Quality Assurance Project Plan (Draft) 

For 

Central Regional Air Planning Association (CENRAP) 

Emissions and Air Quality Modeling

Revision 3 

March 29, 2004 

Prepared for: 

Ms. Annette Sharp 

Executive Director 

and 

Mr. Jeff Peltola 

Technical Director 

Central Regional Air Planning Association 

10005 S. Pennsylvania, Ste. C 

Oklahoma City, OK 73159 

Project Manager and Co-Principal Investigator: 

Mr. Ralph Morris 

ENVIRON International Corporation 

101 Rowland Way, Site 220 

Novato, CA 94945 

Co-Principal Investigator: 

Dr. Gail Tonnesen 

College of Engineering 

Center for Environmental Research and Technology 

University of California at Riverside 

Riverside, CA 92507
Approvals 

Title: Quality Assurance Project Plan for Central Regional Air Planning Association (CENRAP)

Emissions and Air Quality Modeling, November 19, 2004. 

Ms. Annette Sharp Date: 

Technical Director 

Central Regional Air Planning Association (CENRAP) 

(Name here) Date: 

Project Officer 

U.S. Environmental Protection Agency 

Mr. Ralph Morris Date: 

Project Manager and Co-Principal Investigator 

ENVIRON International Corporation 

Dr. Gail Tonnesen Date: 

Co-Principal Investigator 

University of California, Riverside 

EVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, California 94945
TABLE OF CONTENTS 

Page 

1.0 INTRODUCTION .....................................................................................................................1 

 1.1 Problem Definition...............................................................................................................2 

2.0 PROJECT DESCRIPTION........................................................................................................2 

2.1 Background..........................................................................................................................2 

2.2 CENRAP Long Range Plan.................................................................................................4 

 2.2.1 Tier One: Tasks........................................................................................................4

 2.2.2 Tier Two: Activities.................................................................................................4 

 2.2.3 Tier Three: Projects..................................................................................................4 

 2.2.4 Tier Four: Deliverables............................................................................................5 

 2.2.5 Critical Milestones Dates.........................................................................................5 

2.2.6 Role of Emissions and Air Quality Modeling in 

 CENRAP Long Range Plan.....................................................................................6 

2.3 Quality Assurance of the CENRAP 2002 Annual Emissions 

2.3.6 and Air Quality Modeling........................................................................................6 

 2.3.1 Past Related Regional Modeling Studies.................................................................6 

2.3.2 QA for the CENRAP 2002 Annual Emissions and 

 Air Quality Modeling...............................................................................................7 

 2.3.2.1 Data Gatekeeping...............................................................................................8 

 2.3.2.2 Emissions QA/QC..............................................................................................9 

 2.3.2.3 Meteorology QA/QC .......................................................................................10 

 2.3.2.4 Air Quality Modeling QA/QC .........................................................................11 

 2.3.2.5 Overview of Data Flow and Quality Assurance Process.................................12 

2.4 Scope of Work for CENRAP 2002 Annual Emissions and Air 

 Quality Modeling ..............................................................................................................18 

 2.4.1 Project Management ..............................................................................................19 

 2.4.2 Phase I Modeling Tasks.........................................................................................20 

2.4.2.1 Task 1-1: Develop and Update Quality Assurance 

 Project Plan (QAPP) and Modeling Protocol ..................................................20 

2.4.2.2 Task 1-2: Develop Modeling Protocol/Work Plan for 

 Performing 2002 Base Case Modeling for the CENRAP Region ...................20 

 2.4.2.3 Task 1-3: Develop Model-Ready Meteorology Inputs ....................................21 

 2.4.2.4 Task 1-4: Develop Base Case Model-Ready 2002 Emission Inputs ...............22 

 2.4.2.5 Task 1-5: Perform 2002 Base Case Modeling and 

 Model Performance Evaluation .......................................................................25 

 2.4.2.6 Task 1-6: Develop Future Base Case Model-Ready Emission Inputs.............27 

2.4.2.7 Task 1-7: Perform 2018 Modeling, Sensitivity Analysis 

and Control Strategy Evaluation 28 

 2.4.2.8 Task 1-8: Reports and Recommendations .......................................................29 

2.4.3 Phase II Modeling Tasks........................................................................................30 

 2.4.3.1 Task 2-1: Revise and Update the CENRAP Modeling 

 Quality Assurance Project Plan (QAPP)..........................................................30 

 2.4.3.2 Task 2-2: Develop Model-Ready Meteorological Inputs ................................30 

 2.4.3.3 Task 2-3: Generate Model-Ready Emissions ..................................................31 
 2.4.3.4 Task 2-4: Perform 2018 Base Case Modeling .................................................31 

 2.4.3.5 Task 2-5: Refine 2018 Base Case Modeling and 

 Conduct Control Strategy Evaluations.............................................................32 

 2.4.3.6 Task 2-6: Final Report for Inclusion as CENRAP 

 Technical Support Document ..........................................................................32 

3.0 PROJECT MANAGEMENT...................................................................................................33 

3.1 Project Organization ..........................................................................................................33 

 3.1.1 ENVIRON Project Manager and Co-Principal Investigator..................................37 

3.1.2 ENVIRON and UCR Co-Principal Investigators ..................................................37 

3.1.3 CENRAP Technical Director and Executive Director...........................................38 

3.14 CENRAP Project Manager ....................................................................................39 

3.1.5 CENRAP Modeling Workgroup............................................................................39 

 3.2 Project Schedule and Execution.........................................................................................39 

 3.3 Personnel Qualifications and Training...............................................................................40 

 3.4 Communications Plan ........................................................................................................40 

 3.5 Documentation and Records..............................................................................................41 

4.0 MEASUREMENT AND DATA ACQUISTION....................................................................41 

 4.1 Computer Hardware/Systems Administration ...................................................................41 

 4.1.1 ENVIRON Modeling Center Hardware.................................................................41 

 4.1.2 UCR Modeling Center Hardware ..........................................................................42 

 4.1.3 Backup Procedures.................................................................................................42 

 4.2 Sources for Data Used in Modeling...................................................................................43 

5.0 EMISSIONS MODELING ......................................................................................................44 

 5.1 Quality Assurance Components of Emissions Modeling .................................................45 

 5.1.1 Modeling QA .........................................................................................................45 

 5.1.2 System QA.............................................................................................................45 

 5.1.3 Gatekeeping and Outside Review..........................................................................46 

 5.1.4 Documentation.......................................................................................................46 

 5.2 Implementation of the QA System ....................................................................................46 

 5.2.1 The Emissions QA Team.......................................................................................46 

6.0 METEOROLOGICAL MODELING ......................................................................................49 

7.0 CENRAP 2002 AIR QUALITY MODELING........................................................................50 

 7.1 CMAQ Overview ..............................................................................................................50 

 7.2 CMAQ Configuration for CENRAP Modeling .................................................................51 

 7.2.1 Updates to the CMAQ Secondary Organic Aerosol (SOA) Module.....................51 

 7.2.1.1 SOAmods Formation .......................................................................................52 

 7.2.1.2 Quality Assurance of SOAmods......................................................................55 

 7.3 CAMx Overview................................................................................................................57 

 7.4 CAMx Configuration for CENRAP Modeling..................................................................58 

 7.5 Advantages of Operating Multiple Models........................................................................59 

8.0 ASSESSMENTS AND OVERSIGHT ....................................................................................61 

 8.1 Assessment Responsibilities ..............................................................................................61 

 8.2 Assessment Types and Usage ............................................................................................61 

 8.3 Assessment Criteria ..........................................................................................................62 

9.0 REFERENCES ........................................................................................................................62 
FIGURES 

Figure 2-1a Data flow and quality assurance steps in the CENRAP 

 Emissions and Air Quality Modeling ....................................................................16 

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

 CENRAP Emissions and Air Quality Modeling....................................................17 

Figure 3-1 CENRAP Emissions and Air Quality Modeling 

 Project Organizational Chart..................................................................................34 

Figure 5-1 Flow Diagram of Major SMOKE Processing Steps ..............................................45 

Figure 5-2 Diagram of CENRAP Modeling Team Approach 

 to SMOKE Modeling.............................................................................................48 

Figure 7-1. Schematic describing the addition of new SOA processes 

 (bold italic) within the existing CMAQ SOA module (regular font) 

 to treat polymerization and SOA from sesquiterpenes and isoprene.....................56 

TABLES 

Table 2-1. Model Performance goals used in Phase I to help interpret 

 modeling results.....................................................................................................15 

Table 2-2. Task 1-1: Develop Quality Assurance Project Plan (QAPP) 

 and Modeling Protocol...........................................................................................20 

Table 2-3. Task 1-2: Develop Modeling Protocol/Plan for Performing 

 2002 Base Case Modeling for the CENRAP Region.............................................21 

Table 2-4 Task 1-3: Develop Model-Ready Meteorological Inputs ......................................22 

Table 2-5. Task 1-4: Develop Base Case Model-Ready 2002 Emissions Input .....................25 

Table 2-6. Task 1-5: Perform 2002 Base Case and Model Performance Evaluation..............27 

Table 2-7. Task 1-6: Develop Future Base Case Model-Ready Emissions Inputs .................28 

Table 2-8. Task 1-7: Perform 2018 Modeling, Sensitivity Analysis 

 and Control Strategy Evaluation............................................................................29 

Table 2-9. Task 1-8: Reports and Recommendations .............................................................29 

Table 2-10. Task 2-1: Revise and Update CENRAP Modeling QAPP.....................................30 

Table 2-11. Task 2-2: Develop Model-Ready Meteorological Inputs ......................................31 

Table 2-12. Task 2-3: General Model-Ready Emissions ..........................................................31 

Table 2-13. Task 2-4: Perform 2018 Base Case Modeling .......................................................32 

Table 2-14. Task 2-5: Refine 2018 base case modeling and conduct 

 control strategy evaluations ...................................................................................32 

Table 2-15. Task 2-6: Final report/Technical Support documentation (TSD)..........................33 

Table 3-1. CENRAP 2002 Annual Emissions and Air Quality 

 Modeling project participants and contacts. ..........................................................35 

Table 7-1. Parameters use in enhanced SOA module (see Figure 7-1)...................................54 

Table 7-2. Comparison of fractional bias performance metric for Organic 

 Carbon (OC) using the standard CMAQ Version 4.4 (V4.4) and 

 CMAQ V4.4 with he SOAmods enhancement......................................................55 
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1.0 INTRODUCTION 

This Quality Assurance Project Plan (QAPP) describes the quality management functions and 

activities performed by the Central Regional Air Planning Association (CENRAP) 2002 current 

and 2018 future-year annual Emissions and Air Quality Modeling, performed by ENVIRON 

International Corporation and the University of California at Riverside College of Engineering – 

Center for Environmental Research and Technology (UCR/CE-CERT). This is the third 

revision (Revision 3) of the CENRAP Emissions and Air Quality Modeling QAPP and updates 

Revision 2 of the QAPP dated December 23, 2004. Revision 3 of the CENRAP Modeling QAPP 

updates the QAPP to include additional modeling analysis added with the CENRAP additional 

Award of Work dated December 15, 2005. 

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 EPA 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. 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 will perform 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 RHR. 

The quality assurance approach utilized herein is generally based on the national consensus 

standard (ANSI/ASQC, 1994). This standard describes the necessary management and technical 

elements for developing and implementing a quality system. It recommends a tiered approach to 

the design of the specific quality system used in each of the organization’s efforts. This approach 

has been adopted by the ENVIRON/UCR Team and is documented in the CENRAP 2002 

Annual Modeling Protocol (ENVIRON and UCR, 2004) and in UCR’s Quality Management 

Plan (QMP; UCR, 2003). The CENRAP Modeling Protocol and UCR’s QMP formed the basis 

for much of the content in this CENRAP Emissions and Air Quality Modeling QAPP. 

This QAPP was prepared in accordance with the EPA guidelines for quality assurance project 

plans for modeling (EPA, 2002), for QAPPs (EPA, 2001), and the North American Research 

Strategy for Tropospheric Ozone (NARSTO) Quality Handbook for modeling projects 

(NARSTO, 1998). The EPA and NARSTO guidance documents were developed specifically for 

modeling projects, which have different quality assurance concerns than environmental 

monitoring data collection projects. The work performed in this project involves modeling at the 

basic research level and for regulatory/policy applications. In order to utilize model outputs for 

these purposes, it must be established that each model is scientifically sound, robust, and 

defensible. This is accomplished by following a project planning process that incorporates the 

following elements as described in the EPA guidance document for modeling: 

• A systematic planning process including identification of assessments and related 

performance criteria; 
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• Peer reviewed theory and equations; 

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

• Documentation of any changes from original plans; 

• Clear documentation of assumptions, theory, and parameterization that is detailed 

enough so others can understand the model output; 

• Input data and parameters that are accurate and appropriate for the problem; and 

• Output data that can be used to help inform decision makers. 

The purpose of this QAPP is to establish and encourage a continuous improvement process that 

will result in clearly defined data quality objectives, documentation, procedures, and 

requirements for QA benchmarks and reports. A rigorous quality system will assist in ensuring 

that the quality of the project products are known, defensible, and meet the user’s data quality 

objectives. This system will also enable the modeling team to systematically plan to 

accommodate the additional work that will be required to ensure high-quality results. 

1.1 Problem Definition 

The 1990 Amendments to the Clean Air Act and the 1999 U.S. EPA Regional Haze Rule 

establishes special goals for visibility in 156 national parks, wilderness areas, and international 

parks. Through these amendments, 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). States, and potentially Tribes, in the Central 

States are required to submit visibility SIPs under Section 308 of the Regional Haze Rule (RHR). 

The Central Regional Air Planning Association (CENRAP) has implemented a regional air 

quality planning process to provide the necessary technical and policy tools needed by states and 

tribes to comply with Section 308 of the RHR. As part of this effort, CENRAP has funded 

modeling studies to support the development of SIPs and TIPs. In October 2004, CENRAP 

retained ENVIRON International Corporation and the University of California at Riverside 

(UCR) to perform Emissions and Air Quality Modeling of the 2002 annual period. 

The results from these modeling studies will help determine the proper mitigation measures that 

will be necessary to comply with Section 308 of the RHR. 

2.0 PROJECT DESCRIPTION 

2.1 Background 

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, 
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Louisiana, Minnesota, Missouri, Nebraska, Oklahoma and Texas. The governing body of 

CENRAP is the Policy Oversight Group (POG) that is made up of 18 voting members 

representing the 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 five standing workgroups see Table 3-1 for contact information): 

• Monitoring; 

• Emissions Inventory; 

• Modeling; 

• Communications; and 

• Implementation and Control Strategies. 

•

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 has been formed). 

The RHR requires the states (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 five 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 

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 this support is performing emissions and air 

quality modeling to obtain a better understanding of the causes of regional haze and 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 initial current and future year Emissions and Air Quality Modeling for the 2002 

annual period. In December 2005 CENRAP awarded additional work to ENVIRON and UCR to 

perform 2018 control strategy evaluation, PM source apportionment and prepare a Technical 

Support Document (TSD) for the modeling as part of the RHR visibility SIP due in December 

2007. 

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 
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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. 

2.2 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. The CENRAP long-range plan was updated in the spring 2005 and was approved 

by the CENRAP POG on March 2-3, 2005 (http://www.cenrap.org/organization.asp#). 

2.2.1 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): 

• Develop reasonable progress goals; 

• Establish baseline and natural conditions; 

• Develop a long term strategy for reducing regional haze; 

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

• Establish BART requirements. 

Each of these Tasks must be accomplished to achieve the goal of providing states and tribes the 

information upon which supportable and effective RHR SIPs and TIPs will be developed. 

2.2.2 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. 

2.2.3 Tier Three: 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 to provide them to the Emissions and Air 

Quality Modeling Team. Details on specific projects are identified in the Attachments to the 

Updated CENRAP Long Range Plan (CENRAP, 2005). 
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2.2.4 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. Deliverables for the 2002 Annual Emissions and 

Air Quality Modeling activities will include a Modeling Protocol, a QAPP, Base Case Modeling 

and Model Performance Evaluation Report, PowerPoint presentations of future-year base case 

modeling, control strategy evaluations and PM source apportionment modeling and a draft and 

final technical Support Document (TSD) that describes the modeling and is suitable for including 

with the visibility SIP due December 2007. 

2.2.5 Critical Milestones Dates 

The Updated CENRAP Long Range Plan (CENRAP, 2005) contains 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 Updated CENRAP Long Range Plan back-calculated critical milestone dates from the date 

that the RHR SIPs/TIPs are required to be submitted and developed the following timeline (see 

Attachment 2 of Updated CENRAP Long Range Plan): 

SIP/TIPs Adopted and States Submit SIP to EPA – December 17, 2007: Although 

SIP/TIP may be submitted later with the PM2.5 SIP due April 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 – May 1, 2005: States are to provide BART reduction 

estimates and/or market trading estimates for their BART sources. 

Base Case Modeling Begins – February 1, 2005: The CENRAP Modeling Workgroup 

has estimated that it will take six months to perform base year base case modeling and 

model performance evaluation. 
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2.2.6 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. 

2.3 Quality Assurance of the CENRAP 2002 Annual Emissions and Air Quality Modeling 

The CENRAP 2002 annual Emissions and Air Quality Modeling includes annual emissions and 

PM/regional haze simulations using a 36 km grid covering the continental US and adjacent 

regions, plus simulation of additional shorter duration episodes using a 12 km grid 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 Emissions and Air Quality Modeling effort will focus on the use of 

the SMOKE emissions and CMAQ and CAMx air quality modeling systems for calendar year 

2002 over the Inter-RPO continental US 36 km horizontal grid, as well as potentially the 

CENRAP Central States 12 km grid. 

2.3.1 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 efforts performed in the 

Central States and across the United States. The procedures used in these previous studies 

provide a guide to the modeling and QA approach for the CENRAP 2002 annual Emissions and 

Air Quality Modeling: 

Big Bend Regional Aerosol sand Visibility Observational Study (BRAVO): The BRAVO 

study examined the causes and sources 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). 

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). 
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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). 

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

Emissions and Air Quality Modeling Study. Consequently, the quality assurance (QA) and 

quality control (QC) procedures employed are directly relevant to this QAPP. Others are 

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

in the development of this QAPP and the CENRAP Emissions and Air Quality Modeling 

Protocol (ENVIRON and UCR, 2004). 

2.3.2 QA for the CENRAP 2002 Annual Emissions and Air Quality Modeling 

The CENRAP 2002 annual Emissions and Air Quality Modeling Study will use 36km MM5 

meteorological data provided by the Iowa Department of Natural Resources (IDNR) (Johnson, 
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2004), 12km MM5 meteorological data provide by other CENRAP participants and emissions 

data provided by CENRAP contractors (Strait, Roe and Vukovich, 2004) and EPA. These data 

will be used to perform emissions and air quality modeling of the 2002 annual period on a 36 km 

grid and for episodes of shorter duration using a 12 km grid. Additional annual 12 km modeling 

may also be performed. 

Closely integrated with the annual (and possibly episodic) meteorological, emissions and air 

quality modeling will be ongoing project management, technical review, and QA activities 

performed under the guidance of the CENRAP Project Manager, CENRAP Administrative 

Project Manager and the CENRAP Modeling Workgroup. The modeling team members will 

conduct regular monthly conference calls with CENRAP management, as well as ad hoc topical 

conference calls as needed, and will attend periodic meetings with the CENRAP Modeling 

Workgroup as needed. 

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

management activities, the following four other CENRAP Emissions and Air Quality Modeling 

activities/functions will be performed, consistent with the QAPP and CENRAP’s Long Range 

Plan: 

2.3.2.1 Data Gatekeeping 

The CENRAP Emissions and Air Quality Modeling Team are receiving emissions, 

meteorological and air quality data from other CENRAP contractors, participants, or other 

sources. As a first line of QA, we have defined a Gatekeeping function to assure that the data 

have been received correctly, the quality of the data has been evaluated, and that the data 

received have been documented. 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. Obtains air quality data as appropriate for model input 

development and model performance evaluation and assures that the quality of all air 

quality data obtained are consistent with the approved QAPP. Provides documentation of 

evaluation and generate IC/BC inputs for CMAQ and CAMx for all modeling runs. 

3⁄4 Meteorological Gatekeeper. Obtains meteorological data as MM5 files for annual 2002 

modeling runs (36 km) and other episode periods (12 km) and performs data quality 

checks as approved in the QAPP, together with the appropriate documentation of model 

performance evaluation activities. 

3⁄4 Emissions Gatekeeper. Obtains emissions inventory data for CENRAP states from the 

CENRAP emissions contractor (Strait, Roe and Vukovich, 2004) necessary to support 

annual 2002 and future year modeling and recommend source of emissions data to be 

used for non-CENRAP states, Canada and Mexico. Assures quality of all emissions data 

received are consistent with the approved QAPP, and develops all emissions modeling 

files to support modeling runs for 2002. Develops the chemical speciation files and 
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temporal and spatial allocation files necessary to convert annual inventories into hourly 

and daily emissions modeling files, as appropriate. Develops all emissions modeling files 

for non-CENRAP states to support modeling runs for future year base case. 

3⁄4 Data Management Gatekeeper: Maintains the CENRAP Modeling Website including 

posting modeling input and output files, reports, interpretation of results, and other 

documents as requested by CENRAP. 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. 

2.3.2.2 Emissions QA/QC 

Emissions Quality Assurance (QA) and Quality Control (QC) are some of the 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 Emissions and Air Quality Modeling effort we 

will perform a multistep emissions QA/QC approach that was developed under WRAP and 

VISTAS. 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 designed 

to detect and correct errors prior to the air quality model simulations. 

Emissions QA/QC performed as part of the emissions modeling includes: 

EPA Input Screening Error Checking Algorithms: Although the SMOKE emissions 

model will be used for emissions processing, some additional input error checking 

algorithms like those used with the EMS and EPS emission models will be considered 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. 

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. 
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After 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 5 emission categories typically used are biogenic, fires, on-road mobile, 

other low-level anthropogenic (area plus non-road) and point sources. If possible, 

separate spatial QA plots will be generated for low-level and elevated point sources. 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 by source category 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 by source category 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 emissions control strategy is being 

analyzed and there are changes in emissions for other pollutants or for SO2 outside of the 

CENRAP state, problems in emissions processing can be identified prior to the air quality 

model simulation. 

The emissions QA/QC displays are made available to CENRAP participants for review through 

the project website (see: http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml). 

2.3.2.3 Meteorology QA/QC 

Participants performing the 36 km annual and 12 km MM5 modeling will conduct their own 

QA/QC and evaluation of the meteorological fields. For example, Johnson (2004) and Baller 

(2004) have performed an initial evaluation of the 36 km MM5 results. In addition, the 

Emissions and Air Quality Modeling Team will also perform some QA/QC of the meteorological 
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data to assure that it has been 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 Emissions and Air Quality Modeling Meteorological Gatekeeper will perform the 

following: 

3⁄4 Analyses of the MM5 data to assure that it has been transferred correctly. 

 

3⁄4 Evaluation of the MM5 data using METSTAT and the surface meteorological network. 

3⁄4 Evaluation of upper-air MM5 meteorological estimates by comparison them to upper-air 

observations and satellite images. 

3⁄4 Comparison of the CENRAP 2002 36 km MM5 simulation with the ones generated by 

WRAP and VISTAS. 

3⁄4 Generation of the CMAQ-ready meteorological inputs using the MCIP2.3 processor. 

In October 2005 EPA released a new version of the CMAQ modeling system (V4.5) that 

included a new version of MCIP (MCIP3.0). In order to take full advantage of the new features 

of CMAQ V4.5 (e.g., Sea Salt emissions, AE3 aerosol module and spatially varying minimum 

Kz), the MCIP3.0 processor needs to be used. A new version of CAMx is also available (V4.3) 

that requires use of a new version of MM5CAMx to process ice-phase water. Thus, the 

following additional work was added to the Meteorological Gatekeepers with the December 

2005 contract update: 

3⁄4 Process 36 km and 12 km MM5 data for 2002 using the latest MCIP3.0 and 

MM5CAMx processors for CMAQ V4.5 and CAMx V4.3. 

The Meteorological Gatekeeper report evaluation of the CENRAP 36 km MM5 results and 

comparison with the VISTAS and WRAP 36 km MM5 runs can be found on the project website 

(http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_MM5_ev

al.ppt). 

2.3.2.4 Air Quality Modeling QA/QC 

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

3⁄4 Verification that correct configuration and science options are used in compiling and 

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

JPROC, ICON, BCOM and the CCTM. Same procedures for the CAMx modeling 

system that includes MM5CAMx and TUV. 

3⁄4 Verification that the correct configuration and science options are used in running each 

model in the CAMx modeling system where these include MM5CAMx, TUV, CAMx, 

and the CMAQ-to-CAMx emissions and IC/BC processors. 
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3⁄4 Verification that correct input data sets are used when running each model. 

3⁄4 Evaluation of CCTM and CAMx results to verify that model output is reasonable and 

consistent with general expectations. 

3⁄4 Processing of ambient monitoring data for use in the model performance evaluation. 

3⁄4 Evaluation of the CCTM and CAMx results against concurrent observations and each 

other. 

3⁄4 Backup and archiving of critical model input data. 

The most critical element 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 running the model. 

For CMAQ modeling we have employed a system of naming conventions using environment 

variables in the compile and run scripts that guarantee that correct inputs and science options are 

used. Similar procedures are used in CAMx modeling using file and directory naming 

conventions. We also have employed a redundant naming system so that the names 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 will provide a summary that can be useful for quickly comparing 

various model simulations. A spread sheet will be prepared and posted on the project website 

that tracks the model simulations providing a clear definition of the run and its purpose. 

2.3.2.5 Overview of Data Flow and Quality Assurance Process 

Figures 2-1a and 2-1b display 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 QA and QC. Whenever data are received by the Modeling Team, it is 
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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 2-1a 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. After 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), 

and are then subjected to another round of QA to assure the integrity of the data is retained. 

After the model-ready inputs have been developed and subjected to QA/QC, the models (e.g., 

CMAQ and/or CAMx) will be applied using Base Case emissions and the modeling results will 

be subjected to a model performance evaluation. The model performance evaluation (MPE) 

represents an extensive QA effort and is one of the most time consuming components 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 will perform the MPE/QA process using as many different tools 

and analyses as possible in order to fully understand the accuracy, uncertainty and reliability of 

the model simulation. As seen in Figure 2-1b, the MPE process in CENRAP is a multistep 

process using several different techniques: 

UCR Analysis Tools: The University of California at Riverside (UCR) Analysis Tools 

were used extensively in Phase I and are run on a Linux platform separately for each 

network. 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 and similar 

visual tools. 
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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. 

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, which 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 

accurate 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). 

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 2-1, 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 

intercomparisons 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 is greater than 2.5 μg/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.”
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Table 2-1. Model performance goals used in Phase I to help interpret modeling results. 

Fractional 

Bias 

Fractional 

Error Comments 

#±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. 

Model performance evaluation statistical measures will be calculated separately for each 

monitoring network and also stratified temporally and geographically. Because different 

monitoring networks may use different measurement techniques it is important not to mix them 

when calculating statistical measures so that the results can be interpreted correctly. For 

example, the IMPROVE and STN Organic Carbon (OC) measurements contain significant 

differences. In addition, the calculation of statistical measures by network also allows 

comparison of model performance at the more urban STN sites versus the more rural IMPROVE 

and CASTNet sites. The CENRAP region will be divided up into several subdomains for the 

model evaluation. At a minimum this would include north and south subdomain, but there may 

also be advantages to further geographic stratifications (e.g., southern CENRAP subdomain 

could be split into west and east subdomains). Finally, temporal stratifications will be at a 

minimum by month, but further stratifications will also be performed, including separate 

evaluation for the Best 20% and Worst 20% visibility days at the Class I areas (i.e., IMPROVE 

sites). 
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Figure 2-1a. Data flow and quality assurance steps in the CENRAP Emissions and Air Quality M
Quality Assurance Project Plan (Draft) 

CENRAP Emissions and Air Quality Modeling ENVIRON International Corporation 

Figure 2-1b. Concluded. Data flow and quality assurance steps in the CENRAP Emissions and Air Qua
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2.4 Scope of Work for CENRAP 2002 Annual Emissions and Air Quality Modeling 

The CENRAP 2002 annual Emissions and Air Quality Modeling work has been funded in two 

Phases. The first Phase was authorized in a CENRAP Award of Work for ENVIRON/UCR 

dated October 22, 2004 and revised November 16 and December 16, 2004 and was initially 

divided up into eight tasks. A ninth task and additional work in other tasks was added to the 

Phase I work effort in a Change Work Order dated June 8, 2005. The Phase I work effort covers 

the period of October 2004 through April 2006. The nine Phase I tasks are as follows: 

CENRAP Phase I Modeling Tasks

• Task 1-1: Develop Quality Assurance Project Plan (QAPP) 

• Task 1-2: Develop Modeling Protocol/Plan for Performing 2002 Base Case Modeling for 

the CENRAP Region 

• Task 1-3: Develop Model-Ready Meteorological Inputs 

• Task 1-4: Develop Base Case Model-Ready 2002 Emission Inputs 

• Task 1-5: Perform 2002 Base Case Modeling and Model Performance Evaluation 

• Task 1-6: Develop Future-Year Base Case Model-Ready Emission Inputs 

• Task 1-7: Perform 2018 Modeling, Sensitivity Analysis and Control Strategy Evaluation 

• Task 1-8: Reports and Recommendations 

• Task 1-9: Processing, Reformatting and Quality Assurance Check of Midwest RPO 

Emissions Inventory Data 

In December 2005 CENRAP issued ENVIRON/UCR an Award of Work dated December 15, 

2005 for the Phase II 2002 annual modeling efforts. The Phase II CENRAP 2002 annual 

Emissions and Air Quality Modeling work is divided up into six tasks and covers the period of 

December 15, 2005 through December 31, 2006. The six Phase II tasks are as follows: 

CENRAP Phase II Modeling Tasks

• Task 2-1: Develop and Update Quality Assurance Project Plan (QAPP) and Modeling 

Protocol 

• Task 2-2: Develop and Update Model-Ready Meteorological Inputs 

• Task 2-3: Generate Model-Ready Emissions 

o Subtask 3a: Develop 2018 36 km SMOKE Emissions Modeling for CMAQ and 

CAMx 

o Subtask 3b: Develop 2018 12 km Emissions for CAMx using Flexi-Nesting 

• Task 2-4: Perform 2018 Base Case Modeling 

o Subtask 2-4a: 2018 Base Case Modeling using CMAQ 

o Subtask 2-4b: 2018 Base Case modeling using CAMx 

o Subtask 2-4c: Up to 8 Sensitivity Runs with CMAQ 

o Subtask 2-4d: Up to 8 Sensitivity Runs with CAMx 

 Subtask 2-4e1: 2018 PM Source Apportionment (CAMx/PSAT) for SO4, 

NO3, NH4 and Primary PM 
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 Subtask 2-4e2: 2018 PM Source Apportionment (CAMx/PSAT) for SOA 

o Subtask 2-4f: 2018 12 km “Flexi-Nesting” CMAQ and CAMx Model Runs 

o Subtask 2-4g: Generate a Report on Model Results 

• Task 2-5: Refine Perform 2002 Base Case Modeling and Model Performance Evaluation 

• Task 2-6: Generate a Final Report for inclusion in the CENRAP Technical Support 

Document (TSD) 

The previous studies discussed in Section 2.3.1 form the foundation for the CENRAP modeling 

and associated model QA/QC procedures. An important component of the QA/QC process is 

strong project management and project direction, which is discussed next. 

2.4.1 Project Management 

There are three aspects of Project Management that are being performed under this task: (a) 

Project Management of the project’s resources and technical progress keeping CENRAP 

informed of progress; (b) Technical Management of the day-to-day technical activities of the 

ENVIRON/UCR Team keeping clear lines of communication, and (c) Communication 

Management of the data flow and expectations among the other CENRAP contractors. 

Project Management: The Modeling Team Project Manager, Co-Principal Investigators (Co-PIs), 

and Task Managers have frequent communications with the CENRAP Project Manager and 

Administrative Project Manager through regular conference calls, e-mails and meetings as 

needed. The ENVIRON and UCR Modeling Centers are headed by the two Co-PIs (Ralph 

Morris and Gail Tonnesen, respectively) with clear lines of communication and authority. 

Technical Management: The two Co-PIs will be responsible and accountable for the 

management of the technical work within each of their respective research groups. 

Communication Management: Key to the communications management will be weekly internal 

conference calls among the two Co-Principal Investigators and the regular conference calls 

between the two Co-PIs and the CENRAP Project Manager, Administrative Project Manager and 

Modeling Workgroup and other CENRAP contractors and participants. When data are received 

from CENRAP contractors or other sources, the CENRAP Modeling Team’s meteorological or 

emissions Gatekeeper will subject the data to extensive QA screening. Any problems identified 

will be reported to the two Co-PIs who will then immediately relay that information to the 

CENRAP Project Manager and Administrative Project Manager and the relevant 

contractor/participant with the issue addressed in a conference call so that it can be resolved. 

List servers related to emissions, meteorology, and modeling issues will be set up for prompt 

communication among the Modeling Team, CENRAP, and other CENRAP contractors. 
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2.4.2 PHASE I MODELING TASKS 

2.4.2.1 Task 1-1: Develop and Update Quality Assurance Project Plan (QAPP) and 

Modeling Protocol 

The objective of this task is to prepare a Quality Assurance Project Plan (QAPP) and Modeling 

Protocol that documents the procedures for developing all model inputs, identifying sources of 

databases, identifying procedures for QA model inputs, developing model performance metrics 

and methodologies and is approvable by EPA. A QAPP will be prepared in accordance with 

EPA’s Quality Assurance Manuals including the “Guidance for Quality Assurance Project Plans 

for Modeling – EPA QA/G-5M” (EPA, 2002). EPA has developed specific format and content 

for QAPPs that will be followed. The steps that will be carried out to accomplish this task are as 

follows: 

• A preliminary draft CENRAP modeling QAPP and Modeling Protocol will be prepared 

following EPA guidelines using a preliminary CENRAP Modeling Protocol developed 

under Task 1-2, CENRAP SOW and the CENRAP Long Range Plan. 

• The two Co-PIs will review and update the QAPP. 

• A final review of the draft QAPP will be performed to assure it adheres to EPA guidance. 

• The draft QAPP will be submitted to CENRAP. If comments are received from 

CENRAP, the draft QAPP will be revised and submitted to CENRAP who will then 

submit the QAPP to EPA. 

Table 2-2 summarizes the Task 1-1 deliverables and schedule. 

Table 2-2. Task 1-1: Develop Quality Assurance Project Plan (QAPP) and Modeling Protocol. 

Item Description Due Date 

1 Draft QAPP 11/19/04 

2 Draft Final QAPP Within 1 week of comments 

2.4.2.2 Task 1-2: Develop Modeling Protocol/Work Plan for Performing 2002 Base Case 

Modeling for the CENRAP Region 

The objective of this task is to develop a Modeling Protocol/Work Plan (MP/WP) that describes 

the CENRAP modeling approach including a review of recent relevant regional modeling 

studies, sources of data, modeling approach, model evaluation approach and criteria, and 

schedule for completion. 

A Modeling Protocol/Work Plan (MP/WP) will be prepared that summarizes current regional 

modeling studies, proposed modeling domain and grid selection, proposed model configurations, 

details model input preparation, design of model evaluation and sensitivity runs, discussions of 

model performance evaluation criteria, an assessment of uncertainties and actions needed to 

obtain better performance, and recommendations for future control strategy modeling. The 
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MP/WP will also describe the procedures that will be used to complete each task in this project. 

The MP/WP will also identify a list of input files and resource needs that will be used for the 

model assessment, as well as the suite of modeling systems (i.e. air quality, emissions, and 

meteorological modeling platforms) that it intends to evaluate. The Task 1-2 draft MP/WP will 

be prepared concurrently with the QAPP and be submitted to CENRAP for approval before 

initiating Tasks 1-3 through 1-9. CENRAP may provide comments on the draft MP/WP and 

ENVIRON/UCR will revise the MP/WP and submit a revised draft MP/WP to CENRAP within 

one week of receipt of comments. The MP/WP will be updated during the study as needed. 

Specific steps in the development of the CENRAP modeling MP/WP are as follows: 

• The two Co-PIs will be responsible for developing the MP/WP. 

• A preliminary draft CENRAP modeling MP/WP will be prepared by adapting and 

enhancing the VISTAS Phase II Modeling Protocol to include CENRAP-specific issues 

and incorporating additional information from the CENRAP SOW, Long Range Plan and 

preliminary QAPP. 

• We will then discuss the CENRAP MP/WP contents with the CENRAP Project Manager 

and representatives with the results integrated into the draft MP/WP. 

• The draft MP/WP will be prepared following EPA guidance for preparing Modeling 

Protocols for 1-hour ozone (EPA, 1991) and 8-hour ozone (EPA, 1999) and PM2.5 and 

regional haze (EPA, 2001) modeling. 

Table 2-3 summarizes the task deliverables and schedule for this task. The draft MP/WP will be 

submitted to CENRAP within 3 weeks of authorization to proceed with the study. If comments 

are received from CENRAP, then a revised draft MP/WP will be submitted within one week of 

receiving comments. The modeling team will continue to update the MP/WP during the project 

as necessary and in consultation with CENRAP. 

Table 2-3. Task 1-2: Develop Modeling Protocol/Plan for Performing 2002 Base Case 

Modeling for the CENRAP Region. 

Item Description Due Date 

1 Draft Modeling Protocol/Workplan 11/19/04 

2 Draft Final Modeling Protocol/Workplan Within 1 week of comments 

2.4.2.3 Task 1-3: Develop Model-Ready Meteorology Inputs 

The objective of this task is to develop 2002 meteorological inputs for emissions and air quality 

modeling. CENRAP Participants (Iowa Department of Natural Resources, IDNR for the 36 km 

MM5 data, others for the 12 km MM5 data) has conducted MM5 runs to develop 2002 

meteorological fields for CENRAP 36 km modeling grid and will conduct 12 km MM5 modeling 

for episodic periods. The ENVIRON/UCR Team will acquire the CENRAP 2002 36 km and 12 

km MM5 outputs and process them using the latest versions of the Models-3 MCIP and 

MM5CAMx processors to generate hourly 2002 meteorological fields for the CMAQ and CAMx 

models. Note that EPA has a new version of MCIP (Version 2.3) that includes some 

improvements (e.g., graupel pass through) and corrections (e.g., correction to errors in the Pleim-
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Xiu variables). MCIP version 2.3 will be used in this analysis. There is also a new version of 

the MM5CAMx processor that includes a new treatment of subgrid-scale cloud processes that 

will be used in the CENRAP study; this version is currently available on the CAMx website 

(www.camx.com). We anticipate that MCIP and MM5CAMx will be operated using the PBL 

pass through option, although if TKE output data are available we may consider using it. 

Specific steps to be undertaken to complete this task are as follows: 

• Acquisition of the 2002 36 km and 12 km MM5 output from CENRAP; 

• QA of the MM5 output through the Gatekeeper including running through the existing 

set up of METSTAT and comparison of model performance with the 2002 MM5 

simulations performed by WRAP and VISTAS. 

• Processing of the MM5 output using the latest version of the Models-3 MCIP processor 

to generate 2002 36/12 km meteorological inputs for CMAQ. 

• Processing of the MM5 output using the latest version of MM5CAMx to generate 2002 

36/12 km meteorological inputs for CAMx. 

• QA/QC of the CMAQ and CAMx meteorological inputs. 

Assuming receipt of the 2002 MM5 36 km and some of the 12 km model output during 

November 2004, this task will be completed in December 2004. A PowerPoint presentation on 

the summary evaluation and comparison of the CENRAP MM5 model performance with WRAP 

and VISTAS 2002 MM5 runs and QA/QC results will be ready by December 20, 2004. The 

CMAQ and CAMx 36 km meteorological inputs will be ready no later than December 31, 2004. 

At this time it is unclear when CENRAP will provide the episodic 12km MM5 output. We 

anticipate that the Gatekeeper and processing into model-ready inputs will be accomplished 

within 3 weeks of receiving the 12 km MM5 output. Table 2-4 summarizes the deliverables 

under this task. 

Table 2-4. Task 1-3: Develop Model-Ready Meteorological Inputs. 

Item Description Due Date 

1 Presentation (in electronic form) of Meteorological 

Gatekeeper evaluation and MCIP/MM5CAMx processing 

of 2002 36 km MM5 data 

12/20/04 

2 Model-ready 2002 36 km meteorological inputs for CMAQ 

and CAMx 

12/31/04 

3 Model-ready 12km meteorological inputs for CMAQ and 

CAMx and episodes in 2002 

12/31/05 

2.4.2.4 Task 1-4: Develop Base Case Model-Ready 2002 Emissions Inputs 

The objective of this task is to develop 2002 Base Case model-ready hourly speciated gridded 

emissions inputs on the 36 km and 12 km grid for the CMAQ and CAMx models. The CENRAP 

emissions contractors will provide emissions data files for the CENRAP states necessary to 

develop a model-ready emissions inventory for the 2002 base case. Emissions for the other 

states, Mexico and Canada will be acquired from EPA and other sources. These data will be 
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compiled and subjected to QA/QC by the Emissions Gatekeeper using the procedures described 

previously. 

The SMOKE emissions model will be used to process the CENRAP emissions data. The 

existing WRAP and VISTAS SMOKE setups will be used as the starting point for the SMOKE 

emissions modeling and be augmented by emissions for CENRAP states and other information 

provided by CENRAP. There are two 2002 Base Case inventories that need to be developed, an 

“Actual Base Case” to be used for model performance evaluation and a “Typical Base Case” that 

is used for future year projections. These and other details (e.g., source data for fire emissions) 

will be worked out with CENRAP during the development of the Task 2 MP/WP. The 

provisional steps to be taken to generate the CENRAP CMAQ-ready 2002 Base Case emission 

inputs will be the following; more details and refinements will be provided to the Task 2 MP/WP 

after consultation with the CENRAP participants: 

1. Yearly, seasonal, monthly or daily ASCII emissions inventories in IDA format for the 

area and point sources including fire sources. 

a. Area sources will include non-road mobile sources, area sources, wind blown 

dust, and road dust. 

b. Point sources will include stationary sources with large emissions, and fire 

sources. 

2. Yearly, seasonal, or monthly ASCII vehicle miles traveled (VMT) inventory for on road 

mobile sources. 

3. NetCDF files for gridded land use as input for BEIS3 and the emission factors. 

4. Ancillary files which include: 

a. Surrogate files for spatially allocating the area sources emissions, 

b. Temporal and cross reference files for temporal allocation, 

c. Speciation profiles and cross-reference files for chemical allocation. 

5. Fire emissions: Will have prescribed burns and agricultural burns for CENTRAP state 

from CENRAP. WRAP and VISTAS will have prescribed burns, agricultural burns and 

wildfires for their states. 

6. Wind Blown Fugitive Dust: Use data provided by CENRAP, the new ENVIRON/UCR 

2002 EI already prepared for WRAP or leave out. 

7. Ammonia: Use data provided by CENRAP and the new ENVIRON/UCR 2002 EI 

already prepared for WRAP. 

Emissions for the Entire 36 km Unified Grid Domain:

To process the emissions for CENRAP: 

1. All the inventories provided by CENRAP will replace any default CENRAP emissions in 

the current 2002 emissions inventory that was prepared for WRAP. 

2. Any new specific temporal profiles (if provided by CENRAP) will be added to the 

current EPA temporal profiles and cross-reference files (or default to the values currently 

being used for WRAP). 

3. The EPA spatial surrogate data will be used for area sources (as in WRAP and VISTAS 

inventories). 
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4. The EPA Model speciation profiles and cross reference file, which were distributed with 

SMOKE, will be used for chemical speciation. 

5. For the biogenic emissions, the netCDF gridded land use inventory will be used. 

6. All the emissions that are meteorology dependent will be processed using the MCIP2.3 

files created using MM5 output to be provided by CENRAP. 

Emissions Processing QA:

The following QA steps will be performed: 

1. Use SMOKE reports to: 

a. QA the inventory for total emissions per state and/or county for spatial allocation. 

b. Check for irregularities in the temporal allocation. 

c. Check for other irregularities, such as vertical allocation, inventory per SCC, etc. 

2. Use RMC Post processing QA to create and review: 

a. Spatial vertical daily total emissions, 

b. Daily domain total emissions, 

c. Diurnal domain total emissions, and 

d. Domain total vertical distribution. 

Emissions for the 12 km CENRAP Domain: 

To process the emissions for 12 km grid cell the following steps will be taken: 

1. Establish domain definition. 

2. Create new 12 km surrogates by extracting the surrogates for the domain from the EPA 

12 km surrogates using SMOKE or a program written by the RMC. 

3. Create new gridded netCDF biogenic land use inventory for 12 km cells (not yet 

available, we are working on a 1-km projection now of the landuse data). 

4. Use the same temporal and speciation profiles and cross references as in the 36 km grid 

cell. 

5. Perform QA as in the 36 km grid cell domain. 

There are extensive QA protocols in place for emissions modeling that were developed as part of 

the WRAP and VISTAS modeling efforts. These included both internal SMOKE QA procedures 

and post-SMOKE QA summaries. A variety of QA summary plots will be posted to the project 

webpage providing easy access to emissions QA results. In addition, the complete set of 

QA/QC results will be archived as well as made available to the CENRAP project participants 

through ftp transfer or CD. 

Emissions modeling for the 2002 Base Case will be performed during the December 2004 

through February 2005 time period. The deliverables will be a technical memorandum suitable 

for inclusion as a chapter in the final report and PowerPoint presentation summarizing the 2002 

emissions modeling and QA/QC results and the 2002 CMAQ-ready emission inputs for the 36 

km domain. Table 2-5 summarizes the deliverables and schedule for the Task 1-4 emissions 

modeling. 
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Table 2-5. Task 1-4: Develop Base Case Model-Ready 2002 Emission Inputs. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Technical Memorandum on development of 2002 Base 

Case emissions (Actual and Typical) 

02/21/05 

3 Presentation (electronic format) on development of 

2002 Base Case emissions 

02/21/05 

4 Model-ready 2002 Actual Base A emissions completed 01/31/05 

5 Model-ready 2002 Typical Base A emissions completed 02/28/05 

6 Revised model-ready Actual Base Case emissions 

completed 

TBD 

7 Revised model-ready Typical Base Case emissions 

completed 

TBD 

2.4.2.5 Task 1-5: Perform 2002 Base Case Modeling and Model Performance Evaluation 

The objective of this task is to perform 2002 Base Case modeling using the CMAQ and CAMx 

models and to conduct a model performance evaluation. The first step in this task will be to set 

up a webpage for the project to disseminate information and post documents and modeling 

results. The ENVIRON/UCR Team has already set up a CENRAP modeling webpage for the 

initial modeling analysis work conducted previously at: 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml

We will use the meteorological inputs developed in Task 1-3 and the emissions files developed 

in Task 1-4 as inputs to the CMAQ model, and perform an initial 2002 Actual Base Case 

simulation and model evaluation on the 36 km grid. CAMx 2002 emissions and ICBC inputs 

will be prepared using the CMAQ-to-CAMx processors that will be used along with the 

meteorological inputs developed in Task 1-3 to perform a 2002 CAMx Base Case simulation. 

The CMAQ and CAMx model performance evaluation and diagnostic sensitivity tests will be 

conducted following the procedures in the Task 1-2 MP/WP, along with additional diagnostic 

tests that are identified during the course of the study. We will conduct 12 km sensitivity tests 

for several 2002 episodes and the compare model performance with that using a 36 km grid. Our 

experience with 36/12 km modeling in the southeastern US for VISTAS is that although there 

can be large differences in model performance at a given time and location, at most times and 

locations and in general, the performance attributes overall (both good and bad) are very similar. 

Based on the model evaluation and diagnostic tests, an optimal model configuration(s) will be 

identified and, after approval from the CENRAP Project Manager, revised 2002 Actual Base 

Case simulation(s) and model performance evaluation will be performed. We will also conduct 

sensitivity analyses and use available probing tools to conduct sensitivity and source 

apportionment analyses following the procedures in the MP/WP. 
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Model performance evaluation will be completed using the UCR model performance evaluation 

(MPE) software, the CENRAP evaluation tool and ENVIRON’s performance tools for the AQS, 

IMPROVE/IMPROVE Protocol, CASTNet, STN, and NADP networks. Data from the St. Louis 

PM supersite will be analyzed individually taking advantage of the high time, size and species 

resolved measurements. Standard performance evaluation plots will include time-series and 

scatter plots for individual and collective CENRAP and nearby sites for each network, and over 

20 different model performance metrics will be calculated including several different formulas 

for estimating bias and error. A variety of summary plots will also be developed including 

stacked bar plots for individual sites and bugle and soccer plots summarizing performance at all 

sites in the CENRAP region. We will also examine performance in other subregions.

Specific steps to be taken under this task include: 

• Preparation of CMAQ IC/BC and other inputs (e.g., photolysis rates) for 2002 and the 

CENRAP 36 km and 12 km grids. 

• Development of CAMx emissions and IC/BC inputs using the CMAQ-to-CAMx 

processors and the CMAQ-ready emissions and IC/BC inputs, prepare remainder CAMx 

2002 inputs. 

• Performance of initial 2002 CMAQ and CAMx 36 km Actual Base Case simulations 

and 12 km runs for available time periods; conduct model performance evaluation and 

diagnostic sensitivity tests. 

• Performance of diagnostic sensitivity tests, sensitivity analyses and limited source 

apportionment aimed at correcting potential flaws in the modeling system. 

• Identification of optimal model configuration(s) and make recommendations to 

CENRAP Project for revised 2002 Base Case simulation(s). 

• After approval from CENRAP Project Manager, performance of a revised 2002 Actual 

and Typical Base Case simulation with approved model configuration(s) and conduct a 

model performance evaluation (MPE). 

• Performance of sensitivity analysis using either brute force (i.e., emission reduction) or 

DDM sensitivities following procedures in Task 2 MP/WP. 

• Performance of limited source apportionment modeling using TSSA (CMAQ) and/or 

PSAT (CAMx), as available. 

• Performance of typical 2002 CMAQ and CAMx Base Case runs. 

• Preparation of Monthly Progress Reports. 

• Preparation of Draft Final and, after receiving comments from CENRAP, preparation of 

Final Reports. 

Table 2-6 summarizes the deliverables and schedule for Task 1-5. 
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Table 2-6. Task 1-5: Perform 2002 Base Case and Model Performance Evaluation. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Recommendations on model configuration 03/15/05 

3 Draft Report on 2002 Base Case modeling and model 

performance evaluation 

04/30/05 

4 2002 Base Case model simulations completed 04/30/05 

5 Second Draft Report on Revised 2002 Base Case 

modeling and model performance evaluation 

TBD 

2.4.2.6 Task 1-6: Develop Future Base Case Model-Ready Emission Inputs 

The Task 1-6 objective is to develop model-ready 2018 Base Case emission inputs. CENRAP 

will provide 2018 point, area and non-road emissions data files and/or growth and control factors 

for CENRAP states necessary to develop a model-ready emissions inventory for the 2018 future 

case. 2018 emissions for non-CENRAP states will be acquired from the other RPOs (e.g., 

WRAP and VISTAS). The 2018 mobile emissions will be developed using SMOKE to produce 

the model-ready emissions data files for 36km/12km modeling grids. The 2002 biogenic 

emissions prepared under Task 1-3 will be used in the 2018 modeling. It is important that the 

2018 emission scenarios be constructed in a consistent fashion to the 2002 Typical Base Case 

emissions in order to make reliable visibility projections. The steps to be taken to complete this 

task include: 

• Acquisition of 2018 point, area and non-road emissions or projection factors from 

CENRAP. 

• Processing of 2018 point, area and non-road emissions with SMOKE. 

• Acquisition of fire emissions and processing by SMOKE. 

• Review of 2018 on-road mobile source modeling assumptions, and application of 

SMOKE to generate 2018 on-road mobile source emissions. 

• Merging of 2018 Base Case emissions to generate CMAQ-ready 2018 emissions for the 

36 km and 12 km grids. 

• Preparation of quality assurance and quality control (QA/QC) plots of the 2018 Base 

Case emissions. 

• Preparation of technical memorandum documenting the development of the 2018 Base 

Case emissions. 

• Preparation of Monthly Progress Reports. 

The types of QA/QC plots generated for the 2018 Base Case emissions scenario would be similar 

to those prepared for the 2002 Base Case discussed previously. In addition, we would generate 

difference plots of total emissions by major source category (e.g., on-road mobile, fires, point 

and other anthropogenic) between the 2018 Base Case and 2002 Typical Base Case emissions 

scenarios. Animations of differences in hourly emissions between the 2018 Base Case and 2002 
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Typical Base Case emissions scenarios would also be examined to investigate potential errors in 

temporal allocation. 

Table 2-7 summarizes the deliverables under Task 1-6. The 2018 model-ready Base Case 

emissions and technical memorandum on their development will be provided to CENRAP by 

April 30, 2005. 

Table 2-7. Task 1-6: Develop Future Base Case Model-Ready Emissions Inputs. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Technical Memorandum on Future-Year Base Case 

emissions development 

04/30/05 

3 Model-ready 2018 Base A emission inputs 04/30/05 

4 Model-ready Revised 2018 Base Case emission inputs TBD 

2.4.2.7 Task 1-7: Perform 2018 Modeling, Sensitivity Analysis and Control Strategy 

Evaluation 

The objective of Task 1-7 is to perform 2018 sensitivity and control strategy modeling to support 

the development of the visibility SIPs/TIPs. Under this task we will conduct 2018 sensitivity and 

control strategy modeling using the Task 3 meteorological inputs and the Task 6 2018 Base Case 

emissions inputs. Depending on the results of Tasks 1-1 through 1-5, either the CMAQ model or 

the CAMx model or both will be used in the 2018 sensitivity and control strategy analysis 

(decision to be made by CENRAP). 

We will recommend model configuration(s), sensitivity tests, model diagnostic tests and source 

apportionment modeling to the CENRAP Project Manager. Unique features/options of the 

alternative models will be considered and employed if appropriate. A 2018 modeling approach 

will be developed with approval of the CENRAP Project Manager. Results will be compared 

with the annual Base Case simulation and provide a means for corroborative analysis and 

diagnostic evaluation. 

The exact scope of work for the 2018 modeling analysis cannot be completely defined at this 

time. It is expected that a combination of SMOKE emissions control strategy modeling, CMAQ 

and/or CAMx emissions sensitivity modeling and use of Probing Tools, such as Decoupled 

Direct Method (DDM) sensitivity; Process Analysis (PA) and PM Source Apportionment (PSAT 

and/or TSSA) will be used. At this time it is difficult to speculate which of these Probing Tools 

will be operational in the PM portions of CMAQ and/or CAMx models at the initiation of this 

Task in May 2005. 

We will provide a Draft Report to the CENRAP Project Manager and Administrative Project 

Manager summarizing the sensitivity analysis and source apportionment analysis that can be 
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used to help design effective emissions control strategies. A Final Report on the 

sensitivity/source apportionment analysis will be submitted to CENRAP within one week of 

receiving comments. 

CENRAP will then provide a list of the control strategy evaluations to be conducted based on the 

results of the sensitivity and source apportionment analyses that will be modeled using SMOKE 

and CMAQ and/or alternative models as directed by CENRAP. The control strategy results will 

be documented as they are performed with final documentation in the Task 1-8 report. 

The Draft Report on 2018 sensitivity and source apportionment modeling will be submitted to 

CENRAP in April 2006, with the Final Report on the sensitivity/apportionment modeling being 

submitted a week after receiving comments from CENRAP. Results of the 2018 control strategy 

simulations will be provided to CENRAP as they became available and incorporated into the 

Task 1-8 report. The Task 1-7 deliverables and their due dates are given in Table 2-8. 

Table 2-8. Task 1-7: Perform 2018 Modeling, Sensitivity Analysis and Control Strategy 

Evaluation. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Draft sensitivity and source apportionment report 04/01/06 

3 Final sensitivity and source apportionment report Within 1 week of 

comments 

4 2018 model simulations completed 04/30/06 

2.4.2.8 Task 1-8: Reports and Recommendations 

The original intent of Task 1-8 was to document the work performed under Tasks 1-3 through 1-

7 in reports and develop portions of a Technical Support Document (TSD) for the CENRAP 

states RHR SIPs/TIPs. However, because of the need to perform emission updates for CENRAP 

States and other RPOs, the development of the CENRAP RHR SIP/TIP TSD covering the 

modeling will now be performed in Phase II under Task 2-6. 

Table 2-9. Task 1-8: Reports and Recommendations. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Draft Technical Support Document (TSD) See Task 2-6 

3 Final Technical Support Document (TSD) See Task 2-6 
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2.4.3 PHASE II MODELING TASKS 

2.4.3.1 Task 2-1: Revise and Update the CENRAP Modeling Quality Assurance Project 

Plan (QAPP) 

The current CENRAP draft QAPP for Emissions and Air Quality Modeling (Revision 2 dated 

December 23, 2004) will be updated to include the work to be undertaken under Phase II of the 

modeling effort and any changes to the Phase I work. A Draft Revision 3 QAPP will be 

submitted to CENRAP and finalized within one week of receipt of CENRAP comments. Table 

2-10 summarizes the Task 2-1 deliverables. 

Table 2-10. Task 2-1: Revise and Update CENRAP Modeling QAPP 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Draft Updated Revision 3 QAPP March 31, 2006 

3 Final Updated Revision 3 QAPP Within one week of 

receipt of CENRAP 

comments 

2.4.3.2 Task 2-2: Develop Model-Ready Meteorological Inputs

Since the development of the CMAQ and CAMx meteorological inputs using 36 km MM5 data 

for the annual 2002 period and 12 km MM5 data for several 2002 episodes under the Phase I 

Task 1-3, several changes have occurred that require the reprocessing of the MM5 data: 

• A new version of CMAQ (V4.5) has been released along with a new version of the 

CMAQ meteorological processor (MCIP3.0). In order to take advantage of all the 

features of the new version of CMAQ (e.g., the AE3 aerosol module with Sea Salt and 

the spatially varying minimum Kz), the MM5 data must be reprocessed using MCIP3.0. 

• A new version of CAMx (V4.3) has been released that includes treatment of solid phase 

precipitation not previously treated and a new MM5CAMx processor is needed to process 

the MM5 frozen cloud water and precipitation. 

• The MM5 model has now been run for 2002 using both a 36 km and 12 km resolution. 

At the February 6-8, 2006 CENRAP Workgroup meetings it was decided to perform most 

of the model simulations at 36 km resolution, but process the 12 km MM5 data so that 

annual 12 km runs could also be made to corroborate the 36 km runs. 

Based on these new events, the 2002 MM5 36/12 km annual data will be reprocessed using 

MCIP3.0 and the latest MM5CAMx to generate new meteorological inputs that can be used to 

run the latest versions of the CMAQ (V4.5) and CAMx (V4.3) models. Also under this task we 

will evaluate the 12 km MM5 model and compare its performance with the 36 km MM5 results 

that were evaluated under Task 1-3 in Phase I. 

Table 2-11 summarizes the deliverables and schedule for Tasks 2-2. 
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Table 2-11. Task 2-2: Develop Model-Ready Meteorological Inputs 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 PowerPoint Presentation on MM5 12 km Evaluation 

and Comparison with 36 km Results 

April 30, 2006 

3 Model-ready 36 km and 12 km 2002 annual 

meteorological inputs for CMAQ and CAMx processed 

with the latest MCIP3.0 and MM4CAMx processors. 

April 20, 2006 

2.4.3.3 Task 2-3: Generate Model-Ready Emissions 

Under this task we use the SMOKE emissions model to generate CMAQ-ready emissions for the 

revised 2018 Base Case emissions scenario at 36 km and 2002. CAMx-ready emissions for 2018 

Base Case would be generated using the 36 km CMAQ emissions results as was done for 2002. 

In addition, CAMx 12 km emissions would be generated using flexi-nesting. Table 2-12 lists the 

deliverables and schedule for Tasks 2-3. 

Table 2-12. Task 2-3: General Model-Ready Emissions. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Revised 2018 36 km Base Case Model-Ready Emission 

Inputs for CMAQ and CAMx 

Within 6 weeks of 

receipt of 2018 

growth and control 

factors (estimated 

March 31, 2006) 

3 2018 12 km Emissions for CAMx using Flexi-Nesting April 15, 2006 

2.4.3.4 Task 2-4: Perform 2018 Base Case Modeling 

Under this task 2018 Base Case, Sensitivity and Source Apportionment modeling would be 

conducted. There would be several Subtasks performance as part of this task: 

• Subtask 2-4a: 2018 Base Case Modeling with CMAQ 

• Subtask 2-4b: 2018 Base Case Modeling with CAMx 

• Subtask 2-4c: Conduct up to 8 Sensitivity Runs with CMAQ 

• Subtask 2-4d: Conduct up to 8 Sensitivity Runs with CAMx 

• Subtask 2-4e: Conduct 2018 36 km CAMx PM Source Apportionment Technology 

(PSAT) runs for: 2-4e1 – SO4, NO3, NH4 and primary PM; and 4e2 -- SOA. 

• Subtask 2-4f: Conduct 2018 12 km “flexi-nesting” CMAQ and CAMx modeling 

• Subtask 2-4g: Generate report on modeling results that can be included as part of the 

TSD (Task 6) 
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Table 2-13 summarizes the deliverables and schedule for Task 2-4. 

Table 2-13. Task 2-4: Perform 2018 Base Case Modeling. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 2018 Base Case Modeling with CMAQ May 15, 2006 

3 2018 Base Case Modeling with CAMx May 15, 2006 

4 Conduct up to 8 Sensitivity Runs with CMAQ June 15, 2006 

5 Conduct up to 8 Sensitivity Runs with CAMx June 15, 2006 

6 2018 36 km PSAT Runs May 30, 2006 

7 2018 12 km flexi-nesting runs using CMAQ/CAMx July 15, 2006 

8 Draft Report on Modeling Results July 15, 2006 

2.4.3.5 Task 2-5: Refine 2018 Base Case Modeling and Conduct Control Strategy 

Evaluations 

Under this task ENVIRON/UCR would refine the 2018 emissions and air quality modeling. 

2018 control strategy modeling will be conducted using information provided by the CENRAP 

Implementation and Controls Strategies (ICS) Workgroup. The exact control strategies to be 

evaluated are to be determined (TBD). Table 2-14 summarizes the deliverables and schedule for 

Task 2-5. 

Table 2-14. Task 2-5: Refine 2018 base case modeling and conduct control strategy evaluations. 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Control strategy evaluations completed Within 6 weeks of 

receipt of control 

strategy definitions 

from ICS 

2.4.3.6 Task 2-6: Final Report for Inclusion as CENRAP Technical Support Document

Under this task ENVIRON/UCR would document all of the modeling results conducted in Phase 

I and II in a report that would be suitable for inclusion with the CENRAP Regional Haze 

SIPs/TIPs as the modeling Technical Support Document (TSD). A draft final report (TSD) 

would be submitted to CENRAP in MSWORD format. Based on comments received from 

CENRAP, the TSD would be updated and resubmitted. CENRAP and/or CENRAP States and 

Tribes may make further updates and modifications to the document prior to submission to EPA 

as part of each State’s SIP or Tribe’s TIP. 
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Also under this task we would archive all model inputs and scripts and selected model outputs on 

USB drives supplied by CENRAP and transfer the data to CENRAP. 

Table 2-15. Task 2-6: Final report/Technical Support document (TSD). 

Item Description Due Date 

1 Monthly Progress Reports 2nd week of 

following month 

2 Draft Report/TSD submitted to CENRAP July 15, 2006 

3 Final Report/TSD Submitted to CENRAP Within 2 weeks of 

receipt of comments 

on draft report 

4 Disk drives with model inputs and selected outputs June-July 2006 

3.0 PROJECT MANAGEMENT 

3.1 Project Organization 

This project is being performed by the ENVIRON International Corporation and the University 

of California at Riverside (UCR), with input from the CENRAP Project Manager, CENRAP 

Administrative Project Manager, CENRAP Modeling Advisor and CENRAP Modeling 

Workgroup and other CENRAP Workgroups (e.g., ICS). Organizational commitment is an 

essential element for developing and implementing a successful research project. Ralph Morris 

of ENVIRON will be the CENRAP Emissions and Air Quality Modeling Project Manager (PM). 

The CENRAP Modeling Team has two Co-Principal Investigators that coordinate activities at 

each of the modeling centers, Ralph Morris of ENVIRON and Gail Tonnesen at UCR. The PM 

and two Co-PIs will be kept apprised of all project activities, from identifying the need to 

develop sound experimental and project designs to delivering reports. Commitments to research 

and project activities, such as those described in this QAPP are made only after the activities are 

thoroughly reviewed and approved by the PM, Co-PIs and CENRAP Project Manager. Figure 3-

1 presents the organizational chart that shows the lines of responsibility and information flow for 

activities under this project. Table 3-1 lists the project responsibilities for participants in the 

CENRAP Emissions and Air Quality Modeling study, with more details on their roles provided 

next. 
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 Central Regional Air 

Planning Association 

(CENRAP) 

(contracting agency) 

CENRAP 

Modeling Workgroup 

Technical Project 

Manager 

Lee Warden 

Alternate Technical 

Project Manager 

Calvin Ku 

CENRAP 

Executive Director 

Annette Sharp 

Technical Director 

Jeff Peltola 

CENRAP 

Modeling Advisor 

T.W. Tesche 

Alpine Geophysics, LLC 

Other CENRAP 

Contractors 

Contractor 

ENVIRON 

Project Manager and CoPrincipal Investigator 

Ralph Morris 

Staff 

Contractor 

UC Riverside 

Co-Principal Investigator 

Gail Tonnesen 

Staff 

Figure 3-1. CENRAP Emissions and Air Quality Modeling Project Organizational Chart.
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Table 3-1. CENRAP 2002 Annual Emissions and Air Quality Modeling project participants and 

contacts. 

Person & Role Affiliation/Address Contact Information 

Annette Sharp 

(Executive Director) 

CENRAP 

10005 S. Pennsylvania, Ste. C 

Oklahoma City, OK 73159 

(405) 378-7377 

asharp@censara.org 

Jeff Peltola 

(Technical Director) 

CENRAP 

10005 S. Pennsylvania, Ste. C 

Oklahoma City, OK 73159 

(405) 378-7377 or (651) 293 1295 

jpeltola@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 

T.W. Tesche 

(CENRAP Modeling 

Advisor) 

Alpine Geophysics, LLC 

3479 Reeves Drive 

Ft. Wright, KY 41017 

(859) 341-7502 

(Fax) (859) 341-7508 

twt@iac.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

CENRAP Workgroup Chairpersons (www.cenrap.org) 

Brandon Krogh 

(Monitoring) 

Minnesota Power (218) 723-3954 

bkrogh@allete.com 

Ray Bishop Oklahoma Department of (405) 702-4218 
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Person & Role Affiliation/Address Contact Information 

(Monitoring) Environmental Quality Ray.Bishop@deq.state.ok.us 

Calvin Ku 

(Modeling) 

Missouri Department of Natural 

Resources 

(573) 751-8406 

Calvin.Ku@dnr.mo.gov

Lee Warden 

(Modeling) 

Oklahoma Department of 

Environmental Quality 

(405) 702-4201 

Lee.Warden@deq.state.ok.us 

Ron Hoofman 

(Emissions) 

Arkansas Department of 

Environmental Quality 

(501) 682-0357 

hoofman@adeq.state.ar.us 

Lisa Brenneman 

(Emissions) 

Eastern Shawnee Tribe of Oklahoma estoepa@hotmail.com 

John Seltz 

(Implementation and 

Control Strategy) 

Minnesota Pollution Control Agency (651) 296-7801 

John.Seltz@pca.state.mn.us 

Mark McCorkle 

(Implementation and 

Control Strategy) 

Arkansas Department of 

Environmental Quality 

(501) 682-0736 

mac@adeq.state.ar.us 

Scott Weir, 

Communications 

Kansas Department of Health and 

the Environment 

(785) 291-3272 

sweir@kdhe.state.ks.us 
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3.1.1 ENVIRON/UCR Project Manager and Co-Principal Investigators 

Mr. Ralph Morris of ENVIRON is the Project Manager (PM) and Co-Principal Investigator (CoPI) for the CENRAP Emissions and Air Quality Modeling Team. He provides overall direction 

to the project and establishes a policy relationship with the sponsor, ensuring that all issues of 

importance to the CENRAP Project Manager, Executive Director and Technical Director and 

Workgroups are addressed. The PM is responsible for the overall conduct of the project, 

experimental design, reporting of the results, and interacting with the client, consultants, and 

project staff. The specific responsibilities of the PM include, but are not necessarily limited to, 

the following: 

• Directs and coordinates the activities of the project team and computer facilities to 

conduct the test program 

• Ensures that this QAPP and the Modeling Protocol are followed during the course of the 

project

• Guides the overall approach for performing modeling evaluations

• Keeps current on project status and delivers progress reports 

• Conducts initial modeling or analysis of experiments to determine if inconsistencies or 

unexpected results suggest possible experimental or measurement problems 

• Evaluates overall data quality, characterization results, and overall system performance 

with regard to meeting project objectives 

• Reviews and delivers modeling and assessment reports 

• Interacts with external scientific reviewers, collaborators and other external groups in 

their area of expertise in the development of study priorities, reporting of results, and 

obtaining external input 

• Oversees the project team in responding to any issues raised in assessment reports and 

initiates corrective actions as necessary 

• Serves as ENVIRON’s primary point of contact for contract issues 

• Establishes a project budget and monitors the effort to ensure that budget is not exceeded

• Assists in the performance of the modeling program in accordance with its contract and 

the Work Plan 

• Provides information to assist the CENRAP Project Manager, Administrative Project 

Manager and Modeling Workgroup in achieving its goals as stated in its Work Plan and 

Long Range Plan 

• Develops individual test protocols and reports as directed 

• Analyzes modeling data and provides assessment reports 

• Supports the CENRAP Project Manager and CENRAP in responding to any issues raised 

in assessment reports 

3.1.2 ENVIRON and UCR Co-Principal Investigators 

The two Co-Principal Investigators of Ralph Morris and Gail Tonnesen perform the following 

functions: 

• Direct and coordinate the day-to-day project activities of the project team and computer 
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facilities to conduct the test program 

• Ensure that this QAPP and Modeling Protocol are followed during the course of the 

project

• Manage the activities in each of the three modeling centers

• Directly supervise personnel working on this project 

• Guide the approach for performing modeling evaluations following the direction of the 

Project Manager

• Keep current on project status and deliver information to Project Manager for progress 

reports 

• Conduct initial modeling or analysis of experiments to determine if inconsistencies or 

unexpected results suggest possible experimental or measurement problems 

• Evaluate overall data quality, characterization results, and overall system performance 

with regard to meeting project objectives 

• Review and deliver data and sections for integration into modeling and assessment 

reports 

• With the Project Manager, interact with external scientific reviewers, collaborators and 

other external groups in their area of expertise in the development of study priorities, 

reporting of results, and obtaining external input 

• Oversee the project team in each modeling center responding to any issues raised in 

assessment reports and initiates corrective actions as necessary with the Project Manager 

• Monitor the effort to ensure that budget is not exceeded

• Assist in the performance of the modeling program in accordance with its contract and 

the Work Plan 

• Develop individual test protocols and reports as directed 

• Analyze modeling data and provide assessment reports 

3.1.3 CENRAP Technical Director and Executive Director 

The CENRAP Technical Director (Jeff Peltola) and Executive Director (Annette Sharp) serve as 

the primary administrative contact between the Emissions and Air Quality Modeling Team and 

CENRAP and performs the following functions: 

• Provides day-to-day oversight of CENRAP Emissions and Air Quality Modeling Team 

activities 

• Works with the CENRAP Project Manager and Modeling Team Project Manager and CoPrincipal Investigators, CENRAP Modeling Workgroup, States, Tribes oversight groups, 

collaborators, Stakeholders, etc. in assuring that the interests and concerns of all of the 

CENRAP participants are appropriately represented as project priorities are developed or 

modified due to external input 

• Assists in organizing and conducting meetings, conference calls, and workshops where 

this and related projects are discussed 
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3.1.4 CENRAP Project Manager 

The CENRAP Project Manager (Lee Warden) for the Emissions and Air Quality Modeling Team 

works with the CENRAP Administrative Project Manager/Project Coordinator in the day-to-day 

oversight and management of the modeling analysis: 

• Provides day-to-day oversight of CENRAP Emissions and Air Quality Modeling Team 

activities 

• Works with the CENRAP Administrative Project Manager and Modeling Team Project 

Manager and Co-Principal Investigators to assure that the study is being carried out in a 

technically correct fashion following the QAPP and Modeling Protocol 

• Prepares and gives presentations to CENRAP groups on the activities of the Modeling 

team as needed. 

The CENRAP Alternate Project Manager (Calvin Ku) provides backup and seamless 

continuation of work should the primary Project Manager be unavailable. 

3.1.5 CENRAP Modeling Workgroup 

The CENRAP Modeling Workgroup is responsible for overseeing the regional haze and fine 

particulate modeling that will be required for the CENRAP States and Tribes State 

Implementation Plans (SIPs) and Tribal Implementation Plans (TIPs). This workgroup provides 

both emissions modeling and air quality modeling technical support to CENRAP. Modeling 

efforts will include identification, evaluation, and application of air quality modeling tools 

(including emissions, meteorological and air quality models) to quantify the effects of emission 

management options upon air quality in Class I areas in the CENRAP region. Specific activities 

of the CENRAP Modeling Workgroup include:

• Oversees the activities of the CENRAP Emissions and Air Quality Modeling Team 

through the CENRAP Project Manager and Administrative Project Manager, conference 

calls, and periodic in-person meetings and workshops 

• Provides the CENRAP Project Manager, CENRAP Administrative Project Manager, and 

Modeling Team Project Manager and Co-Principal Investigators input on the research 

plans and their ability to meet the needs of the various stakeholders relevant to the overall 

objectives of the project 

• Provides input as needed to assure that the project has effective and appropriate peer 

review 

• Makes the Modeling Team Project Manager and Co-Principal Investigators aware of 

other projects that may be of relevance to this project 

• Reviews the QAPP and conducts critical project reviews 

3.2 Project Schedule and Execution 

The schedule for each of the CENRAP Emissions and Air Quality Modeling Phase I and II Tasks 

are presented at the end of each Task Description in Section 2.4. 
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3.3 Personnel Qualifications and Training 

General education of all project personnel lays the foundation for successful project 

implementation. It is not intended to provide detailed and specific knowledge of all components 

of the project, but it promotes an understanding of the nature of the overall project goals, 

ensuring that all personnel understand the part they are to play in the project. The members of 

this project team include resident experts in emissions, meteorological and air quality model 

development and operations. 

All project personnel must have extensive experience in their particular disciplines. Each team 

member must be familiar with the content of this QAPP and the CENRAP Modeling Protocol 

thus obtaining a project overview, including information on all functions of the modeling 

systems, from experimental design, objectives, and data validation and reporting. Where 

applicable, project personnel must be familiar with the SOPs applicable to their areas of 

responsibility. In addition, if major revisions or enhancements are made to the QAPP and/or 

SOPs, all affected individuals must review those revisions at that time. 

3.4 Communications Plan 

The CENRAP Emissions and Air Quality Modeling Team members, other CENRAP Contractors 

and CENRAP representatives are linked by e-mail correspondence, and also use this as a means 

to communicate and exchange data, either as e-mail attachments, website or by networkaccessible files. A considerable amount of information is exchanged by e-mail within this 

project. The CENRAP Modeling Team maintains listservs to distribute information to 

CENRAP. 

The Modeling Team members and CENRAP Project Manager, Administrative Project Manager 

and other members of the Modeling Workgroup will hold periodic conference calls and meetings 

to report results, discuss project status, and modify work plans as necessary. Unscheduled 

meetings or conference calls will also be held concerning specific issues as the needs arise. In 

addition, periodic project meetings and conference calls will be held. In these meetings detailed 

technical information will be exchanged, project status will be discussed, and project direction 

will be assessed. 

Written progress reports on the CENRAP Emissions and Air Quality Modeling Team activities 

will be submitted to the CENRAP Project Manager and Administrative Project Manager on a 

monthly basis. These reports will summarize project progress, results to date, problems 

encountered and necessary action items, and plans for the upcoming reporting period. 

Key modeling results, quality assurance reports, and related documents will be posted on the 

project web site. 

 http://pah.cert.ucr.edu/aqm/cenrap/index.shtml 
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3.5 Documentation and Records 

After the project tasks described in Section 2.4 that require deliverables are completed, the 

ensuing documents will be posted on the CENRAP Modeling project website. In addition, 

during the project, the CENRAP participants will be provided updates by e-mail or telephone. 

Document control is the system that ensures that the Modeling Team personnel engaging in 

project activities use only the latest revisions of the defined documents. The system includes 

retention of the document with original signed page(s) in a limited access storage area, a unique 

numbering system for all documents (typically identified by revision number and/or date), and 

electronic storage of documents by date so that the latest versions is clearly identifiable. Such 

documents are controlled documents, and can be revised only by the personnel listed within each 

document or the project quality document. The following is a typical list of the controlled 

documents within the project folder: 

1. The quality documents for the project such as the Quality Management Plan, Modeling 

Protocol and Quality Assurance Project Plan. 

2. All applicable and referenced or attached Standard Operating Procedures and/or 

Methods. 

3. Project proposals, contracts, Work Plans, experimental designs, software documentation, 

and/or similar documents. 

4.0 MEASUREMENT AND DATA ACQUISITION 

4.1 Computer Hardware/Systems Administration 

All emissions and air quality modeling will be performed on computer equipment located at 

either the ENVIRON or UCR modeling centers. 

4.1.1 ENVIRON Modeling Center Hardware 

The computing facilities at the ENVIRON Novato, CA office are built on current state-of-the-art 

hardware and software. The networked configuration of personal computers, workstations and 

printers includes Linux and Window PCs, SGI, SUN and DEC Unix workstations and Linux 

Clusters all connected by a fast network with disk access to RAID systems. The ENVIRON 

computer center includes the very latest computing technology including both OMP (shared 

memory) and MPI (cluster) multi-processing capability. ENVIRON currently has three Linux 

Clusters each with > 10 CPUs and each connected to RAID disk systems. All staff also has their 

own state-of-the-art PCs and has access to all workstations at all times, there are no problems 

with computer resource availability. ENVIRON’s high speed Linux and UNIX computing 

environment includes workstations from three top suppliers, Sun, Digital Equipment Corporation 

(DEC) and Silicon Graphics Incorporated (SGI). ENVIRON also uses Linux PC workstations 

with several different distributions of the Linux OS (e.g., Red Hat, Debian, Mandrake) and these 

exceed the performance of fast UNIX workstations. Using workstations from several suppliers 
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allows us to develop and test model codes on multiple computer platforms and means that we are 

experienced with most of the workstations used by our clients. The latest addition to the 

ENVIRON computing facilities is a 16 node Linux cluster using the Athlon MP2200 chip set 

with gigabit Ethernet that is configured as two 8 node MPI multiprocessing platforms. 

4.1.2 UCR Modeling Center Hardware

The UCR computer laboratory is designed to process large data sets for air quality modeling. It 

includes over 30 high-performance, dual CPU Linux workstations configured as several small 

Linux clusters on a private network to facilitate parallel simulations. These systems include a 24 

CPU Athlon 2000MP cluster, an 8 CPU Xeon 2.2GHz cluster and an 8 CPU 64-bit Opteron 

2GHz cluster. The data storage system includes over 23 TB of disk space configured as RAID5 

disk systems. All computers and disk systems are networked 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 is used internally within 

the laboratory so that only project team members have access to project computers and data. 

Data are also routinely transferred from the UCR systems to other organizations using a variety 

of tape formats and portable hard drives. 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. The compute clusters and disk systems 

are located in a locked, secure room with a dedicated climate control system and with backup air 

conditioning. UCR also uses the computer laboratory for air pollution modeling classes for 

graduate students and for professional staff from State and Tribal air pollution agencies. 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. 

4.1.3 Backup Procedures 

Different back-up procedures are applied to three different sets of computers that include (1) file 

servers; (2) user desktop Windows PCs; and (3) RAID5 disks storage systems. Several different 

File servers are used to host systems and user information, websites and ftp sites. 

1. Each server receives a full Level 0 backup at least once every 14 days, and an 

incremental backup at least three times per week to an independent computer. In addition, 

incremental backups are periodically made to tape. 

2. Windows based PC’s are used to for editing project documents and reports. Each PC on 

the LAN receives a full Level 0 backup at least once every 14 days and incremental 

backups every three days. 

3. Most of the project data are stored on RAID5 disk systems. Because of the large volume 

of data (> 20 TBs) it cannot be routinely backed up. Moreover, most of the project data 

are output from model simulations, which can be regenerated more quickly by re-running 

the models than by restoring from tape. Therefore, only critical model input data are 
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backed-up. One 2 TB RAID5 disk system is reserved for backup of critical project data. 

Backups are performed weekly. In addition, critical project input data is archived to IDE 

drives, firewire drives or DLT tape. 

All incoming electronic mail receives virus scanning using commercial software (Norton or 

McAffee Anti-Virus), which is updated routinely. Windows PC receives automated, weekly 

scans for viruses. 

Three types of software are used in the project: (1) commercial software for word processing, 

project management, communications, and commercial software compilers; (2) compilers and 

computer operating systems publicly available through the GNU Public License (GPL) 

distribution channels; and (3) custom software used in computer simulation modeling and data 

analysis. All commercial software used in the CENRAP modeling project has been purchased 

with licenses and is installed and tested as specified by the publisher. Commercial compilers 

include Portland Group compilers, and Intel and IBM FORTRAN compilers. All GPL software 

is obtained from official distribution centers, such as Redhat, and are regularly maintained and 

updated with each official release from the code developers. Custom software is developed by 

UCR, EPA and other researchers using commercial and GPL compilers for FORTRAN, C and 

C++. 

4.2 Sources for Data Used in Modeling 

Most of the data used in the CENRAP Emissions and Air Quality Modeling will be provided by 

other CENRAP contractors or participants. The data will be subjected to QA/QC screening prior 

to delivery to the Modeling Team. In addition, the Modeling Team has formed data 

“Gatekeeper” functions to further screen the data prior to its use. Below the data used in the 

modeling and the function of the Gatekeeper roles is discussed, which are also described in 

Section 2.3.2.1: 

3⁄4 Air Quality Gatekeeper: The Air Quality Gatekeeper will download air quality data 

from the VIEWS AIRS, and St. Louis Supersite websites and subject it to QA/QC. The 

data will be reformatted and processed for use in the modeling. 

3⁄4 Meteorological Gatekeeper: The Meteorological Gatekeeper will evaluate the 36 km 

MM5 data provided by the Iowa Department of Natural Resources (IDNR), a CENRAP 

state, and 12 km MM5 data for 2002 episodes provided by other CENRAP participants, 

subjects it to QA/QC, and processes it for input to the CMAQ/CAMx air quality models. 

3⁄4 Emissions Gatekeeper: The Emissions Gatekeeper will receive emissions data from the 

CENRAP Emissions Contractors (Pechan and STI) for CENRAP states and other sources 

for non-CENRAP states, subjects them to a comprehensive QA/QC, and will reject data 

that doesn’t pass the QA/QC and reformats the data for input into the SMOKE emissions 

model. 
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3⁄4 Data Management Gatekeeper: The Data Management Gatekeeper will maintain the 

project website, keep backups of data, respond to data requests and will update reports 

and files on the website. 

5.0 EMISSIONS MODELING 

The project objective is to integrate mesoscale emissions and transport modeling efforts so that 

predictions of regional haze in Class I areas can be performed under different emissions and 

meteorological transport scenarios. Meeting this objective means that modeling outputs must be 

of known quality. This section addresses emissions modeling, Section 6.0 addresses 

meteorological modeling and Section 7 addresses air quality modeling as they apply to this 

project. 

The CENRAP Modeling Team uses the Sparse Matrix Operator Kernel Emissions (SMOKE) 

emissions model to simulate the anthropogenic and biogenic gas and particulate matter (PM) 

emissions needed for regional haze modeling. A detailed quality assurance protocol for utilizing 

SMOKE has been prepared as part of the CENRAP Modeling Protocol (ENVIRON and UCR, 

2004) and is summarized in this section. 

The purpose of SMOKE is to convert the resolution of the raw emission inventory data to the 

resolution needed by an air quality model. Emission inventories are typically available with an 

annual total emissions value for each emissions source, or perhaps with an average-day 

emissions value. The air quality models, however, typically require emissions data on an hourly 

basis, for each model grid cell (and perhaps model layer), and for each model species. 

Consequently, emissions processing for this project involve transformation of emission inventory 

data by temporal allocation, chemical speciation, spatial allocation, and layer assignment, to 

achieve the input requirements of the air quality model. 

SMOKE formulates emissions modeling in terms of sparse matrix operations. Figure 5-1 

presents an example of how the matrix approach is used by the project team to organize the 

emissions processing steps for anthropogenic emissions, with the final step in creating the 

model-ready emissions being the merge step. This example does not include all processing steps, 

which can be different for each source category in SMOKE, but does include the major 

processing steps listed above, except for the layer assignment. Specifically, the inventory 

emissions are arranged as a vector of emissions, with associated vectors that include 

characteristics about the sources such as its state and county (SCC). SMOKE also creates 

matrices that will apply the gridding, speciation, and temporal factors to the vector of emissions. 

In many cases, these matrices are independent from one another, and can therefore be generated 

in parallel. The processing approach ends with the merge step, which combines the inventory 

emissions vector (now an hourly inventory file) with the control, speciation, and gridding 

matrices to create model-ready emissions. 
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Merge Import

Inventory

Temporal

Allocation

Speciation

Gridding

Inventory

Vectors

Controls

Hourly

Emissions

Speciation

Matrix

Gridding

Matrix

Control

Matrix

Model-ready

Emissions

Program File Shows input or output

Merge Import

Inventory

Temporal

Allocation

Speciation

Gridding

Inventory

Vectors

Controls

Hourly

Emissions

Speciation

Matrix

Gridding

Matrix

Control

Matrix

Model-ready

Emissions

Program File Shows input or output

Figure 5-1. Flow Diagram of Major SMOKE Processing Steps 

5.1 Quality Assurance Components of Emissions Modeling 

The quality assurance (QA) screening steps for the emissions modeling process has been divided 

into the four major classifications defined in the sections below. 

5.1.1 Modeling QA 

Modeling QA involves performing data quality checks, assuring simulation accuracy, and 

recognizing and identifying problems as they happen; it is the process of looking for glaring 

faults in the model input and output data (I/O) and determining whether the input data are 

producing the desired results. Scrutiny of the I/O using standard statistical analyses can reveal 

problems in the data and/or the model setup. Using a standard approach for analyzing emissions 

model I/O establishes reference points to use when scrutinizing the data. Seeking these indicators 

of correct model performance allows project personnel to determine the accuracy of the 

simulations and whether faults in the data or model configuration exist. 

5.1.2 System QA 

System QA addresses model installation and configuration issues, data accounting, and ensures 

that the modeling systems are producing results that are reasonable and reproducible. The first 

step in this process is to properly benchmark the model and assuring that the installation is 

complete. Confirmation of configuration settings, compile options, and other system-related 

parameters must then occur and be documented prior to producing any model results. Archiving 

the model installation at set “freeze points” in the project is also required in order to keep 

accurate records of the modeling installation and run scripts. A key feature of System QA is 

facilitating the reproduction of model results in the future and the ability to revert back to a 

previous configuration or installation after the model has been revised or updated. In addition to 

thorough documentation, version control software is required for archiving model executables, 

run and configuration scripts, and important data files. Combining documentation of the 

modeling procedures with archives of the model installation and data files will ensure that the 
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model installation and configuration are correct and that past simulations are sufficiently 

documented and archived to allow their reproduction. 

5.1.3 Gatekeeping and Outside Review 

Gatekeeping and outside review is the process of ensuring that the data entering and exiting the 

SMOKE modeling process meet a predetermined quality level. A gatekeeper screens model data 

before they pass from one major step of the modeling process to the next. In emissions QA, 

gatekeeping is applied to the emissions inventory and SMOKE input files on the front end of the 

modeling process, and to the SMOKE output files at the back end. A gatekeeper (or gatekeeping 

team) is responsible for performing a series of reasonableness checks on both the input and 

output data streams. Outside reviewers are emissions experts who are not part of the CENRAP 

Modeling Team. They periodically review the entire process—the emissions data, the modeling, 

and the QA steps taken—using their judgment as experts to decide what to review and how to 

review it. 

5.1.4 Documentation 

Documentation, a critical component that is common to all of the other three QA classifications, 

provides the record of the QA process. Establishing a detailed set of requirements for documenting every step in the QA process will ensure not only that the documentation is created as 

expected, but that the processes recorded by the documentation are completed correctly. In 

addition to records or lists of completed QA steps, documentation refers to summaries and 

interpretations of emissions inventory reports and analyses. Covering the entire realm of the 

modeling process, QA documentation includes records of model configuration, details about data 

files, simulation records, and final report generation. 

5.2 Implementation of the QA System 

5.2.1 The Emissions QA Team 

The level of effort required to implement the CENRAP Emissions QA framework requires a 

team consisting of: a data gatekeeper(s), a production modeler(s), a QA manager, outside 

reviewers, and a project manager (See Section 2.3.2.5 and Figure 2-1 for data flow and QA 

overview). Each team member contributes a different critical perspective on the data and 

modeling. 

• The Gatekeeper (or gatekeeping team) is responsible for reviewing the SMOKE I/O data 

streams for correctness. Before new datasets are used in SMOKE modeling, or new SMOKE 

output emissions datasets are used in air quality modeling, they must be reviewed by the 

gatekeeper. For all SMOKE input and output files, the gatekeeper ensures that the data are 

complete, are formatted correctly, and pass reasonableness checks. Checking completeness 

entails examining the files for the necessary data elements, spatial coverage, and temporal 

extent. Data formats will be confirmed using the SMOKE manual to check ASCII files and 

using PAVE to check binary netCDF files, such as the meteorological inputs. Reasonableness 
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checks consist of looking for glaring errors in the file contents and ensuring that the data 

make sense in the context of how they will be used and relative to similar or reference 

datasets. Issues that arise during the gatekeeping process are reported to the Project Manager 

for resolution. 

• The Production Modeler is responsible for receiving input data, maintaining the model run 

scripts, running the model per the work plan, producing default QA reports, delivering model 

output data, and assisting with compiling QA reports into summaries for documentation 

purposes. A Lead Modeler oversees the entire modeling process, performs the majority of the 

SMOKE modeling, and receives and archives input and output data. Secondary Modelers

organize the SMOKE QA reports into emissions summaries for data QA and reporting, and 

generate custom QA summaries and reports for troubleshooting any problems encountered 

during the modeling process. 

• The QA Manager performs and documents all of the checks required for determining model 

accuracy and revealing errors. The QA Manager also ensures that the software is configured 

correctly and that the data and model are being archived consistently and correctly. 

Overseeing the production modelers, the QA Manager verifies that SMOKE is being applied 

correctly and that all QA summaries are consistent with the relevant input data. Leading the 

documentation efforts, the QA Manager ensures that all the necessary QA summaries are 

generated and certified and that they are compiled into the project report. 

• The Project Manager is ultimately responsible for the quality of the final products of the 

modeling process. Providing technical assistance to the production modelers and clarifying 

any uncertainties about model input, configuration, and operation for the QA Manager, the 

Project Manager addresses any questions in the modeling process. Working with the QA 

Manager, the Project Manager approves the results of all QA procedures, and thoroughly 

scrutinizes the QA summaries for problems to determine whether the data are ready to be 

sent to the gatekeepers and outside reviewers for final review before delivery to the air 

quality modelers. For the CENRAP project the Project Manager is the liaison between the 

emissions modeling team and the CENRAP Modeling Workgroup and in this role 

communicates any issues raised during the emissions QA process. 

Figure 5-2 presents the QA framework developed by the CENRAP Modeling Team for SMOKE 

modeling. Identifying the specific participants in the CENRAP Modeling Team for performing 

all of the tasks in the framework is the first step of the QA process for each new emissions 

modeling scenario. Two Production Modelers are shown in the diagram, with the Lead Modeler 

performing the SMOKE simulations and receiving the input data and the Secondary Modeler 

compiling the output of the SMOKE QA programs into the QA products for evaluating the 

quality of the simulations. Gatekeepers are positioned in the SMOKE input and output data 

streams to screen the data before they are used in either SMOKE or CMAQ modeling, 

respectively. Reviewers are solicited from outside the emissions modeling team on a volunteer 

basis to conduct periodic reviews/audits of the data and modeling process. 
Quality Assurance Project Plan (Draft) 

CENRAP Emissions and Air Quality Modeling ENVIRON International Corporation 

CENRAP Modeling

Workgroup

Mo

CENRAP

Figure 5-2. Diagram of CENRAP Modeling Team Approach to SMOKE Modeling
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6.0 METEOROLOGICAL MODELING 

The EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system and the 

Comprehensive Air-quality Model with extensions (CAMx) are the two regional ozone and 

particulate matter (PM) air quality models being used in the CENRAP project. Meteorological 

inputs for CMAQ and CAMx have been generated using output from the fifth generation 

Mesoscale Model (MM5). The 2002 MM5 36 km database for the continental US was 

developed by the Iowa Department of Natural Resources (IDNR) (Johnson, 2004). Whereas, 

other CENRAP participants are developing the 12 km MM5 database. The MM5 model was 

initial applied for the entire year of 2002 on a continental scale domain with 36 km grid spacing. 

Subsequent MM5 model runs were applied to a regional-scale domain with 12 km grid spacing 

covering the central United States for 2002. The CMAQ and CAMx models require inputs of 

three-dimensional gridded wind, temperature, humidity, cloud/precipitation, and boundary layer 

parameters. The MM5 meteorological model was developed and is maintained by the 

Pennsylvania State University and National Center for Atmospheric Research (PSU/NCAR). 

The procedures used for applying MM5 for CENRAP are based on an extensive evaluation of 

MM5 and a series of sensitivity tests conducted by IDNR and the Lake Michigan Air Directors 

Consortium (LADCO) that was consequently updated based on the MM5 sensitivity modeling 

conducted as part of VISTAS (www.baronams. Com/projects/VISTAS). A description of 

CENRAP 36 km MM5 modeling approach including the modeling domain, MM5 physical 

configuration, and a model application approach can be found in Johnson (2004). 

MM5 has proven useful for air quality applications and has been used extensively in past local, 

state, regional, and national modeling efforts. MM5 has undergone extensive peer-review, with 

all of its components continually undergoing development and scrutiny by the modeling 

community. MM5 is the most widely used public-domain prognostic model. In-depth 

descriptions of MM5 can be found in Dudhia (1993) and Grell et al. (1994), and at the following 

web site: 

 http://www.mmm.ucar.edu/mm5. 

The evaluation of the MM5 meteorological modeling will be performed primarily by the 

meteorological modeler. For the CENRAP 2002 36 km MM5 modeling preliminary evaluation 

of the MM5 model can be found at: 

http://www.ladco.org/tech/photo/present/mm5v363_eval.pdf

http://www.ladco.org/tech/photo/present/rainfall_eval.pdf 

In addition, the CENRAP Emissions and Air Quality Modeling Team has a Meteorological 

Gatekeeper role that is described in Section 2.3.2.3 that will evaluate the MM5 fields to provide 

an independent verification step and assure that the data have been transferred correctly to the 

Modeling Team. The Modeling team Meteorological Gatekeeper reports will be posted on the 

project website at: 
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http://pah.cert.ucr.edu/aqm/cenrap/mm5.shtml 

7.0 CENRAP 2002/2018 AIR QUALITY MODELING 

For the 2002 annual modeling on the 36 km grid, the CENRAP Emissions and Air Quality 

Modeling Team will apply and evaluation EPA’s Models-3 Community Multiscale Air Quality 

(CMAQ) modeling system and the Comprehensive Air-quality Model with extensions (CAMx). 

7.1 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 ‘One-Atmosphere’ 

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 stateof-science representations of atmospheric processes in a high performance computing 

environment (Ching, et al., 1998). EPA completed the initial stage of development with Models3 and released the Community Multi-Scale Air Quality model (CMAQ) in mid-1999 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 in October 2004, is the version that will be 

used in the CENRAP Emissions and Air Quality Modeling Study. 

CMAQ consists of a core Chemical Transport Model (CTM) and several pre-processors 

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

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. 

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 aerosols (sulfates, nitrates and organics) are 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 
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included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), 

the latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 

2003 to be a 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. 

A description of the features implemented in the version of CMAQ ver 4.4 (released October 

2004) used in the initial CENRAO 2002 modeling is available from the CMAS Center website 

(www.cmascenter.org). Many of these features are mentioned above; others pertain to details in 

the model’s chemistry, transport, computer implementation, and model operation. In October 

2005, a new version of CMAQ was released (V4.5) that included several updates and was 

subsequently adopted by CENRAP. 

7.2 CMAQ Configuration for CENRAP Modeling 

Based on preliminary modeling performed by CENRAP (Pun, Chen and Seigneur, 2004; 

Tonnesen and Morris, 2004), BRAVO (Pitchford et al., 2004), VISTAS (Morris et al, 2004a) and 

WRAP (Tonnesen et al., 2003) we have identified and justified a set of initial recommended 

science options for the CENRAP CMAQ modeling. In this section we briefly identify the main 

science options recommended for annual PM modeling with CMAQ, more details are provided 

in the Modeling Protocol (ENVIRON and UCR, 2004). The model will be set up and exercised 

on the same 36 km Inter-RPO Continental US modeling domain that is being used by WRAP and 

VISTAS. This domain is larger than the 36 km domain used in the preliminary CENRAP 

modeling (Pun, Chen and Seigneur, 2004). For the 12 km episodic modeling, the CMAQ 

modeling domain will be determined after receipt of the 12 km MM5 data. For the 12 km 

CMAQ modeling, one-way grid nesting will be used whereby the results of the 36 km simulation 

are used to provide hourly BCs to the 12 km simulation. A total of 19 vertical layers will be 

implemented, extending up to a region top of 100 mb (approximately 15 km AGL). 

The latest versions of CMAQ (Version 4.4 and 4.5) will be used in the CENRAP modeling. The 

PPM advection solver will 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 and the latest CMAQ Meteorological-Chemistry Interface Processor (latest 

MCIP2.3) will 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 CMAQ 2002 modeling. Treatment of reversible secondary organic aerosols will be 

simulated by the SORGAM implementation in CMAQ. 

7.2.1 Updates to the CMAQ Secondary Organic Aerosol (SOA) Module 

Initial simulations of the CMAQ V4.4 for the 2002 annual period revealed that Organic Carbon 

(OC) was underestimated in the central and eastern U.S. by approximately 80% to 100% during 

the summer. A review of the CMAQ model formulation and model inputs found that one 

potential reason the model was underestimating OC was because there are several processes 
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associated with SOA formation from biogenic VOCs believed important for SOA formation. 

These processes are not treated in the current CMAQ model. Thus, the CMAQ SOA module 

was enhanced to include several missing processes. The formulation and resulting model 

performance are discussed in detail in a manuscript that has been accepted for publication in 

Atmospheric Environment (Morris, et al., 2006). The SOA module enhancement were first 

added to CMAQ V4.3 and then transferred to CMAQ V4.5 when it became available in October 

2005. 

7.2.1.1 SOAmods Formulation 

The formulation of the standard CMAQ SOA module is described in Binkowski and Roselle 

(2003). SOA can be formed from aromatic VOCs and biogenic terpenes. The biogenic SOA 

precursors were modeled with the Biogenic Emissions Information System – Version 3 (BEIS3) 

model (Pierce et al., 2002). BEIS3 generates three biogenic VOC species: isoprene (ISOP), 

monoterpenes (TERP) and other biogenic VOC (OVOC). For this study, the Carbon Bond IV 

photochemical mechanism was used (Gery et al., 1989) that represents VOC compounds based 

on their carbon bond structure. The BEIS3 ISOP, TERP and OVOC species are speciated into 

the CB4 species for photochemical modeling in CMAQ and CAMx as follows (molar 

speciation): 

• ISOP = ISOP (isoprene is an explicit species) 

• ALD2 = 1.5 x TERP 

• OLE = 0.5 x TERP 

• PAR = 6.0 x TERP 

• NR = 0.5 x OVOC 

• OLE = 0.5 x OVOC 

• PAR = 8.5 x OVOC 

• TERPB = TERP 

Here, ALD2, OLE, PAR and NR are the CB4 chemical mechanism representations of the 

biogenic VOC emissions as high molecular weight aldehydes, olefinic carbon bond, paraffin 

carbon bond and non-reactive functional groups. In CMAQ, the TERPB species is specified in 

the emissions inputs, along with its CB4 representation of ALD2, OLE and PAR, but does not 

participate in the photochemical mechanism and is only used in the SOA formation module. The 

TERPB species forms a SGTOT species based on oxidation parameters extracted from the 

photochemical module. SGTOT consists of the combined gaseous condensable gas (CG) plus 

particle SOA that are assumed to be in equilibrium. CMAQ transports the SGTOT species and 

splits it to a CG gaseous and particle SOA for output. 

 

The CMAQ TERB SOA formation rate is based on a fit to smog chamber data collected at the 

California Institute of Technology for several biogenic monoterpene species (Binkowski and 

Roselle, 2003). A review of recent literature of biogenic SOA measurements identified several 

processes that may be important to biogenic SOA formation that are not treated by the BEIS3 

biogenic emissions and the CMAQ SOA module: 
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Polymerization: Recent measurements indicate that some SOA species may polymerize, 

resulting in species that are no longer volatile and cannot evaporate back to a CG. In this 

case, the equilibrium assumption between the CG and SOA will understate the amount of 

particle SOA present in the atmosphere (Kalberer et al., 2004; Jang et al., 2002). 

Sesquiterpenes: Sesquiterpenes are not accounted for in the BEIS3/CMAQ SOA 

modeling system (Guenther et al., 2000; Vizuete et al., 2004). 

Isoprene: More recent evidence suggests that isoprene can also form particle SOA 

compounds that are not accounted for in CMAQ (Claeys et al., 2004; Matsunaga et al., 

2003; 2005). 

Acid Catalyzation: Recent literature also suggests that some SOA formation may have 

acid catalyzed reactions (Claeys et al., 2004; Jang et al., 2005). 

Heterogeneous Reactions: Recent evidence suggests that some SOA formation may 

occur during heterogeneous aqueous-phase chemical reactions (Yu et al., 2005). 

A prototype module was added to CMAQ that accounted for the first three processes listed 

above. The last two processes were not included in this work because there are not enough 

quantitative experimental data yet to establish a parameterization. Modules were added to the 

CMAQ SOA module under the following constraints: 

• The existing CMAQ SOA module for monoterpenes would remain unchanged; 

• The same CMAQ model inputs would be used; and 

• The basic CMAQ model formulation would remain unchanged, modules would be added 

to account for polymerization and SOA from sesquiterpenes and isoprene. 

Figure 7-1 displays how the prototype representation of new processes to represent SOA 

polymerization and SOA formation from sesquiterpenes and isoprene were added to the CMAQ 

SOA module using the existing CMAQ structure and inputs. The new components of the SOA 

module are indicated in bold italic, whereas the existing CMAQ SOA components (Binkowski 

and Roselle, 2002) use a regular font. There are several parameters that must be defined in the 

new elements of the enhanced SOA module: emission factors (EF), canopy escape efficiencies 

for gases (EEG) and aerosols (EEA) and SOA yields (Y). Based on an analysis of recent 

measurements, primarily from a recent biogenic emissions field study in Duke Forest, North 

Carolina (Stroud et al., 2005; Matsunaga et al., 2005), a range of values for the factors in Figure 

7-1 were developed as shown in Table 7-1. For the initial prototype of the enhanced SOA 

module, we selected the mid-point of the range values for the factors from the measurements 

(Table 7-1). No attempt was made to optimize the parameters in Table 7-1 for OC/TCM model 

performance. 

The emission factors, EF1 and EF2, relate the monoterpene emissions estimated by BEIS3 to 

emissions of monoterpenes, EF1 (e.g., α-pinene), and sesquiterpenes (EF2). Table 2 displays the 

range of EF1 and EF2 factors based on recent field study data (Stroud et al., 2005). Using the 

midpoint of the range results in emission factors of 0.7 for EF1 and 0.4 for EF2. EF1 is assigned 

a value of 0.7 based on field observations that indicate that the BEIS3 terpene emission factors 

are likely overestimated due to a tendency of earlier measurements approaches to artificially 

increase the emissions due to disturbance when leaves were enclosed in the measurement system. 
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As an initial approach for including sesquiterpene emissions, we have assigned EF2 a value of 

0.4 based on the ratio of the observed sesquiterpene emission from the Duke Forest field study 

(Stroud et al, 2005) to the BEIS3 monoterpene emission estimate. The net result is that BEIS3 

TERP emissions are increased by 10% and split 64% as monoterpenes and 36% as 

sesquiterpenes. The CG yields from the sesquiterpenes are assumed to partly condense into a 

non-volatile SOA particle that is modeled in CMAQ using the new secondary organic carbon 

species (SOC2) species and only some of the gas and aerosol species associated with 

sesquiterpenes are assumed to escape from the canopy using the mid-range of the Escape 

Efficiencies (EE) estimated by Stroud et al. (2005). The fraction of BEIS3 TERP emissions that 

are assumed to be monoterpenes (i.e., 64% of the emissions) are treated with the standard CMAQ 

two-product SOA module (Binkowski and Roselle, 2003) assuming equilibrium between the CG 

and SOA with the SOA output in the standard AORGB species (Binkowski and Roselle, 2003). 

The isoprene SOA formation pathway forms a CG using the mid-point yield rate based on the 

range of recent measurements (Stroud et al., 2005) and a CG/SOA partitioning rate based on the 

mid-point of measurements from Matsunaga et al. (2003, 2005) (Table 7-1). The isoprene SOA 

is assumed to be volatile and is modeled as a new secondary organic carbon species in CMAQ 

SOAmods (SOC3). Finally, all SOA species, with the exception of the already non-volatile 

SOC1 (polymerized SOA) and SOC2 (sesquiterpene) species, are assumed to partially 

polymerize into non-volatile particles that are stored in the SOC1 species. The polymerization 

rate is based on the results of Kalberer et al (2004) who found that 50% of the SOA polymerized 

in 20 hours. 

Table 7-1. Parameters use in enhanced SOA module (see Figure 7-1). 

Parameter Mid-Point Range 

EF1 0.7 0.4 ~ 1.0 

EF2 0.4 0.2 ~ 0.6 

EEG1 0.325 0.2 ~ 0.45 

EEA1 0.2 0.05 ~ 0.35 

Y2 0.875 0.75 ~ 1.0 

Y1 0.11 0.06 ~ 0.16 

P1 0.45 0.15 ~ 0.75 

EF1 = emission factor of monoterpenes to the TERP 

emissions estimated by BEIS3 

EF2 = emission factor of sesquiterpenes relative to 

the TERP emissions estimated by BEIS3 

EEG1 = escape efficiency of gas phase precursor of 

sesquiterpenes from canopy 

EEA1 = escape efficiency of SOA from sesquiterpenes 

from canopy 

Y1 = SOA yield of oxidated isoprenes 

Y2 = SOA yield of sesquiterpenes 
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7.2.1.2 Quality Assurance of SOAmods 

Several levels of Quality Assurance and Quality Control of the enhanced SOAmods module in 

the CMAQ model were conducted as follows. 

QA/QC of SOAmods Coding: The SOAmods implementation was conducted at 

ENVIRON. Staff at the University of California at Riverside performed independent 

QA/QC of the SOAmods code implementation and independent testing and evaluation. 

QA of SOAmods Formulation: The new processes being added to the CMAQ SOA 

module was discussed with researchers at EPA’s Office of Research and Development 

(ORD). Although they have not completed all the laboratory tests, the inclusion of SOA 

from sesquiterpene and isoprene have been observed and are supported. 

Peer Review of SOAmods: The formulation of the SOAmods enhancement to the 

CMAQ SOA module was documented and comments were received by several parties. 

The results were also written up and submitted to Atmospheric Environment where it was 

subjected to peer review and will be published (Morris et al., 2006). 

Model Performance Evaluation of SOAmods: The final level of QA of the SOAmods 

was comparisons of CMAQ V4.4 model performance with and without including the 

SOAmods enhancement. Table 7-2 displays fractional bias error for Organic Carbon 

(OC) for regions corresponding to VISTAS, MRPO, MANE-VU and CENRAP states 

using the standard CMAQ Version 4.4 (V4.4) and then CMAQ V4.4 with the SOAmods 

enhancement. Whereas the standard CMAQ V4.4 underestimates OC across IMPROVE 

sites of from –76% (MRPO) to –102% (VISTAS), with the SOAmods enhancement the 

fractional biases centered on zero and ranges from –14% to +8%. Similar results are seen 

for OC fractional bias across the more urban STN sites where the CMAQ V4.4 exhibits 

an underestimation bias of –67% to –105%, when using SOAmods the under-prediction 

bias is –27% to –44%. Note that the continued underestimation of OC across the urban 

STN sites is likely due to missing primary OC emissions and uncertainties in the STN 

OC measurements. 

Table 7-2. Comparison of fractional bias performance metric for Organic Carbon (OC) using 

the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement. 

July 2002 IMPROVE OC STN OC 

Fractional Bias V4.4 SOAmods V4.4 SOAmods 

Southeastern U.S. -102% -2% -105% -32% 

Midwestern U.S. -76% +12% -67% -24% 

Northeast U.S. -82% -14% -95% -44% 

Central U.S. -98% +8% -81% -27% 
Quality Assurance Project Plan (Draft) 

ASIP Emissions and Air Quality Modeling ENVIRON International Corporation Figure 7-1. Schematic describing the addition of new SOA processes (bold italic) within the existing CMAQ font) to treat polymerization and SOA from sesquiterpenes and isoprene.

Biogenic emissions TERP

ISOP ISPD Y1

P1

PAROLE

ALD2

TERPRXN

A_TRP1G_TRP1A_TRP2G_TRP2

ISPRXN SGTOT_ISP

G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 )* Y3*Y4

O

O

O: Oxidation reaction with OH, O3, and NO3ISPRXN: The amount of reacted ISPD with oxidantsTERPRXN: The amount of reacted TERP with oxidantsDotted box: Not implemented in the initial testBold italic: New processes

Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1

Biogenic emissions TERP

ISOP

OVOC

PAROLE

NR

ISPD Y1

P1

PAROLE

ALD2

SGTOT

_

TRP1

TERPRXN SGTOT

_TRP2

ISPRXN SGTOT_ISP

G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) **

O O

O

O: Oxidation reaction with OH, O3, and NO3ISPRXN: The amount of reacted ISPD with oxidantsTERPRXN: The amount of reacted TERP with oxidantsDotted box: Not implemented in the initial testBold italic: New processes

Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1
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7.3 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 has been 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 and PM Source Apportionment 

Technology (OSAT and PSAT). Designed originally to address multiscale ozone issues from the 

urban- to regional-scale, CAMx has been widely used in recent years by a variety regulatory 

agencies for 1-hr and 8-hr ozone SIP modeling studies. Key attributes of the CAMx model for 

simulating gas-phase chemistry include the following: 

• Two-way grid nesting that supports multi-levels of fully interactive grid nesting (e.g., 

36/12/4/1.33 km). 

• CB4 or SAPRC99 Chemical Mechanisms. 

• Two chemical solvers, the CAMx Chemical Mechanism Compiler (CMC) Fast Solver or 

the highly accurate Implicit Explicit Hybrid (IEH) solver. 

• Multiple numerical algorithms for horizontal transport including the Piecewise Parabolic 

Method (PPM), Bott, and Smolarkiewicz advection solvers. 

• Subgrid-scale Plume-in-Grid (PiG) algorithm to treat the near-source plume dynamics 

and chemistry from large NOx point source plumes. 

• 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). 

• 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.). 

• 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. 

• Treatment of particulate matter (PM) using an empirical aerosol thermodynamics 

algorithm. 
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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’ 

model 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: 

• 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). 

• 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. 

• 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. 

• Secondary organic aerosol thermodynamics are represented using the semi-volatile 

scheme of Strader and co-workers (1999). 

• 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 CPUefficient 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 episodes) 

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. 

Several updates have been made to CAMx since Version 4.10a was released. The current 

version is Version 4.3 that includes updates to treat frozen precipitation over Version 4.2. 

Version 4.3 of CAMx will be used in the CENRAP modeling. 

7.4 CAMx Configuration for CENRAP Modeling 

CAMx (ver 4.3) will be applied using many similar science options and input data sets to 

CMAQ. However, in some instances, the CMAQ and CAMx model development teams chose 
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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. 

The latest version of CAMx (ver 4.3 newer) will be employed and the model will be set up and 

exercised on the same 36/12 km grid as CMAQ. However, CAMx will be run using two-way 

grid nesting. The base configuration of CAMx will use 19 vertical layers to a 100 mb region 

(~15 km AGL) that exactly match those used by CMAQ. The PPM advection solver will be 

used along with the spatially varying (Smagorinsky) horizontal diffusion approach. Vertical 

diffusion in CAMx will be modeled by K-theory. The MM5 simulation using the Pleim-Xiu 

Land-Surface Model (LSM) and the ACM Planetary Boundary Layer (PBL) scheme will 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 will be exercised with the 

CB4 gas-phase, RADM aqueous-phase, and CMU/ISORROPIA aerosol chemistry schemes. The 

SOAP secondary organic aerosol scheme will 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 investigate scientific issues that may be of interest that cannot be simulated 

by CMAQ, such as: 

• The full sectional approach could be used to determine whether allowing secondary PM 

to grow into the coarse mode affects the model estimates; 

• The 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, 

especially when looking at clean days or natural background; and 

• The full sectional aqueous-phase chemistry module may be important for sulfate 

formation. 

7.5 Advantages of Operating Multiple Models 

The use of both CMAQ and CAMx in the CENRAP 2002 annual modeling is a powerful and 

comprehensive diagnostic and quality assurance (QA) check of the modeling. There are 

numerous uncertainties in air quality models and the completely independent formulation of 

CMAQ and CAMx allow an independent QA check of one model against the other that will not 

be available if only one model was applied. 

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, CMAQ-AIM, CMAQ-MADRID and 

CAMx modeling systems all meet these requirements. 
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We believe that there is potentially significant value in including two or more regional modeling 

systems. Our testing of the CMAQ and CAMx model in some preliminary CENRAP model 

evaluation analysis (Tonnesen and Morris, 2004) found that their performance was generally 

similar for most species, with one model performing slightly better for some species and vice 

versa. Similar findings were obtained by VISTAS (Morris et al., 2004a). There are numerous 

advantages in applying both the CMAQ and CAMx models in the CENRAP 2002 annual 

modeling: 

• Diagnosis: To serve as an efficient diagnostic tool addressing model performance issues 

that may arise in the establishment of the CMAQ and CAMx annual 2002 base cases. 

CAMx’s suite of diagnostic probing tools plus its flexi-nesting algorithms make it an 

attractive tool for assisting in the diagnosis of model performance should this unexpected 

situation arise. CMAQ and CAMx both possess Process Analysis (PA) that can be a 

powerful diagnostic tool. 

• Model Evaluation Corroboration: To provide corroboration of the base case model 

performance evaluation exercises and help identify any compensatory errors in the 

MM5/SMOKE/CMAQ or SMOKE/MM5/CAMx modeling systems. 

• Emissions Control Response Corroboration: To provide corroboration of the response 

of the CMAQ or CAMx modeling system to generic and specific future year emissions 

changes on modeled gas-phase and particulate aerosol concentrations and resultant 

regional haze impacts. 

• Quantification of Model Uncertainty: To provide one estimate of the range of 

uncertainty that attends statements of CMAQ and/or CAMx model performance in the 

annual and episodic base case simulations, and in the estimate of PM2.5 and visibility 

reductions associated with future emissions change scenarios. 

• Alternative Science: CMAQ and CAMx contain alternative science algorithms that may 

elucidate model performance issues in one model or the other. 

• Consistency with Other RPOs: The Midwest RPO (MRPO) may end up using CAMx 

for their regional haze modeling, whereas the VISTAS and WRAP RPOs are currently 

using CMAQ as their primary model. As sources in the MRPO, VISTAS and WRAP 

likely influence visibility at Class I areas in the CENRAP region, having results from 

both models will be useful for reconciling any differences. 

• Backup Contingency: To provide a ‘backstop’ model in the event that unforeseen 

difficulties with one of the models make it necessary to switch to an alternative ‘OneAtmosphere’ model at some point during the CENRAP modeling effort. 

The benefits of employing a pair of complimentary state-of-science air quality models are thus 

quite significant. Especially considering that the same MM5 output (through MCIP and 

MM5CAMx) and SMOKE output and IC/BC files (through CMAQ-to-CAMx emissions and 
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IC/BC converters) can be used to operate CAMQ and CAMx without performing any additional 

meteorological or emissions modeling. 

8.0 ASSESSMENTS AND OVERSIGHT 

8.1 Assessment Responsibilities 

The primary point of contact for CENRAP Modeling project assessment is that of the Modeling 

Team Project Manager (PM). This PM is available to the CENRAP Project Manager and 

Administrative Project Manager, CENRAP Modeling Workgroup, external reviewers, and 

internal staff by direct contact through e-mail and telephone. These links provide timely reviews 

of the project experimental design, implementation, and interpretation of the experimental 

results. 

The assessments of overall project quality are provided from a number of different sources. 

Internal reviews of selected project components are periodically performed by the Project 

Manager and Co-Principal Investigators. The results of this and other routine (e.g., QC checks) 

and special reviews of project data quality are documented in the monthly reports. 

The project team is committed to achieving and maintaining the highest level of quality possible 

throughout the performance of this program. The modeling information generated will be both 

technically sound, and, where appropriate, defensible from a regulatory perspective. The former 

is an obvious requirement but is not, in and of itself, sufficient to defend the data against an 

adversarial inquiry. The latter will address, through documentation, the level of quality achieved. 

The quality of the project data will be maintained not only through the development and use of 

data quality objectives (DQOs), which place numerical limits on the QC indicators, but also 

through the use of subjective science quality objectives. Science quality objectives are used to 

provide evaluations of the quality of the research project and goals of the study. Evaluations of 

all research activities by internal and external peer review will assure that the methodology, 

experimental processes, conclusions and recommendations provided by this project are 

scientifically sound. 

8.2 Assessment Types and Usage 

Assessments of the quality of the products generated on this project will be made by: 

• Conducting internal performance reviews of the critical components of the experimental 

setup and data processing systems. Where applicable, adherence to SOPs will be 

evaluated. The results of these reviews will contain any suggested corrective actions, and 

be appended to the reports generated in this project. 

• Independent peer reviews of thesis materials, reports, and papers resulting from this 

project. 
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The CENRAP Modeling Workgroup provides external review of all reports and plays an active 

role in the development of experimental matrices and to review research progress and plans for 

future project tasks. 

8.3 Assessment Criteria 

The project assessment criteria address the extent to which the modeling runs and experiments 

provide the quality of data needed to satisfy the objectives of this project. A major objective of 

this project consists of providing the data and information needed for evaluating and improving 

models for predicting the effects of emissions on air quality. Therefore, the evaluation process 

includes assessments of model uncertainties, the extent to which the proposed actions or system 

improvements can reduce these uncertainties, and estimates of the certainty of this work in 

providing the data of the type and quality needed. Criteria for assessing data utility and quality 

will include the following: 

• Utility of the type of measurements for model evaluation. For example, are model 

predictions of the measurements sensitive to the uncertainties in the mechanism that are 

being evaluated? Are the available resources being applied to the highest priority types of 

measurements? 

• Degree of characterization of experimental conditions for modeling. For data to be useful 

for model evaluation, experimental conditions must be sufficiently well characterized, so 

that the data can be used for model evaluation without characterization uncertainties 

dominating the results. 

• Accuracy, precision, and sensitivity of the measurement data. The measurements in the 

concentration region of interest should be sufficiently accurate and precise to provide a 

meaningful test of model predictions. 

• Procedures used to assure data quality, identify data not meeting quality objectives, and 

to minimize errors and other data quality problems. 

• Degree to which the modeling appropriately incorporates the characterization results in 

the inputs, and the analysis of the modeling results appropriately take into account 

characterization and measurement uncertainties and biases. 

• Degree to which the experimental procedures, measurement methods, and data 

processing and analyses, and modeling methods are documented. 

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