vvEPA
United Statss
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
EPA/600/8-91/038
March 1993
Selection Criteria for
Mathematical Models
Used in Exposure
Assessments:
Atmospheric Dispersion
Models
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EPA/600/8-91/038
March 1993
SELECTION CRITERIA FOR MATHEMATICAL MODELS
USED IN EXPOSURE ASSESSMENTS:
ATMOSPHERIC DISPERSION MODELS
Exposure Assessment Group
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Washington, D.C.
Printed on Recycled Paper
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental
Protection Agency policy and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
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CONTENTS
List of Tables vii
List of Figures viii
Foreword ix
Preface x
Authors and Reviewers xi
PART 1. SELECTION CRITERIA FOR ATMOSPHERIC DISPERSION MODELS
1. LIMITATIONS OF ATMOSPHERIC DISPERSION MODELS 1
2. BACKGROUND INFORMATION 3
2.1. Characterization of Contaminant Concentrations in Air . . . 3
2.1.1. Source Characteristics 3
2.1.2. Meteorological Conditions 5
2.1.3. Geographic Scale 6
2.1.4. Topography 7
2.1.5. Contaminant Properties . 8
2.2. Characteristics of Exposure Assessments 9
2.3. Definition of Terms 10
3. GENERAL GUIDELINES AND PRINCIPLES OF MODEL SELECTION 13
3.1. Atmospheric Dispersion Models 13
3.1.1. Buoyant Line and Point Source Dispersion Model (BLP)* 15
3.1.2. CALINE3* 15
3.1.3. Climatological Dispersion Model (COM 2.0)* 15
3.1.4. Offshore and Coastal Dispersion Model (OCD)* 16
3.1.5. Gaussian Plume Multiple Source Air Quality
Algorithm (RAM)* 16
3.1.6. Industrial Source Complex Model (ISC)* 16
3.1.7. Multiple Point Gaussian Dispersion Algorithm with
Terrain Adjustment (MPTER)* 17
3.1.8. Single Source Model (CRSTER)* 17
3.1.9. Urban Airshed Model (UAM)* 17
3.1.10. Air Pollution Research Advisory Committee (APRAC-3) 17
3.1.11. Air Quality Display Model (AQDM) 18
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3.1.12. Air Resources Regional Pollution
Assessment Model (ARRPA) 18
3.1.13. COMPTER 18
3.1.14. ERT Air Quality Model (ERTAQ) 19
3.1.15. HIWAY-2 19
3.1.16. LONGZ 19
3.1.17. Multiple Point Source Diffusion Model (MPSDM) 19
3.1.18. Mesoscale Transport Diffusions and Deposition Model for
Industrial Sources (MTDDIS) 20
3.1.19. MULTIMAX 20
3.1.20. Point, Area, Line Source Algorithm (PAL) 20
3.1.21. Pacific Gas and Electric PLUMES Model (PLUMES) 21
3.1.22. Rough Terrain Diffusion Model (RTDM) 21
3.1.23. Multi-Source Model (SCSTER) 21
3.1.24. SHORTZ 22
3.1.25. Simple Line Source Model (SLSM) 22
3.1.26. Texas Climatological Model (TCM-2) 22
3.1.27. Texas Episodic Model (TEM-8) 22
3.1.28. Models 3141 and 4141 22
3.2. Human Exposure Models 23
3.3. Model Selection Process 23
3.3.1. Geographic Scale 24
3.3.2. Time Scale 24
3.3.3. Land Use 31
3.3.4. Terrain . 31
3.3.5. Source Characteristics 31
3.3.6. Reactivity 33
3.3.7. Form of Release 34
3.3.8. Removal Mechanism 34
4. EXAMPLES OF MODEL SELECTION FOR SPECIFIC EXPOSURE
ASSESSMENTS • 35
4.1. Municipal Waste Combustors .35
4.1.1. Assessment 35
4.1.2. Recommendation . 35
4.2. Perchloroethylene Emissions from Dry Cleaning Facilities 37
4.2.1. Assessment 38
4.2.2. Recommendation 38
IV
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4.3. Exposure to Dioxin from Soils via Vapor Inhalation 40
4.3.1. Assessment 40
4.3.2. Recommendation 40
REFERENCES FOR PART I 41
PART II. GAUSSIAN PLUME MODELS
1. INTRODUCTION 44
1.1. Assumptions 44
1.2. Limitations 47
1.3. Uncertainties 47
1.3.1. Site Conditions 47
1.3.2. Meteorologic Conditions 50
1.3.3. Release Conditions 51
1.3.4. Source Complexity 51
1.3.5. Plume Depletion Processes 52
2. DISPERSION COEFFICIENTS 54
2.1. Pasquill-Gifford Dispersion Coefficients 54
2.2. Briggs Dispersion Coefficients 54
2.3. Klug Dispersion Coefficients 59
2.4. Brookhaven Dispersion Coefficients 59
2.5. St. Louis Dispersion Coefficients 59
2.6. Julich Dispersion Coefficients 60
3. INPUT DATA REQUIREMENTS 61
3.1. Source 61
3.1.1. Typical Source Parameters for Hazardous Waste
Management Facilities 62
3.2. Meteorologic 64
3.3. Receptor 64
REFERENCES FOR PART II 66
PART III. INDOOR AIR MODELING
1. INTRODUCTION 74
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2.
CHARACTERIZATION OF INDOOR AIR EXPOSURE 75
2.1. Identification of Emission Sources 75
2.2. Characterization of Emissions 77
2.3. Determination of Ventilation Rates 77
2.4. Relative Impact of Indoor and Outdoor Emissions on Human Exposure ... 77
3.
MODELING INDOOR AIR 80
REFERENCES FOR PART III 82
APPENDIX 85
REFERENCES FOR APPENDIX " .95
VI
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TABLES
1 Model selection profile 25
2 Characteristics of atmospheric dispersion models 26
3 Site characteristics—atmospheric dispersion models 29
4 Source characteristics—atmospheric dispersion models 30
5 Identification and classification of land use types found in
Metropolitan St. Louis 32
6 Model selection criteria for sample sites 36
7 Estimated ratios of predicted to observed air concentrations using
Gaussian plume atmospheric dispersion models 48
8 Definition of the Pasquill atmospheric stability categories 55
9 Parameters used to compute dispersion coefficients 58
10 Air contaminants by source locations 76
11 Summary of I/O ratios of selected airborne contaminants 79
VII
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FIGURES
Horizontal dispersion coefficient as a function of downwind
distance from the source . . ; 56
Vertical dispersion coefficient as a function of downwind
distance from the source 57
VIII
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FOREWORD
When performing exposure assessments using predictive methods, assessors
frequently ask the following questions: "How do I select the best fate model to use in my
assessment," "How can I tell if the model someone else used in an assessment is
appropriate," and "What are the strengths and weaknesses of these models?" This document
is a first step in addressing these questions as they apply to the dispersion of contaminants in
air.
One of the functions of the Exposure Assessment Group is to develop guidelines for
exposure assessments. On September 24, 1986, the U.S. Environmental Protection Agency
published Guidelines for Estimating Exposures. During the development of the guidelines and
subsequent review and comment, four areas were identified that required further research.
One of these areas was selection criteria for mathematical models. This document, which is
the third selection criteria document in the series, deals with atmospheric dispersion models.
The first two dealt with surface water and ground-water models. On May 29, 1992, the
Guidelines for Exposure Assessment were published in the Federal Register as an update to
the original Guidelines. Also, the computer-based Integrated Model Evaluation System (IMES)
was developed to incorporate the information within these criteria documents. IMES provides
assessors with a simple and up to date mechanism for selecting appropriate models and
obtaining details about them and their validation.
This document, as a foundation of the IMES, is designed to help the exposure
assessor evaluate the appropriateness of models for various situations. The report defines
the terms and discusses the general approaches that modelers take to a problem so that
exposure assessors may more readily evaluate the appropriateness of both new and existing
models. In addition, step-by-step criteria are provided to enable the assessor to answer the
questions posed above.
Michael A. Callahan
Director
Exposure Assessment Group
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PREFACE
This is the third of three documents produced by the Exposure Assessment Group of
the Office of Health and Environmental Assessment addressing selection criteria for
mathematical models used in exposure assessments. These documents serve as technical
support documents for the Guidelines for Estimating Exposures, (first published in 1986 and
replaced by the Guidelines for Exposure Assessment published by the U.S. EPA in 1992) and
the PC-based Integrated Model Evaluation System (IMES) issued by the U.S. EPA in 1991 as
a prototype.
The purpose of this document is to present criteria that provide a means for selecting
the most appropriate mathematical model(s) for conducting an exposure assessment involving
the dispersion of contaminants in air.
The literature search to support the models discussed in this report is current to
January 29, 1989. More recent and additional information is contained in the IMES. It must
be recognized that the Agency is continually improving its models and approaches to
exposure assessment. For example, the Agency is currently re-evaluating air deposition
models. This class of air models was not addressed in this document, but information from
the re-evaluation will be incorporated into IMES as it becomes available.
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AUTHORS AND REVIEWERS
The Exposure Assessment Group within the U.S. Environmental Protection Agency's
Office of Health and Environmental Assessment was responsible for the preparation of this
document and provided overall direction and coordination during the production effort. The
information in this document has been funded, wholly or in part, by the U.S. Environmental
Protection Agency under Interagency Agreement No. DW89931434-01-0. Richard
Walentowicz, Exposure Assessment Group, served as Project Officer.
AUTHORS
D.E. Fields
Health and Safety Research Division
Oak Ridge National Laboratory
Oak Ridge, TN
J.K. Garrett
Office of Risk Analysis
Health and Safety Research Division
Oak Ridge National Laboratory
Oak Ridge, TN
LM. Hively •
Health and Safety Research Division
Oak Ridge National Laboratory
Oak Ridge, TN
C.W. Miller
Illinois Department of Nuclear Safety
Springfield, IL
F.R. O'Donnell
Environmental and Health Protection Division
Oak Ridge National Laboratory
Oak Ridge, TN
C.C. Travis
Office of Risk Analysis
Health and Safety Research Division
Oak Ridge National Laboratory
Oak Ridge, TN
XI
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REVIEWERS
C.F. Baes, III
Environmental Sciences Division
Oak Ridge National Laboratory
Oak Ridge, TN
Randall Bruins
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Cincinnati, OH
Mike Dusetzina
Emission Standards Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
F.C. Kornegay
Environmental and Health Protection Division
Oak Ridge National Laboratory
Oak Ridge, TN
Norman Kowal
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Cincinnati, OH
John Schaum
Exposure Assessement Group
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
John Segna
Environmental Resources Management, Inc.
McLean, VA
XII
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PART I.
SELECTION CRITERIA FOR ATMOSPHERIC DISPERSION MODELS
Part I of this document gives risk analysts the basic techniques and guidance they
need to select site-appropriate atmospheric dispersion models. Exposure assessment, a
critical component of the risk assessment process, determines the extent of human contact
with the released contaminant during a specified time. Specifically, the dose from which
human risk is calculated must be determined. To determine exposure, or "administered dose,"
the risk analyst must be able to quantitate (1) the contaminant concentration in a particular
medium with which humans may have contact, (2) the contact rate (the rates of inhalation,
ingestion, and dermal contact depending on the route of exposure), and (3) the exposure
duration (the length of time that contact with the contaminant lasts).
1. LIMITATIONS OF ATMOSPHERIC DISPERSION MODELS
Mathematical modeling is often used in exposure assessments to provide quantitative
estimates of contaminant concentrations when site measurements do not exist. Atmospheric
dispersion models, designed specifically for regulatory compliance with ambient air quality
standards, are often used to estimate contaminant concentrations in air. Some of these
models are better suited than others for exposure assessment use.
Regulatory atmospheric dispersion models may be formatted to estimate worst-case
concentrations; however, these estimates may represent worst-case exposure, since exposure -
is dependent not only on concentration but also on the rate and duration of human contact.
Furthermore, regulatory atmospheric dispersion models were designed to consider only those
contaminants under regulation for air quality; however, analysts conducting an exposure
assessment must consider a much broader range of contaminants and their fates, including
the transfer of the contaminants to water, soil, and food. Most existing regulatory atmospheric
dispersion models, for example, do not account for deposition.
With these caveats in mind, the risk analyst must select an appropriate model for a
particular exposure situation from the existing atmospheric dispersion models. The U.S.
Environmental Protection Agency's Guideline on Air Quality Models (Revised) (USEPA,
1986a) and the EPA's Supplement A to the Guideline on Air Quality Models (Revised)
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(USEPA, 1987a) provide extensive information on selecting atmospheric dispersion models for
regulatory and compliance uses. In this guideline, EPA's Office of Air Quality Planning and
Standards recommends air quality models, data bases, and requirements for concentration
estimates to use in air pollution control strategy evaluations and new source reviews.
Although the information was not provided for exposure assessment purposes, the risk analyst
will find the Guideline an excellent companion to the present document for identifying key
features of the most often selected air dispersion models. Analysts are not limited to using
the models recommended for regulatory uses, and several of the atmospheric dispersion
models discussed in the Guideline are included in this document.
The remainder of Part I contains background information on air dispersion processes
and characteristics of exposure assessments (Section 2); provides general guidelines and
principles of model selection criteria, including the model selection process and brief
descriptions of representative atmospheric dispersion models (Section 3); and furnishes
examples of model selection for three specific exposure assessments (Section 4).
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2. BACKGROUND INFORMATION
To select an appropriate dispersion model, an analyst must be able to characterize the
factors that affect dispersion of air contaminants. In addition, the route and duration of
exposure as well as the location of the exposed population affect model selection. This
section provides the background information needed to characterize contaminant
concentrations in air and the exposure factors that must be considered when choosing a
model.
2.1. CHARACTERIZATION OF CONTAMINANT CONCENTRATIONS IN AIR
The processes or factors that significantly influence contaminant concentration in the
air, and hence affect human exposure, must be sufficiently characterized so that they can be
described quantitatively by the model. These characterizations may be grouped according to
source characteristics, meteorological conditions, geographic scale, topography, and
contaminant properties.
2.1.1. Source Characteristics
Source characteristics include the type of source, the rate of contaminant release, the
effective plume height, and site-specific parameters.
2.1.1.1. Type of Source
A source can be characterized as point, nonpoint, or multiple.
A point source is a single specific emission point that can be defined with
geographic coordinates plus height above ground, stack diameter, gas velocity,
and temperature.
A nonpoint source, or area source, is either several (usually
small) emission points distributed over a specified surface area
or one large area (e.g., a seepage zone) that is treated as one
source. When many small sources release overlapping
emissions along a specified surface area (e.g., urban traffic),
they are referred to as a line source (Anderson et al., 1983).
A multiple source is a combination of several emission points
that includes both point and nonpoint sources. Multiple sources
are typically found in urban or industrial settings.
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Analysts should note that the way in which a source is typed may depend on the
purpose of a particular exposure assessment. For example, if the assessment is concerned
with exposure of on-site workers to a contaminant being released near ground level, the
emissions would probably be calculated as an area source. However, if we wanted to assess
exposure of a population living a distance from the same release, the emissions could be
calculated as a point source.
2.1.1.2. Hate of Release
The rate of release, or emission rate, is the mass of a contaminant emitted per unit of
time (mass/time). Contaminant mass is expressed in various ways, depending on the type of
source that has been identified. For point sources, the rate of emissions is expressed as
mass per unit of time (e.g., grams/second) or as volume per unit of time (e.g., parts per
million/second). Volume rates are typically used in analyses involving chemical reactions
(Anderson et al., 1983). For nonpoint sources, both mass per unit of time and spatial
distribution of the release must be measured. The latter factor is typically expressed as
micrograms per square mile per second, (|a,g/mi2)/s. For line sources such as urban traffic,
the rate of emissions is expressed in units such as grams per second per kilometer. For
multiple sources, the rate of emissions must incorporate all time and spatial distributions of
emissions from all the relevant sources (Anderson et al., 1983).
2.1.1.3. Effective Plume Height
The height and temperature of the released contaminant affect the dispersion of the
contaminant plume. Effective plume height is the physical height of the stack adjusted by
factors that raise the plume (as a result of buoyancy or momentum) or lower it (as a result of
downwash or deflection). Higher elevation allows more time for released contaminants to be
dispersed before reaching ground level. Thus peak ground-level contaminant concentrations
are not normally found close to an elevated emission source (USEPA, 1987b). Contaminants
that are hotter than the ambient air are buoyant and will rise above stack height for some time
after release (Anderson et al., 1983). Effective plume height can be reduced by stack-tip
downwash, a phenomenon that occurs when the velocity of the contaminant emitted from the
stack is low compared to windspeed. Aerodynamic downwash, caused by structures near the
stack, can also reduce plume height (USEPA, 1986a). The effective plume height for a given
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modeling situation is generally estimated by the atmospheric dispersion model. Most near-
field atmospheric dispersion models incorporate the plume rise methods of Briggs (1975) and
can account for stack-tip downwash. The Industrial Source Complex model (ISC), an often-
used near-field model, also contains algorithms to account for aerodynamic downwash
(USEPA, 1986a).
2.1.1.4. Other Site-Specific Factors
The source characterization is influenced by several other site-specific factors. The
analyst must determine the extent of influence these factors have on the source
characterization.
Variations in emissions and/or meteorological conditions over
time. The emission rate for a continuous source may vary over
time because of operational variables. Changes in
meteorological conditions can also affect ambient concentrations
and dispersion patterns.
Use of emission controls. Emission controls at a facility can
reduce emission rates. Facilities that apply emission controls
should be identified. The analyst may need to differentiate
between exposure to baseline emissions and post-control
emissions for some exposure assessment situations.
Other source emissions. Some emissions can escape through
leaks or from open processes and not be vented to stacks.
These fugitive emissions may be significant at ground level near
the emission source. Storage emissions (e.g., losses from
holding or storage tanks, or from loading and handling) may also
occur. If available, mass balance calculations of a given facility
should provide estimates of contaminant concentrations from all
emissions.
2.1.2. Meteorological Conditions
Wind speed, wind direction, precipitation, and turbulence affect dispersion and ground-
level concentrations. Increased wind speed can reduce ground-level concentrations by
increasing the volume of air into which the contaminant is emitted, enhancing dispersion by
immediately diluting the contaminant concentration. Increased wind speed generally
decreases concentrations of contaminants emitted at ground level, but it can also prevent
stack emissions from being dispersed. Precipitation can wash contaminants from the air,
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increasing concentrations and ground deposition nearer the source. Many models do not
account for precipitation scavenging and washout. The intensity of atmospheric turbulence
has profound effects on dispersion and ground-level concentrations of contaminants.
Atmospheric turbulence is generated by kinetic and thermal energy transfers between the air
and the terrain. Turbulence enhances dispersion of the contaminant plume in the lower
atmospheric levels, in the higher atmospheric levels, however, long-range transport of a
contaminant plume without reduced concentration often occurs under stable atmospheric
conditions and high wind speed (Anderson et al., 1983). Sets of empirically-determined
dispersion coefficients for classes of wind speed and atmospheric stability are used for
modeling purposes. Factors such as averaging time, urban or rural land use, and type of
source may prescribe the choice of specific coefficients used in different models (USEPA,
1986a). Dispersion coefficients used in most atmospheric dispersion models are identical to,
or based on, the Pasquill-Gifford dispersion coefficients for rural areas and the McElroy-Pooler
(St. Louis) dispersion coefficients for urban areas (USEPA, 1986a; see also Part II of this
document).
2.1.3. Geographic Scale
Geographic scale refers to the range of contaminant dispersion that is applicable to the
assessment. Dispersion ranges are described as near-field or far-field (long-range). Most
exposure assessments are conducted in the near-field range.
2.1.3.1. Near-Field Dispersion
Near-field dispersion of a contaminant occurs within 50 km of the source of the
contaminant release. In addition to the emission rate, concentration patterns in the near-field
range are dependent on effective plume height and meteorological conditions. Atmospheric
chemical reactions do not significantly affect concentration patterns in the near-field range.
Most atmospheric dispersion models are designed to estimate concentrations in the
near-field. Gaussian plume models, a class of atmospheric dispersion models made up of
several individual models similar in design and performance, are commonly used to represent
plume dispersion in the near-field and are based on the assumption that the plume will spread
both laterally and vertically in accordance with a Gaussian statistical distribution under steady-
state atmospheric conditions (e.g., constant wind speed) and spatial homogeneity (flat terrain).
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Although such conditions rarely exist, average annual atmospheric concentrations computed
with Gaussian plume models generally are expected to agree with values measured over flat
terrain within a factor of two (National Council on Radiation Protection and Measurement
[NCRPJ, 1984).
2.1.3.2. Far-Field Dispersion
Far-field dispersion refers to transport of the contaminant more than 50 km from the
emission source(s). As travel time increases, diurnal variations in meteorological conditions,
movement of weather systems, mesoscale terrain effects, and transformation and intermedia
transfer processes also increase. These increasing numbers of variables and changes in the
contaminant plume increase the complexity and uncertainty of an analysis. Although there is
some uncertainty in assuming steady-state conditions and spatial homogeneity for near-field
dispersion, the variables encountered in far-field dispersion make such assumptions unsound.
The objectives of a far-field study influence the level of complexity which must be reflected in
the atmospheric dispersion models. Although techniques are available to analyze these
effects (Miller et al., 1981), few analysts are experienced in their use. The complexity, time,
and cost of applying the techniques pose further limitations. Since it is difficult to channel
many complex variables into simplified characterizations appropriate for model application,
near-field models are sometimes used as an admittedly poor substitute until more appropriate
techniques can be determined. Miller et al. suggest that a simple Gaussian plume model may
be adequate to describe intermediate range (50 to 250 km) transport in some situations. A
long-range trajectory model has been developed under EPA sponsorship for use in
radiological assessments. This model could be adapted for use in nonradiological
assessments (Murphy et al., 1984).
2.1.4. Topography
The area surrounding an emission point is characterized as either flat or complex
terrain (USEPA, 1986a). Typically, terrain is defined as being flat where land and building
elevations are below the stack height of the emission source and where steady-state
conditions and uniformity in patterns of air movement can be reasonably assumed. Terrain is
defined as complex where land and building elevations are above the stack height of the
emission source, where there is a wide diversity in terrain features (e.g., mountains, valleys,
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tall buildings, large bodies of water), and where terrain deflects or alters patterns in air
movement. Complex terrain can affect dispersion in ways not encountered in flat terrain.
Effects include: (1) channeling of flows along valleys, (2) trapping of pollutants within basins,
(3) enhanced vertical dispersion through mechanically induced turbulence, (4) large vertical
gradients in wind speed and direction, and (5) flow changes resulting from heating and cooling
of sloping terrain. These complicated atmospheric dispersion patterns and distributions in
areas of complex terrain are difficult to simulate and, therefore, adequate complex terrain
dispersion models have not been developed.
2.1.5. Contaminant Properties
Chemical-specific factors and physical processes influence the contaminant's
environmental fate and transport and affect contaminant concentration. During long-range
transport, in particular, these processes of transformation and intermedia transfer significantly
alter contaminant concentrations in air. When a contaminant has undergone some chemical
reaction or transformation or has been partially removed from the atmosphere, its
concentration in air is reduced. During near-field dispersion, the rates of these processes may
not be sufficient to cause appreciable differences in concentrations. For this reason, most
near-field atmospheric dispersion models account only for simple exponential decay.
Contaminants are characterized as reactive or nonreactive. Reactivity refers to the
potential for chemical change of the contaminant following emission. Although most
contaminants undergo some reactions or transformations after release to the atmosphere,
some react more readily than others. Transformation processes, such as photolysis and
oxidation, may involve multiple contaminants and the formation of reactive secondary
contaminants. Modeling of reactive contaminants in a complex reacting system is more
difficult because of the need to model both the reactive system and the dispersion and fate of
all contaminants involved in the reactive system. Some secondary contaminants resulting
from transformation processes may have different properties, and may be more hazardous,
than the primary contaminants of concern. The best-known example of such a reactive
system is the formation of photochemical smog in which nitrogen oxides, reactive
hydrocarbons, and sunlight interact to create ozone and other reactive secondary
contaminants (Anderson et al., 1983). Intricate models that require involvement by expert
8
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modelers are available to represent these chemical kinetic mechanisms (e.g., the Urban
Airshed Model [UAM]).
Intermedia transfer processes also reduce the contaminant concentrations remaining
airborne at any given time. Dissolution, adsorption, gravitational settling, and precipitation
affect the removal of contaminants from the air and their transfer to other environmental
media. These removal processes are defined in Section 2.3. Definition of Terms. Many near-
field atmospheric dispersion models do not account for these removal mechanisms.
2.2. CHARACTERISTICS OF EXPOSURE ASSESSMENTS
The exposure assessment is often the most critical and complex step in determining
human risk. Such an assessment is often undertaken for a variety of purposes and for
various types of exposures. In most cases, however, this step requires the selection of
appropriate mathematical models that can be used to provide quantitative estimates of
contaminant concentration, population distribution, and exposure. The results from these
models must be able to provide the quantitative data needed to support the intended
purpose(s) of the exposure assessment. The objectives of the exposure assessment should
be explicitly stated so that appropriate models can be selected.
Three key factors of an exposure assessment directly affect model selection: (1) the
duration of exposure, i.e., the time scale of both the release of and human contact with the
contaminant; (2) the routes of exposure; and (3) the location of the population.
1. The time period over which exposure estimates are to be averaged should be
matched with the model's averaging time capability. For long periods of
exposure, the model should have the capability to provide long-term averaging,
i.e., annual average air concentrations. For short or intense periods of exposure
to potentially toxic substances, the model should have the capability to provide
short-term averaging, i.e., 1-, 8-, and 24-hour average air concentrations.
2. All routes of exposure needed to complete the exposure assessment
should be specified. An exposure assessment that is concerned
with exposure to both airborne contaminants and contaminants in
the food chain must use models that can provide both atmospheric
concentrations and deposition rates.
3. The location of the populations at risk and areas used for growing
food must be identified to determine the appropriate number and
locations of model receptors (USEPA, 1987b).
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2.3. DEFINITION OF TERMS
Key terms that are used in the text to describe the processes of air dispersion,
modeling, and exposure assessment are defined below:
ADSORPTION. An intermedia transfer mechanism in which an airborne contaminant in
the vapor phase becomes attached to paniculate matter suspended in the air or to soil
particles at the air/soil interface, thereby removing the adsorbed contaminant from the air
(USEPA, 1988a).
ADVECTION. The movement of contaminants with an air mass in a predominantly
horizontal direction. The process is dependent upon wind speed and direction.
AIR CONTAMINANT. Any substance released into the atmosphere and present in
sufficient quantity to have an adverse effect on human health.
AREA SOURCE. Numerous, usually small, sources of contaminant emissions that are
distributed over a specified surface area.
ATMOSPHERIC STABILITY. A meteorological condition that affects dispersion of airborne
contaminants. The atmosphere is said to be stable when there is little or no vertical
movement of air masses. With little or no vertical mixing of the contaminants with the air,
contaminant concentrations accumulate at ground level. Unstable atmospheric conditions
result in vertical mixing of the air masses.
Box MODEL. A model in which the entire modeling region is contained irt a single cell
(box); it is used to obtain estimates of area source concentrations.
DEPOSITION. The process by which particulates and reactive gases are deposited on
the earth's surface from the atmosphere. Both wet and dry deposition occurs. Wet deposition
occurs sporadically during specific rain or snow events; dry deposition occurs continuously
under dry atmospheric conditions.
DIFFUSION. The dispersion of a contaminant relative to its advective movement.
Diffusion reduces the central concentrations in a contaminant mass (plume), increases the
concentration at the periphery of the mass, and expands the periphery.
DISSOLUTION. A constant, reversible intermedia transfer mechanism in which
contaminants in the gaseous phase are either dissolved into water droplets in the air and
distributed to other media by precipitation or are dissolved directly into surface water at the
air/water interface (USEPA, 1988a).
EFFECTIVE PLUME HEIGHT. The physical height of the stack adjusted by factors that
raise the plume (as a result of buoyancy or momentum) or lower it (as a result of downwash
or deflection).
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EMPIRICAL MODEL. A model that statistically fits simple mathematical forms to data;
sometimes called a statistical model. It is typically used for screening purposes or when the
data are not sufficient to use Gaussian or grid models (USEPA, 1986a).
EXPOSURE. Contact of a chemical or physical agent with the outer boundary of an
organism.
EXPOSURE ASSESSMENT. The determination of the magnitude, frequency, duration, and
route of exposure.
GAUSSIAN PLUME MODEL. A model most commonly used to represent plume dispersion
in the near-field range. Simple expressions (Gaussian functions) are assumed to represent
the dependence of pollutant concentration on lateral or vertical distance from the plume
centerline (i.e., the advective path).
GRAVITATIONAL SETTLING. An intermedia transfer mechanism where particulate
contaminants or contaminants adsorbed onto suspended particulates settle to surface media
via gravitational attraction. The particulates are normally more than 20 (xm in diameter
(USEPA, 1988a).
MODEL. A quantitative or mathematical representation or simulation that attempts to
describe the characteristics or relationship of physical events.
PASQUILL-GIFFORD DISPERSION COEFFICIENTS. Numerical values of the standard
deviations of atmospheric displacements about any point moving with the mean wind. The
values are defined as a continuous, empirical function of downwind travel distance (of
pollutant from a source) for each discrete stability class.
PASQUILL-TURNER ATMOSPHERIC STABILITY CLASSIFICATION. The most often used
inferential method of estimating the turbulent state of the atmosphere, in discrete classes,
using solar intensity and wind speed as surrogate indicators.
PHYSICAL MODEL. A model that uses actual physical simulation for situations involving,
complex terrain and severe or erratic meteorological conditions. Its use is limited because of
cost and other resource constraints.
POINT SOURCE. A specific source of contaminant emissions that can be characterized
as a single point in three-dimensional space (geographic coordinates plus height above
ground) and by the mass rate of its emissions.
PRECIPITATION. An intermedia transfer mechanism that removes particulate and
aerosol matter from the atmosphere. Particulate and aerosol matter serve as nuclei for the
condensation of raindrops. Raindrops generally remove particulates and aerosols greater
than 1.0 |im in diameter from the air (USEPA, 1988a).
REACTIVITY. The potential for change in contaminant identity characteristics following
emission.
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RECEPTOR. A point in space, generally on the ground, where the ambient air
concentration of a pollutant is to be determined. Knowing the number and placement of
receptors in relation to expected peak concentrations of the contaminant and the population
distribution is essential to obtaining reasonable exposure estimates. An individual exposed at
such a point is also known as a receptor.
*
STAR (STABILITY ARRAY) DATA. Summaries of meteorological data including seasonal or
annual joint frequencies for each stability class, wind direction, and wind speed category.
STAR data are available from the National Climatic Center (NCC), Asheville, North Carolina,
for all National Weather Service (NWS) locations in the United States. STAR data from the
NWS Station most representative of the site should be used for modeling purposes.
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3. GENERAL GUIDELINES AND PRINCIPLES OF MODEL SELECTION
The analyst who is familiar with a range of available models is better prepared to
select the most appropriate model to meet the objectives of a given exposure assessment.
To assess exposure to airborne contaminants or contaminants that have entered the food
chain through atmospheric deposition, the analyst may use either an atmospheric dispersion
model that provides estimates of contaminant concentrations or a human exposure model that
incorporates both an atmospheric dispersion model and an exposure model.
3.1. ATMOSPHERIC DISPERSION MODELS
In general, the atmospheric dispersion models most commonly selected for use in
exposure assessments are readily available, widely accepted by both the scientific community
and governmental regulatory agencies, and simple to use (i.e., they require a minimum of
input data and little time or money to run). In addition, they provide information in a form that
can be easily used to estimate exposure (e.g., the number and placement of receptors are
sufficient to estimate maximum individual exposure).
Most near-field exposure assessment situations involving air modeling can be
adequately handled by one of several simple Gaussian plume models. These atmospheric
dispersion models are similar in design and performance, generally conform to a limited set of
well-accepted physical laws and principles (e.g., conservation of mass, simple diffusion), and
do not attempt to account for atmospheric variables that are prevalent in complex situations
(e.g., varied terrain, long-range transport, highly reactive chemical emissions). The Gaussian
plume models are simple representations of dispersion patterns of nonreactive contaminants
within 50 km of the emission source(s), and are generally expected to produce computed
results within a factor of two of the measured values over flat terrain (NCRP, 1984). Several
near-field models are available from User's Network for Applied Modeling of Air Pollution
(UNAMAP) through the National Technical Information Service (NTIS) (USEPA, 1986b). All
the models listed in Table 2 are near-field models.
Although Gaussian plume dispersion models apply to near-field situations, they are not
applicable immediately at the site of the emissions. For example, they would not provide air
concentration estimates over a landfill resulting from vaporization. Gaussian dispersion
modeling cannot be applied to on-site conditions because experimental data needed to
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calibrate the dispersion coefficients used to calculate short-range (<100 m) dispersion are
lacking (Hwang, 1987). Frequently simple mixing models or box models are applied in this
situation. These models typically assume that the emissions are uniformly mixed within a
box-shaped volume and are diluted by the wind. The box model is typically formulated as:
C = Q/(LS)V(MH)
(1-1)
where
C
Q
LS
V
MH
= amount of air concentration of contaminant
= total emission rate (g/sec)
= length of the site perpendicular to the direction of the wind (m)
= average wind speed
= mixing height
An advantage of this model is its simplicity. A disadvantage is the uncertainty arising
from assumptions regarding restrictions on mixing volume, uniform mixing, and wind speed
and direction. The model fails to account for contaminant dispersion but, as stated above,
experimental data are lacking for which to calibrate dispersion coefficients. Thus, despite its
utility, it is important to emphasize the uncertainty resulting from these simplifying
assumptions.
Based on availability, general acceptance, and ease of use, a few models, especially
the Industrial Source Complex model (a Gaussian plume atmospheric dispersion model), are
preferred for use in exposure assessments. The ISC has an extensive range of modeling
options which increases its applicability to different modeling situations. Other models are
also used and may often be more applicable for a given exposure assessment. Regardless of
which atmospheric dispersion model is used, the analyst should remember that the output
from an atmospheric dispersion model is expressed as concentration, not exposure, and that
a model may estimate maximum concentration at a point where there is little or no human
contact.
Several atmospheric dispersion models discussed in the EPA's Guideline on Air
Quality Models (Revised) (USEPA, 1986a) and the EPA's Supplement A to the Guideline on
Air Quality Models (Revised) (USEPA, 1987a) are summarized in the following pages. Models
marked with an asterisk (*) have been designated by the EPA as preferred models for
regulatory uses; the other models listed have been designated by the EPA as alternative
models for regulatory uses.
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3.1.1. Buoyant Line and Point Source Dispersion Model (BLP)*
The BLP is a Gaussian piume dispersion model designed to model aluminum reduction
plants and other industrial sources for which plume rise and downwash effects from stationary
line sources are important. This model is recommended for modeling aluminum reduction
plants that contain buoyant, elevated line sources in rural areas over distances of less than 50
km in simple terrain with averaging times of 1 hour to 1 year.
3.1.2. CALINE3*
CAL1NE3 is a steady-state Gaussian plume model used to estimate concentrations of
nonreactive pollutants from highway traffic. It can be applied to determine air pollutant
concentrations at receptor locations downwind of "at grade," "fill," "bridge," and "cut section"
highways located in relatively uncomplicated terrain. It is applicable for any wind direction,
highway orientation, and receptor location. Other features include the ability to adjust
averaging times and surface roughness, the ability to handle up to 20 highway links and 20
receptors, and the availability of an algorithm to account for deposition and settling velocity
(i.e., the ability to handle particulate concentrations). This model is recommended for
modeling line (highway) sources in rural or urban areas for distances of less than 50 km in
simple terrain with averaging times of 1 to 24 hours.
3.1.3. Climatological Dispersion Model (COM 2.0)*
The COM 2.0 is a Climatological steady-state Gaussian plume model used for
determining long-term arithmetic average pollutant concentrations at any ground level receptor
in an urban area. This model is recommended for modeling point and area sources in urban
areas for distances of less than 50 km in simple terrain with averaging times of 1 month to 1
year, or longer.
The CDMQC, a version of the COM, can also produce a source contribution list and,
using a statistical model, can transform the average concentration data for a fimited number of
receptors into expected geometric mean and maximum concentration values for several
different averaging times.
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3.1.4. Offshore and Coastal Dispersion Model (OCD)*
The OCD is a straight-line Gaussian plume model developed to determine the impact
of offshore emissions from point sources on the air quality of coastal regions. The OCD
incorporates overwater plume transport and dispersion as well as changes that occur as the
plume crosses the shoreline. Hourly meteorological data are needed from both offshore and
onshore locations. These include water surface temperature, overwater air temperature, and
relative humidity. Some of the key features include platform building downwash, partial plume
penetration into elevated inversions, direct use of turbulence intensities for plume dispersion,
interaction with the overland internal boundary layer, and continuous shoreline fumigation.
The Minerals Management Service (U.S. Department of the Interior, 1985) has recommended
the OCD for emissions located on the Outer Continental Shelf. The OCD is applicable for
overwater sources where onshore receptors are below the lowest source height. Where
onshore receptors are above the lowest source height, offshore plume transport and
dispersion may be modeled on a case-by-case basis in consultation with the EPA Regional
Office.
3.1.5. Gaussian Plume Multiple Source Air Quality Algorithm (RAM)*
RAM is a steady-state Gaussian plume model used to estimate concentrations of
relatively stable pollutants. This model is recommended for modeling point and area sources
in urban areas for distances of less than 50 km in simple terrain with averaging times of 1
hour to 1 year. Both RAM and the COM 2.0 are preferred EPA atmospheric dispersion
models for long-term, multiple point sources. The EPA recommends that RAM be used for
areas having only a few sources to be modeled. A rural version of RAM exists, but it is not
recommended for regulatory use.
3.1.6. Industrial Source Complex Model (ISC)*
The ISC is a steady-state Gaussian plume model used to assess pollutant
concentrations from the wide variety of sources associated with industrial complexes. It can
account for settling and dry deposition of particulates; downwash; area, line, and volume
sources; plume rise as a function of downwind distance; separation of point sources; and
limited terrain adjustment. This model is recommended for modeling industrial complexes
(collections of varied sources, including points, areas, lines, and volumes, associated with one
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plant site) in rural or urban areas for distances of less than 50 km in simple terrain. The
ISCST, the short-term version, uses an averaging time of 1 hour and can combine hourly
results to give 1- or n-day averages. The ISCLT, a long-term version, uses averaging times of
1 month to 1 year.
3.1.7. Multiple Point Gaussian Dispersion Algorithm with Terrain Adjustment
(MPTER)*
The MPTER is a steady-state Gaussian plume model used for making hourly estimates
of air concentrations of relatively nonreactive pollutants. This model is recommended for
modeling point sources in rural or urban areas for distances of less than 50 km in simple
terrain with averaging times of 1 hour to 1 year.
3.1.8. Single Source Model (CRSTER)*
The CRSTER is a steady-state Gaussian plume model recommended to estimate
single point sources in rural areas for distances of less than 50 km in simple terrain. Highest
and second-highest concentrations are calculated at each receptor for 1-, 3-, and 24-hour and
1-year averaging times.
3.1.9. Urban Airshed Model (UAM)*
The UAM is an urban scale, three-dimensional, grid type, numerical simulation model.
It incorporates.a condensed photochemical kinetics mechanism for urban atmospheres. It is
designed for computing ozone concentrations under short-term, episodic conditions lasting 1
or 2 days resulting from emissions of oxides of nitrogen (NOX) and volatile organic compounds
(VOCs). The model treats urban VOC emissions as their carbon-bond surrogates.
The UAM is recommended for modeling single urban areas having significant ozone
attainment problems and no interurban emission transport with an averaging time of 1 hour.
3.1.10. Air Pollution Research Advisory Committee (APRAC-3)
APRAC-3 is a Gaussian plume model that computes hourly average carbon monoxide
concentrations for any urban location. It calculates concentrations from dispersion on three
scales: extraurban (mainly from sources upwind of the city of interest), intraurban (from
freeway, arterial, and feeder street sources), and local (from within a street canyon). Its use
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requires an extensive traffic inventory for the city of interest. An emissions model,
incorporating MOBILE2, is used to compute emission factors. The documentation now
contains a revised table (Inputs for preprocessor program) for accommodating additional
MOBILE2 inputs.
3.1.11. Air Quality Display Model (AQDM)
The AQDM is a climatological, steady-state Gaussian plume model that estimates
annual arithmetic average sulfur dioxide and paniculate concentrations at ground level in an
urban area. A statistical model is used to transform the average concentration data from a
limited number of receptors into expected geometric mean and maximum concentration values
for several different averaging times.
3.1.12. Air Resources Regional Pollution Assessment Model (ARRPA)
The ARRPA is a medium- to long-range Gaussian segmented-plume model designed
to compute air concentrations and surface dry mass deposition of sulfur dioxide and sulfate.
The model is unique in its use of prognostic meteorological output from the National Weather
Service Boundary Layer Model (BLM), which computes boundary layer conditions on a grid
with spatial resolution of 80 km and archives them in intervals of 3 hours. The ARRPA uses
the three-dimensional wind field components and potential temperature at 10 height levels,
from the surface through 2000 meters above the.surface. It is recommended, possibly, for
transport distances less than 50 km.
3.1.13. COMPTER
COMPTER is a Gaussian plume model. It is applicable to both urban and rural areas,
calculates maximum 24-, 3-, 1-, and variable-hour concentrations for both block and running
averages, considers elevated terrain using either the standard plume chopping technique or a
stability-dependent plume path trajectory, uses annual hourly meteorological data in the
CRSTER preprocessor format, uses Pasquill-Gifford stability curves, and allows for stability
class substitution in the stable categories. Typical use is in rural areas with moderate to low
terrain features.
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3.1.14. ERT Air Quality Model (ERTAQ)
ERTAQ is a multiple point, line, and area source dispersion model based on the
univariate form, with multiple reflections, of the Gaussian plume dispersion model. With the
fugitive dust option, entrainment of particulates from ground level sources and their
subsequent deposition are accounted for. An urban/rural option is available. Long-term or
worst-case concentrations due to arbitrarily located sources may be calculated at arbitrarily
located receptors, which may be at or above ground level. The user can specify background
concentrations and calibration factors at each receptor. Unique flexibility is afforded by a
postprocessing storage and manipulation capability.
3.1.15. HIWAY-2
HIWAY-2 is a steady-state Gaussian plume model that can be used to estimate
concentrations of nonreactive pollutants from highway traffic. It considers receptors located
downwind of "at grade" and "cut section" highways located in relatively uncomplicated terrain.
It is applicable for any wind direction, highway orientation, and receptor location. It was
developed for situations where horizontal wind flow dominates, but it cannot handle complex
terrain or large obstructions to the flow (e.g., buildings or large trees).
3.1.16. LONGZ
LONGZ uses a steady-state, univariate Gaussian plume formula to calculate ground
level air concentrations for both urban and rural areas in flat or complex terrain due to
emissions from up to 14,000 arbitrarily placed sources (stacks, buildings, and area sources).
The output consists of the total concentration at each receptor due to emissions from each
user-specified source or group of sources, including all sources. An option that considers
losses due to deposition for complex terrain is considered inappropriate by the EPA.
3.1.17. Multiple Point Source Diffusion Model (MPSDM)
The MPSDM is a steady-state Gaussian dispersion model designed to calculate, in
sequential mode or in "case-by-case" mode, concentrations of nonreactive pollutants resulting
from single or multiple source emissions. It may be used for sources located in flat or
complex terrain in a univariate (a , az) mode. Sufficient flexibility is allowed in the
specification of model parameters to allow the user to duplicate results that would be obtained
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from other Gaussian point source models. A number of features are incorporated to facilitate
site-specific validation studies.
3.1.18. Mesoscale Transport Diffusions and Deposition Model for Industrial Sources
(MTDDIS)
The MTDDIS is a variable trajectory Gaussian puff model applicable to long-range
transport of point source emissions over level or rolling terrain. It can be used to determine 3-
hour maximum and 24-hour average concentrations of relatively nonreactive pollutants from
up to 10 separate stacks.
3.1.19. MULTIMAX
MULTIMAX is a Gaussian plume model applicable to both urban and rural areas. It
can be used to calculate highest and second-highest concentrations for each of several
averaging times for up to 100 arbitrarily located sources.
3.1.20. Point, Area, Line Source Algorithm (PAL)
The PAL is a method of estimating short-term dispersion using Gaussian plume
steady-state assumptions. It can be used for estimating concentrations of nonreactive
pollutants at 99 receptors for averaging times from 1 to 24 hours, and for a limited number of
point, area, and line sources (99 of each type). It is not intended for application to entire
urban areas, but rather is intended to assess the impact on air quality (on scales of tens to
hundreds of meters) in portions of urban areas such as shopping centers, large parking areas,
and airports. Level terrain is assumed. Concentrations from point sources are estimated after
determining the effective height of emissions and the upwind and crosswind distance from the
source to the receptor. Numerical integration of the Gaussian point source equation is used
to determine concentrations from the four types of line sources. Subroutines are included that
estimate concentrations for multiple land line and curved path sources, special line sources
(those with endpoints at different heights above ground), and special curved path sources.
Integration over the area source, which includes edge effects from the source region, is done
by considering finite line sources perpendicular to the wind at intervals upwind from the
receptor. The- crosswind integration is done analytically; integration upwind is done
numerically by successive approximations.
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3.1.21. Pacific Gas and Electric PLUMES Model (PLUMES)
PLUMES is a steady-state Gaussian plume model applicable to both urban and rural
areas in uneven terrain. It calculates pollutant concentrations at 500 receptors for up to 10
sources with up to 15 stacks each and uses up to 5 meteorological inputs. PLUMES is similar
to the CRSTER and the MPTER but contains several options that may allow for better
simulation of atmospheric conditions and improved model outputs. These options allow plume
rise into or through a stable layer and crosswind spread of the plume by wind directional
shear with height, initial plume expansion, mean (advective) wind speed, terrain
considerations, and chemical transformation of pollutants. PLUMES and the CRSTER differ in
the stability class determination, hourly mixing height schemes, hourly stable layer data,
randomization of wind direction, extent of data set required for preprocessing, and
meteorological data inputs.
3.1.22. Rough Terrain Diffusion Model (RTDM)
Version 3.0 of the RTDM is a sequential Gaussian plume model designed to estimate
ground-level concentrations in rough or flat terrain near one or more collocated point sources.
It is specifically designed for applications involving chemically stable pollutants and is best
suited for evaluation of buoyant plume behavior within about 15 km of the source(s) location.
Results for receptors between 15 and 50 km can be used with caution; results beyond 50 km
can be used for screening purposes. Special algorithms deal with plume behavior in complex
terrain.
Although the RTDM 3.0 is designed for use with sequential data sets, it can be used in
a case-study mode. Optional features make it useful for either research/sensitivity
applications or routine evaluations of source compliance. It can use hourly, on-site
measurements of turbulence intensity, vertical temperature difference, horizontal wind shear,
and wind speed profile exponents. However, it is flexible enough in specifying model inputs to
let the user obtain results similar to many other Gaussian point source models. Its ability to
read hourly emissions data makes it useful for site-specific model evaluation studies.
3.1.23. Multi-Source Model (SCSTER)
The SCSTER is a modified version of the CRSTER model which includes the ability to
consider multiple sources that are not necessarily collocated, enhanced receptor
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specifications, variable plume height terrain adjustment procedures, and plume distortion from
directional wind shear.
3.1.24. SHORTZ
SHORTZ uses the steady-state bivariate Gaussian plume formulation for both urban
and rural areas in flat or complex terrain to calculate ground-level ambient air concentrations.
It can calculate 1-, 2-, 3-, and n-hour average concentrations due to emissions from stacks,
buildings, and area sources for up to 300 arbitrarily placed sources. Output consists of total
concentration at each receptor due to emissions from each user-specified source or group of
sources, including all sources. If the option for gravitational settling is used, analysis in
complex terrain cannot be accomplished without violating mass continuity.
3.1.25. Simple Line Source Model (SLSM)
The SLSM is a simple, steady-state Gaussian plume model that can be used to
determine hourly (or half hourly) averages of exhaust concentrations within 100 meters of a
roadway on relatively flat terrain. The model allows for plume rise due to heated exhaust,
which can be important when the crossroad wind is very low. It also uses a new set of
vertical dispersion parameters that reflects the influence of traffic-induced turbulence.
3.1.26. Texas Ciimatoiogical Model (TGM-2)
The TCM-2 is a climatoiogical, steady-state Gaussian plume model for determining
long-term (seasonal and annual arithmetic) average pollutant concentrations of nonreactive
pollutants.
3.1.27. Texas Episodic Model (TEM-8)
The TEM-8 is a steady-state Gaussian plume model for determining short-term
concentrations of nonreactive pollutants.
3.1.28. Models 3141 and 4141
Models 3141 and 4141 are modifications of the CRSTER that are applicable to
complex terrain, particularly where receptor elevation approximately equals or exceeds the
stack-tip elevation. It uses intermediate ground displacement procedures and dispersion
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enhancements developed from an aerial trace study and ground level concentrations
measured for a power plant located in complex terrain.
3.2. HUMAN EXPOSURE MODELS
In conducting exposure assessments, the analyst may decide to use a model designed
specifically to estimate human exposure. These models incorporate a population distribution
data base with an atmospheric dispersion model and include mechanisms for calculating
exposure from paired contaminant concentrations and population location. Two such models
are the Human Exposure Model (HEM) and the Inhalation Exposure Methodology (IEM).
The HEM was specifically designed by the EPA to estimate the population exposed to
air pollutants emitted from stationary sources and the carcinogenic risk associated with this
exposure (USEPA, 1986c). The HEM is comprised of two basic exposure assessment models
that differ slightly in purpose and scope. The Systems Applications Human Exposure and
Dosage (SHED) model is generally used to model single pollutants from major point sources
on a nationwide basis. The Systems Applications Human Exposure and Risk (SHEAR) model
is used to model single or multiple pollutants in more restricted geographic areas for major
point, prototype, and area sources. The population distribution data base is comprised of the
1980 Census Data Base, which is broken down by block group/enumeration district (BG/ED).
The User's Manual for the Human Exposure Model (HEM) (USEPA, 1986c) discusses the
characteristics of the two basic models and the population data base in detail. Arrangements
for use of the HEM can be obtained through the NTIS (USEPA, 1986c).
The IEM uses the Industrial Source Complex Long-Term model to calculate annual
average ground level air concentrations of contaminants. These concentrations are entered
into a concentration-exposure program that calculates population exposures to the
contaminants. The method provides interactive access to selected STAR data, the ISCLT
model, and the 1980 census (population) data (USEPA, 1983).
3.3. MODEL SELECTION PROCESS
The atmospheric dispersion model selected, whether used alone or as a component of
a human exposure model, must adequately characterize specific characteristics of site,
source, and emissions. Several key factors, processes, and assumptions that affect
contaminant concentrations were discussed in the previous section and serve as the basis for
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model selection. However, the model user need not determine all of these factors, processes,
and assumptions. The atmospheric dispersion models incorporate several of these factors in
their codes (e.g., dispersion coefficients).
The model selection process can be simplified by developing a profile of the features
needed (Table 1) and comparing it with the capabilities of various models (Table 2). In some
cases, several models may meet the general criteria, and the reader should coihsult the
appropriate model descriptions in Section 3.1 and supplemental information in Tables 3 and 4.
In other cases, the situation may call for a model feature available only in one or a few
models. For some modeling situations, such as those involving complex terrain, no one
model may be appropriate.
The EPA's Guideline on Air Quality Models (Revised) (USEPA, 1986a) and EPA's
Supplement A to the Guideline on Air Quality Models (Revised) (USEPA, 1987a) provide
extensive information on the selection of atmospheric dispersion models for regulatory and
compliance uses. Although the information provided is for purposes different from those of an
exposure assessment, the risk analyst will find the Guideline to be an excellent resource for
identifying key features of the most often selected air dispersion models. The Guideline
should be used in conjunction with this document and should prove useful to persons filling
out the Model Selection Profile (Table 1). A brief summary of the items in the profile follows.
3.3.1. Geographic Scale
Most exposure assessments consider only near-field (<50 km) dispersion, since
individual exposures are highest near the emission sources and these areas usually include
most of the exposed population. Far-field (>50 km) dispersion is becoming more of a concern
because of the potential for widespread emission and long-term accumulation of contaminants
at low concentrations. Since far-field dispersion modeling is complex and not extensively
tested, analysts wishing to use this method should get professional help in defining the scope
of the study and selecting appropriate modeling techniques.
3.3.2. Time Scale
The time period over which exposure estimates are to be averaged should be matched
with the model's averaging time capacity. For long-term exposure, models should be able to
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Table 1. MODEL SELECTION PROFILE
GEOGRAPHIC SCALE
Near-field (<50 km)
Far-field (>50 km)
TIME SCALE
Long-term exposure
Short-term exposure
LAND USE
Urban
Rural
TERRAIN
Flat/rolling
Complex
SOURCE CONFIGURATION
Point
Elevated point
Nonpoint (e.g., area, line)
Multiple sources treated individually
REACTIVITY
Low or nonreactive primary contaminant
Reactive primary contaminant
Formation of secondary contaminants
FORM OF RELEASE
Gaseous
Paniculate
REMOVAL MECHANISMS
Decay
Reaction
Deposition
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Table 2. CHARACTERISTICS OF ATMOSPHERIC DISPERSION MODELS
Model
GEOGRAPHIC SCALE
Near-field (£50 km)
Far-field (>50 km)
TIME SCALE
Long-term exposure
Short-term exposure
LAND USE
Urban
Rural
TERRAIN
Flat/rolling
Complex
SOURCE CONFIGURATION
Point/Line
Elevated point
Nonpoint (e.g., area, line)
Multiple sources treated individually
REACTIVITY
Low or nonreactive primary contaminant
Reactive primary contaminant
Formation of secondary contaminants
FORM OF RELEASE
Gaseous
Paniculate
REMOVAL MECHANISMS
Decay
Reaction
Deposition
BLP
Yes
No
Yes
Yes
No
Yes
Yes/Yes
No
Yes/Yes
No
No
No
Yes
Yes
No
Yes
—
Linear
No
No
CALINE3
Yes
No
Yes
No
Yes
Yes
Yes/No
No
No/Yes
No
No
No
Yes
Yes
No
Yes
—
No
No
9
COM 2.0
Yes
No
.
Yes
Yes
Yes
No
Yes/No
No
Yes/No
No
Yes
No
Yes
Yes
No
Yes
—
Exponential
No
No
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Table 2. (continued)
Model
GEOGRAPHIC SCALE
, Near-field (<50 km)
Far-field (>50 km)
TIME SCALE
Long-term exposure
Short-term exposure
LAND USE
Urban
Rural
TERRAIN
Flat/rolling
Complex
SOURCE CONFIGURATION
Point/line
Elevated point
Nonpoint (e.g., area, line)
Multiple sources treated individually
REACTIVITY
Low or nonreactive primary contaminant
Reactive primary contaminant
Formation of secondary contaminants
FORM OF RELEASE
Gaseous
Paniculate
REMOVAL MECHANISMS
Decay
Reaction
Deposition
CRSTER
Yes
No
Yes
Yes
Yes
Yes
Yes/Yes
No
Yes/No
No
No '
No
Yes
Yes
No
Yes
—
Exponential
No
No
ISCLT
Yes
No
Yes
Yes
Yes
Yes
Yes/Yes
No
Yes/Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Exponential
No
Yes
ISCST
Yes
No
No
Yes
Yes
Yes
Yes/Yes
No
Yes/Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Exponential
No
, Yes
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Table 2. (continued)
Model
GEOGRAPHIC SCALE
Near-field (<50 km)
Far-field (>50 km)
TIME SCALE
Long-term exposure
Short-term exposure
LAND USE
Urban
Rural
TERRAIN
Flat/rolling
Complex
SOURCE CONFIGURATION
Point/line
Elevated point
Nonpolnt (e.g., area, line)
Multiple sources treated individually
REACTIVITY
Low or nonreactive primary contaminant
Reactive primary contaminant
Formation of secondary contaminants
FORM OF RELEASE
Gaseous
Participate
REMOVAL MECHANISMS
Decay
Reaction
Deposition
aUSEPA, 1980a
MPTER
Yes
No
No
Yes
Yes
Yes
Yes/Yes
No
Yes/No
No
No
Yes
Yes
Yes
No
Yes
—
Exponential
No
No
RAM
Yes
No
No
Yes
Yes
No
Yes/No
No
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provide annual average concentrations. For short-term or brief, intense exposures to
potentially toxic substances, the model should be able to provide 1-, 8- and 24-hour average
air concentrations.
3.3.3. Land Use
Selecting the type of land use for the, modeling area determines the appropriate
dispersion coefficients. Use the meteorological land use type scheme (e.g., Table 5) to
classify the area within a 3-km radius of the source. If land use types 11, 12, C1, R2, and R3
account for more than 50% of the total area, the area is considered urban. Otherwise, the
area is considered rural. For multiple sources, use the parameter that defines the location of
most of the sources; that is, if most of the sources are located in urban areas, the entire
source is modeled as an urban area (USEPA, 1986a).
3.3.4. Terrain
Many models can be used for flat or rolling terrain, but no dispersion models for
complex terrain currently exist. However, for regulatory purposes, the EPA has designated
existing models that can be adapted to site-specific complex terrain for screening purposes to
calculate concentrations at receptors located at or above plume height (i.e., in complex
terrain).
3.3.5. Source Characteristics
Point sources may be elevated or may be located at the site of process vents or near
other ground level release points. Models for point sources should account for effective plume
rise.
Nonpoint or area sources are evaluated using a simplified box model in which
Air Concentration = Total Emission Rate (g/s)
of Contaminant Area Length X Average Wind Speed X Mixing Height
Box models assume a well-mixed volume and do not account for contaminant
dispersion. The Gifford-Hanna algorithm (Gifford and Hanna, 1973), which contains a
31
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Table 5. IDENTIFICATION AND CLASSIFICATION OF LAND USE TYPES FOUND
IN METROPOLITAN ST. LOUIS
Type
Description
Use and structures
Vegetation
11
12
C1
R1
R2
R3
Heavy industrial
Major chemical, steel, and
fabrication industries;
generally 3-5 story buildings,
flat roofs
Light-moderate industrial
Rail yards, truck depots, ware-
houses, industrial parks,
minor fabrication; generally
1 -3 story buildings, flat roofs
Commercial
Office and apartment buildings,
hotels; >10 story heights, flat
roofs
Common residential
Single family dwellings with
normal easements; generally
one story, pitched roof
structures; frequent driveways
Compact residential
Single, some multiple, family
dwelling with close spacing;
generally <2 story, pitched
roof structures; garages (via
alley) and ashpits, no driveways
Compact residential
Old multi-family dwellings with
close (<2) lateral separation;
generally 2 story, flat roof
structures; garages (via alley)
and ashpits, no driveways
Grass and tree growth extremely
rare; <5% vegetation
Very limited grass, trees almost
totally absent; <5% vegetation
Limited grass and trees; <15%
vegetation
Abundant grass lawns and lightly
to moderately wooded; >70%
vegetation
Limited lawn sizes and shade
trees; <30% vegetation
Limited lawn sizes, old
established shade trees;
<35% vegetation
32
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Table 5. (continued)
Type
Description
Use and structures Vegetation
R4
A1
Estate residential
Expansive family dwelling on
multi-acre tracts
Abundant grass lawns and
lightly wooded; >80% vegetation
Metropolitan natural
Major municipal, state, or Nearly total grass and lightly
federal parks, golf courses, wooded; >95% vegetation
cemeteries, campuses; occasional
single story structures
A2
A3
A4
A5
Agricultural rural
Undeveloped
Uncultivated; wasteland
Undeveloped rural
Water surfaces
Rivers, lakes
Local crops (e.g., corn, soybean);
>95% vegetation
Mostly wild grasses and weeds,
lightly wooded; >90% vegetation
Heavily wooded; >95% vegetation
SOURCE: Auer, 1978.
coefficient for urban size, is widely used to model area source releases in urban areas. Some
models also use the virtual point source concept (e.g., the ISCLT).
3.3.6. Reactivity
Since dispersion of airborne contaminants is affected by their ability to interact with the
environment, the analyst must assess the reactivity of the primary contaminant as well as the
formation of secondary contaminants. Modeling reactive contaminants in a complex reactive
system requires monitoring of both the reactive system and the dispersion and fate of all the
contaminants involved.
33
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3.3.7. Form of Release
Although all recommended systems model gases, few can model participates. Thus
the form of the contaminant release may be a significant limiting factor in model selection.
3.3.8. Removal Mechanism
Dissolution, adsorption, gravitational settling, and precipitation affect the removal of
contaminants from the air and their environmental fate. Many near-field atmospheric
dispersion models do not account for these removal mechanisms. If the exposure
assessment is intended to estimate contaminants in the food chain, the model sliould account
for removal mechanisms in the near-field range.
34
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4. EXAMPLES OF MODEL SELECTION FOR SPECIFIC
EXPOSURE ASSESSMENTS
This section provides examples of how to select the appropriate air dispersion model
for contaminants emitted from various sites. Profiles were developed for three situations by
using the Model Selection Profile (Table 1). These profiles (Table 6) were compared to the
model characteristics in Table 2 to determine which models were appropriate for each task.
The following sections provide an overview of municipal waste combustors,
perchloroethylene emissions from dry cleaning facilities, and exposure to dioxin from soils via
vapor inhalation. For each situation, the model requirements and the relative merits of various
models are discussed to illustrate the reason for the final model selection.
4.1. MUNICIPAL WASTE COMBUSTORS
Incineration of municipal solid waste releases both metals and organics into the
environment. Approximately 100 municipal waste combustors (MWCs) are currently operating
in the United States, having the incineration capability of more than 47,000 tons of waste. By
the year 2000, that capacity is estimated to reach 250,000 tons (USEPA, 1987d). As the use
of municipal waste incineration for a waste management alternative has increased, so has
public concern over possible environmental and human health effects. Of particular concern
are persons living near incinerators, who are exposed via the inhalation and ingestion routes.
4.1.1. Assessment
Although all recommended models can be used for near-field situations, only the
CRSTER and the ISCLT provide long-term averaging and apply to rural and urban settings
and stack sources. No models are appropriate for complex terrain, so this need cannot be
addressed. Of these two models, only the ISCLT handles both particulate emissions and
associated deposition.
4.1.2. Recommendation
Based on these criteria, the ISCLT is the model of choice. The ISC atmospheric
dispersion model is well-suited for use in risk assessment at MWCs because it is a point
source model and can allow for stack heights, exit velocities, and other source term
35
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Table 6. MODEL SELECTION CRITERIA FOR SAMPLE SITES
Site
Geographic scale
Time scale
Land use
Municipal
waste
combustors
near-field3
long-term
urban and
rural possible
PCE emissions
from dry-
cleaning
near-field3
long-term13
urban for most
facilities ,
Dioxin
in
soils
on-site and
near-field"
long-term
rural location
for landfills
Terrain
Source
Source config.
Emissions
Reactivity
Form of release
Removal
mechanisms
flat or complex
possible
elevated point
source for
stacks
low0
particulate and
gaseous
particulate
deposition
flat or complex
possible
single point source
and collection of
point sources acting
as an area
low0
gaseous emissions
only
none
flat or complex
ground level area
source (i.e.,
landfills)
low0
gaseous, since
vapori2:ation
process used
none0
a Usually where highest exposure occurs.
b Since interest is in long-term health effects.
0 Conservative estimate.
d Workers are onsite and residents nearby.
36
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characterization parameters. The ISC is a near-field model, i.e., it can estimate
concentrations within 20 to 50 km of a MWC. The ISC computes both short-term (maximum
24-hour concentrations) and long-term (yearly average) atmospheric concentrations around
the facility. The ISC also accounts for buoyancy-induced dispersion that is likely to be
significant from combustion processes and the aerodynamic effects of building downwash,
which is likely to be significant for many MWCs with short stacks.
Although the ISC is often the model selected for estimating concentrations of
contaminants from MWCs, it has some limitations in its use for exposure assessments. The
ISC is an atmospheric dispersion model, not a human exposure model, and it cannot estimate
exposure. As a note, the Human Exposure Model (HEM), developed by the EPA specifically
to estimate exposure, combines atmospheric concentration estimates with population statistics
for the entire United States. Although HEM currently uses another point source atmospheric
dispersion model, the EPA is in the process of incorporating the ISC into the HEM code.
Once these modifications have been made, the HEM model will provide a valuable addition to
the modeling techniques available for estimating human inhalation exposures around MWCs.
Another limitation of the ISC is that it currently uses reflection coefficients to estimate
deposition velocities of pollutants. The use of reflection coefficients is questionable for
estimating deposition impacts on the food chain. A more reliable method, employed by the
California Air Resources Board (CARB) Model (Sehmel and Hodgson, 1978; CARB, 1986),
accounts for particle size deposition velocities.
The ISC is intended for urban or rural situations where the terrain elevations do not
exceed stack height; it is not designed to handle complex terrain situations. However, the
complex terrain screening models that could be used to model complex terrain, such as
LONGZ and COMPLEX I, do not account for particulate deposition and therefore are not
useful for estimating food chain impacts in complex terrain situations. In addition, these
complex terrain screening models do not account for building downwash.
4.2. PERCHLOROETHYLENE EMISSIONS FROM DRY CLEANING FACILITIES
Perchloroethylene (PCE), a volatile chlorinated hydrocarbon, is used extensively as a
solvent in dry cleaning operations; an estimated 40% of all PCE produced annually in the
United States is used by the dry-cleaning industry. PCE emissions are almost exclusively
37
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atmospheric and, in vapor form, PCE is readily absorbed through the lungs. PCE is classified
as a probable human carcinogen via inhalation.
4.2.1. Assessment
The EPA performed an inhalation risk assessment for PCE emissions from dry-
cleaning facilities. The documentation of exposure and cancer risk assessment for the dry
cleaning source category was described in an EPA internal memorandum dated December 10,
1987, from J.J. Vandenberg, Pollutant Assessment Branch, to M.L. Meech, Chemicals and
Petroleum Branch, Office of Air Quality Planning and Standards. Quantitative estimates for
both annual average exposure from a single source and national aggregate exposure from
multiple, unspecified sources were needed to adequately assess human exposure nationwide
to emissions of PCE from dry-cleaning facilities.
In addition to the requirements outlined in Table 1, the model needs to estimate the
aggregated exposure for the United States. This requires a model that can incorporate a
population distribution data base with an atmospheric dispersion model and limits selection to
the HEM and Inhalation Exposure Model (see Section 3.2).
4,2.2. Recommendation
Of these two models, the HEM specifically addresses the two-point source
configuration requirements and is thus the logical choice.
The general HEM model contains an atmospheric dispersion component including
meteorological data, a population distribution data file for the entire United States, and an
automated method for calculating human exposure and subsequent risk. It includes two basic
human exposure models, SHED and SHEAR. To estimate exposures to PCE, SHED was
used to model point sources and SHEAR was used to model area sources.
For point source modeling, SHED employs a Gaussian atmospheric dispersion model
similar to the Climatological Dispersion Model (COM) (USEPA, 1986a) to estimate the annual
average ground level concentrations resulting from emissions of point sources. It assumes
that the terrain is flat. The CDM-type atmospheric model used in SHED also accounts for
enhanced dispersion caused by building wake effects; however, as is common for Gaussian
plume models, it cannot realistically estimate concentrations at elevated receptors, such as
multi-storied apartment buildings.
38
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Input data required by this atmospheric model include source location (specifically,
longitude/latitude coordinates); emission rate; source parameter data (e.g., stack height and
diameter, gas exhaust velocity and temperature); and the appropriate Stability Array (STAR)
station code number. Because PCE has a low rate of reactivity, input for atmospheric
reactivity was not required. The STAR data sets for meteorological data and the population
data base, which includes the 1980 population of each Block Group/Enumeration District in
the United States, are permanent parts of the internal computer data base for the HEM.
To compute exposure, SHED pairs concentration with population at the same location
or point. The smallest population "point" is the BG/ED level of census data (1980). Although
this is the smallest point that can be determined from census data, each BG/ED contains an
average of 800 people. These population points are paired with the concentration array for
160 receptors around the point source (10 receptors along each of the 16 wind directions of
STAR). Two interpolation approaches are used to define the best concentration/population
relationship and are described in detail in the HEM User's Manual (USEPA, 1986c).
For area source modeling, the SHEAR model was used to estimate national aggregate
exposure from multiple, unspecified sources of PCE emissions. Input requirements for area
source modeling using SHEAR include location (i.e., defining the area or "box" by determining
the specific coordinates of the southwest corner of the box and the length and width of the
box), emission rate (determined as emissions/area or emissions/person), and code number of
the closest (usually) STAR station. Source parameters and atmospheric reactivity values
were not required in this application. The defined boxes were restricted to urban areas, i.e., to
areas where sources of PCE emissions are most prevalent.
To model dispersion from area sources, SHEAR incorporates a simplified Gifford-
Hanna urban area dispersion algorithm (Gifford and Hanna, 1973). This model was chosen
because it is can estimate atmospheric pollutant concentrations caused by area source
emissions in urban areas (USEPA, 1986c). The basic Gifford-Hanna equation is given as:
X = CQ0/U
(I-2)
where X is air pollutant concentration, C is the Gifford coefficient, QQ is the effective emissions
rate per unit area, and U is the wind speed.
39
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Exposure to area emissions is a product of the PCE concentration per area of each
BG/ED and the number of people in each BG/ED. National aggregate exposure is a
summation of human exposure for each BG/ED (USEPA, 1987b).
4.3. EXPOSURE TO DIOXIN FROM SOILS VIA VAPOR INHALATION
In a recent publication, Estimating Exposures to 2,3,7,8-TCDD (USEPA, 1988b), the
EPA describes the use of various mathematical modeling approaches to estimate exposure to
dioxin, a highly carcinogenic, organic compound, via all probable routes of exposure to
contaminated soils and sediments as well as through bioaccumulation of dioxin in fish and
cattle. The EPA's modeling treatment of exposure to dioxin from soils via vapor inhalation is
used here to represent an area source modeling situation. Dioxin emitted from soil surfaces
in the form of vapors or particulates can be an exposure hazard to on-site workers and nearby
residents. Estimating average lifetime exposure to dioxin via vapor inhalation is the objective
of this particular assessment (USEPA, 1988b).
4.3.1. Assessment
The on-site geographic scale requirement means that Gaussian plume models are not
applicable (see Section 3.1.) and that all models except the Urban Airshed Model (UAM) are
not appropriate. However, the UAM does not provide long-term averages or apply to rural
settings, and thus it is also not appropriate. On-site emission models are not well developed
and are usually approached by use of simple box models. These models are well suited to
area sources and, accordingly, are the best approach for this situation.
4.3.2. Recommendation
Based on these evaluation factors, the UAM is the best available model. However,
the modeler should emphasize the uncertainty caused by its simplifying assumptions.
40
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41
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USEPA. 1979. Industrial source complex (ISC) dispersion model user's guide, Vol. 1. Office
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USEPA. 1980a. Modeling of simulated photochemical smog with kinetic mechanisms, volume
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USEPA. 1980b. User's guide for MPTER. U.S. Environmental Protection Agency, Research
Triangle Park, NC. EPA-600/8/80-016. NTIS PB80-.197361.
42
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USEPA. 1980c. Guidelines for applying the airshed model to urban areas. U.S.
Environmental Protection Agency, Research Triangle Park, NC. EPA-450/4-80-020.
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EPA-600/2-83-029.
USEPA. 1986a. Guideline on air quality models (Revised). Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.
EPA-450/2-78-027R. (Supplement A, 1987)
USEPA. 1986b. User's network for applied modeling of air pollution (UNAMAP) (Version 6).
U.S. Environmental Protection Agency, Research Triangle Park, NC. NTIS
PB86-222361.
USEPA. 1986c. User's manual for the human exposure model (HEM). Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency, Research Triangle
Park, NC. EPA-450/5-86-001.
USEPA. 1986d. Industrial source complex (ISC) dispersion model user's guide, Second
Edition, Vol. 1 and 2. U.S. Environmental Protection Agency, Research Triangle Park,
NC. EPA-450/4-86-005a and 005b. NTIS PB86-234259 and PB86-234267.
USEPA. 1987a. Supplement A to guideline on air quality models,(Revised). Office of Air
Quality Planning and Standards, U.S. Environmental Protection Agency, Research
Triangle Park, NC. EPA-450/2-78-027R.
USEPA. 1987b. Sensitivity analysis for application of the inhalation exposure methodology
(IEM) to studies of hazardous waste management facilities. Prepared for the
Hazardous Waste Engineering Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Cincinnati, OH.
EPA/600/2-87/071.
USEPA. 1987c. Industrial source complex (ISC) dispersion model. Addendum to the User's
Guide. U.S. Environmental Protection Agency, Research Triangle Park, NC.
USEPA. 1987d. Characterization of the municipal waste combustion industry. Prepared by
Radian Corporation for the Office of Air and Radiation, U.S. Environmental Protection
Agency, Washington, DC. EPA Contract No. 68-02-4330.
USEPA. 1988a. Superfund exposure assessment manual. Office of Emergency and
Remedial Response, U.S. Environmental Protection Agency, Washington, DC.
EPA/540/1-88/001.
USEPA. 1988b. Estimating exposures to 2,3,7,8-TCDD. Office of Health and Environmental
Assessment, U.S. Environmental Protection Agency, Washington, DC.
EPA/600-6-88/005A. NTIS PB 88-231196/AS.
43
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PART II.
GAUSSIAN PLUME MODELS
1. INTRODUCTION
Part II of this document discusses the assumptions, limitations, and uncertainties
associated with Gaussian plume models, several Gaussian plume dispersion coefficients, and
typical model input data requirements. The Gaussian plume model is the most widely used
method of estimating downwind concentrations of nonreactive pollutants released to the
atmosphere within 50 km of the source. This class of models is used frequently because (1) it
produces results similar to those produced by other models when comparisons are made to
experimental data, (2) its mathematical operations are easily performed, (3) it is appealing
conceptually, (4) it is consistent with the random nature of turbulence, (5) it is a solution to the
simple diffusion equation for constant diffusion and wind speed, (6) other so-caSled theoretical
formulas also contain large amounts of empiricism in their final stages, and (7) it is the model
most often referred to in government guidebooks (Hanna et al., 1982). Specific examples of
Gaussian models are the Industrial Source Complex (ISC) and the Climatological Dispersion
Model (COM 2.0).
1.1. ASSUMPTIONS
The Gaussian class of models is based on the assumption that the plume will spread
both laterally and vertically in accordance with a Gaussian statistical distribution under
conditions of constant wind speed, no wind shear, flat terrain, and simple diffusion along the
direction of the mean wind. The Gaussian equation for the ground level concentration
downwind from a continuously emitting ground-level point source is (Gifford, 1968):
where
X =
Q =
X=-
Q
Ttuayaz
exp
ground level air concentration (units/m3),
pollutant emission rate (units/s),
(11-1)
44
-------
°y =
u =
H =
y =
standard deviation of a Gaussian distribution in the crosswind
direction (m),
standard deviation of a Gaussian distribution in the vertical
direction (m),
wind speed (m/s),
pollutant plume height (m),
crosswind distance (m).
The ,oy and oz values are not defined explicitly by the mathematical assumptions and,
therefore, must be determined empirically. Thus, a number of different field experiments have
been conducted to determine o and oz as functions of atmospheric stability and downwind
distance. The set of empirically determined atmospheric dispersion values that will yield a
predicted value of downwind pollutant concentration from a Gaussian plume model in closest
agreement with measured values will vary for differing meteorologic, topographic, or release
conditions. A common challenge is choosing the dispersion parameters that most closely suit
a poorly characterized emission site. Several empirically determined atmospheric dispersion
parameters are discussed in the next section.
Equation (11-1) applies to a release sufficiently long in duration that a plume-like
distribution of pollutants results. However, many of the Gaussian models calculate the
average concentration around a source for extended periods (e.g., a year). Consequently,
Equation (11-1) can be modified to eliminate its oy dependence based on long-term historical
weather data for most U.S. cities. Usually these data are expressed as joint frequency
distributions of wind direction, wind speed class, and atmospheric stability class. Normally, 16
wind directions are reported in U.S. data, allowing a 22.5°-sector average air concentration to
be determined.
The crosswind-averaged concentration due to a continuous source is obtained by
integrating Equation (11-1) over -«. < y < +00. For long-term, continuous exposure, an average
concentration is required over the extended period, (i.e., long compared to the period during
which the mean wind conditions are predominant). This average is estimated by multiplying
crosswind-averaged concentration by the frequency with which the wind blows toward a given
sector and dividing by the width of that sector at the downwind distance of interest (Gifford,
1968). The resulting long-term air concentration is
45
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X = 2.032Qf expM/2(H/g,n
(11-2)
xuo,
where
time fraction that the wind blows toward a given receptor,
distance between release point and receptor (m).
If the vertical dispersion becomes large (e.g., oz > K x L)
where
K = some numerical factor (on the order of unity),
L = depth of the tropospheric boundary layer,
then vertical distribution of the pollutant is considered to be uniform. Under these conditions,
Equation (11-2) becomes
X = 2.032Qf
xuozL
411-3)
Equations (11-2) and (11-3) are no longer truly Gaussian plume models. Furthermore, an
implicit problem in applying these equations is that there is no consideration of wind velocity
persistence. That is, in most cases, the wind does not actually blow in one direction at a
specific speed for as long a time as assumed here. Hence, there will never be uniform
vertical mixing, even at very large distances. Moreover, concentrations at very large distances
will be unduly influenced by stable wind classes and low wind speeds.
The height (H) of the release is another sensitive parameter in the Gaussian plume
model. The value of H includes not only the physical height of the stack but also any
additional height due to the rise of the plume as a result of its buoyancy or momentum. The
amount of this plume rise is usually estimated through the use of models such as those
suggested by Briggs (1969). For example, nuclear power plants seldom have either large-
momentum buoyant plumes or stacks associated with their routine releases; as a result,
plume rise is not usually critical for estimating air concentrations resulting from these facilities
(Crawford, 1978). However, fossil-fueled power plants often have large plume rises for which
46
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an accurate estimate can be critical in determining the predicted air concentration (Bowne,
1981).
1.2. LIMITATIONS
The Gaussian plume model often is applied when it theoretically should not be, but this
practice has been accepted when predicted impacts are so small that the importance of
potential errors would not affect the conclusions reached. Conditions for which the Gaussian
plume model is not expected to apply include situations involving complex terrain or meteoro-
logic conditions, long-range transport, and short-term releases. As a result, a large number of
more complex, seemingly more realistic, dispersion models have been or are being developed
for use in these obviously non-Gaussian situations (Miller et al., 1981).
1.3. UNCERTAINTIES
The best overall uncertainty determination for an atmospheric dispersion model (e.g.,
Gaussian plume) is a comparison of modeling predictions with environmental measurements
for conditions similar to those assumed by the model. These comparison studies should
include various terrains, release heights, and meteorologic conditions. Unfortunately, not
enough model validation studies under varying conditions have been performed to allow
reliable statistical analysis of the uncertainties associated with the Gaussian plume model
(Fabrick et al., 1977a,b,c; USEPA, 1977; Crawford, 1978; Hilst, 1978). Several validation
studies (and other estimates of the Gaussian plume model accuracy) have been reviewed and
are summarized in Table 7. The references in Table 7 include individual validation studies as
well as reviews of such studies. The range of ratios (predicted air concentration/observed air
concentration) is based on all the literature cited for that condition and is not necessarily
representative of any one reference. The results of the validation studies indicate that
modeling uncertainties depend on site, meteorologic and release conditions, and source
complexities. Little data are available to validate plume depletion processes used in atmo-
spheric dispersion models to predict deposition rates.
1.3.1. Site Conditions
The estimate given for a highly instrumented flat-field site (Table 7) assumes that
previous data on meteorologic conditions and airborne concentrations are also available.
47
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Table 7. ESTIMATED RATIOS OF PREDICTED TO OBSERVED AIR CONCENTRATIONS
USING GAUSSIAN PLUME ATMOSPHERIC DISPERSION MODELS
Conditions
Range
References
Highly instrumented site
centerline concentration
within 10 km of a continuous
point source
ground level releases
elevated releases
maximum air concentration
for elevated releases
Annual average for a specific
point, flat terrain, within
10 km downwind of the release
point
Annual average for a specific
point, flat terrain, 10-150 km
downwind of the release point
Specific hour and receptor point,
flat terrain, steady meteorologic
conditions
elevated releases without
building wake effects
0.8 -1.2
0.65-1.35
0.5 - 1.5
0.5 -2.0
0.25 - 4.0
0.1 - 10.0
Pasquill, 1974
Pasquill, 1974
Pasquill, 1974; Wendell et al., 1976
Gifford and Pack, 1962; Gogolak et al.,
1981; Lindeken et al., 1980;; Ruff et al.,
1980
Buckner, 1981; Draxler, 1980; Fields et al.,
1981 a; Fields et al., 1984; Huang, 1980;
Pendergast, 1979; Pendergast, 1982;
Raridon and Murphy, 1982; Telegadas et
al., 1978; Weber et al., 1982; Yildiran and
Miller, 1984
elevated releases with
building wake effects
0.01 -100.0
Bowne, 1981; Cotter et al., 1985; Fields et
al., 1981b; Gogolak, 1973; Guzewich and
Pringle, 1977; Lewellen et al., 1985; Miller
et al., 1980b; Miller and Colter, 1982;
Nickola, 1979; Partridge et al., 1976;
USEPA, 1981; Vankatram and Vet, 1981;
Yildiran and Miller, 1983
Fields et al., 1984; Howroyd and Gutfreund,
1980; Koziar et al., 1980; Miller, 1981;
Miller, 1983; Start et al., 1978
48
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Table 7. (continued)
Conditions
Range
References
Short-term, surface-level releases
with building wake effects using
temperature gradient method of
estimating atmospheric stability
wind speeds over 2 m/s 0.7 -100.0
wind speeds under 2 m/s 1.0 - 100.0
Short-term, surface-level releases
without building wake effects
using temperature gradient method
of estimating atmospheric stability
wind speeds over 2 m/s 0.3 - 10.0
wind speeds under 2 m/s 1.0 -100.0
Deluca et al., 1981; Fackrell, 1984; Fields
et al., 1984; Huber, 1984; Start et al., 1978;
Van der Hoven, 1981;
Van der Hoven, 1981
Miller and Little, 1980; Miller et al., 1982;
Van der Hoven, 1981;
Miller and Little, 1980; Van der Hoven,
1981
Complex terrain or meteorology
(e.g., sea breeze regimes)
annual average concentrations 0.1 - 10.0
short-term releases 0.01 -100.0
Urban releases
annual average concentrations 0.25 - 4.0
<24 hr concentrations
0.1 - 10.0
Miller and Moore, 1977; Smith and Ruch,
1979; Veilleque et al., 1981; Yildiran and
Miller, 1984
Bendel and Cresswell, 1977; Egan et al.,
1979; Elliott et al., 1977; Iwanchuk et al.,
1980; Lavery and Schulman, 1977; Lott,
1984; Miller et al., 1980a; Septoff et al.,
1977; Veilleque et al., 1981; Weil and
Jepsen, 1977; Wilson et al., 1977
Gifford and Hanna, 1973; Nieuwstadt and
van Dop, 1975
Hanna, 1971; Pendergrass and Rao,
1984a; Pendergrass and Rao, 1984b;
Turner, 1964
49
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Pasquill (1974) has also estimated an uncertainty of 10%-20% for short downwind distances
(<10 km), steady winds, and ground level releases. He suggests that an error of 30%-35%
may be more appropriate for elevated releases. A more appropriate value for the uncertainty
associated with the maximum air concentration value from elevated sources may be 50%
(Pasquill, 1974; USEPA, 1976). The meteorologic and terrain conditions specified in these
idealistic estimates seldom occur in the real world. Consequently, these uncertainty estimates
represent the best accuracy that can be expected by present atmospheric dispersion models.
1.3.2. Meteorologic Conditions
Vogt (1977) has compared the short-term diffusion factors Xu/Q(x, y = 0, z = 0) computed
from six such sets of curves assuming a 100-m release height and using one method of
determining atmospheric stability. He found that maxima in Xu/Q generally agreed within a
factor of two for each set of curves and each stability category considered. However, the
downwind location of the maxima differed by as much as an order of magnitude. Vogt (1977)
also calculated annual average diffusion factors for each of the same six sets of dispersion
parameters and annual average meteorologic statistics for Julich, Germany: In this case, the
maxima differed by over an order of magnitude and their location by a factor of five,
depending upon which set of dispersion parameters was used.
Vogt's comparisons were based on one method of determining the stability of the
atmosphere. However, a variety of methods have been proposed for classifying the stability
of the atmosphere (Hanna et al., 1977). It has been shown that these different methods can
give significantly different results when applied to the same meteorologic data set
(Pendergast, 1982; Miller et al., 1978; Miller and Little, 1980; Miller and Cotter, 1982). There
are indications that the selection of a stability category alone can result in an order of
magnitude or more difference between the lowest and highest annual average air
concentration calculated using a given set of dispersion parameters. To avoid these large
differences, the dispersion parameters should be chosen on the basis of as much site-specific
information (e.g., wind velocity, topography, and release height) as possible (Hanna et al.,
1977).
50
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1.3.3. Release Conditions
85.
1.3.3.1. Long-term Releases Over Flat Terrain
Data have been obtained in the Savannah River Plant (SRP) for the release of °°Kr;
corresponding air concentrations were measured within 150 km of the SRP (Telegadas et al.,
1978; Gogolak et al., 1981). These data have been used in a number of Gaussian plume
model validation studies, which have included areas within 10 km of the release point
(Gogolak et al., 1981) and 30 to 150 km downwind of the release point (Telegadas et al.,
1978; Pendergast, 1979; Draxler, 1980; Huang, 1980; Fields et al., 1981 a, 1984; Miller et al.,
1981; Buckner, 1981; Weber et al., 1982). The results generally support the summaries
presented in Table 7 for ensemble averages, especially for the annual average predictions.
Ruff et al. (1980) also were able to predict annual average air concentrations within approxi-
mately a factor of 2 at 13 SO2 monitoring stations around St. Louis, Missouri. These and
other model validation studies support the continued use of the Gaussian plume model for
assessing the impact of long-term releases of pollutants from facilities located in relatively flat
terrain (Miller et al., 1981; Buckner, 1981). The main advantage of flat terrain is that the wind
direction (and other weather conditions) remain relatively constant with increasing distance.
1.3.3.2. Short-term Releases Over Flat Terrain
More comparisons are available for short-term than for long-term releases. As the
averaging time for the air concentration calculation decreases, the uncertainty in that concen-
tration increases. The primary factor in predicting short-term air concentrations seems to be
an accurate description of the wind field, assuming that the release rate is well measured
(Buckner, 1981). While the given references generally support the uncertainty estimates in
Table 7, individual comparisons for releases lasting less than one hour show deviations larger
than the one order of magnitude (e.g., Miller and Cotter, 1982).
1.3.4. Source Complexity
The basic Gaussian plume model was never intended for use with complex conditions
such as mountainous terrain, lake or seashore environments, low wind speed, inversion
conditions, and urban environments or within complexes of buildings and other physical
structures. However, many facilities with a potential for atmospheric pollutant release are
51
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located in areas that have complex conditions. Therefore, versions of the Gaussian plume
model have been applied in these situations. Available data for validating models under
complex conditions are very limited. Often, only a small number of monitoring stations are
available for comparisons during any given set of releases. Limited tests in complex terrain
(Egan et al., 1979; Iwanchuk et al., 1980) and near large bodies of water (Wilson et al., 1977;
Lavery and Schulman, 1977; Septoff et al., 1977; Lague et al., 1980) indicate that, under
some conditions, Gaussian type model predictions have less uncertainty than the four orders
of magnitude shown in Table 7 for short-term releases. Other studies, however, indicate that
the uncertainty estimate shown in Table 7 is not too large at all (e.g., Elliott et al., 1977). For
annual average comparisons, an uncertainty range of two orders of magnitude may be more
appropriate (Wilson et al., 1977; Smith and Ruch, 1979). In general, results of model
validation studies under these release conditions are highly dependent on the exact site
involved, the meteorologic conditions during the release, and the exact algorithm used in the
calculation (Bendel and Cresswell, 1977). For example, Van der Hoven (1981) has shown
that the predicted-to-observed ratios can cluster more closely about one for low wind speed
with inversion conditions if atmospheric stability is estimated on the basis of more than just
temperature gradient information.
Many pollutant sources are located within complexes of buildings and other physical
structures. A limited number of field studies indicate that standard Gaussian modeling
techniques may overpredict the downwind air concentration near the point of release from
short-term releases by as much as a factor of 1000 (Start et al., 1978). This large discrepan-
cy occurs because wind turbulence around buildings increases the diffusion rate as compared
to the simple dispersion from isolated release points that is typically used in standard
Gaussian-plume modeling. However, it may be possible to reduce this uncertainty by a factor
of 10 to 100 using modified procedures, depending upon the complexity of the site, the
release height, and meteorologic conditions (Howroyd and Gutfreund, 1980; Koziar et al.,
1980; Miller, 1981).
1.3.5. Plume Depletion Processes
Particulates and reactive gases may deposit on the surface of the earth through the
processes of dry and wet deposition. Wet deposition occurs sporadically during specific rain
or snow events, but dry deposition processes occur continuously. A number of different
52
-------
measurements of dry deposition processes have been made (McMahon and Denison, 1979;
Sehmel, 1980). Also, a number of different models for estimating plume depletion have been
proposed (Hosker, 1984). However, during the course of this project no data have been found
that are adequate for validating plume depletion processes. For example, recent sodium
release tests to the atmosphere (Johnson et al., 1978; Johnson and Nelson, 1979) have
shown that sodium aerosols tend to form agglomerates that fall out rapidly. However, more
tests are needed to establish quantitatively the effect of this mechanism on subsequent
downwind exposure to sodium. Also, calculations have been performed in which inclusion of
depositional processes in the model predictions improved agreement with the observed air
concentrations of fluorescein particles (Fields et al., 1981b; Little et al., 1982; Piepho, 1981).
However, no deposition measurements were taken during the field studies considered in these
comparisons. Good agreement also has been reported between three observed and
predicted values of 131I concentration on grass, but because of the input data used in the
calculation, the authors do not consider their methods to constitute a validation study
(Hubschmann and Miller, 1980). Until such data sets become available, one cannot specify
the accuracy associated with a calculation of concentration on the ground or with an air
concentration involving significant plume depletion.
53
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2. DISPERSION COEFFICIENTS
Changes in dispersion coefficients, ay and az, strongly affect the resulting air concen-
trations calculated by the Gaussian plume model (Pasquill, 1974; USEPA, 197(5). Values for
a and az in the Gaussian equation are not defined explicitly and must be determined
empirically. A number of different field experiments have been conducted to determine ay and
a as functions of atmospheric stability and downwind distance. The set of empirically
determined atmospheric dispersion values that will yield a predicted value of downwind
contaminant concentration from a Gaussian plume model in closest agreement with measured
values will vary for differing meteorologic, topographic, and release conditions. A description
of several empirically determined atmospheric dispersion values follows. Table 8 lists the
values used to compute dispersion coefficients by various methods and shows their
correspondence to the Pasquill stability classes.
2.1. PASQUILL-GIFFORD DISPERSION COEFFICIENTS
Pasquill (1961, 1962) developed a weather classification scheme that uses surface
wind speed, sunshine, and cloud cover as simple measurements of atmospheric stability. Six
atmospheric stability classes, ranging from A through F, or most unstable to most stable, were
determined (Table 8). Dispersion values for oy and az (nomograms) to be associated with
each stability classification type were determined based on ground-level emission tracer
studies and wind direction fluctuation data (Pasquill, 1961, 1962). The values are
approximate for ground-level emissions of low surface roughness (Vogt, 1977) and were
devised from small source distances (<1 km). The values for ay and oz associated with each
stability classification type are given in Figures 1 and 2. Various equations and parameter
sets are used to replicate the empirical curves in the available models. Some parameteriza-
tions are very complicated.
2.2. BRIGGS DISPERSION COEFFICIENTS
Briggs' values for horizontal and vertical dispersion coefficients are available in two
forms, one for releases in rural settings and one for releases in urban settings. For each of
these forms, the horizontal dispersion coefficient, ay, may be computed from the formulas
contained in Moore (1977). These values are represented as a different function for
54
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Table 8. DEFINITION OF THE PASQUILL ATMOSPHERIC STABILITY CATEGORIES
Surface wind
speed at 10 m
(mis)
<2
2-3
3-5
5-6
>6
Incoming
Day
solar radiation
Strong Moderate Slight
A
A-B
B
C
C
A-B B
B C
B-C C
C-D D
D D
Night
Thinly overcast
or
>4/8 low cloud
cover
E
D
D
D
<3/8 Cloud cover
F
. E .
D
D
SOURCE: Pasquill, 1961.
Note: Types a, b, and c are associated with strong, moderate, and slight instability,
respectively; type d corresponds to a neutral lapse and should be assumed for
overcast conditions during day or night; and types e and f are associated with slight
and moderate inversions, respectively.
each stability class corresponding to the six classes defined by Pasquill (1961). The surface
roughness is also considered. It is expected that this parameterization is more appropriate
than the Pasquill values for distances between 1 and 50 km. The horizontal dispersion
coefficient, ay (m), for rural conditions is calculated for the downwind distance, x (m), using
the formula
a =ccx(1 + 0.0001 x)°-5
(II-4)
The vertical dispersion coefficient, az (m), for rural conditions is calculated using the formula
o = p x (1 + qx)r.
(II-5)
The values of a, p, q, and r are defined in Table 9.
55
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10,000-
1000
100
V
7
w
-V
c .•
a
•4
10
-1—r
0,1
10
Oistanca Downwind, km
100
Figure 1. Horizontal dispersion coefficient as a function of downwind distance from the
source. Lines A through F represent dispersion coefficient functions for atmospheric stability
classes A through F.
SOURCE: Turner, 1970 (as cited in USEPA, 1988).
56
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1,000-
E
N 100-
10-
1.0-
XX
X
X
X
XI
X
X
J
/
|F~
0.1
1-1
B,^-
1 10
Distance Downwind, km *
100
Figure 2. Vertical dispersion coefficient as a function of downwind distance from the source.
Curves A through F represent d;spersion coefficient functions for atmospheric stability classes
A through F.
SOURCE: Turner, 1970 (as cited in USEPA, 1988).
57
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Table 9. PARAMETERS USED TO COMPUTE DISPERSION COEFFICIENTS
Pasquill stability class
(see Figures 1 and 2)
B
D
Briggs parameterization class
Klug parameterization class
Brookhaven gustiness class
a
P
q
r
P
q
r
s
P
q
r
s
A
0.22
0.20
0.00
1.00
V
0.469
0.903
0.017
1.380
St. Louis parameterization class
JQIich parameterization class
(50 meter)
(100 meter)
P
q
r
s
P
q
r
s
P
q
r
s
A
0.87
0.81
0.22
0.97
0.23
1.00
0.097
1.15
B
0.16
0.12
0.00
1.00
IV
0.306
0.885
0.072
1.021
B2
0.400
0.910
0.411
0.907
B
1.700
0.717
0.079
1.200
B
0.87
0.81
0.22
0.97
0.23
0.97
0.16
1.02
C
0.11
0.08
0.0002
-0.50
Ill2
0.230
0.855
0.076
0.879
B'l
0.360
0.860
0.326
0.859
C
1.440
0.710
0.131
1.046
C
0.72
0.78
0.21
0.94
0.22
0.94
0.25
0.89
D
0.08
0.06
0.0015
-0.50
1111
0.219
0.764
0.140
0.727
C
0.320
0.780
0.223
0.776
D
0.910
0.729
0.910
0.702
D
0.62
0.76
0.20
0.94
0.22
0.90
0.40
0.71
E
0.06
0.03
0.0003
-1.00
II
0.237
0.691
0.217
0.610
D
0.310
0.710
0.062
0.709
E
1.020
0.648
1.930
0.465
E
1.69
0.62
0.16
0.81
1.69
0.62
0.16
0.81
F
0.04
0.016
0.0003
-1 .00
I
0.273
0.594
0.262
0.500
F
5.38
0.58
0.40
0.62
5.38
0.58
0.40
0.62
SOURCE: Vogt, 1977.
58
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2.3. KLUG DISPERSION COEFFICIENTS
The Klug (1969) dispersion coefficients are intended primarily for short-term ground-
level releases over terrain having low surface roughness. Klug dispersion coefficients are
normally determined by considering several observables, such as insolation, sun altitude
angle, wind velocity, and site-specific equation coefficients. Vogt (1977) infers a correspon-
dence between Klug and Pasquill stability classes and defines parameters used in the
dispersion coefficients functions
oy =
= rxs
(II-6)
(II-7)
These results are summarized in Table 9.
2.4. BROOKHAVEN DISPERSION COEFFICIENTS
The Brookhaven dispersion coefficients as formulated by Singer and Smith (1966)
were derived from experiments made using a 108-m stack height and one-hour emission
period over terrain of "medium" roughness. Vogt fitted these values to Equations (II-6) and (II-
7) with the values shown in Table 9; correspondences between the Brookhaven gustiness
classes and the Pasquill dispersion categories are also shown.
2.5. ST. LOUIS DISPERSION COEFFICIENTS
The St. Louis dispersion coefficients, described by McElroy and Pooler (1968), were
compiled under urban experimental conditions including one-hour emission duration, source
near ground level, and relatively flat terrain of high surface roughness. One may use Vogt's
correspondences between McElroy and Pooler along with Pasquill's dispersion classes to
compute the St. Louis dispersion coefficients using Equations (II-6) and (ll-7) with Vogt's
parameters listed in Table 9.
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2,6. JULICH DISPERSION COEFFICIENTS
Experiments performed near the Julich Nuclear Research Center with emission heights
of 50 m and 100 m have been evaluated by Geiss et ai. (1979). They considered wind
velocity, sun angle and insolation, and the vertical temperature gradient and derived two
dispersion classifications that correspond closely to the Pasquill categories. Julich
parameterizations are expected to be appropriate for "medium to higher" surface roughness.
This scheme uses Equations (II-6) and (II-7) of the above form with parameters as given in
Table 9 to compute the dispersion coefficients.
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3. INPUT DATA REQUIREMENTS
Data input requirements for Gaussian plume models typically fall into one of three
categories: source data (i.e., stack parameters for point sources, dimensional characteristics
for area sources), meteorologic data, and receptor data.
3.1. SOURCE
Input requirements of Gaussian plume models are dependent on source type. For
point sources, input requirements typically include stack location, physical stack height, stack
inside diameter, stack gas exit velocity, stack gas exit temperature, and pollutant emission
rate. Optional input may include source elevation, building dimensions such as average
building width and average spacing between buildings, particle size distribution with corre-
sponding settling velocities, or surface reflection coefficients.
Input requirements for area sources typically include location, size, average emission
rates, and heights of emissions. For line sources, input requirements typically include
coordinates of the end points of the line, release height, emission rate, average line source
width, and average line source buoyancy parameter.
Values for input parameters can be determined by direct measurement, sampling,
estimates based on sound engineering principles, or calculation, and may be found in a
variety of sources. Values for typical source parameters at hazardous waste management
facilities are compiled below. Values for emission rates for sources under specific operating
conditions, a major source input parameter, can be found in published literature by the EPA
(Godish, 1985). Emission controls and operational variations may affect actual emissions
from a source and should be taken into consideration when selecting values for emission
rates. Most models use the time-weighted average value as the source emission rate. This
value is appropriate for long-term assessment of cumulative risk but may not be appropriate to-
use in assessment of significant short-term emissions where the maximum risk high popula-
tion areas need to be identified.
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3.1.1. Typfcal Source Parameters for Hazardous Waste Management Facilities
Typical hazardous waste management facilities include landfills, land treatment areas,
surface impoundments, treatment plants (wastewater and other), and incinerators. The
following sources may be found either singly or in various combinations at these facilities and
may be modeled using one or more stack, point, area, or line source configurations. It also
may be necessary to model some of the sources using a combination of the above source
representations. Some typical source parameters for these hazardous waste management
facilities are provided below (USEPA, 1987).
Drum Piles
Typical parameters:
Area covered, m2: 1300 (typical)
Release height, m: 6.1 (typical)
Comments:
Releases may be from specific locations within the pile or from the pile in general.
Incinerator Stacks
Typical stack parameters:
Diameter, m: 0.7 to 2.0 (range); 1.1 (average)
Height, m: 9.0 to 30.4 (range); 22.0 (average)
Gas exit velocity, m/s: 3.0 to 7.0 (range); 4.0 (average)
Gas exit temperature, °K: 290 to 1000 (range); 500 (average)
Comments:
Stack emissions could be affected by wake effects due to adjacent or nearby buildings.
Landfills
Typical parameters:
Area covered, m2: 900 to 100,000,000 (range); 7000 (average)
Release height, m: 0 (ground level)
Comments:
Releases may occur at/or below ground level. Part of the area may be undisturbed,
part may be active (wastes being deposited), and part may be inactive (wastes
deposited and covered). Characteristics of undisturbed, active, and inactive areas will
be different.
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Land Treatment Areas
Typical parameters:
Area covered, m2: 4000 to 400,000,000 (range); 3,300,000 (avg)
Release height, m: 0 (ground level)
Comments:
Emission rates may vary over the area. Waste application operations could produce
emissions at slightly above ground level.
Process Commercial Buildings
Typical parameters:
Area covered, m2: 70 to 300 (range); 150 (average)
Release height, m: 3.0 to 18 (range); 9.0 (average)
Comments:
Emissions could come from vents or openings in the roof or in the walls of the building.
The building likely will influence emission characteristics. Commercial buildings might
also have small stacks with somewhat higher release heights.
Stack Structures (Other Than Incinerators)
Typical parameters:
Area covered, m2: 100 to 300 (range); 200 (average)
Release height, m: 4.0 to 20 (range); 12.0 (average)
Comments:
Emissions likely come from discrete sources (e.g., valves and vents) within the
structure. Many structures have no walls.
Storage Tanks
Typical parameters:
Area covered, m2: 50 to 1400 (range); 300 (average)
Release height, m: 2.0 to 10.0 (range); 7.0 (average)
Stack diameter, m: 0.02 to 0.1 (range); 0.06 (average)
Stack gas exit velocity, m/s: 0.103 to 1.65 (range); 0.878 (avg)
Stack gas exit temperature, °K: 280.0 (typical ambient)
Adjacent building height, m: 2.0 to 10.0 (range); 7.0 (average)
Adjacent building width, m: 3.0 (typical)
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Comments:
Emissions likely come from process and emergency valves. Individual tanks or entire
tank farms can be involved. Adjacent or nearby structures could affect emission
behavior. May be modeled as stacks, areas, or volumes.
Surface Impoundments
Typical parameters:
Area covered, m2: 20 to 75,000 (range); 2000 (average)
Release height, m: 0 (ground level) or height of spray
Comments:
Releases may occur below ground level for static impoundments. Releases may occur
above ground level for dynamic impoundments (e.g., for spray ponds).
Waste Piles
Typical parameters:
Area covered, m2: 30 to 400,000 (range); 300 (average)
Release height, m: 3.0 (typical)
Comments:
Part of the area may be undisturbed, part may be active (wastes being deposited), and
part may be inactive (wastes deposited and covered). Emission characteristics of
undisturbed, active, and inactive areas will be different.
3.2. METEOROLOGIC
Meteorologic parameters are required in Gaussian plume models to reasonably predict
the transport, dispersion, and depletion characteristics of the pollutant plume. Some models
may accept hourly surface weather data from a preprocessor program (RAMMET) that
provides hourly stability class, wind direction, wind speed, temperature, and mixing height. In
addition, the actual height of the anemometer may be required for some models. For models
using long-term averages, STAR (Stability Array) summaries provide necessary meteorologic
data.
3.3. RECEPTOR
Typical atmospheric dispersion models provide estimates of maximum or annual-
average ground-level concentrations at individual receptor sectors. The number and place-
ment of receptors will vary depending on risk assessment needs (see USEPA, 1987 for a
64
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discussion of optimal receptor placement). AH receptor locations relevant to an exposure
assessment should be identified, i.e., areas of high population and expected maximum
ground-level concentrations. Receptor input requirements for most Gaussian plume models
include location (coordinates) and elevation data for individual receptors (although most
models assume the receptors to be located at ground level) or the location and size of a
receptor grid. Some models have options for automatic placement of receptors near expected
maximum concentrations. Models that use a grid system to identify the relationship between
the source and receptor use either a polar coordinate or a Cartesian coordinate system. The
grid can consist of a sector (22.5° angle and 5-mile width) or areas with square, rectangular,
or hexagonal shapes.
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PART III.
INDOOR AIR MODELING
1. INTRODUCTION
Contaminants in residential indoor air have been identified as a major source of human
exposure. Two significant factors indicate indoor air as a major exposure pathway: (1)
residences contain numerous sources of airborne contaminants, and (2) people spend a high
percentage of time each day at home (approximately 67%). Recent studies (Merlhave and
Moller, 1979; Repace and Lowrey, 1980; Committee on Aldehydes, 1981; Committee on
Indoor Pollutants, 1981; Leaderer, 1982; Seifert and Abraham, 1982; Spengler and Sexton,
1983; Spengler et al., 1983, 1985; Lebret et al., 1984; Gammage et al., 1984; Monteith et al.,
1984; Leaderer et al., 1986; Wallace et al., 1985) show that residential indoor air
concentrations of several contaminants (e.g., NO2, CO, SO2, respirable particles, radon,
formaldehyde, volatile brganics, bioaerosols) are higher than, sometimes as much as 10
times, concentrations found in outdoor air. These studies show that some residential indoor
air contaminant concentrations exceed limits set by ambient air quality standards and, in some
cases, limits set by occupational standards (Leaderer, 1987). While these standards may be
violated by indoor air concentrations, the standards are nonenforceable because exposure
occurs inside residences (Sexton, 1986).
The use of source modeling to predict indoor air concentrations of contaminants is a
relatively new research topic (Sexton and Hayward, 1987). While some work has been done
in this area, no particular model or modeling technique has emerged to adequately address
indoor air modeling. A brief overview of the current work being undertaken to determine
human exposure to contaminant concentrations in indoor air is included in this document to
emphasize the importance of indoor contaminant concentrations on human exposure.
74
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2. CHARACTERIZATION OF INDOOR AIR EXPOSURE
Four major factors are necessary to determine the extent of human exposure to
contaminants in indoor air: (1) identification of home-generated emission sources, (2)
characterization of emissions, including estimation of emission rates (Wallace et al., 1985;
Leaderer, 1987; Sexton and Hayward, 1987), (3) determination of ventilation rates, and (4)
determination of the relative impact of indoor and outdoor emissions on human exposure
(Sexton and Hayward, 1987).
2.1. IDENTIFICATION OF EMISSION SOURCES
Contaminants found in indoor air comprise a broad spectrum of organic and inorganic
chemicals in gaseous and particulate forms, as well as a range of viable particulates. Indoor
emission sources can be characterized as point (e.g., space heaters) or area sources (e.g.,
use of aerosols) (Leaderer, 1987). Typical sources of contaminants in indoor air include
migrating outdoor air, occupant activity-related sources (e.g., tobacco smoking, cooking, space
heating, use of propellants, showering), environmental control sources (e.g., heating, cooling),
and the building itself (e.g., construction materials such as concrete, chipped board, insulation)
or its contents (e.g., furnishings, dry-cleaned clothes, moth crystals, printed materials). Table
10 lists specific air contaminants by common source locations.
Because thousands of consumer products and building materials are in use in private
and public buildings and may be used differently in each household, the sources of indoor
contaminants have only been partially characterized. A complete characterization of emission
sources is needed to better estimate possible risks to public health from all potential sources
of human exposure (Wallace et al., 1985; Leaderer, 1987).
75
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Table 10. AIR CONTAMINANTS BY SOURCE LOCATIONS
Contaminants
Sources
Group I—Predominantly Outdoor Sources
Sulfur oxides (gases, particles)
Ozone
Pollens
Lead, manganese
Calcium, chlorine; silicon, cadmium
Organic substances
Fuel combustion, nonferrouis smelters
Photochemical reactions
Trees, grass, weeds, plants
Automobiles
Soil suspension, industrial emission
Solvents, nature, fuel vaporization
Group II—Both Indoor and Outdoor Sources
Nitric oxide, nitrogen dioxide
Carbon monoxide
Carbon dioxide
Particles
Water vapor
Organic substances
Spores
Fuel burning
Fuel burning
Combustion, metabolic activity
Resuspension, vapor condensation, and
combustion products
Biological activity, combustion, and evaporation
Volatilization, combustion, paint, metabolic
action, pesticides, insecticides, fungicides
Fungi, molds
Group III—Predominantly Indoor Sources
Radon
Formaldehyde
Asbestos, mineral/synthetic fibers
Organic substances
Ammonia
Polycyclic hydrocarbons, arsenic, nicotine, acrolein
Mercury
Aerosols
Viable organisms
Allergens
Building construction materials (e.g., concrete,
stone), water, soil
Particleboard, insulation, gas stoves, tobacco
smoke, furnishings
Fire-retardant, insulation (acoustic, thermal,
electrical)
Adhesives, solvents, cooking, cosmetics
Metabolic activity, cleaning products
Tobacco smoke
Fungicides in paints, thermometer breakage,
spills (labs and dental)
Consumer products
Infections
House dust, animal dander
SOURCE: National Academy of Sciences, 1981,
76
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2.2. CHARACTERIZATION OF EMISSIONS
The emission rate of any emission source is a crucial factor for determining the extent
of human exposure. Emission rates must be determined for the various indoor air
contaminants before modeling can be used to predict contaminant concentrations. However,
emission rates for only a relatively small number of identified indoor emission sources have
been determined (Leaderer, 1987). Indoor contaminant concentrations tend to be both
building- and occupant-specific (Sexton and Hayward, 1987). Contaminant concentrations
from indoor sources can vary from home to home because of variables in building
construction, air exchange rates, and occupant activities and use patterns. Because of the
complexities involved in determining the actual emission rates for each indoor contaminant
from each identified source in each indoor setting, modelers must rely on measurements from
controlled laboratory studies. Some of these studies have measured emissions from gas
ranges (Himmel and DeWerth, 1974; Yamanaka et al., 1979; Traynor et al., 1982;
Moschandreas et al., 1985) and from unvented gas or kerosene-fired heaters (Girman et al.,
1982; Porter, 1984). Emission rates determined in a controlled laboratory setting can only be
assumed to be equivalent to actual emission rates in a home. Field studies conducted to
measure actual emission rates are needed to corroborate laboratory results.
2.3. DETERMINATION OF VENTILATION RATES
Ventilation rates, or air exchange rates, are determined by the amount of mechanical
or natural ventilation, the building tightness, the indoor/outdoor temperature differences, and
the wind speed and direction (Sexton and Hayward, 1987). Specific air exchange rates are
usually unknown, but can be estimated using tracer gas decay methods (Borrazzo et al.,
1987). In turn, the ventilation pattern, the size and layout of rooms, and temperature
stratification affect the mixing conditions of inside air (Sexton and Hayward, 1987).
2.4. RELATIVE IMPACT OF INDOOR AND OUTDOOR EMISSIONS ON HUMAN
EXPOSURE
Airborne contaminants from outdoor sources may penetrate the indoors and adversely
affect the quality of indoor air. the combined effects of outdoor and indoor sources determine
air contaminant exposures inside buildings. The relationship of indoor and outdoor emissions
to contaminant concentrations in indoor air is generally described in terms of indoor/outdoor
77
-------
(I/O) ratios. Use of I/O ratios indicates the .relationship of indoor and outdoor emissions on
the quality of indoor air when concentrations are averaged over a long period of time. If the
I/O ratio is greater than 1, the excess is assumed to be due to indoor emissions, if the I/O
ratio is less than 1, it is assumed that the penetration of outdoor-generated contaminants is
primarily responsible for the measured indoor levels (Sexton and Hayward, 1987). Yocum
(1982) provides a discussion of I/O relationships for several airborne contaminants. A
summary of typical I/O ratios is provided in Table 11.
78
-------
Table 11. SUMMARY OF I/O RATIOS OF SELECTED AIRBORNE CONTAMINANTS
Contaminant
Usual Typical
range I/O ratio
Comments
Carbon dioxide
Carbon monoxide
Hydrocarbons
Lead
Nitrates
Nitrogen dioxide
Ozone
Particulates respirable
Particulates (total)
Sulfates
Sulfur dioxide
1.0
1.0
1.5
0.3
0.6
0.5
0.1
0.4
0.1
0.5
0.1
-4.0
-2.0
-2.0
-1.0
- 1.0
-2.0
- 1.0
-5.0
- 1.2
-1.0
-1.0
3
1
1.5
0.7
0.8
0.5
0.2
<1
—
0.7
0.5
I/O >1 0 without ventilation
I/O >2 during unvented combustion
Numerous indoor sources
No significant indoor sources
I/O >7 near gas stoves
I/O >2 near unvented combustion
sources
Smoking can raise I/O to >5
Little I/O relationship
SOURCE: Yocum, 1982; Dudney and Hawthorne, 1985; Gammage and Kaye, 1985.
79
-------
3. MODELING INDOOR AIR
Atmospheric dispersion models and other model types (i.e., the proportional rollback
model and receptor models), designed to model contaminant concentrations in ambient air,
may be used as modeling tools for indoor air. Sexton and Hayward (1987) provide a detailed
analysis of the advantages and disadvantages of each model type and possible applications
of these techniques to indoor modeling practices. However, the use of models to determine
indoor contaminant concentrations is a relatively new research topic (Sexton and Hayward,
1987) and modeling methodologies are not well established.
One potential drawback to the use of models to predict indoor air concentrations of
contaminants is the lack of quantitative data for site parameters. Quantitative data are
needed for source strengths of the contaminant, ventilation rates, and-penetration factors of
contaminants from outdoor sources. Little actual site-specific data exist for these parameters.
The modeler must, therefore, rely on measurements conducted in testing chambers that may
not be equivalent to actual values.
Specifically, five major parameters of indoor contaminant concentrations must be
included in any indoor air quality model: (1) outdo.or pollutant concentration; (2) ventilation
rate; (3) indoor source strength(s) (i.e., types of sources and use patterns and resulting
emission rate); (4) inside mixing conditions; and (5) contaminant decay rate (i.e., chemical
transformations, physical deposition). An indoor air quality model should be capable of
incorporating these five major parameters of indoor contaminant levels into a mass-balance
equation. The equation is expressed
= R(Cnut-Cin)-KCi
'in
(111-1)
where
'in
!out ~
volume of the indoor space
indoor contaminant concentration
ventilation rate
outdoor contaminant concentration
contaminant decay rate
source emission rate
80
-------
Use of this equation for modeling employs the following assumptions: the contaminants are
well-mixed; they do not undergo significant chemical reactions; they are not lost from the
system; all sources are known and their emission rates are quantified; and quantification of
outdoor contaminant concentration is applicable to the indoor modeling site.
Because the use of models to predict indoor air concentrations of contaminants is
relatively new, no specific indoor modeling selection criteria can be provided. It is still
questionable if currently used atmospheric dispersion models, which have been validated for
use in outdoor environments, can be effectively used to predict indoor air concentrations.
Risk analysts should seek the assistance of expert modelers and indoor air specialists when
conducting exposure assessments involving contaminant sources in an indoor environment.
81
-------
REFERENCES FOR PART III
Borrazzo, J. E., J. F. Osborn, R. C. Fortmann, R. L. Keefer, and C. I. Davidson. 1987.
Modeling and monitoring of CO, NO and NO2 in a modern townhouse. Atmos.
Environ. 21 (2):299-311.
Committee on Aldehydes. 1981. Formaldehyde and Other Aldehydes.
Washington, DC: National Academy Press.
Committee on Indoor Pollutants. 1981. Indoor Pollutants. Washington, DC: National
Academy Press.
Dudney, C. S. and A. R. Hawthorne. 1985. Analysis of Indoor Air Quality Data from East
Tennessee Field Studies. Oak Ridge National Laboratory, Oakridge, TN. ORNL/TM-
9588.
Gammage, R. B. and S. V. Kaye, eds. 1985. Indoor Air and Human Health. Chelsea, Ml:
Lewis Publishers.
Gammage, R. B., D. A. White, and K. C. Gupta. 1984. Residential measurements of high
volatility organics and their sources. In Indoor Air, Vol. 4, pp. 157-162. Swedish
Council for Building Research, Stockholm, Sweden.
Girman, J. R., M. G. Apte, G. W. Traynor, J. R. Allen, and C. D. Hollowell. 1982. Pollutant
emission rates from indoor combustion and sidestream cigarette smoke. Environ. Int.
8:213-221.
Himmel, R. L. and D. W. DeWerth. 1974. Evaluation of the pollutant emissions from gas-fired
ranges. American Gas Association Laboratories, Report No. 1492.
Leaderer, B. P. 1982. Air pollutant emissions from kerosene space heaters. Science
218:1113-1115.
Leaderer, B. P. 1987. Forward. Atmos. Environ. 21 (2):279-280.
Leaderer, B. P., R. T. Zagraniski, M. Berwick, and J. A. J. Stolwijk. 1986. Assessment of
exposure to indoor air contaminants from combustion sources: Methodology and
application. Am. J. Epidemiol. 124:275-289.
Lebret, E., H. J. Van de Wiel, H. P. Bos, D. Noij, and J. S. M. Boleij. 1984. Volatile
hydrocarbons in Dutch homes. In Indoor Air, Vol. 4, pp. 169-174. Swedish Council for
Building Research, Stockholm, Sweden.
M0lhave, L. and J. Moller. 1979. The atmospheric environment in modern Danish dwellings:
Measurements in 39 flats. In Indoor Climate, pp. 171-186. Danish Building Research
Institute, Copenhagen, Denmark.
82
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Monteith, D. K., T. H. Stock, and W. E. Seifert, Jr. 1984. Sources and characterization of
organic air contaminants inside manufactured housing. In Indoor Air, Vol. 4, pp. 285-
290. Swedish Council for Building Research, Stockholm, Sweden.
Moschandreas, D. J., S. M. Relwani, H. J. O'Neill, J. T. Cole, R. H. Elkins, and R. A. Macriss.
1985. Characterization of emission rates from indoor combustion sources. Gas
Research Institute, Report GRl-85/0075.
National Academy of Sciences. 1981. Indoor Pollutants. Washington, DC: National Academy
Press.
Porter, W. K. 1984. Pollutant emissions from kerosene heaters and unvented gas space
heaters. In Indoor Air, Vol. 4, pp. 265-270. Swedish Council for Building Research,
Stockholm, Sweden.
Repace, J. L and A. H. Lowry. 1980. Indoor air pollution, tobacco smoke, and public health.
Science 208:464-472.
Seifert, B. and H. J. Abraham. 1982. Indoor air concentrations of benzene and some other
aromatic hydrocarbons. Ecotoxicol. Environ. Safety 6:190-192.
Sexton, K. 1986. Indoor air quality: An overview of policy and regulatory issues. Science
Technol. Human Values 11:53-67.
Sexton, K. and S. B. Hayward. 1987. Source apportionment of indoor air pollution. Atmos.
Environ. 21 (2):407-418.
Spengler, J. D. and K. Sexton. 1983. Indoor air pollution: A public health perspective.
Science 221:8-17.
Spengler, J. D., C. Duffy, R. Letz, T. W. Tibbitts, and B. G. Ferris, Jr. 1983. Nitrogen dioxide
inside and outside 137 homes and implications for ambient air quality standards and
health effects research. Environ. Sci. Technol. 17:164-168.
Spengler, J. D., R. D. Treitman, T. D. Tosteson, D. T. Mage, and M. L. Soczek. 1985.
Personal exposures to respirable particulates and implications for air pollution
epidemiology. Environ. Sci. Technol. 19:700-706.
Traynor, G. W., M. G. Apte, J. F. Dillworth, C. D. Hollowell, and E. M. Sterling. 1982. The
effects of ventilation on residential air pollution from a gas-fired range. Atmos. Environ.
16:2979-2987.
Wallace, L., E. E. Pellizzari, T. D. Hartwell, C. M. Sparacino, L. S. Sheldon, and H. Zelon.
1985. Personal exposures, indoor-outdoor relationships, and breath levels of toxic air
pollutants measured for 355 persons in New Jersey. Atmos. Environ. 19:1651-1661.
Yamanaka, S., H. Hirose, and S. Takada. 1979. Nitrogen oxides emissions from domestic
kerosene-fired and gas-fired appliances. Atmos. Environ. 13:407-412.
83
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Yocum, J. E. 1982. Indoor-outdoor air quality relationships. A critical review. J. Air
Pollut. Control Assoc. 32:500-520.
84
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APPENDIX
ADDITIONAL INFORMATION ON AIR MODEL SELECTION
This appendix, consisting of five tables and one figure from other documents, provides
alternative presentations of air model selection criteria. Readers may find these tables useful as
both additional information and as a supplement or second opinion to the decision-making
process described in the main text of the document. The tables are basically self-explanatory and
are briefly described below:
Table A-1. Capabilities of Selected Models-
technical features of 11 common air models.
-This chart compares the
Figure A-1. Decision Tree for Selection of Air Transport Models—A flow
diagram which leads to a quick initial selection of models on the basis of
averaging time, urban/rural setting, and point/area source.
Table A-2. Preferred Models for Selected Application in Simple
Terrain—Recommends models on the basis of averaging time, source
configuration, and land use.
Table A-3. Resource Requirements and Information Sources:
Atmospheric Fate Models—Summarizes capabilities and assumptions,
resource requirements in terms of computing equipment and input data, and
references for 11 atmospheric fate models.
Table A-4. Features of Atmospheric Models—Compares technical
features/capabilities and output forms of nine models.
Table A-5. Data Requirements for Atmospheric Models—Compares various
data requirements for the nine models in Table A-4.
85
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Single Source
Multiple Source
Complicated Sources"
Buoyant Industrial Line Sources
Land Use
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Rural/Urban
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Model
CRSTER
RAM
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RAM
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BLP
CRSTER
RAM
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COM 2.0 or RAM0
ISCLT
BLP
•Several of these models contain options that allow them to be
interchanged. For example, ISCST can be substituted for CRSTER, and
equivalent, if not identical, concentration estimates can be obtained.
Similarly, for a point source application, MPTER with an urban option
can be substituted for RAM. Where a substitution is convenient to
the user and equivalent estimates are assured, it can be made. The
models as listed here reflect the applications for which they were
originally intended.
bComplicated sources are sources with special problems such as; aero-
dynamic downwash, particle deposition, volume and area sources, etc.
clf only a few sources in an urban area are to be modeled.
SOURCE: Versar. 1988.
88
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REFERENCES FOR APPENDIX
Bonazountas M., J. Fiksel, et al. 1982. Environmental Mathematical Pollutant Fate Modeling
Handbook/Catalogue. Draft. Prepared for the Office of Policy and Resource
Management, U.S. Environmental Protection Agency, Washington, DC, Contract No.
68-01-5146. As cited in USEPA (1988).
General Software Corporation (GSC). 1982. Graphical Exposure Modeling System (GEMS)
User's Guide. Prepared for the Office of Pesticides and Toxic Substances, U.S.
Environmental Protection Agency, Washington, DC, Contract No. 68-01-6618. As cited
in USEPA (1988).
Texas Air Control Board. 1980. User's Guide to the Texas Climatological Model. Texas Air
Control Board, Austin, TX. As cited in USEPA (1988).
USEPA. 1976a. Design and application of the Texas episodic model. Proceedings of the
Conference on Environmental Modeling and Simulation. U.S. Environmental Protection
Agency, Washington, DC. EPA-600/9-76-016. As cited in USEPA (1988).
USEPA. 1976b. User's Guide for the Climatological Dispersion Model. U.S. Environmental
Protection Agency, Research Triangle Park, NC. EPA-R4-73-024. As cited in USEPA
(1988).
USEPA. 1977a. VALLEY Model User's Guide. U.S. Environmental Protection Agency,
Washington, DC. EPA-450/2-77-018. As cited in USEPA (1988).
USEPA. 1977b. User's Manual for Single Source (CRSTER) Model. Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency, Research Triangle
Park, NC. EPA-4502-77-013. As cited in USEPA (1988).
USEPA. 1978. User's Guide for RAM. Vols. a and b, U.S. Environmental Protection Agency,
Research Triangle Park, NC. EPA-600/8-78-016. As cited in USEPA (1988).
USEPA. 1979a. Industrial Source Complex (ISC) Dispersion Model User's Guide. Vols. 1
and 2. Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC. EPA 450/4-79-030. As cited in USEPA (1988).
USEPA. 1979b. Environmental Modeling Catalogue. Prepared for the Management
Information and Data Systems Division, U.S. Environmental Protection Agency,
Washington, DC, Contract No. 68-01-4723. As cited in USEPA (1988).
USEPA. 1980. User's Guide for MPTER. U.S. Environmental Protection Agency, Research
Triangle Park, NC. EPA-600/8-80-016. NTIS PB80-176361. As cited in USEPA
(1988).
95
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USEPA. 1982. Chemical Fate Test Guidelines. Test Guideline CG-1700. U.S.
Environmental Protection Agency, Washington, DC. EPA 560/6-82-003. NTIS PB82-
233008. As quoted in Leifer et al. (1983).
USEPA. 1986. Addendum to the User's Guide for MPTER. U.S. Environmental Protection
Agency, Research Triangle Park, NC. EPA-600/8-86/021. NTIS PB86-217163. As
cited in USEPA (1988).
USEPA. 1988. Technical Support Document. Incineration of Sewage Sludge. Office of
Water Regulations and Standards, U.S. Environmental Protection Agency,
Washington, DC. NTIS PB89-136592.
Versar, Inc. 1988. Air Dispersion Modeling as Applied to Hazardous Waste Incinerator
Evaluations: An Introduction for the Permit Writer. Prepared for the Office of Solid
Waste, U.S. Environmental Protection Agency, Washington, DC.
96
<> O.S, GOVERNMENT PRINTING OFFICE: 1993—750-002/60,180
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