United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-450/4-92-Qlla
June 1992
Air
& EPA
GUIDELINE FOR REGULATORY
APPLICATION OF THE
URBAN AIRSHED MODEL
FOR AREAWIDE CARBON
MONOXIDE
VOLUME I: TECHNICAL
REPORT
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EPA-450/4-92-0113
GUIDELINE FOR REGULATORY
APPLICATION OF THE
URBAN AIRSHED MODEL
FOR AREAWIDE CARBON
MONOXIDE
VOLUME I: TECHNICAL REPORT
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Technical Support Division
Research Triangle Park, NC 27711
U.fc. Environmental Protection Agency
June 1992 Region 5, Library (PL-12J)
77 West Jackson Boulevard, 12th Floor
Chicago, IL 60604-3590
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Notice
This report has been funded by the United States Environmental Protection Agency (EPA) under
contract 68D00124 to Systems Applications International (SAI). Thomas N. Braverman served
as the EPA work assignment manager. Any mention of trade names or commercial products is
not intended to constitute endorsement or recommendation for use.
ii
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CONTENTS
Page
NOTICE ii
TABLES vi
ABBREVIATIONS vii
ACKNOWLEDGMENTS ix
1 INTRODUCTION _ 1
1.1 Background 3
1.2 Application of the UAM 5
2 MODELING PROTOCOL 7
2.1 Protocol Development Process 8
2.2 Contents of Protocol Document 9
3 DOMAIN AND DATA BASE ISSUES 11
3.1 Overview of Model Inputs 11
3.2 Episode Selection 14
3.3 Selection of Modeling Domain and Resolution 16
3.3.1 Domain Definition 16
3.3.2 Horizontal Grid Cell Size 17
3.3.3 Number of Vertical Layers 17
3.3.4 Time Span 18
3.4 Preparation of Meteorological Inputs 19
3.4.1 Surface Roughness and Deposition (TERRAIN) 19
3.4.2 Diffusion Break (DIFFBREAK) 20
3.4.3 Top of the Modeling Domain (REGIONTOP) 21
3.4.4 Winds (WINDS) 22
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3.5 Preparation of Air Quality Inputs 25
3.5.1 Air Quality (AIRQUALITY) 25
3.5.2 Top Concentration (TOPCONC) 26
3.5.3 Boundary Conditions (BOUNDARY) 26
3.6 Performance Evaluation Data 29
3.7 Emission Inventory 30
3.7.1 Use of Surrogate Factors to Spatially Grid Area Sources 32
3.7.2 Development of Mobile Source Inventories 32
3.7.3 Episode-Specific Adjustments 34
3.7.4 Consistency with National Inventories 35
4 DATA QUALITY ASSURANCE .AND MODEL DIAGNOSTIC ANALYSES . . 37
4.1 Step 1: Quality Assurance Testing of Component Fields 38
4.2 Step 2: Diagnostic Testing of the Base Case Meteorological Epiodes 40
4.3 Step 3: Additional Base Meteorological Episode Sensitivity Testing 41
5 MODEL PERFORMANCE EVALUATION 45
5.1 Performance Measures 46
.5.1.1 Graphical Performance Procedures , 46
5.1.2 Statistical Performance Measures 47
5.2 Assessment of Model Performance 48
6 ATTAINMENT DEMONSTRATION 53
6.1 Developing Attainment Year Base Case Emission Inventories 53
6.2 Developing Future Year Emission Control Strategies 54
6.3 Performing Attainment-Year Simulations to Assess Various Control Strategies 56
6.4 Using Modeling Results in the Attainment Demonstration 56
6.5 Exceptions to Guidance Document 57
REFERENCES 59
IV
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VOLUME E. APPENDIXES
APPENDIX A: RECOMMENDED MODELING PROTOCOL CONTENTS
APPENDIX B: PERFORMANCE MEASURE FORMULATIONS
APPENDIX C: TECHNICAL DISCUSSION OF UAM MODEL INPUTS WITH
EXAMPLE APPLICATION
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TABLES
2-1 Example table of contents for protocol document 10
3-1 Overview of the 13 Urban Airshed Model input files 12
3-2 Recommended background concentrations for carbon monoxide 27
VI
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ABBREVIATIONS
AIRS
CAAA
CARB
CMSA
CO
DWM
FTP
LAV
MSA
NAAQS
NTIS
NWS
OAQPS
OMS
ORD
RWC
SAI
SCRAM BBS
SEP
TAZ
TCM
Aerometric Infonnation Retrieval System
Clean Air Act Amendments
California Air Resources Board
Consolidated Metropolitan Statistical Area
Carbon Monoxide
Diagnostic Wind Model (UAM preprocessor program)
U.S. Environmental Protection Agency
Emissions Preprocessor System for the UAM
Federal Implementation Plan
Link Attribute Value
Metropolitan Statistical Area
National Ambient Air Quality Standard(s)
National Technical Information Service
National Weather Service
EPA Office of Air Quality Planning and Standards
EPA Office of Mobile Sources
EPA Office of Research and Development
Residential Wood Combustion
Systems Applications International
EPA Support Center for Regulatory Air Models
Bulletin Board System
State Implementation Plan
Transportation Analysis Zones
Transportation Control Measures
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UAM Urban Airshed Model
USGS United States Geologic Survey
VMT Vehicle Miles Traveled
viii
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ACKNOWLEDGMENTS
Much of the format, style, and, where applicable, content of this guidance document was
borrowed from .the EPA guidance document titled "Guideline for Regulatory Application of the
Urban Airshed Model." The principal authors of that document were Mr. Dennis C. Doll (U.S.
Environmental Protection Agency (EPA), Office of Air Quality Planning and Standards
(OAQPS)), Dr. Richard D. Scheffe (EPA, OAQPS), Dr. Edwin L. Meyer (EPA, OAQPS), and
Mr. Shao-Hang Chu (EPA, OAQPS).
Mr. Edward L. Carr (Systems Applications International (SAI)) is the principal contributor to
this document. Significant contributions were also made by Julie L. Fieber and Robert C.
Kessler (SAI). In addition, the document was reviewed and commented upon by Mr. Thomas
N. Braverman (EPA, OAQPS), Mr. Jay L. Haney (SAI), Mr. Henry Hogo (South Coast Air
Quality Management District), Dr. Robert G. Ireson (SAI), Mr. William Ryan (EPA, Region
X), and Mr. Robert Wilson (EPA, Region X).
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CHAPTER 1
INTRODUCTION
Under the Clean Air Act Amendments of 1990, over 40 urban areas are classified as
nonattainment with respect to the 8-hour National Ambient Air Quality Standard (NAAQS) for
carbon monoxide (CO). Of these urban areas, those whose 8-hour average design value exceeds
12.7 ppm are recommended to use an urban areawide model to address attainment of the CO
NAAQS of 9.0 ppm in the revision of their State Implementation Plan (SIP). SIP revisions
demonstrating attainment of the CO NAAQS are required by the Clean Air Act Amendments of
1990. The Urban Airshed Model (UAM) has been identified as an effective tool for evaluating
emission control requirements needed to attain and maintain the CO NAAQS.
The purpose of this document is to provide guidance in the procedures used to apply the
UAM for CO SIP attainment demonstrations and to ensure national consistency in model
applications.
Methodologies and procedures used in the preparation of UAM inputs and applications
to carbon monoxide regulatory issues are addressed in this report. This document also describes
recommendations for the preparation of all input files needed to exercise the UAM. The
technical explanation and rationale for the development of UAM inputs are discussed in
Appendix C.
The UAM source code is maintained and distributed by the Source Receptor Analysis
Branch, Technical Support Division, of the EPA Office of Air Quality Planning and Standards
(OAQPS). Users will be informed of modifications or enhancements to the UAM through the
Support Center for Regulatory Air Models Bulletin Board System (SCRAM BBS). Additionally,
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the UAM source code, user's guide, and test case data base are available from the National
Technical Information Service (NTIS)(703-737-4600).
Steps needed to conduct an urban-scale modeling study consist of the following:
1. Establish a protocol for the modeling study in which candidate modeling episodes
are identified.
2. Compile air quality, meteorological, and emissions data to develop UAM input
files for each meteorological episode to be used in the attainment demonstration
model simulations.
3. Execute the model for each meteorological episode.
4. Conduct diagnostic analyses on each meteorological episode simulation. The
principal purpose of diagnostic analyses is to ensure that the model properly
characterizes physical phenomena (e.g., wind fields, spatial and temporal
emission patterns) instrumental in leading to observed CO concentrations. The
visible product is enhanced model performance (i.e., better spatial and temporal
agreement with observed data). Diagnostic model simulations are intended to
uncover potential model input data gaps that, when corrected, may lead to
improved model performance.
5. Exercise the model for each meteorological episode and use a series of
performance measures to determine overall model performance in replicating
observed CO concentrations and patterns.
6. For each meteorological episode, estimate emissions and air quality for the
projected attainment year required under the CAAA. Perform model simulations
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for each episode to determine whether the CO NAAQS can be met in the
attainment year.
7. If the model simulations for the attainment year do not show attainment for each
modeled episode, develop additional emission control measures on selected source
categories.
8. Perform model simulations for the emission control measures to demonstrate
attainment of the CO NAAQS for each meteorological episode. If the control
measures do not show attainment, repeat steps 7 and 8 as an iterative process
until attainment is shown for each modeled episode.
This report is divided into six sections. Section 1 provides background information about
air quality problems related to high CO. concentrations and describes the philosophies underlying
UAM areawide CO applications. Section 2 describes the modeling protocol and development
processes. Section 3 discusses domain and data base issues. Section 4 discusses data quality
assurance and model diagnostic analysis. Section 5 discusses model performance evaluation and
Section 6 describes the methodology to be followed in attainment demonstration.
1.1 Background
High 8-hour CO concentrations in urban areas often result from periods of high emissions
(the afternoon and evening mobile traffic peak) coinciding with adverse meteorological
conditions (low wind speeds and poor vertical dispersion). Under such conditions,
concentrations can increase rapidly near heavy traffic areas. The greatest problem in achieving
ambient 8-hour CO standards in an urban area is controlling the neighborhood-scale buildup and
persistence (over 8 hours or more) of elevated CO concentrations. Under meteorological
conditions conducive to high concentrations, CO emissions become trapped in a shallow, stable
surface layer caused by the radiational cooling of the air next to the ground. When winds are
light and variable, this high-concentration air mass may extend over a large portion of an
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urbanized area depending upon topography and emission distribution and can remain relatively
stationary or meander during nighttime hours, with the result that one or several areas may
experience high CO concentrations throughout the night and early morning hours. Maximum
8-hour average CO concentrations result from the combined effect of microscale and
neighborhood-scale processes. For some urban areas, sites on the downwind edge of high
emission density regions can experience extended periods of high concentrations even if wind
speeds are moderate.
Carbon monoxide emissions result from incomplete combustion of fossil fuels which
include residential wood combustion and industrial emissions. However, motor vehicle exhaust
accounts for most of the CO emissions in urban areas. To reduce motor vehicle emissions
injected into the atmosphere, either the emission rates of individual vehicles must be reduced or
traffic conditions (volumes, speeds, stops and idling periods) must be modified. To estimate the
effects of these control measures, both microscale and neighborhood-scale modeling must be
conducted. Areas with significant stationary sources as determined under Section 187c (Laxton,
1991a) should perform modeling following the techniques cited in the Guideline on Air Quality
Models (Revised) (EPA, 1986).
Previous UAM modeling studies of CO have been performed in Phoenix (Haney, 1988;
Causley et al., 1991) and Denver (Anderson et al., 1977; Rogers, 1986). In the studies
conducted in Denver, only neighborhood-scale modeling was performed. In later studies,
neighborhood-scale and microscale processes were modeled separately, and the results of each
were added to estimate the total concentrations at selected roadway intersections to assess
attainment. This approach allowed use of the UAM to describe the accumulation of emissions
over several hours and kilometers within a three-dimensional modeling grid, as well as the
separate estimation of roadway impacts within a few hundred meters of the roadway intersection.
The combined model results were then used to evaluate the effectiveness of control measures.
Procedures for determining the roadway intersection concentration for CO SIP applications are
addressed in the guidance document Guideline for Modeling Carbon from Roadway Intersections
(EPA, 1992a).
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1.2 Application of the UAM
Conditions that often are conducive to high areawide 8-hour CO concentrations (low wind
speeds, stable conditions) are the same conditions for which the steady-state assumption inherent
in Gaussian formulated models is invalid. The steady-state assumption results in the
accumulation of large errors due to the inability of the Gaussian models to gradually build
carbon monoxide concentrations from hour to hour. The grid cell and time step modeling
approach of the UAM is more appropriate for CO modeling because it is capable of handling
low wind speeds and stable conditions by allowing concentrations to accumulate over time.
A number of past studies involving the UAM relied on intensive and expensive
monitoring studies to provide information to adequately prepare model inputs. Recent studies
have shown that routine meteorological and air quality observations are adequate for UAM SIP
applications. This set of procedures (known as PLANR, or Procedures for Low-cost Airshed
applications to Nonattainment Regions) was applied to determine its feasibility in future SIP
efforts (Morris et al., 1990). Although the PLANR methodology was initially confined to ozone
nonattainment problems, the same general methodology can be applied equally well to carbon
monoxide nonattainment areas. The method relies on the ability of the UAM to accurately
predict hourly concentrations within a certain distance and time using routine data gathered from
the monitoring station measuring the highest carbon monoxide values. Although the PLANR
method may prove to be applicable to most CO nonattainment areas, some areas with complex
terrain and/or meteorology may still require more intensive data for successful application of the
UAM.
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CHAPTER 2
MODELING PROTOCOL
The UAM modeling domain may encompass multiple geopolitical boundaries (counties,
cities, and states) with a potentially large regulated community. Therefore, the development of
a modeling protocol is recommended to (1) promote technical credibility, (2) encourage the
participation of all interested parties, (3) provide for consensus building among all interested
parties concerning modeling issues, and (4) provide documentation for technical decisions made
in applying the model as well as the procedures followed in reaching these decisions.
The protocol should detail and formalize procedures for conducting all phases of the
modeling study, such as (1) describing the background and objectives of the study, (2) creating
a schedule and organizational structure for the study, (3) developing the input data, (4)
conducting diagnostic and model performance evaluations, (5) interpreting modeling results, (6)
describing procedures for using the UAM and roadway intersection models to demonstrate
whether proposed strategies are sufficient to attain the CO NAAQS, and (7) producing
documentation and data analyses that must be submitted for EPA regional office review and
approval.
All issues concerning the modeling study must be thoroughly addressed during the
protocol development. Thus, modifications to the protocol as the study progresses should not
be needed unless unforeseen procedural and/or technical issues are encountered. All parties
involved in the study should agree to protocol modifications through the modeling policy
oversight committee, if applicable (see below). It is especially important that the state/local
agencies and EPA regional office(s) overseeing the study concur on protocol modifications.
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2.1 Protocol Development Process
The state agency responsible for developing the CO State Implementation Plan (SIP) is
usually the lead agency responsible for developing the modeling protocol. For domains
encompassing parts of more than one state, the responsible state agencies need to develop the
modeling protocol jointly. Since the protocol should describe the modeling policy and technical
objectives of the study, input will be required from various EPA and state/local personnel
dealing with regulatory policy issues and from others with modeling expertise. In some cases
a modeling policy oversight and technical committee will need to be organized to address these
issues. The composition and responsibilities of the committee should be defined in the modeling
protocol.
Responsibilities of the modeling policy oversight committee will be, at a minimum, to
set the objectives of the study, set the schedule, determine resource needs, and implement any
modifications to the protocol as the modeling study proceeds. The committee should include
representatives from the appropriate EPA regional office(s), state/local agencies, the regulated
community, and public interest groups. It is important that appropriate policy-oriented personnel
be identified for membership on the committee.
Responsibilities of the technical committee will be, at a minimum, to develop the
protocol's technical specifications concerning emission inventories, meteorological data, air
quality data, data quality assurance, emission control strategies, model diagnostic analyses,
model performance evaluation procedures, and interpretation of model results. The technical
committee should include appropriate technically oriented members from the EPA regional
office(s), state/local agencies, the regulated community, and public interest groups.
The modeling protocol must be submitted to the appropriate EPA regional modeling
contact for review and approval. The EPA regional modeling contact should be a member of
the policy oversight and/or technical committee so that rapid review and approval of the protocol
is assured.
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Recommendations
A protocol document is recommended for each UAM application used for a CO
attainment demonstration. This protocol should describe the methods and procedures to
be used for conducting the CO modeling study.
Additionally, it is suggested that both a policy oversight committee and a technical
committee be established to develop the modeling protocol. The composition and
responsibilities of the committees should be defined in the protocol.
The modeling protocol and any modifications to it should be agreed upon by all parties
involved in the study through the policy oversight committee. It is especially important
that the state/local agency participants and EPA regional office(s) overseeing the
modeling study concur on any protocol modifications. Protocol modifications should be
documented for subsequent public review.
The modeling protocol must be submitted to the appropriate EPA regional modeling
contact for review and approval.
2.2 Contents of Protocol Document
Recommendations
It is recommended that the applicable components listed in Table 2-1 be included in the
protocol document for each attainment demonstration modeling study. A description of
each component is presented in Appendix A.
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TABLE 2-1
EXAMPLE TABLE OF CONTENTS FOR PROTOCOL DOCUMENT
1. UAM Modeling Study Design
Background and Objectives
Schedules
Deliverables
Management Structure/Technical Committees
Participating Organizations
Relationship to Planning/Strategy Groups
2. Domain and Data Base Issues
Data Bases:
• Air quality
•Meteorology
Base Meteorological Episode Selection
Modeling Domain
Horizontal Grid Resolution
Number of Vertical Layers
Emission Inventory
Specification of Initial arid Boundary Conditions
Wind Field Specification
Inversion Depth
Sources of Other Input Data
3. Quality Assurance and Diagnostic Analyses
Quality Assurance Tests of Input Components
Diagnostic Tests of Base Case Simulation
Test Results/Input Modifications
4. Model Performance Evaluation
Performance Evaluation Tests
5. Roadway Intersection Modeling
Selection Methodology for Intersections Modeled
Modeling Methodology
6. Attainment Demonstrations
Identification of Attainment-Year Mandated Control Measures
Methodologies for Generating Control Strategy Emission Inventories
Procedures for Attainment Demonstration
7. Submittal Procedures
Data Analysis Review
Documentation Review and Approval
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CHAPTERS
DOMAIN AND DATA BASE ISSUES
Described in this chapter are the following topics: episode selection, domain selection,
meteorological inputs, air quality data, and emissions inventories. Choices made in each topic
area are often interrelated. Accordingly, decisions concerning a particular topic area probably
will be based on consideration of several areas. In several topic areas, recommendations are
made concerning minimum requirements for data availability and modeling resolution. To
reduce uncertainties in modeling inputs and outputs, users are encouraged to exceed these
minimum recommendations whenever possible.
3.1 Overview of Model Inputs
This section provides an overview of the 13 input files required to exercise the Urban
Airshed Model (UAM). Table 3-1 presents a list of these input files and indicates the amount
of effort required to create each one. A more complete reference for the UAM is contained in
the UAM User's Manual (EPA, 1990).
Of the 13 input files, two are considered "universal" in that they do not vary from one
simulation to another. The first file, CHEMPARAM, run in the unreactive mode for carbon
monoxide modeling, contains the list of species to be modeled. Other unreactive species may
be simulated for little additional cost if the emission inventory data are available. The other
input file, SIMCONTROL, contains parameters controlling the UAM simulation. In general,
the date and time will vary for different meteorological episodes but not for the same episode.
11
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Of the other 11 UAM input files, the most time-consuming and resource-intensive to
create is the low-level carbon monoxide emission file, EMISSIONS. This file is usually
prepared concurrently with the preparation of the meteorological and air quality files. State/local
agency participants using the UAM for areawide CO modeling should concentrate their efforts
on the emissions inventory development, especially the mobile source component, preparation
of the 3-dimensional wind fields, ground-level inversion height, and specification of thickness
of model layers.
3.2 Episode Selection
This subsection describes the criteria to be "considered in selecting carbon monoxide (CO)
meteorological episodes appropriate for modeling. The modeling protocol should include a
complete discussion regarding episode selection. A trained meteorologist familiar with local and
regional weather patterns should be consulted in the episode selection process. The following
approach is recommended for selecting episodes for use in modeling:
1. Use the most recent years (1988 to present) of CO monitoring data as the period
from which to select candidate modeling episodes.
2. Select as candidate modeling episodes the three highest non-overlapping 8-hour CO
episodes from each year as determined by the highest monitored concentration per
episode.
3. Examine the meteorological conditions for each candidate modeling episode.
4. Determine the different types of meteorological regimes for the candidate episodes.
Conditions resulting in dissimilar source-receptor configurations should be the prime
consideration in distinguishing different meteorological regimes. In areas dominated
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by mobile source CO emissions and stagnation conditions, there may be only one
meteorological regime. In areas with major point sources of CO and/or significant
wood-burning CO emissions in addition to mobile source CO emissions, there may
be multiple meteorological regimes because of dissimilar source-receptor
configurations.
5. Rank each candidate episode within each meteorological regime according to the
magnitude of the peak 8-hour CO concentration.
6. Model a minimum of one episode for each meteorological regime. Select the
episode(s) for modeling from among the three highest ranked episodes from each
meteorological regime. In general, the highest ranked episode within the
meteorological regime should be selected for modeling. However, there may be
circumstances in which the second or third ranked episode is more appropriate for
modeling, e.g. (1) the air quality and meteorological data base is much better than
that of the first ranked episode, (2) the second or third ranked episode is from a
later year that includes controls that were not in effect in the year of the highest
ranked episode; consequently, the use of the second or third ranked episode would
be more conservative, and (3) the CO pattern for the highest ranked episode is
distinctly different from all other candidate episodes (i.e., weekend instead of
weekday).
States may want to consider a procedure other than the one outlined in Steps 1-6 for
selecting modeling episodes. Any such procedure should be described in the modeling protocol
and approved by the appropriate EPA Regional Office.
Recommendations
The modeling protocol should include a complete discussion of the choice of modeling
episodes. It is recommended that episodes selected for modeling be from the four most
recent years, 1988 to present. A trained meteorologist should assist in determining the
different types of meteorological regimes under which 8-hour exceedances occur during
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the four-year period. At a minimum, one episode for each meteorological regime should
be modeled.
3.3 Selection of Modeling Domain and Resolution
3.3.1 Domain definition
The size and location of the modeling domain define the data requirements for the
modeling. Definition of the modeling domain (both horizontal and vertical dimensions and
duration of simulation) depends upon the meteorology of the particular episode to be modeled.
Criteria that play an important role in determining the modeling domain include:
Stagnation period and duration of elevated CO levels
Location of major current and future emission sources
Wind flow pattern
Size of recirculation pattern
Available aerometric data
Preassumed emission inventory region
Generally, the domain should be set as large as feasible in order to reduce the dependence of
predictions on uncertain boundary concentrations and to provide flexibility in simulating different
meteorological episodes. It is generally much easier to subsequently reduce the size of a
modeled area than it is to subsequently increase it.
Recommendations
It is recommended that the domain be set large enough to encompass all major current
and future emission sources, and reduce the dependence of predictions on uncertain
boundary concentrations.
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3.3.2 Horizontal grid cell size
The horizontal dimension of each model grid square is based upon (1) the sensitivity of
predicted concentrations to horizontal grid size, (2) the resolution of observed meteorological
and air quality data and/or estimated emissions data, and (3) limitations imposed by other
considerations such as a required minimum domain size. Air parcel trajectories can be
performed using wind observations taken on each day of the candidate carbon monoxide episodes
to determine the horizontal limits of the modeling domain.
Relatively high horizontal grid cell resolution should be used in UAM carbon monoxide
modeling applications. It is recommended that at a minimum the horizontal resolution should
be no greater than 2x2 km. Model applications with finer grid resolution lead to slightly
higher peak values because artificial dilution is kept to a minimum. Larger (or coarser)
horizontal grid resolutions (> 2 km) are not recommended because (1) artificial dilution will
result for point emissions sources, and (2) loss of spatial resolution will result in less effective
evaluation of control strategy effectiveness.
Recommendations
It is recommended that, at a minimum, horizontal grid cell resolution should be no
greater than 2x2 km. Smaller grid cell sizes are encouraged because they allow more
accurate gridding of area and mobile sources. Additionally, point sources are better
characterized by smaller grid cell sizes.
3.3.3 Number of vertical layers
In most UAM CO applications, nearly all CO emissions are confined to the near surface;
as a result, model applications will need at a minimum two vertical layers: a lower layer
extending from the surface to the diffusion break, and an upper layer extending from the
diffusion break to the top of the model domain. However, in some cases additional model layers
may be warranted. At a minimum, these situations would include (1) multi-day elevated CO
events in which carryover from the previous day's emissions suspended aloft is believed to play
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a significant role; (2) episodes where vertical wind shear is significant, resulting in differential
transport of CO emissions within each model layer; (3) episodes where elevated point source
emissions are significant, resulting in emissions being suspended aloft under stable nighttime
conditions and not mixing downward until the surface-based inversion is fully eroded; and (4)
episodes where the vertical CO concentration profile changes rapidly below the diffusion break.
To accurately describe the vertical structure, it is recommended that data be collected
describing both the vertical temperature and CO concentration profile. The number of vertical
layers below the inversion base should characterize the vertical CO concentration profile. The
'modeling protocol document should include a complete discussion regarding the basis for
choosing the number of vertical layers.
Recommendations
Based on previous UAM CO applications, it is recommended that a minimum of two
vertical model layers be used in the modeling study. However, additional layers may be
warranted in cases where meteorology or emission source type justifies the use of
additional layers. The modeling protocol document should discuss the basis for choosing
the number of vertical layers. It is recommended that 20 m be used as the minimum
depth of the vertical layers below the diffusion break and 20 m for the vertical layers
above the diffusion break.
3.3.4 Time span
The time span of the UAM simulations should be at least 18 hours. Longer time spans
may be required to examine episodes where carryover from earlier emissions is believed to play
a role in producing high carbon monoxide concentrations. For example, an 8-hour exceedance
ending in mid morning may include substantial contributions from the previous evening. These
emissions must be modeled to properly treat future changes in daily emissions. If an episode
is selected that shows persistent recirculation of materials or lengthy stagnation, longer
simulation periods may have to be defined.
A primary reason for extending the simulation period is to reduce the influence of the
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initial conditions on the predicted carbon monoxide concentration. The starting time of the
simulation should be during the mid-afternoon hours when ambient concentrations are still low,
just prior to the development of the ground-level inversion. The ending time usually extends
into the mid-morning hours.
Recom mendation s
Simulations should extend for at least 18 hours. Longer time spans may be preferable
when one day's exceedance can be traced to emissions from the previous day.
3.4 Preparation of Meteorological Inputs
The availability of meteorological data varies widely among prospective modeling
domains. Also, a variety of techniques are available for developing wind fields, temperature
fields, and mixing heights. Although high resolution and confidence for all meteorological data
are desirable, time and resource constraints force a compromise between desirable and
acceptable methods. Historically, measured meteorological data have been spatially and
temporally interpolated for most UAM applications. More recently, diagnostic and prognostic
meteorological modeling techniques have been explored as possible means to develop input fields
(particularly wind fields) for air quality models. Procedures for preparation of the
meteorological inputs are presented in this section. As shown in Table 3-1, with the exception
of the EMISSIONS file, the WIND file is the most resource-intensive file to prepare.
3.4.1 Surface roughness and deposition (TERRAIN)
The TERRAIN file contains (1) surface roughness lengths and (2) deposition factors.
The surface roughness lengths are used in computation of vertical exchange coefficients. Both
the roughness lengths and deposition factors are used in computing deposition of gaseous
species.
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Because deposition of CO is negligible for most CO UAM applications, detailed representation
of surface characteristics is not crucial. The magnitudes of vertical exchange coefficients are
unimportant, given that maximum CO concentrations occur at the surface under very stable
conditions when mixing between UAM layers is minimal. It may be sufficient to assume values
of surface roughness and deposition factors typical of urban land use over an entire CO modeling
domain. Appendix C details a methodology for developing surface roughness and deposition
factors.
Recommendations
Previous applications have found that using surface roughness and deposition factors
typical of "urban" land use over the entire CO modeling domain is sufficient because
deposition of CO is slight.
It is recommended that, unless important urban versus rural land use areas are contained
within the modeling domain, a surface roughness length of 0.5 m and a deposition factor
of 0.3 be used.
3.4.2 Diffusion break (DIFFBREAK)
In UAM terminology, the diffusion break (DIFFBREAK) is the height at which the upper and
lower layers are divided. The value of DIFFBREAK affects the way many of the other
parameters are used by the UAM.
Predictions from the UAM have been shown to be fairly sensitive to the diffusion break
field. Therefore, the temporal variations in the diffusion break field over the UAM domain
should be described as realistically as possible. The UAM modeling system contains a
methodology for deriving the diffusion break based on surface temperatures, vertical sounding
measurements of temperature, and cloud cover (EPA, 1990). Appendix C contains a technical
discussion of important considerations for determining the diffusion break for CO applications.
However, because of the diversity of techniques and data bases that may be available on a case-
by-case basis, a specific procedure for deriving the diffusion break field cannot be recommended
in all cases.
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Recommendations
It is recommended that, at a minimum, the techniques described in the UAM User's
Guide and/or Appendix C be used in establishing the diffusion break field in the domain.
The choice of upper-air stations to be used in the diffusion break calculations should be
based on prevailing wind fields and location of the upper-air stations relative to the UAM
domain. If there are no upper-air stations within the domain, stations outside the domain
may need to be used. An experienced meteorologist should be consulted on the selection
of upper-air stations for use in determining the diffusion break.
The techniques for generating the diffusion break field should be described in the
Appendix C protocol document. Techniques other than that described in the UAM
User's Guide or should be documented and justified.
Because the assignment of DIFFBREAK in CO UAM applications is relatively uncertain,
it is recommended that the sensitivity of CO predictions to DIFFBREAK assumptions be
assessed by repeating the UAM simulations with DIFFBREAK values (1) decreased by
50 percent; and (2) increased by 100 percent.
3.4.3 Top of the modeling domain (REGIQNTOP)
A spatially and temporally constant top of the modeling region (REGIONTOP) is
recommended for virtually all UAM applications. Most 8-hour CO violations of the National
Ambient Air Quality Standards (NAAQS) occur during nighttime hours following the evening
traffic rush. Thus, it is recommended that for most CO applications (nighttime) the
REGIONTOP be set at 200 m. However, this value may be low when: (1) episodes extend
over several days, (2) high CO events are not associated with strong surface-based inversions,
or (3) elevated point source CO contributions are significant. In these cases the REGIONTOP
should be specified above the highest DIFFBREAK height by at least the depth of one upper-
layer cell.
Recommendations
For most CO applications (nighttime) set the REGIONTOP to 200 m.
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3.4.4 Winds (WINDS)
Methodologies to construct wind fields for UAM applications have historically fallen into
three categories:
1. Objective analyses that interpolate observed surface and aloft data throughout the
modeling domain
2. Diagnostic wind models in which physical constraints are used in conjunction with
objective analyses to determine the wind field
3. Prognostic models based on numerical solution of the governing equations for mass,
momentum, energy, and moisture conservation along with numerical solutions for
thermodynamic processes
Objective analysis. These procedures generally involve straightforward interpolative
techniques. They have the advantage of being relatively simple and inexpensive to use. The
primary disadvantages are that these analyses contain limited physical concepts, and results are
highly dependent upon the temporal and spatial resolution of the observed values. Thus, in
domains containing sparse observational data or complex topography, results may be
unsatisfactory.
Diagnostic wind models. These models improve mass consistency for the flow fields.
This may be addressed through parameterizations for terrain blocking effects and upslope and
downslope flows, as in the UAM Diagnostic Wind Model (EPA, 1990). Diagnostic models
generally require minimal computer resources and can produce a three-dimensional wind field;
however, they need representative observational data to generate features such as land and sea
breezes. Appendix C contains a detailed technical discussion of the use of the DWM for CO
model applications.
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Prognostic models. These models simulate relevant atmospheric physical processes while
requiring minimal observational data. Prognostic models require specification of the synoptic-
scale flow. Reliability of these approaches is usually enhanced if sufficient observations are
available to "nudge" solutions closer to observations. Since these models can simulate
temperature fields in addition to the wind field, it is possible to determine stabilities and
inversion depth, thus eliminating the need to generate these from sparse observational data.
Another significant advantage is that interdependencies of various meteorological inputs with one
another are considered in prognostic models. A major disadvantage is the extensive
computational resources needed to run a prognostic model. Additionally, the availability of
evaluated models and expertise needed to apply them for general application with grid models
is limited.
Selection of a specific technique for generating the domain wind field depends largely on
(1) the spatial and temporal resolution of surface and upper-air observations, (2) available
modeling expertise in applying alternative meteorological models, and (3) available computer
resources. However, some guidelines on preferences for generating the wind fields are as
follows.
The development of a wind field for each modeling episode depends upon ground-level
and elevated wind observation data. It is preferred that a surface-based monitoring network
report wind speed and direction as hourly averages because an hour is the time period
commensurate with most UAM concentration output analyses. The surface monitoring network
should be broad and dense so that diagnostic models can depict major features of the wind field.
Data representing vertical profiles of wind speed and direction are required in order to establish
upper-level wind fields. Preferably, data should provide adequate spatial (horizontal) and
temporal resolution. Results of UAM -applications are often criticized because of inadequate
meteorological data, and lack of sufficient meteorological data often prevents definitive
diagnostic analyses. Thus, the need for adequate meteorological data cannot be overstated.
Appendix C contains a detailed technical discussion about the data needs for the DWM for CO
applications.
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Time and/or resource constraints may preclude consideration of new meteorological
monitoring stations. Thus, it is likely that the base case to be used in the attainment
demonstration will be from a historical episode for which model performance has been deemed
acceptable.
Recommendations
It is recommended that the DWM be used to generate the UAM gridded wind fields. As
discussed in Appendix C, the DWM modeling domain should encompass terrain features
outside the UAM modeling domain which may affect the DWM representation of the
flow field. The use of other techniques for deriving the wind field, such as prognostic
wind models or other objective techniques, may be employed on a case-by-case basis,
subject to approval from the appropriate EPA regional office.
Meteorological data routinely available for a UAM modeling demonstration usually
consist of National Weather Service (NWS) hourly surface and upper-air observations
(for winds aloft). If these data are the only data available for use in a modeling
demonstration, they may have to suffice. However, the NWS data consist of
observations made over very short periods rather than hourly averaged values. An
assumption that wind velocity measured over a very short period persists unaltered over
an hour may lead to an overestimate of transport. Therefore, whenever possible, hourly
averaged meteorological data (e.g., from an intensive field study) should be used.
Additional meteorological data may be available from other sources in the domain (e.g.,
an on-site meteorological monitoring program at an industrial facility). These data may
be used to supplement the NWS data, provided the data have been adequately quality
assured. Additionally, the EPA guideline entitled On-Site Meteorological Program
Guidance for Regulatory Modeling Application (EPA, 1987) should be consulted to
assess whether the supplementary data reflect proper siting of meteorological instruments
and appropriate data reduction procedures.
In planning a special field study to provide a more spatially and temporally dense
meteorological data base, the number of surface meteorological monitoring stations
should be sufficient to describe the predominant wind flow features within the modeling
domain. An experienced meteorologist familiar with local climatic patterns should be
consulted concerning the location and suitability of the surface meteorological stations.
Vertical sounders or profilers are highly encouraged in a special field study to resolve
winds aloft and vertical temperature gradients. Any special field study and monitoring
program should be planned in consultation with the appropriate EPA regional office
before implementing the study.
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3.5 Preparation of Air Quality Inputs
Ambient air quality data are generally used to specify initial- and boundary-condition
concentrations. In the UAM, these functions are performed by the Air Quality, Top
Concentration and Boundary Condition files. In addition, air quality data are needed to diagnose
problems in setting up model applications and assessing model performance for the
meteorological episodes being considered in the attainment demonstration. A lean air quality
data base may introduce significant uncertainties in characterizing model performance. This
section discusses the procedures for preparing air quality inputs needed by the UAM.
3.5.1 Air quality (AIROUALITY)
The air quality (AIRQUALITY) file specifies the initial pollutant concentrations to be
modeled by the UAM. The concentrations are specified for the full three-dimensional grid.
A routine monitoring data base usually does not, by itself, make a complete, three-
dimensional specification of initial conditions possible. For instance, spatial coverage provided
by monitoring stations is generally incomplete, with most stations located within urban centers
and therefore strongly influenced by local emissions sources. Monitors located in rural areas,
which can give an indication of appropriate local background concentrations, are rare. As a
rule, only surface measurements are available, so no direct indicator of the vertical distribution
of carbon monoxide will be available. The problem, therefore, is to specify the three-
dimensional concentration field from a very sparse data set. A detailed technical discussion is
presented in Appendix C for developing initial conditions from a sparse data set.
Recommendations
At the inflow boundaries, air quality data at the surface and aloft should be used
whenever available to specify the initial boundary conditions.
For values near the surrounding perimeter of the model domain, default values as shown
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in Table 3-2 may be used where necessary. Selection of these values should be based
on how pristine the surrounding areas are considered to be. For example, if the area
near the boundary can be classified as rural, then the low CO value should be used; if
that area is better classified as suburban, then the middle value should be used.
To diminish dependence on arbitrary specification of initial conditions, a simulation
should begin prior to the buildup of high carbon monoxide concentrations.
3.5.2 Top concentration (TOPCONC)
The recommended method for setting the top concentrations in cells above the
DIFFBREAK is to set them equal to the observed concentration at the top of the region. When
top concentration is not known, the concentration should be no higher than the middle
concentration level of Table 3-2. If measurements are available indicating higher concentrations
exist at the top of the region, they can be used, but the user should verify that they are
representative of more than just a local value before using them for the entire grid.
In the absence of upper air concentration data, the values in TOPCONC would be the low
values from Table 3-2 for a relatively remote area. If there is evidence that considerable
recirculation of carbon monoxide pollutants may occur, these values should be raised to the mid-
level values. The mid-level values should also be used if the region is surrounded by other
urban/suburban areas that would elevate the background pollutant concentrations.
Recommendations
It is recommended that the concentration at the top of the region be used to set
concentrations in cells above the DIFFBREAK. If no data are available for upper air
concentrations, values should be set by referring to Table 3-2, considering the level of
urbanization and the importance of recirculation.
3.5.3 Boundary conditions (BOUNDARY)
The BOUNDARY file contains both the definition of the physical boundaries of the
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TABLE 3-2. Recommended background
concentrations for carbon monoxide
(Killusetal., 1982).
Area Concentration
Value Classification (ppm)
Low Rural 0.1
Middle Suburban 0.2
High Urban 0.5
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region to be modeled and the concentrations along each of the lateral boundaries. When there
are no available data near the lateral boundaries of the region, specification of the boundary
concentrations can be difficult.
Two approaches for specifying boundary conditions for UAM simulations are as follows:
(1) use objective/interpolation techniques with a sufficient amount of measured data (i.e., data
from an intensive field program) and (2) use default background values and expand the upwind
modeling domain to mitigate uncertainties due to paucity of measurements.
Ideally, the preferred technique would be based on an intensive field program. However,
this approach is seldom feasible for historical episodes. Presented next are recommendations
for implementing each technique just identified for deriving boundary conditions, including
discussion of the advantages and disadvantages of each technique. A detailed technical
discussion for developing boundary conditions for a sparse data set is presented in Appendix C.
Use of measured data. All sources of air quality data for a particular modeling domain
should be evaluated for applicability in establishing boundary conditions. Unfortunately, most
ongoing monitoring programs have been designed (understandably so) with a receptor-based
orientation. While available monitoring data are useful for evaluating model performance, they
usually are not adequate for establishing boundary concentrations.
Use of default values - Some urban areas may lack adequate data suitable for establishing
boundary conditions. Section 3.3 on domain selection and Chapter 4 on diagnostic analyses
recommend constructing domains large enough to minimize the sensitivity of inner core and
downwind concentrations to assumed boundary conditions.
Boundary-condition concentrations are influenced by large- and small-scale weather
patterns and emissions distributions that are unique to each modeling domain. Thus, case-
specific attributes should be used in estimating these concentrations whenever feasible. For
example, boundary concentrations of regions where residential wood combustion is significant
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are likely to be higher during very cold conditions than during periods of more mild
temperatures.
Recommendations
To develop boundary conditions, it is recommended that one or more monitoring stations
be sited upwind of the central urban area along prevailing wind trajectories that give rise
to carbon monoxide exceedances.
At the inflow boundaries, air quality data at the surface and aloft should be used
whenever available to specify the boundary conditions.
Those having to use default values should plan to perform diagnostic/sensitivity
simulations (see Chapter 4) to evaluate the sensitivity of domain-interior model
predictions to the boundary conditions.
Table 3-2 lists the recommended default boundary values depending on the area
classification. When using default values, the boundary of the domain should extend as
far upwind as practicable.
3.6 Performance Evaluation Data
Ambient carbon monoxide measurements are needed to diagnose problems in setting up
model applications and assessing model performance for the meteorological episodes being
considered in the attainment demonstration. A lean air quality data base may introduce
significant uncertainties in characterizing model performance. The Technical Committee should
agree on the adequacy of the existing data for assessing model performance.
Recommendations
For lean data bases the Technical Committee should scrutinize in detail the adequacy of
the data base to ensure that model performance that appears to be acceptable has not
actually resulted from compensating errors in the data bases. Additional diagnostic
analyses may be necessary for lean data bases from historical episodes.
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3.7 Emission Inventory
The credibility of UAM applications is directly tied to formulating the best possible
emission inputs. Model performance may hinge on how well emissions are estimated. Also,
in the attainment demonstration, modeling results are used to determine emission scenarios that
lead to improved air quality levels consistent with the NAAQS. A faulty emission inventory
could lead to erroneous conclusions about the extent of needed controls.
Carbon monoxide emissions in most regions are primarily from motor vehicles.
However, in some regions emissions from residential wood combustion may contribute
significantly to the total carbon monoxide emissions. Stationary sources are composed of either
point sources, which are sizeable stationary emission sources at specific locations, or area
sources, which are emissions from stationary and non-roadway mobile sources that are too small
and/or too numerous to be included in the point source inventory (e.g., wood stoves). For most,
but not all, areas of the country, the peak CO season is the wintertime months. Therefore, the
focus of CO emission inventory development is the on-road vehicle emissions for wintertime CO
episodic conditions. However, discussion will begin with an overview of information sources
that are available to explain the development of stationary and area source CO emission
inventories suitable for use with UAM.
Much of the information required to assemble CO modeling inventories will have already
been assembled for the base year inventory, which is required for all CO nonattainment areas
under the Clean Air Act Amendments of 1990 (CAAA). Specific information on base year
inventory requirements is contained in Emission Inventory Requirements for Carbon Monoxide
State Implementation Plans (EPA, 1991b). The base year inventory for the CO SIP submittals
due November 1992 will be from the 1990 base year. Other documents (EPA, 199Ic; EPA,
1988; Laxton, 199Ib) describe the development of emission inventories from raw data, and
specifically address questions regarding the methodology to spatially and temporally resolve CO
emission estimates contained in the base year inventory so that they can be used with the UAM.
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For use in regulatory applications of the UAM, the base year modeling inventory will
have to undergo several adjustments. First, the inventory needs to be adjusted to be consistent
with meteorological conditions during each selected episode (i.e., "base year day-specific
emissions"). Second, the resulting "base year day-specific emissions" should be adjusted to
reflect control programs and activity levels prevailing during the year(s) of selected episodes.
For example, if a selected episode occurred in 1988, the "base year day-specific emissions"
would be further adjusted to reflect controls and activity levels prevailing in 1988. This latter
adjustment is needed to provide an estimate of emissions most suitable for evaluating
performance of the UAM.
As noted in Chapter 1, once the UAM's performance has been evaluated and the model
has been determined to perform satisfactorily, it is used to derive control strategies to attain the
NAAQS. This requires another adjustment to the "base year day-specific emissions" that entails
use of growth factors, ongoing control programs and retirement rates for obsolete sources of
emissions to project "base year day-specific emissions" to the years by which the CAAA specify
that the NAAQS must be attained. The resulting attainment year modeling inventory is used as
a starting point from which to construct a control strategy inventory, which is obtained by
superimposing additional control measures on sources of emissions in the attainment year
modeling inventory.
In summary, a base year modeling inventory is first adjusted to evaluate UAM
performance. The base year modeling inventory is then readjusted to reflect emissions most
likely to occur at the time the CAAA require attainment of the NAAQS.
Two emission files drive the UAM~a file of emissions that are injected into the first,
surface-based vertical layer, and a file of elevated point source emissions that are injected into
vertical layers above ground level. The UAM Emissions Preprocessing System (EPS) (EPA,
1992b) reads county-level area- and point-source files and performs three major functions: (1)
area sources and point sources are allocated to grid cells; (2) temporal profiles are assigned to
source categories; and (3) point sources with effective plume heights greater than a prescribed
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cutoff level are assigned to the elevated point source file, and the remaining point sources are
assigned to the surface-layer emissions file.
Emission inventories are developed for stationary area, stationary point, and off-road
emission inventories that are appropriate for the UAM. The following issues arise in developing
emission input data: (1) use of surrogate factors to grid area sources, (2) treatment of mobile
sources, (3) episodic adjustment of inventories to day-specific modeling inputs, and (4)
consistency with national inventories.
3.7.1 Use of surrogate factors to spatially grid area sources
Area source emission data, including motor vehicle emission data, are often supplied on
a county basis. Spatial allocation of county-level emission estimates to grid cells is performed
for each identified area source category and requires use of surrogate distribution factors such
as population distribution, land use, and road type. The UAM EPS (EPA, 1992b) contains a
program that uses gridded surrogate factors to allocate county-level emissions data to the grid
cell size of the modeling domain.
Recommendations
It is recommended that the emission inventory guidance document (EPA, 199 Ic) be
consulted for alternative surrogate factor choices and sources of information for
assimilating surrogate data. The EPA is currently developing a utility to provide gridded
surrogate data. States will be notified of the availability of gridded surrogate data
through the EPA regional offices.
3.7.2 Development of mobile source inventories
Under the requirements of the CAAA, all CO nonattainment areas are required to
develop base year inventories of mobile sources. These represent base year emission levels for
a typical operating day during the designated peak CO season. As part of the development of
a base year inventory, estimates of VMT must be developed from traffic ground counts
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consistent with the existing Highway Performance Monitoring System (HPMS). Projections of
vehicle miles traveled (VMT) growth must be developed for moderate CO nonattainment areas
through historically based extrapolation techniques if better methods are not locally available.
Non-exempt serious areas must use a transportation demand model to project VMT growth
(EPA, 1991a). These VMT estimates will serve as the basis of the modeling inventory.
Guidance for making projections is detailed in Procedures for Preparing Emissions Projections
(EPA, 199 If).
The mobile source CO emission are complex and difficult to prepare because they are
strongly affected by the ambient temperature and motor vehicle speed. There are two primary
ways to prepare a mobile source inventory. One relies upon county-wide estimates of VMT,
which are multiplied by a "composite" emission factor (expressed as grams pollutant emitted per
mile of travel). The emission factor is referred to as "composite" in that it is a weighted
average value that is representative of the average vehicle operated under average conditions
(Ireson et al., 1991). The other method relies upon the output of transportation models. These
models can provide roadway link-specific estimates of distance, traffic volumes, and speed, and
estimates of trip ends for specific transportation analysis zones (TAZ), which are combined with
"composite" emission factors to arrive at regional mobile source emission inventories. Examples
of commonly used travel demand models are the Urban Transportation Planning System (UTPS),
which is maintained by the U.S. Department of Transportation, and commercial travel demand
modeling software packages such as TRANPLAN (UAG, 1990) and MINUTP (Comsis Corp.,
1991). These models have traditionally been used in regional transportation planning offices as
planning tools for roadway expansions, highway development, land use, etc. However, the
information provided by travel demand models, coupled with EPA's MOBILE model (EPA,
199Id), can be used to produce better estimates of mobile source emissions rather than estimates
based on county-wide VMT estimates.
Recommendations
Use of output from a transportation demand model is the preferred approach for
estimating vehicle activity levels and emission factors because this method allows
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resolution of variations in speed and vehicle miles traveled (VMT) among different grids
over hourly time slices. The transportation demand model approach is the most
appropriate for addressing the inner urban core of modeling domains. Peripheral, less
dense traffic areas can be treated by disaggregating county-level estimates of VMT. Care
should be taken not to double count mobile source emissions as part of both the
transportation demand model approach and county level disaggregation approach (EPA,
1992b). Exceptions to these recommendations should be considered by the Technical
Committee on a case-by-case basis. Justification for more extensive use of
disaggregating county-level emissions should be sought in discussions with the
appropriate EPA regional office.
Appendix C contains a detailed technical discussion of procedures specific to the use of
EPA's latest MOBILE model to develop emission factors for area-wide CO modeling.
3.7.3 Episode-specific adjustments
Both motor vehicle and residential wood combustion emissions are sensitive to ambient
temperature. Thus, it is important for modeling inventories to reflect episode-specific ambient
temperature. In addition, known episode-specific events such as changes in process operations
for point sources affect emissions rates and should be reflected in the episode modeling
inventory.
Recommendations
Mobile-source emissions should be adjusted for episode-specific temperatures. Emission
factors for deriving episode-specific mobile-source emissions should use the latest
MOBILE model. Use of models other than the latest EPA MOBILE model should be
reviewed by the Technical Committee on a case-by-case basis, and is subject to approval
by the EPA regional office.
If available, episode-specific operating rates for point sources are preferable for
estimating temporal point-source emissions. Procedures for temporally adjusting point
and area sources are also provided in the emission inventory guidance document.
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3.7.4 Consistency with national inventories
Comparisons should be made between the modeling inventory and the 1990 SIP and RFP
tracking emission inventories reported in the EPA Aerometric Information Retrieval System
(AIRS). Although these inventories will not be identical, such a check can be considered part
of the quality assurance process. Major inconsistencies should be noted and documented.
Recom mendation s
For an acceptable attainment demonstration, documentation should be provided that
shows that the modeling emission inventory is consistent with the emission inventory
being reported in AIRS in accordance with applicable guidance and regulations.
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CHAPTER 4
DATA QUALITY ASSURANCE AND MODEL DIAGNOSTIC ANALYSES
This chapter provides general guidance for quality assurance testing of component data
input fields and diagnostic testing of base case episodes. These analyses are designed to
establish and improve reliability of the input data and proper functioning of the model.
Although the UAM has been evaluated on a number of historical data bases, measures
of model behavior with respect to observed data are necessary for new applications. Model
developers and users perform diagnostic tests to uncover potential input data gaps that, when
corrected, may lead to unproved treatment of model processes. Regulators need some indication
that the model captures the key features of the base meteorological episodes being applied in the
model simulations in order to have confidence in the model's ability to predict future carbon
monoxide (1) after applying projected growth and planned emission controls, and (2) after
applying alternative emission control strategies.
Important prerequisites for a model performance evaluation (see Chapter 5) are (1)
quality assurance testing of model inputs and (2) diagnostic testing of the base meteorological
episode simulation to ensure that the model is functioning properly and that apparently accurate
model results are being obtained for the right reasons. For example, quality assurance testing
of input data helps to ensure that apparently good model results have not resulted from
compensating errors in input data.
An excellent compilation of model performance evaluation techniques, including
diagnostic tests and related issues, is contained in Tesche et al. (1990). Although the Tesche
study was developed for photochemical modeling, much of what is presented is applicable to
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area-wide CO modeling. The study by Tesche also serves as the basis for the model
performance evaluation described in Chapter 5 of the work reported here. Various graphical
and numerical measures described in the following paragraphs are treated in detail in Tesche et
al. (1990). Two graphical displays used for both quality assurance and diagnostic testing are
mapping and time-series plots.
Mapping is a two- or three-dimensional spatial display of values illustrated with various
contouring and tiling methods. These displays may depict political boundaries and monitoring
site locations as well. Mapping capability is a multipurpose tool applicable for all forms of
gridded data, such as future-year emission control strategy results and most input data fields
(e.g., gridded wind fields, temperatures, and emission densities). Point- source locations may
also be depicted to ensure that they are properly located. Spatial displays of predicted and
observed carbon monoxide patterns are particularly useful as part; of a model performance
evaluation.
Time-series plots display hourly and eight-hourly measured and predicted carbon
monoxide values for specific locations such as monitoring sites. Time-series plots provide an
overview of the temporal performance of the model predictions. Comparison of time-series plots
across multiple monitoring sites provides an indication of spatial response. These plots may
provide insights to carbon monoxide prediction patterns and also to data base inconsistencies
requiring further investigation.
The following sections describe steps recommended for conducting diagnostic testing of
each base case meteorological episode simulation.
4.1 Step 1; Quality Assurance Testing of Component Fields
Starting with initial, quality-assured data, input data are developed for use in various
UAM preprocessors. The first stage of diagnostic testing should focus on assessing the accuracy
of major UAM input fields produced by the UAM preprocessors. Generally, the testing is
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qualitative in nature and based on comparing visual displays of preprocessor outputs with
patterns exhibited by the observed data. Prior to conducting a base case meteorological episode
simulation, individual air quality, meteorological, and emissions fields should be reviewed for
consistency and obvious omission errors. Both spatial and temporal characteristics of the data
should be evaluated. These checks may be only cursory, but errors uncovered by this
component testing might be extremely difficult to diagnose later in the modeling process, when
errors could arise from any subset of the data inputs. Examples of component testing include
the following:
Air Quality: Check for correct order of magnitude, especially when using background
values
Emissions: Plot various source types by grid cell and review major source locations
with local emissions patterns; check major highway routes; generally, look
for obvious omission errors; plot CO by grid cell and cross-check with
source distribution for logical patterns, such as high motor vehicle
emissions near the urban core
Meteorology: Plot surface and elevated wind vectors and compare with monitoring
stations and weather maps for consistent patterns; compare diffusion
break heights with sounding data; check temperature fields
In quality assurance testing of component input fields, the emphasis is on capturing large
errors before performing model simulations.
Recommendations
It is recommended that quality assurance testing of the air quality, emissions, and
meteorological data input files be conducted before proceeding to diagnostic testing of
the base case meteorological episodes. At a minimum, emissions data should be quality
assured by looking at emission distribution maps and known source locations and
emission strengths.
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4.2 Step 2; Diagnostic Testing of the Base Case Meteorological Episodes
After confidence in the accuracy of UAM input fields has been achieved, the UAM
should be exercised for each base case meteorological episode. The initial run is termed a
diagnostic simulation because review of initial base case simulations may uncover additional
input errors requiring correction before an acceptable set of base case inputs can be derived.
During this stage of the process, emphasis is placed on assessing the model's ability to correctly
depict area-wide distribution and the timing of observed carbon monoxide maxima.
Accordingly, visual methods such as mapping and time-series plotting, using measured data as
reference marks, may be used to assess model behavior.
Recommendations
To aid in interpreting simulation results, it is recommended that predicted and observed
carbon monoxide concentration maps be constructed for each base meteorological episode
simulation. Concentration maps present spatial information on the structure of the carbon
monoxide cloud. Maps at 1-hour intervals should be constructed over the modeling
period.
Consideration should also be given to constructing a map that depicts the highest
predicted 1-hour and 8-hour maximum carbon monoxide value for each grid cell.
Examples of various mapping techniques are described in Tesche et al. (1990).
It is also recommended that the method used to predict concentrations used in time-series
plots be consistent with the method for deriving predicted concentrations for the model
performance evaluation described in Chapter 5. Time-series of both 1- and 8-hour
average periods should be constructed.
Other methods for deriving predicted concentrations for time-series comparisons may be
judged appropriate by the Technical Committee; some suggestions are contained in
Chapter 5. These methods should be described in the Modeling Protocol.
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4.3 Step 3; Additional Base Meteorological Episode Sensitivity Testing
In addition to running the base meteorological episode diagnostic simulation, other
episode diagnostic simulations that perturb levels of emissions, initial and boundary conditions,
and meteorological inputs may provide valuable information for identifying critical input areas
and ensuring proper domain and episode selection. The following simulations, which are
equivalent to sensitivity tests on major model inputs, illustrate the utility of this exercise.
1. Zero emissions. To indicate levels of sensitivity to emissions, all emissions are
set to zero and the resulting predicted concentrations are compared with the base
meteorological episode predictions that include emissions. A lack of substantial
sensitivity may indicate a need to reexamine the selection of episodes or domains.
Variations can be performed by zeroing out emission subsets, such as mobile-
source emissions, and individual source categories.
2. Zero boundary concentrations. Inflow concentrations at the lateral boundaries and
top of the modeling domain are reduced to zero or low background levels.
Sensitivity of concentrations in the inner core and downwind portions of the
modeling domain provide a measure of the boundary conditions' influence. This
simulation provides assurance that the upwind extent of the domain is adequate.
3. Zero initial concentrations. Initial concentrations for all grid cells are reduced to
zero or low background levels. Sensitivity of concentrations within the modeling
domain provides a measure of the initial conditions' influence. Changes of less
than a few percent indicate that the initial conditions are not dominating
concentration estimates with the domain.
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\ 4. Diffusion break and wind speed variations. Much uncertainty is associated with
the diffusion break and wind speeds, and simulated concentrations are often
sensitive to these inputs. Simulations that test the sensitivity of model estimates
to variations in wind speed and/or diffusion break provide bounds on some of the
uncertainty resulting from these parameters. Large sensitivity values may suggest
that future model applications will need improvement in the meteorological data
bases.
Certain numerical measures, which are recommended in the discussion of model
performance evaluation in Chapter 5, are also useful diagnostic tools. For example, consistent
underpredictions usually produce numerical values greater than zero using these measures (see
Appendix B). This phenomenon could be due to various factors, such as overstatement of wind
speeds or diffusion break heights, or underestimation of emissions or the number of vertical
layers. Modelers are encouraged to use numerical as well as graphical techniques in the
diagnostic process.
The diagnostic analyses described in this chapter are considered to be a starting point for
a specific modeling study. Diagnostic tests discussed in Tesche et al. (1990) should be
considered whenever possible.
Recommendations
Diagnostic testing of the model should begin with quality assurance testing on input data
files (Section 4.1). Diagnostic testing of each base meteorological episode should follow
(Section 4.2). Additional diagnostic sensitivity tests for the base episode should also be
considered (Section 4.3), including using zero emissions and/or zero boundary
conditions, zero initial conditions, and varying diffusion break and wind speed estimates.
•
Agreement should be obtained among members of the Technical Committee concerning
input field modifications arising from the quality assurance testing. These modifications
should be based on scientific or physical reasoning and not just on what will improve
model performance. All changes to the data that result from the diagnostic testing should
be documented and justified.
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In addition, all diagnostic steps should be documented to avoid misinterpretation of model
performance results. After confidence is gained that the simulation is based on
reasonable interpretations of observed data, and model concentration fields generally
behave both spatially and temporally with known carbon monoxide distribution, a
performance evaluation based on numerical measures is conducted for each base
meteorological episode (see Chapter 5).
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CHAPTERS
MODEL PERFORMANCE EVALUATION
The purpose of the UAM application is to simulate a historical CO episode in order to
estimate the area-wide contribution to concentrations measured at neighborhood-scale monitors.
Once the model has adequately simulated observed CO magnitudes and spatial and temporal
patterns for a historical day, greater confidence can be placed in its ability to provide reliable
estimates of concentrations under the same meteorological conditions for a variety'of emission
scenarios. Thus UAM applications can serve as predictors of future concentrations and assist
in the formulation of control strategies for attaining CO air quality standards.
Comparison of model predictions with observed values may lead to uncertainty, especially
if samplings are from locations not representative of area-wide concentrations. Furthermore, the
UAM output represents a volumetric (typically, 8 x 107 m3 ) concentration, whereas air quality
data represent point locations that may or may not represent the same volume. Because of these
uncertainties, specification of rigid rejection/acceptance criteria has not been generally supported
by model developers or decision makers participating in previous modeling efforts. Instead,
performance measures based on past modeling applications are thought to provide a reasonable
benchmark for acceptable model performance.
Poor performance may necessitate (1) delaying model applications until further diagnostic
testing and quality assurance checks are reflected in the input data base, or (2) selecting another
meteorological episode for modeling. Cases where good model performance is shown should
also be reviewed because compensating errors can induce spurious agreement among observed
and predicted values.
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5.1 Performance Measures
This section describes recommended graphical and statistical performance measures for
carbon monoxide predictions.
The measures used in the performance evaluation should include both qualitative (e.g.,
graphical) and quantitative (e.g., statistical) analyses. Statistical measures may provide a
meaningful test of model performance for dense monitoring network!!, such as those for special
field studies. However, for some routine monitoring networks where coverage may be sparse,
statistical measures may provide a distorted view of model performanee, especially for paired
values.
Tesche et al. (1990) provides detailed descriptions of graphical and statistical measures
available for assessing model performance. Although these methods were formulated primarily
for photochemical grid models, most of the model performance measures are applicable to area-
wide carbon monoxide modeling. The Technical Committee should consult the Tesche study
when formulating model performance evaluation methods, and may want to use it for developing
additional performance evaluation procedures other than those recommended in this guidance
document.
5.1.1 Graphical performance procedures
Graphical displays can provide important information on qualitative relationships between
predicted and observed concentrations. At a minimum, the following graphical displays should
be developed for each meteorological episode: time-series plots and ground-level isopleths.
Time-series plots. The time-series plot, developed for each monitoring station in the
modeling domain, depicts the hourly predicted and observed concentrations for the simulation
period. The time series reveals the model's ability to reproduce the peak prediction, the
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presence of any significant bias within the diurnal cycle, and a comparison of the timing of the
predicted and observed maxima.
Ground-level isopleths or tile maps. Ground-level isopleths or tile maps display the
spatial distribution of predicted concentrations at a selected hour. Isopleths of predicted maxima
may also be constructed. The isopleths provide information on the magnitude and location of
predicted carbon monoxide concentration. Superimposing observed hourly or daily maximum
concentrations on the predicted isopleths reveals information on the spatial alignment of
predicted and observed concentrations.
Recommendations
At a minimum, the following graphical displays are recommended in the evaluation of
each meteorological episode:
Time-series plots of predicted* and observed hourly carbon monoxide values should be
constructed for each simulation period for each monitoring station where data are
available.
Ground-level isopleths or tile maps of the spatial distribution of predicted concentrations
should be constructed for selected hours. Also, ground-level isopleths or tile maps of
the carbon monoxide maxima should be constructed. The corresponding observed
concentrations should be superimposed on the predicted concentration isopleths to analyze
spatial patterns and carbon monoxide magnitudes.
Additional graphical displays such as scatterplots of predictions and observations may
also be used to assess model performance. The graphical displays to be used in the
model performance evaluation should be described in the modeling protocol.
5.1.2 Statistical performance measures
Statistical measures provide a useful measure of model performance for spatially dense
monitoring networks; however, for routine urban area CO monitoring networks, the typically
*For this purpose, the predicted value is the weighted average of the predictions from the
four grid cells nearest to the monitoring station. The four-cell weighted average is derived
from bilinear interpolation as described in EPA (1991e).
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sparse coverage may result in a statistically distorted view of model performance. However,
on the basis of UAM applications in past area-wide CO modeling, it is recommended that the
following three statistical criteria be applied to all neighborhood-scale monitors (and, if
applicable, roadway intersection monitors showing persistently high CO values during low traffic
volumes):
Recommendations
It is recommended that, at a minimum the following three formulations be applied as
measures for model performance evaluation.
1. Unpaired (time or space) highest 8-hour prediction accuracy. This measure
quantifies the difference between the highest observed 8-hour value and the
highest predicted 8-hour value over all hours and monitoring locations.
2. Average absolute error in 8-hour peak prediction accuracy paired ftime and
space') values greater than 5.0 ppm . This measure quantifies the difference
between the highest observed 8-hour value and the highest predicted 8-hour value
at the time and location of each observed maximum.
3. Average absolute error in the predicted time of the 8-hour peak concentration.
paired by station values greater than 5.0 ppm . This measure quantifies the
difference between the highest observed 8-hour value and the highest predicted
8-hour value at the location of each observed maximum within a window of time.
Additional statistical measures may also be applied. Other available measures are listed in the
Guideline for Regulatory Application of the Urban Airshed Model (EPA, 1991e).
5.2 Assessment of Model Performance
As noted, both graphical and statistical performance measures should be used for the
performance evaluation. However, statistical measures should be used with caution when
interpreting results derived from sparse monitoring networks. The Technical Committee should
consider the monitoring network design in interpreting statistical measures.
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Although only a limited number of UAM area-wide CO modeling evaluations have been
conducted, the following statistical performance measures should be achievable for SIP
applications:
• Unpaired (time or space) highest 8-hour prediction accuracy: ± 30-35
percent
• Average absolute error in 8-hour peak prediction accuracy for paired
values (time and space) > 5.0 ppm: 25-30 percent
• Average absolute error in the predicted time of the 8-hour peak
concentration, paired by station > 5.0 ppm: 2 hours
In general, performance results that fall within these ranges would be acceptable. However,
caution is urged in using these ranges as the sole basis for determining the acceptability of model
performance. These ranges were derived from limited past model performance evaluations with
varying densities of air quality and meteorological monitoring networks and corresponding
variations in the quality and quantity of aerometric model input data. In some cases, they reflect
use of earlier versions of the UAM. Thus, these ranges should be used in conjunction with the
graphical procedures to assess overall model performance.
If statistical results are worse than the above ranges and graphical analyses also indicate
poor model performance, users should consider choosing an alternative meteorological episode
for modeling. Performance evaluations should be done on other candidate episodes to identify
those that might result in better model performance.
If statistical results are worse than the above ranges for any of the three statistics, but
graphical analyses generally indicate acceptable model performance, simulation results used for
attainment demonstration should be applied with caution. Users may consider conducting
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performance evaluations on other candidate episodes to identify any that might yield improved
model performance.
When performance is less than expected, the assumptions and data used as model inputs
should be assessed, and probable causes of poor performance should be examined before the
model is applied to attainment demonstrations. If performance is poorer than expected using the
existing aerometric data as model input and the existing data base is considered inadequate, the
responsible regulatory agency should consider an enhanced monitoring program to improve the
aerometric data base for future attainment demonstration modeling studies.
Decisions regarding the acceptability of using a modeling episode with poor performance
should be reviewed and approved by the EPA regional office. Supporting documentation should
include a discussion as to why performance was poorer than expected, and the potential adverse
effects of poor model performance on control strategy evaluations.
Recommendations
It is recommended that the model performance for each meteorological episode be
assessed as follows:
1. The graphical performance procedures specified in Section 5.1.1 should be conducted
for each meteorological episode. To assess model performance, the Technical Committee
should review the time-series plots and ground-level isopleth plots.
2. The statistical performance measures specified in Section 5.1.2 should also be derived
and evaluated for each meteorological episode. When interpreting these measures, the
monitoring network density and design should be considered. Caution is urged when
interpreting the statistical measures for a sparse monitoring network.
It is recommended that the statistical performance measures be compared with the
following ranges:
• Unpaired highest 8-hour prediction accuracy: ±30-35 percent
• Average absolute error in 8-hour peak prediction accuracy for paired (time
and space) values > 5.0 ppm: 25-30 percent
• Average absolute error in the predicted time of the 8-hour peak
concentration, paired by stations > 5.0 ppm: 2 hours
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If all of these statistical measures are within the ranges shown, and the graphical
performance procedures also are interpreted to yield acceptable results, then the model
is judged to be performing acceptably.
If any of the statistical measures are worse than the above ranges, or the graphical
procedures are interpreted to yield unacceptable performance, users should consider
choosing an alternative highly ranked meteorological episode for the attainment
demonstration. Performance evaluations should be conducted on a prospective alternative
episode to determine whether it yields improved model performance.
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CHAPTER 6
ATTAINMENT DEMONSTRATION
The primary purpose for conducting area-wide and roadway intersection modeling is to
demonstrate the effectiveness of control strategies in attaining the National Ambient Air Quality
Standard (NAAQS) for CO. Such demonstration of effectiveness consists of four parts:
1. Development of attainment-year base case emission inventories;
2. Development of future-year emission control strategies;
3. Performing attainment year model simulations to assess control strategies;
4. Use of modeling results in the attainment demonstration.
6.1 Developing Attainment Year Base Case Emission Inventories
Base year inventories and initial and boundary concentrations must be projected to the
future attainment year. The future modeling year is a function of the attainment dates required
in the CAAA of 1990. Projections of base year inventories reflect the net effect of existing
required controls and growth projections for all source types.
The methodology for creating future year emission inventories is contained in Procedures for
the Preparation of Emission Projections (EPA, 1991f), which covers development of emission
projections for stationary and mobile sources. When transportation model outputs are used to
develop the mobile source portion of the inventory through the use of a mobile source emission
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model (e.g., TRFCONV, Causley, 1992), questions of which base inventory to use for
developing vehicle emissions growth estimates must be resolved. Appendix C provides a
technical discussion of procedures for developing vehicle emissions growth estimates.
Projection to future year boundary and initial concentrations is usually accomplished by
applying factors that are the ratio of the future year emission totals to the base year totals. An
exception to this occurs when low values have been specified for all boundary conditions. These
conditions would not be expected to become even cleaner, so they would remain the same.
Before applying the factors, a background value that is assumed to be unaffected by the emission
controls being considered is subtracted. Then the factor is applied to the remaining values and
the background is added back on. If the concentration is already at or below the background
value, it is left unchanged. The background values used are normally the mid-level
concentrations of 0.2 ppm. In a few cases, there might be some question about whether the
boundary conditions should be scaled. For example, if the boundary concentrations are affected
by an upwind urban area, will control measures similar to those being applied in the current
simulation be in effect in the upwind area? If so, should the current simulation reflect the
change due to local emission controls or changes due to all controls? If upwind controls will
be in effect, boundary concentrations should be reduced to reflect control changes; simulation
results should reflect all changes, not only local ones.
Recommendations
It is recommended that the EPA guidance document entitled Procedures for Preparing
Emissions Projections EPA (1991f) be consulted for developing attainment-year
inventories. The guidance document provides procedures for projecting point-source,
area-source, mobile-source, and addresses projections of spatial and temporal, changes
between the base year inventory and the attainment-year inventory.
6.2 Developing Future Year Emission Control Stratc
Numerous future year emission control strategies can be developed and simulated using
the area-wide model and the roadway intersection model. Eventually, a modeling analysis must
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be submitted for approval for a SIP demonstration. The effectiveness of a given set of control
measures in reducing CO is an important factor in selecting the final strategy.
Prior studies have used a number of control measures to ascertain an effective control
strategy for attainment. Because of the low reactivity of CO in the wintertime, there is a near
linear relationship between domain-wide predicted changes in CO emissions and the domain-wide
predicted changes in CO concentrations for future year simulations. However, depending upon
the control strategy employed, highly nonlinear changes may occur within each model grid cell.
For example, control strategies which employ the use of such transportation control measures
(TCM's) as a parking management program (PMP) will reduce CO emissions locally and within
and near a model grid cell, but will have much less effect elsewhere in the modeling domain.
Prior studies have typically used a progression of control strategy scenarios in the
modeling to ascertain an effective strategy for attainment. A suggested logical progression is
the following:
1. Simulate the CAAA and other mandated control measures for the attainment year
to determine if these measures are sufficient to demonstrate attainment.
2. If mandated controls are insufficient to demonstrate attainment, then the
approximate emission-reduction targets may be estimated by the use of linear
rollback.
3. Once an approximate target range is ascertained in steps 1 and 2, simulate control
strategies that reflect source-specific or source-category-specific control measures
that realize the approximate emission reductions identified as sufficient to reduce
maximum 8-hour CO concentration to 9.0 ppm or less.
4. Adjust the strategy chosen in step 3 until it is sufficient to demonstrate attainment
of the CO NAAQS, as described in Section 6.4.
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Recommendations
Find the emission reduction target needed to establish attainment by using linear rollback
of emissions. Using this as an estimate, the level of emission reductions needed for
control strategies can be assessed. Adjustments to the strategy should continue until it
is sufficient to demonstrate attainment. An outline of procedures for deriving control
strategies for evaluation in the attainment demonstration must be specified in the model
protocol.
6.3 Performing Attainment-Year Simulations to Assess Various Control Strategies
Many graphical display and numerical procedures are available for illustrating the effects
of alternative emission control strategies on predicted concentrations of carbon monoxide. For
example, the emission levels in the control strategies are often compared with the attainment-
year base emissions. Also of interest are comparisons with the inventory derived for purposes
of model performance evaluations and corresponding base-case UAM results. Difference maps
are extremely useful for illustrating changes in the daily 8-hour maximum carbon monoxide
predictions throughout the modeling domain.
Recommendations
The primary focus of the carbon monoxide attainment demonstration is on the maximum
8-hour concentration predicted at each location in the modeling domain. However, in
some cases the scope of the attainment demonstration should be broader to assess the
effects on the subdomain and temporal impacts.
6.4 Using Modeling Results in the Attainment Demonstration
To demonstrate attainment of the carbon monoxide NAAQS, the combined results from
the areawide and roadway intersection modeling should show no predicted 8-hour maximum
carbon monoxide concentrations greater than 9.0 ppm anywhere in the modeling domain for the
episode modeled. Procedures for combining the area wide and roadway intersection modeling
are given in EPA (1992c). Alternative methods for demonstrating attainment must be approved
by the appropriate EPA regional office on a case-by-case basis.
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The attainment test described in the preceding paragraph is consistent with the flexibility
allowed in the choice of episode day (see Section 3.2) and reflects concerns over the difficulty
of accurately estimating emissions inputs to the model.
Recommendations
To demonstrate attainment of the carbon monoxide NAAQS, the combined results of
areawide and roadway intersection modeling should show no predicted 8-hour maximum
carbon monoxide concentrations greater than 9.0 ppm anywhere in the modeling domain
for the episode modeled.
States may opt to conduct more comprehensive statistical testing of the modeling results
for the attainment demonstration. Any alternative methods for attainment demonstration
must be approved by the appropriate EPA regional office on a case-by-case basis. Any
optional methods should be agreed upon during the development of the modeling
protocol.
6.5 Exceptions to Guidance Document
It is not possible in a general guidance document like this to anticipate all contingencies
associated with developing an attainment demonstration study. The modeling policy oversight
and technical committees responsible for a specific modeling study may propose an alternative
modeling approach provided that (1) the modeling protocol requires consensus on the proposed
alternative approach within the Technical Committee, and (2) justification for the proposed
approach is documented. Application of any alternative grid modeling approach must first
receive concurrence in writing from the responsible EPA regional office(s).
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO. 2.
EPA-450/4-92-011a
4. TITLE AND SUBTITLE
Guideline for Regulatory Application of the Urban
Airshed Model for Areawide Carbon Monoxide
Volute Tt TeH-m-iral Report
7. AUTHOR(S)
Edward L. Carr, Julie L. Fieber and Robert C. Kessler
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Systems Applications International
San Rafael, California 94903
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, N.C. 27711
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE
June 1992
6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT N
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
13. TYPE OF REPORT AND PERIOD COVERE
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
State implementation Plan (SIP) revisions demonstrating attainment of'the
Carbon Monoxide (CO) National Ambient Air Quality Standards (NAAQS) are required
under the Clean Air Act Amendments of 1990. Urban areawide modeling and
intersection modeling are recommended to address attainment of the CO NAAQS. The
Urban Airshed Model (UAM) has been identified as an effective urban areawide
model for evaluating emission control requirements needed to attain the CO NAAQS.
The purpose of this document is to provide guidance in the procedures used to apply
the UAM for CO SIP attainment demonstrations.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
Atmospheric Dispersion Models
Carbon Monoxide (CO)
State Implementation Plan (SIP)
Clean Air Act Amendments
Urban Areawide Modeling
18. DISTRIBUTION STATEMENT
b. IDENTIFIERS/OPEN ENDED TERMS
19. SECURITY CLASS (This Report!
20. SECURITY CLASS (This page)
c. COSATl Field/Group
21. NO. OF PAGES
65
22. PRICE
EPA Form 2220-1 (R«v. 4-77) PREVIOUS ECITION is OBSOLETE
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