EPA-450/4-74-013
SEPTEMBER 1974
(OAQPS NO. 1.2-031)
GUIDELINES FOR AIR QUALITY
MAINTENANCE PLANNING AND ANALYSIS
VOLUME 12 : APPLYING
ATMOSPHERIC SIMULATION MODELS
TO AIR QUALITY MAINTENANCE AREAS
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EPA-450/4-74-013
(OAQPS ISO. 1.2-031)
GUIDELINES FOR AIR QUALITY
MAINTENANCE PLANNING AND ANALYSIS
VOLUME 12 : APPLYING
ATMOSPHERIC SIMULATION MODELS
TO AIR QUALITY MAINTENANCE AREAS
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
September 1974
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OAQPS GUIDELINE SERIES
Tho guideline series of reports is being issued by the Office of Air Quality
I'ir-nning arid Standards (OAQPS) to provide information to state and local
;»i nolluiion control agencies; for example, to provide guidance on the
.-> quisition and processing of air quality data and on the planning and
analysis requisite for the maintenance of air quality. Reports published in
•Hs series will be available - as supplies permit - rrom the Air Pollution
">. "rhiiifdi Information Center, Research Triangle Park, North Carolina
/:'-!!; or. for a nominal fee, from the National Technical Information
Service, 5285 Port Royal Road, Springfield, Virginia 22151.
Publication No. EPA-"50/4-74- 013
(OAQPS Guideline No. 1.2-031)
11
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FOREWORD
This document is the twelfth in a series comprising Guidelines for Air
Quality Maintenance Planning and Analysis. The intent of the series is to
provide State and local agencies with information and guidance for the prepa-
ration of Air Quality Maintenance Plans required under 40 CFR 51. The volumes
in this series are:
Volume 1: Designation of Air Quality Maintenance Areas
Volume 2j_ Plan Preparation
Volume 3_:_ Control Strategies
Volume 4: Land Use and Transportation Consideration
Volume 5: Case Studies in Plan Development
Volume 6: Overview of Air Quality Maintenance Area Analysis
Volume 7j_ Projecting County Emissions
Volume 8: Computer-Assisted Area Source Emissions Cridding
Procedure
Volume 9j_ Evaluating Indirect Sources
Volume 10: Reviewing New Stationary Sources
Volume ljj_ Air Quality Monitoring and Data~^Analysis
Volume 12: Applying Atmospheric Simulation Models to Air Quality
Maintenance Areas
Additional volumes may be issued.
All references to 40 CFR Part 51 in this document are to the regulations
as amended through July 1974.
NOTE
This guideline is being released in its present form in order to
allow its immediate use by State and local agencies. This guideline
may be reissued in the near future in order to incorporate comments
and suggested improvements offered by the EPA Regional Offices and by
State and local agencies and other concerned groups.
ill
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VOLUME 12. APPLYING ATMOSPHERIC SIMULATION MORELS TO
AIR QUALITY MAINTENANCE AREAS
PREFACE . '
Volumes 7, 8 and 13 discuss methods v;hich can be used to
estimate future county-wide emissions and to allocate these
emission projections within a county. Volume 11 presents guidance
for locating representative air duality monitoring stations and
for interpreting the resulting data. The information contained
in this Volume 12 is intended to provide insight into how the emission
and air quality information thus obtained might be combined to estimate
air quality levels in future years. The vehicle for estimating future
air quality ib cue atmospheric simulation model.
This guideline is not intended as a critical review of atmospheric
simulation models, but only as a guide to the types of models, associated
data requirements, and levels of detail which are available for use in
developing control strategies. Key simulation model parameters and the
data needed to estimate these are first identified. Next, three
tateooHes of models are distinguished in accordance with the amount
and detail of emissions data which are required to use the model.
Finally, examples of models from each category are identified and
*r ioeti in terms of their applicability to different pollutants,
averaging times, emissions and meteorological data requirements, use,
reliability and air quality maintenance analysis.
IV
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The models described in this volume are those which are likely
to be the most available to air pollution control agencies. Alternate
models may be used provided available data are consistent with the
model's requirements and the model is appropriate for the pollutant
and sampling time being analyzed.
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CONTENTS
Page
I. Introduction 1
II. Simulation Model Parameters 2
A. Pollutant Types and Averaging Times 2
B. Emission Inventory Requirements 3
C. Meteorological Data 4a
III. Types of Simulation Models . 6
IV. Applicability of Models 8
V. Background Concentrations 11
VI. Model Validation/Calibration Procedures 11
VII. Summary 14
VIII. Caution 14
IX. Appendix: Model Descriptions 17
Rollback Model 18
Appendix J HC-0X Relationship 21
Miller-Holzworth Model 23
Hanna-Gifford Model 25
HIWAY Model. 28
Air Quality Display Model 31
Sampled Chronological Input Model 34
APRAC-1A Model 36
SAI Photochemical Model. , 38
X. References 39
Bibliographical Data Sheet 43
VI
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TABLES
Table.!. Summary of Simulation Model Characteristics
Table 2. Models Applicable to Specific Pollutants and
Averaging Times
FIGURES
Figure 1. Dispersion Node! Flow Diagram
Figure 2. Flow Diagram for the Development of a Control Strategy.
vii
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APPLYING ATf'OSPHERIC SIMULATION MODELS
TO AIR DUALITY MAINTENANCE AREAS
I. INTRODUCTION
The development of an air quality maintenance plan is strongly
dependent on en adequate methodology for relating pollutant emissions
to ambient air quality. The methodology is required so that con-
clusions can be reached regarding (1) the effect of anticipated growth
in an area on air quality during some specified future period and (2)
the ability of selected control strategies to maintain air quality
standards throughout this period. The most commonly used tool for
relating emissions and air quality is an atmospheric simulation or
dispersion model.
An atmospheric simulation model can be defined as a mathematical
description of the transport, dispersion and transformation processes
that occur in the atmosphere. In its simplest form, such a model
relates pollutant concentrations (x) to pollutant emission rates
(Q) and a background concentration (b),
x = kQ+b (1)
The constant k is a function of atmospheric conditions and the
spatial relationship between source and receptor. Atmospheric
simulation models are ultimately concerned with the variability of
k, and of emission rate and their impact on pollutant concentrations.
This review is intended only as a guide to the types of models,
associated data requirements, and levels of detail which are available for
use in developing control strategies. It is not intended as a critical
review of atmospheric simulation models.
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II. SIMULATION MODEL PARAMETERS
The type of simulation model v/hich is used to examine a specific
growth situation or maintenance plan depends on a number of parameters.
These parameters include the pollutant and averaging times under
consideration, the detail of the emissions inventory, and available
meteorological data.
A. Pollutant Types and Averaging Times
The types of pollutants that are commonly considered in
dispersion model applications are: (1) S02 and total suspended particu-
lates (TSP) as pollutants emitted primarily by stationary sources, (2)
CO and fine particulate matter such as that emitted from mobile sources,
(3) pollutants such as HC and NO which are emitted from both stationary
/\
and point sources and react in the atmosphere to form oxidants.
For purposes "f discussion here, CO and particulate matter are
treated as pollutants which do not transform or decay in the atmosphere.
However HC and N0x are involved in numerous chemical reactions in the
atmosphere; these reactions can be properly accounted for only by highly
sophisticated, complex models. S02 1s known to decay in the atmosphere
and to form other sulfur compounds. However such chemical changes are
not well documented. Some models attempt to account for these changes by
ihe use of a half-life term; usually half-life is assumed to be on the
order of a few hours. Some models completely ignore such a decay. It
should be noted that N02, emitted as a pollutant from an isolated point
source in a non-reactive atmosphere, may be treated in a manner similar to
S0?; this should be done with great care.
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The averaging times for which dispersion models must be used vary
with the National Ambient Air Quality Standards applicabl e to those
pollutants. Dispersion models which allow concentration estimates to be
made for the following combinations of pollutants and averaging times are
needed: S02 and particulate matter for 3-hour (S02 only), 24-hour and annual
averages; CO for one-hour end eight-hour averages; oxidants for one-hour
averages; and N02 for annual averages. Dispersion models estimate con-
centrations for a one-hour period or for seasonal or annual averages. If
an average concentration for a period of intermediate length (e.g., 3-, 8-,
24-hour is required, two options are available. One, the model can be used
to estimate concentrations hour-by-hour for the period under consideration
and an average of all hours taken; this is the preferred method. Two,
statistical techniques suggested by Larsen for urban areas or empirical
o
techniques suggested by Turner and others for point sources can be used
to convert a concentration for one averaging time to a concentration for
longer or shorter averaging times.
Emissions Inventory Requirements
The detail of the emissions inventory is a determining factor
of the dispersion model used to estimate concentrations. Pollutant
sources generally can be separated into point sources, Hne sources and
area sources. Point sources are defined as those which have emissions
of a pollutant greater than some limit, such as 100 tons per year. Any
space heating, process or incineration source which emits this amount
of pollutants or more is considered a point source. Line sources are
generally confined to roadways and streets along which there are
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well defined straight movements of motor vehicles and their emissions.
Area sources include the multitude of minor sources with small emissions
which are impractical to consider as individual point or line sources.
Such area sources are typically treated as a grid network of square
areas, with pollutant emissions distributed uniformly within each grid
square. Commonly, emissions from such sources are prorated over areas
of one to IOC square kilometers, depending on the available detail of
emissions and desired refinement of modelling results. In cases where
there are no data on the spatial distribution of emissions, the emissions
inventory may be limited to one average emission for the entire area
of concern.
For a point source the following minimum data are required as
input to a dispersion model: pollutants emittedjaverage emission rates
(some identi-Mcatlr-r, of shorter term variations may be desirable);
physical stack parameters such as stack height, stack diameter, stack
exit velocity and stack exit temperature; and location of the source in
appropriate grid coordinates. Desirably, a point source emissions
inventory should include these data for all sources with emissions greater
than 100 tons per year. However a less precise estimate can be made using
only data from larger sources.
Typically for line sources, data are required on the width of the
roadway and its center strip, the types and amounts (grams per second
per meter) of pollutant emissions, the number of lanes and the emissions
from each lane and the height of emissions. The location of the ends of the
straight roadway segments must be specified in appropriate grid coordinates.
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Area source information required are types and amounts of
pollutant emissions, the physi.cal size of the area over v/hich em'ssions
are prorated, a representative average stack height for the area sources,
the location of the centroid or the southwest corner of the source area
in appropriate coordinates. For circumstances where only total area-
wide emissions ere available, supplementary information on the dimensions
of the area are required.
It should be noted that the emissions inventory available
for use with most dispersion models is representative of annual average
pollutant emissions. A level of specification greater than this is
generally not available, simply due to e lack of data. The model used
to estimate pollutant concentrations cannot be contingent upon whether
or not an hourly emissions inventory is available. In some cases
factors which specify the diurnal variations of certain source classes
are built into a model. However unless otherwise identified it will
be assumed in this review that the same inventory is used to estimate
hourly as well as annual average concentrations.
C. Meteorological Data
Meteorological data required to describe transport and
dispersion in the atmosphere are wind direction, wind speed, atmospheric
stability and mixing height. Wind direction determines the direction
4a
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of movement of the plume. Wind speed affects the initial dilution
of the pollutant as it is emitted from the stack. The atmospheric
stability determines the rate of dilution as the plume moves downwind.
Mixing height determines the depth of the atmosphere through which
pollutants can be dispersed in the vertical. These parameters are
routinely measured by National Weather Service (NWS) stations.
They are available both as individual observations and in summarized
form from the National Climatic Center in Asheville, North Carolina.
Other sources of similar data may be local universities, industrial
companies, pollution control agencies and consultants. However, data
from these sources are likely to be less comprehensive in scope than
that from the NWS. Simple dispersion models require, as a minimum, data
on wind speed. The more sophisticated models require much more detailed
data on the temporal and spatial variations of the above meteorological
parameters.
For a dispersion model to be useful and valid the meteorological
data input to the model must be representative of the transport and
dispersion conditions which the model is attempting to simulate. The
representativeness of the data is dependent on (1) the proximity of the
meteorological monitoring site to the area under consideration, (2) the
complexity of the terrain in the area, and (3) the immediate surroundings
of the monitoring site. The representativeness of the data can be
adversely affected by distance betv/een the source areas and receptors
of interest, valley-mountain terrain, presence of large bodies of water
and urban-rural differences.
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The r-ost representative meteorological monitoring site(s)
should be selected. For the models to be considered in these
guidelines (except for the SAI model) only one representative, site is
required. Hov;ever, in an area encompassing widely differing meteorological
regimes attention should be given to applying the model with more than
one set of meteorological data. Meteorological consultation for the
application of rodels is required in this circumstance, and is highly
desirable in any case.
The detail of the emissions and meteorological data will
determine which dispersion models can be used to estimate concentrations.
For example, given an urban area for which (1) all ooint/line sources have
been identified, (2) all other emissions are accounted for as area
sources and (3) all meteorological parameters are available on an hourly
basis, specific detailed dispersion models can be used. However, if it
is not possu'iv. to define the locations and existence of point sources,
it becomes necessary to estimate emissions and distribute these as area
sources without regard to specific site Ideations. Thus a dispersion
model requiring less detailed emissions data may be necessary. In cases
where only the crudest area-wide emissions are available, very elementary
models must be used. Obviously, the greater detail with which a model
considers spatial and temporal variations in emissions and meteorological
conditions, the greater its ability to distinguish between the effects
of various air ouality control strategies.
Atmospheric simulation models can be categorized into three general
groupings. They are: (1) models requiring only total area-wide emission
data, (2) models requiring specific information about point/line and area
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sources and (3) models requiring area source emissions allocated on a
sub-county basis.
The first category encompasses models which are limited to
consideration of only total area-wide emissions. There is no identification
of individual point sources or specific area sources. The meteorological
input, if required at all, is in terms of very general parameters such as
average wind speed and average rrixinq height, Concentrations estimated
with such models are either averages for the whole area or are site
specific and apply only where there are air duality data. These models
include the Rollback Model;3 the 40 CFR Part 51, Appendix J HC-0
A
4 5
Relationship; and the Miller-Holzworth Model. These models have limited
usefulness for evaluating individual air quality control strategies. They
can only determine the impact of total pollutant emissions on air quality.
They cannot consider in any way how those emissions or the resulting
air quality levels are spatially distributed across an area.
The second class of models is that which considers in detail
point/line source and area source emissions of pollutants. These models
have detailed requirements for meteorological inputs and consider complex
atmospheric mechanisms for estimating the downwind transport, dispersion and
transformation of pollutants. These models can be used to estimate con-
centrations at any site in an area for which estimates are desired. This
type of model includes the Air Quality Display Model ' the Sampled
Chronological Input Model7 and the APPAC-1A Model.9'10'11 For cases where
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there is a lack of detailed point/line source data, the input to these
models can be limited to area source emissions, wherein all point source
emissions are summed in the area sources. It should be noted that such
a summation, should it be necessary, will considerably detract from the
reliability of the dispersion models.
The last class of dispersion model is that which considers
only area source emissions. This includes the SAI Photochemical
Simulation Model12 and the Hanna-Gifford Model.13'14 The SAI model
is a sophisticated photochemical dispersion model which does not
specifically consider point sources. The Hanna-Gifford model is a
simplified area source model for stable pollutants. While the
latter model does not consider point/line sources, the impact of point
or line sources can be individually determined by the application of a
point source m r!e1 '' or a line source model such as HIWAY. '
Both the SAI and the Hanna-Gifford models allow concentrations to be
estimated for a receptor in any of the designated source areas; however
only one representative air quality value for each area can be determined.
IV. APPLICABILITY OF MODELS
For those areas with very poor information on the spatial
distribution of pollutant emissions the application of the first type
of simulation model (e.g., Rollback, Appendix J, Miller-Holzworth) is
suggested. Where possible the Miller-Holzworth model is preferred for
estimating area-wide average concentrations of SOp and TSP. Where oxidant
concentrations must be estimated, either the Appendix J or rollback
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approach may be necessary; those models allow reduction in HC emissions
to be determined directly from the required reduction in 0 concentrations.
/v
Rollback should be used for estimating CD concentrations in accordance
with procedures described in Volume 1.
In those areas where there is detailed information on pollutant
emissions (current emissions and projected emissions to 1985), the AQDM,
SCIM, APRAC-1A or SAI models may be used, depending on the averaging
times and the pollutants to be considered. However, it should be noted
that the SCIK and SAI models are currently in a developmental and debugging
phase; they are not now available for general distribution as computer
programs. In those cases where the pollutant emissions projected to
1985 can only be known on an area-source basis, it is recommended that
either the Hanna-Gifford or the AQDM be used with just area sources.
The data requirements, model outputs and general performance
of these various models are summarized in Table 1. The summary is
structured from the most elementary to the most sophisticated models.
In the table, "Vs" indicate factors which are the most general or the
easiest to work with. Increasing numbers indicate factors which are more
detailed or difficult to work with. The Appendix to this guideline (1)
examines the models listed in Table 1 in greater detail, (2) discusses
the emissions and meteorological requirements to operate these models, and
(3) identifies the availability and reliability of these models. For
easy reference, Table 2 indicates the models which are applicable to specific
pollutants and averaging times. The models are listed according to level
of detail and applicability.
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Table I
Sunnary of Simulation f'odel Characteristics
Pollutant
Model St"-_.:ifi-
Nai'ic Cdl'C'i
Rollback A:iy
Appendix J QX
Miller- SO?,TSP
Holzv/orth
Hanna- SO,,,TSP
Gifford CO
Hanna-
Gifford S)^,T£P
W. Point SOUIL"
model jJ~,7S?
w. HIWAY CO
AQDM SO?,TSP
SCIM* SJ2,TbP
APRAC-1A CO
5AI* • CO,N02,0X
Averaging
Tine
Snscifi- emission
cation ujta
Any
1 Hour
1 Hour,
Annuul
Annual
1-24 Hour
1-24 Hour
1-24 Hour
Annual
1-24 Hour
1-24 Hour
1-10 Hour
1
1
1
1
2
3
3
3
3
3
2
Keteor-
oloqical
p.it.1
•— ' ' *
1
1
3
2
5
5
5
4
5
5
5
Concen-
tration
tstimtcs
3
3
3
3
2
1
1
1
1
1
2
Ease of
Use
1
1
1
1
2
2
2
3
3
3
3 '
Avail-
abillty
1
1
1
1
1
2
2
2
3
2
3
— : . j t _ i i
Reli-
ability
3
3
1
1
1
1
1
1
2
2
2
e —
Appl icabili ty
to AQ'1
3
3
3
3
2
1
1
1
1
1
2
_ l ,,.—.,,— .
*1t:ese models, arc currently in a dpveloorental and debugging phase"; ti.ty are not available for general
distribution as cc-nputer programs.
Key to Table 1
,;o,)
t. Concentration Estimates
1. Estimates at any specified point
2. One estimate for each area source grid
3. One estimate applicable to entire ACTA
F. Ease of Use
1. Slide-rule
2." Small computer effort
3. Major computer effort
G. Availability
1. Open literature
2. National Technical Information Service
3. EPA, upon request
H. Reliability
1. Can be verified and calibrated
2. Verification 1s Incomplete, possibility of callbrat
1s uncertain
3. Questionable; acceptable for crude estimates only
var1at.1ors of wind direction, wind speed, stability'- Applicability to AQM
,; h, u't ^- Can distinguish letwcen specific source anJ lend i,
2. Can distinguish between land use types only
3. Considers no distinction between sources or land u1
A. Pollutant Specification
Any pollutant
Specific PolTutap.ts (V>n. -
B. JveTM(j1r.]-t1me Specification
t. i iveraging-tline
i a ?1 hour Average
!r:!s '1on Data
1 .*rea-wHe Emissions Total
> t.iial rrrlsslon distributed as finite area sources
i Metalled point, line and area sources
: . f;.^ro'njlc#i Data
i •' ^
K t^ra^e ^.f^d speed
,'•»!'-' w1''d ','^r-rd and rlxing heiqht
•f,(j,--r,,^ di'tricutlon of v.ird direction, wind speed,
•t«b;) • ng r.eight
ion
r<;
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Table 2
Models* Applicable to Specific Pollutants and Averaging Times
S02 end TSP
Annual Averaqe
AOPM
Hanna-Gifford
Miller-Holzworth
Rollback
S02 and TSP
24-Hour Averaae
SCIfl***
Henna-Gifford***
with point source
model
AQDM**
Rollback
S02 and TSP
3-Hour Average
SCIM***
Henna-Gifford***
with point source
model
AQDM**
Miller-Holzworth***
Rollback
CO
1-and 8-Hour Averaae
APRAC-1A***
Hanna-Gifford***
with HIWAY
Rollback
1-Hour Average,
SAI
Appendix J
Rollback
N02
Annual Average
Rollback
* Listed according to level of detail and applicability.
** Statistical conversion of averaging times required.
*** Repetitious application of model to each hour under consideration
is required for averaging times longer than 1-hour.
lOa
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It should not be construed that the models discussed here
are the only ones for relating emissions to air Quality. Other models
which have been summarized and discussed by Lamb, et al, Calder,
on 21
Stern, and Moses are available from private consultants and other
governmental agencies. Exclusion of these other models from this volume
is not meant to imply that they cannot or should not be used. The input
and output of these models should be compared with that of the models
described herein to determine whether they are being used in an appropriate
fashion. The models discussed in this Volume are (1) those most readily
available to air pollution control agencies and (2) representative of
the state-of-the-art for atmospheric simulation models.
V. BACKGROUND CONCENTRATIONS
It must be noted in the application of these simulation models
that background concentrations of pollutants must also be specified. The
simulation models estimate concentrations only for pollutants which have
Identified sources. If pollutants occur naturally in the atmosphere or
are the result of unidentified distant pollutant sources, these pollutant
concentrations must be accounted for and separately added to the dispersion
model estimates. For example it is commonly assumed that the natural
background concentration of TSP is 30-40 yg/m over much of the Eastern
22
United States. It is usually necessary to add a concentration of this
magnitude to any concentration estimates for TSP.
VI. MODEL VALIDATION/CALIBRATION PROCEDURES
To be certain that the dispersion model estimates are as accurate
as possible, validation-calibration is required. Any model may have
faults v;hich cause estimated concentrations to be in error. Therefore,
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it is necessary to validate and calibrate the model estimates. The
availability and accuracy of the input data to these models will sig-
nificantly influence the accuracy of the model estimates. "The source
factors which have the greatest impact on the accuracy of the estimates
are the accuracy and completeness of the emissions data, the accuracy and
completeness of data for the physical plant parameters, and the exactness
with which the location of the sources is determined. The accuracy of the
concentration estimates are also affected by the location, exposure, and
representativeness of the meteorological monitoring sites and the overall
accuracy and completeness of the meteorological data. Similarly, the
validation of the dispersion model is affected by the location, exposure, and
representativeness of the air quality sampling sites and by the accuracy
and completeness of the air quality data itself. These data should be
available for the same averaging times as the concentration estimates. For
proper validation u, the more sophisticated dispersion models, air quality
data should describe the spatial variation of pollutant concentration
across the area. In short, if the air quality data are in any way unsuitable
or incorrect, the validity of the dispersion model estimates cannot be
determined. Statistical methods available for validation of models include
skill scores, contingency tables, correlation analyses and comparisons of
?1
timo series and spatial variations.
If evaluation by one or more of these statistical techniques
indicates that the concentration estimates are not a satisfactory repre-
sent,-.* • , of observed concentrations, even though all source, emissions,
meteorological and air quality data are reliable and complete, it must be
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assumed that the dispersion node! itself is inadequate or inappropriate
for the area under consideration. This is likely to occur (1) in areas
with complex topographic/meteorological conditions, (2) where model
inputs do not allow a complete description of significant source variations,
(3) where atmospheric reactions or rates of removal of the pollutant from
the atmosphere are not adequately accounted for in the model, or (4) for
pollutants which were not considered in development of the model. In
such cases it will be necessary to find a more appropriate model or make
appropriate changes to available models.
Once the dispersion model estimates have been determined to be
acceptable, they must be calibrated. The calibration should account for
systematic errors in the estimates. A model is calibrated by comparing the
calculated concentration estimates to observed air Quality. Then by use of
a regression equation or averaged correction factor, a means for adjusting
the individual model estimates is developed. It should be emphasized that
once a model is calibrated, all future uses of the model must employ a data
base which is similar to that on which the calibration was based. For
example if AQDM were calibrated for a 1970 point/area source data base,
the calibration could then be used to estimate 1985 concentrations with an
appropriate emission inventory projected to 1985. However for consistency,
the projected inventory should identify all point sources in 1985. If due
to a lack of information, the inventory for 1985 must be generalized to
area source emissions, then the base year (1970) should consider an emissions
inventory which also has been generalized to area source emissions.
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VII. SUMf'ARY
The use of the above data and dispersion models in air quality
maintenance should proceed as follows. First, a base year is selected.
The dispersion model is applied to emissions and meteorological data for
this base year and the dispersion estimates compared to the observed air
quality. In this way the model can be validated and deficiencies in the
dispersion model can be identified and corrected (Figure 1). In addition,
any inadequacies in the basic emissions inventory, the meteorological
data, or available air quality data can be identified. Next, the dispersion
model is applied to the projected 1985 emissions inventory. For this
application, meteorological data which can be considered representative
of atmospheric conditions in 1985 are required. This necessitates the
use of climatological means and extremes or the use of data from a year
which exhibited average dispersion conditions. With these data the
likelihood of exceeding the air quality standards in 1985 can be ascertained.
This is done with the calibrated model so that some confidence in the
estimates is assured. Next, if it is determined that the air quality is
likely to be greater than National Ambient Air Quality Standards in 1985,
this dispersion model can be used to determine the ability of alternative
strategies for emissions control in 1985 (Figure 2). Thus, the dual
purpose of air quality maintenance evaluation can be met.
VIII. CAUTION
Like any "tool," atmospheric simulation models are useful only if
the user understands how to apply them and is aware of their vagaries.
They are highly specialized tools which require, in many cases, large
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amounts of detailed technical data. As such they should not be used
indiscriminately. It is strongly urged that the services of competent
• ' *
air pollution meteorologists, engineers and air quality analysts be
solicited in the application of atmospheric simulation models. This
need is especially critical in the application of the more sophisticated
models and in complex meteorologic/topographic locales. Without the
availability of such expertise, a model applied improperly or with
incorrect data, can lead to erroneous conclusions about the ability to
maintain air quality.
14a
-------
-------
H. , • f — * , ^.
-Vcr.'i
Gsy
1 D!S?E
5 SIGN
DEL
J
METEOROLOGICAL
A;.;D TCrGcHAPHICA
DATA
3DT~r*t''*Tr-T-\
r r. c U i v..; c D
A! F; O JI •'" ! 7 v
*->.iu L; J i-. i_ I j j
EXISTING
AIS G'JAL.'TY
VALID
MODEL
COnRECTlC.M
Fig. 1 - Dispersion mode! flow diagram.
15
-------
OF
r
0
i r -\ r •>
.»!;•> >.,.
F sour.
CES
i
FO;-".:!JLAT!GM
DATA Orj
AVAIL A'JL
T ;; ^- ;• "^' i
1 L \«»4 . . • I V- w*
E
ES
OF CCi.'TfiOL
^ STRATEGIES
v
D'JE TO
liRSA,-JiZATIO;\!
STRATEGY 4*
s:?.:uLAno.\f
CC;.'TOL7.SOF
[ <\ • •>
sr;:A7i cv
SELEC: -5
Fig. 2 - Flow diagram for the devslopmcnt of a control strategy.
16
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IX. Model Descriptions
17
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ROLLBACK f-'ODEL
The rollback rrodel is based on the simple expression relating
pollutant concentrations (X) to pollutant emission rates (0) and a
background concentration (b).
X = kO + b.
The rollback model assumes that the dispersion parameter k
does not vary with time or with the source-receptor relationship and
that changes in emission rates are uniform across the area. Thus the
relationship of emissions (Q8g) and air quality in a future year (X85)
to the emissions (Obase) and air quality (Xbase) in a base year can be
expressed by the following proportionality:
Xbase'b QBase
The basic assumption in the model is that a given percent reduction
or increase in pollutant emissions will result in a similar reduction
or increase in pollutant concentrations. It is simply a tool for
scaling concentrations up or down to reflect similar changes in the
gross emission rates.
The rollback model is applicable to most pollutants and
averaging times for which appropriate data are available. Input to
tne rollback model requires total area-wide emissions for the base
year end for 1985 or other years of interest. A pollutant concentration
rx.....ative of air quality for the area and the averaging time
18
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of interest is also necessary. It should be noted that since there is
no allowance for specifying the dispersion parameter k or other
meteorological parameters, this model cannot be used to estimate
concentrations at sites where representative air quality data do not
exist.
An expansion of the simple rollback procedure is call ed Modified
2
Rollback. Modified rollback is a technique for considering a range of
source categories for the pollutant being considered. It can be
expressed as
N
'"1 Gi Fi'85
where G is a growth factor, e. g. the ratio of population projected
to 1985 versus population in the base year,
F is the emission factor ratio, e.g., the ratio of expected
emissions per unit of population in 1985 to emissions
per unit of population in the base year,
N is the number of source categories,
i is a particular source category, e.g. light-duty vehicles,
stationary sources, etc.
In cases where the area-wide pollutant emissions are contributed by a
variety of source types with differing emissions, growth rates and
applicable controls, the modified model will allow the situation to
be studied in more detail.
The rollback models are applicable anywhere for which there are
basic data on area-wide emissions and representative air quality for
a particular base year. The simple rollback model can be applied with
19
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simplified hand calculations and is widely used, Modified rollback
has been computerized and documented. The programs and associated
documentation on the ."Motor Vehicle Emission Estimation Program" and
the "Modified Rollback Computer Program", has been made available to
all EPA Regional Offices.
The rollback rrodels in general are valid for the simplified
case of only one type of source uniformly distributed across an area
affecting a receptor. . Mobile sources and,associated CO emissions are
an example of the type of source which may approximate this situation.
Accuracy is lost as the variability of source types and emission rates
increase and the impact of atmospheric processes'on pollutant concentrations
increase. Thus due to the importance of point sources for TSP a nd
S02 and the reactive nature of N02 and px, this model can provide only
very crude esti^tFc -f concentrations for these pollutants.
20
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APPENDIX J HC-0 RELATIONSHIP
A
Appendix J of 40 CFR Part 51, the Requirements for Preparation,
Adoption, and Subidttal of Implementation Plans (36 P.P.. 15486), is a
graphical presentation of the percent reduction in hydrocarbon (HC)
emissions required to reduce an observed peak hourly average oxidant
(Ox) concentration to the national Ambient Air Quality Standard (NAAQS)
for f>x. The relationship assumes that the maximum 1-hour 0 concentration
A
is directly affected by the quantity of HC emitted during the morning
hours. This assumption is based on the maximum observed relationship
of HC and GX concentrations for selected cities. This relationship can
be used to determine the effect a change in HC emissions will have on peak
oxidant concentrations.
To use the Appendix graph it is assumed that HC emissions are uniform
across the whole area and that the peak Ov concentration in the area
A
has been identified. Inherent in the use of Appendix J is the assumption
that meteorological conditions and the source-receptor relationship are
invariant. Thus there is no need for input of meteorological data.
Similarly this approach cannot be used to estimate concentrations at sites
where air quality data does not exist. It too is simply a means of
scaling 0^ concentrations to variations in HC emissions. Its application
is restricted to peak hourly average 0 concentrations. However it is
A
generally applicable to any area v/here the formation of 0 is a problem.
A
As noted above the Appendix J relationship is presented in the
Federal Pecistor. Its application is elementary. The reliability of
21
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this relationship for determining the effect of HC emissions on GX
concentrations has not been identified. There is no allowance for
the photochemical reactions which take 'place between HC and NOX and
no consideration of 0 concentrations due to natural sources. Thus
/^
any conclusions concerning the HC reduction required to achieve given
0 levels derived from this relationship should be considered as very
/\
rough first approximations.
22
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MILLER-HOLZWORTH f'ODEL
The Miller-Holzworth dispersion node! is a step in sophistication
above the basic rollback rrodel for estimating pollutant concentrations.
In this model the dispersion parameter k is expressed in terns of mixir-r-
heiqht, wind speed and the geographical size of the city. The node! c
-------
to this node! is the uniform average area emission rate (0) in units
of grains per second per square rreter. Mixing height (H), wind speed
through the mixing layer (U) and city size (S) (distance across the
city for a given direction) are also required to estimate pollutant
concentrations (X). A discussion of the dispersion model and appropriate
seasonal average mixing heights and wind speeds is given in EPA
5
publication AP-101. This publication also provides median, upper
quartile and upper decile (X/Q) values for various city sizes. Thus a
range of pollutant concentrations can be estimated for the more
restrictive meteorological dispersion conditions. However, neither
the emissions inventory used as input nor the output of the dispersion
model makes it possible to estimate spatial variations in pollutant
concentrations across the area. In this sense, any set of emission
control strategic i.r.ich allow similar total area-wide pollutant
emissions will result in the same air quality for the area. No matter
how different the strategies are, if the total emissions are the same,
then the air quality estimated will be the same with this model.
This is a simple model for which concentration estimates can be
made with a slide rule or from the summaries in AP-101. The reliability
of the dispersion model when applied to specific areas to estimate
1-hour concentrations has not been determined. However, as noted
earlier, the model has been calibrated. Such a calibration is presented
23
in Anp- l,n A to Requirements for Preparation, Adoption and Submittal
of Implementation Plans (36 F.R. 15486) for a range of city sizes
so that estimates of annual average concentrations can be made.
24
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HANNA-GIFFORD MODEL
The Hanna-Gifford dispersion model is the next step upward in
dispersion model sophistication. The Hanna-Gifford model is .most
readily applicable to stable pollutants such as SOp, particulates,
and CO. It has been used to estimate 1-hour and annual average con-
centrations of such pollutants. The model can be used to estimate
an average concentration for any defined area. This model differs
from the previous models in the form of the dispersion constant (k).
In the basic Hanna-Oifford model the constant is a function of
stability, wind speed and the size and number of area sources. The
equation relating concentrations to emissions is
N
Q + [ Q [(21 + I)1'13- (21 - I)1"15
° 1=1 i
X =
*x
where
a
f2]
H
(Ax /2)1-b
a(l-b) U
a, b are empirically determined constants used to specify
dispersion, they are functions of the atmospheric stability
AX is the size (width) of the area sources,
N is the number of upwind sources,
i is a specific upwind source,
QQ is the pollutant emissions for the area in which the
receptor site is located,
^i are the emissions for upwind areas.
The model is applied to each area which encompasses a receptor site.
The application is made for the wind direction, wind speed and stability
class for each meteorological situation under consideration. All sources
upwind of the receptor area are included in determining the pollutant impact,
This approach is used to estimate hourly average concentrations for
25
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all situations of interest. If data on diurnal variations in emission
rates are available, they can be used with the model. Concentrations for
other averaging times can be obtained by estimating concentrations for
each hour of the period and averaging the hourly concentrations.
Annual average concentrations can be obtained in a manner similar
to that described for AQDM. However Hanna-Gifford have simplified
this procedure by substituting a more elementary form of the dispersion
equation. For annual average concentrations the model takes the
r
simplified form of x* = - 0, where C is a constant dependent on the
_ u
pollutant, u is the annual average wind speed, and 0 is the average
o
emission density (g/sec/m ) for the whole area of interest. However
this form of the model can only be used to estimate a representative
average concentration for the whole area.
In examinations of the reliability of this model, it has been shown
that the more detailed formulation provides an accuracy similar to
that of much more sophisticated models. Correlations generally
varying between .60_ and .95_ depending on the pollutant, averaging time
14
and area have been found. For the simplified model, from an application
to a large number of areas, values of the constant C equal to 225 and
50 respectively for particulates and S02 were found. However the
standard deviation of the error in concentration estimates obtained
when the mean value for the constant is used, has been found to be
about 50 vg/m for TSP. Further analysis has shown that the constant
is overly simplistic and that a relationship of x = 52 + 91.7 Q/u
provides a better estimate for particulate matter. The use of this
relationship in place of an average for C reduces the standard
deviation of the error to 10 ug/m . For SOp a similar
26
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relationship could not be derived because the variation of the
constant C was too random. This reflects the importance of a
largo point source emissions in determining SCL concentrations and
indicates that caution should be exercised in the use of a value
of 50 for C when considering S0?.
The model is basically applicable for areas where there is
no point source information available so that all emissions are grouped
into area source emissions. However, if specific point sources
or line sources are known the impact of these sources can be
estimated independently through the use of an appropriate model and
concentrations added to those caused by area sources. Diurnal variations,
where available^in emission rates should be considered in the application
of such specific models to point and line sources. Appropriate
point source models are discussed in guidelines for Reviewing New
1 n
Stationary Sources. These models are generally accepted to be
accurate within a factor of two. The most basic point source
dispersion models are available through the National Technical
Information Service. An appropriate line source model is discussed
in this Guideline under the HIWAY model.
Discussions of the Hanna-Gifford dispersion model are available
from various literature sources. ' The model is simple enough
to estimate concentrations with a slide rule and hand tabulations.
27
a
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HIWAY KODEL
The HIWAY model is a basic line source dispersion rnodel .
It is applicable to mobile source pollutant emissions along
roadways such as highways and streets and is applicable only to stable
pollutant emissions (CO and fine particulate matter) from
automotive sources. The model is applicable to estimate one-hour
average concentrations of these pollutants. By repetitively
simulating each hour in an 8-hour or 24-hour period, 8-hour and
24-hour concentrations of the pollutants of interest can be estimated.
In this way concentrations at specified distances downwind from the
roadway or line source can be obtained.
The basic formulation for this line source model is
X - il 1 y \2
i
J
A
where x is the concentration at the receptor
u is wind speed
qe is the constant line source emission rate
ty oz are the standard deviations of the concentration
distribution in the crosswind and vertical directions;
they are dependent on atmospheric stability and on
downwind distance from sources to the receptor
A, B are the endpoints of the line source
28
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L is the length of the line source from point A to the ith
element of the line source
y is crosswind distance from source element to the receptor.
The model is applied to each lane of traffic which makes up the line
source to estimate the contribution to concentrations at specified
downwind distances. Concentrations can be calculated for any
specified combination of meteorological conditions.
Basic inputs into this dispersion model are emissions in terms
of grams per meter per second for each lane making up the roadway
for each hour being considered. Due to the diurnal variations in
emission rates of automotive sources, emissions should be appropriately
varied with time of day. In addition, the length of roadway, its width,
and other physical parameters of the roadway need to be specified.
Meteorological inputs into the model are stability class, wind direction
and wind speed for the hour of interest. The output of the model is
one-hour average concentrations at specified points downwind from the
roadway. Concentrations estimated for each hour in a given sequence of
hours make it possible to obtain 8-hour and 24-hour concentrations of
pollutants. The model is not readily applicable to estimating annual
average concentrations.
To determine the impact of line sources, in addition to more
generalized area source emissions, the HIWAY model can be used in
conjunction with the Hanna-Gifford rrodel. The Hanna-Gifford model
is used to determine the impact of all other sources in an area while
the HIWAY model is used to determine the impact of specific nearby
roadways.
29
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The model is available from the National Technical Information
Service. HIK'AY has been compared with preliminary data which
indicate that this model has an accuracy comparable to that of point
source dispersion models.
30
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AIP OU/UITY DISPLAY f'ODFL
The Air Ouality Display Model (AODr) is a long-term average
concentration urban dispersion model which is best used to determine
the impact of a wide variety of stationary source classes on annual
averaqe concentrations of S02 and TSP. The model has been widely
applied to areas with numerous point and area sources; it has been
validated and calibrated for these areas.
The model is based on the standard long-term concentration
equation
2 Q f
where f(e, S, N) is frequency during the period of interest that
the wind is from direction e, for the stability
condition S, and the wind -speed class N.
oz is the vertical dispersion parameter at distance x
for stability condition S
u is the representative wind speed for class N
H is the effective stack height for wind speed u.
The model is used to determine the impact of all sources at a oiven
receptor, for a ciiven set of nieteoroloaical conditions. It then
weights this concentration by the frequency with which that particular
set of meteorological conditions occurs and then sums over all
31
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meteorological conditions, thus producing a long-term average
concentration. Basic inputs to the model are a comprehensive emissions
inventory including both noint'sources and area sources. Meteorological
input is a joint frequency distribution of wind speed (6 classes), wind
direction (16 cardinal points), and stability class (Pasquill classes
A-F) along with an annual average mixing height. The dispersion model can
be used to estimate concentrations at any point downwind that is specified.
When adequate point source information is not available a properly
calibrated model could be used with just area source information,
that is, all point sources included in area source emissions. However,
it must be recognized that the reliability of this model or any model
will be adversely affected by such an assumption, namely that all
point sources can be considered in the area source emissions.
This model is available from National Technical Information Service6
and as a computer system. A similar model, called Climatological Dispersion
Model, is also available.8' The two models have differences in calculation
techniques, however they require basically the same inputs and have the
same type of outputs. A significant difference betv/een the two is
that there is a source contribution file produced by A(W which allows
the impact of each individual source on air quality to be obtained.
The same sort of output cannot readily be obtained from CDM. It should
be noted that the AODM was originally developed with the Holland plume
rise equation for calculating effective stack heights. A. change to the
computer program is now available for the inclusion of the Briggs1 plurr.e
32
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rise equation. Ihis latter equation is preferred, especially for
large sources, e.q. power plants.
AOPr can he calibrated using regression analysis techniques.
In nurerous applications of the model a correlation coefficient of
C.8 or greater has been found to be typical.
33
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SAMPLED CHRONOLOGICAL INPUT MODEL
The Sampled Chronological Input Model (SCIM) is one of several
20
dispersion models which are applicable to both point and area source
emissions in urban areas. These models as a class are basically
used to estimate hourly average concentrations of SC>2 and particulate
matter which are emitted from stationary sources. SCIM specifically
determines e frequency distribution of one-hour concentrations. If
this model or any other model is to be used to estimate 24-hour
concentrations, a series of hourly observations must be used to
determine 24 1-hour concentrations. While this model does not
sppr.ifically pstimate 24-hour concentrations, it provides a summary
of all estimated hourly concentrations. Thus estimates of con-
centrations for averaging times longer than 1 hour can be tabulated.
Through the use of standard statistical techniques SCIM summarizes the
1-hour estimates as a distribution of concentrations. This model
can be used to estimate concentrations at any desired site. The model
is based on the standard Gaussian dispersion equation;
-V Q F1/'V\2]F1/'H\-1
y^ c\-p „- — - tvp --A —
» «r.
-------
no allcvvnce for diurnal variations in emissions. Limited tests with
variations in emissions based on time of day did not show a consistent
improvement in concentration estimates. The meteorolooical data
required e.re specific hourly data on wind speed, wind direction,
stability class and mixing height. The model does provide the option
of estii..£'ting concentrations for selected intervals, e.p. every 3
hours to lessen the computational burden. /* judiciously selected
interval, which insures that estimates for all times of day for a
sufficiently long period of time, will not adversely influence the
frequency distribution for hourly concentrations. Documentation of
this model is in progress.
The reliability of this model has not been widely determined;
howpvpr. annl lV;>t-j npc tn thrpo riti'oc i prH r:\-t-rNc -i-ha-t- +-Kn •Fv^^i.r.M,-,/
> ~r[~- -'-'•— -~ «.-.---, ^-.w.^i i I, v- i w v. w i. ^ 1.11 w> w LIIV. iii.t^wC'ii^^
distributions of 1-hour concentrations estimated by the dispersion
model approximately reproduce the observed frequency distributions.
35
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APRAC-1A KODEL
APRAC-1A is a dispersion model which is applicable only to line
and area sources of stable automotive pollutants (CO and participate
matter). The model can be used to determine 1-hour averages of these
pollutant concentrations or a frequency distribution of such concentra-
tions. It too can be used to estimate concentrations for other
averaging times by averaging a series of hourly estimates. It can
be used to estimate concentrations at any desired site.
APRAC-1A is based on a modification of the Gaussian dispersion
equation. The basic input to this dispersion model is in terms of
individual street segments or line sources. The model takes these
segments and determines area source emissions from these line sources;
these emissions are combined with other specified area source emissions
to indicate total emissions. A method for determining diurnal variations
in emissions is included in this model. These area sources are oriented
in the upwind direction. A logarithmic spacing of the area sources
allows the nearby sources to be considered in greater detail than those
farther sources, whose individual contributions tend to be merged
during longer travel. Meteorological input to the model is the same
:s with other dispersion models; wind direction, wind speed, stability
class and mixing height on an hourly basis.
"uus model also incorporates a street effects submodel. This
submodel allows the impact of localized emissions and wind circulations
36
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on pollutant concentrations to be determined. It basically takes the
form of a box model with slightly different methods for calculating
concentrations on opposite sides of the road. This difference is an
attempt to reflect the impact of the complex v/ind circulation which
has been identified in street canyons.
The model is available from National Technical Information
Service/' ' This dispersion model has been applied to St.
Louis and San Jose and estimates compared to observed data; it can
be expected to have a reliability similar to that of other dispersion
models.
37
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SAI PHOTOCHEMICAL f'ODEL
The SAI model is a ohotochemical dispersion model. It not
only considers the transport and dispersion of pollutants but also
the transformation of HC and NOX into photochemical oxidant pollutants.
It estimates the hourly concentration variations in these pollutants;
CO, HC, NO, NOp and 0 .
^- J\
The mathematical formulation for this model is considerably
different from that discussed for the other dispersion models. This
model uses finite difference techniques over a qrid of area sources
to solve the classical equations of conservation of mass v/hich
include local change, advection, diffusion, photochemical reaction
and emission.
The emissions input to this dispersion model is specified as
uniform gridded area sources; hourly emission rates and their diurnal
variations are specified. Meteorological input are hourly data
on the spatial variation of wind direction, wind speed and mixing
height. The model is capable of estimating for each hour an
average concentration for each area identified as an area source.
'fr.is model is most applicable when large amounts of data are available
:,.,* a sophisticated analysis of a region. To this date it has only
reen applied to areas in California.
This model is one of the better sophisticated ohotochemical
models currently available. The reliability of this model has not
been thoroughly examined.
38
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VIII. REFERENCES
(1) l.ersen, R. I.; "A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards"; Office of Air Programs
Publication I'o. AP-£9 (NTIS PB 205277); Office of Technical
Information and Publications; U. S. F.PA; Research Triangle Park,
N. C. 27711; (November 1971).
(2) Turner, P. B.; "Workbook of Atmospheric Dispersion Estimates";
PUS Publicetion No. 999-AP-26 (NTIS PB 191482); Office of Technical
Information and Publications, U. S. EPA; Research Triangle Park,
N. C. 27711; (1969).
(3) de Nevers, f!. and J. P. Morris, "Rollback Modeling—Basic and
Modified"; Paper Number 73-139; Presented at 1973 Annual Air
Pollution Control Association Meeting; Chicago, 111.; (June 1973).
(4) U. S. EPA; "Requirements for Preparation, Adoption, and
Submittal of Implementation Plans—Appendix J"; Federal Register 36;
No. 158; p. 15502; (August 14, 1971).
(5) Holzworth, G. C.; "Mixing Heights, Wind Speeds, and Potential
for Urban Air Pollution Throughout the Contiguous United States";
Office of Air Programs Publication Ho. AP-101 (NTIS PB 207103);
Office of Technical Information and Publications; U. S. EPA; Research
Triangle Park, N. C. 27711; (January 1972).
(6) TRW Systems Group; "Air Ouality Display Model"; Prepared for
the rational Air Pollution Control Administration under Contract Ho.
PH-22-68-60 (NTIS PB 189194), DHEW, U. S. Public Health Service,
Washington, 0. C. (November 1949).
39
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(7) Koch, R. C. and G. H. Stadsklev; "A User's Manual for the
Sampled Chronological Input Model (SCIM)"; GEOMET Report No. E-261
prepared for U. S. FPA Under Contract Number 68-02-0281; U. S. EPA,
OAQPS, Research Triangle Park, N. C. 27711; (August 1973) (available
in draft form only) .
(8) Busse, A. D., and J. R. Zimmerman; "User's Guide for the
Climatological Dispersion flodel"; Environmental Monitoring Series
EPA-R4-73-024 (NTIS PB 227346AS) NERC, EPA, Research Triangle Park,
N. C. 27711 (December 1973).
(9) Ludwig, F. L. and R. L. Mancuso; "User's Manual for the
APRAC-1A Urban Diffusion Model Computer Program," Prepared for
U. S. EPA Division of Meteorology Under Contract CAPA-3-68 (1-69)
(NTIS PB 213091); U. S. EPA, Research Triangle Park, N. C.
27711; (September 1972).
(10) Ludwia, F. L. and W. F. Dabberdt; "Evaluation of the APRAC-1A
Urban Diffusion Model for Carbon Monoxide"; Prepared for U. S.
EPA, Division of Meteorology Under Contract CAPA-3-68 (1-69) (NTIS
PB 210819); U. S. EPA, Research Triangle Park, N. C. 27711
(February 1972).
(11) U. S. EPA; User's Network for Applied Modeling of Air Pollution
fUNAMAP); (Computer Programs on Tape for Point Source Models, HIWAY,
Clinatological Dispersion Model and APRAC-1A) NTIS PB 229771
National Technical Information Service, Springfield, Virginia 22151
40
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(12) Systorrs Applications Inc.; Urban Air Shed Photochemical
Sinuletion Model Study—Volumes I-I II"; Prepared for U. S.
EPA, CPD Under Contract Nurrber 68-02-0339; EPA-P4-73-030g;
Washington, D. C. 20460; (July 1973). (Available from APTIC).
(13) Hanna, S. P.; "A Simple f-'ethod of Calculating Dispersion
from Urban Area Sources"; J. Air Pollution Control Ass'n.,
Vol. 21, No. 12, pp. 774-777; (December 1971).
(14) Gifford, F. A., and S. R. Hanna; "Modeling Urban Air Pollution";
Atnospheric Environment, Vol. 7, pp. 131-136; (1973).
(15) U.S. EPA, OAQPS; "Reviewing New Stationary Sources";
Guidelines for Air Quality Maintenance Planning and Analysis, Volume 10;
OAQPS No. 1.2-029; (September 1974).
(16) Zimmerman, J. R. end R. S. Thompson; "User's Guide for
HIWAY: A Highway Air Pollution Model"; Environmental Monitoring
Series EPA-650/4-008, NERC, EPA, Research Triangle Park, N. C.
27711 (in preparation).
(17) U. S. EPA, OAQPS, CPDD; "Guidelines for Designation of Air
Quality Maintenance Areas"; OAQPS Publication Mo. 1.2-016; U. S.
EPA, OAQPS, Research Triangle Park, N. C. 27711; (January 1974).
(18) Lamb, D. V., et al; "A Critical Review of Mathematical
Diffusion Modeling Techniques for Air Quality with Relation to Motor
Vehicle Transportation"; A Study Prepared for the Washington State
Highway Commission, Department of Highways; University of
Washington, Seattle, Washington (June 1973).
41
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(19) Calder, K. L.; "Mathematical Modeling of Air Quality
Through Calculation of Atmospheric Transport and Diffusion" in
Proceedings of the Third Meeting of the Expert Panel on Air
Pollution Modeling, CCMS/NATO, Paris, France (October 2-3, 1972).
(20) Stern, A. C., "Proceedings of Symposium on Multiple-Source
Urban Diffusion Models"; Air Pollution Control Office Publication
No. AP-86 (NTIS PB 198400), Office of Technical Information and
Publications; U.S. EPA; Research Triangle Park, N. C. 27711;
(1970).
(21) Moses, H.; "Mathematical Urban Air Pollution Model";
Argonne National Laboratory Peport ANL/ES-PPY-001; Argonrie
National Laboratory, Argonne, Illinois, 60439 (April 1969)
(Limited Distribution Only).
(22) McCormick, R. A ; "Air Pollution Climatology in Ai_r
Pollution Volume 1, Edited by A. C. Stern, Academic Press,
New York, flew York 10003 (1968).
(23) U.S. EPA; "Requirements for Preparation, Adoption
and Submittal of Implementation Plans—Appendix A"; Federal
Register, 36. No. 158; pp. 15494-15495; (August 14, 1971).
42
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing!
' -. " ; NO 2.
PA .1^0/4-74-013
j -'.;-. fl\D SUBTITLE
•auin-'i jnes for Air Quality Maintenance Planning and
. Vo^o'fz: Applying Atmospheric Simulation Models to
A"'-"1 'Tniity Mai nienance Areas
. -< ' 3 '
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