west virginia department of environmental protection



# Appendix E

ASIP Quality Assurance Project Plan

Promoting a healthy environment.[This page intentionally left blank.]

### Quality Assurance Project Plan (Draft)

For

Association for Southeastern Integrated Planning (ASIP)

Emissions and Air Qualit y Modeling to Address

8-Hour Ozone and PM 2.5 

### 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 28714Approvals

Title: Quality Assurance Project Plan for Asso ciation for Southeastern Integrated Planning (ASIP) Emissions and Air Quality Mode ling to Address 8-Hour Ozone and PM 2.5 Nonattainment

in Southeastern States October 6, 2005.

Ms. Patricia Brewer Date:

Technical Coordinator

Association for Southeaste rn Integrated Planning

Mr. Ralph Morris Date: Project Manager and Co-P rincipal 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 94945TABLE 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 a nd Co-Principal Investigator ..............................24 3.1.2 ENVIRON and Alpine Geophysic s 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/Sys tems Administration .......................................................29 4.1.1 ENVIRON Modeling Center Hardware .............................................................29 4.1.2 Alpine Geophysics M odeling Center Hardware ................................................294.1.3 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 Treat ment 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 Evalua tion 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 nonattainmen t counties in the US designated by EPA in April 2004. .............................................................................................5 Figure 1-2. PM 2.5 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 Qua lity Modeling Project Organizational Chart ............22 Figure 5-1. Flow Diagram of Major SMOKE Processing Steps ..............................................33 Figure 5-2. Diagram of ASIP Modeli ng Team Approach to SMOKE Modeling ....................36Figure 7-1. Schematic describing the addi tion of new SOA processes (bold italic) within the existing CMAQ SOA module (regular f ont) 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 Manage ment task deliverables and due dates ......................20

Table 2-3. ASIP Task 2 2002/2009 Emissions Modeling task deliverab les 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 Mode ling 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 ma intained by the Emissions and Air Quality Modeling Team ...................................................................................27

Table 7-1. Parameters use in enha nced SOA module (see Figure 7-1) ..................................43 Table 7-2. Comparison of fractiona l bias performance metric for Organic Carbon (OC) using th e standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement ..............................................44Quality Assurance Project Plan (Draft) Page 1 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

1.0 INTRODUCTION

This Quality Assurance Project Plan (QAPP) de scribes the quality mana gement functions and activities performed by th e Association for Southeastern Inte grated Planning (ASIP) to address emissions and air quality modeling associat ed with attaining the 8-hour ozone and PM 2.5 standards in states in the southeastern United States. The ASIP 8-hour ozone and PM 2.5 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 Corporati on (Prime Contractor), Alpine Geophysics, LLC (Subcontractor) and the Univers ity of California at Riverside College of Engineering – Center for Environmenta l Research and Technology (UCR/CE-CERT; Subcontractor). The ASIP regional 8-hour ozone and PM 2.5 modeling will be conducted by ENVIRON (Prime Contractor) and Alpine Geophysics (Subcontractor) under contract to

SESARM.

1.1 SESARM Organization

Southeastern States Air Res ource Managers, Inc., commonly known as SESARM, is a Georgia corporation organized and operated under the pr ovisions 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, Su bchapter 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. It s tax-exempt status was most recently reconfirmed in correspondence from the United St ates Internal Revenue Service dated December 12, 2001.

SESARM directly represents the eight southeastern state air po llution control agencies located within Region 4 of the United States Environm ental Protection Agency (EPA). The member states are Alabama, Florida, Georgia, Kentucky , Mississippi, North Ca rolina, 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 regiona l air pollution goals, conduct and facilitate research and training necessary to m eet its purposes, evalua te air quality issues,

recommend actions to resolve ai r quality problems, and develop steps to accomplish air quality improvements.

When EPA promulgated regional haze regulations on July 1, 1999, it es tablished requirements that states and tribes submit implementation plans to demonstrate reasonable progress towards the ultimate visibility goals of the rule. The fi rst demonstration of reasonable progress is to beQuality Assurance Project Plan (Draft) Page 2 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

made for the year 2018 in state implementati on 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. S outheastern States Air Re source Managers Inc. (SESARM) accepted responsibility fo r 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 pr ogram beyond their member boundaries and invited Virginia and West Virginia to join the gr oup. A memorandum of agreement was arranged among the agencies and executed on August 22, 2001. The effort was na med the Visibility Improvement State and Tribal A ssociation of the Sout heast (VISTAS). Since that time, the agencies have worked together to organize their efforts, develop work ta sk lists and schedules, procure professional services, and support administrative operations. By laws were developed and agreed upon by the participa ting members. An organiza tional structure was created including an oversight committee, an operations committee, and various wo rk groups. This early planning has served the project well a nd much progress has been made.

1.2 ASIP Project Background

On December 17, 2004, EPA made fine particle (PM 2.5 ) nonattainment determinations for at least one area in seven of the states participa ting in the VISTAS regional haze project. They are Alabama, Georgia, North Carolin a, Kentucky, Tennessee, Virgin ia, and West Virginia. In addition, South Carolina has one three-county area that was desi gnated as unclassifiable in the

same action. EPA’s Clean Air Interstate Ru le (CAIR) modeling i ndicated 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 PM 2.5 compliance date is April 2010 unless a state demonstrates th at more time is necessary in which case up to five additional years may be granted. The nona ttainment designations triggered the requirement for development of stat e implementation plans (SIPs) that will be due in April 2008. The draft guidance from EPA i ndicates that a significa nt requirement of PM 2.5 SIPs will be attainment demons trations using, at le ast in part, modeling analyses to define effective emissions control strategies and c onfirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling year for the PM 2.5 attainment demonstration and also is an interim analysis ye ar 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 ar eas in the Southeast ar e Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tenness ee, 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 modeli ng 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.Quality Assurance Project Plan (Draft) Page 3 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

The states participating in the VISTAS project (the SESARM EPA Region 4 states plus Virginia and West Virginia from Region 3) have conclude d that a collaborative process will be the most efficient approach for the coll ective states to develop inform ation upon which to base the PM 2.5 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 beco me 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 h aze project tasks.

The name of this collaborative effort is the Association for Southeaste rn Integrated Planning (ASIP). SESARM was awarded a grant from EPA on February 8, 2005 to conduct what was originally called the fine partic le 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 desc ribes 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 (ENVI RON, 2004a,b). The VISTAS Mode ling Protocol and QAPP formed

the basis for much of the content in this AS IP Emissions and Air Quality Modeling QAPP.

This QAPP was prepared in accordance with th e 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 doc uments 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 ap plications. In order to utilize model outputs for these purposes, it must be established that each model is scientif ically sound, robust, and defensible. This is accomplished by following a pr oject planning process that incorporates the following elements as described in th e EPA guidance document for modeling:

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

• Peer reviewed theory and equations;

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

• Documentation of any change s from original plans;

• Clear documentation of assumptions, theory, and parameterization that is detailed enough so others can under stand the model output;

• Input data and parameters that are accura te 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 en courage a continuous improvement process that will result in clearly defined data qual ity objectives, documentation, procedures, andQuality Assurance Project Plan (Draft) Page 4 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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 displa y the 8-hour ozone and PM 2.5 nonattainment areas designed by EPA in April and December 2004, respectively. Of th e 10 States in the VISTAS region, 8 include counties that have been designed as nonattainme nt 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 co unties that are designated as nonattainment for

PM 2.5 (Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, an d West Virginia). Of the 10 VISTAS states only Florida and Mi ssissippi do not have any 8-hour ozone and PM 2.5 nonattainment areas, South Carolina includes 8-hour ozone nonattainment counties (near Charlotte) but no PM 2.5 nonattainment areas.

The states need to submit the 8-hour ozone Stat e Implementation Plans (SIPs) to EPA by June 2007; the PM 2.5 SIPs are due by April 2008. Some of th e states involved in the ASIP ozone/PM modeling have two-year legislative review proce sses. Thus, the definition of the SIP control plans is needed in early 2006. Consequently, th e ASIP regional ozone and PM modeling has an aggressive schedule.Quality Assurance Project Plan (Draft) Page 5 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Figure 1-1. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004.Quality Assurance Project Plan (Draft) Page 6 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Figure 1-2. PM 2.5 nonattainment counties designed by EPA in December 2004.Quality Assurance Project Plan (Draft) Page 7 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

2.0 PROJECT DESCRIPTION

2.1 Background

The Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality Modeling Team is operating regional scale, thr ee-dimensional air quality models for ozone and fine particulate matter (PM 2.5 ) 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 an d models with regional transport models. The general services provi ded by the ASIP Emissi ons and Air Quality Modeling Team include, but are not limited to:

• Emissions processing and modeling;

• Air quality modeling simulations;

• Analysis, display, and reporti ng 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 pr imary and secondary fine part icles and is simulated using “one-atmosphere” regional photochemical grid m odels. Such models also simulate regional

ozone and fine PM so can also be used to address the ASIP 8-hour ozone and PM 2.5 issues. VISTAS initiated their regi onal haze modeling in 2003 usi ng a two-phase approach.

2.2 VISTAS Two-Phased Approach

The VISTAS Emissions and Air Quality Modeling ac tivities 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 id entify the optimal model configuration(s) for simulating regional haze, ozone and fine PM in th e southeastern US. Phase II, initiated in 2004, consists of operating the emissions and air qual ity 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 PM 2.5 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 determ ine the optimal modeling configuration for use in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a comprehensive

literature review of recent rele vant visibility studies usi ng various photochemical/aerosol modeling platforms in order to assess and identif y 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 el ements of Phase I included:Quality Assurance Project Plan (Draft) Page 8 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

3⁄4 Review all relevant air quality model simula tions that have been completed related to regional haze and PM 2.5 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 regiona l 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 eval uating one-atmosphere PM/ozone models (ENVIRON, 2003c);

3⁄4 Develop and implement a plan or Modeling Protocol for testing and evaluating alternative science configurat ions of the recommended Phas e I model(s) and document the results (ENVIRON, 2003a); and

3⁄4 Prepare a Task 6 Modeling Protocol prescribi ng the model set-up, data base development, performance testing, and control strategy eval uation procedures to be implemented in

VISTAS Phase II (ENVIRON, 2004a).

VISTAS formed three standing work groups to plan and direct the pr oject. These included: (a) the Technical Analysis (emissions and modeling) Wo rkgroup; (b) the Data (monitoring) Workgroup; and (c) the Planning Workgroup. Under Phase I, the VISTAS Technical Analysis Workgroup (TAWG) managed the comprehensive model conf iguration testing program aimed, as noted above, at evaluating the capabilities of current state-of-science regional emissions, prognostic meteorological and PM/visibility models. The re sultant 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 Phas e II Modeling Protocol (ENVIRON, 2004a).

For the meteorological component of the Phas e 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 (Ole rud, 2003a-f). The emissions modeling component of VISTAS Phase I was carried out by the research team of ENVIRON/Alpine/UCR w ith staff at Alpine Geophysics taking the lead role in setting up, tes ting, and applying the emissions modeling system. The air quality modeling compone nt was performed by the team at the ENVIRON/Alpine/UCR modeling cente rs. A dominant theme during Phase I was the exchange of modeling codes, databases, a nd evaluation software between th e three modeling centers as the air quality modeling was carried out.

2.2.2 VISTAS Phase II

The VISTAS Phase II modeling in cludes annual PM/regional haze simulations plus potentially additional shorter duration episode s. Detailed performance testing has been completed. The modeling system has been demonstrated using se veral inventory versions for the base yearQuality Assurance Project Plan (Draft) Page 9 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

(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 syst em 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 quali ty 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 us e 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 simulati ons and performance evaluations using the 2002 inventory for VISTAS and n on-VISTAS states, Ca nada and Mexico. Multiple iterations of the 2002 annual simula tion have been required to confirm the appropriateness of the model science configur ation(s) recommended by the Phase I work, to evaluate updates to the model and mode l 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 modele d air quality condition against which future year modeling runs will be compared to develop relative reduction factors for each pollutant species.

3⁄4 2018 Future Year Annual Runs. 2018 future year emission inventory simulations using the 2002 calendar year meteorol ogical conditions involving a base case inventory of typical EGU and fire emissions. Initi ally a 2018 On-the-Books (OTB) base case scenario, which consisted of all promulgate d 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 usi ng 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 wi ll be defined after th e foregoing simulations and analyses have been completed. Current ly, a 2018 CAIR plus BA RT control strategy is being developed.Quality Assurance Project Plan (Draft) Page 10 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Closely integrated with the a nnual meteorological, emissions a nd air quality modeling will be ongoing project management, techni cal review, and quality assura nce activities performed under the guidance of the VISTAS C ontracting 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 atte nd periodic meetings with the TAWG members throughout Phase II.

Complementing the data acqui sition, modeling input developm ent activities, and project management activities, four other Phase II activi ties will be performed, consistent with the VISTAS Quality Assurance Project Plan ( QAPP) (ENVIRON, Alpi ne and UCR, 2004).

2.2.3 ASIP Modeling

The ASIP regional ozone and PM modeling bu ilds 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 ev aluated, and that the data receiv ed have been documented. The same Gatekeeper QA approach will be used in the ASIP ozone and PM 2.5 modeling. Separate air quality, meteorological and emissions Gatekeeper s 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 archiv e all key data genera ted in the project.

3⁄4 Air Quality Data Gatekeeper . As necessary, obtain air quality data as appropriate for model input developm ent and model performance evaluation and assure that the quality of all air quality data obtained is consistent with the approved QAPP. Provide documentation of evaluati on 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 documenta tion 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 sup port modeling runs for 2002 and future years. Develop the chemical specia tion files and temporal and spatial allocation files necessary to convert a nnual inventories into hourly and dailyQuality Assurance Project Plan (Draft) Page 11 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

emissions modeling files, as appropri ate. Develop all emissions modeling files for non-VISTAS states to support m odeling 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

PM 2.5 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 elect ronic files to ASIP states , EPA, other contractors,

and stakeholders.

2.2.3.2 Emissions QA/QC

Emissions Quality Assurance (QA) and Quality Contro l (QC) are the single most critical steps in performing air quality modeling studies. B ecause emissions processing is tedious, time consuming and involves complex manipulation of ma ny different types of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected. In ASIP we will continue with the multistep emissions QA/QC approach applied in the VISTAS Phase I and II modeling. This includes the in itial emissions QA/QC by the Emissions Gatek eeper described above, as we ll 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 file s. This multistep process with three separate groups involved in the QA/QC of the emissions is intended to detect and co rrect errors prior to the air quality model simulations.

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

EMS and EPA Input Screenin g 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 th e data and identify potential emission input errors. Additi onally, 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 redi rect the SMOKE output to log files and review the log files for serious error message s. An archive of the log files will be maintained so that the error messages can be reviewed at a later date if necessary.

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

Once the CMAQ-ready emission inputs have b een prepared, we will perform additional emissions QA/QC as follows:Quality Assurance Project Plan (Draft) Page 12 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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

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

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

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

the domain as a function of time. The objective of this step is to identify particular days for which emissions appear to be inconsiste nt with other days fo r no reason (e.g., not a weekend) and compare 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 im mediately identify a problem in a control strategy. For example, if a state’s SO 2 control strategy is bei ng analyzed and there are changes in emissions for ot her pollutants or for SO 2 outside of the state under study problems in emissions processing can be id entified 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 pe rformed 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.Quality Assurance Project Plan (Draft) Page 13 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

3⁄4 Evaluated upper-air MM5 meteorological es timates by comparing them to upper-air observations and satellite images.

3⁄4 Compared the VISTAS 2002 MM5 simulati on 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 m odeling input and output data include the following:

3⁄4 Verification that correct configuration and science options are us ed 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 i nput 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 cr itical model input data.

The most critical element for ASIP CMAQ simu lations is the QA/QC of the emissions input files, which is discussed above. The major QA issu e 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 us ed 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 co rrect inputs and science options are used. We also employ a redundant naming system so that the names of ke y science options or i nputs 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 direct ory structure that were used in performing a model simulation.

We will also perform a post-processing QA of th e CMAQ output files similar to that described for the emissions processing. We will generate anim ated 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 ca n 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.Quality Assurance Project Plan (Draft) Page 14 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

2.2.3.5 Overview of Data Flow and Quality Assurance Process

Figure 2-1 displays an overview of the data fl ow and quality assurance process in the ASIP Emissions and Air Quality Mode ling study. The ASIP Modeling T eam receives different types

of data from various contract ors and other sources that have performed their own Quality Assurance (QA) and Quality Control (QC). Wh enever data are receive d by the Modeling Team, it is first subjected to a QA check by a Gatekeep er who assesses the accur acy 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 Gate keeper 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 pe rforms 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 a ssure 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 mode ling 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 st udy. 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 ma ny different tools and analysis as possible in order to fully understand the accuracy and reliabili ty 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 genera ted using gnuplot and the software generates the following:

• Tabular statistical measures;

• Time Series Plots; and

• Scatter Plots by allsite_allday, al lday_onesite and allsite_oneday.

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

runs under Linux and is based on Fortran an d NCAR Graphics. It was originally developed for evaluating ozone models and ha s been extended to treat PM species as well. In addition to calculating similar statis tics, 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 anal ysis of peak concentr ations and includes a Flying Data Grabber (FDB) for comparing modeling results with aircraft data.Quality Assurance Project Plan (Draft) Page 15 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

ENVIRON Analysis Tools : ENVIRON has developed speci alized 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 particul ate and regional haze modeling guidance that model pe rformance 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 de monstrating their descriptive and complimentary nature.

The issue of model performance goals for PM speci es is an area of ongoing research and debate. For ozone modeling, EPA has estab lished performance goals for 1- hour ozone normalized mean bias and gross error of ≤± 15% and ≤ 35%, respectively (EPA, 1991). EP A’s draft fine particulate modeling guidance notes that pe rformance goals for ozone shoul d 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 tec hniques for some species likely exceed the more stringent perfor mance 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% (SO 4 ) to ± 50% (EC) (Solomon et al., 2004).

In the VISTAS/ASIP 2002 CMAQ Ba se Case modeling, we have adopted three levels of model performance goals for bias and gross error as liste d in Table 2-1 that are used to help evaluate model performance. Note that we are not sugge sting that these performance goals be generally adopted or that they are the most appropriate goal s 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. A ccordingly, we are also using performance goals that are a continuous function of average observed concentr ations proposed by Dr. James Boylan at the Georgia Department of Natural Resour ces that have the following features:

• Asymptotically approaching propos ed performance goals or crit eria when the mean of the observed concentrations are greater than 2.5 ug/m 3

.

• Approaching 200% error and ± 200% bias when the mean of the observed concentrations are extremely small.Quality Assurance Project Plan (Draft) Page 16 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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 th e mean observed concentration approaches zero the bias performan ce 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 performa nce 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) Page 17 of 57ASIP Emissions and Air Quality ModelingRevision 0ENVIRON International CorporationNovember 28, 2005Figure 2-1a.Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.Quality Assurance Project Plan (Draft) Page 18 of 57ASIP Emissions and Air Quality ModelingRevision 0ENVIRON International CorporationNovember 28, 2005Figure 2-1b.Concluded.Data flow and quality assurance steps inthe ASIP Emissions and Air Quality Modeling.Quality Assurance Project Plan (Draft) Page 19 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

2.3 ASIP Modeling Protocol

As recommended in EPA’s draft PM 2.5 modeling guidance (EPA, 2001) and final 8-hour ozone modeling guidance (EPA, 2005), the ASIP modeling efforts will be initiat ed by developing a Modeling Protocol that describes th e procedures to be used to c onduct, quality assure and quality control (QA/QC), evaluate and use the modelin g results (Morris and St ella, 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 th e 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 Projec t Plan (QAPP) and M odeling Protocol.

The management structure for the ASIP Emissions and Air Quality Modeling is similar to what was used in VISTAS with Ralph Morris of E NVIRON serving as Proj ect Manager and Ralph Morris and Gregory Stella of Alpine Geophysics se rving 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 contract s between SESARM and ENVIRON and

subcontracts between E NVIRON and Alpine.

• Develop and refine the Scope of Work a nd conduct contract discussions with the ASIP modeling team and the ASIP t echnical and project representatives.

• Preparation of monthly reports, invoici ng and payments to subcontractors.

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

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

• Develop the ASIP Modeling Protocol.Quality Assurance Project Plan (Draft) Page 20 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Table 2-2 summarizes the deliverables and expect ed delivery dates unde r Task 1 of the ASIP regional ozone and PM 2.5 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 Pr oject 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 Invoi ces 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 syst em will be used to generate 2002 Typical 12

km on-road mobile sources emissions usi ng 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 gene rated 2002 Typical Base F 36/12 km emissions for the other source categories (e.g., area, point, non-roa d, etc.). Under ASIP this task will provide supplemental 2002 daily MOBILE m odeling for the 12-km grid. The 2002 12 km Typical daily mobile source emissions will be merged with the other source categor ies to generate 2002 Typical 12 km emissions inputs ba sed on daily mobile source modelin g 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 fo r 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 othe r 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-rea dy emissions w/daily mobile

sources 12/15/05Quality Assurance Project Plan (Draft) Page 21 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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 usi ng 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 m odeling results to the observed PM concentrations. Extractions are made at the ozone and PM monitoring sites, wh ere values at the monitoring site are extracted from the 36 km results and a 3 x 3 grid cell aver age concentration centered on the monitoring site is extracted for the 12 km modeling results. Th e 12 km modeling results will be processed to generate boundary condition inputs for the Alab ama/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 resu lts of the Task 2 emissions and Task 3 CMAQ air quality modeling, archive and dist ribute the modeling results.

The SMOKE emissions and CMAQ air quality mode ling results will be an alyzed. 8-hour ozone projections and annual PM 2.5 projections will be made at all m onitoring 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 pr esented 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 PM 2.5 projections 1/31/06

3 Archive disks of ASIP 2002/ 2009 modeling results 2/10/06Quality Assurance Project Plan (Draft) Page 22 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

3.0 PROJECT MANAGEMENT

3.1 Project Organization

The ASIP ozone and PM modeling project is c onducted by ENVIRON Inte rnational 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 AS IP 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 de velop 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 th oroughly reviewed and approved by the PM

and Co-PIs and SESARM and the ASIP States. Fi gure 3-1 presents the organizational chart that shows the lines of responsibility and information fl ow for activities under this project. Table 3-1 lists the project responsib ilities 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

Technical Contact:

James Boylan, Georgia ASIP

Contracting Officer:

Patricia Brewer, ASIP

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.Quality Assurance Project Plan (Draft) Page 23 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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.netQuality Assurance Project Plan (Draft) Page 24 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

3.1.1 ENVIRON Project Manager a nd Co-Principal Investigator

Mr. Ralph Morris of ENVIRON is the Project Manager (PM) and Co -Principal Investigator (CoPI) for the Association for South eastern Integrated Planning (A SIP) Emissions and Air Quality Modeling Team. He provides overall direction to the project and es tablishes a policy relationship with the sponsor, ensuring that a ll issues of importance to the ASIP group are addressed. The PM is responsible for the overa ll conduct of the project, experimental design, reporting of the results, and interacting with th e client, consultants, a nd project staff. The specific responsibilities of the PM include, but are not necess arily limited to, the following:

• Directs and coordinates the activities of th e project team and co mputer facilities to conduct the test program

• Ensures that this QAPP and the Modeling Prot ocol are followed during the course of the project

• Guides the overall approach fo r performing modeling evaluations

• Keeps current on project status and delivers progress reports

• Conducts initial modeling or analysis of expe riments to determine if inconsistencies or unexpected results suggest possible e xperimental or measurement problems

• Evaluates overall data quality, characterizat ion results, and overall system performance with regard to meeti ng project objectives

• Reviews and delivers modeli ng 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 a ny 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 protoc ols 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 Geophysi cs Co-Principal Investigators

The two Co-Principal Investigat ors 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 programQuality Assurance Project Plan (Draft) Page 25 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

• Ensure that this QAPP and Modeling Protoc ol 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 ev aluations following th e 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 experi ments to determine if inconsistencies or unexpected results suggest possible e xperimental or measurement problems

• Evaluate overall data quality, characteriza tion results, and overall system performance with regard to meeti ng project objectives

• Review and deliver data and sections for integration into mode ling and assessment

reports

• With the Project Manager, interact with exte rnal 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 ce nter responding to any issues raised in assessment reports and initiate corrective acti ons 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 protoc ols and report as directed

• Analyze modeling data and provide assessment reports

3.1.3 ASIP Contracting Officer

The ASIP Contracting Officer (P atricia Brewer) serves as the primary contact between the Emissions and Air Quality Modeling Team a nd ASIP and performs the following functions:

• Provides day-to-day oversigh t of the ASIP Emissions an d Air Quality Modeling Team

activities

• Works with the Project Manager, Co-Princip al 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 participan ts are appropriately re presented as project priorities are developed or m odified due to external input

• Assists in organizing and conducting meeti ngs, 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:Quality Assurance Project Plan (Draft) Page 26 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

• Provides day-to-day oversight of ASIP Ph ase 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 bei ng carried out in a tech nically correct fashion following the QAPP and Modeling Protocol

• Prepares and gives presentati ons to VISTAS groups on th e activities of the Modeling

team.

• Reviews work products.

3.1.5 ASIP States

The primary purpose of the ASIP Emissions and Air Quality M odeling work is to develop the regional modeling component for 8-hour ozone and PM 2.5 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, Virgin ia, and West Virginia have PM 2.5 non-attainment areas and Alabama, Georgia, Kentuc ky, North Carolina, South Carolin a, 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 identifi cation, evaluation, and application of air quality modeli ng tools to quantify the effects of emission management options upon air quality in 8-hour ozone and PM 2.5 nonattainment areas in th e southeastern United States. Specific activities of the ASIP States include:

• Oversees the activities of th e ASIP Emissions and Air Qu ality Modeling Team through the Contracting Officer, conference calls, and periodic in-person meetings and workshops

• Provides the Contracting Offi cer, technical Contact, Project Manager and Co-Principal Investigators input on the research plans and th eir ability to meet th e needs of the various

stakeholders relevant to the ove rall objectives of the project

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

review

• Makes the Project Manager and Co-Principal Inves tigators aware of other projects that may be of relevan ce 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 pr oject personnel lays the founda tion for successful project implementation. It is not intende d to provide detailed and specif ic knowledge of all componentsQuality Assurance Project Plan (Draft) Page 27 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

of the project, but it promotes an understandi ng of the nature of the overall project goals, ensuring that all personnel unders tand the part they are to play in the project. The members of this project team include resi dent experts in emissions, mete orological and air quality model development and operations.

All project personnel must have extensive experien ce 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 info rmation on all functions of the modeling systems, from experimental design, objectives, and data validation and reporting. Where applicable, project personnel must be familia r with the SOPs applicable to their areas of responsibility. In addition, if ma jor revisions or enhancements are made to the QAPP and/or SOPs, all affected individuals must re view those revisions at that time.

3.4 Communications Plan

The ASIP Emissions and Air Qu ality Modeling Team members, other ASIP Contractors and ASIP representatives are linked by e-mail corr espondence, 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 with in this project. The ASIP Modeling Team will use the same four lis tservs 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 part icipants including Modeling Team, VISTAS

TAWG and Stakeholders Vistas-modeling@cert.ucr.edu Contacts M odeling 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 meteor ology 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 proj ect status, and modify wo rk plans as necessary. Unscheduled meetings or conference calls are also held concerning specific issues as the needs arise. In addition, periodic project meetings a nd conference calls are held. In these meetings

detailed technical information is exchanged, project status is disc ussed, 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 m onthly basis. These reports summarize project progress, results to da te, problems encountered and necessar y action items, and plans for theQuality Assurance Project Plan (Draft) Page 28 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

upcoming reporting period.

All modeling results, quality assu rance reports, and related docu ments 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 personne l engaging in project activities. The system includes retention of the document with original signed page(s) in a limite d access storage area, a unique numbering system for all documents (typi cally identified by revision number and/or date), and electronic storage of documents by da te so that the latest versions is clearly

identifiable. Such documents are controlle d documents, and can be revised only by the personnel listed within each document or the proj ect 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.Quality Assurance Project Plan (Draft) Page 29 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

4.0 MEASUREMENT AND DATA ACQUISITION

4.1 Computer Hardware/Systems Administration

All emissions and air quality modeling is performe d on computer equipment located at either the ENVIRON or Alpine m odeling 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 configur ation 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 co mputing technology including both OMP (shared memory) and MPI (cluster) multi-processing capabilit y. 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, Di gital Equipment Corporation (DEC) and Silicon Graphics Incorporated (SGI). ENVIRON also us es 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 wo rkstations 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 c lients. 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 c lient 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 th at is physically disconnected from the power supply when not doing an activ e transfer. This way a cata strophic power fa ilure will not compromise the ability for Alpine Geophysics to deliver.

To efficiently share network resources, 100-Base T fast ethernet switches interconnect the computers within each office. Additionally, the Arvada based computers have both the Parallel Virtual Machine (PVM) software package for co mputation on multiple nodes on the network and Open MP capabilities for shared memory multip rocessing within each computer. For the majority of model simulations, using a job level parallelizati on, 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.Quality Assurance Project Plan (Draft) Page 30 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

A key feature of the AG computer network is th e ability for AG to add computational resources quickly and cost effectively. Alpine scie ntists have been using Linux and Unix for meteorological and air quality mo deling for over 16 years. Th is knowledge base, along with existing relationships with hardware vendors, so ftware vendors, and system specialists, enable AG to meet the computational requir ements of the VISTAS modeling study.

4.1.3 Backup Procedures

Different back-up procedures are ap plied to three different sets of computers that include (1) file servers; (2) user desktop Windows PCs; and (3) RAID5 disks storage syst ems. 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 co mputer. In addition, incremental backups are peri odically 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 RAID 5 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. Th erefore, only critical model input data are backed-up. One 2 TB RAID5 disk system is rese rved for back up of critical project data. Back-ups are performed weekly. In addition, critical project i nput data is archived to IDE drives, firewire drives or DLT tape.

All incoming electronic mail receives virus sc anning using commercial software (Norton or McAffee Anti-Virus), which is updated routin ely. Windows PC receives automated, weekly

scans for viruses.

Three types of software are used in the projec t: (1) commercial software for word processing, project management, communications, and commer cial software compilers; (2) compilers and computer operating systems that are either pub licly available through the GNU Public License (GPL) distribution channels or commercially av ailable; and (3) custom software used in computer simulation modeling and data analysis. All commercial software used in the ASIP modeling is purchased with licen ses and is installed and tested as specified by the publisher. Commercial compilers include Portland Gr oup compilers, and Intel and IBM FORTRAN compilers. All GPL software is obtained from o fficial 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++.Quality Assurance Project Plan (Draft) Page 31 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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 subj ected 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 fu rther QA/QC provided to the Modeli ng 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 deta il in Section 2.2.3.1:

Air Quality Gatekeeper : The Air Quality Gatekeeper downl oads 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 Mete orological 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), subj ects them to a comprehensive QA/QC and rejects data that doesn’t pa ss the QA/QC and reformats th e 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 upda tes reports and files

on the website.Quality Assurance Project Plan (Draft) Page 32 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

5.0 EMISSIONS MODELING

The project objective is to inte grate regional-scale emissions a nd transport modeling efforts so

that non-attainment areas for 8-hour ozone or PM 2.5 can be evaluated unde r different emissions and meteorological transport scen arios. Meeting this objective means that modeling outputs must be of known quality. This section addresse s emissions modeling and Section 6.0 addresses meteorological modeling as they are applied to this project.

The ASIP Modeling Team uses the Sparse Ma trix Operator Kernel Emissions (SMOKE) emissions model to model the anthropogenic a nd biogenic gas and particulate matter (PM) emissions needed for regional ozone and fine particulate matter (PM 2.5 ) modeling. In September 2005 a new versions of SMOKE was released (V ersion 2.2). However, ASIP is using the SMOKE setup developed by VISTAS and switch ing to the new vers ion of SMOKE would interfere with the ASIP schedule and potentially in troduce errors in the analysis. Thus, Version 2.1 of SMOKE will be used in the ASIP emissions modeling.

The purpose of SMOKE is to c onvert the resolution of the em ission inventory data to the resolution needed by an air quality model. Emissi on 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 perhap s model layer), and for each model species. Consequently, emissions processing for this proj ect involve transformati on of emission inventory data by temporal allocation, chemical speciati on, 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 or ganizes the emissions processing steps for anthropogenic emissions, with the final step in creating the model-read y emissions being the merge step. This example does not include all pr ocessing steps, which can be different for each source category in SMOKE, but does include the ma jor processing steps list ed above, except the layer assignment. Specifically, the inventory emi ssions are arranged as a vector of emissions,

with associated vectors that include characteris tics 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 ca ses, these matrices are independent from one another, and can therefore be generated in parall el. The processing approach ends with the merge step, which combines the inventory emissions v ector (now an hourly i nventory file) with the control, speciation, and gridding matri ces to create model-ready emissions.Quality Assurance Project Plan (Draft) Page 33 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Merge Import Inventory Temporal

Allocation

Speciation

Gridding Inventory

Vectors

Controls Hourly

Emissions

Speciation

Matrix

Gridding

Matrix

Control

Matrix Model-ready

Emissions

Program Shows input or output File Merge Import Inventory Temporal

Allocation

Speciation

Gridding Inventory

Vectors

Controls Hourly

Emissions

Speciation

Matrix

Gridding

Matrix

Control

Matrix Model-ready

Emissions

Program Shows input or output File

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 defi ned in the sections below.

5.1.1 Emissions Modeling QA

Emissions Modeling QA involves performing data quality checks, assuri ng 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. Scruti ny of the I/O using standard st atistical analyses can reveal problems in the data and/or the model setup. Usi ng a standard approach for analyzing emissions model I/O establishes reference poi nts 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 installa tion and configuration is sues, data accounting, and ensures that the modeling sy stems are producing results that are reasonable and reproducible. The first step in this process is to properly benc hmark the model and assuri ng that the installation is complete. Confirmation of configuration se ttings, compile options, and other system-related parameters must then occur and be documented pr ior to producing any m odel results. Archiving the model installation at set “freeze points” in th e 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 mode l results in the futu re and the ability to revert back to a previous configuration or insta llation after the model has been re vised 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 theQuality Assurance Project Plan (Draft) Page 34 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

modeling procedures with archives of the model installation and data files will ensure that the model installation and configura tion 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 th e data entering and exiting the SMOKE modeling process meet a predetermined qua lity 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 em issions inventory and SMOKE input files on the front end of the modeling process, and to the SMOKE output files at the back end. A gate keeper (or gatekeeping team) is responsible for performing a series of reasonableness checks on both the input and output data streams. Outside reviewers are emi ssions experts who are not part of the VISTAS Modeling Team (e.g. VISTAS state emission invent ory staff). They peri odically review the entire process—the emissions data, the mode ling, and the QA steps ta ken—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 processe s 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 inve ntory reports and analyses. Covering the entire realm of the modeling process, QA documentation will include records of model configuration, details about data files, simulation re cords, 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 th e ASIP Emissions QA framework requires a team consisting of: a data gatekeeper (s), a production modele r(s), a QA manager, outside reviewers, and a project manager (See Section 2.2.3 and Figur e 2-1 for data flow and QA overview). Each

team member contributes a di fferent critical perspectiv e 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 us ed in air quality modeling, th ey 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 th e necessary data elemen ts, spatial coverage, and temporal extent. Data formats will be confirmed using the SMOKE manual to check ASCII files and using PAVE to check binary ne tCDF files, such as the meteorology inputs.Quality Assurance Project Plan (Draft) Page 35 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Reasonableness checks consist of looking for glar ing 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 w ith 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 ar chives input and output data. Secondary Modelers organize the SMOKE QA reports into emissi ons 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. Th e 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 modele rs, 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 th e project report.

• The Project Manager is ultimately responsible for the qu ality of the final products of the modeling process. Providing technical assistan ce to the production m odelers and clarifying any uncertainties about model input, configur ation, 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 pro cedures, and thoroughly scrutinizes the QA summaries for problems to de termine whether the data are ready to be sent to the gate keepers and outside reviewers for final review be fore 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 a nd Technical Coordinators and States, and in this role communicates any issues rais ed during the emissions QA process.

Figure 5-2 presents the QA fr amework developed by the ASIP Modeling Team for SMOKE modeling. Identifying the specific pa rticipants in the ASIP Modeli ng 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 po sitioned 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 out side the emissions modeling team on a volunteer basis to conduct periodic reviews/audits of the data and modeling process.Quality Assurance Project Plan (Draft) Page 36 of 57ASIP Emissions and Air Quality ModelingRevision 0ENVIRON International CorporationNovember 28, 2005Figure 5-2.Diagram of ASIP Modeling Team Approach to SMOKE Modeling.VISTAS TAWGVISTASModeling TeamVISTASASIPASIPModeling TeamASIPCoordinatorsand StatesQuality Assurance Project Plan (Draft) Page 37 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

6.0 METEOROLOGICAL MODELING

The EPA Models-3/CMAQ system is the primary regional ozone and partic ulate matter (PM) air quality model being used in ASIP regional modeling. Meteorological inputs for CMAQ were generated by the VISTAS Meteorological Mode ling Contractor using the MM5 meteorological model. ASIP CMAQ modeling will use the use th e same MM5 fields as used by VISTAS. The MM5 model was applied for the entire year of 2002 on two grids: a c ontinental scale domain with 36 km grid spacing and a regional-scale do main 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 Genera tion Mesoscale Model (MM5), developed and maintained by the Pennsylvania State University and National Center for Atmos pheric Research (PSU/NCAR). MM5 is being used to develop hourly meteorol ogical fields on the 36 km and 12 km grids.

A detailed protocol for utilizing MM5 as part of VISTAS has been prep ared. 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 physi cal configuration, and a mode l application approach. The protocol also presents a plan for evaluating th e performance of the m odel in replicating the evolution of observed winds, temperature, humidity, and boundary layer morphology to the

extent that resources and data avai lability allow; this will serve as the primary approach to assess the reliability of the meteorological fields to ad equately 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 qual ity applications and has been used extensively in past local, state, regional, and national mo deling 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.Quality Assurance Project Plan (Draft) Page 38 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

7.0 CMAQ MODELING SYSTEM

EPA’s Models-3 CMAQ modeling system is the pr imary 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 deve loping the Models-3 Community Multiscale Air Quality (CMAQ) modeling system with the ov erarching aim of producing a ‘One-Atmosphere’ air quality modeling system capable of addressi ng ozone, particulate matte r (PM), visibility and acid deposition within a common platform (Denni s, et al., 1996; Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 2003). The original ju stification for the Models-3 development emerged from the challenges posed by the 1990 Cl ean Air Act Amendments and EPA’s desire to develop an advanced modeling framework for ‘ho listic’ 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 Qu ality 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 testi ng and evaluation of the CMAQ model performed by VISTAS. VISTAS is currently (through Se ptember 2005) using Version 4.4 of CMAQ that was publicly released in Octobe r 2004 whose secondary organic ae rosol (SOA) module has been enhanced to include missing processes (CMAQ V4.4 SOAmods). The enhancements to the CMAQ V4.4 SOA module are desc ribed in Section 7.3 belo w. In September 2005, EPA released a new Version 4.5 of CMAQ. VISTAS is currently testi ng and evaluating CMAQ Version 4.5 and depending on that outcome, bot h 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 Transpor t Model (CTM) and several pre-processors including the Meteorological-Chemistry Inte rface Processor (MCIP), initial and boundary conditions processors (ICON and BCON) and a phot olysis rates processor (JPROC). EPA is continuing to improve and develop new modules for the CMAQ model and typically provides a new release each year. In the past EPA has also provides patches for CMAQ as errors are discovered and corrected. More recently EP A has funded the Community Modeling and Analysis Systems (CMAS) cente r to support the coordination, upda te and distribution of the Models-3 system.

A number of features in CMAQ’s theoretical fo rmulation 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 th e PM size distribution using three log-normal modes (2 fine and 1 coarse). Transfer of mass between the aerosol and ga s 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 coupl ed to mass transfer between the aerosol and gas phases. For aqueous phase chemistry, th e RADM model is currently employed. This

scheme includes oxidation of SO 2 to sulfate by ozone, hydrogen peroxide, oxygen catalyzed byQuality Assurance Project Plan (Draft) Page 39 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

metals and radicals. The impact of clouds on the PM size distribution is tr eated empirically. For wet deposition processes, CMAQ uses the RADM/R PM approach. Partic le dry deposition is included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003

to be an reversible semi-volatile scheme wher eby VOCs can be converte d to condensable gases that can then form SOA and then evaporat e back into condensable gases depending on atmospheric conditions. ENVIRON has enhanced the CMAQ SOA module to include additional processes missing in the standard CMAQ ve rsion 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 thes e 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 In itial 2002 CMAQ run versio n 4.3 (released August 2003) was used, whereas for VISTAS Phase II CB4/SAPRC sensitivity tests and 2002 annual CMAQ runs Version 4.4beta (rel eased March 2004) and Version 4.4 (released October 2004), respectively, were used. ASIP and VISTAS ar e currently considering switching to the latest versions of CMAQ (Version 4.5) released in September 2005.

7.2 CMAQ Configuration for AS IP 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 accomplis hments in the VISTAS Phase I modeling was the identification and justification of the CMAQ science options re commended for VISTAS Phase II and ASIP modeling. We summarized th e 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 Prot ocols. For ASIP, the CMAQ model would be set up and exercised on the same nested 36/12 km grid domain us ed 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 woul d be used along with the sp atially 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 subs equent VISTAS modeling is sw itching 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 secondar y organic aerosols would be simulated by the SORGAM implementation in CMAQ with enhancem ents developed under VISTAS (SOAmods). Under the Phase I modeling, VISTAS evaluate d three photochemical mechanisms: CB4, CB4- 2002 and SAPRC99. CB4-2002 produced nearly identi cal results as CB4 but took much longer to run since it is only implemented in the slow er SMVGEAR chemistry solver, compared to CB4Quality Assurance Project Plan (Draft) Page 40 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

that is also implemented in the faster EBI chemistry solver. Thus, CB4-2002 was dropped from consideration in Phase II. The Phase I comp arisons 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 a nd periods. The Phase I testing only evaluated the mechanism’s base case performance, not their re sponse 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 SAP RC99 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 Seco ndary Organic Aerosols in CMAQ (SOAmods)

Initial simulations of the CMAQ V4.3 for the 20 02 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 th e 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 th e CMAQ SOA module to include several missing processes. The formulation and resulting mode l performance are discu ssed 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 de scribed in Binkowski and Roselle (2003).

SOA can be formed from aromatic VOCs and bi ogenic terpenes. The biogenic SOA precursors were modeled with the Biogenic Emissions In formation System – Version 3 (BEIS3) model (Pierce et al., 2002). BEIS3 generates th ree 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 = TERPQuality Assurance Project Plan (Draft) Page 41 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Here, ALD2, OLE, PAR and NR are the CB4 ch emical mechanism representations of the biogenic VOC emissions as high molecular we ight aldehydes, olefinic carbon bond, paraffin

carbon bond and non-reactive functional groups. In CMAQ, the TERPB spec ies is specified in the emissions inputs, along with its CB4 representation of AL D2, 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 equili brium. 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 da ta collected at the California Institute of Technology for severa l 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 S OA 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 ca nnot 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 al so suggests that some SOA formation may have acid catalyzed reactions (Claeys et al., 2004; Jang et al., 2005). Heterogeneous Reactions : Recent evidence suggests th at some SOA formation may occur during heterogeneous aqueous-phase chemical reactions (Yu et al., 2005).

A prototype module was added to CMAQ that acc ounted for the first three processes listed above. The last two processes were not includ ed in this work because there are not enough quantitative experimental data ye t to establish a parameterization. Modules were added to the CMAQ SOA module under the following constraints:

• The existing CMAQ SOA module for m onoterpenes would remain unchanged;

• The same CMAQ model inputs would be used; and

• The basic CMAQ model formulation would re main unchanged, modules would be added to account for polymerization and SOA from sesquiterpenes and isoprene.

Figure 7-1 displays how the prototype repres entation of new processes to represent SOA polymerization and SOA formation from sesquiterp enes and isoprene were added to the CMAQ SOA module using the existing CMAQ structure a nd inputs. The new components of the SOA

module are indicated in bold italic, whereas the existing CM AQ 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: em ission factors (EF), canopy escape efficienciesQuality Assurance Project Plan (Draft) Page 42 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

for gases (EEG) and aerosols (EEA) and SOA yiel ds (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 pr ototype 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 (E F2). Table 2 displays the range of EF1 and EF2 factors based on recent fi eld study data (Stroud et al., 2005). Using the midpoint of the range results in emission factor s of 0.7 for EF1 and 0.4 for EF2. EF1 is assigned

a value of 0.7 based on field obs ervations that indicate that th e BEIS3 terpene emission factors are likely overestimated due to a tendency of earlier measurements approaches to artificially

increase the emissions due to disturbance when l eaves were enclosed in the measurement system. As an initial approach for including sesquiterp ene emissions, we have assigned EF2 a value of 0.4 based on the ratio of the observed sesquiterp ene emission from the Duke Forest field study (Stroud et al, 2005) to the BEIS3 monoterpene emi ssion 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 sesquite rpenes 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 St roud et al. (2005). The fracti on 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 yi eld 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, w ith the exception of the already non-volatile SOC1 (polymerized SOA) and SOC2 (sesquite rpene) species, are as sumed to partially polymerize into non-volatile partic les that are stored in the SO C1 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.Quality Assurance Project Plan (Draft) Page 43 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Table 7-1. Parameters use in enhanced SOA module (s ee 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 se squiterpenes 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 Qualit y Control of the enhanced SOAmods module in the CMAQ model were conducted as follows.

QA/QC of SOAmods Coding : The SOAmods implemen tation was conducted at ENVIRON. Staff at the University of Ca lifornia at Riverside performed independent QA/QC of the SOAmods code implementati on and independent te sting 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 comment s 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 perfo rmance 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 statesQuality Assurance Project Plan (Draft) Page 44 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

using the standard CMAQ Version 4.4 (V4.4) and then CMAQ V4.4 with the SOAmods

enhancement. Whereas the standard CM AQ V4.4 underestimates OC across IMPROVE sites of from –76% (MRPO) to –102% (VIS TAS), with the SOAmods enhancement the

fractional biases centere d on zero and ranges from –14% to +8%. Similar results are seen

for OC fractional bias across the more ur ban 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 fo r Organic Carbon (OC) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement. July 2002

Fractional Bias IMPROVE OC STN OC

V4.4 SOAmods V4.4 SOAmods

Southeastern U.S. -102% -2% -105% -32%

Midwestern U.S. -76% +12% -67% -24%

Northeast U.S. -82% -14% -95% -44%

Central U.S. -98% +8% -81% -27%Quality Assurance Project Plan (Draft)Page 45 of 57ASIP Emissions and Air Quality ModelingRevision 0ENVIRON International CorporationNovember 28, 2005Figure 7-1.Schematic describing the addition ofnew SOA processes (bold italic) withinthe existing CMAQ SOA module (regularfont) to treat polymerization and SOA from sesquiterpenes and isoprene.



## Biogenic emissionsTERPISOPISPDY1P1PAROLEALD2TERPRXNA_TRP1G_TRP1A_TRP2G_TRP2ISPRXNSGTOT_ISPG_ISPA_ISPEF2 * ( EEG1 * Y2 + EEA1 )AORGBASOC2ASOC3*Y3*Y4OOO: 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 processesASOC1PolymerizationAll SOAs except SOC1 & SOC2FPOLYEF1



## Biogenic emissionsTERPISOPOVOCPAROLENRISPDY1P1PAROLEALD2SGTOT_TRP1TERPRXNSGTOT_TRP2ISPRXNSGTOT_ISPG_ISPA_ISPEF2 * ( EEG1 * Y2 + EEA1 )ASOC2ASOC3**OOOO: 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 processesASOC1PolymerizationAll SOAs except SOC1 & SOC2FPOLYEF1Quality Assurance Project Plan (Draft) Page 46 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

7.4 Testing and Evalua tion 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 forma tion 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 (fin e 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 diagnos tic variables to calculate PM 2.5 concentrations - Corrected bug in mode merging to reduce mode crossover

- Modified SO4 used in ISORROPIA call

- Corrected inc onsistency 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 cal culation 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 PU RB (% 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 inter polation times for resolved clouds (to time-step midpoint) and for subgrid clouds (to half hour)Quality Assurance Project Plan (Draft) Page 47 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

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 CM AQ 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 systema tic testing of the new CMAQ V4.5 code with the intention of ultimately transitioning the VISTAS/ASIP mode ling 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 w ith 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 vers us heterogeneous formation pathways).

The new CMAQ V4.5 AE4 SOAmods w/ MM5-MCIP 3.0 would then be co mpared against the current CMAQ V4.4 SOAmods for the full January and July 2002 periods with a detailed model performance evaluation.Quality Assurance Project Plan (Draft) Page 48 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

8.0 ASSESSMENTS AND OVERSIGHT

8.1 Assessment Responsibilities

The primary point of contact for ASIP Modeli ng project assessment is that of the Project Manager (PM). This PM is available to the ASIP Contracting Officer, T echnical 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 ex perimental results.

The assessments of overall project quality ar e provided from a number of different sources. Internal reviews of selected project compone nts are periodically performed by the Project Manager and Co-Principal Investigators. The resu lts of this and other routine (e.g., QC checks) and special reviews of project data qualit y are documented in the monthly reports.

The project team is committed to achieving and ma intaining the highest level of quality possible throughout the performance of this program. The modeling information generated will be both technically sound, and, where appropriate, lega lly 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 doc umentation, 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 subject ive 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 inte rnal and external peer review will assure that the methodology, experimental processes, conclusions and recommendations provided by this project ar e scientifically sound.

8.2 Assessment Types and Usage

Assessments of the quality of the products ge nerated on this project will be made by:

• Conducting internal performance reviews of the critical com ponents 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 ge nerated in this project.

• Independent peer reviews of thesis material s, 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 develo pment of experimental matrices and to review research progress and plans for future project tasks.Quality Assurance Project Plan (Draft) Page 49 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

8.3 Assessment Criteria

The project assessment criteria address the extent to which th e 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 providi ng the data and information n eeded for evaluating and improving models for predicting the effects of emissions on air quality. Therefore, the evaluation process includes assessments of model uncertainties, the ex tent to which the proposed actions or system improvements can reduce these uncertainties, and es timates of the certainty of this work in providing the data of th e type and quality needed. Criteria fo r assessing data ut ility 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 be ing applied to the highe st priority types of

measurements?

• Degree of characterization of experimental c onditions for modeling. For data to be useful for model evaluation, experiment al conditions must be suffic iently well characterized, so

that the data can be used for model eval uation without characterization uncertainties dominating the results.

• Accuracy, precision, and sensitivity of the me asurement data. The measurements in the concentration region of intere st should be sufficiently accu rate and precise to provide a meaningful test of model predictions.

• Procedures used to assure data quality, iden tify data not meeting qua lity objectives, and to minimize errors and other data quality problems.

• Degree to which the modeling appropriately in corporates the characterization results in the inputs, and the analysis of the modeli ng results appropriately take into account

characterization and measurement uncertainties and biases.

• Degree to which the experimental pro cedures, measurement methods, and data processing and analyses, and m odeling methods are documented.Quality Assurance Project Plan (Draft) Page 50 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

9.0 REFERENCES

Adelman, Z. 2004. Quality Assurance Protocol: WRAP RMC Emissions Modeling with SMOKE, Prepared for WRAP Modeling Forum, January.

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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 Summ ary Report for Episode 1: 13-22 July 1999.” Alpine Geophysics LLC, ENVIRON Intern ational Corporation and University of

California at Riverside. Ft. Wright, Kentucky (available at: http://pah.cert.ucr.edu/vistas/docs.shtml ). September 15.

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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 Pa t Brewer, VISTAS, June 13,

2003.

ANSI/ASQC Q9000-1-1994. 1994. Quality Manageme nt 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 Ambien t Observations: Testing the Community Multiscale Air Quality Modeling System (C MAQ) at Selected SOS 95 Ground Sites”,

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

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PM 2.5 Data. Report to VISTAS, August 28, 2002. http://www.vistas-sesarm.org

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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. “Comparis ons of 36 km and 12 km Gr id Resolutions”, PowerPoint presentation prepared for December 19, 2003 VISTAS modeling conference call. Available at: http:/ /pah.cert.ucr.edu/vi stas/ppt_files/12_36.ppt.Quality Assurance Project Plan (Draft) Page 51 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

Boylan, J. W. 2004. “Calculati ng Statistics: Concentration Re lated Performance Goals”, paper presented at the EPA PM Model Performance Workshop , Chapel Hill, NC. 11 February.

Brewer, P., S. Holman and J. Hornback. 200 3. “Vistas Analyses of Regional Haze in the

Southeastern United States.” Presente d at Annual Air and Waste Management Association Meeting and Exhi bition, 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) M odeling System”, EPA/600/R-99/030.

Claeys, M., B. Grahm, G. Vas, W. Wang, R. Vermeylen, V. Pas hynska, J. Cafmeyer, P. Guyon, M. O. Andreae, P. Artaxo and W. Maenhaut . 2004. Formation of Secondary Organic Aerosols through Photoxidation of Isoprene. Science , 303, 1173-1176. February 20.

Dudhia, J. 1993. A non-hydrostatic version of the Penn State/NCAR Mesoscale Model: validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev

. 121, pp.1493-1513.

Edgerton, E. S. 2003. S ources of PM 2.5 Carbon in the Southeast US , presentation at the MARAMA Science Meeting, Ba ltimore, MD, January 22, 2003. http://www.marama.org

.

Emery, C., E. Tai, and G. Yarwood. 2001. “Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Episode s”, report to the Texas Natural Resources Conservation Commission, prepared by ENVI RON, International Corp, Novato, CA.

ENVIRON. 2002. “User’s Guide Comprehensive Air Quality Model With Extensions (CAMx) Version 3.10.” ENVIRON International Corp oration, Novato, California (available at www.camx.com) April.

ENVIRON. 2003a. “VISTAS Emissions and Ai r Quality Modeling – Phase I Task 2 Report: Recommended Model Configurations an d Evaluation Methodology for Phase I Modeling.” Prepared by ENVIRON Interna tional Corporation, Al pine Geophysics, LLC and University of California at Riversid e. Novato, Californi a. (available at: http://pah.cert.ucr.edu/vistas/docs.shtml). August 4.

ENVIRON, Alpine Geophysics, UC Riverside and UC Davis. 2003b. “VISTAS Emissions and Air Quality Modeling – Phase I Task 4a/b Report: Review of Model Sensitivity Simulations and Recommendations of Initial CMAQ Model Configurations and Sensitivity Tests.” Revised Draft July 25.

ENVIRON, Alpine Geophysics , UC Riverside a nd UC Davis. 2003c. “VISTAS Emissions and Air Quality Modeling – Phase I Task 3 Repor t: Review and Assessment of Available Ambient Air Quality Data to Support Modeling and Modeling Performance Evaluation for the Three VISTAS Phase I Episodes.” Revised Draft July 22.Quality Assurance Project Plan (Draft) Page 52 of 57 ASIP Emissions and Air Quality Modeling Revision 0 ENVIRON International Corporation November 28, 2005

ENVIRON. 2003d. “Development of an Advan ced Photochemical Model for Particulate

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