December 2004

Draft 2.0

Modeling Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling

Prepared for:

Annette Sharp

Technical Director Central Regional Air Planning Association 10005 S. Pennsylvania, Ste. C

Oklahoma City, OK 73159

Prepared by:

Ralph E. Morris

Gerard Mansell Bonyoung Koo

ENVIRON International Corporation 101 Rowland Way Novato, California 94945

Gail Tonnesen Mohammed Omary Zion Wang

UC Riverside

CE-CERT

1084 Columbia Avenue Riverside, California 92507

December 8, 2004

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700December 2004

i TABLE OF CONTENTS

List of Tables .............................................................................................................. v List of Figures ............................................................................................................. vi

1.0 INTRODUCTION ......................................................................................................... 1-1 1.1 Background ........................................................................................................ 1-1 1.2 CENRAP Organization Structure And Emissions And Air Quality Modeling . 1-3 1.3 CENRAP Long Range Plan ............................................................................... 1-4

1.3.1 Tier One: Tasks.................................................................................. 1-4

1.3.2 Tier Two: Activities........................................................................... 1-5 1.3.3 Tier Three: Projects ........................................................................... 1-5

1.3.4 Tier Four: Deliverables...................................................................... 1-5

1.3.5 Tier Five: Critical Milestone Dates for CENRAP............................. 1-5 1.3.6 Role of Emissions and Air Quality Modeling in CENRAP Long Range Plan................................................................................ 1-6 1.4 Past Related Regional Modeling Studies ........................................................... 1-6 1.5 Overview of 2002 Annual Emissions and Air Quality Modeling Approach ..... 1-8 1.5.1 Data Gatekeepers ............................................................................... 1-9 1.5.2 Emissions QA/QC............................................................................ 1-10 1.5.3 Meteorology..................................................................................... 1-12 1.5.4 Air Quality Modeling QA/QC ......................................................... 1-12 1.5.5 Overview of Data Flow and Quality Assurance Process................. 1-13 1.5.6 Proposed Model Performance Goals ............................................... 1-14 1.6 CENRAP Annual Modeling Study Participants and Responsibilities ............. 1-18

2.0 MODEL SELECTION ................................................................................................. 2-1

2.1 Recommended Models....................................................................................... 2-1 2.2 MM5 Mesoscale Prognostic Model ................................................................... 2-2

2.2.1 MM5 Overview.................................................................................. 2-2 2.2.2 MM5 Configuration for Phase II Modeling....................................... 2-4 2.3 SMOKE Emissions Modeling System ............................................................... 2-4

2.3.1 SMOKE Overview............................................................................. 2-4 2.3.2 SMOKE Configuration for Phase II Modeling.................................. 2-6 2.4 CMAQ Modeling System................................................................................... 2-6 2.4.1 CMAQ Overview............................................................................... 2-6 2.4.2 CMAQ Configuration for Phase II Modeling.................................... 2-7

2.5 Alternative Models............................................................................................. 2-8

2.5.1 CAMx Overview................................................................................ 2-8 2.5.2 CAMx Configuration for Phase II Modeling................................... 2-10 2.6 Advantages in Operating Multiple Modes ....................................................... 2-11

2.7 Model Limitations............................................................................................ 2-12

2.7.1 MM5 ................................................................................................ 2-12

2.7.2 SMOKE ........................................................................................... 2-12 2.7.3 CMAQ ............................................................................................. 2-13

2.7.4 CAMx .............................................................................................. 2-13 2.8 Model Input Requirements............................................................................... 2-13December 2004

ii 2.8.1 MM5 ................................................................................................ 2-13

2.8.2 SMOKE ........................................................................................... 2-14 2.8.3 CMAQ ............................................................................................. 2-15

2.8.4 CAMx .............................................................................................. 2-15

3.0 EPISODE SELECTION............................................................................................... 3-1

3.1 Overview of EPA Guidance............................................................................... 3-1 3.2 Selection of CY 2002 for CENRAP Annual Modeling ..................................... 3-1

4.0 MODELING DOMAINS AND DATA AVAILABILITY ........................................ 4-1 4.1 Horizontal Modeling Domains........................................................................... 4-1 4.2 Vertical Modeling Domains............................................................................... 4-2 4.3 Data Availability ................................................................................................ 4-5

4.3.1 Emissions Data .................................................................................. 4-5 4.3.2 Air Quality ......................................................................................... 4-5

4.3.3 Ozone Column Data........................................................................... 4-9 4.3.4 Meteorological Data .......................................................................... 4-9 4.3.5 Initial and Boundary Conditions Data ............................................... 4-9

5.0 MODEL INPUT PREPARAT ION PROCEDURES ................................................. 5-1 5.1 Meteorological Inputs to Emissi ons and Air Quality Models .................................. 5-1 5.1.1 MCIP Reformatting Methodology..................................................... 5-1 5.1.2 Products of the CMAQ Meteorological Input Development Process 5-3 5.1.3 MM5 Reformatting Methodology ..................................................... 5-3

5.1.4 Treatment of Minimum K

v ................................................................. 5-4 5.2 Development of Emissions Model I nputs and Resultant Inventories ................ 5-4 5.2.1 Emissions Modeling Methodology.................................................... 5-5 5.2.2 Set-up of SMOKE Over the CENRAP Domain ................................ 5-5 5.2.3 Development of Point Source Emissions........................................... 5-8 5.2.4 Development of Area and Non-Road Source Emissions................... 5-9 5.2.5 Development of On-Road Mobile Source Emissions...................... 5-10 5.2.6 Development of Biogenic Source Emissions................................... 5-10 5.2.7 Wildfires, Prescribed Burns, Wind Blown Dust and Sea

Salt Source Emissions...................................................................... 5-11 5.2.8` Speciation and Reformatting of Emissions...................................... 5-12 5.2.9 Development of Modeling Inventories............................................ 5-13

5.2.9.1 2002 Annual Inventories..................................................... 5-13 5.2.9.2 Future Year Episodic Inventories ....................................... 5-13 5.2.10 Products of the Emissions Inventory Development Process ........... 5-13 5.2.11 Preparation of CAMx Emission Inputs............................................ 5-15 5.3 CMAQ Modeling Methodology....................................................................... 5-15 5.3.1 CMAQ Science Configuration......................................................... 5-15 5.3.2 Spin-Up Initialization ...................................................................... 5-17 5.3.3 Boundary Conditions ....................................................................... 5-17 5.3.4 Photolysis Rates............................................................................... 5-17 5.4 CAMx Modeling Methodology........................................................................ 5-18 5.4.1 CAMx Science Components............................................................ 5-18December 2004

iii 5.4.2 Spin-Up Initialization ...................................................................... 5-19 5.4.3 Boundary Conditions ....................................................................... 5-19 5.4.4 Photolysis Rates............................................................................... 5-19

6.0 QUALITY ASS URANCE PLAN................................................................................. 6-1 6.1 Quality Assurance Objectives ............................................................................ 6-1 6.2 Emissions Model Inputs and Outputs ................................................................ 6-2 6.2.1 Emissions Modeling QA/QC ............................................................. 6-2 6.2.2 QA of the Model-Ready Emissions Impacts ..................................... 6-3 6.3 Meteorological Model Outputs .......................................................................... 6-3 6.4 Air Quality Model Inputs and Outputs................................................................6-5

7.0 MODEL PERFORMANCE EVALUATION............................................................. 7-1

7.1 Overview ........................................................................................................... 7-1

7.2 Context for the CENRAP Model Evaluation ..................................................... 7-3 7.3 Multi-Layered Model Testing Process............................................................... 7-4 7.4 Development of Consistent Evaluation Data Sets ............................................. 7-5

7.4.1 Surface Measurements....................................................................... 7-5

7.4.2 Aloft Measurements........................................................................... 7-6

7.5 Model Evaluation Tools..................................................................................... 7-7

7.5.1 Statistical Performance Metrics......................................................... 7-8 7.5.2 Graphical Representations ................................................................. 7-9 7.5.3 Probing Tools and Allied Methods.................................................... 7-9

7.6 CENRAP 2002 Annual Model Evaluation Procedures.................................... 7-12 7.6.1 Assessment of Ground-Level Gas-Phase and Aerosol Species....... 7-12

7.7 Performance Goals and Benchmarks ............................................................... 7-15 7.8 Diagnostic and Sens itivity Tes ting................................................................... 7-17

7.8.1 Traditional Sens itivity Testing ........................................................ 7-17 7.8.2 Diagnostic Tests............................................................................... 7-19 7.9 Corroborative and Weight of Evidence Modeling Analyses ........................... 7-20

7.9.1 Corroborative Models...................................................................... 7-20 7.9.2 Weight of Evidence Analyses.......................................................... 7-21 7.9.3 Comparison with Other RPOs ......................................................... 7-21 7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes 7-21

8.0 VISIBILITY ASSESSMENT AND COMPARISON................................................. 8-1 8.1 Regional Haze Rule Requirements .................................................................... 8-1 8.2 EPA Guidance for Complying with Regional Haze Rule.................................. 8-2 8.2.1 Procedures for Projecting Visibility Changes.................................... 8-3

8.2.1.1 Calculation of Baseline Conditions ...................................... 8-4 8.2.1.2 Mapping of Modeling Results to the

IMPROVE Measurements ................................................... 8-6 8.2.1.3 Using Modeling Results to Project Changes in Visibility ......................................................................... 8-7December 2004

iv 8.2.1.4 Exclusion of Days from 20% Best/Worst

Conditions............................................................................. 8-8 8.3 CENRAP Future-Year Modeling....................................................................... 8-8

8.3.1 Future-Year to be Modeled................................................................ 8-9

8.3.2 Future-Year Emissions ...................................................................... 8-9 8.3.3 Future-Year Initial and Boundary Conditions ................................... 8-9 8.3.4 Other Future-Year Modeling Inputs ................................................ 8-10

8.4 Presentation of Results..................................................................................... 8-10 8.4.1 Projection of Visibility Improvements at Class I Areas .................. 8-10 8.4.2 Spatial Maps of Results ................................................................... 8-12

8.4.3 Time Series Plots ............................................................................. 8-12 8.4.4 Box Plots and Q-Q Plots.................................................................. 8-13

9.0 DATA MANAGEMENT .............................................................................................. 9-1 9.1 Project Website .................................................................................................. 9-1

9.2 Data Transfer...................................................................................................... 9-1 9.3 Data Backup and Archiving ............................................................................... 9-2

9.3.1 ENVIRON.......................................................................................... 9-2

9.3.2 UC Riverside...................................................................................... 9-2

10.0 DOCUMENTATION.................................................................................................. 10-1

10.1 Planned Documentation ................................................................................... 10-1 10.2 Procedures for Updating Mode ling Protocol and QA Plan..............................10-2

REFERENCES..................................................................................................................... ... R-1December 2004

v LIST OF TABLES

Table 1-1. Federal mandated Class I areas in the CENRAP states....................................... 1-3 Table 1-2. Model performance goals used in Phase I to help interpret modeling results ... 1-15 Table 1-3. Summary of key participants and contact information for the CENRAP 2002 Annual Emissions and Air Quality Modeling Study ............... 1-19

Table 2-1. MM5 Meteorological mode l configuration for CENRAP 2002 annual modeling (Johnson, 2004)...................................................................... 2-16

Table 2-2. SMOKE emissions model confi guration for CENRAP annual modeling......... 2-17 Table 2-3. CMAQ air quality model configuration for CENRAP annual modeling

.......... 2-18 Table 2-4. CAMx air quality model configuration for CENRAP annual modeling ........... 2-19

Table 4-1. RPO Unified grid definition. ............................................................................... 4-1 Table 4-2. Grid definitions for MM5 and CMAQ/CAMx. ................................................... 4-2 Table 4-3. Vertical layer definition for MM5 simulations (left most columns), and approach for reducing CMAQ/CAMx layers by collapsing multiple MM5 layers (right columns).................................................................. 4-4 Table 4-4. Overview of ambient data monitoring networks. ................................................ 4-6

Table 5-1. MCIP V2.2 configuration used in the CENRAP annual modeling. .................... 5-3 Table 5-2. SMOKE modeled holidays. ................................................................................. 5-6 Table 5-3. Proposed CENRAP annual m odel configuration for the CMAQ. ..................... 5-17

Table 7-1. Ground-level ambient data monitoring networks and

stations available in the CENRAP states for CY-2002...................................... 7-23 Table 7-2. Aircraft sampling progr ams performed during CY-2002. ................................. 7-24

Table 7-3. Core statistical measures to be used in the CENRAP 2002 annual air quality model eval uation with ground-level data ............................. 7-25

Table 8-1. Definition of IMPROVE Reconstructed Mass (RCM) species from measured IMPROVE species...................................................................... 8-4 Table 8-2a Proposed mapping of CMAQ V4.4 modeled species concentrations to IMPROVE Reconstructed Mass (RCM) species..................... 8-6 Table 8-2b. Proposed mapping of CAMx V4.10s using M4 (coarse/fine) modeled species concentrations to IMPROVE Reconstructed Mass (RCM) species............................................................................................ 8-7

Table 10-1. Current list of deliverables and schedule under the CENRAP 2002 annual modeling study. ............................................................................. 10-1December 2004

vi LIST OF FIGURES

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling. .............. 1-2 Figure 1-2a. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality Modeling ............................................................... 1-16 Figure 1-2b. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality Modeling (Concluded) ........................................... 1-17

Figure 4-1. Nesting of 36-km CMAQ/CAMx grid in the MM5 36-km grid.......................... 4-3 Figure 4-2.a. Locations of IMPROVE, CASTNet, SEARCH, STN and NADP monitoring sites in and near the CENRAP States.................................... 4-7 Figure 4-2b. Locations of AQS monitoring sites in and near the CENRAP States ................. 4-8

Figure 5-1. EPA 36-km National CMAQ Domain................................................................. 5-6

Figure 8-1. Example calculation of visibility progress goals using IMPROVE data collected at the Grand Canyon National Park for the 20% Worst days...................................................................................... 8-12December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-1 1.0 INTRODUCTION

This report constitutes the s econd draft of the Air Quality Modeling Protocol and Quality Assurance (QA) plan for the CENRAP 2002 a nnual Emissions and Ai r Quality Modeling activities to be performed by th e contractor team of ENVIR ON International Corp and the University of California at Riverside. Deve lopment of this second draft Modeling Protocol governing the CENRAP 2002 annual Emissions a nd Air Quality Modeling Study was performed as Task 2 of the study.

Note that at this writing we have not received all the relevant data and materials necessary to completely define the CENRAP annual modeling approach. Consequently, this draft Modeling Protocol will not necessarily be complete and w ill change as we obtain more information. The second draft Modeling Protocol incorporates comments from CENRAP on the first draft Modeling Protocol.

1.1 Background

CENRAP is one of five Regiona l Planning Organizations (RPOs) that have re sponsibility for coordinating development of State Implemen tation Plans (SIPs) and Tribal Implementation Plans (TIPs) in selected areas of the U.S. to address the requirements of the Regional Haze Rule (RHR). The RHR visibility SIPs/TIPs are due in 2007/2008. CENRAP modeling results may also form the regional component for 8-hour ozone and fine particulate (PM 2.5 ) SIPs/TIPs that are also expected to be due in 2007/2008. CENRAP is a regional partnership of states, tribes, federal agencies, stakeholders and citizen groups established to initiate a nd coordinate activities associated with the management of regional haze and other air quality issues within the CENRAP states. The CENRAP region includes states and tribal lands located within the boundaries of Arkansas, Iowa, Ka nsas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma and Texas. The CENRAP Emissions and Air Qua lity Modeling Team is comprised of staff from ENVIRON International Corpora tion (ENVIRON) and the University of California, Riverside (UCR). The ENVIRON/UCR Team performs the emissions and air quality modeling

simulations for states and tribes within the CENRAP region, providi ng analytical results used in developing implementation plans under the EPA Regional Haze Rule. Figure 1-1 identifies the various Regional Planning Organizations in the U.S, including CENRAP. Table 1-1 lists the

Class I areas within the CENRAP states.

The Clean Air Act establishes special goals for visibility in many national parks, wilderness areas, and international parks. Thro ugh the 1977 amendments to the Clean Air Act, Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of visibility in ma ndatory Class I Federal areas which impairment results from manmade air pollu tion” (40 CFR 51.300). States are required to develop State Implementation Plans (SIPs) to attain visibility standards, and Tribes al so may opt to assume responsibility for visibility programs under 40 CF R Part 49 by developing Tribal Implementation Plans (TIPs). The goal of the Regional Haze Ru le (RHR) is to achieve natural visibility conditions at Federally mandated Class I areas by 2064. To achieve this goal, the RHR has set up several milestone years of 2018, 2028, 2038, 2048, 2058 and 2064 to monitor progress toward natural visibility conditions. Section 308 requir es the first visibility SIP/TIP be submitted toDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-2 EPA by 2007 - 2008 to demonstrate progress toward natural visibility conditions in 2018 using the 2000-2004 five-year baseline. The CENRAP L ong Range Plan (CENRAP, 2003) is to have an evaluation of initial strategies by July 2006 th at States and Tribes can start their planning process for submitting their plans to EPA by December 2007.

Regional haze is linked to fine particulate (PM 2.5 ) for which EPA has a new standard.

PM 2.5 SIPS are to be submitted 3 years after designation of PM 2.5 nonattainment areas. EPA intends to perform the fina l designations of the PM 2.5 nonattainment areas by December 2004,

which would make the PM 2.5 SIP due by December 2007. As re gional haze is intricately linked

to PM 2.5, the PM 2.5 and regional haze SIPs are in the proc ess of being aligned so they would both be due by December 2007, if the PM 2.5 final designations occur as pl anned. EPA designated 8-hour ozone nonattainment areas in April 2004 that makes 8-hour ozone SIPs also due in 2007. States may use the integrated one-atmosphere CENRAP base year modeling for ozone modeling

in addition to PM 2.5 and regional haze. Those decisions w ill be based on schedule constraints, model performance, appropr iateness of the episodes, feasibility and need.

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-3 Table 1-1. Federal mandated Class I areas in the CENRAP States.

Class I Area Acreage Federal Land Manager Public

Law

Arkansas Caney Creek Wilderness Area 4,344 USDA-FS 93-622 Upper Buffalo Wilderness Area 9,912 USDA-FS 93-622

Louisiana Breton Wilderness Area 5,000+ USDI-FWS 93-632

Minnesota Boundary Waters Canoe Area Wilderness Area 747,840 USDA-FS 99-577 Voyageurs National Park 114,964 USDI-NP 99-261

Missouri Hercules-Glade Wilderness Area 12,315 USDA-FS 94-557 Mingo Wilderness Area 8,000 USDI-FWS 95-557

Oklahoma Wichita Mountains Wilderness 8,900 USDI-FWS 91-504

Texas Big Bend National Park 708,118 USDI-NP 74-157 Guadalupe Mountains National Park 76,292 USDI-NP 89-667

1.2 CENRAP Organization Structure and Emissions and Air Quality Modeling

The governing body of CENRAP is the Policy Ov ersight Group (POG) that is made up of 18 voting members representing st ates and tribes within the CENRAP region and non-voting members representing local agencies, the EPA and other federal agencies. The work of CENRAP is accomplished through six standing workgroups:

• Monitoring;

• Emissions Inventory;

• Modeling;

• Communications;

• Implementation and Control Strategies; and

• International.

Participation in workgroups is open to all intere sted parties and the POG may form additional ad hoc workgroups to address specific issues (e.g., a Data Analysis workgroup was formed).

The Regional Haze Rule (RHR) requires the st ates, and the tribes may elect to, submit the first SIPs and TIPs in 2007/2008 that address pr ogress toward natural conditions at federally mandated Class I areas. 40 CFR 51.308 (Secti on 308) discusses the following four core requirements to be included in SIPs/TIPs and Best Available Retrofit Technology (BART) requirements:

1. Reasonable progress goals;

2. Calculations of baseline and natural visibility conditions; 3. Long-term strategy for regional haze; 4. Monitoring strategy and other impl ementation plan requirements; andDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-4 5. BART requirements for regional haze visibility impairment.

One of CENRAP’s goals is to provide support to states and tribes to meet each of these requirements of the RHR and to develop scientif ically supportable, economical and effective control strategies that the states and tribes ma y adopt to reduce manmade effects on visibility impairment at Class I areas. One component of CE NRAP’s support to states and tribes as part of compliance with the RHR is performing emissions and air quality modeling to obtain a better understanding of the causes of pot ential mitigation measures for visibility impairment at Class I areas, to evaluate the effects of alternative control strategies for improving visibility and for projecting future-year air quality and visibility conditions. In October 2004, CENRAP selected a team of ENVIRON and UCR to perform their 2002 annual Emissions and Air Quality Modeling.

The CENRAP Emissions and Air Quality Modeling Team performs regional haze analyses by operating regional sc ale, three-dimensional air quality models that simulate the emissions, chemical transformations, and trans port of gaseous and particulate matter (PM) species and consequently effects on visibility in Class I Areas in the central U.S. A key element of this work includes the integration of emissi ons inventories and models with regional transport models. The general services provided by th e CENRAP Emissions and Air Quality Modeling Team include, but are not limited to:

• Emissions processing and modeling; • Air quality and visibili ty modeling simulations; • Analysis, display, and repor ting of modeling results; and • Storage/quality assurance of th e modeling input and output files.

The CENRAP 2002 annual Emissions and Air Quality Modeling Team performs work for the CENRAP Modeling Workgroup through di rection from the CENRAP Project Manager and CENRAP Administrative Project Manager.

1.3 CENRAP Long Range Plan

CENRAP adopted an initial Long Range Plan in October 2003 (CEN RAP, 2003) and will review this plan each spring and fall and update it as needed. The CENRAP Long Range Plan is organized into four primary tiers of work efforts: (1) Tasks; (2) Activities; (3) Projects; and (4)

Deliverables.

1.3.1 Tier One: Tasks

The first tier of the CENRAP Long Range Plan consists of a series of work efforts or Tasks that comprise the statuary requir ements of the Regiona l Haze Rule (RHR):

• Establish baseline and natural conditions;

• Develop reasonable progress goals;

• Develop a long term strategy for reducing regional haze;

• Develop a monitoring strategy and other SIP/TIP requirements; andDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-5 • Establish BART requirements.

Each of these Tasks must be accomplished to achieve the goal of providing states and tribes the information upon which to develop su pportable and effective RHR SIPs and TIPs.

1.3.2 Tier Two: Activities

The second tier (Activities) c onsists of broad, categorized work efforts that must be completed in order to complete a Task. For example, the 2002 annual Emissi ons and Air Quality Modeling effort is an activity that needs to be completed in order to perform the “develop reasonable progress goals” and “develop a long te rm strategy for reducing regional haze” Tasks

listed above.

1.3.3 Tier 3: Projects

The third tier consists of specific Projects ai med at completing an Activity. For example, to perform the 2002 annual modeling, 2002 emissions are needed, thus a sp ecific project would be to compile 2002 emissions for the CENRAP st ates and provide to the Emissions and Air Quality Modeling Team. Details on specific projects are identified in Attachment 1 of the CENRAP Long Range Plan (CENRAP, 2003).

1.3.4 Tier Four: Deliverables

The fourth and final tier is the deliverables that consist of the individual components of the work effort necessary to complete a given project. For example, Deliverables for the 2002 annual Emissions and Air Qualit y Modeling activity would incl ude a Modeling Protocol, a QAPP, and Base Case Modeling and Model Pe rformance Evaluation Report and the modeling

databases.

1.3.5 Critical Milestone Dates for CENRAP

The CENRAP Long Range Plan has develope d a series of milestone dates by which

critical decisions must be made to address a regulatory or statutory deadline. Critical milestone dates were assigned to the following activities:

• SIP submittal;

• SIP and TIP drafting and approval;

• Conducting future-year modeling; and

• Conducting base case modeling.

The CENRAP Long Range Plan back calculated critical milestone dates from the date that the RHR SIPs/TIPs are required to be submitted to EPA and developed the following

timeline:December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-6 SIP/TIPs Adopted and States Subm it SIP to EPA – December 31, 2007 : Although the actual SIP/TIP submittal date may be extended to 2008, CENRAP is adopting the December 2007 submittal date in developing their critical milestone dates to assure that CENRAP obtains the information necessary to develop the RHR SIP/TIPs in a timely

fashion.

States and Tribes Begin to Draft an d Adopt SIPs and TIPs – July 1, 2006 : A survey of the timelines for SIP approval for states in the CENRAP region indica tes that the states can draft and adopt a SIP as long as the technical analysis has been completed 18 months

in advance.

Future-Year Modeling Begins – March 1, 2005 : The CENRAP Modeling Workgroup estimates that it will take fifteen months to complete the future-year modeling and to conduct and evaluate alternative future-year cont rol strategies. Thus, the base year base case and model performance evaluation need s to be completed and the future-year modeling initiated by March 1, 2005.

Base Case Modeling Begins – October 1, 2004 : The CENRAP Modeling Workgroup estimates that it will take six months to pe rform base year base case modeling and model performance evaluation.

1.3.6 Role of Emissions and Air Quality Mo deling in CENRAP Long Range Plan

As seen above, the 2002 annual Emissions and Air Quality Modeling is a critical part of

the overall CENRAP efforts and is an essential component for the development of the RHR SIPs and TIPs. The modeling information must be of high quality and reliability in order to develop effective RHR control strategies . Thus, comprehensive and exha ustive quality assurance (QA) and quality control (QC) techniques are necessary.

1.4 Past Related Regional Modeling Studies

The CENRAP 2002 annual Emissions and Air Qua lity Modeling activities are built off of previous regional emissions, photochemical PM and visibility modeling performed in the Central States and across the United States. The procedures used in these previous studies help guide the design and form the initial basis for the plan for the CENRAP annual modeling approach. We are in the process of reviewing these other studies and data and not all information have been fully assimilated in this draft Modeling Protocol. Information considered will include, but not be limited to, the following:

Big Bend Regional Aerosol sand Visibility Observational Study (BRAVO) : The BRAVO study examined the causes and source of regi onal haze at the Big Bend National Park, the most southwesterly Class I area in the CENR AP states. It performed data collection activities, modeling and used numerous tec hniques to estimate PM source apportionment (Pitchford et al., 2004).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-7 CENRAP Scoping Study : CENRAP commissioned a scoping study to identify the causes of visibility impairment at Cl ass I areas in the CENRAP stat es and identify the analytical tools that are available to investig ate regional haze (Green et al., 2002).

CENRAP Ammonia Emissions Inventory Study : CENRAP sponsored a study to develop an improved ammonia emissions inventory fo r the CENRAP states (Coe and Reid, 2003).

CENRAP Agricultural and Prescribed Burns Study : In this study improved emissions inventories for prescribed burns and agri cultural burning were developed for the CENRAP states (Reid et al., 2004a).

Evaluation of CMAQ and CAMx Models Over the CENRAP States for Three Episodes : CMAQ and CAMx model simulations of a January 2002, July 1999 and July 2001 episodes were evaluated using measurement data in the CENRAP states (Tonnesen and Morris, 2004).

Development of Enhanced Mobile Source and Agricultural Dust Emissions for

CENRAP : This study developed on-road and non-ro ad mobile source and agricultural

dust emission inventories for the CE NRAP states (Reid et al., 2004b).

Development of 2002 Base Case Modeling Inventory for CENRAP : CENRAP sponsored this study to prepare a 2002 Base Case emissions inventory for the CENRAP

states that can be used in emissions and photochemi cal modeling of the 2002 annual period (Strait, Roe and Vukovich, 2004).

Preliminary PM and Visibility Modeling for CENRAP : Under this study preliminary regional PM and visibility modeling wa s conducted focused on the CENRAP region using the CMAQ and CAMx models (Pun, Chen and Seigneur, 2004).

VISTAS Phase I Model Sensitivity and Evaluation Study : This study, sponsored by VISTAS, performed extensive model sensitivity testing and evaluati on analysis using the CMAQ and CAMx models and three episodes, January 2002; July 1999 and July 2001 (Morris et al., 2004a).

WRAP Section 309 SIP/TIP Modeling Analysis : The WRAP performed a study to generate the necessary modeli ng data needed to develop Se ction 309 SIP/TIP for states that opt-in to this prog ram (Tonnesen et al., 2003).

VISTAS Phase II 2002 Annual Modeling : VISTAS is performing annual modeling of 2002 using a continental US 36 km domain and eastern US 12 km domain with attendant model evaluation and sensitivity an alysis (Morris et al., 2004b).

MRPO Modeling and Analysis : The Midwest RPO is also performing regional haze modeling and analysis that wi ll be integrated into the CENRAP modeling (Baker, 2004).

IDNR 2002 MM5 Modeling : The Iowa DNR is performing 2002 MM5 modeling that will be used in the CENRAP annual modeling (Johnson, 2004).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-8 EPA VII MM5 Modeling : EPA Region VII is performing 12 km MM5 modeling for the Central States and portions of 2002 that wi ll be used in the CMAQ/CAMx modeling analysis.

Many of the above studies above are providing data (e.g., em issions) that w ill be used directly in the CENRAP Emissions and Air Qu ality Modeling Study. C onsequently, the quality assurance (QA) and quality control (QC) procedures are directly relevant to this QAPP. Others are companion modeling studies (e.g., BRAVO, VI STAS and WRAP) that provide information that is used in the development of this QAPP and the CENRAP Emi ssions and Air Quality Modeling Protocol (E NVIRON and UCR, 2004).

1.5 Overview of 2002 Annual Emissions and Air Quality Modeling Approach

The CENRAP 2002 annual modeling will include annual PM/regional haze simulations on a 36 km continental US modeling domain and additional shorter duration episodes on a 12 km domain covering the central states. After detail ed performance testing, the modeling system will then be exercised with a variety of emissions control scenarios aimed at enabling CENRAP to assess the effects of future year emission control strategies on visibility and other air quality issues. The modeling system will also allow CENRAP to track reasonable progress toward regional haze goals. More specifically, th e CENRAP 2002 annual modeling will focus on the

use of the SMOKE emissions a nd CMAQ and CAMx air quality modeling systems for calendar year 2002 over a 36 km horizontal grid system. The CENRAP annual Emissions and Air Quality Modeling activities are being performed in two parts corresponding to fi scal years ending on April 30, 2005 (FY1) and April 30, 2006 (FY2). During FY1 the base year case modeling, model performance evaluation and future year base case modeling will be conducted. Whereas in FY2 future year sensitivity and control stra tegy modeling will be performed. A potentially large number of annual (and episodic) model simulations will be performed; the list below reflects the types of simulations that will be carried out:

3⁄4 2002 Initial Annual Run

. The initial annual model si mulations and performance evaluations using a 2002 actua l emissions inventory prov ided by CENRAP and other sources and 2002 MM5 meteorology provided by CENRAP. Multiple iterations of the 2002 annual simulation may be required to co nfirm the appropriate ness of the model science configuration(s), to ev aluate updates to the model a nd model inputs and to refine model performance.

3⁄4 2002 Actual Base Case Annual Run

. A subsequent annual 2002 simulation using actual 2002 emissions would be carried out using a final model configuration and inputs identified by the initial 2002 runs. The primary objective of this run is model performance demonstration using updated model inputs and best science model configurations. Additional sensitivity test s may be conducted using the actual base case year annual run.

3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 simulation representing the 2000-2004 baseline period for EGU and fire emissions and using 2002 emissions inventory fo r all other source sectors. The primary objective of thisDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-9 inventory is to provide the baseline modele d air quality condition against which future year modeling runs will be compared to develop relative reduction factors for each pollutant species.

3⁄4 Future Year Annual Runs. Future year simulations involving a base case inventory of typical EGU and fire emissions for CENRAP -selected future-year period, for which 2018 is the current thinking. Additional future year inventories may also be modeled. The objective of these future year model runs is to establish the modeled air quality basis against which the effectiveness of emissions control strategies will be evaluated.

3⁄4 Future Year Emission Control Strategies and Sensitivities. Prescription of the future year emissions sensitivity and control strate gies to be performed would occur during the second part (May 2005 to Ap ril 2006) of the CENRAP 2002 annual Emissions and Air Quality Modeling Study and would be better defined at a later date.

Closely integrated with the 2002 36 km an nual continental US and episodic 12 km episodic central states emissions and air quality modeling will be ongoing project management, technical review, and quality assurance activi ties performed under the guidance of the CENRAP Project Manager, Administrati ve Project Manager and Mode ling Workgroup. The Modeling Team members will participate wi th CENRAP management in regul ar conference calls, as well as ad hoc topical conference ca lls as needed, and will attend periodic meetings with the CENRAP Modeling Workgroup.

Complementing the data acquisi tion, modeling input developm ent activities, and project management activities, four other CENRAP 2002 annual Emissions and Air Quality Modeling activities will be performed, consistent with th e Quality Assurance Proj ect Plan (QAPP; Morris and Tonnesen, 2004):

1.5.1 Data Gatekeepers

The CENRAP Emissions and Air Quality Modeling Team are receiving emissions, meteorological and air quality da ta from other CENRAP contra ctors and other sources (e.g., EPA, WRAP, IDNR, etc.) and as a first line of QA, we have de fined a Gatekeeper function to assure the data have been received correctly, eval uate the quality of the data, and document the data received. Separate air quality, meteor ological and emissions Ga tekeepers have been

identified whose roles are defined below. In addition, a Data Management Gatekeeper has been defined who will post data, reports and results to the project website and archive all key data generated in the project.

3⁄4 Air Quality Data Gatekeeper . Obtain air quality data as appropriate for model input development and model performance evaluation and assure quality of all air quality data obtained, consistent with QAPP. This gate keeper will also pr ovide documentation of evaluation database for CMAQ and CAMx for all modeling runs.

3⁄4 Meteorological Gatekeeper . Obtain meteorological data as MM5 output files for 36 km annual 2002 modeling runs and other episode periods at 12 km and perform data qualityDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-10 checks as QAPP together with appropria te documentation of model performance

evaluation activities.

3⁄4 Emissions Gatekeeper. Obtain emissions inventory data necessary to support annual 2002 base and future year modeling. Assure quality of all emissions data received, consistent with QAPP and develop all emissi ons modeling files to support modeling runs for 2002. This gatekeeper will also develop the chemical speciation files and temporal and spatial allocation files necessary to conve rt annual inventories into hourly and daily emissions modeling files, as appropriate. CENRAP will provide the Emissions Gatekeeper emissions for CENRAP states and the Emissions Gatekeeper will also develop all emissions modeling files for non- CENRAP states to support modeling runs for base-year and future-year base case and em issions strategies as defined by CENRAP.

3⁄4 Data Management Gatekeeper : This gatekeeper will maintain the CENRAP Modeling Website including posting modeling input and output files, reports, interpretation of results, and other documents as requested by CE NRAP to support all Phas e II tasks. This includes, for example, the storage of model inputs and outputs for annual (and episodic) runs and the transfer (via fire wire or alte rnative media) of elec tronic files to CENRAP states, other regional planning organizations, EPA, other cont ractors, and stakeholders.

1.5.2 Emissions QA/QC

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical steps in performing air quality modeling studies. Because emissions proc essing is tedious, time consuming and involves complex manipulation of ma ny different types of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected. In the CENRAP 2002 annual Emissions and Air Quality Modeling Study we will expand on the multi-step emissions the QA/QC approach the Modeling Team has developed for WRAP and VISTAS emi ssions and air quality m odeling. This includes the initial emissions QA/QC by the Emissions Gate keeper described above, as well as QA/AC by the emissions modeler during the processing of emissions and then additional QA/QC by the air quality modeler of the processed model ready emissi on files. This multi-step process with three separate groups involved in the QA/QC of the emi ssions is much more likely to catch any errors prior to the air quali ty model simulations.

Emissions QA/QC that would be performed as part of the emissions modeling will include:

Input Screening Error Checking Algorithms : Although the SMOKE emissions model will be used for emissions processing, additional input error checking algorithms will be used to screen the data and identify potential emission input errors. Additionally, EPA has issued a revised stack QA and augmentation procedures memorandum that will be used to identify and augmen t any outlying stacks.

SMOKE error messages : SMOKE provides various cautio nary or warning messages during the emissions processing. We will redi rect the SMOKE output to log files andDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-11 review the log files for serious error message s. An archive of the log files will be maintained so that the error messages can be reviewed at a later date if necessary.

SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of pr ocessed emissions as daily totals according to species, source category and county a nd state boundaries. These summaries will then be compared with summary data prepared fo r the pre-processed emissions, e.g., state and county totals for emissions from the augmented emissions data.

Once the CMAQ-ready emission inputs have been prepared, we will perform additional emissions QA/QC as follows:

Spatial Summary : We will sum the emissions for all layers and for all 24 hours that is used to prepare a PAVE plot showing the da ily total emissions spa tial distribution. For a 20 day simulation this produces approximate ly 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simula tions these plots will be presented as tons per day. The objective of this step is to identify errors in spatial distribution of emissions.

Vertical Profile : For point sources the emissions tota l for each layer will be summed and plotted to show the vertical distribution of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis . The objective of this step is to identify possible errors in vertical distribution of emissions.

Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared th at display the diurna l variation in total hourly emissions. The objective of this step is to identify errors in temporal profiles.

Long Term Temporal Summary : The total domain emissions for each day will be accumulated and displayed as time series plots that show the daily total emissions across

the domain as a function of time. The objective of this step is to identify particular days for which emissions appear to be inconsiste nt with other days fo r no reason (e.g., not a weekend) and compare ag ainst the general trend.

Control Strategy Spatial Displays : Spatial summary plots of the daily total emissions differences between a control strategy and base case emissions scenarios will be generated. These plots can be used to im mediately identify a problem in a control strategy. For example, if a CENRAP state’s SO 2 control strategy is being analyzed and there are changes in emissions for other pollutants or for SO 2 outside of the CENRAP state occurs, problems in emissions processing can be identified prior to the air quality

model simulation.

Once the CMAQ-ready emissions have been s ubjected to the above QA, the CMAQ-to-CAMx emissions processor would be exercised to ge nerate CAMx-ready emissi on inputs. The CAMxready emission inputs would then be subjected to an additional round of QA to assure that

identical emissions are contai ned within the CMAQ-ready and CAMx-ready emission inputs.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-12 1.5.3 Meteorology QA/QC

The CENRAP meteorological modeler will ha ve primary responsibility in the QA/QC of the MM5 meteorological fields (Johnson, 2004). However, the CENRAP Emissions and Air Quality Modeling Team will also perform some QA/QC of the meteorological data to assure that it has transferred correctly, to obtai n an assessment of the quality of the data and to assist in the interpretation of the air quality modeling results.

The CENRAP Meteorological Gatek eeper will perform the following:

3⁄4 Analyze the MM5 data to assure it has been transferred correctly;

3⁄4 Evaluate the MM5 using METSTAT and the surface meteorological network;

3⁄4 Evaluate upper-air MM5 meteorological es timates by comparison them to upper-air observations and satellite images;

3⁄4 Compare the CENRAP 2002 MM5 simulation pe rformed with the performance of the WRAP and VISTAS MM5 modeling; and

3⁄4 Generate the CMAQ-ready and CAMx-read y meteorological inputs using the MCIP2.3 and MM5CAMx processors, respectively.

1.5.4 Air Quality Modeling QA/QC

Key aspects of QA for the CMAQ and CAMx i nput and output data include the following:

• Verification that correct conf iguration and science options are used in compiling and running each model of the in the CMAQ and CAMx modeling system.

• Verification that correct input data se ts are used when running each model.

• Evaluation of CMAQ and CAMx results to ve rify that model output is reasonable and consistent with general expectations.

• Processing of ambient monitoring data for use in the model performance evaluation.

• Evaluation of the CMAQ and CAMx resu lts against concurrent observations.

• Backup and archiving of cr itical model input data.

The most critical element in the QA for CM AQ and CAMx simulati ons is the QA/QC of the meteorological and emissions input files, which is discussed above. The major QA issue specifically associated with the air quality mode l simulations is verification that the correct science options were specified in the model itself and that the corre ct input files were used whenDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-13 running the model. For the CMAQ and CAMx modeling we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and scie nce options are used. We also employ a redundant naming system so that the name of key science options or inputs ar e included in the name of the CMAQ and CAMx executable program, in the name of the CMAQ and CAMx output files, and in the name of the directory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files.

A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the original runs scripts and direct ory structure that were used in performing a model simulation.

We will also perform a post-processing QA of the CMAQ and CAMx output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected pa tterns in the CMAQ and CAMx output files. In the case of model sensitivity studies, the animated gi fs will be prepared as difference plots for the sensitivity case minus the base case. Often, errors in the emi ssions inputs can be discovered by viewing the animated GIFs. Finally, we will produce 24-hour average plots for each day of the CMAQ and CAMx simulations. This provides a su mmary that can be useful for more quickly comparing various model simulations.

1.5.5 Overview of Data Flow and Quality Assurance Process

Figure 1-2 displays an overview of the data flow and quality assurance process in the CENRAP Emissions and Air Quality Modeling study. The CENRAP Modeling Team receives different types of data from vari ous CENRAP participants and cont ractors and other sources that have performed their own Quality Assurance (QA) and Quality Control (QC). Whenever data are received by the Modeling Te am, it is first subjected to a QA check by a Gatekeeper who assess the accuracy and quality of the data and prepares a summary presentation on the QA check. Figure 1-2a lists the Gatekeepers in the Modeling Team for emissions, boundary conditions, meteorological, ozone column (TOMS) and air quality data. If the Gatekeeper identifies any problems with the data, the provider of the data is contacted and asked to correct the data. Once the Gatekeeper has conducted a QA check of the data it is passed on to the modeler who performs their own QA of the data. The data are then used in the modeling and resultant output (e.g., model-read y emissions or meteorological f iles) are then subjected to another round of QA to assure the in tegrity of the data is retained.

Once the model-ready inputs have been deve loped and subjected to QA/QC, the models (e.g., CMAQ and/or CAMx) are applied using Base Case emissions and the modeling results subjected to a model performance evaluation. The model performance evaluation (MPE) represents an extensive QA effort and is one of the most time consuming component of the study. EPA has developed draft guidance for ev aluating regional PM and haze models that includes performance goals (EPA, 2001). In addi tion, the Modeling team has adapted EPA MPE approaches and goals for 1-hour (EPA, 1991) a nd 8-hour (EPA, 1999) ozone modeling. The CENRAP Modeling Team performs the MPE/QA process using as many different tools and analysis as possible in order to fully understand the accuracy and reliability of the modelDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-14 simulation. As seen in Figure 1-2b, the MPE pr ocess in CENRAP is a multi-step process using several different techniques:

UCR Analysis Tools : The University of California at Riverside (UCR) Analysis Tools are run on a Linux platform separately for each monitoring network and for different subregions. Graphics are automatically generated using gnuplot and the software generates the following:

• Tabular statistical measures;

• Time Series Plots; and

• Scatter Plots by allsite_allday, al lday_onesite and allsite_oneday.

ENVIRON Analysis Tools : ENVIRON has developed speci alized evaluation tools to analyze visibility model performance for the Best and Worst 20% visibility days that are used in visibility projections for the Section 308 SIPs/TIPs. ENVIRON has also developed “Soccer Plots” that displays m odel performance across networks, episodes, species, models and sensitivity tests and compare them with performance goals. As part of VISTAS, the Georgia DNR has develo ped “Bugle Plots” th at display model performance as a function of observed concen tration that have been integrated with

ENVIRON’s evaluation tools.

CENRAP Model Evaluation Tool : CENRAP has developed a model evaluation tool that includes the observations in a MySQL data base for ease of manipulation of the

observation database. The CENRAP model evaluation tool can interface with Excel and/or gnuplot to generate the usual set of scatter plots, time series plots, etc.

The evaluation of the CENRAP 2002 CMAQ Base Case simulation will use each of the analysis

tools listed above to take advant age of their different descriptive and complimentary nature. The use of multiple model evaluation tools is also a us eful QA/QC procedure to assure that errors are not introduced in the model evaluation process.

1.5.6 Proposed Model Performance Goals

The issue of model performance goals for PM species is an area of ongoing research and debate. For ozone modeling, EPA has establ ished performance goals for 1-hour ozone normalized bias and gross error of # ± 15% and # 35%, respectively (EPA, 1991). EPA’s draft fine particulate modeling guidance notes that pe rformance goals for ozone should be viewed as upper bounds of model performance, that PM models may not be able to always achieve and we should demand better model performance for PM co mponents that make up a larger fraction of

the PM mass than those that are minor contribu tors (EPA, 2001). Measur ing PM species is not as precise as ozone monitoring. In fact, the differences in measurement techniques for some species likely exceed the more stringent perf ormance goals, such as those for ozone. For example, recent comparisons of the PM spec ies measurements using the IMPROVE and STN measurement technologies found differences of approximately " 20% (SO 4 ) to " 50% (EC) (Solomon et al., 2004).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-15 In the CENRAP 2002 CMAQ Base Case modeli ng, we have adopted three levels of model performance goals for bias and gross error as listed in Table 1-2 th at are used to help evaluate model performance. Note that we ar e not suggesting that thes e performance goals be generally adopted or that they are the most approp riate goals to use. Ra ther, we are just using them to frame and put the PM model performance into context and to facilitate model performance intercomparison across episodes, sp ecies, models and sensitivity tests.

As noted in EPA’s draft PM modeling guida nce, less abundant PM species should have less stringent performance goals. Accordingly, we are also using performance goals that are a continuous function of average observed concentr ations proposed by Dr. James Boylan at the Georgia Department of Natural Resour ces that have the following features:

• Asymptotically approaching proposed performan ce goals or criteria when the mean of the observed concentrations are greater than 2.5 ug/m 3

.

• Approaching 200% error and " 200% bias when the mean of the observed concentrations are extremely small.

Dr. Boylan uses bias/error goals and criteria of ± 30%/50% and ± 60%/75% and plots bias and error as a function of average observed concentr ations. As the mean observed concentration approaches zero the bias performanc e goal and criteria flare out to ± 200% creating a horn shape, hence the name “Bugle Plots”.

Table 1-2. Model performance goals used in Phase I to help interpret modeling results.

Fractional

Bias Fractional

Error Comment

#" 15% # 35% Ozone model performance goal for which PM model performance would be considered good.

#" 30% # 50% A level of model performance that we would hope each PM species could meet

#" 60% # 75% At or above this level of performance indicates fundamental problems with the modeling system.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-16 Figure 1-2a. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality Modeling.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-17 Figure 1-2b. Concluded. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality Modeling.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-18 1.6 CENRAP Annual Modeling Study Participants and Responsibilities

The CENRAP 2002 annual Emissions and Air Qu ality Modeling Study is being carried out by ENVIRON and UCR under cont ract to CENRAP. Table 1-3 lists the contact information of the key participants. Their roles in the work are as follows:

3⁄4 Annette Sharp, CENRAP Techni cal Director is the Admini strative Project Office for the study;

3⁄4 Lee Warden of the Oklahoma Department of Environmental Quality is the CENRAP Project Manager;

3⁄4 Calvin Ku of the Missouri Department of Natural Resources is the CENRAP Alternate Project Manager;

3⁄4 Kathy Pendleton of the Texas Commissi on on Environmental Quality is the CENRAP contact for emissions;

3⁄4 Matthew Johnson of the Iowa Department of Natural Resources is the CENRAP contact for 2002 36 km MM5 meteorology;

3⁄4 Bret Anderson of EPA Region VII is the CENRAP contact for 12 km MM5 meteorology;

3⁄4 T.W. Tesche of Alpine Ge ophysics, LLC is the techni cal advisor to CENRAP on modeling activities;

3⁄4 Ralph Morris of ENVIRON is the Project Manager and Co-Principal Investigator

for the CENRAP Emissions and Air Quality Modeling ENVIRON/UCR Team;

3⁄4 Gail Tonnesen of UCR is the Co-Princ ipal Investigator for the CENRAP Emissions and Air Quality M odeling ENVIRON/UCR Team;

3⁄4 Gerard Mansell of ENVIRON is the Emi ssions Coordinator for the Emissions and Air Quality Modeling E NVIRON/UCR Team; and

3⁄4 Mohammed Omary of UCR is the Chief Emissions Modeler for the Emissions and Air Quality Modeling ENVIRON/UCR Team.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 1.doc 1-19 Table 1-3. Summary of key participants and contact information for the CENRAP 2002 Annual Emissions and Air Quality Modeling Study.

Person & Role Affiliation/Address Contact Information Annette Sharp (Administrative Project Manager) CENRAP 10005 S. Pennsylvania, Ste. C Oklahoma City, OK 73159 (405) 378-7377 asharp@censara.org

Lee Warden (Technical Project Manager) Oklahoma DEQ

707 N. Robinson Oklahoma City, OK 73102 (405) 702-4201 Lee.Warden@deq.state.ok.us

Calvin Ku (Alternate Technical Project Manager) Missouri DNR
P.O. Box 176 Jefferson City, MO 65012 (573) 751-4817 calvin.ku@dnr.mo.gov

Kathy Pendleton (Emissions Contact) Texas CEQ 12100 Park 35 Circle, MC-164 Austin, TX 78753 (512) 239-1936 kpendlet@tceq.state.tx.us

Matthew Johnson (36 km MM5 Meteorology Contact) Iowa DNR
7900 Hickman Rd. Ste. 1 Urbandale, IA 50322 (515) 242-5164 matthew.johnson@dnr.state.ia.us

Bret Anderson (12 km MM5 Meteorology Contact) EPA Region VII
901 North Fifth St. Kansas City, KS 66101 (913) 551-7862 Anderson.bret@epa.gov

T.W. Tesche (Advisor on Modeling) Alpine Geophysics, LLC

3479 Reeves Drive Ft. Wright, KY 41017 (859) 341-7502 (Fax) (859) 341-7508 twt@ac.net Ralph Morris (Project Manager and Co-Principal Investigator) ENVIRON 101 Rowland Way Novato, CA 94945 (415) 899-0708 (Fax) (415) 899-0707 rmorris@environcorp.com

Gail Tonnesen (Co-Principal Investigator) UC Riverside
CE-CERT
1084 Columbia Avenue Riverside, CA 92507 (951) 781-5676 (Fax) (951) 781-5790 tonnesen@cert.ucr.edu

Key ENVIRON Participants Gerry Mansell (Emissions Modeling Coordinator) ENVIRON 101 Rowland Way Novato, CA 94945 (415) 899-0727 gmansell@environcorp.com

Bonyoung Koo ENVIRON (415) 899-0727 bkoo@environcorp.com

Edward Tai ENVIRON (415) 899-0725 etai@environcorp.com

Steven Lau ENVIRON (415) 899-0739 slau@environcorp.com Key UCR CE-CERT Participants Mohammed Omary (Chief Emissions Modeler) UC Riverside
CE-CERT
1084 Columbia Avenue Riverside, CA 92507 (951) 781-5652 omary@cert.ucr.edu

Zion Wang UC Riverside (951) 781-5655 zsw@cert.ucr.edu

Chao-Jung Chien UC Riverside (951) 781-5666 chien@cert.ucr.edu Glen Kaukola UC Riverside (951) 781-5630 glen@cert.ucr.eduDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-1 2.0 MODEL SELECTION

This chapter introduces the regional meteorol ogical, emissions and air quality models to be used in the annual PM/regional haze modeling for CENRAP. The specific science configurations for each modeling system are iden tified and discussed briefly, where necessary. Although the initial configuratio ns of each modeling system have been selected as the culmination of a review of previous regional haze modeling studies pe rformed in the CENRAP region (e.g., Pitchford et al., 2004; Pun, Chen and Seigneur, 2004; Tonnesen and Morris 2004) as

well as elsewhere in the United States (e.g., Morris et al, 2004a; To nnesen et al., 2003; Baker, 2004), there remains the possibility that certain algorithms and parameter settings may still be updated in the establishment of the final annual 2002 base case simulation and model performance testing. The CENRAP Emissions and Air Quality Modeling Team will remain alert to progressive model code improvements, data base refinements, and emergent analysis procedures throughout the entire activity. Notable limitations of the mode ls relevant to their intended purpose in CENRAP are identified. We c onclude with a general overview of the input requirements for each system with more details provided in Section 5.

2.1 Recommended Models

Based on the previous CENRAP, WRAP, VISTAS, MRPO, BRAVO, EPA and other work, CENRAP selected the following models for use in modeling particulate matter (PM) and regional haze in the central states:

3⁄4 MM5: The Pennsylvania State University/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorologica l Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale photochemical, fine particulate, and re gional haze regulatory modeling studies.

3⁄4 SMOKE : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that ge nerates hourly gridded speciated emission inputs of mobile, nonroad, area, point, fi re and biogenic emission sources for photochemical grid models.

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemi cal grid model capable of addressing ozone, particulate matter (PM), visibility a nd acid deposition at regional scale for periods up to one year.

3⁄4 CAMx: ENVIRON’s Comprehensive Air Qua lity Model with Extensions (CAMx) modeling system is also a state-of-sci ence ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, part iculate matter (PM), visibility and acid deposition at regional scale fo r periods up to one year.

Application of the MM5 for the 2002 annual modeling on a 36 km gr id for the continental US is being performed by the Iowa Department of Natural Resources (IDNR; Johnson, 2004). Details of the 2002 36 km MM5 model application and evaluation procedures being carried out byDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-2 IDNR may be found in Johnson (2 004). Application of the MM5 model on a 12 km grid covering the Central States for portions of 2002 is being performed by EPA Region VII. For completeness, in this chapter we describe all four regional modeling sy stems and their intended use in the CENRAP 2002 annual modeling.

2.2 MM5 Mesoscale Prognostic Model

Over the past decade, researchers at the Pennsylvania State University (PSU) and the National Center for Atmospheric Research (PSU /NCAR) have collaborated in the refinement and extension of the PSU Mesoscale Meteorological Model leading to the current version of the system, MM5 (ver 3.6, MPP). Originally deve loped in the 1970s at PSU and first documented by Anthes and Warner (1978), the MM5 modeling syst em maintains its status as a state-of-thescience model through enhancements provide d by a broad user community (e.g., Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 2000). The MM5 modeling system is routinely employed in forecasting project s as well as refined investigations of severe

weather. Utilization of MM5 within air quality applications is also a common practice. In recent years, the MM5 modeling system has been succe ssfully applied in c ontinental scale annual simulations for the years 1996 (Olerud et al., 2000), 2001 (McNally and Tesche, 2003), and 2002 (Johnson, 2004). Due to its ongoing scientific development worldwide, extensive historical applications, broad user community support, pub lic availability, and es tablished performance record compared with other applications-oriented prognostic models, CENRAP selected the MM5 as the preferred meteorological model. This section provides an overview of the MM5 and its data input requirements.

2.2.1 MM5 Overview

The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a threedimensional, limited-area, primitive equation, prognos tic model that has been used widely in regional air quality model applications (S eaman, 2000). The basic model has been under continuous development, improvement, testing and open peer-review for more than 20 years (Anthes and Warner, 1978; Anthes et al., 1987) a nd has been used world-wide by hundreds of scientists for a variety of mesoscale studies, including cyclogenesis, polar lows, cold-air damming, coastal fronts, severe thunderstorms, tropical stor ms, subtropical easterly jets, mesoscale convective complexes, desert mixed la yers, urban-scale modeling, air quality studies, frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and operational mesoscale forecasting.

MM5 is based on the prognostic equations for three-dimens ional wind components (u, v, and w), temperature (T), water vapor mixing ratio (q

v ), and the perturbation pressure (p'). Use of a constant reference-state pressure increases the accuracy of the calculati ons in the vicinity of steep terrain. The model uses an efficient semi -implicit temporal integration scheme and has a nested-grid capability that can use up to ten differ ent domains of arbitrary horizontal and vertical

resolution. The interfaces of the nested grids can be either on e-way or two-way interactive. The model is also capable of using a hydrostatic option, if desired, for co arse-grid applications.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-3 MM5 uses a terrain-following non-dimensiona lized pressure, or "sigma", vertical coordinate similar to that used in many operatio nal and research models. In the non-hydrostatic MM5 (Dudhia, 1993), the sigma levels are define d according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmosphere’ air-quality m odels (e.g., CMAQ and CAMx).

Distinct planetary boundary layer (PBL) para meterizations are available for air-quality applications, both of which represent sub-grid -scale turbulent fluxes of heat, moisture and momentum. These parameterizations employ various surface energy budget equations to estimate ground temperature (T g ), based on the insolation, atmos pheric path length, water vapor, cloud cover and longwave radiation. The su rface physical properties of albedo, roughness length, moisture availability, emissivity and therma l inertia are defined as functions of land-use for numerous categories via a look -up table. One scheme uses a first-order eddy diffusivity

formulation for stable and neutral environments and a modified first-order scheme for unstable regimes. The other uses a pr ognostic equation for the second-or der turbulent ki netic energy, while diagnosing the other key boundary layer terms.

Initial and lateral boundary conditions are specified from mesoscale three-dimensional analyses performed at 12-hour intervals on the outermost grid mesh selected by the user. Additional surface fields are analyzed at three-h our intervals. A Cressman-based technique is used to analyze standard surface and radiosonde observations, using the National Meteorological Center's (NMC) spectral analysis as a first guess. The lateral boundary data are introduced into MM5 using a relaxation technique applied in the outermost five rows and columns of the most coarse grid domain.

A major feature of the MM5 is its use of st ate-of-science methods for Four Dimensional Data Assimilation (FDDA). The theory underlying this approach and details on how it has been applied in a variety of applications throughout the country are described in depth elsewhere (Stauffer and Seaman, 1990, 1991; Seaman et al., 1992, 1997).

Results of detailed performance evaluati ons of the MM5 modeling system in regulatory air quality application studies ha ve been widely reported in th e literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic

models such as RAMS and SAIMM. The MM5 enjoys a far richer a pplication history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scal e air quality application studies since 1995, we have generally found that MM5 model tends to produce somewhat better photochemical model inputs than alternative models. For these and other reasons, MM5 was selected as the meteorological modeling system for the CENRAP study.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-4 2.2.2 MM5 Configuration for CENRAP Annual Modeling

Based on the sensitivity test ing carried out by IDNR, LADC O and others, the MM5 (ver 3.63) configuration used by th e IDNR modelers in the CE NRAP 2002 annual 36 km MM5 modeling consist of the following (s ee Table 2-1 for more details):

3⁄4 Nested 36 km grid with 34 vertical layers;
3⁄4 12 km grid for episodic modeling;
3⁄4 For 12 km runs use two way nesting with no feedback;
3⁄4 Initialization and boundary conditions from Eta analysis fields;

o Eta 3D and surface analysis data (ds609.2);

o Not using NCEP global tropospheric SST data (ds083.0) ;

o Observational enhancement (LITTLE_R)

 NCEP ADP surface obs (ds464.0)

 NCEP ADP upper-air obs (ds353.4)
3⁄4 Pleim-Xiu (P-X) land soil model (LSM);
3⁄4 Pleim-Chang Asymmetric Conv ective Mixing (A CM) PBL model;
3⁄4 Kain-Fritsch 2 cumulus parameterization;
3⁄4 Mixed phase (Reisner 1) cloud microphysics;
3⁄4 Raptid Radiative Transfer Model (RRTM) radiation;
3⁄4 No Shallow Convection (ISHALLO=0);
3⁄4 Standard 3D FDDA analysis nudging; and
3⁄4 No surface nudging.

2.3 SMOKE Emissions Modeling System

2.3.1 SMOKE Overview

The Sparse Matrix Operator Kernel Emi ssions (SMOKE) Emissions Processing System Prototype was originally de veloped at MCNC (Coats, 1995; Houyoux and Vukovich, 1999). As with most ‘emissions models’, SMOKE is principally an emission processing system and not a

true emissions modeling system in which emissions estimates are simulated from ‘first principles’. This means that, with the exception of mobile and biogenic s ources, its purpose is to provide an efficient, modern tool for convertin g emissions inventory data into the formatted emission files required by an ai r quality simulation model. For mobile sources, SMOKE actually simulates emissions rates based on input mob ile-source activity data , emission factors and sometimes output from transportation travel-demand models.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing (HPC) as a pplied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach util ized throughout SMOKE permits both rapid and flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexible because the processing st eps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allo cation have been separated into independentDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-5 operations wherever possible. The results from thes e steps are merged together at a final stage of processing.

SMOKE supports area, mobile, fire and poi nt source emission processing and also includes biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory System, version 3 (BEIS3) (see, http://www.epa.gov/ttn/chie f/software.html#pcbeis ). SMOKE has been available since 1996, and it has been us ed for emissions proce ssing in a number of regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with the support of the U.S. Environm ental Protection Agency (EPA), for use with EPA's Models-3/CMAQ ( http://www.epa.gov/asmdnerl/models3 ). The primary purposes of the SMOKE redesign were support of: (a) emissions processing with user-selected chemical mechanisms and (b) emissions processing for reactivity assessments.

SMOKE contains a number of ma jor features that make it an attractive component of the CENRAP modeling system (Seppanen, 2003). The model supports a variety of input formats from other emissions processing systems and m odels including the Inventory Data Analyzer (IDA), Emissions Modeling System—2003 (EMS- 2003), and the Emissions Preprocessor System 2.x (EPS2.x). It supports both gridded and county total land use scheme for biogenic emissions modeling. Although not necessa ry in CENRAP, SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system.

Recent computational improvements to SMOKE include: (a) enhanced disk space requirements compared with other emissions processing software, (b) run-time memory allocation, eliminating any need to recompile the programs for different inventories, grids, or chemical mechanisms, and (c) updated I/O API libraries. A number of science features have been incorporated into the “curr ent” version of SMOKE (ver. 2.0) including: (a) any chemical mechanism can be used to parti tion pollutants to model species, as long as the appropriate input data are supplied, (b) integrati on with the MOBILE6.2 on-road mobile source emissions model including link based processing, (c) support of plume-in-grid (PiG ) processing, (d) integration of

the BEIS3 emissions factors in SMOKE.

A new version of SMOKE (ver.2.1) has just been (Octobe r 2004) released ( www.cmascenter.org ). As with any new release of a model, care must be taken before unilaterally switching from a working version of the model that is already set up to the new just released version. This is particularly important for emissions models that perform extensive data processing and manipulation in multiple steps. If SMOKE ver.2.1 can be easily substituted for SMOKE ver2.0 (e.g., plug and play) then it ma y be considered for the CENRAP annual modeling.

Notable features of SMOKE from an applications standpoint include: (a) improved control strategy input formats and designs, (b) c ontrol strategies can include changes in the reactivity of emitted pollutants, a useful capability, for example, when a solvent is changed in an industrial process, (c) no third party software is required to run SMOKE, although some input file preparation may require other software , (d) fewer SMOKE programs than the SMOKE prototype because programs were combined wh ere possible to be used for multiple source categories, (e) integration with Models-3 file form ats and settings, (f) impr oved data file formats, (g) support of various air qua lity model emissions input formats (e.g., CMAQ, MAQSIP, UAM-December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-6 IV, UAM-V, REMSAD and CAMx), (h) enhanced quality assurance pre- and post-processing, (h) fully integrated with Models-3, which will provide the SMOKE Tool for SMOKE input file preparation, (i) enhanced trea tment of growth and control f actors, (j) improved emissions reporting and QA capabilities, and (k) improved temporal allocation.

Continuing model development activities with SMOKE now occur out of the University of North Carolina (UNC) Carolina Environmenta l Program (CEP). SMOKE ver2.0 was released on 30 Sept ’03, which is the version we are currently planning to employ for the CENRAP annual modeling. Note that SMOKE ver 2.1 has just been released and if set up in time will be

considered for use in the CENRAP annual mode ling. However, SMOKE ver 2.1 needs to be set up and evaluated prior to any commitment to its use. The SMOKE executables, scripts and databases may be downloaded through the Commun ity Modeling and Analys is (CMAS) center’s Model Clearinghouse at http://www.cmascenter.org/modelclear.shtml . The SMOKE user’s guide is available online at the main SMOKE website, http://www.cep.unc.edu/empd/products/smoke

.

2.3.2 SMOKE Configuration for CENRAP Annual Modeling

As an emissions processing system, SMOKE has far fewer ‘scien ce configuration’ options compared with the MM5 and CMAQ mode ls. For a thorough ch aracterization of the

methods that will be used to exercise th e SMOKE system for the annual 2002 emissions processing, see section 5.2, “Development of Emissions Model Inputs and Resultant

Inventories”. Table 2-1 summarizes the versi on of the SMOKE system to be used and the sources of data to be employed in cons tructing the required m odeling inventories.

2.4 CMAQ Modeling System

2.4.1 CMAQ Overview

For more than a decade, EPA has been de veloping the Models-3 Community Multiscale Air Quality (CMAQ) modeling system with the overarching aim of producing a ‘OneAtmosphere’ air quality modeling system capable of addressing ozone, particulate matter (PM), visibility and acid deposition with in a common platform (Dennis, et al., 1996; Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 2003). The or iginal justificati on for the Models-3 development emerged from the challenges posed by the 1990 Clean Air Act Amendments and EPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing state-of-science representations of atmospheric processes in a high performance computing environment (Ching, et al., 1998). EPA completed the initial stage of development

with Models-3 and released the Community Multi-Scale Air Qu ality model (CMAQ) in mid1999 as the initial operating scie nce model under the Models-3 framework (Byun et al., 1998b). The most recent rendition is CMAQ version 4.4, publicly released October 2004 and is the

version to be used in the CENRAP annual modeling.

CMAQ consists of a core Chemical Transpor t Model (CTM) and several pre-processors including the Meteorological-Chemistry Inte rface Processor (MCIP), initial and boundaryDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-7 conditions processors (ICON and BCON) and a phot olysis rates processor (JPROC). EPA is continuing to improve and develop new modules for the CMAQ model and typically provides a new release each year. In the past EPA has also provides patches for CMAQ as errors are discovered and corrected. More recently EP A has funded the Community Modeling and Analysis Systems (CMAS) cente r to support the coordination, upda te and distribution of the Models-3 system (www.cmascenter.org).

A number of features in CMAQ’s theoretica l formulation and technical implementation

make the model well-suited for annual PM modeli ng. In CMAQ, the modal approach has been adapted to dynamically represent the PM size di stribution using three log-normal modes (2 fine and 1 coarse). Transfer of mass between the aerosol and gas phases is assumed to be in equilibrium and all secondary aerosol (sulfate, nitrat e, SOA) is assumed to be in the fine modes. The thermodynamics of inorganic aerosol co mposition are treated using the ISORROPIA module. Aerosol composition is coupled to mass transfer between the aerosol and gas phases. For aqueous phase chemistry, the RADM model is currently employed. This scheme includes

oxidation of SO 2 to sulfate by ozone, hydrogen peroxide, oxygen catalyzed by metals and radicals. The impact of clouds on the PM size distribution is treated empirically. For wet deposition processes, CMAQ uses the RADM/RPM approach. Particle dry deposition is included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003

to be an reversible semi-volatile scheme wher eby VOCs can be converte d to condensable gases that can then form SOA and then evaporat e back into condensable gases depending on atmospheric conditions.

The newest features implemented in the la test CMAQ (ver 4.4 released October 2004) are

described in the release notes available on the CMAS Center website (www.cmascenter.org). Table 2-3 highlights the major options in CMAQ (v er 4.4) for different processes and compares them with the recently released CAMx (ver 4.10s) model in Table 2-4, which is discussed later in this chapter.

2.4.2 CMAQ Configuration for CENRAP Annual Modeling

In this section we identify the main sc ience options we recommend for the CENRAP annual PM modeling with CMAQ. In particular , we propose to run CMAQ (ver 4.4) with the base configuration as shown in Table 2-3. Th e model would be set up and exercised on the 36 km grid continental US Inter-RPO modeling domain that is also used by WRAP and VISTAS. For the 12 km episodic modeling, CMAQ will be se t up on a 12 km domain cover the central

states whose definition is to be determine us ing one-way nesting. That is, boundary conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ BCON processor. A total of 19 verti cal layers would be implemented, extending up to a region top of 100 mb (approximately 15 km AGL).

The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach and K-theory for vertical diffusion. MM5 meteorological output based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer (PBL) scheme will be used (see Table 2-1) and the recently (October 2004) updated CMAQDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-8 Meteorological-Chemistry Interface Processor (MCIP2.3) would pr ocess the MM5 data using the “pass through” option. The CB4 gas-phase , RADM aqueous-phase, and AERO3/ISORROPIA aerosol chemistry schemes are recommended for use in the initial CMAQ 2002 modeling. Treatment of reversible secondary organic aerosols would be simulated by the SORGAM implementation in CMAQ (ver 4.4).

2.5 CAMx Modeling System

2.5.1 CAMx Overview

The Comprehensive Model with Extensions (CAMx) modeling system is a publicly available ( www.camx.com ) three-dimensional multi-scale phot ochemical/aerosol grid modeling system that is developed and maintained by ENVIRON International Corporation. CAMx was developed with all new code during the late 1990s using modern and modul ar coding practices. This has made the model an ideal platform for the extension to treat a variety of air quality issues including ozone, particulate matter (PM), visibility, acid deposition, and air toxics. The flexible CAMx framework has also made it a convenient and robust host model for the implementation of a variety of mass balance and sensitivity analysis techniques including Process Analysis (IRR and IPR), Decoupled Direct Method (DDM), and the Ozone Source Apportionment Technology (OSAT). Designed originally to address multiscal e ozone issues from the urban- to regionalscale, CAMx has been widely used in recent years by a variety regulatory agencies for 1-hr and

8-hr ozone and PM10 SIP modeli ng studies as well as by several RPOs for regional haze modeling. Key attributes of the CAMx model include the following:

3⁄4 Two-way grid nesting that supports multi-leve ls of fully interactive grid nesting (e.g., 36/12/4/1.33 km);

3⁄4 CB4 or SAPRC99 Chemical Mechanisms;

3⁄4 Two chemical solvers, the CAMx Che mical Mechanism Compiler (CMC) Fast Solver or the highly accurate Implic it Explicit Hybrid (IEH) solver;

3⁄4 Multiple numerical algorithms for horizontal transport including the Piecewise Parabolic Method (PPM), Bott, and Smolarkiewicz advection solvers;

3⁄4 Subgrid-scale Plume-in-Grid (PiG) algorithm to treat the near-source plume dynamics and chemistry from large NOx point source plumes;

3⁄4 Ability to interface with a variety of mete orological models including the MM5 and RAMS prognostic hydrostatic meteorological models and the CALMET diagnostic meteorological model (others also compatible);

3⁄4 The Ozone Source Apportionment Technology (OSAT) ozone apportionment technique that identifies the ozone contribution due to ge ographic source regions and source categories (e.g., mobile, point, biogenic, etc.); andDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-9 3⁄4 The Decoupled Direct Method (DDM) se nsitivity method is implemented for

emissions and IC/BC to obtain first-order sensitivity coefficients for all gas-phase species.

3⁄4 Treatment of particulate matter (PM) us ing an empirical aerosol thermodynamics algorithm.

Culminating extensive model development effort s at ENVIRON and other participating groups, the CAMx (ver 4.10s) code was released in th e autumn of 2004 as a truly “One-Atmosphere’ models that rigorously integrates the gas-phase ozone chemistry with the simulation of primary and secondary fine and course particulate aerosols. This ex tension of CAMx to treat PM

involved the addition of several science modules to represent important physical processes for aerosols. Noteworthy among these are:

3⁄4 Two separate treatments of particulat e matter (PM), Mechanism 4 (M4) “oneatmosphere” treatment uses two size secti ons and science modules comparable to CMAQ (e.g., RADM aqueous-phase chemis try and ISORROPIA equilibrium) and a multi-section “full-science” approach using aerosol modules developed at Carnegie Mellon University (CMU).

3⁄4 Size distribution is represented using th e Multi-component Aerosol Dynamics Model (MADM), which uses a sectional approach to represent the aerosol particle size distribution (Pilinis et al., 2000). MADM treats the effects of condensation/evaporation, coagulation a nd nucleation upon the particle size

distribution.

3⁄4 Inorganic aerosol thermodynamics can be represented using ISORROPIA (Nenes et al, 1998; 1999) equilibrium approach with in MADM, or a fully dynamic or hybrid approach can also be used.

3⁄4 Secondary organic aerosol thermodynamics are represented using the semi-volatile scheme of Strader and co-workers (1999).

3⁄4 Aqueous-phase chemical reactions are mode led either using the RADM module (like CMAQ) or the Variable Size-Resolution M odel (VRSM) of Fahey and Pandis (2001), which automatically determine whether wate r droplets can be repr esented by a single ‘bulk’ droplet-size mode or whether it is n ecessary to use fine and coarse droplet-size

modes to account for the different pH effects on sulfate formation.

CAMx (ver 4.10s) provides two key options to users interested in simulating PM. For CPU-efficient annual PM modeling applications , CAMx may be run using Mechanism 4 (M4) with only two size sections (f ine and coarse) and the effici ent RADM bulk aqueous-phase module (as used in CMAQ). Alternatively, more rigorous aerosol simulations (perhaps for shorter episode) may be addressed using the version that treats N-size sections (N is typically 10) and the rigorous, but computa tionally-extensive CMU multi-sec tion aqueous-phase chemistry

module.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-10 A PM Source Apportionment Technology (PSAT) has recently been added to CAMx and extensively tested and evaluated. It is curr ently being used by the MRPO for their BART analysis and may be useful to CENRAP as well. It is currently available on request and will be publicly released on the CAMx website ( www.CAMx.com ) with the next version of CAMx.

2.5.2 CAMx Configuration for CENRAP Annual Modeling

We recommend exercising CAMx (ver 4.10s) us ing similar science options as CMAQ. However, in some instances, the CMAQ and CA Mx model development teams chose different options for characterizing physical and chemical processes, or for implementing the governing equations on modern parallel computers. In these cases, we will utilize the science configurations embodied in the current release of CAMx.

Table 2-4 lists the main CAMx configura tions recommended for the CENRAP annual modeling. The latest version of CAMx (ver 4.10s or newer) will be employed and the model will be set up and exercised on the same 36 and 12 km grids as CMAQ. However, for the 12 km grid episodic simulations CAMx would be run using two-way grid nesting instead of the oneway nesting used by CMAQ. The base configurat ion of CAMx would use 19 vertical layers up to 100mb (~15 km AGL) that ex actly match those used by CMAQ. The PPM advection solver would be used along with the spatially varyin g (Smagorinsky) horizontal diffusion approach.

Vertical diffusion in CAMx would be modeled by K-theory. The MM5 simulation using the Pleim-Xiu Land-Surface Model (LSM) and the ACM Planetary Boundary Layer (PBL) scheme would be used in the CAMx base configuration using the MM5CAMx processor that is similar to the CMAQ MCIP2.3 “pass through” option of the MM5 data invoked. CAMx would be exercised with the CB4 gas-phase, RADM aqueous-phase, and CMU/ISORROPIA aerosol chemistry schemes. The SOAP secondary organi c aerosol scheme would be used for the base configuration in CAMx.

Note that it may be desirable to exercise CA Mx using its “full-science” configuration for selected periods to investigative scientific issues that may be of interest to CENRAP such as:

3⁄4 The full sectional approach could be used to determine whether allowing secondary PM to grow into the coarse mode affects the model estimates;

3⁄4 Model could be exercised with chemical active Sea Salt emissions, this could be important for fine particulate and visibili ty at key coastal sites in the CENRAP domain (e.g., Breton Wilderness Area), espe cially when looking at clean days or natural background; and

3⁄4 The full sectional aqueous-phase chemistr y module may be important for sulfate

formation.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-11 2.6 Advantages in Operating Multiple Models

EPA’s guidance on model selection for PM 2.5 SIPs and Regional Haze “reasonable progress demonstrations” do not identify a pref erred photochemical grid modeling system, recognizing that at present there is “no singl e model which has been extensively tested and shown to be clearly superior or easier to us e than several alternat ives” (EPA, 2001, pg. 169.) The agency recommends that models used for PM 2.5 SIPS or RH reasonable progress requirements should meet the requirements for alternative models. The CMAQ, CMAQMADRID, CMAQ-AIM and CAMx modeling sy stems all meet these requirements.

We believe that there is potentially significant value in including multiple modeling systems in the CENRAP annual modeling analysis . Our testing and comparisons of the CMAQ and CAMx models for WRAP, VISTAS and other recent PM 2.5 /regional haze applications demonstrates that the models are capable of producing results of comparable accuracy and reliability and having results from both models has many benefits, such as:

3⁄4 Diagnosis: To serve as an efficient diagnosti c tool addressing model performance issues that may arise in th e establishment of the mode l annual 2002 and episodic base cases. CMAQ and CAMx both include Proce ss Analysis that can help diagnose model performance. CAMx’s suite of dia gnostic probing tools plus it’s flexi-nesting algorithms make it an attractive tool fo r assisting in the diagnosis of model performance;

3⁄4 Model Evaluation Corroboration: To provide corroboratio n of the base case model performance evaluation exercises to be pe rformed with the two models and help identify any compensatory errors in the modeling systems;

3⁄4 Emissions Control Response Corroboration: To provide corroboration of the response of a modeling system to generic a nd specific future year emissions changes on modeled gas-phase and particulate aerosol concentrations and resultant regional haze impacts;

3⁄4 Quantification of Model Uncertainty: To provide one estimate of the range of uncertainty in the annual and episodic base case simulati ons, and in the estimate of

PM 2.5 and visibility reductions associated with future emissions change scenarios;

3⁄4 Alternative Science: CAMx and CMAQ contain alternative science algorithms that may elucidate model performance issues with one model or the other or provide an alternative approach for simulating aerosols.

3⁄4 Consistency with Other RPOs: The Midwest RPO (MRPO) may end up using CAMx for their regional haze modeli ng, whereas the Western (WRAP) and Southeastern (VISTAS) RPOs are currently using CMAQ. As sources in the MRPO, WRAP and VISTAS likely influence visibility at Class I areas in CENRAP and vice versa, having results from a both models would be useful for reconciling any

differences.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-12 3⁄4 Backup Contingency: To provide a ‘backstop’ model in the event that unforeseen

difficulties with one model occur.

The benefits of employing a pair of compliment ary state-of-science air quality models are thus quite significant and well worth the extra effo rt. Especially considering that the same MM5 output (through MCIP2.3 and MM5CAMx) a nd SMOKE output and CMAQ IC/BC files (through CMAQ-to-CAMx emissions and IC/BC conve rters) can be used to operate CMAQ and CAMx without performing any additional me teorological or emissions modeling.

2.7 Model Limitations

All mathematical models possess inherent limitations owing to the necessary simplifications and approximations made in fo rmulating the governing equations, implementing them for numerical solution on fast computers, and in supplying them with input data sets and parameters that are themselves approximations of the full state of the atmosphere and emissions processes. Below, we list some of the more important limitations of the various modeling systems to be employed by CENRAP.

2.7.1 MM5

MM5 many different physics options that can drastically alter the predicted meteorological fields. MM5 meteor ological estimates are particularly sensitive to the choice of Land Soil Model (LSM) and Planetary Boundary Layer (PBL) model. There are numerous

limitations in the MM5 with the LSM and PBL trea tment being some of the most important. The MM5 Pleim-Xiu/ACM LSM/PBL physic options sele cted by CENRAP frequently predicts very low PBL heights that can appear as “holes” in the spatial distri bution of PBL heights that don’t appear physically realistic and may affect ai r quality modeling. Although the 2002 annual MM5 model performance in the CENRAP region mos tly met performance benchmarks, there were some concerns raised and, in particular, the overstatement of precipitation amounts has been raised as a major concern (Baker, 2004b). Concer ns have also been raised concerning the MM5 performance over the western th ird of the US (Johnson, 2004). The many limitations in MM5 have spawned the development of a new meteorol ogical model, the Weather Research Forecast (WRF) model. However, the WR F model will not be used or te sted in the CENRAP modeling.

2.7.2 SMOKE

In WRAP and VISTAS a number of undocumen ted features of SMOKE necessitated reruns of the emissions processing software to overcome errors and/or ambiguities in source documentation and QA reporting. It is unclear whether similar conditions will be encountered with the SMOKE ver2.0 (or ver2.1) release to be used in CENRAP. Features are continuing to be developed in the SMOKE emissions model. As it is not as mature as some other emission models (e.g., EMS, EPS, etc.) it does not include as many features. We will keep abreast of SMOKE development activities to identify new features that will assist in the CENRAP emissions modeling.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-13 2.7.3 CMAQ

Like all air quality models, a major limitatio n of CMAQ is the emissions, meteorological and IC/BC inputs. Key science limitations in the model itself include the nitrate formation chemistry and the secondary organic aeroso l (SOA) module. The CENRAP preliminary modeling found the CMAQ nitrate performance suspect with winter overestimations and summer underestimations (Pun, Chen and Seigneur, 200 4; Tonnesen and Morris, 2004). The CENRAP preliminary modeling also found the performance fo r Organic Carbon (OC) to be less than ideal; much of the OC performance problems is due to deficiencies in the CMAQ SOA module that fails to account for several known processes im portant to SOA (e.g., polymerization). Other

science limitations in the curren t version of CMAQ include inadequa te treatment of sea salt and the assumption that all secondary PM is in the fine mode. Lack of any two-way grid nesting limits the ability of the model to properly resolv e point source plumes or urban photochemistry

and their effects on more distan t Class I areas without a prohib itive number of grid cells. Another limitation of CMAQ is the computational re quirements, including the need of excessive disk space.

2.7.4 CAMx

The model inputs are also a major limitati on in CAMx and CAMx shares many of the formulation deficiencies of CMAQ. Nitrate formation chemistry is also a major limitation, as evident by the preliminary CENRAP modeling. Although CAMx has some more advanced science modules available, su ch as the VSRM aqueous-phase and MADM dynamic aerosol modules, for annual modeling these modules are too computationally expensive to use.

2.8 Model Input Requirements

Each of the CENRAP modeling system components have significant data base requirements. These data needs fall into two categories: those requi red for model setup and operation, and those required for model evaluation testing. Below, we identify the main input data base requirements for the meteorologi cal, emissions, and air quality models.

2.8.1 MM5

The databases required to set up, exercise, and evaluate the MM5 model for the annual 2002 episode consist of various fi xed and variable inputs.

3⁄4 Topography: High resolution (e.g., 30 sec to 5 mi n) topographic information derived from the Geophysical Data Center global da ta sets from the National Center for Atmospheric Research (NCAR) terrain databa ses are available for prescribing terrain elevations throughout the 36 km and 12 km grid domain.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-14 3⁄4 Vegetation Type and Land Use: Vegetation type and land use information on the 36 km grid may be developed using the NC AR/PSU 10 min. (~18.5 km) databases while for the 12 km grids, the United States Geologi cal Survey (USGS) data are available.

3⁄4 Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from operationally analyzed fields de rived from the National Center for Environmental Predictions (NCEP) ETA (40 km resolution) following the procedures outlined by Stauffer and Seaman (1990). Thes e 3-hr synoptic-scale initialization data the horizontal wind components (u and v), temperature (T), and relative humidity (RH) at the standard pressure levels, plus sea-level pressu re (SLP) and ground temperature (T g ). Here, T g represents surface temperature over land and sea-surface
temperature over water.

3⁄4 Water Temperature: Water temperatures required on both 36 km and 12 km grids can be derived from the ETA skin temperatur e variable. These temperatures are bilinearly interpolated to each model domain and, where necessary, filtered to smooth out irregularities.

3⁄4 Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and precipitation directly through explicit resolv ed-scale and parameterized sub-grid scale processes, the model does not require precip itation or cloud input. The potential for precipitation and cloud formation enters through the thermodynamic and cloud processes formulations in the model. Th e only precipitation-rela ted input required is the initial mixing ratio field that is developed from the NWS and NMC data sets previously discussed.

3⁄4 Multi-Scale FDDA : The standard “multi-scale” data assimilation strategy to be used on the 36 km and 12 km grids will objectiv ely analyzed three-dimensional fields produced every 3-hr from the NWS rawins onde wind, temperature, and mixing ratio data, and similar analyses generated every three hours from the available NWS

surface data.

2.8.2 SMOKE

The databases required to set up and operate SMOKE for the 2002 annual simulation are as

follows:

• Area Source emissions in IDA format

• NonRoad source emissions in IDA format

• Stationary Point Source emissions in IDA format

• CEM emissions, day specific for 2002

• Wildfire, prescribed burns and agricultural burning emissions, day specific for 2002

• On-road Motor Vehicle VMT and activity data

• MOBILE6.2 input parametersDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 2.doc 2-15 Also required for annual modeling are data files specific for:

• Temporal allocation

• Spatial allocation

• Speciation

Chapter 5 discusses the SMOKE data input requirements and data sources in detail.

2.8.3 CMAQ

As described in more detail in Chapter 5, th e CMAQ Chemical Transport Model (CTM) requires the following inputs:

3⁄4 Three-dimensional hourly mete orological fields that will be generated by the CMAQ MCIP2.3 processing of the MM5 output;
3⁄4 Three-dimensional hourly emi ssions generated by SMOKE;
3⁄4 Initial conditions and boundary conditions (IC/BC);
3⁄4 Topographic information;
3⁄4 Land use categories; and
3⁄4 Photolysis rates generated by the CMAQ JPROC processor.

2.8.4 CAMx

CAMx model inputs include (see Chapter 5):

3⁄4 Three-dimensional hourly meteorological fi elds generated by MM5CAMx processing of the MM5 output;
3⁄4 Two-dimensional low-level (surface layer) emissions and elevated point source emissions generated by the CMAQ- to-CAMx emissions processor.
3⁄4 IC/BC inputs generated by the CM AQ-to-CAMx IC/BC processors;
3⁄4 Photolysis rates look up table;
3⁄4 Albedo/Haze/Ozone Column input file;
3⁄4 Land use and topographyDecember 2004H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc2-16Table 2-1.MM5 meteorological model configuration for CENRAP 2002 Annual modeling (Johnson, 2004).Science OptionsConfigurationDetails/CommentsModel CodeMM5 version 3.63 Grell et al., 1994Horizontal Grid Mesh36 km (12 km for episodes)36 km grid165 x 129 cells12 km gridTBDVertical Grid Mesh34 layers Vertically varying; sigma pressure coord.Grid InteractionNo Feedback IFEED=0InitializationEta first guess fields/LittleRBoundary ConditionsEta first guess fields/LittleRMicrophysicsReisner I Mixed Ice Look up tableCumulus SchemeKain-Fritsch 2 On 36 and 12 km GridsPlanetary Boundary LayerACM PBLRadiationRRTMVegetation DataUSGS 24 Category SchemeLand Surface ModelPleim-Xiu Land Surface Model (LSM)Shallow ConvectionNoneSea Surface TemperatureEta Skin Spatially varyingThermal RoughnessGarrattSnow Cover EffectsNone4D Data AssimilationAnalysis Nudging on 36 and 12Surface NudgingNoneIntegration Time Step90 secondsSimulation PeriodsAnnual 2002 for 36 km 12 km episodic onlyPlatformLinux Cluster Done at IDNRDecember 2004H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc2-17Table 2-2.SMOKE emissions model configuration for CENRAP Annual modeling.Emissions ComponentConfigurationDetails/CommentsEmissions ModelSMOKE ver 2.0 SMOKE ver 2.1 may be used if operational in timeHorizontal Grid Mesh36/12 km36 km grid148 x 112 cells RPO Unified Grid12 km gridTBDArea Source EmissionsCENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP statesOther States: '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions: Same as used in WRAPOn-Road Mobile SourcesCENRAP Domain: CENRAP VMT data Updated '02 developed by CENRAP states (Reid et al., 2004)Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions: 2000 Canadian Inventory?Point SourcesCENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states and stakeholdersOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions: 2000 Canadian Inventory?Off-Road Mobile SourcesCENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions: 2000 Canadian Inventory?Biogenic SourcesSMOKE BEIS-3 BELD3 vegetative databaseTemporal AdjustmentsSeasonal, day, hour Based on latest collected information and CEM-based profilesChemical SpeciationRevised CB4 Chemical Speciation Updated January 2004GriddingRevised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/Growth and ControlsCENRAP Contractor TBD Base Cases defined by CENRAP WorkgroupsQuality AssuranceQA Tools in SMOKE 2.0 Follow QAPPSimulation PeriodsAnnual 2002 for 36 km Episodic periods at 12 km TBDDecember 2004H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc2-18Table 2-3.CMAQ air quality model configuration for CENRAP annual modeling.Science OptionsConfigurationDetails/CommentsModel CodeCMAQ (ver 4.4) Available at: www.cmascenter.orgHorizontal Grid Mesh36 km annual/12 km episodic 36 km covering continental U.S; 12 km TBD36 km grid148 x 112 cells RPO National Grid12 km gridTBDVertical Grid Mesh19 Layers First 17 layers sync'd w/ MM5Grid InteractionOne-way nestingInitial Conditions~15 days full spin-up Separately run 4 quarters of 2002Boundary Conditions2002 GEOS-CHEM day-specific 2002 GEOS-CHEM day specific 3-hourlyEmissionsBaseline Emissions ProcessingSee SMOKE (Ver 2.0) model configurationMM5 Meteorology input to SMOKE, CMAQSub-grid-scale PlumesNo Plume-in-Grid (PinG) PinG for PM not available yetChemistryGas Phase ChemistryCBM-IVAerosol ChemistryAE3/ISORROPIASecondary Organic AerosolsSecondary Organic Aerosol Model(SORGAM) Schellet al., (2001)Cloud ChemistryRADM-type aqueous chemistry Includes subgrid cloud processesN2O5 Reaction Probability0.01 – 0.001Meteorological ProcessorMCIP ver 2.3Includes dry deposition and snow coverupdatesHorizontal TransportEddy Diffusivity SchemeK-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approachVertical TransportEddy Diffusivity SchemeK-theoryDiffusivity Lower LimitKzmin = 1.0 Run MCIP2, 3 with Kz-min.=0.1Deposition SchemeM3dryDirectly linked to Pleim-Xiu Land Surface ModelparametersNumericsGas Phase Chemistry SolverEuler Backward Iterative (EBI) solverHorizontal Advection SchemePiecewise Parabolic Method (PPM) schemeSimulation PeriodsAnnual 2002 for 36 km Episodic periods at 12 km TBDIntegration Time StepTBD 15 minute coupling time stepDecember 2004H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc2-19Table 2-4CAMx air quality model configuration for CENRAPannual modeling.Science OptionsConfigurationDetailsModel CodeCAMx (ver 4.10s) Available at: www.camx.comHorizontal Grid Mesh36 km annual/12 km episodic 36 km covering continental U.S; 12 km TBD36 km grid148 x 112 cells12 km gridTBDVertical Grid Mesh19 Layers 17 Layers sync'd w/ MM5Grid InteractionTwo-way nestingInitial Conditions~15 days full spin-up Separately run 4 quarters of 2002Boundary Conditions2002 GEOS-CHEM day-specific 2002 GEOS-CHEM day specific 3-hourlyEmissionsBaseline Emissions ProcessingSee SMOKE (Ver 2.0) model configuraton MM5Meteorology input to SMOKE, CAMxSub-grid-scale PlumesNo Plume-in-Grid (PinG) PinG for PM not available yetChemistryGas Phase ChemistryCBM-IV with Isoprene updatesAerosol ChemistryISORROPIA equilibrium Dynamic and hybrid also availableSecondary Organic AerosolsSOAPCloud ChemistryRADM-type aqueous chemistryAlternative is CMU multi-section aqueouschemistryN2O5 Reaction ProbabilityNoneMeteorological ProcessorMM5CAMxHorizontal TransportEddy Diffusivity SchemeK-theory with Kh grid size dependenceVertical TransportEddy Diffusivity SchemeK-TheoryDiffusivity Lower LimitKzmin = 1.0 (or other) Run MM5CAMx with Kz-min=1.0Planetary Boundary LayerNo PatchDeposition SchemeWeselyNumericsGas Phase Chemistry SolverCMC Fast SolverHorizontal Advection SchemePiecewise Parabolic Method (PPM) schemeSimulation PeriodsAnnual 2002 at 36 km Episodic periods at 12 km TBDIntegration Time StepWind speed dependentDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 3.doc 3-1 3.0 EPISODE SELECTION

This chapter provides a brief overview of reas ons for the selection of the 2002 annual period for the CENRAP regional haze modeling.

3.1 Overview of EPA Guidance

EPA’s current draft guidance on PM2.5/Regi onal Haze modeling (EPA, 2001) identifies specific goals to consider when selecting one or more episodes for use in demonstrating reasonable progress in attaining the regional haze NAAQS. Howe ver, since there is much in common with the goals for selecting episodes for annual and episodi c PM2.5 attainment demonstrations as well as regional haze, EPA’s guidance addresses all three in a common document. More recently, EPA has published an updated summary of PM2.5 and Regional Haze Modeling Guidance (Timin, 2002) that serves, in some respects, as in interim placeholder until the final guidance is issued as part of the PM2.5/regional haze NAAQS implementation process that is expected during 2004.

EPA recommends that episode selection derive from three principal criteria:

3⁄4 A variety of meteorological conditions shoul d be covered, this includes the types of meteorological conditions that produce the Worst 20% and Best 20% visibility days at Class I areas in the CENRAP States;

3⁄4 To the extent possible, the modeling data base should include days for which extensive data bases (i.e. beyond routine aerometric and emissions monitoring) are available; and

3⁄4 Sufficient days should be available such that relative reduction factors (RRFs) can be based on several (i.e., > 15) days.

For regional haze modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year (EPA, 2001, pg. 188). Moreover, the required RRF values should be based on model results averaged over the 20% worst and 20% best visibility days

determined for each Class I are based on mon itoring data from the 2000 – 2004 baseline period. More recent EPA guidance (Timin, 2002) suggests that states should model at least 10 worst and 10 best visibility days at each Class 1 area. EPA also lists several ‘other considerations’ to bear in mind when choosing potential PM/regional haze epis odes including: (a) choose periods which have already been modeled, (b) choose periods which are drawn from the years upon which the current design values are based, (c) include weekend da ys among those chosen, and (d) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment or Class I areas as possible.

3.2 Selection of CY 2002 For CENRAP Annual Modeling

Due to limited available resources CENRAP, at least initially, is limited to modeling a single Calendar Year (CY). The RHR uses the five-year baseline of 2000-2004 as the starting point for

projecting future-year visibility. Thus, the modeli ng year should be selected from this five-yearDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 3.doc 3-2 baseline period. The 2002 calendar year was selected for the following reasons:

3⁄4 Based on available information, appears to be a fairly typical year in terms of meteorology for the future-year baseline of 2000-2004;

3⁄4 2003 and 2004 appeared to be colder and wetter than typical in the eastern US;

3⁄4 The enhanced IMPROVE and IMPROVE Pr otocol and Supersites PM monitoring data were fully operational by 2002; and

3⁄4 2002 is being considered by the other RPOs.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-1 4.0 MODELING DOMAINS A ND DATA AVAILABILITY

This chapter summarizes the model domain definitions for the CENRAP annual modeling including the model dom ain, resolution, map projections and nesting schemes for high

resolution sub-domains.

4.1 Horizontal Modeling Domain

The 36 km continental US horizontal domain fo r each of the models will be identical to those used by WRAP and VISTAS. The CMAQ and CAMx air quality modeling domain is

nested in the MM5 domain. The selection of the MM5 domain is described by Johnson (2004). Figure 4-1 shows the MM5 horizontal domain as the outer most, blue grid. Also shown in Figure 4-1 is the CMAQ and CAMx 36 km domain nested in the MM5 domain. To achieve finer spatial

resolution in the CENRAP states we will also use a nested high resolution grid with a 12 km grid

resolution whose definition will be determined later.

Both MM5 and CMAQ will employ the Region al Planning Organization (RPO) unified grid definition for the 36 km continental domain for the CENRAP annual modeling. The RPO unified grid consists of a Lambert-Confor mal map projection using the map projections parameters listed in Table 4-1.

Table 4-1. RPO Unified grid definition.

PARAMETER VALUE projection Lambert-Conformal alpha 33 degrees beta 45 degrees x center 97 degrees y center 40 degrees

The MM5 36 km grid include 164 cells in the east-west dimens ion and by 128 cells in the north-south dimension. The CMAQ/CAMx 36 km grid include 148 cells in the east-west

dimension and 112 cells in the north-south dimensi on. Because the MM5 model is also nested in the Eta model, there is a possibi lity of boundary effects near th e MM5 boundary that occur as the Eta meteorological variables are being simulate d by MM5 and must come into dynamic balance with MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid cells around each boundary of the CMAQ/CAMx 36 km do main. This is designed to eliminate any errors in the meteorology from boundary effects in the MM5 simulation at the

interface of the MM5 and Eta models. The buffer region used here exceeds the EPA suggestion of at least 5 grid cell buffer at each boundary.

Table 4-2 lists the number of rows and columns and the definition of the X and Y origin (i.e., the southwest corner) fo r the 36 km for both MM5 and CMAQ/CAMx. Note that the CMAQ/CAMx grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are reversed. In Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells while “cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Fi nally, we note that the grid definition for the SMOKE emissions model, CMAQ Meteorology Chemistry Interface Processor (MCIP), CMAQ Chemical Transport Model (CCTM), MM5CAMx processor and CAMx model are identical.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-2 Table 4-2. Grid definitions for MM5 and CMAQ/CAMx.

MODEL COLUMNS DOT(CROSS) ROWS DOT(CROSS) XORIGIN YORIGIN

MM5 36km 129 (128) 165 (164) -2952000 -2304000

CMAQ/CAMx 36km 149 (148) 113 (112) -2736000 -2088000

4.2 Vertical Modeling Domain

The CMAQ and CAMx vertical structure is prim arily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrai n following coordinate system defined by pressure, using 34 layers that extend from th e surface to the 100 mb. Ta ble 4-3 list the layer definitions for both MM5 and for CMAQ and CAMx. We will use the exactly same vertical layer structure in CAMx as in CMAQ. A laye r averaging scheme is adopted for CMAQ/CAMx to reduce the computational cost of the CMAQ and CAMx simulations. The effects of layer averaging were evaluated by WRAP and VISTAS and found to have a relatively minor effect on the model performance metrics when both the 34 layer and a 19 layer CMAQ model simulations were compared to ambient monitoring data (Morris et al., 2004a).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-3 Figure 4-1 . Nesting of 36-km CMAQ/CAMx grid in the MM5 36-km grid.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-4 Table 4-3. Vertical layer definition for MM5 simulations (left most columns), and approach for reducing CMAQ/CAMx layers by collapsing multiple MM5 layers (right columns).

MM5 CMAQ 19L Layer Sigma Pres(mb) Height(m Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m )

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-5 4.3 Data Availability

The CMAQ and CAMx modeling systems requir e emissions, meteorological, initial and boundary condition (IC/BC) and ozone colu mn data for defining the inputs.

4.3.1 Emissions Data

The base year emissions inventory for CE NRAP annual modeling and CENRAP states

will be founded on revised 2002 emissions developed by CENRAP emission inventory contractor (Strait, Roe and Vukovich, 2004). Emissions for th e non-CENRAP states, Mexican and Canadian will be based on the latest available inventories that are being used by WRAP and VISTAS. For purposes of air quality model va lidation, actual 2002 calendar year emissions for Electrical Generating Units (EGU) and fire activity will be used as available. Whereas for strategy and future year emission runs, “typical year” emissions for these categories will be processed for 2002 and the future years. A 2002 CE NRAP state emission inventory is expected by the end of November 2004 and will be us ed in the model performance evaluation

As necessary, all emissions will be conve rted to Inventory Da ta Analyzer (IDA) formatted versions and the data will be proc essed for air quality modeling using Version 2.0 (or Version 2.1) of the Sparse Matrix Operating Kernel Emissions (SMOKE ) model. Included in these runs will be the temporal and speciation profiles and cross-re ference data provided with the version 2.0 (or 2.1) release of the model augmented with any recommended and approved emission profile data provided by the emissions inventory contr actor, obtained from EPA, or prepared by the Study Team pr ior to initial emi ssions modeling. Spatial allocation of the emissions will be based on prof iles and spatial allocation factor s developed for the National RPO grid. Additional description of em issions processing is described in Chapter 5 and emissions QA is described in Chapter 6.

4.3.2 Air Quality

Data from ambient monitoring networks for both gas and aerosol species are used in the model performance evaluation. Table 4-4 summariz es ambient monitoring networks. Data have been compiled for all networks except the PAMS and PM Supersites. Figure 4-2 displays the

locations of monitori ng sites in and near the CENRAP States.December 2004H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 4.doc4-6Table 4-4.Overview of ambient data monitoring networks.Monitoring NetworkChemical Species MeasuredSampling PeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24hr averagehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (seespecies mappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10,PbTypically hourlyaveragehttp://www.epa.gov/air/data/Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)(Southeastern US only)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (SO4, NO3, NH4, elements);Hourly PM2.5 (Mass, SO4, NO3,NH4, EC, TC); Hourly gases (O3,NO, NO2, NOy, HNO3, SO2, CO)Hourly or 24-hour average,depending onparameter.Electric Power Research Institute (EPRI),Southern Company, and other companies.http://www.atmospheric-research.comEPA Particulate Matter Supersites(Includes St. Louis in the CENRAPregion)Speciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htmDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-7 Figure 4-2a. Locations of IMPROVE, CASTNet, SEARCH, STN and NADP monitoring sites in
and near the CENRAP States. -1000 -750 -500 -250 0 250 500 750 1000 -1500 -1250 -1000

-750 -500 -250 0 250 500 750 1000 1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQSDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-8 Figure 4-2b. Locations of AQS monitoring sites in and near the CENRAP States. -1000 -750 -500 -250 0 250 500 750 1000 -1500 -1250 -1000

-750 -500 -250 0 250 500 750 1000 1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQSDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 4.doc 4-9 4.3.3 Ozone Column Data

Additional data used in the air qualit y modeling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data is availabl e for 24-hour average and is obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . The TOMS data is used in the CMAQ (JPROC) and CAMx (TUV) radiation model to calculate photolysis rates.

4.3.4 Meteorological Data

Meteorological data are being generated us ing the MM5 prognostic meteorological model by the Iowa Department of Natural Resources (IDNR for the 2002 36 km continental US and by EPA Region VII for the 12 km episodic modelin g). IDNR is operatin g the MM5 at 5-day increments for 2002 on the 36 km a ~15 day sp in up period for the end of December 2001.

Details on the CENRAP 2002 36 km MM5 mode ling can be found in Johnson (2004). The Region VII 12 km episodic modeling is ongoing.

4.3.5 Initial and Boundary Conditions Data

The CMAQ default Initial Concentrations (ICs) will be used for both CMAQ and CAMx along with a ~15 day spin up period to elimin ate any significant influence of the ICs.

The CMAQ and CAMx Boundary Conditions (BCs ) will be based on results from a 2002 GEOS-CHEM global clime model simulation. The 2002 GEOS-CHEM model output has been processed to define day-specific high time re solved (i.e., 3-hourly) CMAQ and CAMx BCs for

2002.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-1 5.0 MODEL INPUT PREPARATION PROCEDURES

In this section we describe the procedures to be used to develop the CMAQ and CAMx model inputs for the CENRAP 2002 annual mode ling on the 36 km continental US grid and potentially additional ~4 week episodic simulations on a 12 km grid covering the central states. The development of the CMAQ and CAMx meteor ological and emissions inputs are discussed first followed by the science options to be us ed by CMAQ and CAMx. The procedures for developing the initial and boundary conditions and photolysis rate s inputs are then discussed along with the model application procedures.

5.1 Meteorological Inputs to Emi ssions and Air Quality Models

The emissions and air quality models require certain meteorological input data including wind fields, estimates of turbulent eddy disper sion, humidity, temperature, clouds, and actinic flux. Spatially gridded and hourly varying meteorological data are needed to estimate biogenic, mobile source emissions, and plume-rise for large, elevated point sources. Meteorological data are needed to drive chemical transport models for solving atmospheric diffusion and chemistry equations for model species. Because observed da ta are not available fo r the full gridded model domain, numerical meteorol ogical models are used to provide these inputs.

The National Center for Atmospheric Resear ch (NCAR)/Pennsylvania State University (PSU) Fifth-Generation Mesoscal e Model (MM5) (v3.63) is being used by the Iowa Department of Natural Resources (IDNR) fo r CENRAP to simulate meteor ology at a 36-km resolution for calendar year 2002 (plus the end of 2001) over the entire continental United States and including portions of Canada, Mexico, and the Atlantic a nd Pacific Oceans. MM5 is also being applied over the central states using a 12 km resolution grid for portions of the 2002 calendar year. The MM5 is a three-dimensional prognostic meteor ological model that is used not only for meteorology studies but also for ai r quality studies. Some of th e physics used in the simulation include nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and mixing ratio; explicit treatment of moisture; cumu lus cloud parameterization; vertical mixing of momentum in the mixed layer; PBL process para meterization; atmospheri c radiation; sea ice treatment; and snow cover (see Chapter 2 for more details).

After the MM5 simulation is completed, the MM5 output files are transferred to the CENRAP Emissions and Air Quality Modeling Team and analyzed by the Meteorological Gatekeeper. The Meteorological Gatekeeper performs two main roles. To provide an independent evaluation of the 2002 MM5 simulation that also serves to determine whether the MM5 data have been transferred correctly fr om the CENRAP. And to process the 2002 MM5 output using Version 2.3 of the Models-3 CM AQ Meteorological-Chemical Interface Processor (MCIP) and the MM5CAMx processor to generate meteorological fields that will be used for emissions processing and air quality simulations.

5.1.1 MCIP Reformatting Methodology

The Models-3 Community Multiscale Air Qual ity (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, andDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-2 particles) air quality problems. But befo re running the CMAQ Chemical Transport Model (CCTM), the MM5 generated mete orological data must be pr e-processed and converted to

Models-3 consistent data stru ctures. MCIP version 2.3 will be used to preprocess the MM5 meteorological output. The “pass through” option in MCIP will be used in the CENRAP annual modeling. One of MCIP’s functions is to transl ate meteorological parameters from the output of the Pennsylvania State University/National Ce nter for Atmospheric Research (PSU/NCAR) Mesoscale Modeling System Generation 5 (MM5) to the Models-3 input/output applications program interface (I/O API format) which is required for operation of Models-3 CMAQ processors. Some other necessary parameters not available from the meteorological model are estimated with appropriate diagnostic algorithms in the program. The key functions of MCIP

include:

1. Reading in meteorological model output files 2. Extraction of meteorological data for CTM window domain 3. Interpolation of coarse meteorol ogical model output for finer grid 4. Collapsing of meteorological pr ofile data if coarser vertical resolution data is requested 5. Computation or passing through surface and PBL parameters 6. Diagnosing of cloud parameters 7. Computation of species-speci fic dry deposition velocities 8. Generation of coordinate dependent meteorol ogical data for the ge neralized coordinate

CCTM simulation 9. Output meteorological data in Models-3 I/O API format

The MCIP processor transforms the data into I/ O API format while also calculating several new data fields (e.g. low, middle, an d high cloud fractions) that are not readily available in the raw MM5 output. It also interpolates temperature and wind speed to observation height (1.5m and 10m, respectively). The MCIP processor cull s a minimum of six cells about the domain periphery to minimize edge eff ects in the MM5 simulation. MCIP can be used to further reduce the rows or columns in the MM5 data so that the domain definition for the MCIP output files precisely matches the domain used in the air quality modeling. MCIP also allows MM5 layers to be “collapsed” (i.e., some layers can be aggregated). When feasible it is desirable to use the same layer structure in the air qual ity model as in the MM5 to prevent errors associated with aggregating layer data and to maintain consis tency between data produ ced by the meteorological model and those used by the chem istry-transport model. Howeve r, due to computational costs associated with using large number of vertical layers, ve rtical layer collapsing is typically used to reduce the total number of layers used by the CCTM. In the CENRAP annual modeling we will collapse from 34 layers in MM5 output into 19 layers for the CMAQ air quality simulations. The first 8 layers of CMAQ, up to approximately 450 m AGL, will match th e MM5 vertical layer structure exactly. The re gion top for CMAQ is the same as used by MM5, 100mb (approximately 15 km AGL). The 36 km analys is domain contains 148 columns, 112 rows, and 19 layers. The definition of the hor izontal extend of the 12 km doma in is to be determined, but 19 layers will be used. More details on the CM AQ modeling domain defin itions are provided in Chapter 4 with the vertical layer structur e of MM5 and MCIP/CMAQ shown in Table 4-3.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-3 5.1.2 Products of the CMAQ Meteorolo gical Input Development Process

The meteorological input development proce ss produces three two-dimensional and four three-dimensional daily meteorological and ge ophysical output data in the Models-3 I/O API format. These CCTM-ready meteorological input f iles are used in both emissions processing and

the CCTM simulations. The met fields are 36 km and 12 km horizontal resolution on a Lambert Conformal Projection (LCP) coordina te system with 19 vertical si gma layers extending from the surface to the 100 mb pressure level. The data f iles include three-dimensional gridded fields of u- and v-wind components, vertical velocity, te mperatures, Jacobian, Jacobian weighted air density, total air density, water vapor, cloud wate r content, rain water content, ice and snow mixing ratio, layer heights, and vertical exchange coefficients. Two-dime nsional gridded fields of latitude and longitude, squared map-scaled fa ctor, surface temperatures and pressures, 1.5 and 10 meter temperature, planetary boundary heights, rainfall, tota l cloud fraction, snow cover, deposition velocities, u* and w*, surface roughness length, as well as dominant land use category are also developed.

Table 5-1 shows the configuration to be used in MCIP version 2.2 for processing the 2002 MM5 output to produce CCTM-ready meteorology input files.

Table 5-1. MCIP V2.3 configuration used In the CENRAP annual modeling.
Module or option Values or setting Additional Information

PBL value computation option 1 Use PBL value from input meteorology

Radiation fields 1 Use radiation fields from input meteorology

Dry deposition option 2 Use Models-3 (Pleim) dry deposition

routine

Output interval 60 Unit is in minutes

Vertical layer structure 19 layers See Chapter 4

5.1.3 MM5 Reformatting Methodology

MM5 CAMx serves the same purpose as MCIP in the CAMx modeling system. MM5CAMx will be exercised using the same layer structure as MCIP. Two sets of vertical turbulent diffusivity files will be generated:

• Use of the O’Brien scheme (OB70).

• Use of the CMAQ scheme.

MM5CAMx will be operated initially with a 0.1 m 2 /s minimum K V (Kz_min) value, however the CAMx-ready K V files may be updated to a 1.0 m 2 /s Kz_min to be consistent with CMAQ.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-4 5.1.4 Treatment of Minimum K V

The minimum K V value (Kz_min) is an area of ongoing investigation by the CMAQ and CAMx developers. EPA in itially recommended a 1.0 m 2 /s Kz_min for CMAQ modeling, but for their ozone forecasting EPA is using Kz_min values of 0.1 to 2.0 m 2 /s depending on the amount of urban land use present. Thus, to maximi ze flexibility we will process the 2002 MM5 data using MCIP and MM5CAMx using a 0.1 m 2 /s Kz_min and then impose other Kz_min values (e.g., 1.0 m 2 /s) in the model-ready files.

5.2 Development of Emissions Model Inputs and Resultant Inventories

The base year emissions inventory for the CENRP annual modeling will be founded on revised 2002 emissions developed by CENRAP emission inventory (EI) contractors in IDA SMOKE format (Strait, Roe and Vukovich, 2004). These revised emissions were reviewed by CENRAP stakeholders and considered complete in late 2004. The emission inventory data are being reviewed and quality assured by the CENRAP EI Contractor and will be available by the

end of November 2004.

Non-CENRAP state emissions are based on inventories obtained by the Study Team and determined to be representative of the 2002 episod e year. Western State base year point and area source emissions for 2002 were provided by the WR AP RPO. Additionally, an inventory of point source resolved agricultural, prescribed, and wild fire emissions were provided by WRAP

and will be utilized in the modeling. The South Eastern stat es emissions inventory of 2002 were provided by VISTAS RPO.

For the remaining U.S. portion of the domai n, point source projections were based on EPA’s 2001 modeling platform inventories and ar ea source, nonroad mobile, and fire emissions were based on EPA’s preliminary 2002 NEI. VM T and MOBILE input files were taken from EPA’s preliminary 2002 FTP site and used in running MOBILE6 nationally.

Mexican and Canadian emissions are to be based on the latest available inventories obtainable by the Study Team in formats le nding themselves to emissions modeling.

For purposes of air quality model validati on, actual 2002 calendar year emissions for EGU and fire activity will be used, while during st rategy and future year emission runs, “typical year” emissions for these categories will be processed.

A final 2002 CENRAP state emi ssion inventory is expected by November 2004 and will be used in the final model performance demonstr ation and configuration expected to begin in early 2005. Non-CENRAP state emi ssions are expected to be base d on State submittals to EPA under the CERR and will be augmented with a dditional data provided by RPO, State, and international sources. Air quality modeling will use actual 2002 calendar year emissions for EGU and fires, while “typical year” emissions for these categories will be proce ssed during the
strategy runs.

These emissions will then be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for air quality modeling using Version 2.0 (or VersionDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-5 2.1) of the Sparse Matrix Operating Kernel Emi ssions (SMOKE) model. Included in these runs will be the temporal, spatial, and speciation profiles and cross-reference data currently provided with the 2.0 release of the model augmente d with any recommended and approved emission profile data provided by the emi ssions inventory contra ctor or obtained from EPA prior to initial emissions modeling. The processing will be adjusted for each run to account for the specific air quality model (AQM) input required by CMAQ.

5.2.1 Emissions Modeling Methodology

Emissions inventory development for photoc hemical modeling must address several source categories including: (a) st ationary point sources, (b) ar ea sources, (c) on-road mobile sources, (d) non-road mobile sources, and (e) biogeni c sources. For this analysis, these estimates must be developed to support the episode that is being modeled (i.e., the historical base year when the episode actually occurred; 2002).

Development of an emissions inventory cust omized for the CENRAP region requires a merging of: (a) the most recent pertinent regional inventory and (b ) available high-resolution, locale-specific emissions estimated by local, state, and regional agencies in the CENRAP region. Local air regulatory and transportation planning agencies are generally the best sources of domain specific activity and cont rol factors to use in developing the base year emissions. Often,

these local emissions data sets come from a vari ety of sources, frequently in different formats.

Contacts with CENRAP’s emission inventor y contractors and the U.S. EPA will be established and formal requests made for inve ntory corrections, updates and ancillary data pertinent to the modeling of emissions in their jurisdictions. Where feasible and consistent with project resources and schedule, these updated data sets will be acquired and will be used to create day-specific modeling inve ntories specific to the CENRAP domain for the base year episodes to be modeled.

5.2.2 Set-up of SMOKE Over the CENRAP Domain

SMOKE will be configured to generate point, area, nonroad, highway, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs will be maintained in separate source category files in order to allow maximum flexibility in producing alternate strategies. Settings for each of the source categori es are discussed in relevant sections below. With the exception of biogenic and highway mob ile source emissions that are generated using the, respectively, BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions will be processed using the month, day, and hour specif ic temporal profiles of the SMOKE model.

To produce an emissions inventory to support annual modeling, representative time periods will be selected and modeled. Area, nonroad, and point sources will be modeled as a block of Thursday, Friday, Saturday, Sunda y, Monday, one per month (total of 60 days modeled). On-Road motor vehicles will be represen ted by an entire single week for each month.

These selection criteria a llow for the representation of day-of-the-week vari ability in the on-road motor vehicles, and model a representation of th e meteorological variabil ity in each month.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-6 Holidays will be modeled as Sundays. A list of modeled holidays is provided in Table 5-2. The biogenic emissions will be modeled on a day specific basis (365 days).

Table 5-2. SMOKE modeled holidays.

Date Julian Day Holiday Description January 1, 2002 2002001 New Year's Day March 29, 2002 2002089 Good Friday May 27, 2002 2002147 Memorial Day July 4, 2002 2002185 July 4th September 2, 2002 2002245 Labor Day November 28, 2002 2002332 Thanksgiving Thurs November 29, 2002 2002333 Thanksgiving Fri December 24, 2002 2002358 Christmas Eve December 25, 2002 2002359 Christmas Day

Population will be used as a gridding defau lt for all source categories when the assigned surrogate would cause SMOKE to drop emissions . This can be a case when the county-level emission inventories are prepared using surroga tes other than those available for modeling

purposes.

The domain for the air quality modeling wi ll be based on the EPA’s 36-km national CMAQ domain, illustrated in Figu re 5-1 below (details on the m odeling domains are provided in Chapter 4).

-2736 -2304 -1872 -1440 -1008 -576 -144 288 720 1152 1584 2016 2448 -2088 -1872 -1656 -1440 -1224 -1008

-792 -576 -360 -144

72 288 504 720 936 1152 1368 1584 1800

Figure 5-1. EPA 36-km National CMAQ domain.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-7 The parameters for the SMOKE runs are as follows:

Episodes : 2002 Calendar Base Year.

Future Years : To be determined (most likely 2018).

Output Time Zone : Greenwich Mean Time (zone 0)

Projection : Lambert Conformal with Alpha=33, Beta=45, Gamma=-97, and center at (Lon -97, Lat 40).

Domain :

• 36 Kilometer Grid: Origin at (-2736, - 2088) kilometers with 148 rows by 112 columns and 36-km square grid cells.

• 12 Kilometer Grid: To be determined.

Layer Structure : The CMAQ layer structure will include 19 layers, with specific layer positions defined in the meteorology files (see Chapter 4).

CMAQ Model Species : The CMAQ initial configuration will be for the CB-IV chemical mechanism with PM. The model species in the emission input files will be: CO, NO, NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO 3, POA, PSO4, and PMC.

Meteorology Data : Daily (25-hour). SMOKE requires th e following five types of MCIP outputs: (1) Grid cross 2-d, (2) Grid cross 3-d, (3) Met cross 2-d, (4) Met cross 3-d, and (5), Met dot 3-d. These files need to matc h the grid projection and overlap with the emissions modeling region but can be larger in the horizontal directions than the modeling region shown in Figure 5-1. Therefore, the data files for the 36 Kilometer grid domain will be at least 90 columns by 132 rows.

Elevated Sources : All sources will be treated by SMOKE as potentially elevated. No plume-in-grid sources will be modeled. Wildfi re and some prescribed fire emissions will be handled as point sources.

Producing 365 day-specific input fi les for all source categories places a burden on available computing facilities, data management system s, and would adversely affect the Phase II schedule. Selecting representative model days fo r some or all of the source categories reduces the processing and file handling requirements to a more manageable level, and in most cases does not compromise the accuracy of the emissions files.

Other current or recent projects undertaken by the EPA, WRAP, VISTAS and LADCo have used a selection approach for all of th e source categories (excep t biogenics) that use a representative weekday/Saturday/Sunday either for each month or each season to model all of the emissions files. In an attempt to better repr esent the level of temporal and spatial detail available for each source category, we have developed a more detailed strategy.

Biogenic emissions will be modeled for each episode day, using the daily meteorology. Point sources, including CEM and fire emissions will be modeled for each episode day to takeDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-8 advantage of the available day-specific emission s (if available) and meteorology. Area sources, including non-road mobile and dust emissions, w ith the exception of windblown dust emissions, do not utilize meteorological da ta, and are temporally alloca ted by monthly, daily and hourly profiles. Reviewing these prof iles indicate that maximum tempor al definition can be achieved by selecting representative Monday, weekday, Satu rday, Sunday, for each month. Holidays will be treated as Sunday.

Motor vehicle emissions are influenced by me teorological variabilit y, but the processing requirements for daily motor vehicle emissions were determined to be prohibitive under the current schedule. Instead of calculating the emissions factors as a function of the hourly meteorological data, a weekly aver age of these data, and hence weekly emission factors, will be

calculated.

5.2.3 Development of Point Source Emissions

Stack parameters are often more important to the reliability of the air quality modeling results than the emissions rates themselves. Stack parameter data are frequently incorrect, especially in some of the current regional modeling inventories and careful QA is required to assure that the point source emi ssions are properly located both hor izontally and vertically on the modeling grid. SMOKE has a number of built-in QA procedures designed to catch missing or out-of-range stack parameters. These procedures will be invoked in the processing of the point

source data.

Depending on the emissions input files from CENRAP or its contractor, for the initial baseline modeling, we will be separating the point source emissions into EGU and non-EGU categories. The non-EGU category will not be using any day or hour-specific emissions. All non-EGU point source emissions will be temporal ly allocated to month, day, and hours using annual emissions and source category code (SCC) ba sed allocation factors. These factors will be based on the cross-reference and profile data supplied with the SMOKE 2.0 version and will be supplemented with relevant data provided to the study team by CENRAP or its contractors.

For EGU sources with EPA reported CEM da ta, or with hourly emissions provided by stakeholders, actual hourly data will be used. For those s ources where EPA CEM data are utilized, NOx, SO2, and heat input-based hour-speci fic profiles will be developed and applied to NOx, SO2, and all other emissions, re spectively. This will ensure that the a nnual emission values provided by the EI contractor are maintained, but distributed usi ng hourly to annual profiles. For sources providing hour-specific da ta and where they were approve d by the State in which they operated, those data were substituted for EPA CEM-based emissions and distributions.

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input

data were collected from the 2002 Continuous Em issions Monitoring (CEM ) datasets, and used to develop unit-level temporal distributions. The hour, day of week, and monthly specific temporal profiles will be used in conjunction with th e EI supplied emissions data to calculate hourly EGU emissions by unit.

Off-shore point sources for the Gulf of Mexico region will be based on the latest emissions data developed by the Minerals Mana gement Service (MMS). These data includeDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-9 emissions from all platforms in the Gulf of Mexico for calendar year 2000. The data will be processed and re-formatted for input to the SMOKE modeling system.

All point sources will be spatially allocated in the domain based on th e stationary source geographic coordinates. If a point source is missi ng its latitude/longitude coordinates, the source will be placed in the center of its respective county.

5.2.4 Development of Area and Non-Road Source Emissions

All non-road mobile and area source emissi ons, except ammonia emissions (see below), will be temporally allocated to month, day of the week, and hours using annual emissions and source category code (SCC) based allocation factor s. These factors will be based on the crossreference and profile data supplied with the SM OKE 2.0 version and will be supplemented with relevant data provided to the study team by CENRAP or its cont ractors and other RPO’s. Area and non-road sources will be spatially allocated in the domain based on SCC-based spatial

allocation factor files. If an area or non-ro ad source SCC does not have an existing crossreference profile assigned to it, the county-level emissions w ill be allocated by population density in the respective county.

Off-shore area emission sources in the Gulf of Mexico region will be based on the latest emissions data developed by MMS. These data include emissions from all non-platforms

activities in the Gulf of Mexi co including support and survey vessels, helicopters, commercial marine vessels, and exploration and development activities. The data will be processed and reformatted for input to the SMOKE modeling system.

If needed, a crustal PM transport factor w ill be applied to fugitive dust emission sources

that have been identified in U.S. EPA modeli ng to have only a portion of its mass transportable from the source of the emission generation. The EP A’s studies indicate that 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simu lation. For this reason, the CENRAP emissions contractor should apply county-spec ific fugitive dust emissions tran sport factors to these sources to adjust PM emissions accordingly. If the fugitive dust emissions adjustment is done by the CENRAP emissions contractor pr ior to receipt by the emissions and air quality Modeling Team then no additional adjustment will be made in the emissions modeling. (for adjustment factors

see: http://www.epa.gov/ttn/chief/emc h/invent/statusfugdustemissions_082203.pdf ).

Ammonia Emissions

Ammonia emissions for CENRAP states have been developed by the EI contractor and include emissions from livestock, fertilizer appl ication, natural soils, mobile sources, point

sources and other miscellaneous sources. Thes e estimate were developed primarily using the Carnegie Mellon University (CMU) NH3 model. County-level emissions estimates were developed along with associated temporal and sp atial allocation profiles necessary for emissions processing with SMOKE. These data are curren tly being reviewed and quality assured by the

CENRAP EI contractor and will be available by the end of Nove mber 2004. The study team will incorporate these emissions estimate for the CENRAP states in to the regional modeling

inventories.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-10 For all non-CENRAP states, the project team will apply the ammonia emission inventory modeling system recently developed for WRAP. The model treats all major sources of ammonia emissions (livestock, fertilizer application, natura l soils, domestics source s and wild animals). The remaining ammonia emissions source catego ries are based on the latest 2002 inventories

used for the WRAP. The WRAP ammonia model will be run using the latest MM5 meteorological data for all non-C ENRAP states. The model genera tes hourly gridded emissions data using gridded meteorological data to apply various adjust ments to emission factors and temporal allocation factors. Therefore, SMOKE is not required for the generation of these emissions estimates, although these emissions ar e processed through SMOKE in order to merge these source categories with the remaining ar ea source emission estimates to obtain gridded model-ready data files.

5.2.5 Development of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE will be used to develop the base year on-road mobile source emissions estimates for CO, NOx, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates w ill be combined with gridded, episode-specific temperature data to calculate th e gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link loca tion and subsequently summed to the grid cell level, the off-network em issions estimates are spatially allocated based on a combination of the FHWA version 2.0 highw ay networks and population. For the CENRAP 36/12 km modeling, no link-based da ta will be used. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by th e meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:

• Weekly average minimum/maximum temperatures;

• Facility speeds;

• Locale-specific inspection/maintenance (I/M) control programs, if any;

• Adjustments for running losses;

• Splitting of evaporative and exhaust emi ssions into separate source categories;

• VMT, fleet turnover, and changes in fu el composition and Reid vapor pressure (RVP).

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and main tenance program in effect, type of oxygenated fuel program in effect, alternative vehicle mix pr ofiles, RVP of in-use fuel, operating mode) that

direct the calculation of the MOBILE6 emissions factors.

5.2.6 Development of Biogenic Source Emissions

A revised version of a commonly used biogenic emissions model, the Biogenic Emissions Inventory System (BEIS), has recen tly been developed and tested by EPA over two separate modeling domains/episodes. This vers ion of the model (BEIS-3, v1.2) contains several changes over BEIS-2, including the following:December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-11 • Vegetation input data -- are now base d on a 1-km Biogenic Emissions Landuse Database (BELD3) vegetation data base,

• Emission factors – many updates includi ng some recent NARSTO modifications,

• Environmental algorithm -- includes a sunl it/shaded leaf solar radiation model.

For this particular application of BEIS-3, version 1.2 as curren tly incorporated in the SMOKE processor will be used.

The BELD-3 landuse data on a Lambert confor mal grid at 1-km resolution have already been developed, are available, and will be used to estimate biogenic emi ssions in this study. The BEIS model also requires as input hourly, gri dded temperature and solar radiation data to estimate biogenic emissions, and these data will be derived from the MM5 predictions.

5.2.7 Wildfires, Prescribed Burns, Agricultur al Burns. Wind Blown Dust and Sea Salt

Source Emissions

Agricultural Burns

Emissions from agricultural burns in CE NRAP States have been developed by the

CENRAP EI contractor as area sources with annual emissions. These emissions will be temporally and spatially allocated using the temporal profiles, and cross-reference files developed by the EI contractor. Spatial al location surrogates, based on the MM5 modeling domain have been developed by th e contractor at a 12km spatial re solution. The study team will reprocess these surrogates for the unified RPO domain used for air quality modeling at a spatial

resolution of 36km.

Wildfires and Prescribed Burns

Wildfires and prescribed burn emissions will be handled separately from the standard area source input files. Wildfi re emissions currently are not available for CENRAP states. WRAP and VISTAS have developed their own w ild fire emissions as point sources. These emissions will be used for modeling on the unifi ed RPO domain. An effort is being made to develop national wild file emissions. When these emissions are available, they will be used for

CENRAP states.

Prescribed burn emissions for the CENRAP States have been received from the CENRAP EI contractor as both point and area sources. Bu rn emissions represented as area sources will be treated similar to agricultural burning emissions using spatial allocation surrogates. For the point source emissions, the study team will try to resolve the issue of temporal and vertical allocation

of these files. The SMOKE 2.0 can model fire plume rise if provided with the following

variables:

PTOP – Top of the fire plume profile (meters above ground level) PBOT – Bottom of the fire plume pr ofile (meters above ground level) Lay1 – The percent of the emissions en trained in the first modeling layerDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-12 The WRAP Fire Emissions Joint Forum Em issions Inventory Report (FEJF, 2002) has documented an approach to calculating these plum e descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to calcu late representative hourly plume profiles. These profiles are then used by SMOKE2.0 to distribute th e vertical emissions from the fires. To successfully model fires as elevated point source s, the data provided by the EI contractor will need to include both the day, or days, on which the fi re occurs, and a spatial identifier of the fire location. At a minimum, a latitude and longitude of the fire location can be used, although a polygonal coverage would be preferable.

In addition, wildfire and prescribed burn da ta, including emissions estimates and plume rise distributions, will be obtai ned from the WRAP contractor and used to supplement the inventory for the WRAP states.

Windblown Dust

The study team has developed wind blown dust emissions for the entire domain for PM 10 and PM 2.5. The dust emissions from agricultural lands have adjustment factors applied for the WRAP and CENRAP states only. The emissions for th e rest of the states need to be adjusted for agricultural lands. The emissions model develo ped by the project team will be run using the MM5 meteorology from CENRAP. Emissions from wind erosion of natural geogenic sources (SCCs 2730100000 [total] and 2730100001 [dust devils]) will be excluded from the resulting modeling files using a 100 percent reduction in the control packets and replaced with the

modeled estimates

Sea Salt

CMAQ currently treats sea salt as an inert PM species. That is, the sea salt is not allowed to chemically interact with other species, such as producing particulate sodi um nitrate. There are plans to update CMAQ to have chemically active sea salt, but it is unclear whether such an update will occur during the CE NRAP annual modeling. Accordi ngly, the initial modeling will be conducted without any sea salt emissions. If CMAQ is updated to treat chemically active Sea Salt, or if CAMx is run using its full-science op tions, then Sea Salt emissions will be generated using appropriate procedures.

5.2.8 Speciation and Reform atting of Emissions

SMOKE will be run to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM-IV , CB-IV or CB4). The SMOKE model will

also reformat the emissions estimates for use in CMAQ modeling. For each model-ready emissions inventory, SMOKE will produce at a mi nimum five (5) separate air quality modelready files: low-level point so urce, area source, elevated point source, mobile source, and biogenics. Other source categories, such as E GU and fire emissions may also be handled as separate air quality model-ready files.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-13 5.2.9 Development of Modeling Inventories

The emissions inventories developed for the CENRAP modeling can be grouped into three distinct types: 1) 2002 a nnual inventories, 2) Future Y ear episodic inventories, and 3) Alternate Base Year Episodic Inventories. In all cases, the Study Team expects to receive the emissions inventory data for th e CENRAP states from the Emi ssions Inventory contractor, add

non-CENRAP states and Canadian and Mexican data acquired fr om alternate EPA and/or RPO sources, and produce the CMAQ ready emissions files.

5.2.9.1 2002 Annual Inventories

The initial 2002 annual base inventory will be produced for the entire 2002 year. The initial inventory will consist of all of the required source cate gories: area, nonroad, point, motor vehicle and biogenics.

A revised 2002 annual inventory will be develope d in the event that updated, or revised, emission inventory data area re ceived from CENRAP. This may require re-production of all or some of the source categories. A “typical” inventory will be produced, based on the revised 2002, and replacing the revised actual emissions for EGUs and fires with “typical” EGU and fire emissions, or any other source emissions as provi ded by the EI contractor. The Study Team will also produce future year scenario annual i nventories, based on the 2002 meteorology. The composition of these future year sc enarios is yet to be determined.

5.2.9.2 Future Year Episodic Inventories

Utilizing emissions data provided by the EI contractors, the Study Team will model alternate future-year sensitivity and control st rategy scenarios. The specific episodes and emissions scenarios are as yet to be determined. This may require re-modeling of all, or some, of the source categories, with the exception of the biogenics.

5.2.10 Products of the Emissions Inventory Development Process

In addition to the CMAQ-ready input files ge nerated for each hour of the days modeled in the 2002 base year annual run, a number of quality a ssurance (QA) files wi ll be prepared and used to check for gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and tempor al distribution of the emission provides insight into the quality and accuracy of the emissions inputs.

& Visualizing the model-ready emissions with the scale of the plots set to a very low value, we can determine whether there are areas omitted from the raw inventory or if emissions sources are erroneously lo cated in water cells.

& Spot-check the holiday emissions files to c onfirm that they are temporally allocated like Sundays.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-14 & Producing pie charts emission summaries that highlight the contribution of each emissions source component (e.g. nonroad mobile).

& Normalizing the emissions by population for each state will illustrate where the inventories may be deficient and provide a reality check of the inventories.

& Spot-check vertical allocation of point sources using PAVE.

We will use state inventory summaries prepared prior to the emissions processing to compare against SMOKE output report totals generated after each major step of the emissions generation

process.

To check the chemical speci ation of the emissions to CB-IV terms and the vertical allocation of the emissions, we will compare re ports generated with SMOKE reports to target these specific areas of the processing. For spec iation, we will compare th e inventory import state totals versus the same state totals with the speciation matrix applied.

For checking the vertical allocation of the emissions, we will creat e reports by source, hour, and layer for randomly selected states in the domain. We will create these reports for a representative weekday in each of the episodes for each of these selected states.

The quantitative QA analyses of ten reveal significant deficienci es in the input data or the model setup. It may become necessary to tailo r these procedures to tr ack down the source of each major problem. As such, we can only outline the basic quantitative QA steps that we will perform in an attempt to reveal the underlying problems with the inve ntories or processing. Following are some of the reports that may be generated to review the processed emissions:

& State and county totals from i nventory for each source category

& State and county totals after spatia l allocation for each source category

& State and county totals by day after tempor al allocation for each source category for representative days

& State and county totals by model species after chemical specia tion for each source

category

& State and county model-ready totals (after spatial allocation, te mporal allocation, and chemical speciation) for each source categ ory and for all source categories combined

& If elevated source selection is chosen by user, the report indicati ng which sources have

been selected as elevated and plume-in-grid will be included

& Totals by source category code (SCC) from the inventory for area, mobile, and point

sources

& Totals by state and SCC from the invent ory for area, mobile, and point sourcesDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-15 & Totals by county and SCC from the invent ory for area, mobile, and point sources

& Totals by SCC and spatial surrogates code for area and mobile sources

& Totals by speciation profile code for area, mobile, and point sources

& Totals by speciation profile code and SCC for area, mobile, and point sources

& Totals by monthly temporal profile c ode for area, mobile, and point sources

& Totals by monthly temporal profile code and SCC for area, mobile, and point sources

& Totals by weekly temporal profile c ode for area, mobile, and point sources

& Totals by weekly temporal profile code and SCC for area, mobile, and point sources

& Totals by diurnal temporal profile c ode for area, mobile, and point sources

& Totals by diurnal temporal profile code and SCC for area, mobile, and point sources

& PAVE plots of gridded invent ory pollutants for all pollutants for area, mobile, and point

sources

5.2.11 Preparation of CAMx Emission Inputs

Once the CAMx-ready 3D emission i nputs are generated, the CMAQ-to-CAMx emissions converter would be used to genera te the CAMx-ready emission inputs for 2002. This converter performs the following:

• Reads the 3D CMAQ-ready I/O AP2 emission inputs.

• Maps CMAQ species to CAMx species and performs unit conversions.

• Writes out a 2D surface-layer CAMx-rea dy low-level emission input file.

• For each (i,j,k) cell that has non-zero emissi ons, writes out a CAMx-ready elevated point source input file.

5.3 CMAQ Modeling Methodology

5.3.1 CMAQ Science Configuration

This section described the model configuratio n and science options to be used in the CENRAP annual modeling effort. The recomm endations are based on testing and model

evaluations of several models or model confi gurations carried out in BRAVO (Pitchford, 2004), CENRAP preliminary modeling (Pun, Chen and Seigneur, 2004; Tonnesen and Morris, 2004), VISTAS modeling (Morris et al., 2004), MRPO modeling (Bak er, 2004) and WRAP modeling (Tonnesen, 2003). Table 5-3 summarizes the propos ed configuration for CMAQ. The latestDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-16 version of CMAQ is currently Version 4.4 that was released October 2004 and is currently proposed for use in the CENRAP annual modeli ng. However, if EPA releases an updated version of CMAQ in time, CENRAP would likely sw itch to the latest vers ion at that time.

In the CMAQ base configuration we will run the 36 km for the 2002 annual period and run the 12 km grid for the se lected episode(s) using one-way grid nesting where the boundary conditions for the 12 km grid simulation are ex tracted from the 36 km run using the CMAQ BCON processor. The base configuration of CM AQ will use 19 vertical layers up to a region top of 100 mb (approximately 15 km AGL) (see Table 4-3).

The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion. We will initially run MCIP2.3 specifying a minimum eddy diffusion constant (Kz_min) of 0.1 m 2 /s. Note that in the past the CMAQ default Kz_min value is 1.0 m 2 /s. However, EPA has reported improved CMAQ performance using a variable Kz_min value in their ozone forecasting that ranges from 0.1 m 2 /s

to 2.0 m 2 /s depending on the fraction of urban land use in the gr id cell (0% to 100%). By processing the MM5 data with MCIP2.3 using a 0.1 m 2 /s Kz_min we can easily generate CMAQ Kz input files with a 1.0 m 2 /s Kz_min value.

The MCIP2.3 will be used to process the MM5 data using the “pass through” option.

The AERO3/ISORROPIA aerosol chemistry sche me will be used for inorganic aerosol thermodynamics.

The CB-IV gas-phase chemical mechanis m is selected for the CENRAP annual modeling. VISTAS did extensive evaluation of CMAQ using the CB-IV and SAPRC chemical mechanisms and found that they produced sim ilar PM model performance and major responses to emission reductions. Thus, give n that CB-IV runs faster and us es less disk space that SAPRC

and there are more uncertainties in the other co mponents in the model and model inputs than in the gas-phase photochemistry, the more effici ent CB-IV chemical mechanism was selected.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-17 Table 5-3. Proposed CENRAP annual model configuration for the CMAQ. Model Option CMAQ Model Version Version 4.4 (October 2004)

Horizontal Resolution 36/12 km No. Vertical Layers NZ = 19

Horizontal Advection PPM

Vertical Advection PPM Horizontal Diffusion Spatially Varying

Vertical Diffusion K V (Eddy Diffusion) MM5 Configuration Pleim-Xiu/ACM MM5 Processing MCIP2.2 Pass Through Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver EBI Secondary Organic Aerosol SORGAM Aqueous-Phase Chemistry RADM Aerosol Chemistry AE3/ISORROPIA Dry Deposition Pleim-Xiu

Plume-in-Grid Off
Initial Concentrations CMAQ Default Boundary Conditions 3-Hourly 2002 GEOS-CHEM

Emissions CENRAP States 2002
Other States 2002 from RPOs

5.3.2 Spin-Up Initialization

For the 2002 annual CMAQ modeling, the model will be exercised separately for four quarters. The 2002 MM5 modeling started on December 16, 2001 at 12Z. Thus, allowing for 12 hours of spin up of the MM5 model, CMAQ will be initialized at 00Z on December 17, 2001. This results in a 14 day spin up period for CMAQ and the first quarter run segment of 2002. For the other quarter run segments of 2002, CMAQ w ill be initialized with a 15 day spin up period.

5.3.3 Boundary Conditions

Harvard University has been contracted by the RPOs to perform a 2002 GEOS-CHEM global climate model simulation. VISTAS ha s processed the 2002 GEOS-CHEM model output and generated day-specific 3-hourly boundary cond itions (BCs) for the 36 km Inter-RPO grid in the CMAQ BCON format. The CENRAP 2 002 annual modeling will use the 2002 GEOSCHEM day-specific BCs.

5.3.4 Photolysis Rates

Several chemical reactions in the atmosphe re are initiated by the photodissociation of various trace gases. To accurately represent the complex chemical transformations in the atmosphere, accurate estimates of these photodi ssociation rates must be made. The Models-3 CMAQ system includes the JPROC processor, whic h calculates a table of clear-sky photolysis rates (or J-values) for a specific date. JPROC uses default values for total aerosol loading andDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-18 provides the option to use default column O3 data or to use TOMS data for total column O3. Previously, in the Phase I modeling we used TO MS data, and we will continue to use TOMS data for Phase II. There are a few days in 2002 fo r which TOMS data is not available, and we will used default column O3 data or previous days data for those days.

JPROC produces a "look-up" ta ble provides the photolysis rate s as a function of latitude, altitude, and time (in terms of the number of hour s of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six latitudinal bands (10o, 20o, 30o, 40o, 50o, and 60o N), se ven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), and hourly values up to " 8 hours of deviation from local noon. During model calculations, photolysis rates for each model grid cell are estimated by first interpolating the clear-sky photolysis rates from the look-up table using the grid cell lati tude, altitude, and hour angle, followed by applying a cloud correction factor.

The photolysis rates input file must be prep ared as separate look-up tables for each simulation day. The modeling team has already pr epared scripts to automate the production of photolysis rate files for each day of the annual si mulation. Photolysis files are ASCII files, and these will be visually checked for selected days to verify that photolysis are within the expected

ranges.

5.4 CAMx Modeling Methodology

This section described the model configuratio n and science options to be used in the CENRAP CAMx annual modeling effort. Table 5- 4 summarizes the proposed configuration for

CAMx. The latest version of CAMx is curren tly Version 4.10s that was released August 2004 and is currently proposed for use in the CENRAP annual modeling. However, a more appropriate updated version of CA Mx is available, CENRAP woul d likely switch to the latest

version.

5.4.1 CAMx Science Components

In the CAMx base configuration we will run the 36 km for the 2002 annual period and run the 12 km grid for the selected episode( s) using two-way grid nesting. The base configuration of CAMx will use 19 vertical layers up to a region top of 100 mb (approximately 15 km AGL) (see Table 4-3).

The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion. Two sets of CAMx vertical diffusivity inputs will be generated using MM5CA Mx: (1) one using the O’Brien scheme; and (2) the other using the Kv scheme in CMAQ. We will initially run MM5CAMx specifying a minimum eddy diffusi on constant (Kz_min) of 0.1 m 2 /s. As part of the CAMx modeling system there is a utility that produces enhanced minimum Kz (Kz_min) values near the surface to account for increased mixing due to roughness and the urban heat island. The selection of the Kz_min approach will be based on the latest thinking and sensitivity tests.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 5.doc 5-19 The CAMx 4.10s Mechanism 4 (M4) Course/Fine approach will be used for the CENRAP annual modeling that assumes all secondary PM is fine.

The CB-IV gas-phase chemical mechanis m is selected for the CENRAP annual modeling. The RADM aqueous-phase chemistr y and SOAP SOA module will also be used.

Table 5-4. Proposed CENRAP annual model configuration for the CAMx. Model Option CMAQ Model Version Version 4.10s (August 2004)

Horizontal Resolution 36/12 km No. Vertical Layers NZ = 19

Horizontal Advection PPM

Vertical Advection PPM Horizontal Diffusion Spatially Varying

Vertical Diffusion K V (OB70 and CMAQ) MM5 Configuration Pleim-Xiu/ACM MM5 Processing MM5CAMx Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver CMC Secondary Organic Aerosol SOAP Aqueous-Phase Chemistry RADM Aerosol Chemistry ISORROPIA Dry Deposition Wesley

Plume-in-Grid Off
Initial Concentrations CMAQ Default Boundary Conditions 3-Hourly 2002 GEOS-CHEM

Emissions CENRAP States 2002
Other States 2002 from RPOs

5.4.2 Spin-Up Initialization

For the 2002 annual CAMx modeling, the model will be exercised separately for four quarters using a ~15 day initializa tion period the same as CMAQ.

5.4.3 Boundary Conditions

CAMx boundary conditions would be day- specific 3-hourly and based on the 2002 GEOS-CHEM global climate mode l simulation. The CMAQ-to-CAMx BC processor would be used to processor the CMAQ BCON files for input into CAMx.

5.4.4 Photolysis Rates

The TUV photolysis rates processor would be used to generate the photolysis rates input

file for CAMx. TOMS ozone data would be used to develop the CAMx Albedo/Haze/Ozone input file for 2002.December 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-1 6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assurance procedures that will be used in the CENRAP 2002 annual modeling. Mo re details on the quality a ssurance and quality control (QA/QC) procedures to be used are described in the Quality Assurance Project Plan (QAPP; Morris and Tonnesen, 2004).

6.1 Quality Assurance Objectives

In December 2002, the USEPA publish exte nsive guidance on developing a Quality Assurance Project Plan (QAPP) for modeling st udies (EPA, 2002). The objective of a QAPP is to ensure that a modeling study is scientifically sound, robust, and defensible. The new EPA guidance suggests that a QAPP should include the following elements :

• a systematic planning process including id entification of assessments and related performance criteria;

• peer reviewed theory and equations;

• a carefully designed life-cy cle development process that minimizes errors;

• clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can fully unde rstand the model output;

• input data and parameters that are accu rate and appropriate for the problem;

• output data that can be used to help inform decision making; and

• documentation of any changes from the original quality assurance plan.

Moreover, the EPA guidance specifies that differe nt levels of QAPP may be required depending on the intended application of the model, with a modeling study designed for regulatory purposes requiring the highest level of quality assurance.

The QAPP also provides a valuable resource for project management. It can be used to document data sources and assumptions used in the modeling study, and it can be used to guide project personnel through the data processing and model applica tion process to ensure that

choices are consistent w ith the project objectives.

The guidance document also addresses model development, coding and selection of models, and model performance requirements. For the CENRAP 2002 annual modeling we are using existing EPA sponsored models (SMO KE and CMAQ) and a model developed by ENVIRON (CAMx) and have no cu rrent plans for model development activities. Thus, our QAPP focuses primarily on documen ting data sources and QA of da ta processing performed by the model team. In addition, because no official EPA guidance currently exists for visibility model performance, a major objective of ou r QAPP will be to propose and define model performance evaluation procedures. QA objectives for specific aspects of the project are discussed below, and these will be incorporated into a QAPP that conforms to the EPA guidance document for modeling studies.December 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-2 6.2 Emissions Model Inputs and Outputs

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical step in performing air quality modeling studies . Because emissions processing is tedious, time consuming and involves complex ma nipulation of many different type s of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected.

As part of the CENRAP annual modeling effo rt, an “Emissions Gatekeeper” function will be implemented. The Study Team envisions the role of this Gatekeeper as one to perform quality

assurance activities on the followi ng emission inventory (EI) data:

(1) EI data obtained from the CENRAP emissions inventory contractors and other sources; and (2) The emission inventory to be used for m odeling outside of the St ates in the CENRAP region.

Specifically, the Emissions Gatekeeper will re view the content and format of the provided emission inventories ensuring an appropriate appr aisal of the emissions data and estimates for

the CENRAP States. Other tasks will include any additional translation from mass emissions files into the emissions modeling input file st ructure necessary for modeling. The Study Team will supplement these activities with QA checks on the intermediate and model output files using internal and public domain visua lization and diagnostic packages.

We propose a multi-step emissions QA/QC appr oach that involves several staff to QA/QC the emissions as they are processed. Th is includes the initial emissions QA/QC by the Emissions Gatekeeper described above, as we ll as QA/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the air quality modeler of the processed model ready emission files. This multistep proce ss with three separate groups involved in the QA/QC of the emissions is much more likely to catch any errors prior to the air quality model

simulations.

6.2.1 Emissions Modeling QA/QC

Input Screening Erro r Checking Algorithms : Although the SMOKE emissions model will be used for emissions processing, some of additional input error checking algorithms will be used to screen the data and identify potential emissi on input errors. Additionally, EPA has issued a revised stack QA and augmentation procedures me morandum that will be used to identify and augment any outlying stacks.

SMOKE error messages : SMOKE provides various cautionary or warning messages during the emissions processing. We will redirect the SMOKE output to log files and review the log files for serious error messages. An archive of the lo g files will be maintained so that the error messages can be reviewed at a later date if necessary.December 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-3 SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of processed emissions as daily totals according to species, source category and county and state boundaries. These summaries will then be compared with summary data prepared for the pre-processe d emissions, e.g., state and county totals for emissions from the augmented emissions data.

6.2.2 QA of the Model-Ready Emissions Impacts

The goal of the post-processed emissions summary QA is to detect possible errors in the final, model-ready binary emissions files by pr eparing summary plots that characterize spatial and temporal patterns in the emi ssions data. This step is designed to catch errors that may be missed in the internal SMOKE QA procedures. We will use a QA/QC post-processing program that read the CMAQ-ready I/O API emissions file formats for each of the major source categories (mobile, area, point, biogenic , fire) and produce the following plots.

Spatial Summary : We will sum the emissions for all layers and for all 24 hours that is used to prepare a PAVE plot showing the daily tota l emissions spatial distribution. For a 20 day simulation this produces approximately 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simulations these plots will be presented as tons per day. The objective of this step is to identify errors in spatial distribution of emissions.

Vertical Profile : For point sources the emissions total fo r each layer will be summed and plotted

to show the vertical distributi on of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The objective of this st ep is to identify possible errors in vertical

distribution of emissions.

Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared that display the di urnal variation in total hourly emissions. The objective of this step is to identi fy errors in temporal profiles.

Long Term Temporal Summary : The total domain emissions for each day will be accumulated and displayed as time series plots that show th e daily total emissions across the domain as a function of time. The objective of this step is to identify particular da ys for which emissions appear to be inconsistent with other days for no reason (e.g., not a weekend) and compare against the general trend.

Control Strategy Spatial Displays : Spatial summary plots of the da ily total emissions differences between a control strategy and base case emission s scenarios will be generated. These plots can be used to immediately identify a problem in a control strategy.

6.3 Meteorological Model Outputs

As part of the CENRAP annual modeling QA effort, a “Meteorological Gatekeeper” function will be implemented. The task of the Ga tekeeper is to provide an independent review and quality assurance of the meteorological modeling and rela ted data sets developed byDecember 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-4 CENRAP and subsequently by the emissions and ai r quality modeling teams. This Gatekeeper QA review serves two specific purposes: (a) to en sure that any potential problems with the data sets (should they exist) are iden tified and corrected in a timely manner, and (b) to provide the study team with information to support ongoing CMAQ and CAMx model performance testing and sensitivity analyses. In th e case of meteorology, the Gatekeep er’s independent QA analysis of the MM5 meteorological data se ts serves to provide direct a ssistance to the emissions and air quality modeling team as it undert akes to ratify the SMOKE mode l outputs and to diagnose the CMAQ and CAMx model performance and sensitivity analyses.

In addition to having personal responsibility for the quality and chain of custody of the meteorological data sets supplied by CENRAP , the Meteorological Gatekeeper will be responsible for ensuring and mainta ining the integrity of the data files uploaded to the project website. This website, hosted by UCR, serves as the repository of data for the ENVIRON/UCR modeling centers and for the CENRAP Modeling Wo rkgroup participants. In performing the Gatekeeper quality assurance activity, one of the first steps is to conduct an independent operational evaluation on the MM5 model results at 36 km and 12 km grid scale. This evaluation covers surface and aloft wind direction, temperat ure, mixing ratio, precipitation, and planetary boundary layer (PBL) depths on a c ontinental scale (36 km) and subr egional scale (12 km) basis. The Gatekeeper will also perform supplemental, ad hoc analysis of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to exercise the Meteorological Chemistry Interface Processor (MCIP) version 2.3 and MM5CAMx pro cessor to produce binary input files for the CMAQ and CAMx air quality models, respectively.

In summary, the quality assurance plan fo r the meteorological data will include the following elements:

3⁄4 Upon receiving the MM5 output files from CE NRAP, we will verify the integrity of the file transfer (e.g., no missi ng and/or corrupted files);

3⁄4 We will process the 2002 MM5 data using the MCIP2.3 and MM5CAMx processors to generate 2002 model-ready meteor ological inputs for CMAQ and CAMx, respectively.

3⁄4 We will create horizontal and vertical plot s of temperature, pressure, precipitation, modeled flow patterns, PBL heights, etc. to assess whether the MC IP output fields are reasonable;

3⁄4 The CENRAP 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the VISTAS and WRAP 2002 MM5 simulation as an independent QA and evaluation of the database; and

3⁄4 We will make slected plots available on the CENRAP website for viewing and

download.December 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-5 6.4 Air Quality Model Inputs and Outputs

Key aspects of QA for the CMAQ input a nd output data include the following:

• Verification that correct conf iguration and science options are used in compiling and running each model of the in the CMAQ m odeling system, where these include the MCIP, JPROC, ICON, BCON and the CCTM.

• Verification that correct conf iguration and science options are used in compiling and running each model of the in the CAMx modeling system, where these include the MM5CAMx, TUV, CMAQ-to-CAMx IC, BC a nd emissions processors and other

processors.

• Verification that correct i nput data sets are used when running each model.

• Evaluation of CMAQ and CAMx results to ve rify that model output is reasonable and consistent with general expectations.

• Processing of ambient monitoring data for use in the model performance evaluation.

• Evaluation of the CMAQ and CAMx resu lts against concurrent observations.

• Backup and archiving of cr itical model input data.

The most critical element in the QA plan for CMAQ and CAMx simulations is the QA/QC of the meteorological and emissions input files. The majo r QA issue specifically associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ and CAMx modeling we employ a system of nami ng conventions using envi ronment variables in the compile and run scripts that guarantee that co rrect inputs and scien ce options are used. We also employ a redundant naming system so that th e name of key science options or inputs are included in the name of CMAQ and CAMx execu table program, in the name of the CMAQ and CAMx output files, and in the name of the dire ctory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files. For example, if a model simulation is performed using the CB4 mechanism, all compile and run scripts contain th e variable definition “$ MECH = CB4”, and this

variable is hard coded into th e script for the executable name , the output file name, and the output directory name. This procedure produces long file/directory names but it effectively prevents mistakes or makes mistakes readily apparent if they do occur.

A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the original runs scripts and direct ory structure that were used in performing a model simulation. For example, if we perform simula tion with the SAPRC mechanism, instead of editing the original scripts to specify “$MECH = SAPRC” we will creat e a parallel directory structure with a new set of scripts to perform the SAPR C simulations. This provides a pe rmanent archive of the scripts that were used in performing model simulations . In addition, output from the model simulation will be directed to a log file th at provides a record of input file names, warning messages etc that

will be archived.December 2004

H:\CENRAP_Modeling\Modeling_Pro tocol\Draft2.0\Chapter 6.doc 6-6 We will also perform a post-processing QA of the CMAQ and CAMx output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected pa tterns in the CMAQ and CAMx output files. In the case of model sensitivity studies, the animated gi fs will be prepared as difference plots for the sensitivity case minus the base case. Often, erro rs in the emissions inputs can be discovered by viewing the animated GIFs. Finally, we will pr oduce 24 hour average plots for each day of the CMAQ simulations. This provides a summary that can be useful for more quickly comparing

various model simulations.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-1 7.0 MODEL PERFORMANCE EVALUATION

7.1 OVERVIEW

The CENRAP 2002 annual modeling work is br oken down into six Tasks for the current fiscal Year (FY1) that ends April 30, 2005 that focuses on the development of an annual modeling database for 2002 with an attendant model perfor mance evaluation, sensitivity analysis as well as modeling a future-years base case. And work in a second fiscal year (FY2 ) that ends April 30, 2006 that focuses on future-year control strategy evaluation and sensitivity analysis. The ultimate objective of the FY1 CENRAP modeling work effort is to obtain a properly evaluated operational emissions and air quality modeling system for the 2002 annual calendar year so that control strategy evaluation can begin by May 1, 2005. Consistent with the spirit of a modeling protocol for regulatory decision-making, this document lays out th e ‘roadmap’ for achieving an adequately tested modeling system for regulatory usage. But, obvi ously, this does not mean that every analysis identified in this chapter will be carried out or is indeed even possible given the CENRAP annual modeling schedule and resources, the existing aer ometric data bases, and present technology constraints. The roadmap guides the way to the de sired destination – in this case, an evaluated, operational PM/regional haze modeling system – but does not commit the driver to exploring every side street and back country ro ad along the way. Indeed, one e xpectation of the CENRAP Modeling Workgroup is a close working relationship with th e Modeling Team to ensure that the available resources and schedule are applied most efficiently in reaching the aforementioned goal.

This chapter describes a range of model testing methodologies potentially available to the CENRAP Emissions and Air Quality Modeling study team in its efforts to adequately evaluate the performance of the CMAQ and CAMx air quality modeling systems for the 2002 annual period. Since one cannot know at this juncture the speci fic performance problems that may arise in the initial 2002 CMAQ and CMAQ base case simulations or the “final” year 2002 evaluation , we set forth in this chapter a broad range of methods and techniques that may be brought to bear in examining CMAQ and CAMx model performance. We identify the core operational evaluation procedures, recommended in EPA (2001) guidance that will be performed in the model performance

evaluation. We also describe a broad range of additional performance testing methods that may be worth considering, if necessary. Implementation of one or several of these various techniques would have to be performed under separate funding. However, our base effort model performance evaluation is intended to provide a robust asse ssment of the operational ability of the CMAQ and CAMx models to predict fine particulate and visib ility at Class I areas and other monitoring sites in the CENRAP region.

Clearly, not all of the supplemental evaluativ e techniques identified in this chapter will ultimately be performed. There are three reasons for this:

3⁄4 The CENRAP Emissions and Air Quality M odeling available resources places clear limits on the resources available to perfor m model evaluation analyses. Accordingly, some evaluation steps, while desirable, simply may not be possible given current funding levels;

3⁄4 The CENRAP timeline places stringent sc hedule demands on the model evaluation. A number of the model performance evalua tion methods introduced in this chapterDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-2 (e.g. Weight of Evidence analyses, diagnostic testing with individual measurement networks, PM indicator species and ratios an alyses, use of aircraft data) could very likely require more time to carry out given their quasi-research nature. Since CENRAP annual modeling study is not a mode l research and development effort, but rather an operational evaluation of ex isting modeling systems for regulatory decision-making, some interesting, but time consuming analyses simply may not be possible given the present schedule; and

3⁄4 To conform to the EPA PM guidance docum ents requirements for PM model testing, it may not be necessary to conduct many of the diagnostic and Weight of Evidence tests identified in this protocol. Indee d, an adequate evaluation of the CENRAP annual modeling system may be possible th rough straightforward application of the core operational performance evaluation procedures identified in EPA’s 2001 draft guidance.

At a minimum, the evaluation of the CMAQ and CAMx modeling systems for the CENRAP annual

2002 simulations will be consistent with EPA’s dr aft guidance on PM model testing. This guidance essentially calls for an operational evaluation of the model focusing on a specific set of gas phase and aerosol chemical species and a suite of sta tistical metrics for quantifying model response over the annual cycle. The emphasis is on assessing: (a) How accurately the model predicts observed concentrations? and, (b) How accurate ly does the model predict responses of predicted air quality to changes in inputs? States are encouraged to utili ze the evaluation procedures set forth in the earlier 1991 guidance document (EPA, 1991) for gas phase sp ecies and the newer (2001) guidance of PM species. Thus, in carrying out the initial operationa l evaluation and the subsequent final evaluation, we will implement the suggested EPA performance testing methodologies for the key gas phase and aerosol species. Since these methods are explicitly presented in EPA’s guidance document, there is no need to repeat them here.

Subject to the availability of time and curre ntly unallocated resources, the CENRAP annual evaluation will also attempt to employ other te sting methods beyond those in the EPA guidance document. However, the level of this effort without additional resources will depend on how smoothly the integration of other data (e.g., emi ssions and meteorological) are introduced into the Emissions and Air Quality Modeling Study. For exampl e, if emissions are not in an adequate form usable for SMOKE emissions modeling, then current budget resources may have to be reallocated from model performance to fixing the emissions. This discussion is not intended to circumvent a full evaluation of the modeling systems, rather to recognize the very real resource limitations and if resources are diverted to other activities without additional funding, then work is dropped on the back end that usually includes limitations on the model performance evaluation.

We conclude by again emphasizing that mo st important goal of the CENRAP CMAQ and

CAMx evaluation is to determine whether the a ggregate modeling systems (model codes plus input data sets and observational data for testing) offe rs sufficiently reliable and accurate results that public decision-makers may have reasonable confid ence in using the model to help choose between alternative regional haze reduction scenarios. If the CMAQ and CAMx model evaluation, as outlined in this chapter, provides sufficient evid ence that the modeling systems are operating reliably

and in conformance with measurements and scien tific expectations, then specific justifications explaining why the model is acceptabl e for developing regional haze strategies will be offered in theDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-3 2002 CMAQ and CAMx Modeling Summary Report. Conversely, should the evaluation determine that the modeling systems suffers fr om important flaws or errors that undermine its reliability or use, these findings will also be documented, together with recommendations regarding the use of alternate methods, steps to improve the model and/or data base, or other approaches.

7.2 Context for the CENRAP Model Evaluation

We begin the discussion of the CENR AP annual modeling evaluation methodology by reviewing how the CMAQ and CAMx model output is used in regional haze applications to project changes in visibility (this issue is discussed in more detail in Chapter 8). When designing a model performance evaluation, it is important to unders tand how the modeling results will ultimately be used. EPA has published two versions of draft guidance for fine particulate and regional haze modeling (EPA, 2000; 2001), utilizing a Fine Partic ulate Guidance Workgroup to provide technical input in the development of both documents 1 . More recently, EPA has pr ovided an informal update on the PM/regional haze modeling guidance (Timin, 2002) and conducted a PM model evaluation workshop (see, for example, Timin, 2004; Boylan, 2004) shedding additional li ght on what the final guidance document might contain.

A key concept in EPA’s guidance for addressing regional haze issues is that the modeling

results should be used in a relative sense to scal e or roll back the observed particulate matter (PM) concentrations from which light extinction is estimated. Adopting the recommendations from the Federal Land Managers’ Air Quality Related Values Workgroup (FLAG), estimates of visibility are obtained from observed fine particulate concentr ations using the following IMPROVE reconstructed mass extinction equation (FLAG, 2000):

b ext = 3{f(RH)[(NH 4 ) 2 SO 4 ]} + 3{f(RH)[NH 4 NO 3 ]} + 4{f’(RH)[OC]} + 10[EC] + 1[IP] + 0.6[CM] + b rayleigh

where:

b ext is the estimated extinction coefficient (Mm -1 ); [SO 4 ] is the sulfate concentration assumed to be ammonium sulfate; [NO 3 ] is the particulate nitrate concentration assumed to be ammonium nitrate; [OC] is the organic carbon concentration; [EC] is the elemental carbon concentration; [IP] is the inorganic primary fine particulate (< 2.5 : ) concentration excluding primary sulfates and nitrates; [CM] is the coarse particulate (> 2.5 : and < 10 : ) concentration;

b rayleigh is the light-scattering due to Rayleigh scattering (assumed to be 10 Mm -1 ); f(RH) is a relative humidity adjustment factor for the sulfate and nitrates; and f’(RH) is a relative humidity adjustment factor for OC that is assumed to be 1.0.

1 Members of the CENRAP modeling team particip ated on the EPA fine particulate modeling work group over the two-year span of its activities.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-4 The relative humidity (RH) values used in the above equation are monthly- specific and Class I areaspecific values based on a long term average (EPA, 2003a).

The regional haze rule expresses reasonable progress in terms of changes in the deciview ( ) dV) from the current to future year conditions. Deciview (dV) is expressed as the natural logarithm of the extinction coefficient (b ext ) to Rayleigh scattering:

Deciview = 10 ln(b ext /10)

) dV = 10 ln[(b ext ) future / (b ext ) base ]

In this framework, changes in visibility in te rms of percentage changes in extinction over a natural “clean” background visibility rather than ch anges in deciview. Changes in deciview or extinction are essentially mathematically identical with a 1 change in deciview being approximately equivalent to a 10% change in extinction. Both visibility parameters will be calculated in the CENRAP annual model applications.

The CENRAP 2002 CMAQ and CAMx model tes ting will concentrate on an operational evaluation of those model predictions that ar e most necessary for estimating visibility (e.g., SO 4, NO 3, OC, EC, IP and CM). Where feasible and s upported by sufficient measurement data, we will also evaluate the modeling system for its ability to accurately estimate gas-phase oxidant and precursor/product species since correct, unbiased simulation of gas-phase photochemistry is a necessary element of reliable regional haze predicti ons. This evaluation will be carried out across the full CENRAP domain for the entire year and also on other subdomains (e.g., CENRAP-N, CENRAP-S, WRAP, MRPO, VISTAS and MANE-VU) and month-by-month basis to help build confidence that the modeling system is operating corr ectly. With this context in mind, we next turn to the philosophy of the model evaluation process.

7.3 Multi-Layered Model Testing Process

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) affirms the recommendations of numerous modeling scientists over the past decade (see, for example, Dennis et al., 1990; Tesche et al., 1990, 1994; Seigneur et al., 1998, 2000; Russell and Dennis, 2000; Ar nold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a comprehensive, multi-layered approach to m odel performance testing should be performed, consisting of the four components: operational, diagnostic, mechanistic (or scientific) and probabilistic. As applied to regional PM/visibility models, this multi-layered framework may be viewed conceptually as follows:

> Operational Evaluation : Tests the ability of the model to estimate PM concentrations (both fine and coarse) and the components at PM 10 and PM 2.5 including the quantities used to characterize visibility (i.e., sulfate, nitrate, ammonium, or ganic carbon, elemental carbon, PM 2.5 , and PM 10 ). This evaluation examines whether the measurements are properly represented by the model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”;December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-5 > Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 ) and associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; spatial variation; mass fluxes; and component s of light extinction (i.e., scattering and absorption);

> Mechanistic Evaluation : Tests the ability of the model to predict the response of PM and visibility to changes in variables such as emissions and meteorology; and

> Probabilistic Evaluation: Takes into account the uncertainties associated with the model predictions and observations of PM and visibility.

Within the constraints of the CENRAP annual modeling schedule and budget resources, the CENRAP Emissions and Air Quality Modeling effort will attempt to include elements of each of these components. The operational evaluation will obvi ously receive the greates t attention since this is the primarily thrust of EPA’s 2001 PM guidance. However, we will consider, where feasible and appropriate, diagnostic and mechanis tic tests (e.g., use of probing tools, indicator species and ratios, aloft model evaluations, urban vs. rural perf ormance analyses), and traditional sensitivity simulations to explore uncertainty. The scope of these additional diagnostic and mechanistic tests will be shaped by additional resources and the timing of when such analyses are commissioned

relative to the CENRAP schedule.

Before discussing the types of testing pro cedures available for the above evaluation components, we first identify the surface and aloft data sets that are available to support these comparisons.

7.4 Development of Consistent Evaluation Data Sets

7.4.1 Surface Measurements

The ground-level model evaluation database w ill be developed using several routine and research-grade databases. The first is the rou tine gas-phase concentrati on measurements for ozone, NO, NO 2 and CO archived in EPA’s Aerometric Information Retrieval System (AIRS/AQS)

database. Other sources of information come from the various PM monitoring networks in the U.S., with particular emphasis in the central U.S. These include the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends Network (CASTNET), (c) Southeastern Aerosol Research and Characterization (SEARCH), (d) EPA PM 2.5 and PM 10 Mass Networks (EPA-FRM), (e) EPA Specia tion Trends Network (STN); (f) National Acid Deposition Network (NADP) a nd (g) EPA Supersites (EPA-SPEC) networks. Typically, these networks provide ozone, other gas phase precu rsors and product species, PM, and visibility

measurements.

As an example, the IMPROVE network give s daily (24-hour) average mass concentrations every 3 days for SO 4 , NO 3 , organic carbon (OC), elemental carbon (EC), soil (IP), CM, PM 2.5 and

PM 10 . These data are available at approximately 11 sites in the CENRAP states. In addition, hourly values of light extinction and deciview are availabl e at several of these sites. The SEARCH networkDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-6 provides 24-hour as well as continuous ( hourly) speciated measurements of PM 2.5 components and other specifics from 8 stations all located in th e southeastern US outside of the CENRAP states (Hansen et al., 2003). We will use data from thes e and the other observational databases listed in Table 7-1, supplemented with the routine AIRS/ AQS data, as appropria te, for CMAQ and CAMx model performance testing.

Care must be taken in selecting data that are representative of regional concentrations and the prediction of regional haze at the sensitive Class I ar eas within the CENRAP states. For example, the criteria and other pollutants in the AIRS/AQS databa se are typically urban-oriented. Thus, would not expect CMAQ or CAMx predictions for these species using a 36 km (or even 12 km) grid mesh to be ideally suited to simulating urban-scale concen trations. With other data sets (e.g., SEARCH) it may be possible to reproduce the urban-to-rural gradients in PM 2.5 components. While finer grid meshes (e.g., 4 km) might be more appropriate, hi gh-resolution modeling at 4 km scale on an annual time frame for the CENRAP region is prohibitive given current computer technology and model run times. Accordingly, some selectivity is needed in assembling pertinent measurement sites for comparisons with regional-scale model predicti ons and the interpretation of the modeled and measured comparisons must be made with care.

Another important consideration is that diffe rent PM monitoring networks may use different measurement approaches that “measure” different amount s of the same species th at are also different from the modeled species. For example, the IMPROVE network only speciates PM 2.5 so any sulfate or nitrate in the coarse mode (PM 2.5-10 ) is included in the CM species. The CMAQ and CAMx models will be evaluated separately for each ne twork. While the CENRAP annual modeling study is clearly not aimed at new model development or algorithm refinement, information discerned from model performance across the various networks shoul d be useful in later model refinement activities and network design improvements. Finally, the mapping of the modeled species to the monitored data will also have to be performed in a consistent fashion.

7.4.2 Aloft Measurements

In recent years, the use of instrument aircraft in support of regulatory monitoring and research programs has become much more commonplace. Ind eed, in the upper Midwest, the Lake Michigan Air Directors Consortium (LADCo) has been centr ally involved in aircraft programs to support model development and applications studies for seventeen (17) years, beginning with pioneering flights in 1987. Supplementing the long-term sampling performed by LADCo in the Midwest, there

have been other occasional intensive airborne sampling campaigns throughout the easter n U.S. (e.g., the 1999 SOS field program which provided aloft data for our evaluation of CMAQ for the July ’99 episode), that have produced very useful inform ation for air quality model performance testing. Fortunately, during CY-2002, there were at least tw o mature airborne field programs underway in the eastern U.S. One was centered over the Midwest, the other on the mid-Atlantic coast. A brief characterization of these potentially valuable CMAQ and CAMx model evaluation data sets is given

here.

During 2002, the Wisconsin Department of Natural Resources (WDNR) and the Midwest RPO (MRPO) (who funded the Jacko aircraft) co llaborated on the support of airborne sampling using two aircraft that, along with ground-ba sed measurements, provided a 3-dimensionalDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-7 representation of air pollution concentrations acr oss the upper Midwest with some flight paths extending south to include the Mammoth Cave, KY a nd Dolly Sods, WV Class I areas. The goal of the WDNR/MRPO flights was to collect aloft air quality and meteorological data to support model evaluation and data analyses. The aircraft flights were aimed at: (1) characterizing high fine particle and ozone episodes, (2) characterizing air quality over the Class I areas in the upper Midwest (Isle Royale National Park and Seney National Wildlife Refuge in northern Michigan) on both clean and hazy days, and (3) characterizing urban areas in the Midwest.

As indicated in Table 7-2, airborne sampli ng was performed over a broad region of the

Midwest from 1 June to 22 Novemb er. Lasting 3-5 hours, the WDNR and Jacko aircraft sampled a variety of aerometric parameters (depending upon th e flight and aircraft) including wind speed, wind direction temperature, dew point, relative humidity, pressure, O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg, SO 4 , OC, EC, PM 2.5 , and light scattering (Neph). Still photographs documenting visibility were also coll ected. Presently, the full WDNR/MRPO aircraft database, from the first flights in 1987 to the r ecent sampling in 2003 is being aggregated into a

master data base archive.

At the University of Maryland, research ers have been using ground-based monitors, radiosondes, profilers, and instrumented aircra ft to make observations each year since 1992. Parameters measured included meteorology; selected trace gases; fine particulate chemistry, microphysics and optical properties across broad regions of the middle Atlantic coast. During 2002, the University Research Foundation’s Aztec- F aircraft instrument suite included O 3 , NO, CO, SO 2 samplers, as well as a NO 2 closed-path tunable diode laser system, and a differential GPS-based meteorology (T, RH) and horizontal wind ( u and v horizontal components) data system. Aztec-F flights were made from 23 May to 3 October, typically lasting 3 hours.

The volume of aircraft information availabl e for CMAQ/CAMx performance testing during 2002 is quite significant. Historically, aircraft da ta sets have been used only sporadically in evaluating model performance aloft. Based on cu rrent resource and schedule constraints, we do not plan on evaluating the CENRAP CMAQ/CA Mx 2002 runs using aircraft data.

7.5 Model Evaluation Tools

This section introduces the various statistical m easures, graphical tools, and related analytical procedures that have proven useful over the year s in evaluating grid-based chemical transport models. Many of the methodologies mentioned below are being utilized to one degree or another in WRAP and VISTAS. Where appropriate, they will also be used in the CENRAP 2002 annual 36 km and episodic 12 km evaluation of the CMAQ and CAMx modeling systems. However, while we plan on calculating a rich variety of statistical pe rformance metrics, only a very limited subset of these measures will actually be relied upon to form judgments concerning m odel acceptability and in the final reporting.

The current methodologies and statistical me trics may be augmented, as necessary and appropriate, by new measures that may become available during the course of the CENRAP modeling. The CENRAP evaluation will employ si milar methods and evaluation tools used in WRAP, VISTAS and MRPO only focused on the CENR AP states and vicinity, but the evaluationDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-8 will remain open to emergent methods for te sting model performance where necessary and consistent with the project schedule and resources.

7.5.1 Statistical Performance Metrics

EPA’s 2001 PM and regional haze guidance suggests a suite of metrics for use in evaluating model performance. The standard set of statis tical performance measures suggested by EPA for evaluating fine particulate models includes: (a) normalized bias; (b) normalized gross (unsigned) error; (c) fractional bias; (d) fractional gross error; and (e) fractional bias in standard deviations.

These measures are subsumed with in the list of metrics that are calculated on a routine basis using the UCR and CENRAP model evaluation tools (the se are identified in Table 7-3). The UCR evaluation software will generate these statistical measures for each model simulation performed for each analysis region (see below). In parallel, the CENRAP Evaluation Tool will be used to generate complimentary statistical measures. From past regional PM model evaluations we have found the

fractional bias and fractional error to be the mo st useful summary measures and we will focus mainly upon them in the CENRAP modeling, but not to the exclusion of ot hers that are found to yield discriminating power. For ozone and other gas phase species (NO, NO 2 , SO 2 ) we will include use the traditional statistical measures (EPA, 1991, 1999).

Typically, the statistical metrics are calcula ted at each monitoring site across the full computational domain for all simulation days. In the CENRAP CMAQ/CAMx evaluation, we will stratify the performance statistics across relevant space and time scales. As part of the operational evaluation, the gas-phase and aerosol statistical m easures shown in Table 7-3 will be computed for the full 36 km and 12 km domains, as well as fo r the individual RPOs (including CENRAP of course) and on other subdomains as appropriate. Temporally, we will compute the statistical measures for the appropriate averaging times: 1 hr for ozone, and gas-phase precursors such as NO,

NO 2 , CO, SO 2 ; 8-hr for ozone, and 24 hr for sulfate, nitr ate, PM and other aerosol species. These results will then be averaged over annual, m onthly, and seasonal periods for display, further analysis, and reporting. Should it become necessary as part of model performance diagnosis, we will consider aggregating the statistics in othe r ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipitation vs. non-precipitation da ys, (d) month of the year, and (e) the 20% haziest/cleanest days, in order to help elucidate model performance problems. Absent performance difficulties, these supplemental time/space analyses w ould only be considered if additional resources are made available. In subregional performan ce testing, the focus would likely be on the Class I areas and sites where enhanced monitoring (e.g., St. Louis Supersite) is available.

As part of the operational evaluation, the metric s defined in Table 7-3 will be calculated for each gas phase species and each fine particulate species in the extinction equation as well as separately for SO 4 , NO 3 and ammonium (NH 4 ) on both the 36 km and 12 km domains. In any diagnostic evaluations that are performed, we will examine the model’s ability to estimate the gaseous species listed above from EPA’s guidan ce (EPA, 2001). However, in reality ambient gaseous species in 2002 are principally available for ozone, NO 2 , SO 2 , and CO. Since most of the gaseous air quality monitors are located in urban settings, these data sets will be of somewhat restricted value in evaluating the 36/12 km regi onal-scale CMAQ predictions. We would consider the merits of assembling a rural-oriented ga seous species observational model performance evaluation database for use with CMAQ/CAMx. This database could be composited withDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-9 measurement from rural sites from the AQS, CAST Net, NPS, STN, and SEARCH networks. If additional resources are made available, this rural vs. urban scale operational evaluation would be conducted to augment the more traditional operational evaluation using all valid data sets.

7.5.2 Graphical Representations

The CENRAP annual modeling CMAQ and CAMx operational air quality model evaluation will utilize numerous graphical displays to f acilitate quantitative and qualitative comparisons between CMAQ/CAMx predictions and measurements. Together with the statistical metrics listed in Table 7-3, the graphical procedures are inte nded to help: (a) identify obviously flawed model simulations, (b) guide the implementation of pe rformance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ/CAMx simulations. Th ese graphical tools are intended to depict the model’s ability to predict the observed fine particulate and gaseous species concentrations.

The core graphical displays to be consid ered for use in the CENRAP annual modeling include the following:

3⁄4 Scatter plots of predicted and observed concentrations;
3⁄4 Time series plots at monitoring locations;
3⁄4 Spatial maps of ground-level gas-phase and particulate concentration maps (i.e., tile plots);
3⁄4 Bias and error stratified by concentration (Bugle Plots);
3⁄4 Bias and error stratified by time (e.g., Soccer Plots); and
3⁄4 Separate displays of above by monitoring network, subregions and time.

These graphical displays will be generated, were a ppropriate for the full annual cycle as well as for monthly and seasonal periods. The displays will be generated with a consistent suite of products including the UCR analysis tools, CENRAP eval uation tool and ENVIRON evaluation software.

7.5.3 Probing Tools and Allied Methods

The CMAQ/CAMx operational model evaluation in the current fiscal year (FY1 through April 30, 2005) will employ routine operational evaluation methods and standard statistical metrics (Table 7-3) and graphical displays to support th e assessment of whether the models are shown to perform with sufficient accuracy and reliably for its intended purpose. Ideally, this operational evaluation will confirm that the modeling systems are performing consistent with its scientific formulation, technical implementation, and at a level th at is at least as reliable as other current stateof-science methods. Should unforeseen model perf ormance problems arise in the 2002 Base Case model simulations, it may be necessary to draw in to the evaluation supplemen tal diagnostic tools to aid in model testing. These diagnostic techniques are loosely referred to as “probing tools”. The actual need for their use, if any, can only be determined once the initial 2002 CMAQ/CAMx operational evaluation is completed. Should such di agnostic methods actually be needed, their usage will require additional resources. Below, we id entify the types of probing tools that could be brought to bear to enhance the currently planned CENRAP operational evaluation of the CMAQ and

CAMx models.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-10 Current ‘One-Atmosphere” models, such as CMAQ and CAMx, have been outfitted with a number of “probing tools” that have proven to be very useful in testing and improving model performance and in evaluating emissions control strategies. Among the probing tools available in one or both models are: (a) ozone source a pportionment technology (OSAT) and PM source apportionment technology (PSAT) algorithms, (b) process analysis (PA), and (c) the decoupled direct method (DDM) sensitivity analysis. CENRAP may choose to evaluate these tools as part of their FY2 (May 1, 2005 through April 30, 2006) modeling exercise, or with the allocation of further resources under the current FY1 period.

Source Apportionment Technology: CAMx contains a suite of “source attribution”

methods. One such method is Ozone Source A pportionment Technology (OSAT). OSAT tracks ozone formation based on how groups of ozone precursors contributed to ozone formation. Thus, OSAT decides whether ozone formation is NOx or VOC limited in each grid cell at each time step,

and bases ozone attributions on the relative amounts of the limiting precursor from different sources that are present in that grid cell at that time step. These incremental ozone attributions are integrated throughout the model run. The method is generally applicable and has been widely used to aid model diagnosis in the performance testing phase and to guide control strategy formulations as well.

A new PM Source Apportionment Technology (PSAT) has been implemented in CAMx funded by the MRPO that has been fully tested and ev aluated. A Tagged Species Source Apportionment (TSSA) approach has also been implemented in CMAQ and is undergoing further testing.

Decoupled Direct Method (DDM): Various forms of the Decoupled Direct Method (DDM) have been installed in CMAQ and CAMx, based on the original work of Dunker and co-workers (Dunker, 1981; 1984; Dunker et al., 2002) and research ers at Georgia Institute of Technology (GIT). In general, the DDM method: (a) calculates first order sensitivities dC/dP where C is a concentration output and P an input parameter 2 , (b) promotes accuracy by using consistent numerical methods and the same time steps for concentrati ons and sensitivities, (c) optimizes the code for efficiency, but not at expense of accuracy, and (d) calculates sensitivities with respect to parameters representing pollutant sources – emissions, BCs and ICs. Finally, the DDM provides a flexible and powerful user interface for defining various sensitivities including:

> Emissions resolved by geographic area. > Emissions resolved by source category. > BCs optionally resolved by boundary edge (N, S, E, W, Top). > All sensitivities available relative to sources of individual species (NO, PAR, etc.) or species group (VOC, NOx or ALL). > Simultaneously calculate sensitivities to many initial condition, boundary condition and emissions parameters.

In recent comparisons between CAMx DDM sensitivities and brute-force sensitivities (calculated from +/- 20% perturbations) Dunker et al., (2002a,b) reported that sensitivities of ozone with respect to area source NOx and VOC emissions were calculated and results indicated that the

2 Recent research by Prof. Russell and coworkers at GIT has led to the extension of the CMAQ DDM method to include second order sensitivity coefficients (see, Hakami et al., 2003).December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-11 agreement between DDM and brute force sensitiv ities is excellent. DDM implementation into CMAQ is reported by Kumar (2003).

Process Analysis (PA): Photochemical air quality model simulations are usually evaluated primarily in terms of their ability to simulate observed O 3 data. There is an increasing awareness that chemical mechanisms, and air quality models must also be evaluated in terms of their ability to simulate the fundamental chemical processes that control O 3 formation and the sensitivity of O 3 to emissions reductions (Arnold et al., 1998). Proce ss analysis is a method for explaining model simulations by adding algorithms to the AQM to store the integrated rates of species changes due to

individual chemical reactions and other sink a nd source processes (Jeffries and Tonnesen, 1994; Tonnesen, 1995). By integrating these rates over time and outputting them at hourly intervals, process analysis provides diagnostic outputs that can be used to explain a m odel simulation in terms of the budgets of free radicals, production and loss of odd oxygen and O 3 , and conversion of NOx to inert forms, as well as the effects of transport and other sink and source terms. Of particular importance to the CENRAP modeling, process anal ysis can also improve model diagnosis and performance evaluation efforts by identifying proce sses that are ‘out of balance’ (Tesche and Jeffries, 2002), by identifying situations for whic h the model formulation and/or implementation should not be expected to apply and by suggesting how ambient data can be used to evaluate model accuracy for key terms in the chemical processing of VOC and NOx (e.g., Imre et al., 1998).

Process Analysis (PA) is implemented in both CMAQ and CAMx and each model supports three complementary aspects of the method: (a) th e integrated process rate (IPR), (b) integrated reaction rate (IRR) and (c) chemical process analysis (CPA). Several versions of process analysis (PA) have been implemented in air quality m odels (AQMs) including both trajectory models (Tonnesen, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and De nnis, 2000; Arnold et al., 1998; and Wang, 1997).

The fundamental approach in all versions of PA is similar: The AQM is modified to calculate the integral over time of the individua l sink and source processes and each chemical reaction. These integrated sink/source process rate s (IPR) and integrated reaction rates (IRR) can then be stored to a file and analyzed using a pos t-processor, or some processing can be performed internally in the model and a more limited set of process diagnostic information is output directly by the AQM. Chemical process analysis (CPA) is an improvement on the IRR method whereby some of the processing of IRR information is intern alized within the AQM to output chemically meaningful parameters directly (e.g., budget terms for O 3 , NOx and odd oxygen).

Process analysis measures for aerosol chemis try have not been analyzed as much as for ozone chemistry. Although the ozone chemistry proce ss analysis is directly related to secondary sulfate and nitrate formation, there is additional process analysis information available in the aerosol modules that are not extracted in either CMAQ or CAMx. In particular, information on sulfate formation and oxidants from the aqueous-phase m odule and on the sulfate/nitrate equilibrium from the aerosol thermodynamics module would be a usef ul addition to the current process analysis

output.

Because application of all three of thes e probing tools--source apportionment, DDM, and Process Analysis—are computational intensive and require a fair amount of analysis time to reap the benefits of using the methods, they do not lend themselves directly to annual simulations. However,December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-12 each method has potential for use in addressing key episodic periods or geographical locations in the CENRAP domain where performance in the 2002 simulation may present a problem or where particular attention needs to be focused on emissions controls (a specific Class 1 area for example).

In such focused applications, one or more of these probing tools may indeed serve a purpose and will be considered where appropriate.

7.6 CENRAP 2002 Annual Model Evaluation Procedures

EPA guidance (EPA, 2001, pg. 227) suggests that the performance evaluation focus on two

aspects:

3⁄4 How well is the model able to replicate observed concentrations of components of

PM 2.5 , and total observed mass of PM 2.5 ? and
3⁄4 How accurately does the model characterize the sensitivity of changes in component concentrations to changes in emissions?

Recognizing that the former is much easier to accomp lish than the latter, EPA goes on to declare that testing of a model’s reliability in estimating the actual effects of emissions changes is the more important. Over the past 20 years, a substantia l body of information and analytical techniques has been developed to address the first aspect. Unfortunately, even today there are little rigorous methods available for quantifying the accuracy and precision of a model’s predictions of ozone, PM or visibility changes as the result of emissions changes. In this section we explain how the CENRAP annual model testing will address the first aspect of the performa nce evaluation, i.e., how does the model compare against observed data. In section 7.10 we consider the second performance

consideration.

7.6.1 Assessment of Ground-Level Gas-Phase and Aerosol Species

Given that visibility is expressed in terms of extinction and deciview built off of individual components of fine particulate matter, the model should be evaluated separately for each of the key fine particulate matter components that make up th e extinction coefficient. Current EPA guidance suggests that the model should also be evaluated for ammonium as well as several key gas-phase species that are important for fine particulate modeling. For particulate species this includes SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , elemental carbon (EC), organic carbon (OC), IP, mass of individual constitu ents of IP, and coarse matter (CM). The
gaseous species include ozone (O 3 ), HNO 3 , NO 2 , PAN, NH 3 , NOy, SO 2 , CO, and H 2 O 2

.

As indicated above, for some of the CENRAP subregions there are very few measurements, if any, for most of the gaseous species. But ozone measurements are available and should be integrated into the model performance evaluation. Given the importance of the photochemistry and the radical cycle in forming secondary fine par ticulate matter, the ozone evaluation will provide some insight into the model’s ability in this area. However, given the coarse grid resolution to be used (36 km and 12 km) perhaps greater weight s hould be given to the model evaluation results at the more rural ozone monitors (e.g., CASTNet) comp ared to urban ozone monitors. In addition toDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-13 the list of PM and gaseous species from EPA’s guidance document listed above, the CENRAP operational evaluation will also examine model’s ability to estimate PM 2.5 mass, PM 10 mass, extinction (b ext ) and deciview (dV) using the equation above to obtain extinction from fine particulate concentrations.

At some of the IMPROVE sites there are also direct measurements of hourly extinction using transmissometer or nephelometer instruments. T hus, it would be scientifically interesting to evaluate the model estimated extinction with the hourly measured values at these sites. If such a comparison were to be made, day-specific hourly a nd site-specific RH and f( RH) values could be used to convert model estimated fine particulate matter to extinction rather than the monthly f(RH) EPA recommends for projecting visibility improveme nts (EPA, 2003a). Furthermore, there have been some discrepancies reported in the past between the extinction calculated using the reconstructed mass data and the hourly measurements . Thus, it would be interesting to calculate day-specific extinction using day-specific f(RH) values and the reconstructed mass data for comparison with the direct measurements and the m odel estimates. Whether and to what extend this more detailed investigation into light extincti on is pursued would depend upon the availability of

additional resources.

As part of the CMAQ/CAMx operational evaluation, model outputs will be compared statistically and graphically to observationa l data obtained from the IMPROVE, SEARCH, CASTNet, EPA-FRM, EPA-STN, and other monito ring networks. These monitoring data will be obtained from AIRS, VIEWS, and other appropria te organizations. These comparisons will likely

include:

3⁄4 Daily monthly, seasonal and annual averages for SO 2 , SO 4 , NO 3 , EC, OC, PM 2.5 , and

PM 10 , taking care to exclude periods of sampli ng interference in the observational data. We will look for systematic biases between the model results and IMPROVE observations, and if biases are found, identif y possible sources of error in the model inputs.

3⁄4 Hourly, high resolution PM species and gaseous species concentrations at sites where available (e.g., SEARCH, AIRS and EPA-Supersites).

3⁄4 At sites with contrasting aerosol mass loadings , analysis of the temporal behavior of the major scattering and absorbing aerosol constituents along with the visibility trends, to

establish correlations

The optional CMAQ/CAMx diagnostic model evalua tions may entail several components, many of which can be identified presently. Of course, the act ual diagnostic analyses to be performed and the scope of such analyses can only be determined once the initial operational model evaluation is underway. These potential diagnostics analyses will need to be carefully defined and rank-ordered in terms of their priority to ensure that they can be accommodated within available resources and schedule. Among the diagnostic model evaluation analyses that could be considered are:

3⁄4 Evaluate seasonal trends in observations of organic and inorganic aerosol precursors and their effects on PM composition and visibility, and evaluate the ability of the model to capture these seasonal trends.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-14 3⁄4 Evaluate how well the model simulates various physicochemical processes by: (a) examining observed and modeled correlations between various species pairs, and (b) comparing model-predicted ratios of various species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

3⁄4 Investigate the performance of the model at selected observational sites characterized by different chemical regimes that may be enc ountered either spatially or during different seasons to help identify any inadequacies in the model and to provide a better understanding of conditions under which model inferences may be weak.

3⁄4 Compare hourly 24-hour average, and episode averages of the PM constituents for the 2002 36 km annual modeling period as well as the 12 km episodes across all sites.

3⁄4 Create scatter plots of modeled vs. observe d data and hourly and 24-hour averages by site and subregion to help identify any site-specific biases.

3⁄4 Create time series plots of predicted and observed concentrations as appropriate.

3⁄4 Evaluate for total sulfur (SO 2 + SO 4 ), nitrate (HNO 3 + NO 3 ) and ammonia (NH 3 + NH 4 ).

3⁄4 Compare observed versus modeled mass fractions of PM constituents at various sites that are characterized by their proximity or remo teness relative to sources, or by specific meteorological conditions (e.g., frontal passa ge, stagnation, precipitation); these will

enable identification of trends in the mode l of over- or under-prediction of specific PM

constituents under these conditions.

3⁄4 Calculate the measured and predicted relative abundance of key PM components and compare with EPA guideline recommendations and emergent alternative science recommendations (e.g., removing the soil co mponent from the calculations, use of alternative relative importance equations [i.e., Boylan, 2004]).

3⁄4 Pay particular close attention to the model performance at the Class I areas for SO 4 , NO 3 , EC, OC, IP and CM on the 20% best and 20% worst days to document whether certain of these days should be eliminated from the visibility projections due to inadequate model performance.

The suite of statistical metrics and graphical tool s identified in the previous section for the core operational evaluation efforts would likely also be used to diagnose performance problems with the CMAQ/CAMx simulations should they exist and to highlight differences between model runs. Experience in ozone/PM modeling is the best basis upon which to identify obviously flawed simulation results. Efforts to improve the CM AQ/CAMx model’s base case performance will be made, where necessary, warranted (i.e., to reduce the discrepancies between model estimates and observations), and consistent with the project resources and schedule; however, these model performance improvements efforts must be based on sound scientific principles. “Curve-fitting”

exercises will be avoided.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-15 7.7 Performance Goals and Benchmarks

Establishment of performance goals and benc hmarks for regulatory m odeling is a necessary but difficult activity. Here, performance goals re fer to targets that we believe a good performing model should achieve, where as performance benchm arks are based on historical model performance measures for the best performing simulations. Perf ormance goals are necessary in order to provide consistency in model applications and expectations across the country and to provide standardization in how much weight may be accorded modeling study re sults in the decision-making process. It is a problematic activity, though, because many areas present unique challenges (e.g., Houston, San Joaquin Valley, Los Angeles) and no one set of perfor mance goals is likely to fit all needs. Equally concerning is the very real danger that modeling studies will be truncated when the ‘statistics look right’ before full assessment of the model’s reliability is made. This has the potential from breeding built-in compensating errors (Reynolds et al., 1996) as modelers strive to get good statistics as opposed to searching for the explanations for poor performance and then rectifying them. A

NARSTO review of more than two-dozen urban-scal e ozone SIP applications found this tendency to be all too prevalent in the regulatory modeling of the 1990s. (Roth et al, 1997).

Nearly 15 years ago, research sponsored by the California Air Resources Board (Tesche et al., 1990) led to the agency’s adop tion of three performance goals for 1-hour ozone modeling in the

state:

> Unpaired (in time and space) peak prediction accuracy ( ≤ ± 20%); > Mean normalized bias in hourly averaged concentrations ( ≤ ± 15%); and > Mean normalized gross error in hourly concentrations ( ≤ 35%).

These performance goals for 1-hr ozone concentra tions were adapted from previous surveys of several dozen urban-scale photochemical grid modeli ng studies (principally in California) focusing on ozone episodes of 1 to at most 3 days in dura tion. A surprising number of these studies did not include biogenic VOC emissions in the inventory under the then prevailing belief that biogenics were a negligibly small source category compared to automobile emissions. Most of the studies (Tesche, 1985, 1988; Tesche et al., 1985; 1990) compri sing the data base from which the California ozone performance goals were derived entailed hourly ozone concentrations well above background levels (~40-50 ppb). As a result, it was common practice to use a “cutoff values” ranging between 40 ppb to 60 ppb to eliminate prediction-observations pairs that would cause these bias and error residual statistics to become extraordinarily larg e when measured concentrations were low. Accordingly, normalized statistics such as bias and error proved to be suitable in most applications since the observed concentrations were genera lly high. These three California ozone model performance goals were adopted by EPA (1991) as part of the nationwide photochemical modeling guidelines and have been heavily used since.

However, when these evaluation metrics and goals were later adapted to PM and PM species, difficulties arose because performance statistics that divide by low concentration observations become much less useful. Indeed, some PM species may approach zero (e.g., NO 3 ). In time, this

has led to the introduction of the fractional and norma lized mean bias and error metrics in addition to the mean normalized metrics and related perfor mance expectations based on these alternative

measures.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-16 While the 1-hr metrics and goals still have va lue in interpreting ozone and some gas-phase species performance, it has been necessary to de velop new performance metrics and goals for fine particulates. EPA’s PM guidance document (EPA , 2001) guidance document identifies particulate matter components of interest to include: SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , EC, OC, IP, and mass of individual constituents of inorganic primary particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO 3 , NO 2 , PAN, NH 3 , NO y , SO 2 , CO, and H 2 O 2 . In addition, EPA guidance identifies several potentially useful statistical measures including: (a) accuracy of spatially averag ed concentrations near a monitor, (b) fractional bias in means and standard deviations of pred ictions and observations, (c) normalized bias, (d) normalized gross error, (e) unpaired comparisons between predicted and observed peak concentrations. (Interested readers are referred to the EPA guidance document on the details of these metrics including mathematical formulae and implementation methods.)

As with ozone in the 1980s, actual experience with PM models has led to the development of the current performance expectations for these models. For example, PM 10 SIP model performance goals for mean normalized gross error of < 30% for southern California (SCAQMD, 1997; 2003)

and < 50% for Phoenix (ENVIRON, 1998) have been us ed. As correctly poi nted out by Seigneur and co-workers (2003), the current ability of regi onal PM models to predicting regional PM and visibility is an area of research with improve ments needed for characterizing meteorology and emissions as well as PM models themselves. To this list we would add the need for improvements in model evaluation methodologies as well.

When EPA’s draft guidance was developed nearly f our (4) years ago, an interim set of fine particulate modeling performance goals were suggested for aggregated mean normalized gross error

and mean normalized bias as follows:

Pollutant Gross Error Normalized Bias

PM 2.5 ~30-50% ~ " 10%

Sulfate ~30-50% ~ " 20-30%

Nitrate ~20-70% ~ " 15-50%

EC ~15-60% NA

OC ~40-50% ~ " 38%

Because regional-scale fine particulate and re gional haze modeling is an evolving science, and considerable practical application and perfor mance testing has transpired in the intervening years since these goals were postulated, we consider them general guidelines. Results of the WRAP, VISTAS, and MRPO model evaluation together with recommendations from science workshops (e.g., EPA’s PM Model Performance Evaluation Workshop in February 2004) and recently published scientific studies (e.g., Boylan, 2004) will be used to provide support to these

recommendations.

We regard the above goals as simply general interim guidance. Cert ainly, more information on likely performance expectations will be available once the CENRAP ozone and PM performance evaluation and sensitivity results are fully anal yzed. In the CENRAP annual modeling we will generate the model performance statistics listed in Table 7-3 and make comparisons with EPA’s interim goals as one means for:December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-17 3⁄4 Establishing a benchmark on the annual 2002 CENRAP modeling episode, and

3⁄4 Guiding the interpretation of the CMAQ and CAMx modeling results.

Equally as useful, we will endeavor to compare th e results of the CENRAP multi-species evaluations of CMAQ and CAMx with the preliminary CE NRAP modeling (Pun, Chen and Seigneur, 2004; Tonnesen and Morris, 2004), the results from th e BRAVO study (Pitchford et al., 2004) as well as

results from EPA and the other RPOs.

7.8 Diagnostic and Sensitivity Testing

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance e xpectations. Indeed, our experience has been that initial simulations that ‘look very good’, usually do so as the result of comp ensating errors. The norm is to engage in a logical, documented process of model performan ce improvement wherein a variety of diagnostic probing tools and sensitivity testing methods are us ed to identify, analyze, and then attempt to remove the causes of inadequate model performan ce. This is invariably the most technically challenging and time consuming phase of a m odeling study. We anticipate that the annual CMAQ/CAMx model base case simulations will pr esent some performance challenges that may necessitate focused diagnostic and sensitivity testing in order for them to be resolved. Hopefully, these diagnostic and/or sensitivity tests can be ad equately carried out within the resources and schedule of the current work effort. If not, then it may be necessary to draw upon additional resources from the FY2 budget to conduct the nece ssary work. Where practical, diagnostic or sensitivity analyses, if needed, could be performe d on selected episodes within the annual cycle, thereby avoiding the time-consuming task of running CMAQ/CAMx for the fully 2002 period. Below we identify the types of diagnostic and sens itivity testing methods that might be employed in diagnosing inadequate model performance and devising appropriate methods for improving the model response.

7.8.1 Traditional Sensitivity Testing

Model sensitivity experiments are useful in three distinct phases or ‘levels’ of an air quality modeling study and all will be used as appropr iate in the CENRAP modeling with CMAQ and

CAMx. These levels are:

3⁄4 Level I

. Model algorithm evaluation and configuration testing;
3⁄4 Level II . Model performance testing, uncertainty analysis and compensatory error diagnosis, and
3⁄4 Level III. Investigation of model output respons e (e.g., ozone, aerosol, deposition) to changes in precursors as part of emissions control scenario analyses.

Most of the Level I sensitivity tests with CMAQ and CAMx have already been completed by the model developers and the RPOs. However, given the open community nature of the CMAQ andDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-18 CAMx models, and the frequent science updates to the model and supporting da tabases, it is possible that some additional configuration sensitivity testing will be necessary.

Potential Level II sensitivity analyses might be helpful in accomplishing the following tasks:

3⁄4 To reveal internal inconsistencies in the model;
3⁄4 To provide a basis for compensatory error analysis;
3⁄4 To reveal the parameters (or inputs) that dominate (or do not dominate) the model’s operation;
3⁄4 To reveal propagation of errors through the model; and
3⁄4 To provide guidance for model refinement and data collection programs.

At this time, it is not possible to identify one or more Level II sensitivity runs that might be needed to establish a reliable annual 2002 CMAQ or CAMx base case. The merits of performing Level II sensitivity testing will depend upon whether perf ormance problems are encountered in the operational evaluation. Also, the number of tests po ssible, should performance difficulties arise, will be limited by the additional resources and schedule. Thus, at this juncture, one cannot be overly prescriptive on the number and emphasis of sensitivity runs that may ultimately be desirable. However, from past experience with CMAQ, CAMx and other models, it is possible to identify examples of sensitivity runs could be useful in model performance improvement exercises with the annual 2002 CMAQ/CAMx simulation. These include:

3⁄4 Modified biogenic emissions estimates;
3⁄4 Modified on-road motor vehicle emissions;
3⁄4 Modified air quality model vertical grid structure;
3⁄4 Modified boundary conditions;
3⁄4 Modified fire emissions;
3⁄4 Modified EGU emissions;
3⁄4 Modified ammonia emission estimates.
3⁄4 Modified aerosol/N 2 O 5 /HNO 3 chemistry; and
3⁄4 Modified NH 3 and HNO 3 deposition velocities.

If necessary, Process Analysis extraction output s can be included in these Level II diagnostic sensitivity simulations in order to provide insight into why the model responds in a particular way to each input modification. Again, the number, co mplexity, and importance of these types of traditional sensitivity simulations can only be determined once the initial CMAQ/CAMx annual 2002 simulation(s) are executed.

Level III sensitivity analyses have two main pur poses. First, they facilitate the emissions control scenario identification and evaluation pr ocesses. Today, four complimentary sensitivity “tools” can be used in regional photochemical models depending upon the platform being used. These methods include: (a) traditional or ‘brute force’ testing, (b) the direct decoupled method (DDM), (c) Ozone Source Apportionment Technology (OSAT) and PM Source Apportionment Technology (PSAT), and (d) Process Analysis (PA). Each method has its strong points and they will be employed where needed and as resources ar e available. The second purpose of Level III sensitivity analyses is to help quantify the estimated reliability of the air quality model in simulatingDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-19 the atmosphere’s response to significant emissions changes. This important model evaluation need

is addressed in further detail in section 7.9 below.

Based on experience in other regional studies , examples of Level III monthly or annual sensitivity runs for CENRAP annual modeling might include:

3⁄4 Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO 2 emissions;
3⁄4 Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point

source NO

x emissions;
3⁄4 Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level

NO

x emissions; and
3⁄4 Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia.

Of course, traditional ‘brute force’ sensitivity expe riments are just one way of quantifying these or

other Level III sensitivities. Other methods that can be applies include DDM, OSAT, or PSAT

simulations.

The need to perform sensitivity experiment ation (Levels I, II, or III) will depend on the outcome of the CENRAP 2002 annual operational perfor mance evaluations. If such a need arises, the ability to actually carry out selected sensitivity and/or diagnostic experiments will hinge on the availability of additional resources and sufficient time to carry out the analyses. Clearly, selection of the specific analysis method will depend upon the nature of the technical question(s) being

addressed at the time.

7.8.2 Diagnostic Tests

A rich variety of diagnostic probing tools are available for investigating model performance issues and devising appropriate means for improving the model and/or its inputs. Previously, in section 7.4.4 we introduced the suite of ‘probing t ools’ available for use in the CMAQ and CAMx modeling systems. Where the need exists (i.e ., if performance problems are encountered) and assuming CENRAP elects to fund the use of the probi ng tool applications, these techniques could be employed as appropriate to assist in the model performance improvement efforts associated with the annual 2002 CMAQ/CAMx base case development. Here we describe an additional diagnostic method – indicator species and species ratios -- that is potentially useful not only in model performance improvement activities but also in judging the models reliability in estimating the impacts on air quality from future emissions. This method involves the use of so-called ‘indicator species’ and species ratios. If, during the conducting of the CENRAP annual simulations we determine that application of indicator species a nd species ratio techniques would be beneficial to the study (and if existing project resources allo w), we will discuss with the CENRAP Project Manager the merits of including this additional pr obing tool as part of additional work efforts.

Beginning in the mid 1990s, considerable interest arose in the calculation of indicator species and species ratios as a means of diagnosing phot ochemical model performance and in assessing model credibility in estimating the effects of em issions changes. Major contributions to the development and refinement of this general dia gnostic method over the past decade have been made many scientists including Milford et al., ( 1994), Sillman (1995, 1999), Sillman et al., (1997),December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-20 Blanchard (2000), Blanchard and Fairley (2001), and Arnold et al., (2003). Indeed, a recent evaluation of CMAQ using indicator species ratios such as O 3 /NO

x , NO

z /NO y (a measure of chemical aging), and O 3 /NO

z (a measure of the ozone production efficiency per NO

x converted), showed not only good agreement with measurements (Arnold et al., 2003) but also convincingly demonstrated the utility of the method for diagnosing model performance in a variety of ways.

Traditionally, indicator species analyses have focused on ozone and its precursor and product species. However the method is e qually applicable to PM species and species ratios given sufficient measurement data for comparisons. With some of the high-resolutions monitoring data available from the EPA Supersites (e.g., St. Louis in the CENR AP region), it is indeed feasible to compute relevant indicator species and ratios for PM and its component species. For example, Ansara and Pandis (1998) demonstrated how indicator species ra tios could be applied to show how the modeled mass of PM might respond to sulfate, nitrate and ammonia emissions-related reductions.

7.9 Corroborative and Weight of Evidence Modeling Analyses

This section identifies additional modeling an alyses that might be worth pursuing under additional funding to add strength to the core model evaluation efforts already planned as part of the CENRAP 2002 annual operational evaluation.

7.9.1 Corroborative Models

Noteworthy in EPA’s new ozone, PM, and regional haze guidance documents is the encouragement of the use of alternative modeli ng methods to corroborate the performance findings and control strategy response of the primary air qua lity simulation model. This endorsement of the use of corroborative methodologies, stems fr om the common understanding that no single photochemical modeling system can be expected to provide exact predictions of the observed ozone and PM species concentrations in a region th e size of CENRAP, especially over time scales spanning 1-hr to 1 year. Although the photochemi cal/PM models identified in EPA’s PM/regional haze guidance document possess many up-to-date scie nce and computational fe atures, there still can be important differences in modeled gas-phase and aerosol predictions when alternative models are exercised with identical inputs.

Mindful of EPA’s endorsement of corroborativ e modeling methods and the rigorous use of ‘weight of evidence’ investigations, we reco mmend that the most recent version of CMAQ and CAMx be carried through the study, including the ev aluation of emissions c ontrol strategies. Among other things, this will permit us to more explicitly identify the expected range of model uncertainty and to corroborate the general effectiveness of the CMAQ and CAMx regional haze control strategies. Other corroborative modeling me thods such as the CMAQ-AIM and CMAQ-MADRID should also be considered. However, as these models are derivatives of CMAQ they would not provide as robust independent corroboration as CAMx.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-21 7.9.2 Weight of Evidence Analyses

EPA’s guidance recommends three general types of ‘weight of evidence’ analyses in support of the attainment demonstration: (a) use of ai r quality model output, (b) examination of air quality and emissions trends, and (c) the use of corrobora tive modeling such as obse rvation-based (OBM) or observation-driven (OBD) models. We will consider the use of one or more methods in conducting the CMAQ/CAMx modeling because it could signifi cantly strengthen the cr edibility and reliability of the modeling available to the states for their subs equent use. The exact de tails of the ‘weight of evidence’ analyses must wait until the CENRAP mode ling study evolves further. It is premature to prescribe which, if any of the WOE analyses w ould be performed since the model’s level of performance with the 2002 episode is obviously not known at this time and the time and remaining project resources available to support WOE analys es is unknown as well. Nonetheless, we outline below our thoughts regarding what would lik ely be considered should the operational CMAQ/CAMx model evaluation need to be bolstered with WOE analyses.

Use of Emissions and Air Quality Trends. A limited scope emissions and trend analysis could be employed to support the ‘weight of eviden ce’ determinations. However, traditionally, these types of analyses are performed by the lead agency’s own staff. With this expectation, we would

coordinate our efforts with the CENRAP Mode ling Workgroup to develop a trends analysis supporting the future year applications of CMAQ/CAMx.

Use of Corroborative Observational Modeling

. While regulatory modeling studies for ozone attainment demonstrations have traditionally relied upon photochemical models to evaluate ozone control strategies, there has recently been growing emphasis on the use of data-driven models to corroborate the findings of air quality models. As noted, EPA’s guidance now encourages the use

of such observation-based or observation-driven models (OBMs/ODMs). We will consider the merits of using these techniques as supportive we ight of evidence. While the OBD/OBM models cannot predict future year air quality levels, they do provide useful corroborative information on the extent to which specific subregions may be VOC -limited or NOx-limited, for example, or where controls on ammonia or SO2 emissions might be most influential in reducing PM 2.5 . Information of this type, together with results of DDM and trad itional ‘brute-force’ sensitivity simulations, can be extremely helpful in postulating emissions control scenarios since it helps focus on which pollutant(s) to control.

7.9.3 Comparison with Other RPOs

WRAP, VISTAS and MRPO are also mode ling the 2002 annual period that provides an opportunity to compare the CENRAP modeling resu lts with that from the other RPOs as an independent check of the modeling.

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes

EPA identifies three methods (EPA, 2001, pg. 228) potentially useful in quantifying a model’s reliability in predicting air quality respons e to changes in model inputs, e.g., emissions.

These include:December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-22 3⁄4 Examination of conditions for which substa ntial changes in (accurately estimated) emissions occur;

3⁄4 Retrospective modeling, that is, modeling before and after historical significant changes in emissions to assess whether the observed air pollution changes are adequately simulated; and

3⁄4 Use of predicted and observed ratios of ‘chemical indicator species’.

We note that in some urban-scale analyses, th e use of weekday/weekend information has been helpful in assessing the model’s response to emissi ons changes. However, we suspect that this approach would not prove feasible with an a nnual episode over geographic domain as large as

CENRAP.

The first two methods have actually been cons idered for over 15 years and were the subject of intensive investigations in the early 1990s in Southern California in studies sponsored by the South Coast Air Quality Management District (Tes che, 1991) and the American Petroleum Institute (Reynolds et al., 1996). To date, neither method has proven useful largely because of the great difficulty in developing historical emissions inve ntories of sufficient quality to make such an analysis credible and the difficulties in removi ng the influences of different meteorological conditions such that the modeling signal reflects only the model’s response to emissions changes. It is difficult enough to construct re liable emissions inventories using today’s modeling technology let alone construct retrospective inventories 5-10 year s ago prior to the implementation of significant emissions control programs or major land use changes. The use of indicator species, however, offers some promise.

R ecent analytical and numerical modeling studies have demonstrated how the use of ambient data and indicator species ratios can be used to co rroborate the future year control strategy estimates of Eulerian air quality models. Blanchard et al., (1999), for example used data from environmental (i.e., smog) chambers and photochemical models to devise a method for evaluating the 1-hr ozone predictions of models due to changes in precurs or NOx and VOC emissions. Reynolds et al., (2003) followed up this analysis, augmented with proce ss analysis, to assess the reliability of SAQM photochemical model estimate of 8-hr ozone to pr ecursor emissions cutbacks. With respect to secondary aerosol PM, the recent CMAQ evaluation by Arnold et al. (2003) clearly demonstrated how the use of indicator species analysis could be use to develop insight into the expected reliability and adequacy of a photochemical/PM model for simulating the effects of emissions control

scenarios. These researchers used three indicat or ratios (or diagnostic ‘probes’) to quantify the model’s response to input changes:

3⁄4 The ozone response surface probe [O 3 /NO

x ];

3⁄4 The chemical aging probe [NO

z /NO y ]; and

3⁄4 The ozone production efficiency probe [O 3 /NO

z ].December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-23 By closely examining the CMAQ’s response to key input changes, properly focused in time and spatial location, Arnold et al., (2003) were able to conclude that the photochemical processing in CMAQ was substantially similar to that in the atmosphere

Thus, the extension of these techniques to address CMAQ and CAMx predictions for secondary aerosols will doubtless be quite challe nging, but the use of indicator species (e.g.,

ammonia or HNO 3 limitation for nitrate particle formation) and species ratios appears to offer, at this time, the only real opportunity to quantify the e xpected reliability of the air quality model to correctly simulate the effects of emissions changes. In the CENRAP CMAQ and CAMx model evaluation, we will remain alert to opportunities to extend the indicator species ratio analyses to the problem of fine particulate and regional haze. This is one area where technical collaboration between the Emissions and Air Quality Modeli ng team and the CENRAP Modeling Workgroup can be especially fruitful in term s of identifying and testing emergent methods for challenging the model’s ability to correctly simulate the effects of future year emissions changes. Finally, we note that this is truly a current research area and as such falls outside the scope of the current CENRAP annual modeling effort. However, given its impor tance, we will remain alert to opportunities to utilize newly available methods should this prove feasible within the CENRAP resources and

schedule.

Table 7-1 . Ground-level ambient data monitoring networks and stations available in the CENRAP
states for CY-2002.

Monitoring

Network Chemical Species Measured Sampling Frequency;

Duration Approximate

Number of

Monitors

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr 11

CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week 3

SEARCH 24.hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); and Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 0

NADP WSO4, WNO3, WNH4 Weekly 23

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr (?)

EPA-STN Speciated PM 2.5 Varies; Varies 12

AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly 25

EPA-SPEC Various as part of St. Louis Super
Site Various 1+December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-24 Table 7-2 . Aircraft sampling programs performed during CY-2002.

Aircraft Program Meteorological Parameters & Chemical Species Measured Sampling Program & Flight Duration Approximate

Number of Flights; Days;

Aircraft University of Maryland (UMD);

Univ. Research Foundation (URF) Meteorology: WS, WD, Temp, RH, Air Quality : O 3 , NO, NO 2 , CO,
SO 2, aerosol absorption, aerosol scattering. 23 May to 3 Oct; Typically 3 hrs 54 flights, 54 days,
1 aircraft

Midwest RPO &

Wisconsin DNR Meteorology : WS, WD, Temp, RH, dew point, pressure Air Quality : O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg,
SO 4 , OC, EC, PM 2.5 , light scattering (Neph), visibility pictures. 1 June to 22 Nov; Typically 3-5 hrs 133 flights; 29 days;
2 aircraft (WDNR and Jacko Aircraft)December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-25 Table 7-3. Core statistical measures to be used in the CENRAP 2002 annual air quality model evaluation with ground-level data (see ENVIRON, 2003b,d for details).

Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Accuracy of paired peak ( A p ) Paired_Peak

peak peak


## O O P

− P peak = paired (in

both time

and space) peak prediction

Coefficient of determination ( r 2 ) Coef_Determ



# ∑ ∑ ∑

= = =



## − −      

− −

N

i N

i i i N

i i i



## O O P P O O P P

1 1 2 2 2

1



## ) ( ) ( ) )( ( P i = prediction at

time and

location i ; O i = observation

at time

and location

i ;

P = arithmetic average of P i , i=1,2,..., N;

O = arithmetic average of O i , i=1,2,...,N

Normalized Mean Error ( NME ) Norm_Mean_Err



# ∑ ∑

=

= 

## −

N

i i N

i i i



## O O P

1 1 Reported as %

Root Mean Square Error ( RMSE ) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

## 1      

− 

# ∑

= N

i i i 

## O P N Reported as %

Fractional Gross Error ( F E ) Frac_Gross_Err 

# ∑

= 

## + − N

i i i i i 

## O P O P

N 1 

## 2 Reported as %

Mean Absolute
Gross Error ( MAGE ) Mean_Abs_G_Err 

# ∑

= 

## − N

i i i 

## O P N 1



## 1

Mean Normalized
Gross Error ( MNGE ) Mean_Norm_G_Err 

# ∑

= 

# − N

i i i i 

# O O P

N 1



# 1 Reported as %December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 7.doc 7-26 Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Mean Bias ( MB ) Mean_Bias 

# ( ) 

# ∑

= 

## − N

i i i 

## O P N 1



## 1 Reported as

concentration (e.g., μg/m 3 )

Mean Normalized Bias ( MNB ) Mean_Norm_Bias 

# ( ) 

# ∑

= 

## − N

i i i i 

## O O P

N 1



## 1 Reported as %

Mean

Fractionalized Bias (Fractional Bias, MFB ) Mean_Fract_Bias 

# ∑

= 

##        

+ − N

i i i i i 

## O P O P

N 1 

## 2 Reported as %

Normalized Mean

Bias ( NMB ) Norm_Mean_Bias



# ∑ ∑

=

= 

## −

N

i i N

i i i



## O O P

1 1 

## )

( Reported as %

Bias Factor ( BF ) Bias Factor

1 

## 1 N

i

i i 

## P

N O

= 

##       

# ∑ Reported as

BF:1 or 1: BF or
in fractional notation (BF/1 or 1/BF).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-1 8.0 VISIBILITY ASSESSMENT AND COMPARISON

This chapter provides a summary of how th e CENRAP 2002 annual modeling results will be used to satisfy the requirements of the Regi onal Haze Rule (RHR) as part of the Section 308 visibility State Implementation Plans (SIPs) and Tribal Implem entation Plans (TIPs) due in 2007/2008. Note that the CENRAP 2002 modeling result s may also be used in some capacity as the regional component in some State’s fine particulate (PM 2.5 ) and 8-hour ozone SIPs that are also due in 2007/2008. However, how the modelin g results will be used to address PM 2.5 and 8-hour ozone issues is not discussed in this chapter and will be addressed by the pertinent States at a later time. The purpose of this section is to start planning how the modeling results will be used to project visibility changes for th e Section 308 SIPs/TIPs to optimize the presentation of results and identify any potential pitfalls early on.

8.1 Regional Haze Rule Requirements

The Regional Haze Rule (RHR) regulations were published by EPA in 1999 (Federal Register 35769, July 1, 1999) and are designed to address the requirements of Section 169A and 169B of the Clean Air Act (CAA). The CAA and RH R call for the protection of visibility at the 156 “mandatory Federal Class I areas.” 1 Section 169A of the Clean Air Act Amendments (CAAA) established a national visib ility goal to remedy existing impa irment due to air pollution at the Class I areas. This is accomplished by defining a visibility goal of “nat ural conditions” to be achieved at each Class I area by 2064. The RHR require s states with Class I areas to develop SIPs that include reasonable progress goals for improvi ng visibility in each Class I area and emission

reduction measures to meet thos e goals. For the initial contro l strategy SIPs due in 2007/2008, states are required to adopt progr ess goals for improving visibility from baseline conditions. The 2000-2004 five-year period is used to define baseli ne conditions and the first future progress period is 2018, that has been in terpreted as either the 2013-2017 or 2014-2018 five year periods. A state is required to set progress goals for each Class I area in the state for two visibility metrics:

• Provide for an improvement in visibility for the most impaired (i.e., 20% worst) visibility days; and

• Ensure no degradation in visibi lity for the least impaired (i.e., 20% best) visibility days.

The reasonable progress goals must provide for a rate of improvement sufficient to attain “natural conditions” by 2064, or justify any altern ative rate. States are to defi ne controls to meet progress goals every 10 years, starting in 2018, that defines progress periods ending in 2018, 2028, 2038, 2048, 2058 and finally 2064. States will determine whether they are meeting their goals by comparing visibility conditions from one five -year period to another (e.g., 2000-2004 to 2013- 2017). As stated in 40 CFR 51.308 (d) (1), baseli ne visibility conditi ons, progress goals, and

1 Areas designated as mandatory Class I areas are t hose National Parks exceeding 6, 000 acres, wilderness areas and national memorial parks exceeding 5,0 00 acres, and all international parks th at were in existence on August 7, 1977. Since then some new Class I areas have been defined and some existing Class I areas have been expanded. However, these areas are not included as part of the 156 “Federal mandated Class I areas” for which the RHR applies. In this document the term “Class I area” re fers to the 156 “Federal mandated” Class I Areas.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-2 changes in visibility must be expressed in term s of deciview (dv) units. The deciview unit of visibility impairment is de rived from light extinction (b ext ) as follows:

dv = 10 ln (b ext /10)

Where light extinction (b ext ) is expressed in terms of inverse megameters (Mm -1 = 10 -6 m -1 ).

Section 8.2.1.1 below discusses how b ext is calculated using the observed fine particulate

concentrations from the IMPR OVE monitors and S ection 8.2.1.2 below defines how the modeling results will be mapped to the IMPROVE measurements for the pu rpose of projecting visibility improvements.

8.2 EPA Guidance for Complying with Regional Haze Rule

EPA has published three guidance documents that relate to how modeling results should be used to demonstrate compliance with the RHR:

“Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze, Draft 2.1 (EPA, 2001).

“Guidance for Tracking Progress Under the Regional Haze Rule” (EPA, 2003a).

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” (EPA, 2003b).

The first EPA modeling guidance document lis ted above (EPA, 2001) discusses the use of modeling results to demonstrate progress toward the RHR goals. This is a dr aft document that is scheduled to be updated by the end of 2004. Wh en the draft modeling guidance is updated that may necessitate an update to this Modeling Prot ocol to keep it consistent with current EPA guidance. The second document (EPA, 2003a) focuse s on monitored visibility and how to define the visibility baseline and how to track visi bility goals. The third EPA guidance document discusses procedures for defining “natural conditions” for a Class I area that is the visibility goal in 2064. In the discussion below we are assuming that other CENRAP participants would define the

“natural conditions” for each CENRAP Class I area. In this Chapter we address the use of the modeled and monitored fine particulate concentrati ons at the Class I areas for projecting visibility changes from the current to future years.

Below we describe EPA’s approach for projectin g visibility improvements. Some concerns have been raised regarding some of the assumpti ons in EPA’s visibility projection approach, such as: (1) EPA assumes that sulfate is fully neutra lized by ammonium which may not always be the case; (2) EPA’s natural background fails to a ccount for sodium chloride which may be an important component at some site s; and (3) the adjustment fact or assumed to convert organic carbon (OC) to organic matter (OM) (1.4) may be t oo low. There may be some updates to EPA’s visibility projection approach, but the ba sic approach will likely remain unchanged.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-3 8.2.1 Procedures for Projecting Visibility Changes

EPA guidance recommends using the model in a relative sense to project future-year visibility conditions (EPA, 2001; 2003a). This is done through the use of Rela tive Reduction Factors (RRFs) that are defined as the ratio of th e future-year to the current-year modeling results. The RRFs are applied to the baseline visibility c onditions to project future-year visibility. The major features of EPA’s recommended visibil ity projections are as follows (EPA, 2003a,b):

• Monitored data should be used to define current air quality.

• Monitored concentrations of PM 10 are divided into six major components, the first

five of which are assumed to be PM 2.5 and the sixth is PM 2.5-10

. 3⁄4 SO4 (sulfate);
3⁄4 NO3 (particulate nitrate);
3⁄4 OC (organic carbon);
3⁄4 EC (elemental carbon);
3⁄4 OF (other fine particulate); and
3⁄4 CM (coarse matter).

• Models are used in a relative sense to develop relative reduc tion factors (RRFs) between future and current predicte d concentrations of each component.

• Component-specific relative reduction fact ors are multiplied by current monitored values to estimate future component concentrations.

• Estimates of future component concentra tions are consolidated to provide an estimate of future air quality, which can be related to a goal for regional haze.

• Future estimated air quality is compared wi th the goal to see if the simulated control strategy results in the goal being met.

• It is acceptable to assume that all measur ed sulfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].

In order to facilitate tracking visi bility progress, three important vi sibility parameters are required

for each Class I area:

Baseline Conditions : Baseline Conditions represent visi bility for the 20% best and 20% worst days for the initial five-year baseline period of the regional haze program. Baseline

Conditions are calculated us ing monitor data collected during the 2000-2004 five-year period.

Current Conditions : Current Conditions for the best and worst visibility days are calculated from the latest five-year averag e based on the latest five-years of monitored data (which is currently 1999-2003, but expected to be 2000-2 004 by the time the CENRAP future-year visibility projections will be ma de in the latter half of 2005).December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-4 Natural Conditions : Estimates of natural visibility conditions for the 20% best and 20% worst days at a Class I area that is the goal of the RHR is 2064 that has been defined as visibility conditions that would be expe rienced in the absence of human-caused impairment” (EPA, 2003b).

8.2.1.1 Calculation of Baseline Conditions

Baseline and Current Conditions for Class I ar eas are calculated using fine and coarse particulate concentration measurements at In teragency Monitoring of Protected Visual Environments (IMPROVE) monitors. Currently, each Class I area in the CENRAP domain has an

associated IMPROVE PM monitor. The IMPROVE monitors do not directly measure visibility, but instead measure speciated fine particulate (PM 2.5 ) and total PM 2.5 and PM 10 mass concentrations from which visibility is ob tained through the IMPROVE reconstructed mass extinction equation.

Visibility conditions are estimated starti ng with the IMPROVE reconstructed 24-hour average particulate matter (PM) mass measurements for six PM species:

• Sulfate [SO 4 ];

• Particulate Nitrate [NO 3 ];

• Organic Matter [OM];

• Elemental Carbon [EC];

• Other Fine Particulate [Soil]; and

• Coarse Matter [CM].

The IMPROVE monitors do not directly monitor some of these species so assumptions are

made as to how the IMPROVE measurements can be adjusted and combined to obtain these six components of light extinction. For example, sulf ate and particulate nitr ate are assumed to be completely neutralized by ammonium and are assumed to occur solely in the fine particulate mode (that is any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM IMPROVE measurement). Concentrations for the above six components of light extinction in the IMPROVE reconstructed mass ( RCM) extinction equation are obtained from the IMPROVE measured species as follows:

Table 8-1. Definition of IMPROVE Reconstructed Mass (RCM) species from measured IMPROVE species.

IMPROVE RCM IMPROVE Measured Species

SO 4 1.375 x ( 3 x S)

NO 3 1.29 x NO3_

OM 1.4*OC1 + 1.4*OC2 + 1.4*OC3 + 1.4*OC4 + 1.4*OP

EC EC1 + EC2 + EC3 – OP

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI

CM MT – MFDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-5 Where:

• S is elemental sulfur as determined fr om proton induced x-ray emissions (PIXE) analysis of the IMPROVE Module A that is multiplied by 3 to account the presence of oxygen, if S is missing then the sulfate (SO4) measured by ion chromatography analysis of the Module B is us ed to replace (3 x S). It is assumed to be completely neutralized by ammonium (1.375 x SO4).

• NO3_ is the particulate nitrat e measured by ion chromatogr aphy analysis of the Module B. It is assumed to be complete ly neutralized by ammonium (1.29 x).

• The IMPROVE Organic Carbon (OC) measur ements are multiplied by 1.4 to obtain Organic Matter (OM) to adjust the OC mass for other elements assumed to be

associated with OC.

• Elemental Carbon (EC), which is also referre d to as Light Absorbing Carbon (LAC) is determined by TOR analysis and is the su m of EC fractions minus the pyrolized

fraction.

• Soil is determined as a sum of the ma ss of those elements (measured by PIXE) predominately associated with soil, whos e mass is adjusted for oxygen in the common compounds.

• MT and MF are total PM 10 and PM 2.5 mass, respectively.

Associated with each PM species is an extinc tion coefficient that converts concentrations (in

 g/m 3 ) to light extinction (in inverse mega meters, Mm -1 ). Sulfate and nitrate are hygroscopic so relative humidity adjustment factors [f(RH)] are used to modify the extinction coefficients that increase the particle’s extinction efficiency with increa sing RH to account for the particles taking on water and having higher light scattering properties. Note that some Organic Matter (OM) compounds may also have hygroscopic properti es, but the IMPROVE reconstructed mass extinction equations assume OM is non-hygroscopic.

B Sulfate = 3 x f(RH) x [SO 4 ]

b Nitrate = 3 x f(RH) x [NO 3 ]

b EC = 10 x [EC]

b OM = 4 x [OM]

b Soil = 1 x [Soil]

b CM = 0.6 x [CM]

Monthly average f(RH) factors are used as r ecommended in EPA’s guidance (EPA, 2003a). These values have been recently updated (SAIC, 2003) and are available at: ftp://ftp.saic.com/raleigh/Regiona lHaze_2002FRHcurve/fRH_analysis/

.

The total light extinction (b ext ) is assumed to be the sum of the light extinction due to the six PM species listed above plus Rayleigh (blue sky) background (b Ray ) that is assumed to be 10

Mm -1

.

b ext = b Ray + b Sulfate + b Nitrate + b EC +b OM + b Soil + b CM

The total light extinction (b ext ) in Mm -1 is related to visual range (VR) in km using the following relationship:December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-6 VR = 3912 / b ext

The Regional Haze Rule requires that visibility be expressed in terms of deciview (dv) that uses natural logarithms of the extinction as follows:

dv = 10 ln(b ext /10)

The equations above using data from the associated IMPROVE mo nitor are used to estimate the daily average visibility at each Cl ass I area. For each year from the 2000-2004 baseline these daily average visi bility values, in terms of decivi ew (dv), are then ranked from highest to lowest. The 20% worst days visibility for each given year is given as the average visibility across the 20% highest visibility days in deciview. Similarly, the 20% best days are given as the average visibility across the 20% lo west visibility days in terms of deciview. The Baseline Conditions is the average of the 20% be st and 20% worst days across the five-years of

2000-2004. The Current Conditions is the average of the 20% best and 20% worst days based on the latest five-years of available data, wh ich would be either 1999-2003 or 2000-2004 for the CENRAP annual modeling analysis depending on when the 2004 data become available.

8.2.1.2 Mapping of Modeling Results to the IMPROVE Measurements

As noted above, to project future-year visibil ity at Class I areas the modeling results are

used in a relative sense to scale current obser ved visibility for the 20% best and 20% worst visibility days using RRFs that ar e the ratio of modeling results for the future-year to current-year. This scaling is done separately for each of the six components of light extinction in the IMPROVE reconstructed mass extinction equa tions. The modeled species do not necessarily exactly match up with the IMPROVE reconstructed mass species, thus assumptions must be made to map the modeled species to the IMPROVE reconstructe d mass species for the purpose of projecting visibility improvements. For example, the models explicitly simulates ammonium and sulfate may or may not be fully neutralized in the model, whereas the IMPROVE rec onstructed mass equations assume sulfate is fully neutralized by amm onium. For the CMAQ Version 4.4 model and the CAMx V4.10s using the M5 coarse/fine mode conf iguration that are being used in the CENRAP annual modeling, the mapping of modeled speci es to IMPROVE rec onstructed mass (RCM) species listed in Table 8-2.

Table 8-2a. Proposed mapping of CMAQ V4.4 modeled species concentrations to IMPROVE Reconstructed Mass (RCM) species.

IMPROVE RCM CMAQ V4.3 Species

SO 4 1.375 x (ASO4J + ASO4I)

NO 3 1.29 x (ANO3J + ANO3I)

EC AECJ + AECI

OM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI

Soil A25J + A25I

CM ACORS + ASEAS + ASOILDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-7 Table 8-2b. Proposed mapping of CAMx V4.10s using M4 (coarse/fine) modeled species concentrations to IMPROVE Reconstructed Mass (RCM) species.

IMPROVE RCM CAMx V4.10s M4 Species

SO 4 1.375 x PSO4

NO 3 1.29 x PNO3

EC PEC

OM POA + SOA1 + SOA2 + SOA3 + SOA4

Soil FCRS

CM CCRS + CPRM

If a different model is used (e.g., RE MSAD, CMAQ-MADRID, CMAQ-AIM or CAMx using multi-sectional algorithms) or updates to CM AQ or CAMx change its species definitions, then the species mappings would have to be defined specific for that model. Note that in the above species mapping it is assumed that all of the CM AQ and CAMx estimated sulfate and particulate nitrate are in the fine mode. If a fully sec tional model was used (e.g., CMAQ-AIM or CAMx fullscience), then a decision would have to be ma de whether to map the coarse mode sulfate and

nitrate to the IMPROVE fine SO 4 and NO 3 species (as implicitly assumed for CMAQ and CAMx M4 using mappings in Table 8-2) or to include it in the IMPROVE CM species. In fact this is also an issue for the CMAQ V4.3 modal approach in which the lognormal size di stribution for sulfate

and nitrate in the accumulation mo de (i.e., ASO4J and ANO3J) may have some mass greater than

2.5 : m, however the current usual convention for CM AQ is to assume all th e sulfate and nitrate

are in the fine mode.

8.2.1.3 Using Modeling Results to Project Changes in Visibility

Modeling results are used in a relative fashion to project future-year visibility using relative reduction factors (RRFs). RRFs are expressed of the ratio of the modeling results for the futureyear to the results of the base year and are Class I area and PM species specific. RRFs are applied

to the individual 24-hou r observed PM species measurements for the 20% Worst and 20% Best days that make up the Baseline Conditions to proj ect future-year PM levels from which visibility can be assessed using the IMPROVE RCM extincti on equations listed above. EPA has identified the following six steps to project future-year visi bility for the 20% best and 20% worst days (EPA, 2001; 2003a):

1. For each Class I area visibility is ranked using IMPROVE reconstructed PM mass (RCM) extinction equation for each year that compri ses the five-year Baseline Conditions (i.e., 2000-2004).

2. Calculate the arithmetic average of the 20% be st and 20% worst visibility days, in deciview (dv), for each year of the five-year baseline period and then calculate the five-year average of the 20% best and 20% worst days. Docume nt which days during th e five-year baseline period comprise the 20% best and 20% worst observed visibility days.

3. Use an air quality model to simulate th e baseline period emissions and future-year emissions. Extract 24-hour average PM species concentrations “near” each Class I area. Calculate the average PM species concentratio n estimates in the current and future-yearDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-8 simulation across the 20% best and 20% worst observed visibility days for each year in the five-year baseline period. Average across the five-years the average PM species

concentrations for the 20% best and 20% wors t days and for each year in the five-year baseline period. Calculate Class I area and be st/worst 20% days RRFs for each of the six PM components as the ratio of the five-year average estimated PM species concentration in the future-year to current year.

4. Multiply the 20% best/worst days and PM species dependent RRFs by the observed 24- hour PM species concentrations for each day from the 20% best/wor st days to obtain future-year daily average PM species concentr ations for each day from the 20% best and 20% worst visibility days from the five-year baseline period.

5. Using the future-year estimated 24-hour PM sp ecies concentrations for the best/worst 20% observed days, calculate extinction using the IMPROVE reconstructed mass extinction equation and daily deciview, perf orm annual averaging of the visibility (dv) estimates for the 20% best/worst days for each year and then obtain the five-year average visibility (dv) for the future-year.

When selecting model estimated PM species c oncentrations “near” the monitor, EPA (2001) recommends taking a spatial average of PM concen trations across a grid cell resolution dependent NX by NY array of cells centered on the grid cont aining the monitor. For the CENRAP annual modeling using a 36 km grid, just the model estimates for the grid cell containing the monitor will be used (i.e., NX=NY=1). For the 12 km mode ling, EPA recommends that NX=NY=3, that is the model estimated PM species con centrations are averaged across 9 grid cells centered on the IMPROVE monitor (EPA, 2001).

8.2.1.4. Exclusion of Days from 20% Best/Worst Conditions

As discussed in Chapter 7, the model perfor mance evaluation will pay particular close attention to the model’s PM performance at ea ch Class I area for the 20% Best and 20% Worst days. If the model performance is extremely poor fo r one or more of these da ys at a Class I area, it may be desirable to exclude that day when calcu lating the RRFs for visibility projections. For example, if a day is one of th e Worst 20% days at a Class I area and the model estimates clean background concentrations then it would be inappropriate to use the modeling results, even in a relative fashion through the RRFs, to project future-year visibili ty. The Modeling Team would identify such suspect days and work with the CENRAP Modeling Workgroup on whether they should be excluded from the visibility projections on a case-by-case basis.

8.3 CENRAP Future-Year Modeling

The CENRAP future-year modeling will use the 2002 MM5 meteorological conditions. That is, we will assume that the meteorological c onditions for the future-year are the same as for 2002. This will allow for the comparison of the cha nges in visibility at Class I areas from the current (2002) to future-year due to changes in em issions. This means that the effects of climate change, land use variations and climatic variations will not be accounted for in the future-yearDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-9 meteorological inputs. Several other decisions c oncerning the future-year to be modeled, model(s)

to be used and modifications to th e model inputs to reflect future ye ars do need to be made such as:

8.3.1 Future-Year to be Modeled

Visibility projects are needed for the 20% be st and 20% worst days from the five-year baseline period of 2000-2004 to 20 18, which has been interpreted as the five-year future-year periods of 2013-2017 or 2014-2018. The EPA guidance documents are inconsistent on what future-year should be used for the assessment. The 2001 modeling guidance sa ys that the mid-year from the 2013-2017 five-year proj ection period should be used, wh ich would be 2015 (EPA, 2001, pg. 221). The 2003 guidance for tracking progress doesn ’t discuss the future-year that should be modeled, but does mention that go als should be compared across 5- year planning periods “(e.g., 2000-2004 to 2013-2017) and progress should be measur ed as improvement from 2004 to “2018” (EPA, 2003, pg. 1-6). CENRAP and other RPOs will be looking to EPA for providing more definitive guidance as to which future-year should be used for modeling visibility progress and this Modeling Protocol will be updated when such in formation is available, which should be when

EPA released the revised PM 2.5 and regional haze guidance docume nt by the end of 2004. Thus, in the discussion below we just refer to the “future-y ear” with the actual year to be determined during 2004. Current thinking is that the 2018 fu ture-year will be used in CENRAP.

8.3.2 Future-Year Emissions

The future-year emissions or growth and cont rol factors will be provided to the CENRAP annual Emissions and Air Quality Modeling T eam (ENVIRON/UCR) by the CENRAP emissions projection contractor (TBD). Th ey would be processed into th e gridded speciated hourly threedimensional emissions inputs for photochemical grid modeling using the SMOKE emissions model and the procedures discussed in Section 5.2. The same biogenic emissions as used in the 2002 Base Case modeling will be used for the futu re-year modeling. This assumes that the same

land use and biomass distribution as used in th e 2002 Base Case emissions would exist in the future-year emission scenarios. The effects of ur ban sprawl, increased agri cultural, deforestation, locations of new roads, etc. between the curre nt (2002) and future-year would not be accounted for. Typical Year future-year fire emissions that are consistent with the typical year fire emissions used in the 2002 Typical Year emis sions scenario would be used in the future-year. Future-year Typical Year emissions for Electrical Generating Un its (EGUs) would also be used for the futureyear scenarios.

8.3.3 Future-Year Initial and Boundary Conditions

The same Initial Conditions (ICs) as used in the 2002 Base Case would be used in the future-year modeling. Because a ~15 day spin up period is being used, ICs will have minimal if any influence on the model estimated concentrations.

Boundary condition (BC) concentrations that are assumed along the lateral edges of the

continental US national RPO 36 km modeling domain for curren t (2002) year are based on a 2002December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-10 simulation of the GEOS-CHEM global climate mode l. Ideally, the future-year BCs should be based on a GEOS-CHEM simulation using the 2002 meteorology and the future-year global emission inputs. However, currently such a simulati on is not available. In the absence of a futureyear emissions scenario GEOS-CHEM simula tion, we recommend that the same CMAQ/CAMx BCs be used in the future-year as used in the 2002 base year (i.e., 2002 GEOS-CHEM results) as any adjustments to them would be arbitrary and speculative.

8.3.4 Other Future-Year Modeling Inputs

All other future-year CMAQ and CAMx modeli ng inputs would be identical to the 2002 Base Case simulation including meteorology, photo lysis rates, geophysical , and other inputs.

8.4 Presentation of Results

There are various ways that the future-year m odeling results can be presented to display the visibility projections and convey the key fi ndings. Although the proj ection of visibility improvements at the Class I areas is a key elemen t of the Regional Haze Rule (RHR), analyzing the results solely at the Class I areas is a very narrow view of them, thus we intend to present the future-year modeling results in other forms to convey a more complete picture.

8.4.1 Projection of Visibility Improvements at Class I Areas

The results of the visibility projections at each Class I area in and around the CENRAP domain would be displayed graphically as well as in tabular summaries. Spread sheets of the results would be generated orde red by state and provided to CENR AP. The visibility projections at each Class I area would be compared with the 2018 visibility progress goal calculated following EPA’s “Guidance for Tracking Progress Unde r the Regional Haze Rule (EPA, 2003a).

Figure 8-1 displays a sample plot of a visi bility progress goal pr epared for the Grand Canyon National Park as part of the WRAP Section 309 SIP work effort. The modeled projected visibility could be plot ted on the same figure as a graphical way of presenting the results. Shown as a straight line near the botto m of Figure 8-1 is the EPA defaul t “natural conditions” for the 20% worst visibility days as the green line with green triangles that represents the visibility goal at Grand Canyon of 6.97 dv in 2064. The black diamonds on the left side of the plot are the “current

conditions” that are based on IMPROVE observations for the 20% worst days for five-year periods ending in 1993 (i.e., 1989-1993) to 2001 (i.e., 1997-2001). The “curr ent conditions” for the latest five years of data, which is 1997-2001 in this gr aph (the Section 309 SIP analysis was performed

in 2003 before the 2002 IMPROVE data were availa ble), is assumed as the “baseline conditions” (i.e., 2000-2004) that is the star ting point in 2004 (12.00 dv) for th e glide path or linear uniform rate of progress to natural cond itions in 2064 (purple line with s quares). In this example, the visibility progress goal in 2018 would be 10.83 dv so that a 1.17 dv reduction in visibility is needed by 2018 to meet the visibility progress ta rget in 2018 at the Grand Canyon National Park (GMNP). Note that we would exp ect higher observed visibility le vels for the 20% worst days at many of the CENRAP Class I areas due to higher sulfate concentrations in the eastern US, butDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-11 Figure 8-1 illustrates the t ypes of graphs that can be utilized to display visibility progress goals

combined with visibili ty progress projections.

Tabular summaries could list for each Class I area, in terms of deciview, the observed Baseline Conditions, the 2018 Progress Goal, the difference (i.e., visi bility progress target) and then the modeled difference for each future-year emissions scenario. Such a table would clearly state which future-year emission scenarios met the RHR 2018 Progress Go als at which Class I

areas.

Another useful display of the projected visibility improvement s are stacked bar charts of extinction showing the contributio ns of each of the six major PM components to light extinction for the IMPROVE measured current period to th e future-year period and the 20% best and 20% worst days. These side–by-side current to futu re-year extinction compar isons can graphically display which extinction components are getting be tter and which are getting worse. They can also be used to identify why the emission controls may not have had as large an effect on visibility as expected. For example, an SO 2 emissions control strategy may have had the desired effect in reducing sulfate concentrations but the visibilit y estimates remain rela tively unchanged as the lower sulfate concentrations frees up ammonia that then forms particulate nitrate that compensates for the lower sulfate in the visibility equations (i .e., nitrate replacement). Such a chemical shift in the extinction budget would be readily apparent in the stacked bar charts. The most useful forms to convey the modeling results will evolve over the project.December 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-12 Figure 8-1. Example calculation of visibility progress goals using IMPROVE data collected at the Grand Canyon National Park for the 20% Worst days showing EPA default “Natural Conditions” (green triangles) for the 20% Worst days that is the 2064 Goal (6.97 Dv), “Current Conditions” Observed Visibility (Dv) as black triangles wh ose latest value (12.00 Dv) is assumed as the starting point for the 2000-2004 baseline (first purple square), and the glide path from 2004 baseline to “Natural Conditions” in 2064 assuming linear uniform rate of progress.

8.4.2 Spatial Maps of Results

Spatial maps of changes in individual species, total PM mass, extinction and deciview from the 2002 Typical Year to future-year simulations w ould be used to provide a big picture view of the changes in regional air quality and visibility from 2002 to th e future–year. The display of hourly and 24-hour animations as well as longer-term averages (e.g., monthly, seasonal and annual) may provide more insight into the modeling results.

8.4.3 Time Series Plots

Time series plots of concentrations extinction and deciview at key receptor locations, such as Class I areas, may also provide additional insight into the modeling results. Hourly time series for selected periods and locations may provide insi ght into short-term changes, whereas changes in 

### Uniform Rate of Reasona ble Progress Glide Path Grand Canyon NP - 20% Worst Days

12.00 11.66 10.83 9.99 9.15 8.31 7.47 6.97

0 5 10 15 20 25 30

1993 1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063

YearHaziness Index Unit:Deciviews

Glide Path Natural Visibility Conditions ObservationDecember 2004

H:\CENRAP_Modeling\Modeling_Proto col\Draft2.0\Chapter 8.doc 8-13 24-hour concentrations for the year provides inform ation for all days, not just the ~40 days that constitute the 20% best/worst days of the year.

8.4.4 Box Plots and Q-Q Plots

Box and whisker plots of the projected extinctio n and deciview at Class I areas as well as

absolute modeled extinction and deciview at all sites can be used to see changes in visibility across all concentrations, not just the 20% best/worst da ys. Quantile-Quantile plots of the changes in frequency distribution projected using RRFs and absolute results from the model would also provide an indication of the change s in visibility from the curren t to future-year across all days.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 9.doc 9-1 9.0 DATA MANAGEMENT

Data management and data security procedur es are critical com ponents of the CENRAP regional fine particulate and haze modeling. Very la rge data files are used in each component of the modeling process, including processing of the meteorology data, emissions processing, and visibility modeling with CMAQ and CAMx. An annual simulation on the CENRAP 36-km domain requires approximately 2 Terabytes (T b) of disk storage for CMAQ, with CAMx requiring less than half that storage need due to use of more efficient file formats (e.g., doesn’t use 3D emission inputs). This chapter describes data management practices.

For all critical files we w ill maintain backup copies eith er on tapes or redundant disk systems. In addition, because model simulations will be performed separately by ENVIRON and UCR, each institution will maintain its own copy a nd backup of critical input and output files. Because there are differences in system conf igurations at each of the modeling centers (ENVIRON and UCR), the data backup and archivi ng are discussed separately for each center,

below.

CMAQ and CAMx generate large output files of which most information is rarely used (for example, model output for layers other th an the surface layer). A common practice is to extract layer 1 data from the 3D concentrati ons files for analysis and then deleted the 3- dimensional concentration output files. Whether this will be done for the CENRAP runs remains to be seen. In any event, we w ill permanently saved the script, ex ecutables and input data so that

3-D concentration files can be regenerated if needed.

To promote efficient, reliable communicat ion among project participants, the modeling

team will create several different listservs to aid in dissemination of information and as a primary means for distributing emissions and air quality modeling information to the CENRAP Project Manager, Administrative Project Manager and Modeling Workgroup.

9.1 Project Website

For the preliminary CENRAP modeling anal ysis performed by UCR and ENVIRON, a project webpage at UCR was used to publish and disseminate all project results. The CENRAP preliminary modeling webpage address is: http://pah.cert.ucr.edu/ aqm/cenrap/index.shtml . We will establish a new webpage for the CENRAP 2002 annual modeling. Th e project webpage will be the primary mechanism for communicating resu lts of model simulations and analysis to funding agencies and other interested parties.

9.2 Data Transfer

Data transfer among the modeling centers a nd between other CENRAP participants or contractors will be accomplished using a combinati on of email, ftp downloads and portable disk drives depending on the size of the data transfer. For data files smaller than a few MB email typically works well and is most efficient. For da ta files of less than ab out 500 MB file transfer protocol (ftp) is typically the fastest an d most efficient met hod. ENVIRON and UCR eachDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 9.doc 9-2 maintain webpages and ftp pages that can be us ed for exchanging data . In addition, each modeling center has severa l portable disk drives with both USB2 and firewire interfaces that can be FedEx among project participants to exchange large data sets. Portable disk drives range in size from 80 to 300 GB and are adequate for al l large files data transfers. The approach described here has been used throughout the WR AP and VISTAS projects and has proven to be

economical and efficient.

9.3 Data Backup and Archiving

Data backup and archiving will be performed at each of the modeling centers. Copies of critical project data will be maintained at each modeling center to provide redundant backup of key project data. In addition, ea ch modeling center will perform backups of key project data to tape or redundant disk storage systems. Data storage and back up resources at each modeling

center are described next.

9.3.1 ENVIRON

The ENVIRON modeling center has over 15,000 Gi gabytes (>15 Terabytes, Tb) of disk storage available to the UNIX/ Linux workstations. All of th e workstations are networked together and are accessible from each employee’s desktop PC. All workstations have CD-ROM drives and can access DLT, 4mm DAT and 8mm Exa byte tape drives for data backup and data transfer. ENVIRON can also create CDs (C D-R and CD-RW) and DVDs (DVD+ and DVD-) for data backup and distribution. For CENR AP 2002 annual modeling, most CMAQ and CAMx simulations would be performed on one of the 9 node Beowulf Linux Clus ter that includes one master node and 8 processing nodes. Each node consists of two AMD Athlon 2600+ (or faster) processors. The master node has 2 Gb of memo ry and is connected to a 2.8 Tb RAID disk system. Each secondary processing node incl udes 1 Gb of memory. The ENVIRON Novato computing center also includes approximately 10 dual processor Linux workstations with processing speeds of 1700+ to 3000+. Three older Un ix workstations are also available, SUN, DEC and SGI. The SMOKE modeling would likel y be performed using a dual processor Linux box. The Linux computer systems are located in their own room with their own dedicated air conditioning (AC) system. The room includes a te mperature sensitive power shut off device that will shut off the power to all co mputers in case the AC breaks dow n so that catastrophic failure due to too high temperatures does not occur. Backups are made on IDE disk drives that are removed from the computer and st ored on a shelf to protect agains t power surges destroying the backup data.

9.3.2 UC Riverside

Data storage systems at UCR include more than 22 TB of disk space configured as RAID5 disk systems. All computers and disk systems are connected using high speed Gigabit

Ethernet for efficient simulati on and analysis of large datase ts. To provide maximum data security the systems are located behind the UCR firewall and an additional firewall internally within the laboratory. A separate system is used for the project websites and ftp site to allowDecember 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 9.doc 9-3 project data to be accessed thought the UCR T1 internet connection. The data backup/archiving system include 8mm tape drives and DLT and S uper DLT auto loading car tridge system capable of performing unattended archive/ backups of over 1 Tb (uncompressed). Key disk systems have hot-swappable hard drives with stand-by spare drives and redundant power supplies. Copies of critical project data are backed up to tape a nd to a redundant RAID5 disk system. The compute clusters and disk systems are located in a locke d, secure room with a dedicated climate control system and with backup air c onditioning. The laboratory has a full time systems administrator to perform system backups, maintenance and upda tes and Dr. Tonnesen’s group includes a second full time systems administrator who performs we ekly backups of critical data to tape.December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 10.doc 10-1 10.0 DOCUMENTATION

This section describes the documentation th at be provided during CENRAP annual modeling and the potential for modifications to this M odeling Protocol and QAPP that might become necessary as the study unfolds.

10.1 Planned Documentation

Documentation associat ed with the emissions and air quality modeling performed during the CENRAP annual modeling will include all relevant i nput data bases and scripts associated with the pre- and post-processing associated with model input development, model application, sensitivity and diagnostic analyses, and performance evalua tions. PowerPoint presentations, technical memorandums, interim and final reports that desc ribe the methodologies and results of the model performance evaluation, model intercomparison, a nd visibility assessment will be provided. Table

10-1 below lists the current schedule of deliv erables under the CENRAP 2002 annual modeling and analysis study.

Table 10-1. Current list of deliverables and schedule under the CENRAP 2002 annual modeling study.

Deliverable Deliverable Due Date Task 1. Develop QAPP. Draft due to CENRAP November 15, 2004
Final due to CENRAP Within 1 week of

comments Task 2. Develop Modeling Protocol/Work plan. Draft due to CENRAP November 15, 2004
Final due to CENRAP Within 1 week of

comments Task 3. Develop model-ready meteorology inputs. Presentation (in electronic format) due to CENRAP December 20, 2004 Model-ready 2002 meteorology inputs completed December 31, 2004 Task 4. Develop base case model-ready emission inputs. Monthly progress reports due to CENRAP 2 nd week of following

month Technical memorandum due to CENRAP February 21, 2005 Presentation (in electronic format) due to CENRAP February 21, 2005 Model-ready emissions completed January 31, 2005 Task 5. Perform 2002 base case modeling evaluation. Monthly progress reports due to CENRAP 2 nd week of following

month Recommendation on model configuration due to CENRAP March 15, 2005 Draft report due to CENRAP April 30, 2005 Base case model simulations completed April 30, 2005 Task 6. Develop future case model-ready emission inputs. Monthly progress report due to CENRAP 2 nd week of following

month Technical memorandum due to CENRAP April 30, 2005 2018 Model-ready emissions completed April 30, 2005 Task7. Perform 2018 modeling evaluation, sensitivities, and control strategies. Monthly progress report due to CENRAP 2 nd week of following

month Draft sensitivity and source apportionment report due to August 31, 2005December 2004

H:\CENRAP_Modeling\Modeling_Pr otocol\Draft2.0\Chapter 10.doc 10-2

Deliverable Deliverable Due Date

CENRAP Final sensitivity and source apportionment report due to
CENRAP Within 1 week of

comments 2018 model simulations completed April 30, 2006 Task 8. Reports and recommendations Draft report due to CENRAP December 31, 2005 Final report due to CENRAP April 30, 2006

In addition, copies of all scripts, reports and documentation provided to the CENRAP Modeling Workgroup will be maintained on the pr oject website. A final report summarizing all aspects of the project will also be provided.

Reporting on each task in the 2002 CENRAP modeling will consist of documentation of the

 data sources,

 methods,

 results, and findings. Individual task deliverables shall reflect any changes and revisions that occur over duration of study. At the completion of the CENRAP annual modeling, a draft final report will be prepared th at details, documents and summarizes the results.

This documentation will conform to the recommendations set forth in EPA’s "Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze (Jan ‘01)” and any subsequent versions. The final report will contain: (a)

 an executive summary abstract that provides a brief overview and summary of the modeling effort , emissions and air quality models used, model configuration, model performance evaluation overvie w and results, and rationale for the selected configuration, (b) technical detail covering all re levant aspects of the CENRAP 2002 emissions and PM grid modeling, and (c) a discussion on data accessibility and availability for review by the public. The report will be provided in electronic form, (e.g., Word 2000 and pdf formats) and shall be submitted to the CENRAP Project Manager and also posted on the project web site.

10.2 Procedures for Updating Modeling Protocol and QA Plan

One of the underlying realizations stemmi ng from the preliminary CENRAP modeling

activities was the awareness that the science of “One-Atmosphere” PM/regional haze modeling is advancing very rapidly. Part of this stems from the parallel activities being carried out by the RPOs and EPA; some if it is due to other ongoing 8-hr ozone and PM modeling studies being performed by various states. In addition, EPA is in the process of revising its PM 2.5 and regional haze guidance documents. Collectively, it is quite likely that there will be new opportunities to strengthen the modeling algorithms, input data sets, and evalua tion procedures throughout the duration of the CENRAP annual modeling study. Moreover, wh en EPA updates the regional haze modeling guidance that may also trigger a need to upda te the CENRAP modeling approach and Modeling Protocol. Given the ongoing model refinement activ ities and the need for strong coordination with other CENRAP contractors, it may be necessary to modify certain aspects of this Modeling Protocol. In this event, modification will be made in cons ultation with the CENRAP Project Manager and the revised protocol will be submitted to the CENRAP Modeling Workgroup for approval.December 2004

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