MEMORANDUM 

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

RESEARCH TRIANGLE PARK, NC 27711 

MAY 2 n 2014 

SUBJECT: Guidance for PM2.s Permit Modeling 

FROM: Stephen D. Pagel'\, ~J ~ o t<. !' 

Duector fO<' 

TO: Regional Air Division Directors, Regions 1-10 

OFFICE OF 

AIR QUALITY PlANNING 

AND STANDARDS 

This memorandum and attachment, titled "Guidance for PM2.s Permit Modeling," provides 

guidance on demonstrating compliance with the fine particulate matter (PM2.s) National Ambient 

Air Quality Standards (NAAQS) and Prevention of Significant Deterioration (PSD) increments, 

especially with regard to considerations of the secondarily formed component ofPM2.s. This 

document reflects the EPA's recommendations for how a major stationary source seeking a PSD 

permit may demonstrate that it will not cause or contribute to a violation of the NAAQS and 

PSD increments for PM2.s, as required under section 165(a)(3) ofthe Clean Air Act (CAA) and 

40 CFR Sections 51.166(k) and 52.21 (k). 

A draft version of this guidance document was provided to the public on March 4, 2013, for a 

90-day comment period. The document was revised in response to public comments and 

additional information provided through on-going interactions with various stakeholders. 

Noteworthy changes made to the draft version include: 

• Clarifications throughout with respect to procedures for adequately addressing primary 

and secondarily formed PM2.s. 

• Inclusion of an example hybrid (qualitative/quantitative) secondary PM2.s impact 

assessment. 

• Revision of a second tier cumulative PM2.s NAAQS compliance approach. 

• Revision of Section V and other sections relative to PSD increment for PM2.s. 

Please distribute the attached guidance document to state, local, and tribal governments, as 

appropriate. If you have any questions regarding this document, please contact Tyler Fox, Air 

Quality Modeling Group Leader, Air Quality Assessment Division!OAQPS, at (919) 541-5562, 

fox.tyler@epa.gov . 

Attachment 

lntemet Address (URL) • http://www.epa.gov 

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Guidance for PM2.5 Permit Modeling
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EPA-454/B-14-001

May 2014

Guidance for PM2.5 Permit Modeling

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, North Carolina
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i

TABLE OF CONTENTS

Executive Summary......................................................................................................................................v

Acknowledgements......................................................................................................................................xi

I. Background...........................................................................................................................................1

II. Guidance Overview ..............................................................................................................................9

II.1 Significant Emissions Rates........................................................................................................15

II.2 Screening and Source Impact Analysis.......................................................................................15

II.3 Cumulative Impact Analysis.......................................................................................................21

II.4 Assessment Cases for Source Impacts........................................................................................23

III. Source Impact Analysis for the PM2.5 NAAQS ..............................................................................27

III.1 Assessing Primary PM2.5 Impacts...............................................................................................29

III.2 Assessing Secondary PM2.5 Impacts...........................................................................................31

III.2.1 Qualitative Assessments .....................................................................................................31

III.2.2 Hybrid Qualitative/Quantitative Assessment......................................................................35

III.2.3 Full Quantitative Photochemical Grid Modeling................................................................39

III.3 Comparison to the SIL................................................................................................................45

IV. Cumulative Impact Analysis for the PM2.5 NAAQS.......................................................................51

IV.1 Modeling Inventory ....................................................................................................................52

IV.2 Monitored Background ...............................................................................................................54

IV.3 Comparison to the NAAQS ........................................................................................................56

IV.4 Determining Whether Proposed Source Causes or Contributes to Modeled Violations.............64

V. PSD Increments for PM2.5 ...................................................................................................................67

V.1 Overview of PSD Increments .....................................................................................................67

V.2 PM2.5 Increments Considerations................................................................................................69

V.3 Screening Analysis for Increments.............................................................................................72

V.4 PM2.5 Increments Analysis..........................................................................................................73

V.4.1 Source Impact Analysis ..........................................................................................................73

V.4.2 Cumulative Impact Analysis...................................................................................................74

V.4.2.1 Assessing Primary PM2.5 Impacts from Other Sources...................................................74

V.4.2.2 Assessing Secondary PM2.5 Impacts from Other Sources...............................................75

V.4.2.3 Consideration of PM2.5 Ambient Air Quality Monitoring Data ......................................76

V.5 Determining Significant Contribution to an Increment Violation ..............................................77

VI. References.......................................................................................................................................79
ii

Appendix A: Draft Conceptual Description of PM2.5 Concentrations in the U.S. ...................................A-1

Appendix B: General Guidance on Use of Dispersion Models for Estimating Primary PM2.5

Concentrations.............................................................................................................................B-1

Appendix C: Example of a Qualitative Assessment of the Potential for Secondary PM2.5 Formation....C-1

Appendix D: Example of a Hybrid Qualitative/Quantitative Assessment of the Potential for 

Secondary PM2.5 Formation.........................................................................................................D-1

Appendix E: Example of the background monitoring data calculations for a Second Tier 24-hour 

modeling analysis ........................................................................................................................ E-1
iii

Disclaimer

This document recommends procedures for permit applicants and permitting authorities 

to use to show that they have satisfied the criteria for obtaining or issuing a permit under 

applicable regulations. This document is not a rule or regulation, and the guidance it contains 

may not apply to a particular situation based upon the individual facts and circumstances. This 

guidance does not change or substitute for any law, regulation, or any other legally binding 

requirement and is not legally enforceable. The use of non-mandatory language such as 

“guidance,” “recommend,” “may,” “should,” and “can,” is intended to describe EPA policies 

and recommendations. Mandatory terminology such as “must” and “required” are intended to 

describe controlling requirements under the terms of the Clean Air Act and EPA regulations, but 

this document does not establish legally binding requirements in and of itself. This document 

does not create any rights or obligations enforceable by any party or impose binding, 

enforceable requirements on any permit applicant for a PSD permit or PSD permitting authority.
iv

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v

Executive Summary

The U.S. Environmental Protection Agency (EPA) is providing this “Guidance for PM2.5

Permit Modeling” to fulfill a need for additional guidance on demonstrating compliance with the 

fine particulate matter (PM2.5) National Ambient Air Quality Standards (NAAQS) and the 

Prevention of Significant Deterioration (PSD) increments, especially with regard to 

considerations of the secondarily formed components of PM2.5. This guidance incorporates the 

modeling procedures and recommendations from the EPA’s March 23, 2010, guidance 

memorandum, “Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS,” and 

further clarifies procedures for adequately addressing primary and secondarily formed PM2.5 in a 

NAAQS and PSD increments compliance demonstration. This guidance is consistent with the 

EPA’s Guideline on Air Quality Models, also published as Appendix W of Title 40 of the Code 

of Federal Regulations (CFR) Part 51. The release of this “Guidance for PM2.5 Permit Modeling”

is also consistent with the commitments contained in the EPA’s January 4, 2012, grant of a July 

28, 2010, petition filed by the Sierra Club.

Because of the complex chemistry of secondary formation of PM2.5, the EPA's judgment 

in the past has been that it was not technically sound to assign with particularity specific models 

that must be used to assess the impacts of a single source on PM2.5 concentrations. Instead, the 

EPA has determined it was appropriate to satisfy the requirements of Section 165(e)(3)(D) of the 

Clean Air Act (CAA) by recommending that the “[c]hoice of methods used to assess the [PM2.5] 

impact of an individual source depends on the nature of the source and its emissions,” as stated 

in Section 5.2.2.1.c. of Appendix W. As such, the appropriate methods for assessing PM2.5

impacts are determined as part of the normal consultation process with the appropriate permitting

authority. A modeling protocol should be developed by the permit applicant and approved by the 
vi

appropriate permitting authority to ensure that the analysis conducted will conform to the 

recommendations, requirements, and principles of Section 10.2.1 of Appendix W. This guidance 

is intended to inform that process through recommendations regarding appropriate methods to 

assess secondary PM2.5 impacts from the precursor emissions from the new or modifying source

by providing the permit applicant and the appropriate permitting authority with both focus and 

flexibility. As experience is gained with these NAAQS and increments compliance 

demonstrations (and as the EPA moves forward to consider single source modeling techniques 

pursuant to its grant of the petition from the Sierra Club), this guidance will likely evolve such 

that the EPA will be able to provide further specificity on assessing the impacts of a single 

source on PM2.5 concentrations.

This guidance document is broken down into five primary sections:

• I. Background – The first section provides the relevant regulatory actions and 

historical context to this guidance starting with the promulgation of the initial PM2.5

NAAQS in 1997; chronicling the PM10 Surrogate Policy that for a period of time was 

relied upon for demonstrating compliance with the PM2.5 NAAQS; and arriving at the 

present where there is a need for an assessment of both the primary and secondary 

PM2.5 impacts, as appropriate, of a new or modifying source for demonstrating 

compliance with PM2.5 NAAQS and increments.

• II. Guidance Overview – The second section provides a general overview of the steps 

that a permit applicant would routinely take under the PSD program for 

demonstrating compliance with the PM2.5 NAAQS and increments. The concepts of 

significant emissions rates (SERs) and significant impact levels (SILs) are introduced 

and then presented in the context of a source impact analysis and a cumulative impact 
vii

analysis. The ramifications of the January 22, 2013, decision from U.S. Court of 

Appeals for the District of Columbia Circuit on the use of SILs in a source impact 

analysis or otherwise are included for reference and consideration throughout the 

remaining sections. Four assessment cases (Table ES-1) are then introduced with 

respect to assessing the primary and secondary PM2.5 impacts through either the 

source impact analysis or the cumulative impact analysis.

• III. Source Impact Analysis for the PM2.5 NAAQS – The third section provides a

detailed discussion of a screening assessment of primary and secondary PM2.5

impacts from a new or modifying source using a SIL. The specifics of the four 

assessment cases (Table ES-1) are presented along with appropriate approaches for 

assessing the primary and secondary impacts of PM2.5. For assessing the primary 

PM2.5 impacts from the direct PM2.5 emissions from the new or modifying source, the 

typical use of an appropriate preferred dispersion model for near-field PM2.5

modeling listed in Appendix W, currently AERMOD for most applications, or an 

approved alternative model is recommended. For assessing the secondary PM2.5 

impacts from the precursor emissions from the new or modifying source, three 

different approaches are described. These approaches are 1) a qualitative assessment, 

2) a hybrid qualitative/quantitative assessment utilizing existing technical work, and 

3) a full quantitative photochemical grid modeling exercise.

• IV. Cumulative Impact Analysis for the PM2.5 NAAQS – The fourth section provides 

a detailed discussion of the assessment of primary and secondary PM2.5 impacts from 

a new or modifying source with the inclusion of the primary and secondary PM2.5

impacts of nearby sources and of monitored background. There are specific 
viii

discussions of the modeling inventory and the monitored background. Section IV 

concludes with information on determining significant contributions to modeled 

violations.

• V. PSD Increments for PM2.5 – The fifth section provides a detailed discussion of the 

assessment of primary and secondary PM2.5 impacts of a new or modifying source 

with respect to the increments.

Table ES-1. EPA Recommended Approaches for Assessing Primary and Secondary PM2.5

Impacts by Assessment Case

Assessment Case Description of Assessment Case Primary Impacts Approach Secondary Impacts 

Approach

Case 1:

No Air Quality Analysis

Direct PM2.5 emissions < 10 tpy SER

NOx and SO2 emissions < 40 tpy SER N/A N/A

Case 2:

Primary Air Quality 

Impacts Only

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and SO2 emissions < 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

N/A

Case 3:

Primary and Secondary 

Air Quality Impacts

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

Case 4:

Secondary Air Quality 

Impacts Only

Direct PM2.5 emissions < 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER N/A

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

In summary, this “Guidance for PM2.5 Permit Modeling” recommends technical 

approaches for conducting PM2.5 NAAQS and PSD increments compliance demonstrations

which include adequate accounting for contributions from primary PM2.5 concentrations from a 

proposed new or modifying source’s direct PM2.5 emissions and from secondarily formed PM2.5

concentrations resulting from the source’s PM2.5 precursor emissions. This guidance does not 

create any rights or obligations enforceable by any party or impose binding, enforceable 

requirements on any permit applicant for a PSD permit or PSD permitting authority. Since each 
ix

permitting action will be considered on a case-by-case basis, this document does not limit or 

restrict any particular justifiable approach permit applicants and permitting authorities may take 

to conduct the required compliance demonstrations. Each individual decision to issue a PSD 

permit must be supported by a record sufficient to demonstrate that the proposed construction

and operation of a stationary source will not cause or contribute to a violation of the applicable 

PM2.5 NAAQS and PSD increments. While this document illustrates a particular approach that 

the EPA considers appropriate and acceptable as a general matter, permit applicants and 

permitting authorities should examine all relevant information regarding air quality in the area 

that may be affected by a proposed new or modified source and evaluate whether alternative or 

additional analysis may be necessary in a given case to demonstrate that the criteria for obtaining 

a permit are satisfied. This document does not represent a conclusion or judgment by EPA that 

the technical approaches recommended in this document will be sufficient to make a successful 

compliance demonstration in every permit application or circumstance.

Permitting authorities retain the discretion to address particular issues discussed in this 

document in a different manner than the EPA recommends so long as the approach is adequately 

justified, supported by the permitting record and technical literature, and consistent with the 

applicable requirements in the CAA and implementing regulations, including the terms of an 

approved State Implementation Plan (SIP).

Furthermore, this guidance does not represent final agency action with respect to 

applicable legal requirements or the approvability of any particular permit application. To 

improve the quality of this guidance, the EPA has solicited public comment and considered the 

comments submitted. The EPA has revised the draft guidance in response to many points raised 

in public comment, but this document does not reflect a final determination by the EPA as to any 
x

issue raised in public comments. Concerns expressed in public comments about the 

permissibility or sufficiency of the approach recommended in this guidance for making the 

required demonstration in particular circumstances may be raised in the context of each 

individual permit application and should be considered by the permitting authority in light of the 

record in each instance before making a final determination to issue or deny a PSD permit.
xi

Acknowledgements

We would like to acknowledge contributions from members of the National Association 

of Clean Air Agencies (NACAA) PM2.5 Modeling Implementation Workgroup (NACAA 

Workgroup) in providing a detailed set of recommendations (NACAA, 2011) to the EPA with 

regards to PM2.5 permit compliance demonstration modeling. This NACAA Workgroup was 

comprised of state and local air permitting agency dispersion modelers, permit engineers, and 

technical staff from throughout the country. In particular, we recognize Jim Hodina (Linn 

County Public Health), Bob Hodanbosi (Ohio EPA, Division of Air Quality), and Clint Bowman 

(Washington Department of Ecology) for their roles as Chairpersons for the Emissions 

Inventories, Secondary Formation from Project Source, and Representative Background 

Concentrations Sub-workgroups, respectively.

We would also like to acknowledge the contributions of the staff of the EPA Office of 

Transportation and Air Quality (OTAQ) for their input and assistance in the development of this 

document. The EPA’s “Transportation Conformity Guidance for Quantitative Hot-spot Analyses 

in PM2.5 and PM10 Nonattainment and Maintenance Areas” (U.S. EPA, 2013a) guidance 

document served as a foundation for many aspects of the modeling guidance contained within 

this document.

Finally, there were numerous comments received with respect to issues and concerns of 

demonstrating compliance with the NAAQS and increments for PM2.5 during the formal public 

comment period for the 10th Conference on Air Quality Modeling and comprehensive comments 

received specific to the overall guidance and recommendations presented here through the 

comment period for the “Draft Guidance on PM2.5 Permit Modeling” (U.S. EPA 2013b). This 

invaluable feedback along with additional information gleaned through ongoing interactions with
xii

various stakeholders have been particularly useful in the consideration of a range of acceptable

options for PM2.5 NAAQS and PSD increments compliance demonstrations and aided the EPA in 

the completion of this guidance document
1

I. Background

Under Section 165(a)(3) of the CAA, a PSD permit applicant must demonstrate that 

emissions from the proposed construction and operation of a facility “will not cause, or 

contribute to, air pollution in excess of any (A) maximum allowable increase or maximum 

allowable concentration for any pollutant... , [or] (B) national ambient air quality standard...”

This requirement is implemented in the PSD regulations at 40 CFR 52.21(k)(1) (and at 40 CFR 

51.166(k)(1) with slightly different wording) as follows:

(k) Source impact analysis—(1) Required demonstration. The owner or operator of the 

proposed source or modification shall demonstrate that allowable emission increases 

from the proposed source or modification, in conjunction with all other applicable 

emissions increases or reductions (including secondary emissions), would not cause or 

contribute to air pollution in violation of:

(i) Any national ambient air quality standard in any air quality control region; or

(ii) Any applicable maximum allowable increase over the baseline concentration in 

any area.

On July 18, 1997, the EPA revised the NAAQS for particulate matter (PM) to add new 

annual and 24-hour standards for fine particles using particulate matter less than 2.5 micrometers 

or PM2.5 as the indicator.

1 The EPA revised the 24-hour NAAQS for PM2.5 on September 21, 

2006, by lowering the level of the standard from 65 μg/m3 to 35 μg/m3.

2 In the September 21, 

2006, action, the EPA also retained the previous 1997 annual standard for PM2.5 and the 24-hour 

standard for PM10, and revoked the previous annual standard for PM10. Subsequently, the 

 

1 See 62 Fed. Reg. 58652.

2 See 71 Fed. Reg. 61144.
2

Agency revised the PM2.5 standard again on December 14, 2012, by lowering the level of the 

annual PM2.5 NAAQS from 15 μg/m3 to 12 μg/m3 and retaining the 24-hour standards for PM2.5

and PM10.

3 The annual PM2.5 standard is met when the 3-year average of annual arithmetic mean

concentrations is less than or equal to 12.0 μg/m3. The 24-hour PM2.5 standard is met when the 3-

year average of the annual 98th percentile 24-hour concentrations is less than or equal to 

35 μg/m3.

On October 20, 2010, EPA established maximum allowable increases for PM2.5.

4 These 

values are also frequently described as the PSD increments. For Class I areas, the increments for 

PM2.5 are 1 μg/m3 for the annual averaging time and 2 μg/m3 for the 24-hour averaging time. In 

Class II areas, the increments are 4 μg/m3 for the annual period and 9 μg/m3 for the 24-hour 

period.

To address the compliance demonstration for the PM2.5 NAAQS, on October 23, 1997, 

citing significant technical difficulties with respect to PM2.5 monitoring, emissions estimation, 

and modeling, the EPA established a policy known as the PM10 Surrogate Policy (U.S. EPA,

1997). This policy allowed permit applicants to use compliance with the applicable PM10

requirements as a surrogate approach for meeting PM2.5 New Source Review (NSR) 

requirements until certain technical difficulties were resolved. On May 16, 2008, the EPA 

promulgated final rules governing the implementation of the NSR program for PM2.5, which 

facilitated phasing out the application of the PM10 Surrogate Policy to permits involving PM2.5.

5

With regard to nonattainment NSR permits, the rule provided that as of July 15, 2008 (the rule’s 

effective date), permit applicants and permitting authorities would no longer be able to use the 

 

3 See 78 Fed. Reg. 3086.

4 See 75 Fed. Reg. 64864.

5 See 73 Fed. Reg. 28321.
3

PM10 Surrogate Policy to satisfy the NSR requirements for PM2.5. With regard to PSD permits, 

the rule provided that PSD permits issued under the federal PSD program at 40 CFR 52.21 

would no longer be allowed to rely on the PM10 Surrogate Policy as of the effective date of the 

rule. The exception to this outcome was that the rule also provided a “grandfathering provision”

allowing permit applicants for federal PSD permits covered by 40 CFR 52.21, with complete 

permit applications submitted as of July 15, 2008, to continue relying on the PM10 Surrogate

Policy. The 2008 rule also provided that states with approved PSD programs for PM2.5 could 

continue to use the PM10 Surrogate Policy until May 2011 (when SIP revisions containing 

provisions to meet the new requirements in the 2008 rule were due), or until the EPA approved 

the revised SIP for PM2.5, whichever occurred first.

On June 1, 2009, in response to a petition challenging the continued use of the PM10

Surrogate Policy for issuing PSD permits, the EPA issued a 3-month administrative stay of the 

grandfathering provision for PM2.5 affecting federal PSD permits to give the EPA time to 

propose repealing the challenged grandfathering provision.

6 On September 16, 2009, the original 

3-month stay was extended to June 22, 2010, to allow additional time for the EPA to propose 

repealing the grandfathering provision from the PM2.5 NSR implementation rule for federal PSD 

permits issued under 40 CFR 52.21.

7 On February 11, 2010, the EPA published its proposal to 

repeal the grandfathering provision.

8 These actions cite the fact that the technical difficulties that

necessitated the PM10 Surrogate Policy had been largely, although not entirely, resolved. As part 

of the proposed rulemaking to repeal the grandfathering provision contained in the federal PSD 

program, the EPA also proposed to require an early end to the use of the PM10 Surrogate Policy 

 

6 See 74 Fed. Reg. 26098.

7 See 74 Fed. Reg. 48153.

8 See 75 Fed. Reg. 6827.
4

for state PSD programs that the EPA had already approved as part of the SIP required by 40 CFR 

51.166.

On May 18, 2011, the EPA published a final rule, titled “Implementation of the New 

Source Review (NSR) Program for Particulate Matter Less Than 2.5 Micrometers (PM2.5); Final 

Rule to Repeal Grandfather Provision” (76 Fed. Reg. 28646), that repealed the grandfathering 

provision. In that final action, the EPA ended the use of the PM10 Surrogate Policy for PSD 

permits under the federal PSD program for sources that were covered by the grandfathering 

provision (that is, those sources for which a complete permit application was submitted before 

July 15, 2008) and that were not yet issued a permit by the effective date of the final rule. 9 The 

final rule also reaffirmed that as of May 2011, states with SIP-approved PSD programs for PM2.5

could no longer use the PM10 Surrogate Policy. After the final rule became effective, in order for 

any PSD permits to be issued through the federal PSD program or a state SIP, such permit 

applications were to be reviewed directly against the PM2.5 requirements. The demonstration 

must show, at a minimum, that the source's emissions are controlled to a level that satisfies Best 

Available Control Technology (BACT) requirements for PM2.5 and that the emissions (filterable 

and condensable10) would not cause or contribute to a violation of any NAAQS for PM2.5.

On March 23, 2010, the EPA issued a guidance memorandum titled “Modeling

Procedures for Demonstrating Compliance with PM2.5 NAAQS” (U.S. EPA, 2010b) to assist 

sources and permitting authorities in carrying out the required air quality analysis. The guidance 

memorandum recommended certain interim procedures to address the fact that compliance with 

the PM2.5 NAAQS is based on a statistical form, and that there are technical complications 

 

9 Sources that applied for a PSD permit under the federal PSD program on or after July 15, 2008, were already 

excluded from using the 1997 PM10 Surrogate Policy as a means of satisfying the PSD requirements for PM2.5. See 

73 Fed. Reg. 28321.

10 See 40 CFR 51.165(a)(l)(xxxvii)(D), 51.166 (b)(49)(i)(a), and 52.21(b)(50) (i)(a).
5

associated with the ability of existing models to estimate the impacts of secondarily formed 

PM2.5 in the atmosphere resulting from emissions of PM2.5 precursors. For the latter issue, the 

EPA recommended that special attention be given to the assessment of monitored background air 

quality data since such data account for the contribution of both primary and secondarily formed 

PM2.5 in the atmosphere associated with both nearby and regional sources.

On January 7, 2011, the NACAA Workgroup delivered a final report (NACAA, 2011), 

including a set of specific recommendations, to the EPA. The NACAA Workgroup was formed 

in early 2010 with the objective of providing technical recommendations to the Agency to aid in 

further development of PM2.5 permit modeling guidance. The NACAA Workgroup’s final report 

addressed three specific issues regarding PM2.5 modeling implementation: 1) Emissions 

Inventories; 2) Secondary Formation from Project Source; and 3) Representative Background 

Concentrations.

The need for additional clarification on addressing both the primary and secondarily 

formed PM2.5 in NAAQS compliance demonstrations was heightened following an 

administrative action on January 4, 2012, in which the EPA granted a petition submitted on 

behalf of the Sierra Club on July 28, 2010 (U.S. EPA, 2012a). The Sierra Club petition requested 

that the EPA initiate rulemaking to establish air quality models for ozone and PM2.5 for use by all 

major sources applying for a PSD permit. In the petition grant, the EPA committed to engage in 

rulemaking to evaluate updates to the Guideline on Air Quality Models as published as 

Appendix W of 40 CFR Part 51 and, as appropriate, incorporate new analytical techniques or 

models for ozone and secondarily formed PM2.5. As a part of this commitment and in compliance 

with Section 320 of the CAA, the EPA conducted the 10th Conference on Air Quality Modeling 
6

(10th Modeling Conference) in March 2012. 11 At the 10th Modeling Conference, there were 

invited presentations of ongoing research of single source plume chemistry and photochemical 

grid modeling techniques, an overview presentation on the development of the “Draft Guidance 

for PM2.5 Permit Modeling”, and several public forums and subsequently written comments 

given pertaining to PM2.5 NAAQS modeling.

Based on the EPA’s March 23, 2010, guidance memorandum, the NACAA Workgroup 

final report recommendations, input from a mixture of stakeholders through numerous forums, 

and permit applicant-submitted PM2.5 compliance demonstrations up to that point, the EPA 

prepared the “Draft Guidance on PM2.5 Permit Modeling” and released it for public comment on 

March 4, 2013. During the course of the public comment period following the release of the draft 

guidance, the EPA received numerous comprehensive comments that provided invaluable 

feedback on the document and on the newly recommended approaches for PM2.5 NAAQS and 

PSD increments compliance demonstrations. This feedback along with additional information 

gleaned through ongoing interactions with various stakeholders was particularly useful in the 

consideration of a range of acceptable options for PM2.5 NAAQS and PSD increments

compliance demonstrations and aided the EPA in the completion of this guidance document.

This “Guidance for PM2.5 Permit Modeling” recommends appropriate technical 

approaches for conducting a PM2.5 NAAQS and PSD increments compliance demonstration 

which includes adequate accounting for contributions from primary PM2.5 concentration from a 

proposed new or modifying source’s direct PM2.5 emissions and from secondarily formed PM2.5

concentrations resulting from the source’s PM2.5 precursor emissions. This guidance is consistent 

with the EPA’s Guideline on Air Quality Models. The release of this “Guidance for PM2.5 Permit 

 

11 Additional information regarding and presentations from the 10th Modeling Conference can be found on the 

SCRAM website at: http://www.epa.gov/ttn/scram/10thmodconf.htm.
7

Modeling” is also consistent with the commitments contained in the EPA’s January 4, 2012, 

grant of the July 28, 2010, petition filed by the Sierra Club.

Since each permitting action will be considered on a case-by-case basis, this guidance

does not limit or restrict any particular justifiable approach permit applicants and permitting 

authorities may take to conduct the required compliance demonstrations. Prospective permit 

applicants should recognize the importance of the consultation process with the appropriate 

permitting authority. This process will help identify the most appropriate analytical techniques to 

be used for conducting a PM2.5 NAAQS and PSD increments compliance demonstration, 

including addressing the impacts of individual sources on secondary PM2.5 formation, pursuant to 

Section 5.2.2.1.c of Appendix W.

In addition to this guidance, other recently issued EPA guidance of relevance for 

consideration in permit modeling for PM2.5 includes:

• “Model Clearinghouse Review of Modeling Procedures for Demonstrating 

Compliance with PM2.5 NAAQS,” February 26, 2010 (U.S. EPA, 2010a);

• “Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS,” March 

23, 2010 (U.S. EPA, 2010b);

• “Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5

and PM10 Nonattainment and Maintenance Areas,” November 2013 (U.S.EPA, 2013); 

and

• “Interim Guidance on the Treatment of Condensable Particulate Matter Test Results 

in the PSD and NSR Permitting Programs,” April 8, 2014 (U.S. EPA, 2014a).

The guidance listed above, in addition to other relevant support documents, can be found on the 

SCRAM website at http://www.epa.gov/ttn/scram/.
8

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9

II. Guidance Overview

This modeling guidance provides recommendations on how to conduct a PM2.5 NAAQS 

and PSD increments compliance demonstration under the PSD program. It is based on and is 

consistent with Appendix W. Appendix W is the primary source of information on the regulatory 

application of air quality models for SIP revisions for existing sources and for NSR and PSD 

programs for permitting new and modifying sources.

The complexity of secondary PM2.5 formation has historically presented significant 

challenges for the identification and establishment of particular models for assessing the PM2.5

impacts of individual stationary sources (NARSTO, 2004; Seinfeld and Pandis, 1998; Cohan and 

Napelenok, 2011). Because of these considerations, the EPA's judgment in the past has been that 

it was not technically sound to assign with particularity specific models that must be used to 

assess the impacts of a single source on PM2.5 concentrations.

12 Instead, the EPA has chosen to 

satisfy the requirements of the CAA, Section 165(e)(3)(D) through a process of determining 

particular models or other analytical techniques that should be used on a case-by-case basis 

because the “[c]hoice of methods used to assess the [PM2.5] impact of an individual source 

depends on the nature of the source and its emissions,” as stated in Section 5.2.2.1c. of Appendix 

W. As such, the appropriate methods for assessing PM2.5 impacts are determined as part of the 

normal consultation process with the appropriate permitting authority. A modeling protocol 

should be developed by the permit applicant and approved by the appropriate permitting 

authority to ensure that the analysis conducted will conform to the recommendations, 

requirements, and principles of Section 10.2.1 of Appendix W.

 

12 We note that this technical judgment has no effect on the obligation of sources subject to PSD to conduct a source 

impact analysis and demonstrate that a proposed source or modification will not cause or contribute to a violation of 

any NAAQS or applicable increment. See 40 CFR 51.166(k); 52.21(k). That is, the inclusion of a process rather than 

a specific preferred model in Appendix W does not relieve the source of the requirement to make this demonstration, 

which necessarily involves an analysis.
10

Due to the potentially important contribution from secondary formation of PM2.5 and the 

more prominent role of ambient monitoring data in the cumulative analysis to represent 

background PM2.5 concentrations including secondary formation from precursors from nearby 

sources, certain aspects of standard modeling practices used for PM10 and other criteria 

pollutants may not be appropriate for PM2.5. For example, the contribution from secondary 

formation of PM2.5 is not explicitly accounted for by the current preferred dispersion model (i.e., 

AERMOD), which is used to simulate dispersion of direct PM2.5 emissions. Given these issues, 

PSD modeling of secondarily formed PM2.5 should currently be viewed as screening-level 

analyses under Appendix W, analogous to Section 5.2.4 of Appendix W regarding dispersion 

modeling for nitrogen dioxide (NO2) impacts due to the importance of chemistry in the 

conversion of nitric oxide (NO) emissions to ambient NO2 and lack of a specified “refined” 

model.13 The recommendations presented in this guidance for demonstrating compliance with 

the PM2.5 NAAQS through dispersion modeling and other techniques have been developed with 

the factors listed above in mind.

As with any modeling analysis conducted using approved models identified in

Appendix W, alternative models and methods may be considered on a case-by-case basis, subject 

to approval by the EPA Regional Office in accordance with the recommendations in Section 3.2. 

Additionally, Section 10.2.2 of Appendix W could potentially be given consideration in select 

situations. The provisions of Section 10.2.2 acknowledge that there are circumstances where 

there is no applicable model for a particular NAAQS compliance demonstration and that data 

 

13 Section 5.2.4 of Appendix W puts forth a 3-tiered screening approach for NO2 NAAQS compliance 

demonstrations to obtain estimates of NO2 for PSD and SIP planning purposes. The level of conservativeness in the 

tiered approaches decreases as fewer assumptions are made and a more detailed analysis is applied with the 3rd tier 

approach being the use of detailed screening techniques based on dispersion modeling.
11

from an array of ambient monitors surrounding the facility to be permitted could be used in lieu 

of modeling if appropriately justified.

Given the complexity of the technical issues that arise in the context of demonstrating 

compliance with the PM2.5 NAAQS, we strongly encourage following 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.”

Furthermore, we recommend that the consultative process involve regular communication 

between the appropriate permitting authority and the permit applicant at key milestones to ensure 

timely resolution of issues that may arise.

As necessary, the EPA Regional Office may seek clarification from the EPA’s Office of 

Air Quality Planning and Standards (OAQPS) on technical issues and areas of concern in a 

modeling protocol or NAAQS compliance demonstration. Through these interactions and 

subsequent resolutions of the specific issues, clarifications of preferred modeling procedures can 

ultimately become official EPA guidance. This can happen in several ways: 1) the preferred 

procedures are published as regulations or guidelines; 2) the preferred procedures are formally

transmitted as guidance to the Air Division Directors in the EPA Regional Offices; 3) the 

preferred procedures are formally transmitted as guidance to the EPA Regional Office modeling 

contacts as a result of a regional consensus on technical issues; or 4) the preferred procedures are 

relied upon in decisions by the EPA’s Model Clearinghouse that effectively establish national 

precedent that the approach is technically sound. The Model Clearinghouse is the EPA focal 
12

point for the review of the technical adequacy of pollutant modeling to satisfy regulatory criteria 

and other NAAQS compliance demonstration techniques. Model Clearinghouse memoranda 

involving interpretation of modeling guidance for specific applications, as well as clarification 

memoranda addressing needs to clarify guidance more generally, are available at the Support 

Center for Regulatory Atmospheric Modeling (SCRAM) website at:

http://www.epa.gov/ttn/scram.

The guidance that follows is appropriate for those new or modifying sources locating or 

located in an area classified as attainment or unclassifiable for PM2.5. This document is intended 

to provide recommendations on how to conduct PM2.5 NAAQS and PSD increments compliance 

demonstrations under the PSD program following the progressive steps shown in Figure II-1

(NAAQS) and Figure II-2 (Increments). The EPA has historically allowed the use of screening 

tools to help facilitate the implementation of the PSD program and streamline the permitting 

process in circumstances where proposed construction is projected to have an insignificant (or de 

minimis) impact on air quality. These screening tools have included SERs, SILs, and significant 

monitoring concentrations (SMCs). The use of these screening tools at each progressive step on 

the left side (attainment or unclassifiable areas) of Figure II-1 and Figure II-2 are described in 

more detail in Sections II.1, II.2, and II.3.
13

Figure II-1. Overview of PM2.5 NAAQS Compliance Demonstration for New or Modifying

Sources under NSR/PSD Programs

New or Modified

Source

Nonattainment

Area?

Attainment or Unclassified 

Area

Source Emissions

Greater Than or 

Equal to SER(s)?

Analysis of Ambient Air Quality 

Impacts Not Required for the 

Particular Pollutant

Source Impact 

Above SIL? Satisfies NAAQS AQ Analysis

Projected

NAAQS 

Violations?

Satisfies NAAQS AQ Analysis

Source Impact Greater 

Than or Equal to SIL 

at Projected 

Violations?

Compliance Demonstration is Not 

Adequate.

Y

Y

Y

N Y

N

Source Impact Analysis

N

N

Difference Between 

NAAQS and Measured

Background Greater 

Than or Equal to SIL?

Y

Cumulative Impact Analysis

Direct or Interpollutant Offsets

Please reference the PM2.5

NSR Implementation

final rule (73 FR 28321)

N

Nonattainment Area

N

Y

May Satisfy NAAQS AQ 

Analysis. Please reference 

Section IV.4
14

Figure II-2. Overview of PSD Increments Compliance Demonstration for New or 

Modifying Sources under NSR/PSD Programs

Compliance Demonstration is Not 

Adequate.

New or Modified

Source

Nonattainment

Area?

Attainment or Unclassified 

Area

Source Emissions

Greater Than or 

Equal to SER(s)?

Analysis of Ambient Air Quality 

Impacts Not Required for the 

Particular Pollutant

Source Impact 

Above Increment? Satisfies PSD Increment Analysis

Projected

Increment 

Violations?

Satisfies PSD Increment Analysis

Source Impact Greater 

Than or Equal to SIL 

at Projected 

Violations?

Compliance Demonstration is Not 

Adequate.

Y

Y

Y

N Y

N

Source Impact Analysis

N

N

First PSD 

Application in Area 

After Trigger Date?

Cumulative Impact Analysis

Increment Does Not Apply

N

Nonattainment Area

N

Y

May Satisfy PSD Increment 

Analysis. Please reference 

Section V.5

Major Source 

Construction Since 

Major Source Baseline 

Date?

Y N

Y
15

II.1 Significant Emissions Rates

EPA regulations only require an analysis of ambient air quality impacts for pollutants that 

a source emits (or that a modification of a source increases) in an amount equal to or greater than 

the significant emission rate for that pollutant defined in EPA regulations.14 The EPA 

promulgated SERs for PM2.5 and for the PM2.5 precursors, nitrogen oxides (NOx) and sulfur 

dioxide (SO2), in 2008 as part of the first phase of PSD amendments to address PM2.5.

15 (74 Fed. 

Reg. 28321 at 28333). The PM2.5 SER for direct emissions of primary PM2.5, defined as 10 tons 

per year (tpy) of direct PM2.5 emissions, and the PM2.5 precursor SERs, defined as either 40 tpy

of NOx or 40 tpy of SO2, are used to determine whether any proposed new major stationary 

source or major modification will emit sufficient amounts of direct PM2.5 and/or PM2.5

precursors, i.e., equal to or above the respective SERs, to require review for PM2.5 under the PSD 

program.

II.2 Screening and Source Impact Analysis

The EPA has historically supported the use of screening techniques in the PSD program 

to determine the extent of the air quality analysis that must be carried out to demonstrate that the 

source’s emissions will not cause or contribute to a violation of any NAAQS or increment. 16

 

14 See 40 CFR 51.166(m)(1)(i); 40 CFR 52.21(m)(1)(i).

15 The EPA’s final NSR rules for PM2.5 do not require regulation of volatile organic compounds (VOC) or ammonia 

(NH3) as precursors to PM2.5 for the PSD program. However, a state may demonstrate to the Administrator’s 

satisfaction or the EPA may demonstrate that VOC emissions in a specific area are a significant contributor to that 

area’s ambient PM2.5 concentrations. See 74 Fed. Reg. 28321. If so, then permit applicants with project sources 

having emissions of these pollutants should consult with the appropriate permitting authority and EPA Regional 

Office about how to deal with these emissions for the purposes of a NAAQS or PSD increments analysis.

16 This has been consistent with overall support for screening techniques in the modeling guidelines. See, 40 CFR 

Part 51, Appendix W, Sections 2.2 and 4.2.1. The Guideline observes that “use of screening techniques followed, as 

appropriate, by a more refined analysis is always desirable.” 40 CFR Part 51, Appendix W, Section 2.2.c. With 

respect to PSD permit review specifically, the Guideline says the following: “The purpose of [screening] techniques 
16

Using this screening approach, when a proposed source’s modeled impacts are found to be 

greater than the level of a SIL identified by the EPA, the EPA has called for a cumulative impact 

analysis (considering the combined impact of the proposed source and other sources in the 

affected area) to demonstrate that the proposed source will not cause or contribute to a violation 

of the NAAQS. On the other hand, the EPA has generally said that if the proposed source’s 

modeled impacts are found to be below the level of a SIL identified by EPA for the relevant 

pollutant, this showing may be sufficient to demonstrate that the source will not cause or 

contribute to a modeled violation of the NAAQS.17 However, the EPA has also acknowledged 

that there can be circumstances where a showing that the air quality impact of a proposed source 

is less than a SIL value identified by the EPA is not sufficient by itself to demonstrate that a 

source will not cause or contribute to a violation of the NAAQS or increment.

Prior to 2010, EPA had expressed support in guidance for applying the values in Section 

51.165(b)(2) of its regulations as SILs that could be used as part of a demonstration that a source 

does not cause or contribute to a violation of the NAAQS. However, when the EPA added SILs 

for PM2.5 in 2010 to paragraph (k)(2) of its Section 51.166 and 52.21 regulations, the Agency 

observed that “the use of a SIL may not be appropriate when a substantial portion of any 

NAAQS or increment is known to be consumed.” (75 Fed. Reg. 64894). The EPA also said that 

“notwithstanding the existence of a SIL, permitting authorities should determine when it may be 

appropriate to conclude that even a de minimis impact will “cause or contribute” to an air quality 

problem and to seek remedial action from the proposed new source or modification.” (75 Fed. 

Reg. 64892).

 

is to eliminate the need of more detailed modeling for those sources that clearly will not cause or contribute to 

ambient concentrations in excess of either the National Ambient Air Quality Standards (NAAQS) or the allowable 

prevention of significant deterioration (PSD) concentration increments.” Id. Section 2.2.a.

17 See 72 Fed. Reg. 54112 at 54139 and 75 Fed. Reg. 64864 at 64890.
17

In the course of litigation challenging the SILs for PM2.5, the EPA acknowledged that the 

regulatory language the EPA adopted in Sections 51.166(k)(2) and 52.21(k)(2) did not provide 

sufficient flexibility for permitting authorities to exercise discretion to conduct or require 

additional analysis in some circumstances where the EPA had advised doing so. As a result, the 

EPA requested that the U.S. Court of Appeals for the District of Columbia Circuit remand and 

vacate these provisions so the EPA could take corrective action. On January 22, 2013, the court 

granted this request and observed that, under the language in Sections 51.166(k)(2) and 

52.21(k)(2), sources in some scenarios would not be required to demonstrate that they would not 

cause or contribute to a violation of the NAAQS or increments, even though, based on 

Petitioner’s arguments, the sources likely would cause or contribute to a violation in such 

scenarios. The court concluded this would contravene the statutory command in Section

165(a)(3) of the Act. 705 F.3d at 464-65. The court also said that on remand the EPA may 

choose to promulgate regulations that “include SILs that do not allow the construction or 

modification of a source to evade the requirements of the Act as do the SILs in the current rule” 

and that such regulations would be subject to further review by the court. (Id. at 464).

EPA does not interpret the court’s decision to preclude the use of SILs for PM2.5 as part 

of a demonstration that a source will not cause or contribute to a violation of the PM2.5 NAAQS. 

However, to ensure that PSD permitting decisions meet the requirements of the CAA, permitting 

authorities that continue using SILs for PM2.5 must ensure that they select and apply such SILs in 

a manner that is consistent with the court’s decision and the EPA’s statements from the preamble 

of the 2010 regulation adopting SILs for PM2.5. The EPA is developing a proposed rule to 

address the issues identified by the EPA and the court’s decision. If necessary and as appropriate, 

this guidance will be amended after this rulemaking is proposed and subsequently finalized. In 
18

the interim, permitting authorities may not apply the SIL provisions in the vacated and repealed 

Sections 51.166(k)(2) and 52.21(k)(2). Furthermore, permitting authorities should not apply any 

state regulations that have not yet been amended to conform to the repeal of these provisions and 

still contain regulatory text that is the same as or has a similar effect as the paragraph (k)(2) 

language, particularly in the types of scenarios described in the court decision and the EPA’s 

2010 preamble to the PM2.5 Increments, SILs, and SMC Rule.

18 However, with appropriate 

safeguards, the EPA believes permitting authorities may continue to select and apply SILs values 

for PM2.5 to support PSD permitting decisions and to determine the level of analysis needed to 

demonstrate that a source will not cause or contribute to violation of the NAAQS.19 These 

safeguards involve two related considerations – the particular values of the SILs to be used and 

how those values are used.

The court decision does not preclude the use of SILs for PM2.5, but requires that the EPA 

correct the error in the SIL regulations for PM2.5 at 51.166(k)(2) and 52.21(k)(2). As a first step, 

on December 9, 2013, the EPA issued a final rule removing these sections of its regulations from 

the CFR (78 Fed. Reg. 73698). Until the EPA completes a rulemaking to replace these 

provisions, the EPA believes permitting authorities may continue to apply SILs for PM2.5 to 

support a PSD permitting decision, but permitting authorities should take care to ensure that SILs 

are not used in a manner that is inconsistent with the requirements of Section 165(a)(3) of the 

CAA.

Permitting authorities have the discretion to select the particular PM2.5 SIL values that are 

 

18 Prevention of Significant Deterioration (PSD) for Particulate Matter Less Than 2.5 Micrometers (PM2.5) –

Increments, Significant Impact Levels (SILs) and Significant Monitoring Concentration (SMC). See 75 Fed. Reg. 

64864 (October 20, 2010).

19 The topic of the level of analysis needed for PSD increments compliance analysis is discussed in more detail in 

Section V.
19

used to support a permitting decision, but the values used should be supported by either a 

permitting record or regulation that supports the use of those values in the particular manner they 

are used.20 Permitting authorities may not rely on the values contained in the vacated Sections 

51.166(k)(2) and 52.21(k)(2) of the EPA’s regulations as a screening tool without providing 

additional justification in the permitting record. However, with additional justification, it may be 

permissible in some cases for a permitting authority to use the same PM2.5 SIL values as listed in 

the vacated Sections 51.166(k)(2) and 52.21(k)(2) to demonstrate that a full cumulative impacts 

analysis is not needed to make the NAAQS compliance demonstration

To the extent a permitting authority wishes to use any of the SILs values in the vacated 

Sections 51.166(k)(2) or 52.21(k)(2) as a screening tool to determine whether it is necessary to 

conduct a cumulative analysis of NAAQS compliance, the permitting authority must first 

examine background air quality concentrations to determine whether a substantial portion of the 

NAAQS has been consumed.21 For this purpose, the EPA recommends using the preconstruction 

monitoring data compiled to meet the requirements of Section 51.166(m) or 52.21(m) of the 

EPA’s regulations. If the preconstruction monitoring data are sufficiently representative of the 

air quality in existence before the increase in emissions from the proposed source and the 

difference between the PM2.5 NAAQS and the measured PM2.5 background concentrations in the 

 

20 The EPA has previously observed that the absence of an EPA-promulgated SIL does not preclude PSD permitting 

authorities from developing and applying SILs to support permitting decisions. See, Response to Comments, 

Implementation of New Source Review (NSR) Program for Particulate Matter Less Than 2.5 Micrometers in 

Diameter (PM2.5) at 82 (March 2008) [EPA-HQ-OAR-2003-0062-0278]. However, the EPA has also observed that, 

“[t]he application of any SIL that is not reflected in a promulgated regulation should be supported by a record in 

each instance that shows the value represents a de minimis impact.” See, NO2 NAAQS Guidance at 13; and 

Mississippi Lime at 41 (granting the petition for review where the permitting authority failed to substantiate in the 

record which SIL it applied and its reasons for doing so).

21 The recent court decision vacating the PM2.5 SMC from the PSD regulations will mean that each PSD application 

must include ambient monitoring data representative of the area of concern. These data need not be collected by the 

PSD permit applicant if existing data are determined by the permitting authority to represent the air quality in the 

area of concern over the 12-month period prior to the submittal of a complete PSD application.
20

area is greater than or equal to the SIL value selected from the vacated Sections 51.166(k)(2) and 

52.21(k)(2), then the EPA believes it would be sufficient in most cases for permitting authorities 

to conclude that a source with an impact equal to or below that SIL value will not cause or 

contribute to a violation of the NAAQS and to forego a cumulative modeling analysis for PM2.5

with respect to the NAAQS.

The above comparison of background air quality concentrations and the NAAQS would 

not by itself provide adequate justification for foregoing a cumulative modeling analysis for the 

PM2.5 increments. Such an approach would be inappropriate because it would not ensure that 

there is sufficient “headroom” within the allowable increment to absorb a source contribution 

equal to the SIL. However, a permitting authority may still be able to justify reaching a 

determination that a new or modified source will not cause or contribute to a violation of the 

increments without performing cumulative modeling for increments. 

Since the trigger date has only recently been established (i.e., October 20, 2011), for the 

next several years, a new or modified source being evaluated for increments compliance will 

often be the first source with increment-consuming emissions in the area. As indicated in Figure

II-2, under this situation, a permitting authority may have sufficient reason to conclude that the 

impacts of the new or modified source (based on the approach for conducting source impact 

analysis described below) may be compared directly to the allowable increments, without the 

need for a cumulative modeling analysis. Such a situation would involve the new or modified 

source representing the first PSD application in the area after the trigger date, which establishes 

the minor source baseline date and baseline area, and confirmation that no relevant major source 

construction has already occurred since the major source baseline date.
21

II.3 Cumulative Impact Analysis

As part of a NAAQS compliance demonstration, a cumulative impact analysis for PM2.5

accounts for the combined impacts of direct and precursor emissions from the new or modifying 

source, of direct emissions from nearby sources (as appropriate), and of monitored background 

levels of PM2.5 that account for secondary PM2.5 impacts from regional transport, secondary 

PM2.5 impacts from precursor emissions from nearby sources, and primary PM2.5 impacts from

background sources not included in the modeled inventory. The cumulative impacts are then 

compared to the NAAQS to determine whether the source will cause or contribute to a violation 

of the NAAQS. Several aspects of the cumulative impact analysis for PM2.5 will be comparable 

to analyses conducted for other criteria pollutants, while other aspects will differ due to the 

issues identified earlier.

The measured background levels incorporated into a cumulative analysis should be based 

on the preconstruction monitoring data gathered in accordance with the requirements of the EPA 

regulations. 40 CFR 51.166(m)(1)(iii)-(iv); 40 CFR 52.21(m)(1)(iii)-(iv) (2). The EPA 

regulations contain an exemption from the preconstruction monitoring requirements in cases 

where ambient concentrations or the predicted impact of the source are less than the SMC. 40 

CFR 51.166(i)(5)(i) ; 40 CFR 52.21(i)(5)(i). In the decision mentioned above, a U.S. Court of 

Appeals vacated the SMC for PM2.5. Sierra Club v. EPA, 705 F.3d 458. The court concluded that 

the PM2.5 SMC provisions (51.166(i)(5)(i)(c) and 52.21(i)(5)(i)(c)) were inconsistent with the 

requirements of Section 165(e)(2) of the CAA. The EPA has subsequently removed the PM2.5

SMC provisions from the regulation.22 Thus, permitting authorities may no longer rely on the 

SMCs for PM2.5 to exempt permit applicants from compiling preconstruction monitoring data for 

 

22 See 78 Fed. Reg. 73698.
22

PM2.5 in accordance with Sections 51.166(m) and 52.21(m) of the EPA’s regulation. However, 

the EPA believes PSD permit applicants may continue to meet the preconstruction monitoring 

requirements in these regulations by gathering for purposes of the permitting analysis data 

already available from existing monitors that are determined by the applicable permitting 

authority to be representative of background conditions in the affected area.23

Where the screening analysis described in Section II.2 above is insufficient to show that a 

source will not cause or contribute to a violation of the PSD increments, a cumulative impact 

assessment would be necessary to make the demonstration. A cumulative assessment accounts

for the combined impact of the new or modifying source’s emissions and those emissions 

changes from sources that affect the increment. The cumulative impacts are then compared to the 

PSD increments to determine whether the new or modifying source emissions will cause or 

contribute to a violation of the PSD increments.

 

23 “EPA has long implemented the PSD program pursuant to the understanding that representative data may be 

substituted where circumstances warrant.” (In re: Northern Michigan University Ripley Heating Plant, PSD Appeal 

No. 08-02, slip op. at 58 (Feb. 18, 2009));

“...the prospective PSD source must use existing ... representative air quality data or collect ... monitoring data.”

(52 Fed. Reg. 24672 (July 1, 1987) at 24686); and

With regard to the PSD requirement for monitoring data, “use of ‘monitoring data’ refers to either the use of existing 

representative air quality data or monitoring the existing air quality.” (Ambient Monitoring Guidelines for 

Prevention of Significant Deterioration (PSD), EPA-450/4-80-012, November 1980, at page 3).
23

II.4 Assessment Cases for Source Impacts

To support the processes shown in Figure II-1 and Figure II-2, the EPA is recommending

four different assessment cases shown in Table II-1 that define which air quality analyses, if any, 

a permit applicant should conduct to demonstrate compliance with the PM2.5 NAAQS and PSD 

increments.

Table II-1. EPA Recommended Assessment Cases that Define Needed Air Quality Analyses

of Source Impacts

Assessment Case Description of Assessment Case

Assess Primary Impacts

of Direct PM2.5 

Emissions?

Assess Secondary Impacts 

of Precursor Emissions of 

NOx and/or SO2?

Case 1:

No Air Quality Analysis

Direct PM2.5 emissions < 10 tpy SER

NOx and SO2 emissions < 40 tpy SER NO NO

Case 2:

Primary Air Quality 

Impacts Only

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and SO2 emissions < 40 tpy SER YES NO

Case 3:

Primary and Secondary 

Air Quality Impacts

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER YES YES

Case 4:

Secondary Air Quality 

Impacts Only

Direct PM2.5 emissions < 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER NO YES

The four assessment cases presented in Table II-1 include:

• For “Case 1—No Air Quality Analysis,” if direct PM2.5 emissions are less than 

the SER of 10 tpy and both NOx and SO2 emissions are individually less than the 

respective SERs of 40 tpy, then no modeled compliance demonstration is 

required.24

• For “Case 2—Primary Air Quality Impacts Only,” if the direct PM2.5 emissions 

are greater than or equal to the SER of 10 tpy and both NOx and SO2 emissions 

are individually less than the respective SERs of 40 tpy, then a modeled PM2.5

 

24 See 40 CFR 51.166(m)(1)(i); 40 CFR 52.21(m)(1)(i)
24

compliance demonstration is required for only the direct PM2.5 emissions based 

on dispersion modeling and no modeling to account for impacts of precursor 

emissions from the project source is necessary.

• For “Case 3—Primary and Secondary Air Quality Impacts,” if the direct PM2.5

emissions are greater than or equal to the SER of 10 tpy and NOx and/or SO2

precursor emissions are greater than or equal to the respective SERs of 40 tpy, 

then a modeled PM2.5 compliance demonstration is required for the direct PM2.5

emissions based on dispersion modeling and the permit applicant should also 

assess the potential impact of the significant precursor emissions from the project 

source. The accounting of the precursor emissions impact on secondary PM2.5

formation may be: a) qualitative in nature; b) based on a hybrid of qualitative and 

quantitative assessments utilizing existing technical work; or c) a full quantitative 

photochemical grid modeling exercise. The EPA anticipates only a few situations 

would require explicit photochemical grid modeling.

• For “Case 4—Secondary Air Quality Impacts Only,” if the direct PM2.5 emissions 

are less than the SER of 10 tpy, but the NOx and/or SO2 precursor emissions are 

greater than or equal to the respective SERs of 40 tpy, then a modeled PM2.5

compliance demonstration for the direct PM2.5 emissions is not required, but the 

permit applicant should assess the potential impact of the significant precursor 

emissions from the project source. Similar to “Case 3,” the accounting of the 

precursor emissions impact on secondary PM2.5 formation may be: a) qualitative 

in nature; b) based on a hybrid of qualitative and quantitative assessments 

utilizing existing technical work; or c) a full quantitative photochemical grid 
25

modeling exercise. Again, the EPA anticipates that only a limited number of

situations would require explicit photochemical grid modeling.

Details regarding the source impact analysis and cumulative impact analysis associated

with Cases 2, 3, and 4, where project emissions are equal to or greater than the respective SERs

for direct PM2.5 emissions only (Case 2), for both direct PM2.5 and precursor emissions of NOx

and/or SO2 (Case 3), or for precursor emissions of NOx and/or SO2 only (Case 4), are provided in 

Sections III and IV (NAAQS) and Section V (Increments).
26

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27

III. Source Impact Analysis for the PM2.5 NAAQS

This section provides details regarding the recommended approaches for conducting the

source impact analysis associated with each of the four assessment cases presented in Table III-1

so long as the SIL has been appropriately justified for use in each NAAQS compliance 

demonstration as described in Section II.2. In each of the assessment cases, the analysis should 

begin by evaluating the impacts of direct PM2.5 emissions and/or PM2.5 precursor emissions

based upon the total amount of these emissions as compared to the respective SERs.

Table III-1. EPA Recommended Approaches for Assessing Primary and Secondary PM2.5

Impacts by Assessment Case

Assessment Case Description of Assessment Case Primary Impacts Approach Secondary Impacts 

Approach

Case 1:

No Air Quality Analysis

Direct PM2.5 emissions < 10 tpy SER

NOx and SO2 emissions < 40 tpy SER N/A N/A

Case 2:

Primary Air Quality 

Impacts Only

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and SO2 emissions < 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

N/A

Case 3:

Primary and Secondary 

Air Quality Impacts

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

Case 4:

Secondary Air Quality 

Impacts Only

Direct PM2.5 emissions < 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER N/A

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

A modeled NAAQS compliance demonstration is not required for Case 1 since neither 

direct PM2.5 emissions nor PM2.5 precursor (NOx and/or SO2) emissions are equal to or greater 

than the respective SERs. Case 1 is the only assessment case that does not require a modeled 

NAAQS compliance demonstration. Each of the remaining three assessment cases would 

necessitate a source impact analysis.

The simplest or most traditional assessment case is Case 2 where only direct PM2.5
28

emissions are greater than or equal to the SER. For Case 2, the permit applicant would only need

to demonstrate that ambient PM2.5 impacts associated with its increase in direct PM2.5 emissions 

are below a SIL based on dispersion modeling using AERMOD or other appropriate preferred 

model listed in Appendix A of Appendix W, or an alternative model subject to the provisions of 

Section 3.2 of Appendix W.

Since both direct PM2.5 emissions and NOx and/or SO2 precursor emissions are equal to 

or greater than the respective SERs for Case 3, this will likely be the most challenging of the four 

assessment cases. As with Case 2, the ambient PM2.5 impacts associated with direct PM2.5

emissions can be estimated based on application of an appropriate preferred dispersion model for 

near-field PM2.5 modeling listed in Appendix W, currently AERMOD for most applications, or 

an approved alternative model. However, AERMOD does not account for secondary formation 

of PM2.5 associated with the source’s precursor emissions. Since the source also emits quantities 

of PM2.5 precursors above the respective SERs for Case 3, some assessment of their potential 

contribution to secondary PM2.5 is necessary. The assessment of NOx and/or SO2 precursor 

emission impacts on secondary PM2.5 formation may be: a) qualitative in nature; b) based on a 

hybrid of qualitative and quantitative assessments utilizing existing technical work; or c) a full 

quantitative photochemical grid modeling exercise. The EPA anticipates that only a limited 

number of situations would require explicit photochemical grid modeling.

Since direct PM2.5 emissions are below the applicable SER for Case 4, the source impact 

analysis in this case would only address the potential contribution to secondary PM2.5 from NOx

and/or SO2 precursor emissions, and would not require any modeling of direct PM2.5 emissions. 

As discussed above for Case 3, the assessment of the precursor emission impacts on secondary 

PM2.5 formation for Case 4 may be: a) qualitative in nature; b) based on a hybrid of qualitative 
29

and quantitative assessments utilizing existing technical work; or c) a full quantitative 

photochemical grid modeling exercise. As with Case 3, the EPA anticipates that only a few 

situations would require explicit photochemical grid modeling.

III.1 Assessing Primary PM2.5 Impacts

The assessment of primary PM2.5 impacts from the proposed new or modifying source is 

generally the same for the NAAQS and increments and should be consistent with Appendix W. 

As noted above, Appendix W recommends specific models as “preferred” for specific types of 

applications, based on model performance evaluations and other criteria. The purpose of 

recommending the use of a particular preferred model is to ensure that the best-performing 

model is used in assessing PM impacts from a particular project and is employed in a consistent 

fashion.25 In 2005, the EPA promulgated AERMOD as the Agency’s preferred near-field 

dispersion model for a wide range of regulatory applications in all types of terrain based on 

extensive developmental and performance evaluation.26 For NSR/PSD modeling for the PM2.5

NAAQS, the AERMOD modeling system should be used to model direct PM2.5 emissions unless 

another preferred model is more appropriate, such as the Buoyant Line and Point source 

dispersion model (BLP), or the use of an alternative model can be justified consistent with 

Section 3.2 of Appendix W.

 

25 The best performing model is one that best predicts regulatory design values for a particular pollutant. The EPA’s 

Protocol for Determining the Best Performing Model (U.S. EPA, 1992) defines appropriate methodologies and 

statistical criteria for this evaluation. According to the document, “For a pollutant... for which short-term ambient 

standards exist, the statistic of interest involves the network-wide highest concentration...the precise time, location, 

and meteorological condition is of minor concern compared to the magnitude of the highest concentration actually 

occurring.”

26 The final rule can be found at: http://www.epa.gov/scram001/guidance/guide/appw_05.pdf. Extensive 

documentation is available describing the various components of AERMOD, including user guides, model 

formulation, and evaluation papers. See EPA’s SCRAM website for AERMOD documentation: 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod
30

As noted in the EPA’s March 23, 2010, PM2.5 guidance memorandum, although dry 

and/or wet deposition may be important processes when estimating ambient concentrations of 

PM in general, these factors are expected to be minor for PM2.5 due to the small particle size. In 

addition, there may be additional uncertainty associated with deposition modeling for PM2.5 due 

to the fact that deposition properties may vary depending on the constituent elements of PM2.5. 

Therefore, use of deposition algorithms to account for depletion in estimating ambient PM2.5

concentrations should be done with caution and only when clear documentation and justification 

of the deposition parameters is provided.

The AERMOD modeling system includes the following components:

• AERMOD: the dispersion model (U.S. EPA, 2004a; U.S. EPA, 2014b);

• AERMAP: the terrain processor for AERMOD (U.S. EPA, 2004b, U.S. EPA, 2011a); 

and

• AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2004c; U.S. 

EPA, 2014c).

Other components that may be used, depending on the application, are:

• BPIPPRIME: the building input processor (U.S. EPA, 2004d);

• AERSURFACE: the surface characteristics processor for AERMET (U.S. EPA, 2008);

• AERSCREEN: a screening version of AERMOD (U.S. EPA, 2011b; U.S. EPA, 2011c); 

and

• AERMINUTE: a pre-processor to calculate hourly average winds from ASOS 2-minute 

observations (U.S. EPA, 2011d).

Before running AERMOD, the user should become familiar with the user’s guides 
31

associated with the modeling components listed above and the most recent version of the 

AERMOD Implementation Guide (U.S. EPA, 2009). In addition to these documents, detailed 

guidance on the use of the AERMOD modeling system for estimating primary PM2.5 impacts is 

provided in Appendix B. Because AERMOD is limited to modeling only direct PM2.5 emissions,

additional or alternative approaches must be used to provide an assessment of the secondary 

PM2.5 impact from the proposed new or modifying source, as discussed in more detail in the 

following sections.

III.2 Assessing Secondary PM2.5 Impacts

This section provides more detail on the recommended approaches for assessing the 

impacts of precursor emissions on secondary PM2.5 formation for Cases 3 and 4 presented in 

Table III-1 including:

• a qualitative assessment; 

• a hybrid of qualitative and quantitative assessments utilizing existing technical work; and

• a full quantitative photochemical grid modeling exercise.

III.2.1 Qualitative Assessments

In a number of NAAQS compliance demonstrations requiring an assessment of the 

impact from secondary PM2.5 formation, it is anticipated that a holistic qualitative analysis of the 

new or modifying emissions source and the atmospheric environment in which the emissions 

source is to be located will suffice for determining that secondary PM2.5 impacts associated with 

the source’s precursor emissions will not cause or contribute to a violation of the 24-hour or 

annual PM2.5 NAAQS. Each NAAQS compliance demonstration will be unique and may require 
32

multiple factors to be considered and assumptions to be thoroughly justified as a part of the 

qualitative assessment. A well-developed modeling protocol that includes a detailed conceptual 

description of the current air pollution concentrations in the area (see Appendix A for examples 

of elements of a conceptual description) and of the nature of the emissions sources surrounding 

the new or modifying emissions source is paramount for determining the necessary components 

of an acceptable qualitative assessment of the impact from secondary PM2.5 formation.

27 With 

appropriate consultation, submittal, and subsequent approval of the modeling protocol by the 

appropriate permitting authority, many potential problems and unintended oversights in the 

qualitative assessment can be resolved early in the process or avoided all together.

In the development of an appropriate conceptual description of PM2.5 to support a 

qualitative assessment of the impact from secondary PM2.5 formation, it is important to fully 

characterize the current PM2.5 concentrations in the region where the new or modifying 

emissions source is to be located. This characterization should take into consideration not only 

the most current 24-hour and annual PM2.5 design values, which would typically be used as 

background concentrations in a cumulative modeling demonstration, but should also include an 

understanding of the seasonality and speciated composition of the current PM2.5 concentrations 

and any long term trends that may be occurring. Understanding whether or not PM2.5

 

27 For more detailed information on the development of such conceptual descriptions for an area, please refer to the 

following:

Chapter 10 of “Particulate Matter Assessment for Policy Makers: A NARSTO Assessment.” P. McMurry, M. 

Shepherd, and J. Vickery, eds. Cambridge University Press, Cambridge, England (NARSTO, 2004).

Section 11, “How Do I Get Started? 'A Conceptual Description'” of “Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze.” U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina (U.S. EPA, 2007a).

In addition, relevant regional examples include: “Conceptual Model of PM2.5 Episodes in the Midwest”, January 

2009, Lake Michigan Air Directors Consortium; and “Conceptual Model of Particulate Matter Pollution in the 

California San Joaquin Valley,” Document Number CP045-1-98, September 8, 1998.
33

concentrations are higher or lower in certain seasons or fairly uniform throughout a year and 

determining whether there are particular component species (e.g., sulfates, nitrates, and 

elemental or organic carbon) that dominate the makeup of high, low, and average PM2.5

concentrations will help guide the degree of analysis and ultimately the justification that will be 

required in the qualitative assessment based on the magnitude and characteristics of any 

significant precursor emissions from the source. It may also be important to describe the typical 

background concentrations of certain chemical species that participate in the photochemical 

reactions that form secondary PM2.5, such as NH3, VOC, and ozone. It is possible that there are 

mitigating factors for secondary PM2.5 formation given limitations of other chemical species 

important in the photochemical reactions, e.g., minimal NH3 in the ambient environment that 

could limit any precursor pollutant from readily reacting to form secondary PM2.5. The 

qualitative assessment should include a narrative explaining how any identified significant 

precursor emissions and subsequent secondary PM2.5 formation could contribute to the existing 

PM2.5 concentration environment in the region.

A good conceptual description will also characterize the meteorological conditions that 

are representative of the region and are associated with periods and/or seasons of higher and 

lower ambient 24-hour PM2.5 concentrations. Identification of meteorological phenomena that 

typically occur during periods of high 24-hour PM2.5 concentrations, such as low-level 

temperature inversions, stagnant high pressure systems, etc., can be extremely important in 

understanding the importance, or lack thereof, of photochemistry and secondary PM2.5 formation 

for the higher ambient PM2.5 concentrations. The analysis and understanding of meteorological 

conditions will also inform the assessment of the seasonality of the 24-hour PM2.5 concentrations 

in the region. The qualitative assessment should expand upon the characterization of 
34

meteorology described in the conceptual description to explain any meteorological factors that 

could limit or enhance the formation of secondary PM2.5 from any significant precursor 

emissions.

Analysis of existing photochemical grid modeling developed for regional haze, ozone, 

and PM2.5 SIPs or other photochemical grid modeling used in related sensitivity projects or 

analysis to support prior air quality rules may also be considered to help understand the general 

response of secondary PM2.5 formation to certain magnitudes of a precursor pollutant in that 

region. While the new or modifying emissions source may emit a significant level of a precursor 

pollutant under PSD regulations, that level of emission may be extremely small when compared 

against the total emissions of that precursor pollutant throughout the region. The qualitative 

assessment of the impact from secondary PM2.5 formation can be strengthened if substantial 

regional decreases or increases of that precursor pollutant have been demonstrated through 

photochemical grid modeling exercises do not cause significant decreases or increases of 

secondary PM2.5.

An example of a thoroughly developed qualitative assessment of the potential for 

secondary PM2.5 formation to cause or contribute to a violations of the NAAQS was provided by 

the EPA Region 10 Office through a response to public comments document regarding a CAA

permit issued for Shell’s Discoverer drill ship and support fleet to explore for oil and gas in the 

Chukchi Sea off Alaska. While the environment in and around the Chukchi Sea and North Slope 

of Alaska is unique when compared to the rest of the United States, the various components 

contained within this qualitative assessment provide a template that could be followed, with 

appropriate modifications, in the development of other case-specific qualitative assessments. An 

excerpt from this response to public comments document is provided in Appendix C.
35

As shown in the EPA Region 10 example, the qualitative assessment of the potential for 

secondary PM2.5 formation by Shell’s Discoverer drill ship and support fleet was developed in a 

narrative manner integrating numerous factors specific to the North Slope region of Alaska that 

provided sufficient evidence that the PM2.5 NAAQS would not be violated in this particular case. 

The qualitative assessment examined the regional background PM2.5 monitoring data and aspects 

of secondary PM2.5 formation from existing sources; the relative ratio of the combined modeled 

primary PM2.5 impacts and background PM2.5 concentrations to the level of the NAAQS; the 

spatial and temporal correlation of the primary and secondary PM2.5 impacts; meteorological 

characteristics of the region during periods of precursor pollutant emissions; the level of 

conservatism associated with the modeling of the primary PM2.5 component and other elements 

of conservatism built into the overall NAAQS compliance demonstration; aspects of the 

precursor pollutant emissions in the context of limitations of other chemical species necessary 

for the photochemical reactions to form secondary PM2.5; and an additional level of NAAQS 

protection through a post-construction monitoring requirement. While each of the components of 

the EPA Region 10 example may or may not be necessary, this example should provide a useful 

template for other qualitative assessments under this guidance, recognizing that additional 

components may be essential in other qualitative assessments of the potential for secondary 

PM2.5 formation.

III.2.2 Hybrid Qualitative/Quantitative Assessment

The qualitative assessment discussed above is largely focused on a determination that the 

proposed new or modifying source precursor emissions, in combination with the estimated 

primary PM2.5 impacts (if applicable for Case 3), will not cause or contribute to a violation of the 
36

24-hour and/or annual PM2.5 NAAQS. However, it may not always be possible to provide such a 

justification without some quantification of the potential secondary PM2.5 impacts from the 

proposed new or modifying source’s precursor emissions. In such cases, the EPA expects that 

existing air quality model-based information regarding the potential for SO2 and NOx precursor 

emissions to form secondary PM2.5 concentrations may be used to establish an appropriate 

estimate of secondary PM2.5 impacts from the proposed new or modifying source. As described 

above, there may be situations where the proposed new or modifying source’s total ambient 

impact (i.e., primary and secondary impacts) is less than a SIL, and the record demonstrates that 

no further air quality assessment would be needed to demonstrate that the source would not 

cause or contribute to a violation of any NAAQS. Otherwise, a cumulative impact assessment 

would be necessary, which is discussed in Section IV.

To inform a hybrid qualitative/quantitative assessment, the existing air quality modelbased information would need to be appropriate in terms of representing the type of source, its 

precursor emissions, and its geographic location, in addition to those elements of the conceptual 

description discussed above for the qualitative assessment. The quantitative modeling 

information may be available from past or current SIP attainment demonstration modeling, 

published modeling studies, or peer-review literature with estimates of model responsiveness to 

precursor emissions in contexts that are relevant to the new or modifying source. The estimates 

of model responsiveness, such as impact on PM2.5 concentrations per ton of SO2 emissions, could 

then be used in conjunction with the precursor emissions estimates for the proposed new or 

modifying source to provide a quantitative estimate of the impact of such precursor emissions on 

the formation of secondary PM2.5 concentrations. The estimates should be technically credible in 

representing such impacts and it may be advisable for the estimate to reflect an upper bound of 
37

potential impacts.

The NACAA Workgroup final report (NACAA, 2011) provides details on potential 

approaches to quantify the secondary PM2.5 impacts from a proposed new or modifying source 

that may be appropriate to inform a hybrid qualitative/quantitative assessments of PM2.5 impacts

(See Appendix C and D of NACAA, 2011). One suggested method in the final report is to 

convert emissions of precursors into equivalent amounts of direct PM2.5 emissions using 

“pollutant offset ratios” and then use a dispersion model to assess the impacts of the combination 

of direct PM2.5 emissions and the equivalent direct PM2.5 emissions. The “pollutant offset ratios” 

referenced in the final report were those put forth by the EPA in the 2008 “Implementation of the 

New Source Review (NSR) Program for Particulate Matter Less Than 2.5 Micrometers (PM2.5)”

final rule (73 Fed. Reg. 28321) concerning the development and adoption of interpollutant 

trading (offset) provisions for PM2.5 under state nonattainment area NSR programs for PM2.5.

28

The EPA’s July 23, 2007, technical analysis titled “Details on Technical Assessment to Develop 

Interpollutant Trading Ratios for PM2.5 Offsets,” describes the method used to establish the 

original "preferred" precursor offset ratios (U.S. EPA, 2007b).

We do not support using the specific results from the EPA's 2007 technical assessment in 

this context without additional technical demonstration specific to the source(s) and area(s) for

which the ratios would be applied. However, we expect that the EPA Regional Offices, with 

assistance from the OAQPS, may assist state/local air permitting agencies, as necessary, to 

 

28 In the preamble to the 2008 final rule (73 Fed. Reg. 28321), the EPA included preferred or presumptive offset 

ratios, applicable to specific PM2.5 precursors that state/local air agencies may adopt in conjunction with the new 

interpollutant offset provisions for PM2.5, and for which the state could rely on the EPA's technical work to 

demonstrate the adequacy of the ratios for use in any PM2.5 nonattainment area. In a July 21, 2011 memorandum, 

EPA changed its policy and stated that it no longer supported the ratios provided in the preamble to the 2008 final 

rule as presumptively approvable ratios for adoption in SIPs containing nonattainment NSR programs for PM2.5. 

Memorandum from Gina McCarthy, Assistant Administrator to Regional Air Division Directors, “Revised Policy to 

Address Reconsideration of Interpollutant Trading Provisions for Fine Particles (PM2.5)” (U.S. EPA, 2011e).
38

structure appropriate technical demonstrations leading to the development of appropriate source 

and area-specific offset ratios for PM2.5 that may be appropriate for the purposes of estimating 

potential secondary PM2.5 impacts. As described in the EPA’s July 21, 2011, memorandum 

addressing reconsideration of the interpollutant trading provisions for the 2008 final rule, the 

EPA acknowledged that existing models and techniques are adequate to “conduct local 

demonstrations leading to the development of area-specific ratios for PM2.5 nonattainment areas” 

and provided a general framework for efforts that may be relevant in developing appropriate 

“pollutant offset ratios” for use in hybrid qualitative/quantitative assessment of secondary PM2.5

impacts (U.S. EPA, 2011e).

An example of a hybrid qualitative/quantitative assessment of secondary PM2.5 impacts 

was developed by a permit applicant, Sasol, for a major facility expansion in Southern Louisiana 

through close coordination with the EPA Region 6 Office and the Louisiana Department of 

Environmental Quality (LDEQ). Sasol and LDEQ worked closely with Region 6 to ensure that 

the ambient impacts analysis was robust and defendable. In this particular hybrid assessment, 

Sasol took an approach of using the formerly presumptive interpollutant trading ratios for NOx

and SO2 to PM2.5 offsets and conservatively applied them in an illustrative example to 

demonstrate how relatively inconsequential the impacts of secondary PM2.5 formation would be 

in the area of significant impact surrounding their facility. Sasol did not seek to directly apply the 

formerly presumptive interpollutant trading ratios in an absolute sense. Rather, the intention was 

to present the analysis in a manner to determine if further technical justification would be 

required or if the application of the formerly presumptive interpollutant trading ratios was

adequate in a hybrid qualitative/quantitative sense. A more detailed discussion of Sasol’s hybrid 

assessment is provided in Appendix D.
39

The EPA also notes that the NACAA Workgroup “considered, but rejected, other 

methods for assessing secondary PM2.5 impacts, including use of a simple emissions divided by 

distance (Q/D) metric and use of AERMOD with 100 percent conversion of SO2 and NOx

concentrations to (NH4)2SO4 and (NH4)NO3.” The EPA has reviewed the detailed discussion 

provided in Appendix E of the NACAA Workgroup final report and agrees with these 

conclusions.

III.2.3 Full Quantitative Photochemical Grid Modeling

In those rare cases where it is deemed necessary to estimate secondary PM2.5 impacts 

with full quantitative photochemical grid modeling, the candidate model for use in estimating 

single source impacts on secondarily formed PM2.5 should meet the general criteria for an 

“alternative model” outlined in Section 3.2.2 of 40 CFR 51.112 and 40 CFR Part 51, 

Appendix W, for condition (3) where “the preferred model is less appropriate for the specific 

application, or there is no preferred model,” i.e.,

i. The model has received a scientific peer review;

ii. The model can be demonstrated to be applicable to the problem on a theoretical 

basis;

iii. The databases that are necessary to perform the analysis are available and 

adequate;

iv. Appropriate performance evaluations of the model have shown that the model is 

not biased toward underestimates; and

iv. A protocol on methods and procedures to be followed has been established.
40

Section 3.2.1 of Appendix W also discusses appropriate methodologies for evaluating 

performance of models for regulatory applications, including the EPA’s “Protocol for 

Determining the Best Performing Model” (U.S. EPA, 1992). The determination of acceptability 

of a particular model and approach for such an alternative model application is an EPA Regional 

Office responsibility that may also include consultation with the EPA Headquarters, if 

appropriate.

As noted in the NACAA Workgroup final report, photochemical grid models provide a 

complete characterization of emissions, chemical transformation, transport, and deposition using

time and space variant meteorology. The EPA’s modeling guidance for PM2.5 attainment 

demonstrations (U.S. EPA, 2007a) identifies both the Comprehensive Air Quality Model with 

Extensions (CAMx) (ENVIRON, 2011; Nobel et al., 2001; Russell, 2008) and the Community 

Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006; Foley et al., 2010). These stateof-the-science photochemical grid models have been used by the EPA for air quality modeling to 

support federal rulemaking and by state/local air permitting agencies for their air quality 

planning efforts. Some photochemical grid models have been instrumented with extensions that 

allow for the identification of impacts from specific sources to important receptor locations. 

These extensions generally fall in the categories of source apportionment and source sensitivity,

and of sub-grid plume treatment and sampling, as described below.

Based on the current capabilities of photochemical grid models and consistent with the 

NACAA Workgroup report, the EPA recommends the following approaches be considered to 

estimate secondary PM2.5 impacts from a proposed new or modifying source using this type of 

model:

• “Brute force zero-out” or difference method where two model simulations are conducted, 
41

one with all existing sources and a second, counterfactual simulation with all existing 

sources and the new source emissions, with the difference being taken as the contribution 

from the new or modifying source.

• Instrumented techniques such as 

o Source apportionment tools where the precursor emissions from the new or 

modifying source are tracked to provide a contribution estimate for that individual 

source, or 

o Higher-order decoupled direct method (HDDM) which tracks the sensitivity of 

results to the emissions from a new or modifying source to provide coefficients 

relating source emissions to air quality response.

The NACAA Workgroup final report notes that these approaches represent 

fundamentally different methods and may result in different estimates for secondary PM2.5

impacts depending on the non-linear chemical processes. The EPA, state/local permitting 

agencies, and others within the atmospheric modeling community continue to apply these 

techniques to test and evaluate their suitability for estimating single source impacts on

secondarily formed PM2.5. These efforts are critically important to inform current application of 

these models and techniques for purposes of assessing the secondary PM2.5 impacts from a 

proposed new or modifying source, as well as to inform efforts to evaluate updates to 

Appendix W with new analytical techniques or models for ozone and secondary PM2.5 per the 

commitments contained in the EPA’s January 4, 2012, grant of the July 28, 2010, petition filed 

by the Sierra Club.

29

 

29 Several photochemical grid modeling approaches that allow for estimation of the secondary PM2.5 impacts from a 

proposed new or modifying source were presented during the Emerging Models / Techniques Session of the 10th
42

Photochemical grid models that have been instrumented with source apportionment

techniques track emissions from specific sources through the chemical transformation, transport, 

and deposition processes to estimate the source’s contribution to predicted air quality at 

downwind receptors (Baker and Foley, 2011). Source sensitivity approaches provide information 

about how model predicted concentrations change based on an increase or decrease in emissions

from a specific source. The difference in air quality between the original baseline simulation and 

the simulation where emissions are perturbed provides a quantitative estimate of that source’s 

contribution to the cumulative impact estimate.

Another approach to differentiate the contribution of single sources on changes in model 

predicted air quality is the higher-order decoupled direct method (HDDM), which tracks the 

sensitivity of model results to emissions for a specific source through all chemical and physical 

processes in the modeling system (Bergin et al., 2008). Sensitivity coefficients relating source 

emissions to air quality are estimated during the model simulation and output at the resolution of 

the photochemical grid model. An important difference between source apportionment and 

source sensitivity is that source apportionment answers the “contribution” question, “How much 

did a source contribute overall to modeled air quality?” and source sensitivity answers the 

“responsiveness” question, “How will modeled air quality change if the source’s emissions 

change?”

In some instances where the source and critical receptors are in very close proximity, the 

source and receptors may be located in the same photochemical grid model cell. Since physical 

and chemical processes simulated in the model represent a volume average, this may not 

adequately (or appropriately) represent the gradients of pollution that may exist between the 

 

Modeling Conference. Additional information regarding and presentations from the 10th Modeling Conference can 

be found on the SCRAM website at: http://www.epa.gov/ttn/scram/10thmodconf.htm.
43

source and receptors. One approach to more explicitly represent the spatial gradient in sourcereceptor relationships when they are in close proximity would be to use smaller sized grid cells. 

Grid resolution would be defined such that the source and receptors are no longer in the same

grid cell. Ideally, there would also be several grid cells between the source and receptors to best 

resolve near-source pollution gradients. 

In these situations of close proximity between the source and receptors, a photochemical 

grid model instrumented with sub-grid plume treatment and sampling may be an alternative 

approach for characterizing these relationships. Sub-grid plume treatment extensions in 

photochemical grid models typically solve for in-plume chemistry and use a set of physical and 

chemical criteria for determination of when puff mass is merged back into the host model grid. 

However, accounting for source specific impacts both at the sub-grid and grid levels is 

challenging and enhancements to traditional implementations of this approach may be necessary 

to fully capture source impacts for permit applications.

For this guidance, the EPA is not prescribing in detail how photochemical grid models 

(or their instrumented extensions) should be applied for the purposes of conducting a NAAQS 

compliance demonstration since these details may involve case-specific factors that would need 

to be part of the consultative process with the appropriate permitting authority and reflected in 

the agreed-upon modeling protocol. With this in mind, we recommend that the modeling 

protocols for this purpose should include the follow elements:

1. Overview of Modeling/Analysis Project

• Participating organizations

• Schedule for completion of the project

• Description of the conceptual model for the project source/receptor area
44

• Identify how modeling and other analyses will be archived and documented

• Identify specific deliverables to the appropriate permitting authority

2. Model and Modeling Inputs

• Rationale for the selection of air quality, meteorological, and emissions models

• Modeling domain

• Horizontal and vertical resolution

• Specification of initial and boundary conditions

• Episode selection and rationale for episode selection

• Rationale for and description of meteorological model setup

• Basis for and development of emissions inputs

• Methods used to quality assure emissions, meteorological, and other model inputs

3. Details on the approach for comparison to the SIL and/or NAAQS

4. Model Performance Evaluation

• Describe ambient database(s)

• Describe evaluation procedures and performance metrics

As stated previously, we expect that the EPA Regional Offices, with assistance from the 

OAQPS, may assist states, as necessary, to structure appropriate technical demonstrations 

leading to the development of appropriate photochemical grid modeling applications for the 

purposes of estimating potential secondary PM2.5 impacts.
45

III.3 Comparison to the SIL

Where a permit applicant wishes to compare the proposed source’s total ambient PM2.5

impacts to a SIL in order to make the required demonstration that a source does not cause or 

contribute to a violation of the NAAQS, the compliance demonstration will vary depending on 

whether Case 2, 3, or 4, where direct PM2.5 and/or precursor emissions are equal to or greater 

than the respective SERs, is applicable.

For Case 2, where only direct PM2.5 emissions are equal to or greater than the applicable 

(10 tpy) SER, the SIL may be compared to the modeled estimates of ambient primary PM2.5

concentrations due to direct emissions using the preferred AERMOD dispersion model (or 

acceptable preferred or alternative model). The modeling methods used in this initial source

impact assessment phase of the PM2.5 analysis for Case 2 are similar to the methods used for 

other pollutants, including the use of maximum allowable emissions, following Table 8-2 of 

Appendix W. However, due to the form of the PM2.5 NAAQS, we recommend that a SIL be 

compared to either of the following, depending on the meteorological data used in the analysis:

• The highest of the 5-year averages of the maximum modeled 24-hour or annual PM2.5

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

representative National Weather Service (NWS) data; or

• The highest modeled 24-hour or annual PM2.5 concentrations 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 24-hour or annual PM2.5 concentrations 

predicted each year at each receptor, based on 2 or more years, up to 5 complete years 

of available site-specific meteorological data.

These metrics represent the maximum contribution that project emissions could make to the air 
46

quality impact at any receptor, given the form of the NAAQS, and therefore provide an 

appropriate part of the basis for determining whether a cumulative modeling analysis would be 

needed.

For Case 3, where the source’s direct PM2.5 emissions and emissions of at least one 

precursor are equal to or greater than the respective SERs, the comparison of the SIL would need

to address both primary and secondary PM2.5 ambient impacts associated with the proposed 

source. As with Case 2, the ambient impacts due to direct PM2.5 emissions would be estimated 

using the preferred AERMOD dispersion model (or acceptable alternative model). However, the 

comparison to the SIL will depend on the type of assessment conducted for the secondary PM2.5 

impacts from the source. As noted above, the assessment of the precursor emission impacts on 

secondary PM2.5 formation may be: a) qualitative in nature; b) based on a hybrid of qualitative 

and quantitative assessments utilizing existing technical work; or c) a full quantitative 

photochemical grid modeling exercise.

Since any SIL that is used should represent a specific insignificant (or de minimis) 

ambient concentration of PM2.5 that may be used to demonstrate that a source will not cause or 

contribute to a NAAQS violation without conducting a cumulative impact assessment, basing the 

initial source impact analysis for Case 3 on a qualitative assessment (or a hybrid of qualitative 

and quantitative assessments) of secondary PM2.5 ambient impacts may be difficult to justify. 

This is because there would be no specific quantitative estimate of total PM2.5 impacts for 

comparison to the SIL, unless a valid argument can be made that secondary PM2.5 impacts 

associated with the source’s precursor emissions will be very small (e.g., precursor emissions 

barely exceed the respective SERs and/or the chemical environment is not conducive to 

secondary formation). As such, when using either of these approaches, it may be appropriate to 
47

forego the SIL assessment and focus on the NAAQS compliance demonstration using a 

cumulative impact analysis. 

For cases where a full quantitative photochemical grid modeling assessment of secondary 

PM2.5 is conducted, the SIL comparison for Case 3 should be based on the combined ambient 

impacts of primary and secondary PM2.5. However, the primary and secondary PM2.5 impacts 

may be combined in various ways which may entail greater or lesser degrees of conservatism. 

For example, combining the peak estimated primary PM2.5 impact with the peak estimated 

secondary PM2.5 impact, unpaired in time and space would likely result in a conservative 

estimate of combined impacts since, as noted above, peak impacts associated with a source’s

direct PM2.5 and precursor emissions are not likely well-correlated in time or space. On the other 

hand, the conservatism associated with combining peak estimated primary and secondary 

impacts for comparison to a SIL would likely make such an approach easier to justify than other 

approaches for combining estimated primary and secondary PM2.5 impacts. 

The other extreme for combining primary and secondary PM2.5 impacts for comparison to 

a SIL for Case 3, relative to combining peak primary and peak secondary impacts unpaired in 

time and space, would be full temporal and spatial pairing of estimated primary and secondary 

PM2.5 impacts. Such an approach may not be feasible in many cases, given that the dispersion 

modeling and photochemical grid modeling may be based on different data periods. Furthermore, 

full temporal and spatial pairing of primary and secondary PM2.5 impacts may not be appropriate 

in many cases due to the fact that photochemical grid modeling represents gridded concentration

estimates whereas dispersion modeling produces estimates at discrete receptor locations and 

given the limitations in the skill of both the dispersion model and the photochemical grid model 

to accurately predict impacts on a paired in time and space basis. On the other hand, some degree 
48

of temporal pairing of primary and secondary PM2.5 impacts on a seasonal or monthly basis 

should be appropriate in most cases, recognizing the general lack of correlation between primary 

and secondary impacts. 

The permitting authority and the permit applicant should thoroughly discuss the details 

regarding combining modeled primary and secondary PM2.5 impacts for Case 3 and should reach 

agreement on a protocol during the initial review of the modeling protocol. It may be appropriate 

for the protocol to specifically identify multiple tiers for combining the modeled primary and 

secondary PM2.5 impacts with the more conservative approaches being easier to justify. The 

permitting authority should ensure that any approach for combining estimated primary and 

secondary PM2.5 impacts for comparison to a SIL for Case 3 conforms to the recommendations 

described above for Case 2 regarding the form of the modeled estimate. Accordingly, the 

approach should be based on the highest of the multi-year averages of the maximum modeled 

24-hour or annual PM2.5 concentrations predicted each year at each receptor, which represents 

the maximum contribution that the source’s emissions could make in a cumulative impact 

assessment.

For Case 4, where the source’s precursor emissions are equal to or greater than the 

respective SERs but direct PM2.5 emissions are not, the SIL comparison would only address 

secondary PM2.5 ambient impacts associated with the proposed source. The assessment of the 

precursor emission impacts on secondary PM2.5 formation may be: a) qualitative in nature; b) 

based on a hybrid of qualitative and quantitative assessments utilizing existing technical work; or 

c) a full quantitative photochemical grid modeling exercise. As discussed above for Case 3, since 

a SIL should represent a specific insignificant (or de minimis) ambient concentration of PM2.5

that may be used to demonstrate that a source will not cause or contribute to a violation without a 
49

cumulative impact assessment, basing the source impact analysis on a purely qualitative 

assessment of secondary PM2.5 ambient impacts or a hybrid of qualitative and quantitative 

assessments, utilizing existing technical work for Case 4, may be difficult to justify unless a

demonstrably conservative estimate of the secondary PM2.5 contribution can be made that is 

below a SIL. As such, when using either of these approaches, it may be appropriate for the 

permitting authority to recommend the permit applicant to forego the SIL assessment and focus 

on the NAAQS compliance demonstration using a cumulative impact analysis. However, it may 

be more feasible for the permitting authority to allow the permit applicant to apply a SIL to full 

photochemical grid model estimates of secondary PM2.5 for Case 4 than for Case 3 since the 

issues associated with combining modeled estimates of primary and secondary PM2.5 would not 

apply for Case 4. In these cases, the highest of the multi-year averages of the maximum modeled 

24-hour or annual PM2.5 concentrations predicted each year at each receptor should be compared 

to a SIL, since these metrics represent the maximum contribution that the source could make.
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IV. Cumulative Impact Analysis for the PM2.5 NAAQS

Where the screening analysis described in Section II is insufficient to show that a source 

will not cause or contribute to a violation of the NAAQS, a cumulative impact assessment would 

be necessary to make the NAAQS compliance demonstration. A cumulative assessment accounts

for the combined impact of the new or modifying source’s emissions, emissions from other 

nearby sources, and representative background levels of PM2.5 within the modeling domain. The 

cumulative impacts are then compared to the NAAQS to determine whether the new or 

modifying source emissions will cause or contribute to a violation of the NAAQS. This section 

provides details on conducting an appropriate cumulative impact assessment for the PM2.5

NAAQS.

The cumulative impact assessment should include the following components of PM2.5

impacts, as appropriate, for comparison to the NAAQS:

• Proposed new or modifying source

o Primary impacts on PM2.5, i.e., from direct PM2.5 emissions

o Secondary impacts on PM2.5, i.e., from precursor (NOx and/or SO2)

emissions

• Nearby sources

o Primary impacts on PM2.5, as appropriate

• Monitored background of PM2.5 that accounts for secondary PM2.5 impacts from 

regional transport, secondary PM2.5 impacts from nearby sources, and primary 

PM2.5 impacts from background sources not included in the modeled inventory.

As with the source impact analysis discussed previously, the primary impacts related to

direct PM2.5 emissions from the proposed new or modifying source and nearby sources should be 
52

estimated based on the AERMOD dispersion model (or other acceptable preferred model or an 

approved alternative model) while the estimate of secondary PM2.5 impacts from the proposed 

new or modifying source will vary depending on whether the assessment of the proposed 

source’s precursor emission impacts on secondary PM2.5 formation are: a) qualitative in nature; 

b) based on a hybrid of qualitative and quantitative assessments utilizing existing technical work; 

or c) based on a full quantitative photochemical grid modeling exercise. As noted above, 

secondary impacts on PM2.5 from regional transport and precursor emissions from nearby 

sources should be accounted for through representative monitored background concentrations.

IV.1 Modeling Inventory

The current guidelines on emission inventories for purposes of NAAQS compliance 

modeling contained in Section 8.1 of Appendix W will generally be applicable for the PM2.5

modeling inventory. The guidelines in Appendix W address the appropriate emission level to be 

modeled, which in most cases is the maximum allowable emission rate under the proposed 

permit. The remainder of this section will focus on the modeling inventory of direct PM2.5

emissions that should be used in dispersion modeling of primary PM2.5 impacts. Although the 

EPA’s “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of 

Air Quality Goals for Ozone, PM2.5, and Regional Haze” (U.S. EPA, 2007a) provides some 

guidance relevant to applications involving full quantitative photochemical grid modeling, 

additional considerations and guidance regarding modeling inventories for such analyses in 

support of PM2.5 NAAQS compliance demonstrations in PSD permitting under this guidance will 

be provided by EPA on a case-by-case basis.

As discussed in more detail in the EPA’s March 1, 2011, clarification memorandum 
53

regarding Appendix W modeling guidelines for the 1-hour NO2 NAAQS (U.S. EPA, 2011f), 

Section 8.2.3 of Appendix W emphasizes the importance of professional judgment 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 [proposed] source” 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.” (Section 8.2.3.b). The EPA’s 

March 1, 2011, guidance also includes a detailed discussion of the significant concentration 

gradient criterion included in Section 8.2.3.b of Appendix W, indicating that the significant 

concentration gradient criterion suggests that the emphasis on determining which nearby sources 

to include in the cumulative modeling analysis should focus on the area within about 10 

kilometers of the project location in most cases. However, several application-specific factors 

should be considered when determining the appropriate inventory of nearby sources to include in 

the cumulative modeling analysis, including the potential influence of terrain characteristics on 

concentration gradients and the availability and adequacy of ambient monitoring data to account 

for background sources.

Consistent with the March 1, 2011, guidance, the EPA cautions against the application of 

very prescriptive procedures for identifying which nearby sources should be included in the 

modeled emission inventory for NAAQS compliance demonstrations, such as the procedures

described in Chapter C, Section IV.C.1 of the draft “New Source Review Workshop Manual”

(U.S. EPA, 1990). This caution should not be taken to imply that the procedures outlined in the 

draft “New Source Review Workshop Manual” are flawed or inappropriate. 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. 
54

Our main concern is that following such procedures in a literal and uncritical manner may 

increase the likelihood of double-counting modeled and monitored concentrations in many cases, 

resulting in cumulative impact assessments that are overly conservative and would unnecessarily 

complicate the permitting process in some cases. The identification of which sources to include 

in the modeled emissions inventory should be addressed in the modeling protocol and, as 

necessary, discussed in advance with the permitting authority.

Since modeling of direct PM2.5 emissions has not been frequently conducted to date, the 

availability of an adequate direct PM2.5 emission inventory for nearby sources may not exist in 

all cases. Recommendations for developing PM2.5 emission inventories for use in PSD 

applications will be addressed separately, but existing SIP inventories for PM2.5 or statewide 

PSD inventories of sources for refined modeling may provide a useful starting point for this 

effort.

IV.2 Monitored Background

Sections 8.2.2 and 8.2.3 of Appendix W provide recommendations for determination of 

background concentrations for inclusion in cumulative impact assessments for NAAQS

compliance, which should account for impacts from existing sources that are not explicitly 

included in the modeled inventory and natural sources. From newly-acquired pre-construction 

monitoring data and/or existing representative air quality data gathered for purposes of a 

permitting analysis, permit applicants should assess and document what the background 

monitoring data represent to the extent possible, including any information that may be available 
55

from the state or other agency responsible for siting and maintaining the monitor.30 It is also 

worth noting that the relative makeup of PM2.5 components and temporal patterns associated 

with the highest 24-hour PM2.5 levels may differ considerably from the relative amounts of PM2.5

components associated with annual average PM2.5 levels, especially in western states.

The determination of monitored background concentrations of PM2.5 to include in the 

PM2.5 cumulative impact assessment may entail different considerations from those for other 

criteria pollutants and may also depend on whether the application involves full quantitative 

photochemical grid modeling. An important aspect of the monitored background concentration 

for PM2.5 is that the ambient monitoring data should, in most cases account for the contribution 

of secondary PM2.5 formation associated with existing sources impacting the modeling domain in 

addition to the background levels of primary PM2.5 associated with background sources that are 

not included in the modeled inventory. As with other criteria pollutants, consideration should 

also be given to the potential for some double-counting of the impacts from modeled emissions 

that may be contributing to the background monitored concentrations, but this should generally 

be of less importance for PM2.5 than the representativeness of the monitor for secondary 

contributions, unless the monitor is located relatively close to nearby sources of primary PM2.5

that could be impacting the monitor. Also, the nature of secondary PM2.5, monitored background 

concentrations of PM2.5 are likely to be more homogeneous across the modeling domain in most 

cases compared to most other pollutants, although this will also depend on the potential for local 

sources of primary PM2.5 to be contributing to the monitored concentrations.

Depending on the nature of local PM2.5 levels within the modeling domain, it may be 

 

30 Please note in the case of an existing source seeking a permit for a modification, there is potential overlap across 

secondary contributions from monitored background and from precursor emission from the existing source. In such 

cases, recommendations for excluding monitored values when the source in question is impacting the monitor in 

Section 8.2.2.b of Appendix W may need to be modified to avoid overcompensating in cases where the monitored 

concentrations are also intended to account for the existing project source’s contributions to secondary PM2.5.
56

appropriate to account for seasonal variations in monitored background PM2.5 levels which may 

not be correlated with seasonal patterns of the modeled primary PM2.5 levels. For example, 

maximum modeled primary PM2.5 impacts associated with fugitive or other low-level emission 

sources are likely to occur during winter months due to longer periods of stable atmospheric 

conditions, whereas maximum ambient levels of secondary PM2.5 in the eastern United States

typically occur during spring and summer months due to high levels of sulfates. The use of 

temporally-varying monitored background concentrations in a cumulative impact analysis is 

discussed in more detail in Section IV.3.

IV.3 Comparison to the NAAQS

Combining the modeled and monitored concentrations of PM2.5 for comparison to the 

PM2.5 NAAQS entails considerations that differ from those for other criteria pollutants due to the 

issues identified above. The discussion below addresses comparisons to the NAAQS in the 

context of dispersion modeling of direct PM2.5 emissions only (for Case 2), and also provides 

guidance regarding NAAQS comparisons for applications involving qualitative, hybrid 

qualitative/quantitative, or full quantitative photochemical grid modeling assessments of 

secondary PM2.5 impacts (for Cases 3 and 4).

Given the importance of secondary contributions for PM2.5 and the potentially high 

background levels relative to the PM2.5 NAAQS, greater emphasis is generally placed on the 

monitored background contribution relative to the modeled inventory for PM2.5 than for other 

pollutants. This is true for both NAAQS and increments assessments. Also, given the 

probabilistic form of the PM2.5 NAAQS, careful consideration should be given to how the 

monitored and modeled concentrations are combined to estimate the cumulative impact levels.
57

The representative monitored PM2.5 design value, rather than the overall maximum 

monitored background concentration, should generally be used as the monitored component of 

the cumulative analysis. The PM2.5 design value for the annual averaging period is based on the 

3-year average of the annual average PM2.5 concentrations. The PM2.5 design value for the 24-

hour averaging period is based on the 3-year average of the annual 98th percentile 24-hour 

average PM2.5 concentrations. Details regarding the determination of the annual 98th percentile 

monitored 24-hour value based on the number of days sampled during the year are provided in 

the data interpretation procedures for the PM2.5 NAAQS, Appendix N to 40 CFR Part 50.

It should be noted here that although the monitored design values for the PM2.5 standards 

are defined in terms of 3-year averages, this definition does not preempt or alter the Appendix W 

requirement for use of 5 years of representative NWS meteorological data or at least 1 year of 

site-specific data for air quality modeling purposes.

31 The 5-year average based on use of 

representative NWS meteorological data, or an average across one or more (up to 5) complete 

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 as 

recommended in the EPA’s “Guidance on the Use of Models and Other Analyses for 

Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”, is not 

required.32

The EPA’s March 23, 2010, clarification memo recommended as a First Tier that the 

modeled annual (or 24-hour) concentrations of primary PM2.5 to be added to the monitored 

 

31 See 40 CFR Part 51, Appendix W, Section 8.3.1.2.b.

32 The “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for 

Ozone, PM2.5, and Regional Haze” can be found on the SCRAM website at: 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf
58

annual (or 24-hour) design value for comparison to the NAAQS should be based on the highest 

average of the modeled annual (or 24-hour) averages across 5 years for representative NWS 

meteorological data or the highest modeled annual (or 24-hour) average for one year (or multiyear average of 2 up to 5 complete years) of site-specific meteorological data using the same 

procedures recommended for the initial source impact analysis. The memo cited several issues, 

especially the importance of the contribution from secondary formation of PM2.5 from precursor 

emissions and the fact that such contributions are not explicitly accounted for by the dispersion 

model, as the basis for viewing modeling of PM2.5 as screening-level analyses, analogous to the 

screening nature of the guidance in Section 5.2.4 of Appendix W regarding dispersion modeling 

for NO2 impacts, given the importance of chemistry in the conversion of NO emissions to 

ambient NO2.

Recognizing that the primary focus and motivation for this guidance is to provide 

recommendations on appropriate tools and methodologies to account for the potential 

contribution from a new or modifying source’s precursor emissions on ambient PM2.5 levels, it is 

appropriate to reassess the EPA’s March 23, 2010, guidance under this broadened paradigm. 

Since each of the four cases outlined above, based on comparisons of the project’s direct PM2.5

and precursor emissions with the respective SERs, involves some assessment of the source’s 

potential secondary PM2.5 impacts, we recommend as a new First Tier that the modeled design 

value be added to the monitored design value from a representative monitor. This represents no 

fundamental change with respect to the modeled annual concentration. However, the modeled 

24-hour concentration to be added to the monitored design value would now be based on the 

multi-year average of the 98th percentile of modeled annual 24-hour concentrations rather than 

the multi-year average of the highest (100th percentile) of modeled annual 24-hour 
59

concentrations.

For Case 2, where only the project’s direct PM2.5 emissions are equal to or greater than 

the SER, the modeled design value should be based on AERMOD (or other acceptable preferred 

or alternative model) estimates of primary PM2.5 impacts combined with the monitored design 

value. The monitor should be representative in that it accounts for the contribution of secondary 

PM2.5 formation associated with existing sources within the modeling domain, in addition to the 

background levels of primary PM2.5 associated with background sources that are not included in 

the modeled inventory. For Case 3, where both the project’s direct PM2.5 emissions and precursor 

emissions are equal to or greater than the respective SERs, the cumulative impact for comparison 

to the NAAQS should be based on the sum of the modeled design value for primary PM2.5

impacts (from dispersion model estimates based on the project’s and other nearby source’s direct 

PM2.5 emissions), the modeled design value for secondary PM2.5 impacts (from a qualitative, 

hybrid, or quantitative assessment accounting for the project’s precursor PM2.5 emissions), and 

the monitored design value (same representativeness caveats as with Case 2). For Case 4, where 

only the project’s precursor emissions are equal to or greater than the respective SERs, the 

cumulative impact for comparison to the NAAQS should be based on the sum of the modeled 

design value for secondary PM2.5 impacts (from a qualitative, hybrid, or quantitative assessment 

as with Case 3) and the monitored design value (same representativeness caveats as with Cases 2 

and 3). The resulting cumulative PM2.5 concentrations would then be compared to the annual 

PM2.5 NAAQS of 12 μg/m3 and 24-hour PM2.5 NAAQS of 35 μg/m3

.

The recommendations provided above constitute a First Tier modeling analysis for PM2.5

NAAQS compliance demonstrations that should be acceptable without further justification. For 

applications where impacts from primary PM2.5 emissions are not temporally correlated with 
60

background PM2.5 levels, combining the modeled and monitored contributions as described 

above may be overly conservative in some situations. For example, there are areas of the country 

where background PM2.5 levels are substantially higher on average during the summer months as 

compared to the winter months; however, the projected modeled impacts from the new or 

modified source may be substantially greater in the winter rather than in the summer. In such

cases, a Second Tier modeling analysis that would involve combining the monitored and 

modeled PM2.5 concentrations on a seasonal (or quarterly) basis may be considered. The use of a 

seasonally-varying monitored background component is likely to be a more important factor for 

the 24-hour NAAQS analysis than for the annual NAAQS. Careful evaluation of when model 

projections of PM2.5 impacts and background PM2.5 levels peak throughout the year is strongly 

advised before embarking on a Second Tier modeling analysis. This is because the First Tier 

approach may adequately capture the temporal correlation and would otherwise make a Second 

Tier modeling analysis unnecessary. As a part of this evaluation process, consultation with the 

appropriate permitting authority is advised.

The AERMOD model provides several options for specifying the monitored background 

concentration for inclusion in the cumulative impact assessment. The options that are most 

relevant to PM2.5 analyses include an option to specify a single annual background concentration 

that is applied to each hour of the year (appropriate for the First Tier annual and 24-hour 

analyses described above), and an option to specify four seasonal background values that are

combined with modeled concentrations on a seasonal basis (appropriate for a Second Tier 24-

hour analysis). The AERMOD model also allows the user to track the contribution from 

background concentrations to the cumulative modeled design value.

For the Second Tier 24-hour modeling analyses, it is recommended that the distribution 
61

of monitored data equal to and less than the annual 98th percentile be appropriately divided into 

seasons (or quarters) for each of the three years that are used to develop the monitored design 

value. This results in data for each year (for three years) which contains one season (quarter)

with the 98th percentile value and three seasons (quarters) with the maximum values which are 

less than or equal to the 98th percentile value. The maximum concentration from each of the 

seasonal (or quarterly) subsets should then be averaged across these three years of monitoring 

data. The resulting average of seasonal (or quarterly) maximums should then be included as the 

four seasonal background values within the AERMOD model. Therefore, the monitored 

concentrations greater than the 98th percentile in each of the three years would not be included in 

the seasonal (or quarterly) subsets. These excluded monitored concentrations are the same 

values that are excluded when determining the monitored design value. An example of the 

calculations for a Second Tier 24-hour modeling analysis is provided in Appendix E.

For a monitor with a daily (1-in-1 day monitor) sampling frequency and 100% data 

completeness, this would mean that the top seen monitored concentrations for each year would 

be excluded from the seasonal (or quarterly) subdivided datasets. Similarly, for a monitor with 

every third day (1-in-3 day monitor) sampling frequency and 100% data completeness, the top 

two monitored concentrations for each year would be excluded from the seasonal (or quarterly)

subdivided datasets. The monitored concentrations excluded from the subdivided datasets could 

primarily come from one or two seasons (or quarters) each year or could be evenly distributed 

across all four seasons (or quarters) each year. Additionally, the monitored concentrations not 

included in the subdivided datasets could shift seasonally (or quarterly) from one year to the 

next. Given the reasoning for considering a Second Tier 24-hour analysis (lack of temporal 

correlation between modeled and monitored concentrations), it is likely that the monitored data 
62

greater than the 98th percentile would be concentrated in one or two season as opposed to evenly 

distributed throughout the year. As mentioned earlier, one should reference Appendix N of 40 

CFR Part 50 to determine the appropriate 98th percentile rank of the monitored data based on the 

monitor sampling frequency and valid number of days sampled during each year.

Since several recent permit 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 in the context of PM2.5 NAAQS compliance 

demonstrations. On the surface, the hourly pairing or "paired sums" approach could be perceived 

as being a more “refined” method than what is recommended in the First or Second Tier methods 

and, therefore, more appropriate for assessing the impacts from primary PM2.5 emissions. 

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 PM2.5

concentrations across a typical modeling domain on an hourly basis.

The complexities of the PM2.5 ambient monitoring network also present special 

challenges with a "paired sum" approach that are not present with the other NAAQS pollutants. 

The Federal Reference Method (FRM) PM2.5 monitoring network is based on 24-hour samples 

that are taken on average every third day at the 1-in-3 day monitors. The frequency of daily or 1-

in-1 day PM2.5 monitors is steadily increasing but is relatively limited to the largest cities and 

metropolitan regions of the U.S. Various methods to "data fill" the 1-in-3 day monitoring 
63

database to create a pseudo-daily dataset have been explored in a few situations , but none of 

these data filling methods have been demonstrated to create a representative daily PM2.5 dataset 

that the EPA would consider acceptable for inclusion in a PM2.5 NAAQS compliance 

demonstration. The use of continuous PM2.5 monitors, which are more limited in number 

compared to the FRM monitors and may require careful quality assurance of individual hourly 

measurements, may be an option but should be discussed in advance with the appropriate 

permitting authority.

Considering the spatial and temporal variability throughout a typical modeling domain on 

an hourly basis and the complexities and limitations of hourly observations from the current 

PM2.5 ambient monitoring network, we do not recommend a "paired sums" approach on an hourby-hour basis. Furthermore, the pairing of daily monitored background and 24-hour average 

modeled concentrations is not recommended except in rare cases of relatively isolated sources 

where the available 1-in-1 day FRM/FEM monitor can be shown to be representative of the 

ambient concentration levels in the areas of maximum impact from the proposed new source. In 

most cases, the seasonal (or quarterly) pairing of monitored and modeled concentrations 

previously described in the Second Tier method should sufficiently address situations to which 

the impacts from primary PM2.5 emissions are not temporally correlated with background PM2.5

levels. Any monitor-model pairing approach aside from the First or Second Tier methods should 

be justified on a case-by-case basis in consultation with the appropriate permitting authority and 

the appropriate EPA Regional Office.
64

IV.4 Determining Whether Proposed Source Causes or Contributes to Modeled 

Violations

If the cumulative impact assessment following these recommendations results in modeled 

violations of the PM2.5 NAAQS, then the permit applicant will need to determine whether the 

project’s emissions cause or contribute to the modeled violations. The EPA has previously 

supported showing the proposed source does not cause or contribute by showing that the source 

does not make a “significant contribution” to the modeled violation based on a comparison of the 

modeled impacts from the project emissions associated with the modeled violation, paired in 

time and space, to the SIL for the relevant pollutant and averaging period contained in 40 CFR 

51.165(b) of the EPA’s regulations. The EPA has interpreted this regulation to support the 

conclusion that a source with an impact below the relevant value in section 51.165(b)(2) does not 

significantly contribute to either an existing violation of the NAAQS in a nonattainment area or 

violations predicted in an attainment area based on a cumulative analysis.33

The January 22, 2013, court decision did not vacate the PM2.5 SIL value in section 

51.165(b) of the EPA’s regulations. However, the court recognized that the language in section 

51.165(b)(2) operates in a manner different from sections 51.166(k)(2) and 52.21(k)(2), which 

were vacated by the court. The court observed that section 51.165(b)(2) “simply states that a 

source may be deemed to violate the NAAQS if its exceeds the SILs in certain situations.” (705 

F.3d at 465-66). For this reason, the court did not see the need to resolve the Petitioner’s 

challenge to the EPA’s methodology for determining the PM2.5 values in section 51.165(b)(2) of 

the regulations, which are the same as the Class II area values in the vacated sections 

51.166(k)(2) and 52.21(k)(2). The court decision did not directly address the use of the values in 

 

33 See 75 Fed. Reg. at 64,890; 61 Fed. Reg. 38,250, 38,293 (July 23, 1996); In re Prairie State Generating Co., 13 

E.A.D. 1, 103-09 (EAB 2006). EPA has sometimes described this step as a “culpability analysis.”
65

section 51.165(b)(2) to determine whether a source causes or significantly contributes to a 

modeled violation. However, in light of other elements of the court decision, the EPA advises 

permitting authorities to consult with the EPA before using the SIL value for PM2.5 in section 

51.165(b)(2) as the basis for concluding that a source with an impact below this value does not 

cause or contribute to a modeled violation.

A demonstration that a proposed source does not make a significant contribution should 

be based on a comparison of the modeled concentrations at the receptor location showing the 

violation to a SIL, across 5 years for representative NWS meteorological data and the modeled 

concentration for 1 year, or multiyear average of 2 up to 5 complete years, of site-specific 

meteorological data. For a violation of the annual PM2.5 NAAQS, the average of the predicted 

annual concentrations at the affected receptor(s) should be compared to a SIL, while the average 

of the predicted annual 98th percentile 24-hour average concentrations at the affected receptor(s) 

should be used for the 24-hour PM2.5 NAAQS.
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V. PSD Increments for PM2.5

As cited in Section II of this guidance, section 165(a)(3) of the CAA requires that 

proposed new and modified major stationary sources seeking a PSD permit must demonstrate 

that their proposed emissions increases will not cause or contribute to a violation of any NAAQS 

or PSD increment. Based on the flow diagram presented in Figure II-2 in Section II, this section 

describes the EPA’s recommendations for completing the required analysis of the PSD 

increments for PM2.5.

V.1 Overview of PSD Increments

The term “increment” generally refers to what the CAA calls the “maximum allowable 

increase” of an air pollutant that is allowed to occur above the applicable baseline air quality 

concentration for that pollutant. Thus, by establishing the maximum allowable increase for a 

particular pollutant and averaging period, any cumulative increase in the ambient concentration 

of that pollutant that is greater than the amount allowed is considered “significant deterioration.”

In order to apply the increment concept as part of a PSD permit review, it is necessary to 

identify the affected geographic area in which the increment will be tracked and the emissions 

changes that affect increment. The relevant geographic area for determining the amount of 

increment consumed is known as the “baseline area.” 34 The baseline area may be comprised of 

one or more attainment or unclassifiable areas for a particular pollutant that are in a particular 

state. In accordance with the definition of “baseline area,” the area is an “intrastate area” and 

does not include any area in another state. At a minimum, the baseline area is the attainment or 

unclassifiable area in which a PSD source will locate. Within any baseline area, three key dates 

 

34 “Baseline area” is defined in the PSD regulations at 40 CFR 51.166(b)(15) and 52.21(b)(15).
68

will apply in order to track increment: (1) trigger date; (2) minor source baseline date; and (3) 

major source baseline date. The trigger date is a fixed date, which is the earliest date after which 

proposed sources must track increment in the baseline area. In turn, the minor source baseline 

date is the date on which the first PSD application in a baseline area is submitted to the PSD 

permitting authority after the trigger date. Depending upon the number of separate attainment 

areas that exist for a particular pollutant in the state, there may be a number of minor source 

baseline dates that apply to different baseline areas established in that state. Beginning with the 

PSD source whose complete application has established the minor source baseline date in a 

particular area, any increase or decrease in actual emissions from any major or minor source 

henceforth will consume or expand the available PSD increments for that baseline area. Finally, 

the major source baseline date is a fixed date, which precedes the trigger date, after which 

construction related emissions solely from major stationary sources affect increment, as further 

explained below.

PM2.5 emissions changes occurring before the minor source baseline date generally do not 

impact increment in an area, but are considered to contribute to the baseline air quality level also 

known as the baseline concentration, as described in more detail below. However, it is important 

to note that the CAA provides an exception for certain emissions changes that occur specifically 

at major stationary sources regardless of when those emissions changes actually occur. This date, 

as explained above, is the “major source baseline date.” Specifically, for projects at major 

stationary sources on which construction commenced at a date prior to the major source baseline 

date, the emissions increases from such projects should be considered to contribute to the 

baseline air quality level even though the emissions change may not actually occur until after the 

minor source baseline date. Alternately, for projects at major stationary sources on which 
69

construction commenced after the major source baseline date, the project emissions will be 

considered to affect increment, even if the project actually begins operation before the minor 

source baseline date.

V.2 PM2.5 Increments Considerations

In its 2010 PM2.5 Increments, SILs, and SMC Rule, the EPA established PM2.5

increments at the levels shown in Table V-1, as follows:

Table V-1. PM2.5 Increments

Class I Class II Class III

Increments, μg/m3

Annual arithmetic mean................................................................... 1 4 8

24-hour maximum.......................................................................... 2 9 18

Source: Prevention of Significant Deterioration (PSD) for Particulate Matter Less Than 2.5 Micrometers (PM2.5) - Increments,

 Significant Impact Levels (SILs) and Significant Monitoring Concentration (SMC) final rule (75 FR 64864)

The PM2.5 increments analysis includes many of the same technical considerations in 

assessing source impacts as discussed earlier in this guidance for PM2.5 NAAQS compliance 

demonstrations, specifically the assessment cases described in Section II-4 and detailed in 

Table III-1. However, there are some important differences. The main difference is that the 

increments compliance demonstration is based on calculating the change in ambient PM2.5

concentrations over the applicable baseline concentration, which includes proposed emissions 

increases from the new or modified source, increment-consuming emissions from other sources 

that affect increment consumption in the baseline area, and increment-expanding decreases in 

emissions from the same sources. Another key difference is that the cumulative impact analysis 

for increments is based on the actual emission changes occurring after a prescribed minor source 

baseline date (with the stated exception related to major sources commencing construction after 
70

the major source baseline date), whereas NAAQS analyses are generally based on the cumulative 

impact associated with the maximum allowable emissions from the new or modifying source and 

other nearby sources. Finally, it is important to note that the PM2.5 NAAQS and increments for 

the 24-hour averaging period are defined in different forms and therefore must be analyzed 

differently.35 The 24-hour PM2.5 NAAQS is defined based on the 3-year average of the annual 

98th percentile of the 24-hour average concentrations, while the 24-hour PM2.5 increments are 

based on the second highest maximum 24-hour concentration.

The 2010 “PM2.5 Increments, SILs, and SMC Rule” established October 20, 2011, as the 

“trigger date” and October 20, 2010, as the “major source baseline date” for PM2.5 increments. 

The EPA developed the increment system for PM2.5 generally following the same concepts that 

were previously applied for the increments for PM10, SO2, and NO2. In each case, the framework 

reflects the statutory concepts set forth in the definition of “baseline concentration” contained in 

the CAA at section 169(4), which reads as follows:

The term “baseline concentration” means, with respect to a pollutant, the ambient 

concentration levels which exist at the time of the first application for a permit in an area 

subject to this part, based on air quality data available in the Environmental Protection 

Agency or a State air pollution control agency and on such monitoring data as the permit 

applicant is required to submit. Such ambient concentration levels shall take into account 

all projected emissions in, or which may affect, such area from any major emitting 

facility on which construction commenced prior to January 6, 1975, but which has not 

begun operation by the date of the baseline air quality concentration determination (i.e., 

the minor source baseline date). Emissions of sulfur oxides and particulate matter from 

 

35 The annual NAAQS and increments for PM2.5 are both measured as annual arithmetic mean values.
71

any major emitting facility on which construction commenced after January 6, 1975, shall 

not be included in the baseline and shall be counted against the maximum allowable 

increases in pollutant concentrations established under this part.

Thus, from this definition, it can be seen that it is conceptually possible to measure “significant 

deterioration” in at least two separate ways. That is, either as (1) a direct modeled projection of 

the change in air quality after the applicable baseline date caused by all increment-consuming or 

expanding emissions compared to the maximum allowable increase of the air pollutant 

concentration (increment) in the baseline area, or (2) a determination of whether the ambient air 

quality concentration in a baseline area will exceed an allowable ambient air quality ceiling, 

determined by adding the maximum allowable pollutant concentration increase (increment) to 

the baseline air quality concentration (baseline concentration) for the baseline area. 

Historically, because of various limitations associated with the use of ambient air quality 

monitoring data for measuring increment consumption,36 the EPA elected to determine 

significant deterioration exclusively on the basis of the first approach, which models only the

increment-related emissions increases or decreases to determine the resulting ambient air quality 

change and compares this value with maximum allowable pollutant concentration increases 

(increments) for a particular pollutant. However, the present technical challenges associated with 

the ability to estimate the impacts of secondarily formed PM2.5 in the atmosphere resulting from 

emissions of PM2.5 precursors make it necessary to consider alternative methods of assessing 

increments where the increments are affected by both direct PM2.5 emissions and PM2.5 precursor 

 

36 The EPA described certain limitations associated with the use of ambient air quality monitoring data for 

measuring increment consumption in the preamble to its proposed PSD regulations in 1979. For example, the CAA 

provided that certain emissions changes should not be considered to be increment consuming. These limitations 

generally continue to apply to the extent that certain emissions changes detected by an ambient monitor are not 

considered to consume increment. See 44 Fed. Reg. 51924 at 51944 (September 5, 1979).
72

emissions that form secondary PM2.5. Accordingly, the remainder of this section provides 

recommendations for accomplishing the PM2.5 increments analysis.

V.3 Screening Analysis for Increments

The comparison of background air quality concentrations and the NAAQS, as 

recommended in Section II of this document as an initial step for the NAAQS compliance 

demonstration, would not by itself provide adequate justification for foregoing a cumulative 

modeling analysis for the PM2.5 increments. Such an approach would be inappropriate because it 

would not ensure that there is sufficient “headroom” within the allowable increment to absorb a 

source contribution equal to the SIL. However, a permitting authority may still be able to justify 

reaching a determination that a new or modified source will not cause or contribute to a violation 

of the increments without performing cumulative modeling for increments. 

The EPA recommends that a justification for not performing cumulative modeling for 

PM2.5 increments compliance should be based on (1) a comparison of the predicted impacts of 

the new or modified source and the allowable increment values, (2) information on the extent to 

which, if any, increment has already been consumed since either the major source baseline date 

(for major source construction prior to the minor source baseline date) or minor source baseline 

date by nearby sources that have been permitted prior to the source under analysis, and (3) 

information on increment consumption or expansion by more distant sources.

Since the trigger date has only recently been established (i.e., October 20, 2011), for the 

next several years a new or modified source being evaluated for increments compliance will 

often be the first source with increment-consuming emissions in the area. As indicated in Figure 

II-2, under this situation, a permitting authority may have sufficient reason to conclude that the 
73

impacts of the new or modified source (based on the approach for conducting source impact 

analysis described below) may be compared directly to the allowable increments, without the 

need for a cumulative modeling analysis. Such an approach would be appropriate when the new

or modified source represents the first PSD application in the area after the trigger date, which 

establishes the minor source baseline date and baseline area, and no relevant major source 

construction has already occurred since the major source baseline date.

V.4 PM2.5 Increments Analysis

The guidance provided under Sections III and IV regarding NAAQS compliance 

demonstrations should generally be applicable for PM2.5 increments analyses, with the primary 

distinction that actual emission increases (or decreases) from only increment-affecting sources 

may be used instead of maximum allowable emissions in the cumulative impact analysis.

V.4.1 Source Impact Analysis

The EPA’s recommendations on conducting the source impact analysis for PM2.5

increments rely upon the same four assessment cases for NAAQS, as described in Section II.4. 

As shown in Table V-2, a modeled compliance demonstration is not required for Case 1 since 

neither direct PM2.5 emissions nor PM2.5 precursor (NOx and/or SO2) emissions are equal to or 

greater than the respective SERs. Case 1 is the only assessment case that does not require a 

modeled compliance demonstration for PM2.5, whereas each of the remaining three assessment 

cases would necessitate a source impact analysis that should be conducted following the detailed 

recommendations provided in Section III for NAAQS analysis.
74

Table V-2. EPA Recommended Approaches for Assessing Primary and Secondary PM2.5

Impacts by Assessment Case

Assessment Case Description of Assessment Case Primary Impacts Approach Secondary Impacts 

Approach

Case 1:

No Air Quality Analysis

Direct PM2.5 emissions < 10 tpy SER

NOx and SO2 emissions < 40 tpy SER N/A N/A

Case 2:

Primary Air Quality 

Impacts Only

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and SO2 emissions < 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

N/A

Case 3:

Primary and Secondary 

Air Quality Impacts

Direct PM2.5 emissions ≥ 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER

Appendix W preferred or 

approved alternative 

dispersion model

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

Case 4:

Secondary Air Quality 

Impacts Only

Direct PM2.5 emissions < 10 tpy SER

NOx and/or SO2 emissions ≥ 40 tpy SER N/A

• Qualitative

• Hybrid qualitative /

 quantitative

• Full quantitative

 photochemical

 grid modeling

V.4.2 Cumulative Impact Analysis

Where the screening analysis described above is insufficient to show that a source will 

not cause or contribute to a violation of the PSD increments, a cumulative impact assessment 

would be necessary to make the demonstration. A cumulative assessment accounts for the 

combined impact of the new or modifying source’s emissions and those emissions changes from 

sources that affect the increments. The cumulative impacts are then compared to the PSD 

increments to determine whether the new or modifying source emissions will cause or contribute 

to a violation of the PSD increments. This section provides details on conducting an appropriate 

cumulative impact assessment for PM2.5.

V.4.2.1 Assessing Primary PM2.5 Impacts from Other Sources

To assess direct PM2.5 emissions from increment-consuming or increment-expanding 

sources, the PM2.5 increments analysis would follow the traditional approach involving modeling 

of only PM2.5 emissions changes that affect the increment, and should be based on application of 
75

AERMOD (or other appropriate preferred or approved alternative model), using actual emission 

changes associated with any increment-consuming or increment-expanding sources. The 

AERMOD model allows for inclusion of these emissions (represented as negative emissions for 

the sources expanding increment) in the same model run that includes the allowable increase in 

emissions from the project source, and will therefore output the net cumulative concentrations 

(although the “maximum” cumulative impacts will be output as zero if the cumulative impacts 

computed in the model are less than zero).

V.4.2.2 Assessing Secondary PM2.5 Impacts from Other Sources

To assess changes in PM2.5 precursor emissions from increment-consuming or incrementexpanding sources, the assessment of potential impacts of secondary PM2.5 due to those 

emissions changes may be: a) qualitative in nature; b) based on a hybrid of qualitative and 

quantitative assessments utilizing existing technical work; or c) a full source-specific quantitative 

photochemical modeling exercise.

Several promulgated rules have resulted in reductions in precursor emissions affecting 

ambient PM2.5 concentrations across most areas in recent years.

37 This is particularly true in the 

Eastern U.S. As a result, in many cases, the potential for increment consumption due to 

secondary PM2.5 impacts from existing sources may easily be addressed through a qualitative 

assessment, supported by data that generally confirms a downward trend in precursor emissions 

occurring after the applicable PM2.5 minor source baseline date (or the major source baseline 

date). In such cases, the PM2.5 increments modeling analysis may be simplified to focus solely on 

potential increment consumption associated with direct PM2.5 emissions. For areas where PM2.5

 

37 Such rules would include the Clean Air Interstate Rule (CAIR), Mercury and Air Toxics Standards Rule (MATS), 

NOx SIP Call and multiple federal mobile source rules.
76

precursor emissions increases from other sources are known to contribute to PM2.5 concentration 

increases within the baseline area and thus consume PM2.5 increment, the photochemical grid 

modeling methods discussed in Section III may be appropriate for estimating the portion of 

PM2.5 increment consumed due to secondary PM2.5 impacts associated with those increases in 

precursor emissions.

V.4.2.3 Consideration of PM2.5 Ambient Air Quality Monitoring Data

In light of the current technical complications associated with the ability to model 

precursor emissions to estimate secondarily formed PM2.5 in the atmosphere, the EPA believes it 

may be possible under certain circumstances to use ambient air quality monitoring data for PM2.5

as part of the cumulative impact analysis. This involves using ambient monitoring data as the 

primary means of assessing increment consumption or expansion for PM2.5 by measuring 

ambient air quality on the minor source baseline date (baseline concentration) and thereafter to 

determine changes in air quality resulting from direct PM2.5 emissions and PM2.5 precursors. This 

document does not provide detailed recommendations for conducting the PM2.5 increments

analysis in this manner, but simply acknowledges that it may be possible in certain 

circumstances to use this approach for PSD permitting. There would continue to be a need to 

model projected impacts as part of the PM2.5 increments analysis to include consideration of 

increment consumed by emissions that have not yet occurred. One should also consider the 

extent to which the available monitoring data adequately reflect the air quality changes caused by 

direct PM2.5 and precursor emissions from sources impacting the baseline area.

Where the PSD permit applicant believes that this approach is potentially useful for 

conducting the PM2.5 increments analysis for a particular PSD permit review, early coordination 
77

with the permitting authority is strongly encouraged to establish the appropriate baseline 

concentration(s) within the applicable baseline area and how subsequent ambient monitoring 

data in the area, when compared to the baseline air quality data, can be used to assess cumulative 

increment consumption. The EPA will work with air agencies to support this approach on a case 

specific basis. Based on these experiences, it is our intention to provide additional guidance 

setting forth more specific recommendations on this particular approach at a future date. 

V.5 Determining Significant Contribution to an Increment Violation

As previously explained, the EPA does not anticipate the need to complete a cumulative 

increments analysis in most situations due to the recent setting of the trigger date for PM2.5. 

Therefore, most PM2.5 increments analyses will need to consider the emissions increases 

resulting only from the proposed new source or modification that establishes the minor source 

baseline date for an area. Consequently, we believe that permitting authorities will encounter 

few, if any, situations over the next several years in which there is a predicted increment 

violation.

Nevertheless, there may be situations where a cumulative increments analysis is 

necessary and that analysis projects a modeled increment violation. This guidance recommends 

that such violations be addressed in a manner similar to the NAAQS analysis described in 

Section IV of this document; that is, when a PSD applicant elects to use a SIL to show to the 

permitting authority that the source’s emissions do not make a significant contribution to a 

modeled violation, the EPA advises permitting authorities to consult with the EPA before 

allowing the use of a SIL value, including those PM2.5 values contained in section 51.165(b)(2), 

as the basis for concluding that a source with an impact below this value does not cause or 
78

contribute to a modeled violation of the PM2.5 increment.
79

VI. References

U.S. EPA, 1990: New Source Review Workshop Manual: Prevention of Significant 

Deterioration and Nonattainment Area Permitting – DRAFT. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/nsr/gen/wkshpman.pdf

U.S. EPA, 1992: Protocol for Determining the Best Performing Model. September 1992. EPA454/R-92-025. U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina 27711.

U.S. EPA, 1997: Interim Implementation of New Source Review Requirements for PM2.5. John 

Seitz Memorandum dated October 23, 1997. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina, 27711. 

http://www.epa.gov/region7/air/nsr/nsrmemos/pm25.pdf

Seinfeld and Pandis, 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate 

Change. J. Seinfeld and S. Pandis. Wiley Interscience. New York, New York. ISBN 0 47 

117815 2.

Nobel et al., 2001: Accounting for spatial variation of ozone productivity in NOx emission 

trading. C. Nobel, E. McDonald-Buller, Y. Kimura, and D. Allen. Environmental Science 

& Technology. 2001; 35, 4397-4407.

NARSTO, 2004. Particulate Matter Assessment for Policy Makers: A NARSTO Assessment. P. 

McMurry, M. Shepherd, and J. Vickery, eds. Cambridge University Press, Cambridge, 

England. ISBN 0 52 184287 5.

U.S. EPA, 2005. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W. 

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

Byun and Schere, 2006: Review of the governing equations, computational algorithms, and other 

components of the models-3 Community Multiscale Air Quality (CMAQ) modeling 

system. D. Byun and K. Schere. Applied Mechanics Reviews. 2006; 59, 51-77.

U.S. EPA, 2007a: Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. April 2007. EPA454/B-07-002. U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina 27711. http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rhguidance.pdf

U.S. EPA, 2007b: Details on Technical Assessment to Develop Interpollutant Trading Ratios for 

PM2.5 Offsets. Technical Analysis dated July 23, 2007. U.S. Environmental Protection 

Agency, Research Triangle Park, North Carolina 27711.

Bergin et al., 2008. Single Source Impact Analysis Using Three-Dimensional Air Quality 

Models. M. Bergin, A. Russell, T. Odman, D. Cohan, and W. Chameldes. Journal of the 

Air & Waste Management Association. 2008; 58, 1351–1359.

Russell, 2008: EPA Supersites Program-related emissions-based particulate matter modeling: 

Initial applications and advances. A. Russell. Journal of the Air & Waste Management 

Association. 2008; 58, 289-302.

Foley et al., 2010: Incremental testing of the Community Multiscale Air Quality (CMAQ) 

modeling system version 4.7. K. Foley, S. Roselle, K. Appel, P. Bhave, J. Pleim, T. Otte, 

R. Mathur, G. Sarwar, J. Young, R. Gilliam, C. Nolte, J. Kelly, A. Gilliland, and J, Bash. 

Geoscientific Model Development. 2010; 3, 205-226.
80

U.S. EPA, 2010a: Model Clearinghouse Review of Modeling Procedures for Demonstrating 

Compliance with PM2.5 NAAQS. Tyler Fox Memorandum dated February 26, 2010. U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/mch/new_mch/MCmemo_Region6_PM25_NAA

QS_Compliance.pdf

U.S. EPA, 2010b: Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS. 

Stephen Page Memorandum dated March 23, 2010. U.S. Environmental Protection 

Agency, Research Triangle Park, North Carolina, 27711. 

http://www.epa.gov/ttn/scram/Official%20Signed%20Modeling%20Proc%20for%20De

mo%20Compli%20w%20PM2.5.pdf

ENVIRON, 2011: User's Guide Comprehensive Air Quality Model with Extensions. ENVIRON 

International Corporation, Novato, California. http://www.camx.com.

U.S. EPA, 2011a: Addendum - User's Guide for the AERMOD Terrain Preprocessor 

(AERMAP). EPA-454/B-03-003. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermap/aermap_userguide.zip

U.S. EPA, 2011b: AERSCREEN User’s Guide. EPA-454-/B-11-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/screen/aerscreen_userguide.pdf

U.S. EPA, 2011c: AERSCREEN Released as the EPA Recommended Screening Model. Tyler 

Fox Memorandum dated April 11, 2011. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/20110411_AERSCREEN_Release_Memo.pdf

U.S EPA, 2011d AERMINUTE User’s Guide. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aerminute_v11325.zip

U.S. EPA, 2011e: Revised Policy to Address Reconsideration of Interpollutant Trading 

Provisions for Fine Particles (PM2.5). Gina McCarthy Memorandum dated July 21, 2011. 

U.S. Environmental Protection Agency, Washington, District of Columbia 20460. 

http://www.epa.gov/region7/air/nsr/nsrmemos/pm25trade.pdf.

U.S. EPA, 2011f: Additional Clarification Regarding Application of Appendix W Modeling 

Guidance for the 1-hour NO2 National Ambient Air Quality Standard. Tyler Fox 

Memorandum dated March 1, 2011. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/clarification/Additional_Clarifications_Appendix

W_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf

Baker and Foley, 2011. A nonlinear regression model estimating single source concentrations of 

primary and secondarily formed PM2.5. K. Baker and K. Foley. Atmospheric

Environment. 2011; 45:3758–67.

Cohan and Napelenok, 2011. Atmospheric Response Modeling for Decision Support. D. Cohan 

and S. Napelenok. Atmosphere. 2011; 2(3): 407-425.
81

NACAA, 2011: PM2.5 Modeling Implementation for Projects Subject to National Ambient Air 

Quality Demonstration Requirements Pursuant to New Source Review. Report from 

NACAA PM2.5 Modeling Implementation Workgroup dated January 7, 2011. 

Washington, District of Columbia 20001. 

http://www.epa.gov/ttn/scram/10thmodconf/review_material/01072011-

NACAAPM2.5ModelingWorkgroupReport-FINAL.pdf

U.S. EPA, 2012a: “Sierra Club Petition Grant”. Gina McCarthy Administrative Action dated 

January 4, 2012. U.S. Environmental Protection Agency, Washington, District of 

Columbia 20460. 

http://www.epa.gov/ttn/scram/10thmodconf/review_material/Sierra_Club_Petition_OAR11-002-1093.pdf.

U.S. EPA, 2012b: Summary of Public Comments, 10th Conference on Air Quality Modeling. 

U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711. 

http://www.epa.gov/ttn/scram/10thmodconf/10thMC_Summary_of_CommentsRevised_10-05-2012.pdf

U.S. EPA, 2013a: Transportation Conformity Guidance for Quantitative Hot-spot Analyses in 

PM2.5 and PM10 Nonattainment and Maintenance Areas. November 2013. EPA-420-B10-040. U.S. Environmental Protection Agency, Ann Arbor, Michigan 48105. 

http://www.epa.gov/oms/stateresources/transconf/policy/420b13053-sec.pdf and

http://www.epa.gov/oms/stateresources/transconf/policy/420b13053-appx.pdf.

U.S. EPA, 2013b: Draft Guidance for PM2.5 Permit Modeling. March 4, 2013. EPA-454-/B-11-

001. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 

27711. 

http://www.epa.gov/ttn/scram/guidance/guide/Draft_Guidance_for_PM25_Permit_Model

ing.pdf

U.S. EPA, 2014a: Interim Guidance on the Treatment of Condensable Particulate Matter Test 

Results in the PSD and NSR Permitting Programs. Stephen Page Memorandum dated 

April 8, 2014. U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina, 27711. http://www.epa.gov/ttn/emc/methods/psdnsrinterimcmpmemo4814.pdf

U.S. EPA, 2014b: Addendum – User’s Guide for the AMS/EPA Regulatory Model – AERMOD. 

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

North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

U.S. EPA, 2014c: Addendum - User’s Guide for the AERMOD Meteorological Preprocessor 

(AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency, Research 

Triangle Park, NC 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip
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A-1

Appendix A: Draft Conceptual Description of PM2.5 Concentrations in the U.S.

This appendix provides a brief summary of the current PM2.5 monitoring networks and 

characterizes PM air quality in terms of its chemical composition, concentration levels, and 

spatial and temporal patterns across the nation based largely on ambient data and analyses 

contained in the EPA’s The Particle Pollution Report,38 Particulate Matter Staff Paper,39 and new 

ambient data summaries based on 2008-2010 PM2.5 mass and speciation data. It also discusses 

regional and local source contributions to urban PM2.5 concentrations. Such information may be 

useful for permit applicants in preparing conceptual descriptions, as discussed in Section III.2.1 

of this guidance.

1. PM2.5 Monitoring Networks

1.1. PM2.5, PM10 and PM10-2.5 Mass Networks

The 1997 promulgation of a fine particulate NAAQS (EPA, 1997) led to deployment of 

over 1500 PM2.5 sites (about 1000 currently) used to determine whether an area complies with 

the standard. These sites use a Federal Reference Method (FRM) or Federal Equivalent Method 

(FEM), daily sampling over 24-hours, or every third or sixth day. Nearly 300 additional 

measurements not meeting FRM or FEM specifications are provided by the chemical speciation 

sites (Figure A-1). Approximately 600 stations provide indirect measurements of continuous 

(hourly resolution) PM2.5 mass using a variety of techniques.

1.2. Interagency Monitoring of Protected Visual Environments (IMPROVE) Program

The IMPROVE network, with over 100 sites, has provided nearly a two-decade record of 

major components of PM2.5 (sulfate, nitrate, organic and elemental carbon fractions, and trace 

metals) in pristine areas of the United States (Figure A-1). IMPROVE is led by the National Park 

Service; various federal and state agencies support its operations. The primary focus of the 

network is to track visibility and trends in visibility.

1.3. PM2.5 Chemical Speciation Monitoring

In addition to the IMPROVE network, over 300 EPA speciation sites were added from 

2000 - 2002 in urban areas of the United States to assist PM2.5 assessment efforts. No FRM exists 

for particulate speciation, which is not directly required to determine attainment, and there are 

slight differences between monitors and methods used in the Speciation Trends Network (STN). 

However, the network’s coverage (Figure A-1) across urban and rural areas has proved essential 

for a wide range of research and analysis. The speciation networks typically collect a 24-hour 

sample every three, and sometimes six, days.

 

38 The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003.

http://www.epa.gov/airtrends/aqtrnd04/pmreport03/pmcover_2405.pdf#page=1.

39 Particulate Matter Staff Paper: Review completed in 2012. 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html.
A-2

Daily 24-hour speciation collection is limited to occasional efforts in the SEARCH (see 

below) network. Similarly, only a handful of sites provide near continuous speciation data, 

usually limited to some combination of sulfate, carbon (organic and elemental splits) and nitrate. 

This enables insight to diurnal patterns for diagnosing various cause-effect phenomena related to 

emissions characterization, source attribution analysis and model evaluation.

Figure A-1. Locations of chemical speciation sites delineated by program type

1.4. South Eastern Aerosol Research and Characterization (SEARCH) Study

This study experiment is an industry-funded network of 8 sites that originally emerged 

from the Southern Oxidants Study (SOS) in the 1990s and has operated for over a decade in 

response to the 1997 revisions to the national ambient air quality standards for ground-level 

ozone and particulate matter. SEARCH is part of a public-private collaboration that provides an 

array of standard criteria pollutant measurements but also includes daily 24-hour PM speciation 

at selected times and locations, gaseous ammonia, reactive nitrogen (NOy), and true nitrogen 

dioxide (i.e., a measurement of NO2 concentration unaffected by other nitrogen oxides, which 

contaminate FRM NO2 measurements). These measurements had not been available in major 

government-funded routine networks and in order to identify sources of ozone precursors and 

fine particulate matter and to attribute health effects to specific components, the SEARCH 

project sponsors believe that it is necessary to measure pollutant composition as well as mass.
A-3

1.5. PM Supersites Program

This program provided highly resolved aerosol measurements at eight U.S. cities for 

several time periods from 1999 through 2004, with some sites collecting data after 2004.40 A 

number of instrument configurations were deployed, ranging from additional locations for 

standard speciation monitors, to systems capturing near-continuous size-dependent speciation 

profiles.

2. Composition of PM2.5

Particulate matter (PM) is a highly complex mixture of solid particles and liquid droplets 

distributed among numerous atmospheric gases which interact with solid and liquid phases. 

Particles range in size from those smaller than 1 nanometer (10-9 meter) to over 100 microns (1 

micron is 10-6 meter) in diameter (for reference, a typical strand of human hair is 70 microns and 

particles less than about 20 microns generally are not detectable by the human eye). Particles are 

classified as PM2.5 and PM10-2.5, corresponding to their size (diameter) range in microns and 

referring to total particle mass under 2.5 and between 2.5 and 10 microns, respectively.

Particles span many sizes and shapes and consist of hundreds of different chemicals. 

Particles are emitted directly from sources and also are formed through atmospheric chemical 

reactions and often are referred to as primary and secondary particles, respectively. Particle 

pollution also varies by time of year and location and is affected by several aspects of weather 

such as temperature, clouds, humidity, and wind. Further complicating particles is the shifting 

between solid/liquid and gaseous phases influenced by concentration and meteorology, 

especially temperature.

Particles are made up of different chemical components. The major components, or 

species, are carbon, sulfate and nitrate compounds, and crustal materials such as soil and ash 

(Figure A-2). The different components that make up particle pollution come from specific 

sources and are often formed in the atmosphere. Particulate matter includes both “primary” PM, 

which is directly emitted into the air, and “secondary” PM, which forms indirectly from fuel 

combustion and other sources. Primary PM consists of carbon (soot)—emitted from cars, trucks, 

heavy equipment, forest fires, and burning waste—and crustal material from unpaved roads, 

stone crushing, construction sites, and metallurgical operations. Secondary PM forms in the 

atmosphere from gases. Some of these reactions require sunlight and/or water vapor. Secondary 

PM includes:

• Sulfates formed from sulfur dioxide emissions from power plants and industrial 

facilities;

• Nitrates formed from nitrogen oxide emissions from cars, trucks, industrial facilities, 

and power plants; and

 

40 Solomon, P.A., P.K. Hopke, J. Froines, and R. Scheffe, 2008: Key Scientific and Policy and Health-Relevant 

Findings from the U.S. EPA’s Particulate Matter Supersites Program and Related Studies: An Integration and 

Synthesis of Results, J. Air & Waste Manage. Assoc., 58, S-1 – S-92.
A-4

• Carbon formed from reactive organic gas emissions from cars, trucks, industrial 

facilities, forest fires, and biogenic sources such as trees.

Figure A-2. National Average of Source Contribution to Fine Particle Levels

Source: The Particulate Matter Report, EPA-454-R-04-002, Fall 2004. Carbon reflects both organic carbon and 

elemental carbon. Organic carbon accounts for automobiles, biogenics, gas-powered off-road, and wildfires. 

Elemental carbon is mainly from diesel powered sources.

In addition, ammonia from sources such as fertilizer and animal feed operations 

contributes to the formation of sulfates and nitrates that exist in the atmosphere as ammonium 

sulfate and ammonium nitrate. Note that fine particles can be transported long distances by wind 

and weather and can be found in the air thousands of miles from where they were formed.

The chemical makeup of particles varies across the United States (as shown in Figure A3).41 For example, fine particles in the eastern half of the United States contain more sulfates 

than those in the West, while fine particles in southern California contain more nitrates than 

other areas of the country. Organic carbon is a substantial component of fine particle mass 

everywhere. This figure represents the composition of PM2.5 as measured by the PM2.5 FRM.

42

 

41 The 15 cities are the same ones included in the Integrated Science Assessment for Particulate Matter (2009) which 

includes a similar map based on 2005-2007 PM2.5 data.

42 Frank, N. H., Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine 

Particulate Matter for Six Eastern U.S. Cities, 'J. Air & Waste Manage. Assoc.' 2006, '56', 500-511.

Cars, trucks, heavy equipment,

wildfires, wood/waste burning,

and biogenics

Cars, trucks,

industrial combustion, and

power generation

Suspended soil 

and industrial metallurgical 

operations

Industrial combustion and power 

generation
A-5

Figure A-3. Annual Average PM2.5 Composition in 15 Urban Areas: 2008-2010 

3. Seasonal and Daily Patterns of PM2.5

Fine particles often have a seasonal pattern. Both daily values and quarterly average of 

PM2.5 also reveal patterns based on the time of year. Unlike daily ozone levels, which are usually 

elevated in the summer, daily PM2.5 values at some locations can be high at any time of the year. 

As shown in Figure A-4, PM2.5 values in the eastern half of the United States are typically higher 

in the third calendar quarter (July-September) when sulfates are more readily formed from sulfur 

dioxide (SO2) emissions from power plants in that region and when secondary organic aerosol is 

more readily formed in the atmosphere. Fine particle concentrations tend to be higher in the first 

calendar quarter (January through March) in the Midwest in part because fine particle nitrates are 

more readily formed in cooler weather. PM2.5 values are high during the first (January through 

March) and fourth calendar quarter (October through December) in many areas of the West, in 

part because of fine particle nitrates and also due to carbonaceous particles which are directly 

emitted from wood stove and fireplace use. Average concentration from all locations reporting 

PM2.5 with valid design values is shown.
A-6

Figure A-4. Quarterly Averages of PM2.5 Concentration: 2008-2010

The composition of PM2.5 also varies by season and helps explain why mass varies by 

season. Figure A-5 shows the average composition by season (spring, summer, fall and winter) 

for PM2.5 data collected during 2008-10. In the eastern United States, sulfate are high in the 

spring (March-May) and summer (July-September). Nitrates are most evident in the midwest and 

western cities where its percentage is moderately high in the spring and fall (October-and highest 

during the winter.) Organic mass (OM) is high throughout the year.
A-7

Figure A-5. Seasonal Speciation Profiles of PM2.5 for Select Urban Areas: 2008-2010

The composition of the highest daily PM2.5 values may be different than that for the 

annual average. Table A-1 provides 2008-10 data on daily PM2.5 values and their composition on 

high mass days for various sites within large metropolitan areas (in the east: Birmingham, AL; 

Atlanta, GA; New York City, NY; Cleveland, OH; Chicago, IL; and St. Louis, MO; in the west:

Salt Lake City, UT; and Fresno, CA). Mass is proportioned into five components: sulfates, 

nitrates, OM, elemental carbon (EC) and crustal material. For each site, the table shows the 

2008-2010 annual average speciation profile, the breakdown for the top 10 percent of days per 

year and corresponding FRM mass. The table shows some notable differences in the percentage 

contribution of each of the species to total mass when looking at the high end of the distribution 

versus the annual average. Except for the southeast (where there is little nitrate in PM2.5), nitrates 

are slightly higher in the top 10 percent of the PM2.5 days. For the 2008-2010 measurements, the 

percent of sulfates is currently similar or slightly less on the top 10 percent of the days as 

compared to the annual averages. The portion of OM appears to be similar on the high days 

compared to the annual averages.
A-8

Table A-1. PM2.5 Composition on High PM2.5 Mass Days in Select Urban Areas: 2008-2010

Note: The percentages do not add to 100% due to a small amount of passively

collected fine particle mass included in the measurement of PM2.5 by the FRM.

4. Regional and Local Sources of PM2.5

Both local and regional sources contribute to particle pollution. Figure A-6 shows how 

much of the PM2.5 mass can be attributed to local versus regional sources for 13 selected urban 

areas. In each of these urban areas, monitoring sites were paired with nearby rural sites. When 

the average rural concentration is subtracted from the measured urban concentration, the 

estimated local and regional contributions become apparent. Urban and nearby rural PM2.5

concentrations suggest substantial regional contributions to fine particles in the East. The 

measured PM2.5 concentration is not necessarily the maximum for each urban area. Regional 
A-9

concentrations are derived from the rural IMPROVE monitoring network.43

Figure A-6. “Urban excess” of locally generated PM2.5 mass for four chemical components: 

sulfate, nitrate, organic mass (OM) and elemental carbon (EC)

Note: derived as the interpolated difference between urban CSN concentrations (squares) compared with nearby 

IMPROVE site concentrations within 150 km (circles). Annual mean concentrations from 2005-2008 are used. CSN 

sites not used in the analyses are shown as triangles.44

As shown in Figure A-6, we observe a large urban excess across the United State for 

most PM2.5 species but especially for elemental carbon (EC) and organic mass (OM). Large 

excess for OM is observed in California, throughout the Northwest, and in the Southeast. The 

prevalence of urban excess in EC is seen more widely. Large urban excess of nitrates is seen in 

California. These results indicate that local sources of these pollutants are indeed contributing to 

the PM2.5 air quality problem in these areas. As expected for a predominately regional pollutant, 

only a modest urban excess is observed for sulfates.

In the East, regional pollution contributes more than half of total PM2.5 concentrations. 

Rural background PM2.5 concentrations are high in the East and are somewhat uniform over large 

geographic areas. These regional concentrations come from emission sources such as power 

plants, natural sources, and urban pollution and can be transported hundreds of miles. The local 

and regional contributions for the major chemical components that make up urban PM2.5: 

sulfates, carbon, and nitrates.

 

43 Information regarding the IMPROVE monitoring network can be found at the following website: 

http://vista.cira.colostate.edu/improve

44 Hand et. al., Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United 

States: Report V, 2011 (http://vista.cira.colostate.edu/improve/Publications/Reports/2011/2011.htm)
A-10

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B-1

Appendix B: General Guidance on Use of Dispersion Models for Estimating Primary 

PM2.5 Concentrations

This appendix provides general guidance on the application of dispersion models for 

estimating ambient concentrations of PM2.5 associated with direct emissions of primary PM2.5. 

This guidance is based on and is consistent with the EPA’s Guideline on Air Quality Models, 

published as Appendix W of 40 CFR Part 51, and focuses primarily on the application of 

AERMOD, the EPA’s preferred dispersion model for most situations. Appendix W is the 

primary source of information on the regulatory application of air quality models for State 

Implementation Plan (SIP) revisions for existing sources and for New Source Review (NSR) and 

Prevention of Significant Deterioration (PSD) programs. There will be applications of dispersion 

models unique to specific areas, (i.e., there may be areas of the country where it is necessary to 

model unique specific sources or types of sources). In such cases, there should be consultation 

with the state or appropriate permitting authority with the appropriate EPA Regional Office 

modeling contact to discuss how best to model a particular source.

Recently issued EPA guidance of relevance for consideration in modeling for PM2.5

includes:

• “Model Clearinghouse Review of Modeling Procedures for Demonstrating Compliance 

with PM2.5 NAAQS” February 26, 2010 (U.S. EPA, 2010a);

• ”Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS” March 23, 

2010 (U.S. EPA, 2010b); and

• “Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5 and 

PM10 Nonattainment and Maintenance Areas” November 2013 (U.S.EPA, 2013a).

The guidance listed above, in addition to other relevant support documents can be found on the 

SCRAM website at: http://www.epa.gov/ttn/scram/.

The following sections will refer to the relevant sections of Appendix W and other 

existing guidance with summaries as necessary. Please refer to those original guidance 

documents for full discussion and consult with the appropriate EPA Regional Office modeling 

contact if questions arise about interpretation on modeling techniques and procedures.

45

1. Model selection

Preferred air quality models for use in regulatory applications are addressed in Appendix 

A of the EPA's Guideline on Air Quality Models. If a model is to be used for a particular 

application, the user should follow the guidance on the preferred model for that application. 

These models may be used without an area specific formal demonstration of applicability as long 

as they are used as indicated in each model summary of Appendix A. Further recommendations 

for the application of these models to specific source problems are found in Appendix W. In 

 

45 A list of EPA Regional Office modeling contacts is available on the SCRAM website at: 

http://www.epa.gov/ttn/scram/guidance_cont_regions.htm.
B-2

2005, the EPA promulgated the American Meteorological Society/Environmental Protection 

Agency Regulatory Model (AERMOD) as the Agency’s preferred near-field dispersion model 

for a wide range of regulatory applications in all types of terrain based on extensive 

developmental and performance evaluation. For PSD/NSR modeling under the PM2.5 NAAQS, 

AERMOD should be used to model primary PM2.5 emissions unless use of an alternative model 

can be justified (Section 3.2, Appendix W), such as the Buoyant Line and Point Source 

Dispersion Model (BLP).

The AERMOD modeling system includes the following components:

• AERMOD: the dispersion model (U.S. EPA, 2004a; U.S. EPA, 2014a);

• AERMAP: the terrain processor for AERMOD (U.S. EPA, 2004b, U.S. EPA, 2011a); 

and

• AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2004c; U.S. 

EPA, 2014b).

Other components that may be used, depending on the application, are:

• BPIPPRIME: the building input processor (U.S. EPA, 2004d);

• AERSURFACE: the surface characteristics processor for AERMET (U.S. EPA, 2008);

• AERSCREEN: a screening version of AERMOD (U.S. EPA, 2011b; U.S. EPA, 2011c);

and

• AERMINUTE: a pre-processor to calculate hourly average winds from ASOS 2-minute 

observations (U.S. EPA, 2011d).

Before running AERMOD, the user should become familiar with the user’s guides associated 

with the modeling components listed above and the AERMOD Implementation Guide (AIG) 

(U.S. EPA, 2009). The AIG lists several recommendations for applications of AERMOD that

would be applicable for SIP and PSD permit modeling.

1.2. Receptor grid

The model receptor grid is unique to the particular situation and depends on the size of 

the modeling domain, the number of modeled sources, and complexity of the terrain. Receptors 

should be placed in areas that are considered ambient air (i.e., where the public generally has 

access) and placed out to a distance such that areas of violation can be detected from the model 

output to help determine the size of nonattainment areas. Receptor placement should be of 

sufficient density to provide resolution needed to detect significant gradients in the 

concentrations with receptors placed closer together near the source to detect local gradients and 

placed farther apart away from the source. In addition, the user may want to place receptors at 

key locations such as around facility fence lines (which define the ambient air boundary for a 

particular source) or monitor locations (for comparison to monitored concentrations for model 

evaluation purposes). The receptor network should cover the modeling domain. States may 

already have existing receptor placement strategies in place for regulatory dispersion modeling 

under NSR/PSD permit programs.
B-3

If modeling indicates elevated levels of PM2.5 (near the standard) near the edge of the 

receptor grid, consideration should be given to expanding the grid or conducting an additional 

modeling run centered on the area of concern. As noted above, terrain complexity should also be 

considered when setting up the receptor grid. If complex terrain is included in the model 

calculations, AERMOD requires that receptor elevations be included in the model inputs. In 

those cases, the AERMAP terrain processor (U.S. EPA, 2004b; U.S EPA, 2011a) should be used 

to generate the receptor elevations and hill heights. The latest version of AERMAP (version 

09040 or later) can process either Digitized Elevation Model (DEM) or National Elevation Data 

(NED) data files. The AIG recommends the use of NED data since it is more up to date than 

DEM data, which is no longer updated (Section 4.3 of the AIG).

2. Source inputs

This section provides guidance on source characterization to develop appropriate inputs 

for dispersion modeling with the AERMOD modeling system. Section 2.1 provides guidance on 

use of emission, Section 2.2 covers guidance on Good Engineering Practice (GEP) stack heights, 

Section 2.3 provides details on source configuration and source types, Section 2.4 provides 

details on urban/rural determination of the sources, and Section 2.5 provides general guidance on 

source grouping, which may be important for design value calculations.

2.1. Emissions

Consistent with Appendix W, dispersion modeling for the purposes of PSD permitting 

should be based on the use of continuous operation at maximum allowable emissions or federally 

enforceable permit limits (see Table 8-2 of Appendix W) for the project source for all applicable 

averaging periods. Also consistent with past and current guidance, in the absence of maximum 

allowable emissions or federally enforceable permit limits, potential to emit emissions (i.e., 

design capacity) should be used. Maximum allowable emissions and continuous operation should 

also be assumed for nearby sources included in the modeled inventory for the 24-hr PM2.5

NAAQS, while maximum allowable emissions and the actual operating factor averaged over the 

most recent 2 years should be used for modeled nearby sources for the annual PM2.5 NAAQS.

2.2. Good Engineering Practice (GEP) stack height

Consistent with previous modeling guidance and Section 6.2.2 of Appendix W, for stacks 

with heights that are within the limits of Good Engineering Practice (GEP), actual heights should 

be used in modeling. Under the EPA’s regulations at 40 CFR 51.100, GEP height, Hg, is 

determined to be the greater of:

• 65 m, measured from the ground-level elevation at the base of the stack;

• for stacks in existence on January 12, 1979, and for which the owner or operator had 

obtained all applicable permits or approvals required under 40 CFR Parts 51 and 52

Hg=2.5H

provided the owner or operator produces evidence that this equation was actually relied 
B-4

on in designing the stack or establishing an emission limitation to ensure protection 

against downwash;

• for all other stacks,

Hg=H + 1.5L, 

where H is the height of the nearby structure(s) measured from the ground-level elevation 

at the base of the stack and L is the lesser dimension of height or projected width of 

nearby structure(s); or

• the height demonstrated by a fluid model or a field study approved by the EPA or the 

state/local permitting agency which ensures that the emissions from a stack do not result 

in excessive concentrations of any air pollutant as a result of atmospheric downwash, 

wakes, eddy effects created by the source itself, nearby structures or nearby terrain 

features.

For more details about GEP, see the Guideline for Determination of Good Engineering Practice 

Stack Height Technical Support Document (U.S. EPA, 1985).

If stack heights exceed GEP, then GEP heights should be used with the individual stack’s 

other parameters (temperature, diameter, exit velocity). For stacks modeled with actual heights 

below GEP that may be subject to building downwash influences, building downwash should be 

considered as this can impact concentrations near the source (Section 6.2.2b, Appendix W). If 

building downwash is being considered, the BPIPPRIME program (U.S. EPA, 2004d) should be 

used to input building parameters for AERMOD. More information about buildings and stacks is 

provided in Section 6.5.

2.3. Source configurations and source types

An accurate characterization of the modeled facilities is critical for refined dispersion 

modeling, including accurate stack parameters and physical plant layout. Accurate stack 

parameters should be determined for the emissions being modeled. Since modeling would be 

done with maximum allowable or potential emissions levels at each stack, the stack’s parameters 

such as exit temperature, diameter, and exit velocity should reflect those emissions levels. 

Accurate locations (i.e.. latitude and longitude or Universal Transverse Mercator (UTM) 

coordinates and datum)46 of the modeled emission sources are also important, as this can affect 

the impact of an emission source on receptors, determination of stack base elevation, and relative 

location to any nearby building structures. Not only are accurate stack locations needed, but 

accurate information for any nearby buildings is important. This information would include 

location and orientation relative to stacks and building size parameters (height, and corner 

coordinates of tiers) as these parameters are input into BPIPPRIME to calculate building 

parameters for AERMOD. If stack locations and or building information are not accurate, 

 

46 Latitudes and longitudes to four decimal places position a stack within 30 feet of its actual location and five 

decimal places position a stack within three feet of its actual location. Users should use the greatest precision 

available.
B-5

downwash will not be accurately accounted for in AERMOD.

Emission source type characterization within the modeling environment is also important. 

As stated in the AERMOD User’s Guide (U.S. EPA, 2004a; U.S. EPA, 2012a), emissions 

sources can be characterized as several different source types: POINT sources, capped stacks 

(POINTCAP), horizontal stacks (POINTHOR), VOLUME sources, OPENPIT sources, LINE 

sources, rectangular AREA sources, circular area sources (AREACIRC), and irregularly shaped 

area sources (AREAPOLY). Note that POINTCAP and POINTHOR are not part of the 

regulatory default option in AERMOD because the user must invoke the BETA option in the 

model options keyword MODELOPT while not including the “DFAULT” modeling option for 

these options to work properly. While most sources can be characterized as POINT sources, 

some sources, such as fugitive releases or nonpoint sources (emissions from ports/ships, airports, 

or smaller point sources with no accurate locations), may be best characterized as VOLUME or 

AREA type sources. Sources such as flares can be modeled in AERMOD using the parameter 

input methodology described in Section 2.1.2 of the AERSCREEN User’s Guide (U. S. EPA, 

2011b). If questions arise about proper source characterization or typing, users should consult the 

appropriate EPA Regional Office modeling contact.

2.4. Urban/rural determination

For any dispersion modeling exercise, the urban or rural determination of a source is 

important in determining the boundary layer characteristics that affect the model’s prediction of 

downwind concentrations. Figure B-1 gives example maximum 24-hour concentration profiles 

for a 10 meter stack (Figure B-1a) and a 100 m stack (Figure B-1b) based on urban vs. rural 

designation. The urban population used for the examples is 100,000. In Figure B-1a, the urban 

concentration is much higher than the rural concentration for distances less than 750 m from the 

stack but then drops below the rural concentration beyond 750 m. For the taller stack in Figure 

B-1b, the urban concentration is much higher than the rural concentration even as distances 

increase from the source. These profiles show that the urban or rural designation of a source can 

be quite important.

Determining whether a source is urban or rural can be done using the methodology 

outlined in Section 7.2.3 of Appendix W and recommendations outlined in Sections 5.1 through 

5.3 in the AIG (U.S. EPA, 2009). In summary, there are two methods of urban/rural 

classification described in Section 7.2.3 of Appendix W.

The first method of urban determination is a land use method (Appendix W, Section 

7.2.3c). In the land use method, the user analyzes the land use within a 3 km radius of the source 

using the meteorological land use scheme described by Auer (1978). Using this methodology, a 

source is considered urban if the land use types I1 (heavy industrial), I2 (light-moderate 

industrial), C1 (commercial), R2 (common residential), and R3 (compact residential) are 50

percent or more of the area within the 3 km radius circle. Otherwise, the source is considered a 

rural source. The second method uses population density and is described in Section 7.2.3d of 

Appendix W. As with the land use method, a circle of 3 km radius is used. If the population 

density within the circle is greater than 750 people/km2

, then the source is considered urban. 

Otherwise, the source is modeled as a rural source. Of the two methods, the land use method is 
B-6

considered more definitive (Section 7.2.3e, Appendix W).

Caution should be exercised with either classification method. As stated in Section 5.1 of 

the AIG (U.S. EPA, 2009), when using the land use method, a source may be in an urban area 

but located close enough to a body of water or other non-urban land use category to result in an 

erroneous rural classification for the source. The AIG in Section 5.1 cautions users against using 

the land use scheme on a source by source basis, but advises considering the potential for urban 

heat island influences across the full modeling domain. When using the population density 

method, Section 7.2.3e of Appendix W states, “Population density should be used with caution 

and should not be applied to highly industrialized areas where the population density may be low 

and thus a rural classification would be indicated, but the area is sufficiently built-up so that the 

urban land use criteria would be satisfied...” With either method, Section 7.2.3(f) of Appendix W 

recommends modeling all sources within an urban complex as urban, even if some sources 

within the complex would be considered rural using either the land use or population density 

method.
B-7

Figure B-1. Urban (red) and rural (blue) concentration profiles for (a) 10 m buoyant stack 

release, and (b) 100 m buoyant stack release
B-8

Another consideration that may need attention by the user, and is discussed in Section 5.1 

of the AIG, relates to tall stacks located within or adjacent to small to moderate size urban areas. 

In such cases, the stack height or effective plume height for very buoyant sources may extend 

above the urban boundary layer height. The application of the urban option in AERMOD for 

these types of sources may artificially limit the plume height. The use of the urban option may 

not be appropriate for these sources, since the actual plume is likely to be transported over the 

urban boundary layer. Section 5.1 of the AIG gives details on determining if a tall stack should 

be modeled as urban or rural based on comparing the stack or effective plume height to the urban 

boundary layer height. The 100 m stack illustrated in Figure B-1b, may be such an example as 

the urban boundary layer height for this stack would be 189 m (based on a population of 

100,000) and equation 104 of the AERMOD formulation document (Cimorelli, et al., 2004). This 

equation is:

4

1















 =

o

iuc iuo P

P

z z

(B-1)

where ziuo is a reference height of 400 m corresponding to a reference population Po of 2,000,000 

people.

Given that the stack is a buoyant release, the plume may extend above the urban 

boundary layer and may be best characterized as a rural source, even if it were near an urban 

complex. Exclusion of these elevated sources from application of the urban option would need to 

be justified on a case-by-case basis in consultation with the appropriate permitting authority.

AERMOD requires the input of urban population when utilizing the urban option. 

Population can be entered to one or two significant digits (i.e., an urban population of 1,674,365 

can be entered as 1,700,000). Users can enter multiple urban areas and populations using the 

URBANOPT keyword in the runstream file (U.S. EPA, 2004a; U.S. EPA, 2012a). If multiple 

urban areas are entered, AERMOD requires that each urban source be associated with a 

particular urban area or AERMOD model calculations will abort. Urban populations can be 

determined by using a method described in Section 5.2 of the AIG (U.S. EPA, 2009).

2.5. Source groups

In AERMOD, individual emission sources’ concentration results can be combined into 

groups using the SRCGROUP keyword (Section 3.3.11 of the AERMOD User’s Guide (U.S, 

EPA, 2004a). The user can automatically calculate a total concentration (from all sources) using 

the SRCGROUP ALL keyword. For the purposes of design value calculations, source group 

ALL should be used, especially if all sources in the modeling domain are modeled in one 

AERMOD run. Design values should be calculated from the total concentrations (all sources and 

background). Individual source contributions outputs to the total concentration may be necessary 

to determine the culpability to any NAAQS violations.
B-9

3. Meteorological data

This section gives guidance on the selection of meteorological data for input into 

AERMOD. Much of the guidance from Section 8.3 of Appendix W is applicable to SIP and PSD 

permit modeling and is summarized here. In Section 7.2.1, the use of a new tool, AERMINUTE 

(U.S. EPA, 2011d), is introduced. AERMINUTE is an AERMET pre-processor that calculates 

hourly averaged winds from ASOS (Automated Surface Observing System) 1-minute winds.

3.1. Surface characteristics and representativeness

The selection of meteorological data that are input into a dispersion model should be 

considered carefully. The selection of data should be based on spatial and climatological 

(temporal) representativeness (Appendix W, Section 8.3). The representativeness of the data is 

based on: 1) the proximity of the meteorological monitoring site to the area under consideration, 

2) the complexity of terrain, 3) the exposure of the meteorological site, and 4) the period of time 

during which data are collected. Sources of meteorological data are: National Weather Service 

(NWS) stations, site-specific or onsite data, and other sources such as universities, Federal 

Aviation Administration (FAA), military stations, and others. Appendix W addresses spatial 

representativeness issues in Sections 8.3.a and 8.3.c.

Spatial representativeness of the meteorological data can be adversely affected by large 

distances between the source and receptors of interest and the complex topographic 

characteristics of the area (Appendix W, Section 8.3.a and 8.3.c). If the modeling domain is large 

enough such that conditions vary drastically across the domain, then the selection of a single 

station to represent the domain should be carefully considered. Also, care should be taken when 

selecting a station if the area has complex terrain. While a source and meteorological station may 

be in close proximity, there may be complex terrain between them such that conditions at the 

meteorological station may not be representative of the source. An example would be a source 

located on the windward side of a mountain chain with a meteorological station a few kilometers 

away on the leeward side of the mountain. Spatial representativeness for off-site data should also 

be assessed by comparing the surface characteristics (albedo, Bowen ratio, and surface 

roughness) of the meteorological monitoring site and the analysis area. When processing 

meteorological data in AERMET (U.S. EPA, 2004c; U.S. EPA, 2014b), the surface 

characteristics of the meteorological site should be used (Section 8.3.c of Appendix W and the 

AERSURFACE User’s Guide (U.S. EPA 2008)). Spatial representativeness should also be 

addressed for each meteorological variable separately. For example, temperature data from a 

meteorological station several kilometers from the analysis area may be considered adequately 

representative, while it may be necessary to collect wind data near the plume height (Section 

8.3.c of Appendix W). 

Surface characteristics can be calculated in several ways. For details see Section 3.1.2 of 

the AIG (U.S. EPA, 2009). The EPA has developed a tool, AERSURFACE (U.S. EPA, 2008) to 

aid in the determination of surface characteristics. The current version of AERSURFACE uses 

the 1992 National Land Cover Data. Note that the use of AERSURFACE is not a regulatory 

requirement but the methodology outlined in Section 3.1.2 of the AIG should be followed unless 

an alternative method can be justified.
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3.2. Meteorological inputs

Appendix W states in Section 8.3.1.1 that the user should acquire enough meteorological 

data to ensure that worst-case conditions are adequately represented in the model results. 

Appendix W states that 5 years of NWS meteorological data or at least 1 year of site-specific 

data should be used(Section 8.3.1.2, Appendix W) and should be adequately representative of the 

study area. If 1 or more years of site-specific data are available, those data are preferred. While 

the form of the PM2.5 NAAQS contemplates obtaining 3 years of monitoring data, this does not 

preempt the use of 5 years of NWS data or at least 1 year of site-specific data in the modeling. 

The 5-year average based on the use of NWS data, or an average across 1 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.

3.2.1. NWS data

NWS data are available from the National Climatic Data Center (NCDC) in many 

formats, with the most common one in recent years being the Integrated Surface Hourly data 

(ISH). Most available formats can be processed by AERMET. As stated in Section 3.1, when 

using data from an NWS station alone or in conjunction with site-specific data, the data should 

be spatially and temporally representative of conditions at the modeled sources. Key points 

regarding the use of NWS data can be found in the EPA’s March 8, 2013 clarification memo 

“Use of ASOS meteorological data in AERMOD dispersion modeling” (U.S. EPA, 2013b). The 

key points are:

• The EPA has previously analyzed the effects of ASOS implementation on dispersion 

modeling and found that generally AERMOD was less sensitive than ISCST3 to the 

implementation of ASOS. 

• The implementation of the ASOS system over the conventional observation system 

should not preclude the consideration of NWS stations in dispersion modeling.

• The EPA has implemented an adjustment factor (0.5 knots) in AERMET to adjust for 

wind speed truncation in ASOS winds

The EPA has developed the AERMINUTE processor (U.S. EPA, 2011d) to process 2-

minute ASOS winds and calculate an hourly average for input into AERMET. The use of

hourly averaged winds better reflect actual conditions over the hour as opposed to a 

single 2-minute observation.

While the EPA’s March 8, 2013, memo states that ASOS should not preclude the use of 

NWS data in dispersion modeling, and Section 8.3.1.2 of Appendix W recommends the most 

recent five years of NWS data, Section 8.3.1.2 also recognizes cases where professional 

judgment indicates that ASOS data are inadequate and pre-ASOS, or observer based data may be 

considered for use. The appropriate permitting authority and EPA Regional Office modeling 

contact should be consulted when questions arise about the representativeness or applicability of 

NWS data.

3.2.2. Site-specific data
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The use of site-specific meteorological data is the best way to achieve spatial 

representativeness. AERMET can process a variety of formats and variables for site-specific 

data. The use of site-specific data for regulatory applications is discussed in detail in Section 

8.3.3 of Appendix W. Due to the range of data that can be collected onsite and the range of 

formats of data input to AERMET, the user should consult Appendix W, the AERMET User’s 

Guide (U.S. EPA, 2004c; U. S. EPA, 2014b), and Meteorological Monitoring Guidance for 

Regulatory Modeling Applications (U.S. EPA, 2000). Also, when processing site-specific data 

for an urban application, Section 3.3 of the AERMOD Implementation Guide offers 

recommendations for data processing. In summary, the guide recommends that site-specific 

turbulence measurements should not be used when applying AERMOD’s urban option in order 

to avoid double counting the effects of enhanced turbulence due to the urban heat island.

3.2.3. Upper air data

AERMET requires full upper air soundings to calculate the convective mixing height. For 

AERMOD applications in the U.S., the early morning sounding, usually the 1200 UTC 

(Universal Time Coordinate) sounding, is typically used for this purpose. Upper air soundings 

can be obtained from the Radiosonde Data of North America CD for the period 1946-1997. 

Upper air soundings for 1994 through the present are also available for free download from the 

Radiosonde Database Access website. Users should choose all levels or mandatory and 

significant pressure levels47 when selecting upper air data. Selecting mandatory levels only 

would not be adequate for input into AERMET as the use of just mandatory levels would not 

provide an adequate characterization of the potential temperature profile.

4. Running AERMOD and implications for design value calculations

Recent enhancements to AERMOD include options to aid in the calculation of design 

values for comparison with the PM2.5 NAAQS and to aid in determining whether emissions from 

the project source contributed significantly to any modeled violations. These enhancements 

include:

• The MAXDCONT option, which shows the contribution of each user-specified source 

group to the high ranked values for a specified target source group paired in time and 

space. The user can specify a range of ranks to analyze or specify an upper bound rank, 

i.e. 8th highest, corresponding to the 98th percentile for the 24-hour PM2.5 NAAQS, and a 

lower threshold concentration value, such as the NAAQS for the target source group. The 

model will process each rank within the range specified, but will stop after the first rank 

(in descending order of concentration) that is below the threshold value if specified by the 

user. A warning message will be generated if the threshold is not reached within the 

range of ranks analyzed (based on the range of ranks specified on the RECTABLE 

keyword). This option may be needed to aid in determining which sources should be 

considered for controls.

 

47 By international convention, mandatory levels are in millibars: 1,000, 850, 700, 500, 400, 300, 200, 150, 100, 50, 

30, 20, 10, 7 5, 3, 2, and 1. Significant levels may vary depending on the meteorological conditions at the upper-air 

station.
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For more details about the enhancements, see the AERMOD User’s guide Addendum (U. S. 

EPA, 2014a).

Ideally, all explicitly modeled sources, receptors, and background should be modeled in 

one AERMOD run for all modeled years. In this case, one of the above output options can be 

used in AERMOD to calculate design values for comparison to the NAAQS and determine the 

area’s attainment status and/or inform attainment/nonattainment boundaries. The use of these 

options in AERMOD allows AERMOD to internally calculate concentration metrics that can be 

used to calculate design values and, therefore, lessen the need for large output files, i.e. hourly 

POSTFILES.

However, there may be situations where a single AERMOD run with all explicitly 

modeled sources is not possible. These situations often arise due to runtime or storage space 

considerations during the AERMOD modeling. Sometimes separate AERMOD runs are done for 

each facility or group of facilities, or by year, or the receptor network is divided into separate 

sub-networks. In some types of these situations, the MAXDCONT output option may not be an 

option for design value calculations, especially if all sources are not included in a single run. If 

the user wishes to utilize one of the three output options, then care should be taken in developing 

the model inputs to ensure accurate design value calculations.

Situations that would effectively preclude the use of the MAXDCONT option to calculate 

meaningful AERMOD design value calculations include the following examples:

• Separate AERMOD runs for each source or groups of sources.

o SIP modeling includes 10 facilities for 5 years of NWS data and each facility is 

modeled for 5 years in a separate AERMOD run, resulting in ten separate AERMOD 

runs.

• Separate AERMOD runs for each source and each modeled year.

o 10 facilities are modeled for 5 years of NWS data. Each facility is modeled separately 

for each year, resulting in fifty individual AERMOD runs.

In the two situations listed above, the MAXDCONT option would not be useful as the 

different AERMOD runs do not include a total concentration with contributions from all 

facilities. In these situations, the use of 24-hour POSTFILES, which can be quite large, and 

external post-processing would be needed to calculate design values. 

Situations in which the MAXDCONT options may be used but may necessitate some 

external post-processing afterwards to calculate a design value include:

• The receptor network is divided into sections and an AERMOD run, with all sources and 

years, is made for each sub-network.

o A receptor network of 1,000 receptors is divided into four 250 receptor subnetworks. 10 facilities are modeled with 5 years of NWS data in one AERMOD 

run for each receptor network, resulting in four AERMOD runs. After the 
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AERMOD runs are complete, the MAXDCONT results for each network can be 

re-combined into the larger network.

• All sources and receptors are modeled in an AERMOD run for each year.

• Ten facilities are modeled with 5 years of NWS data. All facilities are modeled with all 

receptors for each year individually, resulting in five AERMOD runs. MAXDCONT 

output can be used and post-processed to generate the necessary design value 

concentrations. The receptor network is divided and each year is modeled separately for 

each sub-network with all sources.

• Ten facilities are modeled with 5 years of NWS data for 1,000 receptors. The receptor 

network is divided into four 250 receptor networks. For each sub-network, all ten 

facilities are modeled for each year separately, resulting in twenty AERMOD runs. 

MAXDCONT output can be used and post-processed to generate the necessary design 

value concentrations.
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5. References

Auer, Jr., A.H., 1978: Correlation of Land Use and Cover with Meteorological Anomalies. 

Journal of Applied Meteorology, 17(5), 636-643.

Brode, R., K. Wesson, J. Thurman, and C. Tillerson, 2008: AERMOD Sensitivity to the Choice 

of Surface Characteristics, Paper 811, Air And Waste Management Association Annual 

Conference.

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, EPA-454/R-03-004. U.S. Environmental Protection Agency, Research 

Triangle Park, NC. http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdfU.S

U.S. EPA, 1985: Guideline for Determination of Good Engineering Practice Stack Height 

(Technical Support Document for the Stack Height Regulations), Revised. EPA-450/4-

80-023R. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. 

http://www.epa.gov/ttn/scram/guidance/guide/gep.pdf

U.S. EPA, 1992: Screening Procedures for Estimating the Air Quality Impact of Stationary 

Sources. EPA-454/R-92-019. U.S. Environmental Protection Agency, Research Triangle 

Park, NC 27711. http://www.epa.gov/ttn/scram/guidance/guide/scrng.wpd

U.S. EPA, 1994: SO2 Guideline Document. EPA-452/R-95-008. U.S. Environmental Protection 

Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2000: Meteorological Monitoring Guidance for Regulatory Modeling Applications. 

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

27711. http://www.epa.gov/ttn/scram/guidance/met/mmgrma.pdf

U.S. EPA, 2004a: User's Guide for the AMS/EPA Regulatory Model – AERMOD. EPA-454/B03-001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

U.S. EPA, 2004b: User's Guide for the AERMOD Terrain Preprocessor(AERMAP). EPA454/B-03-003. U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermap/aermap_userguide.zip

U.S. EPA, 2004c: User’s Guide for the AERMOD Meteorological Preprocessor (AERMET). 

EPA-454/B-03-002. U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711. http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip

U.S. EPA, 2004d: User's Guide to the Building Profile Input Program. EPA-454/R-93-038. U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina 27711.

U.S. EPA, 2005. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W. 

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

U.S. EPA, 2008: AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aersurface_userguide.pdf

U.S. EPA, 2009: AERMOD Implementation Guide. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_implmtn_guide_19March 

2009.pdf
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U.S. EPA, 2010a: Model Clearinghouse Review of Modeling Procedures for Demonstrating 

Compliance with PM2.5 NAAQS. Tyler Fox Memorandum dated February 26, 2010. U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/mch/new_mch/MCmemo_Region6_PM25_NAA

QS_Compliance.pdf

U.S. EPA, 2010b: Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS. 

Stephen Page Memorandum dated March 23, 2010. U.S. Environmental Protection 

Agency, Research Triangle Park, North Carolina, 27711. 

http://www.epa.gov/ttn/scram/Official%20Signed%20Modeling%20Proc%20for%20De

mo%20Compli%20w%20PM2.5.pdf

U.S. EPA, 2011a: Addendum - User's Guide for the AERMOD Terrain Preprocessor 

(AERMAP). EPA-454/B-03-003. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermap/aermap_userguide.zip

U.S. EPA, 2011b: AERSCREEN User’s Guide. EPA-454-/B-11-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/screen/aerscreen_userguide.pdf

U.S. EPA, 2011c: AERSCREEN Released as the EPA Recommended Screening Model. Tyler 

Fox Memorandum dated April 11, 2011. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/20110411_AERSCREEN_Release_Memo.pdf

U.S EPA, 2011d AERMINUTE User’s Guide. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aerminute_v11325.zip

U.S. EPA, 2013a: Transportation Conformity Guidance for Quantitative Hot-spot Analyses in 

PM2.5 and PM10 Nonattainment and Maintenance Areas. November 2013. EPA-420-B10-040. U.S. Environmental Protection Agency, Ann Arbor, Michigan 48105. 

http://www.epa.gov/oms/stateresources/transconf/policy/420b13053-sec.pdf and

http://www.epa.gov/oms/stateresources/transconf/policy/420b13053-appx.pdf.

U.S. EPA, 2013b: Use of ASOS meteorological data in AERMOD dispersion modeling. Tyler 

Fox Memorandum dated March 8, 2013. U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.

pdf

U.S. EPA, 2014a: Addendum – User’s Guide for the AMS/EPA Regulatory Model – AERMOD. 

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

North Carolina 27711. 

http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

U.S. EPA, 2014b: Addendum - User’s Guide for the AERMOD Meteorological Preprocessor 

(AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency, Research 

Triangle Park, NC 27711. 

http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip
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Appendix C: Example of a Qualitative Assessment of the Potential for Secondary PM2.5

Formation

In late 2011, the EPA Region 10 Office developed a qualitative assessment of the 

potential for secondary fine particulate matter (PM2.5) formation to cause or contribute to a 

violation of the PM2.5 National Ambient Air Quality Standard (NAAQS) through a response to 

public comments document regarding a Clean Air Act permit issued for Shell’s Discoverer drill 

ship and support fleet to explore for oil and gas in the Chukchi Sea off Alaska. While the 

environment in and around the Chukchi Sea and North Slope of Alaska is unique when 

compared to the rest of the United States, the various components contained within this 

qualitative assessment provide a template that could be followed, with appropriate modifications, 

in the development of other case-specific qualitative assessments. An excerpt from this response 

to public comments document is provided below for reference.

As shown in the EPA Region 10 example, the qualitative assessment of the potential for 

secondary PM2.5 formation by the Shell’s Discoverer drill ship and support fleet was developed 

in a narrative manner integrating numerous factors specific to the North Slope region of Alaska 

that provided sufficient evidence that the PM2.5 NAAQS would not be violated in this particular 

case. The qualitative assessment examined the regional background PM2.5 monitoring data and 

aspects of secondary PM2.5 formation from existing sources; the relative ratio of the combined 

modeled primary PM2.5 impacts and background PM2.5 concentrations to the level of the 

NAAQS; the spatial and temporal correlation of the primary and secondary PM2.5 impacts; 

meteorological characteristics of the region during periods of precursor pollutant emissions; the 

level of conservatism associated with the modeling of the primary PM2.5 component and other 

elements of conservatism built into the overall NAAQS compliance demonstration; aspects of 

the precursor pollutant emissions in the context of limitations of other chemical species 

necessary for the photochemical reactions to form secondary PM2.5; and an additional level of 

NAAQS protection through a post-construction monitoring requirement. While each of the 

components of the EPA Region 10 example may or may not be necessary, this example should 

provide a useful template for other qualitative assessments under this guidance, recognizing that 

additional components may be essential in other qualitative assessments of the potential for 

secondary PM2.5 formation.

Additional information regarding this EPA Region 10 Office permit action can be found 

through the following web link: http://yosemite.epa.gov/R10/airpage.nsf/Permits/chukchiap/.

Region 10 example:

In support of the 2011 Revised Draft Permits, Region 10 provided a detailed 

explanation for why it believes that modeling secondary PM2.5 emissions is not 

needed in order to determine that emissions of PM2.5 precursors from the Discoverer

and Associated Fleet would not, together with emissions of primary PM2.5, cause or 

contribute to a violation of the 24-hour PM2.5 NAAQS. The factors Region 10 relied 

on to reach this conclusion include:
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1) The background PM2.5 monitoring data considered in the air quality analysis is 

quality assured, quality controlled data from monitors operating for more than one 

year that Region 10 believes will have accounted for much of the secondary 

formation from existing regional emission sources that will occur in the Chukchi 

Sea and Beaufort Sea regions. Monitoring data show low levels of daily PM2.5, 

generally in the range of 2 μg/m3, with the higher PM2.5 values generally 

occurring on days where windblown dust or fires are believed to be contributing 

factors. Thus, there is no indication that secondary formation of PM2.5 from 

existing sources in the North Slope is currently causing or contributing to 

exceedances or a violation of the PM2.5 NAAQS in the onshore communities.

2) Modeled primary PM2.5 impacts from the Discoverer and Associated Fleet that, 

when using a conservative “First Tier” approach to combining modeled primary 

PM2.5 impacts with monitored background PM2.5 concentrations, are less than 67 

percent of the PM2.5 NAAQS. Thus, although not expected, considerable 

formation of secondary PM2.5 emissions could occur before the NAAQS would be 

threatened. 

3) Secondary PM2.5 impacts associated with Discoverer and Associated Fleet 

precursor emissions are expected to be low near the emission release points where 

modeled concentrations associated with primary PM2.5 emissions are highest, 

because there has not been enough time for the secondary chemical reactions to 

occur. Conversely, secondary PM2.5 impacts are more likely to be higher farther 

from the Discoverer and the Associated Fleet where impacts from primary PM2.5

emissions from the Discoverer and the Associated Fleet are expected to be lower. 

This makes it unlikely that maximum primary PM2.5 impacts and maximum 

secondary PM2.5 impacts from the Discoverer and the Associated Fleet will occur 

at the same time (paired in time) or location (paired in space). See March 23, 

2010 PM2.5 Guidance Memo at 9. 

4) The relatively small amount of NOX emissions (a PM2.5 precursor) that will be 

authorized under these permits in comparison to existing NOX emissions in the 

North Slope area in general, together with the generally low levels of PM2.5

recorded at monitoring stations in the area, make it unlikely that NOX emissions 

from the Discoverer and the Associated Fleet would cause or contribute to a 

violation of the PM2.5 NAAQS.

5) The background concentrations of certain chemical species that participate in 

photochemical reactions to form secondary PM2.5, including ammonia and volatile 

organic compounds, are expected to be negligible in the offshore air masses 

where the Discoverer will be permitted to operate. The emissions authorized 

under the permits of approximately 43 tons per year of VOC and 0.52 tons per 

year of ammonia [citation omitted] would also not be expected to result in the 

conversion of significant quantities of NOX emissions to secondary particles in 

the areas impacted by primary PM2.5 emissions.
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6) There are several other conservative assumptions incorporated in the modeling 

of primary PM2.5 emissions. These include the conservatism inherent in using a 

“First Tier” approach to combining modeled primary PM2.5 impacts with 

monitored background PM2.5 concentrations; assuming that the Discoverer will be 

operating in a single drilling location for 3 years, when it is more likely that the 

Discoverer will operate in a different location each year (if not more frequently); 

orienting the Associated Fleet with hourly modeled wind direction and using 

emission release characteristics based on actual meteorological conditions; and 

the fact that the background monitored data used to represent offshore conditions 

was collected onshore, where it is influenced by local sources, and is, therefore 

likely to be a conservative estimate of background PM2.5 levels in the area of 

maximum impact near the Discoverer. 

7) With respect to the Chukchi Sea impacts, the predominant easterly wind 

directions in the Chukchi Sea along with the distance between the project location 

and the existing sources in the North Slope oil and gas fields are such that 

emissions from the Discoverer and Associated Fleet are not likely to significantly 

contribute to the maximum ambient concentrations resulting from the existing 

source emissions. 

8) Region 10 required post-construction monitoring in the previous permits 

because the conservative screening modeling resulted in predicted levels that were 

just below the 24-hour PM2.5 NAAQS. With the additional emission reductions in 

direct PM2.5 emissions and the use of a refined model, predicted PM2.5

concentrations are now well below the NAAQS. However, Region 10 has decided 

to retain the post-construction monitoring requirement in order to obtain better 

information on the quantity of secondary particles in the North Slope 

communities. 

Based on these factors, and consistent with current guidance, Region 10 believes that 

an adequate assessment has been made to demonstrate that the PM2.5 NAAQS will be 

protected, accounting for primary PM2.5 impacts and potential contributions due to 

PM2.5 precursors from the Discoverer and the Associated Fleet, and that it is not 

necessary to use a photochemical model to further evaluate secondary PM2.5 formation 

in these permitting actions.
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Appendix D: Example of a Hybrid Qualitative/Quantitative Assessment of the Potential 

for Secondary PM2.5 Formation

In late 2013/early 2014, a permit applicant, Sasol, engaged and closely coordinated with 

the EPA Region 6 Office and the Louisiana Department of Environmental Quality (LDEQ) in the 

development of a hybrid qualitative/quantitative assessment of the potential for secondary fine 

particulate matter (PM2.5) formation to cause or contribute to a violation of the PM2.5 National 

Ambient Air Quality Standard (NAAQS) for their proposed major facility expansions in 

Southern Louisiana. Sasol’s expansion and new plant are a very large investment (up to $18 

Billion), and Sasol and LDEQ worked closely with Region 6 to ensure that the ambient impacts 

analysis was robust and defendable. In this particular hybrid assessment, Sasol took an approach 

of using the formerly presumptive interpollutant trading ratios for NOx and SO2 to PM2.5 offsets 

and conservatively applied them in an illustrative example to demonstrate how relatively 

inconsequential the impacts of secondary PM2.5 formation would be in the area of significant 

impact surrounding their facility. In Sasol’s case, the projected emissions increases of the direct 

PM2.5 emissions and both PM2.5 precursors of NOx and SO2 were above their respective 

Significant Emissions Rates (SERs). Sasol also performed an analysis of PM2.5 speciated 

monitoring data to further support the amount of impacts of nitrates on high PM2.5 values in the 

area is relatively small and corroborate the ratio based analysis. Thus, this situation is an 

example of a Case 3 assessment as presented in Table III-1 of this guidance.

It is important to note that the EPA revised the provisions of the interpollutant trading 

policy for PM2.5 on July 21, 2011, as described in Section III.2.2 of this guidance, to remove the 

general presumptiveness of the interpollutant trading ratios without further technical 

justification. Sasol is located in Southwestern Louisiana near the coast but chose to use the 

western state value of 100 for NOx as a more conservative assessment. Sasol did not seek to 

directly apply the formerly presumptive interpollutant trading ratios in an absolute sense. Rather, 

the intention was to present the analysis in a manner to determine if further technical justification 

would be required or if the application of the formerly presumptive interpollutant trading ratios 

where adequate in a hybrid qualitative/quantitative sense.

Using the formerly presumptive interpollutant trading ratios resulted in total projected 

secondary PM2.5 formation of 0.18 μg/m3 for the 24-hour PM2.5NAAQS and of 0.04 μg/m3 for 

the annual PM2.5 NAAQS. When considered along with the primary PM2.5 impacts and 

representative background data, the secondary PM2.5 impacts with respect to the 24-hour 

NAAQS would have to be on the order of 32 times higher and to the annual NAAQS would have 

to be at least 15 times greater before a potential projected violation might occur. This also 

assumes that the maximum secondary PM2.5 impacts from the NOx and SO2 precursor emissions 

would occur at the same place and time as the maximum primary PM2.5 impacts. Based upon

Sasol’s PM2.5 primary modeling projecting maximum concentrations very close to the facility 

and decreasing 60% within three kilometers of the facility, it is very unlikely that the primary 

and secondary maximums would ever occur at the same receptors. So, it would take a 

considerable and unreasonably conservative change to the interpollutant trading ratios used in 

this example before the NAAQS could be threatened based on the total proposed emissions 

increases from this facility. 
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At the same time Sasol also conducted an analysis of speciated data at a nearby monitor 

to further corroborate the ratio analysis. There is a PM2.5 monitor within 1⁄2 mile of the Sasol’s 

property line, but it does not have speciated data collection. Sasol utilized a representative PM2.5

monitor approximately 25 miles away that did have long-term speciated data. Sasol evaluated the 

PM2.5 speciated data from the nearby monitor to support that nitrate is not a large contributer to 

high PM2.5 values on an annual basis or even on the higher daily values in the colder months 

when nitrates would be expected to yield more secondary PM2.5. 

Given the close coordination with the respective permitting authorities, it was determined 

that a more thorough technical demonstration with respect to interpollutant ratios specific to this 

source and area was not warranted and that the illustrative use of the formerly presumptive 

interpollutant trading ratios was sufficient to demonstrate that secondary PM2.5 formation would 

not cause or contribute to a violation of the NAAQS. The permit applicant’s corroborative 

analysis of the PM2.5 speciated data further supported that the main increase of emissions (NOx) 

would not be expected to yield significant levels of secondary PM2.5 and the applicable ambient 

standards will not be exceeded by this project.

Region 6 example:

Justification on Secondary PM2.5 Approach

At a December 13, 2013, meeting and on a January 17, 2014, conference call EPA 

Region 6 requested an analysis to examine the fraction of sulfate and nitrate in the 

PM2.5 measurements in the study area and additional justification on the modeling 

approach for secondary PM2.5. This document presents the results of the requested 

analysis.

Secondary PM2.5 is formed primarily from reaction of sulfur dioxide (SO2) emissions 

to form particulate sulfate and from nitrogen oxides (NOx) reacting to form 

particulate nitrate. The approach used to estimate the secondary particulate is 

described in Section [Secondary Particulate Estimate (listed below)].

With regard to NOx. and SO2, the Sasol project emissions are dominated by NOx with 

annual emissions of 1,595 tons per year compared to SO2 emissions of only 121 tons 

per year. EPA requested additional information on the secondary PM2.5 formation 

from nitrate in the colder months.

The PM2.5 background monitor is the Westlake monitor located very near the project 

site. However, this monitor did not record speciated PM2.5 data, so it is not possible to 

compute the sulfate and nitrate fractions at this location. Monitors with speciated 

PM2.5 data include the Port Arthur Memorial School (AIRS: 48-245-0021) in Port 

Arthur, Texas, the Capitol Baton Rouge (AIRS: 22-033-0009), and the Shreveport 

(AIRS: 22-015-0008) monitors. The Port Arthur monitor was chosen as being the 

most representative because it is the closest monitor to the Sasol site and is in a less 

urban area than the Capitol Baton Rouge monitor and is not as far inland as the 

Shreveport monitor. The Port Arthur area is also located along the gulf coast and 

most closely represents the combination of a metropolitan size and industrial presence 

when compared to the Calcasieu Parish area where Sasol is located.
D-3

The Port Arthur monitor, located in Port Arthur, Texas, is approximately 25 miles 

west of the project. Given the regional nature of PM2.5 concentrations, this monitor 

should be representative of the study area. The most recently available five years of 

data for this site was for 2006-2010 and was obtained from the USEPA. The data 

shows that nitrate makes up 2.6 percent of the average of the 24-hour concentrations 

of PM2.5 and 2.3 percent of the 5-year average concentration. On the day with the

highest 24-hour average PM2.5 measurement, nitrate was 2.2 percent of the PM2.5

concentration.

In general, the generation of PM2.5 occurs more from nitrate during colder winter 

months than during the summer. Examination of the worst 10% of PM2.5 days during 

the colder months (November through February) at the Port Arthur monitor for 2006-

2010 reveals that the average nitrate contribution is 2.9 percent, only slightly higher 

than the 5-year average concentration. Thus, even on days with high PM2.5

concentrations in the colder months, particulate nitrate is still a relatively small 

portion of the total PM2.5 concentrations.

Based on this relatively low fraction of particulate nitrate in the observed PM2.5, and 

the magnitude of existing NOx emissions in the area, it is clear that secondary 

formation of particulate nitrate is not significant in the project area.

Particulate sulfate makes up 29.6 percent of the 5-year average of the 24-hour 

concentrations and 29.0 percent of the 5-year average concentrations. On the day with 

the highest 24-hour average PM2.5 observation, sulfate was 10.6 percent of the PM2.5

concentration.

Table 1 presents the total PM2.5 ambient air impact estimated using the formerly 

approved interpollutant trading ratios. The nitrate equivalent ratio (1.026) is [6.5]

times greater than the sulfate equivalent ratio ([1.004]). While sulfate does make up a

significant portion of the total PM2.5 mass, the projected increase in SO2 emissions 

(121 tpy) from the Sasol GTL and LCCP projects are a very small fraction of the total 

SO2 emissions in the large industrial area impacting Port Arthur (i.e. Beaumont/Port 

Arthur, Lake Charles, Houston/Galveston).

An implicit conservatism to the ratio approach that was used by Sasol is that the 

primary and secondary impacts occur at the same location at the same time. The 24-

hour average modeled PM2.5 concentration is presented in Figure 1. Examination of

this figure reveals that the highest impact occurs very near the Sasol project border. 

Within a few kilometers of the project site, the concentrations fall significantly from 

the peak of modeled concentration of 9 μg/m3 to less than 3 μg/m3

. Formation of

secondary sulfate and nitrate particulate is a fairly slow process with conversion rates 

taking many hours to days. Thus, the peak secondary impacts are expected to occur 

well downwind of the peak primary impacts.

Given this information, the study team is comfortable that the ambient ratio analysis 
D-4

presented in the ozone and secondary PM2.5 modeling report is an appropriate 

approach to estimating the secondary PM2.5 impacts for the project.
D-5
D-6

[Secondary Particulate Estimate]

Recent EPA guidance (EPA March 2013) has suggested the need to examine 

secondary particulate formation. Directly emitted sulfur or nitrogen compounds are 

likely to react with available water and other pollutants in secondary reactions to form 

particulate ammonium sulfate –(NH4)2SO4 or ammonium nitrate –NH4NO3. These 

latter compounds are formed primarily downwind of the specific sources of concern, 

given reaction times, ambient temperature and other environmental factors. The sulfur 

compounds emitted by the two projects are in the form of SO2. The nitrogen 

compounds emitted by the two projects are in the form of NOx. The Sasol projects 

(GTL and LCCP combined) would have 1,595 tpy of NOx, 121 tpy SO2, and 612 tpy 

of direct PM2.5 emissions.

The NACAA/EPA recommendation to account for secondary PM2.5 formation is to 

divide the projected emissions by a region average offset ratio. The national ratio for 

SO2 is 40 and for NOx is 100 for western states and 200 for eastern states. To be 

conservative, the western value was used in the analysis since it estimates a higher 

secondary ratio. The total PM2.5 emissions are calculated by multiplying the primary 

PM2.5 modeled concentration by the ratio obtained from the secondary equivalent 

PM2.5 calculation.

For the Sasol combined project emissions the formulas are:

Total Equivalent PM2.5 = Primary PM2.5 + (SO2/40) + (NOx/100) = 612 + (121/400) +

(1,595/100) = 631.0 ton/year

Total PM2.5 Impact (μg/m3) = Primary PM2.5 Impact (μg/m3) * (Total Equivalent 

Primary PM2.5 (tpy) / Primary PM2.5 (tpy))

Total Equivalent PM2.5 / Primary PM2.5 = 631.0 tpy / 612 tpy = 1.03

Hence the modeled impacts for PM2.5 could be increased by a factor of 1.03 [(1.004 for SO2 and 

1.026 for NOx)] to account for the secondary formation for those sources emitting significant 

amounts of secondary PM2.5 precursor emissions.
E-1

Appendix E: Example of the background monitoring data calculations for a Second Tier 

24-hour modeling analysis

This appendix provides an illustrative example of the calculations and data sorting 

recommendations for the background monitoring data to be used in a Second Tier 24-hour PM2.5

modeling analysis. In this example, it was determined through discussion and coordination with 

the appropriate permitting authority that the impacts from the project source’s primary PM2.5

emissions were most prominent during the cool season and were not temporally correlated with 

background PM2.5 levels that were typical highest during the warm season. So, combining the 

modeled and monitored contributions through a First Tier 24-hour PM2.5 modeling analysis was 

determined to be potentially overly conservative. Extending the compliance demonstration to a 

Second Tier analysis allows for a more refined and appropriate assessment of the cumulative 

impacts on the primary PM2.5 emissions in this particular situation.

The example provided is from an idealized Federal Reference Method (FRM) PM2.5

monitoring site that operates on a daily (1-in-1 day) frequency with 100% data completeness. In 

this case, the annual 98th percentile concentration is the 8th highest concentration of the year. In 

most cases, the FRM monitoring site will likely operate on a 1-and-3 day frequency and will also 

likely have missing data due to monitor maintenance or collected data not meeting all of the 

quality assurance criteria. Please reference Appendix N to 40 CFR Part 50 to determine the 

appropriate 98th percentile rank of the monitored data based on the monitor sampling frequency 

and valid number of days sampled during each year.

The appropriate seasonal (or quarterly) background concentrations to be included as 

inputs to the AERMOD model per a Second Tier 24-hour PM2.5 modeling analysis are as 

follows:

• Step 1 – Start with the most recent 3-years of representative background PM2.5 ambient 

monitoring data that are being used to develop the monitored background PM2.5 design 

value. In this example, the 3-years of 2008 to 2010 are being used to determine the 

monitored design value.

• Step 2 – For each year, determine the appropriate rank for the daily 98th percentile PM2.5

concentration. Again, this idealized example is from a 1-in-1 day monitor with 100% data 

completeness. So, the 8th highest concentration of each year is the 98th percentile PM2.5

concentration. The 98th percentile PM2.5 concentration for 2008 is highlighted in Table E1. The full concentration data from 2009 and 2010 are not shown across the steps in this 

Appendix for simplicity but would be similar to that of 2008.

• Step 3 – Remove from further consideration in this analysis the PM2.5 concentrations 

from each year that are greater than the 98th percentile PM2.5 concentration. In the case 

presented for a 1-in-1 day monitor, the top 7 concentrations are removed. If the monitor 

were a 1-in-3 day monitor, only the top 2 concentrations would be removed. The resultant 

dataset after the top 7 concentrations have been removed from further consideration in 

this analysis for 2008 is presented in Table E-2.
E-2

• Step 4 – For each year, divide the resultant annual dataset of the monitored data equal to 

or less than the 98th percentile PM2.5 concentration into each season (or quarter). For 

2008, the seasonal subsets are presented in Table E-3.

• Step 5 – Determine the maximum PM2.5 concentration from each of the seasonal (or 

quarterly) subsets created in Step 4 for each year. The maximum PM2.5 concentration 

from each season for 2008 is highlighted in Table E-3.

• Step 6 – Average the seasonal (or quarterly) maximums from Step 5 across the three 

years of monitoring data to create the four seasonal background PM2.5 concentrations to 

be included as inputs to the AERMOD model. These averages for the 2008 to 2010 

dataset used in this example are presented in Table E-4. As noted above, the full 

concentration data from 2009 and 2010 are not shown across the steps in this Appendix 

for simplicity, but the seasonal maximums from 2009 and 2010 presented in Table E-4 

were determined by following the previous five steps similar to that of 2008.
E-3

Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc.

1-Jan 10.4 16-Feb 15.1 2-Apr 10.5 18-May 11.1 3-Jul 17.1 18-Aug 18.7 3-Oct 12.3 18-Nov 4.4

2-Jan 5.4 17-Feb 11.8 3-Apr 8.2 19-May 7.7 4-Jul 19.8 19-Aug 21.5 4-Oct 19.5 19-Nov 8.2

3-Jan 10.0 18-Feb 3.4 4-Apr 9.7 20-May 13.6 5-Jul 14.3 20-Aug 20.1 5-Oct 23.7 20-Nov 11.1

4-Jan 16.4 19-Feb 4.5 5-Apr 6.9 21-May 12.1 6-Jul 11.5 21-Aug 18.4 6-Oct 19.8 21-Nov 5.3

5-Jan 11.2 20-Feb 4.8 6-Apr 6.3 22-May 10.0 7-Jul 14.3 22-Aug 16.7 7-Oct 21.7 22-Nov 8.9

6-Jan 11.1 21-Feb 11.9 7-Apr 7.9 23-May 13.3 8-Jul 12.2 23-Aug 13.8 8-Oct 12.2 23-Nov 14.0

7-Jan 10.2 22-Feb 20.1 8-Apr 9.8 24-May 11.2 9-Jul 11.1 24-Aug 19.0 9-Oct 5.1 24-Nov 12.7

8-Jan 11.4 23-Feb 11.4 9-Apr 16.5 25-May 17.7 10-Jul 9.7 25-Aug 17.6 10-Oct 10.2 25-Nov 9.7

9-Jan 8.1 24-Feb 19.3 10-Apr 13.3 26-May 14.2 11-Jul 16.4 26-Aug 15.4 11-Oct 10.7 26-Nov 12.8

10-Jan 9.4 25-Feb 18.2 11-Apr 11.0 27-May 15.4 12-Jul 21.5 27-Aug 12.6 12-Oct 5.6 27-Nov 16.6

11-Jan 5.7 26-Feb 12.8 12-Apr 8.8 28-May 13.9 13-Jul 25.1 28-Aug 12.1 13-Oct 5.9 28-Nov 17.2

12-Jan 8.9 27-Feb 5.5 13-Apr 6.3 29-May 9.3 14-Jul 11.7 29-Aug 10.1 14-Oct 9.7 29-Nov 16.6

13-Jan 18.1 28-Feb 9.7 14-Apr 5.1 30-May 14.5 15-Jul 18.9 30-Aug 17.2 15-Oct 12.8 30-Nov 4.5

14-Jan 11.0 29-Feb 12.1 15-Apr 7.9 31-May 20.5 16-Jul 28.9 31-Aug 19.9 16-Oct 16.4 1-Dec 7.5

15-Jan 11.8 1-Mar 9.6 16-Apr 8.2 1-Jun 15.3 17-Jul 27.6 1-Sep 19.4 17-Oct 12.0 2-Dec 10.6

16-Jan 10.7 2-Mar 5.6 17-Apr 14.7 2-Jun 11.5 18-Jul 12.8 2-Sep 18.2 18-Oct 7.9 3-Dec 16.7

17-Jan 10.0 3-Mar 12.5 18-Apr 22.5 3-Jun 17.9 19-Jul 6.2 3-Sep 24.0 19-Oct 6.6 4-Dec 12.5

18-Jan 15.6 4-Mar 7.1 19-Apr 12.8 4-Jun 21.1 20-Jul 20.1 4-Sep 15.4 20-Oct 8.1 5-Dec 7.3

19-Jan 18.0 5-Mar 4.9 20-Apr 6.9 5-Jun 17.9 21-Jul 26.5 5-Sep 12.4 21-Oct 12.2 6-Dec 10.4

20-Jan 6.6 6-Mar 9.9 21-Apr 7.5 6-Jun 17.6 22-Jul 16.9 6-Sep 12.5 22-Oct 4.6 7-Dec 13.4

21-Jan 7.4 7-Mar 11.2 22-Apr 6.0 7-Jun 15.0 23-Jul 12.8 7-Sep 15.8 23-Oct 6.1 8-Dec 10.5

22-Jan 13.5 8-Mar 5.5 23-Apr 9.1 8-Jun 22.3 24-Jul 7.9 8-Sep 23.4 24-Oct 4.6 9-Dec 9.3

23-Jan 16.0 9-Mar 8.8 24-Apr 10.3 9-Jun 27.9 25-Jul 15.7 9-Sep 11.5 25-Oct 4.5 10-Dec 6.5

24-Jan 9.4 10-Mar 11.0 25-Apr 12.0 10-Jun 21.6 26-Jul 24.9 10-Sep 6.0 26-Oct 10.5 11-Dec 3.0

25-Jan 12.6 11-Mar 12.1 26-Apr 12.5 11-Jun 19.4 27-Jul 22.2 11-Sep 11.8 27-Oct 6.4 12-Dec 3.5

26-Jan 13.6 12-Mar 9.7 27-Apr 11.3 12-Jun 21.2 28-Jul 17.5 12-Sep 10.7 28-Oct 4.6 13-Dec 10.2

27-Jan 16.1 13-Mar 15.1 28-Apr 7.6 13-Jun 29.1 29-Jul 19.1 13-Sep 7.6 29-Oct 5.6 14-Dec 17.6

28-Jan 10.0 14-Mar 21.6 29-Apr 7.4 14-Jun 15.6 30-Jul 21.1 14-Sep 7.5 30-Oct 7.6 15-Dec 12.4

29-Jan 10.4 15-Mar 16.6 30-Apr 11.4 15-Jun 14.8 31-Jul 18.0 15-Sep 7.1 31-Oct 11.2 16-Dec 9.7

30-Jan 6.9 16-Mar 7.9 1-May 12.6 16-Jun 17.8 1-Aug 16.3 16-Sep 7.7 1-Nov 16.2 17-Dec 7.0

31-Jan 4.9 17-Mar 9.6 2-May 10.0 17-Jun 12.6 2-Aug 19.3 17-Sep 11.3 2-Nov 17.3 18-Dec 7.9

1-Feb 5.4 18-Mar 10.3 3-May 11.2 18-Jun 10.5 3-Aug 17.9 18-Sep 16.8 3-Nov 18.3 19-Dec 6.9

2-Feb 7.1 19-Mar 8.4 4-May 10.4 19-Jun 15.0 4-Aug 25.1 19-Sep 14.8 4-Nov 8.9 20-Dec 8.1

3-Feb 10.9 20-Mar 4.9 5-May 15.7 20-Jun 22.7 5-Aug 29.3 20-Sep 8.0 5-Nov 5.8 21-Dec 4.9

4-Feb 12.1 21-Mar 8.7 6-May 16.1 21-Jun 18.7 6-Aug 19.1 21-Sep 10.8 6-Nov 8.6 22-Dec 7.7

5-Feb 17.1 22-Mar 13.3 7-May 16.8 22-Jun 15.2 7-Aug 14.0 22-Sep 14.5 7-Nov 15.0 23-Dec 7.7

6-Feb 10.3 23-Mar 12.2 8-May 14.5 23-Jun 16.8 8-Aug 10.8 23-Sep 21.2 8-Nov 8.3 24-Dec 10.5

7-Feb 4.0 24-Mar 10.3 9-May 11.7 24-Jun 15.1 9-Aug 15.0 24-Sep 8.6 9-Nov 10.0 25-Dec 6.5

8-Feb 9.7 25-Mar 11.9 10-May 9.0 25-Jun 20.7 10-Aug 21.7 25-Sep 1.2 10-Nov 12.8 26-Dec 7.6

9-Feb 11.5 26-Mar 20.1 11-May 6.7 26-Jun 23.0 11-Aug 14.3 26-Sep 16.0 11-Nov 11.8 27-Dec 13.3

10-Feb 3.0 27-Mar 22.5 12-May 7.9 27-Jun 17.8 12-Aug 14.7 27-Sep 12.1 12-Nov 14.8 28-Dec 6.4

11-Feb 5.5 28-Mar 18.2 13-May 8.3 28-Jun 12.4 13-Aug 13.0 28-Sep 18.0 13-Nov 14.5 29-Dec 3.7

12-Feb 18.9 29-Mar 10.8 14-May 12.2 29-Jun 12.7 14-Aug 13.5 29-Sep 17.8 14-Nov 7.7 30-Dec 4.7

13-Feb 17.6 30-Mar 6.4 15-May 13.1 30-Jun 8.9 15-Aug 17.5 30-Sep 16.4 15-Nov 3.6 31-Dec 4.4

14-Feb 11.2 31-Mar 3.3 16-May 8.8 1-Jul 7.1 16-Aug 23.9 1-Oct 12.3 16-Nov 4.6

15-Feb 14.4 1-Apr 7.8 17-May 8.2 2-Jul 13.8 17-Aug 18.4 2-Oct 8.2 17-Nov 7.8

Annual 98th Percentile Concentration = 21.5 μg/m3

Table E-1. 2008 Daily PM2.5 Concentrations
E-4

Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc.

1-Jan 10.4 16-Feb 15.1 2-Apr 10.5 18-May 11.1 3-Jul 17.1 18-Aug 18.7 3-Oct 12.3 18-Nov 4.4

2-Jan 5.4 17-Feb 11.8 3-Apr 8.2 19-May 7.7 4-Jul 19.8 19-Aug 21.5 4-Oct 19.5 19-Nov 8.2

3-Jan 10.0 18-Feb 3.4 4-Apr 9.7 20-May 13.6 5-Jul 14.3 20-Aug 20.1 5-Oct 23.7 20-Nov 11.1

4-Jan 16.4 19-Feb 4.5 5-Apr 6.9 21-May 12.1 6-Jul 11.5 21-Aug 18.4 6-Oct 19.8 21-Nov 5.3

5-Jan 11.2 20-Feb 4.8 6-Apr 6.3 22-May 10.0 7-Jul 14.3 22-Aug 16.7 7-Oct 21.7 22-Nov 8.9

6-Jan 11.1 21-Feb 11.9 7-Apr 7.9 23-May 13.3 8-Jul 12.2 23-Aug 13.8 8-Oct 12.2 23-Nov 14.0

7-Jan 10.2 22-Feb 20.1 8-Apr 9.8 24-May 11.2 9-Jul 11.1 24-Aug 19.0 9-Oct 5.1 24-Nov 12.7

8-Jan 11.4 23-Feb 11.4 9-Apr 16.5 25-May 17.7 10-Jul 9.7 25-Aug 17.6 10-Oct 10.2 25-Nov 9.7

9-Jan 8.1 24-Feb 19.3 10-Apr 13.3 26-May 14.2 11-Jul 16.4 26-Aug 15.4 11-Oct 10.7 26-Nov 12.8

10-Jan 9.4 25-Feb 18.2 11-Apr 11.0 27-May 15.4 12-Jul 21.5 27-Aug 12.6 12-Oct 5.6 27-Nov 16.6

11-Jan 5.7 26-Feb 12.8 12-Apr 8.8 28-May 13.9 13-Jul RC 28-Aug 12.1 13-Oct 5.9 28-Nov 17.2

12-Jan 8.9 27-Feb 5.5 13-Apr 6.3 29-May 9.3 14-Jul 11.7 29-Aug 10.1 14-Oct 9.7 29-Nov 16.6

13-Jan 18.1 28-Feb 9.7 14-Apr 5.1 30-May 14.5 15-Jul 18.9 30-Aug 17.2 15-Oct 12.8 30-Nov 4.5

14-Jan 11.0 29-Feb 12.1 15-Apr 7.9 31-May 20.5 16-Jul RC 31-Aug 19.9 16-Oct 16.4 1-Dec 7.5

15-Jan 11.8 1-Mar 9.6 16-Apr 8.2 1-Jun 15.3 17-Jul RC 1-Sep 19.4 17-Oct 12.0 2-Dec 10.6

16-Jan 10.7 2-Mar 5.6 17-Apr 14.7 2-Jun 11.5 18-Jul 12.8 2-Sep 18.2 18-Oct 7.9 3-Dec 16.7

17-Jan 10.0 3-Mar 12.5 18-Apr 22.5 3-Jun 17.9 19-Jul 6.2 3-Sep 24.0 19-Oct 6.6 4-Dec 12.5

18-Jan 15.6 4-Mar 7.1 19-Apr 12.8 4-Jun 21.1 20-Jul 20.1 4-Sep 15.4 20-Oct 8.1 5-Dec 7.3

19-Jan 18.0 5-Mar 4.9 20-Apr 6.9 5-Jun 17.9 21-Jul RC 5-Sep 12.4 21-Oct 12.2 6-Dec 10.4

20-Jan 6.6 6-Mar 9.9 21-Apr 7.5 6-Jun 17.6 22-Jul 16.9 6-Sep 12.5 22-Oct 4.6 7-Dec 13.4

21-Jan 7.4 7-Mar 11.2 22-Apr 6.0 7-Jun 15.0 23-Jul 12.8 7-Sep 15.8 23-Oct 6.1 8-Dec 10.5

22-Jan 13.5 8-Mar 5.5 23-Apr 9.1 8-Jun 22.3 24-Jul 7.9 8-Sep 23.4 24-Oct 4.6 9-Dec 9.3

23-Jan 16.0 9-Mar 8.8 24-Apr 10.3 9-Jun RC 25-Jul 15.7 9-Sep 11.5 25-Oct 4.5 10-Dec 6.5

24-Jan 9.4 10-Mar 11.0 25-Apr 12.0 10-Jun 21.6 26-Jul 24.9 10-Sep 6.0 26-Oct 10.5 11-Dec 3.0

25-Jan 12.6 11-Mar 12.1 26-Apr 12.5 11-Jun 19.4 27-Jul 22.2 11-Sep 11.8 27-Oct 6.4 12-Dec 3.5

26-Jan 13.6 12-Mar 9.7 27-Apr 11.3 12-Jun 21.2 28-Jul 17.5 12-Sep 10.7 28-Oct 4.6 13-Dec 10.2

27-Jan 16.1 13-Mar 15.1 28-Apr 7.6 13-Jun RC 29-Jul 19.1 13-Sep 7.6 29-Oct 5.6 14-Dec 17.6

28-Jan 10.0 14-Mar 21.6 29-Apr 7.4 14-Jun 15.6 30-Jul 21.1 14-Sep 7.5 30-Oct 7.6 15-Dec 12.4

29-Jan 10.4 15-Mar 16.6 30-Apr 11.4 15-Jun 14.8 31-Jul 18.0 15-Sep 7.1 31-Oct 11.2 16-Dec 9.7

30-Jan 6.9 16-Mar 7.9 1-May 12.6 16-Jun 17.8 1-Aug 16.3 16-Sep 7.7 1-Nov 16.2 17-Dec 7.0

31-Jan 4.9 17-Mar 9.6 2-May 10.0 17-Jun 12.6 2-Aug 19.3 17-Sep 11.3 2-Nov 17.3 18-Dec 7.9

1-Feb 5.4 18-Mar 10.3 3-May 11.2 18-Jun 10.5 3-Aug 17.9 18-Sep 16.8 3-Nov 18.3 19-Dec 6.9

2-Feb 7.1 19-Mar 8.4 4-May 10.4 19-Jun 15.0 4-Aug 25.1 19-Sep 14.8 4-Nov 8.9 20-Dec 8.1

3-Feb 10.9 20-Mar 4.9 5-May 15.7 20-Jun 22.7 5-Aug RC 20-Sep 8.0 5-Nov 5.8 21-Dec 4.9

4-Feb 12.1 21-Mar 8.7 6-May 16.1 21-Jun 18.7 6-Aug 19.1 21-Sep 10.8 6-Nov 8.6 22-Dec 7.7

5-Feb 17.1 22-Mar 13.3 7-May 16.8 22-Jun 15.2 7-Aug 14.0 22-Sep 14.5 7-Nov 15.0 23-Dec 7.7

6-Feb 10.3 23-Mar 12.2 8-May 14.5 23-Jun 16.8 8-Aug 10.8 23-Sep 21.2 8-Nov 8.3 24-Dec 10.5

7-Feb 4.0 24-Mar 10.3 9-May 11.7 24-Jun 15.1 9-Aug 15.0 24-Sep 8.6 9-Nov 10.0 25-Dec 6.5

8-Feb 9.7 25-Mar 11.9 10-May 9.0 25-Jun 20.7 10-Aug 21.7 25-Sep 1.2 10-Nov 12.8 26-Dec 7.6

9-Feb 11.5 26-Mar 20.1 11-May 6.7 26-Jun 23.0 11-Aug 14.3 26-Sep 16.0 11-Nov 11.8 27-Dec 13.3

10-Feb 3.0 27-Mar 22.5 12-May 7.9 27-Jun 17.8 12-Aug 14.7 27-Sep 12.1 12-Nov 14.8 28-Dec 6.4

11-Feb 5.5 28-Mar 18.2 13-May 8.3 28-Jun 12.4 13-Aug 13.0 28-Sep 18.0 13-Nov 14.5 29-Dec 3.7

12-Feb 18.9 29-Mar 10.8 14-May 12.2 29-Jun 12.7 14-Aug 13.5 29-Sep 17.8 14-Nov 7.7 30-Dec 4.7

13-Feb 17.6 30-Mar 6.4 15-May 13.1 30-Jun 8.9 15-Aug 17.5 30-Sep 16.4 15-Nov 3.6 31-Dec 4.4

14-Feb 11.2 31-Mar 3.3 16-May 8.8 1-Jul 7.1 16-Aug 23.9 1-Oct 12.3 16-Nov 4.6

15-Feb 14.4 1-Apr 7.8 17-May 8.2 2-Jul 13.8 17-Aug 18.4 2-Oct 8.2 17-Nov 7.8

Annual 98th Percentile Concentration = 21.5 μg/m3

RC = Above 98th Percentile and Removed from Consideration

Table E-2. 2008 Daily PM2.5 Concentrations Less Than or Equal to the 98th Percentile
E-5

Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc. Date Conc.

1-Jan 10.4 16-Feb 15.1 1-Apr 7.8 17-May 8.2 1-Jul 7.1 16-Aug 23.9 1-Oct 12.3 16-Nov 4.6

2-Jan 5.4 17-Feb 11.8 2-Apr 10.5 18-May 11.1 2-Jul 13.8 17-Aug 18.4 2-Oct 8.2 17-Nov 7.8

3-Jan 10.0 18-Feb 3.4 3-Apr 8.2 19-May 7.7 3-Jul 17.1 18-Aug 18.7 3-Oct 12.3 18-Nov 4.4

4-Jan 16.4 19-Feb 4.5 4-Apr 9.7 20-May 13.6 4-Jul 19.8 19-Aug 21.5 4-Oct 19.5 19-Nov 8.2

5-Jan 11.2 20-Feb 4.8 5-Apr 6.9 21-May 12.1 5-Jul 14.3 20-Aug 20.1 5-Oct 23.7 20-Nov 11.1

6-Jan 11.1 21-Feb 11.9 6-Apr 6.3 22-May 10.0 6-Jul 11.5 21-Aug 18.4 6-Oct 19.8 21-Nov 5.3

7-Jan 10.2 22-Feb 20.1 7-Apr 7.9 23-May 13.3 7-Jul 14.3 22-Aug 16.7 7-Oct 21.7 22-Nov 8.9

8-Jan 11.4 23-Feb 11.4 8-Apr 9.8 24-May 11.2 8-Jul 12.2 23-Aug 13.8 8-Oct 12.2 23-Nov 14.0

9-Jan 8.1 24-Feb 19.3 9-Apr 16.5 25-May 17.7 9-Jul 11.1 24-Aug 19.0 9-Oct 5.1 24-Nov 12.7

10-Jan 9.4 25-Feb 18.2 10-Apr 13.3 26-May 14.2 10-Jul 9.7 25-Aug 17.6 10-Oct 10.2 25-Nov 9.7

11-Jan 5.7 26-Feb 12.8 11-Apr 11.0 27-May 15.4 11-Jul 16.4 26-Aug 15.4 11-Oct 10.7 26-Nov 12.8

12-Jan 8.9 27-Feb 5.5 12-Apr 8.8 28-May 13.9 12-Jul 21.5 27-Aug 12.6 12-Oct 5.6 27-Nov 16.6

13-Jan 18.1 28-Feb 9.7 13-Apr 6.3 29-May 9.3 13-Jul RC 28-Aug 12.1 13-Oct 5.9 28-Nov 17.2

14-Jan 11.0 29-Feb 12.1 14-Apr 5.1 30-May 14.5 14-Jul 11.7 29-Aug 10.1 14-Oct 9.7 29-Nov 16.6

15-Jan 11.8 1-Mar 9.6 15-Apr 7.9 31-May 20.5 15-Jul 18.9 30-Aug 17.2 15-Oct 12.8 30-Nov 4.5

16-Jan 10.7 2-Mar 5.6 16-Apr 8.2 1-Jun 15.3 16-Jul RC 31-Aug 19.9 16-Oct 16.4 1-Dec 7.5

17-Jan 10.0 3-Mar 12.5 17-Apr 14.7 2-Jun 11.5 17-Jul RC 1-Sep 19.4 17-Oct 12.0 2-Dec 10.6

18-Jan 15.6 4-Mar 7.1 18-Apr 22.5 3-Jun 17.9 18-Jul 12.8 2-Sep 18.2 18-Oct 7.9 3-Dec 16.7

19-Jan 18.0 5-Mar 4.9 19-Apr 12.8 4-Jun 21.1 19-Jul 6.2 3-Sep 24.0 19-Oct 6.6 4-Dec 12.5

20-Jan 6.6 6-Mar 9.9 20-Apr 6.9 5-Jun 17.9 20-Jul 20.1 4-Sep 15.4 20-Oct 8.1 5-Dec 7.3

21-Jan 7.4 7-Mar 11.2 21-Apr 7.5 6-Jun 17.6 21-Jul RC 5-Sep 12.4 21-Oct 12.2 6-Dec 10.4

22-Jan 13.5 8-Mar 5.5 22-Apr 6.0 7-Jun 15.0 22-Jul 16.9 6-Sep 12.5 22-Oct 4.6 7-Dec 13.4

23-Jan 16.0 9-Mar 8.8 23-Apr 9.1 8-Jun 22.3 23-Jul 12.8 7-Sep 15.8 23-Oct 6.1 8-Dec 10.5

24-Jan 9.4 10-Mar 11.0 24-Apr 10.3 9-Jun RC 24-Jul 7.9 8-Sep 23.4 24-Oct 4.6 9-Dec 9.3

25-Jan 12.6 11-Mar 12.1 25-Apr 12.0 10-Jun 21.6 25-Jul 15.7 9-Sep 11.5 25-Oct 4.5 10-Dec 6.5

26-Jan 13.6 12-Mar 9.7 26-Apr 12.5 11-Jun 19.4 26-Jul 24.9 10-Sep 6.0 26-Oct 10.5 11-Dec 3.0

27-Jan 16.1 13-Mar 15.1 27-Apr 11.3 12-Jun 21.2 27-Jul 22.2 11-Sep 11.8 27-Oct 6.4 12-Dec 3.5

28-Jan 10.0 14-Mar 21.6 28-Apr 7.6 13-Jun RC 28-Jul 17.5 12-Sep 10.7 28-Oct 4.6 13-Dec 10.2

29-Jan 10.4 15-Mar 16.6 29-Apr 7.4 14-Jun 15.6 29-Jul 19.1 13-Sep 7.6 29-Oct 5.6 14-Dec 17.6

30-Jan 6.9 16-Mar 7.9 30-Apr 11.4 15-Jun 14.8 30-Jul 21.1 14-Sep 7.5 30-Oct 7.6 15-Dec 12.4

31-Jan 4.9 17-Mar 9.6 1-May 12.6 16-Jun 17.8 31-Jul 18.0 15-Sep 7.1 31-Oct 11.2 16-Dec 9.7

1-Feb 5.4 18-Mar 10.3 2-May 10.0 17-Jun 12.6 1-Aug 16.3 16-Sep 7.7 1-Nov 16.2 17-Dec 7.0

2-Feb 7.1 19-Mar 8.4 3-May 11.2 18-Jun 10.5 2-Aug 19.3 17-Sep 11.3 2-Nov 17.3 18-Dec 7.9

3-Feb 10.9 20-Mar 4.9 4-May 10.4 19-Jun 15.0 3-Aug 17.9 18-Sep 16.8 3-Nov 18.3 19-Dec 6.9

4-Feb 12.1 21-Mar 8.7 5-May 15.7 20-Jun 22.7 4-Aug 25.1 19-Sep 14.8 4-Nov 8.9 20-Dec 8.1

5-Feb 17.1 22-Mar 13.3 6-May 16.1 21-Jun 18.7 5-Aug RC 20-Sep 8.0 5-Nov 5.8 21-Dec 4.9

6-Feb 10.3 23-Mar 12.2 7-May 16.8 22-Jun 15.2 6-Aug 19.1 21-Sep 10.8 6-Nov 8.6 22-Dec 7.7

7-Feb 4.0 24-Mar 10.3 8-May 14.5 23-Jun 16.8 7-Aug 14.0 22-Sep 14.5 7-Nov 15.0 23-Dec 7.7

8-Feb 9.7 25-Mar 11.9 9-May 11.7 24-Jun 15.1 8-Aug 10.8 23-Sep 21.2 8-Nov 8.3 24-Dec 10.5

9-Feb 11.5 26-Mar 20.1 10-May 9.0 25-Jun 20.7 9-Aug 15.0 24-Sep 8.6 9-Nov 10.0 25-Dec 6.5

10-Feb 3.0 27-Mar 22.5 11-May 6.7 26-Jun 23.0 10-Aug 21.7 25-Sep 1.2 10-Nov 12.8 26-Dec 7.6

11-Feb 5.5 28-Mar 18.2 12-May 7.9 27-Jun 17.8 11-Aug 14.3 26-Sep 16.0 11-Nov 11.8 27-Dec 13.3

12-Feb 18.9 29-Mar 10.8 13-May 8.3 28-Jun 12.4 12-Aug 14.7 27-Sep 12.1 12-Nov 14.8 28-Dec 6.4

13-Feb 17.6 30-Mar 6.4 14-May 12.2 29-Jun 12.7 13-Aug 13.0 28-Sep 18.0 13-Nov 14.5 29-Dec 3.7

14-Feb 11.2 31-Mar 3.3 15-May 13.1 30-Jun 8.9 14-Aug 13.5 29-Sep 17.8 14-Nov 7.7 30-Dec 4.7

15-Feb 14.4 16-May 8.8 15-Aug 17.5 30-Sep 16.4 15-Nov 3.6 31-Dec 4.4

22.5 23.0 25.1 23.7

Season / Quarter 4

Seasonal / Quarterly Maximum

Seasonal / Quarterly Maximum Concentration

RC = Above 98th Percentile and Removed from Consideration

Seasonal / Quarterly Maximum Seasonal / Quarterly Maximum

Season / Quarter 1 Season / Quarter 2

Seasonal / Quarterly Maximum

Season / Quarter 3

Table E-3. 2008 Daily PM2.5 Concentrations Less Than or Equal to the 98th Percentile by Quarter
E-6

Table E-4. Resulting Average of Seasonal (or Quarterly) Maximums for Inclusion into AERMOD

(Note, the complete datasets for 2009 and 2010 are not shown in Appendix E but would follow the same steps as for 2008)

Q1 Q2 Q3 Q4

2008 22.5 23.0 25.1 23.7

2009 21.1 20.7 21.2 19.8

2010 20.7 22.6 23.5 20.7

Average 21.433 22.100 23.267 21.400

Seasonal / Quarterly Average Highest Monitored Concentration

(From Annual Datasets Equal To and Less Than the 98th Percentile)

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/B-14-001

[May 2014]