The Atlanta Eight-Hour Ozone Attainment Demonstration 

Appendix P 

Quality Assurance Project Plan 

(Draft) for Association for 

Southeastern Integrated Planning 

(ASIP) Emissions and Air Quality 

Modeling to Address 8-Hour Ozone 

and PM2.5 Nonattainment in 

Southeastern States
Quality Assurance Project Plan (Draft) 

For 

Association for Southeastern Integrated 

Planning (ASIP) 

Emissions and Air Quality Modeling to Address 

8-Hour Ozone and PM2.5 Nonattainment in Southeastern States 

Revision 0 

November 28, 2005 

Prepared for: 

Ms. Patricia Brewer 

Technical Coordinator 

Association for Southeastern Integrated Planning 

Project Manager and Co-Principal Investigator: 

Mr. Ralph Morris 

ENVIRON International Corporation 

101 Rowland Way, Site 220 

Novato, CA 94945 

Contractors: 

Co-Principal Investigator: 

Mr. Gregory Stella 

Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 
Approvals 

Title: Quality Assurance Project Plan for Association for Southeastern Integrated Planning 

(ASIP) Emissions and Air Quality Modeling to Address 8-Hour Ozone and PM2.5 Nonattainment 

in Southeastern States 

October 6, 2005. 

Ms. Patricia Brewer Date: 

Technical Coordinator 

Association for Southeastern Integrated Planning 

Mr. Ralph Morris Date: 

Project Manager and Co-Principal Investigator 

ENVIRON International Corporation 

Mr. Gregory Stella Date: 

Co-Principal Investigator 

Alpine Geophysics, LLC 

Brenda C. Johnson Date: 

Technical Representative 

U.S. Environmental Protection Agency 

Marilyn Thornton _______________________ Date: _____________ 

Regional Quality Assurance Manager 

U.S. Environmental Protection Agency 

Region 4 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, California 94945
TABLE OF CONTENTS 

Page 

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

 1.1 SESARM Organization............................................................................................1 

1.2 ASIP Project Background........................................................................................2 

 1.3 Purpose of the ASIP QAPP......................................................................................3 

 1.4 Problem Definition...................................................................................................4 

2.0 PROJECT DESCRIPTION ..............................................................................................7 

2.1 Background..............................................................................................................7 

2.2 VISTAS Two-Phased Approach..............................................................................7 

 2.2.1 VISTAS Phase I...................................................................................................7 

 2.2.2 VISTAS Phase II..................................................................................................8 

 2.2.3 ASIP Modeling ..................................................................................................10 

 2.2.3.1 Data Gatekeepers ....................................................................................10 

 2.2.3.2 Emissions QA/QC...................................................................................11 

 2.2.3.3 Meteorology QA/QC ..............................................................................12 

 2.2.3.4 Air Quality Modeling QA/QC ................................................................13 

 2.2.3.5 Overview of Data Flow and Quality Assurance Process........................14

2.3 ASIP Modeling Protocol........................................................................................19 

2.4 ASIP Activities ......................................................................................................19 

 2.4.1 ASIP Task 1: Project Management...................................................................19 

 2.4.2 ASIP Task 2: 2002/2009 Emissions Modeling.................................................20 

 2.4.3 ASIP Task 3: ASIP Task 3: 2002/2009 36/12 km Base F CMAQ Modeling .21 

 2.4.4 ASIP Task 4: ASIP II Task 4: Data Analysis and Data Management.............21 

3.0 PROJECT MANAGEMENT..........................................................................................22 

 

3.1 Project Organization ..............................................................................................22 

 3.1.1 ENVIRON Project Manager and Co-Principal Investigator..............................24 

 3.1.2 ENVIRON and Alpine Geophysics Co-Principal Investigators ........................24 

 3.1.3 ASIP Contracting Officer ..................................................................................25 

 3.1.4 ASIP Technical Contact.....................................................................................25 

 3.1.5 ASIP States ........................................................................................................26 

3.2 Project Schedule and Execution.............................................................................26 

3.3 Personnel Qualifications and Training...................................................................26 

3.4 Communications Plan ............................................................................................27 

 3.5 Documentation and Records..................................................................................28 

4.0 MEASUREMENT AND DATA ACQUISITION .........................................................29 

4.1 Computer Hardware/Systems Administration .......................................................29 

 4.1.1 ENVIRON Modeling Center Hardware.............................................................29 

 4.1.2 Alpine Geophysics Modeling Center Hardware................................................29 
 4.1.3 Backup Procedures.............................................................................................30 

 4.2 Sources for Data used in Modeling........................................................................31 

5.0 EMISSIONS MODELING..............................................................................................32 

 5.1 Quality Assurance Components of Emissions Modeling ......................................33 

 5.1.1 Emissions Modeling QA....................................................................................33 

 5.1.2 Emissions System QA........................................................................................33 

 5.1.3 Emissions Gatekeeper and Outside Review ......................................................34 

 5.1.4 Documentation...................................................................................................34 

 5.2 Implementation of the QA System ........................................................................34 

 5.2.1 The Emissions QA Team...................................................................................34 

6.0 METEOROLOGICAL MODELING ............................................................................37

7.0 CMAQ MODELING SYSTEM......................................................................................38 

7.1 CMAQ Overview...................................................................................................38 

7.2 CMAQ Configuration for ASIP and VISTAS 2002 Modeling .............................39 

7.3 Enhancements to the Treatment of Secondary Organic 

Aerosols in CMAQ (SOAmods)............................................................................40 

 7.3.1 SOAmods Formulation ......................................................................................40 

 7.3.2 Quality Assurance of SOAmods........................................................................43 

7.4 Testing and Evaluation of CMAQ V4.5 ................................................................46 

8.0 ASSESSMENTS AND OVERSIGHT............................................................................48 

8.1 Assessment Responsibilities ..................................................................................48 

 8.2 Assessment Types and Usage ................................................................................48 

 8.3 Assessment Criteria ...............................................................................................49 

9.0 REFERENCES.................................................................................................................50 

FIGURES 

Figure 1-1. 8-hour ozone nonattainment counties in the US designated 

 by EPA in April 2004. .............................................................................................5 

Figure 1-2. PM2.5 nonattainment counties designed by EPA in December 2004.......................6 

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

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

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

 Emissions and Air Quality Modeling ....................................................................18 

Figure 3-1. ASIP Emissions and Air Quality Modeling Project Organizational Chart............22 

Figure 5-1. Flow Diagram of Major SMOKE Processing Steps ..............................................33 

Figure 5-2. Diagram of ASIP Modeling Team Approach to SMOKE Modeling ....................36 
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 .................................45 

TABLES 

Table 2-1. Model performance goals to help interpret modeling results ................................16 

Table 2-2. ASIP Task 1 Project Management task deliverables and due dates ......................20 

Table 2-3. ASIP Task 2 2002/2009 Emissions Modeling task deliverables and due dates ....20 

Table 2-4. ASIP Task 3 2002/2009 36/12/ km Base F CMAQ Modeling. .............................21 

Table 2-5. ASIP Task 4 2002 CMAQ Modeling task deliverables and due dates..................21 

Table 3-1. ASIP Emissions and Air Quality Modeling 

 project participants and contacts............................................................................23 

Table 3-2. VISTAS listservs maintained by the Emissions and 

 Air Quality Modeling Team...................................................................................27 

Table 7-1. Parameters use in enhanced SOA module (see Figure 7-1)..................................43 

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 ..............................................44
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1.0 INTRODUCTION 

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

activities performed by the Association for Southeastern Integrated Planning (ASIP) to address 

emissions and air quality modeling associated with attaining the 8-hour ozone and PM2.5

standards in states in the southeastern United States. The ASIP 8-hour ozone and PM2.5

emissions and air quality modeling will build off of the regional modeling being conducted by 

the Visibility Improvements for States and Tribal Associations in the Southeast (VISTAS). The 

VISTAS Phases I and II Emissions and Air Quality Modeling is being performed by a 

contracting team consisting of ENVIRON International Corporation (Prime Contractor), Alpine 

Geophysics, LLC (Subcontractor) and the University of California at Riverside College of 

Engineering – Center for Environmental Research and Technology (UCR/CE-CERT; 

Subcontractor). The ASIP regional 8-hour ozone and PM2.5 modeling will be conducted by 

ENVIRON (Prime Contractor) and Alpine Geophysics (Subcontractor) under contract to 

SESARM. 

1.1 SESARM Organization 

Southeastern States Air Resource Managers, Inc., commonly known as SESARM, is a Georgia 

corporation organized and operated under the provisions of the Official Code of Georgia 

Annotated, Title 14, Chapter 3, also known as the Georgia Nonprofit Corporation Code. 

SESARM qualifies as a charitable, tax-exempt, nonprofit corporation as provided in the United 

States Code, Title 26, Subtitle A, Chapter 1, Subchapter F, Part 1, Section 501(c)(3). SESARM 

does not engage in for-profit activities nor does it use federal funds to influence legislation. 

SESARM was incorporated February 24, 1997. Its tax-exempt status was most recently reconfirmed in correspondence from the United States Internal Revenue Service dated December 

12, 2001. 

SESARM directly represents the eight southeastern state air pollution control agencies located 

within Region 4 of the United States Environmental Protection Agency (EPA). The member 

states are Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

and Tennessee. The Board of Directors of SESARM consists of the air pollution control agency 

director from each state air pollution control agency. 

SESARM’s Articles of Incorporation describe its purposes as being to enhance communication 

and thus promote more effective air pollution management in the Southeast, improve the 

effectiveness of its members in meeting national and regional air pollution goals, conduct and 

facilitate research and training necessary to meet its purposes, evaluate air quality issues, 

recommend actions to resolve air quality problems, and develop steps to accomplish air quality 

improvements. 

When EPA promulgated regional haze regulations on July 1, 1999, it established requirements 

that states and tribes submit implementation plans to demonstrate reasonable progress towards 

the ultimate visibility goals of the rule. The first demonstration of reasonable progress is to be 
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made for the year 2018 in state implementation plans due December 2007. EPA also offered an 

optional approach that groups of states might collaborate in regional analyses of the haze 

problem. This option showed promise in allowing more cost-effective analyses and the 

southeastern states opted to follow this option. Southeastern States Air Resource Managers Inc. 

(SESARM) accepted responsibility for regional planning organization work tasks on behalf it its 

member agencies pursuant to this option. 

The member agencies of SESARM determined that it was appropriate to expand the 

collaborative effort for the regional haze program beyond their member boundaries and invited 

Virginia and West Virginia to join the group. A memorandum of agreement was arranged 

among the agencies and executed on August 22, 2001. The effort was named the Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS). Since that time, the 

agencies have worked together to organize their efforts, develop work task lists and schedules, 

procure professional services, and support administrative operations. Bylaws were developed 

and agreed upon by the participating members. An organizational structure was created 

including an oversight committee, an operations committee, and various work groups. This early 

planning has served the project well and much progress has been made. 

1.2 ASIP Project Background 

On December 17, 2004, EPA made fine particle (PM2.5) nonattainment determinations for at 

least one area in seven of the states participating in the VISTAS regional haze project. They are 

Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, and West Virginia. In 

addition, South Carolina has one three-county area that was designated as unclassifiable in the 

same action. EPA’s Clean Air Interstate Rule (CAIR) modeling indicated that certain 

nonattainment areas may still be in nonattainment after full implementation of CAIR. These 

areas include Jefferson County, Alabama and Clayton and Fulton Counties in Georgia. 

The PM2.5 compliance date is April 2010 unless a state demonstrates that more time is necessary 

in which case up to five additional years may be granted. The nonattainment designations 

triggered the requirement for development of state implementation plans (SIPs) that will be due 

in April 2008. The draft guidance from EPA indicates that a significant requirement of PM2.5

SIPs will be attainment demonstrations using, at least in part, modeling analyses to define 

effective emissions control strategies and confirm that attainment can be achieved after 

implementation of the strategies. 2009 is the modeling year for the PM2.5 attainment 

demonstration and also is an interim analysis year for the VISTAS regional haze demonstration. 

In April of 2004, EPA determined areas that were not meeting the 8-hour ozone standard. States 

having one or more 8-hour ozone nonattainment areas in the Southeast are Alabama, Georgia, 

Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. EPA will 

require attainment of the 8-hour ozone standard in basic nonattainment areas by June 15, 2009 

and in moderate nonattainment areas by June 15, 2010. This will require states with basic 8-

hour ozone nonattainment areas to model 2008 as the SIP modeling demonstration year while 

moderate nonattainment areas will require 2009 as the modeling year. Given that North Carolina 

and Virginia have two year SIP approval processes, there is an immediate need to complete an 

analysis of ozone attainment using air quality modeling. 
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The states participating in the VISTAS project (the SESARM EPA Region 4 states plus Virginia 

and West Virginia from Region 3) have concluded that a collaborative process will be the most 

efficient approach for the collective states to develop information upon which to base the PM2.5

and 8-hour ozone attainment demonstrations. The local air regulatory agencies for Jefferson 

County, AL, Jefferson County, KY, Mecklenberg County, NC, Forsythe County, NC, Knox 

County, TN, and Shelby County, TN have also become signatory parties to this collaborative 

effort. SESARM will coordinate among participating agencies and oversee the performance of 

the inventory and modeling tasks in parallel with the VISTAS regional haze project tasks. 

The name of this collaborative effort is the Association for Southeastern Integrated Planning 

(ASIP). SESARM was awarded a grant from EPA on February 8, 2005 to conduct what was 

originally called the fine particle SIP development support project but is now known as ASIP. 

1.3 Purpose of the ASIP QAPP 

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/Alpine Team and is documented in the VISTAS Modeling 

Protocol and QAPP (ENVIRON, 2004a,b). The VISTAS Modeling Protocol and QAPP formed 

the basis for much of the content in this ASIP 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 particularly 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; 

• 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 making. 

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

that the project products are of known quality, 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. 

1.4 Problem Definition 

Figures 1-1 and 1-2 display the 8-hour ozone and PM2.5 nonattainment areas designed by EPA in 

April and December 2004, respectively. Of the 10 States in the VISTAS region, 8 include 

counties that have been designed as nonattainment of the 8-hour ozone standard (Alabama, 

Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia). 

Similarly, seven of the VISTAS states have counties that are designated as nonattainment for 

PM2.5 (Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, and West Virginia). 

Of the 10 VISTAS states only Florida and Mississippi do not have any 8-hour ozone and PM2.5

nonattainment areas, South Carolina includes 8-hour ozone nonattainment counties (near 

Charlotte) but no PM2.5 nonattainment areas. 

The states need to submit the 8-hour ozone State Implementation Plans (SIPs) to EPA by June 

2007; the PM2.5 SIPs are due by April 2008. Some of the states involved in the ASIP ozone/PM 

modeling have two-year legislative review processes. Thus, the definition of the SIP control 

plans is needed in early 2006. Consequently, the ASIP regional ozone and PM modeling has an 

aggressive schedule. 
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Figure 1-1. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004. 
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Figure 1-2. PM2.5 nonattainment counties designed by EPA in December 2004. 
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2.0 PROJECT DESCRIPTION 

2.1 Background 

The Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality 

Modeling Team is operating regional scale, three-dimensional air quality models for ozone and 

fine particulate matter (PM2.5) that simulate the emissions, chemical transformations, and 

transport of gaseous and particulate matter (PM) species in the eastern United States. A key 

element of this work includes the integration of emissions inventories and models with regional 

transport models. The general services provided by the ASIP Emissions and Air Quality 

Modeling Team include, but are not limited to: 

• Emissions processing and modeling; 

• Air quality modeling simulations; 

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

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

The ASIP regional emissions and air quality modeling is leveraging the modeling databases 

developed by VISTAS to provide the technical basis for the regional haze SIPs due in December 

2007. Regional haze is caused by primary and secondary fine particles and is simulated using 

“one-atmosphere” regional photochemical grid models. Such models also simulate regional 

ozone and fine PM so can also be used to address the ASIP 8-hour ozone and PM2.5 issues. 

VISTAS initiated their regional haze modeling in 2003 using a two-phase approach. 

2.2 VISTAS Two-Phased Approach 

The VISTAS Emissions and Air Quality Modeling activities are being performed in two Phases. 

Phase I, which occurred primarily during the 2003 calendar year, consisted of emissions and 

regional haze modeling for three episodes to identify the optimal model configuration(s) for 

simulating regional haze, ozone and fine PM in the southeastern US. Phase II, initiated in 2004, 

consists of operating the emissions and air quality models for the 2002 calendar year to develop 

the regional haze modeling databases needed to address the requirements of the Section 308 

RHR SIPs and TIPs. The ASIP regional ozone and PM2.5 modeling is building off the VISTAS 

Phase II 2002 36/12 km annual modeling activities. 

2.2.1 VISTAS Phase I 

The objective of VISTAS Phase I was to determine the optimal modeling configuration for use 

in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a comprehensive 

literature review of recent relevant visibility studies using various photochemical/aerosol 

modeling platforms in order to assess and identify model configurations, data bases, and model 

testing methodologies that were appropriate for use in conducting the VISTAS Phase I emissions 

and PM modeling assessment. Key elements of Phase I included: 
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3⁄4 Review all relevant air quality model simulations that have been completed related to 

regional haze and PM2.5 modeling and document the relevant sensitivity analyses, model 

configuration testing, and performance evaluations that have been performed 

(ENVIRON, 2003b); 

 

3⁄4 Review the current science in regional emissions modeling (e.g., EPS, EMS and 

SMOKE) and PM air quality modeling (e.g., CMAQ, CMAQ-MADRID, CMAQ-AIM, 

REMSAD, UAM-V/PM, CAMx4 and PMCAMx) to determine the most appropriate 

model(s) for use by VISTAS (ENVIRON, 2003b); 

3⁄4 Review available ambient data for evaluating one-atmosphere PM/ozone models 

(ENVIRON, 2003c);

3⁄4 Develop and implement a plan or Modeling Protocol for testing and evaluating 

alternative science configurations of the recommended Phase I model(s) and document 

the results (ENVIRON, 2003a); and 

3⁄4 Prepare a Task 6 Modeling Protocol prescribing the model set-up, data base development, 

performance testing, and control strategy evaluation procedures to be implemented in 

VISTAS Phase II (ENVIRON, 2004a). 

VISTAS formed three standing workgroups to plan and direct the project. These included: (a) the 

Technical Analysis (emissions and modeling) Workgroup; (b) the Data (monitoring) Workgroup; 

and (c) the Planning Workgroup. Under Phase I, the VISTAS Technical Analysis Workgroup 

(TAWG) managed the comprehensive model configuration testing program aimed, as noted 

above, at evaluating the capabilities of current state-of-science regional emissions, prognostic 

meteorological and PM/visibility models. The resultant modeling system (models and databases) 

identified and tested in Phase I were intended to be applied in Phase II (discussed below) 

following the procedures set forth in the Phase II Modeling Protocol (ENVIRON, 2004a). 

For the meteorological component of the Phase I modeling, SESARM contracted with Baron 

Advanced Meteorological Systems (BAMS) to apply the PSU/NCAR Mesoscale Model (MM5) 

in multiple configurations and to evaluate its performance against surface and aloft 

meteorological observations (Olerud, 2003a-f). The emissions modeling component of VISTAS 

Phase I was carried out by the research team of ENVIRON/Alpine/UCR with staff at Alpine 

Geophysics taking the lead role in setting up, testing, and applying the emissions modeling 

system. The air quality modeling component was performed by the team at the 

ENVIRON/Alpine/UCR modeling centers. A dominant theme during Phase I was the exchange 

of modeling codes, databases, and evaluation software between the three modeling centers as the 

air quality modeling was carried out. 

2.2.2 VISTAS Phase II 

The VISTAS Phase II modeling includes annual PM/regional haze simulations plus potentially 

additional shorter duration episodes. Detailed performance testing has been completed. The 

modeling system has been demonstrated using several inventory versions for the base year 
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(2002) and future years (2009 and 2018). These results are posted to the VISTAS modeling 

website managed by University of California at Riverside (http://pah.cert.ucr.edu/vistas/vistas2/). 

Beginning in winter 2005-2006, the modeling system will be exercised with a variety of 

emissions control scenarios enabling VISTAS to assess the effects of future year emission 

control strategies on visibility and other air quality issues. The modeling system will also allow 

VISTAS to track reasonable progress toward regional haze goals. More specifically, the 

VISTAS Phase II program will focus on the use of the CMAQ modeling system for calendar 

year 2002 over the same 36/12 km horizontal grid system used in Phase I. A potentially large 

number of annual (and episodic) model simulations has been or will be performed; the list below 

reflects current plans: 

3⁄4 2002 Annual Run. The initial annual model simulations and performance evaluations 

using the 2002 inventory for VISTAS and non-VISTAS states, Canada and Mexico. 

Multiple iterations of the 2002 annual simulation have been required to confirm the 

appropriateness of the model science configuration(s) recommended by the Phase I work, 

to evaluate updates to the model and model inputs (especially emissions inventory 

versions) and to refine model performance. 

 

3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 

simulation representing the 2000-2004 baseline period for EGU and fire emissions and 

using 2002 revised inventory for all other source sectors. The primary objective of this 

inventory is to provide the base line modeled air quality condition against which future 

year modeling runs will be compared to develop relative reduction factors for each 

pollutant species. 

3⁄4 2018 Future Year Annual Runs. 2018 future year emission inventory simulations using 

the 2002 calendar year meteorological conditions involving a base case inventory of 

typical EGU and fire emissions. Initially a 2018 On-the-Books (OTB) base case 

scenario, which consisted of all promulgated regional controls measures as of the 

beginning of 2005, and a 2018 On-the-Way (OTW) scenario that consisted of OTB plus 

regional SOx and NOx controls expected to be part of the Clean Air Interstate Rule 

(CAIR) and Clean Air Mercury Rule (CAMR) were modeled. After the final CAIR was 

released in June 2005, the 2018 OTB scenario was dropped. For the Base D inventory, 

the Integrated Planning Model (IPM) was used to project future EGU emissions; all other 

inventories were forecasted to 2018 using growth and control factors documented by 

MACTEC (2005). 

3⁄4 2009 Intermediate Future Year Annual Runs. Simulations for the 2009 Base D 

future-year were performed to provide estimates of visibility improvements at Class I 

areas for an intermediate future year. 

3⁄4 Future Year Emission Control Strategies. Prescription of the future year emissions 

control strategies to be performed in 2006 will be defined after the foregoing simulations 

and analyses have been completed. Currently, a 2018 CAIR plus BART control strategy 

is being developed. 
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Closely integrated with the annual meteorological, emissions and air quality modeling will be 

ongoing project management, technical review, and quality assurance activities performed under 

the guidance of the VISTAS Contracting Officer and the TAWG. The modeling team members 

will participate with VISTAS management in regular monthly conference calls, as well as ad hoc 

topical conference calls as needed, and will attend periodic meetings with the TAWG members 

throughout Phase II. 

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

management activities, four other Phase II activities will be performed, consistent with the 

VISTAS Quality Assurance Project Plan (QAPP) (ENVIRON, Alpine and UCR, 2004). 

2.2.3 ASIP Modeling

The ASIP regional ozone and PM modeling builds off of the VISTAS Phase II 2002 annual 

modeling and uses many of the same QA/QC procedures. 

2.2.3.1 Data Gatekeepers 

The ASIP and VISTAS emissions and air quality modeling team receive emissions, 

meteorological and air quality data from other contractors or other sources. As a first line of QA, 

we have defined a Gatekeeper function to assure the data have been received correctly, the 

quality of the data has been evaluated, and that the data received have been documented. The 

same Gatekeeper QA approach will be used in the ASIP ozone and PM2.5 modeling. 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. As necessary, obtain air quality data as 

appropriate for model input development and model performance evaluation 

and assure that the quality of all air quality data obtained is consistent with the 

approved QAPP. Provide documentation of evaluation and generate IC/BC 

inputs for CMAQ for all modeling runs. 

3⁄4 Meteorological Gatekeeper. As necessary, obtain meteorological data, as 

MM5 or MCIP files, as appropriate for annual 2002 modeling runs and other 

episode periods and perform data quality checks as approved in the QAPP, 

together with appropriate documentation of model performance evaluation 

activities. 

3⁄4 Emissions Gatekeeper. Obtain emissions inventory data necessary to 

support annual 2002 and future year modeling and recommend sources of 

emissions data to be used for Canada and Mexico. Assure quality of all 

emissions data received is consistent with the approved QAPP, and develop 

all emissions modeling files to support modeling runs for 2002 and future 

years. Develop the chemical speciation files and temporal and spatial 

allocation files necessary to convert annual inventories into hourly and daily 
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emissions modeling files, as appropriate. Develop all emissions modeling 

files for non-VISTAS states to support modeling runs for future year base case 

and emissions strategies. 

3⁄4 Data Management Gatekeeper: Maintain the ASIP results and other 

documents as requested by the ASIP group to support all 8-hour ozone and 

PM2.5 modeling tasks. This includes, for example, the storage of model inputs 

and outputs for annual runs and the transfer (via USB/firewire portable disk or 

alternative media) of electronic files to ASIP states, EPA, other contractors, 

and stakeholders. 

2.2.3.2 Emissions QA/QC 

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical steps in 

performing air quality modeling studies. Because emissions processing is tedious, time 

consuming and involves complex manipulation of many different types of large data sets, errors 

are frequently made in emissions processing and, if rigorous QA measures are not in place, these 

errors may remain undetected. In ASIP we will continue with the multistep emissions QA/QC 

approach applied in the VISTAS Phase I and II modeling. This includes the initial emissions 

QA/QC by the Emissions Gatekeeper described above, as well as QA/AC by the Emissions 

Modeler during the processing of emissions and then additional QA/QC by the Air Quality 

Modeler of the processed model ready emission files. This multistep process with three separate 

groups involved in the QA/QC of the emissions is intended to detect and correct errors prior to 

the air quality model simulations. 

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

EMS and EPA Input Screening Error Checking Algorithms: Although the SMOKE 

emissions model will be used for emissions processing, some of the more advanced EMS 

input error checking algorithms will be used to screen the data and identify potential 

emission input errors. Additionally, EPA has issued 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. 

Once the CMAQ-ready emission inputs have been prepared, we will perform additional 

emissions QA/QC as follows: 
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Spatial Summary: We will sum the emissions for all layers and for all 24 hours that is 

used to prepare a PAVE plot showing the daily total emissions spatial distribution. For a 

20 day simulation this produces approximately 20 days x 20 species x 5 emissions 

categories = 2,000 plots. In our base case simulations these plots will be presented as tons 

per day. The objective of this step is to identify errors in spatial distribution of emissions. 

Vertical Profile: For point sources the emissions total for each layer will be summed and 

plotted to show the vertical distribution of emissions. These plots show the emissions on 

the x-axis for each model layer on the y-axis. The objective of this step is to identify 

possible errors in vertical distribution of emissions. 

Short Term Temporal Summary: The total domain emissions for each hour will be 

accumulated and time series plots prepared that display the diurnal variation in total 

hourly emissions. The objective of this step is to identify errors in temporal profiles. 

Long Term Temporal Summary: The total domain emissions for each day will be 

accumulated and displayed as time series plots that show the daily total emissions across 

the domain as a function of time. The objective of this step is to identify particular days 

for which emissions appear to be inconsistent with other days for no reason (e.g., not a 

weekend) and compare against the general trend. 

Control Strategy Spatial Displays: Spatial summary plots of the daily total emissions 

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

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

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

changes in emissions for other pollutants or for SO2 outside of the state under study 

problems in emissions processing can be identified prior to the air quality model 

simulation. 

2.2.3.3 Meteorology QA/QC 

The meteorological modeling contractor (BAMS) had primary responsibility in the QA/QC of 

the MM5 meteorological fields. ASIP will rely on the QA/QC conducted by the VISTAS 

emissions and air quality modeling team as part of Phase II modeling to assure that the data has 

transferred correctly, to obtain an assessment of the quality of the data and to assist in the 

interpretation of the air quality modeling results. 

The VISTAS Phase II Meteorological Gatekeeper performed the following activities that serve to 

QA/QC the meteorological fields used in the ASIP modeling: 

3⁄4 Analyzed the MM5 data to assure it had been transferred correctly. 

 

3⁄4 Evaluated the MM5 using METSTAT and the surface meteorological network. 
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3⁄4 Evaluated upper-air MM5 meteorological estimates by comparing them to upper-air 

observations and satellite images. 

3⁄4 Compared the VISTAS 2002 MM5 simulation with the one generated by WRAP. 

3⁄4 Generated the CMAQ-ready meteorological inputs using the MCIP2.2 processor. 

The files will be updated to MCIP version 3.0 for VISTAS in fall 2005. 

2.2.3.4 Air Quality Modeling QA/QC 

Key aspects of QA/QC for the ASIP CMAQ modeling 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. 

3⁄4 Verification that correct input data sets are used when running each model. 

3⁄4 Evaluation of CMAQ results to verify that model output is reasonable and consistent 

with general expectations. 

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

The most critical element for ASIP CMAQ simulations is the QA/QC of the 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 the CMAQ model 

we employ a system of naming conventions using environment variables in the compile and run 

scripts that guarantee that correct inputs and science options are used. We also employ a 

redundant naming system so that the names of key science options or inputs are included in the 

name of the CMAQ executable program, in the name of the CMAQ 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 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 output files. In the case of model sensitivity 

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

base case. Often, errors in the emissions inputs can be discovered by viewing the animated GIFs. 

Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This 

provides a summary that can be useful for quickly comparing various model simulations. 
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2.2.3.5 Overview of Data Flow and Quality Assurance Process 

Figure 2-1 displays an overview of the data flow and quality assurance process in the ASIP 

Emissions and Air Quality Modeling study. The ASIP Modeling Team receives different types 

of data from various contractors and other sources that have performed their own Quality 

Assurance (QA) and Quality Control (QC). Whenever data are received by the Modeling Team, 

it is first subjected to a QA check by a Gatekeeper who assesses 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. Once the Gatekeeper has conducted a QA check 

of the data it is passed on to the modeler who performs their QA of the data. The data are then 

used in the modeling and resultant output (e.g., model-ready emissions or meteorological files) 

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

Once the model-ready inputs have been developed and subjected to QA/QC, the CMAQ model is 

applied using Base Case emissions and the modeling results subjected to a model performance 

evaluation. The model performance evaluation (MPE) represents an extensive QA effort and is 

the most time consuming component of the study. EPA has developed draft guidance for 

evaluating regional PM and haze models that includes performance goals (EPA, 2001). In 

addition, the Modeling team has adapted EPA MPE approaches and goals for 1-hour (EPA, 

1991) and 8-hour (EPA, 1999; 2005) ozone modeling. The MPE/QA process is being performed 

under VISTAS since VISTAS and ASIP share the same modeling platform database and 

approach. The MPE/QA approach is using as many different tools and analysis as possible in 

order to fully understand the accuracy and reliability of the model simulation. As seen in Figure 

2-1b, the MPE process in VISTAS/ASIP is a multistep process using several different 

techniques: 

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

were used extensively in VISTAS 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. 

MAPS Analysis Tools: Alpine Geophysics (Alpine) has a MAPS Analysis Tool that also 

runs under Linux and is based on Fortran and NCAR Graphics. It was originally 

developed for evaluating ozone models and has been extended to treat PM species as 

well. In addition to calculating similar statistics, scatter plots and time series plots as the 

UCR Analysis Tools, it also can generate spatially averaged time series plots of 

concentrations, bias and error, performs analysis of peak concentrations and includes a 

Flying Data Grabber (FDB) for comparing modeling results with aircraft data. 
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ENVIRON Analysis Tools: ENVIRON has developed specialized evaluation tools to 

analyze ozone and PM model performance for urban-scale modeling and comparison 

against EPA model performance goals. 

GA DNR Analysis Plots: Dr. James Boylan of the Georgia Department of Natural 

Resources has extended the concept in EPA’s draft PM fine particulate and regional haze 

modeling guidance that model performance for species that make up a major contribution 

to visibility impairment be subjected to more stringent goals than species that are minor 

contributors by developing concentration-dependent performance goals and “Bugle 

Plots” to display them. 

The evaluation of the VISTAS/ASIP 2002 CMAQ Base Case simulation used each of the 

analysis tools listed above demonstrating their descriptive and complimentary nature. 

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 mean 

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 precise as 

ozone monitoring. In fact, the differences in measurement techniques for some species likely 

exceed the more stringent performance goals, such as those for ozone. For example, recent 

comparisons of the PM species measurements using the IMPROVE and STN measurement 

technologies found differences of approximately ±20% (SO4) to ±50% (EC) (Solomon et al., 

2004). 

In the VISTAS/ASIP 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 

intercomparison across episodes, species, models and sensitivity tests. 

As noted in EPA’s draft PM modeling guidance, less abundant PM species should have less 

stringent performance goals. Accordingly, we are also using performance goals that are a 

continuous function of average observed concentrations proposed by Dr. James Boylan at the 

Georgia Department of Natural Resources that have the following features: 

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

observed concentrations are greater than 2.5 ug/m3

. 

• Approaching 200% error and ±200% bias when the mean of the observed concentrations 

are extremely small. 
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Dr. Boylan uses bias/error goals and criteria of ±30%/50% and ±60%/75% and plots bias and 

error as a function of average observed concentrations. As the mean observed concentration 

approaches zero the bias performance goal and criteria flare out to ±200% creating a horn shape, 

hence the name “Bugle Plots”.

Table 2-1. Model performance goals to help interpret modeling results. 

Fractional 

Bias 

Fractional 

Error Comment 

≤±15% ≤35% Ozone model performance goal for which PM model 

performance would be considered good. 

≤±30% ≤50% A level of model performance that we would hope 

each PM species could meet 

≤±60% ≤75% At or above this level of performance indicates 

fundamental problems with the modeling system. 
Quality Assurance Project Plan (Draft) 

ASIP Emissions and Air Quality Modeling ENVIRON International Corporation Figure 2-1a. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling. 
Quality Assurance Project Plan (Draft) 

ASIP Emissions and Air Quality Modeling ENVIRON International Corporation Figure 2-1b. Concluded. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modelin
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2.3 ASIP Modeling Protocol 

As recommended in EPA’s draft PM2.5 modeling guidance (EPA, 2001) and final 8-hour ozone 

modeling guidance (EPA, 2005), the ASIP modeling efforts will be initiated by developing a 

Modeling Protocol that describes the procedures to be used to conduct, quality assure and quality 

control (QA/QC), evaluate and use the modeling results (Morris and Stella, 2005). The ASIP 

Modeling Protocol will be reviewed and commented on by the ASIP states and is updated 

accordingly. 

2.4 ASIP Activities 

Below we discuss the ASIP project tasks and the QA activities for the currently planned ASIP 

modeling work effort. 

2.4.1 ASIP Task 1: Project Management 

The objective of this task is to manage project activities, participate in conference calls, manage 

the ASIP contract and subcontractors, general oversight and overall quality assurance and the 

preparation of the Quality Assurance Project Plan (QAPP) and Modeling Protocol. 

The management structure for the ASIP Emissions and Air Quality Modeling is similar to what 

was used in VISTAS with Ralph Morris of ENVIRON serving as Project Manager and Ralph 

Morris and Gregory Stella of Alpine Geophysics serving as Co-Principal Investigators (Co-PIs) 

and managing the activities in the ENVIRON and Alpine Geophysics modeling centers, 

respectively. Under this task we are performing all management activities for the ASIP modeling 

study, including: 

• Participation in scheduled conference calls to be held approximately once per month 

as well as expected ad hoc conference calls to be held as needed. 

• Preparation and review of contracts between SESARM and ENVIRON and 

subcontracts between ENVIRON and Alpine. 

• Develop and refine the Scope of Work and conduct contract discussions with the 

ASIP modeling team and the ASIP technical and project representatives. 

• Preparation of monthly reports, invoicing and payments to subcontractors. 

• Internal project conference calls and discussions among the ENVIRON/Alpine 

project team. 

• Develop and implement the ASIP Quality Assurance Project Plan (QAPP). 

• Develop the ASIP Modeling Protocol. 
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Table 2-2 summarizes the deliverables and expected delivery dates under Task 1 of the ASIP 

regional ozone and PM2.5 SIP support modeling analysis. 

Table 2-2. ASIP Task 1 Project Management task deliverables and due dates. 

Item Description Due Date 

1 ASIP Quality Assurance Project Plan (QAPP) 11/30/05 

2 ASIP Modeling Protocol 12/15/05 

3 Conference Calls with ASIP States Monthly; 11/05-2006 

4 Monthly Reports and Invoices Monthly; 11/05-2006 

2.4.2 ASIP Task 2: 2002/2009 Emissions Modeling 

The objective of this task is to perform SMOKE emissions modeling to generate 2002 36/12 km 

Typical “Base F” and 2009 36/12 km “Base F” CMAQ-ready emission inputs using the every 

day mobile source emissions modeling approach. 

Version 2.1 of the SMOKE emissions modeling system will be used to generate 2002 Typical 12 

km on-road mobile sources emissions using daily meteorology and the SMOKE MOBILE 

module. VISTAS has generated 2002 Typical Base F emissions using weekly MOBILE model 

run for 36 km and 12 km grid as well has generated 2002 Typical Base F 36/12 km emissions for 

the other source categories (e.g., area, point, non-road, etc.). Under ASIP this task will provide 

supplemental 2002 daily MOBILE modeling for the 12-km grid. The 2002 12 km Typical daily 

mobile source emissions will be merged with the other source categories to generate 2002 

Typical 12 km emissions inputs based on daily mobile source modeling that will be used in the 

ASIP ozone and PM modeling. 

Also under this task we will process the 2009 Base F annual emissions through the SMOKE 

emissions processor to prepare the emissions for use in the CMAQ air quality model. Base F 

emissions include outputs of the Integrated Planning Model (IPM) for utilities and all inventory 

revisions from VISTAS and other RPOs that are available by July 2005. Daily 2009 MOBILE 

run will be used in SMOKE for the 12 km grid and weekly MOBILE run for the 36 km grid. 

Table 2-3. ASIP Task 2 2002/2009 Emissions Modeling task deliverables and due dates. 

Item Description Due Date 

1 2002 12 km Typical Base F daily mobile source SMOKE emissions 

modeling 

11/28/05 

2 2002 36/12 km Typical Base F CMAQ-ready emissions w/daily 

mobile source emissions 

12/3/05 

3 2009 36/12 km Base F CMAQ-ready emissions w/daily mobile 

sources 

12/15/05 
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2.4.3 ASIP Task 3: 2002/2009 36/12 km Base F CMAQ Modeling 

The objective of this task is to run the CMAQ model on the 36 km and 12 km grid for the typical 

2002 and 2009 Base F emission scenarios using every day mobile source emissions. 

The CMAQ model is applied for the 2002 and 2009 Base F emission scenarios with every day 

mobile sources emissions on the 36 km and 12 km grids. QA/QC is performed on the runs 

including comparisons of the modeling results to the observed PM concentrations. Extractions 

are made at the ozone and PM monitoring sites, where values at the monitoring site are extracted 

from the 36 km results and a 3 x 3 grid cell average concentration centered on the monitoring site 

is extracted for the 12 km modeling results. The 12 km modeling results will be processed to 

generate boundary condition inputs for the Alabama/Georgia (ALGA) and North Carolina (NC) 

subdomains and provided to the states of North Carolina and Georgia. 

Table 2-4. ASIP Task 3 2002/2009 36/12/ km Base F CMAQ Modeling. 

Item Description Due Date 

1 CMAQ 36/12 km simulations for 2002 Typical Base F w/everyday 

mobile sources emissions 

12/15/05 

2 CMAQ 36/12 km simulations for 2009 Base F w/everyday mobile 

sources emissions 

1/15/06 

3 Disk with boundary conditions for ALGA and NC subdomains 1/31/06 

2.4.4 ASIP II Task 4: Data Analysis and Data Management 

The objective of this task is to analyze the results of the Task 2 emissions and Task 3 CMAQ air 

quality modeling, archive and distribute the modeling results. 

The SMOKE emissions and CMAQ air quality modeling results will be analyzed. 8-hour ozone 

projections and annual PM2.5 projections will be made at all monitoring sites in and adjacent to 

the VISTAS states using both the 36 km and the 12 km modeling results. These results will be 

documented in PowerPoint presentations and presented ASIP states. The results from the 

emissions and air quality modeling will also be archived on USB portable disks and delivered to 

the ASIP states. Summaries of the results will also be available on the Internet. 

Table 2-5. ASIP Task 4 2002 CMAQ Modeling task deliverables and due dates. 

Item Description Due Date 

1 PowerPoint Presentation on 2009 8-hour ozone projections 1/31/06 

2 PowerPoint Presentation on 2009 PM2.5 projections 1/31/06 

3 Archive disks of ASIP 2002/2009 modeling results 2/10/06 
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3.0 PROJECT MANAGEMENT 

3.1 Project Organization 

The ASIP ozone and PM modeling project is conducted by ENVIRON International Corporation 

(ENVIRON) and Alpine Geophysics, LLC (Alpine), with input from SESARM and the ASIP 

States. Organizational commitment is an essential element for developing and implementing a 

successful research project. Ralph Morris of ENVIRON would be the ASIP Emissions and Air 

Quality Modeling Project Manager (PM). The ASIP Modeling Team has two Co-Principal 

Investigators that coordinate activities at each of the modeling centers, Ralph Morris of 

ENVIRON and Gregory Stella of Alpine. The PM and two Co-PIs are 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 

and Co-PIs and SESARM and the ASIP States. 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 ASIP Emissions and Air Quality Modeling 

study, with more details on their roles provided next. 

Southeastern States Air 

Resources Managers, Inc. 

(Contracting Agency) 

ASIP 

States Coordinating 

Group 

Chairperson: 

Sheila Holman, North 

Carolina 

ASIP 

Contracting Officer: 

Patricia Brewer, ASIP 

ASIP 

Technical Contact: 

James Boylan, Georgia 

Subcontractor 

Alpine Geophysics 

Co-Principal Investigator 

Gregory Stella 

Staff 

Prime Contractor 

ENVIRON 

Project Manager and CoPrincipal Investigator 

Ralph Morris 

Staff 

Figure 3-1. ASIP Emissions and Air Quality Modeling Project Organizational Chart. 
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Table 3-1. ASIP Emissions and Air Quality Modeling project participants and contacts. 

Person & Role Affiliation/Address Contact Information 

Patricia Brewer 

(Contracting Officer) 

ASIP Technical Coordinator 

2090 US Highway 70 

Asheville, NC 28778 

(828) 296-4500 

(Fax) (828) 299-7043 

pat.brewer@ncmail.net

James Boylan 

(Technical Contact for 

Emissions & AQ 

Modeling) 

Georgia DNR 

Air Protection Branch 

4244 International Pkwy, Ste 120 

Atlanta, GA 30354-3906 

(404) 362-4851 

(Fax) (404) 363-7100 

James_Boylan@mail.dnr.state.ga.us

Michael Abraczinskas 

(Technical Contact for 

MM5 Modeling) 

North Carolina DENR 

1641 Mail Service Center 

Raleigh, NC 27699-1641

(919) 715-3743 

Michael.Abraczinskas@ncmail.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

Gregory Stella 

(Co-Principal 

Investigator) 

Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 

(828) 675-9045 

(Fax) (828) 675-5801 

gms@alpinegeophysics.com

Key ENVIRON Participants 

Bonyoung Koo ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0727 

bkoo@environcorp.com

 

Abby Hoats ENVIRON (415) 899-0735 

ahoats@environcorp.com

Steven Lau ENVIRON (415) 899-0739 

slau@environcorp.com

Key Alpine Geophysics Participants 

Dennis McNally Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

(303) 421-2211 

(Fax) (303) 421-9553 

dem@alpinegeophysics.com

Cyndi Loomis Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

(303) 421-2211 

(Fax) (303) 421-9553 

cfl@alpinegeophysics.com

T. W. Tesche Alpine Geophysics, LLC 

3479 Reeves Drive 

Ft. Wright, KY 41017 

(859) 341-7502 

twt@iac.net
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3.1.1 ENVIRON Project Manager and Co-Principal Investigator 

Mr. Ralph Morris of ENVIRON is the Project Manager (PM) and Co-Principal Investigator (CoPI) for the Association for Southeastern Integrated Planning (ASIP) 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 ASIP group 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 

• Serve 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

• Establishes a Subcontract with Alpine Geophysics, LLC to perform the work, and adhere 

to the terms and conditions of that contract 

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

the Work Plan 

• Provides information to assist the ASIP group in achieving its goals as stated in its Work 

Plan and Strategic Plan 

• Develops individual test protocols and reports as directed 

• Analyzes modeling data and provides assessment reports 

• Supports the Principal Investigator and ASIP in responding to any issues raised in 

assessment reports 

3.1.2 ENVIRON and Alpine Geophysics Co-Principal Investigators 

The two Co-Principal Investigators of Ralph Morris and Gregory Stella perform the following 

functions: 

• Direct and coordinate the day-to-day project activities of the project team and computer 

facilities to conduct the test program 
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• Ensure that this QAPP and Modeling Protocol are followed during the course of the 

project

• Manage the activities in each of the two modeling centers

• Direct 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 initiate 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 report as directed 

• Analyze modeling data and provide assessment reports 

3.1.3 ASIP Contracting Officer 

The ASIP Contracting Officer (Patricia Brewer) serves as the primary contact between the 

Emissions and Air Quality Modeling Team and ASIP and performs the following functions: 

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

activities 

• Works with the Project Manager, Co-Principal Investigators, SESARM, ASIP States, 

collaborators, Stakeholders, etc. to define scope of work and to assure that the interests 

and concerns of all of the ASIP 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 

• Reviews work products 

3.1.4 ASIP Technical Contact 

The ASIP Technical Contact (James Boylan) for the Emissions and Air Quality Modeling Team 

works with the ASIP Contacting Officer in the day-to-day oversight and management of the 

modeling analysis: 
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• Provides day-to-day oversight of ASIP Phase II Emissions and Air Quality Modeling 

Team activities 

• Works with the ASIP Contracting Officer, 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 VISTAS groups on the activities of the Modeling 

team. 

• Reviews work products. 

3.1.5 ASIP States 

The primary purpose of the ASIP Emissions and Air Quality Modeling work is to develop the 

regional modeling component for 8-hour ozone and PM2.5 State Implementation Plans (SIPs) 

being developed by several southeastern U.S. states that are due June 2007 and April 2008, 

respectively. The ten (10) VISTAS states also participate in ASIP. Alabama, Georgia, North 

Carolina, Kentucky, Tennessee, Virginia, and West Virginia have PM2.5 non-attainment areas 

and Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and 

West Virginia have 8-hour ozone non-attainment areas. The ASIP states will oversee the 

regional emissions inventory development and ozone and fine particulate modeling that will be 

required for the State Implementation Plans (SIP's). Emissions Inventory efforts include the 

development of 2002 base case emissions inventories and future year forecasts to be utilized in 

the ASIP modeling efforts. Modeling efforts will include identification, evaluation, and 

application of air quality modeling tools to quantify the effects of emission management options 

upon air quality in 8-hour ozone and PM2.5 nonattainment areas in the southeastern United 

States. Specific activities of the ASIP States include: 

• Oversees the activities of the ASIP Emissions and Air Quality Modeling Team through 

the Contracting Officer, conference calls, and periodic in-person meetings and workshops 

• Provides the Contracting Officer, technical Contact, 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 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 ASIP Tasks is presented at the end of each Task Description in 

Section 2.4. 

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 
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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 ASIP Modeling Protocol (Morris 

and Stella, 2005), 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 ASIP Emissions and Air Quality Modeling Team members, other ASIP Contractors and 

ASIP 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 network-accessible 

files. A considerable amount of information is exchanged by e-mail within this project. The 

ASIP Modeling Team will use the same four listservs as used by VISTAS to distribute 

information to different groups as indicated in Table 3-2. 

Table 3-2. VISTAS listservs that will be used to distribute information by the ASIP Emissions 

and Air Quality Modeling Team and other ASIP participants. 

Listserv Purpose 

Vistas-all@cert.ucr.edu Contacts all participants including Modeling Team, VISTAS 

TAWG and Stakeholders 

Vistas-modeling@cert.ucr.edu Contacts Modeling team and VISTAS TAWG Modeling 

Contacts 

Vistas-emissions@cert.ucr.edu Contacts emissions staff in the Modeling team and emissions 

people in the VISTAS TAWG 

Vistas-met@cert.ucr.edu Contacts meteorology staff in the Modeling team and 

meteorology people in the VISTAS TAWG 

The Modeling Team members and ASIP States and Contracting Officer hold periodic conference 

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

Unscheduled meetings or conference calls are also held concerning specific issues as the needs 

arise. In addition, periodic project meetings and conference calls are held. In these meetings 

detailed technical information is exchanged, project status is discussed, and project direction is 

assessed. 

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

submitted to the ASIP Contracting Officer on a monthly basis. These reports summarize project 

progress, results to date, problems encountered and necessary action items, and plans for the 
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upcoming reporting period. 

All modeling results, quality assurance reports, and related documents are posted on the project 

web site, as described in Section 3.5. 

3.5 Documentation and Records 

Document control is the system that ensures that only the latest revisions of the defined 

documents are used by the Modeling Team personnel engaging in project activities. 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, 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. 
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4.0 MEASUREMENT AND DATA ACQUISITION 

4.1 Computer Hardware/Systems Administration 

All emissions and air quality modeling is performed on computer equipment located at either the 

ENVIRON or Alpine 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 two 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. All staff also have their own state-ofthe-art PCs and have 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 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 Alpine Geophysics Modeling Center Hardware 

Alpine Geophysics' computing facilities consist of SUN Microsystems SPARCstation computers 

and a very powerful array 10 multiprocessor Linux-based workstations. The aggregate network 

has over 7.3 Gbytes of memory and 2500 Gbytes of aggregate disk space with over 2000 Gbytes 

of SCSI and IDE Raid-5 protected space. All client data is stored on at least one RAID-5 

protected disk array. To further protect client data, two of our main servers backup their disk 

drives on a weekly basis to a second server that is physically disconnected from the power 

supply when not doing an active transfer. This way a catastrophic power failure will not 

compromise the ability for Alpine Geophysics to deliver. 

To efficiently share network resources, 100-BaseT fast ethernet switches interconnect the 

computers within each office. Additionally, the Arvada based computers have both the Parallel 

Virtual Machine (PVM) software package for computation on multiple nodes on the network and 

Open MP capabilities for shared memory multiprocessing within each computer. For the 

majority of model simulations, using a job level parallelization, where individual periods of a 

simulation are put onto each computational node, maximizes network throughput. However, the 

computational network also has the ability to share the multi-Gbyte memory and 20 processors 

on a single MM5 model simulation. 
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A key feature of the AG computer network is the ability for AG to add computational resources 

quickly and cost effectively. Alpine scientists have been using Linux and Unix for 

meteorological and air quality modeling for over 16 years. This knowledge base, along with 

existing relationships with hardware vendors, software vendors, and system specialists, enable 

AG to meet the computational requirements of the VISTAS modeling study. 

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 (over 23 TB) 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 

backed-up. One 2 TB RAID5 disk system is reserved for back up of critical project data. 

Back-ups 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 that are either publicly available through the GNU Public License 

(GPL) distribution channels or commercially available; and (3) custom software used in 

computer simulation modeling and data analysis. All commercial software used in the ASIP 

modeling is 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 ENVIRON, Alpine, EPA and other researchers using 

commercial and GPL compilers for FORTRAN, C and C++. 
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4.2 Sources for Data Used in Modeling 

Most of the data used in the ASIP Emissions and Air Quality Modeling are provided by other 

ASIP and VISTAS contractors with the data subjected to quality assurance and quality control 

(QA/QC) prior to delivery to the Modeling team. In addition the Modeling Team have formed 

data “Gatekeeper” functions to further QA/QC provided to the Modeling Team prior to its use. 

Below we discuss the data used in the modeling and the function of the Gatekeeper roles, which 

are described in detail in Section 2.2.3.1: 

Air Quality Gatekeeper: The Air Quality Gatekeeper downloads air quality data from the 

VIEWS, SEARCH and AIRS websites or obtains it from the ASIP States and subjects it 

to QA/QC. The data are reformatted and processed for use in the modeling. 

Meteorological Gatekeeper: The Meteorological Gatekeeper evaluates the MM5 data 

provided by the VISTAS Meteorological Modeling Contractor (BAMS), subjects it to 

QA/QC and processes it for input to the CMAQ/CAMx air quality models. 

Emissions Gatekeeper: The Emissions Gatekeeper receives emissions data from the 

ASIP Emissions Contractor (MACTEC), subjects them to a comprehensive QA/QC and 

rejects data that doesn’t pass the QA/QC and reformats the data for input into the 

SMOKE emissions model. 

Data Management Gatekeeper: The Data Management Gatekeeper maintains the project 

website, keeps back ups of data, responds to data requests and updates reports and files 

on the website. 
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5.0 EMISSIONS MODELING 

The project objective is to integrate regional-scale emissions and transport modeling efforts so 

that non-attainment areas for 8-hour ozone or PM2.5 can be evaluated under different emissions 

and meteorological transport scenarios. Meeting this objective means that modeling outputs 

must be of known quality. This section addresses emissions modeling and Section 6.0 addresses 

meteorological modeling as they are applied to this project. 

The ASIP Modeling Team uses the Sparse Matrix Operator Kernel Emissions (SMOKE) 

emissions model to model the anthropogenic and biogenic gas and particulate matter (PM) 

emissions needed for regional ozone and fine particulate matter (PM2.5) modeling. In September 

2005 a new versions of SMOKE was released (Version 2.2). However, ASIP is using the 

SMOKE setup developed by VISTAS and switching to the new version of SMOKE would 

interfere with the ASIP schedule and potentially introduce errors in the analysis. Thus, Version 

2.1 of SMOKE will be used in the ASIP emissions modeling. 

The purpose of SMOKE is to convert the resolution of the 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 shows 

an example of how the matrix approach organizes 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 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) steps for the emissions modeling process has been divided into the 

four major classifications defined in the sections below. 

5.1.1 Emissions Modeling QA 

Emissions 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 Emissions System QA 

Emissions 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 
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modeling procedures with archives of the model installation and data files will ensure that the 

model installation and configuration are correct and that past simulations are sufficiently documented and archived to allow their reproduction. 

5.1.3 Emissions 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 VISTAS 

Modeling Team (e.g. VISTAS state emission inventory staff). 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 component that is common to all of the other three emissions 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 will include 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 ASIP 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 2.2.3 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 will ensure 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 meteorology inputs. 
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Reasonableness 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 gate keeping 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 ASIP work, the Project Manager is the liaison between the 

emissions modeling team and the ASIP Project and Technical Coordinators and States, and in 

this role communicates any issues raised during the emissions QA process. 

Figure 5-2 presents the QA framework developed by the ASIP Modeling Team for SMOKE 

modeling. Identifying the specific participants in the ASIP 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) 

ASIP Emissions and Air Quality Modeling ENVIRON International Corporation 

VISTA ASIPS 

Mo

VISTAS TAWG Mo

ASIP 

Coordinators 

and States 

Figure 5-2. Diagram of ASIP Modeling Team Approach to SMOKE Modeling.
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6.0 METEOROLOGICAL MODELING 

The EPA Models-3/CMAQ system is the primary regional ozone and particulate matter (PM) air 

quality model being used in ASIP regional modeling. Meteorological inputs for CMAQ were 

generated by the VISTAS Meteorological Modeling Contractor using the MM5 meteorological 

model. ASIP CMAQ modeling will use the use the same MM5 fields as used by VISTAS. The 

MM5 model was applied for the entire year of 2002 on two grids: a continental scale domain 

with 36 km grid spacing and a regional-scale domain with 12 km grid spacing covering the 

eastern United States. The CMAQ model requires inputs of three-dimensional gridded wind, 

temperature, humidity, cloud/precipitation, and boundary layer parameters. CMAQ uses output 

fields from the Fifth Generation Mesoscale Model (MM5), developed and maintained by the 

Pennsylvania State University and National Center for Atmospheric Research (PSU/NCAR). 

MM5 is being used to develop hourly meteorological fields on the 36 km and 12 km grids. 

A detailed protocol for utilizing MM5 as part of VISTAS has been prepared. The procedures 

used for applying MM5 for VISTAS are based on an extensive evaluation of MM5 and a series 

of sensitivity tests. The MM5 protocol contains a detailed description of MM5, the VISTAS 

modeling domain, the MM5 physical configuration, and a model application approach. The 

protocol also presents a plan for evaluating the performance of the model in replicating the 

evolution of observed winds, temperature, humidity, and boundary layer morphology to the 

extent that resources and data availability allow; this will serve as the primary approach to assess 

the reliability of the meteorological fields to adequately characterize the state of the atmosphere 

for input to CMAQ. Details on the VISTAS MM5 modeling can be found on the VISTS 

Meteorological Modeling website: 

http://www.baronams.com/projects/VISTAS/

MM5 is an atmosphere model that 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. 

No additional QA or analysis of the MM5 files for 2002 is planned under this ASIP effort. 
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7.0 CMAQ MODELING SYSTEM 

EPA’s Models-3 CMAQ modeling system is the primary air quality modeling system used in the 

ASIP Emissions and Air Quality Modeling activities. 

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 ASIP CMAQ 

modeling will leverage off the extensive testing and evaluation of the CMAQ model performed 

by VISTAS. VISTAS is currently (through September 2005) using Version 4.4 of CMAQ that 

was publicly released in October 2004 whose secondary organic aerosol (SOA) module has been 

enhanced to include missing processes (CMAQ V4.4 SOAmods). The enhancements to the 

CMAQ V4.4 SOA module are described in Section 7.3 below. In September 2005, EPA 

released a new Version 4.5 of CMAQ. VISTAS is currently testing and evaluating CMAQ 

Version 4.5 and depending on that outcome, both ASIP and VISTAS may transition to using 

CMAQ Version 4.5 with the SOA module enhancements. This issue is discussed in Section 7.4 

below. 

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 episodic ozone and 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 (sulfate, nitrate, SOA) 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 
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metals and radicals. The impact of clouds on the PM size distribution is treated empirically. For 

wet deposition processes, CMAQ uses the RADM/RPM approach. Particle dry deposition is 

included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), 

latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 

to be an reversible semi-volatile scheme whereby VOCs can be converted to condensable gases 

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

atmospheric conditions. ENVIRON has enhanced the CMAQ SOA module to include additional 

processes missing in the standard CMAQ version of the model (see Section 7.3). 

A description of the features implemented in CMAQ is available on the CMAS 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. For the 

VISTAS Phase I modeling and the Phase II Initial 2002 CMAQ run version 4.3 (released August 

2003) was used, whereas for VISTAS Phase II CB4/SAPRC sensitivity tests and 2002 annual 

CMAQ runs Version 4.4beta (released March 2004) and Version 4.4 (released October 2004), 

respectively, were used. ASIP and VISTAS are currently considering switching to the latest 

versions of CMAQ (Version 4.5) released in September 2005. 

7.2 CMAQ Configuration for ASIP and VISTAS 2002 Modeling 

ASIP will use the same CMAQ configuration as used in the VISTAS Phase II modeling of the 

2002 annual period. One of the principal accomplishments in the VISTAS Phase I modeling was 

the identification and justification of the CMAQ science options recommended for VISTAS 

Phase II and ASIP modeling. We summarized the VISTAS Phase I findings briefly in the 

introduction. In this section we briefly identify the main science options we recommend for 

annual PM modeling with CMAQ, more details ate provided in the VISTAS (ENVIRON, 2004a) 

and ASIP (Morris and Stella, 2005) Modeling Protocols. For ASIP, the CMAQ model would be 

set up and exercised on the same nested 36/12 km grid domain used in VISTAS Phase I and II, 

employing one-way grid nesting. That is, boundary conditions for the 12 km grid simulation are 

extracted from the 36 km run using the CMAQ BCON processor. A total of 19 vertical layers 

would be implemented, extending up to a region top of 100 mb (approximately 15 km AGL). 

The PPM advection solver would be used along with the spatially varying (Smagorinsky) 

horizontal diffusion approach and K-theory for vertical diffusion. MM5 meteorological output 

based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer 

(PBL) scheme will be used and the CMAQ Meteorological-Chemistry Interface Processor 

(MCIP2.2 for Phase I and MCIP2.3 for Phase II) would process the MM5 data using the “pass 

through” option. ASIP and subsequent VISTAS modeling is switching to CMAQ v4.5 with the 

AE4 aerosol module, which means the MM5 output will have to be reprocessed using MSIP3.0. 

The CB4 gas-phase, RADM aqueous-phase, and AERO3/ISORROPIA aerosol chemistry 

schemes were used in the initial CMAQ 2002 modeling and AERO4 may be used in CMAQ v4.5 

foe ASIP. Treatment of reversible secondary organic aerosols would be simulated by the 

SORGAM implementation in CMAQ with enhancements developed under VISTAS (SOAmods). 

Under the Phase I modeling, VISTAS evaluated three photochemical mechanisms: CB4, CB4-

2002 and SAPRC99. CB4-2002 produced nearly identical results as CB4 but took much longer 

to run since it is only implemented in the slower SMVGEAR chemistry solver, compared to CB4 
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that is also implemented in the faster EBI chemistry solver. Thus, CB4-2002 was dropped from 

consideration in Phase II. The Phase I comparisons of CB4 and SAPRC99 found they produced 

mostly similar but different model performance. However, one mechanism was not performing 

better than the other across all species, sites and periods. The Phase I testing only evaluated the 

mechanism’s base case performance, not their response to emission reductions. Given that CB4 

runs twice as fast as SAPRC99, for Phase II it was decided to perform initial 2002 CMAQ 

modeling using CB4 and evaluate CB4 and SAPRC99 responses to emission reductions using 

the Phase I episodes. These evaluations did not clearly show any technical advantage of SAPRC 

over CB4 so CB4 was adopted for the VISTAS Phase II and ASIP modeling. 

7.3 Enhancements to the Treatment of Secondary Organic Aerosols in CMAQ (SOAmods) 

Initial simulations of the CMAQ V4.3 for the 2002 annual period revealed that Organic Carbon 

(OC) was underestimated in the southeastern U.S. by approximately 80% during the summer. 

When revised 2002 annual simulations were conducted using CMAQ Version 4.4, the summer 

OC underestimation bias increased to 100%. 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 associated with SOA formation from biogenic VOCs believed 

important for SOA formation. These processes are not treated in the current CMAQ model. 

Thus, as part of VISTAS we enhanced the CMAQ SOA module 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., 

2005). 

7.3.1 SOAmods Formulation 

The formulation of the 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., 1987) 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 
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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: 

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 
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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. 

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. 
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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 

7.3.2 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., 2005). 

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 
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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% 
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ASIP Emissions and Air Quality Modeling ENVIRON International Corporation 

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

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.
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7.4 Testing and Evaluation of CMAQ V4.5 

VISTAS is currently (through September 2005) using Version 4.4 of EPA’s Models-3 

Community Multiscale Air-quality (CMAQ) modeling system that has been modified to include 

additional processes associated with the formation of Secondary Organic Aerosol (SOA) from 

biogenic emissions (CMAQ V4.4 SOAmods). In September 2005, EPA released a new version 

of the CMAQ model (V4.5) that included numerous updates, corrections and enhancements of 

CMAQ as follows: 

1) Aerosols 

 - Added sea salt (fine equilibrium; non-interactive coarse 

 mode) -- aero4 

 - Updated aerosol dry deposition algorithm 

 - Updated mechanism include files to remove any aerosol species 

 with zero concentrations for aero4 

 - Updated ISORROPIA to v1.5 (25 Oct 2003) and fixed some 

 discontinuities 

 - Added diagnostic variables to calculate PM2.5 concentrations 

 - Corrected bug in mode merging to reduce mode crossover 

 - Modified SO4 used in ISORROPIA call 

 - Corrected inconsistency in MINL2SG (aerodepv) 

 - Corrected the EMSULF (H2SO4 emissions) unit conversion bug 

2) Chemistry 

 - Added CB4/chlorine chemistry and associated EBI solver 

 - Added CB4/air toxics and SAPRC99/air toxics chemistry and 

 associated EBI solvers 

 - Added degradation algorithm to the generalized solvers 

 - Corrected treatment of convergence failures in EBI solvers 

 - No longer support the RADM2 mechanism 

3) P-in-G 

 - Improved calculation of plume centerline 

 - Fixed bug in the aerosol species array subscripting 

 - Corrected error in non-reactive species NH3 fluxes and dry 

 deposition 

4) PBL modeling 

 - Updated to use PURB (% urban) for setting minimum Kz 

5) Clouds 

 - Added new sub-grid cloud mixing algorithm/module 

 (based on ACM) 

 - Added new cloud diagnostic variables 

 - Corrected the interpolation times for resolved clouds (to 

 time-step midpoint) and for subgrid clouds (to half hour) 
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6) Advection 

 - Added new mass continuity scheme 

7) Other 

 - Added dynamic vertical layer allocation 

 - Added primary carbon source apportionment capability 

 - Added sulfate tracking capability

VISTAS/ASIP desires to migrate from CMAQ V4.4 SOAmods to CMAQ V4.5 with the 

SOAmods enhancement. The procedures for transitioning the VISTAS/ASIP modeling from 

CMAQ V4.4 SOAmods to CMAQ V4.5 SOAmods is described in this section. 

VISTAS is performing a systematic testing of the new CMAQ V4.5 code with the intention of 

ultimately transitioning the VISTAS/ASIP modeling from the CMAQ V4.4 with AE3 (aero3) 

and SOAmods update to the new CMAQ V4.5 with AE4 (aero4) and SOAmods updates. The 

AE4 code includes new treatment of Sea Salt emissions that may be important for some 

southeastern U.S. locations (e.g., coastal). However, use of the AE4 aerosol module in CMAQ 

V4.5 needs the MM5 data to be reprocessed with the new MCIP3.0 meteorological processor. 

The transition from CMAQ V4.4 SOAmods to CMAQ V4.5 SOAmods will involve the 

systematic testing of the model at each step in the update to assure no new errors are introduced. 

Test runs would be performed for portions of Jan/Jul 2002 and a systematic update of the 

modeling system is being performed as follows: 

• CMAQ V4.4 AE3 

• CMAQ V4.5 AE3 

• Process MM5 data with MCIP3 (MM5-MCIP3.0) 

• CMAQ V4.5 AE3 w/ MM5-MCIP3.0 

• CMAQ V4.5 AE4 w/ MM5-MCIP3.0 

• Update CMAQ V4.5 with SOAmods 

• CMAQ V4.5 AE4 SOAmods w/ MM5-MCIP3.0 

• CMAQ V4.5 AE4 SOAmods w/ MM5-MCIP3.0 testing the new SO4 process tracking 

(e.g., track SO4 formation by homogeneous versus heterogeneous formation pathways). 

The new CMAQ V4.5 AE4 SOAmods w/ MM5-MCIP3.0 would then be compared against the 

current CMAQ V4.4 SOAmods for the full January and July 2002 periods with a detailed model 

performance evaluation. 
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8.0 ASSESSMENTS AND OVERSIGHT 

8.1 Assessment Responsibilities 

The primary point of contact for ASIP Modeling project assessment is that of the Project 

Manager (PM). This PM is available to the ASIP Contracting Officer, Technical Contact, States, 

external reviewers, and internal staff by direct contact through e-mail and phone and is linked to 

each of the four VISTAS listservs (see Table 3-2). 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, legally defensible. 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 quality control 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. 

The ASIP contacting officer and technical contact and ASIP states 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. 
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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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9.0 REFERENCES 

Adelman, Z. 2004. Quality Assurance Protocol: WRAP RMC Emissions Modeling with 

SMOKE, Prepared for WRAP Modeling Forum, January. 

Alpine Geophysics. 2003a. “VISTAS Emissions and Air Quality Modeling – Phase I Subtask 

3c-1 Report: MM5 Meteorology QA/QC Summary Report for Episode 1: 2-20 January 

2002.” Alpine Geophysics LLC, ENVIRON International Corporation and University of 

California at Riverside. Ft. Wright, Kentucky (available at: 

http://pah.cert.ucr.edu/vistas/docs.shtml). September 10. 

Alpine Geophysics. 2003b. “VISTAS Emissions and Air Quality Modeling – Phase I Subtask 

3c-1 Report: MM5 Meteorology QA/QC Summary Report for Episode 1: 13-22 July 

1999.” Alpine Geophysics LLC, ENVIRON International Corporation and University of 

California at Riverside. Ft. Wright, Kentucky (available at: 

http://pah.cert.ucr.edu/vistas/docs.shtml). September 15. 

Alpine Geophysics. 2003c. “VISTAS Emissions and Air Quality Modeling – Phase I Subtask 

3c-1 Report: MM5 Meteorology QA/QC Summary Report for Episode 1: 11-27 July 

2001.” Alpine Geophysics LLC, ENVIRON International Corporation and University of 

California at Riverside. Ft. Wright, Kentucky (available at: 

http://pah.cert.ucr.edu/vistas/docs.shtml). September 16. 

Alpine Geophysics. 2003d. Initial Review for 2002 Inventory Development, memorandum 

prepared by Gregory Stella, Alpine Geophysics, LLC for Pat Brewer, VISTAS, June 13, 

2003. 

ANSI/ASQC Q9000-1-1994. 1994. Quality Management and Quality Assurance Standards – 

Guidelines for Selection and Use (revisions and redesignations) of ANSI/ASQC Q90-

1987. Available at: http://webstore.ansi.org/ansidocstore/dept.asp?dept_id=50. 

Arnold, J.R., R.L. Dennis, and G.S. Tonnesen. 2003. “Diagnostic Evaluation of Numerical Air 

Quality Models with Specialized Ambient Observations: Testing the Community 

Multiscale Air Quality Modeling System (CMAQ) at Selected SOS 95 Ground Sites”, 

Atmos. Environ., Vol. 37, pp. 1185-1198. 

ARS. 2002. Air Resource Specialists, Inc. Analysis of IMPROVE and SEARCH Speciated 

PM2.5 Data. Report to VISTAS, August 28, 2002. http://www.vistas-sesarm.org. 

Binkowski, F.S. and S.J. Roselle. 2003. Models-3 Community Multiscale Air Quality (CMAQ) 

Model Aerosol Component -- 1. Model Description. Journal of Geophysical Research, 

Vol. 108(D6), 4163, DOI:10.1029/2001JD001409. March 27. 

Boylan, J. W. 2003. “Comparisons of 36 km and 12 km Grid Resolutions”, PowerPoint 

presentation prepared for December 19, 2003 VISTAS modeling conference call. 

Available at: http://pah.cert.ucr.edu/vistas/ppt_files/12_36.ppt. 
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Boylan, J. W. 2004. “Calculating Statistics: Concentration Related Performance Goals”, paper 

presented at the EPA PM Model Performance Workshop, Chapel Hill, NC. 11 February. 

Brewer, P., S. Holman and J. Hornback. 2003. “Vistas Analyses of Regional Haze in the 

Southeastern United States.” Presented at Annual Air and Waste Management 

Association Meeting and Exhibition, San Diego, CA. June. 

Byun, D.W., and J.K.S. Ching. 1999. “Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030. 

Claeys, M., B. Grahm, G. Vas, W. Wang, R. Vermeylen, V. Pashynska, J. Cafmeyer, P. Guyon, 

M. O. Andreae, P. Artaxo and W. Maenhaut. 2004. Formation of Secondary Organic 

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