MEMORANDUM UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK

, NC 27711



## DEC 03 21M

OFF I CE OF AIR QUALITY PLANNING
AND STANDARDS

SUBJ E CT: Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for
Ozone

, PM2s

, and Regional Haze

FROM: Richard Wayland

, Division

# Director~/l.tt/d...J/ Air Quality Assessment Division 

## {j

----

TO: Regional Air Division Directors, Regions 1-10

The Envi ronmental Protection Agency (EPA) is providing a revised version of Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PMn

. and Regional Haze to the state and local air agencies as well as the public for consideration

, review, and

comment. This document reflects the EPA's recommendations for how air agencies should
conduct air quality mode l ing and related technical analyses to satisfy mode l attainment
demonstration requirements for the 2008 ozone and 2012 PM 2.s National Ambient Air Quality
Standards (NAAQS), as well as for upcoming regional haze reasonable progress analyses. 1 This
document updates the previous version of the modeling guidance which was released in April
2007

.

This document does not substitute for provisions or reg ulations of the Clean Air Act (CAA), nor is it a regulation itself. As the term "g uidance " suggests, it provides recomme ndations on how to implement the modeling req uirements

. Thus, it does not impose binding, enforceable requirements on any party
, nor does it assure that the EPA will approve all instances of its application, as the guidance may not apply to a particular situation based upon the circumstances.
Final decisions by the EPA regarding a particular State Implementation Plan (SIP) demonstration
will only be made based on the statute and applicable regulations

, and will only be made following a final submission by air agencies and after not ice and opportunity for public review
and comment.

1 This draft guidance will be r ev ised

, as needed, once the review of the current ozone standard is completed in Octo b er 2015.

Recycled/Recyclable •Printed wKh Vegetable Oil Based Inks on 100 % Recycled Paper (40% Postconsumer)2

T here are several modeling g uidance issues that are most appropriate to be addressed in a notice and comment rulemaking such as the 2008 ozone and soon

- to

- be proposed 2012 PM 2.5 implementation rules. As such, there are several details that have either been removed from the draft guidance or are pending updates when the 2008 ozone and 2012 PM 2.5 implemenation r ul es are finalized. These are specifically noted in the guidance (in some cases with placeholders). The ozon e implementation rule for the 2008 NAAQS was proposed in June 2013 and is expected to be finalized in early 2015 . The PM 2.5 implementation rule for the 2012 NAAQS i s expected to be proposed in early 2015.

GUIDANCE UPDATES

Like the original 2007 version, the revised guidance is divided into two main sections. The first part describes how to setup and apply a photochemical modeling platform (including meteorological, emissions, and air quality modeling), and the second part describes how to use th e results of the air quality modeling to show whether future attainment of the ozone and/or
PM 2.5 NAAQS is likely. The guidance also describes how to use photochemical grid modeling to evaluate reasonable progress goals for regional haze.

Many of the up dates to the guidance are in the form of updated and reorganized text. In addition, numerous references have been updated and added. The attainment tests for the annual and 24

- hour average PM 2.5 NAAQS are unchanged 2 , but the 8

- hour ozone NAAQS attainment test has been updated to better reflect the level and form of the 2008 ozone NAAQS . More details are provided below.

Specific u pdates to the draft guidance include:

 A complete reorganization of the document including removal of outdated language and references.

 The ozone attainment test has been updated to address the 2008 NAAQS.

o The revised test eliminates the initial concentration threshold (which was formally 85 ppb) and focuses the calculation of relative response factors (RRFs) to the ten highest modeled 8

- hour average daily maximum ozone days.

 This potentially eliminates days in the RRF calculation that are well below the base year observed design values.

o Elimination of the initial threshold concentration allows the test to be easily applie d to any level of the NAAQS. The revised guidance reflects recommendations for the current 75 ppb NAAQS. Minimal (if any) changes to the test will be needed to address potential future ozone NAAQS.

2 The 24

- hour PM 2.5 attainment test is unchanged from the version which is detailed in a June 2011 guidance memo. The attainment t est from the June 2011 memo has been incorporated into th is revised version of the guidance.3

 The emissions modeling section has been extensively updat ed to account for new and improved emissions models and input data. Additional information on SIP emissions inventory requirements is contained in the draft Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Ai r Quality Standards (NAAQS) and Regional Haze Regulations available at: http://www.epa.gov/ttn/chief/eidocs/eiguid/2014revisedeiguidance.pdf . The draft modeling guidance and the draft emissions inventory guidance have been carefully coordinated.

 The Weight of Evidence (WOE) section has been updated to include additional information that may be appropriate for use in a WOE demonstration. The WOE section includes discussion s on emissions controls, modeling

, and data analyses that have been recommended by stakeholders for inclu sion in a WOE demonstration.

The revised document is draft guidance for public review and comment. However, since the revised guidance replaces and updates the existing modeling guidance document without making major changes, EPA encourages states to follow the recommendations in this draft guidance until an updated version is released

. T hese recommendations will be updated, as needed, based on

commen ts received on this guidance and final decisions made in the implementation rules. States with upcoming attainment demonstration deadlines should consult with their EPA Regional Office to determine the appropriate course of action.

REVIEW AND COMMENT

The EPA will accept comments on the draft modeling guidance t hrough the close of business on March 13 , 201 5

. This allows at least 90 days for consideration, review, and comment on the material presented in the draft guidance. For convenience, the draft gui dance document is available electronically on the EPA’s SCRAM website, http://www.epa.gov/ttn/scram/guidance/guide/Draft

- O3

- PM

- RH

- Modeling_Guidance

- 2014.p df

. Comments should be electronically submitted to Brian Timin of EPA’s Air Quality Modeling Group at timin.brian @epa.gov

. EPA will also schedule a conference call in Mid

- December with state and local air agencies to summarize the guidance and allow for questions.

In preparing the final version of the guidance, t he EPA will take into consideration comments received on the dra ft modeling guidance

, as well as decisions made in the final 2008 ozone and /or 2012 PM 2.5 implementation rules. As there are a number of issues that need to be informed by the se ozone and PM 2.5 implementation rules, t he timing for release of a final version of the guidance depends on the timing of the implement ation rules. Depending on the timing of the se final implementation rules (as well as any implementation rule that may be needed following review of the current ozone NAAQS) , the EPA will conside r releasing a 2 nd draft version of the modeling guidance or clarification memos

, as needed.4

If there are any questions regarding the d raft m odeling g uidance, please contact Brian Timin of EPA’s Air Quality Modeling Group at (919) 541

- 1850 or timin.brian@epa.gov

.

cc : Steve Page Mike Koerber Anna Wood Air Program Managers Kristi Smith

, OGC

AttachmentModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

1



# DRAFT December, 2014



# Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 

# , and Regional Haze

NOTE: Ozone NAAQS implementation requirements are curren tly being assessed as part of the Implementation of the 2008 National Ambient Air Qua lity Standards for Ozone: State Implementation Plan Requirements; Proposed Rule (78 FR 34178, June 6, 2013) (Hereinafter, “Proposed Ozone Implementation Rule”). Accordingly , the guidance provided in this document addresses several options that were addressed in th e Proposed Ozone Implementation Rule, which may or may not be adopted in the final rule. To the extent that the Final Ozone Implementation Rule takes a different approach than contained in this guidance, the Final Rule will dictate what is required for air quality model ing. Once the Final Ozone Implementation Rule is published, EPA will (if necessary) revise t his guidance to reflect the regulations and requirements adopted in the final rule.

In addition, PM NAAQS implementation requirements a re currently undergoing review as part of the planned proposed PM 2.5 Implementation Rule. The guidance includes the us e of “[PM Implementation Placeholder]” text where necessary. Once that rule is finalized, this guidance will be updated (if necessary) and the [PM Implemen tation Placeholder] text will be replaced with the PM-specific guidance associated with that rule.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

2 

## Table of Contents
1.0 Introduction ..................................

................................................... .......................................... 5 1.1 What Is The Purpose Of This Document? ............. ................................................. 6 1.2 Does The Guidance In This Document Apply To Me? .. ....................................... 6 1.3 Outline............................................

................................................... ...................... 7 2.0 Building a Model Platform .....................

................................................... ............................... 9 2.1 Conceptual Description ........................

................................................... ...................... 9 2.1.1 Example applications of conceptual models ... ............................................. 11 2.2 Modeling Protocol and Supporting Documentation

................................................... 12 2.2.1 Modeling protocol ...........................

................................................... .......... 12 2.2.2 Attainment Demonstration Documentation Packag e ................................... 13 2.3 Episode Selection .............................

................................................... ........................ 14 2.3.1 Choosing Time Periods to Model ..............

.................................................. 15 2.3.2 Future Year Selection .............................

.................................................. 17 2.4 Modeling Domain Selection .....................

................................................... ............... 18 2.4.1 Domain Size .......................................

................................................... .... 18 2.4.2 Vertical Layer Configuration ...................... .............................................. 19 2.4.3 Horizontal Grid Cell Size ......................... ................................................. 2 0 2.5 Air Quality Model Selection ...................

................................................... ................. 21 2.6 Meteorological Inputs .........................

................................................... ..................... 24 2.6.1 Developing base year meteorological fields ........ ..................................... 24 2.6.2 Assessing Impacts of Future Year Meteorology ...... ................................. 28 2.6.3 Evaluation of base year meteorological fields ............................................. 29 2.7 How Are the Emission Inputs Developed? ........

................................................... ...... 31 2.7.1 What Emissions Inventory Years Should I use f or Base and Future Modeling? .........................................

................................................... ..... 32 2.7.2 What Base Year Emission Inventory Data Are Ne eded to Support Air Quality Models? ...................................

................................................... .. 32 2.7.3 What Pollutants Should Be Included in Base Ye ar Inventories? ................. 33 2.7.4 What Temporal Resolution Should Be Used for B ase Year Inventories? ... 33 2.7.5 What Spatial Coverage Is Need for Base Year I nventories? ....................... 34 2.7.6 How Can The National Emission Inventory by Ai r Agencies? ................... 35 2.7.7 How Should Base Year Emissions Be Created? .. ........................................ 36 2.7.8 What Other Data Are Needed to Support Emissio ns Modeling? ................ 44 2.7.9 How Are Inventory Data Converted Into Air Qua lity Model Input?........... 50 2.7.10 Other Emissions Modeling Issues ............

.................................................. 53 2.7.11 How Are Emissions Estimated for Future Years ? ..................................... 56 2.8 Initial and Boundary Conditions ...............

................................................... ............... 58 3.0 Evaluating Model Performance ..................

................................................... ......................... 62 3.1 Overview of model performance evaluation.......

................................................... ..... 62 3.2 Operational Evaluation ........................

................................................... .................... 64 3.2.1 Metrics .....................................

................................................... ................. 65 3.2.2 Plots........................................

................................................... ................... 68 3.2.3 Ozone Model Performance .....................

................................................... .. 74Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

3

3.2.4 PM2.5 Model Performance .....................

................................................... .. 74 3.3 Ambient Measurement Networks ..................

................................................... .......... 76 3.3.1 AQS................................................

................................................... ........ 76 3.3.2 IMPROVE............................................

................................................... .. 77 3.3.3 CASTNet............................................

................................................... .... 77 3.3.4 CSN ...............................................

................................................... ......... 78 3.3.5 NADP ..............................................

................................................... ....... 78 3.3.6 SEARCH ............................................

................................................... .... 79 3.3.7 Speciated PM 2.5 Data Summary ...................................... ............................. 79 3.4 Diagnostic Evaluation .........................

................................................... ..................... 85 3.4.1 Process Analysis ............................

................................................... ........... 86 3.4.2 Indicator Ratios ............................

................................................... ............. 88 3.4.3 Source Apportionment ........................

................................................... ...... 91 3.4.4 Sensitivity Studies .........................

................................................... ............ 91 3.4.5 Dynamic Model Evaluation ....................

................................................... .. 92 3.5 Evaluation Tools ..............................

................................................... ........................ 94 4.0 Assessing Modeled Attainment ..................

................................................... ......................... 95 4.1 Overview of model attainment test .............

................................................... ............. 95 4.1.1 Establishing the base design value ..........

................................................... .. 97 4.1.2 Calculation of relative response factors .......... .......................................... 99 4.2 Model attainment test for the primary ozone standar d ....................................... 100 4.2.1 Model values to use in the RRF calculation ........ ................................... 100 4.2.2 Grid cells to use in the RRF calculation .......... ....................................... 102 4.2.3 Sample model attainment test calculation........... .................................... 103 4.3 Model attainment test for the secondary ozone s tandard .......................................... 106 4.4 What is the Modeled Attainment Tests for the An nual Average PM 2.5 NAAQS? ... 106 4.4.1 Ambient PM 2.5 Data Used in the Annual PM 2.5 Attainment Test ........... 109 4.4.2 FRM Monitors that Don’t Have Speciation Data ...... ............................. 109 4.4.3 PM 2.5 Species Calculations and Definitions .............. ............................. 110 4.4.4 Recommended Treatment of Species Data ............. ................................ 111 4.5 What Is The Recommended Modeled Attainment Test For The 24-Hour NAAQS? 124 4.6 Local Area Analyses- Special Considerations for Primary PM 2.5 ............................ 130 4.6.1 Analysis of Primary PM 2.5 Impacts at Monitors .............................. ....... 131 4.6.2 Analysis of Primary PM 2.5 Impacts at Monitors using a Gaussian Dispersion Model ..................................

.................................................. 132 4.7 Estimating design values at unmonitored locatio ns ................................................ .. 144 4.7.1 Why does the unmonitored area analysis need to use both ambient data and model output? .................................

.................................................. 145 4.7.2 Implementation of Gradient Adjusted Spatial Fields. ............................. 146 4.7.3 Using the Results of the Unmonitored Area Analysis ............................ 148 4.8 What Is The Recommended Modeling Analysis for R egional Haze? ...................... 149 4.8.1 Regional Haze Rule Background ..................... ....................................... 149 4.8.2 How is Regional Haze Measured? .................... ...................................... 151 4.8.3 How Do I Apply A Model To Calculate Changes i n Visibility? ............... 158 4.8.4 How Do I Select Appropriate Inputs For The Un iform Rate of Progress Analysis? .........................................

................................................... ..... 173Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

4 4.9 “Weight of Evidence”- How Can Additional Analys es Be Used to Support the Attainment Demonstration? .........................

................................................... .... 179 4.9.1 What Types of Additional Analyses Should Be Complet ed as Part of the Attainment Demonstration? ......................... ........................................... 180 4.9.2 What Role Should Weight Of Evidence Play In Visibil ity-Related Analyses? .........................................

................................................... .... 187 4.9.3 Weight of Evidence Summary .................. ................................................. 1 90 References ........................................

................................................... ........................................ 192Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

5 

## 1.0 Introduction

This document describes how to estimate if an emiss ions control strategy will lead to attainment of the annual average and 24-hour averag e national ambient air quality standards (NAAQS) for particles smaller than 2.5 μm in diamet er ( **PM 2.5**  ) and the 8-hour NAAQS for ozone.
We also describe how to use modeled and monitored d ata to estimate visibility improvement in
**Class I areas** (e.g., national parks, wilderness areas) as part o f a reasonable progress analysis. 1

The document describes how to apply air quality mod els to generate the predictions used to evaluate attainment and/or uniform rate of progress assessments. Modeling to show attainment of the NAAQS primarily applies to nonatt ainment areas 2 for which modeling is required, or desired. Modeling to assess reasonabl e progress for regional haze applies to all

states. 3

This guidance is designed to implement national pol icy on air quality modeling requirements as embodied in the Clean Air Act (CAA), the Proposed O zone SIP Requirements Rule (also called the Proposed Ozone Implementation Rule) 4 , the PM 2.5 Implementation Rule 5 , and the Regional Haze Rule. This guidance is intended for use by the United States (U.S.) Environmental Protection Agency (EPA) headquarters and regional o ffices; state, local and tribal air quality management authorities; and the general public. Th is document does not substitute for provisions or regulations of the CAA enumerated abo ve, nor is it a regulation itself. As the term “guidance” suggests, it provides recommendations on how to implement the modeling

1 Modeling is not used to determine reasonable progre ss goals and does not determine whether reasonable progress has been met. Modeling is one part of the reasonable progress analysis. The modeling results are used to determine if future ye ar visibility at Class I areas are estimated to be on a (glide) path towards reaching natural background. This is called a uniform rate of progress analysis or “glidepath” analysis. The uniform rate of progress analysis is described in more detail in section 4. 8.

2 While this guidance document is primarily directed at modeling applications in nonattainment areas, it may also be useful as a guide for modeling to suppo rt NEPA analyses, maintenance areas or to support other rules or provisions of the Clean Air Act.

3 The Regional Haze rule assumes that all 50 states e ither contain a Class I area or impact visibility w ithin a Class I area. Therefore, a regional haze State I mplementation Plan (SIP) is required for all states (40 CFR 50.308(b)).

4 Implementation of the 2008 National Ambient Air Qu ality Standards for Ozone: State Implementation Plan Requirements; Proposed Rule (78 FR 34178, June 6, 2013)

5 Fine Particulate Matter National Ambient Air Quali ty Standards: State Implementation Plan Requirements, proposed rule anticipated in 2015.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

6 requirements. Thus, it does not impose binding, en forceable requirements on any party, nor does it assure that the EPA will approve all instan ces of its application, as the guidance may not apply to a particular situation based upon the circ umstances.

The EPA and state, local, and tribal air agencies ( hereinafter, referred simply as “air agency” or “air agencies”) retain the discretion to adopt appr oaches on a case-by-case basis that differ from this guidance where appropriate. Final decisi ons by the EPA regarding a particular SIP demonstration will only be made based on the statut e and applicable regulations, and will only be made following a final submission by air agencie s and after notice and opportunity for public review and comment. Interested parties are free to raise questions and objections about the appropriateness of the application of this guidance to a particular situation; the EPA and air agencies should consider whether or not the recomme ndations in this guidance are appropriate
in that situation.



### 1.1 What Is The Purpose Of This Document?

This document has two purposes. The first purpose is to describe how to apply an air quality model to produce results needed to support an attai nment demonstration or a uniform rate of progress analysis in a scientifically appropriate m anner. The second is to explain how to interpret whether results of modeling and other ana lyses support a conclusion that attainment of the ozone and/or PM 2.5 NAAQS, and/or uniform rate of progress for regiona l haze will occur by the appropriate date for an area.



### 1.2 Does The Guidance In This Document Apply To M e?

This guidance applies to all locations required to submit a State Implementation Plan (SIP), or Tribal Implementation Plan (TIP) revision with an a ttainment demonstration designed to achieve attainment of the ozone and/or PM 2.5 NAAQS. The guidance also applies to SIPs developed to address regional haze rule requirement s. Areas required to submit an attainment demonstration and/or a reasonable progress demonstr ation are encouraged to follow the procedures described in this document. Details on when a State is required to submit a modeled attainment or reasonable progress demonstra tion can be found in the regional hazeModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

7 rule 6 , the 8-hour ozone implementation rule 7 and the PM 2.5 implementation rule. 8



### 1.3 Outline

Part 1 of the guidance provides an overview of the modeling and attainment demonstration process. Part 2 describes how to build a “modeling platform” and apply air quality models. A “modeling platform” consists of the building blocks of an air quality modeling demonstration. This includes emissions modeling 9 , meteorological modeling, and the development of a ll other inputs needed to run an air quality model. The mod el platform development process consists of the following steps as outlined in Section 2:

Section 2.1 Development of a conceptual description of the problem to be addressed Section 2.2 Develop a modeling protocol Section 2.3 Select appropriate meteorological time periods to model Section 2.4 Choose an appropriate area to model wi th appropriate

horizontal/vertical resolution Section 2.5 Select an appropriate model to support the demonstration Section 2.6 Generate meteorological inputs to the air quality model Section 2.7 Generate emissions inputs to the air q uality model Section 2.8 Develop initial and boundary condition s that are suitable for the

application

Typically, the air quality model process starts wit h the development of base year emissions and meteorology for input to an air quality run to eval uate model performance. Section 3 describes the process for evaluating model performance and pe rforming diagnostic analyses. After evaluating the model and making any necessary input changes or adjustments, the model is run for a future year, which corresponds to the appropr iate attainment year for the area. The air quality model outputs are then used to apply the mo deled attainment test to support an attainment demonstration, as described in Chapter 4 of the guidance. We explain what is

6 40 CFR 51.308

7 40 CFR 50.908 [to be updated when the ozone impleme ntation rule update is finalized]

8 40 CFR 50.1007 [to be updated when the PM2.5 implem entation rule update is finalized]

9 Additional emissions inventory requirements and de tails on emissions modeling are contained in “Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze Reg ulations” at: http://www.epa.gov/ttn/chief/eidocs/eiguid/2014revi sedeiguidance.pdfModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

8 meant by a **modeled attainment demonstration,** a **modeled attainment test** , and a **weight of**
**evidence determination** . Modeled attainment tests are described for the 8 -hr ozone NAAQS,
the annual and 24-hour PM 2.5 NAAQS, and a visibility assessment for regional haz e.

Model applications require a substantial effort. A ir agencies should work closely with the appropriate U.S. EPA Regional Office(s) in executin g each step of the modeling and attainment demonstration process. This will increase the like lihood of approval of the demonstration at the end of the process.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

9 

## 2.0 Building a Model Platform

2.1 Conceptual Description

The first step in developing an attainment demonstr ation should be to construct a conceptual description of the problem that is being addressed. Conceptual descriptions, which are also referred to as conceptual models, are comprehensive summaries of the “state of the knowledge” regarding the influence of emissions, me teorology, transport, and other relevant atmospheric processes on air quality in the area (V ickery, 2004). For a conceptual description to be informative it should identify what processes and sources, in the generic sense, are most responsible for the air quality issue being simulat ed. Well-constructed conceptual models can substantially inform the design of the attainment d emonstration modeling (e.g., episode selection, choice of domain, emissions priorities, evaluation focus) and should be conducted in advance of the development of a modeling protocol. It is worth noting that conceptual descriptions can be valuable in other air quality p lanning efforts besides attainment demonstrations. They can also be useful in determi ning nonattainment area boundaries, investigating emissions control program impacts, an d monitoring network design, among
others.

The following bullets describe some of the key buil ding blocks in developing a conceptual model. The process steps discussed below are mean t to illustrate one possible template for building a conceptual model to inform an air qualit y modeling demonstration; there may be alternate approaches that better suit individual st udy areas or issues, which air agencies should discuss with the appropriate EPA Regional Office.

• Introduce the general nature of the air quality pro blem addressed by the conceptual model:

o What are the pollutants of concern in the area?

o What are the current air quality levels in the area ?

o What is the attainment/nonattainment status of the area?

o What is the geographical scope of poor air quality?

o What is the temporal scope of poor air quality?

o What are the air quality trends in the area? Is th e problem getting better or worse?

o What are the suspected mechanisms for formation of poor air quality levels?

o What are the sources of emissions that may contribu te to poor air quality?

o Are there unique meteorological influences on local air quality levels?Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

10 • Describe the ambient monitoring network used for th e conceptual model:

o Develop a map of monitor sites and types (e.g., FRM , FEM, STN, IMPROVE, etc.)

o Describe characteristics of individual monitoring s ites (scale, frequency, etc.)

• Describe the status and trends of air quality in th e area:

o Summarize the relevant monitoring data for the air quality problem being studied.

o Identify locations that are in violation of the NAA QS

o Describe the spatial pattern in pollutant and precu rsor pollutant levels

o Describe the temporal pattern in pollutant and prec ursor pollutant levels

o Describe the annual trends in concentrations over t he period of interest

o If appropriate, develop fused ambient/model surface s to fill any gaps within the monitoring network.

• Investigate possible relationships between emission s and air quality:

o Examine emissions estimates for the main sector/sou rce categories

o Compare emission trends for annual and or seasonal/ episodic periods to corresponding air quality trends

o Identify key emission sources or source categories

o Assess the historical effectiveness of control prog rams

o Consider how future emissions growth or reductions may affect air quality

o List control programs that are in place or that wil l soon be implemented that may impact emissions sources in the area

• Investigate possible relationships between meteorol ogy and air quality (AQ):

o Describe meteorological characteristics on poor air quality days (e.g., wind speed/direction, temperatures, relative humidity le vels, inversion indicators, etc.)

o Identify any distinct meteorological phenomena that coincide with poor air quality days

o Prepare pollution roses, HYSPLIT back-trajectories (Draxler, 1998) , or any other relevant analysis needed to link poor air quality d ays to specific wind/transport
patterns

o Assess the impact from pollutant transport into are a based on meteorological data and ambient air quality data

• Synthesize all of the relevant information into a d etailed conceptual model:

o Aggregate all elements of the conceptual model and provide the key findings resulting from the analyses completedModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

11 o Characterize the potential factors that influence a ir quality in the area and where possible the rank the importance of those influence s

o Identify areas that require further investigation, either in terms of data collection or chemical transport modeling.

o Discuss how the subsequent SIP modeling and associa ted protocol is impacted by this conceptual description.



### 2.1.1 Example applications of conceptual models

The idea of developing a modeling protocol based up on a conceptual description of the sources and processes that lead to poor air quality is well -established (USEPA, 2007). Numerous excellent conceptual models of local and regional a ir quality issues have been developed over the past decade. Table 2-1 provides a non-comprehe nsive list of example conceptual models that have been developed since the last release of the guidance that may be useful in orienting future SIP modeling applications.

Table 2.1: Examples of recently developed conceptua l models

Air quality issue Reference PM 2.5 / Regional Haze (Northeast U.S.) Downs **et al.** (2012) PM2.5 (Midwest U.S) LADCO (2009) Ozone (Austin TX) McGaughey **et al.** (2010) Ozone (Wyoming) Stoeckenius and Ma (2010) PM2.5 (Southeast Michigan) Turner (2008)Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

12 

### 2.2 Modeling Protocol and Supporting Documentation

As with any technical support document designed to inform air quality planning, an attainment demonstration should be supported by documentation that sufficiently describes the procedures used in the analysis. In order to facil itate the process of EPA Regional Office review and approval, we recommend the preparation of two s eparate supporting documents: one before the modeling analyses are initiated (modelin g protocol) and one after the analyses have been completed (attainment demonstration package).


### 2.2.1 Modeling protocol

Developing and implementing a modeling protocol is an important part of a modeled attainment demonstration. The protocol should deta il and formalize the procedures for conducting all phases of the modeling study, such a s describing the background and objectives for the study, creating a schedule and organization al structure for the study, selection of appropriate period(s) for modeling, developing the input data, conducting model performance evaluations, interpreting modeling results, describ ing procedures for using the model to demonstrate whether proposed strategies are suffici ent to attain the NAAQS and/or regional haze goals, and producing documentation to be submi tted for EPA Regional Office review and approval. The most important function of the model ing protocol is to serve as a blueprint for planning how the modeled attainment demonstration w ill be performed. The protocol should be a valuable communication device by which air age ncies, EPA, and other stakeholders can assess the applicability of default recommendations and develop area-specific alternatives, where needed prior to conducting the work to build the modeling system. A suitable protocol should lead to extensive participation by stakehold ers in developing the demonstration. It should also reduce the risk of spending time and re sources on efforts which are unproductive or inconsistent with EPA rules, policy, and guidanc e.

Meaningful protocols should fully communicate the e xpected scope of the analysis and provide a blueprint for carrying out the needed analyses. There is no one-size-fits-all format for a sufficient modeling protocol as different individua l areas may require specific points of emphasis. However, past attainment demonstrations have yielded several excellent protocol examples that could serve as templates for any grou ps developing new modeling protocols. While not exhaustive, potentially valuable protocol references include recent attainment demonstration modeling efforts completed in the Dal las/Fort Worth area (Environ, 2003), the Denver region (Morris, 2011), and the San Joaquin V alley (CARB, 2012). Based on pastModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

13 experience, EPA recommends that the following topic s be core elements of any modeling protocol:

• Overview of the air quality issue being considered including historical background

• List of the planned participants in the analysis an d their expected roles

• Schedule for completion of key steps in the analysi s and final documentation

• Description of the conceptual model for the area

• Description of periods to be modeled, how they comp ort with the conceptual model, and why they are sufficient

• Models to be used in the demonstration and why they are appropriate

• Description of model inputs and their expected sour ces (e.g., emissions, met, etc.)

• Description of the domain to be modeled (expanse an d resolution)

• Process for evaluating base year model performance (meteorology, emissions, and air quality) and demonstrating that the model is an appropriate tool for the intended

use

• Description of the future years to be modeled and h ow projection inputs will be prepared

• Description of the attainment test procedures and ( if known) planned weight of
evidence

• Expected diagnostic or supplemental analyses needed to develop weight of evidence analyses

• Commitment to specific deliverables fully documenti ng the completed analysis


### 2.2.2 Attainment Demonstration Documentation Packag e

Where the modeling protocol describes the planned s cope of the analysis, the final attainment demonstration package summarizes the actual analysi s conducted (including procedures used) to show that an area will meet the NAAQS under a sp ecific set of future conditions. This
document can follow the same basic outline as the m odeling protocol, highlighting those aspects of the modeling that may have changed from the original plans. Additionally, the attainment demonstration package should have detail ed information on any emission reduction strategies that will be implemented as pa rt of an attainment SIP. Ultimately, the attainment demonstration package should provide a n arrative fully describing the technical rationale behind the projection of attainment of a specific air quality goal in an area. Based on past experience, EPA recommends that the following topics be core elements of any attainment demonstration package:Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

14 • Executive summary that provides an overview of the analysis and the key
conclusions

• Reference to the modeling protocol noting any major deviations from the original plans

• List of the institutional participants in the attai nment demonstration and their roles

• Description of air quality in the area and describe how that shaped the analysis

• Justification for the model, episodes, domain, and grid(s) used in the analysis

• Description of the development of the emissions inp uts used in the base year modeling, including tabular summaries by state/coun ty, as appropriate.

• Description of the development of meteorological in puts used in the base year modeling

• Description of all other base year modeling inputs

• Evaluation of base year model performance (meteorol ogy, emissions, and air quality), including a description of the observatio nal database used in the evaluation and any diagnostic or sensitivity tests used to imp rove the model

• Description of the strategy used to demonstrate att ainment, including speciated emissions summaries for the future year and identif ication of authority for implementing these strategies

• Description of the attainment test results

• Description of any supplemental analyses designed t o bolster the original
attainment test results

• Detailed summary of the entire analysis that leads to the conclusion that the selected attainment demonstration strategy is likel y to produce attainment of the NAAQS and/or uniform rate of progress by the requir ed date



### 2.3 Episode Selection

The modeled attainment test adjusts observed ambien t concentrations during a base case period (e.g., 2009-2013) to a future period (e.g., 2018) using model-derived “relative response factors”. It is important that emissions used in t he attainment modeling correspond with the period reflected by the chosen baseline design valu e period (e.g., 2009-2013). Deviations from this constraint will diminish the credibility of th e relative response factors. The base year inventory typically corresponds to the middle year of the baseline average design value period (e.g. 2011 for a 2009-2013 average design value). T he base emissions inventory is the inventory that is projected to a future year. Alternatively, the base year emissions can reflect average emissions from the base year period, but the emissi ons year or years should be representative of the 5 year design value window.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

15 There is no recommended default base year for model ing. However, it is recommended to use a recent base year period. This ensures that the e missions projection period is as short as possible and the base year ambient data is as curre nt as possible. For example, projecting emissions from 2011 to 2018 (using a 2009-2013 base year average design value) should be less uncertain than projecting emissions from 2005 to 20 18 (using a 2003-2007 base year average design value). The most recent ambient design valu es reflect actual emissions changes that
have occurred over time. It is therefore better to use recent design values, which are actual measurements, and modeled emissions changes to proj ect future concentrations, than to use older ambient data to estimate the change in design values. When selecting a base modeling year, air agencies should review recent ambient dat a and consider the factors below.



### 2.3.1 Choosing Time Periods to Model

In the past, the choice of modeled episode days has been limited by the speed of computers and the ability to store model output files. With t he advancement in computer technology over the past two decades, computer speed and storage is sues are no longer an impediment to modeling long time periods. In fact, the majority o f recent regulatory assessment modeling platforms have been inclusive of entire summers and /or full years (as appropriate) for ozone,

PM 2.5 , and regional haze (Boylan and Russell, 2006; Morr is et al., 2006; Rodriguez et al., 2009; Simon et al., 2012; Tesche et al., 2006; U.S. Envir onmental Protection Agency, 2011a, b).

Ozone based research has shown that model performan ce evaluations and the response to emissions controls need to consider modeling result s from relatively long time periods, in particular, full synoptic cycles or even full ozone seasons (Hogrefe et al., 2000; Vizuete et al., 2011). In order to examine the response to ozone c ontrol strategies, it may not be necessary to model a full ozone season (or seasons), but, at a m inimum, modeling “longer” episodes that encompass full synoptic cycles is advisable. Time p eriods which include a ramp-up to a high ozone period and a ramp-down to cleaner conditions allow for a more complete evaluation of model performance under a variety of meteorological conditions.

Most model applications for the annual PM 2.5 NAAQS have modeled a full year (Morris et al., 2006; Tesche et al., 2006; U.S. Environmental Prote ction Agency, 2011a, b). This is a logical goal since every monitored day of the year is included i n the calculation of the annual NAAQS. The
annual PM 2.5 NAAQS is unique because each and every ambient obs ervation is included in the average. It is therefore likely that a full year o f modeled data is needed to adequately represent the annual average PM 2.5 standard.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

16 Eight-hour ozone and 24-hour PM 2.5 episode selection criteria are similar because bot h standards are based on short term peak concentratio n periods. Regional haze calculations are based on an average of 20 or more days per year (20 % best and 20% worst days). Therefore, regional haze episode selection will likely include more days throughout the year.

At a minimum, several criteria should be used to se lect time periods that are appropriate to
model:

• Model time periods that are close to the most recen tly compiled and quality assured National Emission Inventory (NEI). These comprehens ive inventories are typically generated every 3 years (e.g. 2005, 2008, 2011, etc .) but often contain year specific data for the intermediate years. Since NEI years will ha ve a nationwide complete and comprehensive inventory, selecting a base modeling year that is also an NEI year may save resources. However, other factors should be c onsidered when selecting a base modeling year, such as observed ambient data, meteo rology, and availability of special study data. After consideration of all factors, th e most appropriate base year may or may not be an NEI year. See Section 2.7 for more i nformation on base year inventory
selection issues.

• Model time periods in which observed concentrations are close to the appropriate base year design value or level of visibility impairment and ensure there are a sufficient number of days so that the modeled attainment test applied at each monitor violating the NAAQS is based on multiple days (see sections 4 .2 and 4.5).

• Model time periods both before and following elevat ed pollution concentration episodes to ensure the modeling system appropriatel y characterizes low pollution periods, development of elevated periods, and trans ition back to low pollution periods through synoptic cycles.

• Simulate a variety of meteorological conditions con ducive to elevated air quality.

Primary ozone (8-Hour Ozone) - Choose time periods which reflect a variety of meteorological conditions that frequently correspon d with observed 8-hour daily maxima concentrations greater than the level of the NAAQS at monitoring sites in the nonattainment

area. 24-Hour PM2.5 - Choose time periods which reflect a variety of me teorological conditions which frequently correspond with observed 24-hour a verage concentrations greater than the level of the NAAQS at monitoring sites in the nonat tainment area.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

17 Annual PM2.5 - The best way to represent the meteorological vari ability within season and over an entire year is to model an entire year that has meteorology generally conducive to
elevated PM 2.5 concentrations. Regional Haze - Choose time periods which reflect the variety of meteorological conditions which represent visibility impairment on the 20% be st and 20% worst days in the Class I areas being modeled (high and low concentrations necessar y). This is best accomplished by modeling a full year. Long-term or seasonal ozone – Since long-term (e.g. W126) ozone assessments ar e based on accumulated ozone over 3 month periods during the o zone season the most appropriate way to represent the variability in high and low ozone is to model an entire ozone season that is representative of long term design values. [ Note: This analysis is only applicable if the upcoming final ozone NAAQS rule includes a seasonal secondary ozone NAAQS, such as W126]



### 2.3.2 Future Year Selection

Future emissions should be projected to the attainm ent year or appropriate time period, based
on the area’s classification. The “Final Rule to I mplement the 8-Hour Ozone National Ambient Air Quality Standard, Phase 1" provides a schedule for implementing emission reductions needed to ensure attainment by the area’s attainmen t date (40 CFR 51.908) [ Note: **when the ozone implementation rule for the 2008 NAAQS is fin**  **alized, the draft guidance will be updated to reflect the latest information**  ]. Specifically, it states that emission reduction s needed for attainment must be implemented no later than the be ginning of the ozone season immediately preceding the area’s attainment date. The PM 2.5 implementation rule contains similar provisions. It states that emissions reductions sh ould be in place no later than the beginning of the year preceding the attainment date (40 CFR 51.1 007) [ Note: **when the PM 2.5**  **implementation rule for the 2012 NAAQS is finalized , this draft guidance will be updated to**  **reflect the latest information** ].

In addition to demonstrating attainment, air agenci es are required to conduct a Reasonably Available Control Measures (RACM) analysis to deter mine if they can advance their attainment date by at least a year. A RACM analysis is also r equired for PM 2.5 nonattainment areas. Since areas are required to attain as expeditiously as pr acticable, results of the RACM analysis may
indicate attainment can be achieved earlier.

There are varying requirements related to attainmen t year and RACM analyses for ozone and

PM 2.5 nonattainment areas, depending on classifications a nd other factors. Please see theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

18 appropriate ozone and/or PM 2.5 implementation rule for more details and informatio n. [ Note: **additional details on the RACM analysis will be add ed to this draft guidance when the ozone and** 

**PM 2.5**  **implementation rules are finalized** .]

For regional haze assessments, the first progress g oal review is for 2018, with subsequent reviews every 10 years. The 2028 review will likel y be based on monitored data from the 2023- 2027 period. Therefore, a logical future period to model for the 2018 review is the middle of the five year review period (2025). However, the u niform rate of progress level can be calculated for any year in the future. Unless ther e are reasons to the contrary, any of the years between 2025 and 2028 can be considered to be repre sentative of the regional haze future planning period. The calculation of the uniform ra te of progress should be consistent with the emissions projection period. For example, if a fut ure year of 2025 is modeled, the uniform rate of progress calculation should be based on the numb er of years between the base period and 2025. Similarly, if a future year of 2028 is model ed, then the uniform rate of progress should be based on the number of years between the base perio d and 2028.



### 2.4 Modeling Domain Selection

A modeling domain identifies the geographical bound s of the area to be modeled. Horizontal resolution is the geographic size of individual gri d cells within the modeling domain. Vertical resolution is specified in terms of multiple layers of the atmosphere between the surface and top of the model (usually near the tropopause).



### 2.4.1 Domain Size

The principal determinants of model domain size are the nature of the ozone, PM 2.5 and/or regional haze problem being modeled, and the spatia l scale of the emissions which impact the
nonattainment or Class I area. The influence of bou ndary inflow concentrations should be minimized to the extent possible by allowing for ad equate space between the monitors, key emissions sources, and the model boundaries. Minimi zing boundary influence can be achieved by using a large model domain and using the hourly output from a larger regional or global modeling simulation to feed hourly boundary conditi ons (see section 2.7 for more information on boundary conditions). The grid containing the k ey emissions sources and receptors needs to be large enough to capture recirculation due to shi fting wind directions. An alternative to using a large domain to capture recirculation is to apply a chemical transport model with a smaller fine grid nested inside a coarser grid with feedbac k between the grids (i.e., 2-way nesting). ThisModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

19 option allows pollutants from the fine grid to move into the coarser domain and back into the fine grid depending on wind patterns.

Global models are routinely being used to supply re gional chemical transport models temporally and spatially variant boundary condition s. Since regional and urban model applications for attainment demonstration purposes are typically less than or equal to 12 km sized grid cells it may be worthwhile to apply the regional scale model at a coarser grid resolution (e.g. 36 km) to downscale global model e stimates to the regional model. The coarser simulation results provide a better transition from the global simulation to the nested urban or urban/region of interest both at the boundaries nea r the surface and in the free troposphere. See section 2.8 for more information on using globa l and/or regional models to derive initial and boundary conditions.

Isolated nonattainment areas that have limited impa cts from regional transport of ozone and/or PM 2.5 and its precursors may be able to use a relatively small domain. The modeling domain should be designed so that all major upwind source areas that influence the downwind nonattainment area are included in the modeling dom ain. This is important to be able to directly capture the impact of future year emission s increases or decreases from upwind areas. In addition, all monitors that are currently or rec ently (within any of the years included in the base year period) violating the NAAQS or close to v iolating the NAAQS in the nonattainment area should be contained in the modeling domain far enough from the edge to minimize complications associated with air mass recirculatio n and to minimize boundary influence. Similarly, all Class I areas to be evaluated in a r egional haze modeling application should be sufficiently distant from the edge of the modeling domain.



### 2.4.2 Vertical Layer Configuration

There is no correct maximum or minimum number of ve rtical layers needed in attainment demonstration modeling. However, the specification of the air quality model vertical layer structure should closely match the vertical layer s tructure of the meteorological model used to generate inputs for the air quality model. It is b est to have an air quality model’s vertical layers align with the layers in the meteorological model m atching one-to-one. However, resource constraints typically preclude direct vertical laye r mapping. When vertical layer collapsing is necessary, the highest resolution is most needed to capture the diurnal variability in mixing heights. Recent air quality model applications cons idering the entire troposphere have used anywhere from 14 to 35 vertical layers (U.S. Enviro nmental Protection Agency, 2011a, b).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

20 The top of the modeling domain should typically be set near the tropopause at the 50 or 100 millibar level. The lowest layer (surface layer) i n the air quality model should be no more than
~40 meters thick. The vertical resolution between the surface layer and model top will vary depending on the application. It is important that layer thickness monotonically increases as
altitude increases.

In view of the importance of carefully specifying t he temporal variation in mixing height, high precision below and near the anticipated maximum af ternoon mixing height is ideal. In addition, near-surface vertical resolution is impor tant to capture overnight stable conditions. Layers above the boundary layer are important for c haracterizing clouds and precipitation and layers near the tropopause are important to create a realistic stratification of strongly variant pollutant concentrations in the stratosphere and fr ee troposphere (Emery et al., 2011).

There are some model applications which may not nee d to consider the full extent of the troposphere. These applications typically use verti cal domains which extend up to 4 or 5 km. These types of applications may cover episodes of e levated pollutant concentrations associated with strong surface-based inversions that occur in isolated areas. However, in almost all cases, EPA encourages the use of full-scale one-atmosphere models which account for all atmospheric processes throughout the extent of the troposphere.



### 2.4.3 Horizontal Grid Cell Size

Recent chemical transport modeling applications for ozone, PM 2.5 , and regional haze have commonly used grid cell sizes ranging from 4 km to 12 km resolution (Morris et al., 2006; Rodriguez et al., 2009; Simon et al., 2012; U.S. En vironmental Protection Agency, 2011b; Vizuete et al., 2010). Urban scale assessments have used chemical transport grid models at 1 to 2 km resolution (Vizuete et al., 2011). Intuitively , one would expect to get more accurate results in urban applications with smaller grid cells (e.g. , ~1 or 4 km) provided the spatial details in the emissions and meteorological inputs support making such predictions. However, model performance at 4 km resolution is not always better than 12 km resolution (Simon et al., 2012).

For coarse portions of the regional grids a grid ce ll size of 12km is generally recommended. For urban areas it may be desirable to use grid cells s ized ~1 to 4 km, but not larger than 12 km. The relative importance of using a domain with grid cel ls as small as ~1 km should be weighed on a case-by-case basis. It is likely that spatial grad ients in concentrations are higher for primary particulate matter than for secondary particulate m atter because physical processes dominate concentration rather than chemical processes. Altho ugh it is clear that spatial resolution of primary PM 2.5 will impact the predicted concentrations, it is not clear how it will impact theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

21 relative change in concentrations due to controls. Areas that have large gradients in primary
PM 2.5 may need to use finer resolution (approximately ~1 to 4 km) or may need to supplement the grid modeling with dispersion modeling. This i s particularly true if violating monitors are strongly impacted by local sources of primary PM 2.5 emissions.

The most important factor to consider when establis hing grid cell size is model response to emissions controls. Analysis of ambient data, sensi tivity modeling, and past modeling results can be used to evaluate the expected response to em issions controls at various horizontal
resolutions for both ozone and PM 2.5 and regional haze. If model response is expected t o be different (and presumably more accurate) at higher resolution, then higher resolution modeling should be considered. If model response is expecte d to be similar at both high and low(er) resolution, then high resolution modeling may not b e necessary. The use of grid resolution finer than 12 km would generally be more appropriat e for areas with a combination of complex meteorology, strong gradients in emissions sources, and/or land-water interfaces in or near the nonattainment area(s).

Sensitivity tests comparing relative response facto rs in predicted 8-hour daily maximum ozone
at sites in the eastern United States indicate rela tively small unbiased differences (< .04, in 95% of the comparisons) using a grid with 12 km vs. 4 k m sized grid cells (Arunachalam, 2006). The largest difference in the relative response of seco ndary pollutants at varying resolution is likely to occur in oxidant limited areas (areas that are m ore sensitive to VOC reductions than NOx reductions). In such areas, horizontal resolution may have a large impact on the spatial distribution and magnitude of NOx “disbenefits” (i. e., ozone increases in oxidant limited areas when NOx emissions are reduced).



### 2.5 Air Quality Model Selection

Chemical transport models address the physical proc esses and chemistry that form ozone and

PM 2.5 . Air quality models continue to evolve and each h as their own strengths and weakness (Galmarini et al., 2012; Pirovano et al., 2012; Rus sell, 2008; Simon et al., 2012). The most commonly used chemical transport models for attainm ent demonstrations are the Community Multiscale Air Quality Model (CMAQ; www.cmascenter.org ) (Byun and Schere, 2006; Foley et al., 2010) and the Comprehensive Air Quality Model with Extensions (CAMx; www.camx.com ) (Baker and Scheff, 2007; Vizuete et al., 2011). Th e mention of CMAQ and CAMx is not intended to be a comprehensive list of available chemical tr ansport models and exclusion from this discussion does not imply that an alternative model cannot be used to support a modeled attainment demonstration or reasonable progress ass essment. In the same way, inclusion in this discussion does not imply that a model is “pre ferred” for a particular type of application.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

22 A modeling based demonstration of the impacts of an emissions control scenario for
attainment of the ozone or PM 2.5 NAAQS or as part of a regional haze assessment usua lly necessitates the application of a chemical transpor t grid model. However, it is not necessary to use a model which considers atmospheric chemistry i n addressing changes in primary PM 2.5 components. Either a numerical grid or a Lagrangia n (such as a Gaussian dispersion) model or other empirical techniques could potentially be use d for this purpose. In general, modeling primary PM 2.5 components with a grid model is acceptable, but dis persion modeling may be necessary in areas with large spatial gradients of primary PM 2.5 . Depending on the nature of the problem, an air agency may choose to use a regi onal chemical transport grid model to address both primary and secondary components of pa rticulate matter or they may need to use a chemical transport model to address secondary PM 2.5 and regional primary PM and an inert model applied over a more limited domain to address fine scale primary components of PM.

A model should meet several general criteria in ord er to be considered in an attainment demonstration or reasonable progress assessment. Th ese general criteria are consistent with requirements in 40 CFR 51.112 and 40 CFR part 51, A ppendix W (U.S. EPA, 2005a). 40 CFR Part 51 Appendix W does not identify a “preferred model” for use in attainment demonstrations of the NAAQS for ozone or PM 2.5 or reasonable progress assessments for regional haz e. Therefore, at this time, EPA is not recommending a specific model for use in ozone or PM 2.5 attainment demonstrations or regional haze uniform rate of progress assessments. Models used for these purposes should meet requirements fo r “alternative models” in 40CFR Part 51 Appendix W. Air agencies should use a non-proprie tary model, which is a model whose source code is available for free (or for a reasonable cos t). Furthermore, the user should be able to revise the source code to perform diagnostic analys es and/or to improve the model’s ability to
describe observations in a credible manner. Severa l additional prerequisites should be met for a model to be used to support an attainment demonst ration or reasonable progress assessment (see 40 CFR Part 51 Appendix W § 3.2.2(e)):

1) It should have received a scientific peer review.

An air quality model may be considered to have unde rgone “scientific peer review” if each of the major components of the mode ling system has been described and tested, and the results have been doc umented and reviewed by one or more disinterested third parties, and the model has been revised in response to the review. This review is not the res ponsibility of US EPA. Air agencies should reference this documentation to gai n acceptance of an air quality model for use in a modeled attainment demon stration.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

23 2) It should be appropriate for the specific applicati on on a theoretical basis.

o Prior to use of a selected model’s results in an at tainment demonstration, the model should be shown to perform adequately for the specific application. The model should be scientifically ap propriate and applicable for the intended purpose.

3) It should be used with data bases which are availab le and adequate to support its application.

4) It should be shown to have performed well in past m odeling applications.

o If the application is the first for a particular mo del, then the air agency should demonstrate that the new model is expected to perfo rm sufficiently. For a model to be used in an attainment demonstration, ev idence should be presented that it has been found acceptable for est imating hourly and eighthourly ozone concentrations and/or hourly and 24-ho ur average PM 2.5 and
PM 2.5 component concentrations. In addition to ozone and /or PM 2.5 , the model should be acceptable for estimating precursor species, important chemical intermediates, and in the case of PM 2.5, the main chemical constituents. Preference should be given to models exhibiting satisfactory past performance under a variety of conditions.

5) It should be applied consistently with an establish ed protocol on methods and procedures.

In addition to the criteria in Appendix W, EPA also recommends that the following prerequisites
be met:

1) A user’s guide (including a benchmark example and o utputs) and technical description of the model should be available.

2) The advanced technical features available in a mode l could be a consideration when
other criteria are satisfied. Models are often dif ferentiated by their available advanced science features and tools. For example, some models include advanced probing tools that allow tracking of downwind impac ts from upwind emissions sources. Availability of probing tools and/or scie nce algorithms is a legitimate reason to choose one equally capable model over ano ther.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

24 3) When other criteria are satisfied, resource conside rations may be important. This is a legitimate criterion provided the other listed cr iteria are met.



### 2.6 Meteorological Inputs

In order to solve for the pollutant concentrations over time and space, air quality models require accurate meteorological inputs to properly simulate the formation, transport, and removal of pollutant material. The required meteor ological inputs can vary by air quality model, but all models require parameters such as wi nd, vertical mixing, temperature, humidity, and solar radiation. While model inputs can be cre ated strictly from ambient measurements, a more credible technical approach is to use off-line , dynamic, meteorological grid models to provide the necessary inputs. When these models ar e applied retrospectively (i.e., for historical time periods) they are able to blend ambient data w ith model predictions via four-dimensional data assimilation (FDDA), thereby providing tempora lly and spatially complete data sets that are grounded by actual observations.

This section provides recommendations for generatin g the meteorological data sets needed for regional air quality modeling purposes. In many wa ys, the development of meteorological inputs parallels the steps needed to conduct the ai r quality modeling. A meteorological model platform (episodes, domain, model selection, model configuration, input data, etc.) must be
established and then evaluated. Because of the str ong sensitivity of the eventual air quality results to the input meteorology (Appel, 2007, Appe l, 2010), it is recommended that air agencies spend extensive effort in developing and e valuating the meteorological inputs.



### 2.6.1 Developing base year meteorological fields

The recommended approach for generating the base ye ar meteorological data needed to conduct the attainment demonstration is to apply of f-line, dynamic meteorological models with FDDA. These models use the fundamental equations o f momentum, thermodynamics, and moisture to determine the evolution of specific met eorological variables from a given initial state. When modeling historic episodes, the use of data assimilation helps to "nudge" solutions so that they do not diverge greatly from the actual observed meteorological fields. A major benefit of using dynamic meteorological models is t hat they provide a way of consistently characterizing conditions at times and locations wh ere observations do not exist. Examples of frequently used meteorological models are listed be low:Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

25

• The Weather Research and Forecasting Model (WRF) (S kamarock, 2008),

• The Penn State University / National Center for Atm ospheric Research mesoscale meteorological model v5 (MM5) (Grell, 1994).

The WRF model is a community supported mesoscale nu merical prediction model which replaced the previous community supported MM5 model . The MM5 model is no longer being updated or supported. Other meteorological models can be used as long as they are appropriate for the situation being modeled and are properly evaluated. A number of recent studies (Appel et al., 2010; de Meij et al., 2009; Gilliam et al., 2010) have compared the ability of the WRF and MM5 models to reproduce past meteoro logical conditions and their suitability for use in air quality modeling. Generally speakin g, comparative evaluation efforts have shown that the WRF model outputs represent equivalent or better statistical skill over a variety of meteorological conditions. An additional advantage of the WRF model is that there is a large community of WRF users and developers. As a result , the code is both maintained and frequently updated as new science emerges. EPA rec ommends the use of a well-supported gridded mesoscale meteorological model for generati ng meteorological inputs to regional air quality model attainment demonstrations.

In some cases, however, there may be legitimate rea sons for using an approach other than well-supported gridded mesoscale meteorological mod els to provide the requisite meteorological data. For instance, there may be lo ng-standing local experience for a given domain and/or episode with a different meteorologic al model. In other cases, dynamic meteorological models may not adequately capture ke y meteorological elements of an airshed's conceptual model (e.g., source-receptor t ransport vectors to key monitoring locations). In rare cases such as these, it may be appropriate to blend the dynamic model data with wind data from an objective analysis of observ ed wind fields. The guiding factor in determining which meteorological model or approach to use should be an assessment of which set of inputs will best capture the key meteorologi cal conditions that led to poor air quality. It is recommended that a description of the methods us ed to generate the meteorological fields be included in the modeling protocol. For those ca ses in which an off-line prognostic meteorological model is not used, it is recommended that a detailed description of the alternate technique that will be used to generate t he three-dimensional meteorological fields be shared with the appropriate EPA regional office( s) prior to conducting the meteorological analysis and air quality modeling analysis.

As with other parts of the air quality modeling sys tem, choices made regarding how to configure the meteorological modeling can affect th e quality and suitability of the air quality model predictions. Decisions regarding the configu ration of complex dynamic meteorologicalModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

26 models can be particularly challenging because of t he amount of flexibility available to the user. The goal in establishing the proper configuration f or meteorological modeling should be to obtain the best possible meteorological model perfo rmance, especially for those parameters identified as most important in the conceptual desc ription. As part of the overall chemical transport modeling system evaluation process, perfo rmance issues in the chemical transport model can sometimes be explained by options chosen in the meteorological simulation. Therefore, a feedback between the meteorological mo del evaluation and the air quality model evaluation can be highly beneficial.

2.6.1.1 Selecting a Model Domain The selection of the meteorological modeling domain should closely match the air quality domain. The outermost grid should capture all upwi nd areas that can reasonably be expected to influence local concentrations of ozone, and/or PM 2.5 . In terms of selecting an appropriate meteorological modeling domain, one should extend t he grid at least 3 to 6 cells beyond the domains of each air quality modeling grid to avoid boundary effects. It is recommended that the vertical and horizontal grid structures be gene rally consistent within the meteorological and air quality models to minimize interpolation and ag gregation issues associated with the postprocessing of meteorological model outputs into air quality model inputs.

2.6.1.2 Selecting Physics Options Meteorological models have a suite of physics optio ns that allow users to configure how a given meteorological effect will be simulated. For examp le, there may be several options for specifying the planetary boundary layer scheme or h ow sub-grid cumulus clouds will be handled. In many situations, the "optimal" configu ration cannot be determined without performing an initial series of sensitivity tests w hich consider various combinations of physics options over specific time periods and regions. Wh ile these tests may not ultimately conclude that any one configuration is clearly superior at a ll times and in all areas, it is recommended that these sensitivity tests be completed, as they should lead to a modeling analysis that is bestsuited for the domain and period being simulated. An optimal starting point of physics options may be selected from another modeling application t hat has a similar set of meteorological conditions that lead to elevated pollutant levels. Typically, the model configuration which yields predictions that provide the best statistica l match with observed data over the most cases (episodes, regions, etc.) is the one that sho uld be chosen, although other more qualitative information can also be considered. Additionally, model configurations should be designed to account for the pollutants and time periods that ar e of most interest, per the conceptual description.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

27 2.6.1.3 Use of Data Assimilation As noted above, the use of FDDA helps to keep the m odel predictions from widely diverging from what was actually observed to occur at a parti cular point in time/space. Studies have shown that the incorporation of FDDA into atmospher ic models improves meteorological simulations (Otte, 2008a,b; Pleim and Gilliam, 2009 ; Gilliam and Pleim, 2010). In particular, these studies have shown that better representation of the atmospheric fields aloft through assimilation of data from nationwide atmospheric pr ofiler networks can aid in reproducing certain meteorological features (e.g., low-level je ts) (Godowitch et al., 2011; Gilliam et al., 2012). However, if used improperly, FDDA can signi ficantly degrade overall model performance and introduce computational artifacts (Tesche and M cNally, 2001). Inappropriately strong nudging coefficients can distort the magnitude of t he physical terms in the underlying atmospheric thermodynamic equations and result in " patchwork" meteorological fields with strong gradients between near-site grid cells and t he remainder of the grid. Additionally, if specific meteorological features are expected to be important for predicting the location and amount of pollution formed, based on an area's conc eptual model, then the meteorological modeling should be set up to ensure that FDDA does not prevent the model from forming these features (e.g. lake/sea breeze circulations).

In general, analysis nudging strengths should be no greater than 3.0 x 10 -4 for winds and temperatures and 1.0 x 10 -5 for water vapor mixing ratio. In the case of obse rvation nudging (i.e., FDDA based on individual observations as opp osed to analysis fields), it is recommended that the resultant meteorological fields be examine d to ensure that the results over the entire domain are still consistent. Further, based on pas t experience, we recommend against using FDDA within the boundary layer for thermodynamic va riables like temperature and water vapor mixing ratio because of the potential for spurious convection. If the dynamic model is applied without FDDA, it is suggested that the simulation d urations be no longer than 24 hours. As with selecting physics options, it is often valuable to conduct sensitivity testing of various FDDA approaches to help determine the optimal nudging co nfiguration for any specific case.

Along with application of the meteorological modeli ng, users should be careful when translating the meteorological model outputs into a ir quality modeling inputs. A number of air quality model pre-processors (which are meteorologi cal model post-processors) exist to conduct this off-line coupling, including the Meteo rology-Chemistry Interface Processor (MCIP) (Otte and Pleim, 2010) and WRF2CAMX (ENVIRON, 2011) . These meteorological pre-processors generate the complete set of meteorological data ne eded for the air quality simulation by accounting for issues related to: 1) Data format translation 2) Conversion of parameter unitsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

28 3) Extraction of data for appropriate window domains 4) Reconstruction of the meteorological data on differ ent grid and layer structures 5) Calculation of additional variables As noted above, most meteorological modeling applic ations should be set up to minimize the influence of the pre-processing software. For inst ance, it is recommended that the horizontal grid structures be consistent within the meteorolog ical and air quality models to minimize issues associated with interpolation and aggregatio n. While it is often not feasible to run the air quality model with the same vertical layer stru cture as the meteorological model, it is recommended that vertical layer collapsing between the models be minimized, to the extent possible.

While the traditional approach for attainment demon stration modeling is to convert “off-line” meteorological model outputs into air quality model inputs via specific pre-processing tools, there have been recent efforts to couple “on-line” meteorological models within air quality model applications (Grell et al., 2005; Davidson et al., 2007; Wilczak et al., 2009). The advantage of an on-line meteorological model is tha t one can simulate impacts that air quality changes might have on meteorological conditions (an d vice versa) through direct and indirect radiative feedback mechanisms. In most cases, EPA does not expect emissions reductions from local SIP reductions to appreciably affect future m eteorological conditions. Therefore, off-line meteorological approaches, where the same meteorolo gical fields are used for multiple emissions scenarios, are expected to be sufficient for the majority of SIP modeling exercises.



### 2.6.2 Assessing Impacts of Future Year Meteorology

Traditionally, SIP meteorology simulations have not accounted for the impact of global
emissions of carbon dioxide and other climate force rs on future meteorological conditions. Ozone concentrations have a high correlation with d aily maximum temperatures over many areas in the U.S. (Camalier et al., 2007), suggesti ve of a potential climate connection. Recent research, taking into account temperature and other meteorological variables, indicates that in substantial, populated regions of the country, clim ate change could lead to higher future ozone concentrations (often called a “climate penalty”) ( Jacob and Winner, 2009; Bloomer et al., 2009; EPA, 2009; CARB 2010, Fiore, 2012). Assuming climate change does lead to higher ozone concentrations, there could potentially be a need f or more stringent emissions reductions to counteract the higher ozone potential from warmer c onditions. However, there are significant uncertainties regarding the precise location and ti ming of climate change impacts on air quality. Generally, climate projections are more robust for periods at least several decades in the future because the forcing mechanisms that drive near-term natural variability in climate patternsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

29 (e.g., El Nino, North American Oscillation) have su bstantially larger signals over short time spans than the driving forces related to long-term climat e change. In contrast, attainment demonstration projections are generally for time sp ans of less than 20 years. Given the relatively short time span between base and future year meteorology in most SIP demonstrations, EPA does not recommend that air age ncies explicitly account for long-term climate change in attainment demonstrations. Howev er, air agencies may want to consider potential climate impacts in their specific areas, especially where and when these impacts are believed to be, or are expected to become, potentia lly important.



### 2.6.3 Evaluation of base year meteorological fields

While the air quality models used in attainment dem onstrations have consistently been subjected to a rigorous performance assessment, in many cases the meteorological inputs to these models have received less rigorous evaluation , even though this component of the modeling is quite complex and has the potential to substantially affect air quality predictions (Tesche, 2002). EPA recommends that air agencies d evote appropriate effort to the process of evaluating the meteorological inputs to the air qua lity model as we believe good meteorological model performance will yield more confidence in pre dictions from the air quality model. One of the objectives of this evaluation should be to d etermine if the meteorological model output fields represent a reasonable approximation of the actual meteorology that occurred during the modeling period. Further, because it will never be possible to exactly simulate the actual meteorological fields at all points in space/time, a second objective of the evaluation should be to identify and quantify the existing biases and er rors in the meteorological predictions in order
to allow for an downstream assessment of how the ai r quality modeling results are affected by issues associated with the meteorological data. To address both objectives, it will be necessary to complete both an operational evaluation (i.e., q uantitative, statistical, and graphical comparisons) as well as a more phenomenological ass essment (i.e., generally qualitative comparisons of observed features vs. their depictio n in the model data).

2.6.3.1 Operational Evaluation The operational evaluation results should focus on the values and distributions of specific meteorological parameters as paired with and compar ed to observed data. It is recommended that the observation - model matching be paired as closely as possible in space and time. Typical statistical comparisons of the key meteorol ogical parameters will include: comparisons of the means, mean bias, normalized mean bias, mean absolute error, normalized mean error, and root mean square error. For modeling exercises over large domains and entire ozone seasons or years, it is recommended that the operat ional evaluation be broken down intoModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

30 individual segments such as geographic subregions a nd/or months/seasons to allow for a more comprehensive assessment of the meteorological stre ngths and weaknesses. It may also be useful to breakout model performance aloft, at the surface, during individual episodes (e.g., high ozone / PM2.5 days), over the diurnal cycle, a nd as a function of synoptic regime. Modelers are also encouraged to set aside a portion of the ambient data strictly for evaluation purposes (i.e., data not used in the FDDA). Exampl es of observed meteorological data sets available for operational evaluations include (some of these data sets are not typically used in FDDA and therefore may be serve as an “independent” source of evaluation data):

1. TDL U.S and Canada Surface Hourly Observations (http://rda.ucar.edu/datasets/ds472.0)

2. Cooperative Agency Profilers (https://madis-data.no aa.gov/cap/)
3. NOAA Profiler Network (http://profiler.noaa.gov/npn /)

4. Individual State Climate Office Observation Network s While these data are readily available, it is up to the air agency to determine if the data have undergone appropriate QA/QC procedures through the provider.

It may be helpful when calculating domainwide and/o r regional summary statistics to compare the results against previously generated meteorolog ical model performance "benchmarks" (Emery et al., 2001). However, because of concerns about potentially misleading comparisons of model performance findings across different anal yses with differing model configurations and FDDA strengths, EPA does not recommend using th ese benchmarks as a "pass/fail" indicator of the acceptability of a model simulatio n. The benchmarks should only be used as a means of assessing general confidence in the meteor ological model data. Statistical results that are outside the range of the compiled benchmarks ma y indicate a performance issue that should be given further examination. In some cases , adjustment of input data and/or model settings may be needed in order to improve model pe rformance. The Atmospheric Model Evaluation Tool (AMET) was designed for use in the assessment of meteorological and chemical transport models. AMET provides a method of pairing observed and simulated and performing common analysis techniques to evaluate the performa nce and suitability of atmospheric models (CMAS, 2008).

2.6.3.2 Phenomenological Evaluation As discussed in Section 2.1, it is recommended that a conceptual description of the area's air quality problem be developed prior to the initiatio n of any air modeling study. Within the conceptual description, it is recommended that the specific meteorological parameters that influence air quality be identified and qualitative ly ranked in importance. When evaluating meteorological models or any other source of meteor ological data, the focus of the phenomenological evaluation should be on those spec ific meteorological phenomena that areModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

31 thought to strongly affect air pollution formation and transport within the geographic area of
interest. This event-oriented evaluation should su mmarize model performance in terms of statistical metrics such as probability of detectio n and false alarm rate. As an example of a potential phenomenological analysis, many regional air quality modeling exercises attempt to assess the effects of transport of pollutants from one area to a downwind area with the intent of establishing source-receptor relationships. For these types of analyses, accurate transport wind trajectories are needed to properly establish these source-receptor linkages. A useful event-based meteorological evaluation would be to c ompare model-derived trajectories versus
those based on ambient data to determine what error distance can be associated with the model fields. Other examples of phenomenological ev aluations would be the replication by the model of low level nocturnal jets, cold pool condit ions, fog events, or land/sea breezes.



### 2.7 How Are the Emission Inputs Developed?

Air quality modeling for 8-hour ozone, PM 2.5 , and regional haze requires emissions data for a base year and future modeling years. While not exp licitly a requirement of the implementation rules, the emissions data for modeling are a necess ary input to the modeling that is required by the implementation rules as part of the modeled att ainment demonstration.

A separate EPA guidance document describes the plan ning inventories that are specifically required for SIPs and also provides an overview of all emission inventory requirements, resources, and techniques. That guidance is the "Em issions Inventory Guidance for Implementation of Ozone and Particulate Matter Nati onal Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations" (draft; U.S. EPA, 2014a), and it is used as a key reference throughout this section. Many of the iss ues associated with the planning inventories are also relevant for modeling inventories. We will refer to that guidance here as the “EI SIP
Guidance”.

Table 1 of the EI SIP Guidance provides a list of e missions estimation resources for emissions
inventories. In addition to the resources listed t here, EPA also provides emissions data used in modeling analyses in support of rulemakings. The c urrent available "emissions platforms" are based on 2007, 2008, and 2011 base years. These pl atforms are available publically on the Emissions Modeling Clearinghouse at http://www.epa.gov/ttn/chief/emch/index.html . The emissions modeling data and other data included in these platforms can be a starting point for air agencies in developing their own capabilities f or preparing emissions for use in air quality
models.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

32 

### 2.7.1 What Emissions Inventory Years Should I use f or Base and Future Modeling?

For the base year, the EPA does not require a parti cular year for 8-hour ozone or PM 2.5 SIPs and regional haze plans. However, for several reasons, it is technically appropriate to use a recent year as the base year. Currently, the most recent base National Emissions Inventory (NEI) is 2011 (U.S. EPA 2014b) and NEI data are developed ev ery three years (the next NEI year will be 2014). In most cases, the most recent NEI year wil l be the most appropriate year to use for base case modeling. In some cases, other years (ei ther before or after the most recent NEI year) may be more appropriate for modeling in view of unusual meteorology, transport patterns, exceptional events (i.e. large wildfires) , lack of air pollution events, or other factors that may vary from year to year. See Section 2.3.1 for more information on episode selection. The choice of the base year should be discussed wit h the appropriate EPA regional office as part of the SIP planning process for the attainment demo nstration.

The choice of a future year for an inventory depend s on the nonattainment classification of the nonattainment area, as described in Section 2.3.2. For ozone and PM 2.5 , in most cases the future modeling year will be the expected attainmen t year. Requirements for selecting appropriate future years for modeling are provided in the ozone and PM 2.5 NAAQS implementation rules. For regional haze, the futur e year will depend on the date of the planning period being examined.



### 2.7.2 What Base Year Emission Inventory Data Are Ne eded to Support Air Quality Models?

Air quality models require hourly emissions of mode led species for each grid cell and model layer in the modeling domain. For the base year, t he inventories are called the **base year inventory for modeling.**  To meet the input requirements of models, the inven tory must have pollutants that are the scientific precursors (poll utants that impact the relevant chemistry) to the ozone, PM 2.5 , and/or regional haze being addressed in the model ing demonstration. The time frame of the emissions inventory must be consi stent with the period being modeled. The inventory must include states, counties, and tribes that are covered by the spatial extent of the modeling domain. Additionally, the emissions sourc es included must be comprehensive, including emissions from all source categories. As explained with additional detail below, this includes point sources, nonpoint stationary sources , on-road and nonroad mobile sources, fires, and biogenic sources.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

33 

### 2.7.3 What Pollutants Should Be Included in Base Ye ar Inventories?

Because many sources emit more than one of the prec ursor pollutants, and because the precursor pollutants have the potential to be trans ported across state boundaries, EPA encourages air agencies to develop inventories for all pollutants where possible to support integrated, regional-scale modeling, and control st rategy development for ozone, PM 2.5 , and regional haze. However, there are some consideratio ns for which pollutants to include in inventories for the purpose of supporting air quali ty modeling.

For 8-hour ozone NAAQS modeling, the pollutants to be inventoried are VOC, NO X , and CO. For

PM 2.5 NAAQS modeling, the pollutants to be inventoried a re primary emissions of PM 2.5 and
PM 10 (including both the filterable and condensable por tions) and emissions of SO 2 , NH 3 , VOC,
NO X , and CO. While certain PM 2.5 precursors may not be considered precursors for pl anning purposes in some nonattainment areas 10 , inclusion of all precursors is a prerequisite for appropriate application of air quality models for m odeled attainment demonstrations. For regional haze, the pollutants to be inventoried inc lude all of the pollutants and precursor pollutants identified for ozone and PM 2.5 . While CO does not need to be included in the **planning inventories** associated with the SIP submission (as described i n the EI SIP Guidance), it should be included for modeling purposes because CO plays an important role in the modeled atmospheric chemistry in air quality models.



### 2.7.4 What Temporal Resolution Should Be Used for B ase Year Inventories?

The emissions inventories should cover the time per iods associated with the modeled time period. Different approaches to accomplishing this goal arise because of the nature of ozone,

PM 2.5 , and regional haze problems.

For ozone, the episodic nature of 8-hour ozone exce edances lead to inventories that reflect a specific period being modeled. For this reason, th e **planning** inventories should be provided as an ozone season day inventory 11 (as described in the EI SIP Guidance). For the no nattainment area, the planning ozone season day inventories are also often used for modeling inventories. However, modeling inventories also include areas ou tside the nonattainment area, for which

10 See the PM 2.5 implementation rule for detailed information on th e definition of “planning” precursors for PM 2.5 nonattainment areas.

11 The definition of “ozone season day” emissions spe cifies weekday emissions and is intended to reflect the periods of emissions associated with monitored ambient ozone exceedances.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

34 ozone season day emissions may not be readily avail able. In these instances, air agencies may elect to start with an annual inventory and use an emissions model to adjust the annual emissions to reflect the modeling period. This may not be advisable in every case; in particular, mobile source emissions are highly variable with me teorology and day of the week, and are often available on a monthly or even daily basis th at reflects changes in temperature, traffic patterns, and other factors. In addition, more det ailed temporal data from electric generating units (EGUs), biogenic sources, or other sources sh ould be used when available to enhance the technical quality of the modeled attainment demonst ration.

For PM 2.5 , both annual and daily standards result in differe nt inventory temporal possibilities for the modeling episode, depending on which standa rd(s) a particular nonattainment area must address in their SIP. [Note: **This guidance will be updated (if necessary) when t he PM2.5**  **implementation rule is finalized** ].

For regional haze, the use of annual emissions inve ntories is appropriate given the nature of the regional haze problem. However, the same considerat ions apply for the inventory’s temporal aspects as they do in the cases above: more detail ed temporal data should be used as the inventory starting point when it is available for t he pollutants included in the inventory.



### 2.7.5 What Spatial Coverage Is Need for Base Year I nventories?

Modeling inventories must cover all areas of the mo deling domain, which will include areas
outside of a nonattainment area and most often incl udes areas in neighboring states or tribes.
This subsection addresses areas within and outside the nonattainment area separately.

2.7.5.1 Areas within the nonattainment area As mentioned above, the planning inventories includ e emissions within the nonattainment area. The EPA expects that modeling inventories wi ll be consistent with those planning inventories; however, some exceptions may exist. W here possible, the planning inventories can be a sum (for annual data) or average (for ozone se ason day data) of day-specific or hourspecific data used for modeling. In some cases, ho wever, this approach may not be sufficient for modeling purposes. For example, greater spatial and temporal detail are needed for on-road mobile modeling inventories as compared to the base year (planning) inventory for the nonattainment area. For the planning inventory, on e goal is to allow for the repeatability of the approach in order to create average, seasonal, or a nnual inventories for use in rule requirements, such as reasonable further progress o r conformity demonstrations. That goal is not necessarily compatible with the modeling need f or spatial and temporal detail. In casesModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

35 where some differences are unavoidable, air agencie s should attempt to promote consistency
where feasible.

2.7.5.2 Areas outside of the nonattainment area For air quality modeling, emissions inventories are often needed for areas outside the nonattainment area. Developing modeling inventorie s for these areas should be relatively straightforward and can be included as part of the SIP planning inventories. For areas outside of the air agency’s jurisdiction, however, it can b e more difficult for air agencies to develop the inventories themselves. In these cases, the EPA en courages participation in regional modeling efforts which are designed to allow sharing of data and help promote consistent approaches across state boundaries. When those organizations lack the needed inventories, EPA recommends using the EPA’s publicly available emiss ions, as described in the next section.



### 2.7.6 How Can The National Emission Inventory by Ai r Agencies?

The EPA recommends that state, local, and tribal ag encies start with available inventories suitable for air quality modeling of the selected t ime period(s). If no such inventories are available from a prior state or tribal effort, from regional modeling group, or from some other source, air agencies may derive an inventory suitab le for use with models from EPA-provided
information. Two sources of information are availa ble, as described below. In both cases, the EPA expects that air agencies will only use such da ta after additional review to ensure the appropriate quality of the data for SIP planning pu rposes.

First, the National Emission inventory (NEI) provid es criteria air pollutant and hazardous air pollutant emissions as annual emissions totals for most (if not all) sources of emissions relevant for ozone, PM 2.5 , and regional haze implementation purposes. Air a gencies may use the most recent public release of the NEI as a starting poin t for inventory development. The data and
documentation are available from the latest invento ry page linked at http://www.epa.gov/ttn/chief/eiinformation.html

.

Additionally, the EPA provides emissions modeling " platforms" that are based on the NEI and can be a starting point for air agencies. The mode ling platform data provide additional information not available from the NEI, such as dai ly EGU and fire emissions, monthly nonroad emissions, biogenic emissions and/or input to bioge nic emissions models, and inputs to mobile source models. Furthermore, these modeling platfor ms usually include future-year emissions projections. Both base and future-year data are pr ovided in a format used by emissions models and can therefore be more readily adopted by air ag encies that need to process emissions for use in air quality models. The data and documentat ion are available from the latest platformModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

36 release at the Emissions Modeling Clearinghouse http://www.epa.gov/ttn/chief/emch/index.html ).

The detail and accuracy of the EPA emissions may or may not be sufficient for use in SIPs. Air agencies should review the emissions and associated documentation and make improvements where deficiencies exist. Such improvements will o ften be necessary to demonstrate appropriate model performance, as described in Sect ion 3. Additional degrees of review are
needed for areas closer to the nonattainment area. For example, EPA data may be sufficient for estimating emissions in states that are hundred s of kilometers upwind or downwind of the nonattainment area, but may not be sufficient for e stimating emissions closer to the
nonattainment area because those emissions can have a greater impact on the final results of
the attainment demonstration.



### 2.7.7 How Should Base Year Emissions Be Created?

Emission inventory data from at least five general categories are needed to support air quality modeling: stationary point-source emissions, statio nary area-source emissions (also called nonpoint), emissions for on-road mobile sources, emiss ions for nonroad mobile sources (including railroad and marine vessels), and biogenic/geogenic emissions. A sixth category of wildland fires also exists and includes day-specific emissions fro m wildfires and prescribed burns. These fire
emissions are included as an “event” source in the NEI. Emissions inventories are expected to include “all” sources of emissions; so if an air ag ency wishes to exclude a particular source (e.g., because it is a very small contributor), then the a ir agency should document the reasons such omissions are acceptable. In addition, some “tradi tional” nonroad mobile sources (e.g., airports and rail yards) can also be included in inventories as a point source. For example in the NEI, airports and rail yards are included as point sourc es and railroad and marine vessel emissions as part of the nonpoint data category.

The EI SIP Guidance (EPA, 2014a) addresses numerous details about emissions inventory development. Readers are encouraged to refer to in formation in that document as a resource for building base year emissions inventories. That guidance contains additional information on the definitions of the data categories (point sourc es, nonpoint sources, etc.), provides more
information about emissions estimation for each dat a category, includes a process for prioritization of inventory development, and includ es quality assurance recommendations. All of this information is highly relevant to inventori es developed for air quality modeling. In the remainder of this section, we provide a basic descr iption of the data categories and their base year emissions and additional information needed fo r their use in modeling applications where appropriate.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

37 2.7.7.1 Point Sources Point sources are emissions sources that are attrib uted to specific facilities and emissions release points within those facilities. The Air Em issions Reporting Rule (AERR) (http://www.epa.gov/ttnchie1/aerr/) provides inform ation on how to define which sources to include as specific point sources in the inventory. More information on the relationship
between SIP inventories and the AERR and how that a ffects SIP inventories is available in the EI SIP Guidance. For modeling inventories, data devel opers may include sources with emissions smaller than the required reporting threshold, but should be careful to prevent double counting with nonpoint source categories that may overlap wi th some of these smaller sources. This is especially a concern when compiling point and nonpo int inventories from different sources, because a nonpoint inventory is compiled using cert ain assumptions about what an associated point source inventory includes. This issue is des cribed as “nonpoint reconciliation” and is
discussed further in the EI SIP Guidance.
Point source inventories for modeling should be com piled at a minimum by country, state/tribe, county, facility, unit, process or sou rce category code (SCC) and by “release point” or stack (see references below to point-source inve ntory development). The point source data
should include information on the location of the s ources (e.g., latitude/longitude coordinates); their stack parameters (e.g., stack diameter and he ight, exit gas temperature and velocity); and operating schedules (e.g., monthly, day-of-week, an d diurnal). Information on existing control device types, measures and associated emissions red uctions are also useful to identify sources that might be further controlled and to prepare fut ure year inventories.

For modeling needs, many point sources will also be post-processed for the estimation of plume rise of the emissions vertically through laye rs of the atmosphere as modeled by the air quality model. This plume rise depends on the stac k parameters (listed above) and on meteorological conditions. Plume rise can be calcu lated by emissions models but is more often calculated as part of air quality models. In this l atter case, inventory developers are responsible only for identifying for which sources plume rise w ill be computed using tools available in
emissions models.

Also for modeling needs, hourly emissions data are usually a starting point for inventories of electric generating units (EGUs). This is because s uch data are readily available from Continuous Emissions Monitoring Systems (CEMS) installed as pa rt of EPA trading programs 12 and include hourly emissions of NO X , SO 2 , CO 2 , and heat input. The heat input data can be used to estimate

12 CEM data are provided by EPA’s Clean Air Markets D ivision in a format suitable for use in emissions processing at http://ampd.epa.gov/ampd/ . From this page, click the “Prepackaged data” tab and scroll to the bottom to download the data in a form that can be input into SMOKE.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

38 hourly emissions of other pollutants (such of prima ry PM 2.5 emissions) using facility-specific
emission factors or other emission factors. The EPA ’s modeling platform also includes these hourly data.

2.7.7.2 Nonpoint Sources Nonpoint sources are also called “area sources”. T hese sources collectively represent individual
sources of emissions that have not been inventoried as specific point sources. The individual sources within the nonpoint emissions county totals are typically too small, numerous, or difficult to inventory using the methods for point sources. The nonpoint emissions data should be compiled by country, state/tribe, county, and SC C, and are sometimes further specified by emission type (e.g., hoteling and cruising for comm ercial marine emissions in the NEI).

2.7.7.3 On-Road Mobile Emissions from on-road vehicles are the result of s everal emission processes, including the combustion of fuel while vehicles are starting, idl ing, and moving, evaporation of fuel from the fuel system and during refueling, and from brake we ar and tire wear.

On-road mobile source emissions, including refuelin g, should be estimated with the latest EPA on-road mobile model, which is currently the Motor Vehicle Emission Simulator (MOVES), ( http://www.epa.gov/otaq/models/moves/index.htm) . For on-road emissions in California, the most recent EPA-approved version of the EMission FA Ctors model (EMFAC) should be used (http://www.arb.ca.gov/msei/modeling.htm). The Spa rse Matrix Operator Kernel Emissions (SMOKE) emissions modeling system ( http://www.smoke-model.org/index.cfm ) includes tools that support the use of MOVES emission factors with SMOKE, and thereby allows modelers to account for the sensitivity of on-road emissions to temperature. SMOKE uses county-specific inputs and gridded hourly temperature and other met eorological information, which is typically based on the same meteorological data used for air quality modeling.

The SMOKE-MOVES approach first generates emissions rates from MOVES by process (running, start, vapor venting, refueling, etc.), vehicle typ e, road type, temperature, speed, and hour of day, according to specified fuel parameters and oth er inputs. Then, subsequent steps apply the appropriate MOVES emissions rates along with vehicl e miles traveled (VMT) and vehicle population data for the counties and grid cells in the modeling domain for each hour of the modeling episode. Modelers should consider using t he SMOKE-MOVES system or some equivalent or additionally refined approach for use in emissions modeling needed for modeled
attainment demonstrations.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

39 The MOVES model allows modelers to override defaul t database settings to obtain a localspecific on-road inventory, and the EPA strongly en courages data developers to develop and use MOVES inputs databases that are specific to the states and counties within the modeling
domain. The MOVES Technical Guidance discusses whe n local data should be used in place of model default data, as well as potential sources of local data.

For some input parameters, there is overlap with th e inputs needed for other models, such as the nonroad mobile models. For example, meteorology and fuels information is needed to drive
both the mobile and non-road emissions models. Eff orts should be made to use the same source of data across multiple categories when the inputs are shared by multiple models. Not doing so calls into question the validity of one or both approaches. Air agencies should explain the use of shared inputs, or reasons for not using them, as part of the documentation prepared
with their modeled attainment demonstration.

On-road emissions and VMT should be compiled at lea st at the country, state/tribe, county, and SCC level. Vehicle population data by county and v ehicle type should also be compiled as well as idling hours, if available. For some inputs suc h as VMT, modelers may optionally compile and use data for individual road segments (called “link s”). Link approaches require starting and ending coordinates for each road link. Link-based modeling is typically performed for fine scale (i.e. 4-km or finer) modeling studies. Travel Dema nd Models (TDMs) can be a source of data needed to develop link-based approaches. In region al scale modeling, county-level on-road emissions can be allocated onto road links using sp atial surrogates that are generated based on link data and mapped into the modeling grid cells u sing appropriate weights.

2.7.7.4 Nonroad Mobile Equipment Nonroad mobile equipment emissions result from the use of fuel in a diverse collection of vehicles and equipment, and the EPA’s NONROAD emiss ions model estimates these emissions. These nonroad emissions should be compiled as count ry, state/tribe, county and SCC totals.

The NONROAD model includes more than 80 basic and 2 60 specific types of nonroad equipment and further stratifies equipment types by horsepower rating. Fuel types include gasoline, diesel, compressed natural gas (CNG), and Liquified Petroleum Gas (LPG). NONROAD supports many SIP-related inventory development nee ds, including support for partial counties and seasonal emissions. The EPA provides the latest version of the NONROAD model and all
information related to its use for inventories at http://www.epa.gov/otaq/nonrdmdl.htm . Air agencies should use NONROAD as a resource for estim ating nonroad mobile emissions and
refer to the documentation on the NONROAD website f or more information. The EPA is currently in the process of incorporating NONROAD m odel capabilities into MOVES. When thisModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

40 update is available, guidance on the transition to using MOVES for estimating nonroad
emissions will be included on the EPA’s MOVES and N ONROAD web pages. NONROAD can optionally be run using the National Mobile Invento ry Model (NMIM) (see http://www.epa.gov/otaq/nmim.htm ).
Air agencies have the option of replacing default m odel inputs with more representative data. Common input adjustments include equipment populati on, geographic allocations, and local growth rates. If agencies make changes to default m odel values, the agency should submit the input files to the EPA as well as a description of why the defaults were changed.
In some cases, air agencies have developed their ow n nonroad mobile modeling approaches. For SIPs including these alternative approaches, ai r agencies should make available the details and models for the EPA to consider and explain why their approaches are better than the EPA’s NONROAD approach using custom inputs.

2.7.7.5 Nonroad Mobile: Commercial Marine Vessels, Locomotives, and Aircraft Emissions from commercial marine vessels, locomotiv es, and aircraft are estimated in other ways as described in detail in the EI SIP Guidance. These sources can be particularly important for urban areas (especially in coastal areas) and t herefore can be more important for attainment demonstration modeling than for regional modeling. Thus, air agencies should
consider whether the contributions from these sourc es merit additional effort. The EI SIP
Guidance contains information about methods availab le to estimate emissions from these

sources.

Commercial marine vessel emissions can be a particu lar challenge for nonattainment areas that include ports or are adjacent to major shipping lan es. While the level of emissions from these
vessels can be outside of the control of state air agencies (coming under national rather than state jurisdiction), it is still important to accur ately represent these emissions when they are significant contributors in the vicinity of a nonat tainment area. Vessels are divided into the largest, ocean-going “category 3” (C3) vessels and the smaller “category 1” (C1) and “category 2” (C2) vessels such as tug boats, ferries, researc h vessels, fishing vessels, and others. Commercial marine emissions include both in-port em issions and underway emissions, with most (~90% of nationwide emissions) emissions assoc iated with underway activity. For the planning inventories covered by the EI SIP Guidance , air agencies need to include only those emissions within state waters. For the modeling inv entories, however, air agencies may also
need to include emissions in Federal waters when th e modeling domains extend more than a few miles offshore. This is especially important f or coastal areas where offshore commercialModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

41 marine emissions may be an important contributor to ozone and/or PM in the nonattainment

area.

The current EPA methods for estimating commercial m arine emissions uses top-down approaches for C1, C2, and C3 vessels, with a diffe rent approach being used for C3 vessels since the emissions are affected by international fuel ag reements and more emissions occur far offshore. The methods apportion national activitie s and emissions to counties and geographic information system “shapes” that define waterways a nd offshore shipping lanes. These data are available for use from the EPA’s modeling platf orm data if no other data are available. Since such a top-down approach has high uncertainti es, the EPA strongly encourages air agencies preparing modeling inventories to seek out , develop, and use local-specific information about their ports and waterway vessels to better compute emissions.

2.7.7.5 Biogenic Emissions Biogenic sources are a subset of natural emissions sources that may be an important component of the emissions inventory. Vegetation (i .e., forests and agriculture) is the predominant biogenic source of VOC and is typically the only source that is included in a biogenic VOC emissions inventory. Microbial activit y in the soil contributes to natural biogenic
NOx and CO emissions.

Biogenic emissions from vegetation and soils are co mputed using a model which utilizes spatial information on vegetation and land use and environm ental conditions of temperature and solar
radiation 13 . The model inputs are typically horizontally alloc ated (gridded) data, and the outputs are gridded biogenic emissions which can then be sp eciated and utilized as input to chemical transport grid models. Several models exist, and ar e described more fully in section 2.7.9.2 and
the EI SIP Guidance.

2.7.7.6 Geogenic and Other Natural Sources Geogenic emissions are primarily the result of oil or natural gas seeps, soil wind erosion, and sea salt spray. In addition, lightning may also be an important contributor to natural NO X emissions in an inventory area. Volcanoes and fumar oles (i.e., vapor or gas vents in a volcanic region) can be additional sources of geogenic emiss ions. More information on these sources is provided in the EI SIP Guidance. Of particular rel evance for modeling inventories are soil wind erosion, sea salt, and lighting emissions.

13 In most cases, ozone, PM, and regional haze modeli ng includes the emissions from vegetation and soils, but not other potentially relevant sources s uch as volcanic emissions, lightning, and sea salt.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

42 As a source of ozone precursor emissions, geogenic sources are usually less significant than biogenic sources. However, geogenic wind erosion ma y contribute substantially to PM emissions in an area. In 2012, the EPA released CMA Q v5.0 that includes a module that dynamically estimates hourly natural emissions of f ine and coarse dust particles due to wind action over arid and agricultural land. This allows the contribution of this source to be included
in a modeled attainment demonstration without the n eed to prepare an emissions inventory first. The windblown dust approach is based on the work of Tong et al. (2011) (see also the CMAQ wiki at http://www.airqualitymodeling.org/cmaqwiki/ index.php?title=CMAQv5.0_Windblown_Dust )

. Air agencies using this feature should evaluate the results of the CMAQ-based approach to determine if it provides a reasonable representation of dust emissions in their modeling domain. Often times, CMAQ overestimates
ambient crustal matter concentrations without the a ddition of windblown dust and so adding emissions may not be advisable.

The interaction of sea-salt particles in coastal en vironments with air chemistry can impact
concentrations of PM 2.5 and gas-phase species in the atmosphere. The CMAQ model has been updated to account for these emissions, so that emi ssion inventory developers are able to rely primarily on the air quality model to internally ca lculate sea-salt emissions. Additional information for CMAQ is available in Kelly et al., (2010). Additional information on sea salt in CAMx simulations is available in Athanasopoulou et al. (2008). If other models are used, modelers should consider the possible need for incl uding sea salt emissions data as an input to the air quality model.

Lightning produces NO, which is oxidized to NO 2 in the presence of ozone or in a photochemically reactive atmosphere. The NO and NO 2 is formed in the mid-upper troposphere and can impact the vertical distributio n of reactive nitrogen as well as ozone. Because lightning is not a direct source of NO 2 , accounting for this source category is more important for air quality modeling purposes than fo r SIP planning inventory purposes. The EPA’s CMAQ v5.0 includes a parameterized approach to esti mate NO X emissions and its vertical distribution that is generated from lightning. The peer reviewed literature contains both a description of the approach (Allen, 2012) and a mod el evaluation (Appel, 2010). More information is available from the CMAQ v5.0 wiki (s ee http://www.airqualitymodeling.org/cmaqwiki/index.ph p?title=CMAQv5.0_Lightning_NO ).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

43 2.7.7.7 Wildland Fires (Events) Inventories used for ozone, PM 2.5 , and regional haze modeling purposes should includ e emissions for fires. Thus, wildfire and prescribed fire emissions should be included in the
nonattainment area inventories when theese emission s occur within the modeling domain during time periods being modeled. Detailed inform ation about how the EPA develops
wildland fire inventories can be found in the lates t version of an NEI Technical Support Document, such as that provided with the 2011 NEI. EPA’s approach relies on a combination of satellite detection of fires merged with on-the-gro und observational data where available. The EPA encourages the use of ground-based observations and local fuel information whenever possible as these factors can have a large impact o n the emissions.

Wildland fires (WLFs) are generally defined as any non-structure fire that occurs in the wild land (an area in which development is essentially non-ex istent, except for roads, railroads, power lines, and similar transportation facilities). The National Wildfire Coordinating Group has identified the following two types of WLFs :

• Prescribed fire: Any fire ignited by management ac tions to meet specific objectives (generally related to the reduction of the biomass potentially available for wildfires). A written, approved prescribed fire plan must exist , and National Environmental Policy Act requirements must be met (where applicab le) prior to ignition.

• Wildfire: An unplanned, unwanted WLF including una uthorized human-caused fires, escaped WLF use events, escaped prescribed fire pro jects, and all other WLFs where the objective is to put the fire out.

Wildland fires are a unique source of emissions wit h respect to NAAQS attainment planning. For ozone, PM2.5, and regional haze planning purposes, these fires are major sources of primary

PM 2.5 and VOC and a moderate source of NO X . They can consequently impact the ambient PM 2.5 and ozone; however, they have a number of unique pr operties:

• Depending on the fuel source and acreage burned, bo th prescribed and wildfires can emit significant emissions, but emissions from pres cribed fires are generally lower due to the burning characteristics and the relative amounts of flaming and smoldering emissions. In addition, prescribed fires can be used to prevent future wildfires, which are generally larger, hotter, more dangerous, and more polluting.

• Wildfires are considered to be naturally occurring events for which the emissions are generally not reasonable to prevent or control. As such, air agencies may request that the EPA exclude from regulatory determinations ambient data influenced by the wildfire if the request meets the criteria, sch edule, and procedural requirementsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

44

for an exceptional event set forth in 40 CFR 50.14. In some cases, prescribed fires can also be considered to be exceptional events (al so subject to the criteria of 40 CFR 50.14).

• Fire emissions can rise very high in the atmosphere on plumes, and the pollutants can be transported far beyond state boundaries. Fir es occurring outside NAAs can have impacts within NAAs both on days that have bee n determined to be exceptional events and days that may not be determi ned to be exceptional events.

• Since wildfires and prescribed fires generally occu r in forests and rangelands, they are less likely to be within the boundaries of NAAs , which are usually urbanized

areas.

• Fires can also reduce ozone formation because their smoke can block sunlight that
otherwise would result in more ozone.

Since fire emissions can be a large source of emiss ions, the treatment of fires should be discussed with the relevant EPA Regional Office(s) as part of the modeling protocol development process.



### 2.7.8 What Other Data Are Needed to Support Emissio ns Modeling?

The emission inventories should be converted, throu gh emissions modeling, from their original resolution (e.g., annual emissions by point source or count) to input files for air quality models. Air quality models generally require emissions inpu ts to be specified by model grid cell, hour, and model chemical species. Modeling inventories a lso identify which point sources should have their plume rise computed. This section descr ibes the ancillary data that modelers should collect and prepare to allow emissions models to co nvert the emission inventory data into air quality model inputs.

2.7.8.1 Temporal allocation Ancillary data for temporal allocation are necessar y for stationary point, stationary area, and all mobile sources. To facilitate temporal allocation of the emissions, factors called “temporal profiles” should be created to convert annual emiss ions to specific months (i.e., monthly profiles), average-day emissions to a specific day of the week (i.e., weekly profiles), and daily emissions to hours of the day (i.e., hourly profile s). Additionally, a cross-reference file is needed to assign the temporal profiles to the inven tory records by SCC, facility, geographic area such as state or county, or some other inventory ch aracteristics. Where available, the operating information that may be available from th e point-source inventory should be used to create inventory-specific temporal factors. The EP A provides a starting point for the temporalModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

45 profiles and cross-reference files as part of the l atest modeling platforms (previously described), available at http://www.epa.gov/ttn/chief/emch/index.html

.

The EPA has developed a SMOKE utility program (Gent pro) that estimates temporal profiles for residential wood combustion (RWC), agricultural NH 3 from animals, and other generic (userdefined) area sources by relating meteorology to ai r emission fluxes. Gentpro reads in hourly gridded meteorological data (temperature and wind) from the Meteorology-Chemistry Interface Processor (MCIP) 14 and a gridded spatial surrogate to produce tempora l profiles and cross-reference data. Annual MCIP input data are r equired to calculate temporal profiles with Gentpro and the spatial surrogates are needed to id entify the grid cells for computing countyaveraged meteorology variables. The Gentpro progra m is described in the latest SMOKE User’s Manual ( http://www.smoke-model.org ).

For RWC, Gentpro allocates emissions to specific da ys from annual totals based on a regression equation that was developed by relating daily minim um temperatures and chemical tracers of RWC. This approach results in more RWC emissions a ssigned to periods of colder temperatures using a user-defined temperature threshold that res tricts RWC emissions to days with morning lows below a specific value. Air agencies that use EPA's emissions data for this category should note that this approach has been used to temporally allocate the RWC emissions.

For agricultural NH 3 from animals (i.e. from concentrated animal feeding operations- CAFOs), Gentpro allocates annual, monthly or daily emission s to hourly based on empirical equations derived from investigations of emissions from anima l waste decomposition as a function of temperature and wind speed. The resolution of emis sions input to Gentpro for this sector depends on the available data. Air agencies that u se EPA’s emissions data for this category should note that this approach has been used to tem porally allocate agricultural NH3 emissions
from CAFOs.

For point sources, hourly CEMS data are recommended for use in base year emissions modeling rather than temporal profiles, where such CEMS data are available. CEMS data are collected as part of emissions trading programs. Because these programs require complete hourly data, alternative emissions values are assigned to hours when CEMS devices are not functioning. These alternative values can be extremely high valu es (compared to actual emissions rates), in order to motivate CEMS operators to provide a compl ete data record to the extent possible. As a result, CEMS data may have some anomalies and sho uld be reviewed prior to use in a

14 MCIP is a meteorology postprocessing program. It i s typically used to convert WRF meteorological model output data to CMAQ input format. The latest version of MCIP can be found here: http://www.cmascenter.org/index.cfm

.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

46 modeling study. The CEMS data fields include an em issions method code, which identifies the hours with alternative values. Furthermore, CEMS d ata may be available for only part of a year; therefore, for annual modeling, users should be car eful to ensure CEMS data are used for the appropriate periods and total annual emissions are temporally allocated to fill in the other hours of the year.

For wildland fire emissions, the EPA recommends usi ng daily, location-specific fire data such as
the NEI “events” sources. These sources can have a large impact on ambient levels of ozone,

PM 2.5 , and regional haze. Although emissions from these sources are largely uncontrollable, the inclusion of all emissions sources (including bioge nic and uncontrollable sources) is needed to accurately predict pollutant concentrations and der ive relative response factors for both PM
and ozone.

For future-year modeling there are two choices for representing EGU emissions derived from
CEMs data and wildfire emissions. Modelers can use day specific emissions for both the base and future years, or a “typical year” temporal allo cation (averaging) approach can be used to create representative emissions. If a typical year approach is used, it should include similar daily temporal variability as could be expected for any given year (in most cases, the daily temporal variability also needs to be consistent wi th the base year meteorology). Care should be taken to preserve the day-to-day variability in either the day specific or averaging approach, with the exception of eliminating year-specific out ages or other year-specific anomalies 15 using an averaging approach. Air agencies should discuss the temporal approach to fires and point sources with the appropriate EPA regional office as part of the modeling protocol development

process.

2.7.8.2 Chemical speciation The emissions models also need information about th e chemical species of the VOC and PM 2.5 emissions for all emissions sources. These data ar e used to disaggregate the total VOC and

PM 2.5 emissions to the chemical species expected by the air quality model and are called speciation “factors” or “profiles”. The EPA provid es a starting point for the VOC and PM 2.5 speciation data, which are available with the model ing platforms (previously described), at http://www.epa.gov/ttn/chief/emch/ . The speciation profiles are based on the SPECIAT E

15 The baseline inventory should contain emissions est imates that are consistent with emissions patterns which might be expected in the future year(s). If unplan ned outages or shutdowns at point sources occurred in the base year, then it may not be appropriate to project tho se emissions to the future. Using a typical or ave rage year baseline inventory can provide an appropriate platf orm for comparisons between the base year and futur e years. However, certain emissions strategies may depend on capturing temporal emissions patterns on days with peak ambient concentrations. This should be considered when creating typical or average year inventories.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

47 database, which is available at http://www.epa.gov/ttn/chief/software/speciate/ . In addition to the speciation profiles, a speciation cross-refe rence file assigns speciation profiles. The default EPA speciation cross-reference assigns prof iles to sources using SCCs.

For large or critical VOC and PM 2.5 sources in the modeling domain, air agencies shoul d consider determining the individual chemical compounds contr ibuting to the total VOC and PM 2.5 . If collected, this information should then be used to compile speciation profiles for the critical facilities or source categories. These speciation profiles should be assigned to the facilities by updating the speciation cross-reference file to sup port a facility-specific or facility-SCC-specific assignment.

Additionally, the EPA encourages the use of HAP emi ssions data when available to augment VOC speciation; therefore, we suggest using the inv entory HAPs where criteria air pollutants (CAPs) and HAPs were provided by the same data sour ce. When possible, the EPA’s modeling platforms generally process the following HAPs rath er than relying on VOC speciation: benzene, acetaldehyde, formaldehyde and methanol. These 4 HAPs are collectively known as “BAFM” and are removed from VOC speciation calculat ions for the remaining VOC HAPs needed for the air quality models. Ethanol can als o be added to this “VOC integration” process
where ethanol emissions are available. It is entir ely up to the user to determine the “integration” status of specific sources in each in ventory. A simple test for possible integration of VOC HAPs is the following. For each inventory u nique source (i.e., a stack or nonpoint/mobile FIPS+SCC): 1) confirm that VOC HAP s and VOC were reported by the same data provider, and 2) compute VOC HAPs to ensure th ey are non-zero and less than inventory VOC. A detailed methodology is provided in Section 3.2.1 of the 2011 emissions modeling platform technical support document available at: http://www.epa.gov/ttn/chief/emch/2011v6/outreach/ 2011v6_2018base_EmisMod_TSD_26feb2014.pdf

.

County-specific speciation profile combinations are also available for cases where user-defined mixtures of two or more speciation profiles are nee ded. The “GSPRO_COMBO” feature in SMOKE ( http://www.smoke-model.org/version3.1/html/ch08s05s 03.html ) can be applied by state and county and time period (e.g., month) and is currently used for on-road and nonroad mobile and gasoline-related stationary sources wher e the emissions sources use fuels with varying ethanol content (e.g., E0 and E10 profiles) . Ethanol content varies spatially by state and county via nonattainment areas or local programs, t emporally (e.g., during ozone season months), and by modeling year because future years have more ethanol. Certain source categories will require more careful review of spec iation assignments. For example, the oil and gas sector (point, area and nonroad) inventories ar e generally constructed for specific drillingModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

48 areas (i.e. drilling basins) which may include mult iple counties with unique production gas characteristics. Knowledge of the type of gas (e.g ., dry, wet, coal bed methane) or oil produced,
flared and vented in these basin inventories is hel pful for developing basin specific speciation assignments.

2.7.8.3 Spatial allocation For all source sectors that are compiled at a count y resolution, the emissions models also need information about allocating the countywide emissio ns to individual grid cells that intersect the county boundaries. Such sectors include stationary area, nonroad mobile, and (non-link) onroad mobile sources. The spatial allocation proces s assigns fractions of county-total emissions to the model grid cells intersecting the county bas ed on a “surrogate” data type (e.g., population, housing data). The appropriate types o f surrogate data to use for each SCC in the inventories should be identified for this processin g step. Spatial surrogates can be created using Geographic Information Systems (GISs) to calc ulate the fraction of countywide emissions to allocate to each grid cell based on the surrogat e type. These calculations can also be made using EPA’s Surrogate Tool ( http://www.ie.unc.edu/cempd/projects/mims/spatial/ ), which is based on the Multimedia Integrated Modeling System (MIMS) Spatial Allocator. In addition, all SCCs needing spatial surrogates should be assigned a surrogate in a cross-reference file. Point sources do not need spatial surrogates, since the e missions models assign the grid location based on the latitude and longitude of the point so urces. EPA provides spatial surrogates and
cross-references at http://www.epa.gov/ttn/chief/emch/index.html . The same spatial surrogate data is normally used in both the base an d future year modeling. However, emissions developers can choose to alter the spatial surrogat e data based on predicted changes in land use patterns, population growth, and demographics, although the impact and utility of such approaches are not well characterized 39 , so their use needs to be well documented and explained.

2.7.8.3 Biogenic ancillary inputs For biogenic emissions modeling, a biogenic emissio ns model (as described in Section 2.7.9.2)
must be used. These models come with some ancillar y data, with exception of the meteorology data and (in some cases) the gridded land-use data.

39 At the time this document was written, tools to rea dily predict future-year land use patterns are not readily available in a form for use in emissions mo deling.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

49 For biogenic emissions model inputs, the meteorolog ical data should be consistent with the data used for the air quality model. Biogenic emis sions models are generally set up to use gridded hourly meteorological data, so inventory de velopers should be able to convert their meteorological inputs from air quality modeling inp uts for use in their selected biogenics
emissions model.

Land use data can be created with the MIMS Spatial Allocator ( http://www.ie.unc.edu/cempd/projects/mims/spatial/ ), using raw Biogenic Emissions Landcover Data (BELD), version 3 ( http://www.epa.gov/ttn/chief/emch/biogenic/ ). Based on inputs from the emissions developer, the Spatial Al locator will select the 1-km grid cells from the BELD data that intersect the modeling domain an d (if necessary) aggregate to the grid resolution of the modeling domain. For future-year modeling, land use data is typically held constant (same as the base case). Emissions develo pers can choose to change their land cover data based on predicted changes in land use pattern s, however, the impact and utility of such approaches is not well characterized, so their use needs to be well documented and explained.

2.7.8.4 Other ancillary inputs On-road emissions for fine-scale model grids (e.g., 4-km grid cells or smaller) may be based on a link-based approach as mentioned in Section 2.7.7.3 . The VMT and speed data needed for a link-based approach can be provided using a Travel Demand Model (TDM). These models require their own sets of inputs, which depend on t he specific TDM used. Details on using
TDMs for link-based on-road mobile emissions are av ailable from the EIIP document “Use of Locality-Specific Transportation Data for the Devel opment of Source Emission Inventories” ( http://www.epa.gov/ttn/chief/eiip/techreport/volume 04/iv02.pdf ). Additional information on
the use of TDMs can be found in “Use of Travel Dema nd Model Data to Improve Inventories in Philadelphia” ( http://www.epa.gov/ttnchie1/conference/ei15/session 1/cook.pdf ),

Emissions models have other input files that must b e created. Previously we noted that for point sources, emissions developers will have to id entify which sources should be treated as elevated sources by an air quality model. To do th is, data developers will select criteria such as stack height or a screening plume height value to a llow an emissions model to flag point sources as elevated sources. In the SMOKE modeling system for example, the Elevpoint program can be used for this purpose.

The emissions models have a large number of files a nd settings which work together in fairly complex ways; therefore users should be careful to select the appropriate files needed for the emissions model, and to prepare all input files in a way that will support using the emissionsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

50 model for the specific air quality model applicatio n. A possible shortcut can be starting with the emissions modeling scripts included with the EPA’s modeling platforms.



### 2.7.9 How Are Inventory Data Converted Into Air Qua lity Model Input?

Emissions models are used to convert inventory data to inputs for air quality modeling. As described in Section 2.7.8, as part of the emission s modeling process, additional ancillary data are needed to augment the raw emissions inventories . The emissions data for each of the six emissions sectors (point, area, on-road mobile, non road mobile, biogenics, and wildland fires) are temporally allocated, chemically speciated, and spatially allocated by the emissions model. The resulting hourly, gridded, and speciated emissi ons from all sectors are then combined before being used by an air quality model. In this section, we will provide information on several emissions models and summarize some key iss ues with the application of emissions
models.

2.7.9.1 Emissions models Several emissions models are available for use in d eveloping SIPs. While no single model has been specifically created for all situations, each model is generally capable of performing the necessary emissions processing steps, including tem poral, chemical, and spatial allocation. Users of such models are responsible for ensuring t hat the emissions processing steps
transform the emission inventories as intended and are not changing the emissions in any unexpected way. Each model has different capabilit ies, limitations, and nuances. Therefore, when choosing an emissions model, it is worthwhile to discuss the choice with the developers of these systems and/or with EPA to determine which model is best suited for a particular application. Note that there are a number of progr ams that process emissions for individual
source sectors. These are discussed below in the s ector-specific subsections. Currently, SMOKE is the primary emissions model used to develop emis sions data for input into chemical transport grid models. Other emissions models can also be used, such as the Consolidated Community Emissions Processing Tool (CONCEPT)).

SMOKE supports processing of criteria, mercury, and toxics inventories for stationary point, stationary area, mobile, and biogenic emissions. T his emissions model can create input files for the CMAQ and CAM X air quality models. Applications of SMOKE have be en presented at several of the International Emissions Inventory Workshops (Houyoux, 2000; Strum, 2003, Zubrow, 2012, Baek, 2010, Adelman, 2008, Baek, 2007a, Baek, 2007b, Baek, 2006, Eyth, 2006, Pouliot, 2005). SMOKE is available for UNIX and Linux opera ting systems, but is not recommended for use on PCs running Windows. It does not require th ird party software. The SMOKE softwareModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

51 and User’s Guide are available at http://www.smoke-model.org/index.cfm . Support for the SMOKE system is available through the Community Mod eling and Analysis System (CMAS) help desk ( http://www.cmascenter.org/html/help.html ), hosted by the University of North Carolina at Chapel Hill’s Institute for the Environment ( http://www.ie.unc.edu ).

Utilities for creating speciation profiles, biogeni c land use, or spatial surrogates are not included within SMOKE. However, the Speciation Tool can cre ate speciation profile inputs for SMOKE (Eyth, 2006), and biogenic land use and spatial sur rogates can be built using the Multimedia Integrated Modeling System (MIMS) Spatial Allocator Tool ( http://www.ie.unc.edu/cempd/projects/mims/spatial/) along with the related Surrogate Tools.

The CONCEPT emissions model was developed as an ope n source model written primarily in PostgreSQL. The database structure of the model ma kes the system easy to understand, and the modeling codes are documented to encourage user participation in customizing the system for specific modeling requirements. The CONCEPT mo del structure and implementation allows for multiple levels of QA analysis during every ste p of the emissions calculation process. Using the database structures, an emissions modeler can t race a process or facility and review the calculation procedures and assumptions for any emis sions value. CONCEPT includes modules for the major emissions source categories: area sou rce, point source, on-road motor vehicles, non-road motor vehicles and biogenic emissions, as well as a number of supporting modules, including spatial allocation factor development, sp eciation profile development, growth and control for point and area sources, and CEM point s ource emissions handling. Additional tools include CEM preprocessing software, graphical QA to ols, and an interface to the travel demand models for estimating on-road motor vehicle emissio ns. Note that the CONCEPT website ( http://www.conceptmodel.org ) has not been updated since 2005.

2.7.9.2 Biogenic emissions models There are several available biogenic emissions mode ls that can be used to develop biogenic
emissions for modeled attainment demonstrations. T he most commonly used model by EPA is the Biogenic Emission Inventory System (BEIS), vers ion 3.14 (BEIS3.14) (Geron, et al., 1994 and ( http://www.cmascenter.org/conference/2008/slides/po uliot_tale_two_cmas08.ppt ). BEIS comes with all needed ancillary data, except the gr idded land-use data and meteorology data for a specific air quality modeling domain and grid resolution (as described previously in Section 2.6.1).

The BEIS3 model estimates CO, NO X , and VOC emissions from vegetation and soils in th e gridded, hourly, and model-species forms needed for air quality modeling. Guenther, et al.,Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

52 (2000) contains the technical development and revie w of BEIS3. Vukovich, et al., (2002) summarizes input data and algorithms as implemented within SMOKE. Arunachalam, et al., (2002) presents the impact of BEIS3 emissions on oz one. For more detailed local estimates, air agencies should review the biogenic emissions on a county basis and update the land use data
as needed. BEIS3.14 was the latest version of BEIS 3 in use by EPA at the time this document was written and is integrated directly into SMOKE v 3.0 and later versions.

Other biogenic models such as Model of Emissions of Gases and Aerosols from Nature (MEGAN) ( http://acd.ucar.edu/~guenther/MEGAN/MEGAN.htm ) can be used as well. In all cases, users should demonstrate the suitability of the biogenic emissions model for the particular modeling application (Baker, 2011).

2.7.9.3 Agricultural ammonia emissions Ammonia emissions from agricultural operations can be estimated using standalone models. A commonly used model to estimate ammonia from animal s and fertilizer application is the Carnegie Mellon University Ammonia Model ( http://www.cmu.edu/ammonia/ ). Another model is a process-based model developed by LADCO a nd other collaborators, most recently summarized by: http://www.ladco.org/about/general/2014%20Midwest%2 0Air%20Quality%20Workshop/Prese ntations_files/B_Ammonia/McCourtney_Ammonia_Model.p df

.

In addition, recent improvements to the CMAQ model (v5.0) have included bi-directional NH 3 flux computations directly from hourly meteorology and land use information. 16 The CMAQ bidirectional NH 3 flux option only calculates emissions for fertiliz er ammonia. Using this CMAQ option requires the user to remove all anthropogeni c NH 3 fertilizer application emissions from the emissions input to CMAQ when running the model with bi-directional flux. An evaluation of
the bidirectional NH 3 exchange can be found here: http://www.cmascenter.org/conference/2011/slides/ba sh_evaluation_bidirectional_2011.pdf

.

2.7.9.4 Other methods The Industrial Sector Integrated Solutions (ISIS) ( EPA, 2010) model is under development by EPA for specific industrial source categories. Current ly, ISIS can be used to generate base and future year emissions data for the Portland Cement industr y. ISIS is also being developed for the Iron and Steel sector as well as the Pulp and Paper sect or. ISIS baseline and policy case emissions

16 The CMAQ ammonia bi-directional flux option calcul ates emissions only for fertilizer ammonia, although the flux calculations consider all sources of ammonia.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

53 are currently not provided in a SMOKE-ready format and therefore require some formatting and data conversion preprocessing.



### 2.7.10 Other Emissions Modeling Issues

In addition to the emissions modeling process and t ools described above, there are several other important issues to consider. In the remainder of this section, we briefly address each of these
issues.

2.7.10.1 Elevated Point Sources Point sources need to be assigned to an appropriate model layer 17 (the vertical modeling dimension). Depending on the air quality model tha t is being used, different emissions modeling steps can be taken. Models such as CMAQ and CAM X expect input emissions files separately for layer one emissions versus the elevated point-sourc e emissions. Additionally, elevated point sources may be flagged for treatment with a plume-i n-grid (PinG) approach. Emissions modelers must supply criteria for specifying which point sou rces will be treated as elevated and/or as PinG

sources 18 . For elevated sources, the air quality model calc ulates the plume rise of the point
source emissions when the model is run. For PinG s ources, the model does additional calculation for modeling the plume at a scale small er than a grid cell, releasing the resulting computed concentrations to the neighboring cells on ly after accounting for in-plume chemistry
and other sub-scale factors.

2.7.10.2 Treatment of Fires For the purpose of model evaluation, the EPA recomm ends that detailed point-format dayspecific wildfire and prescribed burning emissions be used. These data are available using the latest version of the Satellite Mapping Automated R eanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Du et al., 2013; Sullivan, et al., 2008). Detailed information on the SMARTFIRE system is available in Raffuse et al, 2007. Additional references and

17 Point sources generally comprise most of the eleva ted emissions (above layer 1), although other area sources, such as fires and aircraft, may also have emissions assigned to elevated layers.

18 Plume rise is calculated based on point source sta ck parameters provided as inputs to the air quality model. Stack parameter information (especially for large sources) should be reviewed in order to ensu re that emissions are placed in the correct vertical l ayer. In addition, stack parameters (such as stack height, diameter, and exit velocity) can change ove r time due to re-configuration of the stack(s) afte r installation of control devices such as scrubbers.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

54 information are available as part of the 2011 NEI T echnical Support Document, http://www.epa.gov/ttn/chief/net/2011inventory.html #inventorydoc

.

Due to the potential large local and regional impac t from fires, for some modeling applications it may be appropriate to reduce the acute temporal and spatial impacts of wildfires and prescribed burning emissions by averaging them spat ially and temporally. The EPA Fire Averaging Tool (FAT) has been developed to convert one or more years of SMARTFIRE point and day-specific fire inventories into daily nonpoint ( county-level) inventories. The FAT user also has the option to apply an averaging technique to f urther smooth wildfire and prescribed burning emissions to a selected averaging period in days. For example, in the 2007v5 platform, EPA created a 29-day averaging period with 7 years of available wildfire data (http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020ba se_EmisMod_TSD_13dec2012.pdf).

The techniques referenced above represent EPA’s tre atment of “actual” and “average” fire emissions in modeling to support EPA rules, but oth er techniques can be used to represent fire emissions. Since fire emissions can be a large sou rce of PM 2.5 and VOC emissions, the treatment of fires should be discussed with the app ropriate EPA Regional Office(s) as part of the modeling protocol development process.

2.7.10.3 Treatment of ‘atypical’ Sources of Emissi ons In the NEI, there are several sources of emissions that are reported by only one state or a small number of states. These sources typically have ver y low emissions, so their removal typically has little or no impact on air quality modeling. E xamples of these sources include domestic and wild animal waste emissions (primarily NH 3 ), human perspiration and cigarette smoke, and swimming pools. In some inventories, large catastr ophic/accidental releases are reported (e.g., car accidents, tire fires). It is generally consid ered inappropriate to include these sources in emissions processing for air quality models. Howev er, some source categories may be important or unique to a particular area. Before r emoving seemingly unimportant source categories, the relative importance (e.g. as a perc entage of emissions in the area) of each category should be evaluated.

In addition, air agencies should review and conside r the EI SIP Guidance related to startup and
shutdown emissions from individual facilities. Thi s guidance provides information on when such emissions should be included in planning inven tories. Since modeling inventories are expected to be consistent with the planning invento ries where possible, air agencies should attempt to include any such emissions included in t he planning inventories in their modelingModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

55 inventories as well. Such emissions may need a det ailed temporal treatment to account for the variable nature of the startups and shutdowns.

Similarly, air agencies should review and consider the draft EI SIP Guidance and the ozone implementation rule regarding EGU emissions during High Electric Demand Day (HEDD) periods. Such emissions can be crucial for having a complete understanding of relevant emissions sources within a modeling domain. Where these emis sions are important for assessing a nonattainment problem, air agencies should consider the potential impact of HEDD emissions when selecting time periods (e.g. episodes, season, year) to model.

2.7.10.4 Transport and Meteorology-based Reduction s in Fugitive Dust Emissions Fugitive dust emissions are another large source of primary PM 2.5 emissions. Past modeling experience has shown large modeled PM 2.5 concentration over-predictions from these emissions (Pouliot et al, 2010). Therefore EPA rec ommends reducing area-source fugitive dust emissions prior to air quality modeling to account for “capture” by the terrain and removal by meteorology (such as precipitation or frozen ground ). These two components should be accounted for when adjusting the inventory PM emiss ions from PM sources such as paved and unpaved roads, construction, mining, agricultural a ctivities, and industrial dust and other lowlevel fugitive dust source categories. The “captur ed” portion of the reduction can be based on gridded land use data such as vegetation and buildi ng characteristics. The EPA has developed a methodology within SMOKE to adjust the fugitive dus t emissions. The “meteorological” component of the reduction requires intermediate SM OKE processing scripts to merge hourly, gridded precipitation data and snow cover values to zero-out all fugitive dust emissions where appropriate. To avoid double-counting the precipit ation-based reduction, the user should make sure that the inventory fugitive dust inventor y does not already include a “MET-adjusted” reduction prior to these manipulations. This combi ned transport and met-based reduction approach is documented in Pouliot et al., 2010.

2.7.10.5 Quality Assurance Quality assurance (QA) is a key component of any su ccessful emissions modeling effort. A brief synopsis of appropriate quality assurance (QA) appr oaches for emissions modeling is available in Section 2.20 of the SMOKE manual ( http://www.smokemodel.org/version3.1/html/ch02s20.html ). The purpose of QA for emissions modeling is to ensure that the inventories are correctly processed using the information the modeler intended. (It is assumed here that the inventory i tself has already been QA’d and erroneous outliers removed through inventory QA procedures, a s described in the EI SIP Guidance .)Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

56 Emissions modeling QA includes such activities as:

• Reviewing log files for errors and warnings and add ressing problems;

• Comparing emissions between each of the processing stages (e.g., data import, speciation, temporal allocation) to ensure mass is conserved;

• Checking that the correct speciation, temporal allo cation, and spatial allocation factors were applied;

• Ensuring that emissions going into the air quality model are consistent with expected results; and

• Reviewing the modeling-specific parameters such as stack parameters.

It is also useful to compare air quality modeling-r eady summaries to previous emissions modeling efforts to verify expected emissions chang es by source category(s) and geographic area(s) of interest. In addition, the process of r unning emissions inventories through emissions models and air quality models often provides insigh ts into the emission inventories themselves. These insights can lead to inventory changes that i mprove the quality of inventories for additional modeling iterations.

In general, this guidance also encourages the use o f graphical analysis and Geographic Information Systems (GIS) for improved QA of emissi ons data and processing. A commonly used analysis tool for model-ready emissions data i s the EPA sponsored Visualization Environment for Rich Data Interpretation (VERDI) to ol. The VERDI software package provides for efficient, flexible and modular capabilities of overlaying meteorology, emissions and air quality modeling data with GIS shapefiles. More in formation on VERDI is available here: http://www.verdi-tool.org/index.cfm . VERDI can be downloaded from here: https://www.cmascenter.org/verdi/. In addition to VERDI, the older Package for Analysis and Visualization of Environmental Data (PAVE) tool is also available at http://www.ie.unc.edu/cempd/EDSS/pave_doc/index.sht ml

.



### 2.7.11 How Are Emissions Estimated for Future Years ?

Emissions estimates for future years are called “em issions projections”. The inventory developed for modeled attainment demonstrations is called the **attainment projected inventory for modeling**  . These emissions projections include emissions ch anges (due to increased or decreased activities), facility and/or unit-level s hutdowns due to settlements (which can be due to settlements, consent decrees, or ownership decis ions), and emissions controls (which can be due to regulations, settlements or fuel-switchings that reduce emissions in specific ways in theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

57 future). The goal in making projections is to obta in a reasonable estimate of future-year emissions that accounts for the key variables that will affect future emissions. Each air agency is encouraged to incorporate in its analysis the va riables that have historically been shown to drive its economy and emissions, as well as the cha nges in growth patterns and regulations that are expected to take place between the time of the base year and future years. Complete guidance on emissions projections is provided in th e EI SIP Guidance. In addition, several
additional issues are listed here for consideration for modeling inventories.

Emissions models (e.g., SMOKE) provide the capabili ty to create future-year inventories using base year inventories and projection information. Emissions modelers will need to convert the projection data into specific formats for input to these models. Inventory developers should determine which emissions model will be used to per form the calculations and make sure that the type(s) of information needed by the model is b eing collected.

For EGUs, models are available that can not only es timate annual emissions, but also can estimate summer/winter or even day-specific emissio ns. The EPA uses the Integrated Planning Model (IPM) to model future electricity demand and emissions trading programs that now dominate the prediction of future-year emissions fr om EGUs. More information on IPM is available at ( http://www.epa.gov/airmarkt/progsregs/epa-ipm/index .html ). Additionally, IPMbased emissions are posted by CAMD on the EPA’s web site ( http://ampd.epa.gov/ampd/)

. Other future-year EGU emissions models can be used, and should include explanations regarding the approaches taken and the expected imp rovements over the EPA-developed approaches.

The mobile sources models (e.g., MOVES and NONROAD/ NMIM) are designed to give different results based on the output year selected. MOVES a ccount for expected changes in vehicles over time, with newer (more controlled) vehicles gr adually replacing older vehicles and newer nonroad engines replacing older ones. These models must be rerun for the future year emissions using the same resolution as was used for the base year modeling inventories. It is generally not appropriate to simply apply projectio n factors to these sources. For example, if SMOKE-MOVES is used in the base year (with base yea r emission factors generated by MOVES),
then SMOKE-MOVES would need to be also run for the future year (using future-year emission factors).

Ancillary input files for inputs to emissions model s, such as spatial surrogates, temporal allocation factors and especially speciation profil es may also need to be adjusted to reflect conditions expected in the future. The EPA modelin g platforms, for example, consider theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

58 impact on speciation of fuels used in the future, w hich are expected to change over time due to regulations.

Once a future-year inventory and other data have be en created, it should undergo the same steps as for the base-year modeling, including temp oral allocation, speciation, spatial allocation, elevated source selection, special on-r oad mobile processing, any other custom processing steps (e.g. applying meteorology-based i nformation to fugitive dust, agricultural NH 3 and residential wood combustion), and quality assur ance
. Except where intentional, every attempt should be made to use consistent approaches between the future year and the base year for all of these modeling steps. Inconsistenc ies in approaches between the future-year modeling and the base-year modeling can lead to art ificial differences in air quality modeling
results that can affect conclusions 19 . Therefore, it is critical to avoid such differen ces whenever possible. If inconsistent base and future year app roaches are necessary, they should be well documented and explained.



### 2.8 Initial and Boundary Conditions

The behavior of a dynamic system is strongly depend ent upon its initial state. In order to solve the requisite chemical and physical equations in sp ace and time, air quality models require specification of the initial state of the chemical conditions within the domain at the first time step of the modeling period. This model input is t ypically referred to as the initial condition file. Additionally, air quality models also require infor mation regarding the time-varying chemical state of the atmosphere just outside the edges of t he modeling domain in order to properly account for transport of material into the area of interest. This model input is typically referred to as the boundary condition file. As with other m odel inputs, the establishment of the initial and boundary condition (IC/BC) data should be guide d by the conceptual model of the air quality issue being simulated. For instance, more attention will be needed in developing accurate boundary conditions for cases in which amb ient data analyses (or previous modeling scenarios) have indicated the potential for long-ra nge transport of pollutant material, compared to cases in which the air quality problem has been identified as largely local in nature. Numerous studies have highlighted the imp ortance of accurately representing boundary conditions in optimizing model performance and identifying the most effective path to attaining the NAAQS (Jacob **et al.** , 1999; Tang **et al.** , 2009; Hogrefe **et al.** , 2011).

19 This is especially important due to the use of the “relative” change in modeled concentrations as par t of the modeled attainment test. An inconsistency i n base and future year emissions may lead to a very large modeled percent change in ozone and/or PM 2.5 concentrations.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

59 Given limitations in available ambient data, it is impossible to exactly specify the complex threedimensional chemical characteristics of the initial or boundary conditions. As a result, two basic approaches to establishing these fields have been u sed within the modeling community. The first approach is to attempt to minimize the impact s of the initial and boundary conditions by configuring the domain and modeling period such tha t the air quality concentrations in the locations and times of interest are largely unaffec ted by the IC/BC inputs. The second approach is to characterize the IC/BC data as accurately as possible given the existing state of knowledge
about ambient conditions at those times and in thos e locations.

In most cases, it will be possible to minimize the impacts of the initial conditions data via the use of a model “spin up” period. A spin up period is defined as some number of modeling days that precede the time period of actual interest. T he model outputs for this spin up period are discarded from any analytical post-processing, as t hey are potentially affected by the uncertain initial conditions. The length of the needed model spin up period will vary as a function of: the domain size, prevailing meteorological conditions, and the chemical lifetimes of the pollutants being simulated. It is a simple modeling exercise to test the sensitivity of the key time periods to the initial conditions. Previous sensitivity m odeling has determined that the initial conditions are typically “washed out” of the system in as few as 3-5 days for a local-scale modeling analysis; approximately 10 days for a regi onal-scale analysis; and on the order of six months for a hemispheric-scale analysis (Berge **et al.** , 2001; USEPA 2010; Anenberg et al., 2011). If there is any question as to the appropri ate length of the spin-up period, then sensitivity modeling tests should be conducted. Ot herwise, the above guidelines should be
sufficient for most ozone and PM 2.5 attainment demonstrations and reasonable progress

assessments.

For the determination of base year boundary conditi ons, it will typically be a case-by-case determination, guided by the conceptual model, as t o whether “impact minimization” or “accurate characterization” should the preferred ap proach. In some situations, it may be possible to construct a modeling platform in which the boundary conditions will not substantially influence the air quality issue under consideration. For instance, areas for which there is no evidence of pollutant transport from th e far edges of the model domain to the local area may be able to demonstrate that the determinat ion of boundary condition data will not impact the eventual attainment demonstration. As w ith the initial conditions data, this conclusion can be proven via simple sensitivity tes ts in which the boundary conditions are
modified from a base state and the results between the two runs are compared (e.g., Appel **et al**  , 2007). For situations in which alterations to th e boundary condition data will not impact key findings, it is acceptable to use simple generic pr ofiles of species data in the boundary condition
file.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

60 In other cases, it will not be feasible to build a large enough domain to minimize the impacts
from emissions outside the domain. For these cases , in which accurate characterizations of boundary conditions are important, it will be neces sary to downscale the requisite species concentration data from well-performing, coarser-gr id, larger domain modeling runs. (In rare cases, it may be possible to assign boundary condit ions from detailed field study species data, but most domains will not have complete chemical pr ofiles in space and time along the domain edges.). These larger “parent” grids may be genera ted by the same regional scale model being
used for the attainment demonstration at coarser re solution; or they can be generated via global or hemispheric models. In recent years, sev eral global/hemispheric air quality models have been used to generate regional boundary condit ion inputs. A list of these models include: GEOS-Chem (Bey et al., 2001), the AM3 atmospheric c omponent of the GFDL global coupled model (Donner, et al., 2011), the Model for Ozone A nd Related Tracers (MOZART, Emmons et al, 2010), the WRF-Chem model (Zhang, et al., 2012a ), and the hemispheric CMAQ model (Byun and Schere, 2006; Fu et al., 2012). EPA does not s pecifically recommend any one particular global/hemispheric model. Each regional modeling e xercise should individually assess whether the chosen global/hemispheric or coarse regional mo del accurately captures inflow boundary
conditions.

For cases in which the boundary conditions will be extracted from larger-scale regional/hemispheric modeling, EPA recommends two e fforts to optimize the BC characterizations. First, modelers should try to m inimize any disconnects in the downscaling process. Wherever possible, one should attempt to utilize the same species, chemical mechanism, layer depths, and meteorological inputs in the regional/hemispheric model as are being used in the finer-scale attainment demonstrat ion modeling (Lam and Fu, 2009). This will
minimize the number of uncertain translations neede d to convert coarse-scale outputs to finerscale BC inputs. In particular, careful considerat ion of the stratosphere in the two modeling systems will be needed to ensure that regional/hemi spheric ozone that is stratospheric in origin is not excessively mixed down to the surface in the finer-scale modeling due to disconnects between the two systems 20 . Second, there should be a comprehensive evaluati on of the regional/hemispheric model performance (Bey **et al** ., 2001). While there may not be a detailed

20 In establishing the layer structure for both globa l and regional models, it is advisable to avoid sel ecting upper model layers that span both the upper troposp here and lower stratosphere. It has been seen that “hybrid” layers can allow for inappropriate diffusi ve mixing between those two generally decoupled sections of the atmosphere. In cases where stratos pheric intrusions are not expected to influence ozone in the area, it may be appropriate to artific ially cap stratospheric ozone concentrations to avo id ozone performance issues. This should be a “last r esort” fix, only applied after detailed assessments of the vertical diffusion and vertical advection schem es within the model.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

61 data set available for model evaluation along the d omain boundaries in most cases, consideration of model performance across the domai n will provide a first-order indication of the ability of the coarser-scale model to character ize air quality at the boundaries. Additionally, it may also be possible to infer information about the quality of the coarse-model estimates via comparisons with satellite data (Liu **et al.** , 2006, Henderson **et al.** , 2013).

Due to the potential impact of boundary conditions on model response to emissions controls, it is important to accurately characterize the relativ e influence of boundary conditions versus local emissions. For example, if the impacts of bo undary conditions on local air quality are overestimated, it will be more difficult to reduce design values from local controls. Conversely, if the modeling system underestimates the role of i nflow air quality, local emissions reductions may appear more effective in the model than they wi ll be in practice. The selection of future year boundary conditions al so has potentially important ramifications on the relative response factors derived from the m odeling for the various control scenarios. If boundary conditions are known to impact local ai r quality, then attainment demonstration modelers may need to consider how these contributio ns will change between the base year and the attainment year (Adelman **et al.** , 2011). Recent ambient trends analyses have indicated that free tropospheric ozone levels above the western U.S. have increased by approximately 0.5 ppb/year over the past two decade s (Cooper et al., 2012; Cooper et al., 2010). If these trends continue into the next deca de, or were they to be reversed, they have the potential to impact attainment of the NAAQS in some areas. It is left to the discretion of the air agency to determine whether varying future boundary conditions are needed as part of
an attainment demonstration. This decision should consider both the length of the projection period and the potential for significant air qualit y changes outside the modeling domain. Additionally, careful consideration of upstream emi ssions will be needed as part of any additional future-year regional/hemispheric modelin g conducted to develop future boundary
conditions.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

62 

## 3.0 Evaluating Model Performance

3.1 Overview of model performance evaluation

The results of a model performance evaluation (MPE) should be considered prior to using modeling to support an attainment demonstration or regional haze assessment. The objective
of the MPE is to demonstrate that the base case mod el can simulate observed pollution concentrations during historical pollution episodes , and to develop confidence that the model can reliably predict how future pollution levels wi ll change in response to changes in emissions. A particular concern in photochemical models is tha t compensating errors in the model can cause the model to reproduce observed pollution con centrations in the base case while incorrectly representing the emissions, dispersion and chemistry processes that control pollution formation. Models that achieve good perf ormance through compensating errors will generally not be reliable for predicting how pollut ion levels will respond to future emissions reductions. Thus, a key goal of the MPE is to demon strate that the model is getting good results for the right reason (Russell and Dennis, 2000). I n addition, the MPE provides air agencies with tools to identify and fix causes of poor model perf ormance such as problems in the meteorological, emissions, or boundary condition in put files.

Dennis et al. (2010) describe an MPE framework that includes four different approaches that can be used to evaluate air quality models:

1. Operational evaluation techniques include statistic al and graphical analyses aimed at determining whether the modeled simulated variables are comparable to

measurements.
2. Diagnostic evaluation focuses on process-oriented a nalyses that determine whether the individual processes and components of the model sy stem are working correctly, both independently and in combination.
3. Dynamic evaluation assesses the ability of the air quality model to predict changes in air quality given changes in source emissions or meteor ology, the principal forces that drive the air quality model.
4. Probabilistic evaluation attempts to assess the lev el of confidence in the model predictions through techniques such as ensemble mod el simulations.

The goal of the operational evaluation is to determ ine how well the model replicates observed
concentrations of PM 2.5 components, ozone and their precursors. Diagnostic and dynamic
evaluations are used to determine whether the model accurately represents the processes that determine pollutant concentrations, including emiss ions, dispersion, chemical reactions, andModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

63 deposition, i.e., to determine if the model is gett ing good results for the right reason. For cases in which the model performs poorly in the operation al evaluation, the diagnostic and dynamic evaluation can provide insights into the causes of the poor performance and to improve model performance. Ultimately, the goal of these evaluat ion methods is to assess the degree of
confidence in the use of the model as a tool to inf orm the planning process, and more specifically, to determine how reliably the model p redicts the response of ozone and/or PM 2.5 to changes in emissions. The modeled attainment tes ts use models to predict the response of
ozone and PM 2.5 to changes in emissions and then applies the result ing relative response factors (RRFs) to observed (rather than modeled) am bient data. Thus, while historically, most of the effort has focused on the operational evalua tion, the relative attainment test makes the diagnostic and dynamic evaluation also important.

At a minimum, a model used in an attainment demonst ration should include a complete operational MPE using all available ambient monitor ing data for the base case model simulation period. Section 3.2 describes the types of metrics and plots that are typically developed as part of an operational MPE, and Section 3.3 describes am bient data that is available through continuously operating monitoring networks. If avai lable, monitoring data from special field
studies and other research data should also be used in the operational MPE.

Where practical, the MPE should also include some l evel of diagnostic evaluation. The use of sensitivity studies, process analysis, indicator ra tios and source apportionment approaches for diagnostic evaluation, including dynamic and probab ilistic evaluations are described in Section 3.4. Given that air agencies might have limited re sources and time to perform diagnostic and dynamic evaluation, the use of these methods may be limited in scope in a typical SIP modeling application. However, more comprehensive diagnosti c testing of the model can be a valuable component of the weight of evidence analysis used t o support a SIP model attainment
demonstration.

It is clear that there is no single definitive tes t for evaluating model performance. All of the tests mentioned here have strengths and weaknesses. Further, even with a single performance test, it is not appropriate to assign “bright line” criteria that distinguish between adequate and inadequate model performance. In this regard, EPA recommends that a “weight of evidence” approach be used to determine whether a particular modeling application is valid for assessing
the future attainment status of an area. EPA recom mends that air agencies conduct a variety of performance tests and weigh them qualitatively to a ssess model performance. Provided suitable databases are available, greater weight sh ould be given to those tests which assess the model capabilities most closely related to how the model is used in the modeled attainment test (i.e. tests that provide insight into the accu racy of the model’s relative response to emissions reductions). Generally, additional confi dence should be attributed to modelModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

64 applications in which a variety of the tests descri bed here are applied and the results indicate that the model is performing well. From an operati onal standpoint, EPA recommends that air agencies compare their evaluation results against s imilar modeling exercises to ensure that the model performance approximates the quality of other applications. A recent literature review (Simon et al, 2012) summarized photochemical model performance for applications published in the peer-reviewed literature between 2006 and 20 12. This review may serve as a resource for identifying typical model performance for state of the science modeling applications.



### 3.2 Operational Evaluation

An operational evaluation is used to assess how acc urately the model predicts observed concentrations. The underlying rationale is that if we are able to correctly characterize historical concentrations of ozone, PM and their pr ecursors for a variety of meteorological conditions, this gives us some confidence that we c an correctly characterize future
concentrations under alternative emissions levels. Typically, this type of evaluation is comprised of statistical assessments of modeled ver sus observed data. Operational evaluations are generally accompanied by graphical and other qu alitative descriptions of the model's ability to replicate historical air quality patterns. An op erational evaluation provides a benchmark for model performance and can therefore identify model limitations and uncertainties that may require further model development/improvement and/o r diagnostic evaluation

The robustness of an operational evaluation is dire ctly related to the amount and quality of the ambient data available for comparison. An operatio nal evaluation for ozone should include collocated measurements of ozone precursors NOx, CO and VOC, and ideally, vertical profile
measurements that can be used to determine the exte nt of vertical mixing of pollutants and the concentration of O3 and precursors above the bounda ry layer.

An operational evaluation for PM 2.5 and regional haze is similar to that for ozone, ho wever, an important difference is that PM 2.5 consists of many component species and is typicall y measured with a 24-hour averaging time. The indivi dual components of PM 2.5 should be evaluated individually. In fact, in undertaking an operation assessment, it is more important to evaluate the components of PM 2.5 than to evaluate total PM 2.5 . Apparent “good performance”
for total PM 2.5 does not indicate whether the model is predicting the proper mix of components, which is important for the modeled atta inment test. If performance of the major components is good, then it follows that performanc e for total PM 2.5 will also be good. In addition to measurements of VOC and NOx, which are precursors for organic aerosols and nitrate, a PM 2.5 model evaluation should also include measurements of gaseous precursors SO 2Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

65 and (where available) NH 3 . Due to the influence of initial conditions, mode l predictions from the spin-up days should be excluded from the analys is of model performance.



### 3.2.1 Metrics

Operational evaluations quantify model performance through a variety of statistical metrics.
Recommended metrics are described below and shown i n Table 3.1. It is important to include multiple statistical measures in any operational ev aluation to fully characterize model performance. At a minimum, we recommend evaluating : Mean obs, Mean Model, Mean Bias, Mean Error and/or Root Mean Square Error, Normalize d Mean Bias and/or Fractional Bias, Normalized Mean Error and/or Fractional Error, and the correlation coefficient. The equations for calculating each of these metrics are given in Table 3.1.

Mean Obs: The time-averaged mean observed value (in ppb or μg/m3).

Mean Model: The time-averaged mean predicted value (in ppb or μg/m3) paired in time and space with the observations.

Mean Bias (MB): This performance statistic averages the model/observation residual paired in time and space. A value of zero would indicate tha t the model over-predictions and model under predictions exactly cancel each other out. An advantage of this metric is the bias is reported in the unit of measure (ppb or μg/m3) maki ng interpretation simpler.

Mean (Gross) Error (ME/MGE): This performance stati stic averages the absolute value of the model/observation residual paired in time and space . A value of zero would indicate that the model exactly matches the observed values at all po ints in space/time. An advantage of this metric is the bias is reported in the unit of measu re (ppb or μg/m3) making interpretation simpler.

Root Mean Square error (RMSE): This performance sta tistic (in units of ppb or ug/m3) is a measure of the average distance between predicted a nd observed values. It is calculated as the
standard deviation of the difference between modele d and observed values.

Normalized Mean Bias (NMB): This statistic (given i n units of percent) normalized MB to the average observed value. NMB values range from -100% to +infinity. Consequently, negative and positive bias values using this metric are not symm etrical around 0. NMB is a useful model performance indicator because it avoids over inflat ing the observed range of valuesModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

66 Normalized Mean Error (NME): This performance stati stic (given in units of percent) is used to normalize the mean error relative to the average ob servation. This statistic averages the absolute value of the difference (model - observed) over the sum of observed values. NME values range from 0 to +infinity. NME is a useful model performance indicator because it avoids over inflating the observed range of values.

(Mean) Fractional Bias (MFB/FB): Fractional bias i s determined by normalizing the MB by the average of observed and modeled concentrations. Si nce normalized bias can become very large when a minimum threshold is not used, fractio nal bias may be used as a substitute. The range of FB is -200% to +200%. The fractional bias for cases with factors of 2 under- and overprediction are -67 and + 67 percent, respectively ( as opposed to -50 and +100 percent, when using normalized bias). Fractional bias is a useful indicator because it has the advantage of equally weighting positive and negative bias estima tes (underestimates and overestimates are symmetrical around 0). The single largest disadvant age is that the predicted concentration is
found in both the numerator and denominator.

(Mean) Fractional Error (MFE/FE): Fractional error is determined by normalizing the ME by the average of observed and modeled concentrations. Si nce normalized error can become very large when a minimum threshold is not used, fractio nal error may be used as a substitute. The range of values for FE is 0 to 200%. It is similar to the fractional bias except the absolute value
of the difference is used so that the error is alwa ys positive.

Correlation Coefficient (R 2 ): This performance statistic measures the degree t o which two variables are linearly related. A correlation coef ficient of 1 indicates a perfect linear relationship; whereas a correlation coefficient of 0 means that there is no linear relationship
between the variables.

Table 3.1: Definitions of recommended statistical metrics

Abbreviation Term Definition

MB mean bias 1
 (   −   )

ME mean error 1
 |   −   |Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

67 RMSE root mean squared error ∑ (   −   )
FB fractional bias 100% × 2
 (   −   ) (   +   )

FE fractional error 100% × 2
 |   −   | (   +   )

NMB normalized mean bias 100% × ∑ (   −   ) ∑  

NME normalized mean error 100% × ∑ |   −   | ∑  

r 2 coefficient of determination  ∑  (   −   ) × (   − 

 )     ∑ (   − 

 )   ∑ (   − 

 )   

M i = modeled concentration i. O i = observed concentration i. N = number of paired obs/model

concentrations. M = mean modeled concentration. O = mean observed concentration.

Observations and model predictions should initially be paired in time and space, matching the temporal resolution of the raw observation data. T he units of time associated with model and observed concentrations can be 24-hour (usually for particulate matter and its species), hourly (usually for species with continuous measurements, like ozone) or sometimes weekly (such as for CASTNet filter pack measurements). Temporal av eraging is appropriate when used to match a relevant regulatory metric such as maximum 8-hr daily average ozone. As appropriate, air agencies should then aggregate the raw statisti cal results into meaningful groups of subregions or sub-periods. For example, examining mod el performance within and near the nonattainment area(s) of interest may be more importan t than performance in other parts of theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

68 modeling domain. For larger areas with more spatia l variation, it may also be informative to evaluate sub-regions within the non-attainment area . In addition to any spatially aggregated statistics, model performance should also be evalua ted at individual monitors within the nonattainment area. Similarly, priority may be pla ced on examination of the days that are potentially used in the attainment test (i.e. base period days with 8-hour ozone > 60 ppb). That is not to say that model performance evaluations sh ould ignore performance on lower ozone days or in areas outside of the nonattainment areas . Model performance on lower concentration days and in areas outside of the nona ttainment area are still important, but it is appropriate to give more attention to the model out puts that most directly impact the outcome
of the attainment test.

In terms of pairing model predictions with monitore d observations, EPA recommends that the grid cell value in which the monitor resides be use d for the calculations. It would also be acceptable to consider bi-linear interpolation of m odel predictions to specific monitoring
locations 21 . Air agencies should recognize that, even in the case of perfect model performance, model-observed residuals are unlikely to result in exact matches due to measurement uncertainty and differences between the model predi ctions which are volume averages and the observations which are point values.



### 3.2.2 Plots

In addition to statistical summaries, graphical dis plays of data allow for a fuller characterization of model performance. Therefore, plots play a key role in any model performance evaluation. Below are examples of some types of plots which hav e been useful in past evaluations.

Time series plots of model and predicted hourly and/or daily (1-hour or 8-hour) maximum ozone for each monitoring location in the nonattain ment area, as well as key sites outside of the nonattainment area. These plots can indicate i f there are particular times of day or days of the week when the model performs especially poorly.

21 In certain instances, air agencies may also want to conduct performance evaluations using a “near the monitor” grid cell array (i.e. the best match withi n a 3X3 grid cell array centered on the monitor). A “near the monitor” analysis may be useful when stro ng ozone gradients are observed, such as in the presence of a sea breeze or in strongly oxidant lim ited conditions.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

69 Figure 3.2.1 Example time series plot of daily 1-hour maximum o zone concentrations

Scatter plots of predicted and observed concentrations at each s ite. These plots are useful for indicating if there is a particular part of the dis tribution of observations that is poorly represented by the model. It may also be useful to develop separate plots for individual time periods or key sub-regions.

Figure 3.2.2 Example scatter plot of 8-hour daily maximum ozone concentrationsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

70 Density plots are a variation on scatter plots which are especia lly useful with large amounts of data when individual points overlap on a scatter pl ot. Density plots color code each point on the scatter plot based on the number of points fall ing in that location.

Figure 3.2.3 Example density ply of 8-hour daily maximum ozone concentrations

Box plots can be developed for model performance evaluation. These types of plots show the distribution of observations, model estimates, or p erformance metrics, which can be grouped in a variety of ways, most commonly by observed ozo ne concentration, month, or hour of the day. Box plots show several quantities: the 25% to 75% percentiles, the median values, and outliers. The monthly box plot can be used to quic kly visualize model performance across the entire year, highlighting the seasonal change in mo del performance. The hourly or “diurnal” box plot is constructed using hourly data, and show s how the model predictions compare against observations throughout an entire day.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

71 Figure 3.2.4 Example box plots of (a) monthly average sulfate c oncentrations, (b) daily diurnal average ozone concentrations, and (c) seasonal ozon e bias binned by concentration range

(a) (b)

(c)Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

72 Daily gridded tile plots of predicted concentrations across the modeling do main with the actual observations as an overlay. Plots should be prepar ed for relevant averaging time (i.e. 1-hour maxima, daily 8-hour maxima, 24-hour average, etc.) . These plots can reveal locations where the model performs poorly. Superimposing observed concentrations on the predicted isopleths reveals useful information on the spatial alignment of predicted and observed plumes.

Figure 3.2.5 Example gridded tile plot of daily maximum 1-hour ozone concentrations (with overlaid observations)

Animations of predicted hourly concentrations for all episode days or for certain periods of interest. Animations are useful for examining the timing and location of pollutant formation. Animations may also reveal transport patterns (espe cially when looking at aloft layers). Animations can also be used to qualitatively compar e model outputs with the conceptual model of particular pollution episodes. These plot s may also reveal whether an important feature was simulated but was spatially or temporal ly displaced.

The soccer plot (Tesche et al, 2006) is so named because the dotte d lines illustrating performance goals resemble a soccer goal. The plot is a convenient way to visualize model performance of both bias and error on a single plot . As bias and error approach zero, the points are plotted closer to or within the “goal”, represe nted by the dashed boxes.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

73 Figure 3.2.6 Example soccer plot of sulfate normalized mean bia s and error.

The bugle plot (Boylan and Russell, 2006; Tesche et al., 2006), n amed for the shape formed by the criteria and goal lines, is another plot availa ble for model performance evaluation. The bugle plots are shaped as such because the goal and criteria lines are adjusted based on the average concentration of the observed species. As the average concentration becomes smaller, the criteria and goal lines become larger to adjust for the model’s ability to predict at low
concentrations.

Figure 3.2.7 Example bugle plot of sulfate normalized mean bias as a function of modeled
concentration.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

74 

### 3.2.3 Ozone Model Performance

It is recommended that, at a minimum, statistical p erformance metrics be calculated for hourly ozone and 8-hourly maxima ozone for each day of the model simulation used to support the
attainment demonstration. EPA recommends that the metrics be calculated both for all pairs of modeled and observed data and for pairs in which th e hourly or 8-hr daily max values are above 60 ppb. Another acceptable approach would be to es timate averaged metrics binned by
observed ozone concentration. This would allow for a thorough evaluation of the model
tendencies to estimate ambient concentrations in di fferent observed ranges of interest. Wherever possible, these types of performance measu res should also be calculated for ozone precursors and related gas phase oxidants.

Ozone precursors and related gas-phase oxidants (pa rtial list) are listed below.

• Total Volatile Organic Compounds (VOCs)

• Speciated VOC

• Nitric acid (HNO3)

• Nitric oxide (NO)

• Nitrogen dioxide (NO2)

• Peroxyacetyl nitrate (PAN)

• NOy (sum of NOx and other oxidized compounds)

• Carbon monoxide (CO)



### 3.2.4 PM2.5 Model Performance

Performance metrics for PM and regional haze are si milar to those calculated for ozone. Metrics should be estimated for components of PM 2.5 , and PM precursors. Since modeling for
PM 2.5 and regional haze will likely require modeling dif ferent times of year, season-specific statistics and graphical displays are helpful for e valuating and diagnosing model performance. Statistics and graphics can be averaged for various time scales depending on the model application. For example, statistical metrics and scatterplots can show daily averaged ambientmodeled pairs or annual average pairs for attainmen t demonstration of the daily and annual
PM 2.5 standards respectively. In addition, EPA recommen ds that daily pairs be grouped by season or month and statistics be given for differe nt times of the year since different processes
dominated PM 2.5 formation and fate in different seasons. Note tha t in this case, pairings should be performed on 24-hour average concentrations and statistics may then be reported for subgroups (seasons, regions etc.) of this data. Th is is different than pairing monthly or seasonally averaged data since statistics and plots tend to look “better” as the averaging timeModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

75 increases from daily to monthly to quarterly to ann ual. As such, daily pairs should always be examined to ensure a detailed look at model perform ance on the time scale of the FRM and CSN measurements (24-hour average). Finally, air a gencies may also want to subset the 20% best and worst visibility days or the “high” modele d PM 2.5 days (e.g. the 10% highest modeled days in each quarter) to examine model performance on the days which will potentially be used
in the attainment test.

Because PM 2.5 is a mixture, a meaningful performance evaluation should include an assessment of how well the model is able to predict individual chemical components that constitute PM 2.5
. Components of PM 2.5 of interest include:

• Sulfate ion (SO4)

• Nitrate ion (NO3)

• Ammonium ion (NH4)

• Elemental Carbon (EC)

• Organic Carbon (OC) and/or Organic Carbon Mass 22

• Crustal (weighted average of the most abundant trac e elements in ambient air)

• Sea salt constituents (Na and Cl) 23

There are a number of gas phase species that can be used to evaluate PM 2.5 . Their presence may affect the formation of secondary PM and the re sponse of secondary PM components to emissions strategies. Also, the model's performance in predicting certain gaseous species may provide diagnostic clues which help explain poor pe rformance. Gas phase species of most interest for performance evaluation due to being a direct precursor to secondarily formed PM 2.5 are listed below.

• Nitric acid (HNO3),

• Ammonia (NH3)

• Sulfur dioxide (SO2)

• Volatile Organic Compounds (VOCs)

Other related gas-phase oxidants (partial list) tha t indirectly influence PM 2.5 formation and
destruction are listed below.

22 For predicted/observed comparisons, organic carbon is preferred over organic carbon mass to allow for transparency between model predictions and obse rved measurements. Ambient measurements will generally only account for the carbon portion of or ganic carbon. Organic carbon mass may include hydrogen, oxygen, and other components.

23 White (2008) recommends the use of 1.8xCl

- to characterize ambient sea salt concentrations at coastal IMPROVE sites due to uncertainty in the XRF Na meas urements.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

76 • Ozone (O3)

• Nitric oxide (NO)

• Nitrogen dioxide (NO2)

• Peroxyacetyl nitrate (PAN)

• NOy (sum of NOx and other oxidized compounds)

• Carbon monoxide (CO)

• Hydrogen peroxide (H2O2)

Some of the species listed above are currently avai lable from either existing monitoring network systems and/or special studies. However, i t is important to note that many of the species listed above are not routinely measured (e. g. H2O2, HNO3, and NH3) or co-located with speciated PM measurements. Model performance can b est be assessed using extensive data bases, such as those obtained in major field studie s. Where possible, model time periods can be prioritized to correspond to periods when field study or special study data was collected. However, it is recognized that many of the precurso r species will not be available for every model application.



### 3.3 Ambient Measurement Networks

Provided below is an overview of some of the variou s ambient air monitoring networks currently available. Network methods and procedure s are subject to change annually due to systematic review and/or updates to the current mon itoring network/program. Please note, there are other available monitoring networks which are not mentioned here and more details
on the networks and measurements can be obtained fr om other sources, if necessary.


### 3.3.1 AQS

The Air Quality System (AQS) is a repository of amb ient air pollution data and related meteorological data collected by EPA, state, local and tribal air pollution control agencies from tens of thousands of monitors (it is not an actual monitoring network). The monitoring data in AQS are the result of the various Clean Air Act req uirements to provide a national database of ambient air pollution data. This information manag ement system contains over 1000 pollutants from 1957 through the present day. AQS contains all the routine hourly gaseous pollutant data collected from State and Local Air M onitoring Stations (SLAMS) and National Air Monitoring Stations (NAMS) sites. SLAMS is a dynami c network of monitors for state and local directed monitoring objectives (e.g., control strat egy development) which consists ofModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

77 approximately 4,000 monitoring stations. A subset o f the SLAMS network, the NAMS has an emphasis on urban and multi-source areas (i.e., are as of maximum concentrations and high population density).

The AQS database includes criteria pollutant data ( SO2, NO2, O3, CO, PM10, PM 2.5 and Pb) and speciation data of particulate matter (SO4, NO3, NH 4, EC, OC, and crustal Material), air toxic data, photochemical assessment data, and meteorolog ical data. The data are measured and reported on an hourly or daily average basis. The AQS system continues to expand to include more ambient air pollutants. An overview of the AQ S can be found at http://www.epa.gov/ttn/airs/airsaqs/index.htm . For querying the database or viewing the data one can use EPA's AirData website ( http://www.epa.gov/airdata/ ) which is a collection of user-friendly visualization tools for air quality a nalysts. The tools generate maps, graphs, and data tables dynamically. An AQS User’s Guide is av ailable at: http://www.epa.gov/ttn/airs/airsaqs/manuals/docs/us ers_guide.html



### 3.3.2 IMPROVE

The Interagency Monitoring of PROtected Visual Envi ronments (IMPROVE) network began in 1985 as a cooperative visibility monitoring effort between EPA, federal land management agencies, and state air agencies (IMPROVE, 2011). Data are collected at Class I areas across the United States mostly at National Parks, National Wi lderness Areas, and other protected pristine areas. Currently, there are approximately 181 IMPR OVE sites that have complete annual PM 2.5 mass and/or PM 2.5 species data. There are 110 IMPROVE monitoring si tes which represent air quality at the 156 designated Class I areas. The 7 1 additional IMPROVE sites are “IMPROVE protocol” sites which are generally located in rura l areas throughout the U.S. These sites use the IMPROVE monitoring samplers and collection rout ines, but are not located at Class I areas. The website to obtain IMPROVE documentation and/or data is http://vista.cira.colostate.edu/improve/ . Data advisories are posted at: http://vista.cira.colostate.edu/improve/Data/QA_QC/ Advisory.htm

.


### 3.3.3 CASTNet

Established in 1987, the Clean Air Status and Trend s Network (CASTNet) is a dry deposition monitoring network where PM data are collected and reported as weekly average data (U.S. EPA, 2012a). In addition, this network measures an d reports hourly ozone concentrations. CASTNet provides atmospheric data on the dry deposi tion component of total acid deposition, ground-level ozone and other forms of atmospheric p ollution. The data (except for ozone) are collected in filter packs that sample the ambient a ir continuously during the week. CASTNetModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

78 now comprises 90 monitoring stations across the Uni ted States. The longest data records are primarily at eastern U.S. sites. More information can be obtained through the CASTNet website

at http://www.epa.gov/castnet/

.


### 3.3.4 CSN

The Chemical Speciation Network (formerly known as STN: The Speciation Trends Network) began operation in 1999 to provide nationally consi stent speciated PM 2.5 data for the assessment of trends at representative sites in urb an areas in the U.S. The CSN was established by regulation and is a companion network to the mas s-based Federal Reference Method (FRM) network implemented in support of the PM 2.5 NAAQS. As part of a routine monitoring program, the CSN quantifies mass concentrations and PM 2.5 constituents, including numerous trace elements, ions (sulfate, nitrate, sodium, pot assium, and ammonium), elemental carbon, and organic carbon. There are currently 52 trends sites in the CSN nationally in operation. CSN sites are largely static urban monitoring stations with protocols for sampling methods; dedicated to characterizing aerosol mass components in urban areas of the U.S. to discern longterm trends and provide an accountability mechanism to access effectiveness of control programs. In addition, there are approximately 150 supplemental speciation sites which are part of the CSN and are SLAMS sites. The CSN data at trends sites are collected 1 in every 3 days, whereas supplemental sites collect data eithe r 1 in every 3 days or 1 in every 6 days. Comprehensive information on the CSN and related sp eciation monitoring can be found at http://www.epa.gov/ttn/amtic/specgen.html

.


### 3.3.5 NADP

Initiated in the late 1970s, the National Acid Depo sition Program (NADP) monitoring network began as a cooperative program between federal and state agencies, universities, electric utilities, and other industries to determine geogra phical patterns and trends in precipitation chemistry in the U.S. NADP collects and reports we t deposition measurements as weekly average data (NADP, 2013). The network is now know n as NADP/NTN (National Trends Network) with nearly 200 sites in operation. The N ADP analyzes the constituents important in precipitation chemistry, including those affecting rainfall acidity and those that may have ecological effects. The NTN measures sulfate, nitr ate, hydrogen ion (measure of acidity), ammonia, chloride, and base cations (calcium, magne sium, potassium). Detailed information regarding the NADP/NTN monitoring network can be fo und at http://nadp.sws.uiuc.edu/

.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

79 

### 3.3.6 SEARCH

The South Eastern Aerosol Research and Characteriza tion (SEARCH) monitoring network was
established in 1998 and is a coordinated effort bet ween the public and private sector to characterize the chemical and physical composition as well as the geographical distribution and long-term trends of PM 2.5 in the Southeastern U.S (Edgerton, 2005; Hansen, 2 003). SEARCH data are collected and reported on an hourly/daily basis. A total of eight sites have historically been operated by SEARCH, although only six are curr ently operating: Birmingham, Alabama (urban), Centreville, Alabama (rural), Gulfport, Mi ssissippi (urban), Jefferson Street, Atlanta, Georgia (urban), Oak Grove, Mississippi (rural) (no t operational as of 2010), Yorkville, Georgia (rural) (not operational as of 2009), suburban Pens acola, Florida (suburban), and downtown Pensacola, Florida (urban). Background information regarding standard measurement techniques/protocols and data retrieval can be foun d at http://www.atmosphericresearch.com/studies/SEARCH/index.html

. 

### 3.3.7 Speciated PM 2.5 

### Data Summary

Speciated PM 2.5 data play an especially important role in performa nce evaluation, as the deterministic models predict exact chemical compone nts which can be compared to some of the corresponding measured analytes. There are kno wn positive and negative sampling artifacts as well as analysis artifacts associated with FRM and speciation monitors (Frank, 2006). Due to the complex nature of the measurements, a ba sic understanding of the various PM 2.5 and speciation monitoring technology and measuremen ts are needed before undertaking a
model evaluation

.

Table 3.2: Known measurement artifacts for PM 2.5 and speciated PM for different monitoring
networks.

Network Measurement Notes Sampling Time

FRM PM 2.5 - Negative artifacts: ammonium nitrate, organic carbon

- Positive artifacts: organic carbon,

water 24-hour average 1/3 days or 1/6 days (some daily sites)

PM 2.5Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

80 Network Measurement Notes Sampling Time
CSN (formally STN) - Negative artifacts: ammonium nitrate, organic carbon

- Positive artifacts: organic carbon,

water -Note that positive and negative
artifacts for PM 2.5 may be different
from artifacts for individual PM components because the speciated

PM 2.5 samples are collected on different types of filters than are used
for total PM 2.5 gravimetric

measurements

-CSN PM 2.5 mass may be different from
FRM measurements due to differences in handling procedures 24-hour average 1/3 days or 1/6 days

SO4

NO3

NH4

Organic Carbon - Blank correction is needed to account

for positive artifact. 24

- Before 2007, OC/EC split was operationally defined using TOT

24 CSN OC measurements reported in AQS do not currentl y have any artifact correction applied, although ambient data provided in MATS does already include a blank correction. Simultaneous with the switch from TOT to TOR, CSN OC samplers were also updated so that the same artifact correction techniques could be applied to measurements from CSN and IMPRO VE networks. The OC artifact from CSN monitors after the change in samplers is generally smaller than OC artifact from CSN monitors before the change in samplers, although this difference ha s not been fully characterized at this point. Work by Malm et al (2011) suggests that CSN OC from 2005 an d 2006 (before the switch in samplers) likely had a positive multiplicative bias of ~1.2 and a positive additive bias between 1.0 and 1.9 depending on the time of year. Frank (2012) recommended updating a rtifact correction methodologies for both CSN and IMPROVE. These recommendations have not yet been i mplemented. Until further network-wide artifact policy has been put in place, we recommend adjusting uncorrected AQS OC data by subtracting 1.4 for measurement before the switch to IMPROVE-li ke measurements and by subtracting 0.12 from measurements after the switch based on Frank (2012)

.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

81 Network Measurement Notes Sampling Time

CS N (formerly STN)

(con’t.) method (NIOSH). CSN monitors switched to using the TOR method (consistent with IMPROVE monitors)
between 2007and 2009. 25

Elemental Carbon - Before 2007, OC/EC split was

operationally defined using TOT method (NIOSH). CSN monitors switched to using the TOR method (consistent with IMPROVE monitors) between 2007 and 2009(see footnote 23)

CASTNet SO4

weekly average

NO3 Split between NO3 and HNO3 may not

be reliable in warm weather due to
conversion from NO3 to HNO3. Recommend comparing total nitrate (NO3 + HNO3) between model and
observations.

NH4

SO2

HNO3 Split between NO3 and HNO3 may not

be reliable in warm weather due to
conversion from NO3 to HNO3. Recommend comparing total nitrate (NO3 + HNO3) between model and
observations.

PM 2.5 - Negative artifacts: ammonium nitrate,

organic carbon

25 The transition from TOT to TOR for CSN monitors occ urred in shifts: 56 sites switched in May 2007, 63 sites switched in April 2009, and 78 sites switched in October 2009.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

82 Network Measurement Notes Sampling Time

IMPROVE - Positive artifacts: organic carbon,

water -Note that positive and negative
artifacts for PM 2.5 may be different
from artifacts for individual PM components because the speciated

PM 2.5 samples are collected on different types of filters than are used
for total PM 2.5 gravimetric

measurements 24-hour average 1/3 days

SO4 and S -IMPROVE monitors measure both

elemental S (by XRF) and ionic SO 4 - by ion chromatography. Generally either measurement is reliable and may be used for model evaluation purposes although some caveats should be
noted. 26

NO3

NH4 -Only available at a limited number of

sites for a limited number of years

Organic Carbon - OC/EC split is operationally defined

using Thermal Optical Reflectance (TOR) analysis.

- Blank corrections based on a median measured positive OC artifact from back-up filters at six sites are applied on a month-specific basis to all data before they are reported (Chow et al,

26 Hyslop et al (2012) report that XRF elemental sulfu r measurements are somewhat affected by changes in XRF methodologies over time. Also, prior to 200 0, filters used to collect sulfate for ion chromatography became clogged during high sulfate e pisodes. Changes in filter collection techniques eliminated this problem after 2000. In addition, r ecent literature by Tolocka and Turpin (2012) sugge sts that differences between XRF sulfur and ion chromat ography sulfate are due to the presence of organic sulfur in aerosol (which would be measured by XRF b ut not IC). If this is the case, than the IC sulfa te measurement is more appropriate for comparison with modeled sulfate which generally does not include formation of organic sulfur compounds.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

83 Network Measurement Notes Sampling Time

IMPROVE (con’t.) 2010; Watson et al., 2009). Frank (2012) recommended changing blank correction procedures for both
IMPROVE and STN OC measurements. These recommendations have not yet been implemented.

Elemental Carbon - OC/EC split is operationally defined

using Thermal Optical Reflectance (TOR) analysis.

Sodium XRF analysis is not as sensitive to

sodium as other methods and generally under-reports sodium
concentrations.

Silicon XRF silicon measurements are

unreliable at high sulfur conditions (when S/Si > about 7)

Aluminum Elemental concen trations above the

MDL can go undetected. IMPROVE has published an alternate soil
calculation which estimates Al
based on Si concentrations. 27

In general, the speciated PM 2.5 data can be directly compared to the model outputs . Of note, care should be taken in examining issues with blank corrections for organic carbon (especially when measured OC is low) as well as the EC/OC split . Prior to 2006, the CSN and IMPROVE networks used different analysis techniques when es timating EC and OC (CSN monitor locations
switched to a similar method to IMPROVE between 200 7 and 2009, depending on the monitor). The EC/OC analysis method will generally have a lar ger impact on EC concentrations than OC concentrations. The difference in OC/EC splits bet ween the two techniques has been investigated in depth in the literature and by IMPR OVE staff (Chow, 2001, Chow, 2010, White,

27 Due to difficulties in detecting Al, IMPROVE somet imes uses an alternative SOIL calculation which doe s not include Al: SOIL = 3.48*Si + 1.63*Ca + 2.42*Fe + 1.94*Ti (http://vista.cira.colostate.edu/improve/publicatio ns/SOPs/ucdavis_sops/sop351_V2.pdf)Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

84 2007). It may not be appropriate to compare EC con centrations between networks for years prior to 2009 (although the data can still be used to evaluate model performance).

Total PM 2.5 mass measured gravimetrically at CSN and IMPROVE m onitors is known to have several measurement artifacts (both positive and ne gative) (Frank, 2006, Simon, 2011, Malm, 2011). Consequently, summing the mass of all PM 2.5 species components is not likely to give
the same answer as the total measured PM 2.5 mass from a Teflon filter. Therefore, model evaluations should concentrate on comparisons with speciated PM 2.5 components at these sites, rather than total PM 2.5 mass.

FRM monitors do not measure all of the PM 2.5 in the air, and the speciation samplers don't
measure PM 2.5 in the same way as the FRM monitors (Frank, 2006). Due to these positive and negative artifacts, the FRM data may not be directl y comparable with model outputs without adjustment. The models predict PM species as they might be measured in the ambient air, but the FRM measurements may not always measure what is in the air (due to known positive and negative artifacts). As part of the PM 2.5 attainment test we recommended default methodologies to adjust speciation (CSN and IMPROVE ) data to better approximate the FRM data (see sections 4.4 and 4.5). A similar adjustm ent could be made to the model output data to allow the direct comparison to FRM measurements. However, as noted earlier, there may be limited value of information gained by evaluatin g daily average total measured PM 2.5 mass (as compared to PM 2.5 components). More meaningful information may be gl eaned from
continuous PM 2.5 ambient data.

Another issue to consider when comparing modeled an d observed PM concentrations is the
size definitions for PM. IMPROVE and CSN ambient m onitors measure PM 2.5 using inlets which screen out any particles with aerodynamic diameters greater than 2.5 μm. Air quality models use varying schemes to characterize particulate mat ter size distributions. The two most comment modeling schemes are the sectional treatmen t and the modal treatment. The default configuration for CAMx includes a sectional aerosol treatment while the default configuration for CMAQ includes a modal aerosol treatment, althou gh other versions of these models exist
with alternate aerosol treatments. Sectional aeros ol modules split particles into bins defined using discrete size cutpoints based on aerodynamic diameter (e.g. particles with aerodynamic diameters between 1 μm and 2.5 μm). One common sec tional set-up includes 2 bins, one containing particles with aerodynamic diameters < 2 .5 μm (fine PM) and one containing particles with aerodynamic diameters > 2.5 μm (coar se particles). Other models have split the fine PM into more than two bins (Nolte et al., 2008 ; Pandis et al., 1993). With sectional models, the comparison to measured PM 2.5 is straightforward so long as one of the PM bins h as a cutoff at an aerodynamic diameter of 2.5 μm (i.e. add up P M mass in all bins less than 2.5 μm). The modal approach treats PM as occurring in distinct “ modes”, each one having a lognormalModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

85 distribution. Each PM mode is characterized by the geometric mean diameter and the geometric standard deviation of the diameter (the g eometric mean and standard deviation can be tracked for particle number, surface area, mass or all three). With modal aerosol treatment,

PM 2.5 can be estimated in several ways. One method is t o simply sum the mass in the modes that generally contain fine PM. Another method wou ld be to use the geometric mean and
standard deviation to calculate the fraction of PM mass in each mode which is associated with particles less than 2.5 μm. However there are seve ral complicating factors with taking the second approach. First, the EPA’s National Emissio ns Inventory and many other inventories
define PM 2.5 as being entirely less than 2.5 μm in diameter. I f these primary emissions are injected into a modal model (generally into a fine PM mode), they are instantaneously mixed with existing aerosol mass in that mode. The tail of the fine PM mode will include particles greater than 2.5 μm. Therefore, a fraction of the PM that was operationally defined as primarily emitted PM 2.5 (diameter < 2.5 μm) would be incorrectly distribute d into the > 2.5 μm size range based on the mode distribution. If prim ary emissions were quantified based on modes versus a discrete cutpoint, then this would n ot be a problem. Secondly, some modal models have been shown to over-predict the peak-con centration diameter and distribution width of the fine PM mode (Kelly et al., 2011; Kell y et al., 2010). This would lead to underpredictions of the PM 2.5 fraction by increasing the mass fraction in the >2 .5 micron diameter range. Based on these two complications, at this t ime we do not recommend calculating the fraction of PM mass in each mode below the 2.5 μm c utpoint unless air agencies have specific analysis for their application to show that method to be more accurate than simply summing mass over the fine PM mode(s).

Finally, although we recommend weighing performance of speciated PM components more heavily than total PM, some additional insight can be gained by looking at FEM continuous PM2.5 measurements. Since speciated measurements a re generally derived from filters, these measurements are usually taken as 24-hour averages. The FEM continuous measurements provide a diurnal profile of PM2.5 concentrations t hat can be compared against hourly model outputs to determine whether the model is replicati ng the measured temporal pattern.



### 3.4 Diagnostic Evaluation

The goal of a diagnostic model evaluation is to det ermine whether the model correctly represents the physical and chemical processes that control the ambient concentrations of precursors and secondary pollutants. It is possibl e that a model can incorrectly represent the relative amount of primary emissions versus seconda ry production or that a model canModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

86 incorrectly represent the importance of dispersion, deposition or chemical reaction as a sink. Thus, the ability of a model to accurately reproduc e the observed concentration of a particular pollutant does not guarantee that the model correct ly simulated the processes that determine the concentration of the pollutant. The goal of a diagnostic evaluation is to investigate the processes that determine the ambient concentrations of ozone, PM 2.5 and their precursors, and to develop confidence that the model can accurately predict how these concentrations will change in the future (e.g., Arnold et al., 2006). It is likely that research grade measurements needed to perform a diagnostic evaluation will not be available in many SIP modeling studies, however, it can still be valuable to perform diagno stic analyses and compare the results with previous modeling research studies.

Several different tools and analysis methods are av ailable for use in diagnostic model evaluation including process analysis, indicator ra tios, source apportionment methods, model sensitivity studies, and retrospective studies.



### 3.4.1 Process Analysis

Both the CMAQ and CAMx models include a process ana lysis tool (Jeffries and Tonnesen, 1994) that provides additional diagnostic information on the chemical reactions and physical processes (e.g., emissions, deposition, advection, vertical mixing, and net change from chemistry) that determine species concentrations. Chemical reaction data can be output either as the hourly integrated reaction rate (IRR) for ea ch reaction or as a summary of key reactions in the chemical process analysis (CPA). Physical p rocesses can be output as hourly concentration changes in the integrated process rat e (IPR) file. Detailed analysis off IRR and IPR outputs have been demonstrated in research studies (Jang et al., 1995; Vizuete et al., 2008; Henderson et al., 2011), however, significant time and effort are required to complete these analyses. The CPA output provides a more limited s et of chemical process information that is more accessible and that can be visualized using th e same tools used to analyze model concentration output files. Minimal computational cost is required to produce the CPA files and they are in the same format as the model concen tration files and can be view using the same visualization tools. Below are examples of th ese plots comparing modeled ozone concentration, daily total Ox production, and net O 3 production in NOx limited and oxidant limited chemical regimes. The CPA tool can also pro vide information on the chemical reactions of VOC, NOy and radical species.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

87 Figure 3.4.1 Chemical process analysis plots showing modeled oz one concentration, daily total Ox production, and net ozone production under VOC sensitive and NOx sensitive
conditions.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

88 

### 3.4.2 Indicator Ratios

One way to evaluate the response of the model is to examine predicted and observed ratios of indicator species. Indicator species techniques ha ve been developed for both ozone and secondary PM species (in particular nitrate) (Sillm an, 1995, 1998; Ansari and Pandis, 1998; Blanchard et al., 2000; Tonnesen and Dennis, 2000a, b). If ratios of observed indicator species are very high or very low, they provide a sense of whether further ozone or secondary PM 2.5 production at the monitored location is likely to b e limited by availability of NOx or VOC (or NH 3 in the case of particulate nitrate). Agreement bet ween paired observed and predicted ratios suggests a model may correctly predict the sensitiv ity of ozone or secondary PM 2.5 at the monitored locations to emission control strategies. Thus, the use of indicator species aims to validate the modeled chemical regime and may help t o build confidence in the RRFs predicted from the specific model application.

For ozone, measurements of certain “indicator speci es ratios” are a potentially useful way to
assess whether local ozone formation is VOC or NOx limited at any particular point in space and time as well as, help reveal whether the model is c orrectly predicting the sensitivity of ozone to VOC and/or NOx controls (e.g., comparisons between modeled and observed ratios: O 3 /NOy, O3/HNO3) (Millford et al., 1994; Sillman, 1995, 199 7, 1998, 2002; Lu and Chang, 1998). Tonnesen and Dennis (2000a,b) distinguished between short lived indicators of instantaneous rates of production of O3 (PO3) or odd oxygen (POx) versus long lived indicators of peak O 3 concentration and found that the ratio of productio n of H2O2 to production of HNO3 (PH2O2/PHNO3) is strongly associated with POx sensi tivity to VOC and NOx. Tonnesen and Dennis also found that the ratio of formaldehyde to NO 2 was useful as an indicator of O 3 sensitivity, and this ratio can be evaluated using satellite data (Martin et al., 2004; Duncan et al., 2010). For PM, such a comparison may reveal w hether the model is predicting sensitivity of secondary components of PM 2.5 to changes in SO 2 , NH 3 , VOC and/or NOx controls correctly (Ansari and Pandis, 1998; Blanchard et al., 2000; P un and Seigneur, 1999, 2001). If a model accurately predicts observed indicator species rela tionships, then one can conclude with additional confidence that the predicted change in future year ozone or PM may be accurate.

Although ratios of indicator species have the poten tial to be quite useful, they should be used
with caution since different studies have identifie d different ranges of values to characterize VOC and NOx limited regimes in different locations. Table 3.3 summarizes the range of values determined for 10 indicator species or ratios in va rious studies. In cases where the NOx and VOC limited ranges are broad or overlap, air agenci es should attempt to use values from studies in the local area. At a minimum, air agencies shou ld be careful with respect to the use of indicator species when the range of observed values for which the preferred direction ofModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

89 control is not clear. When this occurs, agreement between predictions and observations does not necessarily imply that the response to controls , as predicted by the model is correct (especially for secondary particulate matter to cha nges in precursors). If a model predicts observed values of indicator species relationships such that observed and predicted value fall within the same range, this provides some confidenc e that the predicted change in ozone or secondary particulate matter may be accurate.

A second precaution is that this method often requi res more types of measurements than are commonly available. In some cases, it may be diffi cult to calculate meaningful indicator ratios from relatively imprecise measurements from routine monitoring networks. Finally, much of the work done to date with indicator species has fo cused on peak concentrations of ozone. Limited research has been conducted on development of indicator ratios for secondary PM.

Table 3.3: Ranges of indicator species/ratios that have been r eported for VOC and NOx limited regimes.

indicator median VOC

- sensitive transition median NO x - sensitive

O 3 /NO y 1.6

- 8.8 a,b,d,f

- m 6

– 15 c,d,m,n,q 8.8

– 26 a,b,d,f

- m

O 3 /NO z 5.8

– 16 a,d,f,j

- m 7

– 100 c

- f, n,q 10.1

– 35 a,d,f,j

- m

O 3 /NO x < 15 15

– 60 n,o,q,t > 40

O 3 /HNO 3 7

- 33.9 a,d,j,k,m 12

– 25 c,d 17.2

- 62.9 a,d,j,k,m

H 2 O 2 /HNO 3 0.3

- 1.5 d,j

- m 0.2

- 2.4 c,e,h,n

- q 0.4

- 3.6 d,j

- m

H 2 O 2 /NO z 0.2

- 1.1 a,j 0.2

- 0.25 c 0.6

- 1.3 a,j

H 2 O 2 /NO y 0.2

- 0.4 a,j 0.12

- 0. 17 c 0.6

- 1.0 a,j

HCHO/NO 2 < 1 1

- 2 n,o,u > 2

NO z /NO y < .5 0.5

- 0.6 f > 0.6

NOy 7.7

- 12.2 j 5

- 2 0 c,e,f,n,r,s 3.2

- 5.7 j

a Castell et al., 2009; b Xie et al., 2011; c Torres-Jardon and Keener, 2006; d Sillman and He, 2002; e Sillman, 1995; f Lu and Chang, 1998; g Stein et al, 2005; h Sillman et al., 1997; i Martilli et al., 2002; j Jimenez and Baldasano, 2004; k Torres-Jardon et al, 2009; l Chock et al, 1999; m Sillman et al., 2003; n Zhang et al, 2009;Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

90 o Tonnesen and Dennis, 2000a; p Hammer et al., 2002; q Liang et al., 2006; r Milford et al, 1994; s Vogel et al, 1995; t Tonnesen and Dennis, 2000b; u Martin et al., 2004

Blanchard et al., (2000) have examined how indicato r species might be used to assess whether particulate nitrate concentrations are limited by N Ox or by ammonia emissions using mechanisms which incorporate reactions dealing with secondary particulate matter. These authors identify several indicators which appear po tentially useful for identifying limiting precursors for secondary nitrate particulate matter . The indicators include:

(1) excess NH 3 defined in Equation 3-1 (values greater than zer o indicate NOx limited regimes and values less than 0 indicate ammonia limited reg imes);

	! " = 	 [NH3(g)]	+	 [NH4 + (a)]	– 	2 ∗ [SO42 − (a)]	− 	 [NO3 − (a)]	– [HNO3(g)]

− 	 [HCl(g)]	+ 	2 ∗ [Ca2+]	+ 	2 ∗ [Mg2+]	+	 [Na+]	+ 	 [K+]	+ 	 [ Cl−]

[3-1] 28

(2) The ratio of particulate to total nitrate. Val ues above 0.9 at high temperatures indicate NOx limited regimes (values below 0.3 to 0.5 were indic ative of ammonia limited regimes.)

Ansari and Pandis (1998) also have suggested the us e of the gas ratio (GS), defined in equation 3-2, as an indicator of PM formation regimes

.



### 45 = 6 7

8

69 7 : [3-2] 29

Despite these precautions, comparing predicted and observed relationships of indicator species provides a means of assessing a model's ability to accurately characterize the sensitivity of predicted ozone and predicted secondary components of PM 2.5 to changes in precursors. Where observations are available, it is recommended that SIP modeling demonstrations show reasonable agreement between observations and ratio s of indicator species.

28 Concentrations are in μmol/m 3 , (g) represent gas-phase and (a) represents aeroso l phase.

29 NH 3 F is free ammonia (total ammonia – 2*sulfate). HNO 3 T is total nitrate (gas + PM).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

91 

### 3.4.3 Source Apportionment

Source apportionment methods and model sensitivity studies are two complementary approaches that can be used to identify emissions s ources that contribute to high pollutant concentrations. The CMAQ and CAMx models both inclu de options to use source apportionment tools to identify sources that contri bute to the production of ozone (ENVIRON, 2014; Kwok et al, 2014) and some components of PM 2.5 including sulfate, nitrate, ammonium and primary emissions of PM2.5 (ENVIRON, 2014; Kwok et al., 2013). Source apportionment tools employ a set of model tracers to attribute po llutant concentrations to user selected emissions source sectors and geographic areas. This approach can be useful both as a diagnostic tool to identify sources that contribute to ozone and PM 2.5 and also as an aid in regulatory analysis to identify emissions control m easures that will be effective for attaining air quality standards. An advantage of source apportion ment approaches is that tracers attribute the mass contribution from a source to a receptor, and therefore this approach is less susceptible to the effects of non-linear chemistry that can complicate the interpretation of model sensitivity simulations.



### 3.4.4 Sensitivity Studies

Model sensitivity simulations can be used to identi fy model inputs (IC/BC, meteorological data, chemical reactions, and emissions) that have the gr eatest impact on model predicted concentrations. The objective of sensitivity tests is to identify whether the inputs have a large enough influence that errors in these inputs could be a driving influence on the model predictions. In “brute-force” sensitivity simulatio ns, the model input data is modified directly, e.g., increasing or decreasing emissions in the mod el input file. Brute force simulations are relatively easy to implement but are resource inten sive when many different inputs must be evaluated. The Direct Decoupled Method (DDM) is an approach that can be used to evaluate the first order sensitivity to multiple model input s in a single model simulation (e.g., Cohan et al., 2005; Napelenok et al., 2006). Because of non- linearity in chemical reactions, first order sensitivity is not always sufficient, and high-orde r DDM (HDDM) can be used to evaluate second order sensitivities to inputs that are subje ct to non-linear chemistry (Zhang et al., 2012b).

In cases where the model performs poorly in the bas e case, sensitivity simulation may be useful to identify potential causes for poor model perform ance. However, caution should be used in modifying inputs to achieve good model performance because arbitrarily “tuning” of modelModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

92 input data to fit the observations can introduce co mpensating errors. For example, if a model
underestimates observed ozone because the meteorolo gical data is overly dispersive, the model performance could be improved by increasing e missions or BC concentrations, but this model would not be reliable for predicting future c oncentrations because its good performance in the base case was achieved through compensating errors in the input. An appropriate use of sensitivity approaches is to identify each of the p rocesses or inputs that has a large effect on model predictions, and then perform additional anal ysis of those inputs to determine if they are accurately represented in the base case model



### 3.4.5 Dynamic Model Evaluation

A second method for assessing a model's performance in predicting the sensitivity of ozone or
PM 2.5 species to changes in emissions is to perform a ret rospective dynamic analysis. This involves comparing model predicted historical trend s with observed trends. Retrospective analyses provide potentially useful means for diagn osing why a strategy did or did not work as expected. They also provide an important opportuni ty to evaluate model performance in a way which is closely related to how models are used to support an attainment demonstration.

A dynamic evaluation uses various analyses to asses s the accuracy of the model in characterizing the sensitivity of ozone and/or PM 2.5 to changes in emissions. Although dynamic evaluation is always recommended, the measurements and resources needed for a dynamic evaluation may not be readily available to many air agencies performing SIP modeling. Additional challenges for retrospective analyses in clude identifying appropriate case studies for which responses to emissions changes are large enou gh to be distinguished in ambient data, developing or obtaining equivalent and appropriate emissions for multiple historical years, and disentangling the confounding influences of year to year changes in meteorology and emissions. Consequently, retrospective dynamic eva luations may be beyond the scope of many SIP modeling demonstrations. Major challenges in e valuating indicator species include variability in the characteristic ratios for VOC an d NOx limited regimes in different locations and the availability of ambient data. Where appropriat e observational data are available, modelers are encouraged to evaluate these indicators.

A retrospective analysis is intended to examine the ability of the model to respond to emissions changes by comparing recent trends in observed ozon e or PM 2.5 concentrations to the modelpredicted trend over the same time period. The app roach is a direct assessment of what isModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

93 most important in an attainment demonstration: does the model accurately predict changes in air quality as a result of changes in emissions? As part of a retrospective the model is run for (a) current episodes or time periods and (b) episod es in one or more historical time periods using the emissions and meteorological inputs appro priate for each time period modeled.

While retrospective analyses may be useful, it may be difficult to obtain meteorological and emissions inputs for the historical time period(s) that are calculated using techniques and assumptions which are consistent with the calculati on of these same inputs for the current time period. Using inconsistent inputs will confound th e interpretation of the predicted trend. One method for ensuring consistent methodology for emis sions estimates is to backcast emissions from the most recent year to the earliest year. Th e model should respond in a predictable way if the emissions changes are large enough. Hence, modeling an emissions change of only a few percent would not be viable. However, if NOx or SO 2 emissions have been reduced by a large percentage (e.g. 30% or more) over a relatively sho rt time period, then that may be a good time period to test the response of the model.

Because differences in meteorology between years ca n confound the apparent change in pollutants, a complete retrospective analysis could include multiple sensitivity runs using both year-specific meteorology and constant meteorology. The year-specific meteorology would allow the modeler to compare modeled and observed t rends most accurately, and the constant meteorology would help inform the analysis on what portion of the change in pollutant
concentrations were due to emissions versus meteoro logical differences.

Retrospective analyses are one of the few tools tha t can be used to determine if the model is responding “adequately” to control measures. Retro spective analyses may be an important tool to show that the models are responding “correc tly” to emissions changes. Recent efforts to perform this type of analysis have been undertak en by Zhou et al (2013), Gilliland et al (2008), Godowitch et al (2010), Pierce et al. (2010 ) and (Foley et al, 2014). These published analyses may be used to better understand the abili ty of certain models to properly capture relationships between ambient pollution levels and emissions. However, since model response is highly dependent on the meteorology and emission s inputs, past studies will not necessarily provide insight on specific SIP demonstration model ing episodes which rely on independently derived meteorology and emissions.

Another dynamic evaluation approach is to look at o perational performance under varying conditions, e.g., by day of the week, by season, an d regionally (Marr and Harley, 2002; Murphy et al, 2007; Harley et al, 2005). The mix of pollut ants vary by day of the week and from city to city so when a model shows good operational perform ance across these different chemical environments, this supports the assertion that it w ill respond appropriately to changes inModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

94 emissions. Two recent studies have performed model evaluations that focused on the model’s ability to capture weekend/weekday changes (CRC, 20 11; Pierce et al, 2010)



### 3.5 Evaluation Tools

One available software tool to assist in model perf ormance evaluations is the Atmospheric Model Evaluation Tool (AMET) (Appel, 2011) ( www.cmascenter.org ). AMET is built on several open-source software packages and uses the MySQL da tabase software to store paired observation and model output data. There are modul es in AMET for evaluating both meteorological and air quality model output. AMET provides a useful set of scripts for creating various types of plots and statistics.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

95 

## 4.0 Assessing Modeled Attainment

A **model attainment demonstration** usually consists of two key components: a) analyse s which
estimate whether a set of simulated emissions reduc tions will result in ambient concentrations that meet the National Ambient Air Quality Standard s (NAAQS) and b) an identified set of control measures which will result in the required emissions reductions. This guidance focuses on the first component of a model attainment demons tration, that is, the completion and interpretation of analyses to estimate the amount o f emission reduction needed to attain the
NAAQS.

Air agencies should estimate the amount of emission reductions needed to demonstrate attainment of the NAAQS using the **model attainment test** . The model attainment test is a technical procedure in which an air quality model i s used to simulate base year and future air pollutant concentrations for the purpose of demonst rating attainment of the relevant NAAQS.
The recommended test uses model estimates in a “rel ative” rather than “absolute” sense to estimate future year design values. As explained i n more detail in subsequent sections, the fractional changes in air pollutant concentrations between the model future year and model base year are calculated for all valid monitors. T hese ratios are called **relative response factors** (RRF). Future ozone and/or PM 2.5 design values are estimated at existing monitoring sites by multiplying the modeled relative response factor fo r each monitor by the monitor-specific base year design value. The resulting estimates of futu re concentrations are then compared to the NAAQS. If the future estimates of ozone and/or PM 2.5 design values do not exceed the NAAQS, then this provides evidence that attainment will be reached.

In addition to the model attainment test, air agenc ies should also consider performing a set of corroboratory analyses to further assess whether a proposed set of emission reductions will lead to attainment of the NAAQS (or uniform rate of progress for regional haze). As discussed later in this section (see section 4.9), the model attainment test and these supplemental analyses should be aggregated into a **weight of evidence determination** to evaluate whether the selected emissions reductions will yield attainment

.



### 4.1 Overview of model attainment test

There are four reasons why the recommended model at tainment test is based on application of the model in a relative sense. First and foremost, this approach has the effect of anchoring the
future concentrations to a “real” measured ambient value, which is important given model biasModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

96 and error in the base year simulation(s). It is re asoned that factors causing bias (either under or over-predictions) in the base case will also affect the future case. While good model performance remains a prerequisite for use of a mod el in an attainment demonstration, problems posed by imperfect model performance on in dividual days are expected to be reduced when using the relative approach. An inter nal EPA analysis (USEPA, 2014b) considered whether daily ratios of model future/current maximu m daily 8-hour ozone averages (MDA8) varied strongly as a function of site-specific base case model performance. This analysis was completed using a national model simulation that pr ojected 2020 ozone concentrations from a 2007 base case. The analysis determined that when modeled MDA8 ozone bias was relatively small (e.g., less than +/- 20 ppb), the average res ponse ratios were not a strong function of the model MDA8 bias. This provides confidence that the model can detect the air quality response
in the midst of reasonable levels of absolute bias and error. Second, the relative model attainment test allows for a future projection of 3 -year average ozone and PM 2.5 design values without explicitly having to model a three-year per iod. Third, because PM 2.5 concentrations typically consist of a diverse mix of primary and s econdary components, the relative model attainment test decreases the possibility of choosi ng ineffective control strategies based on
inaccurate model estimations of PM 2.5 composition by assessing component specific RRFs. Fourth and finally, there is evidence, based on a r etrospective modeling analysis of ozone changes between 2002 and 2005 (Foley et al, 2014b) and 2002 and 2010 (Hogrefe et al, 2014), that using the model in a relative sense provides b etter estimates of future ozone design values than using the absolute future year simulation. Th e correlation between model-projected and actual design values, as well as the accuracy of mo del projections of attaining a 75 ppb NAAQS both appear to be slightly improved when a relative model attainment test is utilized.

Equation (4.1) describes the recommended model atta inment test in its simplest form, as applied for monitoring site **i** : 

### (DVF) > 

### = ( ??@ )  

### ∗ (ABC)  [4.1]

where DVF > is the estimated design value for the future year in which attainment is required at monitoring site **i** ; RRF > is the relative response factor at monitoring site **i** ; and DVB > is the **base design value**  monitored at site **i** . Each of these terms is discussed in more detail below.

EPA has developed the Model Attainment Test Softwar e (MATS) tool to enable completion of
the model attainment tests for PM 2.5 and ozone, as well as for calculating changes in v isibility in Class I areas as part of the uniform rate of progre ss analysis for regional haze (Abt, 2014). This tool can be found on EPA’s Support Center for Regul atory Atmospheric Modeling (SCRAM) website in the section on “Modeling Guidance and Su pport” http://www.epa.gov/scram001/modelingapps_mats.htmModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

97 

### 4.1.1 Establishing the base design value

The base design value for each monitoring site ( ABC  ) is the anchor point for estimating future year projected concentrations. Because the modelin g is being used in a relative sense to determine how the modeled emissions changes will af fect air quality design values in an area, it is important to match the base design value as clos ely as possible to the base year for which future/base ratios will be assessed. That is, if o ne is estimating the expected air quality change from emissions reductions between 2010 and 2020, it is important to establish an air quality anchor that represents 2010 emissions levels as clo sely as possible. However, it is wellestablished that inter-annual variability in meteor ological conditions often leads to year-to-year differences in design values, even with static emis sions levels (USEPA, 2012). In addition, there is also year to year variability in emissions due t o economic factors (such as recessions) and compliance with regulations. We therefore recommen d using the base design value approach
described below.

For the model attainment tests for ozone and PM 2.5 , we recommend using the average of the three design value periods which include the base e missions inventory year. 30 This average is expected to best represent the air quality resultin g from base year emissions with some consideration of meteorological and emissions varia bility. For example, if the model attainment test uses base year emissions from 2011, then the base design value would be calculated as shown in equation 4.2 for each site **i** :

30 EPA recommends using ambient design values that ar e consistent with the official design values as calculated according to 40 CFR part 50 Appendix N ( PM2.5 NAAQS) and Appendix P (8-hour ozone NAAQS). This includes flagging and removing event- influenced data that meet the requirements set forth in the Exceptional Events Rule (40 CFR 50.14) . In general, air agencies flag data that they bel ieve may qualify for removal as an exceptional event and are then responsible for developing and providing documentation to EPA to support these requests for exclusion. EPA Regional Offices review exceptional events claims and decide whether to concur with eac h individual claim. Once EPA concurs with an air agency’s request, the event-influenced data are off icially noted and removed from the data set used to calculate official design values. In some cases, h istorical ambient data may meet the requirements of the Exceptional Events Rule, but remain in the data set used to calculate official design values. Air agencies may or may not have flagged these data as being potentially influenced by exceptional events or may have flagged these data, but not submitted t he required documentation. Air agencies may not closely examine potential event-influenced data tha t do not affect attainment/nonattainment decisions. However, the influence of potential eve nt-influenced data may affect future year projections that are part of the modeled attainment demonstration. If potential exceptional eventinfluenced data from the historical record may affe ct the outcome of the modeled attainment demonstration, we encourage air agencies to consult with their EPA regional office to determine how best to handle this situation.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

98 

### ( ABC ) F = (( GH	
I ) J K ( GH	
I ) J K ( GH	
I" ) J )

" [4.2] 31

This has the desired effect of weighting the projec ted ozone or PM 2.5 base design values towards the middle year of a five year period (2009 -2013), in this example for a 2011 base emissions inventory year. Additionally, an average of 3 design values will be more stable (less year-to-year variability) than any single design va lue period. An EPA analysis of recent design values data at 761 ozone monitors over the period b etween 2002 and 2011 concluded that the median standard deviation of individual 3-year ozon e design values was 5.3 ppb, whereas the standard deviation of the average of multiple 3-yea r ozone design values was only 4.0 ppb.

In cases in which there are less than five years of valid monitoring data at a site, we recommend that base design values be calculated only when the re are at least three years of consecutive valid data (i.e., at least one complete design valu e). If a location has less than three consecutive years of data, then that site should not ordinarily be used in the attainment test 32 . An example
calculation for DVB **i**

is shown in Table 4.1, and includes the number of significant digits to
include for ozone and each form of the PM2.5 standa rd.

Table 4.1: Example illustrating the calculation of base design values.

2011 DV 2012 DV 2013 DV DVB
O3 NAAQS (ppb) 33 76 76 78 76.7

Annual PM 2.5 NAAQS (μg/m 3 ) 12.9 13.1 12.6 12.87

24-hour PM 2.5 NAAQS (μg/m 3 ) 32 33 32 32.3

The measured 3-year average design values use the r ounding/truncation rules established in 40 CFR Part 50 Appendix P (8-hour ozone) and Appendix N (PM 2.5 ). The resultant 5-year weighted average base design value should carry one digit to the right of the decimal point for ozone and
24-hour PM 2.5 and two digits to the right of the decimal for ann ual PM 2.5 . There are several

31 Note: for nomenclature purposes, the three-year de sign value is referred to by the last year of the averaging period. That is, DV 2011 represents air quality data averaged over 2009, 2010, and 2011 for ozone and PM 2.5 . 32 Monitoring sites with less than 3 years of data ca nnot ordinarily be used to determine attainment status. Therefore, in most cases, those sites shou ld not be used in the attainment demonstration. However, there may be cases where a monitoring site with incomplete data indicates an area of high ozone or PM concentration. Further examination of the monitoring data and modeled response (RRF) in the area is recommended as part of either the monit or based attainment test or an unmonitored area analysis, as appropriate.

33 The current ozone NAAQS is defined in units of pp m. For ease of use, this document will use ppb units.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

99 calculations in the modeled attainment test which c arry the tenths digit or hundredths digit. Prematurely rounding and/or truncating concentratio ns and RRFs can lead to an overestimate or underestimate of the modeled impact of emissions controls. In some cases, a few tenths of a ppb (or μg/m3) change (or a few tenths of a perce nt reduction) can affect the outcome of the model attainment test. Rounding or truncating can make the change appear to be equal to a full ppb (or 1%) or equal to zero change. For 8-ho ur ozone, it is recommended to round to the tenths digit until the last step in the calculation when the final future design value is truncated.
For annual PM 2.5 it is recommended to round to the hundredths digit until the last step when the final future design value is rounded to the nea rest tenths place. For daily PM 2.5 it is recommended to round to the tenths digit until the last step when the final future design value
is rounded to the nearest whole number.

In practice, the choice of the base design value ca n be critical to the determination of the estimated future year design values (Cohan, 2006) a nd careful consideration should be given to the calculation of base year values. There is no s ingle methodology that can derive a “correct” base design value, but the 5 year weighted average value establishes a relatively stable value that is weighted towards the emissions and meteorol ogical modeling year. In most cases, this provides an appropriate anchor point in which to ba se the future year design value calculations. Alternate, equally plausible, calculations of base design values may be considered as part of the corroborating analyses that comprise the aggregate weight of evidence determination. For instance, one may want to consider establishing the base design value on the highest of the relevant design values to ensure that the emissions measures will yield attainment even in periods with meteorological conditions especially c onducive to poor air quality.


### 4.1.2 Calculation of relative response factors

The relative response factor for each monitoring si te ( ??@ )  is the fractional change in air quality concentrations that is simulated due to emi ssions changes between a base and a future year emissions scenario. The specific RRF calculat ion techniques vary by pollutant and are
described in more detail in the remainder of sectio n 4 below. The most important
consideration associated with the RRF calculation i s determining which model values are most representative of the expected air quality change f or a given location. This requires consideration of all of the varying changes in hour ly pollutant concentrations between the base and future cases and a determination of the most ap propriate summary (average) value to apply to the base design value. As with the select ion of a base design value, there may be plausible alternative means of calculating the rela tive response factors that can differ from the approaches recommended below (e.g., Kulkarni **et al** ., 2014, Porter **et al** ., 2014). WhereModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

100 justified, alternate RRF calculation techniques can be included as corroborative analyses as part of a weight of evidence determination.


### 4.2 Model attainment test for the primary ozone sta ndard

The 8-hour ozone design value is currently calculat ed as the three-year average of the fourth highest monitored daily 8-hour maximum value at eac h monitoring site. The standard is considered to be attained if the observed design va lue does not exceed the level of the ozone
NAAQS. 34 Similarly, the model attainment test is considere d satisfied when the (DVF) > is less than or equal to the current NAAQS level. The reco mmended test is described below.



### 4.2.1 Model values to use in the RRF calculation

Given the formulation of the model attainment test, it is important to identify which model days are best suited for determining the expected a ir quality response resulting from a set of emissions changes. On any given modeled day, meteo rological conditions may or may not be conducive to high concentrations at a particular mo nitor. If ozone predicted near a monitor on a particular day is much less than the design value , the model predictions for that day could be unresponsive to controls (e.g., the location could be upwind from most of the emissions in the nonattainment area on that day). If these days are included in the RRF calculation, they will likely lead to a higher RRF (closer to one) and a p otential overestimation of the future design
value.

Internal EPA analyses (USEPA, 2014b) have shown tha t model response to decreasing emissions is generally most stable when the base ozone predic tions are highest. The greater model response at higher concentrations is likely due to more “controllable” ozone at higher concentrations. In addition, the high model days a re selected individually for each monitor. Meteorological conditions that lead to high ozone m ay differ between monitors in an area. For example, monitors that are typically “upwind” or “d ownwind” of an area may experience high ozone on different days, depending on the prevailin g wind direction. In most urban areas, on days with high ozone concentrations, there is a rel atively high percentage of locally generated ozone compared to days with low base case concentra tions (U.S. EPA 2014c). Days with low

34 At the time of publication of this guidance, the ozone NAAQS was 0.075 ppm per 40 CFR Part 50.15. Design values for the .075 ppm NAAQS are truncated to the 3 rd decimal digit. Therefore, 0.0759 ppm (75.9 ppb) is considered attainment and 0.0760 (76. 0 ppb) is considered nonattainment. The same rounding/truncation procedures should be applied to the modeled attainment test. A detailed description of the methodology to calculate ambient ozone design values is provided in 40 CFR Appendix P to Part 50.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

101 ozone concentrations are more likely to have a high percentage of ozone due to background and boundary conditions. Since the form of the sta ndard is also focused on the highest days of an ozone season (i.e., the fourth highest MDA8), th e RRF calculation should also focus on days when the model predicts the highest ozone concentra tions.

As the ozone NAAQS has been lowered, a key question is whether the attainment test should use an average RRF based on all modeled days that are greater than the NAAQS, as i n the previous guidance approach. In some areas of the c ountry, this could lead to RRF calculations that might include upwards of 100 days per ozone se ason (for the 75 ppb NAAQS). In these cases, similar to RRFs based on low modeled concent rations, too many days in the RRF calculation may lead to higher (closer to 1) RRFs. Using the highest modeled days at each monitor is most likely to represent the response of the observed design value at a monitor. Since the design value is based on the seasonal 4 th high observed values, we recommend selecting a set of modeled days that are likely to encompass a range of values that are somewhat higher than and somewhat lower than the 4 th high value. We therefore recommend calculating the RRF based on the highest 10 modeled days in the simulated period (at each monitoring site). We believe this balances the des ire to have enough days in the RRF to generate a robust calculation, but not so many days that the RRF does not represent days with concentrations near the observed design values. In addition, use of the highest 10 days (without an initial threshold) allows the attainmen t test to be easily adapted to any level of the NAAQS with little or no change in the methodology. In support of the revised recommendation, a recent assessment of modeled ozone changes betwee n two ozone seasons (Foley et al., 2014) suggests that the use of the highest 10 days in the mean RRF calculation yields a slightly better estimate of the actual observed ozone change than t he previous guidance approach.

EPA recommends that the ozone RRF calculations be b ased on the 10 highest days in the base year modeling at the monitor location, as long as t he base MDA8 values are greater than or equal to 60 ppb for that day. In cases for which t he base model simulation does not have 10 days with MDA8 values greater than or equal to 60 p pb at a site, then EPA recommends using all days where MDA8 >= 60 ppb, as long as there are at least 5 days that meet that criteria. If there are less than 5 days with MDA8 >= 60 ppb, EPA recommends that air agencies do not
calculate a DVF for the site. 35 If the model attainment test is based on less tha n 10 days, then other assessments in the aggregate weight of eviden ce demonstration will need to be more rigorous to counter the reduced robustness of the m odel attainment test.

35 A scenario in which the 5 th highest base model MDA8 values is less than 60 ppb either indicates an extremely low ozone site or poor model performance. In either case, projections of DVF values are lik ely not reliable.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

102 In addition, as discussed by Vizuete et al. (2011), there can be days in which the modeled source-receptor relationships may not yield a repre sentative response for a particular cell or array of grid cells. This can result from small in consistencies between the model representation of transport patterns (or chemical formation) and w hat actually occurred on that day. As part of the weight of evidence demonstration, if there i s compelling evidence to determine that a particular day, while being among the 10 highest MD A8 values at a location, is not representative of the expected source-receptor rela tionship at that location; then that day can
be considered for removal from the RRF calculation. Air agencies should document the evidence that argues for the exclusion of any other wise-eligible days. As noted earlier, poor model performance on individual days can lead to ra tios of future to base ozone that may be biased on those days. For example, underprediction of ozone concentrations may lead to ratios that are unresponsive to emissions controls. Air a gencies may want to examine the day- and site-specific model performance for days that are p art of the RRF calculation. Where feasible, it is recommended that days with model biases greater than +/- 20 percent be examined to make sure that they can be appropriately used in calcula ting the expected response.



### 4.2.2 Grid cells to use in the RRF calculation

It is recommended that ozone RRF calculations consi der model response in grid cells immediately surrounding the monitoring site along w ith the grid cell in which the monitor is located. There are two primary reasons why we beli eve it is appropriate to include predictions from grid cells near a monitor rather than just the cell containing the monitor. First, limitations in the inputs and model physics can affect model pr ecision at the grid cell level. Allowing some leeway in the precision of the predicted location o f daily maximum ozone concentrations can help assure that possibly artificial, fine scale va riations do not inadvertently impact an assessment of modeled ozone response. Second, some ozone monitors and important emission sources may be located very close to the b order of a grid cell. Considering multiple cells near a monitor rather than the single cell co ntaining the monitor diminishes the likelihood of inappropriate results which may occur from the a rtificial geometry of the superimposed grid
system.

Based on the above considerations, it is recommende d that the RRF be based on a 3 x 3 array of cells centered on the location of the grid cell con taining the monitor. This is a change from the 2007 guidance (USEPA, 2007) which pre-identified 15 km as an appropriate distance for grid cell proximity to a monitor and therefore recommended th e selection of RRF grid cells to be contingent upon the chosen model grid resolution (i .e., a finer grid resolution considered a larger array of grid cells to maintain the pre-iden tified 15 km distance). The revised approach allows for small spatial differences in the respons e to be considered in the RRF calculation andModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

103 presumes that the chosen model grid resolution is a ble to appropriately capture model response at the location of the monitoring site. A dditionally, as described in section 4.2.3, the guidance now recommends that the grid cell with the highest base year MDA8 ozone value in the 3 x 3 array be used for both the base and futur e components of the RRF calculation, as opposed to the 2007 guidance approach which recomme nded selecting the peak value in the base and future cases, even if they were in differe nt grid cells.

The selection of grid cells to use in the RRF calcu lation is especially important in “oxidant limited” areas (i.e., areas where incremental NOx d ecreases may lead to ozone increases) because these areas are likely to have grid cells t hat respond to VOC controls, with NOx sensitive grid cells nearby (perhaps even in the sa me 3 x 3 array). The 3 x 3 array methodology could lead to unrealistically small or large RRFs i n these locations, depending on the specific case. Internal EPA analyses with a 12 km grid reso lution have shown that on average, modelestimated future design values vary by less than 1 ppb when a single cell is used as opposed to the recommended array, although higher variability can occur. (USEPA, 2014b).

While the use of a 3 x 3 array of cells is the reco mmended approach for the model attainment test, air agencies are encouraged to consider the a ctual spatial scales of the monitors at which the model attainment test is being applied. Most o zone monitors are generally sited to be representative of an urban or neighborhood scale. In cases in which the spatial representativeness of a monitoring location is much smaller or larger than the area covered by the 3 x 3 array of cells, air agencies may consider assessing site-specific model response over an alternative grid cell array as part of corroborativ e analyses that inform the aggregate weight of
evidence determination.



### 4.2.3 Sample model attainment test calculation

Once the appropriate RRF days are determined at eac h site, the calculation is straightforward. Relative response factors are not calculated on a d aily basis, but instead are based on the mean 8-hour daily maximum values on all eligible calcula tion days. At the last step in the process, the
final DVF should be calculated in accordance with t he data handling conventions associated with the NAAQS (e.g., truncated to the third decima l in ppm units as per current Appendix P). An example is provided below for a 2011 base year a nd 2020 future year simulation based on an annual model simulation. This hypothetical mode l attainment test considers an area with
three ozone monitors.

Step 1: Compute site-specific base year design values (DVB s) from observed data at all eligible sites in the area by calculating the average of the design value periods which include the baseModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

104 year inventory year. All averaged values should be rounded to the tenth of a ppb. See Table 4.2 for the calculation in this sample case. The b ase design value is the average of the 3-year design values for 2011, 2012, and 2013.

Table 4.2: Ambient ozone base design values in example calcula tion.
Monitoring site 201 1 DV (pp b ) 201 2 DV (pp b ) 201 3 DV (pp b ) Base design value (pp b )
A 84 79 73 7 8.7
B N/A 67 83 75.0 C 86 76 80 8 0.7

Step 2: Compute site-specific relative response factors (R RFs) from modeled data at all eligible sites (i.e., A, B, and C). This is done by first i dentifying the 10 days with the highest daily 8-hou r maximum ozone concentrations greater than 60 ppb (o r subset of days if there are 5-9 days greater than 60 ppb) in the base year. For each of these days, identify the grid cell with the highest base year MDA8 ozone in the 3 x 3 cell arra y surrounding the monitor cell location and use that value in the calculation of a multi-day av erage. 10-day averages (where possible)
should be calculated for both the base and future s imulations. The future case average should be based on MDA8 values paired to the same cell as used in the base average. Figure 4.1 below
shows an abbreviated subset of the RRF calculation for three days at a single sample site. Model estimates of MDA8 (in ppb) should be calculat ed to at least two places to the right of the decimal, with the last digit rounded.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

105 Figure 4.1. Example demonstration of daily selections of 8-hour maxima to be used in RRF
calculation

Base

6/21/2011

60.2 4 68. 1 5 66.2 0

73.41 76.2 6 71.27

67.6 2 74.0 2 73.2 4

6/26/2011

76 .2 0 75

. 3 5 7 7

. 4 1

78.51 76.79 75.92

76

. 6 6 75

. 9 5 77 .2 1

7/17/2011

5 0. 2 2 5 8. 5 5 5 6. 2 2

5 3.4 7 5 6.2 2 5 1.2 8

57.76 59

. 1 0 53.29 Future

6/21/2011

56

. 8 9 59

. 2 2 58

. 0 5

63.69 68

. 4 6 63.39

61

. 8 6 67

. 1 6 66

. 8 0

6/26/2011

6 9

. 8 4 67

. 7 1 68. 14

68.30 66.27 67.95

64

. 2 8 65

. 1 5 66. 5 8

7/17/2011

4 6.8 0 4 9.2 7 4 8.5 5

5 1 .9 1 5 0.4 1 5 0 .3 4

53.62 5 4. 1 0 50.62 Day 1 is straightforward. The daily maximum 8-hour average is > 60 ppb and is in the same cell in both
the base and future case.

For day 2, the peak location shifts
from the base to another cell in the array in the future (underlined cell). However, the center left cell is used for base and future values (paired in space).

Day 3 would not be eligible for the
RRF calculation because there are no base values > 60 ppb.

Step 3: Once the MDA8 values are selected for each appropr iate day for each monitoring site, they should be averaged together over the days whil e maintaining at least two places to the right of the decimal (round to the last decimal dig it). The RRF is the ratio of the average future MDA8 values to the average base MDA8 values. Table 4.3 shows an example RRF calculation, using the results from days 1 and 2 from Figure 4.1 , plus the other eight (top 10) days in which the daily model predictions were deemed appropriate for RRF calculation. RRF values should be estimated to four decimal places (rounded to the 4 th decimal digit).

Table 4.3. Relative response factor (RRF) calculation in examp le calculation.

Date Base (ppb) Future (ppb) 6/21/2011 76.26 68.46 6/26/2011 78.51 68.30 6/27/2011 75.22 65.43 6/28/2011 70.05 62.76 7/11/2011 76.33 68.18 7/12/2011 81.67 72.62 7/13/2011 78.31 64.52 8/02/2011 73.69 69.96 8/03/2011 75.83 65.05 8/21/2011 71.44 59.36 Average 75.73 66.46
RRF i 0.8776Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

106 The last step of the process is to multiply all of the site-specific RRFs by the corresponding site-sp ecific DVBs and compare the results against the current le vel of the ozone NAAQS. In the final step, as note d in footnote 34, the design values are truncated to the integer ppb unit. Table 4.4 shows those result s, assuming that the sample RRF shown above is for sit e C in the example. The final DVF for this sample site would be 70 ppb or 0.070 ppm. This would be c onsidered a passing model attainment test for an ozone NAAQS of 0.075 ppm.

Table 4.4. Calculation of DVF **i** for the three sites in the sample exercise.

Monitoring site Base design

value (ppb) RRF Future design value , pre-truncation (ppb) Final Future des ign value (ppb)
A 78.7 0.8933 70.3 70
B 75.0 0.9138 68.5 68 C 80.7 0.8776 70.8 70
Note: The “final” future year design value represents t he truncated value to compare to the NAAQS. However, it is recommended to retain the tenths (of a ppb) digit in all documentation, so that small ozone changes can be gleaned from control strategy and sensitivity analyses. 

### 4.3 Model attainment test for the secondary ozone s tandard

At the time this guidance document was written, the secondary ozone NAAQS is the same as the primary NAAQS. However, in the past, EPA has c onsidered setting a secondary ozone NAAQS that is based on a cumulative measure of ozon e concentrations, as opposed to the three-year average of the annual fourth-highest dai ly maxima. If EPA were to establish a distinct secondary standard, it is possible that ai r agencies would need to separately show that the planned emissions reductions would be sufficien t to meet both the primary and secondary NAAQS. If a new secondary standard is subsequentl y developed, EPA will provide supplemental guidance on how to assess attainment o f that new standard at the time of its promulgation.



### 4.4 What is the Modeled Attainment Tests for the An nual Average PM 2.5 

### NAAQS?

Since PM 2.5 consists of multiple PM species, the modeled attai nment test for PM 2.5 utilizes both

PM 2.5 and individual PM 2.5 component species. A separate RRF is calculated f or each PM 2.5 species. In order to perform the recommended model ed attainment test, air agencies should
divide observed mass concentrations of PM 2.5 into 8 components (plus passive mass):Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

107 • Sulfates

• Nitrates

• Ammonium

• Organic carbon

• Elemental carbon

• Particle bound water

• Salt

• “Other” primary inorganic particulate matter

• Passively collected mass (blank mass)

To apply the attainment test, air agencies must fir st have outputs from air quality model simulations for a base year and a future year emiss ions scenario. We recommend a modeled attainment test which has 4 basic steps, as describ ed in this section.

Step 1. Compute observed quarterly mean PM 2.5 and quarterly mean composition for each
monitor.

Derive base year quarterly mean concentrations 36 for each of the major components of PM 2.5
. This is done by multiplying the monitored quarterly mean concentration of Federal Reference Method (FRM) derived PM 2.5 (40 CFR Part 53) by the monitored fractional composition of PM 2.5 species for each quarter. (e.g., 20% sulfate x 15.0 μg/m 3 PM 2.5 = 3.0 μg/m 3 sulfate).

Step 2. Using air quality model results, derive co mponent-specific relative response factors (RRF) at each monitor for each quarter.

Use the air quality model predictions to calculate the quarterly mean concentration for each

PM 2.5 component for the base year and future year scenari o. Take the ratio of future to base year predictions for each component. The result is a component-specific relative response

36 Concentrations should be calculated based on calend ar quarters for two reasons. First, the NAAQS is calculated for a calendar year, so the quarters nee d to fit evenly within a year. Second, the monitor ed data used to calculate design values is averaged on a calendar quarter basis before calculating annual
averages.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

108 factor (RRF).

The relative response factor for component j at a site i is given by the following expression:



### (RRF) ij 

### = [C ij, projected 

### ]/[C ij, base year 

### ] [4.3]

where C i j, base year is the quarterly mean concentration predicted at o r near the monitoring site 37 with emissions characteristic of the period used t o calculate the base year design value for annual PM 2.5.

C i j, projected is the future year quarterly mean concentration pr edicted at or near the monitoring site.

(e.g., given model predicted base year sulfate of 10.12 μg/m 3 and future year concentration of 8.23 μg/m 3, then RRF for sulfate is 0.8132).

Step 3. Apply the component specific RRFs to obser ved air quality to obtain projected quarterly species estimates.

For each quarter, multiply the base year quarterly mean component concentration (step 1) times the component-specific RRF obtained in step 2 . This leads to an estimated future quarterly mean concentration for each component. (e .g., 3.0 μg/m 3 sulfate x 0.8132 = future sulfate of 2.44 μg/m 3 ).

Step 4. Calculate a future year annual average PM 2.5 estimate.

Sum the quarterly mean components to get quarterly mean PM 2.5 values. Then average the quarterly mean PM 2.5 concentrations to get a future year annual average PM 2.5 estimate for
each FRM site.

37 For greater than 12km grid resolution, use the sin gle grid cell. For 12km or less grid resolution, u se the mean of the 3 x 3 array of the grid cells surro unding the monitor.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

109 Compare the projected average annual arithmetic mea n PM 2.5 concentration obtained in Step 4 with 12.0 μg/m 3 . If all values are < 12.0 μg/m 3 , the test is passed.



### 4.4.1 Ambient PM 2.5 

### Data Used in the Annual PM 2.5 

### Attainment Test

PM 2.5 data collected at FRM and FEM sites are used for n onattainment designations. Therefore we recommend using FRM and FEM data as the base dat a for projecting future PM 2.5 concentrations. As can be seen from the list of st eps, the modeled attainment test is critically dependent on the availability of species component mass at FRM sites. This raises several issues. First, the majority of FRM sites do not ha ve co-located chemical speciation network (CSN 38 ) samplers. 39 And second, the FRM filter measurements and PM 2.5 speciation measurements do not always measure the same mass (F rank, 2006). There are numerous known issues with positive and negative sampling ar tifacts. Both of these issues are addressed
below.



### 4.4.2 FRM Monitors that Don’t Have Speciation Data

Species concentration data and/or species fractions are needed in order to apply the PM2.5 attainment test. There are approximately 1200 FRM and FEM PM 2.5 monitoring sites, but only ~200 urban speciation monitoring sites. This makes it difficult to apply the attainment test at the majority of FRM/FEM sites. There are several possible ways to estimate species concentrations at FRM monitors that lack speciation data. Among them are:

1) Use concurrent data from a nearby speciation monito r to estimate species concentrations and/or fractions at one or more FRM sites. 2) Use representative speciation data (from a differen t time period) collected in an area to estimates species data at FRM sites. 3) Use interpolation techniques to create spatial fiel ds using ambient speciation data. 4) Use interpolation techniques to create spatial fiel ds using ambient speciation data and

38 References to CSN monitors in this document refers to the overall speciation network which includes
both trends sites and SLAMS sites.

39 There are ~1100 FRM measurement sites and ~200-250 urban speciation sites (trends and SLAMS).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

110 gridded model outputs to adjust the species concent rations based on the modeled spatial gradients.

In general, we recommend using spatial interpolatio n techniques to estimate species concentrations at FRM sites that do not have specia tion data (numbers 3 and 4 above). But in some cases, interpolating data from nearby sites ma y not be appropriate, may not be feasible, or simply may not be necessary. In the EPA’s Model Attainment Test Software (MATS), a relatively simple interpolation technique is used t o estimate species concentrations at all FRM sites in the country. The analysis uses a Voronoi Neighbor Averaging (VNA) technique. Other interpolations have been done using Kriging and oth er more complicated methodologies such as “Downscaler” (Berrocal, 2010a and 2010b). Air a gencies are encouraged to explore techniques that are most appropriate for their area and situation. EPA’s MATS software is available for air agencies to use as a default tech nique.

For areas which contain one or more speciation site s, and where FRM sites exhibit very little spatial gradients, it may be appropriate to assume that the speciation site is representative of the entire area. Areas which exhibit strong spatia l gradients in PM 2.5 concentrations should strongly consider using more sophisticated techniqu es to estimate species concentrations. Combining ambient data with model output concentrat ions (number 4 above) may help adjust concentrations in areas with strong gradients, but limited speciation data. This technique has been used extensively to create spatial fields of P M 2.5 concentrations for the purpose of generating health benefits calculations, but it has generally not been used to generate species fractions at FRM sites. The technique is further l imited by uncertainties in the representativeness of the model outputs and emissio ns inventories.



### 4.4.3 PM 2.5 

### Species Calculations and Definitions

Data analyses (Frank, 2006) have noted that the FRM monitors do not measure the same components and do not retain all of the PM 2.5 that is measured by routine speciation samplers and therefore cannot be directly compared to specia tion measurements from the Chemical Speciation Network (CSN). 40 The FRM mass measurement does not retain all ammo nium

40 The information in this section applies to the most common samplers in the CSN. Some networks use alternative special purpose samplers to collect bot h PM 2.5 and PM 2.5 speciation data. The characteristics of the sampler and the analytical procedures used t o produce chemical speciation data should beModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

111 nitrate and other semi-volatile materials (negative sampling artifacts) and includes particle bound water associated with sulfates, nitrates and other hygroscopic species (positive sampling artifacts). This results in FRM measured concentrat ions (and percent contributions to PM 2.5 mass) which may be different than the ambient level s of some PM 2.5 chemical constituents.

Because the FRM data are used to determine complian ce with the NAAQS, it is critical to estimate the species composition as measured on the FRM filters. In addition, for the purposes of predicting changes in PM 2.5 components, constructed PM 2.5 mass should match the composition of mass retained by the FRM.

The goal is to reconstruct the measured species com ponents so that they add up to the measured FRM mass. This concept can generally be represented by equation 4.4:

RCFM FRM = [Ammoniated Sulfate Mass] + [Retained Ammoniated Nitrate Mass] + [Retained Carbonaceous Mass] + [Other Primary PM 2.5 ] + [Other Components] [4.4]

In the above characterization, RCFM FRM equals reconstructed fine mass and all of the list ed chemical components reflect those retained during s ampling and equilibration on the FRM’s
Teflon filter. Sulfate and nitrate mass include ass ociated ammonium which may be different
than assumed ammonium sulfate and ammonium nitrate compounds. Carbonaceous mass includes elemental carbon (EC), organic carbon (OC) , and hundreds of different carbon compounds which are not directly measured, and whic h may not match the carbon mass in speciated measurement data. Also, sulfates, nitrat es and carbonaceous mass includes particle bound water associated with these hygroscopic aeros ols. In this characterization, other primary
PM 2.5 mass is intended to be a more general term that in cludes fine soil, and oxides that result
from other PM emissions.



### 4.4.4 Recommended Treatment of Species Data

We recommend an adjustment technique based on an FR M mass construction methodology which results in reduced nitrates (relative to the amount measured by routine speciation

considered in determining which, if any adjustments are appropriate to make the data useful for comparison to FRM data.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

112 networks), higher mass associated with sulfates and nitrates (reflecting water included in gravimetric FRM measurements) and a measure of orga nic carbonaceous mass which is derived
from the difference between measured PM 2.5 and its non-organic carbon components (Frank, 2006). This characterization of PM 2.5 mass also reflects “other” primary inorganic PM 2.5 and other minor constituents. Frank (2006) terms this approach “sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance appro ach (SANDWICH)”. The resulting characterization provides a complete mass balance. It does not have any unknown mass which is sometimes presented as the difference between me asured PM 2.5 mass and the characterized chemical components derived from routine speciation measurements. The recommended SANDWICH based characterizations should yield more accurate assessments of future PM 2.5 concentrations as measured by FRM monitors, compare d to using unadjusted CSN data.

4.4.4.1 Retained Nitrate Mass The first step in the procedure for identifying FRM mass components is to estimate the retained
nitrate mass on the FRM filters. The FRM does not capture all of the semi-volatile components of the ambient air, such as ammonium nitrate. The retained amount of nitrate ion, however, can be estimated by a simple thermodynamic model th at involves 24-hr ambient nitrate speciation concentrations (as measured by a standar d speciation sampler using a nylon filter preceded by a HNO3 denuder) together with hourly am bient temperature and humidity. Atmospheric nitrates are highest during the cooler months. Retention on the FRM is also higher during the cooler months and essentially all the nitrates are lost during the summer. The retention does not appear to depend on ambient NH 3 or HNO 3 concentrations. More NO 3 is retained at low temperatures and high humidity, whi ch varies by sampling location and time of

year.

Because nitrate retention varies by site and season , a simple ammonium nitrate equilibrium model can be used to predict the amount of nitrates retained on the FRM Teflon filter. As used by Hering (Hering, 1999; Zhang, 1992),

24

delta NO 3 (μg/m3)= 745.7/T R * 1/24* Σ ( K i 1⁄2 ) [4.5]
i=1
where delta NO3 is the amount of volatilized nitrat e mass, T R is the reference temperature forModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

113 the sampled air volume in degrees Kelvin and K i is the dissociation constant for ammonium nitrate evaluated at the ambient temperature and re lative humidity for hour i. The nitrate loss can be predicted for each day, based on hourly temp erature and relative humidity data. It must
be subtracted from the NO 3 measured at speciation sites to estimate the FRM r etained nitrate. The MATS software is populated with a speciation am bient data file with daily average retained nitrate already calculated for all CSN and IMPROVE sites. The analysis uses National Weather Service temperature and relative humidity data from the closest station. Other sources of meteorological data may also be appropriate. Furth er details on the nitrate loss calculations can be found in (Frank, 2006).

4.4.4.2 Ammonium Associated with Sulfates and Retai ned Nitrates There are several ways to estimate ammonium mass fo r use in attainment test. The most direct way is to use measured ammonium concentratio ns from the CSN network (IMPROVE does not measure ammonium ion). A second, more ind irect method is to estimate the ammonium associated with nitrate, and to calculate the ammonium associated with sulfate and the degree of neutralization of sulfate (DON), and then use the resulting information to
calculate ammonium mass. Due to uncertainties asso ciated with the ammonium
measurements and the lack of ammonium measurements in rural areas, this indirect method is
recommended as the default attainment test methodol ogy.

To determine the mass associated with nitrates, we assume retained nitrate is all ammonium nitrate. Thus, using the ratio of the molecular wei ght of ammonium to that of nitrate, the ammonium associated with nitrates (NH4 NO3 ) can be derived directly from the estimated retained nitrate (NO3 FRM ) as:

NH4 NO3 = 0.29 * NO3 FRM [4.6]

The difference between total FRM NH 4 (amount associated with nitrates and sulfates), te rmed
NH4 FRM , and the measured NH 4 , termed NH 4CSN , is needed to determine the ammoniated form of sulfates as described by equation 4.4. A measu rement study by Collett (Collett, 2004) shows
that NH 4 may not be completely retained during collection o n nylon filters preceded by a nitric acid denuder, such as are used at CSN sites. At se veral FRM study sites, the CSN NH 4 which is adjusted for evaporated NH 4 NO 3 tends to more closely correspond to the measured N H 4 from the FRM Teflon filter. However, for other sites, t he measured CSN NH 4 appears to agree withModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

114 FRM NH 4
.

The available information suggests that using measu red ammonium (assuming none is volatilized) may be the best assumption. We theref ore recommend using unadjusted ammonium, but further analysis of this issue is war ranted.

To calculate the ammonium associated with sulfate, we recommend using the degree of neutralization of sulfate (DON). This is defined as the ammonium associated with sulfate (i.e.,
total ammonium less the ammonium associated with ni trate), divided by sulfate. This reflects the degree to which sulfate has been neutralized by ammonium. The ambient data is such that
almost all of the ammonium data is from urban sites (CSN), but the sulfate and nitrate data is from both urban (CSN) and rural (IMPROVE) sites. Th is may lead to an overestimation of
ammonium concentration in rural areas when ammonium is directly interpolated. Therefore, we recommend using calculated DON with SO 4 and NO3 FRM, and interpolating to each FRM site to get quarterly average concentrations. The interp olated DON and species concentrations are
used to calculate NH4 FRM using the following equation:

NH4 FRM = DON * SO4 + 0.29*NO3 FRM [4.7]

The indirect calculation of ammonium mass from inte rpolated fields tends to smooth out the gradients in mass. This is deemed to be beneficial , due to the uncertainty in the

measurements.

4.4.4.3 Particle Bound Water Because ammoniated sulfate and ammonium nitrate are hygroscopic, the retained sulfate and
nitrate mass will include water. Particle bound wa ter (PBW) can be estimated using an equilibrium model. The recommended approach is to use the Aerosol Inorganic Model (AIM) (Clegg, 1998) to calculate PBW. PBW is derived from quarterly av erage FRM concentrations of sulfate, ammonium, and nitrate as described above. Estimated hydronium ion, H+, needed to
achieve ionic balance is derived from the latter va lues. The model enables the distribution of water and ions to be calculated between liquid, sol id and vapor phases for specific temperature and relative humidity conditions. Typical FRM filt er equilibration conditions of 35% RH and 22 deg C (295 deg K) temperature are used to estimate water concentrations at those conditions.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

115 Application of AIM at the specified FRM filter equi libration conditions show that PBW is much more dependent on sulfate concentration compared to nitrate and that the relationship varies somewhat by season. There is proportionally less e stimated PBW water for wintertime aerosol which has higher NO 3 and lower SO 4 . The PBW concentrations are also sensitive to the degree of neutralization of the sulfate particles (determi ned by the relative concentration of NH4 FRM ).

We recommend calculating PBW as a component of PM 2.5 mass. The AIM model (or other equilibrium models) can be used, or a regression eq uation can be developed to simplify the process. For computational convenience, EPA derive d a polynomial regression equation fit to
the calculated water mass from AIM and the three in put values to AIM (sulfate, nitrate and ammonium). The polynomial equation is used in the MATS software to calculate PBW. See the
MATS User’s Guide for more details on the default P BW equation.

4.4.4.4 Salt In the PM 2.5 attainment test methodology, salt is represented as a species which accounts primarily for sea salt at sites near the oceans. T he salt species is estimated using the CSN
measured Cl + ion concentration multiplied by 0.56. The multipl ier accounts for the sodium contribution to the species. In areas distant from oceans, measured Cl + may represent PM mass
that is not associated with salt water. For instan ce, there may be chloride emissions resulting from various industrial processes. Therefore, the use of measured salt in the attainment test
calculations should be evaluated on an individual a rea/monitor basis.

Measured salt is included by default in the MATS sp ecies ambient data file. The inclusion of modeled salt (and hence an RRF for salt) is optiona l and can be included if there are accurate
modeled salt concentrations. If modeled salt is no t included in the MATS input files, an RRF of
1.0 used in the MATS calculations.

4.4.4.5 Other Primary PM2.5 The terms “crustal”, “fine soil”, “major metal oxid es”, “inorganic particulates”, and “other PM” are sometimes used interchangeably. For PM 2.5 design value calculations we will refer to this material as “other primary PM 2.5 "(OPP). For regional haze calculations, we will co ntinue to
refer to this material as “fine soil”.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

116 For the purposes of estimating OPP for the PM 2.5 attainment test, all measured non-carbon mass that is not organic in nature (not associated with sulfate and/or nitrate) should be
counted. As with the other PM 2.5 components measured on the FRM filter, there is un certainty
associated with this estimate. The “crustal” or “f ine soil” definition from IMPROVE can be used
to estimate OPP 41 or an alternative formula can be defined which bet ter estimates the urban
nature of the FRM measurements. The IMPROVE defini tion of “fine soil” accounts for the typical crustal components (and attached mass) that would be expected in remote Class I areas. Fine soil is represented as five elements (Al, Si, Fe, Ti, and Ca) with coefficients to represent various oxides and material which may be attached t o or associated with the major elements. In urban areas, inorganic PM which is not elemental carbon, or associated with sulfate or nitrate, may come from many sources such as re-susp ended dust or industrial sources (stack or fugitives). It is generally “crustal” in nature (d ominated by silicon), but urban PM is more likely to contain heavy metals and industrial components.

Although the composition of inorganic PM may differ between urban and rural (remote) areas, we recommend using an equation similar to the IMPRO VE fine soil equation to estimate OPP
for the PM 2.5 attainment test. The recommended equation is sugg ested by (Frank, 2006) and uses only four elements. The equation removes alum inum (and accounts for associated mass by increasing the coefficient for Si), due to the f act that aluminum is often missing from the speciation measurements. This allows for more comp lete data.

The recommended equation is as follows:

Other primary PM 2.5 mass = 3.73 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti] [4.8]

4.4.4.6 Blank mass The other quantifiable components of PM 2.5 mass include passively collected mass, represented by a field blank concentration of 0.3-0.5μg/m3 (U.S . EPA, 2002). This appears to constitute a contamination of the filter resulting from handling or contact with the FRM cassette. This value is deemed to be an important constituent of PM 2.5 mass (it is assumed to not be dependent on pollutant emissions). We recommend using a default nominal blank mass value of 0.5 μg/m3.

41 IMPROVE estimates fine soil as: 2.2 × [Al] + 2.49 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti]Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

117 This value can be modified based on local FRM blank mass measurements. The blank mass is assumed to remain constant through time (RRF=1.0).

4.4.4.7 Calculation of Carbonaceous Mass Organic carbon mass is typically estimated from bla nk corrected speciation data, where organic carbonaceous mass is first estimated by multiplying the organic carbon concentrations by 1.4 or alternative factors to account for the oxygen, hydr ogen and other elements associated with ambient carbon particles.

There are many uncertainties in estimating carbonac eous mass from carbon measurements (Turpin, 2001; Chow, 2004). Uncertainties include differences in carbon measurement protocol between urban and rural monitoring locations; a rel atively “bumpy” surface of urban carbon concentrations as derived from urban and rural orga nic carbon measurements; and lack of
carbon measurements at all FRM locations. We there fore recommend an alternative approach to estimate the organic carbon contribution to PM 2.5 mass.

The recommended attainment test approach estimates organic carbon by **mass balance**

. Precisely measured FRM PM 2.5 mass (U.S. EPA, 2003) is compared to the sum of its non-organic carbon components. The latter are sulfates, ammoni um, nitrates, estimated particle bound water, elemental carbon, estimated other primary PM 2.5 material, salt, plus 0.5 μg/m3 blank
mass as discussed earlier.

This approach estimates retained organic carbon FRM mass and explicitly accounts for the following important and difficult to estimate carbo n mass properties: (1) regional and urbanrural differences in the mix of carbonaceous aeroso ls, i.e. the amount of oxygen, hydrogen, etc; (2) retained water associated with hygroscopic carb on compounds (Saxena, 1996; Yua, 2004 **)** ; (3) volatile carbonaceous material measured by spec iation samplers, but not retained in FRM mass; and (4) uncertainties associated with blank corrections of measured organic carbon.

Organic Carbon Mass by mass balance (OCM mb ) is defined as,

OCM mb = PM 2.5 - { [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [EC] + [OPP] + [salt] + [0.5] } [ 4.9]

In this expression, all of the above components rep resent the mass retained on FRM Teflon
filters.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

118 This approach completely accounts for FRM mass 42 and OCMmb is often greater than the amount that would be derived directly from speciati on measurements. Because of uncertainties in speciation measurements and their estimates from interpolated surfaces, setting a lower limit (floor) may be necessary so t hat the OCMmb is not unreasonably low. In some cases, the OCMmb could be lower than the measu red OC or OCM. Since measured OC (without accounting for mass associated with OC) is a conservative minimum estimate of OC mass, we recommend setting the OCMmb “floor” to be equal to measured OC.

There may also be situations where an OCMmb “ceili ng” is needed. In remote urban areas with relatively high FRM concentrations that may be surrounded by rural background concentrations, the OC by mass balance technique ma y apportion 95%+ of the PM 2.5 mass to OCM. If this is not a reasonable assumption, then a ceiling may be needed to cap the OCM as a percentage of PM 2.5 mass. Based on measured data (FRM sites with co-l ocated speciation data), it appears that on a quarterly average basis , OCM is rarely more than 80% of total PM 2.5 mass. This may be a reasonable default ceiling, bu t a lower value (or in rare circumstances a higher value) may be more appropriate in some regio ns of the country.

4.4.4.8 Summary of PM 2.5 Composition Calculations The terms of equation 4.10 reflect the final estima ted composition of the particles measured by the FRM (for each quarter). Quarterly average FRM mass is equal to the sum of the eight species plus blank mass.

PM 2.5FRM = { [OCMmb] + [EC] + [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [OPP] + [salt] + [0.5] } [4.10]

42 The OC by mass balance technique assumes that all o ther mass is accounted for and therefore all remaining mass is OCM. This may not always be a go od assumption. The results of the technique should be carefully evaluated to ensure that OC mas s is not overestimated (and therefore other mass components are underestimated). This may be an iss ue in areas that do not have nearby speciation data and have relatively large concentrations of pr imary PM 2.5 . The OC by mass balance technique may inadvertently apportion mass to organic carbon whic h may actually be EC or “other” primary PM 2.5 mass components (such as heavy metals). All available a mbient data and modeling data should be used to evaluate the species apportionment results.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

119 The recommended order to generate the data is as fo llows:

1) Calculate adjusted nitrate using hourly meteorol ogy and 24-hour average nitrate

measurements.

2) Calculate quarterly averages for adjusted nitrat e, sulfate, elemental carbon, ammonium (or degree of sulfate neutralization (DON) ), OPP, salt, and measured OCM. 43

3) Quarterly average ammonium is calculated from th e adjusted nitrate, sulfate, and DON values (if measured ammonium is not used direct ly).

4) Ammonium, sulfate, and nitrate concentrations ar e input into the polynomial water equation to derive particle bound water concentrati ons.

5) Carbon mass by difference (OMCmb) is calculated from the PM 2.5 mass, adjusted nitrate, ammonium, sulfate, water, elemental carbon , salt, other primary PM 2.5 , and blank mass values. The sum of the 8 species plus bl ank mass is equal to the FRM mass.

We illustrate application of the recommended test i n example 4.4.1.



### Example 4.4.1

Given: (1) Area “C” has 2 monitoring sites.

(2) Monitored FRM air quality data show the followi ng average quarterly mean PM 2.5 concentrations based on a 5 year weighted average o f observations from 2005-2009 at each site (values are in μg/m 3 ).

43 The measured OCM is only used to calculate the “flo or” for OCMmbModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

120 Table 4.4.1 Quarterly average PM 2.5 FRM mass at 2 example sites

Site Quarter 1 Quarter 2 Quarter 3 Quarter 4

1 17.21 μg/m3 16.34 20.30 14.76

2 15.39 17.98 18.23 13.76

(3) The area has 1 urban speciation site which is c o-located with FRM site 2. The speciation data is available for the entire time period, but t he species concentrations for the 2006-2008 time period have been deemed to be most representat ive of the 2005-2009 5 year weighted average. Therefore, the species data for the avera ge three year period (2006-2008) has been interpolated to derive estimated species concentrat ions at each site. The species concentrations are matched up with FRM data for the same 2006-2008 period to derive representative species fractions. The average monit ored air quality for the 3 rd quarter of 2006-
2008 at site 1 is as follows 44 :

Table 4.4.2 Average monitored 3 rd quarter ambient data at site 1

FRM
Mass (μg/m3) Blank

Mass (μg/m3) Nonblank
Mass (μg/m3) Sulfate (μg/m3) Nitrate

(μg/m3) Organic

Carbon
Mass (μg/m3) Elemental

Carbon (μg/m3) Water

(μg/m3) Ammonium

(μg/m3) Salt

(μg/m3) OPP

(μg/m3)

22.92 0 .5 22.42 8.51 1.11 5.21 0.91 2.31 3.31 0.35 0.71

The blank mass is subtracted before species fractio ns are calculated because the blank mass is held constant at 0.5 μg/m3 throughout the analysis. In the example, the measured FRM mass for quarter 3 is 22.92 μg/m3. The non-blank FRM ma ss is 22.42 μg/m3. The mass of the eight species add up to the non-blank mass.

44 The species concentrations can be derived from co-l ocated speciation data, nearby speciation data or from interpolated spatial fields. FRM mass is not interpolated.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

121 (4) Species fractions are calculated for each quart er for each species. In the example below, a
fraction of non-blank mass is calculated for each o f the eight species. Blank mass remains fixed at 0.5 μg/m3.

Table 4.4.3 3 rd quarter average species fractions for 2006-2008 at site 1

FRM
Mass (μg/m3) Blank

Mass (μg/m3) Nonblank
Mass (μg/m3) %

Sulfate %

Nitrate % Organic aerosol %

Elemental Carbon %

Water % Ammonium %

Salt % OPP

22.92 0 .5 22.42 37.96 4.95 23.24 4.06 10.30 14.76 1.56 3.17

The percentages in table 4.4.3 above are the relati ve composition for the 3 rd quarter of 2006- 2008. It is assumed that these species fractions a re representative of the 2005-2009 time period. 45

(5) The weighted quarterly average FRM design value s are used as the base year FRM value for each monitoring site (2005-2009). The species frac tions from the 2006-2008 speciation data were used to estimate the species concentrations fo r the base year FRM PM 2.5 data. The percentage compositions for 2006-2008 are applied t o the quarterly weighted average design values as shown in table 4.4.4. In the example bel ow, the weighted average design value for
the 3 rd quarter for the site from table 4.4.1 is 20.30 μg/ m3. This leads to the following
concentrations of PM 2.5 species:

45 The default assumption is that the average of the m iddle three years of speciation data is representative of the 5 year weighted average FRM d ata. However, other assumptions (number of years of speciation data) can be used. It is gener ally assumed that the inter-annual variability of t he species fractions is small compared to the variabil ity of species concentrations.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

122 Table 4.4.4 Calculation of the “base case” species concentratio ns at site 1

Weighted Avg.
FRM Mass (μg/m3) Blank

Mass (μg/m3) Nonblank
Mass (μg/m3) Sulfate

(μg/m3) Nitrate (μg/m3) Organic

aerosol (μg/m3) Elemental

Carbon (μg/m3) Water

(μg/m3) Ammoni

um (μg/m3) Salt OPP

(μg/m3)

20.30 0 .5 19.80 7.52 0.98 4.60 0.80 2.04 2.92 0.31 0.63

This procedure is repeated for each PM 2.5 site and quarter to complete the calculation of ba se year ambient concentrations used as the basis for f uture estimates of PM 2.5 mass and its components.

(6) Modeled results show the following relative res ponse factors (RRF) for predicted mass of 5 components of PM 2.5 for the 3rd quarter: 46

Table 4.4.5 3 rd quarter modeled RRFs for site 1

RRF Sulfate RRF
Nitrate RRF Organic
aerosol RRF Elemental
Carbon RRF

Salt RRF
OPP

0.876 0.943 0.971 0.932 1.0 1.042

(7) The quarterly mean RRFs from table 4.4.5 are mu ltiplied by the weighted quarterly average species concentrations from table 4.4.4 to derive f uture year concentrations.

46 The default assumption for the annual average PM 2.5 NAAQS test is to calculate RRFs from the mean of th e 3 X 3 matrix of grid cells surrounding each FRM monitorin g site.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

123 From the example above, the future year 3 rd quarter concentrations are:

Sulfate Future = 7.52 * 0.876 = 6.58 μg/m3
Nitrate Future = 0.98 * 0.943 = 0.92 μg/m3 Organic carbon mass Future = 4.60 * 0.971 = 4.47 μg/m3
Elemental Carbon Future = 0.80 * 0.932 = 0.75 μg/m3
Salt Future =0.31*1.0 = 0.31 μg/m3
OPP Future = 0.63 * 1.042 = 0.65 μg/m3

(8) The future year concentrations derived above ar e used to calculate the future year concentration of ammonium (if the direct ammonium R RF is not used) and particle bound

water.

The future year ammonium concentrations are calcula ted from the sulfate, nitrate, and (base year) DON values. Assuming that the DON is unchang ed from the base year 47 , the ammonium is calculated using the following formula:

NH4 future = DON * SO4 future + 0.29*NO3 future , [4.7]

In the example above, assuming the base year DON is 0.336,

Ammonium Future = 0.336 * 6.58 + 0.29 * 0.92 = 2.48 μg/m3

The NH4 future , SO4 future, and NO3 future concentrations can then be input into an equilibri um

47 Due to the uncertainty in the ammonium measurements , by default, the DON is assumed to stay constant through time. The water calculation is se nsitive to the ammonium (and therefore the DON value) concentrations. Holding the DON constant al lows for the future year ammonium and water values to be solely a function of the change in sul fate and nitrate concentrations. Otherwise, the wa ter concentration can go up when the sulfate and nitrat e concentrations go down (and vice versa). This may occur if sulfate becomes more neutralized in th e future. It is a somewhat illogical outcome (although scientifically possible) and is highly de pendent on an uncertain measurement (ammonium). Therefore, use of a constant DON creates a more sta ble set of calculations. If the measured and modeled ammonium concentrations are believed to be accurate and respond in a reasonable way to emissions controls, then it would be more scientifi cally credible to use the model predicted change in ammonium. Otherwise, it is a reasonable assumption to keep the DON constant over time.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

124 model (AIM or another alternative model) or through a polynomial regression equation to predict future year particle bound water concentrat ion (based on the default equation, the future year water concentration in this case is 1.7 3 μg/m3).

(9) The future species concentrations at each FRM site are then summed over the eight species plus blank mass to estimate the future quarterly av erage PM 2.5 concentration.

In the example above, the total PM 2.5Future = 6.58 + 0.92 + 4.47 + 0.75 + 0.65 + 2.48 + 1.73 + 0. 5 = 18.08 μg/m3

(10) The same calculations are completed for the ot her 3 quarters to get a future year PM 2.5 concentration for each quarter. The 4 quarters are then averaged to get a final future year annual average PM 2.5 concentration for each FRM site.

(11) The future year annual average concentration i s compared to 12.0 μg/m3 48

.



### 4.5 What Is The Recommended Modeled Attainment Test For The 24- Hour NAAQS?

Our recommended modeled attainment test for the 24- hour NAAQS for PM 2.5 is similar to the previously described test for the annual NAAQS in t hat it uses model predictions in a relative sense to reduce site-specific **observations** (averaged over 5 years). In the test, we are interested in reducing the base year design values 49 at each site to < 35 μg/m 3 (the 2006 24
hour PM 2.5 NAAQS).

Ideally, the modeled attainment test should reflect model results obtained for days in each season having observed PM 2.5 concentrations above the design value. Even thoug h the 24-hour

48 In the final step, the future year concentration sh ould be rounded to the tenths digit. A (rounded) value of 12.0 μg/m3 meets the NAAQS. A value of 12 .1 μg/m3 or greater violates the NAAQS.

49 The PM 2.5 24-hour standard was lowered to 35 μg/m3 in 2006 ( 71 FR 61224) (40 CFR 50.13).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

125 NAAQS is based on the single 98 th percentile value of all days in the year, it is im portant to perform the test on a seasonal basis, since PM 2.5 consists of a mixture of pollutants whose composition can vary substantially from season to s eason. There could be a substantial amount of uncertainty associated with predictions on any s ingle day. Thus, our test is most likely to be reliable when relative response factors (RRFs) refl ect composite responses from multiple days. Therefore, we recommend modeling as many days as fe asible where observed PM 2.5 is greater than 35 μg/m 3 . Alternatively, the test can focus on the high e nd of the distribution of days in each quarter 50 (e.g. the top 10% of PM 2.5 days 51 ) assuming that the high days are representative of days that violate the NAAQS. As with the annual NAAQS (and for the same reasons), the preferred approach is to develop RRFs which are sea son (i.e., quarter) specific. 52

The 24-hour PM 2.5 attainment test should be based on the same 5 year weighted average methodology that was used for the annual standard, with some slight modifications. The 24- hour design values are calculated from the 98 th percentile value for each year. In the 24-hour

PM 2.5 calculations, the 98 th percentile value from each year is used in the fin al calculations.
Since the 98 th percentile value can come from any day in the year , all quarters and years should be carried through to near the end of the calculati ons. To calculate final future year design

50 Similar to the annual PM 2.5 NAAQS, the default recommended procedures for the 2 4-hr. NAAQS assume handling of the ambient data and model data on a calendar quarter basis. However, in some nonattainment areas, 24-hr PM 2.5 NAAQS exceedances may occur during a very limited t ime of the year or season. Processing the data (both ambient and m odel) on a sub-quarterly basis may be appropriate in certain circumstances.

51 The top 10% of days may seem like a large number of days per quarter to use for an annual 98 th percentile based standard, but for sites with a 1 i n 3 day sampling schedule, the top 10% of days is o nly 3 days per quarter. For most sites, the top 10% of monitored days per quarter will represent between 3 and 8 ambient measurement days.

52 In some areas it may not be necessary to model and evaluate the 24-hour NAAQS for all quarters. For example, if PM 2.5 concentrations only exceed the NAAQS in the 1 st and 4 th quarters, and concentrations in the 2 nd and 3 rd quarters are very low, then it may not be necessar y to model the full year. But for areas that have monitored violations (or high value s that are close to the NAAQS) in all 4 seasons, th e entire year should be evaluated.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

126 values, the 98 th percentile day for each year is identified and the n a five year weighted average
of the 98 th percentile values for each site is calculated to d erive the future year design value 53

. In nonattainment areas that measure violations of t he daily PM2.5 NAAQS in both the summer and winter, the highest PM days can sometimes shift between seasons when days are projected to the future. A consequence of this shift is that a base year 98 th percentile winter day may have a higher future year concentration than a summ er day that started with a higher concentration in the base year. Therefore, the rec ommended attainment test methodology does not assume that the temporal distribution of h igh days in the base and future periods will
remain the same.

In the 24-hour attainment test methodology (describ ed below), the eight highest ambient PM 2.5 days in each quarter at each site are projected to the future (32 days per year) using species specific quarterly relative response factors. Afte r all 32 days are projected to the future, the days are re-ranked to determine the future year 98 th percentile day. 54 The rank of the future year 98 th percentile day is selected based on the rank of th e 98 th percentile day in the base year ambient data. For example, at monitoring site A, i f there are 120 observations in year 1 then
the 98 th percentile day is the 3 rd high observation day for the year. In that case, the future year
98 th percentile day is selected as the 3 rd high future day for the year (out of the 32 highes t days).

Similar to the annual PM 2.5 attainment test, we recommend interpolation techni ques for FRM monitors that do not have co-located speciation dat a. Because the 24-hour standard is a 98 th percentile based value, the species composition on high concentration days may be highly variable from day to day and from site to site. Th erefore, while interpolation techniques may be needed, we strongly recommend collecting speciat ion data at all FRM sites that violate the 24-hour NAAQS. A precise estimate of the PM 2.5 components at violating sites will help reduce uncertainty in projecting the future year concentra tion estimates.

53 Similar to the annual average PM 2.5 attainment test, design values are calculated for c onsecutive three year periods. From the five year base period, thre e design values are calculated and then averaged together to create a five year weighted average.

54 The observed 98 th percentile day is always between the 1 st and 8 th high for the year, depending on the sampling schedule. Therefore, projecting the 8 hig hest days in each quarter ensures that the observed 98 th percentile day is always captured. More days coul d be projected, but a maximum of 32 days per year is needed to guarantee that the 98 th percentile observation day is projected to the fut ure.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

127 We recommend a modeled attainment test for the 24-h our PM 2.5 NAAQS with the following
steps.

Step 1. Identify the high observed PM 2.5 days at each FRM monitoring site for each year.

The first step in projecting the daily design value is to identify at each FRM site, the eight highest observed 24-hour PM 2.5 concentration days in each quarter for each year o f the base period (up to 5 years), and identify the day rank o f the observed 98 th percentile value for each year based on the number of collected ambient sampl es (i.e. 3 rd highest day of the year). This results in a data set containing 32 days of data fo r each year (for up to 5 years) for each site. 55

The test should be performed for each monitoring si te that meets the data completeness criteria for calculating a design value for the 24- hour NAAQS. There may not always be data available for all four quarters and all five years. We recommend using eight days per quarter
because the 98 th percentile day for a year is always one of the 8 h ighest days of the year. 56 If all of the high days occur in a single quarter, then us ing the 8 highest days from each quarter will
ensure that the actual 98 th percentile day is always captured. This may resul t in processing more days than necessary, but effectively limits th e design value calculations to a reasonably small number of days.

Step 2. Calculate “high days” species fractions fo r each quarter for each FRM monitor.

In this step, quarterly ambient species fractions o n “high” days are calculated for each of the major component species of PM 2.5 (i.e. sulfate, nitrate, ammonium, elemental carbon , organic carbon mass, particle bound water, salt, and blank mass). This calculation is performed by multiplying the average monitored concentrations of FRM-derived total PM 2.5 mass on the high
PM 2.5 days at each site for each quarter, by the average monitored species composition on the

55 The methodology does not assume that the temporal distribution of high days in the base and future periods will remain the same. We recommend projecti ng a larger set of ambient days from the base period to the future and then re-rank the entire se t of days to find the new future year 98 th percentile value (for each year).

56 If there are 365 samples in a year, then the 98 th percentile is the eighth high day.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

128 high PM 2.5 days for each quarter. 57 The default recommendation for identification of “high” days is the top 10% of days in each quarter. This results in a relatively robust calculation which typically uses between 3 and 9 days per quarter (de pending on the sampling frequency). The end result is a set of quarterly “high days” specie s fractions for each FRM site.

Step 3. Calculate species concentrations for each of the high ambient days.

Multiply the quarterly “high day” species fractions from step 2 by the PM 2.5 mass concentration for the 8 high days per quarter identified in step 1. This results in a set of species concentrations for each of the 32 days per year ide ntified in step 1.

Step 4. Calculate component specific RRFs for the high days in each quarter.

For each quarter, calculate the ratio of future yea r to base year modeled predictions for sulfate, nitrate, elemental carbon, organic carbon, salt, an d OPP for the top 10 percent of modeled

PM 2.5 days based on modeled concentrations of 24-hour av erage PM 2.5 . The result is a set of species-specific “high day” relative response facto rs (RRF) for each quarter.

The relative response factor for component j at a s ite i is given by the following expression:



### (RRF) ij 

### = ([C j, projected 

### ]/[C j, base year 

### ]) i

where C j, base year is the base year mean species concentration (for t he high modeled
PM 2.5 days for each quarter) predicted at the (single) g rid cell which contains monitoring
site i
.

C j, projected is the future year mean species concentration (for the high modeled days for each quarter) predicted at the (single) grid cell w hich contains monitoring site i
.

57 Similar to the annual average calculations, for FRM sites that do not have co-located species data, we recommend calculating the quarterly species fractio ns using interpolated species data. For the 24-hou r species interpolations, we recommend interpolating the average of the highest 10% of monitor days in each quarter.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

129 For example, assume that base year predicted sulfat e mass on the 10 percent highest PM 2.5 days for quarter 3 for a particular location is 20. 0 μg/m 3 and the future year modeled sulfate concentration is 16.0 μg/m 3 , then the RRF for sulfate for quarter 3 is 16.0/20 .0 or 0.80. Due to potentially large spatial and temporal gradients, w e recommend 24-hour PM 2.5 NAAQS RRFs to
be calculated based on the modeled concentrations a t the single grid cell where the monitor is
located.

Step 5. Apply the component specific RRFs to obser ved air quality by quarter.

For each of the 8 days in each quarter, multiply th e daily species concentrations from step 3 by the quarterly species-specific RRFs obtained in ste p 4. If there is one modeled base year, then there will be four quarterly RRFs at each monitor. The modeled quarterly RRF for quarter 1 is multiplied by the ambient data for quarter 1 (8 day s in each year) for each of the 5 years of ambient data. For example, for day A, 21.0 μg/m 3 nitrate x 0.75 = future nitrate of 15.75 μg/m 3 . The same procedure is applied for the 8 high day s per quarter in the other 3 quarters.
This leads to an estimated future concentration for each component for each day.

Step 6. Calculate remaining future year PM2.5 speci es.

The future year concentrations for the remaining sp ecies are then calculated for each of the days. 58 Similar to the annual PM 2.5 attainment test, we recommend that the future year ammonium is calculated based on the future year sul fate and nitrate concentrations, using a constant value for the degree of neutralization of sulfate (from the ambient data). The future year particle bound water concentration is then cal culated from an empirical formula derived from the AIM model. The inputs to the formula are the future year concentrations of sulfate, nitrate, and ammonium calculated in step 5.

Step 7. Sum the species components to get total PM 2.5 concentrations for each day.

Sum the species concentrations for each day to obta in total PM 2.5 values for the 32 days per year per site.

58 If salt is not explicitly modeled, then the salt RR F should be held constant. Blank mass is assumed t o be constant between the base and future year.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

130 Step 8. Determine future year 98 th percentile concentrations for each site year.

Sort the 32 days for each site for each year by tot al PM 2.5 concentration. For each site year, the
monitored 98 th percentile rank (for each year) is used to identif y the 98 th percentile rank for each year. For example, if the base year 98 th percentile value for year 1 was the 3 rd high concentration, then the future year 98 th percentile concentration is identified as the 3 rd high future year PM 2.5 concentration (out of the 32 days).

Step 9. Calculate future 5 year weighted average 2 4-hour design values and compare to the NAAQS.

The estimated 98th percentile values for each of th e 5 years are averaged over 3 year intervals to create 3 year average design values (e.g. the 98 th percentile values from year 1, year 2, and year 3 are averaged. Then the 98 th percentile values from year 2, year 3, and year 4 are averaged, etc.). These design values (up to 3) are then averaged to create a future year 5 year weighted average design value for each monitoring s ite.

The preceding steps for determining future year 24- hour PM 2.5 concentrations are applied for
each FRM site. The 24-hour PM 2.5 design values are truncated after the first decima l place. This approach is consistent with the truncation and roun ding procedures for the 24-hour PM 2.5 NAAQS. Any value that is greater than or equal to 35.5 μg/m 3 is rounded to 36 μg/m 3 and is considered to be violating the 24-hour NAAQS. Simi larly, a value of 35.4 μg/m 3 is rounded down

to 34 μg/m 3 and is considered to be attaining the 24-hour NAAQ S.



### 4.6 Local Area Analyses- Special Considerations for Primary PM 2.5

Primary particulate matter does not undergo chemica l transformation between being emitted and its arrival at a receptor location. Thus, high concentrations of primary particulate matter can occur without relatively lengthy travel times f rom source to receptor. Unlike secondary particulate matter or ozone, we would expect concen trations of primary particulate matter to increase the closer one gets to its source(s) of em issions. Therefore, if a designated
nonattainment area contains concentrated sources of primary particulate matter, we would expect there to be relatively large spatial gradien ts near sources of the primary portion of the organic carbon, elemental carbon and other particul ate matter components of ambient PM 2.5Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

131 (these localized areas of high concentrations are o ften called “hotspots”).

Note that the PM 2.5 NAAQS ambient monitoring rule language 59 indicates that some monitoring locations may not be comparable to the annual PM 2.5 NAAQS. PM 2.5 measurement data from monitors that are not representative of “area-wide” air quality but rather of relatively unique micro-scale, or localized hot spot, or unique middl e-scale impact sites are not eligible for comparison to the annual PM 2.5 NAAQS. The ambient air quality monitor siting rul es define the appropriate scales of influence for the PM 2.5 monitoring network. 60



### 4.6.1 Analysis of Primary PM 2.5 

### Impacts at Monitors

The majority of FRM and FEM PM 2.5 monitors are located at “urban scale” sites. Thes e sites tend to represent relatively large spatial scales a nd do not have large gradients compared to
other monitors in the area. Some sites are classif ied as “neighborhood” scale and may be influenced by more local sources. For reasons stat ed above, local influences creating large spatial gradients are likely to consist mostly of p rimary PM 2.5 (OC, EC, and OPP). These sources may be point sources, or they may be nearby roads o r other mobile or area sources.

As part of the attainment demonstration modeling, i t may be necessary to evaluate the localscale impacts of primary PM 2.5 sources for contributions to the 24-hour and/or th e annual NAAQS. As stated earlier, for the purposes of dete rmining attainment with the NAAQS, it may not always be appropriate to compare ambient data f rom PM 2.5 monitoring sites that are dominated by point sources to the annual NAAQS. Bu t there are numerous cases where local source contributions may not be dominant, but are a sizable contributor to total PM 2.5 (~10- 30% of total annual average PM 2.5 ). In these cases, a more refined analysis of the contribution of local primary PM 2.5 sources to PM 2.5 at the monitor(s) will help explain the causes of nonattainment at and near the monitor and may lead to the more efficient ways to attain the NAAQS by controlling emissions from local sources w hich may be important contributors to the violating area.

There are several modeling tools that can be used t o evaluate contributions of local PM 2.5

59 40 CFR part 58 subpart D 58.30

60 40 CFR part 58 Appendix EModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

132 sources. A grid model can be run at very high hori zontal resolution (1 or 2 km or finer) or a Gaussian dispersion model can be used. Grid based models simulate chemical transformation and complex meteorological conditions, while disper sion models are generally more simplistic; being limited to a local-scale, using Gaussian appr oximations with little or no chemistry. Therefore, while dispersion models may not be an ap propriate tool for determining secondary

PM 2.5 or ozone concentrations, they work well for use in determining local primary PM 2.5 impacts. The model(s) and model setup should be ev aluated to determine the most appropriate tools for a specific situation.

Regardless of which type of models are used to eval uate changes in primary PM 2.5 at monitoring locations, we recommend that the model r esults be used in a relative manner. This is consistent with the already described attainment tests. If a grid model is used at very high resolution, the attainment test as described in Sec tions 4.4 and 4.5 should be followed. If a Gaussian dispersion model is used then the applicat ion of the attainment test will vary slightly.
The test will need to combine results from the chem ical transport grid based modeled
attainment test and the results from local-scale di spersion modeling. If such an approach is taken, the suggested variations on the attainment t est are discussed in the following section.



### 4.6.2 Analysis of Primary PM 2.5 

### Impacts at Monitors using a Gaussian Dispersion Model

To apply a dispersion model in an attainment test, it is important to determine the local component of primary PM 2.5 at the monitor and the sources that are contributi ng to this component. There is no single, simple method for q uantifying this contribution, but detailed analysis of ambient data and advanced data analysis techniques such as receptor modeling may help quantify the contribution. EPA provides softw are and information on the CMB, UNMIX, and PMF receptor models at: http://www.epa.gov/scram001/receptorindex.htm

The simplest method for identifying the local compo nent of PM 2.5 is to examine local ambient
PM 2.5 concentrations. For this type of analysis, it is important to identify the local contributions from as small a source area as possible. This will make it easier to identify contributing sources. One aspect of this analysis is to examine monitored concentration differences between urban monitors (with the highest concentrations) and more suburban measurements. This is likely to provide a representative indication of the excess c ontribution from a relatively local area.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

133 “Urban excess” calculations which pair an urban mon itor with an appropriate rural background monitor (U.S. EPA, 2004b) are likely to indicate “l ocal” contributions that may be representative of a metropolitan area. In most cases, the local c omponent will include contributions from
more than one source.

Sources identified as contributing to the monitor t hrough emissions analysis or data analysis (such as receptor modeling) can then be modeled wit h a dispersion model (or alternatively, a very high resolution grid model). 61 It is common practice to run dispersion models fo r a limited number of major point sources in relatively small a reas (out to a maximum distance of ~50 km). Dispersion models can also be run for all sources o f primary PM 2.5 in a limited area (including mobile, non-road, and area sources).

When applying a model to evaluate the impacts from emissions of primary PM 2.5 , one should
determine the PM 2.5 species that make up the local primary particulate matter. 62 We recommend that the individual components of primary PM 2.5 be tracked separately in the dispersion model. This will allow for a more thorou gh evaluation of the dispersion modeling results (comparing model output to speciated ambien t data) and may aid in the development of primary PM 2.5 control strategies.

The majority of the primary PM 2.5 will consist of the primary portion of the organic carbon component, elemental carbon (EC), and the general c ategory of “other primary particulate matter” (OPP). Speciated PM 2.5 measurements may be able to identify some of the m ore important “other” inorganic components such as heav y metals or salt. In some cases, directly emitted sulfate (and in rare cases nitrate) may als o be a significant component of the local primary PM 2.5
.

The dispersion modeling results should be evaluated to ensure adequate model performance. Similar to grid modeling, the dispersion model resu lts should be compared to ambient data to

61 This guidance only applies to demonstrating attain ment of the PM 2.5 NAAQS to satisfy requirements in CAA Section 189. Other guidance documents and proc edures are applicable for SO 2 , NO 2 , CO, and/or Pb attainment demonstrations and PM 2.5 permitting applications.

62 The PM 2.5 emissions should be broken out into individual spe cies using the best information available. Typically, SCC specific speciation profiles are use d to “speciate” the PM into individual components. Local, source specific information should be used w henever possible.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

134 ensure that the model is providing concentration re presentative of observed values. Although section 3 of this guidance is geared towards evalua ting grid models, many of the same statistical calculations can be made for primary PM 2.5 and PM 2.5 components predicted by a Gaussian dispersion model. Since secondary PM 2.5 is often a large component of total PM 2.5 concentrations, it may be difficult to separate the primary and secondary components of
ambient PM 2.5 . EC and OPP should be considered to be primary PM 2.5 . Much of the rest of the primary PM 2.5 concentration will likely be primary OC.

As part of the local-source impact analysis, an est imated concentration of primary OC is
needed. There are several methods available for es timating the primary vs. secondary portion of ambient OC. Among these are the EC tracer metho d and receptor modeling. The EC tracer
method is the most common method used to estimate s econdary and primary OC concentrations (Turpin, 1995), (Strader, 1999), (Ca bada, 2004), (Chu, 2005), (Saylor, 2006). The
method uses measurements of OC and EC and calculate d OC to EC ratios to identify periods when OC is likely to be mostly primary. This infor mation is then used to calculate the secondary contribution to OC. Receptor models such as CMB an d PMF have also been used to estimate secondary organic concentrations (Na, 2004), (Yuan, 2006).

The following sections describe a suggested methodo logy for quantifying the impacts on PM concentrations from local primary sources of PM for the Annual and 24-hour PM 2.5 NAAQS. Because each nonattainment area has unique emission s sources and source-receptor relationships, air agencies should work closely wit h their EPA Regional Office in developing local area analysis applications.

4.6.2.1 Methodology for the Annual PM2.5 NAAQS The suggested steps outlined below assume that the attainment test has been performed using chemical transport modeling and that the annual DV has been computed as discussed in
Section 4.4.

(1) Identify local primary PM 2.5 sources that are thought to be contributing to the violating monitor(s) and causing non-attainment of the annual NAAQS.

(2) By quarter, identify at the monitor, primary v ersus secondary components of

PM 2.5
.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

135 (3) Identify the percentage of total primary PM 2.5 at the monitor due to the local sources identified in step (1). Convert the percentage to an estimated concentration in μg/m3.

(4) Run a dispersion model to estimate base year a nd future concentrations from the identified
local sources. -Place receptors at and near the monitor location(s ). 63 -The receptor spacing should be fine enough to get a representative picture of the
concentration at the monitor. -Model the base year and future year annual average -For both base and future year use the same 1 year of meteorological data – preferably the same year as used for the chemical transport mo deling (if available, use more than 1 year of meteorological data).

(5) From the modeling in step (4), calculate quart erly RRFs 64 for total primary PM 2.5 modeled for the selected sources (using concentration value s for receptors located at monitors).

(6) Multiply the primary PM 2.5 identified in step (3) by the RRFs from step (5).

(7) Subtract the result in step (6) from the primar y PM 2.5 identified in step (3)

(8) For each quarter, subtract the amount in step (7) from the future year quarterly DV obtained from the chemical transport modeling.

(9) Average the four quarters to get an annual ave rage DV.

63 Additional receptors should be placed near monitors to examine predicted concentration gradients. Receptors should only be located in areas that are appropriate for placing FRM monitors for comparison to the annual NAAQS (see EPA, 1997).

64 RRFs should be calculated as a ratio of the base an d future mean concentrations (a ratio of the means, not a mean of the daily ratios).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

136 Annual NAAQS Example:

Based on ambient monitoring data, for quarter 4, a monitor has a 5 year weighted mean concentration of 17.0 μg/m3.

(1) Based on emissions and ambient data analysis, i t has been determined that several steel mills in the area are contributing to the local com ponent of PM 2.5 at a violating monitor.

(2) Based on comparisons to other monitors in the a rea, it is believed that the local PM 2.5 component at the monitor for quarter 4 is 2 μg/m3.

(3) Using receptor modeling, it is determined that, on average, the steel mills are contributing a
local PM 2.5 component in quarter 4 of approximately 1 μg/m3.

(4) Modeling with a dispersion model shows the foll owing contribution at the monitor from the identified local sources (steel mills): Base year: 2.0 μg/m3 Future year: 1.5 μg/m3

(5) Quarter 4 RRF local = 1.5/2.0 = 0.750.

(6) 1 μg/m3 * 0.750 = 0.75 μg/m3.

(7) 0.75 μg/m3 - 1 μg/m3 = -0.25 μg/m3 (total Q4 re duction from identified local sources is 0.25 μg/m3)

(8) Quarter 4 New DV = 17.0 – 0.25 μg/m3 = 16.75 μg /m3

(9) Steps 1-8 are repeated for each quarter with si milar results. The annual DV = (Q1 New DV + Q2 New DV + Q3 New DV + Q4 New DV)/4

4.6.2.2 Methodology for the 24-Hour NAAQS The suggested steps outlined below assume that the attainment test has been performed using chemical transport modeling and that the quarterly DV’s have been computed as discussed inModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

137 Section 4.5. As with the annual design value, moni toring data for 5 years are used as the basis for the projection.

(1) Using emissions and/or data analyses described above, determine the local primary PM 2.5 sources that are thought to be contributing to the monitor for each of the 4 quarters. 65

(2) For each of the four quarters, determine the pr imary versus secondary components of PM 2.5 at the monitor on the high observed PM 2.5 days (e.g. the high 10% of monitored days in each quarter)

(3) Identify the percentage of total primary PM 2.5 at the monitor due to the local sources identified in step (1). Convert the percentage to an estimated concentration in μg/m3.

(4) Model with a dispersion model the base year and future concentrations of these local sources using emissions that are representative of emissions on high PM 2.5 days. -Place receptors at and near the monitor location(s ). 66 -The receptor spacing should be fine enough to get an accurate picture of the
concentration at the monitor. -Model the base and future year. -For both base and future year use the same 1 year of meteorological data used for the chemical transport modeling (if available, use more than 1 year of met data). -Calculate the 24-hour average concentration for ea ch day. Average the high days 67 within each quarter to determine a modeled high con centration for each quarter in the

65 The selection of these sources should be representa tive of the 5-year base period used to calculate th e 5 year weighted average design values. If large em issions changes have occurred over the 5 year perio d then the identification of the sources contributing to the monitor should be most influenced by the ba se modeling year (the middle year of the 5 year period ) since it is the year being modeled and it has the strongest weighting in the design value calculation s.

66 Additional receptors should be placed near monitors to examine predicted concentration gradients. Receptors should only be located in areas that are appropriate for placing FRM monitors for comparison to the 24-hour NAAQS (see EPA, 1997).

67 High days may be all days > 35 μg/m3 or the high en d of the distribution of modeled days (i.e. top 10% days).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

138 base and future year.

(5) From the modeling in step (4), calculate quarte rly “high days” RRFs 68 for total primary PM 2.5 modeled for the selected sources.

(6) Multiply the primary PM 2.5 identified in step (3) by the RRFs from step (5).

(7) Subtract the result in step (6) from the primar y PM 2.5 identified in step (3)

(8) For each of the 5 years used to calculate the 5 year weighted average DV, subtract the result from step (7) from each of the 8 high days per quar ter, for each year. Re-sort the 32 high days per year to get the new 98 th percentile value for each year. Repeat for all 5 years of ambient
data.

(9) Average the annual 98 th percentile concentrations to get 3 DVs and then av erage the 3 DVs to get the final future year 5 year weighted averag e DV.

24 Hour NAAQS Example:

The base year design values are calculated from the 5 year base period centered around the base emissions year. The dispersion model is run w ith emissions and meteorology for year 3 (the center year of the 5 year period).

(1) It is believed that several steel mills in the area are contributing to the local component of
PM 2.5 at the monitor.

(2) Based on a comparison to other monitors in the area, it is believed that the local primary component of PM 2.5 at the monitor on high PM days is the following fo r each of the quarters in
Year 3: Quarter 1: 15 μg/m3 Quarter 2: 13 μg/m3

68 RRFs should be calculated as a ratio of the base an d future mean concentrations (a ratio of the means, not a mean of the daily ratios).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

139 Quarter 3: 8 μg/m3 Quarter 4: 20 μg/m3 It is believed that this is representative of the o ther four years within the 5-year period.

(3) Using receptor modeling, it is determined that the steel mills are contributing a local

PM 2.5 component (on high PM days) of the following for e ach of the quarters in Year 3: Quarter 1: 8 μg/m3 Quarter 2: 4 μg/m3 Quarter 3: 4 μg/m3 Quarter 4: 5 μg/m3 It is believed that this is representative of the o ther four years within the 5-year period.

(4) A dispersion model was run for a base year and a future year. For each quarter, compute the 24-hr average modeled concentration for each da y and compute the mean of the high days for each quarter to get the base and future year co ncentrations. The dispersion modeling shows the following contribution at the monitor (on high days) from the identified local sources (steel mills) for total primary PM 2.5 in each of the quarters in Year 3: Quarter 1: Base: 11.7 μg/m3 Future: 8.3 μg/m3 Quarter 2: Base: 6.3 μg/m3 Future: 4.4 μg/m3 Quarter 3: Base: 5.3 μg/m3 Future: 3.1 μg/m3 Quarter 4: Base: 8.3 μg/m3 Future: 6.2 μg/m3

(5) Based on the results from the modeling in (4), the RRF values are: Quarter 1: 8.3/11.7 = 0.709 Quarter 2: 4.4/6.3 = 0.698 Quarter 3: 3.1/5.3 = 0.585 Quarter 4: 6.2/8.3 = 0.747

(6) Multiplying the primary PM 2.5 identified in (3) by the RRFs from (5). Quarter 1: 8 μg/m3 * 0.709 = 5.7 μg/m3 Quarter 2: 4 μg/m3 * 0.698 = 2.8 μg/m3 Quarter 3: 4 μg/m3 * 0.585 = 2.3 μg/m3 Quarter 4: 5 μg/m3 * 0.747 = 3.7 μg/m3Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

140 (7) Subtracting the result in (6) from the primary PM 2.5 identified in (3) Quarter 1: 5.7 μg/m3 - 8 μg/m3 = -2.3 μg/m3 Quarter 2: 2.8 μg/m3 - 4 μg/m3 = -1.2 μg/m3 Quarter 3: 2.3 μg/m3 - 4 μg/m3 = -1.7 μg/m3 Quarter 4: 3.7 μg/m3 - 5 μg/m3 = -1.3 μg/m3

(8) The results from (7) are subtracted from the fu ture year concentration for each of the 8 days per quarter (32 days per year) for each of the 5 ye ars. Re-sort the 32 high days per year to get
the new 98 th percentile value for each year. Repeat for all 5 years of ambient data.

Below is an example for the year 1 ambient data:

Quarter Future year Concentration

(based on ambient data projected to future year)

(μg/m3) Modeled reduction

from dispersion model (μg/m3) Adjusted future year

concentration (μg/m3)
1 45.57 -2.3 43.27
1 44.3 -2.3 42
1 39.58 -2.3 37.28
1 35.86 -2.3 33.56
1 34.87 -2.3 32.57
1 32.87 -2.3 30.57
1 31.87 -2.3 29.57
1 31.6 -2.3 29.3

Quarter Future year

Concentration (μg/m3) Modeled reduction from dispersion model (μg/m3) Adjusted future year

concentration (μg/m3)
2 14.06 -1.2 12.86
2 12.8 -1.2 11.6
2 12.51 -1.2 11.31
2 11.93 -1.2 10.73
2 10.86 -1.2 9.66
2 10.67 -1.2 9.47
2 10.18 -1.2 8.98
2 10.09 -1.2 8.89Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

141 Quarter Future year

Concentration (μg/m3) Mode led reduction from dispersion model (μg/m3) Adjusted future year concentration (μg/m3)
3 37.92 -1.7 36.22
3 22.39 -1.7 20.69
3 21.92 -1.7 20.22
3 18.93 -1.7 17.23
3 16.12 -1.7 14.42
3 15.93 -1.7 14.23
3 15.28 -1.7 13.58
3 15 -1.7 13.3

Quarter Future year

Concentration (μg/m3) Modeled reduction from dispersion model (μg/m3) Adjusted future year

concentration (μg/m3)
4 52.79 -1.3 51.49
4 50.8 -1.3 49.5
4 45.73 -1.3 44.43
4 44.92 -1.3 43.62
4 44.65 -1.3 43.35
4 42.66 -1.3 41.36
4 42.21 -1.3 40.91
4 41.57 -1.3 40.27

The year one 98 th percentile value for the monitoring site is the 3 rd high value. Therefore the adjusted “new” 98 th percentile value for year 1 is 44.43 μg/m3 (the 3 rd highest adjusted future high day out of the 32 days for year 1).

The same calculations are completed for the other 4 years of data with the following result:

Year 1: 44.43
Year 2: 47.34
Year 3: 50.22
Year 4: 41.68
Year 5: 49.77Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

142 (9) Averaging the 3 design values: DV1 = (44.43 + 47.34 + 50.22)/3 = 47.3 DV2 = (47.34 + 50.22 + 41.68)/3 = 46.4 DV3 = (50.22 + 41.68 + 49.77)/3 = 47.2

Averaging the 3 DV to get the final future year 5 y ear weighted average DV: (47.3 + 46.4 + 47.2)/3 = 46.9 = 47 μg/m3

In the above examples, emissions reductions from st eel mills were modeled with a dispersion model, which provided additional reductions to the future 5 year weighted average annual and 24-hour design values. Changes in DVs derived from chemical transport modeling should be examined to quantify the impact of emissions change s from the flagged steel mills. The change in concentration in the chemical transport model fr om steel mill emissions controls may need to be adjusted in order to ensure that there is no double counting of emission reductions (see below).

4.6.2.3 Double Counting The methodology discussed above may result in doubl e counting of local emissions sources and reductions. The change in emissions from local sou rces is accounted for in the chemical transport modeling and again in the dispersion mode ling. There are several ways to examine
this issue.

1) Run the chemical transport model with the primary P M 2.5 emissions from the flagged source(s) in the base case and hold the emissions f rom the flagged source(s) constant in the future case(s). 2) Run the grid model with a source tagging approach. The flagged sources can be tagged so that their contribution in the grid model can be explicitly tracked (Environ, 2006), (Environ, 2014). 3) Employ a methodology to combine the results of the grid and dispersion models so that double counting is eliminated or minimized. Severa l techniques have been developed to do this (Isakov, 2007).

The potential for double counting can be evaluated by considering the grid resolution of the chemical transport model and the number of flagged sources in the dispersion model. The finerModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

143 the resolution of the grid model, the more double c ounting will be a problem (because the local sources will be more finely resolved). Additionall y, the more sources that are flagged, the more double counting will be a problem. If the grid cel l size is relatively large (e.g. 12km) and the primary emissions are relatively small, it may be p ossible to assume that double counting is small enough to be ignored. But the nature of the issue should be evaluated on a case by case
basis.

The simplest way to evaluate the magnitude of the p roblem is to run the chemical transport model with and without the flagged sources from the dispersion model. This will indicate the maximum impact that the sources can have on the che mical transport modeling results. A very small impact means that double counting is not a pr oblem. A large impact indicates that a more explicit analysis is needed in order to resolv e double counting issues.

4.6.2.4 Analysis of Primary PM 2.5 at Monitors- Quality Assurance As with any modeling exercise, it is important to q uality assure the model inputs and outputs. In particular, we recommend focused quality assuran ce checks on emissions from sources flagged for a dispersion modeling analysis. Prior to applying a model, air agencies should
review available information to ensure that there a re no major discrepancies between modeled estimates and nearby monitored data for particulate matter. The emission factors, activity data, and speciation profiles of the PM 2.5 emissions should also be analyzed for accuracy. I f a speciation monitor exists, the speciated data from the monitor can be compared to the speciation profiles of the flagged sources. Recept or models can also be used as a QA tool. Discrepancies between receptor modeling results (wh ich are based on analyzing ambient data) and speciated emissions may indicate a problem with the magnitude and/or the speciation of the emissions sources. If discrepancies are found, those implementing the modeling protocol (see section 2.1) should consult with the appropria te EPA regional office to reach agreement on what, if any, adjustments should be made to the emi ssions estimates for the identified sources.

It is also important to quality assure the model ou tputs. Modeling each species of primary

PM 2.5 separately within the dispersion model should aid in this analysis, especially if the selected control strategies applied in the future y ear do not control each primary species to the same degree. If a speciation monitor exists, the s peciated data from the monitor may also help quality assure the dispersion model results. Howev er, the application of the dispersion modelModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

144 results in a relative sense will help to reduce the impact of possible over- or under-estimations by the dispersion model due to emissions, meteorolo gy, or general selection of other model input parameters.



### 4.7 Estimating design values at unmonitored locatio ns

High ozone and/or PM 2.5 concentrations can occur at (or near) existing moni tors or in
unmonitored areas. The modeled attainment test is primarily a monitor based test. As such,
the focus of the attainment test is whether attainm ent can be reached at existing monitors. An additional “unmonitored area analysis” can also be performed to examine ozone and/or

PM 2.5 concentrations in unmonitored areas. The unmonito red area analysis is intended to be a means for identifying high ozone and/or PM 2.5 concentrations outside of traditionally monitored locations, particularly in nonattainment areas where modeling or other data analyses have indicated potential high concentratio n areas of ozone and/or PM 2.5 outside of the existing monitoring network. An unmonitored area a nalysis may also be more important in
areas where the ozone or PM 2.5 monitoring networks just meet or minimally exceed the minimum required network in a nonattainment area.

This review can help ensure that a control strategy leads to reductions in ozone or PM 2.5 at other locations which could have base case (and fut ure) design values exceeding the NAAQS were a monitor deployed there. This document descr ibes how an unmonitored area analysis can be performed for a particular area. See the re spective ozone and PM 2.5 implementation rules (including the preamble and response to comme nts) for more information on when an unmonitored area analysis is recommended or require d.

We use the term “unmonitored area analysis” to desc ribe an analysis that uses a combination of model output and ambient data to identify areas that might exceed the NAAQS in areas that not currently monitored. The unmonitored area anal ysis for a particular nonattainment area is intended to address the potential for air quality p roblems within or near that nonattainment area. The analysis should include, at a minimum, a ll nonattainment counties and counties surrounding the nonattainment area, as appropriate. It is possible that unmonitored area violations may appear in counties far upwind or dow nwind of the local area of interest. In those cases, the distance to the nonattainment area and ability of the modeling to represent farModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

145 downwind areas should be evaluated on a case-by-cas e basis.

The spatial resolution of the unmonitored area anal ysis should be consistent with the appropriate model resolution for the attainment dem onstration. For example, if the
attainment demonstration for ozone or PM 2.5 is conducted at 12km grid resolution, the unmonitored area analysis should also be performed at the same horizontal resolution. [ Note:
When the PM 2.5 implementation rule for the 2012 NAAQS is finalized , this draft guidance will be updated to reflect additional recommendations and/o r requirements for near-road PM 2.5 modeling].



### 4.7.1 Why does the unmonitored area analysis need to use both ambient data and model output?

When performing an unmonitored area analysis, it is recommended to use interpolated spatial fields of ambient data in combination with gridded modeled outputs (i.e. fused surfaces). Ambient data can be interpolated to provide a set o f spatial fields. The resultant spatial fields will provide an indication of pollutant concentrati ons in monitored and unmonitored areas. But interpolated ambient data cannot identify unmonitor ed areas with higher concentrations than those measured at monitors. The interpolated conce ntration between monitors will generally
be the same or lower than the measured concentratio n at the monitors (assuming that more sophisticated statistical techniques are not used). The interpolation technique does not account for emissions or chemistry information that may be needed to identify high
concentrations in unmonitored areas.

In addition to interpolated surfaces, the gridded m odel output (absolute) concentrations can be
used to examine unmonitored area concentrations. T he model provides an hourly concentration for every grid cell. The concentrati ons can be analyzed to determine unmonitored areas where the model predicts high val ues. But the absolute predictions from the model may not be entirely accurate. But unlike the interpolated ambient data, the model explicitly accounts for emissions, chemistry, and m eteorology over the entire domain.

When considered together, interpolated ambient data and absolute model outputs both have strengths and weaknesses. We can take advantage of the strengths of each dataset by combining the two types of data. Interpolated spat ial fields of ambient data provide a strongModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

146 basis for estimating accurate pollutant concentrati ons at and near monitor locations. The model outputs can be used to adjust the interpolate d spatial fields (either up or down) so that
more accurate estimates can be derived in the unmon itored areas. 69 A recommended way to use the modeling in the creation of fused surfaces is to adjust the ambient spatial fields using
the concentration **gradients** derived from the model predictions

**.** It is preferable to assume that the model is accurately predicting areas of general ly high or low ozone or PM, rather than to assume that the absolute predictions from the model are correct. For example, in unmonitored areas where the model predicts relatively high ozon e or PM concentrations, the spatial fields can be adjusted upward. In unmonitored areas where the model predicts relatively low ozone or PM concentrations, the spatial fields can be adj usted downward. In this way, it is be possible to predict unmonitored areas that may have high concentrations. At the same time, concentrations in rural areas, (which may be overly influenced by high monitored concentrations near urban areas), may be adjusted d ownward. We refer to this combination of interpolated spatial fields and modeled output a s “gradient adjusted spatial fields”


### 4.7.2 Implementation of Gradient Adjusted Spatial Fields

Gradient adjusted spatial fields are first created for the base year. Future year estimates can then be created by applying gridded RRFs to the gra dient adjusted spatial fields. The basic steps are as follows:

1) Interpolate base year ambient data to create a set of spatial fields. 2) Adjust the spatial fields using gridded model-based spatial gradients (base year values). 3) Apply gridded model-based RRFs to the gradient adju sted spatial fields. 4) Determine if any unmonitored areas are predicted to exceed the NAAQS in the future.

EPA’s “Modeled Attainment Test Software” (MATS) wil l spatially interpolate data, adjust the spatial fields based on model output gradients, and multiply the fields by model calculated RRFs (steps 1-3 above) (ABT, 2014). Air agencies can us e the EPA-provided software or are free to develop alternative techniques that may be appropri ate for their areas or situations. Air Agencies should consult with the appropriate EPA re gional office to determine how an

69 The accuracy of interpolated fields can be evaluate d by removing data from monitors (either in groups or one at a time) to see how well the interpolation scheme estimates known concentrations at ambient monitoring sites. This is known as a cross validat ion.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

147 unmonitored area analysis should be conducted (in c ases where an unmonitored area analysis is recommended or required).

Step 1

The first step in developing an unmonitored area an alysis is to interpolate ambient data. Ideally, design values should be interpolated. Eit her the 5 year weighted average design values
that are used in the monitor based model attainment test or a single design value period can be used in the development of ambient spatial fields. Care should be taken so that the interpolated fields are not unduly influenced by mo nitoring sites that do not have complete data. Since the design values can vary significant ly from year to year, it is important to use a
consistent set of data.

We are not recommending a single interpolation tech nique. EPA’s MATS uses the Voronoi Neighbor Averaging (VNA) technique. Past analyses have used a Kriging interpolation technique (U.S.EPA, 2004).

For ozone analyses, a single spatial field of ozone values is needed. But for the PM 2.5 NAAQS, we recommend creating interpolated spatial fields f or PM 2.5 (FRM) and for each component species of PM 2.5 . For the annual PM 2.5 NAAQS, a set of quarterly average spatial fields c an be created. The 4 quarters are averaged to get an ann ual average set of fields.

For the 24-hour PM 2.5 standard, a spatial field can be created using the high end of the
distribution of 24-hour PM 2.5 concentrations. This is best represented by inter polating the measured high values from each quarter. For the PM 2.5 component species, we recommend interpolating the high PM 2.5 days in each quarter. This can be based on the hi ghest monitored days in each quarter.

Step 2

The second step in the process involves the use of gridded model output to adjust the spatial
fields derived from ambient data. MATS uses the eV NA technique, where the basic VNA interpolation is “enhanced” by adjusting the interp olated values based on the modeled ozone or PM gradient. See the MATS user’s guide for more details (Abt, 2014). Other “modelModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

148 adjusted” spatial fields techniques include Hierarc hical-Bayesian (McMillan, 2010) and “Downscaler” (Berrocal, 2010a and 2010b).

A specific metric is needed to determine the model predicted gradient in concentrations for each of the NAAQS. For ozone, a logical metric is the 4 th highest ozone prediction in each grid
cell. 70 For the annual PM 2.5 NAAQS, the model predicted quarterly mean concentr ations for
PM 2.5 and PM 2.5 species can be used to adjust the ambient spatial fields. For the 24-hour PM 2.5 NAAQS, the gradient adjusted fields can be derived from the high end of the distribution of daily averages in each quarter. This could be for the top 10% of modeled days in each quarter or for all days with 24-hour average concentration > 35 μg/m3 (or some other relatively high value).

Step 3

The next step is to create future year fields by mu ltiplying the base year gradient adjusted spatial fields by model derived gridded RRFs. The RRFs for the unmonitored area analysis are calculated in the same way as the monitored based a ttainment test (except that the grid cell array is not used in the spatial fields based analy sis). The future year concentrations are equal to the base year concentration multiplied by the RR F in each grid cell. For ozone, a single spatial field is multiplied by a single set of mode l derived RRFs. For PM 2.5 , the RRFs for each of the species, for each quarter, are multiplied by th e spatial fields for each species, for each year. The future year gradient adjusted spatial fields ar e then analyzed to determine if any grid cells are predicted to remain above the NAAQS.



### 4.7.3 Using the Results of the Unmonitored Area Ana lysis

States should consult with their EPA regional offic e to determine if an unmonitored area analysis should be conducted for a particular nonat tainment area, and how the results of an unmonitored area analysis should be used. Note: The guidance will be updated to include

70 The metric should approximate the measured design v alues at monitoring sites. Depending on the days modeled, other metrics, such as the average of the top 4 modeled days may be a better proxy for the design value.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

149 additional information on the appropriate use of un monitored area analysis results when final
decisions are made in the ozone and PM 2.5 implementation rules.



### 4.8 What Is The Recommended Modeling Analysis for R egional Haze?

This document focuses on the modeling analysis requ ired to assess future visibility improvement relative to the uniform rate of progres s or “glidepath” (for each Class I area) as part of a reasonable progress analysis. In this se ction, we describe the recommended modeled analysis to assess visibility improvement. The vis ibility analysis has many similarities to the
PM 2.5 attainment tests described in sections 4.4 and 4.5. The recommended visibility analysis
and the attainment tests both use monitored data to define base year air quality. They divide

PM 2.5 into major species, and component-specific relativ e response factors are multiplied by base year monitored values to estimate future conce ntrations.

Section 4.8.1 provides background information on th e requirements of the Regional Haze rule and defines the components of a reasonable progress analysis. The rest of the section describes the recommended modeling analysis that ai r agencies should perform as part of their reasonable progress analysis.



### 4.8.1 Regional Haze Rule Background

Section 169A(a)(1) of the Clean Air Act states “Con gress hereby declares as a national goal the prevention of any future, and the remedying of any existing, impairment of visibility in mandatory class I Federal areas which impairment re sults from manmade air pollution.” Section 169B calls for EPA to “carry out the Admini strator's regulatory responsibilities under [section 169A], including criteria for measuring ‘r easonable progress’ toward the national goal.”

In response to these mandates, EPA promulgated the regional haze rule (RHR) on July 1, 1999. 71 Under section 51.308(d)(1) of this rule, states mus t “establish goals (expressed in deciviews) that provide for reasonable progress towards achiev ing natural visibility conditions for each

71 64 FR 35714 (codified at 40 CFR 51.300-309).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

150 Class I area within a state.” These reasonable pro gress goals (RPGs) must provide for an improvement in visibility for the most impaired day s over the period of the implementation plan and ensure no degradation in visibility for th e least impaired days over the same period. 72 RPGs, measured in deciviews, are interim goals that represent incremental visibility improvement over time toward Congress’s goal of nat ural background conditions and are developed in consultation with other affected air a gencies and Federal Land Managers. 73

In determining what would constitute reasonable pr ogress, section 169A(g)(1) of the Clean Air Act requires air agencies to consider the following four factors:

• The costs of compliance;

• The time necessary for compliance;

• The energy and non-air quality environmental impact s of compliance; and

• The remaining useful life of existing sources 74

States must demonstrate in their SIPs how these fac tors are taken into consideration in selecting the RPG for each Class I area in the stat e. More details on the setting of RPGs and the appropriate use of the four factors is contained in “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” (U.S. EPA, 2 007b).

As explained in the RHR, the baseline for each Clas s I area is the average visibility (in deciviews) on the 20% most impaired days, or “worst days”, and on the 20% least impaired days, or “best days,” for the years 2000 through 2004. 75

The regional haze rule also establishes an addition al analytical requirement in the process of establishing RPG. States are required to determine the rate of improvement in visibility needed

72 40 CFR 51.308(d)(1).

73 40 CFR 51.308(d)(1)(iv) and 51.308(i).

74 42 U.S.C. § 7491(g)(1); 40 CFR 51.308(d)(1)(i)(A).

75 64 FR at 35730.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

151 to reach natural conditions by 2064, and to set eac h RPG taking this “glidepath” into account. 76 The glidepath is the amount of visibility improveme nt needed in each review period to stay on a linear path towards visibility improvement to natur al conditions by 2064. The glidepath represents a linear or uniform rate of progress. T herefore, the amount of visibility improvement needed to stay on the glidepath is also referred to as “uniform rate of progress”. The terms “glidepath” and “uniform rate of progress ” are used interchangeably in this
document.

This section does not address the process of calcul ating the glidepath or setting and evaluating
RPGs. 77 The determination of reasonable progress goals is a multi-step process that assesses the uniform rate of progress, the predicted rate of progress, and the four factor test. The RPGs are determined by air agencies for each Class I are a by considering the factors and the modeling analysis. The results of the modeling analyses wil l be one of several components in the
determination of RPGs.



### 4.8.2 How is Regional Haze Measured?

Regional haze is measured by an **extinction coefficient** (b ext ) which represents light attenuation resulting from scattering and absorption of light f rom ambient PM plus scattering of light due to gas molecules in the air (i.e., Rayleigh scatter ing). Although b ext can be estimated by several different methodologies, the RHR requires that it b e estimated using measured ambient PM. This follows since, for a given set of meteorologic al conditions, visibility can be improved by reducing concentrations of ambient PM. Thus, deriv ing b ext in this manner provides a direct link between regional haze and related pollutant concent rations. Equation (4.11) is applied for each
Class I area to estimate b ext (IMPROVE, 2000). The equation reflects empirical relationships derived between measured mass of PM components and transmissometer measurements of
b ext at monitoring sites in Class I areas within the IM PROVE monitoring network.

b ext = 3( **f(rh)** )[SO 4 ] + 3(

**f(rh)** )[NO 3 ] + 4(

**f’(rh)** )[OC] + 10[EC] + 1[Fine Soil] + 0.6[CM] + b rayleigh [4.11]

76 40 CFR 51.308(d)(1)(i)(B).

77 For more details on calculating the glidepath and setting reasonable progress goals see (U.S. EPA 2007b, U.S. EPA 2003b and U.S. EPA 2003c).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

152 where the numerical coefficients on the right hand side of the equation represent the light scattering or absorption efficiency, m 2 /gm of the corresponding component of particulate
matter,

**f(rh)**

, **f’(rh)** are relative humidity adjustment factors applied to the light scattering efficiency (to be described in greater detail shortly), dimensionl ess,

SO 4 is the mass associated with sulfates, μg/m 3

,

NO 3 is the mass associated with nitrates, μg/m 3

,

OC is the mass associated with organic carbon, μg/m 3

,

EC is the mass associated with elemental carbon, μg /m 3

,

Fine Soil is inorganic primary particulate matter ( excluding primary sulfate and nitrate particles) associated with soil components with aerodynamic di ameter < 2.5 um, μg/m 3

,

CM is coarse PM with aerodynamic diameter > 2.5 um, but < 10 um, μg/m 3

,

b rayleigh is light-scattering attributable to Rayleigh scat tering, Mm -1 (i.e., inverse “megameters”), assumed to be 10 Mm -1

.

b ext is the estimated extinction coefficient, Mm -1

.

4.8.2.1 “Revised” IMPROVE Algorithm The IMPROVE program revised the IMPROVE algorithm i n 2006 (Hand, 2006); (Pitchford, 2007). The revised algorithm is intended to reduce biases in light extinction estimates compared to the old algorithm. The revised algorithm, which is som ewhat more complicated than the old algorithm, is as follows:



### bext ≈ 2.2 x f s 

### (RH) x [Small Sulfate] + 4.8 x f L 

### (RH) x [Large Sulfate]
+ 2.4 x f s 

### (RH) x [Small Nitrate] + 5.1 x f L 

### (RH) x [Large Nitrate]Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

153 

### + 2.8 x {Small Organic Mass] + 6.1 x [Large Organic Mass] + 10 x [Elemental Carbon] + 1 x [Fine Soil]
+ 1.7 x f ss 

### (RH) x [Sea Salt] + 0.6 x [Coarse Mass] + Rayleigh Scattering (site specific) + 0.33 x [NO 2 

### (ppb)] [4.12]

The total sulfate, nitrate and organic carbon compo und concentrations are each split into two fractions, representing small and large size distri butions of those components. The organic mass concentration used in the new algorithm is 1.8 times the organic carbon mass concentration (changed from 1.4 times carbon mass c oncentration used for input into the old IMPROVE algorithm). New terms have been added for sea salt (important for coastal locations) and for absorption by NO 2 (only used where NO 2 data are available). Site-specific Rayleigh scattering is calculated based on the elevation and annual average temperature of each IMPROVE monitoring site.

The apportionment of the total concentration of sul fate compounds into the concentrations of the small and large size fractions is accomplished using the following equations.

[Large Sulfate] = [Total Sulfate]/20 μg/m3 x [Total Sulfate], for [Total Sulfate] < 20 μg/m3

[Large Sulfate] = [Total Sulfate], for [Total Sulfa te] ≥ 20 μg/m3

[Small Sulfate] = [Total Sulfate] - [Large Sulfate]

The same equations are used to apportion total nitr ate and total organic mass concentrations into the small and large size fractions.

Sea salt is calculated as 1.8 x [Chloride] or 1.8 x [Chlorine] if the chloride measurement is below detection limits, missing or invalid. The algorith m also uses three new water growth adjustment terms. They are for use with the small size distribution and the large size distribution sulfate and nitrate compounds and for sea salt (f S (RH), f L (RH) and f SS (RH) respectively).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

154 Testing of the revised algorithm reveals that, in m ost regions of the country, the revised equation gives somewhat higher light extinction, es pecially on the worst visibility days. But compared to the old equation, the relative percenta ge of extinction attributed to the various PM components is nearly unchanged (Hand, 206). The refore, the revised equation is not expected to make a large difference in the **rate** of visibility changes resulting from potential control strategy simulations. However, the revised equation could lead to differences in the relative change in light extinction predicted by th e models. As of 2014, the IMPROVE program has stopped reporting visibility data using the old IMPROVE equation. Therefore, due to the improvements associated with the revised equation a nd unavailability of routine calculations using the old equation, we recommend using the revi sed IMPROVE equation for all reasonable progress related modeling calculations. If states want to use the old equation or a variation of the revised equation, they should provide documenta tion concerning the reasons why an alternative is more appropriate than the “revised” IMPROVE calculation.

4.8.2.2 Deciview Haze Index The RHR stipulates that reasonable progress is to b e measured in terms of changes in a
**deciview haze index**

or simply “deciviews”(dv). Deciviews are defined as the natural logarithm of the ratio of the extinction coefficient to Rayle igh scattering (Pitchford and Malm, 1994)



### Deciview = 10 ln(b ext 

### /10) [4.13]

Where the units of b ext and light scattering due to Rayleigh scattering 78 (i.e., the “10" in the denominator of the logarithmic expression) are both expressed in Mm -1

.

A **change** in deciviews, which is how progress is tracked is given by Equation (4.14). A one deciview change is equivalent to ~10% change in b ext .

78 Even though the “revised” IMPROVE equation uses a s ite-specific Rayleigh scattering value, the denominator in the deciview equation should always be 10. In this way, the deciview calculation will be consistent across all Class I areas. Under very cl ean conditions, when the site-specific Ralyeigh scattering is less than 10, the deciview value can be negative, but this should not be considered a problem.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

155 

### Δdv = dv future 

### - dv baseline [4.14]

A negative number indicates a reduction in deciview s, which is an improvement in visibility.

4.8.2.3 Estimating Mass Associated With Components of Particulate Matter All Class I areas either have on-site speciated PM 2.5 measurements or have been assigned a representative IMPROVE monitoring site with measure ments. 79 Therefore, it is generally not necessary to interpolate measured PM 2.5 species data to a site. The existing IMPROVE data base
of PM 2.5 measurements should be adequate to provide data fo r all Class I areas. 80

FRM data is not used in the regional haze analysis. Therefore, it is not necessary for the sum of species components to equal gravimetric measurement s obtained with a Federal Reference or equivalent method for measuring PM 2.5 . Therefore, for regional haze calculations, it is not necessary to use the speciated data adjustment proc edures introduced in Section 4.4 (the “SANDWICH” adjustments).

Hand (2006) has developed a set of default assumpti ons for mass associated with each of the components of PM for Equation (4.12). These are pr esented in Table 4.8.1. The components in
Table 4.8.1 are similar to those used in the modele d attainment demonstrations for the PM 2.5 NAAQS. Notice however, that sulfate and nitrate ma ss are assumed to be fully neutralized (therefore, ammonium is not needed as a separate co mponent); organic carbon is assumed to be equal to 1.8 times measured OC mass (1.4 times OC in the old IMPROVE equation) ; there is no water component; and there is a term for coarse PM.

79 See U.S. EPA 2003b, Appendix A, Table A-2.

80 There are some IMPROVE sites that don’t have enough complete data to provide baseline condition information for the 2000-2004 period. It may be ne cessary to substitute data from other monitoring networks or to interpolate data to the site(s). Dat a substitution is discussed in (U.S. EPA 2007b).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

156 Table 4.8.1. Default Assumptions Used To Derive Aerosol Species Concentrations for Estimating Extinction Coefficients

(1) Species (2) Formula (3) Assumptions

Sulfate 1.375 *measured SO4 Sulfate is assumed to be fully neutralized (ammonium sulfate)

Nitrate 1.29 * measured nitrate Denuder efficiency is ~100% & all nitrate is from ammonium nitrate

EC high + low temperature EC All high temperature carbon is elemental

OC 1.8 * organic carbon (or 1.4 * OC

for the old IMPROVE equation) Average organic molecule is 56% carbon

Fine Soil 2.2*Al + 2.49*Si + 1.63*Ca +

2.42*Fe + 1.94*Ti (Soil K)=0.6(Fe), FeO & Fe 2 O 3 are equally abundant, a factor of 1.16 is used for MgO, Na 2 O, H 2 O & CO 3

Sea Salt 1.8*ClSea salt is 55% chloride ion by weight

PM 2.5 -Measured gravimetrically -Represents dry ambient fine aerosol mass for continental sites

CM (coarse mass) (PM 10 ) - (PM 2.5 ) Consists only of insoluble soil particles

Throughout this section we make every attempt to be consistent with the “Guidance for Tracking Progress under the Regional Haze Program” (hereafter referred to as the “TrackingModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

157 Guidance”) (U.S. EPA, 2003b). It will be easier to interpret the modeling results and compare
them to future measured values if the data are calc ulated in a similar manner.

4.8.2.4 Normalizing trends in regional haze- f(rh) factors It is clear from equations 4.11 and 4.12 that rela tive humidity can have a substantial effect on estimated extinction coefficients, as well as the r elative importance of changes in different components of PM on trends in regional haze. Becau se of the importance of relative humidity as a determinant of regional haze, it is necessary to normalize any apparent trend in the
estimated extinction coefficient for differences in relative humidity. This enables an assessment of whether an emissions control strategy will reduce regional haze, without confounding effects of different relative humidity during the base and future periods.

There are two possible approaches to normalize tren ds in visibility for changes in relative humidity. The first is to assume that the same day to day changes in relative humidity which were observed in the base period calculations will occur in future years. Thus, one would use the relative humidity observations made on a specif ic day together with measured components of particulate matter on that day to compute the da y specific visibility extinction coefficient on that day. However, the approach could lead to misl eading conclusions if humidity observations were missing for some days or if the humidity obser vations are atypical in some way. Further, if an air agency wanted to perform visibility calculat ions in a number of Class I areas, they would need to obtain hourly relative humidity data for ea ch area.

The second approach is to review relative humidity data over a long period of record to derive climatological estimates for relative humidity adju stment factors. These climatological estimates would then be used in Equations 4.11 and/ or 4.12 to estimate visibility extinction coefficients. These estimates are more likely to reflect “typical” relative humidity at different times of year and, thus, expected future visibility extinction.

The need to use climatological f(rh) data for track ing progress of measured data is obvious. The measured relative humidity on the 20% worst days in a baseline period will be different than the measured relative humidity in a future period. Therefore the only way to normalize for relative humidity between the base years and the fu ture is to use a single climatological value. However, in the context of modeling, there is a cho ice. Either climatological data or modeled (or measured) relative humidity data from a meteoro logical model can be used. Since theModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

158 meteorological data is held constant when predictin g future year air quality, the modeled relative humidity values would also be constant. T his would be one way to normalize the data. But since the Tracking Guidance recommends tracking visibility using climatological f(rh) values, it will be easier to interpret the modeling results and compare them to future measured values
if the data are calculated in a similar manner. Th erefore, we recommend using climatological f(rh) values to calculate baseline and future visib ility. Appendix A (Table A-1) from the Tracking Guidance displays the relationship between relative humidity and f(rh) values. The f(rh) values were calculated from data reported by Tang (1996). The f(rh) values are 1.00 up to 36% relative humidity and grow to a maximum value of 7.40 at a r elative humidity of 95%. 81 The Tracking Guidance contains monthly climatological average f( rh) values for each Class I area. These values were calculated using 10 years of meteorolog ical data.

For calculations with the revised IMPROVE equation, three separate f(rh) curves are needed. These can be found in (Hand, 2006). The monthly f( rh) values for both the old and revised IMPROVE equations have been incorporated into pre-c alculated daily extinction and deciview
data that can be found on the IMPROVE website at: http://vista.cira.colostate.edu/improve/Data/IMPROV E/summary_data.htm . The use of precalculated f(rh) and visibility data makes it more simple for air agencies and regional organizations to calculate visibility changes and w ill also provide for more consistency.



### 4.8.3 How Do I Apply A Model To Calculate Changes i n Visibility?

The purpose of a regional haze modeling assessment is to project future visibility to determine progress relative to the glidepath. In the procedu res below, the observed visibility data should be linked to the base modeling year. Similar to th e ozone and PM 2.5 attainment tests, the 5 year ambient data period should be centered about t he base modeling year. For example, for a base modeling year of 2011, the ambient IMPROVE dat a should be from the 2009-2013 period. 82 However, unlike the ozone and PM 2.5 attainment tests, the calculation is a 5 year

81 For the regional haze calculations, the f(rh) facto rs have been capped at the f(rh) value associated w ith 95% relative humidity. Relative humidity value abo ve 95% use the same value.

82 The base period for the regional haze program cont inues to be 2000-2004, and the uniform rate of progress is calculated using that historical data. However, the modeled reasonable progress visibilit y projections should use ambient data that correspond s to the modeled base year meteorological and emissions data.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

159 mean where each year counts equally (unlike the 5-y ear weighted average values for the ozone
and PM 2.5 attainment test). This is consistent with the regi onal haze rule ambient data calculations for the 20% best and worst days. The analysis we recommend has 6 steps.

1) For each Class I area, rank observed visibility (i n deciviews) on each day using observed speciated PM 2.5 data plus PM 10 data for each of the 5 years comprising the base p eriod. 2) For each of the 5 years comprising the base period, calculate the mean deciviews for the 20% of days with worst and 20% of days with best vi sibility. For each Class I area, calculate the 5 year mean deciviews for worst and b est days from the 5 year-specific
values. 3) Use an air quality model to simulate base period em issions and future emissions. Use the resulting information to develop relative respo nse factors for each component of PM identified on the right hand side of Equation (4 .12). 4) Multiply the relative response factors by the measu red species concentration data during the base period (for the measured 20% best a nd worst days). This results in daily future year species concentrations data. 5) Using the results in Step 4 and the IMPROVE algorit hm (equation 4.12), calculate the future daily extinction coefficients for the 20% be st and worst visibility days in each of the five base years. 6) Calculate daily deciview values (from total daily e xtinction) and then compute the future average mean deciviews for the worst and best days for each year. Average the five years together to get the final future mean decivie w values for the worst and best days.

We describe each of these steps more fully below. The methodology follows the same basic procedures outlined in the Tracking Guidance. Furt her details (for steps 1 and 2) can be found
in that document. We conclude this subsection with an example illustrating the recommended modeled uniform rate of progress analysis.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

160 **Step 1. Using monitored data, rank baseline visibi lity for each day with PM**  **10 , PM**  **2.5 and**  **speciated PM 2.5**  **measurements within a Class I area**

Ranking should be performed separately for each of the 5 years comprising the base period. 83 The deciview (dv) should serve as the basis for ran king. Day-specific observations for mass
associated with SO 4 , NO 3 , OC, EC, soil, and CM, as defined in Table 4.7 or 4.8, should be used to
calculate b ext for each day. The appropriate month- and area-spe cific climatological relative humidity adjustment factor(s) ( **f(rh)** ) should be used. Total b ext for all components should be converted to deciviews for each day to get a daily deciview value.

**Step 2. Calculate the average baseline deciviews f or the 20% of days with worst and the 20%**  **days with best visibility.**

For each of the 5 years in the base period, order a ll days considered in Step 1 from worst (highest deciview value) to best (lowest deciview v alue) visibility. For each year, note the 20% of days with worst and the 20% of days with best vi sibility. Calculate the arithmetic mean
deciview value for the identified 20% worst- and be st visibility days in each year. Average the resulting 5 yearly mean deciview values reflecting worst visibility. This represents the value subject to improvement (i.e., reduction) to meet th e glidepath for regional haze. Average the 5 yearly mean deciview values reflecting mean visibil ity on the days with best visibility.

**Step 3. Estimate relative response factors (RRF) f or each component of PM2.5 and for CM.** 

The RRFs should be determined using results from ai r quality modeling of base year and future year emissions. These model simulations should be performed for a large number of days. 84 The (temporal) arithmetic mean concentration for ea ch PM 2.5 component (and coarse mass) computed near the Class I monitoring site from the future year modeling is divided by the

83 Pre-calculated and ranked extinction and deciview c alculations for all Class I areas is available on t he
IMPROVE website at http://vista.cira.colostate.edu/improve/Data/IMPROV E/summary_data.htm

. This data can be used to satisfy steps 1 and 2.

84 How many and which days to simulate is discussed in Section 2.3.1.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

161 corresponding arithmetic mean concentration for eac h component from the base year modeling. The resulting ratios are the component-s pecific RRF’s. A separate set of RRF values are calculated for the worst and best visibility da ys identified in step 2. The RRFs are calculated using the identified 20% best and 20% worst visibil ity monitored days at each Class I area. This will likely be a different set of days at each moni tor.

**Step 4. Using the RRFs and ambient data, calculate future year daily concentration data for the**  **best and worst days.**

Multiply the relative response factors derived in S tep 3 by measured daily concentration data for each component of PM 2.5 and CM to get future daily estimates of species co ncentrations for
PM 2.5 components and CM on worst visibility and best visi bility days. These multiplications produce future concentration estimates for SO 4 , NO 3 , OC, EC, Soil and CM for each of the previously selected worst and best visibility days. This calculation is performed for each best and worst day for the five year period using an RRF for each PM component (a separate set of RRFs for the best days and the worst days).

**Step 5. Use the information developed in Step 4 to compute future year daily bext values for the**  **best and worst days.**

Use the future year concentration data calculated i n step 4 to calculate future year daily b ext values for each PM component for each of the best a nd worst days for the five year period. This is accomplished by applying the IMPROVE visibi lity algorithm (equations 4.11 and 4.12).

**Step 6. Use the daily total bext values from step 5 to calculate future mean deciview values for**  **the best and worst days.**

The total daily b ext for each day is converted to deciviews. This give s a future year daily deciview value for each of the best and worst days.

Next, compute the arithmetic mean future deciview v alue for the worst and best visibility days for each year. This leads to 5 future estimated me an deciview values for the worst and 5 future
estimated mean deciview values for the best visibil ity days. Compute the arithmetic mean of the 5 mean values for deciviews on the worst days, and the arithmetic mean of the 5 meanModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

162 deciview values estimated for the best visibility d ays.

The resulting 5 year average mean values for decivi ews on worst and best visibility days can be compared to the baseline mean deciview values calcu lated in step 2. If the resulting change in deciviews is a negative number (future - base), thi s represents an improvement in visibility. The future year visibility values can also be compared to the glidepath to determine if the Class I area is expected to be on, above, or below the glid epath in the future planning period.

**Example 4.8.1**

We use example 4.8.1 to illustrate the modeled unif orm rate of progress assessment. For ease of presentation, we assume there are only 10 specia ted samples for PM in the 5 years comprising the base period and the example uses the old IMPROVE equation. 85 Since sampling occurs every third day, a typical sample size is ab out 120 days per year. We illustrate the calculations for the first base year and then furni sh information regarding mean deciviews for the other four base years to illustrate subsequent steps in the test. For simplicity sake, the example uses the old IMPROVE algorithm. The proced ure is the same for the revised IMPROVE algorithm.

**Given:** Ten days have measured components of PM in a Class I area during the first year of a 5-year base period. Table 4.8.2 below shows the measureme nts (in μg/m 3 ) for each of the 10 days.
Table 4.8.2 also shows the date of each measurement and the corresponding climatological relative humidity adjustment factor (made up for th is example) for the appropriate month and

area.

85 We recommend use of the revised IMPROVE equation. However, the old IMPROVE equation is used in the example for simplicity.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

163 Table 4.8.2 Example observed IMPROVE data at a Class I area

Day Date f(rh) SO 4

(μg/m 3 ) NO 3

(μg/m 3 ) OC

(μg/m 3 ) EC

(μg/m 3 ) Soil

(μg/m 3 ) CM

(μg/m 3 ) Rayleigh

Scattering (Mm -1 )

1 2/15 1.7 4.53 2.23 3.37 0.89 0.32 7.33 10

2 3/15 2.9 5.12 1.78 2.92 0.78 0.44 9.17 10

3 4/15 3.5 5.67 1.25 2.44 0.54 0.43 10.25 10

4 5/15 3.7 6.59 1.12 2.86 0.50 0.66 9.80 10

5 6/15 4.3 7.47 0.93 3.49 0.61 0.98 10.99 10

6 7/15 4.6 8.33 0.79 3.50 0.69 1.08 11.38 10

7 8/15 4.4 9.22 0.89 3.24 0.67 0.89 10.78 10

8 9/15 4.1 8.05 0.97 3.12 0.73 0.71 8.25 10

9 9/30 4.1 6.84 1.15 3.21 0.85 0.42 8.82 10

10 10/30 2.7 5.32 1.41 3.26 0.76 0.64 8.11 10

Using the IMPROVE data for the Class I area and the estimated natural background values, the uniform rate of progress on the 20% worst days for the future year has been calculated as 26.7 dv. In addition, the mean visibility on the 20% be st visibility days should not deteriorate.

Find: Does the control strategy simulated in this model analysis l ead to future year visibility that is on, or below the glidepath in this Class I area?Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

164 Solution:

**Step 1. Using monitored data, rank baseline visibil ity for each day with PM**  **10 , PM**  **2.5 and**  **speciated PM 2.5**  **measurements within a Class I area.**

First, estimate the extinction coefficient for each day with the needed PM measurements. This is done using the information in table 4.8.2 w ith Equation (4.11). For day 1 in year 1, the
current extinction coefficient is:

b ext = (3)(1.7)[4.53] + (3)(1.7)[2.23] + (4)(1)[3.37] + (10)[0.89] + (1)[0.32] + (0.6)[7.33] + 10

b ext = 71.6 Mm -1

Then convert extinction into deciviews:

dv = 10 * ln(71.6/10) = 19.7 dv

Base year extinction coefficients and deciviews for the remaining 9 days with monitored data in year 1 are calculated in a similar manner. The day s are then ranked. The day with the highest deciviews (i.e., worst visibility) is given a rank of “1". The results of these calculations are displayed in the table 4.8.3 below. Based on these rankings, days 6 and 7 comprise the 20% of days with worst visibility. Days 1 and 10 comprise the 20% of days with best visibility

Table 4.8.3 Example observed IMPROVE extinction, deciviews, and rank at a Class I area

Day Date f(rh) SO 4 (Mm -1 ) NO 3

(Mm -1 ) OC

(Mm -1 ) EC

(Mm -1 ) Soil

(Mm -1 ) CM

(Mm -1 ) b ext

baseline (Mm -1 ) Decivi

ews Rank

1 2/15 1.7 23.1 11.4 13.5 8.9 0.3 4.4 71.6 19.7 10

2 3/15 2.9 44.5 15.5 11.7 7.8 0.4 5.5 95.5 22.6 8Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

165 3 4/15 3.5 59.5 13.1 9.8 5.4 0.4 6.2 104.4 23.5 7

4 5/15 3.7 73.1 12.4 11.4 5.0 0.7 5.9 118.6 24.7 6

5 6/15 4.3 96.4 12.0 14.0 6.1 1.0 6.6 146.0 26.8 4

6 7/15 4.6 115.0 10.9 14.0 6.9 1.1 6.8 164.7 28.0 2

7 8/15 4.4 121.7 11.7 13.0 6.7 0.9 6.5 170.5 28.4 1

8 9/15 4.1 99.0 11.9 12.5 7.3 0.7 5.0 146.4 26.8 3

9 9/30 4.1 84.1 14.1 12.8 8.5 0.4 5.3 135.3 26.1 5

10 10/30 2.7 43.1 11.4 13.0 7.6 0.6 4.9 90.7 22.0 9

Step 2. Calculate the average base year deciviews for the 20% of days with worst visibility and the 20% days with best visibility.

For year 1, mean worst visibility = (28.0 + 28.4) / 2 = 28.2 dv, and

mean best visibility = (19.7 + 22.0) / 2 = 20.9 dv

Mean worst and best visibility for years 2-5 is pro vided in table 4.8.4. Table 4.8.4 below summarizes mean worst and best visibility for each of the 5 years in the base period.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

166 Table 4.8.4 Example mean worst days visibility and best days vi sibility at a Class I area

Year Mean dv current ,

Worst Visibility Days Mean dv current , Best Visibility Days

1 28.2 20.9

2 29.1 20.4

3 30.2 18.7

4 27.8 19.1

5 28.5 19.5

The average mean base period worst and best visibil ity is obtained by taking the arithmetic mean of the average worst and best visibility for t he 5 years. Thus, the 5 year mean of the average worst visibility is given by

dv baseline = (28.2 + 29.1 + 30.2 + 27.8 + 28.5) / 5 = 28.8 dv

The average mean best visibility is

dv baseline = (20.9 + 20.4 + 18.7 + 19.1 + 19.5) / 5 = 19.7 dv

Step 3. Apply a model to develop component specifi c RRF’s for SO 4 , NO 3 , OC, EC, Soil and CM.

Tables 4.8.5 and 4.8.6 show the procedure for calcu lating component-specific relative response factors using an air quality model. The example sh ows the calculation of RRFs for the worst days in the year modeled. The same calculation is repeated for the best days.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

167 Table 4.8.5 Base year modeled species concentrations on worst d ays (in μg/m3)

Modeled Output SO 4 NO 3 OC EC Soil CM

Day 1 2.12 6.54 3.56 0.87 1.23 5.43

Day 2 8.33 2.11 4.67 1.23 0.34 7.32

Day 3 9.33 1.23 6.32 1.45 0.87 8.21

Day 4 6.43 1.67 4.56 0.54 1.07 4.67

Day 5 10.53 0.57 4.12 0.69 1.54 10.32

Mean Base
Concentration 7.35 2.42 4.65 0.96 1.01 7.19

Table 4.8.6 Future year modeled species concentrations on worst days (in μg/m3)

Modeled Output SO 4 NO 3 OC EC Soil CM

Day 1 2.01 4.23 3.45 0.77 1.21 5.75

Day 2 7.56 1.89 4.55 1.01 0.32 7.54

Day 3 6.54 1.11 5.99 1.09 1.02 8.71

Day 4 5.32 1.45 4.23 0.35 1.1 5.02Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

168 Day 5 7.23 0.43 3.99 0.45 1.51 10.47

Mean Future
Concentration 5.73 1.82 4.44 0.73 1.03 7.50

Table 4.8.7 Component specific RRFs for the 20% worst days

Worst days

RRF SO 4 NO 3 OC EC Soil CM

RRF (mean future/mean base) 0.780 0.752 0.956 0.768 1.022 1.043

**Step 4. Multiply the relative response factors tim es the measured daily species concentration**  **data during the base period to compute future daily species concentrations.** 

In year 1, we previously identified days 6 and 7 as those included in the 20% of days with worst visibility (i.e., see Step 1). Similarly, days 1 a nd 10 are the 20% of days with best visibility. In this step, we estimate future concentrations for co mponents of PM 2.5 and for CM for these two sets of days. This is done using information shown in tables presented in Steps 1 and 3 as well as the best days RRFs given in table 4.8.8 below:

Table 4.8.8 Component specific RRFs for the20% best days

Best days
RRF SO 4 NO 3 OC EC Soil CM

RRF (mean future/mean base) 0.870 0.823 0.976 0.784 1.112 1.058Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

169 Worst Days

Day 6: [SO 4 ] future = (RRF) SO4 [SO 4 ] baseline = (0.780) [8.33] = 6.50 μg/m 3 [NO 3 ] future = (0.752) [0.79] = 0.59 μg/m 3 [OC] future = (0.956) [3.50] = 3.35 μg/m 3 [EC] future = (0.768) [0.69] = 0.53 μg/m 3 (Soil) future = (1.022) [1.08] = 1.10 μg/m 3 [CM] future = (1.043) [11.38] = 11.87 μg/m 3

Day 7: [SO 4 ] future = (0.780) [9.22] = 7.19 μg/m 3 [NO 3 ] future = (0.752) [0.89] = 0.67 μg/m 3 [OC] future = (0.956) [3.24] = 3.10 μg/m 3 [EC] future = (0.768) [0.67] = 0.51 μg/m 3 (Soil) future = (1.022) [0.89] = 0.91 μg/m 3 [CM] future = (1.043) [10.78] = 11.24 μg/m 3

Best Days

Day 1: [SO 4 ] future = (0.870) [4.53] = 3.94 μg/m 3 [NO 3 ] future = (0.823) [2.23] = 1.84 μg/m 3 [OC] future = (0.976) [3.37] = 3.29 μg/m 3 [EC] future = (0.784) [0.89] = 0.70 μg/m 3 (Soil) future = (1.112) [0.32] = 0.36 μg/m 3 [CM] future = (1.058) [7.33] = 7.76 μg/m 3

Day 10: [SO 4 ] future = (0.870) [5.32] = 4.63 μg/m 3 [NO 3 ] future = (0.823) [1.41] = 1.16 μg/m 3 [OC] future = (0.976) [3.26] = 3.18 μg/m 3 [EC] future = (0.784) [0.76] = 0.60 μg/m 3 (Soil) future = (1.112) [0.64] = 0.71 μg/m 3 [CM] future = (1.058) [8.11] = 8.58 μg/m 3Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

170 Similar calculations (using the same model derived component specific RRFs) are performed for each of the worst and best days in each of the othe r 4 years in the base period. 86

**Step 5. Using the results in Step 4, calculate the future year extinction coefficients for the 20%**  **best and 20% worst visibility days in each of the f ive base years.** 

Using future PM components obtained in Step 4, we c an estimate future daily total b ext . For year 1

Worst Days

Day 6: b ext = (3)(4.6)[6.50] + (3)(4.6)[0.59] + (4)[3.35] + (1 0)[0.53] + 1.10 + (0.6)[11.87] + 10 =
134.8 Mm -1

Day 7: b ext = (3)(4.4)[7.19] + (3)(4.4)[0.67] + (4)[3.10] + (1 0)[0.51] + 0.91 + (0.6)[11.24] + 10 =
139.0 Mm -1

Best Days

Day 1 : b ext = (3)(1.7)[3.94] + (3)(1.7)[1.84] + (4)[3.29] + (1 0)[0.70] + 0.36 + (0.6)[7.76] + 10 =
64.6 Mm -1

Day 10 : b ext = (3)(2.7)[4.63] + (3)(2.7)[1.16] + (4)[3.18] + (1 0)[0.60] + 0.71 + (0.6)[8.58] + 10 =
81.4 Mm -1

86 Unless multiple meteorological years are being mode led, the same mean RRFs (for each component) are applied to the concentrations on all of the wor st days for each of the 5 years of data. The mean RRFs are derived from the modeled days. A separate set of RRFs are derived (in the same manner) for the best days.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

171 **Step 6. Use the daily total bext values from step 5 to calculate future mean deciview values for**  **the best and worst days.**

Calculate daily deciview values (from total daily e xtinction) and then compute the future average mean deciviews for the worst and best days for each year. Then average the 5 years together to get the final future mean deciview valu e for the worst and best days.

For Year 1

Worst days

Day 6 : 134.8 Mm -1 = 10* ln(134.8/10) = 26.0 dv

Day 7: 139.0 Mm -1 = 10* ln(139.0/10) = 26.3 dv

Future mean visibility on worst days = (26.0 + 26.3 ) / 2 = 26.2 dv

Best Days

Day 1: 64.6 Mm -1 = 10* ln(64.6/10) = 18.7 dv

Day 10: 81.4 Mm -1 = 10* ln(81.4/10) = 21.0 dv

Future mean visibility on best days = (18.7 + 21.0) / 2 = 19.8 dv

Similar calculations are performed for previously s elected worst and best days in each of years 2-5. To illustrate the uniform rate of progress as sessment, assume these other calculations yield the following estimates for future mean dv on worst and best visibility days.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

172 Table 4.8.9 Future year mean deciviews on the worst and best da ys for each of the 5 years

Year Future Mean dv On Worst Visibility Days Future Mean dv On Best Visibility Days

1 26.2 19.8

2 27.3 19.3

3 27.6 17.6

4 25.9 18.3

5 26.1 18.6

Using results in table 4.8.9, we see that the estim ated future average mean visibility for the 20% days with worst visibility is

dv future = (26.2 + 27.3 + 27.6 + 25.9 + 26.1) / 5 = 26.6 dv

The estimated future average mean visibility for th e 20% days with best visibility is

dv future = (19.8 + 19.3 + 17.6 + 18.3 + 18.6) / 5 = 18.7 dv

The worst days results generated in step 6 can then be compared to the uniform rate of progress for the particular future year for the Cla ss I area. The future year best days are compared to the base year best days to ensure that visibility on the best days is not forecast to degrade.

The future year strategy leads to an estimated visi bility impairment of 26.6 dv, which is lower than the calculated uniform rate of progress value of 26.7 dv. Also visibility is not predicted to deteriorate on the best days, and is in fact estima ted to improve by 1.0 dv (from 19.7 dv in the base year to 18.7 in the future year).Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

173 This information is used in the process of setting reasonable progress goals, in conjunction with other analyses, as described in “Guidance for Setti ng Reasonable Progress Goals Under the Regional Haze Program” (U.S. EPA, 2007). The other steps in the process are beyond this modeling guidance. The modeling can be used to det ermine the predicted improvement in visibility and whether the visibility levels are on , above, or below the glidepath. It cannot by itself determine the reasonable progress goals or d etermine whether the reasonable progress goal is met, and it does not satisfy the requiremen ts for the statutory four factor analysis. See the Regional Haze Rule and related guidance documen ts for more information on the four factor analysis, including control strategy analysi s for single sources.



### 4.8.4 How Do I Select Appropriate Inputs For The Un iform Rate of Progress Analysis?

In Section 4.8.3, we described the recommended regi onal modeling analysis to evaluate future year visibility relative to the glidepath. An impo rtant part of the analysis requires using component-specific RRFs, obtained with models, to e stimate future concentrations of these components and, subsequently, future visibility. I n this subsection, we address more details concerning the calculation of visibility RRFs. Sec ond, there are several assumptions inherent in the recommended modeled uniform rate of progress an alysis. We identify these in this subsection and comment on their underlying rational e. More specifically, we address seven
issues:

1. How to estimate base period air quality in a Class I area without monitored data;
2. How to handle a base year without data or with a sm all sample size;
3. Use of the same days to estimate changes in visibil ity for the worst days and a different set of same days to estimate changes in visibility for the best days;
4. Which predictions to use to derive relative respons e factors;
5. How many and what kind of days to use to develop RR F values;
6. RRFs when modeled species concentration are very lo w; and

7. Alternative RRF calculationsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

174 **Estimating baseline worst and best visibility in a Class I area without monitors.**  There are 156 officially designated Class I areas a nd 110 IMPROVE sites in or near Class I areas. Therefore, approximately 46 Class I areas do not ha ve co-located IMPROVE monitors. EPA’s Tracking Guidance recommends using IMPROVE data fro m a nearby site to represent the visibility at each Class I area that does not have ambient data. Table A-2 in Appendix A of the Tracking Guidance lists the recommended monthly f(r h) values 87 for each Class I area as well as the representative site for each Class area. The r epresentative IMPROVE site data will be used to track regional haze progress for the Class I are as. Therefore, it follows that visibility improvement for tracking purposes should be predict ed at the location of the IMPROVE monitor, not at the actual Class I area. For the p urposes of deriving ambient data for modeling, we recommend following the same representative site assignments contained in the Tracking Guidance. In this way, the 20% worst and best days can be derived for each Class I area from
the network of 110 IMPROVE sites. 88 Similarly, the modeling results should be extract ed for the location of the representative monitor, not the act ual location of the Class I area. 89

**Considering a base year with little or no monitored particulate matter or missing data**  The Tracking Guidance recommends calculating baseli ne visibility values for sites with at least 3 out of 5 complete years of data. It further contai ns recommendations for determining if a year has complete data. In general, a site should have 50% data completeness in all quarters and meet a 75% completeness criteria for the full year. There should be no periods with more than 30 consecutive days without data. The guidance ass umes that all IMPROVE sites will have at

87 The f(rh) values in the Tracking Guidance were deve loped for the “old” IMPROVE algorithm and should not be used to calculate visibility using the “new” IMPROVE algorithm.

88 Bering Sea Wilderness (Alaska) is the only Class I area that has no IMPROVE monitor and no representative IMPROVE site. On-site IMPROVE or re presentative IMPROVE data can be found for the other 155 sites.

89 There may be other reasons to calculate visibility improvement at the actual Class I area. The MATS provides options for calculating visibility changes at both the Class I area grid cell center and at t he representative IMPROVE monitoring site. It may be informative to compare model response at the representative IMPROVE site versus the actual locat ion of the Class I area. Large differences in the model response may indicate that the “representativ e” site may not adequately represent visibility at the Class I area.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

175 least 3 complete years in the base period (which ma y or may not be true). The guidance also contains procedures for filling in missing data as part of the calculation of the 20% best and worst days.

There are several data completeness issues that may cause problems within the visibility modeling analysis. First, a site may have less tha n 3 years of complete data. It is possible to calculate visibility improvement based on as little as 1 year of ambient data. States should work with their EPA Regional Office and Federal Land Man agers to determine how to estimate baseline visibility for these area(s).

Another issue that is a more specific problem for m odeling analyses occurs when data is missing during the meteorological time period that was mode led. It is likely that most air agencies will only be modeling a single year of meteorology. The refore it is possible that the ambient data at one or more Class I areas is incomplete during that year. Without ambient data, it is impossible to identify the 20% best and worst days which are u sed to calculate modeled RRFs.

Again, if this occurs, states should work with thei r Regional Office and FLMs to determine the best way to calculate visibility on the best and wo rst days. Potential options are to use data from another nearby IMPROVE site, use nearby data f rom a different ambient data network, or interpolate ambient data to the site.

**Using a constant sample of days to estimate baselin e and future visibility**  For a typical Class I area, there will be about 120 days per year having measurements needed to estimate (dv) baseline with Equation (4.11) or (4.12). Thus, there shoul d be about 24 worst and 24 best visibility days for each of the 5 years in the base period. It is conceivable that the identity of these worst and best days could change if emissi ons were altered to reflect net effects of controls and growth. The recommended test describe d in Section 4.8 assumes that the identity of the worst and best days remains unchanged. This is done primarily to avoid having to perform iterative analyses to identify future worst and best visibility days and to keep the test relatively simple and more readily understood. Thi s assumption could cause improvement in visibility to be overestimated for the worst days a nd could also cause the test to overestimate the difficulty in preventing deterioration of visib ility on the best days. However, for the reasons described below, we do not believe the effe cts of this assumption are substantial.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

176 It is unlikely that there would be any wholesale ch ange in the identity of worst or best days with future vs. base year emissions. Analyses performed by Meyer, et al. (1997) have shown that the predicted ranked severity of high ozone days is largely unaffected by simulated controls and growth (i.e., highest days tend to remain the highe st days after the effects of growth and controls are simulated). There is no reason to exp ect a different outcome for other secondary pollutants. If there are differences, we would exp ect these to occur near the borderline between the worst days and more moderate days.

Because the uniform rate of progress analysis relie s on **mean** visibility values on 20 or more worst visibility days and most of these days are un likely to change, we would expect little difference in the outcome of the analysis. Further , because of the shape of the distribution of extinction coefficients, the mean of the worst days is more heavily influenced by extreme days
rather than those on the borderline between worst a nd more moderate light extinction. There could be differences in some best visibility days c orresponding with pre- and post-control emissions. However, because the differences in con centrations of particulate matter on such days are likely to be relatively low, differences i n the computed mean visibility for best days are likely to be small. Further, any resulting differe nce in the progress analysis for best days is likel y to be protective of the environment. If our recomm ended procedure leads to suspected problems in the outcome of a test, an air agency ma y perform a more rigorous version of the analysis (in which the identity of pre-control and post-control days changes) as part of a weight
of evidence determination.

**Selecting predictions to use in deriving RRF** Relative response factors should be developed for e ach Class I area. When a Class I area contains a monitoring site, the RRF estimates shoul d be derived using predictions which are made “near” that site. For each day, daily average surface predictions of each component of
PM made near a monitor should be estimated. Simila r to the annual PM 2.5 NAAQS attainment test, nearby grid cells should be averaged. For 12 km or finer resolution, we recommend averaging the modeled concentrations for a 3 x 3 ar ray of grid cells surrounding the monitor. Note that for cells larger than 12 km on a side, no spatial averaging is necessary—states should just use the prediction in the cell containing the monitor. These nearby estimates should then be spatially averaged to estimate a spatially repre sentative daily concentration. Spatially representative daily concentrations obtained for ea ch modeled day with monitored data should then be averaged. This final average should be use d to compute the RRF. Thus, component-Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

177 specific RRF values for a Class I area with a monit or are the ratio of the temporally averaged spatial mean of nearby concentrations predicted wit h future emissions to that predicted with base year emissions.

**Selecting days to derive RRF values** RRF values should be estimated by taking the ratio of future predictions averaged over at least several days to base year predictions averaged over the same several days. It may often happen that a planning organization or a group of s tates decides to model the visibility impacts of a strategy for numerous Class I areas simultaneo usly. As we note in Section 2.3.1, this may make it advisable to simulate (at least) a full yea r so that relative response factor (RRF) values
for each Class I area is based on a substantial num ber of observed best and worst days. For the worst days in the chosen year, the RRF for a compon ent of PM should be estimated as the ratio of its arithmetic mean predicted value on the 20% w orst days with future emissions to that with base year emissions. Thus, the RRF should reflect values averaged over ~ 24 worst days in that year. The same procedure is followed to derive RRF s over the ~24 best days in the year.

Since meteorological conditions and/or emissions ma y be markedly different on best visibility vs. worst visibility days, we recommend calculation of a separate set of RRF values for best visibility days. As with worst days, the preferre d approach is to model an entire year and select an RRF value for concentrations averaged over the 2 0% best visibility days for each Class I area.
The RRF values are the ratios of the future to base year modeled averages. The appropriate
RRF values should then be used in concert with each observed best day to estimate future concentrations for each component on each identifie d best day.

**RRFs when modeled species concentration are very lo w**  In most cases, the RRFs derived for visibility comp onents are either less than 1.0 or slightly more than 1.0. However, large RRFs (much greater t han 1.0) can occur when the mean modeled concentration of a PM component on the 20% best or worst days is very small and the concentration of that component increases in the fu ture year case. This most often occurs with the nitrate component. On both the 20% best and wo rst days, the modeled nitrate concentrations at Class I areas can be very small ( i.e. < .01 μg/m3). These low modeled concentrations can sometimes increase in future yea r scenarios by orders of magnitude. This
occurs most often in ammonia limited areas when sul fate concentrations are reduced in the future (due to SO 2 emissions reductions) causing nitrate increases du e to an increase inModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

178 available ammonia. Large RRFs should be closely ex amined to identify the cause of the issue. In many cases, large RRFs are caused by poor model performance (i.e. underpredicted base case nitrate concentrations) or instability in one or more model components (i.e. the nitrate partitioning model). One solution is to cap the RR F at 1.0 or close to 1 in cases where nitrate (or any other PM 2.5 component) increases in the future by an unrealisti c amount due to very low modeled concentrations. Any adjustments to the default calculations should be documented and discussed with the appropriate EPA r egional office and the FLMs.

**Alternative RRF calculations** The default analysis is relatively simple in that a single mean RRF is calculated for each PM component (separate RRFs on worst and best days). A series of tests with more complicated methods has shown that 1) the difference between va rious versions of the test are usually small and 2) each of the alternative tests has limi tations in its applicability (Environ, 2005). Possible variations include the use of day specific RRFs, season (or quarter) specific RRFs, or climatological based RRFs. In some cases, these mo re complicated techniques may provide different answers, but sometimes not. There are sp ecific limitations noted with each of these alternatives. We have chosen to keep the single me an RRF test as the default recommendation. States are encouraged to explore o ther methods for estimating RRFs if it is thought that the default recommendation is too simp listic to accurately capture the change in future visibility at any particular Class I area. The SIP demonstration should use the most appropriate method of projecting future concentrati ons for the characteristics of each Class I

area. 90 Alternative methods should be discussed in the mo deling protocol and discussed with the appropriate EPA Regional Office and FLMs.

90 In particular, issues may arise when dealing with l arge visibility contributions from fires, coarse ma ss and fine soil (from wind-blown dust), and internati onal transport (and possibly other issues). Each o f these issues should be addressed in the modeling pr otocol and solutions should be discussed with the appropriate EPA regional office(s) and FLMs on a ca se by case basis.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

179 

### 4.9 “Weight of Evidence”- How Can Additional Analys es Be Used to Support the Attainment Demonstration?

By definition, models are simplistic approximations of complex phenomena. The modeling analyses used to assess whether emission reduction measures will bring an individual area into attainment for the NAAQS contain many elements that are uncertain (e.g., emission projections, meteorological inputs, science formula tions, etc.). These uncertain aspects of the analyses can sometimes prevent definitive assessmen ts of future attainment status. The confidence in the representativeness of the quantit ative results from a modeled attainment test should be a function of the degree to which th e uncertainties in the analysis were minimized. In general, by following the recommenda tions contained within this guidance document, EPA expects that the attainment demonstra tions will mitigate the uncertainty as much as is possible given the current state of mode ling inputs, procedures, and science. However, while air quality models represent the bes t tools for integrating emissions and meteorological information with atmospheric chemist ry, and no single additional analysis can match the expected reliability of these models’ res ults, EPA believes that all attainment demonstrations and reasonable progress assessments will be strengthened by additional analyses that can supplement the modeling to enhanc e the assessment of whether the planned emissions reductions are likely to result in attain ment (or expected visibility progress).

Supplemental evidence should accompany all model at tainment demonstrations. Generally, those modeling analyses that show that attainment w ill be reached in the future with some margin of safety will need more limited supporting material. For other attainment cases, in which the projected future design value is closer t o the NAAQS, more rigorous supporting analyses should be completed. There may be some ar eas for which the supplemental evidence is persuasive enough to support a conclusion that t he area can expect to achieve timely attainment despite failing the model attainment tes t, and other areas for which the modeled attainment test demonstrates attainment but the sup plemental evidence casts significant doubt on that result. This section of the guidance will discuss some specific information and additional analyses that can be used to supplement the model projections. Of particular interest are analyses that help determine whether t he modeling-based projections are likely to provide a prediction of the **air quality improvement** that is likely to occur by the attainment date. Air agencies should review these supplementa l analyses, in combination with the modeling analysis, in a “weight of evidence” assess ment of whether each area is likely toModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

180 achieve timely attainment. Again, it should be not ed that no single supplemental analysis can serve as an adequate substitute for the air quality model; however, in aggregate, supplemental analyses may provide information which may indicate a different outcome than the modeled

test.

In considering the use of supplemental analyses, an important factor is the time (number of years) until the attainment date. In general, mode ling and related analyses are the most useful corroborative analyses for areas with attainment da tes which are more than several years in the future. In contrast, ambient data and emission s trends become more important (and hence model results become less important) the closer the area is to their attainment date. For example, if an area is only one or two years away f rom their attainment date, ambient data is in most cases the best predictor of likely air quality levels in the near future. However, this may not be true if there are large emissions reductions that are expected to occur in the near term period. In that case, modeling is needed to estima te additional ozone and/or PM reductions that are likely to occur due to the emissions contr ols in the short term. Similarly, if the attainment date is 5 or 10 years or more in the fut ure, modeling will likely be the most reliable indicator of future year ozone, PM, and/or regional haze levels. Emission changes over a relatively long time period are likely to overwhelm the influence of ambient data trends and meteorological variability, making ambient data and emissions trends analyses relatively less important. 

### 4.9.1 What Types of Additional Analyses Should Be C ompleted as Part of the
Attainment Demonstration?

There are three basic types of analyses that are re commended to supplement the primary modeling analysis. They are:

1) Additional modeling analyses

2) Analyses of trends in ambient air quality and emiss ions

3) Additional emissions controls/reductionsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

181 4.9.1.1 Modeling Analyses The relative attainment tests described in sections 4.2, 4.4, and 4.5 are the primary modeling tools used in an attainment demonstration. The app lication of a chemical transport grid model, applied on a regional or local scale is the best to ol available to judge the impacts of changes in future year emissions on concentrations. In additi on to this “primary” modeling analysis, there are various other models, model applications, and t ools that can be used to supplement the
results of the modeled attainment test. These incl ude, but are not limited to:

• Available regional or national scale modeling appli cations that are suitable 91 for the local area. For example, modeling in support of EPA rule makings or regional multi-jurisdictional organization modeling that may be available for the appropriate future year of interest. Modeling analyses may be available that used differ ent models and/or inputs.

• Use of other appropriate local modeling that includ es the nonattainment area of interest. This may include applications using alternative mod els and/or inputs or research oriented analyses.

• Use of photochemical source apportionment, DDM, and /or process analysis modeling tools to help explain why attainment is (or is not) demon strated.

• Use of multiple air quality models / model input da ta sets (e.g., multiple meteorological data sets, alternative chemical mechanisms or emiss ions inventories, etc.). Multiple model configurations can be used to estimate sensitivity and uncertainty of future year design value predictions.

• For results to be most relevant to the way we recom mend models be applied in attainment demonstrations, it is preferable that su ch procedures focus on the sensitivity of estimated relative response factors (RRF) and resulting projected design values to the variations inputs and/or model formulations.

91 The resolution, emissions, meteorology, and other model inputs should be evaluated for applicability to the local nonattainment area. Additionally, mod el performance of the regional modeling for the loc al nonattainment area should be examined before determ ining whether the regional model results are suitable for use in the local attainment demonstrat ion.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

182 • Application of the attainment test with alternative procedures compared to the default recommendations in Sections 4.2, 4.4, and 4.5 of th is guidance. Any alternate approaches should be accompanied with a technical justificatio n as to why the approach is appropriate for the area in question and should be discussed wi th the appropriate EPA regional office.

 Alternative RRF calculations using non-default assu mptions or more detailed temporal and spatial analysis of RRFs at one or more monitor ing locations. If a sound technical argument can be made for why atypically high RRFs a t any particular location are not reasonable, then these types of supplemental analys es would suggest that attainment is more likely to be achieved than the default modelin g analysis alone would indicate.  Alternate base year design values that may differ f rom the 5-year weighted average
value. Alternative values could include different methodologies for determining base year design values or removal of potential exceptio nal events data (see footnote 30).

• Use of dispersion models to address primary PM 2.5 contributions to PM 2.5 concentrations. In areas with large spatial gradients of primary PM 2.5 , dispersion models are best suited to characterizing the change in primary PM 2.5 in the future. A local area analysis may be usefu l as a supplemental analysis (in either monitored or unmonitored areas, as appropriate) for areas that at least partially rely on local primary PM controls to reach attainment and did not otherwise perform and submit a local area analy sis of part of the attainment
demonstration.

EPA has determined that the best approach to using models to demonstrate attainment of the NAAQS is to use a model in a relative mode. Howeve r, some types of “absolute” model results may be used to assess general progress towards atta inment from the baseline inventory to the projected future inventory. 92 Example metrics include:

92 Care should be taken in interpreting absolute metr ics if the model evaluation shows a large underprediction or overprediction of ozone or PM 2.5 concentrations. An underprediction of observed concentrations will make it artificially easy to sh ow progress towards absolute attainment levels and an overprediction will make it artificially difficult to show progress towards attainment.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

183 • Percent change in total amount of ozone or PM 2.5 >= NAAQS 93 within the nonattainment

area

• Percent change in number of grid cells >= NAAQS wit hin the nonattainment area

• Percent change in grid cell-hours (days) >= NAAQS w ithin the nonattainment area

• Percent change in maximum modeled 8-hour ozone with in the nonattainment area

While these metrics can be used to estimate the mag nitude, frequency, and relative amount of
ozone or PM 2.5 reductions from any given future emissions scenari o, there are no threshold quantities of these metrics that can directly trans late to an attainment determination. Generally, a large reduction in the frequency, magn itude, and relative amount of 8-hour ozone nonattainment (i.e., >= 76 ppb) or PM 2.5 nonattainment (24-hour and/or annual) is consisten t with a conclusion that a proposed strategy would me et the NAAQS. In the context of a weight of evidence determination, these metrics could be u sed to suggest that a particular location may be “stiff” or relatively unresponsive to emissi ons controls, while the rest of the modeling domain/nonattainment area is projected to experienc e widespread reductions.

4.9.1.2 Analyses and Trends in Ambient Air Quality and Emissions Generally, air quality models are regarded as the m ost appropriate tools for assessing the expected impacts of a change in emissions. However , it may also be possible to evaluate progress towards attainment of the ozone or PM 2.5 NAAQS based on measured historical trends of air quality and emissions. It may be possible t o develop a relationship between past emissions changes and historical and current air qu ality. Once the relationship between past/present emissions and air quality is establish ed, a response to the expected emissions reductions from a particular control strategy can b e estimated. There are several elements to this analysis that are difficult to quantify. Firs t, in most cases, the ambient data trends are best assessed by normalizing to account for year-to-year meteorological variations. Second, one must have an accurate accounting of the year-to-yea r changes in actual emissions (NOx, VOC, and/or SO 2 and NH 3 ) for the given area and any surrounding areas whos e emissions may impact local concentrations. Third, one must have a solid conceptual model of how ozone or PM 2.5 is

93 For each of these metrics, the appropriate comparis on to the level of the NAAQS is 75 ppb for 8-hour ozone; 35 μg/m3 for 24-hour PM 2.5 ; and 12 μg/m3 for annual PM 2.5 .Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

184 formed in the local area (e.g., influence of meteor ology, NOx-limited, ammonia limited, transport-influenced, etc.).

If available, meteorologically adjusted ozone and PM 2.5 concentrations can be used to establish air quality trends. There are several techniques t hat have been used to examine the influence of meteorology on air quality. Among them are (a) statistical modeling (U.S. EPA, 2005b ); (b) filtering techniques (Rao, 1995; Flaum, 1996; Milan chus, 1998; and Hogrefe, 2000), (c) using a probability distribution of meteorological severity based on climatological data (Cox and Chu, 1993, 1996), (d) and using CART analysis to identif y meteorological classes and selecting days from each year so that the underlying frequency of the identified meteorological classes remains the same (Deuel and Douglas, 1996). Most o f this work has examined the relationship between ozone and meteorology. Only recently have analyses examined the relationship between meteorology and PM 2.5 . Additionally, compared to PM 2.5 , the established relationship between ozone and meteorological variables is gener ally stronger (higher r-square values). In
the case of PM 2.5 , the relationship between concentration and meteor ology is complicated by
the fact that PM 2.5 components experience high concentrations at diffe rent times of the year
and for different reasons. This makes it more diff icult to meteorologically adjust PM 2.5 concentrations.

If a meteorologically adjusted trend in ozone or P M 2.5 can be estimated, then the information
can be used to establish a link between emissions a nd air quality trends. This is not always straightforward due to the multitude of emissions p recursors that may lead to high ozone and
PM 2.5 concentrations. A careful analysis of (meteorolog ically adjusted) air quality trends and emissions trends of each of the ozone and PM precur sors (as well as primary PM) is needed to fully establish relationships. Detailed emissions information as well as a solid understanding of the conceptual model of ozone or PM 2.5 formation is needed. If a trend can be establishe d based on past emissions changes and air quality cha nges, then future year predicted emissions levels can be used to extrapolate future air qualit y.

Meteorologically adjusted trends in ozone and/or PM 2.5 attempt to adjust observed
concentrations to levels which would have been obse rved during average meteorological conditions (Camalier, 2007). This makes it easier to observe trends primarily due to emissions changes and also allows identification of years wit h above average or below average meteorologically conducive conditions. Identificat ion of “extreme” meteorological conditionsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

185 which are not likely to re-occur in a particular de sign value period can be used as part of a weight of evidence analysis. For example, the pres ence of one or more “extreme” (high concentration) meteorological years in either the b ase year period or the most recent design value period may make it appear that an area is far ther away from attaining the NAAQS than would otherwise be the case if more typical meteoro logical conditions had occurred. It is important to note that one or more years of meteoro logical conditions which are not conducive to ozone formation will have the opposite effect. An area may appear to be on track to attain the NAAQS (or close to attaining), but in reality m ay need substantial additional emissions reductions in order to attain under average or abov e average meteorological conditions. Meteorological adjusted concentration and/or design value calculations can help explain both
of these situations. More information on meteorolo gically adjusted trends can be found here: http://www.epa.gov/airtrends/weather.html

.

A simpler (and more uncertain) way to qualitatively assess progress toward attainment is to examine recently observed air quality and emissions trends. Downward trends in observed air quality and in emissions (past and projected) are c onsistent with progress towards attainment. Strength of the evidence produced by emissions and air quality trends is increased if an extensive monitoring network exists and if there is a good correlation between past emissions
reductions and current trends in ozone or PM 2.5 . Until recently, EPA prepared annual air quality trends reports for all criteria pollutants. The la test trends report analyzes ambient data through 2010 and can be found here: http://www.epa.gov/airtrends/2011/report/fullreport .pdf . The formal trends reports have been replaced by less formal annual updates which a re contained on the Air Trends website. Pollutant specific trends updates can be found here : http://www.epa.gov/airtrends/index.html

Weight given to trend analyses depends on several f actors. Analyses that use more air quality data and apply a greater variety of trend parameter s provide more credible results. More weight can be placed on the results if the procedur e used to normalize the trend for meteorological differences explains much of the var iability attributable to these differences. In addition, trend analysis is more believable if the extrapolation (as applicable) does not extend very far into the future. Finally, trend analysis is most credible if the contemplated strategy is similar to a past strategy (e.g., both strategies f ocus on reducing sulfates for PM or NOx for ozone). For example, if a past strategy focused on reducing sulfates, but a future one envisionsModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

186 controlling OC, there is no guarantee that ambient OC will respond similarly to changes in past
emissions.

In addition to ambient data trends, further analysi s of ambient data can provide information on ozone production efficiency, evidence of transport and assist in the QA of emissions inputs. In particular, photochemical assessment monitoring sta tion (PAMS) data, special study data, research and non-routine ambient data (i.e. NOy, an d VOC data) can provide insights into the ozone and/or PM 2.5 concentrations observed in the nonattainment area a nd can help assess the accuracy of the modeling results and control strate gies that are being relied upon for
attainment.

4.9.1.3 Additional Emissions Controls/reductions Models are used to predict the future expected conc entrations of ozone and/or PM 2.5 , based on modeled emissions changes between a base year and f uture year. It is expected that emission inventories are as accurate as possible and represe nt emissions changes (including growth and controls) that are expected to occur. However, the re may be various emissions sources and/or controls that are difficult to represent in the mod eling analysis and the effects of some emissions controls may be difficult to quantify. A dditionally, there may be uncertainty in how emissions controls may be implemented either in the home state or in upwind regions which may contribute ozone to the nonattainment area.

The following are some of the types of control meas ures that may be appropriate to include in a weight of evidence demonstration:

• Measures that are difficult to quantify or may not be enforceable in the SIP. Examples
include:

o Energy efficiency or renewable energy (EE/RE) progr ams may be implemented statewide (and in fact may be enforceable). But em issions reductions from such programs are hard to quantify and it may be difficu lt to assign reductions to particular utility sources. EPA has put together a n “EE/RE Roadmap Manual”
which can be found here: http://epa.gov/airquality/eere/manual.html . The roadmap includes a weight of evidence “pathway” for air agencies that want to acknowledge the emissions benefits and potential re ductions from EE/RE measures, but are unable to accurately quantify the emissions reductions in theirModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

187

SIP.

o Smart growth initiatives such as land use planning and transportation planning.

o Truck stop electrification.

o Emissions reductions from idling regulations which may not be quantified in the mobile emissions modeling.

• Voluntary measures

o Air agencies may have numerous voluntary measures t hat are not enforceable in
the SIP. These could include measures such as “ozo ne action days”, voluntary no burn days, telework programs, idling reduction init iatives, etc. Even though these programs may be voluntary, they can still lea d to positive actions in the
nonattainment area that can lead to lower emissions . Voluntary measures should be documented to the fullest extent possible , including estimates of emissions benefits, where possible.

• Regional/super-regional and/or national programs th at may not have been accounted
for in the attainment demonstration.

o Federal measures which were not accounted for in th e modeling because they were proposed and/or finalized after the state mode ling was completed.

o Upwind regional, state, and/or local measures which were not known or not able to be quantified in the attainment demonstration du e to timing or lack of
information.



### 4.9.2 What Role Should Weight Of Evidence Play In Visibility-Related Analyses?

We believe a weight of evidence analysis is an appr opriate option for air agencies to use when examining visibility goals. In this subsection, we note some potential supplemental analyses that may be used to examine visibility trends.

**4.9.2.1 Additional air quality modeling** Sensitivity tests can be performed to see if conclu sions about trends in the 20% worst and best visibility days are robust. One example of such an analysis is applying a model with and without a more finely resolved nested grid near source area s or near Class I areas. A purpose of thisModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

188 would be to see whether conclusions are affected by the degree of detail in which nearby sources are considered. A second example of an ana lysis would be to consider alternative future emissions and/or differing growth rate assum ptions. This may be a particular concern for regional haze analyses because the emissions pr ojection period is generally longer than for
most ozone and PM 2.5 attainment demonstrations. Uncertainty in emissio ns and growth rates become more important as the projection period is l engthened.

If trends in visibility for worst and/or best days are similar using sensitivity tests, alternative models and/or alternative modeling approaches, this finding supports conclusions reached in the uniform rate of progress analysis.

**4.9.2.2 Review of trends** A review of trends generally involves a comparison, sometimes qualitative, between past trends in reconstructed visibility and estimated ch anges in emissions (e.g., early ‘00’s to early ‘10's). This information could be used to confirm that more control measures on previously reduced emissions of a component or its precursors is likely to be useful. It may also be used to see whether certain PM components are becoming incr easingly important sources of light
extinction.

**4.9.2.3 Other models and tools** Trajectory models may be useful for identifying the types of meteorological conditions most often corresponding to observed worst and best visi bility in various Class I areas. This, in turn, may enable air agencies to draw inferences about th e orientation of areas containing sources most likely to influence visibility in a Class I ar ea on days with “poor” and “good” visibility. Grid model based techniques such as DDM or source apport ionment may also be useful in identifying areas and emissions sources most respon sible for visibility impairment on the worst or best days.

**4.9.2.4 Refinements to the recommended uniform rate of progress analysis**  If a modeled control strategy appears generally suc cessful, but the glidepath is not met in certain Class I areas, states may consider refining the recommended modeling analysis in some manner. Refinements are best made if they are base d on local observations/analyses which suggest that some of the underlying assumptions in the recommended assessment may not beModeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

189 applicable. We list some potential refinements tha t could be considered. The list is intended to illustrate types of additional analyses that could be performed.

• Use an alternative light extinction equation or an area-specific version.

• Available speciated data and other information may be reviewed to see whether the outcome of the test is being influenced by includin g one or more days with extraordinary events (e.g., a major wildfire lastin g a number of days or transported dust events). If convincing arguments can be made that the event is a “natural” one, excluding these days from the calculations should b e discussed with the appropriate EPA regional office and FLMs.

• Daily component specific RRFs can be examined to de termine if one or more days are responding in a different way compared to the major ity of the best or worst days. Determining why days may be more or less responsive to emissions controls may lead to conclusions regarding the suitability of particular days to be represented in the mean response. It may be appropriate, in some cases, to re-calculate mean RRFs with suspect days removed.

• Re-rank future estimated light extinction (i.e., b ext values) for all days with current measurements and re-compute mean future 20% best an d worst visibility (i.e., do not assume that the identity of baseline 20% best and w orst days remains the same).

**Concerns about modeling days with best visibility** In some parts of the United States, concentrations of the components of PM used in visibility calculations may be very close to background levels on days with best visibility. Measurements and model estimates may be subject to more relative uncertainty (i.e., lower signal to noise ratio) on days where observed concentrations of PM are very low (and light extinction is also low). The utility of a WOE determination is height ened in such cases. A state should see whether a model’s inability to accurately predict o ne or more individual components of PM has
a substantial effect on the extinction coefficient calculated with Equation (4.12). If it does, and diagnostic tests are unable to resolve a performanc e problem, a state may need to address the goal for best visibility days in the particular Cla ss I area(s) without using results from a grid
model.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

190 

### 4.9.3 Weight of Evidence Summary

A WOE determination examines results from a diverse set of analyses, including the outcome of the primary attainment test, and attempts to summar ize the results into an aggregate conclusion with respect to whether a chosen set of control strategies will result in an area attaining the NAAQS by the appropriate year. The s upplemental analyses discussed above are intended to be part of a WOE determination, althoug h the level of detail required in a WOE submittal will vary as a function of many elements of the model application (e.g., model performance, degree of residual nonattainment in th e modeled attainment test, amount of uncertainty in the model and its inputs, etc.). Ea ch WOE determination will be subject to areaspecific conditions and data availability. Area-sp ecific factors may also affect the types of analyses that are feasible for a nonattainment area , as well as the significance of each. Thus, decisions concerning which analyses to perform and how much credence to give each need to be made on a case by case basis by those implementi ng the modeling/analysis protocol. Air agencies are encouraged to consult with their EPA R egional office (and FLMs, when appropriate) in advance of initiating supplemental analyses to determine which additional analyses may be most appropriate for their particul ar area.

The most useful supplemental analyses are those pro viding the best evidence as to how much air quality improvement can be expected as compared to the improvement projected by the air quality modeling analysis. Each analysis is weighe d qualitatively, depending on: 1) the capacity of the analysis to address the adequacy of a strate gy and 2) the technical credibility of the analysis. If the overall WOE produced by the combi nation of the primary modeling analysis and the various supplemental analyses supports the atta inment hypothesis, then attainment of the NAAQS is demonstrated with the proposed strategy. The end product of a weight of evidence determination is a document which describes analyse s performed, data bases used, key assumptions and outcomes of each analysis, and why an air agency believes that the evidence, viewed as a whole, supports a conclusion that the a rea will, or will not, attain the NAAQS. In conclusion, the basic criteria required for an atta inment demonstration based on weight of
evidence are as follows:

1) A fully-evaluated, high-quality modeling analysis that projects future values that are close to the NAAQS.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

191 2) A description of each of the individual supplementa l analyses, preferably from multiple categories. Analyses that utilize well-established analytical procedures and are grounded with sufficient data should be weighted ac cordingly higher.

3) A written description as to why the full set of evi dence leads to a conclusive determination regarding the future attainment statu s of the area that differs from the
results of the modeled attainment test alone.Modeling Guidance for Demonstrating Attainment of A ir Quality Goals for Ozone, PM 2.5 , and Regional Haze- December 2014 DRAFT

192 

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