed above, EPA believe s the most appropriate data to use for compliance demonstrations for the 1-hour NO 2 NAAQS are those based on emissions scenarios that are continuous enough or frequent enough to contribute significan tly to the annual distribution of daily maximum 1-hour concentr ations. Section 8.1.1.b of the guideline also provides that “[t]he appropriate reviewing authority should be cons ulted to determine appropriate10 source definitions and for guidance concerning the determination of emissions from and techniques for modeling various source types.” When EPA is the reviewing authority for a permit, for the reasons described above, we w ill consider it acceptable to limit the emission scenarios included in the modeling comp liance demonstration for the 1-hour NO 2 NAAQS to

those emissions that are continuous enough or frequent enough to c ontribute signif icantly to the annual distribution of daily maximum 1-hour concentr ations. Consistent w ith this rationale, the language in Section 8.2.3.d of Appendix W states that “ [i]t is appropriate to model nearby sources only during those times when they, by th eir nature, operate at the same time as the primary source(s) being modeled. ” While we recognize that thes e intermittent emission sources

could operate at the same time as the primary source(s), the discussion above highlights the

additional level of conservatism in the modeled im pacts inherent in an as sumption that they do in

fact operate simultaneously and conti nuously with the primary source(s).

The rationale regarding treatment of interm ittent emissions applies for both project emissions and any nearby or other background s ources included in the modeling analysis. However, this rationale does not apply to the load analysis recommended in Table 8-2 of Appendix W, since various operating loads are not by design intended to be intermittent. Appendix W, Section 8.1.2.a. With respect to the operating level, for the proposed new or modified source, Table 8-2 calls for using “[d]esign capacity or federally enforceable permit condition.” With respect to nearby sources, the gu idelines call for estimating emissions based on “[a]ctual or design capacity (whichever is greate r), or federally enforceable permit condition.” Footnote 3 to the table notes that “[o]perating levels such as 50 percent and 75 percent of capacity should also be modeled to determine th e load causing the highest concentration.” The justification for not including certain intermittent operations described in this memo does not apply to these guidelines that ad dress analyzing the lo ad causing the highest concentration.

We recognize that case-specific issues and factors may arise th at affect the application of this guidance, and that not all facilities requi red to demonstrate compliance with the 1-hour NO 2 NAAQS will fit within the scenario described above with clearly defined continuous/normal operations vs. intermittent/infrequent emissions. Additional discretion may need to be exercised in such cases to ensure that public health is prot ected. For example, an intermittent source that is permitted to operate up to 500 hours per year, but typically operates much less than 500 hours per year and on a random schedule that cannot be controlled would be appr opriate to consider under this guidance. On the other hand, an “inter mittent” source that is permitted to operate only 365 hours per year, but is operated as part of a pro cess that typically occurs every day, would be less suitable for application of this guidance sinc e the single hour of emissions from each day could contribute signific antly to the modeled design value based on the annual distribution of daily maximum 1-hour concentrations. Similarl y, the frequency of startup/shutdown emission scenarios may vary significantly depending on the t ype of facility. For example, a large baseload power plant may experience startup/shutdo wn events on a relatively infrequent basis whereas as a peaking unit may go through much more frequent startup/shutdown cycles. It may be appropriate to apply this guidance in the former case, but not the latter.

Another aspect of intermitt ent emissions worth noting is the distinction between

intermittent emissions that can be scheduled wi th some degree of flexibility vs. intermittent emissions that cannot be scheduled. For exampl e, a portion of emissions from an emergency11 generator are likely to be associated with regular testing of the equipment that may be required to ensure its reliable operation, while that porti on of emergency generator emissions associated with actual emergency use typically cannot be scheduled. In this case it may be appropriate to include a permit condition that re stricts operation of the emerge ncy generator during testing to certain hours of the day, which may mitigate that source’s contribution to ambient NO 2 levels based on dispersion conditions. Limiting operation to specific time periods is an appropriate permit condition under Appendix W gui dance and would not constitu te a “dispersion technique” subject to Section 123 of the CAA. In this ca se the portion of the emissions associated with scheduled testing can be accounted for more r ealistically by limiting the hours modeled to account for meteorological conditions that are more representative of actual operations.

Another approach that may be considered in cases where there is more uncertainty regarding the applicability of this guidance would be to model impacts from intermittent emissions based on an average hourly rate, ra ther than the maximum hourly emission. For example, if a proposed permit includes a lim it of 500 hours/year or less for an emergency generator, a modeling analysis could be based on assuming conti nuous operation at the average hourly rate, i.e., the maximum hourly rate tim es 500/8760. This approach would account for potential worst-case meteorologi cal conditions associated with emergency generator emissions by assuming continuous operation, while use of the average hourly emission represents a simple approach to account for the probability of the emergency generator actually operating for a given

hour. Also note that the contribution of intermitte nt emissions to annual impacts should continue to be addressed as in the past to de monstrate compliance with the annual NO 2 standard.

A final point of clarification regarding intermittent emissions that deserves some emphasis is that the guidance provided here in relation to determining compliance with the 1-

hour NO 2 NAAQS through dispersion modeling has no effect on or relevance to the existing policies and guidance regarding excess emissions that may occu r during startup and shutdown, where such excess emissions violate applicable emission limitations 4 . In other words, all emissions from a new or modified source are subject to the applicable permitted emission limits and may be subject to enforcemen t action regarding such excess em issions, regardless of whether a portion of those emissions are not included in the modeling demonstration based on the guidance provided here.

Given the added complexity of the technical issues that arise in the context of demonstrating compliance with the 1-hour NO 2 NAAQS through dispersion modeling, we strongly encourage adherence to the recommen dations in Section 10.2.1. of Appendix W that

“ [e]very effort should be made by the Regional Offi ce to meet with all par ties involved in either a SIP revision or a PSD permit application prior to the start of any work on such a project. During this meeting, a protocol should be es tablished between the preparing and reviewing parties to define the procedures to be followed, the data to be co llected, the model to be used, and the analysis of the s ource and concentration data. ”

4 While excess emissions during malfunctions are also addressed in the policy related to excess emissions, Appendix W explicitly excludes emissions due to malfunction from the modeling analysis to demonstrate compliance with the NAAQS, unless the excess emissions are the result of poor maintenance, careless operation, or other preventable conditions. See Section 8.1.2.a, footnote a.12 DETERMINING BACKGROUND CONCENTRATIONS

Unless a facility can demonstrate that ambien t impacts associated from its emissions will not exceed the appropriate SIL, a cumulative anal ysis of ambient impacts will be necessary, and the determination of background concentrations to include in that cumulative impact assessment will be a critical component of the analysis . The June 29, 2010 memorandum addressed some aspects of this issue, but given the stringency of the new 1-hour NO 2 standard, the “margin for error” in this aspect of the analysis is much sma ller than it has been in the past. As a result, we believe it is necessary to provide additional clarification and a more detailed discussion of the factors associated with this aspect of the pe rmitting process. We hope that this additional discussion will serve to more clearly define some of the key steps and considerations in the process that could form the basis of a generic modeling protocol. We also provide suggestions regarding some of the documentation related to this component of the modeling analysis that may facilitate and expedite the review process.

The goal of the cumulative impact assessment should be to demonstrate with an adequate degree of confidence in the result that the proposed new or modifi ed emissions will not cause or significantly contribute to violations of the NAAQS. In ge neral, the more conservative the assumptions on which the cumulative analysis is based, the more confidence there will be that the goal has been achieved and the less controve rsial the review process will be from the perspective of the reviewing authority. As less conservative assumptions are implemented in the analysis, the more scrutiny those assumptions may require and the review process may tend to be lengthier and more controversial as a result. We expect that by providing a more detailed

discussion of the factors to be considered in the cumulative impact assessment, permit applicants and permitting authorities will be able to find th e proper balance of the competing factors that contribute to this analysis.

Identifying Nearby Sources to Include in Modeled Inventory

As noted in the June 29, 2010 memo, Sec tion 8.2.3 of Appendix W emphasizes the importance of professional judgment by the review ing authority in the id entification of nearby

and other sources to be included in the m odeled emission inventory, and establishes “a significant concentration gradient in the vicinity of the source” under consideration as the main

criterion for this selection. A ppendix W also suggests that “the number of such [nearby] sources is expected to be small except in unusual situ ations.” See Section 8.2.3.b . In light of this guidance, the June 29, 2010 memo cautioned against the literal and uncritical application of very prescriptive procedures for identifying which background sources should be included in the modeled emission inventory for NA AQS compliance demonstrations, such as those described in Chapter C, Section IV.C.1 of the draft New Source Review Workshop Manual (EPA, 1990). This caution should not be taken to imply that the procedures outlined in the NSR Workshop Manual are flawed or inappropriate in themselves. Cu mulative impact assessments based on following such procedures will generally be acceptable as the basis for permitting decisions, contingent on an appropriate accounting for the monitored cont ribution. Our main concern is that following such procedures in a literal and uncritical manner may in many cases result in cumulative impact assessments that are overly conservative a nd could unnecessarily co mplicate the permitting13 process in some cases. Such pro cedures might be characterized as being sufficient in most cases, but not always necessary to fulfill the requirements of a cumulative impact assessment.

A fundamental challenge in developing more detailed general guidance on the issue of determining background concentrations as part of a cumulative impact assessment is that the

factors that need to be consid ered are very case-specific in nature. These factors include foremost the nature of the source being permitted, including the source characteristics and local meteorological and topographical fa ctors that determine the spatial and temporal patterns of the source’s ambient impacts. The in itial significant impact assessme nt should serve to characterize these factors, and we w ould suggest the following:

1. As a standard practice contour plots of modeled concentrations should be prepared which clearly depict the imp act area of the source, prefer ably overlaid on a map of the area that identifies key geographical feat ures that may influence the dispersion patterns. The concentrati on contour plot also serves to visually depict the concentration gradients associated with the source’s impact.

2. We also recommend that the controlling meteorological conditi ons for the project impacts be identified as clearly as possibl e. The probabilistic form of the 1-hour NO 2 standard complicates this assessment so mewhat, but the recent update to the

AERMOD model includes new mo del output options (MAXDAILY and MXDYBYYR keywords) that identify the specific time periods on which the modeled design value is based.

3. As an aid to interpreting this informati on, we also suggest in cluding the location of the meteorological monitoring station used in the modeling analys is on the plot of source impacts, as well as a wind rose depicting general flow patterns.

If a cumulative impact assessment is require d due to the source’s impacts exceeding the interim SIL, the applicant will need to identify and acquire data on the two main components of the cumulative impact assessment, namely th e location and emissions from nearby background sources that may need to be included in th e modeled component of the cumulative ambient impact assessment, and the location and magn itude of air quality data from ambient NO 2 monitors located within the ar ea. Section 8.2.1.b of Appendix W states that “[t]ypically, air quality data should be used to es tablish background concentrations in the vicinity of the source(s)

under consideration.” Section 8.2.1.c further states that “[i]f the source is not isolated, it may be necessary to use a multi-source model to establish the impact of nearby sources.” While many applications will be required to include both mon itored and modeled contributions to adequately account for background concentrations in the cu mulative analysis, we believe that these statements imply a preference for use of ambi ent air quality data to account for background concentrations where possible.

Many of the challenges and more controversia l issues related to cumulative impact

assessments arise in the context of how best to combine a monito red and modeled contribution to account for background concentrations. Addressing these issues requires an assessment of the spatial and temporal representativeness of the background monitored concentrations for purposes of the cumulative impact assessment and the potential for double counting of impacts from modeled sources that may be contributing to the monitored concentrations. This assessment may14 involve significant technical deta ils which could complicate the revi ew process. Therefore, the more thoroughly and clearly thes e issues are documented the more efficient and effective the review process is likely to be.

A key point to remember when assessing th ese issues is their interconnectedness – the question of which nearby background sources should be included in the cumulative modeling analysis is inextricably linked w ith the question of what ambient monitoring data is available and what that data represents in relation to the a pplication. Furthermore, the question of how to appropriately combine monitored and modeled c oncentrations (temporal ly and spatially) to determine the cumulative impact depends on a clear understanding of what the ambient monitored data represents in relation to th e modeled emission inventory. A more detailed temporal pairing of monitored and modeled conc entrations may be acceptable in one case given the extent of the modeled emission inventory, while a more conservative assumption for combining monitored and modeled concentrations using high ranked monitored concentrations may be sufficient to justify a more limited m odeling inventory. As noted above, the stringency of the new standard may require a more detailed a nd refined analysis of these issues in order to demonstrate compliance with the standards than wa s necessary in the past, and these refinements will generally increase the burden on the applicant to adequately demonstrate that the net result of the analysis is protective of the standard. A detailed analysis and explanation of any potential bias to the net result introduced by proposed re finements will be importa nt to facilitate the review process. The issues associated with determining an appropriate method for combining

modeled and monitored contributions to a cumula tive impact assessment are discussed in more

detail in the next section.

Building on the geographical information recommended above for the initial SIL analysis, we suggest including the following documentation:

1. A geographical depiction of the location and magnitude of nearby emission sources, along with the location and magnitude of any ambient monitored data as part of the documentation submitted with a cumulative impact assessment.

2. Depicting the impact area and pattern of the project impacts on such a figure along

with a wind rose should be useful in asse ssing many of the issues touched on above, such as what nearby sources are likely to cause significant concentration gradients in the vicinity of the project source, or more specifically in the areas of high impacts associated with the project source. This figure should also help to identify what nearby source’s impacts are likely to be adequately represented in the available monitored data and the potential for double counting of impacts from modeled background sources if certain ambi ent background data are used.

3. In addition to a standard wind rose, pollu tion roses (i.e., a depiction of monitored pollutant concentrations as a function of wind direction a nd/or other meteorological factors) should also be useful for purposes of assessing the representativeness of the monitoring background concentrations in relation to the cumulative impact

assessment.15 Finally, we reiterate the impor tance of close coordination wi th the appropriate reviewing authority in the determination of nearby or ot her sources to include in the modeled emission inventory.

Significant Concentration Gradient Criterion

While Appendix W (Section 8.2.3.b) identifies “a significant concentr ation gradient in the vicinity of the source” as th e sole criterion in relation to determining which nearby sources should be explicitly modeled as part of the cumu lative impact assessment, little else has been written to explain what “significant” means in this context or even what the relevance of a “significant concentration gradient” is for this purpose. In fact, A ppendix W states that no attempt was made to “comprehensively define” the term, “owing to both the uniqueness of each modeling situation and the large number of variables involved in identifying nearby sources.” Section 8.2.3.b. Nothing has fundamentally changed to alter this characte rization, but given the issues and challenges arising from the implementation of the new 1-hour NO 2 standard, we feel compelled to offer some additional explanation regarding what this guidance means and how it should be applied.

One definition of the term “gradient” that applie s in this context is “t he rate of change of a physical quantity . . . with distance 5 .” In this case the physical quantity is the ground-level concentration of the pollutant bei ng assessed. The first point worth noting is that the gradient of the ground-level concentration has two dimensions , a longitudinal (along-wind) gradient and a lateral (cross-wind) grad ient. Appendix W makes no distincti on as to which gradient is more important or whether both gradients should be co nsidered. Before offering any suggestions on that question, it might be helpful to offer so me thoughts on the question of why a significant concentration gradient is mentione d as the sole criterion. Since an ambient monitor is limited to characterizing air quality at a fixed location, the impact from a near by source that causes a significant concentration gradient in the vicin ity of the project source is not likely to be characterized very well by the m onitored concentration in terms of its potential for contributing to the cumulative modeled design value due to th e high degree of variability of the source’s impact. In this sense both the longitudinal a nd lateral gradients could be of importance. However, since the location of impacts from a pa rticular source relative to other sources being

modeled or relative to the ambi ent monitor location is strongly influenced by the transport wind direction, relatively minor changes in wind direction can result in significant changes in modeled concentrations at a particular time and point in space, such as the monitor location. The longitudinal gradient will also vary as a result of changes in wind speed and atmospheric stability, but in general the impact of this longitu dinal variability on concentr ations at a particular time and point in space will be less significant than the variability associated with the lateral gradient. From this perspective it would appear that the lateral gradient may be more important to consider for purposes of assessing which back ground sources should be explicitly modeled.

Concentration gradients associated with a particular source will generally be largest

between the source location and the distance to the maximum gr ound-level concentrations from the source. Beyond the maximum impact distance, concentration gradients will generally be much smaller and more spatially uniform. A general “rule of t humb” for estimating the distance

5 Webster's New World College Dictionary, Copyright © 2010 by Wiley Publishing, Inc., Cleveland, Ohio.16 to maximum 1-hour impact and the region of sign ificant concentration gr adients that may apply in relatively flat terrain is approximately 10 ti mes the source release height. For example, the maximum impact area and region of significant concentration gr adients associated with a 100 meter stack in flat terrain would be approxima tely 1,000 meters downwind of the source, with some variation depending on the source characte ristics affecting plume rise. However, the potential influence of terrain on maximum 1-hour pollutant impacts may also significantly affect the location and magnitude of concentration gradient s associated with a particular source. Even accounting for some terrain influences on the location and gradients of maximum 1-hour concentrations, these consider ations suggest that the empha sis on determining which nearby

sources to include in the mode ling analysis should focus on the area within about 10 kilometers of the project location in most cases. The routine inclusion of all sources within 50 kilometers of the project location, the nominal distance for which AERMOD is a pplicable, is likely to produce an overly conservative result in most cases.

The relative importance of the lateral vs. th e longitudinal gradient will also depend on terrain effects and other factors, such as the at mospheric stability associated with worst-case impacts. The importance of the lateral gradie nt relative to the longitudinal gradient will generally increase for sources where maximum h ourly impacts occur under stable conditions due to the narrowness of the plume under such condi tions. The contour plots of modeled design values suggested above provide a method for examining concentrati on gradients more explicitly.

The AERSCREEN model should also serve as a us eful tool for identif ying the worst-case meteorological conditions for individual sources, as well as determining locations of maximum impact and areas of significant concentration gradients.

A final point to mention in relation to this topic is that the pattern of concentration gradients can vary significantly based on the av eraging period being assessed. In general, concentration gradients will be smaller and more spatially uniform for annual averages than for short-term averages, especially hourly averages. The spatial distribution of annual impacts around a source will typically have a single p eak “downwind” of the source based on the prevailing wind direction, except in cases wher e terrain or other geographical effects are important. By contrast, the spatial distributi on of peak hourly impacts will typically show several localized concentration peaks with more significant gradients. The number of peaks and the magnitude of the gradients will be somewhat smaller for modeled design values based on the

form of the 1-hour NO 2 standard than for overall peak hourly values, due to the smoothing effect of using a multiyear average of the 98 th -percentile from the annual distribution of daily maximum values. One implication of these di fferences between long-term and short-term concentration patterns is that the factors affecting which sour ces should be included in the modeled inventory and the method for combining modeled with monitored concentrations are more complex for the 1-hour NO 2 standard than for the annual standard.

While we hope this discussion provides some useful insight into this issue, we also caution against interpreting this guidance too lit erally or too narrowly, and emphasize that a “large number of variables” (A ppendix W, Section 8.2.3.b) are invol ved in this assessment.17 COMBINING MODELED RESULTS A ND MONITORED BACKGROUND TO

DETERMINE COMPLIANCE

One important aspect of the cumulative impact assessment that also deserves further discussion and entails new challenges with the 1-hour NO 2 NAAQS is the method for combining modeled concentrations with monitored backgro und concentrations to determine the cumulative ambient impact. The June 29, 2010 memo indicated that a “first tier” assumption for a uniform monitored background contribu tion that may be applied without fu rther justificati on is to add the overall highest hourly background NO 2 concentration (across the most recent three years) from a representative monitor to the modeled design value 6 for comparison to the NAAQS. Use of a single uniform monitored background contribution is the simplest approach to implement since it can be applied outside of the modeling system. We recognize that use of the overall highest hourly background concentration may be overl y conservative in many cases, but that conservatism also provided the basis for indicati ng that this approach could be used without further justification. As explained above, the more conservative the assumptions on which the cumulative analysis is based, the more confidence there will be that the goal of demonstrating

that the source will not cause or contribute to violations of the NAAQS has been achieved and the less controversial the review process will be fr om the perspective of the reviewing authority. The June 29, 2010 memo also indicated that additiona l refinements to this “first tier” approach based on some level of temporal pairing of mode led and monitored values may be considered on a case-by-case basis, with adequate justifica tion and documentation. Given the importance of this aspect of the analysis and the challenges th at have arisen in appli cation of the guidance to date, we feel compelled to offer a dditional guidance on this issue.

While the “first tier” assumption from th e June 29, 2010 memo of using a uniform monitored background contribu tions based on the overall highest hourly background NO 2 concentration should be acceptable without further ju stification in most cas es, we recognize that this approach could be overly conservative in many cases and may also be prone to reflecting source-oriented impacts from nearby sources, increasing the potential for double-counting of

modeled and monitored contributions. Based on these considerations, we believe that a less

conservative “first tier” for a uniform m onitored background contribution based on the monitored design value from a representative mo nitor should be acceptable in most cases. The

monitored NO 2 design value, i.e., the 98 th -percentile of the annual distribution of daily maximum 1-hour values averaged across the most recent three years of monitored data 7 , should be used irrespective of the mete orological data period used in the dispersion modeling. This somewhat

less conservative “first tier” for a uniform monitored backgr ound contribution retains the advantage of being relatively easy to implement.

6 The 1-hour NO 2 “modeled design value” refers to the highest (across all modeled receptors) of the 5-year average

of the 98 th -percentile (8 th -highest) of the annual distribution of daily maximum 1-hour values based on NWS meteorological data, or the multiyear average of the 98 th -percentile of the annual distribution of daily maximum 1- hour values based on one or more complete years (up to 5 years) of site-specific meteorological data. The1-hour

SO 2 “modeled design value” follows the same form except that the multiyear averages of the 99 th -percentile (4 th

highest) values are used.

7 The monitored design value for the 1-hour SO 2 standard is based on the 99 th -percentile of the annual distribution of daily maximum 1-hour values averaged across the most recent three years of monitored data.18 Depending on the circumstances of a particul ar application, use of a “first tier” assumption for a uniform monitored backgroun d contribution may represent a level of

conservatism that would obviate the need to include any bac kground sources in the modeled inventory if, for example, the number of nearby sources which could cont ribute to the cumulative impact is relatively few and the available ambien t monitor would be expected to reflect their cumulative impacts reasonably well or conservative ly in relation to the modeled design value based on the project emissions. At the othe r extreme, if the b ackground source inventory included in the modeling is complete enough a nd background levels due to mobile sources and/or minor sources that are not explicitly modeled is expected to be small, an analysis based solely on modeled emissions and no monitored background might be considered adequate for purposes of the cumulative impact assessment.

One of the important factors to consider in re lation to this issue is that the standard is based on the annual distribution of daily maxi mum 1-hour values, which implies that diurnal patterns of ambient impacts could play a signifi cant role in determining the most appropriate method for combining modeled and monitored c oncentrations. For example, if the daily maximum 1-hour impacts associated with the pr oject emissions generally occur under nighttime

stable conditions whereas maximum monitored c oncentrations occur during daytime convective conditions, pairing modeled and monitored concen trations based on hour of day should provide a more appropriate and less conservative estima te of cumulative impacts than a method that ignores this diurnal patt ern. This situation could occur for applications dominated by low-level sources and for elevated releases subject to pl ume impaction on nearby complex terrain. It is also important to cons ider the role of NO

x chemistry for applications using the Tier 3 options in AERMOD since diurnal patterns of background ozone concentrations may also factor into the diurnal patterns of modeled impacts. Give n the potential contribution of background ozone levels to the temporal variability of modele d impacts, the seasonal variability of background monitored values could also be important. Inco rporating a seasonal compone nt to the variability of background monitored concentr ations will also account for so me of the variability in meteorological conditions that may contribute to high hourly impacts.

Another situation where understa nding the temporal variability of modeled vs. monitored concentrations could be importa nt in determining the most a ppropriate method for combining

modeled and monitored concentrat ions is where contributions from mobile source emissions contribute significantly to either the monitore d background concentrations and/or the modeled concentrations. In these cases, diurnal variabil ity of emissions associated with morning and afternoon rush hours could contribute to the tempor al variability of ambient impacts in addition to meteorological factors associated with the dispersion and conversion of NO

x emissions. Since rush hours tend to be relatively fixed in terms of time of day and also occur near the transitions from nighttime stable to daytime convective c onditions, and vice versa, incorporating a seasonal or monthly element to the temporal variability should account for the va riable effect that dispersion conditions may have depending on wh ether rush hour occurs during stable or

convective hours.

With these general considerations in mind, we now examine the following guidance in

relation to the use of background monitored concentrations in a cumulative impact assessment, from Section 8.2.2 of Appendix W, which applies to applications for isolated sources and for the19 contribution of “other sources” consisting of “[t] hat portion of the background attributable to all other sources (e.g., natural sources, minor sources and distant major sources)” in a multi-source

area:

b. Use air quality data collected in the vicin ity of the source to determine the background concentration for the averaging times of concern. Determine the mean background concentration at each monitor by excluding values when the source in question is impacting the monitor. The mean annua l background is the average of the annual

concentrations so determined at each monitor. For shorter averaging periods, the meteorological conditions accompanying the concentrations of concern should be identified. Concentrations for meteorologi cal conditions of concern, at monitors not impacted by the source in question, should be averaged for each separate averaging time to determine the average background value. Monitoring sites inside a 90° sector downwind of the source may be used to determine the area of impact. One hour concentrations may be added and averaged to determine longer averaging periods.

c. If there are no monitors loca ted in the vicinity of the s ource, a ‘‘regional site’’ may be used to determine background. A ‘‘regional site’’ is one that is located away from the area of interest but is impacted by sim ilar natural and distant man-made sources.

The key principle in this guidan ce in relation to short-term aver aging periods is to determine background concentrations associated with “meteorological conditions accompanying the

concentrations of concern.” The concentrations thus determined “should be averaged for each separate averaging time to determin e the average background value.”

Based on this guidance, we believe that an appropriate methodology for incorporating background concentrations in the cumulative impact as sessment for the 1-hour NO 2 standard would be to use multiyear averages of the 98 th -percentile 8 of the available background concentrations by season and hour-of-day, exclud ing periods when the source in question is expected to impact the monitored concentration (w hich is only relevant for modified sources). For situations involving a significant mobile source component to the background monitored concentrations, inclusion of a da y-of-week component to the tem poral variability may also be appropriate. The rank associated with the 98 th -percentile of daily maximum 1-hour values should be generally consistent with the number of “samples” within that distribution for each combination based on the temporal resolution but also account for the number of samples “ignored” in specifying the 98 th -percentile based on the annual di stribution. For example, Table 1 in Section 5 of Appendix S specifi es the rank associated with the 98 th -percentile value based on the annual number of days with valid data. Sinc e the number of days per season will range from 90 to 92, Table 1 would indicate that the 2 nd -highest value from the seasonal distribution should be used to represent the 98 th -percentile. On the other hand use of the 2 nd -highest value for each season would effectively “ignore” only 4 values fo r the year rather than the 7 values “ignored” from the annual distribution. Balancing thes e considerations we recommend that background values by season and hour-of-day used in this context should be based on the 3 rd -highest value for each season and hour-of-day combination, whereas the 8 th -highest value should be used if values vary by hour-of-day only. For more deta iled temporal pairing, such as season by hour-of-

8 The 99 th -percentile should be used for the 1-hour SO 2 standard.20 day and day-of-week or month by hour-of-day, the 1 st -highest values from the distribution for each temporal combination should be used. 9

Figure 1 shows the background monitored conc entrations by season and hour-of-day for the Salt Lake City, UT monitor for the peri od 2005-2007 based on these recommendations. The values labeled “Average Winter”, “Average Sp ring”, etc. are the 3-year averages of the 3 rd

highest values by hour-of-day for each s eason; the values labeled “Average 98 th %” (the dashed line) are the 3-year average of the 8 th -highest values by hour-of-day only; and the values labeled “Overall Average” are the averages across all values by hour-of- day. These results illustrate the significant temporal variability captured by the multiyear averages of the 98 th -percentile values by season and hour-of-day. Also note that values for the 98 th -percentile by hour-of-day only show little variation by hour-of-day, while valu es by season and hour-of-day show significant diurnal variability for some seasons.

It should also be noted here that the c onventions regarding ob servation reporting timediffer between ambient air quality monitoring, where the observation time is based on the hourbeginning convention (EPA, 2009; see Section 3.20) , and meteorological monitoring where the observation is based on the hour-ending conventi on (EPA, 2000; see Section 7.1). Thus, ambient monitoring data reported for hour 00 should be paired with modele d/meteorological data for hour 01, etc. The recent update to the AERMOD mo del (dated 11059) provides an option (the BACKGRND keyword on the SO pathway) to include temporally-varying background concentrations within the cumulative impact asse ssment based on these temporal factors, similar

9 For 1-hour SO 2 analyses, use the 2 nd -highest value for each season and hour-of-day combination, or the 4 th -highest value for hour-of-day only. Use the 1 st -highest value for more detailed pairing, such as month by hour-of-day or season by hour-of-day and day-of-week. 0 25 50 75 100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23NO2Concentration (ppb)

Hour 

### Figure 1. Monitored Background Concentrations for Salt Lake City, UT Monitor

2005-2007 One-Hour NO 2 Concentrations

NAAQS

98th % Winter

98th % Spring

98th % Summer

98th % Fall

98th % Annual

Overall Average

1-hr DV21 to the options that have been available in pr evious versions of the model to vary source emissions using the EMISFACT ke yword. We believe that this technique provides a reasonable and efficient method for ensuring that the m onitored contribution to the cumulative impact assessment will be representative of the “meteorological conditions accompanying the

concentrations of concern” sin ce the monitored values will be te mporally paired with modeled concentrations based on temporal factors that are associated with meteorological variability, but

will also reflect worst-case mete orological conditions in a manner that is consistent with the probabilistic form of the 1-hour NO 2 standard. The use of multiyear-averaged monitored values for the meteorological conditions of concern is consistent with the language in Appendix W related to this issue, and al so consistent with the intent of using monitored background concentrations, which is to reflect the contribu tion from natural or regional levels of pollution

and the net contribution of minor emission sources which are not explicitly accounted for in the modeled inventory.

Since several applications have come to our attention proposing to combine monitored background and modeled concentrations on an hour-by-hour basis, us ing hourly monitored background data collected concurrently with th e meteorological data period being processed by the model, we feel compelled to include a di scussion of the potential merits and concerns regarding such an approach. On the surface this approach could be perceived as being a more

“refined” method than what is recommended above , and therefore more appropriate. However, the implicit assumption underlying this approach is that the background monitored levels for each hour are spatially uniform and that the m onitored values are fully representative of background levels at each receptor for each hour. Such an assumption cl early ignores the many

factors that contribute to the te mporal and spatial variability of ambient concentrations across a typical modeling domain on an hourly basis. Ther efore we do not recommend such an approach except in rare cases of relatively isolated sources where the availa ble monitor can be shown to be representative of the ambient co ncentration levels in the area s of maximum impact from the proposed new source. Another situation where such an approach may be justified is where the modeled emission inventory clearly represents the majority of em issions that could potentially contribute to the cumulative impact assessm ent and where inclusion of the monitored background concentration is intende d to conservatively represent the potential contribution from minor sources and natural or regional background le vels not reflected in the modeled inventory. In this case, the key aspect which may ju stify the hour-by-hour pa iring of modeled and

monitored values is a demonstr ation of the overall conservatism of the cumulative assessment

based on the combination of modeled and monitored impacts. Except in rare cases of relatively isolated sources, a single ambient monitor, or even a few monitors, will not be adequately representative of hourly concentrations across the modeled domain to preclude the need to include emissions from nearby backgr ound sources in the modeled inventory.

REFERENCES

Cimorelli, A. J., S. G. Perry, A. Venkatram, J. C. Weil, R. J. Paine, R. B. Wilson, R. F. Lee, W. D. Peters, R. W. Brode, and J. O. Paumier, 2004. AERMOD: Description of Model Formulation with Addendum, EPA-454/R-03-004. U.S. E nvironmental Protection Agency, Research Triangle Park, NC.22 EPA, 1988. Air Quality Analysis for Prevention of Significant Dete rioration (PSD). Gerald A. Emison memorandum, dated July 5, 1988. U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA, 1990. New Source Review Workshop Manual: Prevention of Significan t Deterioration and Nonattainment Area Permitting – DRAFT. U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA, 2000. Meteorological Monitoring Guidan ce for Regulatory Modeling Applications.

EPA-454/R-99-005. U.S. Environmental Protecti on Agency, Research Triangle Park, NC.

EPA, 2004. User's Guide for the AMS/EP A Regulatory Model – AERMOD. EPA-454/B-03- 001. U.S. Environmental Protection Agen cy, Research Triangle Park, NC.

EPA, 2008. Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient Air Quality Standard. EPA-452/R-08-0 08a. U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA, 2009. AQS Data Dictiona ry. Version 2.21. U.S. Envir onmental Protection Agency, Research Triangle Park, NC.

EPA, 2010a. Applicability of the Federal Pr evention of Significant Deterioration Permit Requirements to New and Revised National Ambien t Air Quality Standards. Stephen D. Page Memorandum, dated April 1, 2010. U.S. Environm ental Protection Agency, Research Triangle Park, NC.

EPA, 2010b. Addendum – User's Guide for the AMS/EPA Regulatory Model – AERMOD.

EPA-454/B-03-001. U.S. Environmental Protecti on Agency, Research Triangle Park, NC.

Hanrahan, P.L., 1999. The Plume Volume Molar Ratio Method for Determining NO 2 /NO

x Ratios in Modeling – Part II: Evaluation Studies. J. Air & Waste Manage. Assoc.

, 49 , 1332-1338.

Janssen, L.M.J.M., F. Van Haren, P. Bange, and H. Van Duuren, 1991. Measurements and modelling of reactions of nitrogen oxides in power-plant plumes at night. Atmos. Env.

, 25A , No. 5/6, 829-840. MACTEC, 2005. Evaluation of Bias in AERM OD-PVMRM. Final Report, Alaska DEC Contract No. 18-9010-12. MACTEC Federal Progr ams, Inc., Research Triangle Park, NC.

Wang, Y.J., A. DenBleyker, E. McDonald-Bulle r, D. Allen and K. Zhang, 2011. Modeling the chemical evolution of nitrogen oxides near roadways. Atmos. Env.

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cc: Richard Wayland, C304-02 Scott Mathias, C504-01 Lydia Wegman, C504-0223 Raj Rao, C504-01 Roger Brode, C439-01 Dan deRoeck, C504-03 Elliot Zenick, OGC Brian Doster, OGC Melina Williams, OGC EPA Regional M odeling ContactsA-1 ATTACHMENT A

Summary of AERMOD Model Performance for 1-hour NO2 Concentrations

As noted in the June 29, 2010 memo, limited evaluations of the Plume Volume Molar Ratio Method (PVMRM) for estim ating conversion of NO to NO 2 have been completed which show encouraging results, but the amount of data currently available is too limited to justify a designation of PVMRM as a refined method for NO 2 (Hanrahan, 1999; MACTEC, 2005). The original evaluations of PVMRM also focused on model performance for annual averages since the only NO 2 standard in effect at the time was annual. These evaluations have recently been updated to reflect the current AERMOD modeling system components and extended to examine model performance for hourly NO 2 concentrations and to include the Ozone Limiting Method (OLM). Preliminary results from these recent ev aluations are presented below in the form of QQ plots of ranked hourly NO 2 concentrations for the two moni tors included in the New Mexico Empire Abo field study and for the single monito r included in the Palaau, HI field study.

Evaluation results are also summarized in the form of predicted vs. observed 1-hour Robust Highest Concentrations (RHC), a model evaluation metric that represents an exponential tail fit to the top 26 ranked values in the distribution of hourly concen trations. Note that the OLM results presented here inco rporate an equilibrium NO 2 /NO

x ratio of 0.90, consistent with the PVMRM option.

Figures A-1 and A-2 show results in the form of hourly Q-Q plots for the North monitor and the South monitor, respectively, from the Ne w Mexico field study based on the Tier 1 option

of full conversion of NO to NO 2 , the OLM option applied on a s ource-by-source basis, the OLM option applied using OLMGROUP ALL (OLMGR P), as recommended in the June 29, 2010,

NO 2 clarification memorandum, and the PVMRM opti on. The New Mexico results clearly show

the conservatism associated with the Tier 1 assumption of full conversion and the OLM option on a source-by-source basis, with both options sh owing a significant bias to overpredict hourly

NO 2 concentrations. The OLMGRP option exhibits the best pe rformance for both New Mexico monitors, with nearly unbiased results for the No rth monitor and a slight bias to overpredict for the South monitor. The PVMRM option show s significantly better pe rformance than full conversion or source-by-source OLM for both m onitors, but not as goo d performance as the OLMGRP option.

Figure A-3 shows the hourly Q-Q plot for Pa laau based on the same range of options shown in Figures A-1 and A-2. Similar to the New Mexico results, the Tier 1 option of full conversion and the OLM option applied on a source- by-source basis show a significant bias to overpredict hourly NO 2 concentrations at Palaau. The PVMRM option shows the best performance for this field study with very good agreement between predicted and observed concentrations. The use of the OLMGRP op tion clearly improves model performance as compared to application of the OLM option on a source-by-source basis, w ith the peak predicted concentrations within a factor of 2 higher th an observed. These Q-Q plot comparisons are consistent with the comparisons of RHCs summarized in Table A-1, where the average (geometric mean) ratios of Predicted/Observ ed RHCs for PVMRM and OLMGRP are about 1.5 and 1.2, respectively, and the average RHC ratios for OLMGRP and FULL conversion are much higher at 4.5 and 5.0.A-2 Since these Tier 3 options in AERMOD are intended to estimate the conversion of

ambient NO to NO 2 , it is also useful to compare the modeled vs. observed NO 2 /NO

x ratios since offsetting errors in dispersion vs. conversion co uld mask poor model performance. Table A-2 summarizes the observed vs. predicted NO 2 /NO

x ratios for the three monitors included in these

Palaau and New Mexico field stud ies. These results are generall y consistent with the hourly Q-Q plots of NO 2 concentrations, and clearly indicate th at the OLM option on a source-by-source basis significantly overestimates the conversion of NO to NO 2 . However, results for the New Mexico South monitor are interesting in that the PVMRM option shows much better agreement

with observed NO 2 /NO

x ratios than the OLMGRP option, whereas the OLMGRP option indicates better performance th an PVMRM in terms of hourly NO 2 concentrations.

These preliminary model eval uation results of hourly NO 2 predictions for Palaau and New Mexico show generally good performance for the PVMRM and OLMGROUP ALL options in AERMOD; however, it should be emphasized that these results are very limited in terms of the number of monitors. Although the scope of the field study data is limited, this level of model performance on a paired-in-space basis is impr essive, especially for the PVMRM option at Palaau and for the OLMGROUP ALL option for th e North monitor at New Mexico. We believe

that these additional model evaluation results le nd further credence to the use of these Tier 3 options in AERMOD for estimating hourly NO 2 concentrations and to the recommendation to use the OLMGROUP ALL option whenever OLM is applied.A-3 10 100 1000

10 100 1000Predicted Conc (μg/m3)

Observed Conc (μg/m 3 ) Figure A-1. AERMOD Model Evaluation - New Mexico North Monitor - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRM

10 100 1000

10 100 1000Predicted Conc (μg/m3)

Observed Conc (μg/m 3 ) Figure A-2. AERMOD Model Evaluation - New Mexico South Monitor - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRMA-4 Table A-1. 1-hour NO 2 Robust Highest Concentrations (μg/m 3 )

Observed PVMRM OLMGRP OLM FULL

New Mexico Abo

North Monitor RHC 117.87 116.26 108.38 444.87 449.24

New Mexico Abo

South Monitor RHC 70.10 218.98 104.81 440.96 454.68

Hawaii Palaau

Monitor RHC 95.42 101.57 113.18 368.57 480.38

Geometric Mean

Pred/Obs RHC --- 1.486 1.177 4.510 4.993

Table A-2. Average Unpaired NO 2 /NO

x Ratios for Monitored Values of NO

x > 20 ppb

Monitored

NO 2 /NO

x PVMRM

NO 2 /NO

x OLMGRP

NO 2 /NO

x OLM

NO 2 /NO

x New Mexico Abo North Monitor (n=772) 0.455 0.377 0.669 0.976

New Mexico Abo South Monitor (n=262) 0.363 0.437 0.491 0.950

Hawaii Palaau Monitor (n=672) 0.138 0.163 0.376 0.854

Geometric Mean

Pred/Obs Ratios --- 1.056 1.756 3.263 1 10 100 1000

1 10 100 1000Predicted Conc (μg/m3)

Observed Conc (μg/m 3 ) Figure A-3. AERMOD Model Evaluation - Palaau, HI - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRM