Draft Modeling Protocol
Emissions and Air Quality Modeling for
SEMAP
Contract # S-2010-04-01
Prepared by:
Georgia Institute of Technology
University of North Carolina
Colorado State University
Prepared for:
Souteastern States Air Resource Managers, Inc.
526 Forest Parkway, Suite F
Forest Park, GA 30297-6140
February 17, 2011
Project Manager:
Associate Manager:
Talat Odman
Zac Adelman
School of Civil & Environmental Engineering
Georgia Institute of Technology
311 Ferst Drive, Atlanta, GA 30332-0512
odman@gatech.edu
Phone: 404-894-2783 Fax: 404-894-8266
Center for Environmental Modeling
University of North Carolina–Chapel Hill
137 E. Franklin St., Chapel Hill, NC 27599
zac@unc.edu
Phone: 919-966-2206
SEMAP Modeling Protocol
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SEMAP Modeling Protocol
Table of Contents
Section 1:
Introduction ....................................................................................................................... 1-1
Section 2:
Project Overview .............................................................................................................. 2-1
2.1 Management Structure .................................................................................................................. 2-1
2.2 Communication Procedures .......................................................................................................... 2-4
2.3 Participating Organizations .......................................................................................................... 2-4
2.4 Project Schedule ........................................................................................................................... 2-5
2.5 Conceptual Description ................................................................................................................ 2-8
Section 3:
Models and Model Inputs ................................................................................................. 3-1
3.1 Meteorology, Emissions and Air Quality Models ........................................................................ 3-1
3.2 Modeling Domains ..................................................................................................................... 3-13
3.3 Episode Selection........................................................................................................................ 3-16
3.4 Input Databases........................................................................................................................... 3-16
3.5 Quality Assurance and Error Correction Protocol ...................................................................... 3-26
Section 4:
Technical Approach .......................................................................................................... 4-1
4.1 Simulation Identification .............................................................................................................. 4-1
4.2 General Modeling Procedures ...................................................................................................... 4-1
4.3 Initial Base Case Simulation ......................................................................................................... 4-5
4.4 Diagnostic Model Sensitivities ..................................................................................................... 4-8
4.5 Final Base Case Simulation ........................................................................................................ 4-12
4.6 Typical Base Case Simulation .................................................................................................... 4-12
4.7 Future OTB and Emissions Control Scenarios ........................................................................... 4-13
4.8 Emissions Sensitivities ............................................................................................................... 4-13
4.9 Interactive Database Tool ........................................................................................................... 4-14
Section 5:
Air Quality Model Evaluation Protocol ............................................................................ 5-1
5.1 Ambient Air Quality Data ............................................................................................................ 5-1
5.2 Evaluation Tools ........................................................................................................................... 5-4
5.3 Evaluation Procedures .................................................................................................................. 5-5
Section 6:
Supplemental Analyses ..................................................................................................... 6-1
6.1 Future Attainment Demonstration Analysis Protocol ................................................................... 6-1
Section 7:
Data Backup and Archival Protocol.................................................................................. 7-1
7.1 Georgia Tech ................................................................................................................................ 7-1
7.2 UNC .............................................................................................................................................. 7-1
Section 8:
Computer Resources ......................................................................................................... 8-1
8.1 Georgia Tech Computing Resources ............................................................................................ 8-1
8.2 UNC Computing Resources ......................................................................................................... 8-2
Section 9:
Project Team Roles and Responsibilities .......................................................................... 9-1
Section 10:
Documentation ................................................................................................................ 10-1
Appendix A. SEMAP Emissions QA Worksheets ................................................................................ A-1
Appendix B. SMOKE Emissions QA Checklist and Log for Warnings and Notes .............................. B-1
Appendix C. References ........................................................................................................................ C-1
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List of Tables
Table 2-1. Project schedule ........................................................................................................................ 2-5
Table 3-1. Projection parameters for the CMAQ and CAMx modeling domains ................................... 3-14
Table 3-2. SEMAP WRF and CMAQ Vertical Layer Configurations .................................................... 3-15
Table 3-3. Emissions processing sectors for the SEMAP project ............................................................ 3-19
Table 3-4. 2007 Actual inventory data..................................................................................................... 3-22
Table 3-5. 2007 Typical inventory data ................................................................................................... 3-23
Table 3-6. 2017 OTB inventory data ....................................................................................................... 3-23
Table 3-7. 2017 OTW inventory data ...................................................................................................... 3-24
Table 3-8. 2020 OTB inventory data ....................................................................................................... 3-25
Table 3-9. 2020 OTW inventory data ...................................................................................................... 3-25
Table 4-1. CMAQ and CAMx configuration options ................................................................................ 4-6
Table 5-1. Surface Air Quality Data available for model performance evaluation and input to receptor
models for the Southeast ............................................................................................................................ 5-3
Table A-1. Configuration settings worksheet for SMOKEv2.7b .............................................................. A-2
Table A-2. Inventory file recording worksheet for SMOKEv2.7b (sample entry shown in gray) ........... A-7
Table A-3. Ancillary input file recording worksheet for SMOKEv2.7b (sample entry shown in gray) ... A-8
Table A-4. Binary meteorology input and SMOKE output file recording worksheet for SMOKEv2.7b
(sample entries shown in gray) ................................................................................................................. A-9
Table A-5. Emissions simulation tracking worksheet (sample entries shown in gray) ......................... A-10
List of Figures
Figure 2-1. SEMAP project organization chart.......................................................................................... 2-3
Figure 3-1. SMOKE modeling system ....................................................................................................... 3-5
Figure 3-2. SEMAP 36-km (grey) and 12-km (white) modeling domains .............................................. 3-14
Figure 3-3. Regional Planning Organization map.................................................................................... 3-18
Figure 4-1. Modeled (red solid line) and observed (blue dots) 24-hr average organic carbon
concentrations (gm-3) at South DeKalb, GA from May 1st to July 15th, 2009. ...................................... 4-11
Figure 6-1. Location of various PM2.5 monitors in the U.S. for SMAT..................................................... 6-2
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List of Acronyms
AIRS
AMET
AOD
AQM
BC
BEIS3
CAMx
CB05
CEM
CEMPD
CENRAP
CIRA
CoST
CMAS
CMAQ
CTM
DDM
DVC
DVF
EC
EGU
EMF
EMS
EMP
FAQS
FB
FE
GIS
GIT
HAP
IC
IDT
I/O API
IMPROVE
JFSP
MAQSIP
MAT
MCIP
MEGAN
MSA
NAAQS
NASA
NATA
NCDAQ
Atmospheric Infrared Sounder
Atmospheric Model Evaluation Tool
Aerosol Optical Depth
Air Quality Model
Boundary Conditions
Biogenic Emission Inventory System, version 3
Comprehensive Air quality Model with extension
Carbon Bond—Version 2005
Continuous Emissions Monitor
Center for Environmental Modeling for Policy Development
Central Regional Air Planning (association)
Cooperative Institute for Research in the Atmosphere
Control Strategy Tool
Community Modeling and Analysis System
Community Multiscale Air Quality
Chemistry-Transport Model
Decoupled Direct Method
Current Year Design Value
Future Year Design Value
Elemental Carbon
Electrical Generating Unit
Emissions Modeling Framework
Emissions Modeling System
Emissions Modeling Platform
Fall-line Air Quality Study
Fractional Bias
Fractional Error
Geographic Information System
Georgia Institute of Technology
Hazardous Air Pollutant
Initial Conditions
Interactive Database Tool
Input/Output Applications Programming Interface
Interagency Monitoring of Protected Visibility Environments
Joint Fire Science Program
Multiscale Air Quality Simulation Platform
Modeled Attainment Test
Meteorology-Chemistry Interface Processor
Model of Emissions and Gases from Nature
Metropolitan Statistical Area
National Ambient Air Quality Standards
National Aeronautics and Space Administration
National-scale Air Toxics Assessment
North Carolina Division of Air Quality
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NEI
netCDF
NIF
NMIM
NMOC
OAQPS
OC
ORD
ORL
OSAT
OTAQ
PAMS
PAVE
PM
PPTM
PSAT
QA
QAPP
QMP
QC
RDBMS
RFP
RHR
ROSES
RPO
RRF
SAMI
SANDWICH
SDBMS
SEARCH
SEMAP
SERDP
SIP
SLAMS
STN
TAWG
UNC
VIEWS
VISTAS
WRAP
WRF
National Emissions Inventory
network Common Data Form
National Inventory Format
National Mobile Inventory Model
Non-Methane Organic Compounds
Office of Air Quality Planning and Standards
Organic Carbon
Office of Research and Development
One-Record-per-Line
Ozone Source Apportionment Technology
Office of Transportation and Air Quality
Photochemical Assessment Monitoring Stations
Package for Analysis and Visualization of Environmental Data
Particulate Matter
Particle and Precursor Tagging Methodology
PM Source Apportionment Technology
Quality Assurance
Quality Assurance Project Plan
Quality Management Program
Quality Control
Relational Database Management System
Request for Proposal
Regional Haze Rule
Research Opportunities in Space and Earth Sciences
Regional Planning Organization
Relative Reduction Factor
Southern Appalachian Mountains Initiative
Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous Mass
and estimated aerosol acidity (H+)
Spatial Database Management System
Southeastern Aerosol Research and Characterization Study
Southeastern Modeling Analysis and Planning
Strategic Environmental Research and Development Program
State Implementation Plan
State and Local Air Monitoring Stations
Speciated Trends Network
Technical Analysis Work Group
University of North Carolina
Visibility Information Exchange Web System
Visibility Improvement State and Tribal Association of the Southeast
Western Regional Air Partnership
Weather Research and Forecasting model
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Section 1: Introduction
In 2009, SESARM initiated a new Southeastern Modeling, Analysis and Planning (SEMAP)
project to produce technical analyses to aid member states, in the development of SIPs for O3 and
PM2.5, and in the demonstration of reasonable progress for the RHR, required under the Clean Air
Act Amendments. Specifically, since the precursor emissions and some of the atmospheric
chemical processes are interlinked in the way one can control the chemical formation and
transport for fine particles, ozone and regional haze, an integrated assessment using a oneatmosphere modeling approach is needed to address the air pollution problems in the SESARM
states. It is anticipated that this analysis will help the SESARM state agencies to protect human
health as well as the environment from the impacts of air pollutants.
To address the air quality and emissions modeling needs of the SEMAP project, Georgia
Institute of Technology (Georgia Tech), the University of North Carolina Institute for the
Environment (UNC) and the Colorado State University Cooperative Institute for Research in the
Atmosphere (CSU) formed a team. A contract (Contract # S-2010-04-01) was signed on April
13, 2010 between SESARM and Georgia Tech, the lead organization of the team. Dr. Talat
Odman from Georgia Tech is the project principal investigator. Mr. Zac Adelman from UNC and
Mr. Shawn McClure from CSU are co-investigators. They are joined by a large number of
investigators and staff from all three institutions to perform the tasks of this project.
The objective of the SEMAP project is to provide data and technical support to the SESARM
members in the development of state implementation plans (SIPs) for O3 and PM2.5, and in the
demonstration of reasonable progress for the regional haze rule (RHR). The Project Team will
pursue this objective through the collection and preparation of meteorology and emissions data,
the development and analysis of air quality modeling simulations, and the distribution and
documentation of all modeling results. The SEMAP project team will meet this objective using a
modeling system configuration chosen in collaboration with SESARM. The following modeling
systems will be used to accomplish the goals of the SEMAP project:

Weather Research and Forecasting (WRF) model – meteorological model

Meteorology-Chemistry Interface Processor (MCIP) – prepare Weather Research and
Forecasting (WRF) model data for emissions and air quality modeling

Sparse Matrix Operator Kernel Emissions (SMOKE) System – convert emissions
inventories into formats for air quality modeling

MOtor Vehicle Emissions System (MOVES) – on-road mobile source emissions model

Biogenic Emissions Inventory System (BEIS) – biogenic emissions model

Community Multiscale Air Quality (CMAQ) model – Eulerian air quality model

Comprehensive Air Quality Model with eXtensions (CAMx) – Eulerian air quality model
This document presents the modeling protocol for the SEMAP project. Its intent is to detail the
data collection, modeling approaches, and quality assurance/quality control (QA/QC) processes
that will be used in the project. Following from U.S. EPA guidance documents on air quality
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modeling and analysis (EPA, 2007; EPA, 1999; EPA, 1991), this protocol is a comprehensive
approach to the acquisition, production, assessment, archival, and documentation of all data used
for the SEMAP project. After presenting the modeling periods and domains this protocol details
the modeling systems that will be used during the SEMAP project, how the input databases will
be compiled. Protocols for model performance evaluation, future attainment demonstration,
quality assurance and error correction, and data backup and archival are then followed by
descriptions of the computer resources and the project team. The project schedule is then
followed by a description of the documentation that will be produced for the project. This
document concludes with two appendices of emissions QA/QC worksheets that will be used
during the SEMAP project.
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Section 2: Project Overview
2.1
Management Structure
The SEMAP tasks present a large and complex work effort with significant data throughput and
an aggressive schedule of deliverables. To maintain coordinated workflow, meet the project
milestones, and ensure adherence to the project Quality Assurance Project Plan (QAPP), a tiered
management structure is proposed that focuses on the primary data elements of the project. The
project Principal Investigator (PI), Dr. Talat Odman, has the ultimate responsibility for ensuring
that the Project Team properly works toward the project goals and objectives. The project
Quality Assurance (QA) Manager, Dr. Adel Hanna, is independent of the PI and will act with the
Co-Investigators, Shawn McClure and Zac Adelman, to ensure that the work and deliverables
produced during this project meet all requirements of the QAPP. Ms. Jeanne Eichinger, as the
Documentation and Deliverables Manager, will serve as an intermediary between the project CoInvestigators and the data managers to ensure that all deliverables are generated on time and in
accordance with the project QA/QC requirements. Dr. Sarav Arunachalam, Ms. Uma Shankar,
Dr. Aijun Xiu, and Mr. Zac Adelman will serve as the Air Quality Data, Observational Data,
Meteorology Data, and Emissions Data Managers, respectively; as data managers they will
supervise the acquisition, storage, generation, transfer, and documentation of all project data
relevant to their data elements.
Figure 2-1 summarizes the project organizational structure and the communication lines for all
key project staff. In general, the flow of information and communication for the project will be
downward, initiated at SESARM and going through the project PI, Dr. Odman. The CoInvestigators will be responsible for managing the work at their organizations and enforcing the
QA/QC requirements at the individual level. The project QA Manager will oversee and audit the
QA/QC process for the entire Project Team. The data mangers will be responsible for performing
and documenting QA/QC on individual data elements in accordance with the QAPP. The
Documentation and Deliverables Manager will ensure proper record keeping of all QA/QC
processes and that all products delivered as part of the SEMAP project meet the requirements of
the QAPP. Note the central role played by the Documentation and Deliverables Manager as the
coordinator between the technical staff/data managers and the project investigators/QA manager.
The QA Manager is responsible for the development, assessment, implementation, and approval
of the Quality Management Program (QMP). In the event that the QA Manager believes the project
activities do not meet program QA/QC policies or requirements, he may recommend, following
consultation with the project PI and the SESARM Project Coordinator, suspension of projects
and request corrective actions. Remedies and actions will be identified to ensure that project
activities meet QA/QC policies.
Mr. John Hornback, SESARM Contract Officer will oversee all activity on the contract, and
supervise managerial aspects of the project. He will help facilitate communications among
State/local agencies, the Georgia Tech/UNC/CSU project team, and the SESARM Project
Coordinator.
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Mr. Ron Methier, SESARM Project Coordinator will work with the SESARM Contract Officer
and State/local Agency Coordinators to define the air quality and emissions modeling activities
needed to support regional haze modeling and planning activities. He will be the responsible
official for this project overseeing the overall project and budget, as well as tasking Georgia
Tech with work required to complete this project. He will communicate project needs to Georgia
Tech's project coordinator.
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Figure 2-1. SEMAP project organization chart
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2.2
Communication Procedures
The components of the SEMAP project are implemented using a task structure and approach that
can very effectively complete the work. Each task/subtask is measured and documented for QC,
and corrective action will be taken immediately if a problem is found. Prior to delivery to
SESARM and other interested parties, all outputs from the SEMAP project tasks will be assessed
against the objectives stated in the project work plan to ensure that the work has been completed
and quality assured. If SESARM requests that the work products are to be distributed to specific
parties, a usability assessment will be completed to ensure that the products are commensurate
with their needs. It is necessary to have feedback at all steps in the workflow to provide specific
QC at the task level, assessment of the program objectives, and determination of usability to
assure that the air quality modeling needs of the states, tribes, and stakeholders in the SESARM
region (henceforth referred to as SESARM members) are being fulfilled.
Each task in the work plan will have appropriate entries for objectives, subtasks, and deliverables. The formal review and reporting is the project PI’s responsibility. The QA Manager is
responsible for verifying that all tasks are appropriately documented and reported, and will report
any discrepancies to the PI along with recommendations for correction. The project PI will note
the QA Manager’s recommendations in the monthly report to SESARM along with the
corrective actions taken.
A monthly report containing the following elements for each of the 16 tasks in the SEMAP
project will be distributed by the 15th of each month to the SESARM Project Coordinator:





2.3
Work completed the previous month
Expected work during the next month
Difficulties encountered and corrective actions taken
Actions awaiting SESARM authorization
Status of project deliverables
Participating Organizations
The following organizations are participating in the SEMAP project:

Southeastern States Air Resource Managers, Inc. (SESARM) – SEMAP project sponsor
and technical advisor

The Georgia Institute of Technology (Georgia Tech) – SEMAP project lead contractor,
air quality modeling and analysis, project management

The University of North Carolina (UNC) – emissions and meteorology data preparation,
air quality modeling and analysis

The Cooperative Institute for Research in the Atmosphere (CIRA) – web-based data
display, distribution, and analysis
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2.4
Project Schedule
The original project schedule can be found in Section 2.2 of the Project Work Plan. Since then,
the schedule of deliverables has been revised. Those revisions are reflected in the schedule of
deliverables below.
Table 2-1. Project schedule
Projected
completion
date
Deliverable
Task 1: Project Management
Draft project work plan to SESARM
4/28/2010
Draft QAPP to SESARM
4/27/2010
Revised/final QAPP incorporating reviewers comments
6/11/2010
Revised/final project work plan
6/11/2010
Participation on conference calls with the SEMAP Project Coordinator, TAWG, and
other contractors (monthly)
Ongoing
function
Prompt responses to questions raised by SESARM and other contractors (as needed)
Ongoing
function
Monthly project reports summarizing work performed on each task, progress on
deliverables, and expected progress during the next month (monthly)
Ongoing
function
Participation in up to two project meetings
TBD
Task 2: Modeling Protocol
Initial version of the emissions and air quality modeling protocol
6/13/2010
Wiki version of the reviewed protocol accessible through project website
2/21/2011
Task 3: Data Acquisition/Dataset Preparation for Modeling and Evaluation
MCIP outputs for SMOKE and CMAQ models
7/16/2010
Observational datasets in formats that are suitable for the model evaluation software
and receptor models to be used in Task 10
7/16/2010
CMAQ-ready ICs/BCs generated from the GEOS-Chem model
8/13/2010
SMOKE-ready 2007 SEMAP emission inventories and ancillary data
2/18/2011
SMOKE-ready future-year SEMAP emission inventories and ancillary data
4/15/2011
SMOKE-ready inventory data for the non-SEMAP regions, including Canada and
Mexico, to use for the 2007 modeling
2/18/2011
SMOKE-ready inventories for the non-SEMAP regions to use for the future-year
modeling
4/15/2011
Latest BELD and MEGAN land use and emissions factor data gridded to the SEMAP
modeling domains
2/18/2011
MCIP outputs for SMOKE and CMAQ models
1/14/2011
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Photolysis rate inputs for CAMx model
3/4/2011
WRFCAMx outputs for CAMx model
3/4/2011
Gridded satellite data for column NO2 and HCHO from OMI and extinction AOD
from MODIS and OMI for selected periods
2/18/2011
Task 4: Conceptual Description
Draft interim report prepared according to EPA guidance and submitted to the
SEMAP TAWG
2/4/2011
Revised/final interim report incorporating reviewers’ comments
3/4/2011
Task 5: 36-km/12-km Emissions/AQ Annual Actual Base-Year Simulations
Emissions and air quality model results on the SEMAP 36-km and 12-km modeling grids for the following simulations:
Initial “2007 Actual” SMOKE and CMAQ
5/6/2011
Final “2007 Actual” SMOKE and CMAQ
9/2/2011
Final “2007 Actual” SMOKE and CAMx
9/2/2011
Task 6: Model Configuration Diagnostic Sensitivity Modeling
CMAQ-ready emissions with 34 vertical layers
4/15/2011
Evaluation of the CMAQ predictions and comparison to the model performance
between the 19- and 34-layer simulations
5/27/2011
CMAQ-ready emissions with biogenic CO, NO, and VOC estimates calculated with
MEGAN
4/22/2011
Evaluation of the MEGAN and BEIS CMAQ predictions and recommendations on
which model to use to estimate biogenic emissions for the SEMAP project
6/3/2011
Diagnosis of vertical mixing/mixing height problems through analysis of long-range
plume transport
6/10/2011
Comparison of the new SOA mechanism by Baek (2009) to the standard CMAQ
mechanism and evaluation with OC data
6/17/2011
Comparison of ACM cloud with RADM cloud and evaluation with wet deposition
data
6/24/2011
Final recommendation on model configuration, with supporting evidence
7/1/2011
Task 7: Emissions and Air Quality Benchmark Simulations
Document summarizing the technical transfer protocol and all model utilities and
scripts used by the project team for the modeling tasks
12/2/2011
Task 8: 36-km/12-km Annual Typical/Future-Year Emissions AQ Modeling
Emissions and air quality model results on the SEMAP 36-km and 12-km modeling grids for the following simulations:
“2007 Typical” SMOKE, CMAQ, and CAMx
“2017 OTB” SMOKE, CMAQ, and CAMx
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“2020 OTB” SMOKE, CMAQ, and CAMx
12/2/2011
“2017 Control” SMOKE, CMAQ, and CAMx
3/2/2012
“2020 Control” SMOKE, CMAQ, and CAMx
3/2/2012
Task 9: Emission Sensitivity Modeling
Annual CAMx PSAT simulation on the SEMAP 36-km modeling grid with analysis
products posted on the project website
11/4/2011
Emissions sensitivities for daily and annual PM2.5 obtained from the brute force
CMAQ simulations on the 36-km grid, processed in a similar way to VISTAS
11/4/2011
Emissions sensitivities for 8-hour maximum ozone obtained from the DDM method
on the 12-km grid, processed at selected SEMAP sites
Emissions sensitivities for 8-hour maximum ozone obtained from the brute force
method on the 12-km grid, processed at selected SEMAP sites
11/18/2011
12/2/2011
Task 10: Model Performance Evaluation
All standard analyses specified in RFP Attachment A for (a) ozone and precursors;
(b) fine PM, coarse PM, and constituent mass concentrations; and (c) regional haze
metrics
9/30/2011
Diagnostic interpretation of ozone performance regarding the impact of NOx- and
VOC-limited regimes on the model performance using “photochemical day” analysis
9/30/2011
Interpretation of the modeled AOD vs. those reported from OMI and MODIS, using
complementary analyses of model performance at the surface
9/30/2011
Task 11: Future Year Model Projections
Relative Reduction Factors and Future-Year Design Values for O3, PM2.5, and
reasonable rate of progress for regional haze goals at all monitored locations, MATS
inputs and outputs, and UAA for O3 and PM2.5, from CAMx and CMAQ model
outputs for 2017 and 2020 future-year scenarios
3/16/2012
Task 12: Interactive Database Tool
Analyze and understand SEMAP data and metadata
3/18/2011
Enhance and extend VIEWS/TSS database
5/20/2011
Develop import routines and procedures
6/17/2011
Test and refine the import system
8/12/2011
Develop Interactive Database Tool(s) (IDT) by leveraging existing ones
8/12/2011
Test and refine the IDT(s)
Ongoing
function
Collaborate with UNC to import and integrate modeling and emissions data
Ongoing
function
Task 13: Receptor Modeling
Apply CMB and PMF to speciated PM2.5 data from 20 locations, and compare results
to each other and to related studies; prepare displays of daily source contributions
using CMB and PMF, as well as seasonal and annual averages, along with
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uncertainties as provided by both techniques
Apply the chosen CMB approach to up to 20 locations (depending upon data
availability) to provide estimates of source contributions to ozone increases and how
those contributions vary with ozone level and time of year
12/2/2011
Task 14: Development of Technical Web Site
Create FTP site for organizing and disseminating data products
8/31/2010
Develop technical website by leveraging existing resources
5/20/2011
Integrate IDT(s) with technical website
3/25/2011
Package IDT(s) and technical website for delivery to SESARM
5/6/2011
Collaborate with SESARM to transfer and configure finished system
5/20/2011
Test and refine website and FTP site
Ongoing
function
Reliable methods for transferring data between modeling contractors and SEMAP
members (as needed)
Ongoing
function
Archive of all SEMAP modeling input and output data (as needed)
Ongoing
function
Task 15: Other Tasks as Assigned
(Deliverables will be determined when tasks are assigned)
TBD
Task 16: Reporting
Interim task reports documenting each major task milestone (as needed)
2.5
Ongoing
function
Draft final project report
3/30/2012
One hard copy of final project report, and PDF version posted on project website
5/25/2012
Conceptual Description
A qualitatively description of the ozone, PM2.5, and regional haze problem in the SEMAP region
was prepared. This description was derived from the air quality data provided by the SEMAP
states, the answers by the states to the questions in Chapter 11 of EPA’s Guidance for
preparation of conceptual descriptions and relevant results of recent modeling efforts in the
SEMAP region. A summary of this conceptual description is included here.
Statewide average ozone design values trend downward for all SEMAP states after the peaks in
1999 and 2000. This trend is most likely due to the reductions in ozone precursor emissions. The
ozone trends of urban, suburban and rural sites are generally very similar with a few exceptions.
On the other hand, geographic grouping of the sites as coastal, inland and mountain showed that
ozone values at coastal sites of Georgia and South Carolina and inland sites of Virginia have not
been decreasing as rapidly as at other sites.
In the last decade, annual PM2.5 has been decreasing in all SEMAP states. A slight increase has
been observed between 2005 and 2007 but this is most likely due to the influence of adverse
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weather conditions during those years. Except in Florida where annual PM2.5 at rural sites have
been decreasing at a much slower pace, the trends among urban and rural sites are very similar.
The downward trend is more noticeable at the mountain sites of Georgia than inland or coastal
sites of the state and the coastal sites of Virginia experience a slower decrease than inland and
mountain sites of the same state.
The statewide average 24-hr PM2.5 has also been decreasing in all SEMAP states during the last
decade. However, unlike the trends in annual PM2.5, there are significant differences between the
rates of decrease at urban and rural sites. The rural sites experience a slower decrease rate in
Florida, Kentucky, South Carolina, Tennessee and Virginia. In Mississippi and North Carolina,
on the other hand, the rate of decrease of the 24-hr PM2.5 design values is faster at rural sites
compared to urban sites. The 24-hr PM2.5 at coastal sites has been decreasing at a much slower
rate compared to inland and mountain sites, and has been actually increasing at one coastal
location in North Carolina.
Both local and regional factors play a role in the SEMAP region’s 8-hour ozone nonattainment
problem. As emissions are being reduced in individual SEMAP states, regional factors become
increasingly more important. The concentrations of ozone and its precursors are also high at
higher elevation sites and aloft based on a limited number of observations aloft. In general,
violations of the 8-hour ozone National Ambient Air Quality Standard occur at several
monitoring sites throughout nonattainment areas. While the exceedances are frequent in some
states (e.g., AL, TN, VA) they are less frequent (e.g., KY, MS) or rare in other states (e.g., FL,
SC, NC, WV), especially in recent years. There are some characteristic spatial patterns
associated with the exceedances such as grouping in the downwind vicinity of metropolitan
areas. Typically, violations do not occur because of mesoscale wind patterns as high
temperatures and low winds are the most general accompanying meteorological patterns, except
in locations like Birmingham AL, Hampton Roads VA, Ohio and Kanawha River Valleys in
WV.
There have been significant reductions in NOx emissions in or near the 8-hour ozone
nonattainment areas, except in MS and TN, as well as some VOC reductions, as a result of many
controls on both industry and motor vehicles and some plant shutdowns. The most notable
reductions in EGU emissions came after the NOx SIP Call and other regulations such as North
Carolina’s Clean Smokestacks Act. As a result of these emission reductions, the ozone design
values in all s SEMAP states have been trending downward. The biggest decreases were
observed at some inner city monitors. The coastal areas of the region have decreased the least
over the last 5 years. The trends of the precursor concentrations are mostly unknown but VOC
concentrations have been trending downward at a few speciation sites where they are measured
with no apparent changes in VOC profiles.
The results of past modeling studies in the region suggest that there is both a local and a regional
component to ozone, each playing a role in nonattainment. Reducing NOx emissions is much
more effective than VOC reduction at reducing ozone. In general, biogenic VOC reductions are
more beneficial than anthropogenic VOC reductions. High temperature, light winds and clear
skies are the most common meteorological conditions coinciding with 8-hour ozone
exceedances. In coastal regions, the position of pressure ridges matter because they determine
how far inland the see breeze can penetrate and break up the ozone buildup.
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The annual PM2.5 nonattainment in the SEMAP region is caused both by local and regional
factors. While regional factors are believed to play a bigger role in general, sometimes local
factors such as wood burning can lead to exceedances. The relative importance of primary and
secondary components of PM2.5 is not known for sure. Measurements at a limited number of sites
suggest that sulfates and nitrates, both secondary, are significant while primary components such
as soils are small. Sulfates and organics are the most prevalent component of measured PM2.5.
Sulfate concentrations are similar between urban and rural areas while organic and elemental
carbon concentrations are generally higher in urban areas. This suggests that sulfate is widely
distributed throughout the region while urban areas see additional incremental PM2.5 loadings
from local emissions sources.
The measured PM2.5 components roughly agree with the mix of emissions surrounding the
monitoring site. For example, metals are high in Birmingham AL. There are also large gradients
of primary PM2.5 around monitors near urban centers, industrial areas, and rail yards. There are
some indications that SO2 emissions limit the formation of secondary sulfate formation.
Violations at monitors located in valleys and surrounded by mountains are believed to be caused
by mesoscale wind patterns. Economically driven plant shutdowns, consent decrees, and SO2 and
NOx controls significantly reduced PM and precursor emissions. The downward trend in PM2.5
levels are believed to be tied directly to these emission reductions. Regional SO2 emission
reductions are believed to play the biggest role in this decrease. The decrease in design values is
observed throughout the SEMAP region with no apparent spatial patterns. Recent modeling
studied by VISTAS and ASIP suggested that, in general, regional SO2 sources and local primary
PM2.5 sources contribute to the PM2.5 problem. Ammonium sulfate is the predominant
contributor to PM2.5. The models predict that regional SO2 emission reductions along with local
elemental and organic carbon emission reductions will have the greatest benefits.
In general light winds associated with high pressure centered overtop lead to high PM2.5
concentrations. During the warm season, PM2.5 increases with moisture. PM2.5 concentrations are
also higher ahead of approaching cold fronts. During the cold season, strong nocturnal inversions
often lead to high PM2.5 concentrations. Especially in summer, high PM2.5 concentrations often
correspond with high ozone concentrations.
Ammonium sulfate is the major contributor to visibility impairment followed by nitrates and
organic carbon. Visibility, good or poor, appears to track well among nearby class I areas of the
SEMAP region. There is a clear distinction between coastal and mountainous Class I areas. Poor
visibility generally occurs at stagnant high pressure conditions when it is hot and humid. Poor
visibility generally coincides with high observed concentrations of ammonium sulfate and
sometimes organic carbon. The meteorological conditions leading to good visibility are cool
temperatures, low humidity, cold frontal passage and high wind speeds. On good visibility days
ammonium sulfate is still the largest constituent of particulate matter.
Recently, Visibility Improvement States and Tribal Association on the Southeast (VISTAS), the
regional planning organization for the SEMAP region, supported regional scale modeling and
assessment of the regional haze problem. VISTAS projected responses to emissions reductions
expected by the year 2018 under existing federal and state regulations for the southeastern
United States. This analysis determined that ammonium sulfate, predominantly from sulfur
dioxide emissions from electric generating utilities and industrial sources, contributed 60 to 70%
to the visibility impairment on the 20% haziest days; particulate organic matter, predominantly
from biogenic emissions and biomass burning, was the second largest contributor (Brewer and
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Moore, 2009). As for controls, sulfur dioxide emission reductions are projected to result in
nearly one-to-one regional sulfate reductions; oxides of nitrogen emission reductions have
minimal impact on nitrate concentrations and haze.
One modeling study supported by VISTAS estimated PM and visibility at Class I areas of
Southeastern U.S. in the year 2018 (Odman et al, 2007). According to the 2018 on-the-way
scenario, which included the Clean Air Interstate Rule, no Class I area in the region is expected
to experience visibility degradation on 20% best visibility days. However, SO2 controls and
increasing ammonia emissions may increase nitrate levels at a number of sites. Additional
controls may be necessary for reasonable progress towards the goal of achieving natural
visibility conditions on 20% worst visibility days by the year 2064.
In the same study, using 2018 emission projections as the base case, various emission reduction
options were simulated to evaluate each option in terms of visibility improvement on 20% worst
visibility days and degradation, if any, on 20% best visibility days. Domain-wide SO2 reductions
showed the largest benefits at the majority of Class I areas on worst 20% visibility days followed
by NOx reductions at mountain sites and PC reductions at other sites. NH3 reductions were
somewhat effective at coastal sites but not at mountain sites and VOC reductions were
ineffective. None of the reductions made visibility worse than current conditions at any Class I
area. When more detailed analysis of the SO2 reductions was conducted, it was found that
reductions in elevated SO2 emissions were significantly more effective than ground-level SO2
reductions. Of these elevated SO2 sources, coal-fired power plants generally had the largest
effect on visibility. When the coal-fired power plants of the region were broken down by state
visibility at each Class I area was affected at a different level by emissions from different states.
In general, the largest impacts could be attributed to the closest states however the contributions
of distant states were often significant. When the non-EGU emissions were broken down by
state, the contribution from distant sources became more obvious. This result points to the
regional nature of the haze problem and the need for regional control strategies.
Another VISTAS modeling study estimated the responses and sensitivities (responses
normalized by emissions reduced) of O3, PM2.5, and Bext (calculated from PM2.5 components) to
SO2, NOx, NH3, VOC, and PC emissions in the Southeastern US for the year 2009 (Odman et al.,
2009). The analysis of the modeling results focused on areas of concern (AOC) and Class I areas
in the VISTAS region. The findings of this study are as follows.
The sensitivity of O3 to VOC emissions is 2-3 orders of magnitude smaller than the sensitivities
to elevated and ground-level NOx; therefore, VOC controls would be much less effective than
NOx controls for reducing O3 levels in Southeastern US. Although sensitivities to ground-level
NOx emissions from AOC are larger, state-wide reductions of elevated NOx emissions by the
same ratio (30%) are generally more effective in reducing summertime O3. Reducing one state’s
ground-level NOx emissions generally produces the largest effect in that state. Elevated NOx
reductions have a less localized and more regional effect. Responses are larger in southern
VISTAS states where O3 forming potentials are greater. It is possible to achieve as much as 3.5
to 5 ppb decreases in 2009 O3 levels by reducing ground-level or elevated NOx emissions by
30% throughout the VISTAS region.
In the VISTAS states, sensitivity of PM2.5 is generally largest to PC emissions (sensitivity to
ground-level sources in AOCs being larger than elevated sources statewide), followed by SO2
emissions (sensitivity to non-EGU being larger than EGU emissions), and NOx emissions
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(sensitivity to ground-level being larger than elevated sources). There are several exceptions to
this rule depending on the season of the year, magnitude of emissions from different source
categories in each state, and the proximity of the sources to the sampling sites. The sensitivities
provide a means of comparing the controls of different pollutants on a per-unit-mass basis but,
ultimately, the amounts of controllable emissions determine the achievable PM2.5 reductions.
Since PC emissions are limited but there are ample SO2 emissions that can be controlled,
responses to 30% SO2 reductions are generally larger than responses to 30% PC reductions. In
this respect, SO2 reductions are ultimately more “beneficial” in summer. As much as 1.12 g/m3
decrease in 2009 PM2.5 levels can be achieved by reducing EGU SO2 emissions by 30%
throughout the VISTAS region. The benefits generally decrease from north to south and from
west to east. The second most beneficial summertime strategy in the region is the reduction of
non-EGU SO2 emissions, followed by ground-level and elevated PC controls, respectively. The
impacts of PC reductions are highly localized; as much as 0.69 g/m3 decrease in PM2.5 levels
can be expected. The impacts of elevated and ground-level NOx controls are generally smaller.
In winter, ground-level PC controls are generally more beneficial than EGU SO2 controls. PM2.5
levels can be decreased by as much as 1.61 g m-3 by reducing the ground-level PC emissions by
30%. The maximum response to 30% EGU SO2 reductions is a PM2.5 decrease of 0.32 g/m3.
Colder temperatures in northern VISTAS states limit secondary sulfate formation and the
potential benefits of SO2 emissions reductions. It is possible to achieve greater impacts in some
states by reducing elevated NOx emissions than by reducing EGU SO2 emissions (however note
that the uncertainties in sensitivities to NOx are larger than those to SO2).
When PM2.5 responses are normalized by total emissions reductions over the VISTAS region,
sensitivity to NH3 emissions ranks second after sensitivity to ground-level PC in winter and is
larger than sensitivities to ground and elevated NOx emissions in summer. In the Southeastern
US, NO3NH4 levels are generally limited by the availability of NH3, so controlling NH3
emissions can be a more effective strategy for PM2.5 reduction than controlling NOx emissions.
Wintertime PM2.5 levels can be reduced by as much as 0.75 g/m3 on average in VISTAS states
by reducing NH3 emissions from those states by 30%. VOC controls would have no significant
impact on PM2.5 levels in this region.
At the mountain Class I areas, which have their worst visibility days in summer, the greatest
visibility benefits would likely be from EGU SO2 reductions within the VISTAS states. At the
costal Class I areas, which generally have their worst visibility days in winter, controlling NH3
emissions would be more beneficial for reducing NO3NH4 contributions to visibility impairment
than controlling NOx emissions from either ground-level or elevated sources. This is consistent
with Odman et al. (2007), which suggests NH3 control can be a very effective strategy for
reducing regional haze in winter. Controlling anthropogenic VOC emissions or PC emissions
from elevated or ground-level sources would have little if any visibility benefits at Class I areas
of the Southeastern US.
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Section 3: Models and Model Inputs
3.1
Meteorology, Emissions and Air Quality Models
This section describes the modeling systems that will be used in the modeling and analysis of
the air quality data for the SEMAP project. We have selected the following modeling systems
because they present the best combination of scientific and computational formulations to satisfy
the requirements of the SEMAP project. The air quality, meteorological, and emissions models
selected for this project follow the recommendation in the EPA Modeling Guidance (i.e.,
Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air
Quality Goals for Ozone, PM2.5, and Regional Haze) and Appendix W to Part 51 of 40 Code of
Federal Regulations: Guideline on Air Quality Models.
3.1.1 WRF
3.1.1.1 Description
The WRF v. 3.1.1 mesoscale meteorology model is a fully compressible, nonhydrostatic model
with an Eulerian mass dynamical core (Skamarock et al. 2008). Two dynamical cores are
available: the Advanced Research WRF (ARW) core, developed and supported by NCAR, and
the Nonhydrostatic Mesoscale Model (NMM) core, whose development is centered at NCEP’s
Environmental Modeling Center (EMC) and support is provided by NCAR’s Development
Testbed Center (DTC). The WRF-NMM is designed to be a real-time forecast model so physics
schemes are preset. SEMAP chose WRF-ARW (hereinafter WRF) because it includes multiple
physics options for turbulence/diffusion, radiation (long and shortwave), land surface, surface
layer, planetary boundary layer, cumulus, and microphysics. The major physics options used
are: microphysics – Morrison(option 10); cumulus – Kain-Fritsch(1); PBL – ACM2/PleimXiu(7); surface layer – Pleim-Xiu(7); surface – Pleim-Xiu(7); longwave and shortwave radiation
– RRTMG(4). Vertical velocity damping was used to help prevent excessive vertical motion
which may cause the model to crash. Per US EPA, 6th order diffusion was used to prevent gridscale artifacts when using the Pleim-Xiu PBL Scheme. Additional details can be found in
“Protocol for WRF Meteorological Modeling in Support of Regional SIP Air Quality Modeling
in the SEMAP” (SEMAP, 2011).
WRF was selected for use in upcoming SIP air quality modeling by a consensus of several RPOs
because the model is:

Generally considered the most technically advanced public-domain prognostic model
available for operational use in preparing inputs to urban- and regional- scale photochemical
air quality models.

Suitable for a broad spectrum of applications across scales ranging from meters to thousands
of kilometers.

Flexible and efficient computationally, while offering the advances in physics, numerics, and
data assimilation contributed by the research community.
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In addition, EPA expects that air quality modelers will also shift from MM5 to WRF
(http://www.epa.gov/scram001/metmodel.htm ).
The computational domain for the WRF annual simulation consists of a coarse and fine grid
nested domains. The coarse domain consists of a Lambert conic conformal projection with
specifications used in previous regional modeling studies, which is centered at 40° N and 97° W
with true latitudes of 33 and 45° N. The coarse grid structure consists of an array of 165 cells in
the east-west direction and 129 cells in the north-south direction with a grid spacing of 36
kilometers (km). This domain was designed to maximize the usage of the Eta analysis region.
A nested domain is implemented to resolve finer scale meteorological features. The nested
domain grid structure is a 250 by 250 array of grid cells with a grid spacing of 12 km. This
domain begins at node (66, 18) of the course domain. The nested domain shares the northern,
eastern, and southern boundaries of the EPA 12 km Eastern domain and western boundary of the
Iowa/LADCO 12 km domain.
This 35-layer vertical structure was designed to provide increased resolution in the PBL and near
the tropopause. The second level is placed at ~20 meters so that the midpoint of the grid cells in
the vertical direction is located at 10 meters and corresponds to the standard National Weather
Service (NWS) anemometer height. This avoids the use of a surface layer parameterization to
vertically interpolate horizontal wind components to the standard NWS observational height. The
top of the model is fixed at 50 millibars (mb).
The geographic data for the outer domain was at 10 m resolution and for the inner nest was 2 m
resolution. The North American Mesoscale (NAM) model fields with 40 km horizontal
resolution, 27 vertical levels, and 3-hour intervals were used for initial and boundary conditions.
The map projection was Lambert conformal conic projection with the first true latitude of 33° N
and second true latitude of 45°N with 97°W as the longitude parallel to the conic axis. The
model was reinitialized after each segment of 5.5 days (132 hours). The first 12-hour period is
for the model spin-up (i.e., the first 12 hours of data were discarded in the application of the
datasets).
3.1.2 MCIP
3.1.2.1 Description
The Meteorology-Chemistry Interface Processor (MCIP) (Otte and Pleim, 2010) is the software
program linking the meteorological model and the air quality model. It takes MM5/WRF outputs
and processes them into CMAQ ready data sets. MCIP is designed to maintain dynamic
consistency between the meteorological model and the chemical transport model as much as
possible. MCIP reads in meteorological model outputs in their native formats and then
transforms horizontal (CMAQ domains is smaller than MM5/WRF domains) and vertical
coordinates (vertical collapsing) according to the user’s definition. Gridding parameters are
defined accordingly. MCIP not only pass through all the necessary meteorological variables but
also derive variables needed by CMAQ, such as Jacobian and contravariant vertical velocity.
Certain additional atmospheric fields such as cloud information are diagnosed in MCIP. Final
products from MCIP are the meteorological variables in IO/API format required by the CMAQ
modeling system.
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We will use the latest version of MCIP (Version 3.6, release on March 2010) to process the
provided WRF model outputs to prepare meteorological fields for CMAQ simulations. The
horizontal and vertical coordinates are defined according to SESARM’s guidance.
3.1.2.2 Limitations
In WRF model, users have the option to add or reduce output variables through the Registry
system (Skamarock et al., 2008), but MCIP code need to be modified to ingest these changes.
Also when new physics packages added into WRF model, MCIP needs to be changed as well to
reflect the new schemes. Nevertheless, these limitations should not affect this project.
3.1.2.3 Input Requirements
MCIP needs WRF model outputs in netcdf format. Also it requires WRF model outputs these
variables specific for CMAQ model: fractional land use (LANDUSEF), leaf area index (LAI),
Monin-Obukhov length (RMOL), aerodynamic resistance (RA), stomatal resistance (RS), and
vegetative fraction (VEGF_PX). These variables are in the WRF Registry and need to be output.
Availability: MCIP is freely available for download from http://www.cmascenter.org
3.1.3 WRFCAMx
3.1.3.1 Description
The WRFCAMx program generates CAMx meteorological input files from WRF (ARW core)
hourly output files. A single WRFCAMx run provides meteorological fields for a single CAMx
grid, whether defined as a CAMx master coarse grid, or a nested grid. The program generates the
files for any duration (from a single hour to several days). For a single day, 25 hours of
meteorology must be present (midnight through midnight, inclusive) as these fields represent
hourly instantaneous conditions and CAMx internally time interpolates these fields to each
model time step. Precipitation fields are not time-interpolated, but rather time-accumulated, so
cloud/precipitation files contain one less hour than other met files (e.g., 24 hours of
cloud/precipitation vs. 25 hours other met). The CAMx physical height layer structure (meters
AGL) is defined from the geopotential heights at each of WRF's "eta" levels, and thus varies in
space and time. Vertical averaging of WRF layers to a coarser CAMx layer structure is allowed.
Variables in each WRF layer are mass-weighted to each bulk CAMx layer. Several options are
available to derive Kv (diffusivity) fields from WRF output. If TKE is available, Kv can be
derived from this; otherwise, other options diagnose Kv from wind, temperature, and PBL
parameters. WRFCAMx outputs can be converted into a format viewable in PAVE using the
METPAVE utility.
The development of photolysis rate inputs for CAMx is crucial for the photochemical
mechanisms. The AHOMAP program (version 3) prepares albedo/haze/ozone column input files
for CAMx, and must be run prior to running the Total Ultraviolet (TUV) radiative transfer model
as it defines the albedo, haze, and ozone column intervals based on input data. A CAMx-ready
landuse file (from WRFCAMx) and total integrated ozone column (TOMS) data files (available
at http://jwocky.gsfc.nasa.gov/) must be supplied as input. The program assumes a constant haze
turbidity value for the entire grid. It should be noted that gaps in day-specific TOMS data at low
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latitudes, and high latitudes during winter, may result in zero ozone column values over portions
of the modeling grid. These gaps result from the inability of polar-orbiting satellite platforms to
cover the entire globe on a daily basis. Seasonal-average TOMS input files (no data gaps) may
be used instead; alternatively, AHOMAP provides an option to fill these day-specific gaps with
an average determined from valid data processed for the extraction domain. Additionally, the
Total Ultraviolet (TUV) radiative transfer program (version 4.0.1) develops photolysis rate
inputs for all CAMx CB4, CB05, and SAPRC99 photochemical mechanisms via a deltaEddington radiative transfer model. TUV originates from UCAR
(http://cprm.acd.ucar.edu/Models/TUV/). The program uses the albedo, ozone column, and haze
turbidity intervals defined in AHOMAP to generate a look-up table of photolysis rates to be used
in CAMx. The table defines photolysis rates for several CB05 photolytic reactions over a range
of solar zenith angles, altitudes, ozone column, surface UV albedo, and haze turbidity. CAMx
internally adjusts the photolysis rates for cloud cover according to the cloud inputs provided to
CAMx (from WRFCAMx).
We will use the latest WRFCAMx (version 2.2), AHOMAP program (version 3), and TUV
program (version 4) to process the provided WRF outputs to CAMx ready meteorological fields.
The horizontal and vertical structures will be defined by SESARM.
3.1.3.2 Input Requirements
WRFCAMx reads in WRF outputs in netcdf format. In addition to the WRF outputs, the
AHOMAP and TUV programs need TOMS data from NASA web site
(http://jwocky.gsfc.nasa.gov). The sequence of running these programs should be: WRFCAMx,
AHOMAP, and TUV.
Availability: All programs and utilities described above can be downloaded at
http://www.camx.com/down/support.php. TOMS data can be obtained from
http://jwocky.gsfc.nasa.gov
3.1.4 SMOKE
3.1.4.1 Description
The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is a set of programs
that is used by the U.S. EPA, Regional Planning Organizations (RPOs), and State environmental
agencies to prepare emissions inventory data for input to an air quality model such as CAMx.
SMOKE converts annual or daily estimates of emissions at the state or county level to hourly
emissions fluxes on a uniform spatial grid that are formatted for input to an air quality model.
SMOKE integrates annual county-level emissions inventories with source-based temporal,
spatial, and chemical allocation profiles to create hourly emissions fluxes on a predefined model
grid. For elevated sources that require allocation of the emissions to the vertical model layers,
SMOKE integrates meteorology data to derive dynamic vertical profiles. In addition to its
capacity to simulate emissions from stationary area, stationary point, and on-road mobile sectors,
SMOKE is also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3)
for estimating biogenic emissions fluxes (U.S. EPA, 2004) and the both MOBILE6 and the
Motor Vehicle Emission Simulator (MOVES) 2010 model for estimating on-road mobile
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emissions fluxes from county-level vehicle activity data. SMOKE can additionally be used to
calculate future-year emissions estimates, if the user provides data about how the emissions will
change in the future (Figure 3-1).
Figure 3-1. SMOKE modeling system
SMOKE uses C-Shell scripts as user interfaces to set configuration options and call executables.
SMOKE is designed with flexible QA capabilities to generate standard and custom reports for
checking the emissions modeling process. After modeling all of the emissions source categories
individually, SMOKE creates two files per day for input into CMAQ or CAMx: (1) an elevated
point source file for large stationary sources, and (2) a merged gridded source file of low-level
point, mobile, non-road, area, and biogenic emissions. The efficient processing of SMOKE
makes it an appropriate choice for handling the large processing needs of regional and seasonal
emissions processing, as described in more detail by Houyoux et al. (1996, 2000).
We will use the latest version of SMOKE (version 2.6 released on October 2009) for the SEMAP
modeling. Additional technical details about this version of SMOKE are available from CMAS
website (CMAS, 2010).
3.1.4.2 Limitations
SMOKE is a software tool and not a set of data files; therefore, SMOKE relies on user-provided
data files for emission inventories and factors to apply to those emissions. The factors assign the
annual inventory data to the hours, grid cells, and model species and can be adjusted by the user
in a way that is the most appropriate for the inventory sources included in the air quality
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modeling domain. In addition SMOKE requires meteorology data in the Input/Output
Application Programmers Interface (I/O API) format to process meteorology-dependent
emissions sectors. The temporal and spatial extents of the SMOKE modeling periods are dictated
by the input meteorology. SMOKE can neither interpolate between different grid resolutions nor
project/backcast to dates that are not covered in the input meteorology. SMOKE has strict
requirements for the nature and formats of the inventory data that it can use.
3.1.4.3 Input Requirements
SMOKE primarily uses two types of input file formats: ASCII files and I/O API files. Input files
are files that are read by at least one core SMOKE program, but are not written by a core
program. SMOKE uses strict rules that define the format and content of the input files. These
rules are explicitly laid out in the SMOKE Users Manual (CMAS, 2010). All data input to
SMOKE must be either formatted to one of the prescribed input file types or converted to an
intermediate form, such as a gridded I/O API inventory file, before it can be input to SMOKE.
In general SMOKE requires an emissions inventory, temporal allocation, spatial allocation, and
chemical allocation data to prepare emissions estimates for an air quality model. For some source
categories, such as on-road mobile and stationary point sources, SMOKE also requires
meteorology data to calculate emissions. SMOKE calculates biogenic emissions estimates with
gridded land use, vegetative emissions factors, and meteorology data. Further details about the
SMOKE input requirements are available at CMAS website.
We will use the latest version of BEIS (version 3.14 released on October, 2008 as a part of
SMOKE model) for the SESARM modeling. Additional technical details about this version of
BEIS are available from CMAS website (CMAS, 2010).
Availability: SMOKE is freely available for download from http://www.smoke-model.org
3.1.5 BEIS
3.1.5.1 Descriptions
The Biogenic Emissions Inventory System (BEIS) model was developed to estimate emissions of
VOCs from biological activity from land-based vegetative species and nitric oxide emissions
from microbial activity in certain soil types. BEIS version 3 (BEIS3) was deployed within the
SMOKE modeling system and included support for coupling with predicted hourly meteorology
data. BEIS3 uses land use data projected to an air quality modeling grid to compute normalized
gridded biogenic emissions for different land use categories. The normalized emissions are
adjusted for temporal variability using gridded, hourly meteorology data and speciated using
precomputed VOC profiles for different photochemical mechanisms. BEIS outputs air quality
model-ready hourly, gridded biogenic emissions files.
The primary inputs to BEIS3 are:

Spatially and temporally resolved meteorological data including temperatures, solar
radiation and surface pressures.
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
Species-specific leaf area indices (LAI)

Spatially resolved, species-specific vegetation categories

Species-specific biogenic emissions factors for NO and VOCs

Chemical speciation profiles to chemically allocate biogenic VOC emissions to species
recognized by the chemistry mechanism in the desired air quality model.
Overall, BEIS3 uses spatially and temporally resolved meteorology data to estimate hourly
emissions factors of NO and VOCs using species-specific LAI for each land use type. The user
can assign the units for the output emissions as gram-moles per hour or gram- moles per second.
The output is in I/O API format and can be input into both CMAQ and CAMx modeling systems.
To support CAMx model, SMOKE needs to use an utility program (CMAQ2CAMx) that
converts the BEIS3 I/O API output files into a binary format recognized by the CAMx.
Availability: BEIS3 is freely available for download as part of the SMOKE distribution from
http://www.smoke-model.org
3.1.6 MEGAN
The model of Emissions of Gases and Aerosols from Nature (MEGAN) has been developed to
estimate biogenic emissions of reactive gases and aerosols needed for both regional air quality
models and global chemistry transport models (Guenther et al., 2006). MEGAN uses an
approach similar to previous terrestrial biogenic emission models, like BEIS3 and the Global
Biogenic Emissions Investigation System (GLOBEIS) (Guenther et al., 1999) but is easier to
update, use, and expand to other compounds. MEGAN is designed to be continually updated as
understanding of chemical emissions from plants expands, and further, to be useable by both
research scientists and regulatory air quality modelers.
One of the biggest changes in MEGAN as compared to the BEIS family of models is in its
treatment of plant species area coverage. In the BEIS family of models, plant species are mostly
treated explicitly (e.g., California live oak, corn row crop, loblolly pine) where in MEGAN
plants are grouped by the following six plant functional types (PFTs): 1) broadleaf trees, 2) fine
leaf evergreen trees, 3) fine leaf deciduous trees, 4) shrubs, 5) grass, and non-vascular plants and
other ground cover and 6) crops.
The MEGAN biogenic emissions estimates are based on data prepared by Guenther et al. (2006):




Emission factors available for six PFTs;
Soil wilting point data;
PFT area coverages;
Leaf area index (LAI) for vegetated surfaces.
These data sets are currently available for much of the planet in ARC/Grid format and are
gridded on a scale of 30 seconds by 30 seconds (approximately 1,000 meters by 1,000 meters).
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The MEGAN chemical speciation processing step for VOCs is similar to BEIS3. MEGAN
estimates emission factors from biogenic activities for fifty- seven chemicals (e.g., isoprene,
ethanol, α-cedrene) using a speciation profile file that supports the speciation of the chemicals
from biogenic activities into the species recognized by the CB-IV, CB05 and SAPRC chemical
mechanisms.
We will use a GIS to convert version 2.1 of the MEGAN emission factors and plant functional
types and version 2.0 of the MEGAN leaf area index data to the SEMAP grids and combine
these with the SEMAP meteorology data to estimate biogenic emissions for the summer and
winter sensitivity periods.
Availability: MEGAN can be downloaded at http://bai.acd.ucar.edu/Megan/index.shtml
3.1.7 MOVES
In December 2009, a new version of MOVES was released: the Motor Vehicle Emission
Simulator 2010 model (MOVES2010) (OTAQ, 2009). MOVES is the next generation of mobile
source emissions model developed by U.S. EPA. In the modeling process, the user specifies
vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and
road types to be modeled. The model then performs a series of calculations, which have been
carefully developed to accurately reflect vehicle operating processes (such as cold start or
extended idle) and provide estimates of bulk emissions or emission rates.
An important feature of MOVES2010 is that it allows users to choose between (1) the Inventory
calculation type, which provides emission rates in terms of total quantity of emissions for a given
time, and (2) the Emission Rate calculation type, which gives emission rates in terms of
grams/mile or grams/vehicle/hour. For large-scale emissions modeling such as that needed for
regional and national-scale air quality modeling projects, it is desirable to use the Emission Rate
calculation type, which populates emission rate lookup tables that can then be applied to many
times and places, thus reducing the total number of MOVES runs required.
The approach for running MOVES for SMOKE relies on the concept of reference counties.
These are counties that are used during the creation and use of emission factors to represent a set
of counties called a “county group.” All counties in a county group share the same fuel
parameters, fleet age distribution, and inspection/maintenance (I/M) programs. The use of
modeling emission factors for one reference county to represent an entire county group avoids
unnecessary duplicative MOVES runs. Each reference county is modeled at a range of speeds
and temperatures to produce emission rate lookup tables (grams/mile or grams/vehicle/hour,
depending on emission process). This approach allows any county with unique distributions of
vehicle miles traveled (VMT), vehicle population, roadway speed, and grid cell temperatures to
be modeled in SMOKE without having to rerun MOVES.
In addition to the emission factor tables for the reference counties, SMOKE requires other input
data in order to use the MOVES-based emission factor tables; these additional data include
county- and SCC-specific VMT and vehicle population, average speed by roadway type, and
gridded temperature. Finally, some inputs used to create the MOVES emission factor tables, such
as the mapping of counties to their reference counties, are also used as input to SMOKE.
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The outputs (emissions estimates) from MOVES can be used as inputs to larger emissions
modeling systems that model many different types of emissions and provide results that are used
in performing air quality modeling. In SMOKE version 2.7b, the new feature has been developed
to facilitate the process of using MOVES to create emissions estimates appropriate for air quality
modeling. Additional technical details of how to process SMOKE-MOVES integration approach
will be available from CMAS website (CMAS, 2010).
Availability: MOVES-SMOKE tool is currently not available for distribution, but will be
released with SMOKE at http://www.smoke-model.org.
3.1.8 CMAQ Modeling System
The Community Multiscale Air Quality (CMAQ) modeling system (Byun and Ching, 1999;
Byun and Schere, 2006) has been under development for over a decade by the U.S. EPA and
other members of the community, and has been the primary model used in several regulatory
applications both by the U.S. EPA and local/state/regional agencies for air quality management.
CMAQ includes treatment of ozone, particulate matter, visibility, acid deposition and several air
toxics to study air pollution in a ‘one atmosphere’ mode at multiscales from local to regional to
intercontinental. The treatment of aerosols in CMAQ and the initial evaluation is described by
Binkowski et al (2003) and Mebust et al (2003). CMAQ has been evaluated extensively for
several applications both within the U.S. and outside the U.S. (Hanna and Benjey, 2006).
CMAQ consists of a core Chemistry Transport Model (CCTM), and a suite of preprocessors that
prepare various other inputs for the model. These include the a) Meteorology-Chemistry
Interface Preprocessor (MCIP), b) Initial Conditions Preprocessor (ICON), c) Boundary
Conditions Preprocessor, and d) Photolysis Rates Preprocessor (JPROC). Emissions are prepared
using SMOKE discussed above.
The very first release of CMAQ came out in 1998. Starting in 2001 with the release of CMAQ
v4.1, EPA has released several versions of CMAQ via the CMAS Center at UNC, with
incremental updates and bug fixes along the way. For the SEMAP project, we will use the latest
version of CMAQ (v4.7.1) expected to be released by the CMAS Center during June 2010. The
changes and new features of this release are given below. Foley et al (2010) describe the results
from incremental testing of the various enhancements made in CMAQ v4.7.
3.1.8.1 Features of CMAQ V4.7.1


Aqueous chemistry
o Mass conservation improvements
o Imposed one second minimum timestep for remainder of the cloud lifetime after 100
"iterations" in the solver
o Force mass balance for the last timestep in the cloud by limiting oxidized amount to
mass available
o Implemented steady state assumption for OH
o Only allow sulfur oxidation to control the aqueous chemistry solver timestep
(previously, reactions of OH, GLY, MGLY, and Hg for multipollutant model also
controlled the timestep)
Advection
o Added additional divergence-based constraint on advection timestep
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






o Vertical advection now represented with PPM to limit numerical diffusion
Model time step determination
o Fixed a potential advection time step error reported by Talat Odman
 The sum of the advection steps for a given layer time step might not equal the
output time step duration in some extreme cases
 Ensured that the advection steps sum up to the synchronization ste
Horizontal diffusion
o Fixed a potential error reported by Talat Odman
o Concentration data may not be correctly initialized if multiple sub-cycle time
steps are required
o Fix to initialize concentrations with values calculated in the previous sub-time
step
Emissions
o Bug fix in EMIS_DEFN.F to include point source layer 1 NH3 emissions
o Bug fix to calculate soil NO "pulse" emissions in BEIS
o Remove excessive logging of cases where ambient air temperature exceeds 315.0
Kelvin. When this occurs, the values are just slightly over 315
Multipollutant model updates
o Bug fix to facilitate in-line emissions option
o Mercury
 Dicarboxylic acid reduction mechanism applied to aqueous phase oxidized
mercury species
 Cl oxidation pathway based on Donohoue et al 2005 J. Phys. Chem. A
109(34)
o Lowered CB05 mechanism unit yields for acrolein from 1,3-Butadiene tracer
reactions to be consistent with laboratory measurements
Possible parallel processing problem
o Processor 0 creates the NetCDF, I/O-API file header when opening new output
files
o On some parallel platforms, message-passing latency causes other processors to
not have imminent access to the file header, resulting in a "hung" execution or a
crash.
o Fixed by deferring the other processor's access until later in the computation
Photolysis
o JPROC/phot_table and phot_sat options
 Expanded lookup tables to facilitate applications across the globe and
vertical extent to 20km
 Updated temperature adjustments for absorption cross sections and
quantum yields
 Revised algorithm that processes TOMS datasets for OMI data format
o In-line option
 Asymmetry factor calculation updated using values from Mie theory
integrated over log normal particle distribution; added special treatment
for large particles in asymmetry factor algorithm to avoid numerical
instabilities
Instrumented Models
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o DDM-3D
o Sulfur Tracking
o Primary Carbon Apportionment
3.1.8.2 Known Issues in CMAQ version 4.7.1

When using the inline option for biogenic emissions, only one hour output timesteps are
supported

DDM-3D is currently only operational with the "eddy" vertical diffusion module (DDM for
the acm2 or acm2_inline modules may be available in the next release)
Availability: CMAQ is freely available for download from http://www.cmaq-model.org
3.1.9 CAMx
The Comprehensive Air quality Model with extensions (CAMx) is also an Eulerian
photochemical dispersion model that includes an integrated “one-atmosphere” assessment of
gaseous and particulate air pollution (ozone, PM2.5, PM10, air toxics, mercury) over many
scales ranging from sub-urban to continental (Morris et al, 2003a). The CAMx model and the
associated pre- and post-processors are also in the public domain, and developed and supported
by Environ, Inc. In addition to the PM treatment available in this release that is used by most
regulatory applications, CAMx also has an alternate sectional treatment of PM that is used for
several research applications (Morris et al, 2003b). The CAMx model will be used to
supplement the CMAQ modeling and help provide a weight-of-evidence model attainment
demonstration.
The CAMx extensions include:
a) Ozone Source Apportionment Technology (OSAT)
b) Particulate Source Apportionment Technology (PSAT) (Yarwood et al, 2004)
c) Decoupled Direct Method (DDM) and Higher Order Decoupled Direct Method (HDDM)
for ozone (Dunker et al, 2002)
d) Process Analyses (PA) and
e) Reactive Tracer (RTRAC) Source Apportionment (Morris et al, 2003c)
3.1.9.1 Overview of Version 5.20
V5.20 provides two updates and several modifications and bug fixes from the previous release
(v5.10). The new capabilities in CAMx include the linkage of PM chemistry to the SAPRC99
gas-phase mechanism, and the addition of mineral nitrate.
While there are important chemistry changes for v5.20, there are no changes to other input file
formats from versions 4.3 through 5.1. There are no changes to core model output file formats
from versions 4.4 through 5.1, and no changes to Probing Tool outputs from version 5.1.
Since v5.0, CAMx is capable of distributed multi-processing using MPI. To use MPI one must
have the MPICH or MPICH2 utility installed on the system. Guidance for running MPI is
provided in the README file located in the source code directory, and in the CAMx User's
Guide.
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3.1.9.2 Features of Version 5.20

















PM chemistry has been linked to the SAPRC99 gas-phase mechanism (mechanism 5).
CAMx now determines the uptake of nitrate by calcium in fine mineral (crustal) dust
particles. Calcium in soil dust particles reacts with nitric acid to form calcium nitrate. Since
the current ISORROPIA implementation in CAMx does not consider mineral cations other
than sodium, nitrate uptake by calcium in soil dust is calculated external to ISORROPIA.
CAMx outputs total particulate nitrate, i.e., the sum of particle nitrate determined by
ISORROPIA and calcium nitrate.
Numerous modifications were made to improve the vertical transport solution.
o The vertical velocity diagnostic algorithm and associated vertical advection solver
were altered to improve the vertical transport solution. The vertical solver changed
from an upstream-donor scheme to a centered scheme in space (continues to be
backwards-Euler implicit in time). The top boundary condition was changed to a
"zero-gradient" approach, alleviating the need for a top boundary condition input file.
o The manner in which the "super-stepping" driving time step is calculated has been
modified to improve coupling between the horizontal and vertical advection
algorithms.
o The order of X and Y advection is now constant.
o Nested grid boundary winds are no longer set from their parents.
All CB and SAPRC gas-phase chemistry mechanisms were improved by removing the
"NxOy" species and adding explicit species for NO3 and N2O5.
Oxidation of SOA precursors was added to PiG chemistry.
The wet deposition routine was improved to more correctly track accumulated gas-phase
mass within falling precipitation through a grid column. The gas scavenging rate into
precipitation was simplified.
Fixed several bugs in the OMP section of the PiG chemistry module.
Fixed a bug in the OMP section of the core chemistry driver that affected the chemical
apportionment of ozone production in OSAT.
Improved the accumulation of mass summary fluxes within several OMP sections of the
model.
Fixed bugs in the application of OMP for RTCMC.
Changed some data structures that caused large applications of source apportionment
(OSAT/PSAT) to crash with segmentation faults.
Implemented a major update to source apportionment timing tracers.
Fixed bug in reading boundary conditions for purposes of calculating the OH reactivity of
OSAT boundary tracers.
Fixed error in routine that updates boundary condition DDM sensitivities while advancing
the timestep.
Converted large data structures used in calculating kOH reactivity for OSAT/PSAT from
static to dynamically allocated.
Fixed bug in reading the boundary conditions for RTRAC.
Improved memory management of temporary arrays that are used for message passing in
MPI.
Availability: CAMx is freely available for download from http://www.camx.com
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3.1.10 MATS
The Modeled Attainment Test Software (MATS) is a tool made available by the U.S. EPA to
perform the modeled attainment tests for ozone and fine particulate matter as discussed in the
U.S. EPA guidance for demonstrating attainment of the NAAQS for ozone and PM2.5. (U.S.
EPA, 2007).
While the attainment test for ozone is relatively straight forward, the test for PM2.5 is slightly
more complicated. MATS includes the Speciated Modeled Attainment Test (SMAT) for PM2.5
by taking speciated components of PM2.5 from CMAQ for each of the base and future years and
computes species specific relative reduction factors and uses them in conjunction with ambient
data from FRM, STN and IMPROVE monitors to compute future year design values. MATS
thus applies modeling data in a relative sense rather than an absolute sense to investigate air
quality changes between two scenarios. The various calculations with MATS using the Sulfate,
Adjusted Nitrate, Derived Water, Inferred Carbonaceous material balance approach
(SANDWICH) are described in Frank (2006). SMAT has been used in a number of EPA policy
relevant studies, such as regulatory impact analyses performed to support the Clear Skies, the
former Clean Air Interstate Rule (CAIR), and Low Sulfur Diesel Rule, and for modeling
performed by the various Regional Planning Organizations including the Visibility Improvement
State and Tribal Association of the Southeast (VISTAS). Furthermore, the EPA requires states to
apply SMAT in their SIP in conjunction with air quality modeling to demonstrate attainment of
the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants, including
PM2.5, as part of the Clean Air Act (U.S. EPA, 2007). SMAT results are available as point
estimates and spatial estimates. Point estimates are calculated at CMAQ grid cells containing
Federal Reference Method (FRM) monitoring sites while spatial estimates are calculated at each
grid cell in the CMAQ domain. MATS is needed because of its previous use in policy relevant
work and is considered best practice by the EPA, its ability to produce speciated PM2.5 fields,
and its combination of ambient data and modeling results.
The publicly available version V2.2.1 can perform attainment tests for ozone, annual PM2.5, and
visibility. As part of a group of beta testers, UNC has access to a beta version of MATS (V2.2.3)
that can perform attainment test for daily average PM2.5 as well, but is being worked on for a
few bug fixes. The installer package from EPA includes all ambient datasets that are necessary
for performing the attainment tests. MATS is a PC-based software, and takes CMAQ model
outputs that are post-processed in an ASCII format suitable for MATS.
Availability: MATS is freely available for download from:
http://www.epa.gov/ttn/scram/modelingapps_mats.htm
3.2
Modeling Domains
This section describes the horizontal and vertical modeling domains that will be simulated for the
SEMAP project.
3.2.1 Horizontal Domain
The SEMAP modeling domain is on a Lambert Conformal Conic projection centered on 40°N
and 97°W. A one-way nested 36-km grid resolution parent domain covering the continental
United States will provide boundary conditions to a 12-km grid resolution domain covering the
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Southeastern U.S. The projection and grid definition information for the two domains is given in
Table 3-1, and Figure 3-2 shows the nested SEMAP modeling domains.
Table 3-1. Projection parameters for the CMAQ and CAMx modeling domains
Parameter
Map Projection
P-alpha
P-beta
P-gamma
X-cent
Y-cent
X-orig
Y-orig
dx
dy
Columns
Rows
Layers
36-km domain
12-km domain
Lambert Conformal Conic
33°N
45°N
97°W
97°W
40°N
-2,736,000 m
108,000 m
-2,088,000 m
-1,620,000 m
36,000 m
12,000 m
36,000 m
12,000 m
148
168
112
177
24
Figure 3-2. SEMAP 36-km (grey) and 12-km (white) modeling domains
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3.2.2 Vertical Domain
Table 3-2 is the vertical layer structures of the WRF and CMAQ/CAMx modeling domains. The
initial configuration collapses 34 layers from the WRF results into 22 layers for the air quality
modeling.
Table 3-2. SEMAP WRF and CMAQ Vertical Layer Configurations
WRF
CMAQ
Height Pressure Depth Level
(m)
(mb)
(m)
Sigma
Height (m)
Pressure
(mb)
Depth
(m)
23
0
18663
50
5264
22
0.1056
13399
150
3775
21
0.2378
9624
276
2870
20
0.3895
6754
420
2150
19
0.5406
4604
564
1100
18
0.6323
3504
651
880
17
0.7133
2624
728
700
16
0.7828
1924
794
552
15
0.841
1372
849
228
14
0.8659
1144
873
200
13
0.8882
944
894
174
12
0.9079
770
913
150
11
0.9252
620
929
128
10
0.9401
492
943
108
Level
Sigma
35
0
18663
50
2034
34 0.0332
16629
82
1715
33 0.0682
14914
115
1515
32 0.1056
13399
150
1375
31 0.1465
12024
189
1255
30 0.1907
10769
231
1145
29 0.2378
9624
276
1045
28 0.2871
8579
323
955
27 0.3379
7624
371
870
26 0.3895
6754
420
790
25 0.4409
5964
469
715
24 0.4915
5249
517
645
23 0.5406
4604
564
580
22 0.5876
4024
608
520
21 0.6323
3504
651
465
20 0.6742
3039
690
415
19 0.7133
2624
728
370
18 0.7494
2254
762
330
17 0.7828
1924
794
293
16 0.8133
1631
823
259
15
0.841
1372
849
228
14 0.8659
1144
873
200
13 0.8882
944
894
174
12 0.9079
770
913
150
11 0.9252
620
929
128
10 0.9401
492
943
108
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9 0.9528
384
955
90
8 0.9635
294
965
74
7 0.9723
220
974
60
6 0.9796
160
981
48
5 0.9854
112
986
38
4
0.99
74
991
30
3
0.994
44
994
24
2 0.9974
20
998
20
0
1000
1
3.3
1
9
0.9528
384
955
90
8
0.9635
294
965
74
7
0.9723
220
974
60
6
0.9796
160
981
48
5
0.9854
112
986
38
4
0.99
74
991
30
3
0.994
44
994
24
2
0.9974
20
998
20
1
1
0
1000
Episode Selection
This section describes the modeling periods that will be simulated for the SEMAP project.
The base year modeling and analysis period for the SEMAP project is annual 2007. Calendar
year 2007 was selected by the SEMAP states after careful consideration of (1) regional air quality
measurements and trends, (2) meteorological conditions, (3) emission trends, and (4) available emission
inventories. For additional details on the selection of CY2007, please refer to “Selection of CY2007 as
Base Year for SEMAP Modeling Project” (GA DNR, 2011).
As has been general practice in the air quality modeling community, in the interest of being able
to complete the numerous simulations on time, we propose to model the year in 4 quarters as
follows with a 2-week spin-up period for each quarter
 Quarter 1: 12/16/2006 to 3/31/2007
 Quarter 2: 3/16/2007 to 6/30/2007
 Quarter 3: 6/16/2007 to 9/30/2007
 Quarter 4: 9/16/2007 to 12/31/2007
For each of the 4 quarters, we will exclude the first 2 weeks to avoid the effects of initial
conditions, and include only the full 3 months for all analysis purposes. Although future year
inventories will be simulated for the SEMAP project, the modeling periods are determined by the
meteorology data used for the simulations, and these periods will not change across the SEMAP
simulations.
3.4
Input Databases
This section describes the input data that will be collected and processed to support the SEMAP
project air quality modeling tasks.
3.4.1 Initial and Boundary Conditions
This section describes how we will create the necessary initial and boundary conditions (IC/BCs)
for the CMAQ 36-km domain from GEOS-Chem (Bey et al, 2001). Recently, GA EPD has
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performed GEOS-Chem simulations for the year 2007, and once the model vertical resolution
has been finalized, has agreed to extract CMAQ-ready IC/BCs for our use. UNC will review the
GEOS-Chem based 36-km initial and boundary condition profiles generated for SEMAP and
quality assure them. We will first run the I/OAPI utility m3stat on each day’s file to obtain
summary statistics for each day, and plot them to visually verify the ranges of IC and BCs
obtained from GEOS-Chem. In addition to the above, we will perform the following QA of the
boundary conditions:

Create timeseries plots (by hour and by day and by month for the entire year) of all
chemical species for each of the model layers and for each of the 4 edges of the domain
to verify the possible inflow conditions coming into the domain.

Create a profile based ICON file and compare with the GEOS-Chem based ICON file to
assess the effects of using initial conditions from a global model such as GEOS-Chem.

Review satellite data for the year 2007, to specifically see if there are any dust storms that
impacted the Southeastern U.S., and see if GEOS-Chem captured those events, and
further verify if they are reflected in the CMAQ-ready IC/BCs. If we find any issues
with the GEOS-Chem based IC/BCs, we will alert SESARM to these, and suggest
alternate approaches to address them.
3.4.2 Emission Inventories and Ancillary Data
3.4.2.1 General Approach
The principal objective of the SEMAP emissions modeling tasks is to generate emissions inputs
for simulating air quality in the SESARM region for multiple modeling scenarios. This objective
will be fulfilled through the development of emissions modeling platforms (EMPs) that represent
North American air pollutant emissions for the SEMAP modeling periods. An EMP is a
collection of data and modeling tools needed to simulate emissions in support of air quality
modeling studies. In addition to being useful for organizing emissions modeling tasks, EMPs
provide a clean method for packaging and distributing all of the data and scripts needed to
reproduce an emissions simulation. The following eight EMPs will be developed to produce
annual emissions input files for CMAQ and CAMx:
1. 2007 Actual
2. 2007 Typical
3. 2017 OTB
4. 2017 OTW
5. 2020 OTB
6. 2020 OTW
Each of these EMPs will include the following items:

Emissions inventory data with coverage for the entire SEMAP 36-km modeling domain

Land use data gridded to the SEMAP 36-km and 12-km modeling domains and formatted
for input to both BEIS3.14 and MEGAN
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
Temporal, speciation, and spatial allocation data

General SMOKE input data, such as SCC description and FIPS codes identification files

SMOKE scripts and binary executables
The data needed to build the EMPs for the SEMAP project will include a combination of new
inventories and ancillary data for the SESARM region and existing datasets that are available
from different federal, state, and private organizations. Since many of the emissions datasets
outside of the SESARM region will not be available for the four modeling years identified for
the SEMAP project (2007, 2017, 2020) and/or the development of these data are not on the same
schedule as the SEMAP project, different approaches will be used to obtain the best available
emissions data at the time the SEMAP emissions modeling tasks begin. Figure 3-3 shows a map
of the regional planning organizations (RPOs) and will be used in the following sections to
identify different regional sources of emissions data that will be collected for the SEMAP project
modeling.
Figure 3-3. Regional Planning Organization map
3.4.3 Emissions Processing Sectors
We will refine the inventory categories from the standard four operational categories (area,
mobile, point, biogenic) to more specific source sectors. Refinements to the inventory sectors
provide flexibility in the design of emissions sensitivities, including source apportionment
studies, and to the approaches for performing quality assurance on the emissions data. As the
stationary-area-source emissions sector has traditionally been a catch-all for many types of
sources, this is the inventory sector that will be refined the most.
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The six sectors listed in the SEMAP RFP (EGU point, non-EGU point, area, non-road mobile,
on-road mobile, and biogenic) provide the bare-minimum inventory categorization needed for
optimizing the SMOKE configuration and probing model performance in CMAQ. We will
develop a more detailed set of inventory categories to provide flexibility to the diagnostic
modeling and the emissions sensitivity modeling tasks. Given the impact that wildfires had on
Southeastern air quality in 2007 (http://alg.umbc.edu/usaq/archives/2007_05.html), there should
at least be one explicit fire inventory category that is processed separately from the rest of the
area and non-EGU point emissions for the initial CMAQ simulation. Additional inventory
sectors can be determined through discussion with the SEMAP Project Coordinator and TAWG
following analysis of the results from the initial Actual 2007 annual simulation.
Table 3-3 lists the emissions processing sectors that will use for the initial Actual 2007 SEMAP
simulation and includes the operational category applicable to the sector and the temporal
processing approach (described in Section 5) applied to each sector.
Table 3-3. Emissions processing sectors for the SEMAP project
ID
Processing Sector
Operational Category
ar
arfire
nusar
nr
nusnr
US Non-point area sources
US Fire area sources
Canada/Mexico area sources
US Off-road mobile sources
Canada/Mexico off-road mobile
sources
On-road mobile sources
Canada/Mexico on-road mobile
sources
Electricity generating unit point
sources
Non-electricity generating unit point
sources
Fire point sources
Canada/Mexico point sources
Commercial marine vessels
Biogenic sources
Area
Area
Area
Area
Area
Temporal
Approach
MWSS
Daily
MWSS
MWSS
MWSS
Mobile
Area
Hourly
MWSS
Point
Hourly
Point
Daily
Point
Point
Gridded area
Biogenic
Hourly
MWSS
Single day
Hourly
mb
nusmb
egupt
negupt
ptfire
nuspt
cmv
bg
3.4.4 SEMAP Project Emissions Modeling Platforms
This section describes the EMPs that will be developed for the different modeling scenarios
under the SEMAP project. In general, only the inventory data will differ between the EMPs; the
ancillary data, including the spatial, temporal, and chemical allocation data, and the modeling
software will be constant. Upon receipt of all inventory data they will be formatted for input to
SMOKE and summary reports will be generated for use during the QA/QC of the emissions
processing. The SMOKE-ready inventories will be installed in directory locations on the UNC
computers that are consistent with the simulations that will use the data. The SMOKE scripts,
including the Assigns and run scripts, will be configured to process the inventories. The ancillary
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emissions data will be installed in the appropriate locations in the SMOKE directory structure on
the UNC computers and SMOKE will be configured to process these data.
Because the available inventories from RPOs for the non-SEMAP regions of the modeling
domain do not exactly match the SEMAP project modeling years, we will explore a few of
options for populating the base and future year EMPs for the other regions of the modeling
domain:

Option 1 – For regions with emissions inventories that fall on both sides of a SEMAP
modeling year, interpolate the existing inventories to the actual SEMAP year. For
example, LADCO is distributing 2008, 2012, and 2018 inventories for the MWRPO
states. We could develop a projection trajectory from these three years to develop 2017,
and 2020 inventories for the MWRPO states.

Option 2 – Use existing inventory data that match the SEMAP modeling periods as
closely as possible. For example, the LADCO 2008 inventory could be used for 2007 and
their 2018 inventory for 2017 and 2020.

Option 3 – Keep the inventory data constant, using the most recent, best quality data. For
inventories, such as the non-mobile WRAP data, which are only available for 2002 and
2018, applying a high quality alternative like the final version of the NEI 2005 to this part
of the modeling domain may be the best option.
We will contact the data providers for the non-SEMAP inventories to determine the best
available data for each region. The approaches that will be used to compile and prepare the
general emissions data are presented below followed by the approaches for preparing data
specific to the different SEMAP modeling scenarios.
3.4.4.1 General Emissions Data
The general emissions data in the SEMAP EMPs include data that will not change between
simulations. All of the non-inventory data, including the temporal, spatial, and chemical
allocation data, will be held constant across all of the SEMAP simulations. The land use data
used to compute biogenic emissions with BEIS and MEGAN will also remain constant across the
SEMAP simulations. The U.S. EPA 2005 emissions modeling platform version 4
(http://www.epa.gov/ttn/chief/emch/index.html#2005) (2005EMPv4) will be used as the starting
point for the ancillary emissions data that will be used for the SEMAP project. In particular, the
following data will be used directly from the 2005EMPv5:







Chemical speciation profiles for the Carbon Bond 05 photochemical mechanism and the
CMAQ and CAMx aerosol mechanisms
Temporal profiles by state, county, and source classification code
Cross referencing files for associating speciation, temporal, and spatial allocation data to
inventory sources
Stack defaults for elevated point sources
Federal Implementation Standards (FIPS) codes (i.e., country/state/county codes)
definitions
SCC definitions
Pollutant definitions
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Temporally allocating annual, daily, or hourly emissions inventories in SMOKE involves
combining a temporal cross-reference file and a temporal profiles file.

Temporal cross-reference files associate monthly, weekly, and diurnal temporal profile
codes with specific inventory sources, through a combination of a FIPS
(country/state/county) code, an SCC, and sometimes for point sources, facility and unit
identification codes.

Temporal profiles files contain coded monthly, weekly, and diurnal profiles in terms of a
percentage of emissions allocated to each temporal unit (e.g., percentage of emissions per
month, weekday, or hour).
As a starting point for the temporal allocation data for the SEMAP simulations, we will use files
from the 2005EMPv5. Based on guidance from the developers of the SEMAP inventory files and
analysis of these inventories by UNC, we may update the temporal profiles and assignments for
some source categories.
Chemical speciation follows a similar approach of using profiles and cross-reference data to split
inventory pollutants to the emissions species required by CMAQ and CAMx. These data will
also be taken directly from the 2005EMPv5 and updates will be made as needed to accommodate
new sources or guidance provided with the SEMAP project inventories.
SMOKE uses spatial surrogates and SCC cross-reference files to allocate county-level emissions
inventories to model grid cells. Geographic information system (GIS)-calculated fractional land
use values define the percentage of a grid cell that is covered by standard sets of land use
categories. For example, spatial surrogates can define a grid cell as being 50% urban, 10% forest,
and 40% agricultural. In addition to land use categories, demographic or industrial units, such as
population or commercial area, can also define spatial surrogates. Similar to the temporal and
chemical allocation data, an accompanying spatial cross-reference file associates the spatial
surrogates (indexed with a numeric code) to SCCs in the inventory. Spatial allocation with
surrogates is applicable only to area and mobile sources. Point sources are located in the model
grid cells by SMOKE based on the latitude-longitude coordinates of each source. Biogenic
emissions are estimated based on gridded land use information that is mapped to the model grid
using geographic information system (GIS).
Spatial surrogates for allocating area and mobile source inventories to the SEMAP project
modeling grids will be developed using the best available North American GIS Shapefile data.
The U.S. EPA Shapefile database version 2004
(http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html) will be used as the starting point
for the SEMAP project surrogates. None of the census-based surrogates, such as housing,
residential heating, and population, will be updated since 2010 census data will not be available
during the time frame of this project. To represent the significant changes in population density
patterns in the SESARM region between 2000 and 2007, we have explored the availability of
updated population data for 2007. The projected population data available from the U.S. Census
are based on an extrapolation of Census data collected in 1990 and 2000. The projected data are
only accurate if the population change trajectory observed from 1990 to 2000 continued into the
2000’s. As extrapolated data, the projected population data don’t consider the impacts of actual
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economic trends on population changes. Despite these issues with the projected data, since the
2010 U.S. Census data will not be available in time for this project, we will use the extrapolated
2007 population data as it presents the best alternative to actual 2007 census tract population
information.
Updates will be made to the surrogates calculated from land use/land cover and rail/roadway data
using geospatial data that have been updated since the release of the EPA 2004 Shapefiles.
National Land Cover Database (NLCD) information from the year 2006 are available to
characterize the location of forest, agricultural, urban, water, and other land use/land cover data
that are relevant to emissions. The NLCD is a very high-resolution (30-m) land cover database
for North America that will result in a significant improvement over the land use/land coverbased surrogates in the EPA 2004 Shapefile database. All of the land area, water area, and land
use classification surrogates will be updated to 2006 levels for the SEMAP project. The Census
Bureau TIGER data for 2007 will be used to create surrogates for road, rail, and waterways for
the SEMAP project.
Biogenic emissions will be calculated using the best available land use and emissions factors
data for BEIS and MEGAN. The BELD3 land use tiles and BEIS3.14 emissions factors will be
used to compute biogenic emissions for the SEMAP project modeling. The BELD3 tiles will be
projected and regridded to the SEMAP modeling domains using the Spatial Allocator. For the
biogenic emissions sensitivity simulation, MEGAN gridded input files (plant functional types
2.1, leaf area index 2.0, emissions factors 2.1) will be projected to the SEMAP modeling
domains using a GIS. Meteorology data processed through MCIP as part of this project will be
used in the calculation of all biogenic emissions. The biogenic emissions will be held constant
through all of the SEMAP project simulations.
3.4.4.2 2007 Actual
Table 3-4 shows the inventory configuration that will be used for the 2007 Actual SEMAP
project SMOKE simulations. These inventories represent the best available information for this
simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation.
Table 3-4. 2007 Actual inventory data
Region
SESARM
MANE-VU
Inventory Year
2007
2007
CENRAP
2005
MWRPO
WRAP
2007
2002 typical
Fires
2007
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Description
Actual 2007 inventory provided by SESARM
Initial 2007 modeling will use the screening
2007 inventory developed for OTC modeling
by NY DEC; final 2007 modeling will use the
2007 MARAMA inventory if available
The NEI2005v4 will be used in the absence of
an updated regional inventory
Base C provided by LADCO
WRAP planning 2002 inventory version C as
recommended by Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
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Mexico
Canada
Offshore
1999
2010
2001,2002
Mexico NEI 1999v3 available from WRAP
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.4.3 2007 Typical
Table 3-5 shows the inventory configuration that will be used for the 2007 Typical SEMAP
project SMOKE simulations. These inventories represent the best available information for this
simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation.
Table 3-5. 2007 Typical inventory data
Region
SESARM
Inventory Year
2007
MANE-VU
CENRAP
2007
2005
MWRPO
WRAP
2007
2002 typical
Fires
2007
Mexico
Canada
Offshore
1999
2010
2001,2002
Description
Typical 2007 inventory provided by
SESARM
The NEI2005v4 will be used in the absence of
an updated regional inventory
Base C provided by LADCO
WRAP planning 2002 inventory version C as
recommended by Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
Mexico NEI 1999v3 available from WRAP
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.4.4 2017 OTB
Table 3-6 shows the inventory configuration that will be used for the 2017 OTB SEMAP project
SMOKE simulations. These inventories represent the best available information for this
simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation
Table 3-6. 2017 OTB inventory data
Region
SESARM
MANE-VU
CENRAP
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Inventory Year
2017
2017
2015
Description
2017 OTB inventory provided by SESARM
The NEI2005v4 projected to 2015 will be
used in the absence of an updated regional
inventory
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MWRPO
2017
WRAP
2018
Fires
2007
Mexico
Canada
Offshore
2018
2020
2001,2002
2007->2017 growth factors provided by
LADCO
WRAP preliminary reasonable progress
PRP2018cmv inventory as recommended by
Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
Mexico NEI 1999v3 available from WRAP
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.4.5 2017 OTW
Table 3-7 shows the inventory configuration that will be used for the 2017 OTW SEMAP project
SMOKE simulations. These inventories represent the best available information for this
simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation.
Table 3-7. 2017 OTW inventory data
Region
SESARM
MANE-VU
CENRAP
Inventory Year
2017
2017
2015
MWRPO
2017
WRAP
2018
Fires
2007
Mexico
Canada
Offshore
2018
2020
2001,2002
Description
2017 OTW inventory provided by SESARM
The NEI2005v4 projected to 2015 will be
used in the absence of an updated regional
inventory
2007->2017 growth factors provided by
LADCO
WRAP preliminary reasonable progress
PRP2018cmv inventory as recommended by
Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
Mexico NEI 1999v3 available from WRAP
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.4.6 2020 OTB
Table 3-8 shows the inventory configuration that will be used for the 2020 OTB SEMAP project
SMOKE simulations. These inventories represent the best available information for this
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simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation.
Table 3-8. 2020 OTB inventory data
Region
SESARM
MANE-VU
CENRAP
Inventory Year
2020
2020
2020
MWRPO
2020
WRAP
2018
Fires
2007
Mexico
Canada
Offshore
2018
2020
2001,2002
Description
2020 OTB inventory provided by SESARM
The NEI2005v4 projected to 2020 will be
used in the absence of an updated regional
inventory
2007->2020 growth factors provided by
LADCO
WRAP preliminary reasonable progress
PRP2018cmv inventory as recommended by
Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
Mexico NEI 1999v3 available from WRAP
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.4.7 2020 OTW
Table 3-9 shows the inventory configuration that will be used for the 2020 OTW SEMAP project
SMOKE simulations. These inventories represent the best available information for this
simulation year that we anticipate being available at the beginning of this simulation. This table
will be update if new inventory data become available prior to beginning the simulation.
Table 3-9. 2020 OTW inventory data
Region
SESARM
MANE-VU
CENRAP
Inventory Year
2020
2020
2020
MWRPO
2020
WRAP
2018
Fires
2007
Mexico
2018
GIT/UNC/CIRA-SEMAP 004.v3
Description
2020 OTW inventory provided by SESARM
The NEI2005v4 projected to 2020 will be
used in the absence of an updated regional
inventory
2007->2020 growth factors provided by
LADCO
WRAP preliminary reasonable progress
PRP2018cmv inventory as recommended by
Tom Moore
2007 SESARM inventory and 2007 Blue Sky
inventory for the rest of the CONUS domain
Mexico NEI 1999v3 available from WRAP
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Canada
Offshore
2020
2001,2002
Canada NEI 2010 available from U.S. EPA
2001 offshore point inventory from the Gulf
of Mexico; 2002 gridded 36-km commercial
shipping lanes for the Pacific, Gulf, and
Atlantic
3.4.5 Landuse Data
Different landuse data sets will be collected for the SEMAP project. The landuse data required
for estimating biogenic emissions are described in Section 4.1. Landuse data needed for the air
quality modeling will be provided by SESARM as part of the WRF meteorology data. The WRF
model uses landuse data from NCAR and outputs them in the WRF output files. These landuse
data will be processed by MCIP and WRFCAMx and used by CMAQ and CAMx for calculating
dry deposition parameters.
3.4.6 Meteorology Data
All meteorology data required for the emissions processing and air quality modeling will be
provided by SESARM. All the necessary meteorological variables will be processed through
MCIP and WRFCAMx along with the two programs (AHOMAP and TUV). The CMAQ and
CAMx ready meteorology data will be use by SMOKE, CMAQ, and CAMx.
3.5
Quality Assurance and Error Correction Protocol
3.5.1 Quality assurance and Quality control
Quality assurance in the context of emissions and air quality modeling refers to ensuring that the
results of the simulations are of the highest quality possible with respect to the data. Quality
control, on the other hand, refers to ensuring that the model simulations completed successfully
and without errors. QA focuses on the quality of the data; QC focuses on the process of
modeling, archiving, and documenting the data. Some analysis products, like emissions spatial
plots and simulation web sites, are used for both QA and QC. Other products, such as documents
of model settings are used for QC only. This section describes both the QA and QC products that
we will use during the emissions and air quality modeling for the SEMAP project to ensure high
quality model results and that the modeling software performed as we expected. Specific
examples showing how to apply QA/QC concepts in the application of the modeling software
provide a framework for guiding the QA process during the production of model-ready
emissions and air quality model results. The QA/QC framework begins with the installation of
the modeling software and concludes with compiling QA summaries and notes into a final
report. The result of following the framework will be highly scrutinized data that were generated
by well-documented QA procedures.
All quality assurance and quality control procedures applied during the SEMAP project will
adhere to the SEMAP quality assurance project plan (QAPP).
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3.5.2 Meteorology Data and Modeling
Quality assurance and quality control for meteorological data processing are needed to ensure the
meteorological inputs into air quality models are correct and consistent with what the
meteorological model generated. This especially important when vertical collapsing is
performed. We will compare the results from MCIP and WRFCAMx on full 34 WRF layers and
the collapsed vertical layers, especially near the model top where potential problems arise
(Emery et al. 2009). We will make certain all the necessary variables CMAQ needs are correctly
output from the WRF model. We will generate runtime log files when we run MCIP and
WRFCAMx programs to QA the variables in printouts and routinely ensure no error messages
were generated by the programs. If errors were generated, we will diagnose the problems and
address them.
3.5.3 Emissions Quality Assurance
Adelman (2004) presented the first systematic QA/QC protocols for emissions modeling. The
concepts presented there will provide the basis for the QA/QC procedures that we will use for the
emissions processing for this project.
3.5.3.1 QA Classifications
This protocol’s quality assurance plan for emissions modeling focuses on the following four
classifications of QA, to ensure that datasets are both of high quality and reproducible:
(1) accuracy assurance and problem identification (termed “modeling QA”); (2) software and
data tracking (“system QA”); (3) independent data review (“gatekeeping and outside review”);
and (4) documentation. Described in detail and presented as actual QA procedures in Adelman
(2004), these four areas cover all aspects of emissions modeling. Splitting the QA into different
classifications is not an attempt to prioritize the QA effort; instead, it is a way of presenting the
information that allows those performing QA to focus on each area individually as it arises in the
modeling process.
3.5.3.1.1 Modeling QA
Modeling QA involves performing data quality checks, assuring simulation accuracy, and
recognizing and identifying problems as they happen; it is the process of looking for glaring
faults in the model input and output data (I/O) and determining whether the input data are
producing the desired results. Scrutiny of the I/O using standard statistical analyses can reveal
problems in the data and/or the model setup. Using a standard approach for analyzing emissions
model I/O establishes reference points to use when scrutinizing the data. Seeking these indicators
of correct model performance allows QA personnel to determine the accuracy of the simulations
and whether faults in the data or model configuration exist. Several indicators of model
performance are presented in Adelman (2004).
3.5.3.1.2 System QA
System QA addresses model installation and configuration issues, data accounting, and ensures
that the modeling systems are producing results that are expected and can be reproduced in the
future. Properly benchmarking a model and assuring that the installation is complete is the first
step in the system QA process. Confirmation of configuration settings, compile options, and
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other system-related parameters must occur and be documented prior to producing any model
results. Archiving the model installation at set “freeze points” in the project is also required in
order to keep accurate records of the modeling installation and run scripts. A key feature of
system QA is facilitating the reproduction of model results in the future and the ability to revert
back to a previous configuration or installation after the model has been revised or updated. In
addition to thorough documentation, version control software is required for archiving model
executables, run and configuration scripts, and important data files. Combining documentation of
the modeling procedures with archives of the model installation and data files will ensure that the
model installation and configuration are correct and that past simulations are sufficiently documented and archived to allow their reproduction.
3.5.3.1.3 Gatekeeping and Outside Review
Gatekeeping and outside review is the process of ensuring that the data entering and leaving the
SMOKE modeling process meet a predetermined quality level. A gatekeeper screens model data
before they pass from one major step of the modeling process to the next. In emissions QA,
gatekeeping is applied to the emissions inventory and SMOKE input files on the front end of the
modeling process, and to the SMOKE output files at the back end. A gatekeeper (or gatekeeping
team) is responsible for performing a series of “sanity checks” on both the input and output data
streams. Outside reviewers are emissions experts who are not part of the RMC modeling team.
They periodically review the entire process—the emissions data, the modeling, and the QA steps
taken—using their judgment as experts to decide what to review and how to review it.
3.5.3.1.4 Documentation
Documentation, a component that is common to all of the other three QA classifications,
provides the record of the QA process. Establishing a detailed set of requirements for documenting every step in the QA process will ensure not only that the documentation is created as
expected but that the processes recorded by the documentation are completed correctly. In addition to records or lists of completed QA steps, documentation refers to summaries and interpretations of emissions inventory reports and analyses. Covering the entire realm of the modeling
process, QA documentation will include records of model configuration, details about data files,
simulation records, and final report generation.
3.5.3.2 Implementing the QA Framework
With the QA classifications conceptually defined in the previous section, this section focuses on
the specifics of how to implement them. Beginning with details about the modeling team
requirements of the QA framework and the sources of the QA information, this section then
presents specific step-by-step instructions on performing emissions QA. This section concludes
with discussion about how and where to document the QA process.
3.5.3.2.1 The Emissions QA Team
The level of effort required to implement this QA framework requires a team consisting of a data
gatekeeper(s), a production modeler(s), a QA manager, outside reviewers, and a project manager.
Each member of the team contributes different perspectives on the data and modeling.
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The gatekeeper (or gatekeeping team) is responsible for reviewing the SMOKE I/O data streams
for correctness. Before new datasets are used in SMOKE modeling, or new SMOKE output
emissions datasets are used in air quality modeling, they must be reviewed by the gatekeeper.
For all SMOKE input and output files, the gatekeeper will ensure that the data are complete, are
formatted correctly, and pass sanity checks. Checking completeness entails examining the files
for the necessary data elements, spatial coverage, and temporal extent. Data formats will be
confirmed using the SMOKE manual to check ASCII files and using PAVE and/or VERDI to
check binary netCDF files, such as the meteorology inputs. Sanity checks look for glaring errors
in the file contents and ensure that the data make sense in the context of how they will be used
and relative to similar or reference datasets. Issues that arise during the gate keeping process will
be reported to the project manager for resolution. UNC staff will serve as the gatekeepers for the
SEMAP emissions data.
The production modeler is responsible for receiving input data, maintaining the model run
scripts, running the model per the work plan, producing default QA reports, delivering model
output data, and assisting with compiling QA reports into summaries for documentation
purposes. At least two people are needed in this role in all emissions modeling projects because
of its broad scope; a single person cannot complete all of the production modeler functions. A
lead modeler will oversee the entire modeling process, perform the majority of the SMOKE
modeling, and receive and archive input and output data per the procedures. Secondary modelers
will organize the SMOKE QA reports into emissions summaries for data QA and reporting, and
will generate custom QA summaries and reports for troubleshooting any problems encountered
during the modeling process.
The QA manager enforces adherence to this QA protocol. Consistent with the protocol, the QA
manager performs and documents all of the checks required for determining model accuracy and
revealing errors. The QA manager also ensures that the software is configured correctly and that
the data and model are being archived consistently and correctly. Overseeing the production
modelers, the QA manager will verify that SMOKE is being applied correctly and that all QA
summaries are consistent with the relevant input data. Leading the documentation efforts, the QA
manager ensures that all the necessary QA summaries are generated and certified and that they
are compiled into a final project report. Zac Adelman from UNC will serve as the QA manager
for the SEMAP emissions modeling.
Outside reviewers will be required to both systematically confirm and periodically audit the
work of the emissions modeling team for consistency with the modeling data and adherence to
the QA protocol. An outside perspective of the emissions modeling process will be used to
subject the emissions modeling data and process to screening procedures for correctness.
SESARM technical staff will serve as outside reviewers of the SEMAP emissions data.
The final member of the modeling team is the project manager. The project manager is
ultimately responsible for the quality of the final products of the modeling process. Providing
technical assistance to the production modelers and clarifying any uncertainties about model
input, configuration, and operation for the QA manager, the project manager will be able to
address any questions in the modeling process. Working with the QA manager, the project
manager approves the results of all QA procedures, and thoroughly scrutinizes the QA
summaries for problems to determine whether the data are ready to be sent to the gatekeepers
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and outside reviewers for final review before delivery to the air quality modelers. The project
manager is responsible for keeping the project on schedule and within budget.
For each of the QA classifications presented in Section 5.1.2.1 (modeling QA, system QA,
gatekeeping and outside review, and documentation), the following sections provide procedural
information and describe the role of each member of the modeling team. These sections are
preceded by an outline of the types of information and the components of the SMOKE modeling
environment that are involved in the QA procedures.
3.5.3.2.2 Information Required for Performing Quality Assurance
Various sources of QA information are available from SMOKE. The nature of the information,
how it is generated and where to obtain it, and what it will be used for are explained in Adelman
(2004). The following data will be used in the SMOKE QA process.







Log files
SMOKE QA reports
Run Scripts
Assigns files
Inventory summaries
QA Documents
Emission QA plots
3.5.3.2.3 Modeling QA
Modeling QA involves procedures for determining whether SMOKE is working correctly and
producing expected results. These procedures must be completed with every new emissions
inventory modeled with SMOKE, with new base-year simulations, new sensitivity simulations,
reruns to correct errors in past simulations, and changes to the base SMOKE configuration. This
section applies these components to a detailed set of procedures for checking SMOKE modeling.
There are several indicators of model performance that must be reviewed to confirm that
SMOKE is configured and applied correctly. In general these indicators will be used to confirm
the following items:






Confirming SMOKE configuration and input files
Confirming that SMOKE executed without errors or unexpected warnings
Checking the SMOKE ancillary inputs
Emission modeling QA checks
Confirming that SMOKE processed the emissions inventory correctly
Compare the SMOKE QA reports and model-ready emissions for consistency
The QA steps detailed in this section coincide with those in the SMOKE emissions QA checklist
in Appendix B (Table B-1), and are to be performed by the production modelers. After the
production modeler completes the entire checklist, it will be passed to the QA manager for
verification. The QA manager will re-check all of the individual items in the checklist.
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When all of the steps have been completed for a new inventory or sensitivity and the checklist is
filled out in its entirety, the production modeler and QA manager must sign off on the checklist
and deliver it to the project manager. As an important last step in the SMOKE QA procedure, the
project manager validates the checklist using the accompanying documentation to determine
whether the emission modeling results are ready to pass to the gatekeepers and outside
reviewers. He/she then certifies the model-ready emissions as final, interim, or not ready for air
quality modeling. The emissions may be deemed interim if resource availability (time,
computing, funding) or scheduling requirements outweigh the severity of the emissions issues.
The project manager will document the caveats in the interim emissions dataset and ensure that
they are either resolved in a subsequent emissions modeling simulation and/or presented as
caveats in the emissions data to air quality modelers. If the project manager deems that the
emissions are not ready for air quality modeling, he/she will also document the problem(s) that
lead to this determination and a solution will be worked out by the modeling team. After the
problem is corrected, the emissions category that contained the problem will be subjected to the
appropriate QA procedures and the model-ready emissions regenerated.
Complete details of the model performance indicators are provided in Adelman (2004).
3.5.3.2.4 System QA
System QA refers to the accounting process for modeling software, tools, and data, beginning
with installation or import onto the computing system of choice and completing with archival
using an online version control system. Version control in the context of emissions modeling is
the concept of enumerating every instance of a model configuration or data file and storing the
configuration information and data with associated version numbers. All changes made to the
model configuration or installation and updates made to the data are documented and tracked
using the project website.
The QA checklist in Appendix B (Table B-1) contains entries for system QA. Each of the major
steps detailed in this section correspond to entries in the QA checklist. This protocol requires
that all of the steps outlined in this section be completed and verified by the QA manager in the
checklist in Table B-1.
3.5.3.2.5 Gatekeeping and Outside Review
The third major classification of QA is gatekeeping and outside review. As significant time,
effort, and funding are involved in emissions modeling, air quality modeling, and the subsequent
analysis of the results, it is very important to ensure that the data are correct before they enter the
modeling and analysis process. To supplement the above QA procedures performed by the
production modelers and the QA manager, perspectives from people outside of the modeling
process are needed and must be part of the QA protocol. These external participants are the
gatekeeper and the outside reviewers. We will make QA products available through the Web to
facilitate review by the gatekeepers.

The gatekeeper’s function is to review the SMOKE input and output data streams
before they are used in modeling. A gatekeeper will be designated by the project
manager of the emissions modeling team. The gatekeeper should be a person who
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understands the data requirements and the operational aspects of SMOKE, has
worked with the same or similar datasets as those entering and leaving the SMOKE
modeling process, and a person involved in the modeling project and who knows the
modeling domain and the nature of the inventory data. The gatekeeper will perform a
high-level review of SMOKE inventory files for formatting errors and for data
“sanity” (defined below). He or she will also review the CMAQ-ready SMOKE
output files for correctness and completeness.

While the gatekeeper focuses on the I/O data streams, outside reviewers are called
upon to periodically review the entire process: the emissions data, the modeling, and
the QA steps taken.
3.5.3.2.6 Documentation
Documentation is the fourth key component of the emissions modeling QA protocol; it
encompasses all aspects of the QA process. From recording the results, progress, and key
findings of the accuracy assurance and problem identification tasks to embodying the central
issue of the software and data tracking tasks, documentation is important for ensuring the
accuracy of the model results and providing tangible records of how they were obtained and how
they can be reproduced. Three levels of documentation are required in this protocol:
1. The first level consists of worksheets for recording SMOKE configuration settings and
details about the input and output data. These are working documents that change as the
project evolves and new data become available.
2. The second level of documentation involves forms for recording the QA process; these
are also working documents. Recording this information ensures that the primary QA
goals are fulfilled. This second level of documentation records both the completion of
procedures and the creation of documentation as defined by this protocol.
3. The last level of documentation is the generation of a final report. All of the information
recorded in the previous two levels of documentation must be compiled into a final report
at the end of a modeling project.
These documentation levels are described in detail in Adelman (2004).
3.5.4 Air Quality Data and Modeling
We will adopt rigorous QA procedures to verify the quality and accuracy of all model inputs and
outputs that will be developed for the project. This will be mainly accomplished using statistical
and graphical approaches.
3.5.4.1 AQM Input Files
IC/BC files: Once the IC/BC files are created, we will create graphical outputs – tile plots for
initial conditions and time-series plots of boundary conditions for every model day for all 4
edges of the domain. We will also compute min and max for each hour for each edge of the
domain and verify if these lie within reasonable bounds for the entire modeling period. A sample
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time-series plot of boundary conditions is shown in Figure xx. We will archive all run logs from
the processing of the data and screen them for any error messages.
3.5.4.2 AQM Output Files
We will use a rigorous naming convention (see Section 4.1) for distinguishing between all model
scenarios and between the CAMx and CMAQ simulations. We will create individual directories
for each model scenario, and retain unique scripts for each scenario, each month, each year, and
never reuse any script for multiple scenarios. We will retain all runtime logs from the models and
routinely parse them for any error/warning messages. We will flag runs/scenarios/days that have
error messages and take appropriate action to address them.
Once the model simulations are complete, we will use automated post-processing tools that UNC
has used extensively in numerous projects to perform time aggregation (daily maxima, daily
average, etc.), create tile plots, timeseries plots and standard min/max statistics for all species to
QA them for reasonableness. We will create automated ways to parse these min/max values for
each and every simulation and report anomalies to the AQM data manager for possible corrective
action. All plots will be posted to the interactive project website for SEMAP and the QA
manager will be informed for review.
The model executable, run scripts and log directories will be periodically synchronized to the
archival system at GIT and UNC (mass storage system) in near real-time. Thus, in case of disk
failure, etc., we can easily reproduce these runs as necessary.
The following general procedures will be used to QA all air quality modeling simulations
completed for this project.






Verification that correct configuration and science options are used in compiling and
running each component of the modeling system, where these components include the
meteorology pre-preprocessor, initial/boundary conditions processors, photolysis rate
calculators, chemistry-transport model (CTM), and any other preprocessor used to
prepare data for the CTM.
Verification that correct input data sets are used when running each model.
Evaluation of model results to verify that the output is reasonable and consistent with
general expectations.
Processing of ambient monitoring data for use in the model performance evaluation.
Evaluation of the air quality model results against observations.
Backup and archiving of critical model input and output data.
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Section 4: Technical Approach
4.1
Simulation Identification
We will use a naming scheme for the CMAQ and CAMx simulations to identify the versions of
the input data, inventory year, and inventory version used in the simulation. The model used to
generate the results with version number (i.e., CMAQ4.7.1) will be included in the output file
names along with the julian date of the simulation. A three letter input data versioning prefix in
the simulation identification (ID) will be used to track the version of the meteorology (digit 1),
emissions (digit 2), and air quality data (digit 3). A two-digit number will then denote the
inventory year used in the simulation. The last digit(s) will identify the version of the inventory,
i.e. actual (A), typical (T), on the books (OTB), on the way (OTW). For example simulation ID
adb07T can be interpreted as:




Meteorology data/configuration version a
Emissions data/configuration version d
Air quality data/configuration version b
2007 typical (07T) inventory
The justification for using different version IDs for the input data is based on our experience with
similar modeling projects that required the ability to track numerous iterations of the input data
used in the air quality modeling simulations. When one of the input streams to the air quality
model changes, either through changes to the data or model configuration, the ID for that data
will be incremented. The version IDs and simulation IDs will be documented and tracked on the
SEMAP project website.
4.2
General Modeling Procedures
This section describes the general procedures that we will use to process data during the SEMAP
project. The modeling procedures described here will be used throughout all of the modeling
applications in the SEMAP project. Alternative procedures that diverge from those documented
in this section are described in the applicable task descriptions below.
4.2.1 Meteorology Data Preparation
4.2.1.1 MCIP
We will acquire the namelist and registry files and WRF outputs for the year 2007 from the
SESARM meteorological modeling contractor. We will select two ten day periods from both
summer and winter months to test the MCIP make certain the WRF model was run with all the
physics options and outputted necessary variables needed for CMAQ simulations. While we run
MCIP we will window out the CMAQ domain mentioned in Section 2 from the WRF domain
which has been chosen by SESARM and the meteorological modeling team. We will also
acquire the WRF modeling protocol and evaluation/analysis documentations and then carefully
review the evaluations already done by the WRF modeling team and perform extra
evaluation/analysis if needed.
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We will have conference calls and emails with the metrological modeling team regarding the
CMAQ vertical layers. According to EPA scientists, layer collapsing in the first 1km near the
surface is not recommended. After the vertical layer structure is determined we will run MCIP
for the two testing periods using vertical collapsing. Since the vertical collapsing has the
potential to cause problems at the model top which can be propagate to lower model layers
(Emery et al. 2009), we will compare the metrological variables from the original WRF outputs
and the MCIP outputs to assure the quality of the met data going into SMOKE and CMAQ.
After we are satisfied with the MCIP outputs for the test cases, we will perform MCIP
processing with the year 2007 WRF output data. We will report our findings to SESARM and
external sources.
4.2.1.2 WRFCAMx
We will test the WRFCAMx program with the same testing cases mentioned in 4.1. We will also
examine the outputs after vertical collapsing and compare them to the original WRF outputs. We
will also run the two programs (AHOMAP and TUV) to prepare photolysis rate inputs for
CAMx model.
After we make certain the results from WRFCAMx, AHOMAP, and TUV are satisfactory, we
will process the whole year of 2007 WRF output data through these programs.
4.2.2 Emissions Data Preparation
4.2.2.1 SMOKE
UNC will conduct all emissions modeling for the SEMAP project using the SMOKE version
2.7beta (May 2010), which supports the processing of MOVES-based lookup tables. Before
beginning operational SMOKE modeling to develop annual, multi-scale emissions for CMAQ
and CAMx, UNC will build an emissions modeling infrastructure on UNC computers to support
the SEMAP project. The sequential process of developing this infrastructure encompasses the
installation of SMOKE on the UNC computers and creating the 2007 SEMAP emissions
modeling platform. SMOKE installation requires that after the binary executables are copied to
the system, that the program be benchmarked on the UNC computers before it is used for
operational modeling. Benchmarking is the process of confirming that a model is installed on a
new computing system correctly by comparing the results from a test simulation run on the new
system against results from the same simulation that are distributed with the model. UNC will
use the preliminary 2006 version A (Pre06a) RoMANS emissions modeling platform as the
starting point for the development of all of the SEMAP emissions modeling (Adelman, 2007).
The scripts and SMOKE configuration used for RoMANS Pre06a will be transferred directly to
the UNC computing systems for the 2007, 2017 and 2020 SEMAP modeling.
After SMOKE is benchmarked on the UNC computers, we will build an emissions modeling
platform for developing emissions for the SEMAP project on UNC computing system.
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4.2.2.2 MEGAN
We will use a GIS to convert version 2.1 of the MEGAN emission factors and plant functional
types and version 2.0 of the MEGAN leaf area index data to the SEMAP grids and combine
these with the SEMAP meteorology data to estimate biogenic emissions for the summer and
winter sensitivity periods.
4.2.2.3 Blue Sky Framework
The Blue Sky Framework (http://blueskyframework.org/) is a modeling system designed to
provide estimates of wildfires and other large fire events using high-resolution satellite-based
data and ground-based observations. We will use the Blue Sky Framework in conjunction with
the Fire Emission Production Simulator (FEPS) to generate 2007 annual fire inventories for input
to the SMOKE processing system. These fires data will be used to simulate fire emissions for the
regions of the modeling domain outside of the area covered by the SEMAP fire inventories
provided by SESARM. The Blue Sky Framework generates hourly fire and plume rise
parameters that SMOKE can use to produce hourly, gridded, 3-D emissions for input to CMAQ
and CAMx.
4.2.2.4 Spatial Allocator
The Spatial Allocator is an open-source GIS that we will use to prepare emissions and air quality
input data. The Surrogate Tool component of the Spatial Allocator will be used to convert GIS
Shapefiles to spatial surrogates for the SEMAP modeling domains. These spatial surrogates will
be used to allocate the county-level inventory data to the 36-km and 12-km modeling grid cells.
We will also use the Spatial Allocator to project the BELD tiles to the SEMAP modeling grids
for input to BEIS3. Finally, we will use the Spatial Allocator to generate an ocean mask input
file to CMAQ which is used to calculate surf zone and open ocean sea salt emissions. We will
explore the options for calculating offline sea salt emissions to use in CAMx.
4.2.3 Air Quality Modeling
4.2.3.1 CMAQ
CMAQ version 4.7.1 will be used for conducting the SEMAP air quality modeling. As described
above, meteorology and emissions inputs to CMAQ are prepared using MCIP and SMOKE,
respectively. Initial and boundary conditions for the CMAQ 36-km domain are extracted from
GEOS-Chem outputs. Here we will use CMAQ’s preprocessors ICON and BCON, respectively,
to prepare initial and boundary conditions for the CMAQ 12-km domain. We will use JPROC to
prepare the photolysis rates tables. Finally, the air quality modeling will be conducted using the
CMAQ’s core Chemistry-Transport Model CCTM. CMAQ version 4.7.1 system will be installed
on Georgia Tech’s computer system and will be benchmarked against release data. We choose
ifort as the Fortran compiler for the system.
4.2.3.1.1 Initial and Boundary Conditions
We will use ICON and BCON of CMAQ version 4.7.1 to prepare initial and boundary conditions
for the CMAQ 12-km domain. The input datasets to ICON and BCON will be the output fields
of instantaneous concentrations from the corresponding CMAQ modeling of the 36-km domain.
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Initial conditions will be prepared for the beginning hour of each simulation period and boundary
conditions will be prepared for every hour during each simulation period. Run scripts, log files
and all outputs will be archived and the generated initial and boundary conditions will be
visualized using graphics tools for quality assurance.
4.2.3.1.2 Photolysis Rates
Photolysis rates tables will be prepared by using JPROC of CMAQ version 4.7.1. The same
chemical mechanism to be used in CCTM (i.e. CB05 with Chlorine) will be used here to prepare
photolysis rates tables for every day of each simulation period. These photolysis rates tables will
be first reviewed and then used for both the 36- and 12-km CCTM modeling. Photolysis rates
tables will be prepared only once for the entire SEMAP project. Run scripts, log files and
photolysis rates tables will be archived for quality assurance.
4.2.3.1.3 Chemistry-Transport Model
We will use CCTM of CMAQ version 4.7.1 to conduct the air quality modeling. All the CMAQ
ready inputs will be prepared as described previously and will be reviewed for quality control
purposes. CCTM build-it script and run-together scripts will be developed for the SEMAP
project. The CCTM configuration for the initial base case simulation will be determined by
recommendation using all available information in the literature, consultation with EPA, and our
own experiences and by approval of the SEMAP Project Coordinator and TAWG. The final
CCTM configuration will be determined after the diagnostic model sensitivities tests and by the
SEMAP approval. The source code of each set of CCTM configuration including those for the
diagnostic tests will be kept for quality assurance. Run scripts, log files and most importantly the
CCTM output files will also be archived. Quality review will be made during each CCTM
simulation and after the simulation is finished. Log file will be first reviewed for checking if
appropriate input files are used in the simulation and then checked for any suspicious total mass
reporting. Simulated concentration fields from output files will be statistically analyzed using
m3stat tool after the simulation finished and will be processed for necessary temporal averaging
and species merging. The processed concentration fields will be visualized using PAVE and
other graphic tools as a standard procedure for further quality assurance.
4.2.3.2 CAMx
4.2.3.2.1 Initial and Boundary Conditions
We will use the initial and boundary condition files generated for CMAQ (from GEOS-Chem)
and convert them using the CMAQ2CAMx utility to CAMx-ready files for the 36-km domain.
We will then use the CAMx-based 36-km concentration files to create the ICON/BCON for the
12-km CAMx modeling applications. Run scripts, log files and all outputs will be archived and
the generated initial and boundary conditions will be visualized using graphics tools for quality
assurance.
4.2.3.2.2 Other Inputs
We will use the CAMx utility AHOMAP to generate additional inputs for CAMx, which include
UV albedo, haze turbidity, and ozone column. While recent versions of CAMx can support the
use of additional inputs such as snow cover, surface roughness, land-ocean mask, and drought
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stress, ahomap does not support the creation of these since they are not essential. We will look
into available data from the National Land Cover Database (NLCD) and WRF outputs to see if
we can create these.
4.2.3.2.3 Chemical Mechanism
Unlike CMAQ which uses a specific chemical mechanism module during compilation, CAMx
needs a chemical parameter file as an input to the run script. From the various options, we will
use the mechanism file CAMx5.2.chemparam.6_CF, included with the distribution. This
mechanism includes the Carbon Bond 2005 gas-phase chemical mechanism with PM treatment.
4.2.3.2.4 Chemistry-Transport Model
We will use CAMx V5.20 for conducting the SEMAP air quality modeling. As described above,
meteorology and emissions inputs to CAMx are prepared using WRFCAMx and SMOKE,
respectively. All other inputs such as IC, BC and photolysis rate files will be developed as
discussed above. Finally, the air quality modeling will be conducted using CAMx V5.20. UNC
has already installed and configured this model on the UNC computer cluster. We are in the
process of performing comparative performance evaluation using OpenMP and MPI to obtain the
best performance. We will benchmark the final configuration outputs against available test data
provided by the developers before starting modeling for SEMAP. We will use the Intel Fortran
Compiler ifort as the Fortran compiler for the system.
4.3
Initial Base Case Simulation
The objective of this task is to develop an initial annual “Actual” 2007 base year air quality
model simulation (herein referred to as Base2007) on the SEMAP 36-km and 12-km resolution
modeling grids (Section 2). Many of the modeling approaches discussed in this section are
applicable to the other modeling tasks in the project. The modeling approaches for tasks that
differ from those presented in this section will be presented with the applicable task.
We will divide the work on this task between Georgia Tech and UNC. UNC will conduct the
modeling that prepare all inputs to CMAQ, including IC, BC, JPROC, MCIP and SMOKE
modeling; Georgia Tech will conduct all CMAQ modeling. External drives will be used for data
transfer between UNC and Georgia Tech.
We will use SMOKE version 2.7beta with the SESARM “Actual” 2007 emissions inventory to
prepare gridded emissions inputs, GEOS-CHEM to prepare BCs, and the latest versions of
ICON, JPROC and MCIP to prepare the other inputs for CMAQ. Details of the input databases
that will be compiled to prepare inputs to simulation Base2007 are provided in Section 4,
including the compilation of inventories outside of the SESARM region. To select a specific
configuration of CMAQ for the SEMAP project, we will start with the standard configuration of
the CMAQ version 4.7.1 released by EPA through the CMAS Center at UNC. Features of this
version were announced at the CMAS Conference in October 2009, which include updated SOA
and cloud modules, support for biogenic sesquiterpene emissions, and a fully incorporated DDM
module for both gas and aerosol phases.
The initial Base2007 CMAQ simulations, which will be identified as CMAQ4.7.1.aaa07ACT
will be performed using a configuration based on a literature review of the relevant air quality
model sensitivity simulations including the latest evaluation articles on the most recent
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developments in CMAQ (i.e. Foley et al (2009)) and through communication with the EPA
Office of Research and Development (ORD) scientists who developed CMAQ. In consideration
that CMAQ version 4.7.1, due to its recent release, will not have accompanying publications in
the primary literature, we will use a combination of literature reviews of modeling studies that
used previous CMAQ versions (i.e. Foley et al., 2009, Wilczak et al., 2009, Byun and Shere,
2006) and discussion with EPA ORD to determine the recommended optimal configuration for
the SEMAP project. In general, the base model configuration specifications outlined in the
SEMAP RFP will be used for the initial CMAQ simulation with the following modifications:

As of version 4.7 the RADM cloud module is no longer an option in CMAQ. We will
use the ACM cloud routine that includes extensions for the AERO5 heterogeneous
chemistry mechanism.

The module specified in the RFP, AERO4, has been replaced by AERO5 in CMAQ
version 4.7. We will use the CMAQ AERO5 module for the SEMAP project.
We will select a CMAQ configuration using all available information in the literature,
consultation with EPA, and our own experiences to provide a recommendation to the SEMAP
technical analysis working group (TAWG) along with the rationales used to recommend the
configuration. After approval by the SEMAP Project Coordinator and TAWG, we will finalize
the initial air quality model configuration and provide our insights on conducting the necessary
diagnostic model configuration tests. After completing the CMAQ simulations we will wait for
guidance from SESARM about how to proceed with the CAMx simulations. The CAMx
simulation will use a model configuration and input data that are as close to the CMAQ
simulation as possible. Table 12 shows the proposed configuration options for CMAQ and
CAMx that will produce results that are normalized as much as possible.
Table 4-1. CMAQ and CAMx configuration options
Model Parameter
Horizontal Advection
CMAQ_v4.7.1
Yamartino (hyamo)
Vertical Advection
Yamartino (vyamo)
Horizontal Diffusion
Vertical Diffusion
Multiscale
Advanced Convective Method
(ACM2)
CB05 with Chlorine
(cb05cl_ae5_aq)
Euler Backward Iterative
(ebi_cb05cl_ae5)
CMAQ 5th Generation (aero5)
ACM clouds with aero5
(cloud_acm_ae5)
none
Gas Chemistry Mechanism
Gas Chemistry Solver
Aerosol Mechanism
Clouds/Aqueous Chemistry
Plume in Grid
CAMx_v5.20
Piecewise Parabolic Method
(PPM)
Piecewise Parabolic Method
(PPM)
Implicit
Advanced Convective Method
(ACM2)
CB05 with Chlorine
(Mechanism 6)
Euler Backward Iterative
(EBI)
Two static modes (CF)
Implicit
none
We will use the “Actual 2007” emissions modeling platform (EMP) with SMOKE version
2.7beta to prepare all of the emissions data for input to CMAQ. The inventory and ancillary
emissions data used in the Actual 2007 EMP are detailed in Section 4. We will generate both the
CMAQ and CAMx emissions directly with SMOKE. The CMAQ2CAMx utility will only be
used to process certain inventory sectors and the GEOS-CHEM BCs for input to CAMx. Fire
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inventories that are processed through SMOKE do not have the typical stack parameters needed
to select the elevated versus low level sources needed to support CAMx. In the case of fire
emissions, 3-d CMAQ-ready fire emissions files will be processed with the CMAQ2CAMx
utility to split the 3-d CMAQ emissions into elevated and low level CAMx emissions files. The
vertically split fire emissions will be merged with the rest of the CAMx-ready emissions files
using SMOKE. For the rest of the emissions data, the conversion of the CMAQ emissions files to
CAMx is an incremental change that happens during the SMOKE processing sequence. Minor
speciation changes, output units, and file format conversions to produce CAMx-ready emissions
will be handled by SMOKE for all but the fire inventories. We will also use the CMAQ2CAMx
utility to convert the GEOS-CHEM BC data into the CAMx format. Because processing
emissions for CAMx with SMOKE and using the CMAQ2CAMx utility is redundant, we are
proposing only a small amount of work for the CMAQ2CAMx subtasks to support the
conversion of the BCs and a few of the inventory sectors to CAMx format. The combination of
SMOKE and CMAQ2CAMx will allow us to produce input data for both CMAQ and CAMx
that are nearly identical. Because SMOKE-generated CAMx emissions and CMAQ2CAMx
converted emissions are identical, we do not see the need for pursuing the full CMAQ2CAMxbased simulations requested in the RFP.
Efficiencies in the emissions processing will be gained by minimizing the number of modelready emissions files required to represent the temporal variability of individual sources. For
example, commercial marine emissions use flat temporal profiles across the months of the year
and days of the week. Since this configuration results in uniform emissions for every day of the
year, only a single day of processed emissions is required to represent this sector in an annual
simulation. In addition to a single representative day for the entire year, there are several other
temporal configurations for emissions, such as representative weeks (7 days/month),
representative Monday-Weekday-Saturday-Sunday (MWSS) days (4 days/month), representative
days per month (1 day/month), etc. The different temporal configurations used for simulated
emissions for this project and referenced in Table 2 are described below.

Single day – one representative day per year; applies to sources with flat monthly,
weekly, and daily temporal profiles

MWSS – Representative Monday, weekday, Saturday, Sunday per month; includes
representing holidays as Sundays

Daily – one emissions file per simulation day

Hourly – hourly inventories or hourly-meteorology-based emissions
We will conduct systematic and automated quality assurance/quality control (QA/QC) in the
emissions modeling process. The SMOKE QA sequence focuses on identifying errors in the
input data or use of the input data, such as the incorrect assignment of temporal profiles. The
SMOKE QC sequence focuses on ensuring that the emissions modeling process is well
documented, reproducible, and did not insert errors into the modeling results. Details of the
QA/QC procedures that we will use for the SEMAP project emissions processing are presented
in Section 8.2.
We will use the automated analysis approaches described in Section 6 “Air Quality Model
Evaluation Protocol” to produce all of the evaluation products needed to diagnose model biases.
The techniques that we will use to identify biases in the initial CMAQ and CAMx simulations
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are detailed in Section 6. We will target these biases in the design of the sensitivity simulations
that will be performed under SEMAP project Task 6 (Model Configuration Diagnostic
Sensitivity Modeling). All model inputs, including BCs, meteorology, and emissions and model
configuration options will be analyzed for their contribution to both model biases and errors. If
we identify potential biases that result from the meteorology inputs, we will provide our analysis
results to the SEMAP Project Coordinator to initiate dialogue with the SESARM meteorology
contractor about how the meteorology could be improved. Biases and errors that appear to be
resulting from the emissions and BCs will be addressed in the design of the sensitivity
simulations conducted under the diagnostic modeling task.
The Base2007 simulations will produce emissions and air quality model output on the SEMAP
36-km and 12-km modeling grids for CMAQ and CAMx that will be used for model
performance evaluation and in the design of sensitivity simulations for diagnosing air quality
model performance.
4.4
Diagnostic Model Sensitivities
The objective of this task is to determine the final model configuration that will be used through
the rest of this project, including the 2007 “actual” and “typical” simulations, and future year
simulations as well as emissions sensitivity modeling.
The necessary diagnostic tests will be decided according to prediction deficits revealed by the
model performance evaluation on the initial annual “actual” 2007 base year simulation. All the
tests to be conducted here will use the initial “actual” 2007 base year simulation as the base case
and will be conducted for selected month-long periods during winter and summer times. This
task may involve model modification as well as re-processing of emissions and meteorological
inputs depending on specific sensitivity tests. Both Georgia Tech and UNC have tremendous
experience with tests that require model development, meteorological modeling, and emissions
processing capabilities. Our team has proven experience in testing and applying CMAQ in
various configurations, developing new modules, and finding and fixing bugs in existing
modules of CMAQ that we will use to recommend the most effective and absolutely necessary
diagnostic tests. Following discussion with the SEMAP Project Coordinator, we will execute the
carefully selected sensitivity tests. After thorough investigations on all the tests results, we will
recommend a final optimized model configuration and/or input data improvements for the
SEMAP 2007 final simulation and the rest simulations.
The determination of the most necessary diagnostic tests will be based on our interpretation of
the model performance after evaluation of the initial annual simulation results. We will conduct
the following model configuration tests, if necessary, as suggested in the RFP and as based on
our past experiences using CMAQ to simulation in the Southeast.
4.4.1 Number of Vertical Layers (19 vs. 34)
More vertical layers in CMAQ lead to better resolution of emissions plumes and pollutant
transport and can result in better predictions at the surface. The sacrifice of using more vertical
layers is increased computational costs, both in terms of slower model speeds and a larger data
storage burden. Therefore, CMAQ applications are typically run with less than 20 layers, with
higher resolution (more layers) inside the mixed layer (0-3 km). While this type of configuration
was believed to be working well for capturing most of the emission impacts on the ground level
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concentrations, a recent SERDP project conducted by Georgia Tech to enhance the capability of
simulating prescribed forest fires, has found that increasing the number of vertical layers can
significantly improve the prediction of the air quality impacts of fire plumes. A recent modeling
study at Georgia Tech comparing CMAQ results using 34 and 80 layers showed significant
improvement going from 13 to 34 layers for the February 28, 2007 Atlanta smoke incidence.
The May 21-22, 2007 period during Georgia-Florida wildfires is an ideal case to test vertical
resolution, as well as fire emissions and vertical mixing. The fire plume, which southeasterly
winds carried to northern Alabama on May 21, entered Georgia under westerly winds on May 22
and hit Atlanta. Our first modeling attempt using our 12-km forecasting grid was not successful
due to limited vertical resolution (13 layers only). Of note, an attempt by Yang et al. (2009)
focusing on Tallahassee, FL did not track the long-range transport either, although it used 20
vertical layers. We are currently re-simulating this event with 34 layers as part of the JFSP
project. We expect to see improvement in the long-range transport of the wildfire plume due to
increased vertical resolution.
Team members Georgia Tech and UNC are experienced in applying CMAQ with different
number of vertical layers (e.g. 9, 13, 19, 21, 34 and 80 vertical layers in various past and ongoing projects). It should be noted that switching between different numbers of vertical layers is
limited by the layering in the original WRF or MM5 simulations. Supported by the layer
configuration of the SEMAP project WRF data, we will conduct a test with an increased number
of vertical layers 34 in CMAQ by reprocessing the meteorology through MCIP, reprocessing the
elevated emissions sectors through SMOKE, and requesting reprocessed GEOS-Chem IC/BC
data from the SEMAP Project Coordinator using the new layer configuration. We will focus our
typical analyses of vertical layer sensitivities on the impacts of fire and power plant plumes on
surface layer model performance.
4.4.2 MEGAN vs BEIS biogenic emissions
MEGAN and BEIS are two biogenic emissions models that predict fluxes of the VOC pollutants
that are critical to O3 and SOA formation in the atmosphere. MEGAN and BEIS use different
datasets to characterize land use and also differ in the algorithms that they use to compute
emissions. The biogenic emissions predictions from the two models are quite different and can
cause significant difference in ozone and SOA predictions in both spatial patterns and
magnitudes ((Sakulyanontvittaya, Duhl et al. 2008; Sakulyanontvittaya, Guenther et al. 2008)).
The latest versions of both MEGAN and BEIS estimate monoterpene and sesquiterpene
emissions, which are important precursors to SOA formation in the Southeast.
In this sensitivity we will prepare the 30-second resolution ESRI GRID emission factor and
landuse data to run MEGAN for the 36-km and 12-km SEMAP modeling grids. We will use a
GIS to convert version 2.1 of the MEGAN emission factors and plant functional types and
version 2.0 of the MEGAN leaf area index data to the SEMAP grids and combine these with the
SEMAP meteorology data to estimate biogenic emissions for the summer and winter sensitivity
periods. After merging the MEGAN emissions in with the anthropogenic and pyrogenic
emissions using SMOKE they will be used to simulate air quality with CMAQ for the two
sensitivity periods.
Evaluation of the BEIS and MEGAN biogenic emissions will be conducted by comparing the
CMAQ non-methane hydrocarbon (NMHC) predictions of biogenic species to ground-based
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ambient monitoring data from the Photochemical Assessment Monitoring Stations (PAMS). We
will also use the predicted formaldehyde (HCHO) from CMAQ to calculate tropospheric HCHO
columns for comparison to remote sensed HCHO data from the Ozone Monitoring Instrument
(OMI) on the Aura satellite (Stavrakou, T., et al, 2009). Along with the standard model
performance evaluation metrics for ozone and PM, we will use these additional metrics to
determine which biogenic emissions model produces the most accurate estimates of biogenic
VOC’s for the SEMAP project. Our analysis will also focus on differences in O3 and SOA
predictions between the CMAQ simulations that used biogenic emissions estimated with BEIS
and MEGAN.
4.4.3 Vertical Mixing/Mixing Heights
Model parameters that describe vertical mixing, such as the vertical diffusivity coefficient and
mixing height, are critical for accurately predicting surface pollutant concentrations in air quality
models. Knowledge of the nocturnal planet boundary layer (PBL) and of the characteristic
changes in PBL during the transition times at sunset and sunrise is limited. As a result, the
vertical mixing parameters are poorly predicted in meteorology models during these periods.
The inability of meteorology models to accurately simulate the nocturnal PBL is linked to poor
nighttime air quality model performance ((Hogrefe, Rao et al. 2001; Hogrefe, Rao et al. 2001)).
This weakens the accuracy of not only the 24-hr PM2.5 predictions but also the 8-hour ozone
predictions.
In this sensitivity we will diagnose potential vertical mixing and mixing height problems in the
initial CMAQ run. For example, the May 21-22 period with its remarkable long-range transport
of the Georgia-Florida wildfire plume will be scrutinized. We will analyze how diagnosed
problems are impacting model performance. Depending on the nature of the identified problems,
we will test a different model configuration, conduct further diagnostics, reiterate if necessary,
and recommend the most appropriate configuration for the final CMAQ simulation.
4.4.4 Updated SOA formation
It is well known that insufficient SOA formation mechanisms in CTMs contribute to
underpredictions of organic carbon (OC) ((Eder, Kang et al. 2006; Tesche, Morris et al. 2006)).
The modifications that EPA added to CMAQ version 4.7 to include SOA formation from
sesquiterpene emissions and to consider aged SOA, among other modifications, had small
impacts on SOA predictions ((Napelenok, Carlton et al. 2008; Bhave, Carlton et al. 2009)).
Recent field and laboratory studies have provided new information on SOA formation that may
be useful for enhancing CTM SOA mechanisms ((Jimenez, Canagaratna et al. 2009)). Georgia
Tech recently developed a new SOA mechanism that includes all SOA formation pathways and
also considers multi-generations of aged SOA ((Baek 2009) ). We have tested this new
mechanism using 2007 forecast simulations and found improvements in 24-hr PM2.5 and OC
predictions compared to the original forecasts that used default CMAQ SOA mechanism ((Hu,
Baek et al. 2008)). We implemented this new SOA mechanism in our forecasting system for the
Southeast starting in the 2009 O3 season. Figure 4-1 illustrates how the model performance in
predicting 24-hr PM2.5 and OC concentration was significantly improved in the Atlanta, GA area
in comparison to the forecasts for previous years ((Hu, Baek et al. 2009)).
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Figure 4-1. Modeled (red solid line) and observed (blue dots) 24-hr average organic carbon
concentrations (gm-3) at South DeKalb, GA from May 1st to July 15th, 2009.
In this sensitivity we will conduct a full comparison of CMAQ SOA predictions using the SOA
mechanism in CMAQv4.7.1 and SOA mechanism developed by Georgia Tech. We will use both
the standard observational data as well as other observed information in the Southeast that we
have access to, such as WSOC, molecular marker observations, and, if available, Aerosol Mass
Spectrometer (AMS) and C14 data.
4.4.5 Updated cloud parameters
Past applications of CMAQ in the Southeast, such as the VISTAS modeling, favored the RADM
module over the ACM module. The recent updates to ACM in CMAQ warrant testing the
impacts of the new features on model performance in the Southeast and comparison to the old
RADM cloud module. We will apply the new ACM cloud module in the initial 2007 annual
simulation and comparing the results to a sensitivity simulation with the RADM cloud module.
Georgia Tech will execute the test simulations and, if necessary, will update the CMAQ RADM
cloud module accordingly. The evaluation will focus on not only the wet deposition but also
aerosol and related gaseous model species.
In this sensitivity we will conduct the simulations for each selected diagnostic test, and as stated
above use our expertise along with the standard model performance evaluation procedures
described in section 6 “Air Quality Model Evaluation Protocol” to assess the different
configuration choices. The performance evaluation, however, will emphasize specially on the
individual subject in each test. Note that in the “Number of Vertical Layers (19 vs. 34)” test, we
will re-process the IC, BC, meteorology and emissions inputs since the change of the vertical
layers. In the “MEGAN vs BEIS biogenic emissions” test we will prepare the additional
MEGAN biogenic emissions. And in the other three tests we will integrate corresponding
modules or fixes into the standard CMAQ 4.7.1 codes. After finishing all of the tests, we will
combine the assessment information to recommend a final model configuration to be used for the
final annual 2007 and subsequent CMAQ simulations.
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4.5
Final Base Case Simulation
The objective of this task is to develop a final annual “actual” 2007 base year air quality model
simulation on 36-km and 12-km resolution modeling grids using the final recommendation on
model configurations from the previous diagnostic modeling.
After optimizing the model configuration and input data for the “Actual 2007” simulations using
the sensitivity results from the previous task, we will produce final annual SMOKE, CMAQ and
CAMx simulations of 2007 air quality. The selection of configuration options in CAMx will be
based on the final recommendation on model configurations for CMAQ and will be discussed
with SEMAP TAWG before application.
After preparing the final “Actual 2007” emissions data and GEOS-CHEM BCs for both CMAQ
and CAMx, we will run annual simulations with both models on the 36-km and 12-km SEMAP
grids and prepare performance metrics for comparing the predictions from the simulations, as
described in section 6 “Air Quality Model Evaluation Protocol”. All products will be posted to
the project web site
4.6
Typical Base Case Simulation
The objective of this task is to develop an annual “typical” 2007 base year air quality model
simulation on 36-km and 12-km resolution modeling grids using the final recommendation on
model configurations. The results from this task and future year simulations will provide air
quality simulations in support of SIPs by SEMAP states.
We will divide the modeling and analysis work on this task between Georgia Tech and UNC.
Georgia Tech will serve as the overall task lead and perform the CMAQ modeling and UNC will
conduct the SMOKE and CAMx modeling. The emissions modeling platforms (EMPs) for the
typical year emissions data prepared by UNC will be combined with the actual 2007
meteorology and BCs used in the “Final Base Case Simulation” to simulate typical year air
quality with CMAQ and CAMx. We will use the same final model configurations applied in the
“Final Base Case Simulation” task for CMAQ and CAMx to perform the annual simulations for
this task on both SEMAP modeling grids. The same SMOKE configuration, including the use of
representative days to optimize the emissions processing tasks, will be applied to the typical year
simulations. We will plan on producing daily emissions files for the meteorology-dependent and
episodic emissions sectors, such as elevated point sources and fires. We don’t anticipate
rerunning the biogenic emissions for this task.
Similar to the previous task, we do not recommend using CMAQ2CAMx to produce separate
emissions data for explicit CAMx simulations. We will use the same approach of using
CMAQ2CAMx to process the fire emissions only, otherwise producing the non-pyrogenic
CAMx emissions with SMOKE.
The typical year simulations conducted here will take advantage of the SMOKE, CMAQ, and
CAMx configurations developed under the “Diagnostic Model Sensitivities” task and applied in
the “Final Base Case Simulation” task. We are able to offer cost savings for these simulations
because they represent incremental changes to the modeling set up. By only requiring the
replacement of certain emissions sectors, the production of the typical year simulations relative
to the actual year simulation require less resources to complete, an important consideration given
the large number of model runs requested for this Task.
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All analysis products outlined in section 6 “Air Quality Model Evaluation Protocol” will be
produced for the simulations conducted here and posted to the project web site.
4.7
Future OTB and Emissions Control Scenarios
The objective of this task is to develop annual future year air quality model simulations on 36km and 12-km resolution modeling grids using the final configuration used in the base year
modeling using both CAMx and CMAQ modeling systems. The results from this task will
provide air quality simulations in support of the development of O3 and PM2.5 SIPs by SEMAP
states. We will perform the following model simulations for this task.

“2017 OTB” SMOKE, CMAQ, and CAMx

“2017 Control” SMOKE, CMAQ, and CAMx

“2020 OTB” SMOKE, CMAQ, and CAMx

“2020 Control” SMOKE, CMAQ, and CAMx
We will divide the modeling and analysis work for this task between Georgia Tech and UNC.
Georgia Tech will serve as the overall task lead and perform the CMAQ modeling and UNC will
conduct the SMOKE and CAMx modeling. The emissions modeling platforms (EMPs) for the
future year emissions data prepared by UNC will be combined with the actual 2007 meteorology
used in the “Final Base Case Simulation” to simulate future year air quality with CMAQ and
CAMx. We will use the same final model configurations applied in the “Final Base Case
Simulation” task for CMAQ and CAMx to perform the annual simulations for this task on both
SEMAP modeling grids. The same SMOKE configuration, including the use of representative
days to optimize the emissions processing tasks, will be applied to the future year simulations.
We will plan on producing daily emissions files for the meteorology-dependent and episodic
emissions sectors, such as elevated point sources and fires. We don’t anticipate rerunning the
biogenic emissions for this task.
All of the EMPs used for the future year simulations are detailed in Section 4.1. The emission
inventory contractor will keep track of the emission reductions, control factors and efficiencies
that will be applied in the emissions modeling for controls that are applied either through on-thebooks federal and state controls and any new state controls and the shutdowns of emission
sources.
Similar to the base year modeling, we do not recommend using CMAQ2CAMx to produce
separate emissions data for explicit CAMx simulations for the various future year scenarios. We
will use the same approach of using CMAQ2CAMx to process the fire emissions only, otherwise
producing the non-pyrogenic CAMx emissions with SMOKE.
4.8
Emissions Sensitivities
To provide useful information in determining the most effective control strategies for specific
non-attainment areas, a series of emission sensitivities will be performed. A CAMx PSAT
simulation will be performed using 20 categories, selected from a combination of source regions
and emissions sectors, to generate results for an annual simulation on the 36-km SEMAP
modeling grid. The PSAT categories will be selected based on discussions with the SEMAP
Project Coordinator. In addition, five brute force CMAQ simulations of emissions sensitivities
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for daily and annual PM2.5 using the 36-km SEMAP grid will be performed. For ozone, CMAQ
will be used to perform five DDM and five brute force simulations of emissions sensitivities for
8-hour maximum O3 using the 12-km SEMAP grid during the 5-month ozone season. Addition
sensitivities may be performed under the direction of the SEMAP Project Coordinator.
4.9
Interactive Database Tool
This section describes the products that will be made available through the Interactive Database
Tool (IDT).
Ambient Data
 Display 2006-08, 2007-09, and 2008-10 (when available) design values for ozone and
PM2.5 for all monitors.
o Bar Charts
o Table
 Display measured daily maximum 1-hour ozone values, daily maximum 8-hour ozone
values, daily W126 ozone values, daily PM2.5 values (total and by species), quarterly
PM2.5 values (total and by species), annual PM2.5 values (total and by species), and
visibility (total and by species) for each monitor for years 2006-2010.
o Bar Charts
o Table
Emissions Data
 Display emissions by pollutant (SO2, NOx, VOCs, NH3, OC, EC, Soils) and source
category (EGU, non-EGU point, area sources, on-road mobile, non-road mobile, and
fire). Determine for annual and ozone season (summer day).
o Spatial Plots
o Tables
Modeling Results
 Display modeled daily maximum 1-hour ozone values, daily maximum 8-hour ozone
values, daily W126 ozone values, daily PM2.5 values (total and by species), quarterly
PM2.5 values (total and by species), and visibility (total and by species) for all days in
modeled year (base year or future year).
o Spatial Plots (grid cells)
o Bar Charts (monitors)
o Table (monitors)
 Capable of showing the differences between the base year and the future year projections
on an absolute and relative (percentage) basis.
o Spatial Plots (grid cells)
o Bar Charts (monitors)
o Table (monitors)
 Display quarterly and annual RRFs for each scenario.
o Spatial Plots (grid cells)
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o Bar Charts (monitors)
o Table (monitors)
 Capable of displaying the in-state and out-of-state impacts of sensitivity (e.g. 30% NOx
reduction, IC/BC, anthropogenic/biogenic) and source apportionment runs.
o Spatial Plots (grid cells)
o Bar Charts (monitors)
o Table (monitors)
 Introduce a metric that accounts for the overall impact of emission reductions over all
grid cells over a prescribed area as opposed to a single grid cell associated with a monitor
location. (e.g., the sum over all grid cells of ozone reduction in ppb in each grid cell for a
specific scenario or sensitivity in county X).
o Spatial Plots (grid cells)
o Bar Charts (user defined area)
o Table (user defined area)
Model Performance
 Capable of displaying model performance statistics and graphics
o Spatial Plots (observations overlaid on model results)
o Scatter Plots
o Time Series Plots
o Q-Q Plots
o Stacked Bar Charts
o Bugle Plots
o Soccer Plots
o Summary Statistical Tables
General
 All of the above functions should be capable of analyzing the results by monitor, county,
nonattainment area, or other group of counties (as defined by the user).
 All of the above functions should be capable of doing sub-portions of the year (e.g., date1
to date2).
 Ability to export the data to spreadsheets for additional analysis or processing.
 Ability to save plots and charts to PC.
 Ability for user to import their own data sets into the tool.
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Section 5: Air Quality Model Evaluation Protocol
The model evaluation described in this section applies to the 2007 actual CMAQ modeling. All
products, including performance statistics and graphics, will be prepared for the 12 km domain
only.
5.1
Ambient Air Quality Data
We will collect the available surface air quality measurement data, measured atmospheric
deposition data, and satellite data for the 2007 SEMAP modeling periods and domains.
Particularly, we will collect surface measurement data of air quality from the following sources:
EPA database AIRS (including STN, PAMS, SLAMS and Air Toxics networks) and networks
CASTNet, IMPROVE and SEARCH, and will collect atmospheric deposition data from the
network NADP and NASA’s satellite air quality data from OMI and MODIS. The following
sections describe the data that we will collect for this project and the types of evaluation that can
be performed with the data.
5.1.1 AIRS
AIRS compiles and provides access to datasets from multiple national observational
networks/programs including STN, PAMS, SLAMS and Air Toxics. The STN network provides
24-hr (midnight to midnight) measurements of PM2.5 and its composition including trace metals
at every 3rd day or every 6th day. The SLAMS network provides hourly measurements of criteria
air pollutants including PM2.5, PM10, O3, SO2, NO2, CO etc. The PAMS network measures
photochemical smog related species such as O3, NO, NO2, NOy, Total NMOC, and 60 PAMS
target VOC compounds in non-attainment areas. The AIRS datasets are spatially dense in
populated areas with intensive sampling frequency in the SEMAP states and will be the
backbone for model performance evaluation and receptor modeling. In addition the monitoring
data from the Air Toxics program provides a critically important role by characterizing HAPs
concentrations to support exposure assessments, and can be a supplement for air quality model
evaluation and input for receptor modeling. The AMET evaluation tool developed at UNC can
take all AIRS datasets in their original AQS engineering format directly.
In addition, AIRS includes meteorological data at STN and PAMS sites that can be used for
quality assurance of the MCIP files and as an independent evaluation of WRF performance,
especially if the meteorological modeling contractor is not using this dataset.
5.1.2 IMPROVE
The IMPROVE network was developed to monitor visibility degradation at wildness and remote
areas in the nation. IMRPOVE includes measurements of chemical constituents including trace
metals of PM2.5 as well as related gaseous species at each site. From these measurements, light
extinction values are also calculated and provided. Note that different carbonaceous analysis
methods have been used among the major national networks with IMPROVE using TOR and
STN using TOT in measuring EC/OC.
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5.1.3 CASTNet
CASTNet was developed to monitor dry and wet acid deposition. Monitoring site locations are
predominantly rural by design to assess the relationship between regional pollution and changes
in regional patterns in deposition. CASTNet also includes measurements of rural ozone and the
chemical constituents of PM2.5.
5.1.4 NADP
NADP provides atmospheric deposition data. The weekly cumulative deposition data will be
extracted from CMAQ/CAMx outputs between the hours of data collection and assigned to the
measured species.
5.1.5 SEARCH
SEARCH provides continuous and speciated PM data in one urban and one rural location in each
one of the following four SEMAP states: Alabama, Georgia, Florida and Mississippi. In addition
to 24-hr particulate matter mass (fine and coarse) and composition (EC, OC, sulfate, nitrate,
ammonium and metals) SEARCH also measures hourly PM mass and ion components (BC, OC,
sulfate, nitrate, ammonium) and a wide range of complementary gaseous species (O3, NO, NO2,
NOy, HNO3, SO2, CO, CO2). This dataset will be extremely useful to evaluate how well the
models reproduce the diurnal variability (which is not possible with the daily averaged data),
spatial variability (urban versus rural), and if the secondary PM formation processes were
simulated correctly. We can speciate the SEARCH OC data (and OC data from ASACA sites
which are operated by Georgia Tech) into individual compounds for use as molecular markers
for periods of the February 2007 prescribed burning event and the May-June 2007 FloridaGeorgia wildfire episodes ((Yan, Zheng et al. 2008)). These additional datasets will be very
useful for evaluating the model performance in capturing the important Southeastern fire events
during 2007. In addition, the HAP and other VOC data collected at the SEARCH sites in the
Atlanta, GA metropolitan area are valuable for both model performance evaluation and receptor
modeling.
5.1.6 Satellite Data
Air quality data collected by satellites provide a powerful supplement to ground-based air quality
observations. Where ground-based observations are finite spatial measurements at the earth’s
surface, satellite data provide atmospheric measurements on spatial grids that are commensurate
with regional air quality modeling grids. Further, satellite data observe three-dimensional
columns of pollutants. Satellite air quality data can be processed to match the spatial and vertical
resolution of air quality model predictions, providing an observational dataset that is more
spatially commensurate than ground-based measurements.
We will use satellite observations of gases and particulate matter to provide supplemental
evaluation data for diagnosing CMAQ and CAMx model performance. While these data will not
be used to replace the ground-based monitoring networks, they will provide an additional
dimension of atmospheric measurements to use in the model evaluations as they observe the
entire tropospheric column of pollution. In particular satellite data will be useful in the SEMAP
project to diagnose the ability of CMAQ and CAMx to simulate the impacts of the southern
Georgia wildfire events in May-July 2007, the impacts of dust transport into the region, and
other important episodic pollution events. These data are not intended for use in the SIP process,
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they are only going to be used to lend insight into the skill of the air quality models to simulate
air quality in the Southeast.
We will acquire ambient air quality data, including satellite data from the OMI instrument on
tropospheric NO2 and formaldehyde (HCHO), and aerosol optical depth (AOD) data from the
OMI and MODIS instruments. Advanced post-processing tools developed by UNC are available
to process the satellite data products, the Level 2 swath, or gridded data (Level 2 and Level 3),
from their native lat-long grid to the Lambert conformal conic grid used by CAMx and CMAQ,
for a user-specified domain and grid resolution. The UNC satellite data toolkit also consists of an
advanced version of AMET that allows statistical comparisons of satellite data against CMAQ
for selected sub-domains of the data. CMAQ output is not directly comparable with columnintegrated trace-gas concentrations, e.g., tropospheric column measurements of NO2, and HCHO.
We will download the satellite data from the data warehouses (GIOVANNI or VIEWS as
appropriate) for the base year 2007, re-grid them to the model grid, and post-process CMAQ and
CAMx output for comparison with these data for the specified sub-yearly periods.
For detailed information, the surface ambient air quality measurement data that will be used for
evaluating model performance and input to receptor models are summarized in Table 5-1.
Table 5-1. Surface Air Quality Data available for model performance evaluation and input to
receptor models for the Southeast
Variable
PM10
PM2.5
Sulfate
Nitrate
Ammonium
EC (BC)
OC
Trace Metals in PM2.5
NO, NO2 and NOy
SO2
VOCs, HAPs
Total Nitrate (c)
Averaging time
daily, hourly
daily, hourly
daily, hourly, weekly
daily, hourly
daily, hourly, weekly
daily, hourly
daily, hourly
daily
hourly
hourly, daily, weekly
hourly
daily, weekly
Database/Networks
AIRS, IMPROVE, SEARCH
AIRS, IMPROVE, SEARCH
AIRS, IMPROVE, SEARCH, CASTNet
AIRS, IMPROVE, SEARCH
AIRS, SEARCH, CASTNet (a)
AIRS, IMPROVE, SEARCH
AIRS, IMPROVE, SEARCH
AIRS, IMPROVE, SEARCH
AIRS, SEARCH
AIRS, CASTNet, IMPROVE, SEARCH
AIRS, SEARCH (b)
CASTNet, SEARCH
(a) Ammonium data from IMPROVE are estimated from sulfate and nitrate based on complete neutralization of
those ions. Independent measurements from CASTNet are more appropriate for model evaluation purposes
(b) ARIES program measures HAPs and other VOCs at the SEARCH sites in Atlanta, GA area.
(c) CASTNet weekly measurements of PM nitrate and HNO 3 will be evaluated as total nitrate due to issues relating
to filter-based methods to collect volatile nitrate species.
As existing data, surface measurements from the above specific networks or from EPA AIRS
database are already quality-checked and well documented. Use of these existing data will be
fully documented with data’s original documentation archived. In addition we will use the
provided measuring uncertainty information to further screen the datasets and to decide
excluding particular data points. Spatial plots and time series plots will be made for visual check
of outliers that seems less reasonable. Additional documentation will be provided when
necessary to address the issue of particular data points that seem less reliable than others. Also,
descriptive statistics (mean, standard deviation, frequency distributions, etc.) will be calculated
and be used for data presentation and cross comparison and to facilitate peer review. CrossGIT/UNC/CIRA-SEMAP 004.v3
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checking among the networks will be made too if applicable for further assurance of the data
reliability.
Special cautions will be used to double-check the quality of the acquired satellite data from OMI
and MODIS instruments. The cloud cover information will be used in combination of other
information to decide uncertainty for each individual data point. This uncertainty information
will then be used for screening the final data to be used and be used to weight the reliability of
each data point. Descriptive statistics (mean, standard deviation, frequency distributions, etc.) of
the data will be also calculated and compared to the surface measurement, which is considered
more reliable. Spatial plots will be made for satellite data and further compared with surface
measurements for data screening purpose. All of the above procedures will be documented for
quality assurance.
5.2
Evaluation Tools
This section describes the software that will be used to conduct the analyses of the meteorology,
emissions, and air quality data for the SEMAP project.
5.2.1 Atmospheric Model Evaluation Tool (AMET)
The Atmospheric Model Evaluation Tool (AMET) (Appel et al., 2005) is a suite of software
designed to facilitate the analysis and evaluation of meteorological and air quality models.
AMET matches the model output for particular locations to the corresponding observed values
from one or more networks of monitors. These pairings of values (model and observation) are
then used to statistically and graphically analyze the model’s performance.
More specifically, AMET is currently designed to analyze outputs from the PSU/NCAR
Mesoscale Model (MM5), WRF, CMAQ, and MCIP-postprocessed meteorological data (surface
only). The basic structure of AMET consists of two fields and two processes.

The two fields (scientific topics) are MET and AQ, corresponding to meteorology and air
quality data.

The two processes (actions) are database population and analysis. Database population
refers to the underlying structure of AMET; after the observations and model data are
paired in space and time, the pairs are inserted into a database (MySQL). Analysis refers
to the statistical evaluation of these pairings and their subsequent plotting.
Practically, a user may be interested in using only one of the fields (either MET or AQ), or
maybe interested in using both fields. That decision is based on the scope of the study. The three
main software components of AMET are MySQL (an open-source database software system), R
(a free software environment for statistical computing and graphics), and perl (an open-source
cross-platform programming language).
5.2.2 Package for Analysis and Visualization of Environmental (PAVE) data
PAVE is a flexible open source application to visualize multivariate gridded environmental
datasets. It will be used for the SEMAP project to create 2-d spatial plots of meteorology,
emissions, and air quality data.
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5.3
Evaluation Procedures
5.3.1 Ozone and precursors (AQS and PAMS).
5.3.1.1 Data Specifications.
For each SEMAP and non-SEMAP state, we will create a table of statistics including mean
normalized bias, mean normalized gross error, mean observation, mean prediction, ratio of
predicted to observed means, mean bias, normalized mean bias, mean fractional bias, mean error,
normalized mean error, mean fractional error, and correlation coefficient. All statistics will
focus on the grid cell of the monitor, not the array of grid cells near the monitor. Separate
summary tables will be created for 1-hour, 8-hour, 1-hour maximum, and 8-hour maximum
ozone values using (1) no threshold and (2) a threshold of ≥60 ppb (observed value). In addition,
separate summary tables will be created for daily ozone W126 and seasonal ozone W126. By
state, monthly values will be calculated for all sites/all days, all sites/one day, and all days/one
site.
Depending on the availability of observations, these same statistics will be calculated for hourly
(1-hour) ozone precursors and related gas-phase oxidants (e.g., CO, HNO3, H2O2, NOx, NOy,
VOCs, and VOC species).
The individual hourly and daily measured/modeled values used to perform these calculations will
be delivered for all states in the 12 km domain in a separate text file for each state. States can
then calculate additional statistics appropriate to their nonattainment areas.
5.3.1.2 Approach
For each state, we will create monthly scatter plots of observed versus predicted values for 1hour ozone/8-hour maximum ozone values (all values, no threshold) and daily ozone W126 for
March 1 through October 31 for all sites/all days, all sites/one day, and all days/one site.
For each site, we will create monthly time series plots of observed versus predicted values for 1hour ozone/8-hour maximum ozone values (no threshold) and daily ozone W126 for March1 to
October 31 for all days/one site. We will address whether the y-axis scale can be specific to each
site so that observed values cover the full range of the axis even if some outlier data points are
lost.
For each state, we will create monthly Q-Q plots of observed versus predicted values for 1-hour
ozone/8-hour maximum ozone values (no threshold) and daily ozone W126 for March 1 to
October 31 for all sites/all days.

For each state, we will create scatter plots, time series, and Q-Q plots as described above
for available hourly (1-hour) ozone precursors and related gas-phase oxidants from AQS
and PAMS sites (e.g., CO, HNO3, H2O2, NOx, NOy, VOCs, and VOC species).

Daily and monthly average tile plots of monitored and modeled 8-hour maximum ozone
values for March 1 through October 31.
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5.3.1.3 Diagnostic Evaluation
As data are available (from PAMS and possibly SEARCH sites for continuous NOy, VOCs, etc.),
we will develop ratios of indicator species to determine if ozone nonattainment areas are NOx- or
VOC-limited. For each site, we will provide monthly scatter plots and monthly time series plots
for all days. Plots will be accompanied by an interpretation of results (e.g., does the data suggest
that areas are NOx- or VOC-limited or transitional?).
5.3.2 Fine Particulate Matter (FRM, STN, CASTNET)
For FRM and STN sites, we will provide statistics for each state. For IMPROVE sites, we will
provide statistics for all monitors in the 12 km domain, and for three subcategories: coastal
states, interior SEMAP states, and neighboring SEMAP states. For the CASTNET, NADP (wet
deposition), and SEARCH networks, we will provide statistics for all monitors collectively in
the 12 km domain.
5.3.2.1 Approach
We will create monthly tables of statistics including mean normalized bias, mean normalized
gross error, mean observation, mean prediction, ratio of predicted to observed means, mean bias,
normalized mean bias, mean fractional bias, mean error, normalized mean error, mean fractional
error, correlation coefficient. We will present separate tables for PM2.5 and speciated
components of PM by network and month. The values will be calculated for all sites/all days, all
sites/one day, and all days/one site where “all sites” are defined above for each monitoring
network.
The individual hourly and daily measured/modeled values used to perform these calculations will
be delivered for all states in the 12 km domain in a separate text file for each state. States can
then calculate additional statistics appropriate to their nonattainment areas.
We will create the following plots:




monthly scatter plots of observed versus modeled PM2.5 and speciated components of
PM2.5, for all sites/all days, all sites/one day, and all days/one site, by state for the FRM
and STN networks, and for the 12 km domain for other networks.
annual time series plots for PM2.5 and speciated components of PM, for all days/one site.
monthly Q-Q plots of observed versus predicted PM2.5 and speciated components of PM
for all sites/all days, by state for the FRM and STN networks, and for 12 km domain for
other networks.
tile plots of monitored and modeled PM2.5 and speciated components of PM, on a daily
basis for the FRM, IMPROVE, and STN networks, and on a monthly basis for all
networks separately.
5.3.2.2 Soccer and Bugle Plots.
We will create the following plots and post to the project website:
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




For all ten SEMAP states together, soccer plots by speciated components of PM2.5 and
total PM2.5 for all monitoring networks (CASTNET, IMPROVE, NADP, SEARCH, and
STN with, each network having a distinct color or shape), by month with 3 months per
plot.
For each of the ten SEMAP states, soccer plots for PM2.5 mass for FRM monitors, by
month, with all 12 months per plot.
For all ten SEMAP states together, bugle plots by speciated components of PM2.5 and
total PM2.5 including all monitoring networks, by month with 12 months per plot.
For all ten SEMAP states together, bugle plots for all speciated components of PM2.5 on
one plot, with separate plots for the IMPROVE network and STN network.
For each of the ten SEMAP states, bugle plots for PM2.5 mass for FRM monitors, by
month with 12 months per plot.
5.3.2.3 STN and IMPROVE Data.
For each STN and IMPROVE site, we will create stacked bar charts of observed versus modeled
speciated components of PM2.5 for each monitored day in a month, separate plots for each
month.
5.3.2.4 Diagnostic Evaluation
EPA Modeling Guidance recommends evaluating “indicator species ratios” to determine if
NH4NO3 formation is limited by NH3 or NO3. Because CASTNET data is reliable for total N,
but not components of particulate NO3 versus HNO3, and because NH3 measurements are not
available in 2007 (with the exception of SEARCH sites), this analysis may not be feasible.
Additional alternatives for this evaluation could be sought.
5.3.3 Regional Haze (IMPROVE).
For the 12 km domain (21 IMPROVE monitors in SEMAP and neighboring states), we will use
the new IMPROVE equation with monthly f(RH) values for both observed and modeled data.
5.3.3.1 Table of Statistics.
We will create a table of statistics including mean normalized bias, mean normalized gross error,
mean observation, mean prediction, ratio of predicted to observed means, mean bias, normalized
mean bias, mean fractional bias, mean error, normalized mean error, mean fractional error, and
correlation coefficient. We will present separate tables for total bext and speciated components
of bext for 20% worst days/one site and 20% best days/one site.
5.3.3.2 IMPROVE Monitor Data.
The individual daily values used to perform these calculations will be included in a single text
file for all IMPROVE monitors. We will complete the following tasks:

Create scatter plots of observed vs. predicted total bext and speciated components of bext
for 20%W days/one site and 20%B days/one site.
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


Create bugle plots for total bext and speciated components of bext, using the average of
all IMPROVE sites in the SEMAP 12 km domain and monthly average values (12 month
averages for all sites by 6 components). See reference to Boylan and Russell 2006,
Atmos. Environ.
Create bugle plots for total bext and speciated components of bext, using the IMPROVE
sites in the SEMAP 12 km domain and site average values for the 20% worst days by
component. (21 site averages for all months by 6 components).
Create bugle plots for total bext and speciated components of bext, using IMPROVE sites
in the SEMAP 12 km domain and site averages for the 20% best days by component. (21
site averages for all months by 6 components).
For the individual IMPROVE sites in the SEMAP states and for 6 neighboring IMPROVE sites,
we will create:

For each of the 20% worst and 20% best days in 2007 (defined by the new IMPROVE
equation), a stacked bar chart for speciated components of PM2.5 (including sea salt) plus
coarse mass for observed versus modeled 2007 actual results versus modeled 2007
typical results.
5.3.4 Model Performance Goals.
While there are no bright line statistical measures to determine if model performance is
acceptable for regulatory application, SEMAP will compare ozone and PM2.5 modeled
statistics to some commonly used evaluation criteria.
For ozone model performance, the traditional ≤ ±15% and ≤ 35% performance goals (Tesche,
1990) for mean normalized bias (MNB) and mean normalized gross error (MNGE) using
hourly predicted and observed ozone pairs with the observed ozone value greater than 60 ppb
will be used. For PM2.5, performance statistics of <±30% mean fraction bias and <50%
mean fraction error indicate good performance, while performance statistics of <±60% mean
fraction bias and <75% mean fraction error indicate average performance (Boylan, 2006).
Recommended performance goals and criteria will be used to help identify areas that can be
improved upon in future modeling. Exceeding these levels of performance may indicate
fundamental concerns with the modeling system. Extended diagnostic evaluation and
sensitivity tests should be performed to address extremely poor performance.
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Section 6: Supplemental Analyses
6.1
Future Attainment Demonstration Analysis Protocol
The overall goal of this task is to analyze future year model outputs to compute future year
design values to demonstrate modeled attainment of the NAAQS. We will rely on guidance
provided by the U.S. EPA (U.S. EPA, 2007) for demonstrating modeled attainment of air quality
goals for O3, PM2.5 and regional haze to analyze the CMAQ and CAMx outputs from the future
year simulations, where the emphasis is on using model outputs in a relative sense. In this
guidance, the attainment test methodology uses model outputs and ambient data to estimate
future year concentrations. Specifically,
1. Relative Reduction Factor (RRF) = Model predicted change (as a ratio) from base year to
future year
2. Future Year Design Value (DVF) = Base Year Design Value (DVC) X RRF
This procedure is fairly straightforward for O3, since there is only one component, and no
speciation is involved. However, for PM2.5 and the regional haze reasonable progress, the
attainment test needs to use all the PM2.5 species, where the RRFs need to be calculated for each
individual PM2.5 speciated component, and total PM2.5 is reconstructed from the sum of all PM2.5
components. This procedure called the Speciated Modeled Attainment Test (SMAT) can be
directly applied where speciated PM2.5 information is available. Since FRM monitors are the
only ones that can be used to demonstrate attainment, the SMAT process can be applied
relatively easily if the FRM monitors have collocated STN monitors. However, speciated
information is not available at most FRM locations. Further, the measurements collected at the
STN and IMPROVE monitors are not directly comparable to FRM measurements. To give a
rough idea of this discrepancy, there are ~1200 FRM monitors in the country, but only ~250
STN and ~165 IMPROVE monitors, and over 75% of the FRM monitors do not have a
collocated STN monitor. Figure 6-1 shows a map of the U.S. with the locations of the various
monitors.
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Figure 6-1. Location of various PM2.5 monitors in the U.S. for SMAT.
While there is a need to perform interpolation of the STN data to the FRM locations, there are
issues with using speciated data from monitoring networks due to issues with sampling
methodologies. For example, sampling filters do not retain portions of volatile compounds, such
as ammonium nitrate, and therefore lead to negative sampling artifacts. To alleviate this and
other issues, SMAT uses the Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous
Mass and estimated aerosol acidity (H+) (SANDWICH) approach developed by Frank (2006).
The speciation of SO4, NO3, EC, crustal material and salt are relatively straight forward and the
information from the speciated monitors (with the exception of an adjustment for nitrate) are
used. For nitrate, the speciated monitor input data is adjusted to account for volatilization.
Because volatility is also an issue for monitor measurements for ammonium, ammonium is
interpolated using the Degree of Neutralization (DON). DON is defined as DON = NH4,so4 /
SO4, where NH4,so4 is ammonium associated with sulfate and SO4 is sulfate.
To help with the SANDWICH application and to perform the interpolation of STN data to the
FRM locations, we will use the latest version of the U.S. EPA Modeled Attainment Test
Software (MATS) (Abt Associates, 2009) with the post-processed CMAQ outputs. The
interpolation is done using the Voronoi Neighborhood Averaging (VNA) scheme, which is also
used in the BenMAP modeling system used for performing health and economic benefits
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analyses of the air quality modeling. We will use all FRM monitors with valid data in the
modeling domains. We will also repeat these calculations using Excel and compare the results.
For each future year modeling scenario (at the two model grid resolutions of 36k and 12k, and
for each of CAMx and CMAQ), we will analyze the model outputs using MATS and create
various analyses products. We will develop routines to develop visualization outputs of the RRFs
and DVFs for O3, PM2.5 and the glide slopes for demonstrating rate of progress for addressing
regional haze goals using the best 20% and worst 20% visibility days. The output products for
this task are given below.





Tile-plots showing daily, monthly, quarterly and annual average changes in key pollutant
species (including daily maximum 8h O3, daily average PM2.5 and the speciated
components)
Tables of Relative Reduction Factors (RRFs) and Future Year Design Values (DVFs) for
8hO3, PM2.5 for all sites in the SEMAP states
Graphics showing progress towards goals for regional haze
Spatial fields of RRFs and DVFs (which will address the unmonitored area analyses
using the gradient adjusted option in SMAT.
Comparison of DVFs between 36k and 12k model outputs, and between CMAQ and
CAMx.
We will post all analyses products on the interactive website that will be developed for this
project. We will enable the interface to be queried from the web based upon a specific state or
monitor that may be of interest to the SESARM team.
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Section 7: Data Backup and Archival Protocol
7.1
Georgia Tech
We pack our data (typically emissions are zipped, meteorology and air quality data are not) in 1
TB external hard drives and shelve them in a locked faculty office of the Ford Environmental
Science and Technology (ES&T) Building in steel bookcases. Each drive is labeled properly and
the list of contents is inserted in the package. Electronic copies of the contents are saved on
regularly backed-up drives. External drives containing a second copy of critical data (e.g.,
irreproducible or costly to reproduce data) are stored in steel drawers in a separate locked office
on a different wing of the same building. In case of fire, this wing is separated from the wing
where the first office is located.
7.2
UNC
At the end of each model simulation (MCIP, SMOKE and CAMx), we will archive all model
inputs and outputs on the UNC ITS mass storage system and retrieve as necessary later. Every
file that is copied to UNC’s MS is copied onto 2 tapes, and one tape is stored off site in a remote
location for redundancy. Thus, in the event of one tape failing, there is always a backup option
for accurate and timely retrieval of the data. All files copied to MS are also automatically
compressed during the archival process. The copy process from the compute servers’ scratch
disks to MS and back to the disks is done seamlessly with a simple UNIX ‘cp’ command, thus
making the data available in near real-time during the entire process.
At the end of SMOKE and MCIP processing, we will also make a copy of the outputs for CMAQ
onto external hard drives, make a list of contents on the drive, label them accordingly and ship to
GIT for CMAQ modeling. We will maintain a catalog of all data drives that will be created along
with their contents on the project website for easy tracking and retrieval if necessary.
At the end of each AQ simulation, we will run a series of post-processors to create layer one
extractions along with aggregate metrics such as daily average, daily maxima, and these postprocessed AQ outputs will also be copied to an external hard drive and shared with GIT, for
model inter-comparisons and QA.
We will also create an archive of all model code and run scripts used in the modeling along with
all logs that will be generated during the modeling. We will set up an automated way of rsyncing the run directories periodically with the mass storage system for continued archival of the
code and scripts.
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Section 8: Computer Resources
Since the modeling activities are shared by both UNC and GIT, we describe below the specific
resources that are available at each of these 2 institutions.
8.1
Georgia Tech Computing Resources
The computational resources of LAMDA consist of one 8-node cluster (each node has dual 32bit 2.8 GHz Intel Xeon processors with 4 Gigabytes memory); one SunFire X4100 server (dual
dual-core 64-bit AMD Opteron processors with 16 Gigabytes memory); two SunFire X4150
servers (each has dual quad-core 64-bit Intel Xeon X5450 processors with 16 Gbytes memory);
one Penguin Computing Altus 1704 server (quad AMD Opteron 8431 6-core processors with 32
Gbytes memory); fifteen 2.2 to 3.0 GHz dual-processor Linux workstations (each has 2G-4G
memory); and three Sun UltraSPARC-III Unix workstations. The total disk space on these
systems is approximately 5 Tera-bytes. A separate data storage facility, consisting of six RAID
systems, has the capacity of holding over 20 Terabytes of data. All these systems are supported
and secured by the Information Systems Group (ISG), an independent unit of the School of Civil
and Environmental Engineering at Georgia Tech.
LAMDA is housed in the Environmental Science and Technology Building, a 287,000-square
foot facility devoted only to environmental research. This 5-year old facility brings together,
under one roof, researchers from the School of Earth and Atmospheric Sciences, the School of
Chemical Engineering, and programs in Environmental Biology, Environmental Chemistry, and
Environmental Engineering. The building is located in the main Georgia Tech campus within
easy access of other facilities. The ES&T Building is equipped with 1-Gbps Ethernet which
forms the backbone of LAMDA’s computational network. Georgia Tech is connected to the
outside world through the Internet 2 (I2) network.
The following server was ordered, in part using SERDP funds ($6,000) to be used for SEMAP
modeling.
[1] Relion 2712 Server
Qty: 1
Unit Price: $8,255.06
Ext. Price: $8,255.06
Standard Features:
- One or Two Intel Xeon 5500 Series ('Nehalem') Processors
- QuickPath Interconnect, up to 6.4GT/s (GigaTransfers per second)
- Up to 96GB of DDR3 RAM
- Up to 12 Hot Swap 3.5" SATA or SAS Hard Drives, optional hardware RAID
- Dual Integrated 10/100/Gigabit Ethernet, Dedicated 10/100 Management
NIC
- Integrated IPMI 2.0 Management Processor
- Redundant 2 x 720W Redundant Power Supplies
Selected Features:
- Relion 2712, Base System
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- Dual Intel Xeon E5520 Quad Core 2.26GHz 8MB max RAM speed 1066MHz
- 6GB, DDR3-1333 ECC (3 x 2GB)
- Hardware RAID 0/1/5/6/10/50: Adaptec 5445 w/ 512MB cache and BBU
- RAID 6 Volume: 17166 GB (11+1 x 2TB SATA2)
- Preload, CentOS, Version 5
- Standard 3-Year Warranty
8.2
UNC Computing Resources
In addition to each UNC staff member having his or her own desktop workstation, UNC staff
members have access to very high-end computing resources to support a project of this scope.
The most important of these is access at no cost on a first-come first-served basis to UNC’s
Information Technology Services (ITS) resources for research computing services. This includes
an Applications Support Group (ASG) with excellent support for user’s problems and helping
with benchmarking, optimizing models on different platforms. However, for some of the work
that needs to be performed for the SEMAP project, we have budgeted to purchase dedicated
resources (computer blades and scratch disk space to add to the computing cluster, tapes for mass
storage, and disk space for dedicated web server) as necessary to ensure timely project
deliverables.
The most relevant computing resources that ITS maintains and supports, and that are available
for this project are:

Dell Cluster (topsail) with 4,168 CPUs

o Login node : 8 CPUs @ 2.3 GHz Intel EM64T with 2x4M L2 cache (Model
E5345/Clovertown), 12 GB memory
o Compute nodes : 4,160 CPUs @ 2.3 GHz Intel EM64T with 2x4M L2 cache
(Model E5345/Clovertown), 12 GB memory
o Shared Disk : (/ifs1) 39 Terabyte IBRIX Parallel File System
o Interconnect: Infiniband 4x SDR
o Resource management is handled by LSF v.6.2 for serial and parallel batch jobs
Linux Beowulf Cluster (Emerald) with a total of 340 processors
o
o
o
o
o
o
o
o
o
o
o
2 Login Nodes and 168 Compute Nodes
18 Dual-CPU AMD Athlon 1600+ 1.4 GHZ MP nodes (2.0GB memory)
6 Dual-CPU AMD Athlon 1800+ 1.6 GHz MP nodes (2.0 GB memory)
25 Blade Centers, each having Dual-CPU Xeon 2.4 GHz/2.5GB MP nodes
96 Blade Centers, each having Dual-CPU Xeon 2.8 GHz/2.5GB MP nodes
15 Blade Centers, each having Dual-CPU Xeon 3.2 GHz/4.0GB MP nodes
8 Blade Centers, each having Dual-Core Xeon 2.0 GHz/4.0GB MP nodes
2GB DDR RAM on each node (total of 352 GB memory)
20GB EIDE Hard Drive for OS
Myrinet Fiber PCI Adapter with 2MB, Myrinet Switch with 64 port enclosure
Each master node installed with RAID 5 SCSI Controller connected to 3 76GB
hard drives for scratch
o Supports Load Sharing Facility (LSF) for serial and parallel batch jobs
o Myrinet Switch with 64 port enclosure
o OS: Redhat Enterprise Linux (RHEL 3) with XFS filesystem
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
o 20.0 TeraByte RAID array for scratch space
Linux Beowulf Cluster (kure) with a total of 480 processors

o 2 Login nodes and 62 Compute Nodes
o 60 HP Quad-core blade servers each with 64 MB memory
o 2 HP Quad-core blade servers each with 192 MB memory
o 34.0 TeraByte RAID array for scratch space (Fiber Channel)
o 40. 0 TeraByte RAID array for scratch space (SATA disks)
o Additional 25.0 TeraByte space on GPFS
Mass Storage silo

o 175.0 Terabyte capacity using a SAM-FS file system
o Provides near-line access for archived data
o Access speeds from 19 to 30 MB/sec (much faster than LTOs or DLTs)
o StorageTek Powderhorn 9840 tape cartridges of 20GB capacity
o Dual backup of all data (redundancy in storage helps in data recovery)
Software Resources
o Portland Group Fortran Compilers
o Intel Fortran Suite of Compilers
o Complete Installation of ArcGIS, MATLAB, SPlus, SAS, etc.
Additional details of UNC’s ITS hardware and software resources can be found at:
http://its.unc.edu/Research/hardware/index.htm.
Given the available resources above, we will perform all SMOKE and MCIP runs on the
Emerald cluster, and the CAMx runs and all post-processing on the new Kure cluster at UNC. At
the time of release of this protocol, the scratch disks on the two clusters are being reconfigured,
and will all share the same disks facilitating easy access of the SMOKE outputs for the CMAQ
simulations.
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Section 9: Project Team Roles and
Responsibilities
Task 1. Project Management
Task Leader: T. Odman (Georgia Tech)
Other staff: Z. Adelman, S. Arunachalam, J. Eichinger, A. Hanna
Task 2. Modeling Protocol
Task Leader: S. Arunachalam (UNC)
Other staff: Z. Adelman, J. Eichinger, A. Hanna, T. Odman
Task 3. Data Acquisition/Dataset Preparation for Modeling and Evaluation
 Emissions
Task Leader: Z. Adelman (UNC)
Other staff: B. Baek, T. Odman, Research Assistant
 Meteorology
Task Leader: A. Xiu (UNC)
Other staff: K. Talgo
 Initial and Boundary Conditions
Task Leader: S. Arunachalam (UNC)
Other staff: E. Adams
 Ambient Air Quality Data
Task Leader: Y. Hu (Georgia Tech)
Other staff: N. Davis, T. Odman, U. Shankar, A. Valencia
Task 4. Conceptual Description
Task Leader: T. Odman (Georgia Tech)
Other staff: Research Assistant
Task 5. 36 km/12 km Emissions/AQ Annual Actual Base Year Simulations
Task Leader: T. Odman (Georgia Tech)
Other staff: E. Adams, Z. Adelman, S. Arunachalam, B. Baek, Y. Hu, K. Talgo, A. Valencia,
Research Assistant
Task 6. Model Configuration Diagnostic Sensitivity Modeling
Task Leader: T. Odman (Georgia Tech)
Other staff: E. Adams, Z. Adelman, B. Baek, Y. Hu, B. Naess
GIT/UNC/CIRA-SEMAP 004.v3
9-1
April 29, 2011
SEMAP Modeling Protocol
Task 7. Emissions and Air Quality Benchmark Simulations
Task Leader: S. Arunachalam (UNC)
Other staff: B. Baek, K. Talgo, A. Zubrow
Task 8. 36 km/12 km Annual Typical/Future Year Emissions AQ Modeling
Task Leader: T. Odman (Georgia Tech)
Other staff: E. Adams, Z. Adelman, S. Arunachalam, B. Baek, Y. Hu, K. Talgo, A. Valencia,
Research Assistant
Task 9. Emission Sensitivity Modeling
Task Leader: T. Odman (Georgia Tech)
Other staff: Z. Adelman, S. Arunachalam, B. Baek, Y. Hu, A. Valencia, Research Assistant
Task 10. Model Performance Evaluation
Task Leaders: T. Odman (Georgia Tech) and S. Arunachalam (UNC)
Other staff: N. Davis, U. Shankar, A. Valencia, Research Assistant
Task 11. Future Year Model Projections
Task Leader: S. Arunachalam (UNC)
Other staff: N. Davis, T. Odman, A Valencia
Task 12. Interactive Database Tool
Task Leader: S. McClure (CIRA)
Other staff: N. Davis, D. Del Vecchio, J. Huddleston, T. Odman, R. Ross, A. Zubrow
Task 13. Receptor Modeling
Task Leader: T. Russell (Georgia Tech)
Other staff: Y. Hu, T. Odman, Research Assistant
Task 14. Development of Technical Web Site
Task Leaders: S. McClure (CIRA) and Z. Adelman (UNC)
Other staff: J. Huddleston, B. Naess, T. Odman
Task 15. Other Tasks as Assigned
Task Leader: T. Odman (Georgia Tech)
Other staff: TBD, based on types of tasks assigned by SESARM
Task 16. Reporting
Task Leader: J. Eichinger (UNC)
Other staff: Z. Adelman, S. Arunachalam, Y. Hu, T. Odman, T. Russell, U. Shankar, A. Zubrow
GIT/UNC/CIRA-SEMAP 004.v3
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April 29, 2011
SEMAP Modeling Protocol
Section 10: Documentation
This project will generate the following reports:

Draft project work plan to SESARM

Final project work plan that incorporates SESARM Contract Officer’s comments and
follows review of the draft project work plan by SESARM and its participating agencies

Draft QAPP to SESARM

Final QAPP that incorporates SESARM Contract Officer’s comments and follows review
of the draft QAPP by SESARM and its participating agencies

An initial version of the emissions and air quality modeling protocol.

An interim report with a conceptual description of the O3, PM2.5, and regional haze
problem in the SEMAP region will be prepared according to EPA guidance and
submitted to the SEMAP TAWG, then finalized in response to their comments

Document summarizing the technical transfer protocol and all model utilities and scripts
used by the Project Team for the modeling tasks

Results of all standard analyses specified in Attachment A of the RFP for (a) O3 and
precursors; (b) fine PM, coarse PM, and constituent mass concentrations; and (c) regional
haze metrics, as well as other analyses performed

Monthly project reports summarizing the work performed on each task, progress on the
deliverables, and expected progress during the next month

Interim task reports documenting each major task milestone

Draft of the final project report

Fifteen hard copies of the final project report and a Microsoft Word or PDF version of the
report posted to the project website
Reports on tasks that use or develop specific data sets will include descriptions of data source(s)
and methodologies used in their preparations and any information that might be critical to their
use in the project (for examples, data gaps or errors). If possible, the documentation of the data
will also include references (reports or publications) that may have further information on the
data validity and usability.
Copies of all project reports (including monthly reports) will be retained in electronic format
through the duration of the project and sent to SESARM for archival as they are generated. The
three universities participating in this project have access to advanced computer backup systems.
For example, UNC utilizes a 175-terabyte mass-storage silo for data backup and near-line access
of archived data. The availability of such a system is critical to keeping large volumes of data
available in near real-time during the progress of the project. SESARM stores both financial and
programmatic files for the appropriate length of time as determined in SESARM's Federal
Assistance Agreements.
10-1
SEMAP Modeling Protocol
Appendix A. SEMAP Emissions QA Worksheets
These four worksheets are filled out as part of Level 1 of the QA documentation process (see
Section 5.1.2.2.3).
Table A-1 is the SMOKE configuration settings worksheet. All configuration options for each
SMOKE program are contained in Table A-1. Fill in the configuration settings for each source
category in the appropriate columns in the worksheet. Before SMOKE is run for the first time,
these configuration settings must be confirmed by the project manager. Additional details about
completing and archiving Table A-1 are provided in the body of the protocol in Section
5.1.2.2.3.1.
Table A-2 is the inventory file recording worksheet. Used for recording SMOKE inventory files,
Table A-2 requires the creation/receipt date, data source, file name, number of records, brief file
description, and a listing of the variables/file contents for all inventory files used. Additional
details about completing and archiving Table A-2 are provided in the body of the protocol in
section 5.1.2.2.6.1.
Table A-3 is the ancillary input file recording worksheet. Used for recording SMOKE ancillary
input files, Table A-3 requires the file name, the SMOKE logical name, the creation/receipt date,
data source, and a brief description of the file contents. Additional details about completing and
archiving Table A-3 are provided in the body of the protocol in section 5.1.2.2.6.1.
Table A-4 is the binary meteorology input and SMOKE output file recording worksheet. This
worksheet requires the file type, creation date and source, file name, location on the RMC file
servers, file ID, and any comments about the file. Additional details about completing and
archiving Table A-4 are provided in the body of the protocol in section 5.1.2.2.6.1.
Each worksheet is to be completed in full electronically and then printed for obtaining the
various signatures. The completed soft copies of these worksheets will be kept in the centralized
emissions QA documentation directory on the RMC compute servers. The completed and signed
hard copies will be archived by the QA manager to include in the project final report.
A-1
SEMAP Modeling Protocol
Table A-1. Configuration settings worksheet for SMOKEv2.7b
SMOKE Simulation ID:
Checked?*
Environment variable
Description
Setting to use
Comments/exceptions
Time-independent programs
RUN_SMKINVEN
Run inventory import program
RUN_SPCMAT
Run speciation matrix program
RUN_GRDMAT
Run gridding matrix program
RUN_CNTLMAT
Run control matrix program
Area and point only
RUN_ELEVPOINT
Run elevated/PinG sources selection
Point only
RUN_MBSETUP
Run on-road mobile setup for MOBILE6
On-road only
RUN_NORMBEIS3
Runs normalized emissions program
Biogenic only
RUN_MET4MOVES
Run temperature preprocessing for MOVES
On-road only
RUN_PREMOBL
Run temperature preprocessing
On-road only
RUN_EMISFAC
Run MOBILE6 emission factor generation
On-road only
RUN_LAYPOINT
Run layer fractions program
Point only
RUN_TEMPORAL
Run temporal allocation program
RUN_TMPBEIS3
Runs temporal adjustments and speciation
RUN_MOVESMRG
Run MOVES merging program
RUN_SMKMERGE
Run merging program
Time-dependent programs
Biogenic only
Quality assurance
RUN_SMKREPORT
Run for quality assurance reports
QA_TYPE
Produces particular reports
Will run this several times to get
different QA reports
all
Will also probably use custom as
well
Program-specific controls
For Smkinven
FILL_ANN_WSEAS
Fill annual emissions based on seasonal values
Setting
HOURLY_TO_DAILY
Y reads daily total only from hourly file
Point only
HOURLY_TO_PROFILE
Y converts hourly data to source-specific profiles
Point only
A-2
A
F
RD
NR
M
P
B
SEMAP Modeling Protocol
SMOKE Simulation ID:
Checked?*
Environment variable
Description
Setting to use
Comments/exceptions
IMPORT_AVEINV_YN
Y for importing annual-average inventories
SMK_ARTOPT_YN
Y uses area-to-point conversion
IMPORT_GRDIOAPI_YN
Y for imported pregridded emissions in I/O API
format
IMPORT_VMTMIX_YN
Y imports VMT mix – EMS-95 inputs
Mobile only
SMK_VMTMIX_FIXFMT
Y uses fixed-format VMT Mix file
Mobile only
RAW_DUP_CHECK
Y for errors on duplicate records
SMK_NHAPEXCLUDE_YN
Y uses NonHAP exclusions file
SMKINVEN_FORMULA
Formula for calculating an additional pollutant
SMK_BASYR_OVERRIDE
Override value for base year of all inventories
WKDAY_NORMALIZE
Y normalizes weekly profiles for weekdays
WEST_HSPHERE
Y converts all stack coordinates to western
hemisphere
0=Laypoint sets elev srcs;
1=use PELVCONFIG
UNIFORM_STIME
-1 or HHMMSS for uniform start hour for daily
emissions days
Setting
For Cntlmat
Set to VOC or ROG
For Temporal
UNIFORM_TPROF_YN
Y makes all temporal profiles uniform
ZONE4WM
Y uses time zones for start of day/month
This assumes that area- and pointsource input formats will be provided
as average-weekday emissions and
VMT will be provided as averageday VMT. This should be confirmed.
Point only
POLLUTANT_CONVERSION Y uses ROG-to-TOG file
Y renormalizes temporal profiles
M
P
N
N
N
N
0
Default surrogate code
RENORM_TPROF
NR
PMC=PM10-PM2_5 Not used for mobile VMT
For Spcmat
REACTIVITY_POL
RD
Point only
For Grdmat
SMK_DEFAULT_SRGID
F
Setting
For Elevpoint
SMK_ELEV_METHOD
A
A-3
N
B
SEMAP Modeling Protocol
SMOKE Simulation ID:
Checked?*
Environment variable
Description
Setting to use
Comments/exceptions
For Tmpbeis3
BG_CLOUD_TYPE
Method used to calculate PAR
Biogenic only
BIOG_SPRO
Speciation profile code to use for biogenics
Biogenic only
BIOMET_SAME
Y uses temperature and rad/cld data in same file
Biogenic only
BIOSW_YN
Y for using seasons file in Tmpbeis3
Biogenic only
OUTZONE
Output time zone
Biogenic only
RAD_VAR
Name of radiation/cloud variable
Biogenic only
TMPR_VAR
Name of temperature variable
Biogenic only
PRES_VAR
Name of surface pressure variable
Biogenic only
REP_LAYER_MAX
Layer number for reporting high plume rise
Point only
SMK_SPECELEV_YN
Y uses Elevpoint output file to set elevated
sources
Point only
HOUR_PLUMEDATA_YN
PHOUR file contains hourly plume data
For Laypoint
For Met4moves
EPI_STDATE
Define start dates of MOVES lookup table
processing
MOVES only
EPI_ENDATE
Define end dates of MOVES lookup table
processing
MOVES only
TVARNAME
Define the name of temperature variable
MOVES only
AVERAGING_METHOD
Define the averaging method
(Episode/Monthly/Dayily)
MOVES only
For Movesmrg
MRG_TEMPORAL_YN
Y merges with hourly emissions
MRG_SPCMAT_YN
Y merges with speciation matrix
MRG_GRDOUT_YN
Y outputs gridded file
MRG_REPSTA_YN
Y outputs for state totals
MRG_REPCNY_YN
Y outputs for county totals
MODE_RPD
Y process MOVES rate-per-distance lookup table
MODE_RPV
MOVES only
MOVES only
Y process MOVES rate-per-vehicle lookup table
A-4
A
F
RD
NR
M
P
B
SEMAP Modeling Protocol
SMOKE Simulation ID:
Checked?*
Environment variable
MODE_RPP
Description
Setting to use
Comments/exceptions
MOVES only
MRG_GRDOUT_UNIT
Y process MOVES rate-per-profile lookup table
Units of gridded output file
moles/s
MRG_TOTOUT_UNIT
Units of report file
tons/day
For Smkmerge
MRG_TEMPORAL_YN
Y merges with hourly emissions
MRG_SPCMAT_YN
Y merges with speciation matrix
MRG_LAYERS_YN
Y merges with layer fractions
MRG_GRDOUT_YN
Y outputs gridded file
MRG_REPSTA_YN
Y outputs for state totals
MRG_REPCNY_YN
Y outputs for county totals
SMK_ASCIIELEV_YN
Y outputs ASCII elevated file
MRG_GRDOUT_UNIT
Units of gridded output file
moles/s
MRG_TOTOUT_UNIT
Units of report file
tons/day
MRG_REPORT_TIME
Hour in OUTZONE for reporting emissions
230000
MRG_MARKETPEN_YN
Apply reactivity controls market penetration
Point only
Reports will be generated by
Smkreport
So daily totals will be from 0-23 EDT
Apply reactivity controls market
penetration
AREA_SURROGATE_NUM Number for land-area surrogate
Biogenic only
Multiple-program controls
DAY_SPECIFIC_YN
Y imports day-specific inventory
EXPLICIT_PLUME_YN
Y for special wildfire processing or
UAM/REMSAD
HOUR_SPECIFIC_YN
Y imports hour-specific inventory
REPORT_DEFAULTS
Y reports default profile application
SMK_AVGDAY_YN
Y uses average day emissions instead of annual
SMK_PING_METHOD
0=no PinG source, 1=use PELVCONFIG file
SMK_EMLAYS
Number of emissions layers
SMK_MAXWARNING
Maximum number of warnings in log file
SMK_MAXERROR
Maximum number of errors in log file
OUTZONE
Output time zone of emissions
Not processing wildfires
Setting
No PinG sources to be run
GMT
A-5
A
F
RD
NR
M
P
B
SEMAP Modeling Protocol
SMOKE Simulation ID:
Checked?*
Environment variable
Description
Setting to use
Comments/exceptions
SMK_DEFAULT_TZONE
Time zone to fix if missing COSTCY file
VELOC_RECALC
Y recalculates velocity from diameter and flow
Point only
USE_SPEED_PROFILES
Y uses speed profiles instead of inventory
speeds
Mobile only
A
F
RD
NR
M
P
Script settings
SRCABBR
Abbreviation for naming log files
Setting
QA_TYPE
None, all, part1-part4, or custom
PROMPTFLAG
Never set to Y for batch processing
AUTO_DELETE
Y deletes SMOKE I/O API output files
AUTO_DELETE_LOG
Y automatically deletes logs without asking
DEBUGMODE
Y changes script to use debugger
DEBUG_EXE
Sets debugger to use when DEBUGMODE = Y
NONROAD
Y resets area files to nonroad files
Episode settings
G_STDATE
Julian start date
G_STTIME
Start time (HHMMSS)
G_TSTEP
Time step (HHMMSS)
G_RUNLEN
Run length (HHMMSS)
Daily files are from 0-23Z, and they
need an extra hour!
*A=Area; F=Fire, RD=Road dust, NR=Nonroad mobile, M=On-road mobile, P=Point, B = Biogenic
We certify that all of the configuration settings listed in the worksheet above for the specified SMOKE simulation have been completed and
verified.
Production Modeler:
QA Manager:
Project Manager:
__________________________________
__________________________________
__________________________________
Signature
Signature
Signature
Date
Date
A-6
Date
B
SEMAP Modeling Protocol
Table A-2. Inventory file recording worksheet for SMOKEv2.7b (sample entry shown in gray)
SMOKE Simulation ID:
Date,
source
10/29/01,
PES
# of
records
File name
pt96_wrap_102901a.ida
64,999
Description
Species/contents
Full 1996 point source inventory; WRAP, Tier1, Tier2
states (1 of 4)
CO, NOx, SO2, VOC,
NH3, PM10, PM2.5
We certify that the worksheet above lists all of the emissions inventory files used for the specified SMOKE simulation.
Production Modeler:
QA Manager:
Project Manager:
__________________________________
__________________________________
__________________________________
Signature
Signature
Signature
Date
Date
A-7
Date
SEMAP Modeling Protocol
Table A10-3. Ancillary input file recording worksheet for SMOKEv2.7b (sample entry shown in gray)
SMOKE Simulation ID:
SMOKE logical
name
AGREF
File name
amgref.m3.us+mex.txt
Creation date,
source
Date unknown,
CEP
Description
Spatial cross-reference distributed with
SMOKEv2.7b
We certify that the worksheet above lists all of the ancillary SMOKE input files used for the specified SMOKE simulation.
Production Modeler:
QA Manager:
Project Manager:
__________________________________
__________________________________
__________________________________
Signature
Signature
Signature
Date
Date
A-8
Date
SEMAP Modeling Protocol
Table A-4. Binary meteorology input and SMOKE output file recording worksheet for SMOKEv2.7b (sample entries shown in gray)
SMOKE Simulation ID:
File
type*
P
Creation date,
source
10/31/03, CEPAdelman
Met
11/01/01,
RMC-Wang
File name
Location on RMC servers
File ID
Comments
pgts.wrap96h.$jdate.ncf
/home/aqm4/edss2/cmaq/data/wrap96h/s
moke/output
wrap96ptV3
Merged point source file;
based off of inventory file
wrap96pointV3.022803.ida
METCRO2D.wrap96.ncf
/home/aqm4/edss2/cmaq/data/met/1996
wrap96
Converted from MM5 to
Models-3 with MCIPv2.1 by
Zion Wang (RMC)
*A=Area, B=Biogenic, M=On-road mobile, P=Point, NR=Non-road mobile, RD=Road dust, WB=Windblown dust, F=Fire, Met=Meteorology, Mrg=Model-ready/merged
We certify that all of the meteorology and SMOKE output files listed in the worksheet above have been documented and verified.
Production Modeler:
QA Manager:
Project Manager:
__________________________________
__________________________________
__________________________________
Signature
Signature
Signature
Date
Date
A-9
Date
SEMAP Modeling Protocol
Table A-5. Emissions simulation tracking worksheet (sample entries shown in gray)
SMOKE Simulation ID: Pre2002v1
Date
01/02/10
Months Completed
Jan, Feb, Mar, Apr….
Source Category: Ar,Mb,MbvPt…
Modeler
AH
QA
BH
EI Type: Annual, seasonal, monthly, daily
Comments
Reprocessed to fix a problem with the formatting of the raw inventory
file.
A-10
SEMAP Modeling Protocol
Appendix B. SMOKE Emissions QA Checklist and
Log for Warnings and Notes
Table B-1 is the emissions QA checklist. Detailed explanations of the features to examine in
each QA step in the table are provided in Section 5.1.2.2.3 and 5.1.2.2.4 The production modeler
checks off and initials each QA step in Table B-1 as it is completed. Comments about the QA
step or answers to the questions asked in each QA step can be added to the comments field in the
checklist. After completion of the QA checklist, the production modeler will pass it off to the QA
manager who will then verify and initial each step.
Table B-2 is the gatekeepers QA checklist. Detailed explanations of the features to examine in
each QA step in the table are provided in Sections 5.1.2.2.5.1. The gatekeeper checks off and
initials each QA step in Table B-2 for each source category as it is completed.
Table B-3 is the log for recording SMOKE warnings and notes. The purpose of this table is to
ensure that the emissions modeling team is aware of all messages reported by SMOKE. The
production modeler and QA manager are responsible for filling out Table B-2 and confirming all
of the SMOKE messages (see procedure in Section 5.1.2.2.3.2).
The QA checklists and the log are to be completed in full electronically and then printed. On the
checklist only, the last step is to obtain the various signatures. The completed soft copies of the
checklist and log will be kept on the RMC compute servers. The completed hard copies will be
archived by the QA manager to include in the project final report.
B-1
SEMAP Modeling Protocol
Table B-1. Step-by-step emissions QA checklist for SMOKEv2.7b; subheadings list the section numbers of the protocol where
detailed explanations of each step are provided.
PM*
QA*
QA step
Comments
Model QA
Configuration settings worksheet (Table A-1)
Inventory files worksheet (Table A-2)
Ancillary input files worksheet (Table A-3)
Meteorology/Output files worksheet (Table A-4)
Look for unexpected errors, warning, and notes
Warnings and notes worksheet (Table B-1)
Spatial surrogate/cross-reference checks
Temporal profile/cross-reference checks
Speciation profile/cross-reference checks
Inventory import (Smkinven)
Inventory data check
Inventory coverage
Annual, daily or hourly emissions
Smkinven formula
Resetting negative formula values
Weekday normalization
Missing rule fields
Inventory records dropped
Duplicate sources
Missing or negative inventory records
B-2
SEMAP Modeling Protocol
PM*
QA*
QA step
Comments
Bad stack coordinates
Missing stack parameters
Missing/bad SIC codes
Chemical allocation (Spcmat)
Pollutant conversion
Default profile assignment
Bad profile assignment
No speciation cross-reference/no default
Incorrect NOx speciation
Horizontal spatial allocation (Grdmat)
Default surrogate
Bad surrogate assignment
No gridding cross-reference/no default
Temporal allocation (Temporal)
Output time zone
Bad profile assignment
No temporal cross-reference/no default
Inventory base year inconsistent with start
Redundant activity and inventory data
Inventory vehicle types not in MOBILE
B-3
SEMAP Modeling Protocol
PM*
QA*
QA step
Comments
Elevated source selection (Elevpoint)
Is Elevpoint needed
Elevated source selection method
PinG source selection method
Vertical layer allocation (Laypoint)
Hourly plume data method
Bad plume values
Special source selection file
Vertical layer fractions sum to unity
Emissions control and growth (Cntlmat)
Control type
Bad control packets
Duplicate control packets
Packet/inventory mismatch
Bad output years in control packets
Control packet years inconsistent with inventory
Badly named control packet
Pollutant-specific entries skipped
MOBILE6
Meteorology variables
MOBILE6 environment variables
Group type
MOBILE6 temperatures
B-4
SEMAP Modeling Protocol
PM*
QA*
QA step
Comments
Freeway ramp modeling
MOBILE6 speeds
MOVES
Preprocessing Meteorology data : Met4moves
Met data averaging method/Temperature variables
Reference county fuel month
Cross reference county
MOVES rate-per-distance lookup table
MOVES rate-per-vehicle lookup table
MOVES rate-per-profile lookup table
BEIS3
Meteorology variables
Speciation profile code
Biogenic season switch
Area surrogate number
Fire emissions
Spatial and temporal locations
Layer allocation
Fire temporal and chemical profiles
Fugitive/windblown dust emissions
Merging emissions components (Smkmerge/Mrggrid/Movesmrg)
Emissions units
Elevated/PinG source selection
B-5
SEMAP Modeling Protocol
PM*
QA*
QA step
Comments
Plume rise method
Emissions components
Relative magnitude of component emissions files
Comparisons to base inventory
Types of MOVES lookup tables
B-6
SEMAP Modeling Protocol
PM*
QA*
QA step
Comments
Report generation (Smkreport)
Qualitative Analyses
Check spatial coverage of emissions
Check temporal variability in emissions
Check vertical distribution of emissions
Create inventory regressions for all source categories
Compare SMOKE reports vs. model-ready emissions
values
Quantitative Analyses
Create Emissions regressions
Create annual county/state total using daily modelready input files
System QA: Software and Data Tracking
Run scripts checked into CVS
Assigns file checked into CVS
SMOKE binary executables checked into CVS
SMOKE source code checked into CVS
Installation freeze point created
Final files in $GE_DAT directory added to CVS
Final inventory files added to CVS
*The “PM” column is initialed by the production modeler after the QA step has been completed. The “QA” column is initialed by the QA manager
after he/she has verified completion of the step.
B-7
SEMAP Modeling Protocol
We certify that all of the emissions QA steps listed in the checklist above have been completed and verified.
Production Modeler:
QA Manager:
Project Manager:
__________________________________
__________________________________
__________________________________
Signature
Signature
Signature
Date
Date
B-8
Date
SEMAP Modeling Protocol
Table B-2. Gatekeepers emissions QA checklist for SMOKEv2.7b
GK*
QA step
A
RD
SMOKE Inputs
Inventory file formats
Emissions inventory sanity checks
SMOKE Outputs
Create and review total domain hourly plots
Create and review total domain daily layer fractions
Compare daily domain totals from ASCII to netCDF
files
*The “GK” column is initialed by the gatekeeper after the QA step has been completed.
B-9
NR
M
P
Ag
B
SEMAP Modeling Protocol
Table B-3 . Warnings and notes log for SMOKEv2.7b
SMOKE Simulation Name:
Modeler:
Date:
Comments:
Program
Warnings
Notes
Smkinven:
Grdmat:
Spcmat:
B-10
SEMAP Modeling Protocol
Program
Warnings
Notes
Temporal:
Cntlmat:
Elevpoint:
Emisfac:
B-11
SEMAP Modeling Protocol
Program
Warnings
Notes
Grwinven:
Laypoint:
Mbsetup:
Met4moves:
B-12
SEMAP Modeling Protocol
Program
Warnings
Notes
Movesmrg:
Mrggrid:
Normbeis3:
Premobl:
B-13
SEMAP Modeling Protocol
Program
Warnings
Notes
Rawbio:
Smkmerge:
Tmpbeis3:
Tmpbio:
B-14
SEMAP Modeling Protocol
Appendix C. References
Abt Associates, Inc. (2009) Modeled Attainment Test Software (MATS) User’s Guide prepared for the
U.S. EPA Office of Air Quality Planning and Standards, Bethesda, MD, March 2009.
Adelman, Z., Quality Assurance Protocol – WRAP RMC Emissions Modeling with SMOKE, Prepared
for the WRAP Modeling Forum by the WRAP RMC, Riverside, CA, 2003.
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