GUIDELINE FOR REGULATORY APPLICATION OF THE

 URBAN AIRSHED MODEL

 U.S. ENVIRONMENTAL PROTECTION AGENCY

 Office of Air Quality Planning and Standards

 Technical Support Division

 Source Receptor Analysis Branch
 Research Triangle Park, NC 27711

 July 1991
 DISCLAIMER

 This report has been reviewed by the Office of Air Quality

Planning and Standards, U.S. Environmental Protection Agency, and

has been approved for publication. Any mention of trade names or

commercial products is not intended to constitute endorsement or

recommendation for use.
 CONTENTS

 Page

DISCLAIMER . . . . . . . . . . . . . . . . . . . . . . . . . . ii

TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

ABBREVIATIONS. . . . . . . . . . . . . . . . . . . . . . . . .vii

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . ix90

1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . 1

2 MODELING PROTOCOL . . . . . . . . . . . . . . . . . . . . 5

 2.1 Protocol Development Process . . . . . . . . . . . . 6

 2.2 Contents of Protocol Document. . . . . . . . . . . . 8

3 DOMAIN AND DATA BASE ISSUES . . . . . . . . . . . . . . . 11

 3.1 Episode Selection. . . . . . . . . . . . . . . . . . 11

 3.2 Size of the Modeling Domain. . . . . . . . . . . . . 15

 3.3 Horizontal Grid Cell Size. . . . . . . . . . . . . . 16

 3.4 Number of Vertical Layers. . . . . . . . . . . . . . 17

 3.5 Meteorological Data. . . . . . . . . . . . . . . . . 19

 3.5.1 Wind fields. . . . . . . . . . . . . . . . . 19

 3.5.2 Data needs for wind field

 development . . . . . . . . . . . . . . . . 23

 3.5.3 Mixing heights . . . . . . . . . . . . . . . 24

 3.5.4 Clear-sky assumption for photolysis rate

 calculations . . . . . . . . . . . . . . . . 25

 3.6 Air Quality. . . . . . . . . . . . . . . . . . . . . 26

 3.6.1 Initial and boundary conditions. . . . . . . 26

 3.6.2 Performance Evaluation Data. . . . . . . . . 31

 3.7 Emissions. . . . . . . . . . . . . . . . . . . . . . 32

 3.7.1 VOC speciation . . . . . . . . . . . . . . . 35

 3.7.2 Spatial gridding of area sources . . . . . . 35

 3.7.3 Mobile sources . . . . . . . . . . . . . . . 36

 3.7.4 Episode-specific adjustments . . . . . . . . 37
 3.7.5 Biogenic emissions . . . . . . . . . . . . . 38

 3.7.6 Point-source and plume-rise cutoff levels. . 39

 3.7.7 Consistency with national inventories. . . . 40

CONTENTS (Continued)

 Page

4 DATA QUALITY ASSURANCE AND MODEL DIAGNOSTIC ANALYSES. . . 41

 4.1 Step 1: Quality Assurance Testing of Component

 Fields. . . . . . . . . . . . . . . . . . . 43

 4.2 Step 2: Diagnostic Testing of the Base Case

 Meteorological episodes . . . . . . . . . . 44

 4.3 Step 3: Additional Base Meteorological Episode

 Sensitivity Testing . . . . . . . . . . . . 46

5 MODEL PERFORMANCE EVALUATION . . . . . . . . . . . . . . . . 49

 5.1 Performance Measures . . . . . . . . . . . . . . . . 49

 5.1.1 Graphical performance procedures . . . . . . 51

 5.1.2 Statistical performance measures . . . . . . 53

 5.2 Assessing Model Performance Results. . . . . . . . . 55

6 ATTAINMENT DEMONSTRATION. . . . . . . . . . . . . . . . . 59

 6.1 Developing Attainment-Year Model Inputs. . . . . . . 59

 6.2 Construction of Attainment Year Emission Control

 Strategies . . . . . . . . . . . . . . . . . . . . . 60

 6.3 Performing Attainment-Year Simulations to Assess

 Various Control Strategies . . . . . . . . . . . . . 62

 6.4 Using Modeling Results in the Attainment

 Demonstration. . . . . . . . . . . . . . . . . . . . 63

 6.5 Exceptions to Guidance Document. . . . . . . . . . . 64

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . 65

APPENDIX A RECOMMENDED MODELING PROTOCOL CONTENTS . . . . . . 69

APPENDIX B IDENTIFICATION OF METEOROLOGICAL REGIMES

 CORRESPONDING WITH HIGH OBSERVED OZONE . . . . . . 77
APPENDIX C PERFORMANCE MEASURE FORMULATIONS . . . . . . . . . 81
TABLES

 Number 

 1 Example Table of Contents for Protocol

 Document . . . . . . . . . . . . . . . . . . . .9

 2 Default Boundary Condition Concentrations for

 Carbon Bond-IV Species . . . . . . . . . . . 28

 ABBREVIATIONS

AIRS Aerometric Information Retrieval System

BEIS Biogenic Emissions Inventory System

CAAA Clean Air Act Amendments

CARB California Air Resources Board

CMSA Consolidated Metropolitan Statistical Area

CO Carbon Monoxide

CSC Computer Sciences Corporation

DWM Diagnostic Wind Model (UAM preprocessor program)

EKMA Empirical Kinetic Modeling Approach

EPA U.S. Environmental Protection Agency

EPS Emissions Preprocessor System for the UAM

GMISS Gridded Model Information Support System

MSA Metropolitan Statistical Area

NAAQS National Ambient Air Quality Standard(s)

NO Nitric Oxide

NOx Nitrogen oxides

NO2 Nitrogen Dioxide

NSR New source review

NTIS National Technical Information Service

NWS National Weather Service

OAQPS EPA Office of Air Quality Planning and Standards

OMS EPA Office of Mobile Sources

ORD EPA Office of Research and Development

PWD Predominant wind direction

RACT Reasonably available control technology

RFP Reasonable further progress (a type of tracking

 required under Section 182 of the CAAA)

ROM EPA Regional Oxidant Model

SAI Systems Applications International
ABBREVIATIONS (Continued)

SCC Source Category Code

SCRAM BBS EPA Support Center for Regulatory Air Models

 Bulletin Board System

SIP State Implementation Plan

UAM Urban Airshed Model

UV Ultraviolet

VOC Volatile Organic Compound

VMT Vehicle Miles Traveled
ACKNOWLEDGEMENTS

 Mr. Dennis C. Doll is the principal author of this document. 

Dr. Richard D. Scheffe, Dr. Edwin L. Meyer, and Mr. Shao-Hang Chu

also contributed significantly to the guidance document's content. 

Mrs. Cynthia J. Baines was responsible for typing and assembling

the manuscript.

 A draft version of this guidance document has been subject to

public review and comment through inclusion in Docket A-88-04 for

public comment on the Fifth Conference on Air Quality Modeling held

in March 1991. In addition, the authors would like to acknowledge

the efforts of a working group set up to review and advise them

regarding the issues addressed in the guidance document. While all

the recommendations do not necessarily reflect the views of each

working group member nor their organizations, members' free

exchange of ideas and the generous amount of time they spent in

reviewing various drafts added materially to the guidance

document's content. Working group members (in alphabetical order)

included Dr. C.S. Burton (Systems Applications International

Incorporated (SAI, Inc.)), Mr. C. Durrenberger (Texas Air Control

Board), Mr. G. Gipson (U.S. Environmental Protection Agency (EPA),

Office of Research and Development (ORD)), Mr. T. Helms (EPA,

Office of Air Quality Planning and Standards), Dr. H. Jeffries

(University of North Carolina), Mr. M. Koerber (Lake Michigan Air

Directors Consortium), Dr. C. Liu (South Coast Air Quality

Management District), Mr. T. McGuire (California Air Resources

Board), Mr. R. Morris (SAI, Inc.), Dr. S. T. Rao (New York

Department of Environmental Conservation), Dr. P. Roth (private

consultant), Mr. K. Schere (EPA, ORD), Dr. T. Tesche (private

consultant), Mr. J. Vimont (U.S. National Park Service), Mr. D.

Wackter (Connecticut Department of Environmental Protection), and

Dr. S. Ziman (Chevron USA, Inc.).

 Finally, the authors would like to thank Ms. Jeanne R.

Eichinger (CSC) for her thorough review and many helpful editorial

comments.
 CHAPTER 1

 INTRODUCTION

 The Clean Air Act Amendments (CAAA) of 1990 require ozone

nonattainment areas designated as extreme, severe, serious, or 

multi-State moderate to demonstrate attainment of the ozone

National Ambient Air Quality Standard (NAAQS) through photochemical

grid modeling or any other analytical method determined by the

Administrator to be at least as effective. The Environmental

Protection Agency (EPA) has adopted the Urban Airshed Model (UAM)

as the guideline model for photochemical grid modeling applications

involving entire urban areas. Procedures described in this

guidance document are intended to satisfy the CAAA attainment

demonstration requirements, foster technical credibility, and

promote consistency among UAM regulatory applications.

 This guidance document provides recommendations and general

procedural guidance for exercising the UAM (described in 

References 1-5) in regulatory applications. However, methodologies

and procedures discussed in this guidance document generally apply

for other urban-scale photochemical grid models as well. 

Acceptance criteria for alternative models is beyond the scope of

this guidance document. Use of alternative urban-scale

photochemical grid models as well as regional-scale photochemical

grid models other than the EPA Regional Oxidant Model (ROM) must be

addressed on a case-by-case basis through the EPA Regional Offices.

 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, the UAM source code, user's

guide, and test case data base are available from the National

Technical Information Service (NTIS)(703-737-4600). The NTIS

document numbers are noted in the reference list.

 This guidance document provides recommendations and procedures

for conducting an ozone analysis with the UAM for ozone attainment

demonstrations. Some of the recommendations and procedures

described were adopted from the California Air Resources Board

(CARB) Technical Guidance Document: Photochemical Modeling6, and

will be referenced as such throughout the text.
 Steps needed to conduct an urban-scale photochemical modeling

study using the UAM typically consist of the following:

 1. Establish a protocol for the modeling study. 

 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 UAM 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 and chemical phenomena (e.g., wind

 fields, spatial and temporal emission patterns)

 instrumental in leading to observed ozone concentrations. 

 The visible product is enhanced model performance (i.e.,

 better spatial and temporal agreement with observed

 data). Diagnostic model simulations uncover potential

 model input data gaps that, when corrected, may lead to

 improved model performance.

 5. Exercise the UAM for each meteorological episode and

 conduct a series of performance measures to determine

 overall model performance in replicating observed ozone

 concentrations and patterns. Model performance evalua-

 tion should also be done for ozone precursors (e.g., NO,

 NO2) if suitable monitoring data are available. 

 6. For each meteorological episode, estimate emissions and

 air quality for the projected attainment year required

 under the CAAA. Perform model simulations for each

 episode to determine whether the ozone 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

 emission control measures on selected source categories

 (for example, volatile organic compound (VOC) and/or NOx

 controls on selected source categories, alternative fuel

 scenarios, etc.).

 8. Perform model simulations for the emission control

 measures to demonstrate attainment of the ozone 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.

 These steps are addressed in subsequent chapters of this

guidance document as follows:

 Chapter 2: Modeling Protocol

 Chapter 3: Domain and Data Base Issues

!Episode selection

 !Domain selection/grid spatial allocation

 !Meteorological/air quality data

 !Emission inventories

 Chapter 4: Data Quality Assurance and Model Diagnostic

 Analyses

 Chapter 5: Model Performance Evaluation

 Chapter 6: Attainment Demonstration
 CHAPTER 2

 MODELING PROTOCOL

 Regulatory application of the UAM potentially affects a broad

spectrum of society. The UAM modeling domains may encompass

multiple geopolitical boundaries (counties, cities, and States)

with a potentially large regulated community. Therefore, the

development of a Modeling Protocol is required. This Protocol is

necessary 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 for 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 model to demonstrate whether proposed

strategies are sufficient to attain the ozone 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. Modifications to the

Protocol as the study progresses should not be needed unless

significant, 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 (see below). It is especially important that the State/

local agencies and EPA Regional Office(s) overseeing the study

concur on Protocol modifications.

2.1 Protocol Development Process

 Ordinarily, the State agency responsible for developing the

ozone State Implementation Plan (SIP) is also 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. The

Protocol should describe the modeling policy and technical

objectives of the study. This will require input from various EPA

and State/local personnel dealing with regulatory policy issues and
from others with modeling expertise. It is likely that Modeling

Policy Oversight and Technical Committees will be needed for

addressing these issues. The composition and responsibilities of

the Committees should be defined in the Modeling Protocol.

 Responsibilities of the Modeling Policy Oversight Committee

may 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 policyoriented personnel be identified for membership on the Committee.

 Responsibilities of the Technical Committee may be, at a

minimum, to develop the Protocol's technical specifications

concerning emission inventories, meteorological data, air quality

data, data quality assurance, development of 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.

 For some areas, regional modeling is being planned to

establish initial and boundary conditions for urban-area modeling

attainment demonstrations. The urban-area Modeling Protocol

development should be coordinated with the regional Modeling

Protocol. Some members of the urban-area Policy Oversight and

Technical Committees would probably also be members of the regional

Policy Oversight and Technical Committees.

 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 Committees so that rapid review and

approval of the Protocol is assured.

 Recommendations

 A Protocol Document is required for each UAM application used

 for an ozone attainment demonstration. This Protocol should

 describe the methods and procedures to be used for conducting

 the photochemical modeling study. 

 Additionally, it is recommended 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.

 For some nonattainment areas, regional modeling is being

 planned to provide initial and boundary conditions as well as

 other inputs for the urban-area modeling attainment

 demonstrations. Procedures for coordinating the development

 of the urban-area Modeling Protocol with the regional Modeling

 Protocol should be clearly described.

 It is especially important that close technical coordination

 be maintained during the Protocol development among nearby

 urban-area domains within a regional modeling domain. 

 Procedures should be established for coordinating the Modeling

 Protocols among these areas, and these coordination procedures

 should be clearly specified in each nonattainment area's

 Modeling Protocol.

 The Modeling Protocol must be submitted to the appropriate EPA

 Regional Modeling Contact for review and approval.

2.2 Contents of Protocol Document

 The recommended contents of the Protocol Document (Table 1)

are patterned after those described in a CARB Technical Guidance 

Document.6

 Recommendations

 It is recommended that, at a minimum, the components listed in

 Table 1 be included in the Protocol Document for each

 attainment demonstration modeling study. A description of

 each component is presented in Appendix A.

 
 TABLE 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 Regional Modeling Protocols

 Relationship to Other Urban-Area Modeling Protocols

 Relationship to Planning/Strategy Groups

 2. Domain and Data Base Issues

 Applicable Preprocessor Programs (e.g., ROM-UAM 

 Interface System)

 Data Bases:

 !Air quality

 !Meteorology

 Base Meteorological Episode Selection

 Modeling Domain

 Horizontal Grid Resolution

 Number of Vertical Layers

 Emission Inventory

 Specification of Initial and Boundary Conditions

 Wind Field Specification

 Mixing Depths

 Sources of Other Input Data

 3. 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. Attainment Demonstrations

 Identification of Attainment-Year Mandated Control 

 Measures

 Methodologies for Generating Control Strategy

 Emission Inventories

 Procedures for Attainment Demonstration

 
 6. Submittal Procedures

 Data Analyses Review

 Documentation Review and Approval
 CHAPTER 3

 DOMAIN AND DATA BASE ISSUES

 Described in this chapter are the following topics: episode

selection, domain selection, meteorological data, 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 Episode Selection

 A major component of the Modeling Protocol is selection of

meteorological episodes. In general, episode selection involves a

review (described below) of several multiday periods during which

high ozone was monitored. At least 1 day is chosen as the day of

primary interest for each selected episode. Model simulations

typically begin at least one day prior to the day of primary

interest. This minimizes the effects of assumed initial conditions

on predicted concentrations for the critical day. The length of a

modeled episode is generally a minimum of 48 hours, and the last

day in this period--the day of primary interest--is referred to as

the "primary day."

 Episodes that have a high probability of covering different

sets of meteorological conditions corresponding with high ozone

concentrations should be modeled. Clearly, a trained meteorologist

familiar with local and regional weather patterns should be

consulted in the selection process. Conditions resulting in

distinctly different source-receptor configurations should be the

prime consideration in distinguishing different meteorological

regimes. Generally, conditions reflecting both poorly defined wind

flow (stagnation) and better defined flow (transport) will need to

be included. It is important to coordinate episode selection with

those responsible for Modeling Protocols in nearby domains,

particularly when observed exceedances may result from

"overwhelming transport."7

 The following approach is recommended for selecting episode

days for use in modeling:
 1. Identify the meteorological regimes associated with high-

 ozone episodes. The procedure recommended for

 identifying meteorological regimes is described in

 Appendix B.

 2. Select candidate episode days for modeling from the

 period from 1987 to the present time. Place each

 candidate episode day in the appropriate meteorological

 regime (see Appendix B).

 3. Rank each candidate episode day within each regime

 according to the magnitude of the peak observed ozone

 concentration (ranked highest are days with the highest

 observed daily maximum ozone from among all sites in or

 near a nonattainment Consolidated Metropolitan Statis-

 tical Area/Metropolitan Statistical Area [CMSA/MSA]).

 4. Select the episode days for modeling from among the three

 highest ranked episode days from each meteorological

 regime. In choosing from among the top-ranked episode

 days, consider the availability and quality of air

 quality and meteorological data bases, the availability

 of supporting regional modeling analyses, the number of

 monitors recording daily maximum ozone concentrations

 greater than 0.12 ppm (i.e., pervasiveness), number of

 hours for which ozone in excess of 0.12 ppm is observed,

 frequency with which the observed meteorological condi-

 tions correspond with observed exceedances, and model

 performance (discussed in Chapter 5). For example, the

 top-ranked episode day within a meteorological regime may

 have only routine air quality and meteorological data

 bases available for use in the modeling. The third-

 highest day, however, may have occurred during an

 intensive field study, so that a more comprehensive data

 base is available. Thus, the third-highest day may be

 more desirable for modeling than the top-ranked day. As

 another example, the three highest-ranked episode days

 may have air quality and meteorological data bases of

 similar quality and quantity, and the number of

 monitoring sites recording daily maximum ozone greater

 than 0.12 ppm may also be similar. If model performance

 on the initially chosen day is questionable, the

 Technical Committee may wish to consider a second- or

 third-ranked day from the three highest-ranked days for

 a regime. The day with the overall best model

 performance may be selected as the primary day for
 modeling in the attainment demonstration. Note that a

 more comprehensive model performance evaluation may be

 needed for the selected day, as described in Chapter 5.

 5. At least 1 day should be modeled from each identified

 meteorological regime. Further, a minimum of 3 days from

 among all meteorological regimes should be modeled for

 the attainment demonstration (e.g., three meteorological

 regimes each containing 1 primary episode day, or two

 meteorological regimes with at least 2 primary days from

 one of those regimes). Using the model results in the

 attainment demonstration is described in Section 6.4.

 

 States may want to consider a technique other than the one

outlined in steps 1-5 for selecting modeling episodes. Any such

techniques should be described in the Modeling Protocol and

approved by the appropriate EPA Regional Office.

 Consideration of several meteorological regimes that

correspond to observed daily maximum ozone levels above 0.12 ppm is

important, because certain emission control strategies that are

effective in reducing peak ozone under some meteorological

conditions may be less so under others. The goal is to develop

strategies that are robust with respect to effectiveness over most

scenarios.

 Recommendations

 It is recommended that episodes for modeling be selected from

 the period from 1987 to the present time. Selected episodes

 should represent different meteorological regimes observed to

 correspond with ozone >0.12 ppm (as described above). When

 selecting episodes, both stagnation and transport conditions

 should be examined. A minimum of 3 primary episode days

 should be simulated.

 Primary episode days falling within each meteorological regime

 are ranked according to the highest observed daily maximum

 ozone concentration measured within or near the nonattainment

 CMSA/MSA. Episodes may be chosen to include any of the three

 top-ranked days in each regime. In addition to considering

 the magnitude of the highest observed daily maximum ozone

 concentration in making this choice, data availability and

 quality, model performance, availability of regional modeling

 analyses, pervasiveness, frequency with which observed

 meteorological conditions coincide with exceedances, and

 duration of observations >0.12 ppm may be considered.
 Other techniques for selecting episodes should be described in

 the Protocol Document and approved by the appropriate EPA

 Regional Office.

3.2 Size of the Modeling Domain

 The size and location of the modeling domain define the data

requirements for the modeling. In selecting a modeling domain,

consideration should be given to (1) typical wind patterns, (2) the

location of major area and point emission sources, (3) the location

of air quality monitors and important receptor locations, and 

(4) the need to mitigate effects of uncertainty in upwind boundary

conditions. 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.

 Once UAM input data for a sufficiently large domain have been

assimilated and processed, the size of the modeling domain can be

reduced for modeling purposes by specifying domain boundary values

in the UAM. Procedures for reducing the size of the domain are

described in Reference 2. This could save resources in simulating

modeling episodes in which light or poorly defined wind fields

result in a smaller domain being adequate. In contrast, expanding

domain dimensions would require reconstructing most of the UAM

input files.

 Recommendations

 It is recommended that the domain's downwind boundaries be

 sufficiently far from the CMSA/MSA that is the principal focus

 of the modeling study to ensure that emissions from the

 CMSA/MSA occurring on the primary day for each selected

 episode remain within the domain until 8:00 p.m. on that day. 

 The extent of the upwind boundaries will depend on the

 proximity of large upwind source areas and the adequacy of

 techniques7 used to characterize incoming precursor

 concentrations. Large upwind emission source areas should be

 included in the modeling domain to the extent practicable. 

 Also, if large uncertainty is anticipated for domain boundary

 conditions, the upwind boundaries should be located at a

 distance sufficient to minimize boundary effects on the model

 predictions in the center of the domain. Sensitivity analyses

 described in Section 4.3 assist in determining the effects of

 boundary conditions on predicted values.
3.3 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. Generally, large grid square dimensions result in smoothing

of the emission gradients, wind fields, and spatially varying

mixing heights, which in turn leads to a smoothing of the predicted

concentration field. Also, larger grid cell dimensions reduce both

computer storage space and computational time.

 The following should be considered when selecting the

horizontal grid cell size:

 1. The grid cells should be small enough to reflect emission

 gradients and densities in urban areas, particularly

 those resulting from large point sources and major

 terrain or water features that may affect air flow

 2. Sensitivity studies conducted by the EPA suggest that

 peak ozone predictions may increase as grid size

 decreases

 3. Practical limitations on the grid cell size are the

 resolution of the emission inventories and the density of

 meteorological and air quality monitoring networks

 Previous modeling studies have used horizontal grid cell sizes

generally in the range of 2 x 2 km to 8 x 8 km. A grid size of 

5 x 5 km has generally been compatible with computer resource

requirements and emission inventory development.

 Recommendations

 It is recommended that the size of the horizontal grid cells

 should not be greater than 5 x 5 km. Grid cell sizes coarser

 than this should be justified and should, at a minimum,

 address items 1-3 above. Smaller grid cell sizes are

 encouraged because they allow more accurate gridding of area

 and mobile sources. Additionally, emissions from major point

 sources are better characterized by smaller grid cell sizes. 

 However, grid cell sizes smaller than 2 x 2 km are not

 recommended because of potential model formulation

 inconsistencies for those grid sizes.
3.4 Number of Vertical Layers

 In specifying the number of vertical layers, issues analogous

to those raised for horizontal grid cell dimensions must be

addressed. Again, a compromise is generally needed between the

number of vertical layers and the adequacy of available data bases

and computer resources. It is important that sources with tall

stacks or sources having plumes with high buoyancy be assigned to

an appropriate altitude. Pollutants in elevated, buoyant, pointsource plumes often have effective release heights in layers well

above the surface. Increased vertical resolution allows more

accurate representation of the vertical layer at which these

emissions interact with emissions occurring closer to the ground. 

Also, increased vertical resolution minimizes dilution that may

result from placing emissions into artificially large vertical

layers. Finally, increased vertical resolution improves the

simulation of when and where plumes are mixed to ground level. 

Simulation of the chemistry between individual plumes and the

environment can be greatly affected by how well the model simulates

mixing of these plumes with the ambient air.

 Previous applications of the UAM have generally used four or

five vertical layers, with two layers between the surface and the

morning mixing height (diffusion break in the UAM) and three layers

between the mixing height and the top of the modeling domain.

Sensitivity studies suggest that using fewer than three layers

above the mixing height may artificially dilute elevated pointsource plumes, which may cause the model to underpredict nearsurface ozone and precursor concentrations.

 Users of the UAM should consider specifying greater detail for

the horizontal and vertical grid cell size than the minimum

recommended in this guidance document. This is encouraged

particularly in modeling domains containing complex terrain or

land/water interfaces. Wind field models can typically produce

wind fields for many more vertical layers than the minimum number

given here.3 The number of vertical layers considered in the UAM

is more likely to be constrained by the time-consuming calculations

needed to simulate atmospheric chemistry.

 Recommendations

 Based on previous model applications, it is recommended that

 a minimum of five vertical layers be used in the modeling

 study, with at least three layers above the morning mixing

 height (diffusion break in the UAM). Additionally, it is

 recommended that the top of the modeling domain (region top in
 the UAM) be specified above the mixing height by at least the

 depth of one upper-layer cell. This can be done by setting

 the region top value equal to the maximum mixing depth plus

 the minimum depth of the upper-layer cells.

 Previous applications have typically used 50 m for the minimum

 depth of the vertical layers below the diffusion break and 100

 or 150 m for the vertical layers above the diffusion break. 

 It is recommended that 50 m be used as the minimum thickness

 for layers below the diffusion break and 100 m as the minimum

 thickness for layers above the diffusion break.

3.5 Meteorological Data

 The availability of meteorological data varies widely among

prospective modeling domains. Also, there are a variety of

techniques 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 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.

 Wind fields and mixing heights are two of the most important

meteorological inputs that significantly affect photochemical model

predictions. Methodologies and recommendations for determining

these inputs are described below.

 3.5.1 Wind fields

 Methodologies to construct wind fields for the UAM

applications have historically fallen into three categories:6

 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

 More recently, an additional methodology has been developed in

areas where the EPA ROM has been applied. Computer software has

been developed to map a ROM diagnostic gridded wind field into a

nested UAM domain.5

 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.3 Diagnostic models generally

require minimal computer resources and can produce a threedimensional wind field. However, diagnostic models need

representative observational data to generate features such as land

and sea breezes.

 Prognostic models - These models simulate relevant atmospheric

physical processes while requiring minimal observational data. 

Prognostic models require a 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 mixing heights, 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 photochemical grid models is limited.

 The ROM-UAM Interface System - This system can develop a UAM

gridded wind field from a diagnostically derived wind field used in

the ROM. Such a ROM-derived wind field can be applied for a UAM

domain that is nested within a ROM domain, provided ROM data are

available for identical episode periods. Use of ROM data has the
advantage of being easy to implement and also provides a

consistency between ROM model predictions used to specify UAM

boundary conditions and the corresponding wind fields. The ROM

data are based on an approximately 18 x 18 km horizontal grid cell

size. Thus, one disadvantage is that ROM gridded wind fields may

not sufficiently describe detailed features such as land/sea

circulations. A more finely resolved wind field may be obtained

by using the ROM-gridded winds as the initial wind field for the

UAM's Diagnostic Wind Model (DWM) preprocessor (see Reference 3). 

This provides a means for mass consistency when using the ROM data

as boundary conditions in conjunction with another wind model.

 The selection of a specific technique for generating the

domain wind field depends largely on (1) availability of concurrent

ROM diagnostic wind fields, (2) the spatial and temporal resolution

of surface and upper-air observations, (3) available modeling

expertise in applying alternative meteorological models, and (4)

available computer resources. However, some guidelines on

preferences for generating the wind fields are as follows.

 Recommendations

 The ROM-UAM Interface System should be used to derive the UAM

 gridded wind fields when the UAM domain is nested within a ROM

 domain for concurrent time periods and ROM predictions are

 used to derive the hourly UAM boundary conditions. If it is

 judged by the Technical Committee (and identified in the

 Protocol) that a wind field derived from the UAM DWM is more

 representative of the domain-scale flow, then this wind field

 may be used in lieu of the ROM diagnostic wind field. To

 minimize mass inconsistency problems, the ROM-gridded winds

 may be used as the initial wind field in the DWM (see

 Reference 3) when generating the UAM gridded wind field.

 For cases in which concurrent ROM applications are

 unavailable, it is recommended that the DWM be used to

 generate the UAM gridded wind fields. 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.

 3.5.2 Data needs for wind field development

 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 (if that is the technique chosen) 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.

 Time constraints imposed by the 1990 CAAA will probably

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 an historical episode for which model

performance has been deemed acceptable.

 Recommendations

 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 Application9 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 mixing heights. Any special field study and

 monitoring program should be planned in consultation with the

 appropriate EPA Regional Office before implementing the study.

 3.5.3 Mixing heights

 Predictions from the UAM have been shown to be sensitive to

the mixing height field.6 Therefore, the temporal variations in

the mixing height field over the UAM domain should be described as

realistically as possible. The UAM modeling system contains a

methodology for deriving mixing heights (diffusion break in the

UAM) based on surface temperatures, vertical sounding measurements

of temperature, and cloud cover (see Reference 2). However,

because of the diversity of techniques and data bases that may be

available on a case-by-case basis, we cannot recommend a specific

procedure for deriving the mixing height field in all cases.

 Recommendations

 It is recommended that, at a minimum, the techniques

 described in Reference 2 be used in establishing the mixing

 height field in the domain.

 The choice of upper-air stations to be used in the mixing 

 height 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. A trained

 meteorologist should be consulted on the selection of upper-

 air stations for use in determining mixing heights.

 The techniques for generating the mixing height field should

 be described in the Protocol Document. Techniques other than

 that described in Reference 2 should be documented and

 justified.

 3.5.4 Clear-sky assumption for photolysis rate calculations

 For regulatory UAM applications, clear-sky conditions have

typically been assumed for photolysis rate calculations in the
METSCALARS processor. The UAM's current structure does not allow

for spatial variation in cloud cover, so the choice is either

uniformly clear or a uniform cloud cover based on a mean cloud

cover over the domain. Use of mean cloud cover could significantly

understate reaction activity in "clear" patches of the domain.

Potentially, this could be a more serious error than assuming

clear-sky conditions and simulating an overall excess of "domainwide" insolation. Additionally, the ROM-UAM Interface System

IMETSCL processor assumes clear-sky conditions for photolysis rate

calculations.

 Recommendations

 For applications involving the current regulatory version of

 the UAM, it is recommended that clear-sky conditions be

 assumed for calculating photolytic rate constants in the

 METSCALARS processor.

3.6 Air Quality

 Ambient air quality data are generally used for two purposes:

(1) to specify initial- and boundary-condition concentrations, and

(2) to assess the model performance for each meteorological episode

to be used in the attainment demonstration. These topics are

addressed in the following two subsections.

 3.6.1 Initial and boundary conditions

 Three general 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), (2) use default background

values and expand the upwind modeling domain and simulation period

to mitigate uncertainties due to paucity of measurements, and (3)

use regional-scale model predictions of ozone and precursor

concentrations. Initial conditions for UAM simulations are handled

in one of two ways: (1) use regional-scale model predictions to

derive initial conditions, and/or (2) begin the UAM simulation

sufficiently far in advance of the primary day to eliminate

sensitivity of results to arbitrary assumptions regarding initial

conditions. 

 Clearly, the nature of case-specific applications will

determine what approaches should be taken for establishing initial
and boundary conditions for particular domains. Ideally, the

preferred technique would be based on an intensive field program

with regional-scale modeling used to fill in spatial and temporal

gaps. This approach is seldom feasible, however, particularly for

historical episodes. Presented below are recommendations for

implementing each of the three techniques identified above for

deriving boundary conditions, including discussion of the

advantages and disadvantages of each technique. Default boundarycondition values for ozone and precursor concentrations are also

provided. Finally, recommendations are provided on the approach

most likely to be feasible for specifying the initial and boundary

conditions for modeling historical episodes in most locations.

 Use of measured data - All sources of air quality data for a

particular modeling domain should be evaluated for applicability in

establishing initial and 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 initial and boundary concentrations.

 Recommendations

 To develop initial and 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 ozone exceedances.

 The sampling and analysis program should provide data to

 calculate hourly values for ozone, NO, NO2, and speciated

 hydrocarbons. 

 At the inflow boundaries, air quality data at the surface and

 aloft should be used whenever available to specify the

 boundary conditions. Default values (Table 2) may be used

 where necessary. 

 Use of default values - Some urban areas may lack adequate

data suitable for establishing initial and boundary conditions. 

Section 3.2 on domain selection and Chapter 4 on diagnostic

analyses recommend constructing domains and simulation periods

large enough to minimize the sensitivity of inner core and downwind

concentrations to assumed initial and boundary conditions.
 TABLE 2

 DEFAULT BOUNDARY CONDITION CONCENTRATIONS FOR

CARBON-BOND-IV SPECIES (SEE REFERENCES 1 AND 7)

 Species Species Name Concentration (ppbC)

 OLE Olefins 0.60

 PAR Paraffins 14.94

 TOL Toluene 1.26

 XYL Xylene 0.78

 FORM Formaldehyde 2.1

 ALD2 Higher Aldehydes 1.11

 ETH Ethene 1.02

 CRES Cresol,Higher Phenols 0.01

 MGLY Methyl Glyoxal 0.01

 OPEN Aromatic ring fragment 0.01

 acid

 PNA Peroxynitric acid 0.01

 NXOY Total nitrogen 0.01

 compounds

 PAN Peroxyacyl nitrate 0.01

 HONO Nitrous acid 0.01

 H2O2 Hydrogen peroxide 0.01

 HNO3 Nitric acid 0.01

 MEOH Methanol 0.1

 ETOH Ethanol 0.1

 O3 Ozone 40.0 (ppb)

 NO2 Nitrogen Dioxide 2.0 (ppb)

 NO Nitric oxide 0.0 (ppb)

 CO Carbon monoxide 350.0 (ppb)

 ISOP Isoprene 0.1 (ppb)
 Initial- and boundary-condition concentrations are influenced

by large- and small-scale weather patterns and emissions distributions that are unique to each modeling domain. Thus, casespecific attributes should be used in estimating these concentrations whenever feasible. For example, boundary concentrations of

hydrocarbons, particularly those species (or intermediate products)

emitted from vegetation, are likely to be higher in urban areas

surrounded by dense vegetation than in areas surrounded by sparse

vegetation.

 Recommendations

 It is recommended that use of default values to establish

 boundary conditions be limited to areas surrounded by large

 expanses of low-density anthropogenic emissions. Accordingly,

 the modeling domain may need to envelop rural areas.

 Those choosing 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 2 lists the recommended default boundary values for the

 chemical species used in the model. Use of default boundary

 values under regional transport conditions should be closely

 evaluated. When using default values, the boundary of the

 domain should extend as far upwind as practicable.

 To diminish dependence on arbitrary specification of initial

 conditions, a simulation should begin at least 1 day prior to

 the primary day.

 Use of regional model concentration predictions - Output from

regional-scale models such as the EPA ROM provides estimates of

initial and boundary conditions (as well as certain meteorological

inputs) for urban-scale models. This is especially important under

regional transport conditions. The ROM-UAM Interface System

referred to in Section 3.5.1 can use ROM concentration predictions

to develop UAM input files of initial and boundary conditions. 

This interfacing software should be used for UAM domains nested

within more extensive ROM domains. Using the ROM is the

recommended approach for generating boundary conditions. It is the

most technically defensible approach for estimating future boundary

conditions for the attainment year.

 Certain considerations arise when using interfacing methods. 

First, selection of historical episodes is limited to those that 
have been modeled on a regional scale. Second, there may be

inconsistencies in mass conservation when applying ROM-derived

initial and boundary conditions in conjunction with wind fields not

derived from the ROM wind field (see Section 3.5.1). The 

combinations of concentrations and wind velocities produced by the

ROM-UAM Interface System represent mass fluxes passing through the

urban-scale modeling domain. In cases where ROM-derived initial

and boundary conditions are applied without ROM-generated wind

fields, locally developed wind fields may need to be evaluated for

mass consistency throughout the urban-scale domain. Methods for

addressing this problem will need to be chosen on a case-by-case

basis. A general procedure for enhancing mass consistency is

described in Section 3.5.1. Additionally, initial and boundary

conditions derived from the ROM data should be compared with

corresponding monitoring data wherever available. This will ensure

that the ROM wind fields adequately represent the transport of

ozone and precursors into the domain region.

 Recommendations

 It is recommended that, whenever feasible, the ROM-UAM

 Interface System be applied to derive the initial and boundary

 conditions for the episode(s) being modeled. If the Interface

 System is used to derive the initial and boundary conditions,

 it is also recommended that it be used to derive the UAM

 gridded wind field, unless there is sufficient justification

 that other techniques for deriving the wind field are more

 accurate.

 In cases for which ROM predictions are not available, it is

 generally recommended that measured data be used to establish

 the initial and boundary conditions, provided the Technical

 Committee identified in the Protocol determines the data are

 adequate. If measured data are not adequate, the default

 values may be used. To diminish sensitivity of results to

 assumed initial conditions, simulations should begin 1 day

 prior to each primary day.

3.6.2 Performance Evaluation Data

 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.

 Under Title I, Section 182 of the CAAA of 1990, the EPA is
required to develop regulations for enhanced monitoring of ozone

and ozone precursors in serious, severe, and extreme ozone

nonattainment areas. When promulgated, these regulations will

specify criteria for network design, monitor siting, monitoring

methods, operating schedule, quality assurance, and data

submittal.11 The enhanced ozone monitoring system is designed to

provide a more comprehensive data base for model input and to

improve model performance evaluation.

 Recommendations

 It is recommended that the data base used in the attainment

 demonstration modeling meet the requirements for the

 enhanced ozone monitoring system to be promulgated by the EPA. 

 However, the EPA recognizes that some historical episodes that

 will be used in the attainment demonstration modeling for the

 November 1994 ozone SIP submittals may have data bases that

 would not meet the requirements for an enhanced ozone

 monitoring system. Under these conditions, the data bases

 should be scrutinized in detail by the Technical Committee to

 help 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.

 If it is determined that the existing air quality monitoring

 program does not meet the requirements for the enhanced ozone

 monitoring system, responsible regulatory agencies should

 begin planning for development of an enhanced ozone monitoring

 system for potential future modeling studies. 

3.7 Emissions

 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 and,

in some cases, errors in judgment about the need to control certain

classes of precursors (e.g., NOx). 

 Developing photochemical model emission input data is the most

intensive task of model applications, and requires consideration of

many issues. The source of the UAM modeling emission inventory

will be the 1990 SIP nonattainment base year inventory required
under the CAAA of 1990 for all ozone nonattainment areas. A

further discussion of the 1990 base year inventory is contained in

Emission Inventory Requirements for Ozone State Implementation

Plans.12 It is important to note that the 1990 modeling inventory

will not be identical to the 1990 nonattainment area inventory

required for reasonable further progress (RFP) tracking under

Section 182 of the CAAA. For example, the modeling inventory will

probably have to cover a larger geographical area than that

required for the nonattainment area inventory. The discussion of

modeling domain and boundary-condition issues in Sections 3.2 and

3.6 makes it clear that the modeling inventory must encompass a

larger area than the nonattainment MSA. A complete description of

relationships between the modeling inventory and the nonattainment

area inventory is provided in Procedures for the Preparation of

Emission Inventories for Volatile Organic Compounds, Volume II: 

Emission Inventory Requirements for Photochemical Air Quality

Simulation Models (Revised).13 Additional guidance for developing

the modeling emission inventory is found in Reference 4.

 For use in regulatory applications of the UAM, the 1990

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., "1990

day-specific emissions"). Second, the resulting "1990 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 "1990 dayspecific 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 "1990 dayspecific emissions" described above. This adjustment entails use

of growth factors, ongoing control programs and retirement rates

for obsolete sources of emissions to project "1990 day-specific

emissions" to the years by which the CAAA specify that the NAAQS

must be attained. Reference 14 describes the appropriate

methodology for making emission projections. The resulting

"attainment year modeling inventory" is used as a starting point

from which to construct "strategy inventories." A "strategy

inventory" is obtained by superimposing additional control measures

on sources of emissions in the "attainment year modeling
inventory."

 In summary, a 1990 modeling inventory is first adjusted to

evaluate UAM performance. The 1990 modeling inventory is then

readjusted to reflect emissions most likely 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 an

elevated point source file of emissions that are injected into

vertical layers above ground level. The UAM Emissions Preprocessing System (EPS)4 reads county-level area- and point-source

files and performs four major functions: (1) area sources and point

sources are allocated to grid cells; (2) temporal profiles are

assigned to source categories; (3) hydrocarbon speciation profiles

are assigned to source categories, and (4) point sources with

effective plume heights greater than a prescribed cutoff level are

assigned to the elevated point source file and the remaining point

sources are assigned to the surface-layer emissions file.

 Addressed below are the following issues that arise in

developing emission input data: (1) use of speciation profiles,

(2) use of surrogate factors to grid area sources, (3) treatment of

mobile sources and top/down versus bottom/up approaches, (4)

episodic adjustment of inventories to day-specific modeling inputs,

(5) treatment of biogenic emissions, (6) cutoff levels for NOx

point sources, and (7) consistency with national inventories.

 3.7.1 VOC speciation

 The EPA provides "default" nationwide VOC speciation profiles

for various source category codes (SCCs).13 Use of local speciation

information, especially for major emitters, is preferable to

national default profiles. If feasible, major VOC point- and areasource categories should be surveyed to determine appropriate VOC

composition profiles. In many cases, both the quantity and the 

composition of emissions change as process operations are modified. 

To the extent feasible, this should be accounted for when deriving

local speciation profiles and in simulating control strategies. 

The emissions inventory guidance document13 provides details on

developing local speciation profiles.

 Most current-year applications are likely to rely on existing

default data for speciating mobile-source emissions. Projected

future-year mobile-source emissions files may be based on different
formulations of gasoline and use of alternative fuels. Speciation

guidance for these fuels will be provided by the EPA Office of

Mobile Sources (OMS) through the appropriate EPA Regional Office.

 Recommendations

 It is recommended that local speciation profiles for point-

 source and area-source categories be used whenever feasible. 

 The Technical Committee should determine the appropriateness

 of using local or national default speciation profiles. 

 Profiles used in the modeling demonstration must be

 documented, and any changes assumed in profiles as the result

 of control strategies must be identified and justified.

 3.7.2 Spatial gridding of 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; the allocation requires use of

surrogate distribution factors such as population distribution,

land use, and road type. The UAM EPS4 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

 document13 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.

 3.7.3 Mobile sources

 Development of gridded, time-variant mobile-source inputs

raises several concerns and often represents the largest fraction

of effort when assimilating mobile-source emissions inputs. 

Mobile-source emissions have been compiled from original data or

from existing county-level emissions.13 Developing gridded mobilesource emissions from original data requires aggregating sub-gridcell-level components. This may require exercising transportation

models that produce inputs for the mobile-source emissions model

(i.e., the latest EPA MOBILE model), and then performing the

necessary spatial allocations to grid cells and temporal

distribution over every hour. This practice is far from
standardized. Also, in certain areas, execution of transportation

models is restricted by lack of appropriate traffic count and speed

data.

 The emission inventory guidance document13 provides direction

for developing mobile-source inputs from original data (referred to

as a bottom/up method) or from an existing county-level inventory

(referred to as a top/down method).

 Recommendations

 Bottom/up methods are the preferred approach for estimating

 vehicle activity levels and emission factors because these

 methods have potential for resolving variations in speed and

 vehicle miles traveled (VMT) among different grids over hourly

 time slices. Bottom/up approaches are most appropriate for

 addressing the inner urban core of modeling domains. 

 Peripheral, less dense traffic areas can be treated with

 top/down methods. Exceptions to these recommendations should

 be considered by the Technical Committee on a case-by-case

 basis. Justification for more extensive use of top/down

 methods should be sought in discussions with the appropriate

 EPA Regional Office.

 3.7.4 Episode-specific adjustments

 Several source categories of VOC emissions are sensitive to

meteorological conditions. Thus, it is important for modeling

inventories to reflect episode-specific meteorological conditions.13 

For example, biogenic emissions, mobile-source evaporative

emissions, and solvent categories will need to reflect specific

modeling days. 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. This is done by running the latest EPA

 MOBILE model using episode-specific maximum and minimum

 temperatures. Chapter 7 of the emission inventory guidance

 document13 describes the procedures for deriving episode-

 specific mobile-source emissions using 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.
 Biogenic emissions must reflect episode-specific conditions

 (see Section 3.7.5).

 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.13

 3.7.5 Biogenic emissions

 Biogenic emissions can be a significant portion of the overall

VOC emission inventory for a given domain, particularly in areas of

high vegetative density. The EPA provides the Biogenic Emissions

Inventory System (BEIS), which can develop day-specific, hourly,

gridded, speciated inputs (see Reference 4), and also provides a

national data base of land use distributions with this system. 

Spatial variability is limited to the county level (i.e., emissions

are evenly spread throughout the grids within a specific county). 

 The EPA is currently modifying the BEIS to allow users to

input user-derived and possibly more up-to-date land use

distribution data. Users will be advised of the expected delivery

date of the modified processor via the SCRAM BBS and EPA Regional

Offices. 

 Recommendations

 Biogenic emissions must be included in the emission inventory

 developed for each model simulation (i.e., base case and

 control strategy). The biogenic emission processor (BEIS)

 that is part of the EPA Emissions Preprocessor System4 should

 be used to derive the inventory. Use of alternative land use

 factors in the BEIS should be described and documented in the

 Protocol Document. 

 Also, methods other than the BEIS may be considered for

 deriving the biogenic emissions. These methods must be

 described in the Protocol Document along with justification

 for using them.

 3.7.6 Point-source and plume-rise cutoff levels

 Guidance for initiating ozone/CO SIP emission inventories

pursuant to the 1990 CAAA15 specifies point-source cut-off levels

of 10 tons/yr and 100 tons/yr for VOCs and NOx, respectively. Any

source may be treated as a point source as long as stack data are
specified that allow derivation of effective plume height, and

source location is provided.4 In some cases, the Technical

Committee may wish to treat selected smaller sources as point

sources.

 The UAM EPS4 requires the specification of a plume-rise cutoff

level for delineating elevated point sources from area sources. If

the plume rise that the EPS calculates for a given point source is

below the user-specified level, then the point-source emissions are

placed in the area-source emissions file. If the plume rise is

above the level, the emissions are treated as coming from elevated

point sources and are then placed within the appropriate UAM

vertical layer.

 Recommendations

 Point-source cutoff levels of 10 tons/yr for VOCs and no

 greater than 100 tons/yr for NOx are recommended for inclusion

 in the modeling emission inventory. Point sources must have

 the stack data needed to calculate effective plume height, so

 that the heights at which emissions are injected into the

 modeling system can be determined.

 The Technical Committee may consider using a lower plume-rise

 cutoff level, particularly in areas where there may be a high

 density of point sources. Additionally, the CAAA specifies

 "major source" definitions that have lower cutoff limits for

 purposes such as application of reasonably available control

 technology (RACT), new source review (NSR) and creation of

 Emission Statements.15 The Technical Committee may consider

 using these lower cutoff limits in the modeling inventory. 

 The Technical Committee should specify the plume-rise cutoff

 level to be used in delineating point-source and area-source

 emissions, and the level should be identified in the Protocol

 Document.

 3.7.7 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).16 Although

these inventories will not be identical, such a check can be

considered part of the quality assurance process. Major

inconsistences should be noted and documented. It is especially

important that those planning to use ROM-derived air quality data

in the model simulations follow applicable guidance/regulations for

reporting statewide emissions data to AIRS. These national
inventories are used in the ROM modeling. As noted previously,

using the ROM is the preferred procedure for estimating UAM

boundary conditions and meteorological inputs. Attainment

demonstrations will be less consistent if the ROM and the UAM use

significantly different emissions data bases.

 Recommendations

 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.16
 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 improved 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 a model's

ability to predict future ozone (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 Reference 17. This reference serves as the basis for

this chapter and for the model performance evaluation described in

Chapter 5. Various graphical and numerical measures described

below are treated in detail in Reference 17.

 Two useful graphical displays 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 ozone

patterns are particularly useful as part of a model performance

evaluation.

 Time-series plots display hourly measured and predicted ozone

values for specific locations such as monitoring sites. Timeseries 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. Even

though measured VOC or NOx species data may be limited, it may

still be useful to plot time-series plots for some of these

species, particularly for cases where ozone predictions do not meet

expectations. Such plots may provide insights to the ozone

prediction patterns and also to data base inconsistencies requiring

further investigation.

 The following sections describe recommended steps 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 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;

 assure reasonable speciation

 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 VOCs, NOx and CO by grid cell and

 cross-check with source distribution for

 logical patterns, such as high NOx levels

 associated with major power plants

 Meteorology: Plot surface and elevated wind vectors and

 compare with monitoring stations and weather

 maps for consistent patterns; compare mixing

 height fields with sounding data; check

 temperature fields

 In quality assurance testing of component input fields, the

emphasis is on capturing rather 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.

4.2 Step 2: Diagnostic Testing of the Base Case Meteorological

 episodes

 After confidence has been achieved in producing UAM input

fields, 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 usually

uncovers 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 plume orientation and the timing of

observed ozone 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 ozone concentration maps be

 constructed for each base meteorological episode simulation.

 Concentration maps present spatial information on the

 structure of the ozone plume.
 Maps at 1- or 2-hour intervals should be constructed over the 

 periods of most interest. While a typical period might be

 defined as early morning to late afternoon for the day of

 highest ozone, it is also useful to look at most time

 intervals under recirculation, stagnation, and transport

 conditions.

 Consideration should also be given to constructing a map that

 depicts the highest predicted daily maximum ozone value for

 each grid cell. Examples of various mapping techniques are

 described in Reference 17.

 

 It is also recommended that the predicted concentrations used

 in the time-series plots be consistent with the method for

 deriving predicted concentrations for the model performance

 evaluation described in Chapter 5. This method is based on

 Reference 17 and produces a weighted average using bilinear

 interpolation of the predictions from the four grid cells

 nearest to the monitor location.

 

 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.

 If suitable data are available, time-series plots should be

 developed for NO and NO2, and for VOC species at selected

 locations, particularly for cases in which ozone time-series

 or mapping results are not consistent with expectations.

 Comparisons of ozone precursors should be done for

 concentration levels above the detection limits for the

 monitoring equipment.
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 sampling of 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 biogenic emissions, 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 can identify

 transport-dominated episodes and provide assurance that

 the upwind extent of the domain is adequate for episodes

 where intradomain emissions dominate. In minimum trans-

 port conditions, the second- and third-day concentrations

 (inner core and downwind locations) should be relatively

 insensitive to boundary-condition concentration changes.

 

 3. Mixing height and wind speed variations - Much

 uncertainty is associated with mixing heights 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

 mixing height provide bounds on some of the uncertainty

 resulting from these parameters. Large sensitivity may

 suggest that future model applications will need

 improvement in the meteorological data bases. Also,

 large sensitivity may suggest a need to consider

 alternative wind field generation techniques.
 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 bias values less than zero. This phenomenon could

be due to various factors, such as overstatement of wind speeds or

mixing heights, or low emission estimates. 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 Reference 17 should be considered

whenever possible. Also, the EPA is developing a UAM Postprocessing System18 to assist in diagnostic testing of the base

meteorological episodes. Availability of this software will be

announced through the SCRAM BBS.

 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 condi-

 tions, and varying mixing height 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.

 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 track (both spatially and

 temporally) known urban-scale plumes, a performance evaluation

 based on numerical measures is conducted for each base

 meteorological episode (see Chapter 5).

 CHAPTER 5

 MODEL PERFORMANCE EVALUATION

 At some point in the modeling process, an assessment of model

performance is required. Specifying rigid rejection/acceptance

criteria has not been supported by model developers nor by decision

makers participating in previous modeling efforts. Instead,

performance measures derived from previous photochemical model

applications may provide a reasonable benchmark for model performance. Also, graphical procedures reveal qualitative relationships

between predicted and observed concentrations that can be used in

model performance evaluation.

 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. However, this is not

a valid reason for delaying SIP attainment demonstration submittals

beyond the dates required in the 1990 CAAA. Also, cases where good

model performance is shown should be reviewed as well, because

compensating errors can induce spurious agreement among observed

and predicted values. 

5.1 Performance Measures

 This section describes recommended graphical and statistical

performance measures for ozone predictions. These measures should

be applied for modeling results beginning on the second day of the

modeled episode. As described in Section 3.1, the first day is

eliminated to mitigate the effects of specifying initial conditions

arbitrarily. Performance measures should also be considered for

ozone precursors wherever possible, based on availability of

monitored data. Obvious problems exist in comparing model

predictions with observed values. The UAM output represents

volumetric (e.g., 25 km3), 1-hour average concentrations, but air

quality data represent point locations with various sampling

periods. This "incommensurability" may lead to considerable

uncertainty in the comparisons, especially for precursor species

that are not buffered chemically and may have been sampled at

locations not representative of areawide concentrations.

 As part of the UAM Postprocessing System, the EPA is currently

developing a model performance utility that will contain the

performance measures listed below. Users will be able to access

this utility for model performance evaluation testing. This

utility is expected to be available in late 1991. Model users will
be advised on its availability through the EPA SCRAM BBS.

 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 networks, 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 performance,

especially for paired values. 

 Reference 17 provides detailed descriptions of graphical and

statistical measures available for assessing the performance of

photochemical grid models. The Technical Committee should consult

this reference 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,

ground-level isopleths, quantile-quantile plots, and scatterplots

of predictions and observations.

 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 presence of any significant bias within the diurnal

cycle, and a comparison of the timing of the predicted and observed

ozone maxima.17

 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 daily

maxima may also be constructed. The isopleths provide information

on the magnitude and location of predicted pollutant "plumes." 

Superimposing observed hourly or daily maximum concentrations on

the predicted isopleths reveals information on the spatial

alignment of predicted and observed plumes. Subregional biases of

predictions versus observations may result from spatial 

misalignments. 
 Scatterplots of predictions and observations - Scatterplots

depict the extent of bias and error in the ensemble of hourly

prediction-observation pairs. Bias is indicated by the systematic

positioning of data points above or below the perfect correlation

line. The dispersion of points is a measure of the error in the

simulation. The scatterplot also reveals outlier predictionobservation pairs.

 Quantile-quantile plots - Quantile-quantile plots compare the

frequency distributions of rank-ordered observed and rank-ordered

predicted concentrations. The observed and predicted concentrations are sorted from highest to lowest then plotted on an x-y

plot. This graphically depicts any model bias over the frequency

distribution.

 "Paired" predictions of daily maxima - In attainment 

demonstrations, particular interest is focused on daily maximum

ozone concentrations. One test that may provide insight into model

performance is to consider model predictions occurring within 

±1 hour of the observed daily maxima at each monitoring site in the

nine grid squares surrounding and including the monitor. The

"prediction," for purposes of this pairing, would be the one that

agrees most closely with the observed daily maximum for each site. 

This method may be useful for sparse meteorological and air quality

networks, because it recognizes that both the inputs and air

quality observations have some attendant uncertainty. Resulting

comparisons can be superimposed on a map depicting emissions and

monitors to help assess model performance.

 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 ozone

 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 distribu-

 tion of predicted concentrations should be constructed for

 selected hours. Also, ground-level isopleths or tile maps of

 the daily ozone maxima should be constructed. The correspond-

 ing observed concentrations should be superimposed on the

 predicted concentration isopleths to analyze spatial plume

 patterns and ozone magnitudes.

 Scatterplots should be constructed for all hourly prediction-
 observation pairs for each simulation; quantile-quantile

 plots are also recommended for each simulation.

 The development of additional graphical displays, such as the

 paired predictions of daily maxima, is encouraged. The

 graphical displays to be used in the model performance

 evaluation should be described in the Protocol. 

 5.1.2 Statistical performance measures

 Statistical performance measures can provide meaningful

measures of model accuracy for dense monitoring networks, such as

those for special field studies. However, statistical measures may

give a distorted view of model performance in cases of routine

monitoring networks, where coverage may be sparse. The Technical

Committee should evaluate the adequacy of the existing monitoring

network for conducting statistical tests for performance

evaluation.

 Recommendations

 It is recommended that, at a minimum, the following 

 mathematical formulations be applied as measures for model

 performance evaluation. These formulations are detailed in

 Appendix C.

 Unpaired highest-prediction accuracy - This measure quantifies

 the difference between the highest observed value and highest

 predicted value over all hours and monitoring stations. 

 Normalized bias test - This test measures the model's ability

 to replicate observed patterns during the times of day when

 available monitoring and modeled data are most likely to

 represent similar spatial scales.

 Gross error of all pairs above 60 ppb - In conjunction with

 bias measurements, this metric provides an overall assessment

 of base case performance and can be used as a reference to

 other modeling applications. Gross error can be interpreted

 as precision.

 Additional measures may include the following:

 Average station peak prediction accuracy - This is a measure

 of peak performance at all monitor sites, using pairings based

 on time and space. 
 Bias of all pairs above 60 ppb - This bias metric measures the

 overall degree to which model predictions overestimate or

 underestimate observed values. Note, however, that a zero

 bias for several observation-prediction pairs can be caused by

 a canceling effect of overprediction and underprediction in

 different subregions.

 Bias of all station peaks - For this metric, bias calculations

 are performed on observation-prediction pairs associated with

 peak ozone values for each monitoring station. This metric

 provides information on the model's ability to replicate peak

 ozone observations.

 Fractional bias for peak concentration - Fractional bias is

 calculated for both the mean and the standard deviation of

 peak predicted and observed values. This metric provides

 additional information on the model's ability to replicate

 peak ozone observations.

5.2 Assessing Model Performance Results

 Both graphical and statistical performance measures should be

used for the performance evaluation. However, although the

recommended statistical measures should be applied for all

meteorological episodes and monitoring networks, caution is

suggested for interpreting these measures in cases of sparse

monitoring network coverage. The Technical Committee should

consider the monitoring network design in interpreting statistical

measures.

 In assessing model simulation results for the performance

evaluation, there is no rigid criterion for model acceptance or

rejection (i.e., no pass/fail test). Reference 17 states that,

based on past photochemical model evaluations, this type of

modeling "generally produces peak (unpaired) prediction accuracy,

overall bias, and gross error statistics in the approximate ranges

of ±15-20 percent, ±5-15 percent, and 30-35 percent, respectively." 

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 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 performance evaluations on other candidate

episodes to identify any that might yield improved model 

performance.

 A minimum of 3 primary episode days is required for use in the

model simulations for attainment demonstration (Section 6.4). If

fewer than 3 primary episode days can be identified that have

acceptable model performance for the attainment demonstration, the

responsible regulatory agencies are strongly encouraged to take

steps that will improve model performance for any future attainment

demonstrations. For serious and above nonattainment areas, this

may require short, intensive field studies to supplement installation of the enhanced monitoring network required under the CAAA of

1990.

 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

 analyze the time-series plots, ground-level isopleths,

 quantile-quantile plots, and scatterplots. Use of "paired"

 predictions of daily maxima may also be considered.

 2. The statistical performance measures specified in Section

 5.1.2 should also be derived and evaluated for each meteoro-

 logical 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 prediction accuracy: ±15-20 percent

 ! Normalized bias: ±5-15 percent

 ! Gross error of all pairs >60 ppb: 30-35 percent

 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.

 Additional model performance measures are encouraged. These

 should be described in the Modeling Protocol.

 CHAPTER 6

 ATTAINMENT DEMONSTRATION

 This chapter provides guidance on using modeling simulations

for attainment demonstrations. The primary reason for conducting

photochemical modeling is to demonstrate the effectiveness of

alternative control strategies in attaining the NAAQS for ozone

throughout the modeling domain. This demonstration consists of

four main parts: (1) developing attainment-year modeling emission

inventories, (2) developing alternative-control strategy emission

inventories, (3) performing model simulations for the attainment

year with and without alternative control strategies, and 

(4) comparing attainment year and control strategy simulation

results with the ozone NAAQS. Attainment year and control strategy

simulations are conducted for each selected meteorological episode

(see Section 3.1).

6.1 Developing Attainment-Year Model Inputs

 The attainment-year modeling inventory must be derived from

the 1990 SIP nonattainment base year inventory, adjusted for

episode-specific meteorology, and then projected to the attainment

year. Also, to the extent possible, initial- and boundarycondition ozone and precursor concentrations must be projected to

the attainment year. The attainment year is determined by the

nonattainment area designation and the attainment dates specified

in the 1990 CAAA. Projections of emission inventories reflect the

net effect of mandated controls and growth projections for various

source categories. Guidance for projecting inventories is 

available in Procedures for Preparing Emissions Projections.14 The

most direct method for projecting initial- and boundary-condition

precursor concentrations is by applying ROM simulation results for

which the UAM domain is nested within the ROM domain (see 

Chapter 3). In the absence of available ROM data, the projection

of ozone precursor concentrations used for initial conditions

typically has been done by linear scaling based on emission changes

projected to take place from the 1990 base year to the future year. 

For initial ozone concentrations, there is little basis for doing

anything other than assuming initial ozone remains constant. In

the absence of regional modeling results or better information, the

guidance in Reference 7 for specifying future boundary conditions

may be followed.

 Recommendations

 It is recommended that the EPA guidance document entitled

 Procedures for Preparing Emissions Projections14 be consulted
 for developing attainment-year inventories. The guidance

 document provides procedures for projecting point-source,

 area-source, mobile-source, and biogenic emissions, and

 addresses projections of spatial, temporal, and chemical

 composition changes between the 1990 SIP inventory and the

 attainment-year inventory.

 

 Also, if regional modeling predictions for the attainment year

 are available, it is recommended that these be used to derive

 the attainment-year initial and boundary conditions for the

 attainment-year model simulations (see Chapter 3). 

6.2 Construction of Attainment Year Emission Control Strategies

 Many possible attainment-year emission control strategies can

be set up and simulated. Eventually, a modeling analysis must be

submitted for approval as the basis of a SIP demonstration. The

effectiveness of a given set of control measures in reducing ozone

(and perhaps other pollutants) is a major factor in selecting the

final emission control strategy.

 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 of the ozone NAAQS. 

 2. If mandated controls are insufficient to demonstrate

 attainment, superimpose a series of additional, across-

 the-board reductions in VOCs-only, VOCs-plus-NOx, and

 NOx-only strategies, relative to the mandatory CAAA

 controls, to identify a suitable emission-reduction

 target range.

 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 and that realize the approximate emission

 reductions identified as sufficient to reduce daily

 maximum ozone to 0.12 ppm or less.

 4. Adjust the strategy chosen in step 3 until it is

 sufficient to demonstrate attainment of the NAAQS, as
 described in Section 6.4. Adjustments may be needed in

 VOC controls, or NOx controls, or both. 

 Recommendations

 The procedures for deriving control strategies for evaluation

 in the attainment demonstration must be specified in the

 Modeling Protocol.

 Emission control strategies for linked urban-area modeling

 domains (e.g., northeastern U.S. Corridor) should be coordi-

 nated among State agencies having lead responsibility for

 respective domains to ensure consistency among the domains. 

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 ozone and other species. 

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 daily maximum ozone predictions throughout the modeling

domain.

 Recommendations

 The focus of any ozone attainment demonstration is on the

 daily maximum 1-hour concentration predicted at each location

 in the modeling domain. However, it is recommended that

 responsible parties broaden the scope of an attainment demon-

 stration to examine the impact on other important metrics,

 such as different concentration averaging times, population

 exposure, subdomain and temporal impacts, effects on other

 pollutant species, and other important measures that are

 sensitive to emission control strategies.

 For deriving initial and boundary conditions for a particular

 urban-area domain, using appropriate regional model predic-

 tions that reflect control measures applied in other urban-

 area domains within the regional modeling domain is

 recommended.
6.4 Using Modeling Results in the Attainment Demonstration

 As described in Section 3.1, at least 3 primary episode days

should be modeled for the attainment demonstration. Also, a

minimum of 1 primary day should be modeled from each identified

meteorological regime. Therefore, for example, if there are three

meteorological regimes, at least 1 primary episode day from each

regime should be modeled; if there are only two meteorological

regimes, at least 2 primary episode days should be modeled from one

of the regimes and at least 1 primary episode day modeled from the

other regime. Note that the episodes simulated would generally be

at least 48 hours long (i.e., the first day would be an initial

modeling day and the second day would be the primary episode day). 

This would count as simulation of 1 primary episode day.

 To demonstrate attainment of the ozone NAAQS, there should be

no predicted daily maximum ozone concentrations greater than 

0.12 ppm anywhere in the modeling domain for each primary episode

day modeled. Alternative methods for demonstrating attainment must

be approved by the appropriate EPA Regional Office on a case-bycase basis.

 The attainment test described in the preceding paragraph is

consistent with the flexibility allowed in the choice of episode

days (see Section 3.1) and reflects concerns over the difficulty of

accurately estimating emissions inputs to the model.

 Recommendations

 To demonstrate attainment of the ozone NAAQS, there should be

 no predicted daily maximum ozone concentrations greater than

 0.12 ppm anywhere in the modeling domain for each primary

 episode day modeled. At least 3 primary episode days should

 be modeled.

 States may opt to conduct more comprehensive statistical

 testing of the modeling results for the attainment demonstra-

 tion. 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 photochemical 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 photochemical modeling approach must first

receive concurrence in writing from the responsible EPA Regional

Office(s).
 REFERENCES

1. Morris, R.E., T.C. Myers and J.L. Haney, 1990. User's Guide

 for the Urban Airshed Model, Volume I: User's Manual for UAM

 (CB-IV), EPA-450/4-90-007A, U.S. Environmental Protection

 Agency, Research Triangle Park, NC (NTIS No.: PB91-131227).

2. Morris, R.E., T.C. Myers, E.L. Carr, M.C. Causley, S.G.

 Douglas and J.L. Haney, 1990. User's Guide for the Urban

 Airshed Model, Volume II: User's Manual for the UAM (CB-IV)

 Modeling System, EPA-450/4-90-007B, U.S. Environmental

 Protection Agency, Research Triangle Park, NC (NTIS No.: PB91-

 131235).

3. Douglas, S.G., R.C. Kessler and E.L. Carr, 1990. User's Guide

 for the Urban Airshed Model, Volume III: User's Manual for the

 Diagnostic Wind Model, EPA-450/4-90-007C, U.S. Environmental

 Protection Agency, Research Triangle Park, NC (NTIS No.: PB91-

 131243).

4. Causley, M.C., J.L. Fieber, M. Jimenez and L. Gardner, 1990. 

 User's Guide for the Urban Airshed Model, Volume IV: User's

 Manual for the Emissions Preprocessor System, EPA-450/4-90-

 007D, U.S. Environmental Protection Agency, Research Triangle

 Park, NC (NTIS No.: PB91-131250).

5. Tang, R.T., S.C. Gerry, J.S. Newsom, A.R. Van Meter, R.A.

 Wayland, J.M. Godowitch and K.L. Schere, 1990. User's Guide

 for the Urban Airshed Model, Volume V: Description and

 Operation of the ROM-UAM Interface Program System, EPA-450/4-

 90-007E, U.S. Environmental Protection Agency, Research

 Triangle Park, NC (NTIS No.: PB91-131268).

6. DaMassa, J., 1990. Technical Guidance Document: Photochemical

 Modeling. California Air Resources Board, Technical Support

 Division, Sacramento, CA.

7. U.S. Environmental Protection Agency, 1991. Criteria for

 Assessing the Role of Transported Ozone/Precursors in Ozone

 Nonattainment Areas, EPA-450/4-91-015, Office of Air Quality

 Planning and Standards, Research Triangle Park, NC (NTIS No.:

 PB91-195958).

8. U.S. Environmental Protection Agency, 1991. Gridded Model 

 Information Support System (GMISS) User's Guide, Volume II:

 UAM Subsystem, EPA-450/4-91-009, Office of Air Quality

 Planning and Standards, Research Triangle Park, NC (NTIS No.: 
 PB91-206268).

9. U.S. Environmental Protection Agency, 1987. On-Site Meteoro-

 logical Program Guidance for Regulatory Modeling Applications,

 EPA-450/4-87-013, Office of Air Quality Planning and Stan-

 dards, Research Triangle Park, NC (NTIS No.: PB87-227542).

10. U.S. Environmental Protection Agency, 1989. Procedures for

 Applying City-Specific EKMA, EPA-450/4-89-012, Office of Air

 Quality Planning and Standards, Research Triangle Park, NC 

 (NTIS No.: PB90-256777).

11. U.S. Environmental Protection Agency, 1991. Enhanced Ozone

 Monitoring Regulations: Proposed Rule (Draft), 40 CFR Part 58,

 Office of Air Quality Planning and Standards, Research

 Triangle Park, NC.

12. U.S. Environmental Protection Agency, 1991. Emission Inven-

 tory Requirements for Ozone State Implementation Plans, EPA-

 450/4-91-010, Office of Air Quality Planning and Standards,

 Research Triangle Park, NC.

13. U.S. Environmental Protection Agency, 1991. Procedures for

 the Preparation of Emission Inventories for Volatile Organic

 Compounds, Volume II: Emission Inventory Requirements for

 Photochemical Air Quality Simulation Models, EPA-450/4-91-014,

 Office of Air Quality Planning and Standards, Research

 Triangle Park, NC (NTIS No.: PB91-216176).

14. U.S. Environmental Protection Agency, 1991. Procedures for

 Preparing Emissions Projections, Office of Air Quality

 Planning and Standards, Research Triangle Park, NC (Draft).

15. U.S. Environmental Protection Agency, 1991. Guidance for

 Initiating Ozone/CO SIP Emission Inventories Pursuant to the

 1990 Clean Air Act Amendments, Office of Air Quality Planning

 and Standards, Research Triangle Park, NC.

16. U.S. Environmental Protection Agency, 1989. Aerometric

 Information Retrieval System (AIRS), Volume I. Office of Air

 Quality Planning and Standards, Research Triangle Park, NC.

17. Tesche, T.W., P. Georgopoulos, F.L. Lurmann and P.M. Roth,

 1990. Improvement of Procedures for Evaluating Photochemical

 Models. Draft Final Report. California Air Resources Board,

 Sacramento, CA.
18. U.S. Environmental Protection Agency, 1991. Urban Airshed

 Model Postprocessing System User's Guide, Office of Air

 Quality Planning and Standards, Research Triangle Park, NC 

 (Draft).

 APPENDIX A

 RECOMMENDED MODELING PROTOCOL CONTENTS

 Table 1 of Chapter 2 lists recommended contents for a Modeling

Protocol. This appendix gives a general description of each component, to aid in the development of the Protocol. As stated in

Chapter 2, the contents presented here are adopted from the CARB

Technical Guidance Document: Photochemical Modeling.6

UAM MODELING STUDY DESIGN

Background and Objectives

 The Protocol Document should describe the policy and technical

objectives of the study and pertinent background information such

as the legislative mandate under which the study is being done.

Schedule

 Development of a complete schedule for all phases of the

project is needed. The critical paths and deadlines should be

identified and discussed, as should a schedule for addressing

critical issues that require special attention, such as air quality

and meteorological data preparation and quality assurance, episode

selection, and emission inventory preparation and quality

assurance.

Deliverables

 A list of the interim and final deliverables for the modeling

study should be specified.

Modeling Policy Oversight/Technical Committees

 The composition and responsibilities of the Modeling Policy

Oversight and Technical Committees should be specified to the

extent possible. Meeting frequency and circumstances for

convening a meeting should be identified. Because technical

conflicts may arise, a resolution process for handling them should

be included.

Participating Organizations

 The organizations that are sponsoring the modeling study and
those that may contribute to it should be identified.

Relationship to Regional Modeling Protocols

 Procedures for coordinating development of the urban-area

Modeling Protocol with the regional Modeling Protocol should be

described. This would include a description of control strategies,

emission inventories, projection years, modeling episodes, etc. 

The coordination of urban-area Modeling Policy and Technical

Committees with their regional counterparts should be described.

Relationship to Other Urban Area Modeling Protocols

 In some cases, such as the Northeast U.S., nonattainment

MSA/CMSAs required to do attainment demonstrations may be linked to

other nonattainment MSA/CMSAs. It is important that procedures be

established for coordinating the Modeling Protocols among these

areas, and that these procedures be clearly specified in each

nonattainment area Modeling Protocol. It is likely that Modeling

Policy Oversight and Technical Committees will include some joint

membership among the nonattainment areas.

Relationship to Planning/Strategy Groups

 Key planning agencies and others responsible for emission

projections or other model inputs should be identified, and the

means by which these groups interact to obtain realistic growth

projections and control strategies should be discussed.

DOMAIN AND DATA BASE ISSUES

Preprocessor Programs

 The preprocessor programs to be used in constructing any of

the model input fields should be identified and described.

Data Bases

 The proposed air quality and meteorological data bases should

be described. The completeness of the data base, techniques for

filling in missing data, and quality assurance procedures should be

discussed. 

Base Meteorological Episode Selection

 The episode selection criteria should be detailed, including
the methodology to group candidate episodes into meteorological

regimes. How the episodes will be used in the modeling study

should also be described. 

Modeling Domain

 The Protocol should describe the criteria for selecting the

size and location of the modeling domain. This would include a

description of the MSA/CMSA area size, locations of major sources

outside the MSA/CMSA that may affect it, sensitivity analyses that

may be conducted to assess boundary effects on domain predictions,

relationship of domain size to the episodes selected for use in the

modeling study, etc.

Horizontal Grid Resolution

 The Protocol should describe the horizontal grid resolution to

be applied to the modeling domain. If a resolution coarser than 

5 x 5 km is chosen, justification for this choice should be

provided.

Number of Vertical Layers

 The Protocol should specify the number of vertical layers to

be used in the UAM simulations. If a layering scheme other than

the one recommended in Chapter 3 is chosen, justification for using

the alternative layering should be given.

Emission Inventory

 The assumptions, methodologies, and appropriate guidance

references to be used in constructing the modeling emission

inventory should be described. Quality assurance procedures should

also be described.

Specification of Initial and Boundary Conditions

 The techniques to be used to specify the initial and boundary

conditions for the base meteorological episodes and the attainment

year should be described. The assumptions to be used in forecasting attainment-year conditions should be documented. If a nested

grid approach is used (e.g., using predictions from the ROM through

the ROM/UAM Interface System), the details for implementation

should be described (see Chapter 3).
Wind Field Specification

 The proposed techniques for specifying the wind fields should

be described. The procedures to be used to determine the representativeness of the simulated wind fields should be technically

justified and documented (see Chapter 3).

Mixing Heights

 The techniques to be used for deriving the mixing height for

the modeling domain should be described.

Sources of Other Input Data

 The Protocol Document should describe the data and techniques

to be used to specify other input data, such as cloud cover, water

vapor, UV radiation, surface temperature, terrain, and land use and

surface characteristics.

QUALITY ASSURANCE AND DIAGNOSTIC ANALYSES

Quality Assurance Tests of Input Components

 The specific quality assurance tests to be used on the data

input fields should be described (see Chapter 4).

Diagnostic Tests of Base Case Simulation

 The specific diagnostic tests to be used for the base-case

meteorological episode simulations should be described. As

discussed in Chapter 4, these should include, at a minimum, timeseries plots, observed and predicted ozone maps, zero emissions and

zero boundary conditions tests, and tests on the mixing height

variations and wind fields. Additional diagnostic tests are

encouraged and should be described in the Protocol.

MODEL PERFORMANCE EVALUATION

Performance Evaluation Tests

 The graphical, statistical, and other measures to be used in

the model performance evaluation should be specified. At a
minimum, the tests recommended in Chapter 5 should be included. 

Additional measures may also be considered and should be described

if they are to be used.

ATTAINMENT DEMONSTRATIONS

Identification of Attainment-Year Mandated Control Measures

 The Protocol Document should include a description of the 1990

CAAA control measures and other measures mandated to be implemented

by the attainment year.

Methodologies for Generating Control Strategy Emission Inventories

 The procedures for deriving alternative-control-strategy

emission scenarios to meet the study objectives should be described. A description of how the control scenarios would relate

to applicable control strategies for areas adjacent to the modeling

domain (particularly upwind areas) should be included.

Procedures for Attainment Demonstration

 Procedures for using the model simulation results in

demonstrating attainment of the ozone NAAQS should be included. 

SUBMITTAL PROCEDURES

 The documentation and analyses that will be submitted for EPA

Regional Office review should be described. Also, any

documentation other than the Modeling Protocol requiring EPA

Regional Office approval should be described.

 APPENDIX B

 IDENTIFICATION OF METEOROLOGICAL REGIMES

 CORRESPONDING WITH HIGH OBSERVED OZONE

 The following is a procedure that may be used to assist in

selecting modeling episodes. Other techniques may be considered on

a case-by-case basis; they should be described in the Modeling

Protocol and approved by the appropriate EPA Regional Office.

 Identification of meteorological regimes for a given area

under review begins with constructing a climatological windrose of

high ozone days. The windrose is constructed by first selecting

all days from the period 1987 to present during which at least one

ozone monitor within the area recorded an exceedance of the ozone

NAAQS or some other cutoff level (e.g., 100 ppb). Additional years

of data are encouraged in constructing the climatological windrose

(e.g., 1980-1991). Next, for each exceedance day, calculate the

morning (i.e., 7:00 a.m. - 10:00 a.m.) resultant wind velocity. 

Then group the resultant wind velocities for all of the exceedance

days into eight compass directions plus calm, to establish a

climatic windrose of high-ozone days for the area under review. 

Calm winds are defined as those with speeds less than 1.5 m/s and

referred to as the null wind direction. The windrose will include

nine bins (0-8); place the wind directions corresponding to the

eight compass points into bins 1-8, and the calm or null wind

direction into bin 0. The bins with frequencies significantly

higher than the average frequency for all bins should be defined as

the "predominant wind directions" (PWD).

 Next, compare the morning (i.e., 7:00 a.m. - 10:00 a.m.)

resultant wind velocity for each exceedance day during 1987-89 and

more recent years with the climatic windrose of high-ozone days. 

Categorize exceedance days with wind directions corresponding to

previously identified climatic PWD's as belonging to that PWD. 

Lump all other exceedance days occurring during 1987-89 and later

into a category called "other." Rank each exceedance day within

each PWD category and within the "other" category according to its

areawide daily maximum ozone observation. Within each category,

the day with the highest areawide daily maximum concentration is

ranked first. 

 After the steps described in the two preceding paragraphs are

completed, meteorological regimes can be defined for use in the

attainment demonstration test described in Section 6.4. This may

be done as follows:
 1. Choose the two PWD's which contain the highest areawide

 daily maximum ozone values from 1987 to the most recent

 year with data available. These represent two of the

 meteorological regimes to consider in the attainment

 test.

 2. The third "meteorological regime" to be considered in the

 attainment test is comprised of all exceedance days

 previously categorized as "other" plus those belonging to

 any PWD not chosen in step 1.

 3. Identify the top 3-ranked exceedance days from within

 each of the three meteorological regimes identified in

 steps 1 and 2. These days are candidates for modeling in

 the attainment test. Final choice from among these

 candidates is based on criteria identified in 

 Section 3.1.

 4. It may happen that one or more of the meteorological

 regimes identified in step 1 contains fewer than 3

 exceedance days. If this occurs, exceedance days

 included within PWD's which have been lumped in the third

 meteorological regime (see step 2) may be added to one or

 both of the first two regimes. If this proves necessary,

 selection of days to supplement those in one or both of

 the first two regimes needs to be decided on a case-by-

 case basis keeping in mind the goal of this exercise: to

 provide a choice of exceedance days reflecting high ozone

 concentrations with meteorological conditions which

 frequently coincide with observed exceedances.

 APPENDIX C

 PERFORMANCE MEASURE FORMULATIONS

RECOMMENDED PERFORMANCE MEASURES

1. Unpaired Highest-Prediction Accuracy (Au)

 

where

 Au = unpaired highest-prediction accuracy

 (quantifies the difference between the

 magnitude of the highest 1-hour observed

 value and the highest 1-hour predicted value

 co(.,.) = maximum 1-hour observed concentration over

 all hours and monitoring stations

 cp(.,.) = maximum 1-hour predicted concentration over

 all hours and surface grid squares

2. Normalized Bias Test (D*)

where

 D* = normalized bias obtained from all hourly

 prediction-observation pairs

 N = number of monitoring stations

 Hi = number of hourly prediction-observation pairs

 for monitoring station i

 NT = total number of station-hours

 co(i,j) = observed value at monitoring station i for

 hour j

 cp(i,j) = predicted value at monitoring station i for

 hour j

3. Gross Error of All Pairs >60 ppb (Ed*)
where

 Ed* = normalized gross error for all hourly

 prediction-observation pairs for hourly

 observed values >60 ppb

 NT = total number of station hours (defined

 previously)

 N = number of monitoring stations

 Hi = number of hourly prediction-observation pairs

 for monitoring station i

 co(i,j) = observed value >60 ppb at monitoring station

 i for hour j

 cp(i,j) = predicted value at monitoring station i for

 hour j

OTHER SUGGESTED PERFORMANCE MEASURES

 _

1. Average Station Peak Prediction Accuracy 

where

 _

 A = mean paired peak prediction accuracies

 averaged over all monitoring stations

 N = number of monitoring stations

 Co(i,ti) = peak observed value at monitoring station i

 for hour ti

 Cp(i,ti) = predicted value at monitoring station i for

 hour ti

 ti = hour of peak observed value at monitoring

 station i
2. Bias of All Pairs >60 ppb(D60)

where

 D60 = non-normalized bias from all hourly

 prediction-observation pairs for observed 

 values >60 ppb

 NT = total number of station-hours (defined

 previously)

 N = number of monitoring stations

 Hi = number of hourly prediction-observation pairs

 for monitoring station i

 co(i,j) = observed value >60 ppb at monitoring station

 i for hour j

 cp(i,j) = predicted value at monitoring station i for

 hour j

3. Bias of All Station Peaks (Dpeak)

where

 Dpeak = non-normalized bias from all prediction-

 observation pairs for peak observed values

 at all monitoring stations

 N = number of monitoring stations

 co(i,ti) = peak observed value at monitoring station i

 for hour ti)

 cp(i,ti) = predicted value at monitoring station i for

 hour ti

 ti = hour of peak observed value at monitoring

 station i

4. Fractional Bias for Peak Concentration
 The fractional bias is calculated for both the mean and

standard deviation of peak ozone values, as follows:

where

 Fm = fractional bias of means

 Fs = fractional bias of standard deviation

 mo = mean maximum observed concentration

 mp = mean peak predicted concentration

 so = standard deviation of peak observed

 concentrations

 sp = standard deviation of peak predicted

 concentrations

 The means and standard deviations of predicted and observed

concentrations are determined by each of two methods:

Peak station values:

 co(i,.) = maximum observed concentration at monitoring

 station i across all hours

 cp(i,.) = maximum predicted concentration at monitoring

 station i across all hours

 where i = 1,...,N monitoring stations

Peak hourly values:

 co(.,j) = maximum observed concentration at hour j

 across all monitoring stations

 cp(.,j) = maximum predicted concentration at hour j

 across all monitoring stations

 where j = 1,...,H hours

 The fractional bias of the mean and standard deviation

varies from -2 to +2. Negative values indicate overprediction

and positive values indicate underprediction.