UWED STATES ENVIWM3PfAl PRO=- AQENCY 

RESEARCH TRIANGLE PARK, NC 2771 1 

-ICE OF 

MRQUALrrYAND STMEMORANDUM 

SUBJECT : Additional Clarification Regarding Application of Appendix W Modeling 

Guidance for the 1 -hour NOz National Ambient Air Quality St and& 

FROM: 

TO: 

b$LJ--+- Tyler Fox, Leader 

Air Quality Modeling ~roup, C439-01 

Regional Air Division Directors 

INTRODUCTION 

On January 22, 201 0, EPA announced a new 1 -hour nitrogen dioxide (Na) National 

Ambient Air Quality Standard (1 -hour Na NAAQS or 1 -how NO2 standard) that is attained 

when the 3-year average of the 98th-percentile of the annual distribution of daily maximum 

1 -hour concentrations does not exceed 1 00 ppb at each monitor within an area. The final rule for 

the new 1 -bur N& NAAQS was published in the Federal Register on February 9,201 0 (75 FR 

6474-65371, and the standard became effective on April 12,2010 (EPA, 2010a). A 

memorandum was issued on June 29, 2010, clarifying the applicability of current guidance in the 

Guideline oa Air Quality Models (40 CFR Part 5 1, Appendix W) for modeling Na impacts in 

accordance with the Prevent ion of Significant Deterioration (PSD) permit requirements to 

demonstrate compliance with the new 1 -hour NO2 standard. 

This memorandum supplements the June 29,20 1 0 guidance memo by providing further 

clarification and guidance on the application of Appendix W guidance for the 1 -hour Na 

standard. Note that while the discussion of NO, chemistry options in this memo is exclusive to 

the 1 -hour N& standard, the discussion of other topics in this memo should apply equally to the 

1 -hour SOz standard, accounting for the slight differences in the form of the 1 -hour NOz and SO2 

standards I. In summary, the memo: 

1. Clarifies procedures for demonstrating compliance with the f -hour N@ NAAQS 

based on the form of the standard, including significant contrriution analyses using 

the interim Significant Impact Level (SIL) established in the June 29,201 0 memo, 

I The 1 -hour N02 standard is hwl on the 98m-percentile (8'-highest) of the annual distribution of maximum daily 

1 -hour values, whereas the 1 -hour SO2 standard is based on the 99'-prcmtile (4'b-highest) of the annual distribution 

of maximum daily 1 -hour values. 
2 

and details updates to the AERMOD model with an internal post-processor option 

that supports such analyses. 

2. Provides clarification on the use and acceptance of Tier 2 and Tier 3 options for NO2, 

including updated model evaluation results for the OLM and PVMRM options 

incorporated in the AERMOD model. 

3. Recommends that compliance demonstrations for the 1-hour NO2 NAAQS address 

emission scenarios that can logically be assumed to be relatively continuous or which 

occur frequently enough to contribute significantly 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/shutdown operations from 

compliance demonstrations for the 1-hour NO2 standard under appropriate 

circumstances. 

4. Provides additional clarification and a more detailed discussion of the factors to 

consider in determination of background concentrations as part of a cumulative 

impact assessment including identification of nearby sources to be explicitly 

modeled. 

5. Recommends an appropriate methodology for incorporating background 

concentrations in the cumulative impact assessment for the 1-hour NO2 standard and 

details updates to the AERMOD model with an option to include temporally-varying 

background concentrations within the modeling analysis. 

PROCEDURES FOR DEMONSTRATING COMPLIANCE WITH 1-HOUR NO2 NAAQS 

Compliance with the 1-hour NO2 NAAQS is based on the multiyear average of the 98thpercentile of the annual distribution of daily maximum 1-hour values not exceeding 100 ppb. 

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

the 98th-percentile1

. The AERMOD dispersion model, EPA’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 NO2 NAAQS, as well as other analyses that may be needed in order to 

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

interim SIL established in the June 29, 2010, memorandum. 

Application of Interim SIL to Project Impacts

Using the interim 1-hour NO2 SIL, a permit applicant can determine: (1) whether, based 

on the proposed increase in NOx emissions, a cumulative air quality analysis is required; (2) the 

area of impact within which a cumulative air quality analysis should focus; and (3) whether the 

proposed source’s NOx emissions will contribute to any modeled violation of the 1-hour NO2

NAAQS identified in the cumulative analysis. 

To determine initially whether a proposed project’s emissions increase will have a 

significant impact (resulting in the need for a cumulative impact assessment), the June 29, 2010, 

memorandum recommended that the interim SIL should be compared to either of the following: 
3 

• The highest of the 5-year averages of the maximum modeled 1-hour NO2

concentrations predicted each year at each receptor, based on 5 years of National 

Weather Service data; or 

• The highest modeled 1-hour NO2 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 NO2 concentrations predicted each year at 

each 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 receptor. 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 modeled violations of the 1-hour NO2 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 violations of the NAAQS. While not common 

practice in the past, given the more complex analysis procedures associated with the form of the 

1-hour NO2 NAAQS, we deem it appropriate and acceptable in most cases to limit the 

cumulative impact analysis to only those receptors 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 determine all areas of ambient air where the source 

could contribute significantly to modeled violations. This may especially be appropriate for the 

1-hour NO2 standard since the initial modeling of the project emissions without other 

background emission sources may have a tendency to overestimate ambient NO2 concentrations, 

even under Tier 3 applications, by understating the potential ozone limiting influence of the 

background NOx emissions. If modeled violations of the NAAQS are found based on the 

cumulative impact assessment, then the project’s contribution to all modeled 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 indicated that the significant contribution 

analysis should be based on a source’s contribution to the modeled violation paired in time and 

space. The form of the 1-hour NO2 NAAQS complicates this analysis since the modeled 

violation is based on a multiyear 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 meteorological 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 98th-percentile cumulative air quality impact at the receptor, but rather 

must examine all cases where the cumulative impact exceeds the NAAQS at or below the 98th-
4 

percentile. In some cases a source’s contribution to the 98th-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 below the SIL, while the source’s contribution to 

cumulative impacts below the 98th-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 8th-highest (98th-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 2nd, 3rd, 4th-highest 1-hour values during the day, and so on, are not considered in this 

analysis. In addition, for applications with more than one year of meteorological data, the 

significant contribution analysis should only examine ranks paired across the years, i.e., the 

multiyear average of the Nth-highest values across each of the years processed. The recent update 

to the AERMOD model (dated 11059) includes an option (the MAXDCONT keyword) to 

automatically perform this contribution analysis (EPA, 2010b), examining 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 internal post-processor within the model. Other 

options are available in the recent AERMOD update 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 NO2 standard3, 

to modeling applications to demonstrate compliance with the NAAQS, namely the use of 3 years 

of ambient monitoring data as the basis for attainment of the NAAQS using monitoring vs. the 

use of 5 years of meteorological data for modeling demonstrations of compliance with the 

NAAQS. Specifically, the June 29, 2010 memo indicated that “Although the monitored design 

value for the 1-hour NO2 standard is defined in terms of the 3-year average, this definition does 

not preempt or alter the Appendix W requirement for use of 5 years of NWS meteorological data 

or at least 1 year of site specific data. The 5-year average based on use of NWS data, or an 

average across one or more years of available site specific data, serves as an unbiased estimate 

of the 3-year average for purposes of modeling demonstrations of compliance with the NAAQS. 

Modeling of ‘rolling 3-year averages,’ using years 1 through 3, years 2 through 4, and years 3 

through 5, is not required.” 

We would also like to emphasize that other aspects of the ambient monitoring 

requirements for the 1-hour NO2 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 NO2 concentrations, procedures for handling missing 

data periods, and conventions for rounding of monitored 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 sampling 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 SO2 standard the analysis should begin with the 4th-highest, or 99th-percentile value. 3

 Appendix T to 40 CFR Part 50 addresses ambient monitoring requirements for the 1-hour SO2 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 meteorological data which specifically addresses the 

needs of dispersion modeling, including procedures that are explicitly implemented within the 

meteorological processor and dispersion model to account for missing data due to calm winds or 

other factors. Adjustments to the calculation procedures for determining the modeled design 

value for comparison to the NAAQS based on Appendix 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 recognize here is that ambient monitoring 

requirements/procedures and dispersion modeling guidance/procedures address 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 NO2

Given the stringency of the 1-hour NO2 standard relative to the 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 application of these less conservative assumptions for 

estimating ambient NO2 impacts, relative to the Tier 1 option of full conversion, and clarified the 

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

within the AERMOD model. 

In order to ease the burden on permit applicants in addressing the need to demonstrate 

compliance with the 1-hour NO2 NAAQS, as well as the burden on the permitting authority in 

reviewing such applications, we offer additional discussion and recommendations 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 NO2 standard under Tier 2 

without additional justification by applicants; and 

• General acceptance of 0.50 as a default in-stack ratio of NO2/NOx 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 have 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 further credence to the use of these Tier 3 

options for 1-hour NO2 compliance demonstrations. We anticipate that these recommendations 

and updated model evaluations will simplify and facilitate 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-NO2 Conversion

Regarding the Tier 2 option of applying an ambient 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 NO2 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 several references cite ambient ratios of about 0.80 for 

hourly NO2/NOx (e.g., Wang, et al., 2011; Janssen, et al., 1991), and we believe it would be 

appropriate to accept that as a default ambient ratio for the 1-hour NO2 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 and hourly basis, irrespective of the predicted 

hourly NOx concentration, whereas the Tier 2 ARM of 0.80 would be applied to the maximum 

cumulative hourly NOx concentration. 

Tier 3 Options for NO-to-NO2 Conversion

The June 29, 2010 memo clarified that the OLM and PVMRM options in the AERMOD 

model should be considered as Tier 3 applications under Section 5.2.4 of Appendix W. Also, 

since the OLM and PVMRM methods are currently implemented as non-regulatory-default 

options within the AERMOD dispersion model (Cimorelli, et al., 2004; EPA, 2004; EPA, 

2010b), their use requires justification 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 inputs for both the OLM and PVMRM options in 

the context of the 1-hour NO2 standard, namely the in-stack ratios of NO2/NOx emissions and 

background ozone concentrations. This section provides additional discussion of these key 

inputs for OLM and PVMRM and also clarifies the similarities and differences between these 

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

hour NO2 standard. 

As noted in the June 29, 2010 memo, limited 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 NO2 (Hanrahan, 1999; 

MACTEC, 2005). Furthermore, the original evaluations focused on model performance for 

annual averages since the only NO2 standard in effect at the time was annual. We have recently 

updated the evaluations to reflect the current AERMOD modeling system components and 

extended them to examine model performance for hourly NO2 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 performance, these preliminary results of hourly NO2

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 NO2 concentrations, and we recommend that their use should be generally 
7 

accepted provided some reasonable demonstration can be made of the appropriateness of the key 

inputs for these options, the in-stack NO2/NOx ratio and the background ozone concentrations. 

Although well-documented data on in-stack NO2/NOx ratios is still limited for many source 

categories, we also feel that it would be appropriate in the absence of such source-specific instack data to adopt a default in-stack ratio of 0.5 as being adequately 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 reasonable upper bound based on the available in-stack data. We 

hope that over time the range of source categories for which in-stack ratio information is 

available increases and the quality of such information will improve. 

These preliminary model evaluation results 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 NO2 concentrations. The June 29, 

2010 memo indicated that both PVMRM 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 titration, the interaction of NO with ambient ozone 

(O3) to form NO2 and O2. The main distinction between PVMRM and OLM is the approach 

taken to estimate the ambient concentrations of NO and O3 for which the ozone titration 

mechanism should be applied. For isolated elevated point sources, the PVMRM option does 

represent a more refined treatment of ozone titration since it estimates the NO and O3 available 

for conversion based on simulating the actual volume of the instantaneous plume as it is 

transported downwind. As a result, this method will generally provide a more realistic 

simulation of the NO-to-NO2 conversion rate along the path of the plume for a particular source, 

accounting for the influence of meteorological conditions on the entrainment of O3 associated 

with growth of the plume. However, the algorithm 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 model evaluation results for New Mexico, PVMRM 

may not always provide a “better” answer than the OLM option. 

The PVMRM algorithm as currently implemented may also have a tendency to 

overestimate the conversion of NO to NO2 for low-level plumes by overstating the amount of 

ozone available for the conversion due to the manner in which the plume volume is calculated. 

The plume volume calculation in PVMRM does not account 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 NO2. The PVMRM option has further limitations for area source applications, 

especially for elongated area sources that may be used to simulate road segments. In these cases, 

the lateral extent of the plume used in calculating 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 available for conversion of NO to NO2, and would likely 

overestimate ambient NO2 concentrations. In light of these issues, a series of volume sources 

rather than elongated area sources is recommended for simulating NO2 impacts from roadway 

emissions with PVMRM, especially for receptors located relatively close to the roadway. 

Furthermore, the OLM option with OLMGROUP ALL was used to estimate NO2 concentrations 

from mobile source emissions modeled as area sources for the Atlanta area as part of the EPA’s 
8 

Risk and Exposure Assessment (REA) for the most recent NO2 NAAQS review (EPA, 2008). 

Results of model-to-monitor comparisons from the REA show generally good performance, 

suggesting that use of OLM with OLMGROUP ALL 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 undertaking a demonstration of compliance with the 1-

hour NO2 NAAQS. Prior to promulgation of the new 1-hour NO2 standard, the only NAAQS 

applicable for NO2 was the annual standard and these intermittent emissions typically did not 

factor significantly into the modeled 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 required to assume continuous operation of these 

intermittent emissions for purposes of demonstrating compliance with the annual NAAQS. Due 

in part to the relatively low release heights typically associated with emergency generators, an 

assumption of continuous operation for these intermittent emissions would in many cases result 

in them becoming the controlling emission scenario for determining compliance with the 1-hour 

standard. 

EPA’s guidance in Table 8-2 of Appendix W involves a degree of conservatism in the 

modeling assumptions for demonstrating compliance 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 PSD 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 than actual impacts would realistically 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 each of the years associated with the modeled 

design value based on the form of the hourly standard. 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 NO2 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 extreme meteorological events.” 75 FR 6492. Also, the 

Clean Air Science Advisory Committee (CASAC) “recommended a 98th-percentile form 

averaged over 3 years for such a standard, given 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 could 
9 

determine the modeled design value if that episode coincided with worst-case meteorological 

conditions. While the probability of a particular 2-hour emission episode 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 NO2 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 NO2

NAAQS effectively assumes that the intermittent emission scenario occurs on the specific hours 

of the specific days for each of the specific years of meteorological data included in the analysis 

which factor into the multiyear average of the 98th-percentile of the annual distribution of daily 

maximum 1-hour values. The probability of the controlling emission episode occurring on this 

particular temporal schedule to determine the design value under the probabilistic standard is 

significantly smaller than the probability of occurrence under the deterministic standard; thereby 

increasing the likelihood that impact estimates based on assuming continuous emissions would 

significantly overestimate actual impacts for these sources. 

Given the implications of the probabilistic form of the 1-hour NO2 NAAQS discussed 

above, we are concerned that assuming continuous operations for intermittent emissions would 

effectively impose an additional level of stringency beyond that intended by the level of the 

standard itself. As a result, we feel that it would be inappropriate to implement the 1-hour NO2

standard in such a manner and recommend that compliance demonstrations for the 1-hour NO2

NAAQS be based on emission scenarios that can logically be assumed to be relatively 

continuous or which occur frequently enough to contribute significantly to the annual 

distribution of daily maximum 1-hour concentrations. EPA believes that existing modeling 

guidelines provide sufficient discretion for reviewing authorities to exclude certain types of 

intermittent emissions from compliance demonstrations for the 1-hour NO2 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 review of PSD permit applications 

and serves as a “common measure of acceptable 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 circumstances, the guideline is not “a 

strict modeling ‘cookbook’” so that, as the guideline notes, “case-by-case analysis and judgment 

are frequently required.” Section 1.0.c. In particular, with respect to emissions input data, 

section 8.0.a. of Appendix W establishes the general principle that “the most appropriate data 

available should always be selected for use in modeling analyses,” and emphasizes the 

importance of “the exercise of professional judgement by the appropriate reviewing authority” in 

determining which nearby sources should be included in the model emission inventory. Section 

8.2.3.b. 

For the reasons discussed above, EPA believes the most appropriate data to use for 

compliance demonstrations for the 1-hour NO2 NAAQS are those based on emissions scenarios 

that are continuous enough or frequent enough to contribute significantly to the annual 

distribution of daily maximum 1-hour concentrations. Section 8.1.1.b of the guideline also 

provides that “[t]he appropriate reviewing authority should be consulted to determine appropriate 
10 

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 will consider it acceptable to limit the emission 

scenarios included in the modeling compliance demonstration for the 1-hour NO2 NAAQS to 

those emissions that are continuous enough or frequent enough to contribute significantly to the 

annual distribution of daily maximum 1-hour concentrations. Consistent with 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 their nature, operate at the same time as the 

primary source(s) being modeled.” While we recognize that these 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 impacts inherent in an assumption that they do in 

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

The rationale regarding treatment of intermittent emissions applies for both project 

emissions and any nearby or other background sources 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 guidelines call for estimating emissions based on 

“[a]ctual or design capacity (whichever is greater), 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 the load causing the highest concentration.” The 

justification for not including certain intermittent operations described in this memo does not 

apply to these guidelines that address analyzing the load causing the highest concentration. 

We recognize that case-specific issues and factors may arise that affect the application of 

this guidance, and that not all facilities required to demonstrate compliance with the 1-hour NO2

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 protected. 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 appropriate to consider 

under this guidance. On the other hand, an “intermittent” source that is permitted to operate only 

365 hours per year, but is operated as part of a process that typically occurs every day, would be 

less suitable for application of this guidance since the single hour of emissions from each day 

could contribute significantly to the modeled design value based on the annual distribution of 

daily maximum 1-hour concentrations. Similarly, the frequency of startup/shutdown emission 

scenarios may vary significantly depending on the type of facility. For example, a large baseload power plant may experience startup/shutdown 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 intermittent emissions worth noting is the distinction between 

intermittent emissions that can be scheduled with some degree of flexibility vs. intermittent 

emissions that cannot be scheduled. For example, a portion of emissions from an emergency 
11 

generator are likely to be associated with regular testing of the equipment that may be required to 

ensure its reliable operation, while that portion 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 restricts operation of the emergency generator during testing to 

certain hours of the day, which may mitigate that source’s contribution to ambient NO2 levels 

based on dispersion conditions. Limiting operation to specific time periods is an appropriate 

permit condition under Appendix W guidance and would not constitute a “dispersion technique” 

subject to Section 123 of the CAA. In this case the portion of the emissions associated with 

scheduled testing can be accounted for more realistically 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, rather than the maximum hourly emission. For 

example, if a proposed permit includes a limit of 500 hours/year or less for an emergency 

generator, a modeling analysis could be based on assuming continuous operation at the average 

hourly rate, i.e., the maximum hourly rate times 500/8760. This approach would account for 

potential worst-case meteorological 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 intermittent emissions to annual impacts should continue 

to be addressed as in the past to demonstrate compliance with the annual NO2 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 NO2 NAAQS through dispersion modeling has no effect on or relevance to the existing 

policies and guidance regarding excess emissions that may occur during startup and shutdown, 

where such excess emissions violate applicable emission limitations4. In other words, all 

emissions from a new or modified source are subject to the applicable permitted emission limits 

and may be subject to enforcement action regarding such excess emissions, 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 NO2 NAAQS through dispersion modeling, we 

strongly encourage adherence to the recommendations in Section 10.2.1. of Appendix W that 

“[e]very effort should be made by the Regional Office to meet with all parties 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 established between the preparing and reviewing 

parties to define the procedures to be followed, the data to be collected, the model to be used, 

and the analysis of the source 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 ambient impacts associated from its emissions will 

not exceed the appropriate SIL, a cumulative analysis 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 NO2 standard, the “margin for 

error” in this aspect of the analysis is much smaller 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 permitting 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 modified emissions will not cause or 

significantly contribute to violations of the NAAQS. In general, 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 controversial 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 the 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, Section 8.2.3 of Appendix W emphasizes the 

importance of professional judgment by the reviewing authority in the identification of nearby 

and other sources to be included in the modeled emission inventory, and establishes “a 

significant concentration gradient in the vicinity of the source” under consideration as the main 

criterion for this selection. Appendix W also suggests that “the number of such [nearby] sources 

is expected to be small except in unusual situations.” 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 NAAQS 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. Cumulative 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 contribution. 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 and could unnecessarily complicate the permitting 
13 

process in some cases. Such procedures 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 considered 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 factors that determine the spatial and temporal patterns of the 

source’s ambient impacts. The initial significant impact assessment should serve to characterize 

these factors, and we would suggest the following: 

1. As a standard practice contour plots of modeled concentrations should be prepared 

which clearly depict the impact area of the source, preferably overlaid on a map of the 

area that identifies key geographical features that may influence the dispersion 

patterns. The concentration contour plot also serves to visually depict the 

concentration gradients associated with the source’s impact. 

2. We also recommend that the controlling meteorological conditions for the project 

impacts be identified as clearly as possible. The probabilistic form of the 1-hour NO2

standard complicates this assessment somewhat, but the recent update to the 

AERMOD model includes new model 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 information, we also suggest including the location of 

the meteorological monitoring station used in the modeling analysis on the plot of 

source impacts, as well as a wind rose depicting general flow patterns. 

If a cumulative impact assessment is required 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 the location and emissions from nearby background 

sources that may need to be included in the modeled component of the cumulative ambient 

impact assessment, and the location and magnitude of air quality data from ambient NO2

monitors located within the area. Section 8.2.1.b of Appendix W states that “[t]ypically, air 

quality data should be used to establish 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 monitored and modeled contributions to adequately 

account for background concentrations in the cumulative analysis, we believe that these 

statements imply a preference for use of ambient air quality data to account for background 

concentrations where possible. 

Many of the challenges and more controversial issues related to cumulative impact 

assessments arise in the context of how best to combine a monitored 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 may 
14 

involve significant technical details which could complicate the review process. Therefore, the 

more thoroughly and clearly these issues are documented the more efficient and effective the 

review process is likely to be. 

A key point to remember when assessing these issues is their interconnectedness – the 

question of which nearby background sources should be included in the cumulative modeling 

analysis is inextricably linked with the question of what ambient monitoring data is available and 

what that data represents in relation to the application. Furthermore, the question of how to 

appropriately combine monitored and modeled concentrations (temporally and spatially) to 

determine the cumulative impact depends on a clear understanding of what the ambient 

monitored data represents in relation to the modeled emission inventory. A more detailed 

temporal pairing of monitored and modeled concentrations 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 modeling inventory. As noted above, the stringency 

of the new standard may require a more detailed and refined analysis of these issues in order to 

demonstrate compliance with the standards than was 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 refinements will be important to facilitate the 

review process. The issues associated with determining an appropriate method for combining 

modeled and monitored contributions to a cumulative 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 assessing 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 ambient background data are used. 

3. In addition to a standard wind rose, pollution roses (i.e., a depiction of monitored 

pollutant concentrations as a function of wind direction and/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 importance of close coordination with the appropriate reviewing 

authority in the determination of nearby or other sources to include in the modeled emission 

inventory. 

Significant Concentration Gradient Criterion

While Appendix W (Section 8.2.3.b) identifies “a significant concentration gradient in 

the vicinity of the source” as the sole criterion in relation to determining which nearby sources 

should be explicitly modeled as part of the cumulative 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, Appendix 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 characterization, but given the 

issues and challenges arising from the implementation of the new 1-hour NO2 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 applies in this context is “the rate of change of 

a physical quantity . . . with distance5.” In this case the physical quantity is the ground-level 

concentration of the pollutant being 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) gradient. Appendix W makes no distinction as to which gradient is more 

important or whether both gradients should be considered. Before offering any suggestions on 

that question, it might be helpful to offer some thoughts on the question of why a significant 

concentration gradient is mentioned as the sole criterion. Since an ambient monitor is limited to 

characterizing air quality at a fixed location, the impact from a nearby source that causes a 

significant concentration gradient in the vicinity of the project source is not likely to be 

characterized very well by the monitored concentration in terms of its potential for contributing 

to the cumulative modeled design value due to the high degree of variability of the source’s 

impact. In this sense both the longitudinal and lateral gradients could be of importance. 

However, since the location of impacts from a particular source relative to other sources being 

modeled or relative to the ambient 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 longitudinal variability on concentrations 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 background 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 ground-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 thumb” 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 significant concentration gradients that may apply 

in relatively flat terrain is approximately 10 times the source release height. For example, the 

maximum impact area and region of significant concentration gradients associated with a 100 

meter stack in flat terrain would be approximately 1,000 meters downwind of the source, with 

some variation depending on the source characteristics 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 gradients associated with a particular source. Even 

accounting for some terrain influences on the location and gradients of maximum 1-hour 

concentrations, these considerations suggest that the emphasis on determining which nearby 

sources to include in the modeling 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 applicable, is likely to produce 

an overly conservative result in most cases. 

The relative importance of the lateral vs. the longitudinal gradient will also depend on 

terrain effects and other factors, such as the atmospheric stability associated with worst-case 

impacts. The importance of the lateral gradient relative to the longitudinal gradient will 

generally increase for sources where maximum hourly impacts occur under stable conditions due 

to the narrowness of the plume under such conditions. The contour plots of modeled design 

values suggested above provide a method for examining concentration gradients more explicitly. 

The AERSCREEN model should also serve as a useful tool for identifying 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 averaging 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 peak “downwind” of the source based on the 

prevailing wind direction, except in cases where terrain or other geographical effects are 

important. By contrast, the spatial distribution 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 NO2 standard than for overall peak hourly values, due to the smoothing effect 

of using a multiyear average of the 98th-percentile from the annual distribution of daily 

maximum values. One implication of these differences between long-term and short-term 

concentration patterns is that the factors affecting which sources should be included in the 

modeled inventory and the method for combining modeled with monitored concentrations are 

more complex for the 1-hour NO2 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 literally or too narrowly, and emphasize that a 

“large number of variables” (Appendix W, Section 8.2.3.b) are involved in this assessment. 
17 

COMBINING MODELED RESULTS AND 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 NO2 NAAQS is the method for combining 

modeled concentrations with monitored background concentrations to determine the cumulative 

ambient impact. The June 29, 2010 memo indicated that a “first tier” assumption for a uniform 

monitored background contribution that may be applied without further justification is to add the 

overall highest hourly background NO2 concentration (across the most recent three years) from a 

representative monitor to the modeled design value6

 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 overly conservative in many cases, but that 

conservatism also provided the basis for indicating 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 from the perspective of the reviewing authority. 

The June 29, 2010 memo also indicated that additional refinements to this “first tier” approach 

based on some level of temporal pairing of modeled and monitored values may be considered on 

a case-by-case basis, with adequate justification and documentation. Given the importance of 

this aspect of the analysis and the challenges that have arisen in application of the guidance to 

date, we feel compelled to offer additional guidance on this issue. 

While the “first tier” assumption from the June 29, 2010 memo of using a uniform 

monitored background contributions based on the overall highest hourly background NO2

concentration should be acceptable without further justification in most cases, 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 monitored background contribution based on the 

monitored design value from a representative monitor should be acceptable in most cases. The 

monitored NO2 design value, i.e., the 98th-percentile of the annual distribution of daily maximum 

1-hour values averaged across the most recent three years of monitored data7

, should be used 

irrespective of the meteorological data period used in the dispersion modeling. This somewhat 

less conservative “first tier” for a uniform monitored background contribution retains the 

advantage of being relatively easy to implement. 

 

6

 The 1-hour NO2 “modeled design value” refers to the highest (across all modeled receptors) of the 5-year average 

of the 98th-percentile (8th-highest) of the annual distribution of daily maximum 1-hour values based on NWS 

meteorological data, or the multiyear average of the 98th-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 

SO2 “modeled design value” follows the same form except that the multiyear averages of the 99th-percentile (4thhighest) values are used. 

7

 The monitored design value for the 1-hour SO2 standard is based on the 99th-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 particular application, use of a “first tier” 

assumption for a uniform monitored background contribution may represent a level of 

conservatism that would obviate the need to include any background sources in the modeled 

inventory if, for example, the number of nearby sources which could contribute to the cumulative 

impact is relatively few and the available ambient monitor would be expected to reflect their 

cumulative impacts reasonably well or conservatively in relation to the modeled design value 

based on the project emissions. At the other extreme, if the background source inventory 

included in the modeling is complete enough and 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 relation to this issue is that the standard is 

based on the annual distribution of daily maximum 1-hour values, which implies that diurnal 

patterns of ambient impacts could play a significant role in determining the most appropriate 

method for combining modeled and monitored concentrations. For example, if the daily 

maximum 1-hour impacts associated with the project emissions generally occur under nighttime 

stable conditions whereas maximum monitored concentrations occur during daytime convective 

conditions, pairing modeled and monitored concentrations based on hour of day should provide a 

more appropriate and less conservative estimate of cumulative impacts than a method that 

ignores this diurnal pattern. This situation could occur for applications dominated by low-level 

sources and for elevated releases subject to plume impaction on nearby complex terrain. It is 

also important to consider the role of NOx 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. Given the potential contribution of background ozone 

levels to the temporal variability of modeled impacts, the seasonal variability of background 

monitored values could also be important. Incorporating a seasonal component to the variability 

of background monitored concentrations will also account for some of the variability in 

meteorological conditions that may contribute to high hourly impacts. 

Another situation where understanding the temporal variability of modeled vs. monitored 

concentrations could be important in determining the most appropriate method for combining 

modeled and monitored concentrations is where contributions from mobile source emissions 

contribute significantly to either the monitored background concentrations and/or the modeled 

concentrations. In these cases, diurnal variability of emissions associated with morning and 

afternoon rush hours could contribute to the temporal variability of ambient impacts in addition 

to meteorological factors associated with the dispersion and conversion of NOx 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 conditions, and vice versa, incorporating a seasonal 

or monthly element to the temporal variability should account for the variable effect that 

dispersion conditions may have depending on whether 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 the 
19 

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 vicinity 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 annual 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 meteorological 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 located in the vicinity of the source, 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 similar natural and distant man-made sources. 

The key principle in this guidance in relation to short-term averaging 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 determine the average background value.” 

Based on this guidance, we believe that an appropriate methodology for incorporating 

background concentrations in the cumulative impact assessment for the 1-hour NO2 standard 

would be to use multiyear averages of the 98th-percentile8 of the available background 

concentrations by season and hour-of-day, excluding periods when the source in question is 

expected to impact the monitored concentration (which is only relevant for modified sources). 

For situations involving a significant mobile source component to the background monitored 

concentrations, inclusion of a day-of-week component to the temporal variability may also be 

appropriate. The rank associated with the 98th-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 98th-percentile based on the annual distribution. For example, Table 

1 in Section 5 of Appendix S specifies the rank associated with the 98th-percentile value based on 

the annual number of days with valid data. Since the number of days per season will range from 

90 to 92, Table 1 would indicate that the 2nd-highest value from the seasonal distribution should 

be used to represent the 98th-percentile. On the other hand use of the 2nd-highest value for each 

season would effectively “ignore” only 4 values for the year rather than the 7 values “ignored” 

from the annual distribution. Balancing these considerations we recommend that background 

values by season and hour-of-day used in this context should be based on the 3rd-highest value 

for each season and hour-of-day combination, whereas the 8th-highest value should be used if 

values vary by hour-of-day only. For more detailed temporal pairing, such as season by hour-of-

 

8

 The 99th-percentile should be used for the 1-hour SO2 standard. 
20 

day and day-of-week or month by hour-of-day, the 1st-highest values from the distribution for 

each temporal combination should be used.9

 

Figure 1 shows the background monitored concentrations by season and hour-of-day for 

the Salt Lake City, UT monitor for the period 2005-2007 based on these recommendations. The 

values labeled “Average Winter”, “Average Spring”, etc. are the 3-year averages of the 3rdhighest values by hour-of-day for each season; the values labeled “Average 98th %” (the dashed 

line) are the 3-year average of the 8th-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 98th-percentile values 

by season and hour-of-day. Also note that values for the 98th-percentile by hour-of-day only 

show little variation by hour-of-day, while values by season and hour-of-day show significant 

diurnal variability for some seasons. 

It should also be noted here that the conventions regarding observation reporting time 

differ 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 convention (EPA, 2000; see Section 7.1). Thus, ambient 

monitoring data reported for hour 00 should be paired with modeled/meteorological data for hour 

01, etc. The recent update to the AERMOD model (dated 11059) provides an option (the 

BACKGRND keyword on the SO pathway) to include temporally-varying background 

concentrations within the cumulative impact assessment based on these temporal factors, similar 

 

9

 For 1-hour SO2 analyses, use the 2nd-highest value for each season and hour-of-day combination, or the 4th-highest 

value for hour-of-day only. Use the 1st-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 23

NO2 Concentration (ppb)

Hour

Figure 1. Monitored Background Concentrations for 

Salt Lake City, UT Monitor

2005-2007 One-Hour NO2 Concentrations

NAAQS

98th % Winter

98th % Spring

98th % Summer

98th % Fall

98th % Annual

Overall Average

1-hr DV
21 

to the options that have been available in previous versions of the model to vary source 

emissions using the EMISFACT keyword. We believe that this technique provides a reasonable 

and efficient method for ensuring that the monitored contribution to the cumulative impact 

assessment will be representative of the “meteorological conditions accompanying the 

concentrations of concern” since the monitored values will be temporally paired with modeled 

concentrations based on temporal factors that are associated with meteorological variability, but 

will also reflect worst-case meteorological conditions in a manner that is consistent with the 

probabilistic form of the 1-hour NO2 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 also consistent with the intent of using monitored background 

concentrations, which is to reflect the contribution 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, using hourly monitored 

background data collected concurrently with the meteorological data period being processed by 

the model, we feel compelled to include a discussion 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 monitored values are fully representative of 

background levels at each receptor for each hour. Such an assumption clearly ignores the many 

factors that contribute to the temporal and spatial variability of ambient concentrations across a 

typical modeling domain on an hourly basis. Therefore we do not recommend such an approach 

except in rare cases of relatively isolated sources where the available monitor can be shown to be 

representative of the ambient concentration levels in the areas 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 emissions that could potentially 

contribute to the cumulative impact assessment and where inclusion of the monitored 

background concentration is intended to conservatively represent the potential contribution from 

minor sources and natural or regional background levels not reflected in the modeled inventory. 

In this case, the key aspect which may justify the hour-by-hour pairing of modeled and 

monitored values is a demonstration 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 background sources in the modeled inventory. 

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Hanrahan, P.L., 1999. The Plume Volume Molar Ratio Method for Determining NO2/NOx 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 AERMOD-PVMRM. Final Report, Alaska DEC 

Contract No. 18-9010-12. MACTEC Federal Programs, Inc., Research Triangle Park, NC. 

Wang, Y.J., A. DenBleyker, E. McDonald-Buller, D. Allen and K. Zhang, 2011. Modeling the 

chemical evolution of nitrogen oxides near roadways. Atmos. Env., 45, 43-52. 

cc: Richard Wayland, C304-02 

Scott Mathias, C504-01 

Lydia Wegman, C504-02 
23 

Raj Rao, C504-01 

Roger Brode, C439-01 

Dan deRoeck, C504-03 

Elliot Zenick, OGC 

Brian Doster, OGC 

Melina Williams, OGC 

EPA Regional Modeling Contacts 
A-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 estimating conversion of NO to NO2 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 NO2 (Hanrahan, 1999; MACTEC, 2005). The 

original evaluations of PVMRM also focused on model performance for annual averages since 

the only NO2 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 NO2 concentrations and to include the Ozone Limiting Method 

(OLM). Preliminary results from these recent evaluations are presented below in the form of QQ plots of ranked hourly NO2 concentrations for the two monitors included in the New Mexico 

Empire Abo field study and for the single monitor 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 concentrations. Note that the OLM 

results presented here incorporate an equilibrium NO2/NOx 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 New Mexico field study based on the Tier 1 option 

of full conversion of NO to NO2, the OLM option applied on a source-by-source basis, the OLM 

option applied using OLMGROUP ALL (OLMGRP), as recommended in the June 29, 2010, 

NO2 clarification memorandum, and the PVMRM option. 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 showing a significant bias to overpredict hourly 

NO2 concentrations. The OLMGRP option exhibits the best performance for both New Mexico 

monitors, with nearly unbiased results for the North monitor and a slight bias to overpredict for 

the South monitor. The PVMRM option shows significantly better performance than full 

conversion or source-by-source OLM for both monitors, but not as good performance as the 

OLMGRP option. 

Figure A-3 shows the hourly Q-Q plot for Palaau 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 NO2 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 option clearly improves model performance as 

compared to application of the OLM option on a source-by-source basis, with the peak predicted 

concentrations within a factor of 2 higher than 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/Observed 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 NO2, it is also useful to compare the modeled vs. observed NO2/NOx ratios since 

offsetting errors in dispersion vs. conversion could mask poor model performance. Table A-2 

summarizes the observed vs. predicted NO2/NOx ratios for the three monitors included in these 

Palaau and New Mexico field studies. These results are generally consistent with the hourly Q-Q 

plots of NO2 concentrations, and clearly indicate that the OLM option on a source-by-source 

basis significantly overestimates the conversion of NO to NO2. However, results for the New 

Mexico South monitor are interesting in that the PVMRM option shows much better agreement 

with observed NO2/NOx ratios than the OLMGRP option, whereas the OLMGRP option 

indicates better performance than PVMRM in terms of hourly NO2 concentrations. 

These preliminary model evaluation results of hourly NO2 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 impressive, especially for the PVMRM option at 

Palaau and for the OLMGROUP ALL option for the North monitor at New Mexico. We believe 

that these additional model evaluation results lend further credence to the use of these Tier 3 

options in AERMOD for estimating hourly NO2 concentrations and to the recommendation to 

use the OLMGROUP ALL option whenever OLM is applied. 
A-3 

10

100

1000

10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

Figure A-1. AERMOD Model Evaluation - New Mexico North Monitor - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRM

10

100

1000

10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

Figure A-2. AERMOD Model Evaluation - New Mexico South Monitor - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRM
A-4 

Table A-1. 1-hour NO2 Robust Highest Concentrations (μg/m3

) 

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 NO2/NOx Ratios for Monitored Values of NOx > 20 ppb 

 Monitored 

NO2/NOx

PVMRM 

NO2/NOx

OLMGRP 

NO2/NOx

OLM 

NO2/NOx

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 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

Figure A-3. AERMOD Model Evaluation - Palaau, HI - Hourly NO2 Q-Q Plot

FULL

OLM

OLMGRP

PVMRM