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 415.899.0700 
December 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............................................................................... 2-13 
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 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 PREPARATION PROCEDURES ................................................. 5-1 

 5.1 Meteorological Inputs to Emissions 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 Kv................................................................. 5-4 

 5.2 Development of Emissions Model Inputs 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............................................................ 5-18 
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 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 ASSURANCE 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 Sensitivity Testing................................................................... 7-17 

 7.8.1 Traditional Sensitivity 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 ......................................................................... 8-7 
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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 Modeling Protocol and QA Plan..............................10-2

REFERENCES........................................................................................................................ R-1 
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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 model configuration for CENRAP 2002 

 annual modeling (Johnson, 2004)...................................................................... 2-16 

Table 2-2. SMOKE emissions model configuration 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 model 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 programs performed during CY-2002. ................................. 7-24 

Table 7-3. Core statistical measures to be used in the CENRAP 2002 

 annual air quality model evaluation 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. ............................................................................. 10-1
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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...................................................................................... 8-12 
 

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1.0 INTRODUCTION 

This report constitutes the second draft of the Air Quality Modeling Protocol and Quality 

Assurance (QA) plan for the CENRAP 2002 annual Emissions and Air Quality Modeling 

activities to be performed by the contractor team of ENVIRON International Corp and the 

University of California at Riverside. Development of this second draft Modeling Protocol 

governing the CENRAP 2002 annual Emissions and 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 will 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 Regional Planning Organizations (RPOs) that have responsibility 

for coordinating development of State Implementation 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 (PM2.5) 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 and 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, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma 

and Texas. The CENRAP Emissions and Air Quality Modeling Team is comprised of staff from 

ENVIRON International Corporation (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, providing 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. Through 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 mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards, and Tribes also may opt to assume 

responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation 

Plans (TIPs). The goal of the Regional Haze Rule (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 requires the first visibility SIP/TIP be submitted to 
 

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EPA by 2007 - 2008 to demonstrate progress toward natural visibility conditions in 2018 using 

the 2000-2004 five-year baseline. The CENRAP Long Range Plan (CENRAP, 2003) is to have 

an evaluation of initial strategies by July 2006 that States and Tribes can start their planning 

process for submitting their plans to EPA by December 2007. 

Regional haze is linked to fine particulate (PM2.5) for which EPA has a new standard. 

PM2.5 SIPS are to be submitted 3 years after designation of PM2.5 nonattainment areas. EPA 

intends to perform the final designations of the PM2.5 nonattainment areas by December 2004, 

which would make the PM2.5 SIP due by December 2007. As regional haze is intricately linked 

to PM2.5, the PM2.5 and regional haze SIPs are in the process of being aligned so they would both 

be due by December 2007, if the PM2.5 final designations occur as planned. 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 PM2.5 and regional haze. Those decisions will be based on schedule constraints, 

model performance, appropriateness of the episodes, feasibility and need. 

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

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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 Oversight Group (POG) that is made up of 

18 voting members representing states 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 interested 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 states, and the tribes may elect to, submit the 

first SIPs and TIPs in 2007/2008 that address progress toward natural conditions at federally 

mandated Class I areas. 40 CFR 51.308 (Section 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 implementation plan requirements; and 
 

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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 scientifically supportable, economical and effective 

control strategies that the states and tribes may adopt to reduce manmade effects on visibility 

impairment at Class I areas. One component of CENRAP’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 potential 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 scale, three-dimensional air quality models that simulate the 

emissions, chemical transformations, and transport 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 emissions inventories and models with regional transport 

models. The general services provided by the CENRAP Emissions and Air Quality Modeling 

Team include, but are not limited to: 

• Emissions processing and modeling; 

• Air quality and visibility modeling simulations; 

• Analysis, display, and reporting of modeling results; and 

• Storage/quality assurance of the modeling input and output files. 

The CENRAP 2002 annual Emissions and Air Quality Modeling Team performs work 

for the CENRAP Modeling Workgroup through direction 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 (CENRAP, 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 requirements of the Regional 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; and 
 

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• 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 supportable and effective RHR SIPs and TIPs. 

1.3.2 Tier Two: Activities 

The second tier (Activities) consists of broad, categorized work efforts that must be 

completed in order to complete a Task. For example, the 2002 annual Emissions 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 term strategy for reducing regional haze” Tasks 

listed above. 

1.3.3 Tier 3: Projects 

The third tier consists of specific Projects aimed at completing an Activity. For example, 

to perform the 2002 annual modeling, 2002 emissions are needed, thus a specific project would 

be to compile 2002 emissions for the CENRAP states 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 Quality Modeling activity would include a Modeling Protocol, a 

QAPP, and Base Case Modeling and Model Performance Evaluation Report and the modeling 

databases. 

 

1.3.5 Critical Milestone Dates for CENRAP 

The CENRAP Long Range Plan has developed 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: 
 

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SIP/TIPs Adopted and States Submit 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 and Adopt SIPs and TIPs – July 1, 2006: A survey of 

the timelines for SIP approval for states in the CENRAP region indicates 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 control strategies. Thus, the base year base 

case and model performance evaluation needs 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 perform base year base case modeling and model 

performance evaluation. 

1.3.6 Role of Emissions and Air Quality Modeling 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 exhaustive quality assurance (QA) 

and quality control (QC) techniques are necessary. 

1.4 Past Related Regional Modeling Studies 

The CENRAP 2002 annual Emissions and Air Quality 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 regional haze at the Big Bend National Park, the 

most southwesterly Class I area in the CENRAP states. It performed data collection 

activities, modeling and used numerous techniques to estimate PM source apportionment 

(Pitchford et al., 2004). 
 

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CENRAP Scoping Study: CENRAP commissioned a scoping study to identify the causes 

of visibility impairment at Class I areas in the CENRAP states and identify the analytical 

tools that are available to investigate regional haze (Green et al., 2002). 

CENRAP Ammonia Emissions Inventory Study: CENRAP sponsored a study to develop 

an improved ammonia emissions inventory for the CENRAP states (Coe and Reid, 2003). 

CENRAP Agricultural and Prescribed Burns Study: In this study improved emissions 

inventories for prescribed burns and agricultural 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-road mobile source and agricultural 

dust emission inventories for the CENRAP 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 photochemical 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 was 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 evaluation 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 modeling data needed to develop Section 309 SIP/TIP for states 

that opt-in to this program (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 analysis (Morris et al., 2004b). 

MRPO Modeling and Analysis: The Midwest RPO is also performing regional haze 

modeling and analysis that will 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). 
 

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EPA VII MM5 Modeling: EPA Region VII is performing 12 km MM5 modeling for the 

Central States and portions of 2002 that will be used in the CMAQ/CAMx modeling 

analysis. 

Many of the above studies above are providing data (e.g., emissions) that will be used 

directly in the CENRAP Emissions and Air Quality Modeling Study. Consequently, the quality 

assurance (QA) and quality control (QC) procedures are directly relevant to this QAPP. Others 

are companion modeling studies (e.g., BRAVO, VISTAS and WRAP) that provide information 

that is used in the development of this QAPP and the CENRAP Emissions and Air Quality 

Modeling Protocol (ENVIRON 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 detailed 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, the CENRAP 2002 annual modeling will focus on the 

use of the SMOKE emissions and 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 fiscal 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 strategy 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 simulations and performance 

evaluations using a 2002 actual emissions inventory provided by CENRAP and other 

sources and 2002 MM5 meteorology provided by CENRAP. Multiple iterations of the 

2002 annual simulation may be required to confirm the appropriateness of the model 

science configuration(s), to evaluate updates to the model and 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 tests 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 for all other source sectors. The primary objective of this 
 

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inventory is to provide the baseline modeled 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 strategies to be performed would occur during the 

second part (May 2005 to April 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 annual continental US and episodic 12 km 

episodic central states emissions and air quality modeling will be ongoing project management, 

technical review, and quality assurance activities performed under the guidance of the CENRAP 

Project Manager, Administrative Project Manager and Modeling Workgroup. The Modeling 

Team members will participate with CENRAP management in regular conference calls, as well 

as ad hoc topical conference calls as needed, and will attend periodic meetings with the 

CENRAP Modeling Workgroup. 

Complementing the data acquisition, modeling input development activities, and project 

management activities, four other CENRAP 2002 annual Emissions and Air Quality Modeling 

activities will be performed, consistent with the Quality Assurance Project 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 data from other CENRAP contractors and other sources (e.g., 

EPA, WRAP, IDNR, etc.) and as a first line of QA, we have defined a Gatekeeper function to 

assure the data have been received correctly, evaluate the quality of the data, and document the 

data received. Separate air quality, meteorological and emissions Gatekeepers 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 gatekeeper will also provide 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 quality 
 

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checks as QAPP together with appropriate 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 emissions 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 convert 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 emissions 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 CENRAP to support all Phase 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 alternative media) of electronic files to CENRAP 

states, other regional planning organizations, EPA, other contractors, 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 processing is tedious, time 

consuming and involves complex manipulation of many 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 emissions and air quality modeling. This includes 

the initial emissions QA/QC by the Emissions Gatekeeper 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 emission files. This multi-step process 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. 

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 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 
 

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review the log files for serious error messages. 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 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-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 daily total 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 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 that display the diurnal 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 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 daily total emissions 

differences between a control strategy and base case emissions scenarios will be 

generated. These plots can be used to immediately identify a problem in a control 

strategy. For example, if a CENRAP state’s SO2 control strategy is being analyzed and 

there are changes in emissions for other pollutants or for SO2 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 subjected to the above QA, the CMAQ-to-CAMx 

emissions processor would be exercised to generate CAMx-ready emission inputs. The CAMxready emission inputs would then be subjected to an additional round of QA to assure that 

identical emissions are contained within the CMAQ-ready and CAMx-ready emission inputs. 
 

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1.5.3 Meteorology QA/QC 

The CENRAP meteorological modeler will have 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 obtain an assessment of the quality of the data and to assist in the 

interpretation of the air quality modeling results. 

The CENRAP Meteorological Gatekeeper 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 estimates by comparison them to upper-air 

observations and satellite images; 

3⁄4 Compare the CENRAP 2002 MM5 simulation performed with the performance of the 

WRAP and VISTAS MM5 modeling; and 

3⁄4 Generate the CMAQ-ready and CAMx-ready 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 input and output data include the following: 

• Verification that correct configuration 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 sets are used when running each model. 

• Evaluation of CMAQ and CAMx results to verify 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 results against concurrent observations. 

• Backup and archiving of critical model input data. 

 The most critical element in the QA for CMAQ and CAMx simulations 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 model simulations is verification that the correct 

science options were specified in the model itself and that the correct input files were used when 
 

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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 science options are used. We also employ a redundant naming system so that 

the name of key science options or inputs are 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 directory 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 patterns in the CMAQ and CAMx output files. In the 

case of model sensitivity studies, the animated gifs will be prepared as difference plots for the 

sensitivity case minus the base case. Often, errors in the emissions 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 summary 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 various CENRAP participants and contractors and other sources that 

have performed their own Quality Assurance (QA) and Quality Control (QC). Whenever data 

are received by the Modeling Team, 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-ready emissions or meteorological files) are then subjected to 

another round of QA to assure the integrity of the data is retained. 

Once the model-ready inputs have been developed 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 evaluating regional PM and haze models that 

includes performance goals (EPA, 2001). In addition, the Modeling team has adapted EPA MPE 

approaches and goals for 1-hour (EPA, 1991) and 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 model 
 

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simulation. As seen in Figure 1-2b, the MPE process 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, allday_onesite and allsite_oneday. 

ENVIRON Analysis Tools: ENVIRON has developed specialized 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 model performance across networks, episodes, 

species, models and sensitivity tests and compare them with performance goals. As part 

of VISTAS, the Georgia DNR has developed “Bugle Plots” that display model 

performance as a function of observed concentration 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 database 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 advantage of their different descriptive and complimentary nature. The 

use of multiple model evaluation tools is also a useful 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 established 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 performance 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 components that make up a larger fraction of 

the PM mass than those that are minor contributors (EPA, 2001). Measuring PM species is not 

as precise as ozone monitoring. In fact, the differences in measurement techniques for some 

species likely exceed the more stringent performance goals, such as those for ozone. For 

example, recent comparisons of the PM species measurements using the IMPROVE and STN 

measurement technologies found differences of approximately "20% (SO4) to "50% (EC) 

(Solomon et al., 2004). 
 

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In the CENRAP 2002 CMAQ Base Case modeling, we have adopted three levels of 

model performance goals for bias and gross error as listed in Table 1-2 that are used to help 

evaluate model performance. Note that we are not suggesting that these performance goals be 

generally adopted or that they are the most appropriate goals to use. Rather, we are just using 

them to frame and put the PM model performance into context and to facilitate model 

performance intercomparison across episodes, species, models and sensitivity tests. 

As noted in EPA’s draft PM modeling guidance, 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 concentrations proposed by Dr. James Boylan at the 

Georgia Department of Natural Resources that have the following features: 

• Asymptotically approaching proposed performance goals or criteria when the mean of the 

observed concentrations are greater than 2.5 ug/m3

. 

• 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 concentrations. As the mean observed concentration 

approaches zero the bias performance 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. 
 

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Figure 1-2a. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality 

Modeling.
 

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Figure 1-2b. Concluded. Data flow and quality assurance steps in the CENRAP Emissions 

and Air Quality Modeling.
 

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1.6 CENRAP Annual Modeling Study Participants and Responsibilities 

The CENRAP 2002 annual Emissions and Air Quality Modeling Study is being carried 

out by ENVIRON and UCR under contract 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 Technical Director is the Administrative 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 Commission 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 Geophysics, LLC is the technical 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-Principal Investigator for the CENRAP 

Emissions and Air Quality Modeling ENVIRON/UCR Team; 

3⁄4 Gerard Mansell of ENVIRON is the Emissions Coordinator for the Emissions and 

Air Quality Modeling ENVIRON/UCR Team; and 

3⁄4 Mohammed Omary of UCR is the Chief Emissions Modeler for the Emissions 

and Air Quality Modeling ENVIRON/UCR Team. 
 

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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.edu 
 

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2.0 MODEL SELECTION 

This chapter introduces the regional meteorological, 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 identified and discussed briefly, where necessary. 

Although the initial configurations of each modeling system have been selected as the 

culmination of a review of previous regional haze modeling studies performed 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; Tonnesen 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 models relevant to their 

intended purpose in CENRAP are identified. We conclude 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/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, 

prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate, and regional haze regulatory modeling studies. 

 

3⁄4 SMOKE: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system 

is an emissions modeling system that generates hourly gridded speciated emission 

inputs of mobile, nonroad, area, point, fire 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’ photochemical grid model capable of addressing 

ozone, particulate matter (PM), visibility and acid deposition at regional scale for 

periods up to one year. 

3⁄4 CAMx: ENVIRON’s Comprehensive Air Quality Model with Extensions (CAMx) 

modeling system is also a state-of-science ‘One-Atmosphere’ photochemical grid 

model capable of addressing ozone, particulate matter (PM), visibility and acid 

deposition at regional scale for periods up to one year. 

Application of the MM5 for the 2002 annual modeling on a 36 km grid 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 by 
 

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IDNR may be found in Johnson (2004). 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 systems 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 developed in the 1970s at PSU and first documented 

by Anthes and Warner (1978), the MM5 modeling system maintains its status as a state-of-thescience model through enhancements provided 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 projects 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 successfully applied in continental 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, public availability, and established 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, prognostic model that has been used widely in 

regional air quality model applications (Seaman, 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) and 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 storms, subtropical easterly jets, 

mesoscale convective complexes, desert mixed layers, 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-dimensional wind components (u, v, 

and w), temperature (T), water vapor mixing ratio (qv), and the perturbation pressure (p'). Use of 

a constant reference-state pressure increases the accuracy of the calculations 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 different domains of arbitrary horizontal and vertical 

resolution. The interfaces of the nested grids can be either one-way or two-way interactive. The 

model is also capable of using a hydrostatic option, if desired, for coarse-grid applications. 

 
 

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MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical 

coordinate similar to that used in many operational and research models. In the non-hydrostatic 

MM5 (Dudhia, 1993), the sigma levels are defined 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 models (e.g., CMAQ and CAMx). 

Distinct planetary boundary layer (PBL) parameterizations 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 (Tg), based on the insolation, atmospheric path length, water vapor, 

cloud cover and longwave radiation. The surface physical properties of albedo, roughness 

length, moisture availability, emissivity and thermal 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 prognostic equation for the second-order turbulent kinetic 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-hour 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 state-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 evaluations of the MM5 modeling system in regulatory 

air quality application studies have been widely reported in the 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 application history in 

regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations 

of these models in over 60 recent regional scale 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. 
 

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2.2.2 MM5 Configuration for CENRAP Annual Modeling 

 Based on the sensitivity testing carried out by IDNR, LADCO and others, the MM5 (ver 

3.63) configuration used by the IDNR modelers in the CENRAP 2002 annual 36 km MM5 

modeling consist of the following (see 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 Convective Mixing (ACM) 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 Emissions (SMOKE) Emissions Processing System 

Prototype was originally developed 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 sources, its purpose is to 

provide an efficient, modern tool for converting emissions inventory data into the formatted 

emission files required by an air quality simulation model. For mobile sources, SMOKE actually 

simulates emissions rates based on input mobile-source activity data, emission factors and 

sometimes output from transportation travel-demand models. 

SMOKE was originally designed to allow emissions data processing methods to utilize 

emergent high-performance-computing (HPC) as applied to sparse-matrix algorithms. Indeed, 

SMOKE is the fastest emissions processing tool currently available to the air quality modeling 

community. The sparse matrix approach utilized 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 steps of temporal projection, controls, chemical 

speciation, temporal allocation, and spatial allocation have been separated into independent 
 

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operations wherever possible. The results from these steps are merged together at a final stage of 

processing. 

SMOKE supports area, mobile, fire and point 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/chief/software.html#pcbeis). SMOKE 

has been available since 1996, and it has been used for emissions processing 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. Environmental 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 major 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 models 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 necessary 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 “current” version of SMOKE (ver. 2.0) including: (a) any chemical 

mechanism can be used to partition pollutants to model species, as long as the appropriate input 

data are supplied, (b) integration 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 (October 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 may 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) control 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 where possible to be used for multiple source 

categories, (e) integration with Models-3 file formats and settings, (f) improved data file formats, 

(g) support of various air quality model emissions input formats (e.g., CMAQ, MAQSIP, UAM-
 

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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 treatment of growth and control factors, (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 Environmental 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 modeling. 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 Community Modeling and Analysis (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 ‘science configuration’ 

options compared with the MM5 and CMAQ models. For a thorough characterization of the 

methods that will be used to exercise the 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 version of the SMOKE system to be used and the 

sources of data to be employed in constructing the required modeling inventories. 

2.4 CMAQ Modeling System 

2.4.1 CMAQ Overview 

For more than a decade, EPA has been developing 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 within a common platform (Dennis, et al., 1996; Byun et al., 1998a; 

Byun and Ching, 1999, Pleim et al., 2003). The original justification 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 Quality model (CMAQ) in mid1999 as the initial operating science 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 Transport Model (CTM) and several pre-processors 

including the Meteorological-Chemistry Interface Processor (MCIP), initial and boundary 
 

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conditions processors (ICON and BCON) and a photolysis 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 EPA has funded the Community Modeling and 

Analysis Systems (CMAS) center to support the coordination, update and distribution of the 

Models-3 system (www.cmascenter.org). 

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

make the model well-suited for annual PM modeling. In CMAQ, the modal approach has been 

adapted to dynamically represent the PM size distribution 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, nitrate, SOA) is assumed to be in the fine modes. 

The thermodynamics of inorganic aerosol composition 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 SO2 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 whereby VOCs can be converted to condensable gases 

that can then form SOA and then evaporate back into condensable gases depending on 

atmospheric conditions. 

 The newest features implemented in the latest 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 (ver 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 science 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. The 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 set up on a 12 km domain cover the central 

states whose definition is to be determine using 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 vertical layers would be implemented, extending up to a region top of 

100 mb (approximately 15 km AGL). 

The PPM advection solver would be used along 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 CMAQ 
 

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Meteorological-Chemistry Interface Processor (MCIP2.3) would process 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 photochemical/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 modular 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 multiscale 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 modeling 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-levels 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 Chemical Mechanism Compiler (CMC) Fast 

Solver or the highly accurate Implicit 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 meteorological 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 geographic source regions and 

source categories (e.g., mobile, point, biogenic, etc.); and 
 

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3⁄4 The Decoupled Direct Method (DDM) sensitivity 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) using an empirical aerosol thermodynamics 

algorithm. 

Culminating extensive model development efforts at ENVIRON and other participating groups, 

the CAMx (ver 4.10s) code was released in the 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 extension 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 particulate matter (PM), Mechanism 4 (M4) “oneatmosphere” treatment uses two size sections and science modules comparable to 

CMAQ (e.g., RADM aqueous-phase chemistry 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 the 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 and nucleation upon the particle size 

distribution. 

3⁄4 Inorganic aerosol thermodynamics can be represented using ISORROPIA (Nenes et 

al, 1998; 1999) equilibrium approach within 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 modeled either using the RADM module (like 

CMAQ) or the Variable Size-Resolution Model (VRSM) of Fahey and Pandis (2001), 

which automatically determine whether water droplets can be represented by a single 

‘bulk’ droplet-size mode or whether it is necessary 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 (fine and coarse) and the efficient 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 computationally-extensive CMU multi-section aqueous-phase chemistry 

module. 
 

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A PM Source Apportionment Technology (PSAT) has recently been added to CAMx and 

extensively tested and evaluated. It is currently 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) using similar science options as CMAQ. 

However, in some instances, the CMAQ and CAMx 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 configurations 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 configuration of CAMx would use 19 vertical layers up 

to 100mb (~15 km AGL) that exactly match those used by CMAQ. The PPM advection solver 

would be used along with the spatially varying (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 organic aerosol scheme would be used for the base 

configuration in CAMx. 

Note that it may be desirable to exercise CAMx 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 visibility at key coastal sites in the CENRAP 

domain (e.g., Breton Wilderness Area), especially when looking at clean days or 

natural background; and 

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

formation. 
 

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2.6 Advantages in Operating Multiple Models 

 EPA’s guidance on model selection for PM2.5 SIPs and Regional Haze “reasonable 

progress demonstrations” do not identify a preferred photochemical grid modeling system, 

recognizing that at present there is “no single model which has been extensively tested and 

shown to be clearly superior or easier to use than several alternatives” (EPA, 2001, pg. 169.) 

The agency recommends that models used for PM2.5 SIPS or RH reasonable progress 

requirements should meet the requirements for alternative models. The CMAQ, CMAQMADRID, CMAQ-AIM and CAMx modeling systems 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 PM2.5/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 diagnostic tool addressing model performance 

issues that may arise in the establishment of the model annual 2002 and episodic base 

cases. CMAQ and CAMx both include Process Analysis that can help diagnose 

model performance. CAMx’s suite of diagnostic probing tools plus it’s flexi-nesting 

algorithms make it an attractive tool for assisting in the diagnosis of model 

performance; 

3⁄4 Model Evaluation Corroboration: To provide corroboration of the base case model 

performance evaluation exercises to be performed 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 and 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 simulations, and in the estimate of 

PM2.5 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 modeling, 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. 
 

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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 complimentary state-of-science air quality models are 

thus quite significant and well worth the extra effort. Especially considering that the same MM5 

output (through MCIP2.3 and MM5CAMx) and SMOKE output and CMAQ IC/BC files 

(through CMAQ-to-CAMx emissions and IC/BC converters) can be used to operate CMAQ and 

CAMx without performing any additional meteorological or emissions modeling. 

2.7 Model Limitations 

All mathematical models possess inherent limitations owing to the necessary 

simplifications and approximations made in formulating 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 meteorological 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 treatment being some of the most important. The 

MM5 Pleim-Xiu/ACM LSM/PBL physic options selected by CENRAP frequently predicts very 

low PBL heights that can appear as “holes” in the spatial distribution of PBL heights that don’t 

appear physically realistic and may affect air quality modeling. Although the 2002 annual MM5 

model performance in the CENRAP region mostly 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). Concerns have also been raised concerning the MM5 

performance over the western third of the US (Johnson, 2004). The many limitations in MM5 

have spawned the development of a new meteorological model, the Weather Research Forecast 

(WRF) model. However, the WRF model will not be used or tested in the CENRAP modeling. 

2.7.2 SMOKE 

In WRAP and VISTAS a number of undocumented 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_Protocol\Draft2.0\Chapter 2.doc 2-13 

2.7.3 CMAQ 

 

Like all air quality models, a major limitation 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 aerosol (SOA) module. The CENRAP preliminary 

modeling found the CMAQ nitrate performance suspect with winter overestimations and summer 

underestimations (Pun, Chen and Seigneur, 2004; Tonnesen and Morris, 2004). The CENRAP 

preliminary modeling also found the performance for 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 important to SOA (e.g., polymerization). Other 

science limitations in the current version of CMAQ include inadequate 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 resolve point source plumes or urban photochemistry 

and their effects on more distant Class I areas without a prohibitive number of grid cells. 

Another limitation of CMAQ is the computational requirements, including the need of excessive 

disk space. 

2.7.4 CAMx 

The model inputs are also a major limitation 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, such 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 required for model setup and 

operation, and those required for model evaluation testing. Below, we identify the main input 

data base requirements for the meteorological, 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 fixed and variable inputs. 

3⁄4 Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived 

from the Geophysical Data Center global data sets from the National Center for 

Atmospheric Research (NCAR) terrain databases are available for prescribing terrain 

elevations throughout the 36 km and 12 km grid domain. 
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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 NCAR/PSU 10 min. (~18.5 km) databases while 

for the 12 km grids, the United States Geological Survey (USGS) data are available. 

3⁄4 Atmospheric Data: Initial and boundary conditions to the MM5 may be developed 

from operationally analyzed fields derived from the National Center for 

Environmental Predictions (NCEP) ETA (40 kmresolution) following the procedures 

outlined by Stauffer and Seaman (1990). These 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 pressure (SLP) and ground 

temperature (Tg). Here, Tg 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 temperature 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 resolved-scale and parameterized sub-grid scale 

processes, the model does not require precipitation or cloud input. The potential for 

precipitation and cloud formation enters through the thermodynamic and cloud 

processes formulations in the model. The only precipitation-related 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 objectively analyzed three-dimensional fields 

produced every 3-hr from the NWS rawinsonde 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 parameters 

 
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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, the CMAQ Chemical Transport Model (CTM) requires 

the following inputs: 

3⁄4 Three-dimensional hourly meteorological fields that will be generated by the CMAQ 

MCIP2.3 processing of the MM5 output; 

3⁄4 Three-dimensional hourly emissions 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 fields 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 CMAQ-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 topography 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc Table 2-1. MM5 meteorological model configuration for CENRAP 2002 Annual modeling (Johnson, 20

Science Options Configuration Details/Comments 

Model Code MM5 version 3.63 Grell et al., 1994 Horizontal Grid Mesh 36 km (12 km for episodes)  36 km grid 165 x 129 cells  12 km grid TBD Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coord. Grid Interaction No Feedback IFEED=0 Initialization Eta first guess fields/LittleR Boundary Conditions Eta first guess fields/LittleR Microphysics Reisner I Mixed Ice Look up table Cumulus Scheme Kain-Fritsch 2 On 36 and 12 km Grids Planetary Boundary Layer ACM PBL Radiation RRTM Vegetation Data USGS 24 Category Scheme Land Surface Model Pleim-Xiu Land Surface Model (LSM) Shallow Convection None Sea Surface Temperature Eta Skin Spatially varying Thermal Roughness Garratt Snow Cover Effects None 4D Data Assimilation Analysis Nudging on 36 and 12 Surface Nudging None Integration Time Step 90 seconds Simulation Periods Annual 2002 for 36 km 12 km episodic only Platform Linux Cluster Done at IDNR 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc Table 2-2. SMOKE emissions model configuration for CENRAP Annual modeling.

Emissions Component Configuration Details/Comments 

Emissions Model SMOKE ver 2.0 SMOKE ver 2.1 may be used if operational in time Horizontal Grid Mesh 36/12 km  36 km grid 148 x 112 cells RPO Unified Grid  12 km grid TBD Area Source Emissions CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states 

Other States: '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as used in WRAP 

On-Road Mobile Sources CENRAP Domain: CENRAP VMT data Updated '02 developed by CENRAP states (Reid et al., 2

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: 2000 Canadian Inventory? 

Point Sources CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states and stakehol

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: 2000 Canadian Inventory? 

Off-Road Mobile Sources CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: 2000 Canadian Inventory? 

Biogenic Sources SMOKE BEIS-3 BELD3 vegetative database Temporal Adjustments Seasonal, day, hour Based on latest collected information and CEM-based pChemical Speciation Revised CB4 Chemical Speciation Updated January 2004 Gridding Revised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chieGrowth and Controls CENRAP Contractor TBD Base Cases defined by CENRAP Workgroups Quality Assurance QA Tools in SMOKE 2.0 Follow QAPP Simulation Periods Annual 2002 for 36 km Episodic periods at 12 km TBD 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc Table 2-3. CMAQ air quality model configuration for CENRAP annual modeling.

Science Options Configuration Details/Com

Model Code CMAQ (ver 4.4) Available at: www.cmascenHorizontal Grid Mesh 36 km annual/12 km episodic 36 km covering continental  36 km grid 148 x 112 cells RPO National Grid  12 km grid TBD Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MMGrid Interaction One-way nesting Initial Conditions ~15 days full spin-up Separately run 4 quarters oBoundary Conditions 2002 GEOS-CHEM day-specific 2002 GEOS-CHEM day specEmissions  Baseline Emissions Processing See SMOKE (Ver 2.0) model configuration MM5 Meteorology input to S Sub-grid-scale Plumes No Plume-in-Grid (PinG) PinG for PM not available yeChemistry  Gas Phase Chemistry CBM-IV  Aerosol Chemistry AE3/ISORROPIA  Secondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) Schell et al., (2001)  Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud procN2O5 Reaction Probability 0.01 – 0.001 Meteorological Processor MCIP ver 2.3 

Includes dry deposition and updates 

Horizontal Transport  Eddy Diffusivity Scheme K-theory with Kh grid size dependence Multiscale Smagorinsky (19Vertical Transport  Eddy Diffusivity Scheme K-theory  Diffusivity Lower Limit Kzmin = 1.0 Run MCIP2, 3 with Kz-min.=Deposition Scheme M3dry 

Directly linked to Pleim-Xiu 

parameters 

Numerics  Gas Phase Chemistry Solver Euler Backward Iterative (EBI) solver  Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 for 36 km Episodic periods at 12 km TIntegration Time Step TBD 15 minute coupling time ste
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 2.doc Table 2-4 CAMx air quality model configuration for CENRAP annual modeling.

Science Options Configuration Details 

Model Code CAMx (ver 4.10s) Available at: www.camx.com Horizontal Grid Mesh 36 km annual/12 km episodic 36 km covering continental  36 km grid 148 x 112 cells  12 km grid TBD Vertical Grid Mesh 19 Layers 17 Layers sync'd w/ MM5 Grid Interaction Two-way nesting Initial Conditions ~15 days full spin-up Separately run 4 quarters oBoundary Conditions 2002 GEOS-CHEM day-specific 2002 GEOS-CHEM day specEmissions  Baseline Emissions Processing See SMOKE (Ver 2.0) model configuraton MM5 Meteorology input to S Sub-grid-scale Plumes No Plume-in-Grid (PinG) PinG for PM not available yeChemistry  Gas Phase Chemistry CBM-IV with Isoprene updates  Aerosol Chemistry ISORROPIA equilibrium Dynamic and hybrid also av Secondary Organic Aerosols SOAP  Cloud Chemistry RADM-type aqueous chemistry 

Alternative is CMU multi-sechemistry 

N2O5 Reaction Probability None Meteorological Processor MM5CAMx Horizontal Transport  Eddy Diffusivity Scheme K-theory with Kh grid size dependence Vertical Transport  Eddy Diffusivity Scheme K-Theory  Diffusivity Lower Limit Kzmin = 1.0 (or other) Run MM5CAMx with Kz-minPlanetary Boundary Layer No Patch Deposition Scheme Wesely Numerics  Gas Phase Chemistry Solver CMC Fast Solver  Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 at 36 km Episodic periods at 12 km TIntegration Time Step Wind speed dependent 
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 3.doc 3-1 

3.0 EPISODE SELECTION 

This chapter provides a brief overview of reasons 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/Regional 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. However, since there is much in common with the 

goals for selecting episodes for annual and episodic 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 should 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 monitoring 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 episodes 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 days 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 modeling year should be selected from this five-year 
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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 Protocol 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_Protocol\Draft2.0\Chapter 4.doc 4-1 

4.0 MODELING DOMAINS AND DATA AVAILABILITY 

This chapter summarizes the model domain definitions for the CENRAP annual 

modeling including the model domain, resolution, map projections and nesting schemes for high 

resolution sub-domains. 

4.1 Horizontal Modeling Domain 

The 36 km continental US horizontal domain for 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 Regional 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-Conformal 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 dimension 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 dimension. Because the MM5 model is also nested in 

the Eta model, there is a possibility of boundary effects near the MM5 boundary that occur as the 

Eta meteorological variables are being simulated 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 domain. 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) for 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. Finally, 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_Protocol\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 primarily defined by the vertical grid used in 

the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by 

pressure, using 34 layers that extend from the surface to the 100 mb. Table 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 layer 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_Protocol\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_Protocol\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 0
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 4.doc 4-5 

4.3 Data Availability 

The CMAQ and CAMx modeling systems require emissions, meteorological, initial and 

boundary condition (IC/BC) and ozone column data for defining the inputs. 

4.3.1 Emissions Data 

The base year emissions inventory for CENRAP 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 the 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 validation, 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 CENRAP state emission inventory is expected 

by the end of November 2004 and will be used in the model performance evaluation 

As necessary, all emissions will be converted to Inventory Data Analyzer (IDA) 

formatted versions and the data will be processed 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-reference 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 contractor, obtained from EPA, or 

prepared by the Study Team prior to initial emissions modeling. Spatial allocation of the 

emissions will be based on profiles and spatial allocation factors developed for the National RPO 

grid. Additional description of emissions 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 summarizes ambient monitoring networks. Data have 

been compiled for all networks except the PAMS and PM Supersites. Figure 4-2 displays the 

locations of monitoring sites in and near the CENRAP States. 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 4.doc Table 4-4. Overview of ambient data monitoring networks. 

Monitoring Network Chemical Species Measured Sampling Period Data Availability/SouThe Interagency Monitoring of Protected Visual Environments (IMPROVE) 

Speciated PM25 and PM10 (see species mappings) 

1 in 3 days; 24 hr average 

http://vista.cira.colosta

VE/improve_data.htm

Clean Air Status and Trends Network (CASTNET) 

Speciated PM25, Ozone (see species mappings) 

Approximately 1-week average 

http://www.epa.gov/ca

National Atmospheric Deposition Program (NADP) 

Wet deposition (hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium)), Mercury 

1-week average http://nadp.sws.uiuc.e

Air Quality System (AQS) Aka 

Aerometric Information Retrieval System (AIRS) 

CO, NO2, O3, SO2, PM25, PM10, Pb 

Typically hourly average 

http://www.epa.gov/ai

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttnSoutheastern Aerosol Research and Characterization (SEARCH) (Southeastern US only) 

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); Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) 

Hourly or 24-hour average, depending on parameter. 

Electric Power RSouthern Company, ahttp://www.atmospher

EPA Particulate Matter Supersites (Includes St. Louis in the CENRAP region) 

Speciated PM25 http://www.epa.gov/ttn

Photochemical Assessment Monitoring Stations (PAMS) 

Varies for each of 4 station types. http://www.epa.gov/ttn

National Park Service Gaseous Pollutant Monitoring Network 

Acid deposition (Dry; SO4, NO3, HNO3, NH4, SO2), O3, meteorological data 

Hourly http://www2.nature.np
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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

AQS
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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

AQS
 

December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 4.doc 4-9 

4.3.3 Ozone Column Data 

Additional data used in the air quality modeling include the Total Ozone Mapping 

Spectrometer (TOMS). TOMS data is available 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 using 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 modeling). IDNR is operating the MM5 at 5-day 

increments for 2002 on the 36 km a ~15 day spin up period for the end of December 2001. 

Details on the CENRAP 2002 36 km MM5 modeling 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 eliminate 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 resolved (i.e., 3-hourly) CMAQ and CAMx BCs for 

2002. 
 

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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 modeling 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 meteorological and emissions inputs are discussed 

first followed by the science options to be used by CMAQ and CAMx. The procedures for 

developing the initial and boundary conditions and photolysis rates inputs are then discussed 

along with the model application procedures. 

5.1 Meteorological Inputs to Emissions and Air Quality Models 

The emissions and air quality models require certain meteorological input data including 

wind fields, estimates of turbulent eddy dispersion, 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 data are not available for the full gridded model 

domain, numerical meteorological models are used to provide these inputs. 

The National Center for Atmospheric Research (NCAR)/Pennsylvania State University 

(PSU) Fifth-Generation Mesoscale Model (MM5) (v3.63) is being used by the Iowa Department 

of Natural Resources (IDNR) for CENRAP to simulate meteorology 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 and 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 meteorological model that is used not only for 

meteorology studies but also for air quality studies. Some of the physics used in the simulation 

include nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and 

mixing ratio; explicit treatment of moisture; cumulus cloud parameterization; vertical mixing of 

momentum in the mixed layer; PBL process parameterization; atmospheric 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 from the CENRAP. And to process the 2002 MM5 

output using Version 2.3 of the Models-3 CMAQ 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 Quality (CMAQ) modeling system is designed 

to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and 
 

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particles) air quality problems. But before running the CMAQ Chemical Transport Model 

(CCTM), the MM5 generated meteorological data must be pre-processed and converted to 

Models-3 consistent data structures. 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 translate meteorological parameters from the output of 

the Pennsylvania State University/National Center 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 meteorological model output for finer grid 

4. Collapsing of meteorological profile 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-specific dry deposition velocities 

8. Generation of coordinate dependent meteorological data for the generalized 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, and 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 culls a minimum of six cells about the domain 

periphery to minimize edge effects 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 quality model as in the MM5 to prevent errors associated with 

aggregating layer data and to maintain consistency between data produced by the meteorological 

model and those used by the chemistry-transport model. However, due to computational costs 

associated with using large number of vertical layers, vertical 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 the MM5 vertical layer 

structure exactly. The region top for CMAQ is the same as used by MM5, 100mb 

(approximately 15 km AGL). The 36 km analysis domain contains 148 columns, 112 rows, and 

19 layers. The definition of the horizontal extend of the 12 km domain is to be determined, but 

19 layers will be used. More details on the CMAQ modeling domain definitions are provided in 

Chapter 4 with the vertical layer structure of MM5 and MCIP/CMAQ shown in Table 4-3. 
 

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5.1.2 Products of the CMAQ Meteorological Input Development Process 

The meteorological input development process produces three two-dimensional and four 

three-dimensional daily meteorological and geophysical output data in the Models-3 I/O API 

format. These CCTM-ready meteorological input files 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) coordinate system with 19 vertical sigma layers extending from the 

surface to the 100 mb pressure level. The data files include three-dimensional gridded fields of 

u- and v-wind components, vertical velocity, temperatures, Jacobian, Jacobian weighted air 

density, total air density, water vapor, cloud water content, rain water content, ice and snow 

mixing ratio, layer heights, and vertical exchange coefficients. Two-dimensional gridded fields 

of latitude and longitude, squared map-scaled factor, surface temperatures and pressures, 1.5 and 

10 meter temperature, planetary boundary heights, rainfall, total 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 m2

/s minimum KV (Kz_min) value, however the 

CAMx-ready KV files may be updated to a 1.0 m2

/s Kz_min to be consistent with CMAQ. 
 

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5.1.4 Treatment of Minimum KV

 The minimum KV value (Kz_min) is an area of ongoing investigation by the CMAQ and 

CAMx developers. EPA initially recommended a 1.0 m2

/s Kz_min for CMAQ modeling, but for 

their ozone forecasting EPA is using Kz_min values of 0.1 to 2.0 m2/s depending on the amount 

of urban land use present. Thus, to maximize flexibility we will process the 2002 MM5 data 

using MCIP and MM5CAMx using a 0.1 m2

/s Kz_min and then impose other Kz_min values 

(e.g., 1.0 m2/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 episode year. Western State base year point and area 

source emissions for 2002 were provided by the WRAP 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 states emissions inventory of 2002 were 

provided by VISTAS RPO. 

For the remaining U.S. portion of the domain, point source projections were based on 

EPA’s 2001 modeling platform inventories and area source, nonroad mobile, and fire emissions 

were based on EPA’s preliminary 2002 NEI. VMT 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 lending themselves to emissions modeling. 

For purposes of air quality model validation, actual 2002 calendar year emissions for 

EGU and fire activity will be used, while during strategy and future year emission runs, “typical 

year” emissions for these categories will be processed. 

A final 2002 CENRAP state emission inventory is expected by November 2004 and will 

be used in the final model performance demonstration and configuration expected to begin in 

early 2005. Non-CENRAP state emissions are expected to be based on State submittals to EPA 

under the CERR and will be augmented with additional 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 processed 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 Version 
 

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2.1) of the Sparse Matrix Operating Kernel Emissions (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 augmented with any recommended and approved emission 

profile data provided by the emissions inventory contractor 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 photochemical modeling must address several 

source categories including: (a) stationary point sources, (b) area sources, (c) on-road mobile 

sources, (d) non-road mobile sources, and (e) biogenic 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 customized 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 control factors to use in developing the base year emissions. Often, 

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

Contacts with CENRAP’s emission inventory contractors and the U.S. EPA will be 

established and formal requests made for inventory 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 inventories 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 categories are discussed in relevant sections below. 

With the exception of biogenic and highway mobile 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 specific 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, Sunday, Monday, one per month (total of 60 days 

modeled). On-Road motor vehicles will be represented by an entire single week for each month. 

These selection criteria allow for the representation of day-of-the-week variability in the on-road 

motor vehicles, and model a representation of the meteorological variability in each month. 
 

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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 default 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 surrogates other than those available for modeling 

purposes. 

 The domain for the air quality modeling will be based on the EPA’s 36-km national 

CMAQ domain, illustrated in Figure 5-1 below (details on the modeling 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. 
 

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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, PNO3, POA, PSO4, and PMC. 

Meteorology Data: Daily (25-hour). SMOKE requires the 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 match 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. Wildfire and some prescribed fire emissions will 

be handled as point sources. 

Producing 365 day-specific input files for all source categories places a burden on available 

computing facilities, data management systems, and would adversely affect the Phase II 

schedule. Selecting representative model days for 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 the source categories (except 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 represent 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 take 
 

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advantage of the available day-specific emissions (if available) and meteorology. Area sources, 

including non-road mobile and dust emissions, with the exception of windblown dust emissions, 

do not utilize meteorological data, and are temporally allocated by monthly, daily and hourly 

profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved 

by selecting representative Monday, weekday, Saturday, Sunday, for each month. Holidays will 

be treated as Sunday. 

Motor vehicle emissions are influenced by meteorological variability, 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 average 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 emissions are properly located both horizontally 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 temporally allocated to month, day, and hours using 

annual emissions and source category code (SCC) based 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 data, or with hourly emissions provided by 

stakeholders, actual hourly data will be used. For those sources where EPA CEM data are 

utilized, NOx, SO2, and heat input-based hour-specific profiles will be developed and applied to 

NOx, SO2, and all other emissions, respectively. This will ensure that the annual emission values 

provided by the EI contractor are maintained, but distributed using hourly to annual profiles. For 

sources providing hour-specific data and where they were approved 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 Emissions 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 the 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 Management Service (MMS). These data include 
 

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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 the stationary source 

geographic coordinates. If a point source is missing 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 emissions, 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 factors. These factors will be based on the crossreference 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 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-road source SCC does not have an existing crossreference profile assigned to it, the county-level emissions will 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 Mexico 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 will be applied to fugitive dust emission sources 

that have been identified in U.S. EPA modeling to have only a portion of its mass transportable 

from the source of the emission generation. The EPA’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 simulation. For this reason, the CENRAP emissions 

contractor should apply county-specific fugitive dust emissions transport factors to these sources 

to adjust PM emissions accordingly. If the fugitive dust emissions adjustment is done by the 

CENRAP emissions contractor prior 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/emch/invent/statusfugdustemissions_082203.pdf). 

Ammonia Emissions

 Ammonia emissions for CENRAP states have been developed by the EI contractor and 

include emissions from livestock, fertilizer application, natural soils, mobile sources, point 

sources and other miscellaneous sources. These estimate were developed primarily using the 

Carnegie Mellon University (CMU) NH3 model. County-level emissions estimates were 

developed along with associated temporal and spatial allocation profiles necessary for emissions 

processing with SMOKE. These data are currently being reviewed and quality assured by the 

CENRAP EI contractor and will be available by the end of November 2004. The study team will 

incorporate these emissions estimate for the CENRAP states in to the regional modeling 

inventories. 
 

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 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, natural soils, domestics sources and wild animals). 

The remaining ammonia emissions source categories 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-CENRAP states. The model generates hourly gridded emissions 

data using gridded meteorological data to apply various adjustments to emission factors and 

temporal allocation factors. Therefore, SMOKE is not required for the generation of these 

emissions estimates, although these emissions are processed through SMOKE in order to merge 

these source categories with the remaining area 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 will be combined with gridded, episode-specific 

temperature data to calculate the gridded, temporalized emission estimates. Of note, whereas the 

on-network emissions estimates are spatially allocated based on link location and subsequently 

summed to the grid cell level, the off-network emissions estimates are spatially allocated based 

on a combination of the FHWA version 2.0 highway networks and population. For the CENRAP 

36/12 km modeling, no link-based data will be used. The MOBILE6 emissions factors are based 

on episode-specific temperatures predicted by the 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 emissions into separate source categories; 

• VMT, fleet turnover, and changes in fuel 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 maintenance program in effect, type of oxygenated 

fuel program in effect, alternative vehicle mix profiles, 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 recently been developed and tested by EPA over two 

separate modeling domains/episodes. This version of the model (BEIS-3, v1.2) contains several 

changes over BEIS-2, including the following: 
 

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• Vegetation input data -- are now based on a 1-km Biogenic Emissions Landuse 

Database (BELD3) vegetation data base, 

• Emission factors – many updates including some recent NARSTO modifications, 

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

For this particular application of BEIS-3, version 1.2 as currently incorporated in the SMOKE 

processor will be used. 

The BELD-3 landuse data on a Lambert conformal grid at 1-km resolution have already 

been developed, are available, and will be used to estimate biogenic emissions in this study. The 

BEIS model also requires as input hourly, gridded 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, Agricultural Burns. Wind Blown Dust and Sea Salt 

Source Emissions 

Agricultural Burns

 Emissions from agricultural burns in CENRAP 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 allocation surrogates, based on the MM5 modeling 

domain have been developed by the contractor at a 12km spatial resolution. 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. Wildfire emissions currently are not available for CENRAP states. 

WRAP and VISTAS have developed their own wild fire emissions as point sources. These 

emissions will be used for modeling on the unified 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. Burn 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 profile (meters above ground level) 

Lay1 – The percent of the emissions entrained in the first modeling layer 
 

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The WRAP Fire Emissions Joint Forum Emissions Inventory Report (FEJF, 2002) has 

documented an approach to calculating these plume 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 calculate representative hourly plume profiles. These 

profiles are then used by SMOKE2.0 to distribute the vertical emissions from the fires. To 

successfully model fires as elevated point sources, the data provided by the EI contractor will 

need to include both the day, or days, on which the fire 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 data, including emissions estimates and plume 

rise distributions, will be obtained 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 PM10 

and PM2.5. The dust emissions from agricultural lands have adjustment factors applied for the 

WRAP and CENRAP states only. The emissions for the rest of the states need to be adjusted for 

agricultural lands. The emissions model developed 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 sodium 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 CENRAP annual modeling. Accordingly, 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 options, then Sea Salt emissions will be generated 

using appropriate procedures. 

5.2.8 Speciation and Reformatting 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 minimum five (5) separate air quality modelready files: low-level point source, area source, elevated point source, mobile source, and 

biogenics. Other source categories, such as EGU and fire emissions may also be handled as 

separate air quality model-ready files.
 

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5.2.9 Development of Modeling Inventories 

 The emissions inventories developed for the CENRAP modeling can be grouped into 

three distinct types: 1) 2002 annual inventories, 2) Future Year episodic inventories, and 3) 

Alternate Base Year Episodic Inventories. In all cases, the Study Team expects to receive the 

emissions inventory data for the CENRAP states from the Emissions Inventory contractor, add 

non-CENRAP states and Canadian and Mexican data acquired from 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 categories: area, nonroad, point, motor 

vehicle and biogenics. 

 A revised 2002 annual inventory will be developed in the event that updated, or revised, 

emission inventory data area received 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 provided by the EI contractor. The Study Team will 

also produce future year scenario annual inventories, based on the 2002 meteorology. The 

composition of these future year scenarios 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 strategy 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 generated for each hour of the days modeled 

in the 2002 base year annual run, a number of quality assurance (QA) files will 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 temporal 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 located in water cells. 

& Spot-check the holiday emissions files to confirm that they are temporally allocated like 

Sundays. 
 

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& 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 speciation of the emissions to CB-IV terms and the vertical 

allocation of the emissions, we will compare reports generated with SMOKE reports to target 

these specific areas of the processing. For speciation, we will compare the inventory import state 

totals versus the same state totals with the speciation matrix applied. 

 For checking the vertical allocation of the emissions, we will create 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 often reveal significant deficiencies in the input data or the 

model setup. It may become necessary to tailor these procedures to track 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 inventories or processing. 

Following are some of the reports that may be generated to review the processed emissions: 

& State and county totals from inventory for each source category 

& State and county totals after spatial allocation for each source category 

& State and county totals by day after temporal allocation for each source category for 

representative days 

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

category 

& State and county model-ready totals (after spatial allocation, temporal allocation, and 

chemical speciation) for each source category and for all source categories combined 

& If elevated source selection is chosen by user, the report indicating 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 inventory for area, mobile, and point sources 
 

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& Totals by county and SCC from the inventory 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 code 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 code 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 code for area, mobile, and point sources 

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

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

sources 

5.2.11 Preparation of CAMx Emission Inputs 

 Once the CAMx-ready 3D emission inputs are generated, the CMAQ-to-CAMx 

emissions converter would be used to generate 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-ready low-level emission input file. 

• For each (i,j,k) cell that has non-zero emissions, 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 configuration and science options to be used in the 

CENRAP annual modeling effort. The recommendations are based on testing and model 

evaluations of several models or model configurations 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 (Baker, 2004) and WRAP modeling 

(Tonnesen, 2003). Table 5-3 summarizes the proposed configuration for CMAQ. The latest 
 

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version of CMAQ is currently Version 4.4 that was released October 2004 and is currently 

proposed for use in the CENRAP annual modeling. However, if EPA releases an updated 

version of CMAQ in time, CENRAP would likely switch to the latest version 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 selected episode(s) using one-way grid nesting where the boundary 

conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ 

BCON processor. The base configuration of CMAQ 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 along 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 m2

/s. Note that in the 

past the CMAQ default Kz_min value is 1.0 m2/s. However, EPA has reported improved CMAQ 

performance using a variable Kz_min value in their ozone forecasting that ranges from 0.1 m2

/s 

to 2.0 m2/s depending on the fraction of urban land use in the grid cell (0% to 100%). By 

processing the MM5 data with MCIP2.3 using a 0.1 m2

/s Kz_min we can easily generate CMAQ 

Kz input files with a 1.0 m2/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 scheme will be used for inorganic aerosol 

thermodynamics. 

The CB-IV gas-phase chemical mechanism 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 similar PM model performance and major responses 

to emission reductions. Thus, given that CB-IV runs faster and uses less disk space that SAPRC 

and there are more uncertainties in the other components in the model and model inputs than in 

the gas-phase photochemistry, the more efficient CB-IV chemical mechanism was selected. 
 

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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 KV (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 will 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 has processed the 2002 GEOS-CHEM model output 

and generated day-specific 3-hourly boundary conditions (BCs) for the 36 km Inter-RPO grid in 

the CMAQ BCON format. The CENRAP 2002 annual modeling will use the 2002 GEOSCHEM day-specific BCs. 

5.3.4 Photolysis Rates 

Several chemical reactions in the atmosphere are initiated by the photodissociation of 

various trace gases. To accurately represent the complex chemical transformations in the 

atmosphere, accurate estimates of these photodissociation rates must be made. The Models-3 

CMAQ system includes the JPROC processor, which calculates a table of clear-sky photolysis 

rates (or J-values) for a specific date. JPROC uses default values for total aerosol loading and 
 

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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 TOMS data, and we will continue to use TOMS 

data for Phase II. There are a few days in 2002 for 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" table provides the photolysis rates as a function of latitude, 

altitude, and time (in terms of the number of hours 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), seven 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 latitude, altitude, and hour angle, 

followed by applying a cloud correction factor. 

The photolysis rates input file must be prepared as separate look-up tables for each 

simulation day. The modeling team has already prepared scripts to automate the production of 

photolysis rate files for each day of the annual simulation. 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 configuration 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 currently 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 CAMx is available, CENRAP would 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 along 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 MM5CAMx: (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 diffusion constant (Kz_min) of 0.1 m2

/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. 
 

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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 mechanism is selected for the CENRAP annual 

modeling. The RADM aqueous-phase chemistry 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 KV (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 initialization 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 model 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. 
 

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6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assurance procedures that will be used in the 

CENRAP 2002 annual modeling. More details on the quality assurance 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 extensive guidance on developing a Quality 

Assurance Project Plan (QAPP) for modeling studies (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 identification of assessments and related 

performance criteria; 

• peer reviewed theory and equations; 

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

• clear documentation of assumptions, theory, and parameterization that is detailed enough 

so others can fully understand the model output; 

• input data and parameters that are accurate 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 different 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 application process to ensure that 

choices are consistent with 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 (SMOKE and CMAQ) and a model developed by 

ENVIRON (CAMx) and have no current plans for model development activities. Thus, our 

QAPP focuses primarily on documenting data sources and QA of data processing performed by 

the model team. In addition, because no official EPA guidance currently exists for visibility 

model performance, a major objective of our 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. 
 

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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 manipulation of many 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. 

As part of the CENRAP annual modeling effort, 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 following 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 modeling outside of the States in the CENRAP 

region. 

Specifically, the Emissions Gatekeeper will review the content and format of the provided 

emission inventories ensuring an appropriate appraisal 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 structure 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 visualization and diagnostic packages. 

We propose a multi-step emissions QA/QC approach that involves several staff to 

QA/QC the emissions as they are processed. This includes the initial emissions QA/QC by the 

Emissions Gatekeeper described above, as well 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 process 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 Error 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 emission input errors. Additionally, EPA has issued a 

revised stack QA and augmentation procedures memorandum 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 log files will be maintained so that the error 

messages can be reviewed at a later date if necessary. 
 

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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-processed 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 preparing summary plots that characterize spatial 

and temporal patterns in the emissions 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 total 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 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 that display the diurnal 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 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 daily total emissions differences 

between a control strategy and base case emissions 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 Gatekeeper is to provide an independent review 

and quality assurance of the meteorological modeling and related data sets developed by 
 

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CENRAP and subsequently by the emissions and air quality modeling teams. This Gatekeeper 

QA review serves two specific purposes: (a) to ensure that any potential problems with the data 

sets (should they exist) are identified 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 the case of meteorology, the Gatekeeper’s independent QA analysis 

of the MM5 meteorological data sets serves to provide direct assistance to the emissions and air 

quality modeling team as it undertakes to ratify the SMOKE model 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 maintaining 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 Workgroup 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, temperature, mixing ratio, precipitation, and planetary 

boundary layer (PBL) depths on a continental scale (36 km) and subregional 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 processor to produce binary input files for the 

CMAQ and CAMx air quality models, respectively. 

In summary, the quality assurance plan for the meteorological data will include the 

following elements: 

3⁄4 Upon receiving the MM5 output files from CENRAP, we will verify the integrity of 

the file transfer (e.g., no missing 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 meteorological inputs for CMAQ and CAMx, 

respectively. 

3⁄4 We will create horizontal and vertical plots of temperature, pressure, precipitation, 

modeled flow patterns, PBL heights, etc. to assess whether the MCIP 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. 
 

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6.4 Air Quality Model Inputs and Outputs 

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

• Verification that correct configuration and science options are used in compiling and 

running each model of the in the CMAQ modeling system, where these include the 

MCIP, JPROC, ICON, BCON and the CCTM. 

• Verification that correct configuration 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 and emissions processors and other 

processors. 

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

• Evaluation of CMAQ and CAMx results to verify 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 results against concurrent observations. 

• Backup and archiving of critical 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 major 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 were used when 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 science options are used. We 

also employ a redundant naming system so that the name of key science options or inputs are 

included in the name of 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. For example, if a model simulation is performed using the CB4 

mechanism, all compile and run scripts contain the variable definition “$MECH = CB4”, and this 

variable is hard coded into the 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 directory structure that were used in performing a model simulation. For 

example, if we perform simulation with the SAPRC mechanism, instead of editing the original 

scripts to specify “$MECH = SAPRC” we will create a parallel directory structure with a new set 

of scripts to perform the SAPRC simulations. This provides a permanent 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 that provides a record of input file names, warning messages etc that 

will be archived. 
 

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 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 patterns in the CMAQ and CAMx output files. In the 

case of model sensitivity studies, the animated gifs will be prepared as difference plots for the 

sensitivity case minus the base case. Often, errors in the emissions inputs can be discovered by 

viewing the animated GIFs. Finally, we will produce 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. 
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7.0 MODEL PERFORMANCE EVALUATION 

7.1 OVERVIEW

The CENRAP 2002 annual modeling work is broken 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 performance 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 the ‘roadmap’ for achieving an adequately tested 

modeling system for regulatory usage. But, obviously, 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 aerometric data bases, and present technology 

constraints. The roadmap guides the way to the desired 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 road along the way. Indeed, one expectation of the CENRAP Modeling 

Workgroup is a close working relationship with the 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 specific 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 assessment of the operational ability of the CMAQ and 

CAMx models to predict fine particulate and visibility at Class I areas and other monitoring sites in 

the CENRAP region. 

 Clearly, not all of the supplemental evaluative techniques identified in this chapter will 

ultimately be performed. There are three reasons for this: 

3⁄4 The CENRAP Emissions and Air Quality Modeling available resources places clear 

limits on the resources available to perform 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 schedule demands on the model evaluation. 

A number of the model performance evaluation methods introduced in this chapter 
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(e.g. Weight of Evidence analyses, diagnostic testing with individual measurement 

networks, PM indicator species and ratios analyses, 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 model research and development effort, but 

rather an operational evaluation of existing 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 documents 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. Indeed, an adequate evaluation of the CENRAP 

annual modeling system may be possible through 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 draft 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 statistical 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 accurately does the model predict responses of predicted air quality to 

changes in inputs? States are encouraged to utilize the evaluation procedures set forth in the earlier 

1991 guidance document (EPA, 1991) for gas phase species and the newer (2001) guidance of PM 

species. Thus, in carrying out the initial operational 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 currently unallocated resources, the CENRAP annual 

evaluation will also attempt to employ other testing 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., emissions and meteorological) are introduced into the 

Emissions and Air Quality Modeling Study. For example, 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 most important goal of the CENRAP CMAQ and 

CAMx evaluation is to determine whether the aggregate modeling systems (model codes plus input 

data sets and observational data for testing) offers sufficiently reliable and accurate results that 

public decision-makers may have reasonable confidence 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 evidence that the modeling systems are operating reliably 

and in conformance with measurements and scientific expectations, then specific justifications 

explaining why the model is acceptable for developing regional haze strategies will be offered in the 
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2002 CMAQ and CAMx Modeling Summary Report. Conversely, should the evaluation determine 

that the modeling systems suffers from 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 CENRAP 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 understand 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 Particulate Guidance Workgroup to provide technical 

input in the development of both documents1

. More recently, EPA has provided 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 light 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 scale 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 concentrations using the following IMPROVE reconstructed 

mass extinction equation (FLAG, 2000): 

bext = 3{f(RH)[(NH4)2SO4]} + 3{f(RH)[NH4NO3]} 

 + 4{f’(RH)[OC]} + 10[EC] + 1[IP] 

+ 0.6[CM] + brayleigh 

where: 

 bext is the estimated extinction coefficient (Mm-1); 

 [SO4] is the sulfate concentration assumed to be ammonium sulfate; 

 [NO3] 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; 

brayleigh 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 participated on the EPA fine particulate modeling work 

group over the two-year span of its activities. 
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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 (bext) to Rayleigh scattering: 

Deciview = 10 ln(bext/10) 

)dV = 10 ln[(bext)future / (bext)base] 

In this framework, changes in visibility in terms of percentage changes in extinction over a 

natural “clean” background visibility rather than changes 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 testing will concentrate on an operational 

evaluation of those model predictions that are most necessary for estimating visibility (e.g., SO4, 

NO3, OC, EC, IP and CM). Where feasible and supported 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 predictions. 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 correctly. 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 PM2.5 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; Arnold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a 

comprehensive, multi-layered approach to model 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 PM10 and PM2.5 including the quantities 

used to characterize visibility (i.e., sulfate, nitrate, ammonium, organic carbon, elemental 

carbon, PM2.5, and PM10). 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”; 
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> Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, 

PM chemical composition including PM precursors (e.g., SOx, NOx, and NH3) and 

associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; 

spatial variation; mass fluxes; and components 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 obviously receive the greatest attention since this 

is the primarily thrust of EPA’s 2001 PM guidance. However, we will consider, where feasible and 

appropriate, diagnostic and mechanistic tests (e.g., use of probing tools, indicator species and ratios, 

aloft model evaluations, urban vs. rural performance 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 procedures 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 will be developed using several routine and 

research-grade databases. The first is the routine gas-phase concentration measurements for ozone, 

NO, NO2 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 PM2.5

and PM10 Mass Networks (EPA-FRM), (e) EPA Speciation Trends Network (STN); (f) National 

Acid Deposition Network (NADP) and (g) EPA Supersites (EPA-SPEC) networks. Typically, these 

networks provide ozone, other gas phase precursors and product species, PM, and visibility 

measurements. 

 As an example, the IMPROVE network gives daily (24-hour) average mass concentrations 

every 3 days for SO4, NO3, organic carbon (OC), elemental carbon (EC), soil (IP), CM, PM2.5 and 

PM10. These data are available at approximately 11 sites in the CENRAP states. In addition, hourly 

values of light extinction and deciview are available at several of these sites. The SEARCH network 
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provides 24-hour as well as continuous (hourly) speciated measurements of PM2.5 components and 

other specifics from 8 stations all located in the southeastern US outside of the CENRAP states 

(Hansen et al., 2003). We will use data from these and the other observational databases listed in 

Table 7-1, supplemented with the routine AIRS/AQS data, as appropriate, 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 areas within the CENRAP states. For example, the 

criteria and other pollutants in the AIRS/AQS database 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 concentrations. With other data sets (e.g., SEARCH) it 

may be possible to reproduce the urban-to-rural gradients in PM2.5 components. While finer grid 

meshes (e.g., 4 km) might be more appropriate, high-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 predictions and the interpretation of the modeled and 

measured comparisons must be made with care. 

 Another important consideration is that different PM monitoring networks may use different 

measurement approaches that “measure” different amounts of the same species that are also different 

from the modeled species. For example, the IMPROVE network only speciates PM2.5 so any sulfate 

or nitrate in the coarse mode (PM2.5-10) is included in the CM species. The CMAQ and CAMx 

models will be evaluated separately for each network. 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 should 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. Indeed, in the upper Midwest, the Lake Michigan 

Air Directors Consortium (LADCo) has been centrally 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 eastern 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 information for air quality model performance testing. 

Fortunately, during CY-2002, there were at least two 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) collaborated on the support of airborne sampling 

using two aircraft that, along with ground-based measurements, provided a 3-dimensional 
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representation of air pollution concentrations across the upper Midwest with some flight paths 

extending south to include the Mammoth Cave, KY and 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 sampling was performed over a broad region of the 

Midwest from 1 June to 22 November. Lasting 3-5 hours, the WDNR and Jacko aircraft sampled a 

variety of aerometric parameters (depending upon the flight and aircraft) including wind speed, wind 

direction temperature, dew point, relative humidity, pressure, O3, NO, NO2, NOx, NOy, speciated 

VOCs, carbonyls, HNO3, NH3, Hg, SO4, OC, EC, PM2.5, and light scattering (Neph). Still 

photographs documenting visibility were also collected. Presently, the full WDNR/MRPO aircraft 

database, from the first flights in 1987 to the recent sampling in 2003 is being aggregated into a 

master data base archive. 

 At the University of Maryland, researchers have been using ground-based monitors, 

radiosondes, profilers, and instrumented aircraft 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 O3, NO, CO, SO2 

samplers, as well as a NO2 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 available for CMAQ/CAMx performance testing during 

2002 is quite significant. Historically, aircraft data sets have been used only sporadically in 

evaluating model performance aloft. Based on current resource and schedule constraints, we do not 

plan on evaluating the CENRAP CMAQ/CAMx 2002 runs using aircraft data. 

7.5 Model Evaluation Tools 

This section introduces the various statistical measures, graphical tools, and related analytical 

procedures that have proven useful over the years 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 performance metrics, only a very limited subset of 

these measures will actually be relied upon to form judgments concerning model acceptability and in 

the final reporting. 

 The current methodologies and statistical metrics 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 similar methods and evaluation tools used in 

WRAP, VISTAS and MRPO only focused on the CENRAP states and vicinity, but the evaluation 
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will remain open to emergent methods for testing 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 statistical 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 within the list of metrics that are calculated on a routine basis using 

the UCR and CENRAP model evaluation tools (these 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 most useful summary measures and we will focus 

mainly upon them in the CENRAP modeling, but not to the exclusion of others that are found to 

yield discriminating power. For ozone and other gas phase species (NO, NO2, SO2) we will include 

use the traditional statistical measures (EPA, 1991, 1999). 

Typically, the statistical metrics are calculated 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 measures shown in Table 7-3 will be computed for 

the full 36 km and 12 km domains, as well as for 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, 

NO2, CO, SO2; 8-hr for ozone, and 24 hr for sulfate, nitrate, PM and other aerosol species. These 

results will then be averaged over annual, monthly, 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 other ways, e.g., (a) day vs. night, (b) weekday vs. 

weekend, (c) precipitation vs. non-precipitation days, (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 would only be considered if additional resources 

are made available. In subregional performance 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 metrics 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 SO4, NO3 and ammonium (NH4) 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 guidance (EPA, 2001). However, in reality ambient 

gaseous species in 2002 are principally available for ozone, NO2, SO2, 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 regional-scale CMAQ predictions. We would consider 

the merits of assembling a rural-oriented gaseous species observational model performance 

evaluation database for use with CMAQ/CAMx. This database could be composited with 
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measurement from rural sites from the AQS, CASTNet, 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 facilitate quantitative and qualitative comparisons 

between CMAQ/CAMx predictions and measurements. Together with the statistical metrics listed 

in Table 7-3, the graphical procedures are intended to help: (a) identify obviously flawed model 

simulations, (b) guide the implementation of performance 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. These 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 considered 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 appropriate 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 evaluation 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 the 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 that is at least as reliable as other current stateof-science methods. Should unforeseen model performance problems arise in the 2002 Base Case 

model simulations, it may be necessary to draw into the evaluation supplemental 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 diagnostic methods actually be needed, their usage 

will require additional resources. Below, we identify 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. 
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 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 apportionment 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 Apportionment 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 evaluated. 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 researchers 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 parameter2

, (b) promotes accuracy by using consistent numerical 

methods and the same time steps for concentrations 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). 
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agreement between DDM and brute force sensitivities 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 O3 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 O3 formation and the sensitivity of O3 to 

emissions reductions (Arnold et al., 1998). Process 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 and 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 model simulation in terms 

of the budgets of free radicals, production and loss of odd oxygen and O3, 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 analysis can also improve model diagnosis and 

performance evaluation efforts by identifying processes that are ‘out of balance’ (Tesche and 

Jeffries, 2002), by identifying situations for which 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) the 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 models (AQMs) including both trajectory models 

(Tonnesen, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and Dennis, 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 individual sink and source processes and each chemical 

reaction. These integrated sink/source process rates (IPR) and integrated reaction rates (IRR) can 

then be stored to a file and analyzed using a post-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 internalized within the AQM to output chemically 

meaningful parameters directly (e.g., budget terms for O3, NOx and odd oxygen). 

 Process analysis measures for aerosol chemistry have not been analyzed as much as for 

ozone chemistry. Although the ozone chemistry process 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 module and on the sulfate/nitrate equilibrium from 

the aerosol thermodynamics module would be a useful addition to the current process analysis 

output. 

Because application of all three of these 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, 
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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 

PM2.5, and total observed mass of PM2.5? 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 accomplish 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 substantial 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 performance 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 the 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 SO4

and/or S, NH4, NO3, mass associated with SO4, mass associated with NO3, elemental carbon (EC), 

organic carbon (OC), IP, mass of individual constituents of IP, and coarse matter (CM). The 

gaseous species include ozone (O3), HNO3, NO2, PAN, NH3, NOy, SO2, CO, and H2O2. 

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 particulate 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 should be given to the model evaluation results at 

the more rural ozone monitors (e.g., CASTNet) compared to urban ozone monitors. In addition to 
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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 PM2.5 mass, PM10 mass, 

extinction (bext) 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. Thus, 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 and 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 improvements (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 model estimates. Whether and to what extend this 

more detailed investigation into light extinction 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 observational data obtained from the IMPROVE, SEARCH, 

CASTNet, EPA-FRM, EPA-STN, and other monitoring networks. These monitoring data will be 

obtained from AIRS, VIEWS, and other appropriate organizations. These comparisons will likely 

include: 

3⁄4 Daily monthly, seasonal and annual averages for SO2, SO4, NO3, EC, OC, PM2.5, and 

PM10, taking care to exclude periods of sampling interference in the observational data. 

We will look for systematic biases between the model results and IMPROVE 

observations, and if biases are found, identify 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 evaluations may entail several components, many of 

which can be identified presently. Of course, the actual 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. 
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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, SO4/SOx). 

3⁄4 Investigate the performance of the model at selected observational sites characterized by 

different chemical regimes that may be encountered 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. observed 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 (SO2 + SO4), nitrate (HNO3 + NO3) and ammonia (NH3 + NH4). 

3⁄4 Compare observed versus modeled mass fractions of PM constituents at various sites that 

are characterized by their proximity or remoteness relative to sources, or by specific 

meteorological conditions (e.g., frontal passage, stagnation, precipitation); these will 

enable identification of trends in the model 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 component 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 SO4, NO3, 

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 tools 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 CMAQ/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. 
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7.7 Performance Goals and Benchmarks 

 Establishment of performance goals and benchmarks for regulatory modeling is a necessary 

but difficult activity. Here, performance goals refer to targets that we believe a good performing 

model should achieve, where as performance benchmarks are based on historical model performance 

measures for the best performing simulations. Performance 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 results 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 performance 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-scale 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 adoption 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 concentrations were adapted from previous surveys of 

several dozen urban-scale photochemical grid modeling studies (principally in California) focusing 

on ozone episodes of 1 to at most 3 days in duration. 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) comprising 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 large 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 generally 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., NO3). In time, this 

has led to the introduction of the fractional and normalized mean bias and error metrics in addition to 

the mean normalized metrics and related performance expectations based on these alternative 

measures. 
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While the 1-hr metrics and goals still have value in interpreting ozone and some gas-phase 

species performance, it has been necessary to develop 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: SO4 and/or S, NH4, NO3, mass associated with SO4, mass 

associated with NO3, EC, OC, IP, and mass of individual constituents of inorganic primary 

particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO3, NO2, PAN, NH3, 

NOy, SO2, CO, and H2O2. In addition, EPA guidance identifies several potentially useful statistical 

measures including: (a) accuracy of spatially averaged concentrations near a monitor, (b) fractional 

bias in means and standard deviations of predictions 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, PM10 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 used. As correctly pointed out by Seigneur 

and co-workers (2003), the current ability of regional PM models to predicting regional PM and 

visibility is an area of research with improvements 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 four (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 

PM2.5 ~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 regional haze modeling is an evolving science, 

and considerable practical application and performance 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. Certainly, more information 

on likely performance expectations will be available once the CENRAP ozone and PM performance 

evaluation and sensitivity results are fully analyzed. 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: 
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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 the results of the CENRAP multi-species evaluations 

of CMAQ and CAMx with the preliminary CENRAP modeling (Pun, Chen and Seigneur, 2004; 

Tonnesen and Morris, 2004), the results from the 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 expectations. Indeed, our experience has been that initial simulations that 

‘look very good’, usually do so as the result of compensating errors. The norm is to engage in a 

logical, documented process of model performance improvement wherein a variety of diagnostic 

probing tools and sensitivity testing methods are used to identify, analyze, and then attempt to 

remove the causes of inadequate model performance. This is invariably the most technically 

challenging and time consuming phase of a modeling study. We anticipate that the annual 

CMAQ/CAMx model base case simulations will present 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 adequately 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 necessary work. Where practical, diagnostic or 

sensitivity analyses, if needed, could be performed 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 sensitivity 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 appropriate 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 response (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 and 
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CAMx models, and the frequent science updates to the model and supporting databases, 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 performance problems are encountered in the 

operational evaluation. Also, the number of tests possible, 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/N2O5/HNO3 chemistry; and 

3⁄4 Modified NH3 and HNO3 deposition velocities. 

If necessary, Process Analysis extraction outputs 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, complexity, 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 purposes. First, they facilitate the emissions 

control scenario identification and evaluation processes. 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 are available. The second purpose of Level III 

sensitivity analyses is to help quantify the estimated reliability of the air quality model in simulating 
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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 SO2 emissions; 

3⁄4 Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point 

source NOx emissions; 

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

NOx emissions; and 

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

 

Of course, traditional ‘brute force’ sensitivity experiments 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 experimentation (Levels I, II, or III) will depend on the 

outcome of the CENRAP 2002 annual operational performance 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 tools’ 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 probing 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 and species ratio techniques would be beneficial to 

the study (and if existing project resources allow), we will discuss with the CENRAP Project 

Manager the merits of including this additional probing 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 photochemical model performance and in assessing 

model credibility in estimating the effects of emissions changes. Major contributions to the 

development and refinement of this general diagnostic method over the past decade have been made 

many scientists including Milford et al., (1994), Sillman (1995, 1999), Sillman et al., (1997), 
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Blanchard (2000), Blanchard and Fairley (2001), and Arnold et al., (2003). Indeed, a recent 

evaluation of CMAQ using indicator species ratios such as O3/NOx, NOz/NOy (a measure of 

chemical aging), and O3/NOz (a measure of the ozone production efficiency per NOx 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 equally 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 CENRAP 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 ratios 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 analyses 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 modeling methods to corroborate the performance findings 

and control strategy response of the primary air quality simulation model. This endorsement of the 

use of corroborative methodologies, stems from 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 the size of CENRAP, especially over time scales 

spanning 1-hr to 1 year. Although the photochemical/PM models identified in EPA’s PM/regional 

haze guidance document possess many up-to-date science and computational features, 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 corroborative modeling methods and the rigorous use of 

‘weight of evidence’ investigations, we recommend that the most recent version of CMAQ and 

CAMx be carried through the study, including the evaluation of emissions control 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 methods 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_Protocol\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 air quality model output, (b) examination of air quality 

and emissions trends, and (c) the use of corroborative modeling such as observation-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 significantly strengthen the credibility and reliability 

of the modeling available to the states for their subsequent use. The exact details of the ‘weight of 

evidence’ analyses must wait until the CENRAP modeling study evolves further. It is premature to 

prescribe which, if any of the WOE analyses would 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 analyses is unknown as well. Nonetheless, we outline 

below our thoughts regarding what would likely 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 evidence’ 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 Modeling 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 weight 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 PM2.5. Information of 

this type, together with results of DDM and traditional ‘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 modeling the 2002 annual period that provides an 

opportunity to compare the CENRAP modeling results 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 response to changes in model inputs, e.g., emissions. 

These include: 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 7.doc 7-22 

3⁄4 Examination of conditions for which substantial 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, the use of weekday/weekend information has been 

helpful in assessing the model’s response to emissions changes. However, we suspect that this 

approach would not prove feasible with an annual episode over geographic domain as large as 

CENRAP. 

The first two methods have actually been considered 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 (Tesche, 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 inventories of sufficient quality to make such an 

analysis credible and the difficulties in removing 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 reliable emissions inventories using today’s modeling technology let 

alone construct retrospective inventories 5-10 years ago prior to the implementation of significant 

emissions control programs or major land use changes. The use of indicator species, however, offers 

some promise. 

 Recent analytical and numerical modeling studies have demonstrated how the use of ambient 

data and indicator species ratios can be used to corroborate 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 precursor NOx and VOC emissions. Reynolds et al., (2003) 

followed up this analysis, augmented with process analysis, to assess the reliability of SAQM 

photochemical model estimate of 8-hr ozone to precursor 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 indicator ratios (or diagnostic ‘probes’) to quantify the 

model’s response to input changes: 

3⁄4 The ozone response surface probe [O3/NOx]; 

3⁄4 The chemical aging probe [NOz/NOy ]; and 

3⁄4 The ozone production efficiency probe [O3/NOz ]. 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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 challenging, but the use of indicator species (e.g., 

ammonia or HNO3 limitation for nitrate particle formation) and species ratios appears to offer, at this 

time, the only real opportunity to quantify the expected 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 Modeling team and the CENRAP Modeling Workgroup can 

be especially fruitful in terms 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 importance, 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 PM2.5 and PM10 1 in 3 days; 24 hr 11 

CASTNET Speciated PM2.5, 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 (PM2.5) 1 in 3 days; 24 hr (?)

EPA-STN Speciated PM2.5 Varies; Varies 12 

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 25 

EPA-SPEC Various as part of St. Louis Super 

Site Various 1+ 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\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: O3, NO, NO2, CO, 

SO2, 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: O3, NO, NO2, NOx, 

NOy, speciated VOCs, 

carbonyls, HNO3, NH3, Hg, 

SO4, OC, EC, PM2.5, 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_Protocol\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 (Ap) 

Paired_Peak 

peak

peak

O

P − O Ppeak = 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

P P O O

P P O O

1 1

2 2

2

1

( ) ( )

( )( )

Pi = prediction at 

time and 

 location i; 

Oi = observation 

at time 

 and location 

i; 

P = arithmetic 

average of Pi, 

i=1,2,..., N; 

O = arithmetic 

average of Oi, 

i=1,2,...,N 

Normalized Mean 

Error (NME) 

Norm_Mean_Err 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

Reported as % 

Root Mean 

Square Error 

(RMSE) 

Rt_Mean_Sqr_Err 

( )

2

1

1

1 2





 



 ∑ − =

N

i

Pi Oi N

Reported as % 

Fractional Gross 

Error (FE) 

Frac_Gross_Err 

∑= +

− N

i i i

i i

P O

P O

N 1

2 Reported as % 

Mean Absolute 

Gross Error 

(MAGE) 

Mean_Abs_G_Err 

∑=

−

N

i

Pi Oi N 1

1

Mean Normalized 

Gross Error 

(MNGE) 

Mean_Norm_G_Err

∑=

− N

i i

i i

O

P O

N 1

1 Reported as % 
December 2004 

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 7.doc 7-26 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical 

Expression Notes 

Mean Bias (MB) Mean_Bias 

∑( ) =

−

N

i

Pi Oi N 1

1 Reported as 

concentration 

(e.g., μg/m3

) 

Mean Normalized 

Bias (MNB) 

Mean_Norm_Bias ( ) ∑=

− N

i i

i i

O

P O

N 1

1 Reported as % 

Mean 

Fractionalized 

Bias (Fractional 

Bias, MFB) 

Mean_Fract_Bias 

∑= 















+

− N

i i i

i i

P O

P O

N 1

2 Reported as % 

Normalized Mean 

Bias (NMB)

Norm_Mean_Bias 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

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

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8.0 VISIBILITY ASSESSMENT AND COMPARISON 

 

 This chapter provides a summary of how the CENRAP 2002 annual modeling results will 

be used to satisfy the requirements of the Regional Haze Rule (RHR) as part of the Section 308 

visibility State Implementation Plans (SIPs) and Tribal Implementation Plans (TIPs) due in 

2007/2008. Note that the CENRAP 2002 modeling results may also be used in some capacity as 

the regional component in some State’s fine particulate (PM2.5) and 8-hour ozone SIPs that are also 

due in 2007/2008. However, how the modeling results will be used to address PM2.5 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 the 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 RHR 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 visibility goal to remedy existing impairment due to air pollution at 

the Class I areas. This is accomplished by defining a visibility goal of “natural conditions” to be 

achieved at each Class I area by 2064. The RHR requires states with Class I areas to develop SIPs 

that include reasonable progress goals for improving visibility in each Class I area and emission 

reduction measures to meet those goals. For the initial control strategy SIPs due in 2007/2008, 

states are required to adopt progress goals for improving visibility from baseline conditions. The 

2000-2004 five-year period is used to define baseline conditions and the first future progress 

period is 2018, that has been interpreted 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 visibility 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 alternative rate. States are to define 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), baseline visibility conditions, progress goals, and 

 

1

 Areas designated as mandatory Class I areas are those National Parks exceeding 6,000 acres, wilderness areas 

and national memorial parks exceeding 5,000 acres, and all international parks that 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” refers to the 156 “Federal mandated” Class I Areas. 
 

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changes in visibility must be expressed in terms of deciview (dv) units. The deciview unit of 

visibility impairment is derived from light extinction (bext) as follows: 

dv = 10 ln (bext/10) 

Where light extinction (bext) is expressed in terms of inverse megameters (Mm-1 = 10-6 m

-1). 

Section 8.2.1.1 below discusses how bext is calculated using the observed fine particulate 

concentrations from the IMPROVE monitors and Section 8.2.1.2 below defines how the modeling 

results will be mapped to the IMPROVE measurements for the purpose 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 listed above (EPA, 2001) discusses the use of 

modeling results to demonstrate progress toward the RHR goals. This is a draft document that is 

scheduled to be updated by the end of 2004. When the draft modeling guidance is updated that 

may necessitate an update to this Modeling Protocol to keep it consistent with current EPA 

guidance. The second document (EPA, 2003a) focuses on monitored visibility and how to define 

the visibility baseline and how to track visibility 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 concentrations at the Class I areas for projecting visibility 

changes from the current to future years. 

Below we describe EPA’s approach for projecting visibility improvements. Some concerns 

have been raised regarding some of the assumptions in EPA’s visibility projection approach, such 

as: (1) EPA assumes that sulfate is fully neutralized by ammonium which may not always be the 

case; (2) EPA’s natural background fails to account for sodium chloride which may be an 

important component at some sites; and (3) the adjustment factor assumed to convert organic 

carbon (OC) to organic matter (OM) (1.4) may be too low. There may be some updates to EPA’s 

visibility projection approach, but the basic approach will likely remain unchanged. 
 

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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 Relative Reduction 

Factors (RRFs) that are defined as the ratio of the future-year to the current-year modeling results. 

The RRFs are applied to the baseline visibility conditions to project future-year visibility. The 

major features of EPA’s recommended visibility projections are as follows (EPA, 2003a,b): 

• Monitored data should be used to define current air quality. 

• Monitored concentrations of PM10 are divided into six major components, the first 

five of which are assumed to be PM2.5 and the sixth is PM2.5-10. 

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 reduction factors (RRFs) 

between future and current predicted concentrations of each component. 

• Component-specific relative reduction factors are multiplied by current monitored 

values to estimate future component concentrations. 

• Estimates of future component concentrations 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 with the goal to see if the simulated control 

strategy results in the goal being met. 

• It is acceptable to assume that all measured sulfate is in the form of ammonium 

sulfate [(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate 

[NH4NO3]. 

In order to facilitate tracking visibility progress, three important visibility parameters are required 

for each Class I area: 

Baseline Conditions: Baseline Conditions represent visibility for the 20% best and 20% 

worst days for the initial five-year baseline period of the regional haze program. Baseline 

Conditions are calculated using 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 average based on the latest five-years of monitored data (which is 

currently 1999-2003, but expected to be 2000-2004 by the time the CENRAP future-year 

visibility projections will be made in the latter half of 2005). 
 

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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 experienced in the absence of human-caused 

impairment” (EPA, 2003b). 

8.2.1.1 Calculation of Baseline Conditions

Baseline and Current Conditions for Class I areas are calculated using fine and coarse 

particulate concentration measurements at Interagency 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 (PM2.5) and total PM2.5 and PM10 mass 

concentrations from which visibility is obtained through the IMPROVE reconstructed mass 

extinction equation. 

Visibility conditions are estimated starting with the IMPROVE reconstructed 24-hour 

average particulate matter (PM) mass measurements for six PM species: 

• Sulfate [SO4]; 

• Particulate Nitrate [NO3]; 

• 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, sulfate and particulate nitrate 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 

SO4 1.375 x ( 3 x S) 

NO3 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 – MF 
 

December 2004

H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 8.doc 8-5

Where: 

• S is elemental sulfur as determined from 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 used to replace (3 x S). It is assumed to be completely 

neutralized by ammonium (1.375 x SO4). 

• NO3_ is the particulate nitrate measured by ion chromatography analysis of the Module 

B. It is assumed to be completely neutralized by ammonium (1.29 x). 

• The IMPROVE Organic Carbon (OC) measurements 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 referred to as Light Absorbing Carbon (LAC) is 

determined by TOR analysis and is the sum of EC fractions minus the pyrolized 

fraction. 

• Soil is determined as a sum of the mass of those elements (measured by PIXE) 

predominately associated with soil, whose mass is adjusted for oxygen in the common 

compounds. 

• MT and MF are total PM10 and PM2.5 mass, respectively. 

Associated with each PM species is an extinction coefficient that converts concentrations 

(in g/m3) 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 increasing 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 properties, but the IMPROVE reconstructed mass 

extinction equations assume OM is non-hygroscopic. 

BSulfate = 3 x f(RH) x [SO4] 

bNitrate = 3 x f(RH) x [NO3] 

bEC = 10 x [EC] 

bOM = 4 x [OM] 

bSoil = 1 x [Soil] 

bCM = 0.6 x [CM] 

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). These 

values have been recently updated (SAIC, 2003) and are available at: 

ftp://ftp.saic.com/raleigh/RegionalHaze_2002FRHcurve/fRH_analysis/. 

The total light extinction (bext) is assumed to be the sum of the light extinction due to the 

six PM species listed above plus Rayleigh (blue sky) background (bRay) that is assumed to be 10 

Mm-1. 

 bext = bRay + bSulfate + bNitrate + bEC +bOM + bSoil + bCM

The total light extinction (bext) in Mm-1 is related to visual range (VR) in km using the 

following relationship: 
 

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H:\CENRAP_Modeling\Modeling_Protocol\Draft2.0\Chapter 8.doc 8-6

 VR = 3912 / bext

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(bext/10) 

 The equations above using data from the associated IMPROVE monitor are used to 

estimate the daily average visibility at each Class I area. For each year from the 2000-2004 

baseline these daily average visibility values, in terms of deciview (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% lowest visibility days in terms of deciview. The 

Baseline Conditions is the average of the 20% best 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, which 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 visibility at Class I areas the modeling results are 

used in a relative sense to scale current observed visibility for the 20% best and 20% worst 

visibility days using RRFs that are 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 equations. 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 reconstructed 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 reconstructed mass equations 

assume sulfate is fully neutralized by ammonium. For the CMAQ Version 4.4 model and the 

CAMx V4.10s using the M5 coarse/fine mode configuration that are being used in the CENRAP 

annual modeling, the mapping of modeled species to IMPROVE reconstructed 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 

SO4 1.375 x (ASO4J + ASO4I) 

NO3 1.29 x (ANO3J + ANO3I) 

EC AECJ + AECI 

OM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI 

Soil A25J + A25I 

CM ACORS + ASEAS + ASOIL 
 

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H:\CENRAP_Modeling\Modeling_Protocol\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 

SO4 1.375 x PSO4 

NO3 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., REMSAD, CMAQ-MADRID, CMAQ-AIM or CAMx 

using multi-sectional algorithms) or updates to CMAQ 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 CMAQ and CAMx estimated sulfate and particulate 

nitrate are in the fine mode. If a fully sectional model was used (e.g., CMAQ-AIM or CAMx fullscience), then a decision would have to be made whether to map the coarse mode sulfate and 

nitrate to the IMPROVE fine SO4 and NO3 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 distribution for sulfate 

and nitrate in the accumulation mode (i.e., ASO4J and ANO3J) may have some mass greater than 

2.5 :m, however the current usual convention for CMAQ is to assume all the 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-hour observed PM species measurements for the 20% Worst and 20% Best 

days that make up the Baseline Conditions to project future-year PM levels from which visibility 

can be assessed using the IMPROVE RCM extinction equations listed above. EPA has identified 

the following six steps to project future-year visibility 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 comprises the five-year Baseline Conditions (i.e., 

2000-2004). 

2. Calculate the arithmetic average of the 20% best 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. Document which days during the five-year baseline 

period comprise the 20% best and 20% worst observed visibility days. 

3. Use an air quality model to simulate the baseline period emissions and future-year 

emissions. Extract 24-hour average PM species concentrations “near” each Class I area. 

Calculate the average PM species concentration estimates in the current and future-year 
 

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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% worst days and for each year in the five-year 

baseline period. Calculate Class I area and best/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/worst days to obtain 

future-year daily average PM species concentrations 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 species concentrations for the best/worst 20% 

observed days, calculate extinction using the IMPROVE reconstructed mass extinction 

equation and daily deciview, perform 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 concentrations “near” the monitor, EPA (2001) 

recommends taking a spatial average of PM concentrations across a grid cell resolution dependent 

NX by NY array of cells centered on the grid containing 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 modeling, EPA recommends that NX=NY=3, that is the 

model estimated PM species concentrations 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 performance evaluation will pay particular close 

attention to the model’s PM performance at each Class I area for the 20% Best and 20% Worst 

days. If the model performance is extremely poor for one or more of these days at a Class I area, it 

may be desirable to exclude that day when calculating the RRFs for visibility projections. For 

example, if a day is one of the 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 visibility. 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 conditions for the future-year are the same as for 

2002. This will allow for the comparison of the changes in visibility at Class I areas from the 

current (2002) to future-year due to changes in emissions. This means that the effects of climate 

change, land use variations and climatic variations will not be accounted for in the future-year 
 

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meteorological inputs. Several other decisions concerning the future-year to be modeled, model(s) 

to be used and modifications to the model inputs to reflect future years do need to be made such as: 

8.3.1 Future-Year to be Modeled 

Visibility projects are needed for the 20% best and 20% worst days from the five-year 

baseline period of 2000-2004 to 2018, 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 says that the mid-year 

from the 2013-2017 five-year projection period should be used, which 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 goals should be compared across 5-year planning periods “(e.g., 

2000-2004 to 2013-2017) and progress should be measured 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 information is available, which should be when 

EPA released the revised PM2.5 and regional haze guidance document by the end of 2004. Thus, in 

the discussion below we just refer to the “future-year” with the actual year to be determined during 

2004. Current thinking is that the 2018 future-year will be used in CENRAP. 

8.3.2 Future-Year Emissions 

The future-year emissions or growth and control factors will be provided to the CENRAP 

annual Emissions and Air Quality Modeling Team (ENVIRON/UCR) by the CENRAP emissions 

projection contractor (TBD). They would be processed into the 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 future-year modeling. This assumes that the same 

land use and biomass distribution as used in the 2002 Base Case emissions would exist in the 

future-year emission scenarios. The effects of urban sprawl, increased agricultural, deforestation, 

locations of new roads, etc. between the current (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 emissions scenario would be used in the future-year. Future-year 

Typical Year emissions for Electrical Generating Units (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 current (2002) year are based on a 2002 
 

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simulation of the GEOS-CHEM global climate model. 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 simulation is not available. In the absence of a futureyear emissions scenario GEOS-CHEM simulation, 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 modeling inputs would be identical to the 2002 

Base Case simulation including meteorology, photolysis rates, geophysical, and other inputs. 

8.4 Presentation of Results 

There are various ways that the future-year modeling results can be presented to display the 

visibility projections and convey the key findings. Although the projection of visibility 

improvements at the Class I areas is a key element 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 ordered by state and provided to CENRAP. 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 Under the Regional Haze Rule (EPA, 2003a). 

Figure 8-1 displays a sample plot of a visibility progress goal prepared for the Grand 

Canyon National Park as part of the WRAP Section 309 SIP work effort. The modeled projected 

visibility could be plotted on the same figure as a graphical way of presenting the results. Shown 

as a straight line near the bottom of Figure 8-1 is the EPA default “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 “current conditions” for the latest 

five years of data, which is 1997-2001 in this graph (the Section 309 SIP analysis was performed 

in 2003 before the 2002 IMPROVE data were available), is assumed as the “baseline conditions” 

(i.e., 2000-2004) that is the starting point in 2004 (12.00 dv) for the glide path or linear uniform 

rate of progress to natural conditions in 2064 (purple line with squares). 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 target in 2018 at the Grand Canyon National Park 

(GMNP). Note that we would expect higher observed visibility levels for the 20% worst days at 

many of the CENRAP Class I areas due to higher sulfate concentrations in the eastern US, but 
 

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Figure 8-1 illustrates the types of graphs that can be utilized to display visibility progress goals 

combined with visibility 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., visibility 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 Goals at which Class I 

areas. 

Another useful display of the projected visibility improvements are stacked bar charts of 

extinction showing the contributions of each of the six major PM components to light extinction 

for the IMPROVE measured current period to the future-year period and the 20% best and 20% 

worst days. These side–by-side current to future-year extinction comparisons can graphically 

display which extinction components are getting better 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 SO2 emissions control strategy may have had the desired effect in 

reducing sulfate concentrations but the visibility estimates remain relatively 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. 
 

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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 whose 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 would be used to provide a big picture view of 

the changes in regional air quality and visibility from 2002 to the 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 insight into short-term changes, whereas changes in 

Uniform Rate of Reasonable Progress Glide Path

Grand Canyon NP - 20% Worst Days

12.0011.66 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

Year

Haziness Index Unit:Deciviews 

Glide Path Natural Visibility Conditions Observation
 

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24-hour concentrations for the year provides information 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 extinction 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 days. 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 changes in visibility from the current to future-year across all days. 
 

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9.0 DATA MANAGEMENT 

Data management and data security procedures are critical components of the CENRAP 

regional fine particulate and haze modeling. Very large 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 (Tb) 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 will maintain backup copies either 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 and backup of critical input and output files. 

Because there are differences in system configurations at each of the modeling centers 

(ENVIRON and UCR), the data backup and archiving 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 than the surface layer). A common practice is to 

extract layer 1 data from the 3D concentrations 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 will permanently saved the script, executables and input data so that 

3-D concentration files can be regenerated if needed. 

To promote efficient, reliable communication 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 analysis 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. The project webpage will 

be the primary mechanism for communicating results of model simulations and analysis to 

funding agencies and other interested parties. 

9.2 Data Transfer 

Data transfer among the modeling centers and between other CENRAP participants or 

contractors will be accomplished using a combination 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 data files of less than about 500 MB file transfer 

protocol (ftp) is typically the fastest and most efficient method. ENVIRON and UCR each 
 

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maintain webpages and ftp pages that can be used for exchanging data. In addition, each 

modeling center has several 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 all large files data transfers. The approach 

described here has been used throughout the WRAP 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, each 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 Gigabytes (>15 Terabytes, Tb) of disk 

storage available to the UNIX/Linux workstations. All of the 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 Exabyte tape drives for data backup and data 

transfer. ENVIRON can also create CDs (CD-R and CD-RW) and DVDs (DVD+ and DVD-) 

for data backup and distribution. For CENRAP 2002 annual modeling, most CMAQ and CAMx 

simulations would be performed on one of the 9 node Beowulf Linux Cluster 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 memory and is connected to a 2.8 Tb RAID disk 

system. Each secondary processing node includes 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 Unix workstations are also available, SUN, 

DEC and SGI. The SMOKE modeling would likely 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 temperature sensitive power shut off device that 

will shut off the power to all computers in case the AC breaks down 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 stored on a shelf to protect against 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 simulation and analysis of large datasets. 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 allow 
 

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project data to be accessed thought the UCR T1 internet connection. The data backup/archiving 

system include 8mm tape drives and DLT and Super DLT auto loading cartridge 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 and to a redundant RAID5 disk system. The compute 

clusters and disk systems are located in a locked, secure room with a dedicated climate control 

system and with backup air conditioning. The laboratory has a full time systems administrator to 

perform system backups, maintenance and updates and Dr. Tonnesen’s group includes a second 

full time systems administrator who performs weekly backups of critical data to tape.
 

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10.0 DOCUMENTATION 

 This section describes the documentation that be provided during CENRAP annual modeling 

and the potential for modifications to this Modeling Protocol and QAPP that might become 

necessary as the study unfolds. 

10.1 Planned Documentation 

Documentation associated with the emissions and air quality modeling performed during the 

CENRAP annual modeling will include all relevant input data bases and scripts associated with the 

pre- and post-processing associated with model input development, model application, sensitivity 

and diagnostic analyses, and performance evaluations. PowerPoint presentations, technical 

memorandums, interim and final reports that describe the methodologies and results of the model 

performance evaluation, model intercomparison, and visibility assessment will be provided. Table 

10-1 below lists the current schedule of deliverables 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 2nd 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 2nd 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 2nd 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 2nd week of following 

month 

 Draft sensitivity and source apportionment report due to August 31, 2005 
 

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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 project 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 that 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 PM2.5 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 overview and results, and rationale for the selected 

configuration, (b) technical detail covering all relevant 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 stemming 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 PM2.5 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 evaluation procedures throughout the duration of the 

CENRAP annual modeling study. Moreover, when EPA updates the regional haze modeling 

guidance that may also trigger a need to update the CENRAP modeling approach and Modeling 

Protocol. Given the ongoing model refinement activities 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 consultation with the CENRAP Project Manager and the 

revised protocol will be submitted to the CENRAP Modeling Workgroup for approval. 
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