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'\,

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# t<. !' Duector 

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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 Recycled/Recyclable • Printed wHh Vegetable 011 Based Inks on Recycled Paper (Minimum 25% Postconsumer)

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# Guidance for PM 2.5 

# Permit Modeling

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- 001 May 2014



# Guidance for PM 2.5 

# Permit Modeling

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, North Carolina

## This Page Intentionally Left Blanki TABLE OF CONTENTS Executive Summary

................................ ................................ ................................ ................................ ...... v Acknowledgements

................................ ................................ ................................ ................................ ...... xi I. Backgroun d

................................ ................................ ................................ ................................ ........... 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 PM 2.5 NAAQS

................................ ................................ .............. 27 III.1 Assessing Primary PM 2.5 Impacts

................................ ................................ ............................... 29 III.2 Assessing Secondary PM 2.5 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 PM 2.5 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 PM 2.5 ................................ ................................ ................................ ................... 67 V.1 Overview of PSD Increments

................................ ................................ ................................ ..... 67 V.2 PM 2.5 Increments Considerations

................................ ................................ ................................ 69 V.3 Screening Analysis for Increments

................................ ................................ ............................. 72 V.4 PM 2.5 Increments Analysis

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

................................ ................................ ................................ .......... 73 V.4.2 Cumulative Impact Analysis

................................ ................................ ................................ ... 74 V.4.2.1 Assessing Primary PM 2.5 Impacts from Other Sources

................................ ................... 74 V.4.2.2 Assessing Secondary PM 2.5 Impacts from Other Sources

................................ ............... 75 V.4.2.3 Consideration of PM 2.5 Ambient Air Quality Monitoring Data

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

................................ .............. 77 VI. References

................................ ................................ ................................ ................................ ....... 79ii Appendix A: Draft Conceptual Description of PM 2.5 Concentrations in the U.S.

................................ ... A

- 1 Appendix B: General Guidance on Use of Dispersion Models fo r Estimating Primary PM 2.5 Concentrations

................................ ................................ ................................ ............................. B

- 1 Appendix C: Example of a Qualitative Assessment of the Potential for Secondary PM 2. 5 Formation

.... C

- 1 Appendix D: Example of a Hybrid Qualitative/Quantitative Assessment of the Potential for Secondary PM 2.5 Formation

................................ ................................ ................................ ......... D

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

- hour modeling analysis

................................ ................................ ................................ ........................ E

- 1iii 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 do es 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 policie s 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 

## This Page Intentionally Left Blankv Executive Summary The U.S. Environmental Protection Agency (EPA) is providing this “ Guidance for PM 2.5 Permit Modeling ” to fulfill a need for additional guidance on demonstrating compliance with the fine particulate matter (PM 2.5 ) National Ambient Air Quality Standard s (NAAQS ) and the Prevention of Significant Deterioration (PSD) increments , especially with regard to considerations of the secondarily formed component s of PM 2.5 . This guidance incorporates the modeling procedures and recommendations from the EPA’s March 23, 2010, guidance memorandum, “Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS ,” and further clarifies procedures for adequately addressing primary and secondarily formed PM 2.5 in a NAAQS and PSD increment s 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 th is “ Guidance for PM 2.5 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 PM 2.5 , 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 PM 2.5 concentrat ions

. 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 [PM 2.5 ] impact of an individual source depends on the n ature of the source and its e missions,” as stated in Section 5.2.2.1

. c. of Appendix W

. As such, the appropriate methods for assessing PM 2.5 impacts are determined as part of the normal consultation process with the appropriate permit ting authority

. A model ing protocol should be developed by the permit applicant and approv ed by thevi 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 guid ance is intended to inform that process through recommendations regarding appropriate methods to assess secondary PM 2.5 impacts from the precursor emissions from the new or modifying source by providing the permit applicant and the appropriate per mitting a uthority with both focus and flexibility

. As experience is gained with these NAAQS and increment s compliance demonstrations (and as the EPA moves forward to consider single source modeling techn iques 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 PM 2.5 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 sta r ting with the promulgation of the initial PM 2.5 NAAQS in 1997; chronicling the PM 10 Surrogate Policy that for a period of time was relied upon for demonstrating compliance with the PM 2.5 NAAQS; and arriving at the present where the re is a need for an assessment of both the primary and secondary
PM 2.5 impacts , as appropriate, of a new or modifying source for demonstrating compliance with PM 2.5 NAAQS and increment s .

• II. Guidance Overview

– The second section provides a genera l overview of the steps that a permit applicant would routinely take under the PSD program for demonstrating compliance with the PM 2.5 NAAQS and increment s . The concepts of signifi cant emissions rate s (SER s ) and significant impact level s (SIL s ) are introduced and then presented in the context of a s ource impact analysis and a cumulative impactvii analysis

. The ramification s 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 s ource impact analysis or otherwise are included for reference and consideration throughout the remaining sections

. Four a ssessment c ases (Table ES

- 1 ) are then introduced w ith respect to assessing the primary and secondary PM 2.5 impacts throug h either the

s ource impact analysis or the cumulative impact analysis.

• III. S ource Impact Analysis for the PM 2.5 NAAQS

– The third section provides a detailed discussion of a screening assessment of primary and secondary PM 2.5 impacts from a new or modifying source using a SIL

. The specifics of the four
assessment c ases (Table ES

- 1) are presented along with appropriate approaches for assessing the primary and secondary impacts of PM 2.5 . For assessing the primary
PM 2.5 impacts from the direct PM 2.5 emissions from the new or modifying source , the typical use of an appropriate preferred dispersion model for near

- field PM 2.5 modeling listed in Appendix W, currently AERMOD for most applications , or an approved alternative model is recommended

. For assessing the secondary PM 2.5 impacts from the precursor emissions from the new or modifying source, three different approaches are described

. These approaches are 1) a qualitative assessme nt, 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 PM 2.5 NAAQS

– The fourth section provides a detailed discussion o f the assessment of primary and secondary PM 2.5 impacts from a new or modifying source with the inclusion of the primary and secondary PM 2.5 impacts of nearby sources and of monitored background

. There are specificviii discussion s of the modeling inventory and the monitored background

. Section IV concludes with information on d etermining s ignificant c ontributions to m odeled

v iolations

.

• V. PSD Increment s for PM 2.5 – The fifth section provides a detailed discussion of the assessment of primary and secondary PM 2.5 impacts of a new or modifying source with respect to the increment s .

Table ES
- 1 . EPA Recommended Approaches for Assessing Primary and Secondary PM 2.5 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 PM 2.5 Permit Modeling ” recommends technical approaches for conducting PM 2.5 NAAQS and PSD increment s compliance demonstration s which include adequate accounting for contributions from primary PM 2.5 concentrations from a proposed new or modifying source’s direct PM 2.5 emissions and from secondarily formed PM 2.5 concentrations resulting from the source’s PM 2.5 precursor emissions

. T his 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 eachix permitting action will be considered on a case

- by

- case basis, this document does not limit or restrict any particular jus tifiable 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
PM 2.5 NAAQS and PSD increment s . 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 re present 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 comme nt, but this document does not reflect a final determination by the EPA as to anyx issue raised in public comments. C oncerns exp ressed in public comments about the permissib ility or sufficiency of the approach reco mmended 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 permit ting authority in l ight 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) PM 2.5 Modeling Imple mentation Workgroup (NACAA Workgroup) in providing a detailed set of recommendations (NACAA, 2011) to the EPA with regards to PM 2.5 permit compliance demonstration modeling

. This NACAA Workgroup was comprised of s tate 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 Ecolog y) 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 EP A 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 PM 2.5 and PM 10 Nonattainment and Maintenance Areas” ( U .S. EPA, 201 3 a ) 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 increment s for PM 2.5 during the formal public comment period for the 10 th 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 PM 2.5 Permit Modeling” ( U.S. EPA 2013b)

. This invaluable feedback a long with additional information gleaned through ongoing interactions withxii various stakeholders have been particularly useful in the consideration of a range of acceptable options for PM 2.5 NAAQS and PSD increment s compliance demonstrations and aided the EPA in the completion of this guidance document1 I. Background Under Section 165(a)(3) of the CAA, a PSD permit applicant must demonstrate that emissions from the proposed constructio n 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

... ”
Thi s requirement is implemented in the PSD regulations at 40 CFR 52.21(k)(1) (and at 40 CFR 51.166(k)(1) w ith 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 poll ution 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 particul ate matter (PM) to add new annual and 24

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

or PM 2.5 as the indicator

. 1 The EPA revised the 24

- hour NAAQS for PM 2.5 on Sept ember 21, 2006, by lowering the level of the standa rd from 65 μg/m 3 to 35 μg/m 3

. 2 In the September 21,
2006 , action, the EPA also retained the previous 1997 annual standard for PM 2.5 and the 24

- hour standard for PM 10 , and revoked the previous annual standard for PM 10 . Subsequently, the

1 See 62 Fed. Reg. 58652

.
2 See 71 Fed. Reg. 61144

.2 Agency revised the PM 2.5 standard again on December 14, 2012, by lowering the level of the annual PM 2.5 NAAQS from 15 μg/m 3 to 12 μg/m 3 and retaining the 24

- hour standards for PM 2.5 and PM 10 . 3 The annual PM 2.5 standard is met when the 3

- year average of annual arithmetic mean concentrations is less than or equal to 12.0 μg/m 3

. The 24

- hour PM 2.5 standard is met when the 3

- year average of the annual 98 th percentile 24

- hour concentrations is less than or equal to 35 μg/m 3

. On October 20, 2010, EPA established maximum allowable in creases for PM 2.5 . 4 These values are also frequently described as the PSD increments. For Class I areas, the increments for
PM 2.5 are 1 μg/m 3 for the annual averaging time and 2 μg/m 3 for the 24

- hour averaging time. In Class II areas, the increments are 4 μg/m 3 for the annual period and 9 μg/m 3 for the 24

- hour period. To address the compliance demonstration for the PM 2.5 NAAQS, o n October 23, 1997,

c iting significant technical difficulties with respect to PM 2.5 monitoring, emissions estimation, and modeling , the EPA established a policy known as the PM 10 Surrogate Policy (U.S. EPA , 1997)

. This policy allowed permit applicants to use compliance with the applicable PM 10 requirements as a surrogate approach for meeting PM 2.5 New Source Rev iew (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 PM 2.5 , which facilitated ph asing out the application of the PM 10 Surrogate Policy to permits involving PM 2.5 . 5 With regard to nonattainment NSR permits, the rule provided that as of July 15, 2008 (t he 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 PM 10 Surrogate Policy to satisfy the NSR requirements for PM 2.5 . With regard to PSD permits, the rule provided that PSD permits issued under the f ederal PSD program at 40 CFR 52.21 would no longer be allowed to rely on the PM 10 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 PM 10 Surrogate Policy

. The 2008 rule also provided that states with approved PSD progr ams for PM 2.5 could continue to us e the PM 10 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 PM 2.5 , whichever occurred first. On June 1, 2009, i n response to a petition challenging the continued use of the PM 10 Surrogate Policy for issuing PSD permits, the EPA issued a 3

- month administrative stay of the grandfathering provision for PM 2.5 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 addit ional time for the EPA to propose repeal ing the grandfathering provision from the PM 2.5 NSR implementation rule for federal P SD 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 th e technical difficulties that necessitated the PM 10 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 PM 10 S urrogate P olicy

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 a s 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 (PM 2.5 ) ; 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 PM 10 Surroga te Policy for PSD permits under the federal PSD program for sources that were covered by the grandfathering provision (that i s, those sources for which a complete permit application was submitted before July 15, 2008) and that wer e not yet issued a perm it 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 PM 2.5 could no longer us e the PM 10 Surrogate Policy. After the final rule beca me effective, in order for any PSD permits to be issued through the federal PSD prog ram or a state SIP , such permit applications were to be reviewed directly against the PM 2.5 requirements

. The demonstration must show, at a minimum, that the source's emissions are controlled to a level that satisfies Best Available Control Technology (BAC T) requirements for PM 2.5 and that the emissions (filterable and condensable 10 ) would not cause or contribute to a violation of any NAAQS for PM 2.5 . On March 23, 2010, the EPA issued a guidance memorandum titled “Model ing Procedures for Demonstrating Compliance with PM 2.5 NAAQS” (U.S. EPA, 2010b) t o 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 PM 2.5 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, we re already excluded fr om using the 1997 PM10 Surrogate Policy as a means of satisfying the PSD requirements for PM2.5 . See 73 Fed. Re g. 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
PM 2.5 in the atmosphere resulting from emissions of PM 2.5 precursors . For the latter issue, the EPA recommended that special attention be given to the assessment of monito red background air quality data since such data account for the contribution of both primary and secondarily formed
PM 2.5 in the atmosph ere 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 o f providing technical recommendations to the Agency to aid in further development of PM 2.5 permit modeling guidance

. The NACAA Workgroup’s final report addressed three specific issues regarding PM 2.5 modeling implementation: 1) Emissions Inventories; 2) Se condary Formation from Project Source; and 3) Representative Background Concentrations. The need for additional clarification on addressing both the primary and secondarily formed PM 2.5 in NAAQS compliance demonstrations was heightened following an adminis trative action on January 4, 2012, in which the EPA granted a petition submitted on beha lf 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 PM 2.5 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 publis hed as Appendix W of 40 CFR Part 51 and, as appropriate, incorporate new analytical techniques or models for ozone and secondarily formed PM 2.5 . As a part of this commitment and in compliance with Section 320 of the CAA , the EPA conducted the 10 th Conference on Air Quality Modeling6 (10 th Mod eling Conference) in March 2012. 11 At the 10 th 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 PM 2.5 Permit Modeling ” , and several public forums and subsequently written comments given pertaining to PM 2.5 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 PM 2.5 compliance demonstrations up to that point , the EPA prepared th e “ Draft Guidance on PM 2.5 Permit Modeling ” a nd 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 th e document and on the newly recommended approaches for PM 2.5 NAAQS and
PSD increment s compliance demonstrations

. This feedback along with additional information gleaned through ongoing interactions with various stakeholders was particularly useful in the

c onsideration of a range of acceptable options for PM 2.5 NAAQS and PSD increment s compliance demonstrations and aided the EPA in the completion of this guidance document.
T h is “ Guidance for PM 2.5 Permit Modeling ” recommends appropriate technical approaches for conducting a PM 2.5 NAAQS and PSD increment s compliance demonstration which includes adequate accounting for contributions f rom primary PM 2.5 concentration from a proposed new or modifying source’s direct PM 2.5 emissions and from secondarily formed PM 2.5 concentrations resulting from the s ource’s PM 2.5 precursor emissions

. This guidance is consistent with the EPA’s Guideline on Air Quality Models

. The release of th is “ Guidance for PM 2.5 Permit

11 Additional information regarding and presentations from the 10 th Modeling Conference can be found on the SCRAM website at: http://www.epa.gov/ttn/scram/10thmodconf.htm

.7 Modeling ” is al so 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 proce ss with the appropriate permitting authority

. This process will help identify the most appropriate analytical techniques to be used for conducting a PM 2.5 NAAQS and PSD increment s compliance demonstration , including addressing the impacts of individual sources on secondary PM 2.5 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 PM 2.5 includes:

• “ Model Clearinghouse Review of Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS , ” February 26, 2010 (U.S. EPA, 2010a);

• “Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS , ” March 23, 2010 (U.S. EPA, 2010b);

• “Transportation Co nformity Guidance for Quantitative Hot

- spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas , ” November 2013 (U.S.EPA, 2013 ) ; and

• “ Interim Guidance on the Treatment of Condensable Particulate Matter Test Results in the P SD and N SR 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 

## This Page Intentionally Left Blank9 II. Guidance Overview This modeling guidance provides recommendations on how to conduct a PM 2.5 NAAQS and PSD increment s 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 complex ity of secondary PM 2.5 formation has historically prese nted significant challenges for the identification and establishment of particular models for assessing the PM 2.5 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 PM 2.5 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 [PM 2.5 ] impact o f 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 PM 2.5 impacts are determined as part of the normal consultation process with the appropriate perm itting 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 modificat ion 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 PM 2.5 and the more prominent role of ambient monitoring data in the cumulative analysis to represent background PM 2.5 concentrations including secondary formation from precursors from nearby

sources , certain aspects of standard modeling practices used for PM 10 and other criteria pollutants may not be appropriate for PM 2.5 . For example, the contribution from secondary formation of PM 2.5 is not explicitly accounted for by the current preferred dispersion model (i.e., AERMOD) , which is used to simulate dispersion of direct PM 2.5 emissions

. Given the se issues , PSD modeling of secondar ily formed PM 2.5 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 ( NO 2 ) impacts due to the importance of chemistry in the conversion of nitric oxide ( NO ) emissions to ambient NO 2 and lack of a specified “refined” model. 13 The recommendations presented in this guidance for demonstrating compliance with the PM 2.5 NAAQS through dispersion modeling and other techniques have been developed with the factors listed above in mind. As with any modeling analysis conducted u sin g 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 O ffice in accordance with the recommendations in Section 3.2

. Additionally, Section 10.2.2 of Appendix W could potentially be given conside ration in select situations

. The provisions of S ection 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 NO 2 NAAQS compliance demonstrations to obtain estimates of NO 2 for PSD and SIP planning purposes. The level of cons ervativeness in the tiered approaches decreases as fewer assumptions are made and a more detailed analysis is applied with the 3 rd tier approach being the use of detailed screening techniques based on dispersion modeling.11 from an array of ambient monitor s 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 PM 2.5 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 t his 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 O ffice 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
tran smitted as guidance to the Air Division Directors in the EPA Regional O ffices; 3) the preferred procedures are formally transmitted as guidance to the EPA Regional Office modeling

c ontacts 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

. T he Model Clearinghouse is the EPA focal12 point for the review of the technical ade quacy of pollutant modeling to satisfy regulatory criteria and other NAAQS compliance demonstration techniques

. Model Clearinghouse memoranda involving interpretation of modeling guidance for spe cific applications, as well as c larification memoranda addres sing 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 f or those new or modifying sources locating or located in an area classified as attainment or unclassifiable for PM 2.5 . Th is document is intended to provide recommendations on how to conduct PM 2.5 NAAQS and PSD increment s compliance demonstration s under the PSD program following the progressive steps shown in Figure II

- 1 (NAAQS) and Figure II

- 2 (Increment s ). The EPA has historically allowed the use of screening tools to help facilitate the implementation of the PSD program and streamlin e the permit ting
process in circumstances where proposed construction is projected to have an insignificant (or de
minimis ) impact on air quality. These screening tools have include d SERs, SILs, and significant monitoring concentrations (SMCs)

. The use of these screen ing 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 PM 2.5 NAAQS Compliance Demonstration for New or Modif ying Sources u nder NSR/ PSD Program s

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 Y N

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.414 Figure II
- 2. Overview of PSD Increment s Compliance Demonstratio n for New or Modifying Sources u nder 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 Y N

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

Y15 II.1 Significant Emissions Rate s 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 SER s for PM 2.5 and fo r the PM 2.5 precursors, nitrogen oxide s ( NO x ) and sulfur dioxide ( SO 2 ) , in 2008 as part of the first phase of PSD amendments to address PM 2.5 . 15 ( 74 Fed. Reg. 28321 at 28333 )

. The PM 2.5 SER for direct emissions of primary PM 2.5 , defined as 10 tons
per year (tpy) of direct PM 2.5 emissions, and the PM 2.5 precursor SERs, defined as either 40 t py of NO x or 40 t py of SO 2 , are used to determine whether any proposed new major stationary source or major modification will emit sufficient amounts of direct PM 2.5 and/o r PM 2.5 precursors , i.e. , equal to or above the respective SER s , to requi re review for PM 2.5 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 CF R 51.166(m)(1)(i); 40 C FR 52.21(m)(1)(i).

15 The EPA’s final NSR rules for PM 2.5 do not require regulation of volatile organic compounds ( VOC ) or ammonia ( NH 3 ) as precursor s to PM 2.5 fo r the PSD program. However, a s tate 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 PM 2.5 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 modeli ng guidelines

. See, 40 CFR P ar t 51, App endix 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 P ar t 51, App endix W, Section 2.2.c. With resp ect to PSD permit review specifically, the Guideline says the following: “The purpose of [screening] techniques16 Using this screening approach, when a proposed source’s modeled im pacts are found to be greater than the le vel 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 propose d 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 s ou rce 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 PM 2.5 in 2010 to paragraph (k)(2) of its Section 5 1.166 and 52.21 regulations, the Agency observed that “ t he 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

“ n otwithstanding the existence of a S IL, 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. 6 4892 )

.
is to eliminate the need of more detailed modeling for those sources that clearly will not cause or contribute to ambient concentrations in exce ss of either the National Ambient Air Quality Standards (NAAQS) or the allowable prevention of significant deterioration (P SD) 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 PM 2.5 , 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 disc retion 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 coul d take corrective action. On January 22, 2013 , t he 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 contri bute to a violation of the NAAQS or increment s , 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 contrave ne 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 c urrent 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 PM 2.5 as part of a demonstration that a source will not cause or contribute to a violation of the PM 2.5 NAAQS. However, to ensure that PSD permitting decisions meet the requirements of the CAA, permitting authorities that continue using SILs for PM 2.5 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 PM 2.5 . The EPA is developing a proposed rule to address the issue s identified by the EPA and the c ourt’s decision . If necessary and as appropriate, this guidance will be amended after this rulemaking is proposed and subsequently finalized. In18 the interim , permitting authorities may not apply the SIL provisio n s in the va cated and repealed
S ection s 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 s ame 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 PM 2.5 Increments, SILs, and SMC Rule

. 18 However, with appropriate safeguards, the EPA believes permitting authorities may continue to select and apply SILs values for PM 2.5 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 PM 2.5 , but requires that the EPA correct the error in the SIL regulat ions for PM 2.5 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 C FR (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 PM 2.5 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 PM 2.5 SIL values th at are

18 Prevention of Significant Deterioration (PSD) for Particulate Matter Less Than 2.5 Micrometers (PM 2.5 )

– 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 increment s 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 a uthorities 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 , i t may be permissible in some cases for a permitting authority to use the same PM 2.5 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 p ermitting 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 req uirements 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 betwee n the PM 2.5 NAAQS and the measured PM 2.5 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 (PM 2.5 ) at 82 (March 2008) [EPA

- HQ

- OAR

- 2003

- 0062

- 0278]

. However, the EPA has also obser ved 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, NO 2 NAAQS Guidance at 13; and Mississippi Lime at 41 (gran ting 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 PM 2.5 SMC from the PSD regulations will mean that each PSD applic ation 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 o ver 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 permitt ing 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 PM 2.5 with respect to the NAAQS. The above comparison of bac kground air quality concentrations and the NAAQS would not by itself provide adequate justification for foregoing a cumulative modeling analysis for the
PM 2.5 increments. Such an approach would be inappropriate because it would not ensure that there is suf ficient “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 violat ion 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 increment s 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 (bas ed 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 Cumul ative Impact Analysis As part of a NAAQS compliance demonstration, a cumulative impact analysis for PM 2.5 accounts for the combined impact s of direct and precursor emissions from the new or modifying

source, of direct emissions from nearby sources (as appr opriate) , and of monitored background levels of PM 2.5 that account for secondary PM 2.5 impacts from regional transport, secondary
PM 2.5 impacts from precursor emissions from nearby sources, and primary PM 2.5 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 PM 2 .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 m onitoring 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 am bient 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 PM 2.5 . Sierra Club v. EPA , 705 F.3d 458 . The court concluded that the PM 2.5 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 PM 2.5 SMC provisions from the regulation. 22 Thus, permitti ng authorities may no longer rely on the SMCs for PM 2.5 to exempt permit applicants from compiling preconstruction monitoring data for

22 See 78 Fed. Reg. 73698.22 PM 2.5 in accordance with Sections 51.166(m) and 5 2.21(m) of the EPA’s regulation. However, the EPA believes PSD permit ap plicants 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 increment s , a cumulative impact

asse ssment would be necessary to make the demonstration. A cumulative assessment account s 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, 19 87) 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 Preventio n 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 process es sho wn in Figure II

- 1 and Figure II

- 2 , the EPA is recommending four different asses sment cases shown in Table II

- 1 t hat define which air quality analyses, if any , a permit applicant sh ould conduct to demonstrate compliance with the PM 2.5 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 PM 2.5 Emissions? Assess Secondary Impacts of Precursor Emissions of NO x and/or SO 2 ? 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 PM 2.5 emissions are less than the SER of 10 tpy and both NO x and SO 2 emissions are individually less than the respective SER s of 40 tpy, then no modeled complia nce demonstration is required. 24

• For “Case 2

— Primary Air Quality Impacts Only ,” if the direct PM 2.5 emissions are greater than or equal to the SER of 10 tpy and both NO x and SO 2 emissions

are individually less than the respective SER s of 40 tpy, then a modeled PM 2.5

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 PM 2.5 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 PM 2.5 emissions are greater than or equal to t he SER of 10 tpy and NO x and/ or SO 2 precursor emissions are greater than or equal to the respective SER s of 40 tpy, then a modeled PM 2.5 compliance demonstration is required for the direct PM 2.5 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 PM 2.5 formation may be : a ) qualitative in nature; b ) based on a hybrid of qualitative and quantitative assessments ut ilizing 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 PM 2.5 emissions are less than the SER of 10 tpy, but t he NO x and/ or SO 2 precursor emissions are greater than or equal to th e respective SER s of 40 tpy, then a modeled PM 2.5 compliance demonstration for the direct PM 2.5 emissions is not required, but the permit applicant should a ssess 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 PM 2.5 formation may be : a) qualitative in nature; b) based on a hybrid of qualitative and quantitative assessments
ut ilizing existing technical work; or c) a full quantitative photochemical grid25 modeling exercise

. Again, the EPA anticipates that only a limited number of situations would require explicit photochemical grid modeling.
Details regarding the s ource impact analysis and cumulative impact analysis associated with Cases 2, 3, and 4, where project emissions are equal to or greater than the respective SER s for direct PM 2.5 emissions only (Case 2), for both direct PM 2.5 and precursor emissions of NO x and/or SO 2 (Case 3), or for precursor emissions of NO x and/or SO 2 only (C ase 4), are prov ided in Sections III and IV (NAAQS) and Section V (Increment s )

.26 

## This Page Intentionally Left Blank27 III. S ource Impact Analysis for the PM 2.5 NAAQS This section provides details regarding the recommended approaches for conducting the

s ource 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 demonstrat ion as described in Section II.2

. In each of the assessment cases, the analysis should begin by evaluating the impacts of direct PM 2.5 emissions and/or PM 2.5 precursor emissions based upon the total amount of thes e emissions as compared to the respective SERs.

Table III
- 1 . EPA Recommended Approaches for Assessing Primary and Secondary PM 2.5 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 PM 2.5 emissions n or PM 2.5 precursor ( NO x and/or SO 2 ) emissions are equal to or greater than the respective SER s . Case 1 is the only assessment case that does not require a modeled NAAQS compliance demonstration

. E ach of t he remaining three assessment cases would necessitate a s ource impact analysis

. T he simplest or most traditional assessment case is Case 2 where only direct PM 2.528 emiss ions are greater than or equal to the SER

. For Case 2, the permit applicant would only need to demonstrate that ambient PM 2.5 impacts associated with its increase in direct PM 2.5 emissions are below a SIL based on dispe rsion 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 PM 2.5 emissions and NO x and/or SO 2 precursor emissions are equal to or greater than the respective SER s for Case 3, this will likely be the most challenging of the four
assessment cases. As with Case 2, the ambient PM 2.5 impacts associated with direct PM 2.5 emissions can be estimated based on application of an appropriate preferred dispersion model for

near - field PM 2.5 modeling listed in Appendix W , currently AERMOD for most applications , or an approved alternative model

. However, AERMOD does not account for secondary formation of PM 2.5 associated with the source ’s precursor emissions

. S ince the source also emits q uantities of PM 2.5 precursors above the respective SER s for Case 3, some assessment of their potential contribution to secondary PM 2.5 is necessary . The assessment of NO x and/or SO 2 precursor emiss ion impacts on secondary PM 2.5 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 PM 2.5 emissions are below the applicable SER for Case 4, the s ource impact analysis in this case would only address the potential cont ribution to secondary PM 2.5 from NO x and/or SO 2 precursor emissions, and would not require any modeling of direct PM 2.5 emissions

. As discussed above for Case 3, the assessment of the precursor emission impacts on secondary
PM 2.5 formation for Case 4 may be: a) qualitative in nature; b) based on a hybrid of qualitative29 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 fe w situations would require explicit photochemical grid modeling

.

III.1 Assessing Primary PM 2.5 Impacts The assessment of primary PM 2.5 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 pr eferred 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 preferre d 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 PM 2.5 NAAQS, the AERMOD modeling system should be used to model direct PM 2.5 emissions unless

an other preferred model is more appropriate, such as the Buoyant Line and Point s ource d ispersion m odel (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 Prot ocol 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 inte rest 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 paper s. See EPA’s SCRAM website for AERMOD documentation: www .epa.gov/scram001/dispersion_prefrec.htm#aermod30 As noted in the EPA’s March 23, 2010 , PM 2.5 guidance memo randum , 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 PM 2.5 due to the small particle size

. In addition, there may be additional uncertainty associated with deposition modeling for PM 2.5 due to the fact that deposition properties may vary depending on the constituent elements of PM 2.5 . Therefore, use of deposition algorithms to account for depletion in estimating ambient PM 2.5

conce ntrations 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, 2014 b ) ;

• 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, 2014 c )

.

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

• BPIPPRIME: t he 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

- minu te observations (U.S. EPA, 2011d ).
Before running AERMOD, the user should become familiar with the user’s guides31 associated with the modeling components listed above and the most recent version of the AERMOD Implementation Gu ide (U.S. EPA, 2009)

. In addition to these documents, detailed guidance on the use of the AERMOD modeling system for estimating primary PM 2.5 impacts is provided in Appendix B

. Because AERMOD is limited to modeling only direct PM 2.5 emissions , additional o r alternative approaches must be used to provide an assessment of the secondary
PM 2.5 impact from the proposed new or modifying source , as discussed in more detail in the following sections

.

III.2 Assessing Secondary PM 2.5 Impacts This section provide s more detail on the recommended approaches for assessing the impacts of precursor emission s on secondary PM 2.5 formation for Case s 3 and 4 presented in Table III

- 1 including:

• a qualitative assessment ;

• a hybrid of qualitative and quantitative assessments u tilizing 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 PM 2.5 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 PM 2.5 impacts associated with the source’s precursor emissions will not cause or contribute to a violation of the 24

- hour or annual PM 2.5 NAAQS

. Each NAAQS compliance demonstration will be unique and may require32 multiple factors to be considered and assumptions to be thoroughly ju stified 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 (s ee 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 determin ing the necessary components of an acceptable qualitative assessment of the impact from secondary PM 2.5 formation

. 27 With appropriate consultation, submittal, and subsequent approval of the modeling protocol by the appropriate permit ting 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 PM 2.5 to support a qualitative assessment of the impact from secondary PM 2.5 formation, it is important to fully characterize the current PM 2.5 concentrations in the region where the new or mo difying emissions source is to be located

. This characterization should take into consideration not only the most current 24

- hour and annual PM 2.5 design values, which would typically be used as background concentrations in a cumulative modeling demonstrat ion, but should also include an understanding of the seasonality and speciated composition of the current PM 2.5 concentrations and any long term trends that may be occurring

. Understanding whether or not PM 2.5

27 For more detailed information on the development of such conceptual des criptions 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, PM 2.5 , 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 PM 2.5 Episodes in the Midwest”, January 2009, Lake Michigan Air Directors Consortium; and “Conceptual Model of Particulate Matter Pollut ion 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 PM 2.5 concentrations will help guide the degree of analysis and ultimate ly 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 im portant to describe the typical background concentrations of certain chemical species that participate i n the photochemical reactions that form secondary PM 2.5 , such as NH 3 , VOC , and ozone

. It is possible that there are mitigating factors for secondary PM 2 .5 formation given limitations of other chemical species important in the photochemical reactions, e.g. , minimal NH 3 in the ambient environment that could limit any precursor pollutant from readily reacting to form secondary PM 2.5 . The qualitative assessme nt should include a narrative explaining how any identified significant
precursor emissions and subsequent secondary PM 2.5 formation could contribute to the existing
PM 2.5 concentration environment in the region. A good conceptual description will also cha racterize the meteorological conditions that are representative of the region and are associated with periods and/or seasons of higher and lower ambient 24

- hour PM 2.5 concentrations

. Identification of meteorological phenomena that typically occur during periods of high 24

- hour PM 2.5 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 PM 2.5 formation for the higher ambient PM 2.5 concentrations

. The analysis and understanding of meteorological conditions will also inform the assessment of the seasonality of the 24

- hour PM 2.5 concentration s in the region

. The qualitative assessment should expand upon the characterization of34 meteorology described in the conceptual description to explain any meteorological factors that could limit or enhance the formation of secondary PM 2.5 from any significa nt precursor emissions

. Analysis of existing photochemical grid modeling developed for regional haze, ozone, and PM 2.5 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 PM 2.5 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 emission s of that precursor pollutant throughout the region

. The qualitative assessment of the impact from secondary PM 2.5 formation can be strengthened i f 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 PM 2.5 . A n example of a thoroughly developed qualita tive assessment of the potential for secondary PM 2.5 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 C AA 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 modifi cations, in the development of other case

- specific qualitative assessments

. An excerpt from this res ponse 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 PM 2.5 formation by Shell’s Discoverer drill ship and support fleet was developed in a narrative ma nner integrating numerous factors specific to the North Slope region of Alaska that provided sufficient evidence that the PM 2.5 NAAQS would not be violated in this particular case

. The qualitative assessment examined the regional background PM 2.5 monitorin g data and aspects of secondary PM 2.5 formation from existing sources; the relative ratio of the combined modeled primary PM 2.5 impacts and background PM 2.5 concentrations to the level of the NAAQS; the spatial and temporal correlation of the primary and s econdary PM 2.5 impacts; meteorological characteristics of the region during periods of precursor pollutant emissions; the level of conservatism associated with the modeling of the primary PM 2.5 component and other elements of conservatism built into the ov erall NAAQS compliance demonstration; aspects of the precursor pollutant emission s in the context of limitations of other chemical species necessary for the photochemical reactions to form secondary PM 2.5 ; and an additional level of NAAQS protection throug h 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 as sessments under this guidance , recognizing that additional components may be essential in other qualitative assessments of the potential for secondary
PM 2.5 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 PM 2.5 impacts (if applicable for Case 3) , will not cause or contribute to a violation of the36 24

- hour and/or annual PM 2.5 NAAQS

. However, it may not always be possible to provide such a justification without some quantification of the potential secondary PM 2.5 impacts from the proposed new or modifying source’s precursor emis sions

. In such cases, the EPA expects that existing air quality model

- based information regarding the potential for SO 2 and NO x precursor emissions to form secondary PM 2.5 concentrations may be used to establish an appropriate estimate of secondary PM 2.5 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

w ould be necessary , which is discussed in Section IV

. T o inform a hybrid qualitative/quantitative assessment , t he existing air quality model

based information would need to be appropriate in terms of repre senting the type of source, its precursor emissions, and its geographic location , in addition to those elements of the conce ptual 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 mod el responsiveness to precursor emissions in contexts that are relevant to the new or modifying source . The estima tes of model responsiveness, such as impact on PM 2.5 concentrations per ton of SO 2 emissions, c ould 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 PM 2.5 concentrations

. The estimates should be technically credible in
represen ting such impacts and it may be advisable for the estimate to reflect an upper bound of37 potential impacts. The NACAA Workgroup final report (NACAA, 2011) provides details on potential approaches to quantify the secondary PM 2.5 impacts from a proposed new o r modifying source that may be appropriate to inform a hybrid qualitative/quantitative assessments of PM 2.5 impacts (See Appendix C and D of NACAA , 2011 )

. One suggested method in the final report is to convert emissions of precursors into equivalent amount s of direct PM 2.5 emissions using

“ pollutant of fset ratios” and the n use a dispersion model to assess the impacts of the combination of direct PM 2.5 emissions and the equivalent direct PM 2.5 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 (PM 2.5 ) ” final rule (73 Fed. Reg. 28321) concerning the development and adopt ion of interpollutant trading (offset) provisions for PM 2.5 under s tate nonattainment area N SR programs for PM 2.5 . 28
The EPA’s July 23, 2007 , technical analysis titled “ Details on Technical Assessment to Develop Interpollutant Trading Ratios for PM 2.5 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 demonst ration specific to the source(s) and area(s) for which the ratios would be applied

. However, we expect that the EPA Regional O ffices, with assistance from the O AQPS , 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 PM 2.5 precursors that s tate/local air agencies may adopt in conjunction with the new interpollutant offset provisions for PM 2.5 , and for which the state could rely on the EPA's technical work to demonstrate the adequacy of the ratios for use in any PM 2.5 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 PM 2.5 . Mem orandum from Gina McCarthy, Assistant Administrator to Regional Air Division Directors, “ Revised Policy to Address Reconsideration of Interpollutant Trading Provisions for Fine Particles (PM 2.5 ) ” ( U.S. EPA, 2011e )

.38 structure appropriate technical demonstrations leading to the development of appropriate source and area

- specific offset ratios for PM 2.5 that may be appropriate for the purposes of estimating potential secondary PM 2.5 impacts

. As described in the EPA’s Ju ly 21, 2011 , memorandum addressing reconsideration of the interpollutant trading provision s 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

- s pecific ratios for PM 2.5 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 PM 2.5 im pacts (U.S. EPA, 2011 e )

. An example of a hybrid qualitative/quantitative assessment of secondary PM 2.5 impacts was developed by a permit applicant, Sasol, for a major facility expansion in Souther n Louisiana through close coordination with the EPA Region 6 Office and the L ouisiana 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 presu mptive interpollutant trading ratios for NO x and SO 2 to PM 2.5 offsets and conservatively applied them in an illustrative example to demonstrate how relatively inconsequential the impacts of secondary PM 2.5 formation would be in the area of significant impa ct 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 w ould be required or if the application of the formerly presumptive interpollutant trading ratios w as 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 reject ed, other methods for assessing secondary PM 2.5 impacts, including use of a simple emissions divided by distance (Q/D) metric and use of AERMOD with 100 percent conversion of SO 2 and NO x concentrations to (NH 4 ) 2 SO 4 and (NH 4 )NO 3 .” 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 PM 2.5 impacts with full quantitative photochemical grid modeling, the c andidate model for use in estimating singl e source impacts on secondarily formed PM 2.5 should meet the general criteria for an

“ alternative model” outlined in Section 3.2.2 of 40 CFR 51.112 and 40 CFR P art 51, Ap pendix 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 sci entific peer review; ii. The model can be demonstrated to be applicable to the problem on a theoretical basis; iii. The data bases 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 app lications, 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 PM 2.5 attainment demonstrations (U.S. EPA, 2007 a ) 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)

. T hese state

of

- the

- science photochemical grid models have been used by the EPA for air quality modeling to support f ederal rulemaking and by s tate / 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 repor t, the EPA recommends the following approaches be considered to estimate secondary PM 2.5 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 wi th 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 d ecoupled d irect m ethod ( H DDM) 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 esti mates for secondary PM 2.5 impacts depending on the non

- li near chemical processes

. The EPA, st ate / local permitting agencies, and others within the atmospheric modeling community continue to apply these techniques to test and evaluate their suitability for e stimating single source impacts on secondarily formed PM 2.5 . These efforts are critically important to inform current application of these models and techniques for purposes of assessing the secondary PM 2.5 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 PM 2.5 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 app roaches that allow for estimation of the secondary PM 2.5 impacts from a proposed new or modifying source were presented during the Emerging Models / Techniques Session of the 10 th42 P hotochemical 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 a ir 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 ( H DDM), 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, “H ow much did a source contribute overall to modeled air quality?” and source sensitivity answers the “responsiveness” question , “H ow 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 receptor s 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) repres ent the gradients of pollution that may exist between the

Modeling Conference. Additional information regarding and presentations from the 10 th Modeling Conference can be found on the SCRAM website at: http://www.epa.gov/ttn/scram/10thmodconf.htm

.43 source and receptor s . One approach to more explicitly represent the spatial gradient in source

rec eptor 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 receptor s are no longer in the same grid cell

. Ideally, there would also be several grid cells between the source and receptor s to best resolve near

- source pollution gradients. In these situations of close proximity between the source and receptor s , a photochemical grid model instrumented with sub

- grid plume treatment and sampling may be an alternative
app roach for characteriz ing 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 th e 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 application s . 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, w e recommend that the modeling protocols for t his purpose shou ld 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 area44 • Identify how modeling and o ther 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 d escription of meteorological model setup

• Basis for and d evelopment of emissions inputs

• Methods used to quality assure emissions, meteorological, and other model inputs

3. D etails on the approach for com parison 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
O AQPS , may assist states, as necessary, to structure appropriate technical demonstrations leading to the development of appropriate photochemical grid modeling applications for t he purposes of estimating potential secondary PM 2.5 impacts.45 III.3 Comparison to the SIL Where a p ermit applicant wishes to compare the proposed source’s total ambient PM 2.5 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 PM 2.5 and/or precursor emissions are equal to or greater than the respective SER s , is applicable. For Case 2, where only direct PM 2.5 emissions are equal to or greater than the applicable (10 tpy) SER, the SIL may be compared to the modeled estimates of ambient primary PM 2.5 concentrations due to direct emissions using the preferred AERMOD dispersion model (or

acc eptable preferred or alternative model)

. The modeling methods used in this initial s ource impact assessment phase of the PM 2.5 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 PM 2.5 NAAQS, we recommend that a SIL be compared to either of the followi ng, depending on the meteorological data used in the analysis:

• The highest of the 5

- year averages of the maximum modeled 24

- hour or annual PM 2.5

c oncentrations predicted each year at each receptor, based on 5 years of representative National Weather Servic e (NWS) data; or

• The highest modeled 24

- hour or annual PM 2.5 c oncentrations 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 PM 2.5 concen trations 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 air46 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 PM 2.5 emissions and emissions of at least one
p recursor are equal to or greater than the respective SER s , the comparison of the SIL would need to address both primary and secondary PM 2.5 ambient impacts associated with the proposed

source . As with Case 2, the ambient impacts due to direct PM 2.5 emissions would be estimated using the preferred AERMOD disper sion model (or acceptable alternative model)

. However, the comparison to the SIL will depend on the type of assessment conducted for the secondary PM 2.5 impacts from the source

. As noted above, the assessment of the precursor emission impacts on secondary PM 2.5 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 PM 2.5 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 s ource impact analysis for Case 3 on a qualitative assessment (or a hybrid of qualitative and quantitative assessments) of secondary PM 2.5 ambient impacts may be difficult to justify

. This is be cause there would be no specific quantitative estimate of total PM 2.5 impacts for comparison to the SIL , unless a valid argument can be made that secondary PM 2.5 impacts associated with the source’s precursor emissions will be very small (e.g., precursor e missions barely exceed the respective SER s and/or the chemical environment is not conducive to secondary formation)

. As such, when using either of these approaches, it may be appropriate to47 forego the SIL assessment and focus on the NAAQS compliance demons tration using a cumulative impact analysis

. For cases where a full quantitative photochemical grid modeling assessment of secondary
PM 2.5 is conducted, the SIL comparison for Case 3 should be based on the combined ambient impacts of primary and secondary PM 2.5 . However, the primary and secondary PM 2.5 impacts may be combined in various ways which may entail greater or lesser degrees of conservatism

. For example, combining the peak estimated primary PM 2.5 impact with the peak estimated secondary PM 2.5 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 PM 2.5 an d precursor emissions are not likely well

- correlated in time or space . On the other hand, the conservatism associated with combining peak estimated primary and secondar y impacts for comparison to a SIL would likely make such an approach easier to justify t han other approaches for combining estimated primary and secondary PM 2.5 impacts

. The other extreme for combining primary and secondary PM 2.5 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
PM 2.5 impacts

. Such an approach may not be feasible in many cases, given that the dispersion modeling and photochemical gr id modeling may be based on different data periods

. Furthermore, full temporal and spatial pairing of primary and secondary PM 2.5 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 degree48 of temporal pairing of primary and secondary PM 2.5 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 permittin g authority and the permit applicant should thoroughly discuss the d etails regarding combining modeled primary and secondary PM 2.5 impacts for Case 3 and should reach
agree ment on a protocol during the initial review of the modeling protocol

. I t may be appropriate for the protocol to specifically identify multiple tiers for combining the modeled primary and secondary PM 2.5 impacts with the more conservative approaches being easier to justify

. The permitting authority should ensure that a ny appro ach for combining estimated primary and secondary PM 2.5 impacts for comparison to a SIL for Case 3 conform s to the recommendations described above for Case 2 regarding the form of the modeled estimate

. Accordingly, the approach should be based on the highe st of the multi

- year averages of the maximum modeled
24

- hour or annual PM 2.5 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 SER s but direct PM 2.5 emissions are not, the SIL comparison would only address secondary PM 2.5 ambient impacts associated with the proposed source

. The assessmen t of the precursor emission impacts on secondary PM 2.5 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 exe rcise

. As discussed above for Case 3, since

a SIL should represent a specific insignificant (or de minimis ) ambient concentration of PM 2.5 that may be used to demonstrate that a source will not cause or contribute to a violation without a49 cumulative impact assessment, basing the s ource impact analysis on a purely qualitative assessment of secondary PM 2.5 ambient impacts or a hybrid of qualitative and quantitative
assessments , utilizing existing technical work for Case 4 , may be dif ficult to justify unless a demonstrably conservative estimate of the secondary PM 2.5 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 secondar y PM 2.5 for Case 4 than for Case 3 since the issues associated with combining modeled estimates of primary and secondary PM 2.5 would not apply fo r Case 4

. In these cases , the highest of the multi

- year averages of the maximum modeled
24

- hour or annual PM 2.5 concentrations predicted each year at each receptor should be compared
to a SIL , since th e se metrics represent the maximum contribution that the source could make

.50 

## This Page Intentionally Left Blank51 IV. Cumulative Impact Analysis for the PM 2.5 NAAQS Where the s creening analysis described in S ection II is insufficient to show that a source will not cause or contrib ute to a violation of the NAAQS , a cumulative impact assessment would be necessary to make the NAAQS compliance demonstration. A cumulative assessment account s for the combined impact of the new or modifying source ’s emissions, emissions from other nearby sources, and representative background levels of PM 2.5 within the modeling domain

. The cumulative impacts are th en 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
pr ovides details on conducting an appropriate cumulative impact assessment for the PM 2.5 NAAQS. The cumul ative impact assessment should include the following components of PM 2.5 impacts , as appropriate, for comparison to the NAAQS:

• Proposed new or modifying source

o Primary impacts on PM 2.5 , i.e. , from direct PM 2.5 emissions

o Secondary impacts on PM 2.5 , i.e. , from precursor ( NO x and/or SO 2 ) emissions

• Nearby sourc es

o Primary impacts on PM 2.5 , as appropriate

• Monitored background of PM 2.5 that accounts for secondary PM 2.5 impacts from regional transport , secondary PM 2.5 impacts from nearby sources, and primary
PM 2.5 impacts from background sources not included in the modeled inventory.

As with the s ource impact analysis discussed previously , the primary impacts related to direct PM 2.5 emissions from the proposed new or modifying source and nearby sources sh ould b e52 estimated based on the AERMOD dispersion model (or other acceptable preferred model or an approved alternative model) while the estimate of secondary PM 2.5 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 PM 2.5 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 photochemic al grid modeling exercise

. As noted above, secondary impacts on PM 2.5 from regional transport and precursor emissions from nearby

sources should be accounted for through representative monitored background concentrations

.

IV.1 Modeling Inventory The curre nt guidelines on emission inventories for purposes of NAAQS compliance modeling contained in Section 8.1 of Appendix W will generally be applicable for the PM 2.5 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 PM 2.5 emissions that should be used in dispers ion modeling of primary PM 2.5 impacts

. Although the EPA’s “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze” ( U.S. EPA, 2007 a ) provides some guidance relevant to applicat ions involving full quantitative photochemical grid modeling, additional considerations and guidance regarding model ing inventories for such analyses in support of PM 2.5 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 memorandum53 regard ing Appendix W modeling guidelines for the 1

- hour NO 2 NAAQS (U.S. EPA, 2011 f ), 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 selectio n . 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 ne arby 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 inven tory of nearby sources to include in the cumulative modeling analysis, including the potential influence of terrain characteristics on

conce ntration gradients and the availability and adequacy of ambient monitoring data to account for background sources.
C onsistent 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 complian ce 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 “ N ew S ource R eview Workshop Manual ” are flawed or inapp ropriate

. 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 p rocedures in a literal and uncritical manner may
in crease 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 p ermitting 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 PM 2.5 emissions has not been frequently conducted to date, the availability of an adequate direct PM 2.5 emission inventory for nearby sources may not exist in all cases

. Recommendations for developing PM 2.5 emission inventories for use in PSD applications will be addressed separately, but existing SIP inventories for PM 2.5 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 natura l sources

. From newly

- acquired pre - construction monitoring data and/or existing representative air quality data gathered for purposes of a permitting analysis , permit a pplicants should assess and document what the background monitoring data represent to th e extent possible, including any information that may be available55 from the s tate or other agency responsible for siting and maintaining the monitor. 30 It is also worth noting that the relative makeup of PM 2.5 components and temporal patterns associated with the highest 24

- hour PM 2.5 levels may differ considerably from the relative amounts of PM 2.5 components associated with annual average PM 2.5 levels, especially in western states. The determination of monitor ed background concentrations of PM 2.5 to include in the
PM 2.5 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 PM 2.5 is that the ambient monitoring data should , in most cases account for the contribution of secondary PM 2.5 formation associated with existing sources impacting the modeling domain in addition to the background levels of primary PM 2.5 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 PM 2.5 than the representativeness of the monitor for secondary contributions, unless the monitor is located relatively close to nearby sources of primary PM 2.5 that could be impacting the monitor

. Also, the nature of secondary PM 2.5 , monitored background concentrations of PM 2.5 are likely to be more homogeneous across th e modeling domain in m ost

cases compared to most other pollutants, although this will also depend on the potential for local sources of primary PM 2.5 to be contributing t o the monitored concentrations. Depending on the nature of local PM 2.5 levels within the modeling domain, it may be

30 Please note in the case of an existing source seeking a permit for a modificati on, 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 quest ion 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 PM 2.5 .56 appropriate to account for seasonal variations in monitored background PM 2.5 levels which may not be correlated with seasonal patterns of the modeled primary PM 2.5 levels

. For example, maximum modeled primary PM 2.5 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 PM 2.5 in the eastern U nited States typically occur during spring and summer mon ths 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 PM 2.5 for comparison to the
PM 2.5 NAAQS entails considerations that differ from thos e for other criteria pollutants due to the issues identified above

. The discussion below addresses comparisons to the NAAQS in the context of dispersion m odeling of direct PM 2.5 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 P M 2.5 impacts ( for Cases 3 and 4)

. Given the importance of secondary contributions for PM 2.5 and the potentially high background levels relative to the PM 2.5 N AAQ S , greater emphasis is generally placed on the monitored background contribution relative to the modeled inventory for PM 2.5 than for other pollutants

. This is true for both NAAQS and increment s assessments

. Also, given the probabilistic form of the PM 2.5 N AAQS, careful consideration should be given to how the monitored and modeled concentrations are combined to estimate the cumulative impact levels.57 The representative monitored PM 2.5 design value, rather than the overall maximum monitored background concentration, should generally be used as the monitored component of the cumulative analysis

. The PM 2.5 design value for the annual averaging period is based on the
3

- year average of the annual average PM 2.5 concentrations

. The PM 2.5 design value for the 24

- hour averaging period is based on the 3

- year average of the annual 98 th percentile 24

- hour average PM 2.5 concentrations

. Details regarding the determination of the annual 98 th percentile monitored 24

- hour value based on the number of days sampled during the year are provided in the data interpretation procedures for the PM 2.5 NAAQS, Appendix N to 40 CFR Part 50. It should be noted here that although the monitored design values for the PM 2.5 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 PM 2.5 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.pdf58 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) average s across 5 years for representative NWS meteorological data or the highest modeled annual (or 24

- hour) average for one year (or multi

year average of 2 up to 5 complete years) of site

- specific meteorological data using the same procedure s recommended for t he initial s ource impact analysis

. The memo cited several issues, especially the importance of the contribution from secondary formation of PM 2.5 from precursor emissions and the fact that such contributions are not explicitly accounted for by the dispersi on model, as the basis for viewing modeling of PM 2.5 as screening

- level analyses, analogous to the screening nature of the guidance in Section 5.2.4 of Appendix W regarding dispersion modeling for NO 2 impacts, given the importance of chemistry in the conve rsion of NO emissions to ambient NO 2 . Recognizing that the primary focus and motiv ation 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 PM 2.5 level s , 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 PM 2.5

an d precu rsor emissions with the respective SER s , involves some assessment of the source’s potential secondary PM 2.5 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 r ep resents 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 98 th percentile of modeled annual 24

- hour concentrations rather than the multi

- year average of the highest (100 th percentile) of modeled annual 24

- hour59 concentrations

. For Case 2, where only the project’s direct PM 2.5 emissions are equal to or greater than the SER, the modeled design value sh ould be based on AERMOD (or other acceptable preferred or alternative model ) estimates of primary PM 2.5 impacts combined with the monitored design value

. The monitor should be representative in that it accounts for the cont ribution of secondary
PM 2.5 formation associated with existing sources within the modeling domain, in addition to the background levels of primary PM 2.5 associated with background sources that are not included in the modeled inventory

. For Case 3, where both the project’s direct PM 2.5 emissions and precursor emissions are equal to or greater than the respective SER s , the cumulative impact for comparison to the NAAQS should be based on the sum of the modeled design value for primary PM 2.5 im pacts (from dispersion model estimates based on the project’s and other nearby source’s direct
PM 2.5 emissions), the modeled design value for secondary PM 2.5 impacts (from a qualitative, hybrid, or quantitative assessment accounting for the project’s precu rsor PM 2.5 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 SER s , the cumulative impact for comparison to the NAAQS should be based on the sum of the modeled design value for secondary PM 2.5 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 res ulting cumulative PM 2.5 concentration s would then be compared to the annual
PM 2.5 NAAQS of 1 2 μg/m 3 and 24

- hour PM 2.5 NAAQS of 35 μg/m 3

. The recommendations provided above constitute a First Tier modeling analysis for PM 2.5 NAAQS compliance demonstrations that should be acceptable without further justification

. For applications where impacts from primary PM 2.5 emissions are not temporally correlated with60 background PM 2.5 levels , combining the modeled and monitored contributions as described above may be ove rly conservative in some situations

. For example, there are areas of the country where background PM 2.5 levels are substantially higher on average during the summer months as compared to the winter months; however, the projected modele d impacts from the ne w 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 PM 2.5 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 PM 2.5 impacts and background PM 2.5 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 unnece ssary . As a part of this evaluation process, consultation with the
approp riate permitting authority is advised. The AERMOD model provides several options for specifying the monitored background concentration for inclusion in the cumulative impact assessmen t. The options that are most relevant to PM 2.5 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 opti on 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 distribution61 of monitored data equal to and less than the annual 98 th 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 98 th percentile value and three seasons ( quarters ) with the maximum values w hich are less than or equal to the 98 th 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) maxim um s sh ould then be included as the four seasonal background values within the AERMOD model

. Therefore , the monitored concentrations greater than the 98 th percentile in each of the three years would not be included in the seasonal ( or quarterly ) sub sets

. 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 se en monitor ed concentrations for each year would be excluded from the seasonal ( or quarterly ) subdivided datasets

. Similarly, for a m onitor 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 dataset s 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 data62 greater than the 98 th 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 98 th percentile rank of the monitored data bas ed 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 PM 2.5 NAA QS 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 i mpacts from primary PM 2.5 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 r eceptor for each hour. Such an assumption clearly ignores the many factors that contribute to the temporal and spatial variability of ambient PM 2.5 concentrations across a typical modeling domain on an hourly basis. The complexities of the PM 2.5 ambient mo nitoring network also present special challenges with a "paired sum" approach that are not present with the other NAAQS pollutants. The Federal Reference Method (FRM) PM 2.5 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 PM 2.5 monitors is steadily increasing but is relatively limited to the largest cities and metropolita n regions of the U.S . Various methods to "data fill" the 1

- in

- 3 day monitoring63 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 PM 2.5 dataset that the EPA would consider acceptable for inclusion in a PM 2.5 NAAQS compliance demonstration. The use of continuous PM 2.5 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
PM 2.5 ambient monitoring network, we do not recommend a "paired sums" approach on an hour

by

- 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 PM 2.5 emissions are not temporally correlated with b ackground PM 2.5 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 Det ermining Whether Proposed Source Causes or Contributes to Modeled Violations If the cumulative impact assessment following these recommendations results in modeled violations of the PM 2.5 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 base d 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 p redicted in an attainment area based on a cumulative analysis. 33
T he January 22, 2013 , court decision did not vacate the PM 2.5 SIL value in section 51.165(b) of the EPA’s regu la tions

. However, the c ourt recognized that the language in s ection 51.165(b)(2) operates in a man ner different from s ections 51.1 66(k)(2) and 52.21(k)(2) , which were vacated by the court

. The c ourt observed that s ection 51.165(b)(2) “simply states that a source may be deemed to violate the NAAQS if its exceeds th e SILs in certain situ ations.” (705 F.3d at 465

- 66). For this reason, the c ourt did not see the need to resolve the Petitioner ’ s challenge to the EPA’s methodology for determining the PM 2.5 values in s ection 51.165(b)(2) of the regulations, which are the same as the Class II ar ea values in the vacated s ections 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. R eg. 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 h as sometimes described this step as a “culpability analysis.”65 section 51.165(b)(2) to determine whether a source causes or significant ly 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 PM 2.5 in section 51.165(b)(2) as the basis for concluding that a source with an impact below this value does not

ca use 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 PM 2.5 NAAQS, the average of the pred icted annual concentrations at the affected receptor(s) should be compared to a SIL , while the average of the predicted annual 98 th percentile 24

- hour average concentrations at the affected receptor(s) should be used for the 24

- hour PM 2.5 NAAQS.66 

## This Page Intentionally Left Blank67 V. PSD Increment s for PM 2.5 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, t his section describes the EPA’s recommendations for completing the required analysis of the PSD increments for PM 2.5 .

V.1 Overview of PSD Increments The ter m “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 incre ase 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 o f 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 a s 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 mino r 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 emiss ions from any major or minor source henceforth will consume or expand the available PSD increment s 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 increme nt, as further explained below.
PM 2.5 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 qu ality 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 whe n 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 emis sions 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 source s on which69 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 PM 2.5 Increment s Considerations In its 2010 PM 2.5 Increments, SILs, and SMC Rule , the EPA established PM 2.5 increments at the levels shown in Table V

- 1, as follows:

Table V
- 1. PM 2.5 Increments Class I Class II Class III Increments, μg/m 3 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 PM 2.5 increment s analysis includes many of the same technical considerations in assessing source impacts as discussed earlier in this guidance for PM 2.5 NAAQS compliance demonstration s , specifically the assessment cases described in Section II

- 4 and detailed in Table III

- 1. However, there are some important differen ces. The main difference is that the increment s compliance demonstration is based on calculating the change in ambient PM 2.5 concentrations over the applicable baseline concentration, which includes proposed emissions increases from the new or modified sou rce, 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 increment s 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 after70 the major source baseline date), whereas NAAQS analyses are generally based o n 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 PM 2.5 NAAQS and increments for the 24

- hour averaging period are defined in different forms and therefore must be analyzed differently. 35 The 24

- hour PM 2.5 NAAQS is defined based on the 3

- year average of the annual 98 th percentile of the 24

- hour average concentra tions, while the 24

- hour PM 2.5 increments are based on the second highest maximum 24

- hour concentration. The 2010 “ PM 2.5 Increments, SILs, and SMC Rule ” established October 20, 2011, as the “trigger date” and October 20, 2010, as the “major source baseline date” for PM 2.5 increments. The EPA developed the increment system for PM 2.5 generally following the same concepts that were previously applied for the increments for PM 10 , SO 2 , and NO 2 . In each case, the framework reflects the statutory concepts set fort h 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 appl ication 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 baselin e 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 PM 2.5 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 baselin e 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 basel ine 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 technic al challenges associated with the ability to estimate the impacts of secondarily formed PM 2.5 in the atmosphere resulting from emissions of PM 2.5 precursors make it necessary to consider alternative methods of assessing increment s where the increment s are affected by both direct PM 2.5 emissions and PM 2.5 precursor

36 Th e 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 sho uld 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 PM 2.5 . Accordingly, the remainder of this section provides recommendations for accomplishing the PM 2.5 increment s 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

mo deling analysis for the PM 2.5 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 authori ty 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 per forming cumulative modeling for
PM 2.5 increment s 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 sour ce baseline date by nearby sources that have been permitted prior to the source under analysis, and (3) informat ion 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 increment s compliance will ofte n 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 the73 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 PM 2.5 Increment s An alysis The guidance provided under Sections III and IV regarding NAAQS compliance demonstrations should generally be applicable for PM 2.5 increment s analyses, with the primary distinction that actual emission increases (or decreases) from only increment

- af fecting 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 PM 2.5 in crement s 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 PM 2.5 emissions nor PM 2.5 precursor ( NO x and/or SO 2 ) emissions are equal to or greater than the respective SER s. Case 1 is the only assessment case that does not require a modeled compliance demonstration for PM 2.5 , whereas e ach of t he 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 PM 2.5 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 Cum ulative 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 increment s , a cumulative impact assessment would be necessary to make the demonstration. A cumulative assessment account s for the combined impact of the new or modifying source ’s emissions and those emissions changes from sources that affect the increment s . The cumulative impacts are then compared to the PSD increment s to determine whether the new or modifying source emissions will cause or contribute to a violation of the PSD increment s . This section provides details on conducting an appropriate cumulative impact assessment for PM 2.5 .

V.4.2.1 Assessing Primary PM 2.5 I mpacts from Other Sources To assess direct PM 2.5 emissions from increment

- consuming or increment

- expanding sources, the PM 2.5 increment s analysis would follow the traditional approach involving modeling of only PM 2.5 emissions changes that affect the incre ment, and should be based on application of75 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 o f 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 PM 2.5 Impacts from Other Sources
To assess changes in PM 2.5 precursor emissions from increment

- consu ming or increment

expanding sources, the assessment of potential impacts of secondary PM 2.5 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 PM 2.5 concentrations across most areas in recent years

. 37 This is particularly true in the
E astern U.S. As a result, in many cases , the potential for increment consumption due to secondary PM 2.5 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 PM 2.5 minor source baseline date (or the major source baseline date). In such cases, the PM 2.5 increment s modeling analysis may be simplified to focus solely on potential increment consumption associated with direct PM 2.5 emissions. For areas where PM 2.5

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 kn own to contribute to PM 2.5 concentration increases within the baseline area and thus consume PM 2.5 increment, the photochemical grid modeling me thods discussed in Section III may be appropriate for estimating the portion of
PM 2.5 increment consumed due to secondary PM 2.5 impacts associated with those increases in precursor emissions.

V.4.2.3 Consideration of PM 2.5 Ambient Air Quality Monitoring Data In light of the current technical complications associated with the ability to model precursor emissions to estimate secondarily formed PM 2.5 in the atmosphere, the EPA believes it may be possible under certain circumstances to use ambient air quality monitoring data for PM 2.5 as part of the cumulative impact analysis. This involves using ambient monitoring dat a as the primary means of assessing increment consumption or expansion for PM 2.5 by measuring ambient air quality on the minor source baseline date (baseline co ncentration) and thereafter to determine changes in air quality resulting from direct PM 2.5 emis sions and PM 2.5 precursors. This document does not provide detailed recommendations for conducting the PM 2.5 increment s analysis in this manner, but simply acknowledges that it may be possible in certain circumstances to use this approach for PSD permittin g. There would continue to be a need to model projected impacts as part of the PM 2.5 increment s analysis to include consideration of increment consumed by emissions that have not yet occurred

. One should also consider the extent to which the available mon i toring data adequately reflect the air quality changes caused by direct PM 2.5 and precursor emissions from sources impacting the baseline area. Where the PSD permit applicant believes that this approach is potentially useful for conducting the PM 2.5 increment s analysis for a particular PSD permit review, early coordination77 with the permitting authority is strongly encouraged to establish the appropriate baseline concentration(s) within the applicable baseline area and how subsequent ambient monitori ng 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 intent ion 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 increment s analysis in most situations due to the recent setting of the trigger date for PM 2.5 . Therefore, most PM 2.5 increment s 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. Nevertheles s, there may be situations where a cumulative increment s 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 Secti on 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 PM 2.5 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 or78 contribute to a modeled violation of the PM 2.5 incre ment.79 V I . 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

. EPA

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. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711

. U.S. EPA, 1997: Interim Implementation of New Source Review Requirements for PM 2.5 . 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, 4 397

- 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 compo nents 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, 2007 a : Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze

. April 2007

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. http://www. epa.gov/ttn/scram/guidance/guide/final

- 03

- pm - rh

guidance.pdf U.S. EPA, 2007b: Details on Technical Assessment to Develop Interpollutant Trading Ratios for PM 2.5 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

. J ournal of the Air & Waste Manage ment 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 testi ng 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 Devel opment
. 2010; 3, 205

- 226.80 U.S. EPA, 2010a: Model Clearinghouse Review of Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS

. Tyler Fox Mem orandum dated February 26, 2010

. U.S. Environmental Protection Agency, Research Triangle Park, North Ca rolina 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 PM 2.5 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%20PM 2.5 .pdf ENVIRON, 2011: User's Guide Comprehensive Air Quality Model with Extensions

. ENVIRON International Corporation, Nova to, California

. http://www.camx.com

. U.S. EPA, 2011a: Addendum

- User's Guide for the AERMOD Terrain Preprocessor (AERMAP)

. EPA

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- 003

. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 2 7711

. http://www.epa.gov/ttn/scram/models/aermod/aermap/aermap_userguide.zip U.S. EPA, 2011b: AERSCREEN User’s Guide

. EPA

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. 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 Recom mended 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/s cram/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 (PM 2.5 )

. Gina McCarthy Memorandum dated July 2 1, 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 Reg arding Application of Appendix W Modeling Guidance for the 1

- hour NO 2 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 PM 2.5 . K. Baker and K. Foley

. Atmos pheric Environ ment
. 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: PM 2.5 Modeling Implementation for Projects Subject to National Ambient Air Quality Demonstration Requirements Pursuant to New Source Review

. Report from NACAA PM 2.5 Modeling Implementation Workgroup dated January 7, 2011

. Washington, District of Columbia 20001

. http://www.epa.g ov/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 , Distri ct of Columbia 20460

. http://www.epa.gov/ttn/scram/10thmodconf/review_material/Sierra_Club_Petition_OAR

11

- 002

- 1093.pdf

. U.S. EPA, 2012 b : Summ ary of Public Comments, 10 th 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_Comments

Revised_10

- 05

- 2012.pdf U.S. EPA, 201 3 a : Transportation Conformity Guidance for Quantitative Hot

- spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas

. November 2013

. EPA

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10

- 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, 201 3 b: Draft Guidance for PM 2.5 Permit Modeling. March 4, 2013. EPA

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. U.S. Environ mental 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 P SD and N SR 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, 20 14 b : Addendum

– User’s Guide for the AMS/EPA Regulatory Model

– AERMOD

. EPA

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. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711

. ht tp://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip U.S. EPA, 2014 c : Addendum

- User’s Guide for the AERMOD Meteorological Preprocessor (AERMET)

. EPA

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. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

. http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip82 

## This Page Intentionally Left BlankA

- 1 

### Appendix A : Draft Conceptual Description of PM 2.5 Concentrations in the U.S. This appendix provides a brief summary of the current PM 2.5 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 PM 2.5 mass and speciati on data. It also discusses regional and local source contributions to urban PM 2.5 concentrations. Such information may be useful for permit applicants in preparing conceptual descriptions, as discussed in Section I II.2.1 of this guidance

. 1 . PM 2.5 Monitoring Networks 1.1 . PM 2.5 , PM 10 and PM 10
- 2.5 Mass Networks The 1997 promulgation of a fine particulate NAAQS (EPA, 1997) led to deployment of over 1500 PM 2.5 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 speciatio n sites ( Figure A

- 1 )

. Approximately 600 stations provide indirect measurements of continuous (hourly resolution) PM 2.5 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 PM 2.5 (sulfate, nitrate, organic and elemental carbon fractions, and trace metals) in pristine areas of the United States (Figure A

- 1)

. IMPR OVE is led by the National Park Service; various f ederal and state agencies support its operations

. The primary focus of the network is to track visib ility and trends in visibility. 1.3 . PM 2.5 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 PM 2.5 assessment efforts

. No FRM exists for particulate speciation, which is not directly required to determine atta inment, 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, sou rce attribution analysis and model evaluation. Figure A
- 1. Locations of chemical speciation sites delinea ted 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 fo r 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 ( NO y ), and true nitrogen dioxide (i.e., a measurement of NO 2 concentration unaffected by other nitrogen oxides, which contaminate FRM NO 2 measurements)

. These measurements had not been available in major government

- funded routine network s and in order t o identify sources of ozone pre cursors 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 S upersites 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 PM 2.5 Particulate matter (PM) is a highly complex mixture of solid particles and liquid droplets distri buted 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 ey e)

. P articles are classified as PM 2.5 and PM 10

- 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 s hifting 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 c rustal 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 th e 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 crushi ng, 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 i ndustrial 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. Schef fe, 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 sourc es such as trees. Figure A
- 2 . National Average of Source Contr ibution to Fine Particle Levels

Source: The P articulate Matter Report, EPA

- 454

- R

- 04

- 002, Fall 2004

. Carbon reflects both organic carbon and elemental carbon

. Organic carbon accounts for automobiles, biog enics, 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 at mosphere 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 th e United States (as shown in Figure A

3). 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 PM 2.5 as measured by the PM 2.5 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 PM 2.5 data.

42 Frank, N. H., Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine P articulate 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 generationA

- 5 Figure A
- 3 . Annual Average PM 2.5 Composition in 15 Urban Areas: 2008
- 2010

3 . Seasonal and Daily Patterns of PM 2.5 Fine particles often have a seasonal pattern

. Both daily values and quarterly average of PM 2.5 also reveal patterns based on the time of year

. Unlike daily ozone levels, which are usually elevated in the summer, daily PM 2.5 values at some locations can be high at any time of the year

. As shown in Figure A

- 4, PM 2.5 values in the eastern half of the United States are typically higher in the third c alendar quarter (July

- September) when sulfates are more readily formed from sulfur dioxide ( SO 2 ) emissions from power plants in that region and when secondary organic aerosol is more readily formed in the atmosphere

. Fine particle concen trations tend to b e higher in the first calendar quarter (January through March) in the Midwest in part because fine particle nitrate s are more readily formed in cooler weather

. PM 2.5 values are high during the first (January through March) and fourth calendar quarter (Octo ber 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 PM 2.5 wit h va lid design values is shown.A

- 6 Figure A
- 4 . Quarterly Averages of PM 2.5 Concentration: 2008
- 2010

The composition of PM 2.5 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 PM 2.5 data collected during 2008

- 10. In the eastern U nited S tates , 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. ) O rganic mass (O M ) is high throughout the year.A

- 7 Figure A
- 5 . Seasonal Speciation Profiles of PM 2.5 for Select Urban Areas: 2008
- 2010

The composition of the highest daily PM 2.5 values may be different than that for the annual average. Table A

- 1 provides 2008

- 10 data on daily PM 2.5 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: s ulfates, nitrates, OM , elemental c arbon (EC) and crustal material

. For each site, the table shows the 2008

- 20 10 annual average speciation profile, the breakdown for the top 10 percent of days per yea r and corresponding FRM mass

. The table shows some notable differences in the percentage co ntribution 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 PM 2.5 ), nitrates are slightly higher in the top 10 percent of the PM 2.5 d ays . For the 2008

- 20 10 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 average s.A

- 8 Table A
- 1 . PM 2.5 Composition on High PM 2.5 Mass Days in Select Urban Areas: 2008
- 20 10

Note: The percentages do not add to 100% due to a small amount of passively collected fine particle mass included in the measurement of PM 2.5 by the FRM.

4 . Regional and Local Sources of PM 2.5 Both local and regional sources contribute to particle pollution

. Figure A

- 6 shows how much of the PM 2.5 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 PM 2.5 concentrations sug gest substantial regional contributions to fine particles in the East

. The measured PM 2.5 concentration is not necessarily the maximum for each urban area

. RegionalA

- 9 concentrations are derived from the rural IMPROVE monitoring network. 43 Figure A
- 6 . “Urban excess” of locally generated PM 2.5 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 with in 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 U nited State for most PM 2.5 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 PM 2.5 air quality problem in these areas

. As expected for a predominately regional pollutant, only a modest urban excess is observed for su lfates. In the East, regional pollution contributes more than half of total PM 2.5 concentrations

. Rural background PM 2.5 concentrations are high in the East and are somewhat uniform over large geographic areas

. These regional concentrations come from emis sion 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 PM 2.5 : 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 Con stituents in the United States: Report V, 2011 ( http://vista.cira.colostate.edu/improve/Publications/Reports/2011/2011.htm )A

- 10 

## This Page Intentionally Left BlankB

- 1 

### Appendix B : General Guidance on Use of Dispersion Models for Estimating Primary PM 2.5 Concentrations This appendix provides general guidance on the application of dispersion models for estimating ambient concentrations of PM 2.5 associated with direct emissions of primary PM 2.5 . 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 u nique 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

. Recentl y issued EPA guidance of relevance for consideration in modeling for PM 2.5 includes:

• “Model Clearinghouse Review of Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS” February 26, 2010 (U.S. EPA, 2010a) ;

• ”Modeling Procedures for Demonstrating Compliance with PM 2.5 NAAQS” M arch 23, 2010 (U.S. EPA, 2010b) ; and

• “Transportation Conformity Guidance for Quantitative Hot

- spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas” November 2013 (U.S.EPA, 2013 a )

. 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 wit h the appropriate EPA Regional Office modeling c ontact if questions arise about interpretat ion 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 l ist of EPA Regional Office m odeling c ontacts 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 PM 2.5 NAAQS, AERMOD should be used to model primary PM 2.5 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 A ERMOD modeling system includes the following components:

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

• 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, 2014 b). Other components that may be used, depending on the applic ation, 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

- processo r 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 c an 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 toget her near the so urce 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 sour ce) 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 mo deling under NSR/PSD permit programs.B

- 3 If modeling indicates elevated levels of PM 2.5 (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 o f 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, t he 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 Elevatio n 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 appropri ate 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 hei ghts, Section 2

. 3 provides details on source configura tion and source types, Section 2

. 4 provides details on urban/rural determinati on of the sources, and Section 2

. 5 provides general guidance on source grouping, which may be important for design value calculations. 2.1 . E missions Consistent with Appendix W, dispers ion 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 perio ds

. 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 operati on should also be assumed for nearby sources included in the modeled inventory for the 24

- hr PM 2.5 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 PM 2.5 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, H g , is determined to be the greater of:

• 65 m, measured from the ground

- level elevation at the base of the stack;

• f or stacks in existence on January 12, 1979, and for whic h the owner or operator had obtained all applicable permits or approvals required under 40 CFR Parts 51 and 52
H g =2.5H provided the owner or operator produces evidence that this equation was actually reliedB

- 4 on in designing the stack or establishing an emission limitation to ensure protection against downwash;

• f or all other stacks,
H g =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 p roject ed width of nearby structure(s); or

• t he height demonstrated by a fluid model or a field study approved by the EPA or the s tate/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 Techni cal 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 t o 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 para meters 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 imp ortant, 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 AERM OD

. 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 pos ition a stack within three feet of its actual location. Users should use the greatest precision available.B

- 5 downwash will not be accur ately 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, 2012 a), 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, circu lar 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 no t 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 sourc es 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 m odeling c ontact

. 2. 4 . Urban/rural determination For any dispersion modeling exercise, the urban or rural determination of a sourc e 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

- 1 b) 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 s ource can be quit e 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 us ing 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 res idential) 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 circl e of 3 km radius is used

. If the population density within the circle is greater than 750 people/km 2 , then the source is considered urban

. Otherwise, the source is modeled as a rural source

. Of the two methods, the land use method isB

- 6 considered more definitiv e (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 bod y 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 ur ban 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 po pulation 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 sour ces 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 releaseB

- 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 artificia lly 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 b e 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

- 1 b, may be such an example as the urban boundary layer height for this stack would be 189 m (base d on a population of 100,000) and equation 104 of the AERMOD formulation document (Cimorelli, et al., 2004)

. This equation is:

4 1        

=

o iuo iuc P P z z (B

- 1) where z iuo is a reference height of 400 m corresponding to a reference population P o of 2,00 0,000 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 th e 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, 201 2 a)

. 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 A ERMOD, 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) usi ng 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 (a ll 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 s ection gives guidance on the selection of meteorological d ata 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 on site 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 d ata 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 dr astically 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 b e 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 s ite and the analysis area

. When processing meteorological data in AERMET (U.S. EPA, 2004c; U.S. EPA, 2014 b ), the surface characteristics of the mete orological site should be used ( Section 8.3.c of Appendix W and the AERSURFA CE 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 m ay 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 t he AIG should be followed unless an alternative method can be justified.B

- 10 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 represent ed 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 PM 2 .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 NAA QS

. 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 state d 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, 2013 b ). The key points are:

• The EPA has previously analyzed the effects of ASOS implementation on dispersion modeling and found that ge nerally 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 implement ed 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 ASO S data are inadequate and pre

- ASOS, or observer based d ata 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 NW S data. 3.2.2 . Site
- specific dataB

- 11 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, 20 04c; U. S. EPA, 2014 b ), 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 f or 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 levels 47 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 o f 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 PM 2.5 NAAQS and to aid in determining w hether 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. 8 th highest, corresponding to the 98 th percentile for the 24

- hour PM 2.5 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. Si gnificant levels may vary depending on the meteorological conditions at the upper

- air station.B

- 12 F or more det ails about the enhancements , see the AERMOD User’s guide Addendum (U. S. EPA, 2014 a). 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 conc entration 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

. Th ese 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

- network s . 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 s ource 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 th e 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. Sit uations 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 eac h sub

- network.

o A receptor network of 1 ,000 receptors is divided into four 25 0 receptor sub

networks

. 10 facilities are modeled with 5 years of NWS data in one AERMOD run for each rec eptor network, resulting in four AERMOD runs

. After theB

- 13 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 faci lities 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

- netw ork with all sources.

• T en facilities are modeled with 5 years of NWS data for 1 ,000 receptors

. The receptor n etwork is divided into four 25 0 receptor networks

. For each sub

- network, all ten facilities are modeled for each year separately, resulting in twen ty AERMOD runs

. MAXDCONT output can be used and post

- processed to generate the necessar y design value concentrations.B

- 14 5 . References Auer, Jr., A.H., 1978: Correlation of Land Use and Cover with Meteorological Anomalies

. Journal of Applied Meteorology, 1 7(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

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- 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 Regulat ions), Revised

. EPA

- 450/4

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- 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 Pro cedures for Estimating the Air Quality Impact of Stationary Sources. EPA

- 454/R

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- 019

. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

. http://www.epa.gov/ttn/scra m/guidance/guide/scrng.wpd U.S. EPA, 1994: SO 2 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

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- 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/B

03

- 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)

. 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, 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 Pro tection 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/a ppw_05.pdf U.S. EPA, 2008: AERSURFACE User’s Guide

. EPA

- 454/B

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- 001

. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711

. http://www.epa.go v/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.pdfB

- 15 U.S. EPA, 2010a: Model Clearinghouse Review of Modeling Procedures for Dem onstrating Compliance with PM 2.5 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 PM 2.5 NAAQS

. Stephen Page Memorandum dated M arch 23, 2010. U.S. Environme ntal Protection Agency, Research Triangle Park, North Carolina, 27711

. http://www.epa.gov/ttn/scram/Official%20Signed%20Modeling%20Proc%2 0for%20De mo%20Compli%20w%20PM 2.5 .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, Re search 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 Scr eening 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/20110 411_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/tt n/scram/7thconf/aermod/aerminute_v11325.zip U.S. EPA, 201 3 a : Transportation Conformity Guidance for Quantitative Hot

- spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas

. November 2013

. EPA

- 420

- B

10

- 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 A gency, Research Triangle Park, North Carolina 27711

. http://www.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification. pdf U.S. EPA, 2 014 a: 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

. h ttp://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip U.S. EPA, 201 4 b: 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.zipB

- 16 

## This Page Intentionally Left BlankC

- 1 

### Appendix C: Example of a Qualitative Assessment of the Potential for Secondary PM 2.5 Formation In late 2011, the EPA Region 10 Office dev eloped a qualitative assessment of the potential for secondary fine particulate matter ( PM 2.5 ) formation to cause or contribute to a violation of the PM 2.5 N ational Ambient Air Quality Standard (N AAQS ) 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 th e 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 co uld 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 quali tative assessment of the potential for secondary PM 2.5 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 PM 2.5 NAAQS would not be violated in this particular case . The qualitativ e assessment examined the regional background PM 2.5 monitoring data and aspects of secondary PM 2.5 formation from existing sources; the relative ratio of the combined modeled primary PM 2.5 impacts and background PM 2.5 concentrations to the level of the NAA QS; the spatial and temporal correlation of the primary and secondary PM 2.5 impacts; meteorological characteristics of the region during periods of precursor pollutant emissions; the level of conservatism associated with the modeling of the primary PM 2.5 c omponent 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 second ary PM 2.5 ; 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 qualit ative assessments under this guidance , recognizing that additional components may be essential in other qualitative assessments of the potential for secondary PM 2.5 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 provid ed a detailed explanation for why it believes that modeling secondary PM 2.5 emissions is not needed in order to determine that emissions of PM 2.5 precursors from the Discoverer and Associated Fleet would not, together with emissions of primary PM 2.5 , cause or contribute to a violation of the 24

- hour PM 2.5 NAAQS. The factors Region 10 relied on to reach this conclusion include:C

- 2 1) The background PM 2.5 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 le vels of daily PM 2.5 , generally in the range of 2 μg/m 3 , with the higher PM 2.5 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 PM 2.5 from exis ting sources in the North Slope is currently causing or contributing to exceedances or a violation of the PM 2.5 NAAQS in the onshore communities. 2) Modeled primary PM 2.5 impacts from the Discoverer and Associated Fleet that, when using a conservative “Fi rst Tier” approach to combining modeled primary PM 2.5 impacts with monitored background PM 2.5 c oncentrations, are less than 67 percent of the PM 2.5 NAAQS. Thus, although not expected, considerable formation of secondary PM 2.5 emissions could occur before t he NAAQS would be threatened. 3) Secondary PM 2.5 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 PM 2.5 emissions are highest, because there has not been enough time for the secondary chemical reactions to occur. Conversely, secondary PM 2.5 impacts are more likely to be higher farther from the Discoverer and the Associated Fleet where impacts from primary PM 2.5 emissions from the Discoverer and the Associated Fleet are expected to be lower. This makes it unlikely that maximum primary PM 2.5 impacts and maximum secondary PM 2.5 impacts from the Discoverer and the Associated Fleet will occur at the same time (paired in time) o r location (paired in space)

. See March 23, 2010 PM 2.5 Guidance Memo at 9. 4) The relatively small amount of NO X emissions (a PM 2.5 precursor) that will be authorized under these permits in comparison to existing NO X emissions in the North Slope area in general, together with the generally low levels of PM 2.5 recorded at monitoring stations in the area, make it unlikely that NO X emissions from the Discoverer and the Associated Fleet would cause or contribute to a viol ation of the PM 2.5 NAAQS. 5) The background concentrations of certain chemical species that participate in photochemical reactions to form secondary PM 2.5 , 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 ton s per year of ammonia [citation omitted] would also not be expected to result in the conversion of significant quantities of NO X emissions to secondary particles in the areas impacted by primary PM 2.5 emissions.C

- 3 6) There are several other conservative assu mptions incorporated in the modeling of primary PM 2.5 emissions. These include the conservatism inherent in using a “First Tier” approach to combining modeled primary PM 2.5 i mpacts with monitored background PM 2.5 concentrations; assuming that the Discovere r 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 e mission 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 PM 2.5 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 locat ion 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 emission s. 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 PM 2.5 NAAQS. With the additional emission reductions in direct PM 2.5 em issions and the use of a refined model, predicted PM 2.5 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 pa rticles 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 PM 2.5 NAAQS will be protected, accounting for primary PM 2.5 impacts and potential contributions due to PM 2.5 precursors from the Discoverer and the Associated Fleet, and that it is not necessary to use a photochemical model to further evaluate secondary PM 2.5 formation in these permitting actions.C

- 4 

## This Page Intentionally Left BlankD

- 1 

### Appendix D : Example of a Hybrid Qualitative/Quantitative Assessment of the Potential for Secondary PM 2.5 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 (PM 2.5 ) form ation to cause or contribute to a violation of the PM 2.5 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 anal ysis was robust and defendable. In this particular hybrid assessment, Sasol took an approach of using the formerly presumptive interpollutant trading ratios for NO x and SO 2 to PM 2.5 offsets and conservatively applied them in an illustrative example to demonstrate how relatively inconsequential the impacts of secondary PM 2.5 formation would be in the area of significant impact surrounding their facility. In Sasol’s case, the p rojected emissions increases of the direct PM 2.5 emissions and both PM 2.5 precursors of NO x and SO 2 were above their respective Significant Emissions Rates (SERs). Sasol also performed an analysis of PM 2.5 speciated monitoring data to further support the a mount of impacts of nitrates on high PM 2.5 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 tha t the EPA revised the provisions of the interpollutant trading policy for PM 2.5 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 inte rpollutant trading ratios in an absolute sense

. Rather, the inten t ion 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 PM 2.5 formation of 0.18 μ g/m 3 for the 24

- hour PM 2.5 NAAQS and of 0.04 μ g/m 3 for the annual PM 2.5 N AAQS

. When considered along with the primary PM 2.5 impacts and representative background data, the secondary PM 2.5 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 PM 2.5 impacts from the NO x and SO 2 precursor emissions would occur at the same place and time as the maximum primary PM 2.5 impacts. Based upon Sasol’s PM 2.5 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.D

- 2 At the same time Sasol al so conducted an analysis of speciated data at a nearby monitor to further corroborate the ratio analysis

. There is a PM 2.5 monitor within 1⁄2 mile of the Sasol’s property line, but it does not have speciated data collection. Sasol utilized a representative PM 2.5 monitor approximately 25 miles away that did have long

- term speciated data. Sasol evaluated the PM 2.5 speciated data from the nearby monitor to support that nitrate is not a large contributer to high PM 2.5 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 PM 2.5 . Given the close coordination with the respective permit ting 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 P M 2.5 formation would not cause or contribute to a violation of the NAAQS. The permit applicant’s corroborative analysis of the PM 2.5 speciated data further supported that the main increase of emissions (NO x ) would not be expected to yield significant level s of secondary PM 2.5 and the applicable ambient standards will not be exceeded by this project. Reg ion 6 example: Justification on Secondary PM 2 .5 Approach A t a December 13, 2013 , meeting and on a January 17 , 2014 , conference call EPA Region 6 requested an analysis t o examine th e fra c tion of sulfate and nitrate in the PM 2 .5 measurements in the study a r ea and add itional justification on the modeling app ro ac h for seco ndary PM 2 .5 . Th is document presents the re sul t s of the requested analysis. Secondary PM 2 .5 is formed primarily from reaction of s ulfur dioxide (S O 2 ) emissions to f o rm particulate sulfate and from nitrogen oxides (NO x ) reactin g to form particulate nit ra te

. The approach used to estima te th e secondary particulat e i s d escri bed in Section [ Secondary Particulate Estimate (listed below) ]

. With re ga rd to NO x . and SO 2 , the Sasol project emiss ion s a re dominated by NO x w ith annual emissions of 1 , 595 tons per yea r compa red to S O 2 emissions of on ly 121 tons per year. EPA reque s ted additiona l information on the secondary PM 2 .5 form atio n from nitrate in the colder months. The PM 2

. 5 background monitor is th e Westlake mon itor lo ca ted very near th e project s it e. Ho wever, thi s monitor did not record specia ted PM 2.5 data , so it is not po ss ible to compute the s ulfate a nd nitrate fractions at this location. Monitors with speciated PM 2.5 data include the Port Arthur Memorial Sc hool (AIR S: 48

- 245

- 0021) in Port Arthur, Texas, the Capitol Baton Rouge (AIRS: 22

- 033

- 0009), and th e Shreveport (AIRS: 22

- 015

- 0008) monitor s. The Port Arthur monitor was chose n as being th e most representative because it is the closest monitor to the Sasol site and is in a les s 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 , i s approximately 25 miles west of the project. Given the regional natur e of PM 2.5 co n centrat ion s, this monitor should be representati ve of the s tudy area. The most recent ly available fi ve years of data for this site was for 2006

- 20 10 and was obtained from the U SE PA

. The d a ta shows that nitrate make s up 2

. 6 percent of the average of the 24

- h o ur concentrations o f PM 2.5 a nd 2.3 percent of the 5

- year average concentration. On the day w ith the hi g he st 24

- hour average PM 2.5 m easu rement, nitrate was 2.2 p e rcent of the PM 2. 5 concentration. In general, the generation of PM 2.5 occurs more from nitrate during colder winter months than during the summer. Ex am ination of the worst 10% of PM 2.5 days during the colder month s (Nov ember t hrou g h Februa ry ) a t the Port Arthur monitor for 2006

- 2010 reveal s that the average nitrate con tri b ution is 2.9 p e rc e nt , only s li g htly higher than the 5

- year average concentration. Thus, eve n on days with high PM 2

. 5 concentrations in th e co ld e r months, p art i cu l ate nitrate i s s till a r e lativ e l y small portion of the tot a l PM 2.5 conce ntr a tions. Based on this relatively low fraction of particulate nitrate in the observed PM 2.5 , and the magnitude of existing NO x 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 PM 2.5 observation, sulfate was 10.6 percent of the PM 2.5 concentration. Table 1 presents the total PM 2.5 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 PM 2

. 5 mass, the projected increase in SO 2 emissions (121 tpy) from the Sasol GTL and LCCP projects are a very small fraction of the total SO 2 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 lo cation at the same time. The 24

- hour average modeled PM 2

. 5 concentration is presented in Figure 1. Examination of this figure reveals that the h i ghest impact occurs very near the Sasol project border. Within a few kilometers of the project site, the concentrations fal l significantly from the peak of modeled concentration of 9 μ g/m 3 to less than 3 μ g/m 3 . Formation of secondary sulfate and nitrate particulate is a fairly slow process with conver s ion rates taking many hou r s to days. Thus, the peak second ary impacts are expected to occur well downwind of the peak primary impacts. Given this information, the study team is comfortable that the ambient ratio ana l ysisD

- 4 presented in the ozone and secondary PM 2 .5 modeling report is an appropriate approach to e s timating the secondary PM 2

. 5 impacts for the project.D

- 5D

- 6 [ Seconda ry 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 wit h avai lab l e water and ot h e r pollutants in secondary r e actions to form part i culate ammonium sulfate

– (NH 4 ) 2 SO 4 or ammon ium nitrate

– NH 4 NO 3 . These la tte r compounds are formed primarily downwind of the specific sources of concern, g iven rea ctio n times, ambi en t temperature and other environmental factors. The su lfur compounds emitted by the two projects are in the form of SO 2 . The nitrogen compounds emitted b y the two projects are in the form of NO x . The Sasol projects (GTL and LCCP combined ) would have 1,595 tpy of NO x , 121 tpy SO 2 , and 612 tpy of d ir ect PM 2.5 emission s . The NACAA/EPA r ecommendati on to account for secondary PM 2.5 formation i s to divide th e projected em i ssions by a region average offset ratio. The national ratio for S O 2 is 40 and for NO x is 100 for western states and 200 for eastern states. To be conservative, the western value was u se d in the analys i s s in ce it estimates a higher secondary rat io. The tota l PM 2.5 emissions are calculated by mult i plying the primary PM 2.5 mod e l e d concentration by the ratio obtained from the secondary equivalent PM 2.5 calculation.
For the Saso l combin ed project emissions the formula s are: Tota l Equivalent PM 2.5 = Prim ary PM 2.5 + ( S O 2 / 40 ) + ( N O x / 100 ) = 612 + ( 121/ 40 0) + ( 1 ,595 / 100 ) = 631.0 to n/ye ar Total PM 2.5 Impact ( μ g/m 3 ) = Primary PM 2.5 Impact ( μ g/m 3 ) * (Total Equival ent Primary PM 2.5 (tpy) / Pr im ary PM 2.5 (tpy ) ) Total Eq u ivalent PM 2

. 5 / Primary PM 2

. 5 = 631.0 tpy / 612 tpy = 1 .03
Henc e th e modeled impac ts for PM 2.5 could be increased by a factor of 1.03 [(1.004 for SO 2 and 1.026 for NO x )] to account for th e secondary fo rmation for those sources em itting significant amounts of seconda ry PM 2.5 precursor emissions.E

- 1 

### Appendix E : E xample 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 PM 2.5 modeling analysis. In this example, it was determined through discussion and coordination with the appropriate permi tt ing authority that the impacts from the project source’s primary PM 2.5 emissions were most pro minent during the cool s eason and were not temporally correlated with background PM 2.5 levels that were typical highest during the warm season. So, combining the modeled and monitored contributions through a First Tier 24

- hour PM 2.5 modeling analysis was determined to be potentially overly conservative

. Extending the compliance demonstration t o a Second Tier analysis allows for a more refined and appropriate assessment of the cumulative impacts on the primary PM 2.5 emissions in this particular situation. The example provided is from an idealized Federal Reference Method (FRM) PM 2.5 monitoring site that operates on a daily (1

- in

- 1 day) frequenc y with 100% data completeness

. In this case, the annual 98 th percentile concentration is the 8 th 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 crite ria

. Please reference Appendix N to 40 CFR Part 50 to determine the appropriate 98 th 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 PM 2.5 modeling analysis are as follows:

• Step 1

– Start with the most recent 3

- years of representative background PM 2.5 ambient monitoring data that are being used to develop the monitored background PM 2.5 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 98 th percentile PM 2.5 co ncentration. Again, this idealized example is from a 1

- in

- 1 day monitor with 100% data completeness. So, the 8 th highest concentration of each year is the 98 th percentile PM 2.5 concentration. The 98 th percentile PM 2.5 concentration for 2008 is highlighted in Table E

1. 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 PM 2.5 concentrations from eac h year that are greater than the 98 th percentile PM 2.5 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 result ant 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 98 th p ercentile PM 2.5 concentration into each season (or quarter)

. For 2008, the seasonal subsets are presented in Table E

- 3.

• Step 5

– Determine the maximum PM 2.5 concentration from each of the seasonal (or quarterly) subsets created in Step 4 for each year

. Th e maximum PM 2.5 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 datase t 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 deter mined 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 μ g/m 3 Table E
- 1 . 2008 Daily PM 2.5 ConcentrationsE

- 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 μ g/m 3 RC = Above 98th Percentile and Removed from Consideration Table E
- 2 . 2008 Daily PM 2.5 Concentrations Less Than or Equal to the 98 th PercentileE

- 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 PM 2.5 Concentrations Less Than or Equal to the 98 th Percentile by QuarterE

- 6 Table E
- 4 . Resulting Average of Seasonal (or Quarterly) M aximums for Inclusion into AERMOD

(Note, the complete data sets 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 ]