# Attachment C

ASIP Modeling ProtocolJanuary 2006

Draft Report



### Modeling Protocol

For

Association for Southeastern I ntegrated Planning (ASIP)

Emissions and Air Qualit y Modeling to Address

8-Hour Ozone and PM 2.5 

### Nonattainment in the

Southeastern United States

Prepared by:

Ralph E. Morris Bonyoung Koo

Abby Hoats

Steven Lau

ENVIRON International Corporation 101 Rowland Way Novato, California 94945

Gregory Stella Dennis McNally

Cyndi Loomis

T.W. Tesche

Alpine Geophysics LLC 7341 Poppy Way Arvada, CO 80007

January 31, 2006

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700January 2006

i TABLE OF CONTENTS

Page

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

1.1 SESARM Organization ........................................................................................ 1-1 1.2 ASIP Project Background .................................................................................... 1-2 1.3 Purpose of the ASIP Modeling Protocol .............................................................. 1-3

1.4 Problem Definition............................................................................................... 1-3 1.5 Background .......................................................................................................... 1-6 1.5.1 VISTAS Two-Phased Approach ...................................................................... 1-6 1.6 ASIP Modeling Approach.................................................................................... 1-9 1.6.1 Data Gatekeepers ............................................................................................. 1-9 1.6.2 Emissions QA/QC .......................................................................................... 1-10 1.6.3 Meteorology QA/QC ..................................................................................... 1-11 1.6.4 Air Quality Modeling QA/QC ....................................................................... 1-12

1.6.5 Overview of Data Flow a nd Quality Assurance Process ............................... 1-12 1.7 Project Management .......................................................................................... 1-17 1.7.1 Project Organization ...................................................................................... 1-17

1.8 ASIP Project Manager and Co-P rincipal Investigator ....................................... 1-19 1.8.1 ENVIRON and Alpine Geophysics Co-Principal Investigators .................... 1-19 1.8.2 ASIP Contracting Officer .............................................................................. 1-20

1.8.3 ASIP Technical Contact ................................................................................. 1-20

1.8.4 ASIP States .................................................................................................... 1-21

1.9 Communications Plan ........................................................................................ 1-21

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

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

2.2.1 MM5 Overview ................................................................................................ 2-2 2.2.2 MM5 Configuration for ASIP and VISTAS Phase II Modeling ..................... 2-3 2.3 SMOKE Emissions Modeling System ................................................................. 2-4

2.3.1 SMOKE Overview ........................................................................................... 2-4 2.3.2 SMOKE Configuration for ASIP Modeling .................................................... 2-5 2.4 CMAQ Modeling System .......................................................................................... 2-6 2.4.1 CMAQ Overview ............................................................................................. 2-6 2.4.3 CMAQ Version 4.5 .......................................................................................... 2-7

2.4.4 SOAmods Enhancement .................................................................................. 2-9 2.4.5 CMAQ Configuration for ASIP Modeling .................................................... 2-12

2.5 Model Limitations .................................................................................................... 2-12

2.5.1 MM5 .............................................................................................................. 2-13

2.5.2 SMOKE.......................................................................................................... 2-13 2.5.3 CMAQ............................................................................................................ 2-14 2.6 Model Input Requirements ................................................................................ 2-14

2.6.1 MM5 .............................................................................................................. 2-14January 2006

ii 2.6.2 SMOKE........................................................................................................... 2-15 2.6.3CMAQ.............................................................................................................. 2-16

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

3.1 Overview of EPA Guidance................................................................................. 3-1 3.2 Episode Selection Methodology .......................................................................... 3-1

3.3 Selection of CY 2002 For ASIP and VISTAS ..................................................... 3-2

4.0 MODELING DOMAINS AND DATA AVAILABILITY .......................................... 4-1 4.1 Horizontal Modeling Domain .............................................................................. 4-1 4.2 Vertical Modeling Domain .................................................................................. 4-2 4.3 Data Availability .................................................................................................. 4-7

4.3.1 Emissions Data................................................................................................. 4-7 4.3.2 Air Quality ....................................................................................................... 4-7

4.3.3 Ozone Column Data ....................................................................................... 4-10 4.3.4 Meteorological Data ....................................................................................... 4-10 4.3.5 Initial and Boundary Conditions Data ........................................................... 4-10

5.0 MODEL INPUT PREPARATI ON PROCEDURES .................................................. 5-1 5.1 Meteorological Inputs to Emissi ons and Air Quality Models ............................. 5-1 5.1.1 MCIP Reformatting Methodology ................................................................... 5-2 5.1.2 Products of the Meteorological Input Development Process ........................... 5-3 5.2 Development of Emissions Model In puts and Resultant Inventories .................. 5-3 5.2.1 Emissions Modeling Methodology .................................................................. 5-4 5.2.2 Set-up of SMOKE Over the VISTAS Domain ................................................ 5-5 5.2.3 Development of Point Source Emissions ......................................................... 5-8 5.2.4 Development of Area and Non-Road Source Emissions ................................. 5-9 5.2.5 Development of On-Road Mobile Source Emissions ...................................... 5-9 5.2.6 Development of Biogenic Source Emissions ................................................. 5-10 5.2.7 Wildfires, Prescribed Burns, Wind Blown Dust and

Sea Salt Source Emissions ............................................................................. 5-11 5.2.8 Speciation and Reformatting of Emissions .................................................... 5-12 5.2.9 Development of Modeling Inventories .......................................................... 5-12 5.3 Model Configuration an d Modeling Approach.................................................. 5-14 5.3.1 CMAQ Science Configuration ....................................................................... 5-14 5.3.2 Spin-Up Initialization ..................................................................................... 5-16 5.3.3 Boundary Conditions ..................................................................................... 5-16 5.3.4 Photolysis Rates ............................................................................................. 5-16

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

7.0January 2006

iii MODEL PERFORMANCE EVALUATION .......................................................................... 7-1

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

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

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

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

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

7.5.1 Statistical Performance Metrics ....................................................................... 7-7 7.5.2 Graphical Representations ............................................................................... 7-9 7.5.3 MAPS/Flying Data Grabber Routines ........................................................... 7-10 7.5.4 Use of Multiple Evaluation Packages ............................................................ 7-11 7.5.5 Probing Tools and Allied Methods ................................................................ 7-11

7.6 Model Evaluation Procedures ............................................................................ 7-14

7.6.1 Assessment of Ground-Level Gas- Phase and Aerosol Species ..................... 7-14

7.6.2 Assessment of Aloft Gas-Ph ase and Aerosol Species ................................... 7-16

7.7 Performance Goals and Benchmarks ................................................................. 7-16 7-8. Diagnostic and Sensitivity Testing .................................................................... 7-19 7.8.1 Traditional Sensitivity Testing ....................................................................... 7-19 7.8.2 Diagnostic Tests ............................................................................................. 7-21 7.9 Corroborative and Weight of Ev idence Modeling Analyses ............................. 7-22

7.9.1 Corroborative Models .................................................................................... 7-22 7.9.2 Weight of Evidence Analyses ........................................................................ 7-23

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes ........................................................................................ 7-24

8.0 8.0 OZONE AND PM2.5 ATTAIN MENT DEMONSTRATION ............................. 8-1

8.1 8-Hour Ozone Attainment Demonstration ........................................................... 8-1

8.1.1 Modeled 8-Hour Ozone Attain ment Demonstration Test ................................ 8-1 8.1.2 Additional Analysis to Support the Attainment Demonstration ...................... 8-3

8.2 PM2.5 Attainment Demonstration ....................................................................... 8-3 8.2.1 Speciation of FRM PM 2.5 Mass Measurements ............................................... 8-4 8.2.2 Speciated Modeled Atta inment Test (SMAT) ................................................. 8-4 8.2.3 Additional Supporting Analysis ....................................................................... 8-6

9.0 DATA MANAGEMENT ............................................................................................... 9-1

9.1 Project Website .................................................................................................... 9-2

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

9.3.1 ENVIRON........................................................................................................ 9-2 9.3.2 Alpine Geophysics ........................................................................................... 9-3January 2006

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

10.1 Planned Documentation ..................................................................................... 10-1 10.2 Procedures for Updating Modeli ng Protocol and QA Plan ............................... 10-1

11.0 ASIP STATEMENT OF WORK AND SCHEDULE .............................................................. 11-1

11.1 Task 1: Project Management ............................................................................. 11-1 11.2 Task 2: 2002/2009 Emissions Modeling............................................................ 11-2 11.3 Task 3: 2002/2009 36/12 km Base F CMAQ Modeling ................................... 11-3 11.4 Task 4: Data Analysis and Data Management .................................................. 11-3

REFERENCES .................................................................................................................... ...... R-1

TABLES

Table 1-1. Model performance goals to help interpret modeling results ............................ 1-14 Table 1-2. ASIP Emissions and Air Quality Modeling project participants

and contacts. ....................................................................................................... 1-18

Table 1-3. VISTAS listservs that will be used to distribute information by

the ASIP Emissions and Air Quality Modeling Team and other ASIP participants. .............................................................................................. 1-22

Table 2-1. Parameters use in enhan ced SOA module (see Figure 2-1) .............................. 2-16 Table 2-2. 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 .......................................... 2-16 Table 2-3. MM5 Meteorological M odel Configuration for ASIP ....................................... 2-17 Table 2-4. SMOKE Emissions Model Configuration for ASIP .......................................... 2-18 Table 2-5. CMAQ Air Quality Mode l Configuration for ASIP .......................................... 2-19

Table 4-1. RPO Unified Grid Definition. .............................................................................. 4-1

Table 4-2. Grid Definiti ons For MM5 and CMAQ. .............................................................. 4-2 Table 4-3. Vertical Layer Definition For MM5 Simulati ons (Left Most Columns), And Approach For Reducing CM AQ Layers By Collapsing Multiple MM5 Layers (Right Columns)............................................................................. 4-6

Table 4-4. Overview of Ambien t Data Monitoring Networks. ............................................. 4-9

Table 5-1. MCIP V3.0 C onfiguration used In th e ASIP Modeling. ...................................... 5-3 Table 5-2. SMOKE Modeled Holidays ................................................................................. 5-5 Table 5-3. ASIP Model Configuration for the CMAQ Chemical Transport Model. .......... 5-15

Table 7-1. Ground-Level Ambient Data Monitoring Networks and

Stations Available in VISTAS 12 Km Domain for CY-2002. ........................... 7-25 Table 7-2. Aircraft Sampling Progra ms Performed in the VISTAS 12 Km

Domain for CY-2002. ........................................................................................ 7-26January 2006

v Table 7-3. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIRON, 2003b,d for details). ....................................................................... 7-26 Table 11-1. ASIP Task 1 Project Management task deliverables and due dates. ................. 11-2

Table 11-2. ASIP Task 2 2002/2009 Emi ssions Modeling task deliverables

and due dates. ..................................................................................................... 11-2

Table 11-3. ASIP Task 3 2002/2009 36/12/ km Base F CMAQ Modeling. ......................... 11-3 Table 11-4. ASIP Task 4 2002 CMAQ Modeling task deliverables and due dates. ............. 11-3

FIGURES

Figure 1-1. 8-hour ozone nonattainm ent counties in the US designated by EPA in April 2004. ......................................................................................... 1-4 Figure 1-2. PM 2.5 nonattainment counties designed by EPA in December 2004. .................. 1-5 Figure 1-3a. Data flow and quality assura nce steps in the ASIP Emissions and Air Quality Modeling ......................................................................................... 1-15 Figure 1-3b. Concluded. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling. ............................................................... 1-16 Figure 1-4. ASIP Emissions and Air Quality Modeling Project Organizational Chart. ......................................................................................... 1-17

Figure 2-1. Schematic describing the addition of new SOAmods processes (bold italic) within the exis ting CMAQ SOA module (regular font) to treat polymerization and SOA from sesquiterpenes and isoprene (see Table 2-1 for parameters). .......................................................................... 2-21

Figure 4-1. Nesting of 36-km CMAQ Grid in the MM5 36-km Grid. ................................... 4-3 Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid. ............................................. 4-4 Figure 4-3. Domain Definition fo r High Resolution 12-km Grid. ......................................... 4-5 Figure 5-1. EPA 36-km Na tional CMAQ Domain. ................................................................ 5-6January 2006

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1-1 1.0 INTRODUCTION

This report constitutes the first draft of the Air Quality Modeling Protocol for the

Association for Southeastern Inte grated Planning (ASIP) to a ddress the regional component of emissions and air quality m odeling of 8-hour ozone and PM 2.5 nonattainment in the Southeastern United States. The ASIP emissions and air quality modeling activities are being carried out by

the contractor team of ENVIRON Internatio nal Corporation and Alpine Geophysics, LLC. Southeastern States may use the regional emissions and air quality modeling from ASIP and also may conduct more refined ozone and PM 2.5 modeling of their own nonattainment areas. Previously, the ASIP team has prepared a Quality Assurance Project Plan (QAPP) that details the extensive quality assurance (QA) and quality control (QC) activities being performed as part of ASIP (Morris and Stella, 2005).

1.1 SESARM Organization

Southeastern States Air Resource Managers , Inc., commonly known as SESARM, is a Georgia corporation organized and operated under th e provisions of the Offi cial 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 pollution cont rol agencies located within Region 4 of the United States Environmental Protection Agency (EPA). The member states are Alabama, Florida, Georgi a, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee. The Board of Direct ors of SESARM consists of the air pollution control agency director from each state air pollution control agency.

SESARM’s Articles of Incorporation descri be its purposes as being to enhance communication and thus promote more effective air pollution management in the Southeast, improve the effectiveness of its members in meeting national and regional air pollution goals,

conduct and facilitate research a nd training necessary to meet it s purposes, evalua te air quality issues, recommend actions to resolve air quality problems, and develop steps to accomplish air quality improvements.

When EPA promulgated regional haze regulations on July 1, 1999, it established requirements that states and tribes submit im plementation plans to demonstrate reasonable progress towards the ultimate visibility goals of th e rule. The first demonstration of reasonable progress is to be made for the year 2018 in state implementation plans due December 2007. EPA also offered an optional approach that groups of states might collaborate in regional analyses of the haze problem. This option showed promise in allowing more cost-effective analyses and the southeastern states opted to follow this option. S outheastern States Air Re source Managers Inc.January 2006

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1-2 (SESARM) accepted responsibility fo r regional planning organization work tasks on behalf it its member agencies pursuant to this option.

The member agencies of SESA RM 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 administ rative 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 ma de fine particle (PM 2.5 ) nonattainment determinations for

at least one area in seve n of the states participating in the VISTAS regional haze project. They are Alabama, Georgia, North Ca rolina, 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 that more time is necessary in which case up to five additiona l years may be granted. The nonattainment designations triggered the requirement for development of stat e implementation plans (SIPs) that will be due in April 2008. The draft guidance fro m EPA indicates that a significant requirement

of PM 2.5 SIPs will be attainment demonstrations us ing, at least in part, modeling analyses to define effective emissions control strategies a nd confirm 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 areas in the Southeast are Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. EPA will require attainment of the 8-hour ozone standard in basic nonattainment areas by June 15, 2009 and in moderate nonattainment areas by June 15, 2010. This will require states with

basic 8-hour ozone nonattainment areas to mode l 2008 as the SIP modeling demonstration year while moderate nonattainment areas will require 2009 as the modeling year. Given that North Carolina and Virginia have two year SIP approva l processes, there is an immediate need to complete an analysis of ozone attainment using air quality modeling.

The states participating in the VISTAS proj ect (the SESARM EPA Region 4 states plus Virginia and West Virginia from Region 3) have concluded that a collaborative process will be the most efficient approach for the collective states to develop information upon which to baseJanuary 2006

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1-3 the PM 2.5 and 8-hour ozone attainment demonstrations . The local air regulatory agencies for Jefferson County, AL, Jefferson County, KY, Meck lenburg County, NC, Forsythe County, NC, Knox County, TN, and Shelby County, TN have al so become signatory parties to this

collaborative effort. SESARM will coordinate among participa ting agencies and oversee the performance of the inventory a nd modeling tasks in parallel wi th the VISTAS regional haze project tasks.

The name of this collaborative effort is the Association for Southeastern Integrated Planning (ASIP). SESARM was awarded a gr ant from EPA on February 8, 2005 to conduct what was originally called the fine particle SIP development support project but is now known as

ASIP.

1.3 Purpose of the ASIP Modeling Protocol

The ASIP Modeling Protocol sets forth th e procedures, data sources and modeling approach to be used in perfor ming the ASIP 8-hour ozone and PM 2.5 modeling for the Southeastern United States that will be the basis of the regional modeling component for the

Southeastern States 8-hour ozone and PM 2.5 State Implementation Plans (SIPs) due June 2007 and April 2008 respectively. The procedures will be reviewed by Stat es, Federal Agencies,

Stakeholders and others so that a full and complete understanding of the modeling approach will be understood by all. States, Agencies and St akeholders and invited to comment on the procedures outlined in the Modeling Protocol and will warranted the procedures will be refined

to address comments.

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 the 10 States in the VISTAS region, 8 include counties that have been designed as nonattainment of the 8-hour ozone standard (Alabama, Georgia, Kentucky, North Carolina, S outh Carolina, Tennessee, Virginia, and West Virginia). Similarly, seven of the VISTAS states have counties that are designated as

nonattainment for PM 2.5 (Alabama, Georgia, North Carolina , Kentucky, Tennessee, Virginia, and West Virginia). Of the 10 VIST AS states only Florida and Mi ssissippi do not ha ve any 8-hour

ozone and PM 2.5 nonattainment areas, South Carolina in cludes 8-hour ozone nonattainment counties (near Charlotte) but no PM 2.5 nonattainment areas.

The states need to submit th e 8-hour ozone State Implementa tion Plans (SIPs) to EPA by June 2007; the PM 2.5 SIPs are due by April 2008. Some of the states involved in the ASIP ozone/PM modeling have two-year le gislative review processes. Thus, the definition of the SIP control plans is needed in early 2006. Conse quently, the ASIP regional ozone and PM modeling has an aggressive schedule.January 2006

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1-4 Figure 1-1. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004.January 2006

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1-5 Figure 1-2. PM 2.5 nonattainment counties designed by EPA in December 2004.January 2006

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1-6 1.5 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.

1.5.1 VISTAS Two-Phased Approach

The VISTAS Emissions and Air Quality Mode ling activities are bei ng performed in two Phases. Phase I, which occurred primarily durin g 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.

1.5.1.1 VISTAS Phase I

The objective of VISTAS Phase I was to de termine the optimal m odeling configuration for use in the subsequent Phase II visibility assessment. Accordin gly, Phase I entailed a comprehensive literature review of recent relevant visibility studies using various photochemical/aerosol modeling platforms in order to assess and identify model configurations, data bases, and model testing methodologies th at were appropriate for use in conducting the VISTAS Phase I emissions and PM modeling assessm ent. Key elements of Phase I included:

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);January 2006

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1-7 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 following the procedures set forth in the Phase II Modeling Protocol (ENVIR ON, 2004a).

For the meteorological component of the Phase I modeling, SESARM contracted with Baron Advanced Meteorological Systems (BAM S) to apply the PSU/NCAR Mesoscale Model (MM5) in multiple configurations and to eval uate its performance against surface and aloft meteorological observations (Olerud, 2003a-d). 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 component 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.

1.5.1.2 VISTAS Phase II

The VISTAS Phase II modeli ng is performing annual PM/regional haze simulations for the 2002 calendar year. Detailed performance tes ting has been completed. The modeling system has been demonstrated using several inventory vers ions for the base year (2002) and future years (2009 and 2018). These results are posted to the VISTAS modeling website managed by University of California at Riverside ( http://pah.cert.ucr.edu/vistas/vistas2/ ). Beginning in winter 2005-2006, the modeling system will be exer cised with a variety of emissions control scenarios enabling VISTAS to assess the effects of future year emission control strategies on visibility and other air quality issues. The mo deling system will also allow VISTAS to trackJanuary 2006

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1-8 reasonable progress toward regi onal haze goals. More speci fically, the VISTAS Phase II program will focus on the use of the CMAQ mode ling 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. The Integrated Planning Model (IPM) was used to project future EGU emissions; all other inventories were forecasted to 2018 using growth and control factors documented by MACTEC (2005).

3⁄4 2009 Intermediate Future Year Annual Runs. Simulations for the 2009 future-year were performed to provide estimates of visi bility 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.

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.January 2006

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1-9 Complementing the data acquisi tion, 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).

1.6 ASIP Modeling Approach

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.

1.6.1 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 quali ty data as appropriate for model input development and model performa nce evaluation and assure that the quality of all air quality data obtained is cons istent with the approved QAPP. Provide documentation of evaluation and generate IC/BC inputs for CMAQ for all modeling

runs.

3⁄4 Meteorological Gatekeeper . As necessary, obtain meteorological data, as MM5 or MCIP files, as appropriate for annual 2002 m odeling runs and other episode periods and perform data quality checks as approved in the QAPP, together with appropriate documentation of model perfor mance evaluation activities.

3⁄4 Emissions Gatekeeper. Obtain emissions inventory data necessary to support annual 2002 and future year modeling and recommend s ources of emissions data to be used for Canada and Mexico. Assure quali ty of all emissions data rece ived is consistent with the approved QAPP, and develop all emissions m odeling files to support modeling runs for 2002 and future years. Develop the chemical speciation files and temporal and spatial allocation files necessary to convert annual inventories into hourly and daily emissions modeling files, as appropriate. Develop all emissions modeling files for non-VISTAS states to support modeling runs for future year base case and emissions strategies.

3⁄4 Data Management Gatekeeper : Maintain the ASIP results and other documents as requested by the ASIP group to support all 8-hour ozone and PM 2.5 modeling tasks. This includes, for example, the storage of mode l inputs and outputs for annual runs and the transfer (via USB/firewire porta ble disk or alternative media) of electronic files to ASIP states, EPA, other contra ctors, and stakeholders.January 2006

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1-10January 2006

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1-11 1.6.2 Emissions QA/QC

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical steps in performing air quality modeling studies. Because emissions proc essing is tedious, time consuming and involves complex manipulation of ma ny different types of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected. In 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/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the Air Quality Modeler of the processed model ready emission 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 been prepared, we will perform additional emissions QA/QC as follows:

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

Vertical Profile : For point sources the emissions tota l for each layer will be summed and plotted to show the vertical distribution of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis . The objective of this step is to identify possible errors in vertical distribution of emissions.January 2006

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1-12 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.

1.6.3 Meteorology QA/QC

The meteorological modeling contractor (B AMS) 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 assessm ent 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.

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 CMAQ meteorological input files were updated to MCIP version 3.0 in late 2005.January 2006

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1-13 1.6.4 Air Quality Modeling QA/QC

3⁄4 Key aspects of QA/QC for the ASIP CMAQ modeling input and ou tput data include the following:

3⁄4 Verification that correct configuration and science options are used in compiling and running each model in the CMAQ modeling system, where these include the MCIP, JPROC, ICON, BCOM and the CCTM.

3⁄4 Verification that correct input data se ts 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 si mulations is the QA/QC of the emissions input files, which is discussed above. The major QA issue specifi cally associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the names of 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 the CMAQ output files similar to that described for the emissions processing. We will gene rate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prep ared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions inputs can be discovered by viewing the animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This provides a summ ary that can be useful for qui ckly comparing various model

simulations.

1.6.5 Overview of Data Flow and Quality Assurance Process

Figure 1-3 displays an overview of the data flow and quality assurance process in the ASIP Emissions and Air Quality Modeling study. The ASIP Modeling Team receives different types of data from various contractors and other sources that have performed their own Quality Assurance (QA) and Quality Control (QC). Wh enever data are receive d by the Modeling Team,January 2006

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1-14 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 1-3a 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 de veloped and subjected to QA/QC, the CMAQ model is applied using Base Case emissions and the modeling results subjected to a model performance evaluation. The model performance evaluation (MPE) represents an extensive QA effort and is the most time consuming comp onent of the study. EPA has developed draft guidance for evaluating regional PM and haze m odels 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; 2005b) ozone modeling. The MPE/QA process is being performed under VISTAS since VISTAS and ASIP share the same modeling platform database and approach. The MPE/QA approach is using as many different tools a nd analysis as possible in order to fully understand the accuracy and relia bility of the model simulation. As seen in Figure 1-3b, 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.

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 minorJanuary 2006

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1-15 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 species is an area of ongoing research and debate. For ozone modeling, EPA has establ ished performance goals for 1-hour ozone normalized mean bias and gross error of ≤± 15% and ≤ 35%, respectively (EPA, 1991). EPA’s draft fine particulate modeling guidance notes that performance goals for ozone should be viewed as upper bounds of model performance, which PM models may not be able to always achieve and we should demand better model perf ormance 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 monito ring. In fact, the diffe rences in measurement techniques for some species likely exceed the more stringent performance goals, such as those for ozone. For example, recent comparisons of the PM species measurements using the IMPROVE and STN measurement technologies found differences of approximately ± 20% (SO 4 )

to ± 50% (EC) (Solomon et al., 2004).

In the VISTAS/ASIP 2002 CMAQ Base Case modeling, we have adopted three levels of model performance goals for bias and gross error as listed in Table 1-1 that are used to help evaluate model performance. Note that we ar e not suggesting that thes e performance goals be generally adopted or that they ar e the most appropriate goals to us e. Rather, we are just using them to frame and put the PM model performa nce into context and to facilitate model performance intercomparison across episodes, sp ecies, models and sensitivity tests.

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

• Asymptotically approaching 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.

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

Fractional
Bias Fractional

Error Comment

≤ ±15% ≤ 35% Ozone model performance goal for which PM model performance would be considered good.

≤ ±30% ≤ 50% A level of model performance that we would hope each PM species could meetJanuary 2006

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1-16 ≤ ±60% ≤ 75% At or above this level of performance indicates fundamental problems with the modeling system.December 2005F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc1-17Figure 1-3a.Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.December 2005F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc1-18Figure 1-3b.Concluded.Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.December 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-19 1.7 Project Management

1.7.1 Project Organization

The ASIP ozone and PM modeling projec t is conducted by ENVIRON International Corporation (ENVIRON) and Alpi ne 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 ENVIRO N would be the ASIP Emissions and Air Quality Modeling Project Mana ger (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 St ella 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. Co mmitments to research and proj ect activities, such as those described in this QAPP are made only after the activities are thoroughly reviewed and approved by the PM and Co-PIs and SESARM and the ASIP States. Figure 1-4 presents the organizational

chart that shows the lines of res ponsibility and information flow fo r activities under this project. Table 1-2 lists the projec t responsibilities for participants in the ASIP Emissions and Air Quality Modeling study, with more details on their roles provided next.

Southeastern States Air Resources Managers, Inc. (Contracting Agency)

ASIP
States Coordinating Group

Chairperson:

Sheila Holman, North

Carolina ASIP

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 1-4. ASIP Emissions and Air Quality Modeling Project Organizational Chart.December 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-20 Table 1-2. 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 Swannanoa, 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.netDecember 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-21 1.8 ASIP Project Manager and Co-Principal Investigator

Mr. Ralph Morris of ENVIRON is the Pr oject Manager (PM) and Co-Principal Investigator (Co-PI) for the Association for Sout heastern Integrated Planning (ASIP) Emissions and Air Quality Modeling Team. He provides overall directi on to the project and establishes a policy relationship with the sponsor , ensuring that all issues of importance to the ASIP group are addressed. The PM is responsible for the 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 computer 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 mode ling 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 e ffort 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 pr ogram 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

1.8.1 ENVIRON and Alpine Geophysics 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 programDecember 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-22 • 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

1.8.2 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

1.8.3 ASIP Technical Contact

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

Team works with the ASIP Cont acting Officer in the day-to-day oversight and management of the modeling analysis:December 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-23 • Provides day-to-day oversigh t of ASIP 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.

1.8.4 ASIP States

The primary purpose of the ASIP Emissions and Air Quality Modeling work is to develop the regional modeling co mponent for 8-hour ozone and PM 2.5 State Implementation Plans (SIPs) being developed by several southeastern U.S. stat es that are due June 2007 and April 2008, respectively. Most of the ten VISTAS states also participat e in ASIP. Alabama, Georgia, North Carolina, Kentucky, Tennesse e, Virginia, and West Virginia have PM 2.5 nonattainment areas and Alabama, Georgia, Kent ucky, North Carolina, Sout h Carolina, Tennessee, Virginia, and West Virginia have 8-hour ozon e non-attainment areas. The ASIP states will

oversee the regional emissions inventory developmen t and ozone and fine particulate modeling that will be required for the State Implementa tion Plans (SIP's). Emissions Inventory efforts include the development of 2002 base case emissions inventories and future year forecasts to be utilized in the ASIP modeling efforts. Modeling efforts will include identification, evaluation, and application of air quality modeling tools to quantify the effects of emission management options upon air quality in 8-hour ozone and PM 2.5 nonattainment areas in the southeastern United States. Specific activities of the ASIP States include:

• Oversee the activities of the ASIP Emissions and Air Qua lity Modeling Team through the Contracting Officer, conferen ce 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 Modeling Protocol and QAPP and conducts critical project reviews

1.9 Communications Plan

The ASIP Emissions and Air Quality Modeli ng Team members, other ASIP Contractors and ASIP representatives are linked by e-mail corre spondence, 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 1-3.December 2005

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec1.doc 1-24 Table 1-3. VISTAS listservs that will be used to distribute information by the ASIP Emissions and Air Quality Modeling Team and other ASIP participants.

Listserv Purpose Vistas-all@cert.ucr.edu Contacts all participants including Modeling Team, VISTAS
TAWG and Stakeholders Vistas-modeling@cert.ucr.edu Contacts Modeling team and VISTAS TAWG Modeling
Contacts

Vistasemissions@cert.ucr.edu Contacts emissions staff in the Modeling team and emissions people in the VISTAS TAWG Vistas-met@cert.ucr.edu Contacts meteorology staff in the Modeling team and meteorology people in the VISTAS TAWG

The Modeling Team members and ASIP Stat es and Contracting Officer hold periodic conference calls and meetings to report results, discuss project status, an d modify work plans as necessary. Unscheduled meetings or conference calls are also held concerning specific issues as the needs arise. In addition, periodic project mee tings and conference calls are held. In these meetings detailed technical information is exch anged, project status is discussed, and project

direction is assessed.

Written progress reports on the ASIP Emissions and Air Quality Modeling Team activities are submitted to the ASIP Contracting Officer on a monthly basis. These reports summarize project progress, results to date, pr oblems encountered and necessary action items, and plans for the upcoming reporting period.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-1 2.0 MODEL SELECTION

This chapter introduces the regional meteorol ogical, emissions and air quality models to

be used in the 8-hour ozone and PM 2.5 regional modeling for ASIP. The specific science configurations for each modeling system are iden tified and discussed briefly, where necessary. The configurations of each modeling system have been selected as the culmination of the regional modeling performed as part of the cl osely related VISTAS regional haze modeling

efforts.

2.1 Recommended Models

Based on the findings in the VISTAS Phase I and II modeling activities, ASIP selected the following models for use in modeling 8-hour oz one and particulate matter (PM) of size of 2.5 microns or less (PM 2.5 ):

3⁄4 MM5: The Pennsylvania State University/Na tional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Mo del (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urba n- and regional-scale photochemical, fine particulate, and regional haze regulatory modeling studies.

3⁄4 SMOKE : The Sparse Matrix Oper ator Kernel Emissions (S MOKE) modeling system is an emissions modeling system that generate s hourly gridded speciat ed emission inputs of mobile, nonroad, area, point, fi re and biogenic emission sour ces for photochemical grid

models.

3⁄4 CMAQ: EPA’s Models-3/Community Multis cale Air Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid de position at regional scale for periods up to

one year.

Application of the MM5 for the 2002 annual period and the ASIP 36/12 km domains was performed by BAMS under contract to SESARM as part of the VISTAS Phase II activities. Details of the model applicati on and evaluation procedures bei ng carried out by BAMS may be

found at http://www.baronams.com/projects/VISTAS/ . For completeness, in this chapter we describe the three regional modeling systems and their intended use in the ASIP 2002 annual modeling.

2.2 MM5 Mesoscale Prognostic Model

Over the past decade, researchers at the Pennsylvania State University (PSU) and the National Center for Atmospheric Research (PSU /NCAR) have collaborated in the refinement and extension of the PSU Mesoscale Meteorological Model leading to the current version of the system, MM5 (Ver 3.6, MPP). Originally deve loped in the 1970s at PSU and first documented by Anthes and Warner (1978), the MM5 modeling syst em maintains its status as a state-of-thescience model through enhancements provide d by a broad user community (e.g., Chen andJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-2 Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 2000). The MM5 modeling system is routinely employed in forecasting project s as well as refined investigations of severe

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

2.2.1 MM5 Overview

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

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

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

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

MM5 uses a terrain-following non-dimensiona lized pressure, or "sigma", vertical coordinate similar to that used in many operatio nal and research models. In the non-hydrostatic MM5 (Dudhia, 1993), the sigma levels are define d according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmosphere’ air-quality models using this coordinate (e.g., CMAQ). MM5 fields can be easily used in other regional air quality models with different coordinate systems (e.g., CAMx) by performing a vertical inte rpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer (PBL) para meterizations are available for air-quality applications, both of which represent sub-grid -scale turbulent fluxes of heat, moisture and momentum. These parameterizations employ various surface energy budget equations to estimate ground temperature (T g ), based on the insolation, atmos pheric path length, water vapor, cloud cover and longwave radiation. The su rface physical properties of albedo, roughness length, moisture availability, emissivity and therma l inertia are defined as functions of land-useJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-3 for numerous categories via a look -up table. One scheme uses a first-order eddy diffusivity

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

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

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

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

models such as RAMS and SAIMM. The MM5 enjoys a far richer a pplication history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scal e air quality application studies since 1995, we have generally found that MM5 model tends to produce somewhat better photochemical model inputs than alternative models. For these and other reasons set forth in the MM5 modeling protocol developed by BAMS (Olerud an d Sims, 2003), MM5 was selected as the meteorological modeling system for the ASIP/VISTAS study.

2.2.2 MM5 Configuration for ASIP and VISTAS Phase II Modeling

Based on the extensive sensitivity testing ca rried out by Olerud and Sims (2003) as part of VISTAS, the MM5 (Ver 3.6, MMP) configurati on to be used by BAMS modelers in the VISTAS Phase II modeling that will also be used by ASIP will consist of the following (see Table 2-3 for more details):

> Nested 36/12 km grids, with 34 vertical layers; > Two way nesting, no feedback; > Initialization and boundary conditions from Eta analysis fields; > Pleim-Xiu (P-X) soil model; > Asymmetric Convectiv e Mixing (ACM) PBL model; > Kain-Fritsch 2 cumulus parameterization; > Mixed phase (Reisner 1) cloud microphysics; > Raptid Radiative Transfer Model (RRTM) radiation; > Snow effect turned on; > ETA model sea surface temperature; > 24-category USGS vegetation data sets;January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-4 > Thermal roughness by the Garratt method; and > Standard FDDA analysis nudging on 36 km and 12 km grid nests.

2.3 SMOKE Emissions Modeling System

2.3.1 SMOKE Overview

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

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

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

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

SMOKE contains a number of ma jor features that make it an attractive component of the VISTAS modeling system (Seppa nen, 2003). The model supports a variety of input formats from other emissions processing systems and models including the Inventory Data Analyzer (IDA), Emissions Modeling System—2003 (EMS-2003), and the Emissions Preprocessor System 2.x (EPS2.x). It supports both gridded and county total land use scheme for biogenic emissions modeling. Although not necessary in ASIP, SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-5 Recent computational improvements to SMOKE include: (a) enhanced disk space requirements compared with other emissions processing software, (b) run-time memory allocation, eliminating any need to recompile the programs for different inventories, grids, or chemical mechanisms, and (c) updated I/O API libraries. A number of science features have been incorporated into the version 2.0 of SMOKE including: (a) any chemical mechanism can be used to partition pollutants to model species, as long as the appropriate input data are supplied, (b) integration with the MOBILE6.2 on -road mobile source emi ssions model including link based processing, (c) support of plume-in-gri d (PiG) processing, (d) integration of the

BEIS3 emissions factors in SMOKE.

Notable features of SMOKE from an applications standpoint include: (a) improved control strategy input formats and designs, (b) c ontrol strategies can include changes in the reactivity of emitted pollutants, a useful capability, for example, when a solvent is changed in an industrial process, (c) no third party software is required to run SMOKE, although some input file preparation may require other software , (d) fewer SMOKE programs than the SMOKE prototype because programs were combined wh ere possible to be used for multiple source categories, (e) integration with Models-3 file form ats and settings, (f) impr oved data file formats, (g) support of various air qua lity model emissions input formats (e.g., CMAQ, MAQSIP, UAMIV, UAM-V, REMSAD and CAMx), (h) enhanced quality assurance pre- and post-processing, (h) fully integrated with Models-3, which will provide the SMOKE Tool for SMOKE input file preparation, (i) enhanced trea tment of growth and control f actors, (j) improved emissions reporting and QA capabilities, and (k) improved temporal allocation.

Continuing model development activities with SMOKE now occur out of the University of North Carolina (UNC) Carolina Environmenta l Program (CEP). SMOKE beta Version 1.5b

was released 17 March 2003 and this is the versi on employed in the VISTAS Phase I modeling. Several patches to the model were provided during the summer of 2003, and SMOKE Version 2.0 was released on 30 Sept ’03. In 2004 SMOKE Version 2.1 was releas ed that contained further improvements and enhancements. The VISTAS/ASIP modeling adopted SMOKE Version. 2.1 at that time. In Septembe r 2005 SMOKE Version 2.2 was released by the Community Modeling and Analysis (CMA S) center’s Model Clearinghouse at http://www.cmascenter.org/modelclear.shtml . At that time ASIP/VISTAS had expended considerable effort in setting up and perfor ming emissions modeling using SMOKE Version 2.1. Additionally, the upgrades identified in Vers ion 2.2 were options that ASIP/VISTAS had circumvented using existing file code and format s and switching to this latest version was felt could jeopardize the modeling schedule. Conse quently, ASIP elected to continue emissions modeling using SMOKE Version 2.1. The SMOKE us er’s guide is available online at the main SMOKE website, http://www.cep.unc.edu/ empd/products/smoke

.

2.3.2 SMOKE Configuration for ASIP Modeling

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

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

Inventories”. Table 2-4 summarizes the versi on of the SMOKE system to be used and the sources of data to be employed in cons tructing the required m odeling inventories.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-6 2.4 CMAQ Modeling System

2.4.1 CMAQ Overview

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

with Models-3 and released the Community Multi-Scale Air Qu ality model (CMAQ) in mid1999 as the initial operating scie nce model under the Models-3 framework (Byun et al., 1998b). Since the initial CMAQ release in 19 99, there have typically been annual

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. EPA has funded the Community Modeling and Analysis Systems (CMAS) center to suppor t the coordination, update and distribu tion of the Models-3 system.

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

make the model well-suited for annual PM an d 8-hour ozone modeling. In CMAQ, the modal approach has been adapted to dynamically repr esent the PM size distri bution using three lognormal modes (2 fine and 1 coarse). Transfer of mass between the aerosol and gas phases is assumed to be in equilibrium and all secondary aero sol (sulfate, nitrate, SOA) is assumed to be in the fine modes. The thermodynamics of inorga nic 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 by 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. Gas-phase chemistry can be treated by the CB4, RADM or SAPRC

chemical mechanisms.

Pleim et al., (2003) descri be the features implemented in CMAQ Version 4.3 (released August 2003). Many of these fe atures are mentioned above; othe rs pertain to details in the model’s chemistry, transport, computer impl ementation, and model operation. In September 2004 CMAQ Version 4.4 was released that incl uded updates and enhancements (Pleim, 2004). CMAQ Versions 4.3 and 4.4 were used for much of the VISTAS Phase I and II testing andJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-7 model performance evaluation. In October 2005 CMAQ Version 4.5 was released (Pleim, 2005) which has been adopted for the ASIP and VISTAS modeling.

The VISTAS model evaluation indicated fair ly good performance for sulfate, winter

overestimation bias and summer underestima tion bias for nitrat e and reasonably good performance for Elemental Carbon (EC), albeit with lots of scatte r and low correlation. However, Organic Carbon (OC) was underestimated with the summer OC underestimation bias being quite severe. As OC is typically the seco nd most important PM co mponent contribution to visibility impairment and is the most or s econd most important component contributing to PM 2.5 violations in the southeastern US, VISTAS and ASIP were concerned with the large OC underestimation tendency. After an intense focused analysis of the issue, VISTAS identified processes important to the formation of Sec ondary Organic Aerosols (SOA) that were not included in the CMAQ SOA module that may be important to OC in the Southeastern US (Morris et al., 2005). Conse quently, VISTAS enhanced the CMAQ SOA module by adding several new processes. This enhancement, ca lled SOAmods described below, was implemented in CMAQ Version 4.4 and exhibited much impr oved OC model performa nce over the standard CMAQ SOA treatment (Morris et al., 2005b).

In October 2005, the new Version 4.5 of CM AQ was released (Pleim, 2005). CMAQ

Version 4.5 included several enhancements and co rrections, including a co rrection to the CMAQ mass conservation scheme. Given the importance of mass conservation in air quality modeling, ASIP and VISTAS have adopted an enhanced version of CMAQ Version 4.5 for their modeling

and transferred the SOAmods enhancement to it.

2.4.3 CMAQ Version 4.5

CMAQ Version 4.5 has several corrections, updates and enhancements over earlier versions of

the model:

1) Aerosols - Added sea salt (fine equili brium; non-interactive coarse mode) -- aero4 - Updated aerosol dry deposition algorithm - Updated mechanism include file s to remove any aerosol species with zero

concentrations for aero4 - Updated ISORROPIA to v1.5 ( 25 Oct 2003) and fixed some discontinuities - Added diagnostic variab les to calculate PM2.5 concentrations - Corrected bug in mode merging to reduce mode crossover

- Modified SO4 used in ISORROPIA call - Corrected inconsistency in MINL2SG (aerodepv)

- Corrected the EMSULF (H2SO4 emissions) unit conversion bug

2) Chemistry - Added CB4/chlorine chemistry and associated EBI solver

- Added CB4/air toxics and SAP RC99/air toxics chemistry and associated EBI

solvers - Added degradation al gorithm to the generalized solvers - Corrected treatment of convergence failures in EBI solversJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-8 - No longer support the RADM2 mechanism

3) P-in-G - Improved calculation of plume centerline - Fixed bug in the aerosol species array subscripting

- Corrected error in non-r eactive 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)

6) Advection - Added new mass continuity scheme

7) Other - Added dynamic vertical layer allocation - Added primary carbon source apportionment capability - Added sulfate tracking capability

Thus, even though VISTAS Phase I and II mo deling testing and ev aluation and initial ASIP runs were performed using CMAQ Versions 4.3 and 4.4 and considerab le effort has been

committed to these earlier versions of CM AQ, the CMAQ Version 4.5 model updates were significant enough that VISTAS and ASIP decided to switch over to the new version (Ver 4.5) of CMAQ.

In the VISTAS testing and evaluation of the CMAQ V4.3 and V4.4, the model performance for Organic Carbon (OC) was charact erized by a systematic under-prediction bias that was particularly severe in the summer. As OC is one of the two most important PM components for PM 2.5 exceedances and visibility degradation in the Sout heastern US (SO4 being the other), VISTAS/ASIP were concerned with this underestimation tendency. Thus, VISTAS performed research in this area and identified several missing processes related to Secondary Organic Aerosol (SOA) formation in the CMAQ model. In part icular, CMAQ failed to account for SOA from several biogenic emission sources such as sesquiterpenes and isoprene. Consequently, VISTAS enhanced the CMAQ SOA module to treat these missing processes that resulted in improved OC perfor mance (Morris et al., 2006). This SOAmods enhancement is

discussed next.

2.4.4 SOAmods Enhancement

The formulation of the CMAQ SOA module is described in Binkowski and Roselle (2003). SOA is formed primarily from aroma tic VOCs and biogenic te rpenes. The biogenicJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-9 SOA precursors were modeled with the Biogenic Emissions Information System – Version 3 (BEIS3) model (Pierce et al., 2002). BEIS3 ge nerates three biogenic VOC species: isoprene (ISOP), monoterpenes (TERP) a nd 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 b ond structure. The BEIS3 ISOP, TERP and OVOC species are speciated into the CB4 species for photoche mical modeling in CMAQ and CAMx as follows (molar speciation):

• ISOP = ISOP (isoprene is an explicit species)

• ALD2 = 1.5 x TERP

• OLE = 0.5 x TERP

• PAR = 6.0 x TERP

• NR = 0.5 x OVOC

• OLE = 0.5 x OVOC

• PAR = 8.5 x OVOC

• TERPB = TERP

Here, ALD2, OLE, PAR and NR are the CB4 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 base d on a fit to smog chamber data collected at the California Institute of Technology for several biogenic monoterpene species (Binkowski and Roselle, 2003). A review of recent literature of biogenic SOA measurements identified several processes that may be important to biog enic SOA formation that are not treated by the BEIS3 biogenic emissions a nd 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).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-10 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 2-1 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 efficiencies 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 2-1 were developed as shown in Table 2-1. 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 2-1). No attempt was made to optimize the parameters in Table 2-1 for OC/TCM model performance.

The emission factors, EF1 and EF2, relate the monoterpene emissions estimated by BEIS3 to emissions of monoterpenes, EF1 (e.g., α -pinene), and sesquiterpenes (EF2). Table 2-1 displays the range of EF1 and EF2 factors based on recent field study data (Stroud et al., 2005). Using the midpoint of the range results in emission factors of 0.7 for EF1 and 0.4 for EF2. EF1 is assigned a value of 0.7 based on field observations that i ndicate that the BEIS3 terpene emission factors are likely overestimated due to a tendency of earlier measurements approaches to artificially increase the emissions due to di sturbance when leaves were enclosed in the measurement system. As an initial approach fo r including sesquiterpen e emissions, we have assigned EF2 a value of 0.4 based on the ratio of the observed sesquiterpene emission from the Duke Forest field study (Stroud et al, 2005) to the BEIS3 monoterpene emission estimate. The

net result is that BEIS3 TERP emissions are increased by 10% and split 64% as monoterpenes and 36% as sesquiterpenes. The CG yields fr om the sesquiterpenes are assumed to partly

condense into a non-volatile SOA particle that is modeled in CMAQ using the new secondary organic carbon species (SOC2) sp ecies 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).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-11 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 2-1). 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 (sesquiterpen e product) species, are assumed 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.

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 has 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 is aw aiting publication (Morris et al., 2006).

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 2-2 displays fractional bias error for Organic Carbon (OC) IMPROVE and STN monitoring site s in the VISTAS, MRPO, MANE-VU and CENRAP states using the standard CM AQ Version 4.4 (V4.4) a nd then CMAQ V4.4

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

enhancement the fractional biases centered on zero and ranges fr om –14% to +8%.

Similar results are seen for OC fractional bias across the more urban STN sites where the CMAQ V4.4 exhibits an underestimation bi as of –67% to –105%, when using SOAmods the under-prediction bias is –27% to –44%. Note that the c ontinued underestimation of OC across the urban STN sites is likely due to missing primary OC emissions and

uncertainties in the STN OC measurements.

With the release of CMAQ Versions 4.5 in October 2005, the SOAmods enhancement was added to the AERO3 aerosol module in CMAQ Version 4.5 that was compared against the standard CMAQ Versions 4.5 and SOAmods was found to produce similar improvements in OC model performance as seen with CMAQ Versions 4.4. ASIP and VISTAS are now proceeding with their regional haze and 8-hour ozone/PM 2.5 modeling using the CMAQ Versions 4.5 SOAmods.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-12 2.4.5 CMAQ Configuration for ASIP Modeling

The configuration of CMAQ used in the ASIP modeling is based on the extensive testing

and evaluation of several versions and configurations of CMAQ performance as part of Phase I and II of the VISTAS modeling (Morris et al., 2004a,b; Morris et al., 2005a). As part of VISTAS, the science team has tested and ev aluated CMAQ versions 4.3, 4.4beta, 4.4 and 4.5. When the CMAQ treatment of SOA was found to be incomplete, it was enhanced with the SOAmods update, first in CMAQ Version 4.4 and then in CMAQ Version 4.5. In this section we identify the main science options we recommend for 8-hour ozone and PM 2.5 modeling with CMAQ. In particular, we propose to run CMAQ Version 4.5 with th e SOAmods enhancement and the configuration as shown in Table 2-5. The model would be set up and exercised on the same nested 36/12 km grid domain used in VIST AS, 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 horizontal advection solver will be used along with th e spatially varying (Smagorinsky) horizontal diffusion approach an d K-theory for vertical diffusion. The new Yamertino vertical transport scheme of CMAQ Version 4.5 will be used to correct the mass conservation problems in past ve rsions of the model. MM5 meteorological output based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer (PBL) scheme will be used (see Table 2-3) and the recently updated CMAQ Meteorological-Chemistry Interface Processor (MCIP3.0) w ould process the MM5 data usi ng the “pass through” option. The CB4 gas-phase, RADM aqueous-phase, and AERO4/ISORROPIA aerosol chemistry

schemes are recommended for use in the CM AQ 2002 modeling. Treatment of reversible secondary organic aerosols would be simulate d by the SORGAM implementation in CMAQ with

the SOAmods enhancement described above.

2.5 Model Limitations

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

2.5.1 MM5

Four different configurations of the MM5 Land Soil Model (LSM) and Planetary Boundary Layer (PBL) were evaluated as part of the 2002 meteorological modeling. Depending on the meteorological variable (e.g., winds, temper ature, moisture) and lo cation (e.g., mountains, coastal, east, west) different LSM_PBL confi gurations performed better. The Pleim-Xiu Asymmetric Convective Mixing PX_ACM LSM_PB L configuration was selected because it consistency was near the top pe rforming configuration in the VI STAS region across variables and locations and was never the worst performing configuration. However, there are numerousJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-13 limitations in the MM5 with the LSM and PBL trea tment being some of the most important. The MM5 PX_ACM frequently predicts very low PBL heights that can appear as “holes” in the spatial distribution of PBL hei ghts that don’t appear physically realistic and may affect air quality modeling. Although th e MM5 PX_ACM conf iguration model performance in the VISTAS region mostly met performance benchmarks, the performance was much worst in the western U.S. In addition, there is a stochastic component of real world meteorology that is not captured by MM5. For example, for some ozone episodes stagnation is an important attribute

that MM5 fails to simulate well as it tries to organize the flow fields. The MM5 model represents approximately 20 years of development by various researchers and is showing its age. The many limitations in MM5 have spawned the development of a new meteorological model, the Weather Research Forecast (WRF) model. However, the WRF model will not be used or tested in the VISTAS/ASIP modeling.

2.5.2 SMOKE

In the VISTAS Phase I study a number of undocumented features of the SMOKE 1.5b version necessitated re-runs of the emissions pr ocessing software to overcome errors and/or ambiguities in source documentation and QA re porting. Although there were fewer problems with the SMOKE Version 2.0 and 2.1 releases, probl ems were encountered that were not well

documented that necessitated reruns of the m odel. In October 2005 Version 2.2 of SMOKE was released. However, for reasons discussed earlier, ASIP elected to keep using Version 2.1 and not

transition to the new SMOKE Version 2.2. VIST AS has fully set up and evaluated SMOKE Version 2.1, including identificatio n of problems in the modeling that have been corrected. Switching to the new Version 2.2 could not only cau se a serious set back in the ASIP modeling schedule, it may result in picking up additiona l new undocumented errors in the new modeling system that could require rerunning scenarios. Should problems arise or issues be encountered which would require additional SMOKE runs or potential SMOKE modifications or alternate modeling methods, we will immediately notify AS IP and make recommendations for resolving the issues. Upon receipt of tec hnical direction from ASIP, appr opriate corrective action will be

taken.

Features are continuing to be developed in th e SMOKE emissions model. As it is not as mature as some other emission models (e.g., EMS, EPS, etc.) it does not include as many features. We will keep abreas t of SMOKE development activities to identify new features that will assist in the ASIP emissions modeling.

2.5.3 CMAQ

Like all air quality models, a major limitatio n of CMAQ is the emissions, meteorological and IC/BC inputs. Key science limitations in th e model itself include the nitrate formation and Secondary Organic Aerosol (SOA) chemistry. The VISTAS testing found the CMAQ nitrate performance suspect with winter overestima tions and summer underestimations. Improvements in the ammonia emissions inventory and model formulation have improved this performance attribute, especially the wint er overestimation bias. Defici encies in the CMAQ SOA module have been partly corrected w ith the SOAmods enhancement. However, the current SOAmods formulation is based on very lit tle data and more refined SOA e nhancements are needed. LackJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-14 of any two-way grid nesting limits the ability of the model to properl y resolve point source plumes or urban photochemistry a without a proh ibitive number of grid cells. Another limitation of CMAQ is the computational requirements, including the need of ex cessive disk space.

2.6 Model Input Requirements

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

2.6.1 MM5

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

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

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

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

temperature over water.

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

3⁄4 Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and precipitation directly through explicit resolv ed-scale and parameterized sub-grid scale processes, the model does not require precip itation or cloud input. The potential for precipitation and cloud formation enters through the thermodynamic and cloud processes formulations in the model. Th e only precipitation-rela ted input required is the initial mixing ratio field that is developed from the NWS and NMC data sets previously discussed.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-15 3⁄4 Multi-Scale FDDA : The standard “multi-scale” data assimilation strategy to be used on the 36 km and 12 km grids will objectiv ely analyzed three-dimensional fields produced every 3-hr from the NWS rawins onde wind, temperature, and mixing ratio data, and similar analyses generated every three hours from the available NWS

surface data.

2.6.2 SMOKE

The databases required to set up and operate SM OKE for the ASIP 2002 annual simulation are as

follows:

• Area Source emissions in IDA format

• Fugitive Dust Source emissions in IDA format

• Nonroad source emissions in IDA format

• Non-EGU Stationary Point Source emissions in IDA format

• EGU Stationary Point Source emissions in IDA format

• CEM-Based EGU Emissions, hour specific for 2002

• Prescribed, Agricultural, and Wild fire Emissions, day specific for 2002

• Onroad Motor Vehicle VMT and activity data

• MOBILE6.2 input parameters

Also required for annual modeling are data files specific for:

• Temporal allocation

• Spatial allocation

• Speciation

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

2.6.3 CMAQ

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

3⁄4 Three-dimensional hourly meteorological fi elds that will be generated by the CMAQ MCIP3.0 processing of the BAMS MM5 output;
3⁄4 Three-dimensional hourly emi ssions generated by SMOKE;
3⁄4 Initial conditions and boundary conditions (IC/BC);
3⁄4 Topographic information;
3⁄4 Land use categories; and
3⁄4 Photolysis rates generated by the CMAQ JPROC processor.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc 2-16 Table 2-1. Parameters use in enhanced SOA module (see Figure 2-1).

Parameter Mid-Point Range

EF1 0.7 0.4 ~ 1.0

EF2 0.4 0.2 ~ 0.6
EEG1 0.325 0.2 ~ 0.45

EEA1 0.2 0.05 ~ 0.35

Y2 0.875 0.75 ~ 1.0

Y1 0.11 0.06 ~ 0.16

P1 0.45 0.15 ~ 0.75 EF1 = emission factor of monoterpenes to the TERP emissions estimated by BEIS3 EF2 = emission factor of sesquiterpenes relative to the TERP emissions estimated by BEIS3 EEG1 = escape efficiency of gas phase precursor of sesquiterpenes from canopy EEA1 = escape efficiency of SOA from sesquiterpenes from canopy Y1 = SOA yield of oxidated isoprenes Y2 = SOA yield of sesquiterpenes

Table 2-2. Comparison of fractional bias performance metric for Organic Carbon (OC) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement.

July 2002
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%May 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc2-17Table 2-3. MM5 Meteorological Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsModel Code MM5 Version 3.6 (MPP) Grell et al., 1994Horizontal Grid Mesh 36/12 km36 km grid 164 x 128 cells12 km grid 180 x 189 cellsVertical Grid Mesh 34 layers Vertically varying; sigma pressure coord.Grid Interaction NoFeedback IFEED=0Initialization Eta first guess fields/LittleRBoundary Conditions Eta first guess fields/LittleRMicrophysics Reisner I Mixed Ice Look up tableCumulus Scheme Kain-Fritsch 2 On 36/12 GridsPlanetary Boundary Layer ACM PBLRadiation RRTMVegetation Data USGS 24 Category SchemeLand Surface Model P-X Land Surface Model (LSM)Shallow Convection NoneSea Surface Temperature Eta Skin Spatially varyingThermal Roughness GarrattSnow Cover Effects None4D Data Assimilation Analysis Nudging on 36/12Integration Time Step 90 secondsSimulation Periods Annual 2002 Other episodic periods to be definedPlatform Linux Cluster Done at BAMSMay 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc2-18Table 2-4. SMOKE Emissions Model Configuration for ASIPEmissions ComponentConfigurationDetails/CommentsEmissions Model SMOKE Version 2.1 Oct '04 releaseHorizontal Grid Mesh 36/12 km36 km grid 148 x 112 cells RPO Unified Grid12 km grid 168 x 177 cellsVISTAS/ASIP eastern US 12 km gridArea Source Emissions VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02v.1 and RPO interactionMexico/Canada Emissions: Same as Phase I w/ revisions based on available updatesOn-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02v.1 and RPO interactionMexico/Canada Emissions: Same as Phase I w/ revisions based on available updatesPoint Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states and stakeholdersOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02v.1 and RPO interactionMexico/Canada Emissions: Same as Phase I w/ revisions based on available updatesOff-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02v.1 and RPO interactionMexico/Canada Emissions: Same as Phase I w/ revisions based on available updatesBiogenic Sources VISTAS Domain: VISTAS State 2002EI Updated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02v.1 and RPO interactionMexico/Canada Emissions: Same as Phase I w/ revisions based on available updatesTemporal Adjustments Seasonal, day, hour Based onlatest collected information and CEM-based profilesChemical Speciation Revised CB4 ChemicalSpeciation Expected EPA release in Jan '04Gridding Revised EPA Spatial Surrogates Used Gridding ofsurrogates from http://www.epa.gov/ttn/chief/emch/spatial/Growth and Controls VISTAS Special Interest Team Generated Data Base Cases defined by VISTAS Planning WorkgroupQuality Assurance QA Tools in SMOKE 2.1 Independent QA with AG's Established ProtocolSimulation Periods Annual 2002 Other episodic periods to be definedPlatform Dual Athelon 2600 \+ Local 1.8 terabyteUltra 320 RAID 5 system processing and storageMay 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc2-19Table 2-5. CMAQ Air Quality Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsModel Code CMAQ Version 4.5 with SOAmods Pleim et al., (2005)Horizontal Grid Mesh 36/12 km36 km covering cont. U.S; 12 km covering easternUS36 km grid 148 x 112 cells RPO National Grid12 km grid 168 x 177 cells VISTAS/ASIP 12 km gridVertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5Grid Interaction One-way nestingInitial Conditions ~15 days full spin-up Separately run 4 quarters of 2002Boundary Conditions 2002 3-hourly GEOS-CHEM annual runDay-specific 3-hour BCs from Global ClimateModelEmissionsBaseline Emissions Processing SMOKE (Ver 2.1) MM5 Meteorology input to SMOKE, CMAQDust Transport Fraction Applied in emissions before SMOKE Tom Pace updatesNH3 Inventory Adjustment Applied in emissions before SMOKESub-grid-scale Plumes No Plume-in-Grid (PinG)ChemistryGas Phase Chemistry CBM-IVAerosol Chemistry AE4/ISORROPIA Includes active Sea SaltSecondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) Schell et al., (2001)Aerosol Mass Conservation Patch Yes Georgia Institute of Technology UpdateCloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processesN2O5 Reaction Probability 0.01 – 0.001Meteorological Processor MCIP Version 3.0Includes dry deposition for Sea Salt and percenturban minimum Kz (PURB)Horizontal TransportEddy Diffusivity Scheme K-theory with Kh gridsize dependence Multiscale Smagorinsky (1963) approachVertical TransportAdvection Scheme YamertinoV4.5 Mass Conservation (Yamertino)Eddy Diffusivity Scheme K-theoryDiffusivity Lower Limit Kzmin= 0.1 to 2.0 PURB option in Ver 4.5Planetary Boundary Layer No PatchDeposition Scheme M3dry Directly linked to Pleim-Xiu Land Surface ModelMay 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc2-20Table 2-5. CMAQ Air Quality Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsparametersNumericsGas Phase Chemistry Solver Euler Backward Iterative (EBI) solver Hertel et al (1993) EBI solver ~ 2x faster than MEBIHorizontal Advection Scheme Piecewise Parabolic Method (PPM) schemeSimulation Periods Annual 2002 With~15 day spin-up in December 2001Integration Time Step Determined by met conditions 15 minute coupling time stepPlatform Athlon MP 2600+ MPI using 6 processors per quarterMay 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec2.doc2-21Figure 2-1.Schematic describing the addition of new SOAmods processes (bold italic) within the existing CMAQ SOA module(regular font) to treat polymerization and SOA from sesquiterpenes and isoprene (see Table 2-1 for parameters).



## 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 & SOC2FPOLYEF1January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec3.doc 3-1 3.0 EPISODE SELECTION

This chapter provides a brief overview of the process followed by the ASIP in determining the most appropriate period(s) for PM 2.5 and 8-hour ozone modeling to be used in the Southeastern

States PM 2.5 and 8-hour ozone attainment demonstrati ons for their State Implementation Plans (SIPs). As discussed in more detail below, one cr iteria in selecting modeli ng periods for attainment demonstration modeling is the selection of modeli ng periods for which databases already exist, if appropriate. This was a major criteria for the ASIP 8-hour ozone and PM 2.5 modeling that relied heavily on the VISTAS modeling databases. Thus , the ASIP episode selection was based on the analysis by the VISTAS Technical Analysis Work Group (TAWG) selection of the 2002 annual period for regional haze modeling. ASIP reviewed the 2002 annual period and also found it suitable

for PM 2.5 and 8-hour ozone modeling. While ASIP and th e VISTAS TAWG plan to prepare a formal report documenting these activities, this work wa s not available at the time of this writing. However, much of the technical work underpinni ng the ASIP/VISTAS episode selection process has been published and peer-reviewed over the later stag es of the VISTAS work efforts and it is this body of information that we have distilled in prep aring the brief episode selection summary that

follows.

3.1 Overview of EPA Guidance

EPA’s current draft guidance on PM 2.5 /Regional Haze modeling (EPA, 2001) and final guidance for 8-hour ozone modeling (EPA, 2005) identifies specific goals to consider when selecting one or more episodes for use in demonstrating attainment of the PM 2.5 and 8-hour ozone NAAQS. There is much in common with selecting episodes for annual and episodic PM 2.5 and 8-

hour ozone attainment demonstrations, as well as regi onal haze, EPA’s guidanc e addresses all three in a consistent fashion in their guidance documen ts (EPA, 2001; 2005). As an update to the draft

PM 2.5 and regional haze modeling guidance, EPA has published an updated summary of PM 2.5 and Regional Haze Modeling Guidance (Timin, 2002) that serves, in some respects, as in interim placeholder until the final guidance is issued as part of the PM2.5/regional haze NAAQS implementation process that is expected during 2006.

EPA recommends that episode selection derive from three principal criteria:

3⁄4 A variety of meteorological conditions that lead to exeedances of the PM 2.5 and 8- hour ozone NAAQS should be covered;

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

3⁄4 Sufficient days should be available such that relative reduction factors (RRFs) can be based on several days (> 15 days, with at least 5 days being essentially mandatory).

For regional haze and annual PM 2.5 modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year (EPA, 2001, pg. 188). EPA also listsJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec3.doc 3-2 several ‘other considerations’ to bear in mind when choosing potential PM/regional haze and 8-hour ozone modeling episodes including: (a) choose peri ods which have already been modeled; (b) choose periods which are drawn from the years upon which the current design values are based; (c) include weekend days among those chosen; and (d ) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment areas as possible.

ASIP and VISTAS adopted a logical, stepwise approach in implem enting the EPA guidance in order to identify the most pref erable, representative year for PM 2.5 , regional haze and 8-hour modeling. These steps are summarized briefly in this chapter.

3.2 Episode Selection Methodology

The episode selection methodology entailed coordinated investigations by ASIP and

VISTAS contractors and members of the VISTAS Technical Analysis Workgroup (TAWG). To begin, Olerud (2003b) identified important meteorol ogical characteristics and data sets in the Southeastern U. S. directly relevant to the ev aluation of candidate annua l modeling episodes. A separate detailed aerometric analysis and pattern recognition study was carried out by ICF (Douglas et al., 2003) to characterize the extent to whic h days from January-March 2002 and January-March 2003 represent the type of meteorological conditions that are most frequently associated with high

and low values of a haze index and PM 2.5 concentrations. Using the standard Classification and Regression Tree (CART) analysis software, these re searchers characterized each of the days within these two three-month periods relative to haze and PM 2.5 observations as well as other relevant meteorological factors. This work was based on pr evious episode characteri zation work carried out in support of the SEARCH and MARAMA research projects (Douglas et al ., 2003). The analysis was also supported by climate summaries provided by the North Carolina Division of Air Quality (NCDAQ).

In parallel with the CART episode characteri zation analyses, collaborative investigations by ASIP and VISTAS TAWG participants (e.g., NCDAQ, Georgia DNR, FL DEP) intensively studied the availability of PM 2.5 ozone, meteorological, and emissions data and representativeness of alternative Baseline modeling periods from a regul atory standpoint (Boylan et al., Brewer et al., 2003). Daily average speciated PM 2.5 monitoring data in the Southeastern US were review intensively, by site and by monitoring networ k (e.g., IMPROVE, SEARCH, STN, FRM). 8-hour ozone data for the nonattainment areas were also analyzed. In addition to analyzing data representativeness, consideration was given to the timeliness with which new data could be obtained for the ASIP and VISTAS modeling. Also, data availability from parallel meteorological and emissions database acquisition efforts was considered both for the Southeastern US states as well as

for other states and countries in the 36 km domain.

To assess the representativeness of the five year baseline 2000-2004 period that is used in the 8-hour ozone, PM2.5 and regional haze projections (see Chapter 8), temperature and precipitation records were examined over the 108 yr peri od of record and additional high-resolution meteorological analyses were considered (e.g., CA RT analyses for SEARCH and MARAMA sites). For each PM2.5 monitoring site in the VIST AS domain, and for each component of PM 2.5, monthly means and deviations (from the monthly m ean) were calculated for the months within the 2002-2003 period of record. Daily, monthly, and annua l trends of PM 2.5 conc entrations across theJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec3.doc 3-3 three year period were subsequently analyzed (Boylan et al., 2003). Equally as important, the methodologies and decisions underpinning the epis ode selection processes carried out by other RPOs were also considered (several had already chosen CY-2002 as the modeling year).

3.3 Selection of CY 2002 For ASIP and VISTAS

After a lengthy process of integrated studies, th e episode selection process culminated in the selection of calendar year (CY) 2002 (1 Janua ry through 31 December) as the most current, representative, and pragmatic choice for VIST AS regional haze and ASIP 8-hour ozone and PM 2.5 modeling. All of the EPA criteria for 8-hour ozone, PM 2.5 and PM/regional haze episode selection were directly considered in this process togeth er with many other pragmatic considerations (e.g., timing of new emissions or aerometric data deliverie s by EPA or the states to the modeling teams).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-1 4.0 MODELING DOMAINS A ND DATA AVAILABILITY

This chapter summarizes the model domain definitions including the model domain, resolution, map projections and nesting sc hemes for high resolution sub-domains.

4.1 Horizontal Modeling Domain

The ASIP horizontal domains for each of the mode ls will be identical to those used in the VISTAS Phase I and II modeling. As in VISTAS, as well as the CENRAP and WRAP RPOs, a coarse grid continental United States (US) domai n with a 36 km horizontal grid resolution will be used (the Inter-RPO domain). The CMAQ domain is nested in the MM5 domain. The

selection of the MM5 domain is described in the VISTAS MM5 mode ling protocol (Olerud, 2003). Figure 4-1 shows the MM5 horizontal domain as the outer most, blue grid. Also shown in Figure 4-1 is the CMAQ 36 km domain nested in the MM5 domain. To achieve finer spatial

resolution in the Southeastern US States are al so using a one-way nested high resolution grid with a 12 km grid resolution. Figure 4-2 shows the 36 km CMAQ continental grid and the high resolution, nested 12-km grid in the VISTAS states. Figure 4-3 shows in more detail the 12 km grid for the Southeaster US region that is the focus of ASIP and VISTAS.

Both MM5 and CMAQ employ the Regional Pl anning Organization (RPO) unified grid

definition for the 36 km continental domain. Th e RPO unified grid consists of a LambertConformal map projection using the map proj ections parameters lis ted in Table 4-1.

Table 4-1. RPO Unified Grid Definition.

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

The MM5 36 km grid includes 164 cells in the east-west dimension and by 128 cells in the north-south dimension. The CMAQ 36 km grid includes 148 cells in th e east-west dimension

and 112 cells in the north-south dimension. Becaus e the MM5 model is also nested in the Eta model, there is a possibility of boundary effects near the MM5 boundary that occur as the Eta meteorological variables are being simulated by MM5 and must come into dynamic balance with MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid cells around each boundary of the CMAQ 36 km do main. This is designed to eliminate any errors in the meteorology from boundary effects in the MM5 simulation at the interface of the MM5 and Eta models. The buffer region used here exceeds the EPA suggestio n of at least 5 grid cell buffer at each boundary.

Table 4-2 lists the number of rows and columns and the definition of the X and Y origin (i.e., the southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are reversed. In Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells whileJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-2 “cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Finall y, we note that the grid definition for the CMAQ Meteorology Chemistry Interface Processor (MCIP) and CMAQ Chemical Transport Model (CCTM) are identical.

Table 4-2. Grid Definitions For MM5 and CMAQ.

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

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

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

MM5 12km 190 (189) 181 (180) 7200 -1656000

CMAQ 12km 169 (168) 178 (177) 108000 -1620000

4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain follo wing coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. Table 4-3 lists the layer definitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the computational cost of the CMAQ simulations. The effects of layer averag ing were evaluated in the VISTAS Phase I modeling effort and found to have a relatively minor effect on the model performance metrics when both the 34 layer an d a 19 layer CMAQ models were compared to ambient monitoring data.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-3 Figure 4-1 . Nesting of 36-km CMAQ Grid in the MM5 36-km Grid.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-4 Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid

.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-5 Figure 4-3. Domain Definition for High Resolution 12-km Grid.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-6 Table 4-3. Vertical Layer Definition For MM5 Simulations (Left Most Columns), And Approach For Reducing CMAQ Layers By Collapsing Multiple MM5 Layers (Right Columns).

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

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-7 4.3 Data Availability

The CMAQ modeling system requires emissi ons, meteorological, initial and boundary condition (IC/BC) and ozone column data for de fining the inputs to operate the CMAQ Chemical Transport Model and air quality data with whic h to evaluate the CMAQ CTM concentrations and deposition estimates.

4.3.1 Emissions Data

The base year emissions inventory for Phase II of the VISTAS modeling will be the basis for the ASIP modeling. These data are f ounded on 2002 Consolidated Emission Reporting Rule (CERR) inventories submitted to VISTAS by partic ipating state or local agencies and compiled by VISTAS emission inventory co ntractors in NEI Input Format (NIF) 3.0. These emissions were reviewed by VISTAS stakeholders and cons idered complete in Ja nuary of 2004, with minor

modifications submitted since that time. Non-VISTAS state emissions will be based on

inventories obtained by the Study Team from the other RP Os or EPA and determined to be representative of the 2002 episode year. Mexican and Canadian emissions will be based on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. For purposes of air quality model validation, actual 2002 calendar year emissions for EGU and fire activity will be used. For strategy and future year emission runs, “typical year” emissions for these categories wi ll be processed for 2002 and the future years.

A final revised 2002 VISTAS state emission inve ntory is expected in February 2006 and will be used in the final model performance dem onstration and configuration expected to begin in spring 2006. Non-VISTAS state emissions ar e expected to be based on RPO updated base year emissions augmented with additional data provided by RPO, State, and international sources. As in the initial revised modeling, ac tual 2002 calendar year emissions will be modeled for EGU and fires for base case model performa nce evaluation, while “typical year” emissions for these categories for 2002 and the future year s will be processed during the strategy runs.

All emissions will be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed fo r air quality modeling using Version 2.1 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal and speciation profiles and cross-refere nce data provided with the version 2.1 release of the model augmented with any recommended and appr oved emission profile data provided by the emissions inventory contractor, obtained from EPA, or prepar ed by the Study Team prior to initial emissions modeling. Spatial allocation of the emissions will be based on profiles and spatial allocation factors developed for the National RPO grid. Additional description of emissions processing is described in Chapter 5 and emissions QA is described in Chapter 6.

4.3.2 Air Quality

Data from ambient monitoring networks for both gas and aerosol species are used in the model performance evaluation. Ambient monitori ng data are described in detail in the report:

“Review and Assessment of Available Ambien t Air Quality Data to Support Modeling and Modeling Performance Evaluation for the Thre e VISTAS Phase I Episodes” (ENVIRON, UCRJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-8 and Alpine 2003) so are not repeated here. Tabl e 4-4 summarizes ambient monitoring networks. Data have been compiled for all networ ks except the PAMS and PM Supersites.

Of particular note for th e ASIP 8-hour ozone and PM 2.5 attainment demonstration modeling are the locations of th e key ozone monitors and key FRM monitors where ozone and

PM has been determined to be in nonattainment . Also important are the STN speciated PM

monitor that is associated with each FRM PM monitor. ASIP is currently formulating its strategy on how the PM 2.5 attainment demonstration will be performed using associated FRM and STN PM monitoring sites.May 2004F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec4.doc4-9Table 4-4.Overview of Ambient Data Monitoring Networks.Monitoring NetworkChemical Species MeasuredSampling PeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24 hraveragehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (see speciesmappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10, Pb Typically hourlyaveragehttp://www.epa.gov/air/data/PM2.5Federal Reference Method(FRM)Total PM2.5Mass1 in 3 days; 24-hour averagesSpeciation Trends Network(STN)Speciated PM 24-hour average http://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (SO4, NO3, NH4, elements);Hourly PM2.5 (Mass, SO4, NO3,NH4, EC, TC); and Hourly gases (O3,NO, NO2, NOy, HNO3, SO2, CO)Hourly or 24-houraverage,depending onparameter.Electric Power Research Institute (EPRI), SouthernCompany, and other companies.http://www.atmospheric-research.comEPA Particulate Matter Supersites Speciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htmMay 2004

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec4.doc 4-10 4.3.3 Ozone Column Data

Additional data used in the air qualit y modeling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data is availabl e for 24-hour average and is obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . The day-specific TOMS data is used in the CMAQ radiation model (JPROC) to calcul ate photolysis rates. The TOMS data were missing or bad for several periods in 2002: August 2-12; June 10; and November 18- 19. Thus, the TOMS data for August 1, 2002 was used for August 2-7 and TOMS data for August 13 was used for August 8- 12. Similarly, TOMS data for June 9 was used for June 10 and data for August 17 was used for August 18-19.

4.3.4 Meteorological Data

Meteorological data are being generated us ing the MM5 prognostic meteorological model by Baron Advanced Meteorological Systems (BAM S). BAMS is operating the MM5 at 5-day increments for 2002 on the 36 km and 12 km grid with a 14 day spin up period for the end of

December 2001. Details on the VISTAS Phas e II 2002 MM5 modeling can be found at the

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

.

4.3.5 Initial and Boundary Conditions Data

The CMAQ default Initial Concentrations (IC s) will be used along with a ~15 day spin up period to eliminate any signi ficant influence of the ICs.

The CMAQ Boundary Conditions (BCs) for th e Inter-RPO 36 km grid and the ASIP simulations will be based on day-specific 3-hou rly averages from a 2002 GEOS-CHEM global simulation model output. Boundary conditions for the 12 km grid will be based on CMAQ results from the 36 km rid processed with the CMAQ BCON boundary condition processor.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-1 5.0 MODEL INPUT PREPARA TION PROCEDURES

In this section we describe the procedures to be used to develop the CMAQ model inputs

for the ASIP 2002 annual 36/12 km model s imulations to addres s 8-hour ozone and PM 2.5 nonattainment in the Southeaste rn US States. The developm ent of the CMAQ meteorological and emissions inputs are discussed first followed by the science options to be used by CMAQ. The procedures for developing the initial and boundary conditions and photolysis rates inputs are then discussed along with the model application procedures. With the exception of using a 2009 future-year and performing daily mobile source em issions modeling, the proc edures used in the ASIP CMAQ 36/12 km modeling are identical to those used in VISTAS (ENVIRON, 2004; Morris et al., 2004a,b).

5.1 Meteorological Inputs to Emi ssions and Air Quality Models

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

The National Center for Atmospheric Resear ch (NCAR)/Pennsylvania State University (PSU) Fifth-Generation Mesoscale Model (MM5 ) (v3.6) is being used by the VISTAS meteorological modeling contactor, Baron Advan ced Meteorological Systems (BAMS) (Olerud, 2004a-d) to simulate meteorology at a 36-km re solution for calendar year 2002 over the entire

continental United States and incl uding portions of Canada, Mexic o, and the Atlantic and Pacific Oceans. MM5 is also being applied over the s outheastern U.S. using a 12 km resolution grid. The MM5 is a three-dimensional prognostic mete orological model that is used not only for meteorology studies but also for ai r quality studies. Some of th e physics used in the simulation include one-way nesting; nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and mixing ratio; explicit treatmen t of moisture; cumulus cloud parameterization; vertical mixing of momentum in the mixed laye r; PBL process paramete rization; atmospheric radiation; sea ice treatment; and snow cover (see Chapter 2 for more details).

After the MM5 simulation is completed, the MM5 output files are transferred to the emissions and air quality modeling team and anal yzed by the Meteorological Gatekeeper. The Meteorological Gatekeeper performs two main role s; (1) to provide an independent evaluation of

the 2002 MM5 simulation that also serves to determine whether the MM5 data have been transferred correctly from the VI STAS meteorological modeling cont ractor and (2) to process the 2002 MM5 output using Version 2.2 of the Mode ls-3 CMAQ Meteorological-Chemical Interface Processor (MCIP) to generate meteorological fields that will be used for emissions processing and air quality simulation.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-2 5.1.1 MCIP Reformatting Methodology

The Models-3 Community Multiscale Air Qual ity (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particles) air quality problems. But befo re running the CMAQ Chemical Transport Model (CCTM), the MM5 generated mete orological data must be pr e-processed and converted to

Models-3 consistent data stru ctures. MCIP Version 3.0 will be used to preprocess the MM5 meteorological output. The “pass through” option in MCIP will be used in the modeling. One of MCIP’s functions is to translate meteorological parameters from the output of the Pennsylvania State University/National Center for Atmosphe ric Research (PSU/NCA R) Mesoscale Modeling System Generation 5 (MM5) to th e Models-3 input/output applicat ions program interface (I/O API format) which is required for operation of Models-3 CMAQ processors. Some other necessary parameters not availabl e from the meteorological model are estimated with appropriate diagnostic algorithms in the program. The key functions of MCIP include:

1. Reading in meteorological model output files

2. Extraction of meteorological data for CTM window domain

3. Interpolation of coarse meteorol ogical model output for finer grid

4. Collapsing of meteorological pr ofile data if coarser vertical resolution data is requested

5. Computation or passing through surface and PBL parameters

6. Diagnosing of cloud parameters

7. Computation of species-speci fic dry deposition velocities

8. Generation of coordinate depe ndent meteorological data fo r the generalized coordinate

CCTM simulation

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

The MCIP processor transforms the data into I/ O API format while also calculating several new data fields (e.g. low, middle, an d high cloud fractions) that are not readily available in the raw MM5 output. It also interpolates temperature and wind speed to observation height (1.5m and 10m, respectively). The MCIP processor cull s a minimum of six cells about the domain periphery to minimize edge eff ects in the MM5 simulation. MCIP can be used to further reduce the rows or columns in the MM5 data so that the domain definition for the MCIP output files precisely matches the domain used in the air quality modeling. MCIP also allows MM5 layers to be “collapsed” (i.e., some layers can be aggregated). When feasible it is desirable to use the same layer structure in the air qual ity model as in the MM5 to prevent errors associated with aggregating layer data and to maintain consis tency between data produced by the meteorological model and those used by the chem istry-transport model. Howeve r, due to computational costs associated with using large number of vertical layers, ve rtical layer collapsing is typically used to reduce the total number of layers used by the CCTM. In the ASIP a nd VISTAS modeling we will collapse from 34 layers in MM5 output into 19 layers for the CMAQ air quality simulations. The first 8 layers of CMAQ, up to approximately 450 m AGL, will match the MM5 vertical layer structure exactly. The MM5 layers are then “doubled up” in CMAQ, up to a height of approximately 3,500 m AGL. The region top for CMAQ is the same as used by MM5, 100mb (approximately 15 km AGL). The 36 km analys is domain contains 148 columns, 112 rows, and 19 layers. The 12 km analysis domain covers 168 columns, 177 rows, and 19 layers. More details on the CMAQ modeling domain definitions ar e provided in Chapter 4 with the vertical layer structure of MM5 and MCIP/CMAQ shown at Table 4-3.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-3 5.1.2 Products of the Meteorological Input Development Process

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

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

Table 5-1 shows the configuration to be used in MCIP Version 3.0 for processing the 2002 MM5 output to produce CCTM-ready meteorology input files.

Table 5-1. MCIP V3.0 Configuration used In the ASIP Modeling.
Module or option Values or setting Additional Information

PBL value computation option 1 Use PBL value from input meteorology

Radiation fields 1 Use radiation fields from input meteorology

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

routine

Use PURB Kz_min Option True Calculate Kz_min as a function of percent urban land use

Sea Salt Deposition True Output dry deposition parameters for
Na and Cl

Output interval 60 Unit is in minutes

Vertical layer structure 19 layers See Chapter 4

5.2 Development of Emissions Model Inputs and Resultant Inventories

The base year emissions inventory for AS IP modeling are founded on 2002 Consolidated Emission Reporting Rule (CERR) inventories s ubmitted to VISTAS by participating state or local agencies and compiled by VISTAS emission inventory contractors in NEI Input Format (NIF) 3.0. These emissions were reviewed by VISTAS stakeholders and considered complete in January of 2004, with minor modifications submitted since that time.

Non-VISTAS state emissions are based on inventories obtained by the Study Team and determined to be representative of the 2002 epis ode year. Base year 2002 emission inventories for non-mobile source categories were obtained fr om each RPO in the U.S. domain. These dataJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-4 were supplemented with EPA based VMT and MOBILE6 input files necessary to develop onroad mobile source emissions domain-side. Additionally, an inventory of point source resolved agricultural fire emissions were provided by the western state RPO (WRAP) and utilized in the modeling.

Mexican and Canadian emissions are based on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. At this time, these

inventories are the same as in Phase I.

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

A final revised 2002 VISTAS state emission inve ntory is expected in February 2006 and will be used in the final model performance dem onstration and configuration expected to begin in spring 2006. Non-VISTAS state emissions ar e expected to be based on RPO updated base year emissions augmented with additional data provided by RPO, State, and international sources. As in the initial revised modeling, ac tual 2002 calendar year emissions will be modeled for EGU and fires for base case model performa nce evaluation, while “typical year” emissions for these categories for 2002 and the future year s will be processed during the strategy runs.

These emissions will then be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for ai r quality modeling using Version 2.1 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal, spatial, and speciation profiles and cross-reference data currently provided with the 2.1 release of the model augmented with any r ecommended and approved emission profile data provided by the emissions inventor y contractor or obtained from EPA prior to initial emissions modeling. The processing will be adjusted for e ach run to account for the specific air quality model (AQM) input required by CMAQ.

5.2.1 Emissions Modeling Methodology

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

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

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

The study team will acquire emissions es timate data from Emissions Inventory Contractor, in the NIF 3.0 format for purposes of generating the emission inventory base yearJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-5 files necessary for the ASIP cont ract. These data will be augmen ted with highway mobile source data submitted to the ASIP emission inventory co ntractors from the VISTAS participating States.

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

to be modeled.

5.2.2 Set-up of SMOKE Over the ASIP Domain

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

To produce an emissions inventory to support annual modeling, representative time periods will be selected and modeled. Area, nonroad, and point sources will be modeled as a block of Thursday, Friday, Saturday, Sunday, Monda y one per month (total of 60 days modeled). For 36-km modeling, onroad motor vehicles will be represented by an entire single week for

each month. This selection criteria allows for the representation of day-of-the-week variability in the on-road motor vehicles, a nd models a representa tion of the meteorol ogical variability in each month. For 12-km runs, onroad motor vehicl es will be run for every day of the year. Holidays will be modeled as Sundays. A list of modeled holidays is provided in Table 5-2. The biogenic emissions will be modeled on a day specific basis (365 days).

Table 5-2. SMOKE Modeled Holidays.

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

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

purposes.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-6 The domain for the Phase II episode will be identical to the Phase I domain, which is based on the EPA’s 36-km national CMAQ domain, illu strated in Figure 5-1 below (details on the modeling domains are provided in Chapter 4).

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

The parameters for the SMOKE runs are as follows:

Episodes : 2002 Calendar Base Year. Optional tasks for episodic modeling of up to 60 days that will likely be from 2003.

Future Years : To be determined.

Output Time Zone : Greenwich Mean Time (zone 0)

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

Domain :

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

• 12 Kilometer Grid: Origin at (108, -1620) kilometers with 168 rows by 177 columns and 12-km square grid cells.

Layer Structure : The CMAQ layer structure will be 19 layers, with specific layer positions defined in the meteorology files (see Chapter 4).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-7 CMAQ Model Species : The CMAQ initial configuration will be for the CB-IV chemical mechanism with PM. The model species in the emission input files will be: CO, NO, NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO 3, POA, PSO4, and PMC.

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

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

sources.

Producing 365 day-specific input fi les for all source categories places a burden on available computing facilities, data management systems, and would adversely affect the ASIP schedule. Selecting representative model days for some or all of the source categories reduces the processing and file handling require ments to a more manageable le vel and in most cases does not compromise the accuracy of the emissions files.

Other current or recent projects undertak en by EPA, WRAP and MRPO have used a selection approach for all of the source categorie s (except biogenics) that use a representative weekday/Saturday/Sunday either for each month or each season to model all of the emissions files. In an attempt to better represent the level of temporal and spatial de tail available for each source category, we have develope d a more detailed strategy.

Biogenic emissions will be modeled for each episode day, using the daily meteorology. Point sources, including CEM-based and fire emissions will be modeled for each episode day to take advantage of the available day-specific emi ssions and meteorology. Area sources, including nonroad mobile and dust emissions do not utilize meteorological data, and are temporally allocated by monthly, daily and hour ly profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved by selecti ng representative Thursday, Friday, Saturday, Sunday, and Monday profiles for each month.

Motor vehicle emissions are influenced by me teorological variabilit y, but the processing requirements for daily motor vehicle emissions were determined to be prohibitive under the current schedule. Rather than utilizing averag ed meteorological data or pre-calculated motor vehicle emissions, for the 36-km domain, a single week per month was selected for modeling.

This week was selected from mi d-month, to try to best represen t the average temperature ranges for the month, and also adjusted to exclude holid ays that would require atypical processing. As noted above, in the 12-km domain runs, daily modeling will be conducted. The area source modeling dates were also select ed from these ranges to simpli fy data handling procedures.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-8 2002 36-km modeling of Onroad Mobile Sour ces Represented by the Following Weeks : January 13-19 February 10-16

March 10-16 April14-20 May 12-18

June 9-15 July 14-20 August 11-17 September 15-21

October 13-19

November 10-16

December 15-21

2002 12-km onroad Mobile Source Modeling was Performed for Every Day

5.2.3 Development of Point Source Emissions

Stack parameters are often more important to the reliability of the air quality modeling results than the emissions rates themselves. Stack parameter data are frequently incorrect, especially in some of the current regional modeling inventories and careful QA is required to assure that the point source emi ssions are properly located both hor izontally and vertically on the modeling grid. To screen for si mple, but potentially serious invent ory errors such as these, the study team has modified procedures originally developed by EPA to quality assure, augment, and where necessary, revise, stack parameters to examine the accuracy of the point source emissions, as well as standardize procedures to identify and correct stack data errors. These procedures will be implemented in the NIF to IDA conversion step of the inventory development. Additionally, SMOKE has a number of built-in QA procedures designed to catch missing or out-of-range stack parameters. Thes e procedures will also be invoked in the processing of the point source data.

For the ASIP initial baseline modeling, we w ill be separating the point source emissions into EGU and non-EGU categories. The non-EGU category will not be using any day or hourspecific emissions. All non-EGU point source emissi ons will be temporally allocated to month, day, and hours using annual emissions and source cat egory code (SCC) based allocation factors.

These factors will be based on the cross-referenc e and profile data suppl ied with the SMOKE 2.1 version and will be supplemented with relevant da ta provided to the study team by ASIP or its

contractors.

For EGU sources with EPA reported CEM data or with hourly emissions provided by stakeholders, actual hourly data will be used to temporally allocate emissions. For those sources where EPA CEM data are utilized, NOx, SO2, and h eat input-based hour-s pecific profiles were developed and applied to NOx, SO2, and all othe r emissions, respectively. This ensured that the annual emission values provided by the emissions inventory cont ractor were maintained, but distributed using hourly to annual profiles. For sources providing hour-specific data and where they were approved by the State in which they operated, those data were substituted for EPA

CEM-based distributions.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-9 To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input

data were collected from the 2002 Continuous Em issions Monitoring (CEM ) datasets, and used to develop unit-level temporal distributions. The hour, day of week, and monthly specific temporal profiles will be used in conjunction w ith the emissions inventory supplied emissions

data to calculate hourl y EGU emissions by unit.

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

5.2.4 Development of Area and Non-Road Source Emissions

All area and non-road source emissions will be temporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allo cation factors. These

factors will be based on the cr oss-reference and profile data supplied with SMOKE Version 2.1 and will be supplemented with relevant data provided to the study team by ASIP or its

contractors. Area and non-road s ources will be spatially allocat ed in the domain based on SCCbased spatial allocation factor files. If an ar ea or non-road source SCC does not have an existing cross-reference profile a ssigned to it, the county-level emissi ons will be allocated by population density in the re spective county.

A crustal PM transport factor will be applie d to fugitive dust emission sources that have been identified in U.S. EPA modeling to have on ly a portion of its mass transportable from the source of the emission generati on. The EPA’s studies indicate th at 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simulation. For this reason, we will apply county-specific fugitive dust emissions transport factors to these sources in the modeling files to adjust PM emissions accordingly. These factors can be located at http://www.epa.gov/ttn/chief/emch /invent/statusf ugdustemissions_082203.pdf

.

5.2.5 Development of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE will be used to develop the base year on-road mobile source emissions estimates for CO, NOx, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates w ill be combined with gridded, episode-specific temperature data to calculate th e gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link loca tion and subsequently summed to the grid cell level, the off-network em issions estimates are spatially allocated based on a combination of the FHWA Version 2.0 highw ay networks and population. For the ASIP 36/12 km modeling, no link based data will be us ed. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by the meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-10 • Hourly and daily minimum/maximum temperatures;

• Facility speeds;

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

• Adjustments for running losses;

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

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

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

direct the calculation of the MOBILE6 emissions factors.

5.2.6 Development of Biogenic Source Emissions

A revised version of a commonly used biogenic emissions model, the Biogenic Emissions Inventory System (BEIS), has recen tly been developed and tested by EPA over two separate modeling domains/episodes. This vers ion of the model (BEIS-3, v0.9) contains several changes over BEIS-2, in cluding the following:

• Vegetation input data -- are now base d on a 1-km Biogenic Emissions Landuse Database (BELD3) ve getation data base,

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

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

A series of sensitivity modeling simulations has been completed a nd concluded that the more recent BEIS-3 methodology will impact base cas e model ozone predictions in most parts of the U.S. The preliminary tests have also shown that the newer biogenic emissions do not appear to have a large effect on: 1) the control signal response, 2) relative redu ction factors resulting from a projected emissions change, or 3) overall regional model performance in the eastern U.S.

For this particular application of BEIS-3, Version 0.9 as currently incorporated in the SMOKE processor will be used. This means that: 1) soil NO emissions shall be prepared without the input of specific soil moisture a nd precipitation data and 2) MEOH emissions will not be modeled explicitly. Otherwise, the mo deling should be identical to a BEIS-3 (v1.0) application.

The BELD-3 landuse data on a Lambert confor mal grid at 1-km resolution have already been developed, are available, and will be used to estimate biogenic emi ssions in this study. The BEIS model also requires as input hourly, gri dded temperature and solar radiation data to estimate biogenic emissions, and these data will be derived from the MM5 predictions.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-11 5.2.7 Wildfires, Agricultural, and Prescribed Burns, Wind Blown Dust and Sea Salt

Source Emissions

Wildfires, Agricultural, and Prescribed Burns

Wildfire, agricultural, and prescribed burn emi ssions will be handled separately from the standard area source input files. The study team expects to receive monthly estimates of fire emissions from the emissions inventory contra ctor, which include burn acreage and biomass loading information for the VISTAS states. De pending on the completeness and quality of the data received, attempts will be made to calculate spatial and temporal distributions of the fire emissions, rather than relying on standard distri bution profiles. Also, the study team will attempt

to calculate vertical distribution of the fi re emissions, based on fire size and biomass involvement. The SMOKE 2.1 can model fire pl ume rise if provided with the following

variables:

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

The WRAP Fire Emissions Joint Forum Em issions Inventory Report (FEJF, 2002) has documented an approach to calculating these plum e descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to ca lculate representative hourl y plume profiles. These profiles are then used by SMOKE 2.1 to distribute the vertical em issions for the fires. To successfully model fires as elevated point sour ces, the data provided by the emissions inventory contractor will need to include both the day or days on which the fire occurs, and a spatial

identifier of the fire location. At a minimum, a latitude and longit ude of the fire location can be used, while a polygon coverage would be preferable.

In addition, wildfire and prescribed burn da ta, including emissions estimates and plume rise distributions, will be obtaine d from other RPOs and used to supplement the inventory for the

non-VISTAS states.

Windblown Dust

PM 10 and PM 2.5 emissions from wind erosion of natural geogenic sources (SCCs 2730100000 [total] and 2730100001 [dust devils]) will be excluded from the resulting modeling files using a 100 percent reducti on in the control packets.

Sea Salt

CMAQ currently treats sea salt as an inert PM species. That is, the sea salt is not allowed to chemically interact with other species, such as producing particulate sodi um nitrate. There are plans to update CMAQ to have chemically active sea salt, but it is unclear whether such an update will occur during the ASIP modeling. Accordingly, the initia l modeling will be conducted without any sea salt em issions. If CMAQ is updated to treat chemically active Sea Salt, or if CAMx is run using its full-science op tions, then Sea Salt emissions will be generated using appropriate procedures (e.g., as was done in VISTAS Phase I).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-12 5.2.8 Speciation and Reform atting of Emissions

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

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

5.2.9 Development of Modeling Inventories

The emissions inventories modeled for th e VISTAS ASIP can be grouped into four distinct types: (1) 2002 actual annual emissi on inventories, (2) 2002 typical annual emission inventories; (3) 2009 base case emission inventor ies; and (4) 2009 control strategy emission inventories. In all cases, the Study Team expect s to receive the emissions inventory data for the ASIP states from the emissions inventory cont ractor, add non-ASIP stat es and Canadian and Mexican data acquired from alternate EPA a nd/or RPO sources, and produce the CMAQ ready

emissions files.

5.2.9.1 2002 Annual Inventories

2002 36/12 km actual and typical annual invent ories will be developed under ASIP for CMAQ modeling. These i nventories will be identical to the 2002 36/12 km actual and typical inventories developed under VI STAS, only the 2002 12 km ASIP inventories used everyday onroad mobile source emissions modeling.

5.2.9.2 2009 Annual Future Year Inventories

2009 36/12 km annual emission inventories will be develop for an On-the-Books (OTB) base case emissions scenario and, as needed, co ntrol strategy emission scenarios. The 2009 Onthe-Books base case will include growth and cont rols that have been promulgated, such as the following regional rules:

• NOx SIP Call

• Tier 2/Low Sulfur

• Heavy Duty Diesel

• Non-Road Engine

• Clean Air Interstate Rule (CAIR)

Not included in the 2009 OTB base case emissions scenario are Regional Haze controls including Best Available Retr ofit Technology (BART) controls and any additional controls

needed to attain the 8-hour ozone and PM 2.5 standards. Control strategies to be modeled will be determined during the course of the study.

5.2.10 Products of the Emissions Inventory Development ProcessJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-13 In addition to the CMAQ-ready input files ge nerated for each hour of the days modeled in the ASIP annual run, a number of quality assu rance (QA) files may be prepared and used to check for gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distri bution of the emission provides insight into the quality and accuracy of the emissions inputs.

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

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

Χ Producing pie charts emission summaries that highlight the contribution of each emissions source component (e.g. nonroad mobile).

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

Χ Spot check vertical allocati on of point sources using PAVE.

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

process.

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

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

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

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

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

Χ State and county totals by day after tempor al allocation for each source category for representative daysJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-14 Χ State and county totals by model species after chemical specia tion for each source

category

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

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

sources

Χ Totals by state and SCC from the invent ory for area, mobile, and point sources

Χ Totals by county and SCC from the invent ory for area, mobile, and point sources

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

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

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

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

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

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

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

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

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

Χ PAVE plots of gridded inventor y pollutants for all pollutant s for area, mobile, and point

sources

5.3 Model Configuration and Modeling Approach

5.3.1 CMAQ Science Configuration

This section described the model configuratio n and science options to be used in the ASIP modeling effort. The recommendations ar e based on testing and model evaluations of several models or model configurations carried out in the VISTAS study, as well as related studies including WRAP, CENRAP, BRAVO, CAIR and other studies. Ta ble 5-3 summarizes the proposed configuration for CMAQ. The late st version of CMAQ is currently Version 4.5

that was released October 2005. CMAQ Version 4.5 includes several enhancements and corrections over precious versions of the model, including correc tions to the mass conservationJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-15 algorithms and active treatment of Sea Salt. VISTAS has upda ted CMAQ Version 4.5 with the SOAmods secondary organic aerosol (SOA) module enhancements (see Section 1). ASIP has adopted CMAQ Version 4.5 with SOAmods as its primary model for dem onstrating attainment

of the 8-hour ozone and PM 2.5 standards.

In the CMAQ base configuration we will run both the 36 km and 12 km grids using oneway grid nesting where the boundary conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ BCON processor. The base configuration of CMAQ will use 19 vertical layers up to a region top of 100 mb (approximately 15 km AGL).

The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion. The minimum eddy diffusion constant (Kz_min) will be based on the new CMAQ Version 4.5 PURB option that depends on the percent of urban land use in the grid cell with Kz_min ranging from 0.1 m 2 /s to

2.0 m 2 /s when the grid cell is 100% urban.

The MCIP3.0 will be used to process the MM5 data using the “pass through” option and outputting parameters to treat Sea Salt dry deposition and the PURB Kz_min option.

The AERO4/ISORROPIA aerosol chemistry sche me will be used for inorganic aerosol thermodynamics. During the VISTAS testing of the CMAQ Version 4.3 and Version 4.4 models

it was noted that the AERO3/ISORROPIA aerosol modules failed to conserve sulfur and nitrogen mass, an examination of the AERO4/IS ORROPIA module revealed that this is also likely true for the new version in CMAW Vers ion 4.5. A mass conservati on patch developed by the Georgia Institute of Technology (GIT) was a dded to the model that renormalized the total sulfur, reactive nitrogen and re duced nitrogen species after the call to the aerosol modules to conserve sulfur and nitrogen mass in the model. Sensitivity simulations with and without the mass conservation patch showed it has a very small effect and does not affect model performance, however mass conservation is a f undamental characteristic of the real-world atmosphere so ASIP and VISTAS have retain ed the mass conservation patch for the CMAQ aerosol modules in the modeling. Note that the GIT mass conservation patch is different that the corrections to the CMAQ mass conservation in CMAQ Version 4.5, the GI T mass conservation patch corrects for sulfur and nitrogen mass loss or gained within th e CMAQ aerosol modules, whereas the CMAQ Version 4.5 mass conservation update corrects mass conservation problems in earlier versions of CMAQ in the advec tion algorithms. The SORGAM scheme, which includes a reversible thermal equilibrium, will be used for secondary organic aerosols. The CB4 gas-phase mechanism will be used in the ASIP and VISTAS modeling.

Table 5-3. ASIP Model Configuration for the CMAQ Chemical Transport Model. Model Option CMAQ Model Version Version 4.5 (October 2005)

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

Horizontal Advection PPM

Vertical Advection PPM Horizontal Diffusion Spatially Varying Vertical Diffusion (Kz) K V (Eddy Diffusion)

Minimum Kz PURB 0.1 to 2.0 m 2 /sJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-16 Model Option CMAQ MM5 Configuration Pleim-Xiu/ACM MM5 Processing MCIP3.0 Pass Through Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver EBI/Hertel Secondary Organic Aerosol SORGAM w/ SOAmods Aqueous-Phase Chemistry RADM Aerosol Chemistry AE4/ISORROPIA Dry Deposition Pleim-Xiu

Plume-in-Grid Off Initial Concentrations CMAQ Default w/ ~15 day spin-up Boundary Conditions 3-Hourly 2002 GEOS-CHEM

Emissions 2002 VISTAS States
2002 RPO Inventories

5.3.2 Spin-Up Initialization

For the 2002 annual CMAQ modeling, the model will be exercised separately for four quarters. The 2002 MM5 modeling started on December 17, 2001 at 12Z. Thus, allowing for 12 hours of spin up of the MM5 model, CMAQ will be initialized at 00Z on December 18, 2001. This results in a 13 day spin up period for CMAQ and the first quarter run segment of 2002. For the other quarter run segments of 2002, CMAQ will be initialized with a 15 day spin up period.

5.3.3 Boundary Conditions

Boundary Condition (BC) concentrations along the lateral edges of the 36 km Inter-RPO modeling domain were based on a 2002 simulatio n of the GEOS-CHEM gl obal chemistry model (Jacob, Park and Logan, 2005). The 2002 GEOS-C HEM output were processed to the CMAQ 36 km domain boundary cells and ve rtical layer structure (Byun, 2004). The resultant BCs were 3-hourly day-specific based on the 2002 GEOS-CHEM simulation.

5.3.4 Photolysis Rates

Several chemical reactions in the atmosphe re are initiated by the photodissociation of various trace gases. To accurately represent the complex chemical transformations in the atmosphere, accurate estimates of these photodi ssociation rates must be made. The Models-3 CMAQ system includes the JPROC processor, whic h calculates a table of clear-sky photolysis rates (or J-values) for a specific date. JPROC uses default values for total aerosol loading and provides the option to use default column O3 data or to use TOMS data for total column O3.

JPROC produces a "look-up" ta ble provides the photolysis rate s as a function of latitude, altitude, and time (in terms of the number of hour s of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six latitudinal bands (10o, 20o, 30o, 40o, 50o, and 60o N), se ven altitudes (0 km, 1 km, 2 km , 3 km, 4 km, 5 km, and 10 km), and hourly values up to ∀ 8 hours of deviation from local noon. During model calculations,January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec5.doc 5-17 photolysis rates for each model grid cell are estimated by first interpolating the clear-sky photolysis rates from the look-up table using the grid cell lati tude, altitude, and hour angle, followed by applying a cloud correction factor.

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

ranges.

During the VISTAS modeling errors were fo und in the TOMS data for a few short periods that affected the CMAQ photolysis rates input. The days with the bad or missing TOMS ozone column data were identified (June 10, August 2-12; and November 18-19) and the following approach was used to fill the bad data:

• June 9 data were used for June 10;

• August 1 data were used for August 2-7;

• August 13 data were used for August 8-12; and

• November 17 data were used for November 18-19.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-1 6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assuran ce procedures that will be used in the ASIP modeling. More details are provided in the ASIP Quality Assurance Project Plan (QAPP; Morris and Stella, 2005).

6.1 Quality Assurance Objectives

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

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

• peer reviewed theory and equations;

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

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

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

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

• documentation of any changes from th e original quality assurance plan;

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

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

choices are consistent w ith the project objectives.

The modeling team has already developed QA documents and procedures in the VISTAS effort (Morris, Tonnesen and Tesche, 2003) a nd ASIP modeling (Morri s and Stella, 2005).

6.2 Emissions Model Inputs and Outputs

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical step in performing air quality modeling studies. Because emissions processing is tedious, time consuming and involves complex manipulation of ma ny different types of large data sets, errorsJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-2 are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected.

As part of the VISTAS and ASIP QA effo rt, an “Emissions Gatekeeper” function was implemented to QA the emission inventories. The role of this Gatekeeper is to perform quality

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

(1) EI data obtained from the VISTAS and AS IP emissions inventory contractors; and (2) The emission inventory to be used for modeling outside of the States in the Southeastern US region.

Specifically, the Emissions Gate keeper will review the content and format of the provided emission inventories ensuring an appropriate appraisal of the em issions data and estimates for

the Southeastern US States. Other tasks will include any additional translation from mass emissions files into the emissions modeling input file structure necess ary for modeling. The Study Team will supplement these activities wi th QA checks on the intermediate and model output files using internal and public domai n visualization and diagnostic packages.

For ASIP, we propose to continue with multis tep emissions the QA/QC approach applied in the VISTAS modeling. This includes th e initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the ai r quality modeler of the processed model ready emission files. This multistep process with thre e separate groups involved in the QA/QC of the emissions is much more likely to catch any erro rs prior to the air quality model simulations.

6.2.1 Emissions Modeling QA/QC

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

model will be used for emissi ons processing, some of the mo re advanced EMS input error checking algorithms will be used to screen the data and identify potential emission input errors. Additionally, EPA has issued a revised stack QA and augmentation procedures memorandum that will be used to identify and augment any outlying stacks.

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

SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of pr ocessed emissions as daily totals according to species, source category and county and state bo undaries. These summaries will then be compared with summary data prepared for the pre-processe d emissions, e.g., state and county totals for emissions from the augmented emissions data.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-3 6.2.2 QA of the Model-Re ady Emissions Impacts

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

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

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

to show the vertical distribution of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The obj ective of this step is to iden tify possible errors in vertical

distribution of emissions.

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

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

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

SO 2 control strategy is being analyz ed and there are changes in emissions for other pollutants or

for SO 2 outside of the VISTAS states problems in em issions processing can be identified prior to the air quality model simulation.

6.3 Meteorological Model Outputs

As part of the VISTAS effort, a “Meteorolo gical Gatekeeper” function was implemented. As ASIP is using the exactly same 2002 36/12 km meteorological fields as VISTAS, the VISTAS meteorological QA/ QC procedures are also applicab le to ASIP. The task of the VISTAS Gatekeeper was to provide an indepe ndent review and quality assurance of the meteorological modeling and related data se ts developed by the VI STAS meteorological modeling contractor (BAMS) and used subsequently by the emi ssions and air quality modeling teams. This Gatekeeper QA review serves two sp ecific purposes: (a) to ensure that any potential problems with the data sets (should they exist) are identified and correct ed in a timely manner,January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-4 and (b) to provide the st udy team with information to support ongoing CMAQ model performance testing and sensitivity analyses. In the case of meteor ology, the Gatekeeper’s independent QA analysis of the MM5 meteorological data sets serves to pr ovide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model perf ormance and sensitivity analyses.

The Meteorological Gatekeeper also has persona l responsibility for the quality and chain of custody of the meteorological data sets. In performing the Gatekeeper quality assurance activity, one of the first steps is to conduct an independent op erational evaluation on the MM5 model results at 36 km and 12 km grid scale. This evaluation covers surface and aloft wind direction, temperature, mixing ra tio, precipitation, and planetary boundary laye r (PBL) depths on a continental scale (36 km) and subregional scale (12 km) basis. The specific techniques to be used are described in the MM5 model perfor mance protocol prepared for EPA for annual modeling (McNally and Tesche, 2002). The Gate keeper will also perform supplemental, ad hoc analysis of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to exercise the Meteorological Chemistry Interface Processor (M CIP) Version 2.2 is to read the MM5 outputs from BAMS and produce binary input files fo r the CMAQ Chemical Transport Model (CCTM) to provide the complete set of parameters nece ssary in the emissions processing and air quality modeling.

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

3⁄4 Upon receiving the MM5 and MCIP 2.2 output files from BAMS, we will verify the integrity of the file transfer (e.g., no missing and/or corrupted files);

3⁄4 Since the CMAQ modeling domain is a subset of the MM5 domain, we will verify that the modeling domain and vertical layer structures in the MCIP files are identical to the CMAQ modeling domain;

3⁄4 We will select several days of the MM5 output and reprocess the MM5 files with MCIP v2.2 using the predetermined MCIP opti ons. We will then compare the MCIP files with those provided by BAMS to verify that we obtain identical results from the MCIP processing.

3⁄4 We will create horizontal and vertical plots of temperature, pressure, precipitation, modeled flow patterns, PBL he ights, etc. to assess whethe r the MCIP output fields are reasonable;

3⁄4 The VISTAS 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the WRAP 2002 MM5 simulation as an independent QA and evaluation of the database.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-5 3⁄4 We will make the plots available on the VISTAS website for viewing and download.

3⁄4 We will re-process the MM5 output using MCIP3.0 so that ASIP and VISTAS can use the latest (Version 4.5) version of the CMAQ model that includes active treatment

of Sea Salt emissions.

6.4 Air Quality Model Inputs and Outputs

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

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

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

• Evaluation of CCTM results to verify that model output is re asonable and consistent with general expectations.

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

• Evaluation of the CCTM results ag ainst concurrent observations.

• Backup and archiving of cr itical model input data.

The most critical element in the QA plan for CMAQ simulations is the QA/QC of the meteorological and emissions input files. The major QA issue specifi cally associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the name of ke y science options or inputs are included in the name of 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. For example, if a model simulation is performed using the CB4 mech anism, all compile and run scripts contain the variable definition “$MECH = CB4” , and this variable is hard coded into the script for the executable name, the output file name, and the ou tput directory name. This procedure produces long file/directory names but it effe ctively prevents mistakes or ma kes mistakes readily apparent if they do occur.

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

will be archived.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec6.doc 6-6 We will also perform a post-processing QA of the CMAQ output files similar to that described for the emissions processing. We will gene rate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prep ared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions inputs can be discovered by viewing the animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This provides a summary that can be useful for more quickly comparing various

model simulations.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-1 7.0 MODEL PERFORMANCE EVALUATION

7.1 OVERVIEW

A critical component of every air quality modeling study is the model performance evaluation where the modeled estimates for the current year base case are compared against observed values to access the model’s accuracy and pr ovide an indication of its reliability. As noted previously, the ASIP modeling database, models and modeling approach are intricately linked to the VISTAS modeling. Chapter 11 presents a summary of the current Scope of Work (SOW) for the ASIP Emissions and Air Quality Modeling activities. This ASIP SOW delineates the tasks, approach and schedule for carrying out the various technical activities described in greater detail in this Modeling Protocol. Although the ASIP SOW doe s not explicitly include the model performance evaluation (MPE) component of the modeling that is being carried out unde r VISTAS, the MPE is a critically important component of a modeling study so the approaches to be used in the model

evaluation of the ASIP/VISTAS databases are desc ribed in this section of the ASIP Modeling

Protocol.

Consistent with the spirit of a Modeling Protoc ol for regulatory decision-making, this section lays out the ‘roadmap’ for achieving an adequately tested modeling system for regulatory usage. But, obviously, this does not mean that every analysis identified in this chapter will be carried out or is indeed even possible given the ASIP and VIST AS schedule and resources, the existing aerometric data bases, and present technology constraints. The roadmap guides the way to the desired destination – in this case, an evaluated, operati onal PM/regional haze/ozone modeling system – but does not commit the driver to exploring every side street and back country road along the way. Indeed, one expectation of the ASIP and VISTAS is a close working relati onship with the modeling

team to ensure that the available resources and sc hedule are applied most efficiently in reaching the aforementioned goal.

This chapter describes a range of model testing methodologies potentially available to the Emissions and Air Quality Modeling study team in its efforts to adequately ev aluate the performance of the CMAQ air quality modeling system for th e 2002 annual period. The final 2002 actual base case modeling will likely be conducted in early 2006. Preliminary 2002 base case simulations and abbreviated model performance evaluation has uncovere d several performance i ssues that have been attempted to be addressed through improvements in model inputs, formulation or both. In this Section we set forth a broad range of methods and techniques that may be brought to bear in examining CMAQ model performance. We identif y the core operational evaluation procedures, recommended in EPA (2001) PM 2.5 and regional haze modeling guidance that has been and will continue to be performed as part of the VISTAS modeling efforts. We also describe a broad range of additional performance testing methods that ma y be worth considering, if deemed needed and

resources and time are available.

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

3⁄4 The VISTAS/ASIP SOWs places clear limits on the resources available to perform model evaluation analyses. Accordingly, some evaluati on steps, while desirable, simply may not beJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-2 possible given current funding levels;

3⁄4 The VISTAS/ASIP SOW places stringent schedule demands on the model evaluation. A number of the model performance evaluation met hods introduced in this chapter (e.g. Weight of Evidence analyses, diagnostic testing w ith individual measurement networks, PM indicator species and ratios analyses could very likely require more time to carry out given their quasi-research nature. Since VISTAS/ASI P is not a model research and development effort, but rather an operational evaluation of existing modeling systems for regulatory decision-making, some interesting, but time consuming analyses simply may not be possible given the present schedule; and

3⁄4 To conform to the EPA PM guidance documents requirements for PM model testing, it may not be necessary to conduct many of the diagnos tic and Weight of Evidence tests identified in this protocol. Indeed, an adequate eval uation of the VISTAS/ASIP modeling system may be possible through straightforward applica tion of the core operational performance evaluation procedures identified in EPA’s 2001 draft PM 2.5 and regional haze and final 2005 8-hour ozone guidance.

At a minimum, the evaluation of the CMAQ modeling system for the annual 2002 simulation will be consistent with EPA’s draft guidance on PM model testing and final 8-hour ozone modeling guidance. EPA’s guidance essentially calls for an operational evaluation of the model focusing on a specific set of gas phase and aerosol chemical species and a suite of statistical metrics for quantifying model response over the annual cycl e. The emphasis is on assessing: (a) How accurately the model predicts observed concentrations? and, (b) How accurately does the model predict responses of predicted air quality to changes in inputs? States are encouraged to utilize the evaluation procedures set forth in the earlie r 1991 guidance document (EPA, 1991) for gas phase species and the newer (2001 and 2005) guidance for PM and ozone species. Thus, in carrying out the initial operational evaluation and the subse quent final evaluation, we will implement the suggested EPA performance testing methodologies fo r the key gas phase and aerosol species. Since these methods are explicitly presented in EPA’s guidance documents, there is no need to repeat them

here.

We conclude by again emphasizing that most important goal of the CMAQ evaluation is to determine whether the aggregate modeling syst em (model codes plus input data sets and observational data for testing) offers sufficientlyreliable and accurate results that public decisionmakers may have reasonable confidence in using the model to help choose between alternative emission control strategies designed to regiona l haze reduction scenarios. If the CMAQ model evaluation, as outlined in this chapter, provides sufficient evidence that the modeling system is operating reliably and in conformance with measurem ents and scientific expectations, then specific justifications explaining why the model is acceptable for developing 8-hour ozone, PM 2.5 and regional haze strategies. Conversely, should the evaluation determine that the modeling system suffers from important flaws or e rrors that undermine its reliability or use, these findings will also be documented, together with recommendations regard ing the use of alternate methods, steps to improve the model and/or data base, or other approaches that can be used in the analysis.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-3 7.2 Context for the Model Performance Evaluation

We begin the discussion of the CMAQ 2002 modeling evaluation me thodology by reviewing how the model output is used to project future year 8-hour ozone and PM 2.5 levels.

When designing a model performance evaluation, it is important to understand how the modeling results will ultimately be used. EPA has published two versions of draft guidance for fine particulate and regional haze modeling (EPA, 2000; 2001), utilizing a Fine Particulate Guidance Workgroup to provide technical input in the development of both documents 1 . More recently, EPA has provided an informal update on the PM/reg ional haze modeling guidance (Timin, 2002) and conducted a PM model evaluation workshop (s ee, for example, Timin, 2004; Boylan, 2004) shedding additional light on what the final PM 2.5 and regional haze guidance document might contain. After issuing several draft guidan ce documents for 8-hour ozone modeling (EPA, 1999, 2005a, EPA issued the final 8-hour ozone m odeling guidance in 2005 (EPA, 2005b). These modeling guidance documents, along with the 1-hour ozone guidance (EPA, 1991) provide a framework for the VISTAS/ASIP model performance evaluation approach.

A key concept in EPA’s guidance for addressing 8-hour ozone and PM 2.5 issues and regional haze is that the modeling results should be used in a relative sense to scale or roll back the observed 8-hour ozone and individual particulate matter (PM) species concentrations. The modeled derived ratios of future-year to current-year scaling factor s used to project future-year 8-hour ozone and PM species components are called relative reduction fact ors (RRFs). As 8-hour ozone concentrations are made up as an average of 1-hour ozone con centrations and there is a wealth of model performance evaluation for 1-hour ozone, then the model performance for 1-hour ozone is important

also. Since the model is used to project future year PM 2.5 species components rather than total PM 2.5 mass, then the model performance for each of the components that make up PM 2.5 is actually more important than for total PM 2.5 mass which the standard was written for. These components are:

• Sulfate (SO4);

• Nitrate (NO3);

• Ammonium (NH4);

• Organic Carbon (OC);

• Elemental Carbon (EC); and

• Other Inorganic fine Particulate (IP or Soil).

The VISTAS/ASIP model testing will concentrate on an operational evaluation of those model predictions that are most necessary for estimating PM 2.5 (i.e., NH 4, SO 4, NO 3, OC, EC and IP) and 8- hour ozone (i.e., 1-hour and 8-hour ozone) concen trations. Where feasible and supported by sufficient measurement data, we will also evaluate the modeling system for its ability to accurately estimate coarse mass (CM) PM and other gas-phase precursor, product and indicator species. The correct simulation of gas-phase oxidant species is needed for PM since co rrect, unbiased simulation of gas-phase photochemistry is a necessary elemen t of reliable secondary PM predictions. This evaluation will be carried out across the full Southeastern US domain for the entire year and also on subdomains (e.g., ozone and PM 2.5 nonattainment areas) and month-by-month to daily basis to help

1 Members of the VISTAS modeling team participat ed on this work group over the two-year span of

its activities.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-4 build confidence that the modeling system is operating correctly. With this context in mind, we next turn to the philosophy of the model evaluation process.

7.3 Multi-Layered Model Testing Process

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) and final guidance for 8-hour ozone modeling (EPA, 2005b) affirms the recommendations of numerous modeling scientists over the past decade (see, for example, Dennis et al., 1990; Tesche et al., 1990, 1994; Seigneur et al., 1998, 2000; Russell and Dennis, 2000; Arnold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a comprehensive, multi-layered approach to model performance testing should be performed, consisting of the four components: operational, diagnostic, mechanistic (or scientific) and probabilistic. As applied to PM 2.5 and 8-hour ozone modeling, this multi-layered framework may be viewed conceptually as follows:

> Operational Evaluation : Tests the ability of the model to estimate PM concentrations, the components at PM 10 and PM 2.5 (i.e., sulfate, nitrate, ammonium, organic carbon,

elemental carbon and other PM 2.5 ) and 1-hour and 8-hour ozone concentrations. This

evaluation examines whether the measuremen ts are properly represented by the model predictions but does not necessar ily ensure that the model is getting “the right answer for the right reason”;

> Diagnostic Evaluation: For fine PM, this step tests th e ability of the model to predict PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 ) and associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; spatial variation; mass fluxes; and component s of light extinction (i.e., scattering and absorption). For 8-hour ozone the diagnostic evaluation tests the models ability to predict the temporal and spatial variations in ozone, ozone precursor species (e.g., VOC, NOx and CO) and key indicator species that provide indication of key photochemical regimes (e.g., NOx/ozone, HNO3/H2O2, NOy, etc.);

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

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

Within the constraints of the VISTAS/ASIP schedule and resources, the VISTAS/ASIP model evaluation effort will attempt to include elements of each of these components. The operational evaluation will obviously receive the greatest attenti on since this is the primarily thrust of EPA’s

2001 PM 2.5 and 2005 8-hour ozone guidance. However, we will consider, where feasible and appropriate, diagnostic and mechanis tic tests (e.g., use of probing tools, indicator species and ratios, aloft model evaluations, urban vs. rural performan ce analyses), traditional se nsitivity simulations to explore uncertainty, and comparison of the VI STAS/ASIP CMAQ performance with those from other groups (e.g., MANE-VU, MRPO, CENRAP) so me of which the use alternative science platforms (e.g., CAMx). The scope of these additional diagnostic and mechanistic tests will be shaped by the resources available and the timing of when such analyses are commissioned relative toJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-5 the VISTAS/ASIP schedule and resources.

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

7.4 Development of Consistent Evaluation Data Sets

7.4.1 Surface Measurements

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

database. Other sources of information come from the various PM monitoring networks in the U.S. These include the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends Network (CASTNet), (c) Southeastern Aerosol Research and Characterization (SEARCH), (d) EPA PM 2.5 and PM 10 Mass Networks (EPA-FRM), (e) EPA Speciation Trends Network (STN); (f) Nationa l Acid Deposition Network (NADP) and (g) EPA Supersites (EPA-SPEC) networks. Typically, th ese networks provide ozone, other gas phase precursors and product species, PM mass and species, and visibility measurements. Noteworthy for the VISTAS/ASIP evaluation is the 24-hour average and continuous speciated PM 2.5 and continuous gas-phase concentration measurements availa ble from the SEARCH network for 2002 modeling period. For 2003, additional continuous PM speciat ed data will be available from the FOCUS

network and in the Midwest U.S. ammonia measuremen ts are available. However, since these data were not collected during the 2002 modeling year , their use in the evaluation will be more qualitative and diagnostic.

As an example, the IMPROVE network give s daily (24-hour) average mass concentrations every 3 days for SO 4 , NO 3 , organic carbon (OC), elemental carbon (EC), soil (IP) and total, PM 2.5 and PM 10 mass from which CM can be derived. Some IMPROVE sites also have gas-phase species measurements (e.g., ozone and Great Smokey and Shenandoah National Parks). These data are available at approximately 38 sites in the VIST AS/ASIP 12 km domain. In addition, hourly values of light extinction and deciview are available at several of these sites. The SEARCH network provides 24-hour as well as continuous ( hourly) speciated measurements of PM 2.5 components and other specifics from 8 stations, depending on the time period (Hansen et al., 2003). Of key importance for the ASIP modeling is the ozone a nd PM performance of the CMAQ model at the, respectively, AIRS/AQS and FRM, and nearby STN, sites that are measuring violations of the 8-

hour ozone and PM 2.5 NAAQS. We will use data from these and the other observational databases listed in Table 7-1, for CMAQ model performance testing.

Ozone measurements are highly accurate and precise, but can be highly influenced by local conditions, such as local NOx sources that can suppr ess the observed ozone c oncentrations and make them less representative of the surrounding communit y. An important consideration in evaluating models for PM is that different PM monitoring networks may use different measurement approaches that “measure” different amounts of the same species that are also different from the modeledJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-6 species. For example, the IMPROVE network only speciates PM 2.5 so any sulfate or nitrate in the coarse mode (PM 2.5-10 ) is included in the Coarse Mass (CM) species “measurement” (where CM =

PM 10 – PM 2.5 mass). Because the different monitoring networks may use different measurement technology that results in different “measured” values for the same PM component, the CMAQ model will be evaluated separately for each mon itoring network. Ozone is made up of a single species so the mapping of the measured to modeled species is easily accomplished. However, there is sometimes ambiguity in the mapping of modeled PM species to measurements. For example, PM measurements only measure the carbon component of OC, whereas in the model the entire Organic Mass Carbon (OMC) is simulated that includes carbon as well as other elements attached to the carbon (e.g., hydrogen and oxygen). Thus, a factor is assumed to adjust the measured OC to OMC. In the past a 1.4 OMC/OC factor has been used based on urban scale measurements of fresh OC emissions and this is the factor used in the current IMPROVE reconstructed mass equation (Malm, 2000). However, this OMC/OC factor is likely too low, especially for aged OC compounds, and OMC/OC ratios of 1.4 to 2.2 have been observed (T urpin and Lim, 2001) with the current average OMC/OC ratio value of 1.8 being recommended.

The VISTAS Phase I air quality data asse ssment report (ENVIRON, 2003c) provides more details on the ambient monitoring data availa ble for the VISTAS/ASIP model performance evaluation modeling.

7.4.2 Aloft Measurements

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

have been other occasional intensive airborne sampling campaigns throughout the easter n U.S. (e.g., the 1999 SOS field program which provided aloft data for our evaluation of CMAQ for the July ’99 episode), that have produced very useful inform ation for air quality model performance testing. Fortunately, during CY-2002, there were at least tw o mature airborne field programs underway in the eastern U.S. One was centered over the Midwest, the other on the mid-Atlantic coast. A brief characterization of these potentially valuable CMAQ m odel evaluation data sets is given here. Note that the advanced modeling evaluation using non-routin e data sets like aircraft data are not currently planned under the VISTAS/ASIP work efforts. Ho wever, their inclusion in the evaluation would provide valuable information on the model accuracy and reliability.

During 2002, the Wisconsin Department of Natural Resources (WDNR) and the Midwest RPO (MRPO) (who funded the Jacko aircraft) co llaborated on the support of airborne sampling using two aircraft that, along with ground-ba sed measurements, provided a 3-dimensional representation of air pollution concentrations acr oss the upper Midwest with some flight paths extending south to include the Mammoth Cave, KY and Dolly Sods, WV Class I areas in the VISTAS domain. The goal of the WDNR/MRPO flights was to collect aloft air quality and meteorological data to support model evaluation and da ta analyses. The aircraft flights were aimed at: (1) characterizing high fine particle and ozone episodes, (2) characterizing air quality over the Class I areas in the upper Midwest (Isle Royale National Park and Seney National Wildlife RefugeJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-7 in northern Michigan) on both clean and hazy da ys, and (3) characterizing urban areas in the

Midwest.

As indicated in Table 7-2, airborne sampli ng was performed over a broad region of the Midwest (including portions of the VISTAS states) from 1 June to 22 November. Lasting 3-5 hours, the WDNR and Jacko aircraft sampled a variety of aerometric parameters (depending upon the flight and aircraft) including wind speed, wind directi on temperature, dew point, relative humidity, pressure, O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg, SO 4 , OC, EC,

PM 2.5 , and light scattering (Neph). Still photographs documenting visibility were also collected. Presently, the full WDNR/MRPO aircraft database , from the first flights in 1987 to the recent sampling in 2003 is being aggregated into a master data base archive.

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

7.5 Model Evaluation Tools

This section introduces the various statistical m easures, graphical tools, and related analytical procedures that have proven useful over the year s in evaluating grid-based chemical transport models. Many of the methodologies mentioned belo w have been utilized in the VISTAS Phase I preliminary evaluation and have been refined dur ing the course of the VISTAS and other (e.g., WRAP, MRG, CENRAP) studies. While we plan on calculating a rich variety of statistical performance metrics, only a very limited subset of these measures will actually be relied upon to form judgments concerning model acceptability and in the final reporting.

7.5.1 Statistical Performance Metrics

EPA’s 1991 and 2005 ozone and draft 2001 PM and regional haze guidance documents suggests a suite of metrics for use in evaluating model performance. EP A’s 1-hour ozone guidance lists three statistical measures with performance goa ls that a model should achieve before being used

to demonstrate ozone attainment in a SIP:

• Mean Normalized Bias (MNB) < + 15%

• Mean Normalized Gross Error (MNGE) < 35%

• Unpaired Peak Accuracy (UPA) < + 20%

The 1-hour ozone MNB and MNGE performance m easures are typically calculated using anJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-8 observed hourly ozone cutoff threshold of 60 ppb (40 ppb is also used sometimes). These performance measures have also been used for 8-hour ozone model performance evaluation. The newer 8-hour ozone (EPA, 2005b) and draft PM 2.5/regional haze (EPA, 2001) modeling guidance focuses more on a holistic model evaluation approach that assesses not only how well the model

matches the observation but also whether the mode l is correctly simulating the processes that produces the elevated ozone and PM concentrati ons, including a comparison against a conceptual model. In fact some performance measures a nd goals in earlier versions of the 8-hour ozone guidance (e.g., most daily maximum 8-hour ozone c oncentrations near the monitor matching to

within + 20%; EPA, 1999) were not included in the final version (EPA, 2005b).

Table 7-3 lists a standard set of statistical performance measures that will be used to evaluate fine particulate and ozone models. These perfor mance measures will be calculated using several model performance evaluation software tools for ozone and PM species concentrations, including:

UCR Analysis Tool operates on a Linux platform, performs species and temporal matching of the predictions and observations and genera tes statistical performance measures, scatter plots and time series plots for user specified subdomains and across all sites and all days, for each site and all days and for each day across all sites.

Alpine Geophysics MAPS Software also operates on a Linux plat form generating statistical measures, scatter plots, time series plots and spatial comparisons of predictions and observations. MAPS also spatial averaged perf ormance summaries (e.g., time series of bias and error) that are useful for synthesizing model performance.

PAVE by MCNC is used on a Linux platform to generate spatial maps (tile plots) of model predictions with super imposed observations as colored symbols.

ENVIRON Performance Software calculates performance statistics and exports them along with the predictions and observations to be used with macros operating standard Windows software such as Excel and SURFER to genera te graphical displays of model performance that can be customized by the user where the da ta are also available for further analysis if

desired.

The VISTAS/ASIP ozone and PM evalua tion of the 2002 36/12 km CMAQ base case

simulation will strive to use each of these evaluation packages to some extent to elucidate model performance. Although procedures for assessing ozone model performance are well established since ozone SIPs using photochemical grid modeli ng in the attainment demonstration have been developed for over a decade (e.g., dating back to the 1994 SIPs; see Morris, 1995). Procedures for evaluating PM models, however, are much less established and research is ongoing. Morris and coworkers (2005) summarize some of the newer PM model evaluation techniques for assessing regional haze models as part of VISTAS, enhancem ents to these techniques with a focus on urban PM performance will be needed for the ASIP PM2.5 evaluation.

Typically, the statistical metrics are calcula ted at each monitoring site across the full computational domain for all simulation days. In the VISTAS/ASIP CMAQ evaluation, we will stratify the performance statistics across relevant space and time scales. As part of the operational evaluation, the gas-phase and aerosol statistical m easures shown in Table 7-3 will be computed forJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-9 the full 36 km and 12 km domains, as well as for the individual RPOs (VISTAS, WRAP, CENRAP, MRPO and MANE-VU) and on other subdomains as appropriate (e.g., 8-hour ozone and PM 2.5 nonattainment areas). Temporally, we will comput e the statistical measures for the appropriate averaging times: 1-hour and 8-hour for ozone and hourly for other gas-phase precursors such as NO,

NO 2 , CO, SO 2 ; usually 24-hour for sulfate, nitrate, EC , OC, PM and other aer osol speci es, although some SEARCH sites have continuous PM species and the CASTNet monitoring network measures weekly PM species; and weekly for wet deposition of sulfate, nitrate and ammonium from the NADP network. Where appropriate these results w ill then be averaged over annual, monthly, and seasonal periods for display, further analysis, and reporting. Should it become necessary as part of model performance diagnosis, we will consider aggr egating the statistics in other ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipita tion vs. non-precipitation days, (d) month of the year, and (e) exceedance events, in order to help elucidate model performance problems. The amount of these supplemental time/space analyses would depend on available resources. In subregional performance testing, the focus would likely be on the nonattainment areas, Class I areas and sites where enhanced monitoring (EPA ST N and FRM locations, the hourly/daily SEARCH sites) within the VISTAS 12 km domain (Hansen et al., 2003) is available.

As part of the operational evaluation, the metric s defined in Table 7-3 will be calculated for each gas phase species and each fine particulate species in the extinction equation as well as separately for SO 4 , NO 3 and ammonium (NH 4 ) on both the 36 km and 12 km domains. In any diagnostic evaluations that are performed, we will examine the model’s ability to estimate the gaseous species listed above from EPA’s guidan ce (EPA, 1991; 2001; 2005). However, in reality ambient gaseous species in 2002 are principally available for ozone, NO 2 , SO 2 , and CO.

7.5.2 Graphical Representations

The VISTAS/ASIP operational air quality mode l evaluation will utilize numerous graphical displays to facilitate quantitative and qualitativ e comparisons between CMAQ predictions and measurements, many of which were used in th e VISTAS Phase I Final Report (ENVIRON, 2004) and summarized by Morris and co-workers (2005). T ogether with the statistical metrics listed in Table 7-3, the graphical procedures are intende d to help: (a) identify obviously flawed model simulations, (b) guide the implementation of pe rformance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ simulations. Thes e graphical tools are intended to depict the model’s ability to predict the observed fine particulate and gaseous species concentrations.

The VISTAS Phase I modeling helped to refine the suite of graphical tools most effectively in assessing model performance and the differences between the baseline CMAQ runs and sensitivity experiments. The core graphical displays to be considered for use in Phase II include the following:

> Spatial mean concentration time series plots; > Time series plots at monitoring locations; > Ground-level gas-phase and particulate concentration maps (i.e., tile plots); > Concentration scatterplots stratified by station, by time, and by network; > Bias and error stratified by concentration;January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-10 > Bias and error stratified by time; > Histogram plots of the statistical metric s, stratified by day, by pollutant, by subregion (e.g., 12 km vs. 36 km, by RPO), and by monitoring network; and > Quantile-Quantile (Q-Q) plots.

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

7.5.3 MAPS/Flying Data Grabber Routines

This section describes the procedures we w ould implement for the aloft gas phase and PM model evaluation with CMAQ using aircraft data should resources be available to make such a comparison. This aloft performance evaluation w ould employ aircraft data sets from various sampling programs carried out over the Midwest and eastern U.S. during 2002. Details on how these data are used to evaluate CMAQ performance aloft together with findi ngs from our aloft model evaluations with the 10-21 July 1999 episode are described in the VISTAS Phase I Final Report (ENVIRON, 2004).

The principal challenge in using the aircraft data for meteorological and photochemical

model evaluations relates to the ‘incommensurab ility’ of Lagrangian aircraft observations with Eulerian (i.e., fixed location) volume-averaged model estimates (see, for example, Hanna, 1994). Aircraft data are essentially continuous, high fr equency Lagrangian samples having response times

on the order of 30 seconds or less. In contra st, CMAQ model estimates represent hourly-averaged values. Thus, the aircraft data must be averaged in some manner to yield quantities that are at least qualitatively comparable to the air quality fields estimated by CMAQ in the grid volume(s) through which the aircraft passes. The objective is to de velop hourly-average time series of measurements and model estimates that are as nearly comparable as possible.

The procedures proposed for processing the al oft meteorological and air quality observations and CMAQ model predictions have been described in several science reports prepared in connection with the Lake Michigan Ozone Study (Tesche and McNally, 1993a-d, 2001) and more recently the Houston-Galveston 1-hr ozone SIP modeling that utilized the TexAQS 2000 data base (Tesche and Jeffries, 2002). These methods, formalized within AG’s Flying Data Grabber (FDG ) model, were employed in Phase I for the 13-21 July 1999 episode. The methods used are substantial extensions of the techniques pioneered a decade ago by Sche re and Wayland (1989) for the Regional Oxidant Model (ROM2.0) evaluation agains t the NEROS database and by Barchet and Dennis (1990) for the RADM/ADOM evaluation (Dennis et al., 1990).

For a typical aircraft (or helicopter flight), the Flying Data Grabber first identifies the specific time interval during which the aircraft was located in a given CMAQ model grid cell along the flight path. The observations are then integr ated to produce mean, standard deviation, bias and

error estimates for the variable measured within each grid cell of the flight path. This averaging process produces an observed, averaged time series for the above statistical quantities along the flight path. Note, that these time intervals are characteristically much smaller than the one-hour model averaging time. Flight path statistics, together with the mean modeled and observedJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-11 horizontal winds along the flight paths are also produced by the FDG. The maximum and minimum values during each time segment within a grid cell are also recorded.

The FDG methodology further assumes that the air quality model estimates vary approximately linearly during each hour. It is then straightforward to construct an estimated time series of the modeled values that corresponds to the above-described observed time series derived from the aircraft data. The measurements and model estimates, now on roughly comparable time and space scales, are subsequently processed with the MAPS statistical/graphical software tools described in the Phase I Task 4a report (ENVIRON, 2003d). The statistics of principal interest are

the mean values of the observed and modeled concen trations together with estimates of bias and imprecision (i.e., gross error). A va riety of graphical representations are also produced to facilitate evaluation an intercomparison.

7.5.4 Use of Multiple Evaluation Packages

In VISTAS Phase I model evaluation (ENVI RON, 2004) relied principally on the UCR evaluation package that was originally develope d for WRAP and then enhanced by VISTAS. This package produce scatter plots by site, day or all s ites and days, time series plots and statistical

measures In the VISTAS/ASIP model evaluation we will attempt to augment the UCR Analysis

Tool evaluation software with other software ev aluation packages as discussed previously.

7.5.5 Probing Tools and Allied Methods

The VISTAS/ASIP CMAQ model evaluation will employ routine operational evaluation methods and standard statistical metrics (Table 7- 4) and graphical displays to support the assessment

of whether the model is shown to perform with sufficient accuracy and reliably for its intended purpose. Ideally, this operational evaluation will confirm that the modeling system is performing consistent with its scientific formulation, technical implementation, and at a level that is at least as

reliable as other current state-of-science methods . Should unforeseen model performance problems arise in the initial or refined year 2002 model simu lations, it may be necessary to draw into the evaluation supplemental diagnostic tools to aid in model testing. These diagnostic techniques are loosely referred to as “probing tools”. The actual n eed for their use, if any, can only be determined once the initial 2002 CMAQ operational evaluation is completed. Should such diagnostic methods actually be needed, their usage would require a dditional resources not currently allocated under VISTAS/ASIP. Below, we identif y the types of probing tools that could be brought to bear under should their use become necessary.

Current ‘One-Atmosphere” models such as CMAQ and CAMx have been outfitted with a number of “probing tools” that have proven to be very useful in testing and improving model performance and in evaluating emissions control strategies. Among the probing tools available in one or both models are: (a) ozone source a pportionment technology (OSAT) algorithms, (b) PM Source Apportionment Technology (PSAT) and Tagge d Species Source Apportionment (TSSA) PM source apportionment techniques; (c) process analys is (PA), and (d) the direct decoupled method (DDM) for sensitivity analysis.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-12 Source Apportionment Techniques: CAMx contains a suite of “source attribution”

methods for ozone and PM that use reactive tracer s that operate in parallel to the host model. The Ozone Source Apportionment Technology (O SAT) tracks ozone formation from user defined source regions and categories ba sed on how the group’s ozone precursors contributed to ozone formation. Thus, OSAT decides whether ozone formation is NOx or VOC limited in each grid cell at each time step, and bases ozone attributions on the relative amounts of the limiting precursor from different s ources that are present in that grid cell at that time step. These incremental ozone attri butions are integrated throughout the model run. The method is generally applicable and has been widely used to aid model diagnosis in the performance testing phase, to guide control strategy development and for ozone culpability assessments (e.g., NOx SIP Call and CAIR). The PM Source Apportionment Technology (PSAT) also uses reactive tracers to rack PM sp ecies formation back to user defined source regions and categories based on the primary pr ecursor to the PM species (e.g., sulfate is traced back to SO2 emissions, nitrate is traced back to NOx emissions, etc.). A Tagged Species Source Apportionment (TSSA) approach has also been impl emented in CMAQ and tested for sulfate and nitrate (Tonneson, 2004, personal communication). However, TSSA contained unexplained PM source apportionmen t that has been attributable to mass conservation errors in CMAQ that has been fi xed in the latest (Version 4.5, October 2005) of CMAQ (Pleim, 2005).

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

> Emissions resolved by geographic area. > Emissions resolved by source category. > BCs optionally resolved by boundary edge (N, S, E, W, Top).

> All sensitivities available re lative to sources of individual species (NO, PAR, etc.) or species group (VOC, NOx or ALL). > Simultaneously calculate sensitivities to many initial condition, boundary condition and emissions parameters.

In recent comparisons between CAMx DDM sensitivities and brute-force sensitivities (calculated from +/- 20% perturbations) Dunker et al., (2002a,b) reported that sensitivities of ozone with respect to area source NOx and VOC emissions were calculated and results indicated that the agreement between DDM and brute force sensitivities is excellent. DDM implementation into CMAQ is reported by Kumar (2003).

2 Recent research by Prof. Russell and coworkers at GIT has led to the extension of the CMAQ DDM method to include second order sensitivity coefficients (see, Hakami et al., 2003).January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-13 Process Analysis (PA): Photochemical air quality model simulations are usually evaluated primarily in terms of their ability to simulate observed O 3 data. There is an increasing awareness that chemical mechanisms and air qua lity models must also be evaluated in terms of their ability to simulate the fundament al chemical processes that control O 3 formation and the sensitivity of O 3 to emissions reductions (Arnold et al., 1998). Process analysis is a method for explaining model simulations by a dding algorithms to the AQM to store the integrated rates of species changes due to i ndividual chemical reactions and other sink and source processes (Jeffries and Tonnesen, 1994; Tonnesen, 1995). By inte grating these rates over time and outputting them at hourly interv als, process analysis provides diagnostic outputs that can be used to explain a model simulation in terms of the budgets of free radicals, production and loss of odd oxygen and O 3 , and conversion of NOx to inert forms, as well as the effects of transport and other sink a nd source terms. Of particular importance to the VISTAS modeling, process analysis can al so improve model diagnosis and performance evaluation efforts by identifying processes that are ‘out of balance’ (Tesche and Jeffries, 2002), by identifying situations for which the model formulation and/or implementation should not be expected to apply and by suggesti ng how ambient data can be used to evaluate model accuracy for key terms in the chemical processing of VOC and NOx (e.g., Imre et al., 1998). Process Analysis (PA) is implemen ted in both CMAQ and CAMx and each model supports three complementary aspects of the method: (a) the integrated process rate (IPR), (b) integrated reaction rate (IRR) and (c) chemi cal process analysis (CPA). Several versions of process analysis (PA) have been implem ented in air quality models (AQMs) including both trajectory models (Tonnese n, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and Dennis, 2000; Arnold et al ., 1998; and Wang, 1997). The fundamental approach in all versions of PA is similar: The AQM is modified to calculate the integral over time of the individual sink and source processes and each chemical reaction. These integrated sink/source process rates (IPR) and integrated r eaction rates (IRR) can then be stored to a file and analyzed using a post-processor, or so me processing can be performed internally in the model and a more limited set of process di agnostic information is output directly by the AQM. Chemical process analysis (CPA) is an improvement on the IRR method whereby some of the processing of IRR information is internalized within the AQM to output chemically meaningful parameters directly (e.g., budget terms for O 3 , NOx and odd oxygen).

Process analysis measures for aerosol chemistr y have not been analyzed as much as for ozone chemistry. Although the ozone chemistry process analysis is directly related to secondary sulfate and nitrate formation, there is additional process analysis information

available in the aerosol modules that are not extracted in either CMAQ or CAMx. In particular, information on sulfate formation and oxidants from the aqueous-phase module and on the sulfate/nitrate equilibrium from the aerosol thermodynamics module would be a useful addition to the current process analysis output.

Because application of all three of thes e probing tools--source apportionment, DDM, and Process Analysis—are computational intensive and require a fair amount of analysis time to reap the benefits of using the methods, they do not lend themselves directly to annual simulations. However, each method has potential for use in addressing key episodic periods or geographical locations in the VISTAS/ASIP domain where performance in the 2002 simulation may present a problem or where particular attention needs to be focused on emissions controls (a specific nonattainment area for example). In such focused applications, one or more of these probing tools may indeed serve aJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-14 purpose and will be considered where appropriate.

7.6 Model Evaluation Procedures

EPA draft PM modeling guidance (EPA, 2001, pg. 227) suggests that the performance evaluation focus on two aspects (similar suggesti ons are contained in the 8-hour ozone modeling guidance, EPA, 2005b):

> How well is the model able to replicate observed concentrations of components of

PM 2.5 , total observed mass of PM 2.5 ? and

> How accurately does the model characterize the sensitivity of ch anges in component concentrations to changes in emissions?

Recognizing that the former is much easier to accomp lish than the latter, EPA goes on to declare that testing of a model’s reliability in estimating the actual effects of emissions changes is the more important. Over the past 20 years, a substantia l body of information and analytical techniques has been developed to address the first aspect. Unfortunately, even today there are little rigorous methods available for quantifying the accuracy and precision of a model’s predictions of ozone, PM or visibility changes as the result of emissions changes. In this section we explain how the VISTAS/ASIP testing will address the first aspect of the performance evaluation, i.e., how does the model compare against observed data. In s ection 7.9 we consider the second performance

consideration.

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

Given that PM 2.5 mass is the sum of the individual com ponents of fine particulate matter and

the PM 2.5 attainment demonstration test involves the separate projection of each PM component, the model should be evaluated separately for each of the key fine particulate matter components that make up PM 2.5 mass. Current EPA draft PM modeling guidance suggests that the model should also be evaluated for several key gas-phase species that are important for fine particulate modeling. For particulate species this includes SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated

with NO 3 , elemental carbon (EC), organic carbon (OC), IP , mass of individual constituents of IP, and coarse matter (CM). The gaseous species include ozone (O 3 ), HNO 3 , NO 2 , PAN, NH 3 , NOy,

SO 2 , CO, and H 2 O 2

.

For ozone modeling, EPA guidance (EPA, 2001; 2005b) recommends evaluating the model for ozone as well as ozone precursor (e.g., VOC, NOx and CO) as well as key indicator species (e.g., NOy, NOz, HNO3, H2O2, etc.). As noted previous ly, the 1-hour ozone modeling guidance includes model performance goals, whereas more recent 8-hour ozone (EPA, 2005b) and PM 2.5 (EPA, 2001) guidance stresses more confirmatory and corrobora tive techniques and processed based evaluation to assure that the model is getting the right answer for the right reason, in addition to demonstrating that the model exhibits skill in predicting the observed 8-hour ozone and PM 2.5 concentrations.

At some of the IMPROVE sites there are also direct measurements of hourly extinction usingJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-15 transmissometer or nephelometer instruments that will provide another measure of performance for fine particulate Thus, it would be scientifica lly interesting to evaluate the model estimated extinction with the hourly measured values at these sites.

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

include:

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

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

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

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

establish correlations.

3⁄4 For ozone, comparisons against observed hour ly and 8-hour ozone concentrations in

nonattainment areas.

The types of analysis that could be performed as part of the VISTAS/ASIP CMAQ diagnostic model

evaluations that could be considered are:

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

> Evaluate how well the model simulates various physicochemical processes by: (a) examining observed and modeled correlati ons between various species pairs, and (b) comparing model-predicted ratios of vari ous species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

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

> Create scatter plots of modeled vs. obser ved data and hourly and 24-hour averages by site and subregion to help identify any site-specific biases.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-16 > Create time series plots of predicted and observed concentrations stratified by key variables as appropriate.

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

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

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

constituents under these conditions.

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

> Evaluate for ozone precursors and key i ndicator species and ratios (e.g., HNO3/H2O2) as well as product species.

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

exercises will be avoided.

7.6.2 Assessment of Aloft Gas-Phase and Aerosol Species

A substantial number of aircraft flights were conducted during 2002 over the Midwest and Eastern U.S. Should VISTAS elect to fund the op tional aloft model performance evaluation, we will

endeavor to obtain this information and use it in a scientific performance evaluation of aloft gasphase and aerosol species (see section 7.3.2).

7.7 Performance Goals and Benchmarks

Establishment of performance goals and benc hmarks for regulatory m odeling is a necessary but difficult activity. Here, performance goals re fer to targets that we believe a good performing model should achieve, where as performance benchm arks are based on historical model performance measures for the best performing simulations. Perf ormance goals are necessary in order to provide consistency in model applications and expectations across the country and to provide standardization in how much weight may be accorded modeling study re sults in the decision-making process. It is a problematic activity, though, because many areas present unique challenges (e.g., Houston, SanJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-17 Joaquin Valley, Los Angeles) and no one set of perfor mance goals is likely to fit all needs. Equally concerning is the very real danger that modeling studies will be truncated when the ‘statistics look right’ before full assessment of the model’s reliability is made. This has the potential from breeding built-in compensating errors (Reynolds et al., 1996) as modelers strive to get good statistics as opposed to searching for the explanations for poor performance and then rectifying them. A

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

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

state:

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

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

EPA’s 1999 draft 8-hour ozone modeling guida nce adopted the 1-hour performance goals and added additional performance goals related to 8-hour ozone model performance. For example, the draft 8-hour ozone guidance lists a performa nce goal to match the observed daily maximum 8-

hour ozone concentrations near the monitor to within + 20%.

However, when these evaluation metrics and goals were later applied to evaluate PM species, difficulties arose because performance statistics that divide by low concentration observations become much less useful. Indeed, some observed PM species approach zero (e.g., NO 3 ) which results in the MNB and MNGE performance metrics approaching infinity. In time, this has led to the introduction of the fractional and normalized mean bias and error metrics (see Table 7-4) in addition to the mean normalized bias and gro ss error (MNB and MNGE) metrics and related performance expectations based on these alternative measures.

While the 1-hour ozone metrics and goals still have value in interpreting ozone and some gas-phase species performance, it has been necessary to develop new performance metrics and goals for fine particulates. EPA’s PM guidance document (EPA, 2001) guidance document identifiesJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-18 particulate matter components of interest to include: SO 4 and/or S, NH 4 , NO 3 , mass associated with

SO 4 , mass associated with NO 3 , EC, OC, IP, and mass of individual constituents of inorganic primary particulate matter (i.e., IP). Gase ous pollutants of interest include ozone, HNO 3 , NO 2 , PAN,

NH 3 , NO y , SO 2 , CO, and H 2 O 2 . In addition, EPA guidance identifies several potentially useful statistical measures including: (a) accuracy of spatia lly averaged concentrations near a monitor, (b)

fractional bias in means and standard deviations of predictions and observations, (c) normalized bias, (d) normalized gross error, (e) unpaired comp arisons between predicted and observed peak

concentrations.

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

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

When EPA’s draft guidance was developed nearly f our (4) years ago, an interim set of fine particulate modeling performance goals were suggested for aggregated mean normalized gross error and mean normalized bias as follows (EPA, 2001):

Pollutant Gross Error Normalized Bias

PM 2.5 ~30-50%

~ ∀ 10%
Sulfate ~30-50%

~ ∀ 20-30%

Nitrate ~20-70%

~ ∀ 15-50%

EC ~15-60% NA

OC ~40-50%

~ ∀ 38%

Because regional-scale fine particulate and regi onal haze modeling is an evolving science, and considerable practical application and performance testing has transpired in the intervening years since these goals were postulated, we consider them general guidelines. As part of the VISTAS preliminary model performance evaluation effo rts along with the model evaluation studies conducted by WRAP (Tonnesen et al., 2004; CENRAP (Morris et al., 2005), MRPO (Baker, 2005), and other studies has developed model performance goals and criteria for PM species. These goals and a summary of model evaluation display tec hniques are summarized by Morris and co-workers (2005) and consist of the following:

Fractional

Bias Fractional

Error Comment

≤ ±15% ≤ 35% Ozone model performance goal for which PM model performance would be considered good.

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

≤ ±60% ≤ 75% At or above this level of performance indicates fundamental problems with the modeling system.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-19 We regard the above goals and criteria not as a pass/ fail test, but rather as a basis of intercomparing model performance across studies, sensitivity tests and models.

7-8. Diagnostic and Sensitivity Testing

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance e xpectations. Indeed, our experience has been that initial simulations that ‘look very good’, usually do so as the result of comp ensating errors. The norm is to engage in a logical, documented process of model performan ce improvement wherein a variety of diagnostic probing tools and sensitivity testing methods are us ed to identify, analyze, and then attempt to remove the causes of inadequate model performan ce. This is invariably the most technically challenging and time consuming phase of a modeling study. We anticipate that the annual CMAQ

model base case simulations will present some performance challenges that may necessitate focused diagnostic and sensitivity testing in order for them to be resolved. Hopefully, these diagnostic and/or sensitivity tests can be adequately carried out with in the resources and schedule of Tasks 4a/4b. If not, then it may be necessary to draw upon the Optional Task 14 (Enhanced Model Performance Evaluation) and/or Optional Task 15 (Contingenc y) resources to conduct the necessary work. Where practical, diagnostic or sensitivity analyses , if needed, could be performed on selected episodes within the annual cycle, thereby avoi ding the time-consuming task of running CMAQ for the fully 2002 period. Below we identify the types of diagnostic and sensitivity testing methods that might be employed in diagnosing inadequate mode l performance and devisi ng appropriate methods for improving the model response.

7.8.1 Traditional Sensitivity Testing

Model sensitivity experiments are useful in three distinct phases or ‘levels’ of an air quality modeling study and all will be used as appropriate in the VISTAS Phase II modeling with CMAQ.

These levels are:

> Level I

. Model algorithm evaluation and configuration testing;

> Level II . Model performance testing, uncertainty analysis and compensatory error diagnosis, and

> Level III. Investigation of model output respons e (e.g., ozone, aerosol, deposition) to changes in precursors as part of emissions control scenario analyses.

Most of the Level I sensitivity tests with CMAQ have already been completed in the Phase I configuration and diagnostic analyses. However, given that open community nature of CMAQ and the frequent science updates to the model and s upporting data abases, it is possible that some additional configuration sensitivity testing will be necessary in the early months of Phase II Potential Level I sensitivity runs would be carried out at one or more of the Team’s three modeling

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

> To reveal internal inconsistencies in the model; > To provide a basis for compensatory error analysis;January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-20 > To reveal the parameters (or inputs) th at dominate (or do not dominate) the model’s operation; > To reveal propagation of errors through the model; and > To provide guidance for model refinement and data collection programs.

At this time, it is not possible to identify one or more Level II sensitivity runs that might be needed to establish a reliable annual 2002 CMAQ base case. The merits of performing Level II sensitivity testing will depend upon whether performance problem s are encountered in Tasks 4a/4b. Also, the number of tests possible, should performance di fficulties arise, will be limited by the available schedule and Phase II resources under Optional Task 14 (Extended Model Performance Evaluation). Thus, at this juncture, one cannot be overly pr escriptive on the number and emphasis of sensitivity runs that may ultimately be desirable in Phase II. However, from past experience with CMAQ and other models, experience it is possible to identify examples of sensitivity runs could be useful in model performance improvement exercises with the annual 2002 CMAQ simu lation. These include:

> Modified biogenic emissions estimates; > Modified on-road motor vehicle emissions; > Modified air quality model vertical grid structure; > Modified boundary conditions; > Modified fire emissions; > Modified EGU emissions;

> Modified ammonia emission estimates.

> Modified aerosol/N 2 O 5 /HNO 3 chemistry; and

> Modified NH 3 and HNO 3 deposition velocities.

Note that in a few cases (e.g., vertical grid structure, NH 4 emissions estimates), some sensitivity experimentation has already been carried out in Phase I with the Jan ’02, Jul ’01 and Jul ’99 episodes. To the extent that this Phase I in formation can help guide the Phase II diagnostics analyses, we will capitalize on this earlier work.

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

Level III sensitivity analyses have two main pur poses. First, they facilitate the emissions control scenario identification and evaluation pr ocesses. Today, four complimentary sensitivity “tools” can be used in regional photochemical models depending upon the platform being used. These methods include: (a) traditional or ‘brute force’ testing, (b) the direct decoupled method (DDM), (c) Ozone Source Apportionment Technology (OSAT) and PM Source Apportionment Technology (PSAT), and (d) Process Analysis (PA). Each method has its strong points and they will be employed in Phase II where needed. The sec ond purpose of Level III sensitivity analyses is to help quantify the estimated reliability of the ai r quality model in simulating the atmosphere’s response to significant emissions changes. This important model evaluation need is addressed in

further detail in section 7.9 below.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-21 Based on experience in other regional studies, exam ples of Level III monthly or annual sensitivity runs for Phase II might include:

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

source NO

x emissions; > Ozone, sulfate, nitrate, ammonium a nd other aerosol sensitivities to ground level

NO

x emissions; and > Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia.

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

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

simulations.

The need to perform sensitivity experiment ation (Levels I, II, or III) will depend on the outcome of the initial operational performance evalua tions. If such a need arises, the ability to actually carry out selected sensitivity and/or dia gnostic experiments will hinge on the availability of resources and sufficient time to carry out the analys es. Clearly, selection of the specific analysis method will depend upon the nature of the technical question(s) being addressed at the time. Note that as part of VISTAS modeling, Georgia Ins titute of Technology will be performing emissions sensitivities with CMAQ.

7.8.2 Diagnostic Tests

A rich variety of diagnostic probing tools are available for investigating model performance issues and devising appropriate means for improving the model and/or its inputs. Previously, in section 7.4.4 we introduced the suite of ‘probing t ools’ available for use in the CMAQ and CAMx modeling system for use in Phase II. Where the need exists (i.e., if performance problems are encountered) and assuming VISTAS elects to fund th e use of the probing tool applications, these techniques could be employed as appropriate to assi st in the model performance improvement efforts associated with the annual 2002 CMAQ basecase deve lopment. Here we describe an additional diagnostic method – indicator species and species ratios -- that is potentially useful not only in model performance improvement activities but also in judging the models reliability in estimating the impacts on air quality from future emissions . This method involves the use of so-called ‘indicator species’ and species ratios. If, during the conduct of Phase II, we determine that application of indicator species and species ratio t echniques would be beneficial to the study (and if existing project resources allow), we will discuss with the VISTAS TAWG and ASIP the merits of including this additional probing tool as part of the evaluation effort.

Beginning in the mid 1990s, considerable interest arose in the calculation of indicator species and species ratios as a means of diagnosing phot ochemical model performance and in assessing model credibility in estimating the effects of em issions changes. Major contributions to the development and refinement of this general dia gnostic method over the past decade have been made many scientists including Milford et al., ( 1994), Sillman (1995, 1999), Sillman et al., (1997), Blanchard (2000), Blanchard and Fairley (2001), and Arnold et al., (2003). Indeed, a recent evaluation of CMAQ using indicator species ratios such as O 3 /NO

x , NO

z /NO y (a measure ofJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-22 chemical aging), and O 3 /NO

z (a measure of the ozone production efficiency per NO

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

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

7.9 Corroborative and Weight of Evidence Modeling Analyses

This section identifies additional modeling anal yses that might be worth pursuing to add strength to the core model evaluation efforts alrea dy planned as part of the VISTAS/ASIP efforts.

7.9.1 Corroborative Models

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

As we discovered in the VISTAS/Phase I CMAQ/CAMx inter-comparisons, the general

levels of difference revealed between the two mode l’s ozone and PM predictions is typical of what one encounters when inter-comparing alternative state-of-science regional models. These differences provide some insight into the curre nt limits of predictability and reproducibility of today’s best photochemical/PM models. In light of these understandable differences in modeling results between state-of-science “One Atmosphere” models, not only is the i ssue of model selection

for VISTAS/ASIP critical 3, but the procedures for the selected model (CMAQ in this instance) and interpreting its output are important as well. Thus , recognizing the uncertainty that attends even the most sophisticated models, the EPA’s draft PM/regional haze modeling guidance explicitly addresses the issue of modeling uncertainty by recommending that alternative models (photochemical and observation-based) be considered in the attainment demonstration ‘weight of evidence’ analyses. Indeed, the Relative Reduction Factor (RRF) approach in the formal attainment

3 Model selection is a key issue that all RPOs have addr essed and some such as WRAP and VISTAS have chosen CMAQ. Others, such as the MRPO have adopted CAMx inst ead. Still other RPO’s have yet to decide between these two or another model. Clearly, there does not appear to be any one ‘right’ selection at the present time.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-23 demonstration process is designed to address the fact that no model is fully capable of giving precise predictions. In fact the RRF approach was devel oped in response to the findings of many modelers that alternative, comparable models give so mewhat divergent ozone and secondary aerosol predictions.

7.9.2 Weight of Evidence Analyses

EPA’s guidance recommends three general types of ‘weight of evidence’ analyses in support of the attainment demonstration: (a) use of ai r quality model output, (b) examination of air quality and emissions trends, and (c) the use of corroborative modeling such as alternative models including observation-based (OBM) or observation-driven (OBD) models. We will consider the use of one or more methods in conducting the CMAQ modeling because it could significantly strengthen the credibility and reliability of the modeling available to the states for their subsequent use. The exact details of the ‘weight of evidence’ analyses must wa it until ASIP evolves further. It is premature to prescribe which, if any of the WOE analyses w ould be performed since the model’s level of performance with the 2002 episode is obviously not known at this time and the time and remaining project resources available to support WOE analyses is unknown as well. Al so, how much of this WOE analyses will be performed by the modeling team verses states or other has not been determined. Nonetheless, we outline below our t houghts regarding what would likely be considered should the operational CMAQ model evaluation need to be bolstered with WOE analyses.

Use of Air Quality Models

. As just discussed, we recommend augmenting the CMAQ annual 2002 and episodic simulations with th e use of CAMx to provide additional information on model uncertainty and sensitivit y. More specifically the use of CAMx on a

somewhat limited basis to corroborate the ke y model performance evaluation results and emissions control findings of the primary m odel, CMAQ would be useful. Second, applying

the DDM and OSAT/PSAT methods to deve lop corroborative information on sourcereceptor relationships and model sensitivitie s would strengthen the analyses. These supplemental calculations would be performed w ith one or both models for one or more key periods within the annual 2002 cycle. The results of this additional m odeling would be used directly in the ‘weight of evidence’ analyses to quantify the degree of modeling uncertainty and to corroborate appropriateness of the subsequent PM emissions reductions scenarios.

Use of Emissions and Air Quality Trends. A limited scope emissions and trend analysis could be employed in VISTAS/ASIP to support the ‘weight of evidence’ determinations. However, traditionally, these types of analyses are performed by the lead agency’s own staff. With this expectation, we would coordinate our efforts with the States to develop a trends analysis supporting the future year applications of CMAQ.

Use of Corroborative Observational Modeling

. While regulatory modeling studies for

ozone attainment demonstrations have tr aditionally relied upon photochemical models to evaluate ozone control strategies, there has recently been growing emphasis on the use of data-driven models to corroborate the findings of air quality models. As noted, EPA’s guidance now encourages the use of such obs ervation-based or observation-driven models (OBMs/ODMs). As part of VI STAS/ASIP, we will consider the merits of using these techniques as supportive weight of evidence. While the OBD/OBM models cannot predictJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-24 future year air quality levels, they do provide useful corroborative information on the extent to which specific subregions may be VOC-lim ited or NOx-limited, for example, or where

controls on ammonia or SO2 emissions mi ght be most influential in reducing PM 2.5

. Information of this type, together with results of DDM and traditional ‘brute-force’ sensitivity simulations, can be extremely helpfu l in postulating emissions control scenarios since it helps focus on which pollutant(s) to control.

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes

EPA identifies three methods (EPA, 2001, pg. 228) potentially useful in quantifying a model’s reliability in predicting air quality respons e to changes in model inputs, e.g., emissions.

These include:

> Examination of conditions for which subs tantial changes in (accurately estimated) emissions occur;

> Retrospective modeling, that is, modeling before and after historical significant changes in emissions to assess whether the observed air pollution changes are adequately simulated; and

3⁄4 Use of predicted and observed ratios of ‘chemical indicator species’.

We note that in some urban-scale analyses, th e use of weekday/weekend information has been helpful in assessing the model’s response to emissions changes.

The first two methods have actually been cons idered for over 15 years and were the subject of intensive investigations in the early 1990s in Southern California in studies sponsored by the South Coast Air Quality Management District (Tes che, 1991) and the American Petroleum Institute (Reynolds et al., 1996). To date, neither method has proven useful largely because of the great difficulty in developing historical emissions inve ntories of sufficient quality to make such an analysis credible and the difficulties in removi ng the influences of different meteorological conditions such that the modeling signal reflects only the model’s response to emissions changes. It is difficult enough to construct re liable emissions inventories using today’s modeling technology let alone construct retrospective inventories 5-10 year s ago prior to the implementation of significant emissions control programs or major land use changes. The use of indicator species, however, offers some promise.

However, recent analytical and numerical modeli ng studies have demonstrated how the use of ambient data and indicator species ratios can be us ed to corroborate the future year control strategy estimates of Eulerian air quality models. Blanch ard et al., (1999), for example used data from environmental (i.e., smog) chambers and photochemi cal models to devise a method for evaluating the 1-hr ozone predictions of models due to changes in precursor NOx and VOC emissions. Reynolds et al., (2003) followed up this analysis, augmented with process analysis, to assess the reliability of SAQM photochemical model estimate of 8-hr ozone to precursor emissions cutbacks. With respect to secondary aerosol PM, the recen t CMAQ evaluation by Arnold et al. (2003) clearly demonstrated how the use of indicator species anal ysis could be use to develop insight into theJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-25 expected reliability and adequacy of a photoche mical/PM model for simulating the effects of emissions control scenarios. These researchers used three indicator ratios (or diagnostic ‘probes’) to quantify the model’s response to input changes:

> The ozone response surface probe [O 3 /NO

x ]; > The chemical aging probe [NO

z /NO y ]; and > The ozone production efficiency probe [O 3 /NO

z ].

By closely examining CMAQ’s response to key input changes, properly focused in time and spatial location, Arnold et al., (2003) were able to conclude that the photochemical processing in CMAQ was substantially similar to that in the atmosphere

Thus, the extension of these techniques to address CMAQ predictions for secondary aerosols will doubtless be quite challenging, but the use of indicator species (e.g., ammonia or HNO 3 limitation for nitrate particle formation) and species ra tios appears to offer, at this time, the only real opportunity to quantify the expected reliability of the air quality model to correctly simulate the effects of emissions changes. In the CMAQ mode l evaluation, we will remain alert to opportunities to extend the indicator species ratio analyses to th e problem of fine particulate and regional haze.

This is one area where technical collaboration between the Emissions and Air Quality Modeling

team and the VISTAS and ASIP States and Stakehol ders can be especially fruitful in terms of identifying and testing emergent methods for cha llenging the model’s ability to correctly simulate the effects of future year emissions changes. Fina lly, we note that this is truly a current research area and as such falls outside the scope of th e current modeling effort. However, given its importance, we will remain alert to opportunities to utilize newly available methods should this prove feasible within resources and schedule.

Table 7-1 . Ground-Level Ambient Data Monitoring Networks and Stations Available in VISTAS 12
Km Domain for CY-2002. Monitoring

Network Chemical Species Measured Sampling Frequency;

Duration Approximate

Number of

Monitors

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr 38

CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week 74

SEARCH 24.hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); and Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 8

NADP WSO4, WNO3, WNH4 Weekly 100

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr 313 (?)

EPA-SPEC Speciated PM 2.5 Varies; Varies 43 (?)

AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly 6,407

Table 7-2 . Aircraft Sampling Programs Performed in the VISTAS 12 Km Domain for CY-2002. Aircraft Program Meteorological Parameters & Sampling ApproximateJanuary 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-26 Chemical Species Measured Program & Flight Duration Number of Flights; Days;

Aircraft University of Maryland (UMD);

Univ. Research Foundation (URF) Meteorology: WS, WD, Temp, RH, Air Quality : O 3 , NO, NO 2 , CO,
SO 2, aerosol absorption, aerosol scattering. 23 May to 3 Oct; Typically 3 hrs 54 flights, 54 days,
1 aircraft

Midwest RPO &

Wisconsin DNR Meteorology : WS, WD, Temp, RH, dew point, pressure Air Quality : O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls,
HNO 3 , NH 3 , Hg, SO 4 , OC, EC,

PM 2.5 , light scattering (Neph), visibility pictures. 1 June to 22 Nov; Typically 3-5 hrs 133 flights; 29 days;
2 aircraft (WDNR and Jacko Aircraft)

Table 7-3. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIRON, 2003b,d for details).

Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Accuracy of paired peak ( A p ) Paired_Peak

peak peak


## O O P

− P peak = paired (in

both time and space) peak prediction

Coefficient of determination ( r 2 ) Coef_Determ



# ∑ ∑ ∑

= = =



## − − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− −

N

i N

i i i N

i i i



## O O P P O O P P

1 1 2 2 2

1



## ) ( ) ( ) )( ( P i = prediction at

time and

location i ; O i = observation at

time and location i ;

P = arithmetic average of P i , i=1,2,..., N;

O = arithmetic average of O i , i=1,2,...,N

Normalized Mean Error ( NME ) Norm_Mean_Err



# ∑ ∑

=

= 

## −

N

i i N

i i i



## O O P

1 1 Reported as %

Root Mean Square Error ( RMSE ) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

## 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

# ∑

= N

i i i 

## O P N Reported as %January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec7.doc 7-27 Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Fractional Gross Error ( F E

) Frac_Gross_Err 

# ∑

= 

## + − N

i i i i i 

## O P O P

N 1 

## 2 Reported as %

Mean Absolute Gross Error ( MAGE ) Mean_Abs_G_Err 

# ∑

= 

## − N

i i i 

## O P N 1 

## 1

Mean Normalized Gross Error ( MNGE ) Mean_Norm_G_Err 

# ∑

= 

# − N

i i i i 

# O O P

N 1



# 1 Reported as %

Mean Bias ( MB ) Mean_Bias 

# ( ) 

# ∑

= 

## − N

i i i 

## O P N 1 

## 1 Reported as

concentration (e.g., μg/m 3 )

Mean Normalized Bias ( MNB ) Mean_Norm_Bias 

# ( ) 

# ∑

= 

## − N

i i i i 

## O O P

N 1 

## 1 Reported as %

Mean Fractionalized Bias (Fractional Bias,

MFB ) Mean_Fract_Bias 

# ∑

= 

## ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − N

i i i i i 

## O P O P

N 1 

## 2 Reported as %

Normalized Mean

Bias ( NMB ) Norm_Mean_Bias



# ∑ ∑

=

= 

## −

N

i i N

i i i



## O O P

1 1 

## )

( Reported as %

Bias Factor ( BF ) Bias Factor

1 

## 1 N

i

i i 

## P

N O

= 

## ⎛ ⎞ ⎜ ⎟ ⎝ ⎠ 

# ∑ Reported as BF:1 or
1: BF or in fractional

notation (BF/1 or 1/BF).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-1 8.0 OZONE AND PM 2.5 ATTAINMENT DEMONSTRATION

This chapter provides a summary of how the modeling results will be used to demonstrate

attainment of the 8-hour ozone and PM 2.5 standards. The procedures for demonstrating

attainment of the 8-hour ozone standard follo w those outlined in EPA’s final 8-hour ozone modeling guidance (EPA, 2005b). Whereas the proce dures for demonstrating attainment of the

PM 2.5 National Ambient Air Quality Standard (N AAQS) follow those given in EPA’s draft PM 2.5 and regional haze modeling standard (EPA, 2001) with updates from the Clean Air Interstate Rule (CAIR; EPA, 2005c) augmented by more recent procedures that use the SANDWICH (Frank, 2005) and Speciated Model Attainment Te st (SMAT; Timin, 2005). These procedures are being refined and may be updated during the course of the study including the expected

release of EPA’s final PM 2.5 and regional haze modeling guidance in 2006.

8.1 8-Hour Ozone Attainment Demonstration

The procedures for demonstrating attainment of the 8-hour ozone sta ndard are out lined in EPA’s final 8-hour ozone modeling guidance document (EPA, 2005b). These procedures

include a modeled attainment de monstration test along with perf orming additional analysis that can be used to support and corroborate th e modeled attainment demonstration.

8.1.1 Modeled 8-Hour Ozone Attainment Demonstration Test

The 8-hour ozone modeled attainment demonstr ation procedures use the modeling results in a relative sense to scale or project the curr ent baseline observed 8- hour ozone Design Values (DVB) to estimate a future-year 8-hour ozone Design Value (DVF) that is compared with the 8- hour ozone NAAQS to determine wh ether attainment has been de monstrated (DVF < 85 ppb) or not (DVF > 85 ppb). The ratio of future-year to cu rrent year modeling results is called the Relative Reduction Factor (RRF) and the basic Desi gn Value projection appr oach for a site i is expressed as follows:

DVF i = RRF i x DVB i

Where,

DVB i = the baseline (current year) 8-hour ozone Design Value (ppb) at monitor i;

DVF i = the future-year projected 8-hour oz one Design Value (ppb) at monitor i; and

RRF i = the relative reduction factor, calculated n ear site i that is the ratio of future to baseline year predicted concentrations near the monitoring site averaged over multiple days.

Although the basic equation for projecting futu re-year 8-hour ozone Design Values is conceptually simple, there are several assumptions a nd issues that need to be resolved as follows:

How is the site-specific baseline Design Value [DVB i ] calculated? The baseline 8-hour ozone Design Value is calculated as the thr ee–year average of 8-hour ozone Design ValuesJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-2 centered on the 2002 modeling year. As an 8- hour ozone Design Value is the three-year average of the fourth highest daily maximum 8-hour ozone concentrati on at a site, the DVB “5-year Design Value” will weigh the fourth hi ghest 8-hour ozone concentration from 2000 and 2004 once, 2001 and 2003 twice and 2002 three times.

In calculating the site-specific RRF i , what is meant by “near the monitor” and which predicted 8-hour ozone con centrations “near” the monitor should be used? Near the monitor is roughly defined as the highest estimated daily maximum 8-hour ozone concentration

within 15 km of the monitor. This is obta ined by looking for the highest estimated daily

maximum 8-hour ozone concentration in an a rray of NX by NY grid cells centered on the grid cell containing the monitor. EPA guidance pr esents defaults for the number of cells to use in the array as a function of grid reso lution (EPA, 2005b, pg. 16) and for the ASIP 12 km and 36 km resolution grid array sizes centered on the monitor of, respectively, 3 x 3 and 1 x 1 (i.e., just the grid cell containing the monito r) will be used. Thus for each modeling day under consideration, the highest daily maximu m 8-hour ozone concen trations near the monitor is extracted for the baseline-year base case simulation out of the 3 x 3 array of cells

centered on the monitor for the 12 km simulation. Similarly, the highest daily maximum 8- hour ozone concentrations in the 3 x 3 array of 12 km cells is extracted from the future-year simulation; note that the baseline-year and futu re-year cell extractions may not be from the same grid cell.

How are future-year projected 8-hour ozone Desi gn Values calculated in unmonitored areas? EPA’s 8-hour modeling guidance lists a multiste p procedure for projecting future-year 8- hour ozone Design Values away from the monitors:

• Interpolate the observed 8-hour ozone Design Values to gene rate spatial fields of 8- hour ozone Design Values;

• Adjust the spatial fields using the gr idded model output ozone concentrations gradients for the baseline year simulation;

• Apply gridded RRFs to the model adjusted spatial fields of current-year Design Values to obtain gridded fields of future -year projected 8-hour ozone Design Values;

and

• Compare spatial fields of pr ojected 8-hour ozone Design Valu es with the standard to

determine whether attainment has been demonstrated across the region. EPA notes that the Design Value projections in the unmonitored areas is more uncertain and recommends it be done as a separate test to the monitor-based Design Value projections. The procedures for interpolating the observed 8-hou r ozone Design Values using modeled ozone gradient predictions is not well defined at this time and EPA is in the process of developing

software so that a uniform unm onitored ozone attainment test can be defined. Consequently, initially we will just perform the monitor-based attainment te st for the ASIP 8-hour ozone modeling and as a sensitivity we will examine the spatial distribution of the RRFs in each

nonattainment area.

Which days should be used in calculating the RRFs? For the ASIP modeling, RRFs will be

based on the ratio of the average highest daily maximum 8-hour ozone concentrations near the monitor of the future-year to baseline year for all days in which the baseline year modeled estimated highest daily maximum 8-hour ozone concentration is greater or equal to a threshold-ozone concentration. Initially, a th reshold of 85.0 ppb will be used. However, if this results in less than 10 modeling days in the RRF calculation, then the threshold will beJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-3 successively lowered by 1 ppb until at least 10 mode ling days are used in the RRFs or when a minimum threshold of 70.0 ppb is reached. If usi ng a threshold of 70 ppb still results in less than 10 days, then the data will be flagged and di scussed with ASIP, especially if less than 5 days are being utilized.

8.1.2 Additional Analysis to Support the Attainment Demonstration

EPA’s guidance (EPA, 2005b) lists a series of additional analysis that can be conducted to support and corroborate the modeled ozone atta inment demonstration test listed above. EPA notes that cases when the future-year projected 8-hour ozone Design Valu e is well below (< 82 ppb) the ozone NAAQS less supporting evidence is required then if the projected Design Value is closer to the NAAQS. In fact, EPA recommends that a complete suite of Weight of Evidence (WOE) supporting analysis be pe rformed when the future-year projected 8-hour ozone Design Value is in the 82 to 87 ppb range. EPA notes th at with projected Design Values of 88 ppb or above it is very unlikely that supporting analysis could be suffi ciently convincing to conclude that the NAAQS will be attained given the results of the modeled attainment test.

EPA lists several additional modeling metric s, supplemental modeling analysis, use of observational models and analyzing emissions a nd air quality trends data that should be performed as part of the supporting analysis. With the possible exception of calculating additional modeling metrics, most of this additi onal supporting and WOE an alysis will be carried out by the States, although the ENVIRON/Alpine Team may be recruited to assist in this matter

at a future date.

8.2 PM 2.5 Attainment Demonstration

Currently, only the annual average PM 2.5 NAAQS is violated in the ASIP States so only it will be addressed. However, EPA has proposed to lower the 24-hour PM 2.5 standard to it may

have to be addressed in the future. EPA ha s issued draft modeling guidance that describes procedures for combining PM monitoring data w ith modeling results to project future-year PM 2.5 Design Values for comparison with the NAAQS in an attainment demonstration (EPA, 2001). These procedures are called the Speciated Mode led Attainment Test (SMAT). A preliminary version of the SMAT was applied as part of the proposed Clean Ai r Interstate Rule (CAIR; EPA, 2004) with the approach refined in the final CA IR (EPA, 2005c,d). Like the ozone projection procedures described above, SMAT uses the modeli ng results in a relative sense to scale baseline

PM 2.5 Design Values to estimate future-year projected PM 2.5 Design Values. However, unlike
ozone, PM 2.5 consists of several different components (e .g., sulfate, nitrate, ammonium, etc.). The SMAT develops site-specific separate RRF s for each PM component, projects each PM component to the future-year and then recombin es all of the PM components to obtain total

PM 2.5 mass for comparisons with the PM 2.5 NAAQS.

The SMAT procedures consists of two co mponents: (1) the combination of the PM 2.5 mass measurements from the Federal Refere nce Method (FRM) with the speciated PM 2.5 measurements, such as those from the Speciated Trends Network (STN); and (2) the combination of the modeling results with the speciated FRM PM 2.5 Design Values to obtain future-year projected PM 2.5 Design Values.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-4 8.2.1 Speciation of FRM PM 2.5 Mass Measurements

PM 2.5 attainment/nonattainment can only be determined from the FRM PM 2.5 measurements that measure just total PM 2.5 mass. The FRM PM 2.5 measurements are used to develop the PM 2.5 Design Values that determine attainme nt classification and are used as the starting point for projecting future-year PM 2.5 Design Values for demonstrating attainment. Thus, representative speciated PM 2.5 measurements need to be mapped to the FRM measurements. For most FRM sites there is a speciated PM 2.5 in the same general area that can be used in the mapping. However, in some cases, there is no nearby speciated PM 2.5 site so speciated PM 2.5 data must be interpolated fr om sites surrounding the FRM site.

Speciated PM 2.5 measurements are routinely collected on the same 1:3 day sampling frequency as used by the FRM network at two m onitoring networks in the US: the Speciated Trends Network (STN) and the Interagency M onitoring of Protected Visual Environments (IMPROVE) network. Each of these three monitoring networks use different measurement technologies each of which have their own measur ement artifacts. For example, FRM uses a single Teflon filter and includes water in the measurement (after equilibration at ~35% RH) and just measures total PM 2.5 on the filter. Particulate nitrate may volatilize off of the FRM Teflon filter. The STN measurement technology uses Te flon, Nylon and Quartz filters for measuring the speciated data and does not measure the water component of the PM 2.5 . The STN Quartz

filters are also not blank corrected which results in inaccurate OC measurements. IMPROVE also uses multiple filters and does not include ammonium in its measurements.

As the FRM is the de facto regulatory definition of PM 2.5 , EPA has developed procedures for adjusting the STN and IMPROVE speciated PM 2.5 measurements to account for the

measurement artifacts of the different networ ks and make the speciated PM measurements

consistent with the FRM PM 2.5 mass measurements. These adju stments include the following:

• Adjust nitrates downward to account for volatilization off of the FRM nylon filter;

• Add particle bound water (PBW) that are asso ciated with nitrate and sulfate in the FRM measurements; and

• Estimate total carbonaceous mass accounting for lack of blank correction in the

STN measurements.

The resultant fine particle chemical speciation approach has been named the Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous mass and estimated aerosol acidity (H+), or

SANDWICH. Details on the SANDWICH procedures is give n in Frank (2005).

8.2.2 Speciated Modeled Attainment Test (SMAT)

The SMAT procedures have been applied for the proposed and final CAIR analysis (EPA, 2004; 2005c) each time with refinements. A SMAT Tool has been developed using the SAS programming language that is populated with 1999-2003 FRM and SANDWICH speciated

PM 2.5 data. Use of 1999-2003 PM 2.5 data is appropriate for th e CAIR modeling since it is centered on the 2001 CAIR modeling year. However, for the ASIP PM 2.5 projections, FRM andJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-5 speciated PM data are needed for the 2000-2004 peri od so that it is centered on the ASIP 2002 modeling year.

Because the CAIR SMAT projections were looking at all FRM monitors, including many without nearby associated speciated PM 2.5 monitors, the SANDWICH speciated PM 2.5 data were interpolated to obtaine d gridded fields of PM 2.5 speciation that were then used to speciate the

FRM data. EPA used a Voroni Nearest-ne ighbor Approach (VNA) to interpolate the

SANDWICH PM 2.5 speciation components for sulfate, nitrates, OC, EC, crustal/other and a Degree of Neutralization (DON). DON was interpolated instead of ammonium because the

IMPROVE network does not measure ammonium and using the DON ammonium can be backed out of the interpolated su lfate values.

Updating the CAIR SMAT Tool with the 2004 da ta in a consistent fashion and extending it to the 12km grid would be a major undertaking. The interpol ation procedures are not well defined. Furthermore, the ENVI RON/Alpine Team has applied th e CAIR SMAT Tool and been unable to exactly duplicate EPA’s CAIR results, wh ich has been attributable to different versions of SAS used by EPA (PC) and the ENVIRON/Alpine Team (Linux).

ASIP has reviewed the FRM m onitors in nonattainment area s in the Southeast US and

found that most have an associated STN monitor. However, there is a sm all subset of FRM sites

that the ASIP States did not link to a nearby STN site. In these cas es, the State of North Carolina will revise parts of the existing EPA SMAT Tool to get an approximated speciation mix for the

FRM site. The State of North Carolina will work with the ASIP States to identify the FRM-STN pairs and process the STN data for 2000-2004 using the SANDWICH procedures so that the FRM PM 2.5 mass measurements can be speciated for the application of the SMAT. Once we

have the FRM and associated SANDWICH PM 2.5 speciation data for the 2000-2004 period, the SMAT procedures involves the following steps:

1. Derive quarterly mean average concentrati ons for each of the major components of PM 2.5 and each of the quarters from the 2000-2004 ba seline period. This is done by applying the fractional contribution of each major component of PM 2.5 from the SANDWICH

PM 2.5 speciation data for the same quarter a nd year. Major components are as follows:

• Sulfate (SO4);

• Nitrate (NO3);

• Ammonium (NH4);

• Elemental Carbon (EC);

• Organic Mass Carbon (OMC);

• Final Crustal/Other; and

• Particle Bound Water (PBW).

2. Use the model estimated PM 2.5 components near each monito r and for each of the four quarters of 2002 from the 2009 and 2002 simulati ons to develop monitor-, quarter- and

PM 2.5 species-specific Relative Reduction Factors (RRFs) usi ng the ratio of the 2009 to 2002 quarterly average modeling results.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec8.doc 8-6 3. Apply the monitor-, quarter- and species-speci fic RRF to each quarterly average observed

PM 2.5 species concentrations from 2000-2004 to obtain 5-years of quarterly average

PM 2.5 species concentrations repr esentative of 2009 conditions.

4. Recalculate the Particle Bound Water (P RB) component from the 2009 projected quarterly average sulfate and nitrate concentrations.

5. Average the four quarterly mean 2009 species concentrations to ma ke an annual average

PM 2.5 species concentrations at each monitor and for each of the 5 years (2000-2004). Sum the annual average species components for each of the five years and each monitor to obtain five years of annual average total PM 2.5 mass concentrations.

6. Calculate the PM 2.5 Design Values from the five years of 2009 projected annual average

PM 2.5 concentrations and compare against the NAAQS PM 2.5 in the attainment test.

As in the ozone projections, there are a few issues that need to be resolved and defined to apply

the above PM 2.5 attainment test.

What quarterly average model estimated PM 2.5 species components are used “near” the

monitor? As in the ozone projections, a grid re solution dependent array of cells centered on the monitor is used (i.e., 3 x 3 for 12 km grid and 1 x 1 for 36 km grid). However, for

the PM 2.5 projections the average of the estimated PM 2.5 species across the array of cells is used, rather than the highest value that is used in th e ozone projections.

What PM 2.5 Design Values should be used in the projections? An average of the 2001,

2002 and 2003 PM 2.5 Design Values will be used in the PM 2.5 projections. As a Design Value is a three-year averag e of annual values, then this three year average of PM 2.5 Design Values will weig h the annual average PM 2.5 concentrations from the years 2000 and 2004 once, 2001 and 2003 twice and 2002 three times.

When is PM 2.5 attainment demonstrated? The SMAT attainment test is passed when the 2009 projected 3-year average Design Value is 14.9 μ g/m 3 or lower. [should this be 15.4 ug/m3???]

8.2.3 Additional Supporting Analysis

Additional supporting analysis to the SMAT modeled PM 2.5 attainment test will be conducted to corroborate the modeling analysis. The exact definition of the supporting analysis is being analyzed but would like consist of addi tional modeling metrics as well as emissions and air quality trends and alternative modeling and projection approaches. The supporting analysis to be performed as part of the ASIP PM 2.5 attainment demonstration will be better defined once

EPA has released their final PM 2.5 modeling guidance. Currently, this additional supporting analysis is planned to be carried out by the ASIP States, although the ENVIRON/Alpine Team may assist at a future date.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec9.doc 9-1 9.0 DATA MANAGEMENT

Data management and data security proce dures are critical components of the ASIP regional fine particulate and 8- hour ozone modeling. Very large data files are used in each component of the modeling process, including processing of the meteorology data, emissions processing, and PM 2.5 and ozone modeling with CMAQ. An annual simulation on the ASIP 36- km domain requires approximately 2 Terabytes (Tb) of disk storage, whereas an annual simulation on the ASIP 12-km domain requires over 3 Tb of disk storage. This chapter describes data management practices that will be used in the ASIP 8-hour ozone and PM 2.5 modeling analyses.

For all critical files we will maintain bac kup copies either on tapes, storage disks or redundant disk systems. In addition, because ASIP model simulations will be performed separately by the ENVIRON and AG modeling cen ters, each institution will maintain its own copy and backup of critical input and output files. Because ther e are differences in system configurations at each of the modeling centers the data bac kup and archiving are discussed separately for each center, below. Some of the ASIP States will also ma intain copies of the modeling data files relevant to th eir particular nonattainment areas.

CMAQ generates large output files of which most information is rarely used. For example, model output for layers other than the surface layer are typically only used to define boundary conditions for finer grid simulations. Thus, once the boundary condition files have been generated for the ASIP States 12 km a nd 4 km refined modeling domain, the 3-D CONC

files of instantaneous concentrations do not ha ve to be archived. We do not plan redundant archiving for model output files (including output from MCIP and SMOKE) except for key final model outputs (e.g., 2002 and 2009 model-rea dy emissions) because these files can be regenerated by repeating model simulations, and this is approach more efficient and more cost effective than redundant archiving.

To promote efficient, reliable communicat ion among project participants, the modeling

team has created 4 different listservs for VISTAS to aid in dissemination of information and as a primary means for distributing emissions and ai r quality modeling information. As the ASIP

States are a subset of the VIST AS states the same listservs w ill also be used for ASIP. The

listservs are:

• vistas-all@cert.ucr.edu: ge neral project information fo r all interested persons.

• vistas-modeling@cert.ucr.edu: private list for the VISTAS/ASIP project management team and modeling contractors.

• vistas-emissions@cert.ucr.edu: list for sharing information on emissions processing and QA.

• vistas-met@cert.ucr.edu: list for shari ng information on meteorology modeling and processing of MM5 data using MCIP.

These separate listservs are aimed at provi ding better organization of communications and allowing for detailed discussions of sp ecific topics such as emissions QA.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec9.doc 9-2 9.1 Project Website

Depending on the amount of data generate d by ASIP, a project website will be

established to facilitate the distri bution of information. Whether th e ASIP website will be part of the VISTAS website or its own has yet to be determined.

9.2 Data Transfer

Data transfer among the modeling centers a nd between other ASIP participants or contractors will be accomplished using a combinati on of email, ftp downloads and portable disk drives depending on the size of the data transfer. For data files smaller than a few MB email typically works well and is most efficient. For da ta files of less than ab out 500 MB file transfer protocol (ftp) is typically the fastest a nd most efficient met hod. ENVIRON and AG each maintain webpages and ftp pages that can be used for exchanging da ta. In addition, each modeling center has severa l portable disk drives with both USB2 and firewire interfaces that can be FedEx among project participants to exchange large data sets. Portable disk drives range in size from 80 to 300 GB and are adequate for al l large files data transfers. The approach described here has been used throughout the VI STAS project, as well as WRAP, CENRAP and MRPO, and has proven to be economical and efficient.

9.3 Data Backup and Archiving

Data backup and archiving will be performed at each of the modeling centers. Copies of critical project data will be maintained at each modeling center to provide redundant backup of key project data. Each modeling center stores key model inputs on RAID systems that have self re-generation capabilities in case a disk driv e fails. In addition, each modeling center will perform backups of key project data to tape or redundant disk storage systems. Data storage and back up resources at each mode ling center are described next.

9.3.1 ENVIRON

Over 20,000 Gigabytes (>20 Terabytes, Tb) of disk storage are available to the

UNIX/Linux workstations. All of the workstati ons are networked togeth er and are accessible from each employee’s desktop PC. All workstat ions have CD-ROM drives and can access DLT, 4mm DAT and 8mm Exabyte tape drives for data backup and data transfer . ENVIRON can also create CDs (CD-R and CD-RW) and DVDs (DVD+ a nd DVD-) for data bac kup and distribution. For ASIP modeling, all CMAQ simulations will be performed on one of two 13 node Beowulf

Linux Clusters that included one master node and 12 processing node s. Each node consists of two AMD Athlon 2600+ processors. The master node s have 2 Gb of memory and are connected to a ~3-4 Tb RAID disk system. Each seco ndary processing node includes 1 Gb of memory. The ENVIRON Novato computing center also in cludes approximately 10 dual processor Linux workstations with processing sp eeds of 1700+ to 3000+. Three olde r Unix workstations are also available, SUN, DEC and SGI. The Linux computer systems are located in their own room with

their own dedicated air conditioni ng (AC) system. The room in cludes a temperature sensitive power shut off device that will shut off the power to all comput ers in case the AC breaks downJanuary 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec9.doc 9-3 so that catastrophic failure due to too high temp eratures does not occur. Backups are made on

IDE disk drives that are removed from the com puter and stored on a sh elf to protect against power surges destroying the backup data.

9.3.2 Alpine Geophysics

Alpine Geophysics' computing facilities c onsist of SUN Micros ystems SPARCstation computers and a very powerful array of over 20 multiprocessor Linux-based workstations. Disk storage systems include ~20 Tb of aggregate di sk space and over 8 Tb of SCSI and IDE Raid-5 protected space. All client data is stored on at least one RAID-5 protected di sk array. To further protect client data, two of our main servers b ackup their disk drives on a weekly basis to a second server that is physica lly disconnected from the power supply when not doing an active transfer. This way a catastr ophic power failure will not comp romise the ability for Alpine Geophysics to deliver.January 2006

F:\PM25 SIP Docs\ASIP M odeling Protocol\Sec10.doc 10-1 10.0 DOCUMENTATION

This section describes the documentation that be provided during ASIP and the potential for modifications to this Modeling Protocol and Qua lity Assurance Project Plan (QAPP) that might become necessary as this phase of the study unfolds.

10.1 Planned Documentation

Documentation associated with the ASIP emi ssions and air quality modeling will include all relevant input data bases and scripts associated with the pre- and post-processing associated with model input development, model application, sensitivity and diagnostic analyses, and performance

evaluations. At this time the deliverables consists of disk drives with modeling results and databases for the ASIP States and PowerPoint presentations of the 2009 projections of 8-hour ozone and PM 2.5 Design Value Projections. Deliverables under the current ASIP modeling contract are as follows:

• ASIP Modeling Protocol (this document)

• ASIP Quality Assurance Project Plan (QAPP)

• CMAQ-ready 2002 36/12 km Typical emissions with every day mobile emissions on the 12 km grid

• CMAQ-ready 2009 36/12 km Typical emissions with every day mobile emissions on the 12 km grid

• PowerPoint presentations of 2009 8-hour ozone and PM 2.5 Design Value projections

• Disk drive of modeling results and bounda ry conditions for the NC, ALGA and VA

subdomains

• Archive drives of modeling results

Additional deliverables by the modeling team may be added by ASIP at a later date. Chapter 11 contains the current Scope of Work (SOW) for the ENVIRON/Alpine team under the ASIP work

effort and the schedule for the deliverables.

10.2 Procedures for Updating Modeling Protocol and QA Plan

One of the underlying realizations stemming from the VISTAS modeling activities was the awareness that the science of ‘One-Atmosphere’ PM/regional haze modeling is advancing very rapidly. Part of this stems from the parallel ac tivities being carried out by the RPOs; some if it is due to other ongoing 8-hour ozone and PM modeling st udies being performed by various states. In addition, EPA is in the process of revising its PM 2.5 and regional haze guidance documents. Collectively, it is quite likely that there will be new opportunities to strengthen the modeling algorithms, input data sets, and evaluation pro cedures throughout the duration of the ASIP and VISTAS modeling efforts. Moreover, the AS IP emissions and air quality modeling activities

involve the collaboration with the ASIP States as well as other ASIP contractors who will provide the required emissions and meteorological input s to the SMOKE and CMAQ models. Given the ongoing model refinement activities and the need for strong coordination with other ASIP contractors, it may be necessary to modify certain as pects of this modeling protocol. In this event,

modification will be made in consultation with the ASIP project coordinator and the revised protocol will be submitted to the ASIP States for approval.January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec11.doc 11-1 11.0 ASIP STATEMENT OF WORK AND SCHEDULE

This section describes the current AS IP Statement of Work (SOW) for the ENVIRON/Alpine Modeling Team, the schedule and deliverables under the ASIP work effort. This work currently includes four Tasks that fo cus on the emissions and air quality modeling for the 2002 calendar year using a 36/12 km grid fo r two emission scenario s: (1) a 2002 Typical Base Case; and (2) a 2009 Base Case. Unlike VISTAS, ASIP is performing everyday on-road mobile source modeling on the 12 km grid (on the 36 km grid and for VISTAS 36/12 km modeling on-road mobile source emissions mode ling are performed for one week from each month). The 2002 and 2009 modeling results will be used to project 2009 8-hour ozone and

PM 2.5 Design Values for the attainment demonstr ation. The ENVIRON/Al pine Team may be charged with additional Tasks for ASIP in the futu re at which time this Modeling Protocol will be updated accordingly.

11.1 Task 1: Project Management

The objective of this task is to manage proj ect activities, participate in conference calls, manage the ASIP contract and subcontractors, general oversight and overall quality assurance and the preparation of the Quality Assurance Pr oject Plan (QAPP) and Modeling Protocol (this document).

The management structure for the ASIP Emissi ons and Air Quality Modeling is similar to what was used in VISTAS with Ralph Morris of ENVIRON serving as Project Manager and Ralph Morris and Gregory Stella of Alpine Ge ophysics serving as Co-Pri ncipal Investigators (Co-PIs) and managing the activ ities in the ENVIRON and Alpi ne Geophysics modeling centers, respectively. Section 1.7 provides more details of the ASIP Emissions and Air Quality Modeling management structure. 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, invoi cing to SESARM 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).January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec11.doc 11-2 • Develop the ASIP Modeling Protocol.

Table 11-1 summarizes the deliverables and expect ed delivery dates under Task 1 of the ASIP regional ozone and PM 2.5 SIP support modeling analysis.

Table 11-1. 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 Invoices Monthly; 11/05-2006

11.2 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 F4” and 2009 36/12 km “Base F4” CMAQ-ready emission inputs using the every day mobile source emissions modeling approach.

Version 2.1 of the SMOKE emissions modeli ng system will be used to generate 2002 Typical 12 km on-road mobile sources emi ssions using daily meteorology and the SMOKE MOBILE module. VISTAS has generated 2002 Typical Base F emissions using weekly MOBILE model run for 36 km and 12 km grid as well as generating 2002 Typical Base F 36/12 km emissions for the other source categories (e.g., area, point, non-road, etc.). Under ASIP this task will provide supplemental 2002 daily MOBILE modeling for the 12-km grid. The 2002 12 km Typical daily mobile source emissions will be merged with the other source categories to generate 2002 Typical 12 km emis sions inputs based on daily mob ile source modeling that will be used in the ASIP ozone and PM modeling.

Also under this task we will process the 2009 Base F annual emissions through the SMOKE emissions processor for all anthropogeni c emission source categories to prepare the emissions for use in the CMAQ air quality mode l. Base F emissions include outputs of the Integrated Planning Model (IPM) fo r utilities and all inventory re visions from VISTAS and other RPOs that are available by July 2005. Daily 2009 MOBILE run will be used in SMOKE for the 12 km grid and weekly MOBILE run for the 36 km grid.

Table 11-2. ASIP Task 2 2002/2009 Emissions Modeling task deliverables and due dates.

Item Description Due Date
1 2002 12 km Typical Base F daily mobile source SMOKE emissions modeling 11/28/05 2 2002 36/12 km Typical Base F CMAQ-ready emissions w/daily mobile source

emissions 12/3/05

3 2009 36/12 km Base F CMAQ-ready emissions w/daily mobile sources 12/15/05January 2006

F:\PM25 SIP Docs\ASIP Modeling Protocol\Sec11.doc 11-3 11.3 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 scenario s using every day mobile source emissions.

The CMAQ model is applied for the 2002 and 2009 Base F emission scenarios with weekly mobile emissions modeling for the 36 km grid and every day mobile sources emissions on the 12 km grid. QA/QC is performed on the runs including comparisons of the modeling

results to the observed PM c oncentrations. Extractions ar e made at the ozone and PM monitoring sites, where values at the monitoring s ite are extracted from the 36 km results and a 3 x 3 grid cell average concentration centered on th e monitoring site is extracted for the 12 km modeling results. The 12 km modeling results will be processed to generate boundary condition inputs for the Alabama/Georgia (ALGA) and Nort h Carolina (NC) subdomains and provided to the states of North Carolina and Georgia. Tabl e 11-3 list the deliverables for the ASIP Task 3 CMAQ modeling.

Table 11-3. 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 12/21/05

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

11.4 Task 4: Data Analysis and Data Management

The objective of this task is to analyze the results of the Task 2 emissions and Task 3 CMAQ air quality modeling, archive and distribute the modeling results.

The SMOKE emissions and CMAQ air quality modeling results will be analyzed. 8-hour ozone projections and annual PM 2.5 projections will be made at all monitoring sites in and adjacent to the VISTAS states using both the 36 km and the 12 km modeling results. These

results will be documented in Powe rPoint presentations and presente d ASIP states (Table 11-4). 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 al so be available on the

Internet.

Table 11-4. 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 2/10/06January 2006

F:\PM25 SIP Docs\ASIP Mode ling Protocol\References.doc R-1 REFERENCES

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