81. U.S. Environmental Protection Agency, 1981. Guideline for Use of
Fluid Modeling to Determine Good Engineering Practice (GEP) Stack
Height. Publication No. EPA-450/4-81-003. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. (NTIS No. PB 82-
145327).
82. Lawson, Jr., R.E. and W.H. Snyder, 1983. Determination of Good
Engineering Practice Stack Height: A Demonstration Study for a Power
Plant. Publication No. EPA-600/3-83-024. Office of Research and
Development, Research Triangle Park, NC. (NTIS No. PB 83-207407).
83. U.S. Environmental Protection Agency, 1985. Guideline for
Determination of Good Engineering Practice Stack Height (Technical
Support Document for the Stack Height Regulations), Revised.
Publication No. EPA-450/4-80-023R. Office of Air Quality Planning
and Standards, Research Triangle Park, NC. (NTIS No. PB 85-225241).
84. Snyder, W.H. and R.E. Lawson, Jr., 1985. Fluid Modeling
Demonstration of Good Engineering-Practice Stack Height in Complex
Terrain. Publication No. EPA-600/3-85-022. Office of Research and
Development, Research Triangle Park, NC. (NTIS No. PB 85-203107).
85. Briggs, G.A., 1975. Plume Rise Predictions. Chapter 3 in
Lectures on Air Pollution and Environmental Impact

Analyses. American Meteorological Society, Boston, MA; pp. 59-111.
86. Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Plume Rise.
Chapter 2 in Handbook on Atmospheric Diffusion. Technical
Information Center, U.S. Department of Energy, Washington, DC; pp.
11-24. DOE/TIC-11223 (DE 82002045).
87. Weil, J.C., L.A. Corio and R.P. Brower, 1997. A PDF dispersion
model for buoyant plumes in the convective boundary layer. Journal
of Applied Meteorology, 36: 982-1003.
88. L.L. Schulman, D.G. Strimaitis and J.S. Scire, 2002. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air & Waste Management Association, 50: 378-390.
89. U.S. Environmental Protection Agency, 1995. Compilation of Air
Pollutant Emission Factors, Volume I: Stationary Point and Area
Sources (Fifth Edition, AP-42: GPO Stock No.<PHONE_NUMBER>0-1), and
Supplements A-D. Volume I can be downloaded from EPA's Web site at
https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emission-factors.
90. U.S. Environmental Protection Agency, 2014. Draft Emissions
Inventory Guidance for Implementation of Ozone and Particulate
Matter National Ambient Air Quality Standards (NAAQS) and Regional
Haze Regulations. Office of Air Quality Planning and Standards,
Research Triangle Park, NC. https://www.epa.gov/sites/production/files/2014-10/documents/2014revisedeiguidance_0.pdf.
91. U.S. Environmental Protection Agency, 1987. Ambient Air
Monitoring Guidelines for Prevention of Significant Deterioration
(PSD). Publication No. EPA-450/4-87-007. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. (NTIS No. PB 90-
168030).
92. U.S. Environmental Protection Agency, 2011. Additional
Clarification Regarding Application of Appendix W Modeling Guidance
for the 1-hour NO2 National Ambient Air Quality Standard.
Office of Air Quality Planning and Standards, Research Triangle
Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/Additional_Clarifications_AppendixW_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf.
93. U.S. Environmental Protection Agency, 2013. Use of ASOS
meteorological data in AERMOD dispersion modeling. Memorandum dated
March 8, 2013, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.pdf.
94. U.S. Environmental Protection Agency, 2016. User's Guide for the
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-16-010. Office of Air Quality Planning and Standards, Research
Triangle Park, NC.
95. U.S Environmental Protection Agency. 2016. AERMINUTE User's
Guide. Publication No. EPA-454/B-15-006. Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
96. U.S. Environmental Protection Agency, 1993. PCRAMMET User's
Guide. Publication No. EPA-454/R-96-001. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. (NTIS No. PB 97-
147912).
97. U.S. Environmental Protection Agency, 1996. Meteorological
Processor for Regulatory Models (MPRM). Publication No. EPA-454/R-
96-002. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. (NTIS No. PB 96-180518).
98. Paine, R.J., 1987. User's Guide to the CTDM Meteorological
Preprocessor Program. Publication No. EPA-600/8-88-004. Office of
Research and Development, Research Triangle Park, NC. (NTIS No. PB-
88-162102).
99. Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis,
M.T. Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989.
User's Guide to the Complex Terrain Dispersion Model Plus Algorithms
for Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and
User Instructions. Publication No. EPA-600/8-89-041. U.S.
Environmental Protection Agency, Research Triangle Park, NC. (NTIS
No. PB 89-181424).
100. U.S. Environmental Protection Agency, 2008. AERSURFACE User's
Guide. Publication No. EPA-454/B-08-001. Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
101. Brode, R., K. Wesson, J. Thurman, and C. Tillerson, 2008.
AERMOD Sensitivity to the Choice of Surface Characteristics. Paper
#811 presented at the 101st Air and Waste Management Association
Annual Conference and Exhibition, June 24-27, 2008, Portland, OR.
102. Environ, 2015. The Mesoscale Model Interface Program (MMIF)
Version 3.2 User's Manual.
103. U.S. Environmental Protection Agency, 2016. Guidance on the Use
of the Mesoscale Model Interface Program (MMIF) for AERMOD
Applications. Publication No. EPA-454/B-16-003. Office of Air
Quality Planning and Standards, Research Triangle Park, NC.
104. Solar and Meteorological Surface Observation Network, 1961-
1990; 3-volume CD-ROM. Version 1.0, September 1993. Produced jointly
by National Climatic Data Center and National Renewable Energy
Laboratory. Can be ordered from NOAA National Data Center's Web site
at http://www.ncdc.noaa.gov.
105. Hourly United States Weather Observations, 1990-1995 (CD-ROM).
October 1997. Produced jointly by National Climatic Data Center and
Environmental Protection Agency. Can be ordered from NOAA National
Data Center's Web site at http://www.ncdc.noaa.gov.
106. Radiosonde Data of North America, 1946-1996; 4-volume CD-ROM.
August 1996. Produced jointly by Forecast Systems laboratory and
National Climatic Data Center. Can be ordered from NOAA National
Data Center's Web site at http://www.ncdc.noaa.gov.
107. U.S. Environmental Protection Agency, 2000. Meteorological
Monitoring Guidance for Regulatory Modeling Applications.
Publication No. EPA-454/R-99-005. Office of Air Quality Planning and
Standards, Research Triangle Park, NC. (NTIS No. PB 2001-103606).
108. ASTM D5527: Standard Practice for Measuring Surface Winds and
Temperature by Acoustic Means. (2011).
109. ASTM D5741: Standard Practice for Characterizing Surface Wind
Using Wind Vane and Rotating Anemometer. (2011).
110. U.S. Environmental Protection Agency, 1995. Quality Assurance
for Air Pollution Measurement Systems, Volume IV--Meteorological
Measurements. Publication No. EPA600/R-94/038d. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. Note:
for copies of this handbook, you may make inquiry to ORD
Publications, 26 West Martin Luther King Dr., Cincinnati, OH 45268.
111. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability Class
Determination: A Comparison for One Site. Proceedings, Sixth
Symposium on Turbulence and Diffusion. American Meteorological
Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7).
112. U.S. Environmental Protection Agency, 1993. An Evaluation of a
Solar Radiation/Delta-T (SRDT) Method for Estimating Pasquill-
Gifford (P-G) Stability Categories. Publication No. EPA-454/R-93-
055. Office of Air Quality Planning and Standards, Research Triangle
Park, NC. (NTIS No. PB 94-113958).
113. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8:
Estimation of Pasquill Stability Categories. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. (Docket No. A-80-46, II-B-10).
114. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric
Stability Class from Horizontal Wind Fluctuation. Presented at 72nd
Annual Meeting of Air Pollution Control Association, Cincinnati, OH;
June 24-29, 1979. (Docket No. A-80-46, II-P-9).
115. Smedman-Hogstrom, A. and V. Hogstrom, 1978. A Practical Method
for Determining Wind Frequency Distributions for the Lowest 200 m
from Routine Meteorological Data. Journal of Applied Meteorology,
17(7): 942-954.
116. Smith, T.B. and S.M. Howard, 1972. Methodology for Treating
Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena,
CA. (Docket No. A-80-46, II-P-8).
117. U.S. Environmental Protection Agency, 2016. Evaluation of
Prognostic Meteorological Data in AERMOD Applications. Publication
No. EPA-454/R-16-004. Office of Air Quality Planning and Standards,
Research Triangle Park, NC.

118. U.S. Environmental Protection Agency, 1984. Calms Processor
(CALMPRO) User's Guide. Publication No. EPA-901/9-84-001. Office of
Air Quality Planning and Standards, Region I, Boston, MA. (NTIS No.
PB 84-229467).

Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality
Models

Table of Contents

A.0 Introduction and Availability
A.1 AERMOD (AMS/EPA Regulatory Model)
A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)
A.3 OCD (Offshore and Coastal Dispersion Model)

A.0 Introduction and Availability

    (1) This appendix summarizes key features of refined air quality
models preferred for specific regulatory applications. For each
model, information is provided on availability, approximate cost
(where applicable), regulatory use, data input, output format and
options, simulation of atmospheric physics, and accuracy. These
models may be used without a formal demonstration of applicability
provided they satisfy the recommendations for regulatory use; not
all options in the models are necessarily recommended for regulatory
use.
    (2) Many of these models have been subjected to a performance
evaluation using comparisons with observed air quality data. Where
possible, several of the models contained herein have been subjected
to evaluation exercises, including: (1) Statistical performance
tests recommended by the American Meteorological Society, and (2)
peer scientific reviews. The models in this appendix have been
selected on the basis of the results of the model evaluations,
experience with previous use, familiarity of the model to various
air quality programs, and the costs and resource requirements for
use.
    (3) Codes and documentation for all models listed in this
appendix are available from the EPA's Support Center for Regulatory
Air Models (SCRAM) Web site at https://www.epa.gov/scram. Codes and
documentation may also available from the National Technical
Information Service (NTIS), http://www.ntis.gov, and, when
available, are referenced with the appropriate NTIS accession
number.

A.1 AERMOD (AMS/EPA Regulatory Model)

References

U.S. Environmental Protection Agency, 2016. AERMOD Model
Formulation. Publication No. EPA-454/B-16-014. Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
Cimorelli, A., et al., 2005. AERMOD: A Dispersion Model for
Industrial Source Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of Applied Meteorology,
44(5): 682-693.
Perry, S. et al., 2005. AERMOD: A Dispersion Model for Industrial
Source Applications. Part II: Model Performance against 17 Field
Study Databases. Journal of Applied Meteorology, 44(5): 694-708.
U.S. Environmental Protection Agency, 2016. User's Guide for the
AMS/EPA Regulatory Model (AERMOD). Publication No. EPA-454/B-16-011.
Office of Air Quality Planning and Standards, Research Triangle
Park, NC.
U.S. Environmental Protection Agency, 2016. User's Guide for the
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-16-010. Office of Air Quality Planning and Standards, Research
Triangle Park, NC.
U.S. Environmental Protection Agency, 2016. User's Guide for the
AERMOD Terrain Preprocessor (AERMAP). Publication No. EPA-454/B-16-
012. U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
Schulman, L. L., D.G. Strimaitis and J.S. Scire, 2000. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air and Waste Management Association, 50: 378-390.
Schulman, L. L., and Joseph S. Scire, 1980. Buoyant Line and Point
Source (BLP) Dispersion Model User's Guide. Document P-7304B.
Environmental Research and Technology, Inc., Concord, MA. (NTIS No.
PB 81-164642).

Availability

    The model codes and associated documentation are available on
EPA's SCRAM Web site (paragraph A.0(3)).

Abstract

    AERMOD is a steady-state plume dispersion model for assessment
of pollutant concentrations from a variety of sources. AERMOD
simulates transport and dispersion from multiple point, area, or
volume sources based on an up-to-date characterization of the
atmospheric boundary layer. Sources may be located in rural or urban
areas, and receptors may be located in simple or complex terrain.
AERMOD accounts for building wake effects (i.e., plume downwash)
based on the PRIME building downwash algorithms. The model employs
hourly sequential preprocessed meteorological data to estimate
concentrations for averaging times from 1-hour to 1-year (also
multiple years). AERMOD can be used to estimate the concentrations
of nonreactive pollutants from highway traffic. AERMOD also handles
unique modeling problems associated with aluminum reduction plants,
and other industrial sources where plume rise and downwash effects
from stationary buoyant line sources are important. AERMOD is
designed to operate in concert with two pre-processor codes: AERMET
processes meteorological data for input to AERMOD, and AERMAP
processes terrain elevation data and generates receptor and hill
height information for input to AERMOD.

a. Regulatory Use

    (1) AERMOD is appropriate for the following applications:
     Point, volume, and area sources;
     Buoyant, elevated line sources (e.g., aluminum
reduction plants);
     Mobile sources;
     Surface, near-surface, and elevated releases;
     Rural or urban areas;
     Simple and complex terrain;
     Transport distances over which steady- state
assumptions are appropriate, up to 50km;
     1-hour to annual averaging times; and
     Continuous toxic air emissions.
    (2) For regulatory applications of AERMOD, the regulatory
default option should be set, i.e., the parameter DFAULT should be
employed in the MODELOPT record in the COntrol Pathway. The DFAULT
option requires the use of meteorological data processed with the
regulatory options in AERMET, the use of terrain elevation data
processed through the AERMAP terrain processor, stack-tip downwash,
sequential date checking, and does not permit the use of the model
in the SCREEN mode. In the regulatory default mode, pollutant half-
life or decay options are not employed, except in the case of an
urban source of sulfur dioxide where a 4-hour half-life is applied.
Terrain elevation data from the U.S. Geological Survey (USGS) 7.5-
Minute Digital Elevation Model (DEM), or equivalent (approx. 30-
meter resolution), (processed through AERMAP) should be used in all
applications. Starting in 2011, data from the National Elevation
Dataset (NED, https://nationalmap.gov/elevation.html) can also be
used in AERMOD, which includes a range of resolutions, from 1-m to 2
arc seconds and such high resolution would always be preferred. In
some cases, exceptions from the terrain data requirement may be made
in consultation with the appropriate reviewing authority (paragraph
3.0(b)).

b. Input Requirements

    (1) Source data: Required inputs include source type, location,
emission rate, stack height, stack inside diameter, stack gas exit
velocity, stack gas exit temperature, area and volume source
dimensions, and source base elevation. For point sources subject to
the influence of building downwash, direction-specific building
dimensions (processed through the BPIPPRM building processor) should
be input. Variable emission rates are optional. Buoyant line sources
require coordinates of the end points of the line, release height,
emission rate, average line source width, average building width,
average spacing between buildings, and average line source buoyancy
parameter. For mobile sources, traffic volume; emission factor,
source height, and mixing zone width are needed to determine
appropriate model inputs.
    (2) Meteorological data: The AERMET meteorological preprocessor
requires input of surface characteristics, including surface
roughness (zo), Bowen ratio, and albedo, as well as, hourly
observations of wind speed between 7zo and 100 m (reference wind
speed measurement from which a vertical profile can be developed),
wind direction, cloud cover, and temperature between zo and 100 m
(reference temperature measurement from which a vertical profile can
be developed). Meteorological data can be in the

form of observed data or prognostic modeled data as discussed in
paragraph 8.4.1(d). Surface characteristics may be varied by wind
sector and by season or month. When using observed meteorological
data, a morning sounding (in National Weather Service format) from a
representative upper air station is required. Latitude, longitude,
and time zone of the surface, site-specific (if applicable) and
upper air meteorological stations are required. The wind speed
starting threshold is also required in AERMET for applications
involving site-specific data. When using prognostic data, modeled
profiles of temperature and winds are input to AERMET. These can be
hourly or a time that represents a morning sounding. Additionally,
measured profiles of wind, temperature, vertical and lateral
turbulence may be required in certain applications (e.g., in complex
terrain) to adequately represent the meteorology affecting plume
transport and dispersion. Optionally, measurements of solar and/or
net radiation may be input to AERMET. Two files are produced by the
AERMET meteorological preprocessor for input to the AERMOD
dispersion model. When using observed data, the surface file
contains observed and calculated surface variables, one record per
hour. For applications with multi-level site-specific meteorological
data, the profile contains the observations made at each level of
the meteorological tower (or remote sensor). When using prognostic
data, the surface file contains surface variables calculated by the
prognostic model and AERMET. The profile file contains the
observations made at each level of a meteorological tower (or remote
sensor), the one-level observations taken from other representative
data (e.g., National Weather Service surface observations), one
record per level per hour, or in the case of prognostic data, the
prognostic modeled values of temperature and winds at user-specified
levels.
    (i) Data used as input to AERMET should possess an adequate
degree of representativeness to ensure that the wind, temperature
and turbulence profiles derived by AERMOD are both laterally and
vertically representative of the source impact area. The adequacy of
input data should be judged independently for each variable. The
values for surface roughness, Bowen ratio, and albedo should reflect
the surface characteristics in the vicinity of the meteorological
tower or representative grid cell when using prognostic data, and
should be adequately representative of the modeling domain. Finally,
the primary atmospheric input variables, including wind speed and
direction, ambient temperature, cloud cover, and a morning upper air
sounding, should also be adequately representative of the source
area when using observed data.
    (ii) For applications involving the use of site-specific
meteorological data that includes turbulences parameters (i.e.,
sigma-theta and/or sigma-w), the application of the ADJ_U* option in
AERMET would require approval as an alternative model application
under section 3.2.
    (iii) For recommendations regarding the length of meteorological
record needed to perform a regulatory analysis with AERMOD, see
section 8.4.2.
    (3) Receptor data: Receptor coordinates, elevations, height
above ground, and hill height scales are produced by the AERMAP
terrain preprocessor for input to AERMOD. Discrete receptors and/or
multiple receptor grids, Cartesian and/or polar, may be employed in
AERMOD. AERMAP requires input of DEM or NED terrain data produced by
the USGS, or other equivalent data. AERMAP can be used optionally to
estimate source elevations.

c. Output

    Printed output options include input information, high
concentration summary tables by receptor for user-specified
averaging periods, maximum concentration summary tables, and
concurrent values summarized by receptor for each day processed.
Optional output files can be generated for: A listing of occurrences
of exceedances of user-specified threshold value; a listing of
concurrent (raw) results at each receptor for each hour modeled,
suitable for post-processing; a listing of design values that can be
imported into graphics software for plotting contours; a listing of
results suitable for NAAQS analyses including NAAQS exceedances and
culpability analyses; an unformatted listing of raw results above a
threshold value with a special structure for use with the TOXX model
component of TOXST; a listing of concentrations by rank (e.g., for
use in quantile-quantile plots); and a listing of concentrations,
including arc-maximum normalized concentrations, suitable for model
evaluation studies.

d. Type of Model

    AERMOD is a steady-state plume model, using Gaussian
distributions in the vertical and horizontal for stable conditions,
and in the horizontal for convective conditions. The vertical
concentration distribution for convective conditions results from an
assumed bi-Gaussian probability density function of the vertical
velocity.

e. Pollutant Types

    AERMOD is applicable to primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Chemical
transformation is treated by simple exponential decay.

f. Source-Receptor Relationships

    AERMOD applies user-specified locations for sources and
receptors. Actual separation between each source-receptor pair is
used. Source and receptor elevations are user input or are
determined by AERMAP using USGS DEM or NED terrain data. Receptors
may be located at user-specified heights above ground level.

g. Plume Behavior

    (1) In the convective boundary layer (CBL), the transport and
dispersion of a plume is characterized as the superposition of three
modeled plumes: (1) The direct plume (from the stack); (2) the
indirect plume; and (3) the penetrated plume, where the indirect
plume accounts for the lofting of a buoyant plume near the top of
the boundary layer, and the penetrated plume accounts for the
portion of a plume that, due to its buoyancy, penetrates above the
mixed layer, but can disperse downward and re-enter the mixed layer.
In the CBL, plume rise is superposed on the displacements by random
convective velocities (Weil et al., 1997).
    (2) In the stable boundary layer, plume rise is estimated using
an iterative approach to account for height-dependent lapse rates,
similar to that in the CTDMPLUS model (see A.2 in this appendix).
    (3) Stack-tip downwash and buoyancy induced dispersion effects
are modeled. Building wake effects are simulated for stacks subject
to building downwash using the methods contained in the PRIME
downwash algorithms (Schulman, et al., 2000). For plume rise
affected by the presence of a building, the PRIME downwash algorithm
uses a numerical solution of the mass, energy and momentum
conservation laws (Zhang and Ghoniem, 1993). Streamline deflection
and the position of the stack relative to the building affect plume
trajectory and dispersion. Enhanced dispersion is based on the
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is re-emitted to
the far wake as a volume source.
    (4) For elevated terrain, AERMOD incorporates the concept of the
critical dividing streamline height, in which flow below this height
remains horizontal, and flow above this height tends to rise up and
over terrain (Snyder et al., 1985). Plume concentration estimates
are the weighted sum of these two limiting plume states. However,
consistent with the steady-state assumption of uniform horizontal
wind direction over the modeling domain, straight-line plume
trajectories are assumed, with adjustment in the plume/receptor
geometry used to account for the terrain effects.

h. Horizontal Winds

    Vertical profiles of wind are calculated for each hour based on
measurements and surface-layer similarity (scaling) relationships.
At a given height above ground, for a given hour, winds are assumed
constant over the modeling domain. The effect of the vertical
variation in horizontal wind speed on dispersion is accounted for
through simple averaging over the plume depth.

i. Vertical Wind Speed

    In convective conditions, the effects of random vertical updraft
and downdraft velocities are simulated with a bi-Gaussian
probability density function. In both convective and stable
conditions, the mean vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

    Gaussian horizontal dispersion coefficients are estimated as
continuous functions of the parameterized (or measured) ambient
lateral turbulence and also account for buoyancy-induced and
building wake-induced turbulence. Vertical profiles of lateral
turbulence are developed from measurements and similarity (scaling)
relationships. Effective turbulence values are determined from the
portion of the vertical profile of lateral turbulence between the
plume height and the receptor height. The effective lateral
turbulence is then used to estimate horizontal dispersion.

k. Vertical Dispersion

    In the stable boundary layer, Gaussian vertical dispersion
coefficients are estimated as continuous functions of parameterized
vertical turbulence. In the convective boundary layer, vertical
dispersion is characterized by a bi-Gaussian probability density
function and is also estimated as a continuous function of
parameterized vertical turbulence. Vertical turbulence profiles are
developed from measurements and similarity (scaling) relationships.
These turbulence profiles account for both convective and mechanical
turbulence. Effective turbulence values are determined from the
portion of the vertical profile of vertical turbulence between the
plume height and the receptor height. The effective vertical
turbulence is then used to estimate vertical dispersion.

l. Chemical Transformation

    Chemical transformations are generally not treated by AERMOD.
However, AERMOD does contain an option to treat chemical
transformation using simple exponential decay, although this option
is typically not used in regulatory applications except for sources
of sulfur dioxide in urban areas. Either a decay coefficient or a
half-life is input by the user. Note also that the Plume Volume
Molar Ratio Method and the Ozone Limiting Method (section <IP_ADDRESS>)
for NO2 analyses are available.

m. Physical Removal

    AERMOD can be used to treat dry and wet deposition for both
gases and particles.

n. Evaluation Studies

American Petroleum Institute, 1998. Evaluation of State of the
Science of Air Quality Dispersion Model, Scientific Evaluation,
prepared by Woodward-Clyde Consultants, Lexington, Massachusetts,
for American Petroleum Institute, Washington, DC 20005-4070.
Brode, R.W., 2002. Implementation and Evaluation of PRIME in AERMOD.
Preprints of the 12th Joint Conference on Applications of Air
Pollution Meteorology, May 20-24, 2002; American Meteorological
Society, Boston, MA.
Brode, R.W., 2004. Implementation and Evaluation of Bulk Richardson
Number Scheme in AERMOD. 13th Joint Conference on Applications of
Air Pollution Meteorology, August 23-26, 2004; American
Meteorological Society, Boston, MA.
U.S. Environmental Protection Agency, 2003. AERMOD: Latest Features
and Evaluation Results. Publication No. EPA-454/R-03-003. Office of
Air Quality Planning and Standards, Research Triangle Park, NC.
Heist, D., et al, 2013. Estimating near-road pollutant dispersion: A
model inter-comparison. Transportation Research Part D: Transport
and Environment, 25: pp 93-105.

A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)

References

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T.
Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User's
Guide to the Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and
User Instructions. EPA Publication No. EPA-600/8-89-041. U.S.
Environmental Protection Agency, Research Triangle Park, NC. (NTIS
No. PB 89-181424).
Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near
Complex Topography. Part I: Technical Formulations. Journal of
Applied Meteorology, 31(7): 633-645.

Availability

    The model codes and associated documentation are available on
the EPA's SCRAM Web site (paragraph A.0(3)).

Abstract

    CTDMPLUS is a refined point source Gaussian air quality model
for use in all stability conditions for complex terrain
applications. The model contains, in its entirety, the technology of
CTDM for stable and neutral conditions. However, CTDMPLUS can also
simulate daytime, unstable conditions, and has a number of
additional capabilities for improved user friendliness. Its use of
meteorological data and terrain information is different from other
EPA models; considerable detail for both types of input data is
required and is supplied by preprocessors specifically designed for
CTDMPLUS. CTDMPLUS requires the parameterization of individual hill
shapes using the terrain preprocessor and the association of each
model receptor with a particular hill.

a. Regulatory Use

    CTDMPLUS is appropriate for the following applications:
     Elevated point sources;
     Terrain elevations above stack top;
     Rural or urban areas;
     Transport distances less than 50 kilometers; and
     1-hour to annual averaging times when used with a post-
processor program such as CHAVG.

b. Input Requirements

    (1) Source data: For each source, user supplies source location,
height, stack diameter, stack exit velocity, stack exit temperature,
and emission rate; if variable emissions are appropriate, the user
supplies hourly values for emission rate, stack exit velocity, and
stack exit temperature.
    (2) Meteorological data: For applications of CTDMPLUS, multiple
level (typically three or more) measurements of wind speed and
direction, temperature and turbulence (wind fluctuation statistics)
are required to create the basic meteorological data file
(``PROFILE''). Such measurements should be obtained up to the
representative plume height(s) of interest (i.e., the plume
height(s) under those conditions important to the determination of
the design concentration). The representative plume height(s) of
interest should be determined using an appropriate complex terrain
screening procedure (e.g., CTSCREEN) and should be documented in the
monitoring/modeling protocol. The necessary meteorological
measurements should be obtained from an appropriately sited
meteorological tower augmented by SODAR and/or RASS if the
representative plume height(s) of interest is above the levels
represented by the tower measurements. Meteorological preprocessors
then create a SURFACE data file (hourly values of mixed layer
heights, surface friction velocity, Monin-Obukhov length and surface
roughness length) and a RAWINsonde data file (upper air measurements
of pressure, temperature, wind direction, and wind speed).
    (3) Receptor data: Receptor names (up to 400) and coordinates,
and hill number (each receptor must have a hill number assigned).
    (4) Terrain data: User inputs digitized contour information to
the terrain preprocessor which creates the TERRAIN data file (for up
to 25 hills).

c. Output

    (1) When CTDMPLUS is run, it produces a concentration file, in
either binary or text format (user's choice), and a list file
containing a verification of model inputs, i.e.,
     Input meteorological data from ``SURFACE'' and
``PROFILE,''
     Stack data for each source,
     Terrain information,
     Receptor information, and
     Source-receptor location (line printer map).
    (2) In addition, if the case-study option is selected, the
listing includes:
     Meteorological variables at plume height,
     Geometrical relationships between the source and the
hill, and
     Plume characteristics at each receptor, i.e.,

--Distance in along-flow and cross flow direction
--Effective plume-receptor height difference
--Effective [sigma]y & [sigma]z values, both flat terrain and hill
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT.

    (3) If the user selects the TOPN option, a summary table of the
top four concentrations at each receptor is given. If the ISOR
option is selected, a source contribution table for every hour will
be printed.
    (4) A separate output file of predicted (1-hour only)
concentrations (``CONC'') is written if the user chooses this
option. Three forms of output are possible:
    (i) A binary file of concentrations, one value for each receptor
in the hourly sequence as run;
    (ii) A text file of concentrations, one value for each receptor
in the hourly sequence as run; or
    (iii) A text file as described above, but with a listing of
receptor information (names, positions, hill number) at the
beginning of the file.

    (5) Hourly information provided to these files besides the
concentrations themselves includes the year, month, day, and hour
information as well as the receptor number with the highest
concentration.

d. Type of Model

    CTDMPLUS is a refined steady-state, point source plume model for
use in all stability conditions for complex terrain applications.

e. Pollutant Types

    CTDMPLUS may be used to model non- reactive, primary pollutants.

f. Source-Receptor Relationship

    Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15[deg], so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.

g. Plume Behavior

    (1) As in CTDM, the basic plume rise algorithms are based on
Briggs' (1975) recommendations.
    (2) A central feature of CTDMPLUS for neutral/stable conditions
is its use of a critical dividing-streamline height (Hc)
to separate the flow in the vicinity of a hill into two separate
layers. The plume component in the upper layer has sufficient
kinetic energy to pass over the top of the hill while streamlines in
the lower portion are constrained to flow in a horizontal plane
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these
flows.
    (3) The model calculates on an hourly (or appropriate steady
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly
profiles of wind and temperature measurements are used by CTDMPLUS
to compute plume rise, plume penetration (a formulation is included
to handle penetration into elevated stable layers, based on Briggs
(1984)), convective scaling parameters, the value of Hc,
and the Froude number above Hc.

h. Horizontal Winds

    CTDMPLUS does not simulate calm meteorological conditions. Both
scalar and vector wind speed observations can be read by the model.
If vector wind speed is unavailable, it is calculated from the
scalar wind speed. The assignment of wind speed (either vector or
scalar) at plume height is done by either:
     Interpolating between observations above and below the
plume height, or
     Extrapolating (within the surface layer) from the
nearest measurement height to the plume height.

i. Vertical Wind Speed

    Vertical flow is treated for the plume component above the
critical dividing streamline height (Hc); see ``Plume
Behavior.''

j. Horizontal Dispersion

    Horizontal dispersion for stable/neutral conditions is related
to the turbulence velocity scale for lateral fluctuations, [sigma]v,
for which a minimum value of 0.2 m/s is used. Convective scaling
formulations are used to estimate horizontal dispersion for unstable
conditions.

k. Vertical Dispersion

    Direct estimates of vertical dispersion for stable/neutral
conditions are based on observed vertical turbulence intensity,
e.g., [sigma]w (standard deviation of the vertical velocity
fluctuation). In simulating unstable (convective) conditions,
CTDMPLUS relies on a skewed, bi-Gaussian probability density
function (pdf) description of the vertical velocities to estimate
the vertical distribution of pollutant concentration.

l. Chemical Transformation

    Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

    Physical removal is not treated by CTDMPLUS (complete reflection
at the ground/hill surface is assumed).

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation
of the CTDMPLUS Dispersion Model: Daytime Convective Conditions.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of
CTDMPLUS Model Predictions with the Lovett Power Plant Data Base.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A
Dispersion Model for Sources near Complex Topography. Part II:
Performance Characteristics. Journal of Applied Meteorology, 31(7):
646-660.

A.3 OCD (Offshore and Coastal Dispersion Model)

Reference

DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model, Version 4. Volume I: User's Guide, and
Volume II: Appendices. Sigma Research Corporation, Westford, MA.
(NTIS Nos. PB 93-144384 and PB 93-144392).

Availability

    The model codes and associated documentation are available on
EPA's SCRAM Web site (paragraph A.0(3)).

Abstract

    (1) OCD is a straight-line Gaussian model developed to determine
the impact of offshore emissions from point, area or line sources on
the air quality of coastal regions. 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, mixing height, and relative
humidity.
    (2) 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.

a. Regulatory Use

    OCD has been recommended for use by the Bureau of Ocean Energy
Management for emissions located on the Outer Continental Shelf (50
FR 12248; 28 March 1985). 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 appropriate reviewing authority (paragraph
3.0(b)).

b. Input Requirements

    (1) Source data: Point, area or line source location, pollutant
emission rate, building height, stack height, stack gas temperature,
stack inside diameter, stack gas exit velocity, stack angle from
vertical, elevation of stack base above water surface and gridded
specification of the land/water surfaces. As an option, emission
rate, stack gas exit velocity and temperature can be varied hourly.
    (2) Meteorological data: PCRAMMET is the recommended
meteorological data preprocessor for use in applications of OCD
employing hourly NWS data. MPRM is the recommended meteorological
data preprocessor for applications of OCD employing site-specific
meteorological data.
    (i) Over land: Surface weather data including hourly stability
class, wind direction, wind speed, ambient temperature, and mixing
height are required.
    (ii) Over water: Hourly values for mixing height, relative
humidity, air temperature, and water surface temperature are
required; if wind speed/direction are missing, values over land will
be used (if available); vertical wind direction shear, vertical
temperature gradient, and turbulence intensities are optional.
    (3) Receptor data: Location, height above local ground-level,
ground-level elevation above the water surface.

c. Output

    (1) All input options, specification of sources, receptors and
land/water map including locations of sources and receptors.
    (2) Summary tables of five highest concentrations at each
receptor for each averaging period, and average concentration for
entire run period at each receptor.
    (3) Optional case study printout with hourly plume and receptor
characteristics. Optional table of annual impact assessment from
non-permanent activities.
    (4) Concentration output files can be used by ANALYSIS
postprocessor to produce the highest concentrations for each
receptor, the cumulative frequency distributions for each receptor,
the tabulation of all concentrations exceeding a given threshold,
and the manipulation of hourly concentration files.

d. Type of Model

    OCD is a Gaussian plume model constructed on the framework of
the MPTER model.

e. Pollutant Types

    OCD may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

    (1) Up to 250 point sources, 5 area sources, or 1 line source
and 180 receptors may be used.
    (2) Receptors and sources are allowed at any location.
    (3) The coastal configuration is determined by a grid of up to
3600 rectangles. Each element of the grid is designated as either
land or water to identify the coastline.

g. Plume Behavior

    (1) The basic plume rise algorithms are based on Briggs'
recommendations.
    (2) Momentum rise includes consideration of the stack angle from
the vertical.
    (3) The effect of drilling platforms, ships, or any overwater
obstructions near the source are used to decrease plume rise using a
revised platform downwash algorithm based on laboratory experiments.
    (4) Partial plume penetration of elevated inversions is included
using the suggestions of Briggs (1975) and Weil and Brower (1984).
    (5) Continuous shoreline fumigation is parameterized using the
Turner method where complete vertical mixing through the thermal
internal boundary layer (TIBL) occurs as soon as the plume
intercepts the TIBL.

h. Horizontal Winds

    (1) Constant, uniform wind is assumed for each hour.
    (2) Overwater wind speed can be estimated from overland wind
speed using relationship of Hsu (1981).
    (3) Wind speed profiles are estimated using similarity theory
(Businger, 1973). Surface layer fluxes for these formulas are
calculated from bulk aerodynamic methods.

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

    (1) Lateral turbulence intensity is recommended as a direct
estimate of horizontal dispersion. If lateral turbulence intensity
is not available, it is estimated from boundary layer theory. For
wind speeds less than 8 m/s, lateral turbulence intensity is assumed
inversely proportional to wind speed.
    (2) Horizontal dispersion may be enhanced because of
obstructions near the source. A virtual source technique is used to
simulate the initial plume dilution due to downwash.
    (3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement and wind direction shear
enhancement.
    (4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either lateral turbulence
intensity or Pasquill-Gifford curves. The change is implemented
where the plume intercepts the rising internal boundary layer.

k. Vertical Dispersion

    (1) Observed vertical turbulence intensity is not recommended as
a direct estimate of vertical dispersion. Turbulence intensity
should be estimated from boundary layer theory as default in the
model. For very stable conditions, vertical dispersion is also a
function of lapse rate.
    (2) Vertical dispersion may be enhanced because of obstructions
near the source. A virtual source technique is used to simulate the
initial plume dilution due to downwash.
    (3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement.
    (4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either vertical turbulence
intensity or the Pasquill-Gifford coefficients. The change is
implemented where the plume intercepts the rising internal boundary
layer.

l. Chemical Transformation

    Chemical transformations are treated using exponential decay.
Different rates can be specified by month and by day or night.

m. Physical Removal

    Physical removal is also treated using exponential decay.

n. Evaluation Studies

DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model. Volume I: User's Guide. Sigma Research
Corporation, Westford, MA.
Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The
Offshore and Coastal Dispersion (OCD) Model User's Guide, Revised.
OCS Study, MMS 84-0069. Environmental Research & Technology, Inc.,
Concord, MA. (NTIS No. PB 86-159803).
Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer,
1985. Development and Evaluation of the Offshore and Coastal
Dispersion (OCD) Model. Journal of the Air Pollution Control
Association, 35: 1039-1047.
Hanna, S.R. and D.C. DiCristofaro, 1988. Development and Evaluation
of the OCD/API Model. Final Report, API Pub. 4461, American
Petroleum Institute, Washington, DC.

[FR Doc. 2016-31747 Filed 1-13-17; 8:45 am]
BILLING CODE 6560-50-P