Revision to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Revisions

[Federal Register: April 15, 2003 (Volume 68, Number 72)]
[Rules and Regulations]
[Page 18439-18482]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
[DOCID:fr15ap03-27]

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ENVIRONMENTAL PROTECTION AGENCY
40 CFR Part 51
[AH-FRL-7478-3]
RIN 2060-AF01

Revision to the Guideline on Air Quality Models: Adoption of a
Preferred Long Range Transport Model and Other Revisions

AGENCY: Environmental Protection Agency (EPA).
ACTION: Final rule.

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SUMMARY: EPA's Guideline on Air Quality Models (``Guideline'')
addresses the regulatory application of air quality models for
assessing criteria pollutants under the Clean Air Act. In today's
action we promulgate several additions and changes to the Guideline. We
adopt a new dispersion model, CALPUFF, in appendix A of the Guideline.
CALPUFF becomes the preferred technique for assessing long range
transport of pollutants and their impacts on Federal Class I areas.
Action on AERMOD and the Emissions and Dispersion Modeling System
(EDMS) is deferred. We make various editorial changes to update and
reorganize information, and remove obsolete models.

DATES: This rule is effective May 15, 2003. Beginning April 15, 2003
the new model (i.e., CALPUFF) should be used for its intended purposes,
in accordance with today's document. The period before required
implementation of a new model allows user's sufficient time to prepare
meteorological data bases and to become familiar with model operation.
The new model may be used sooner, if desired.

ADDRESSES: All documents relevant to this rule have been placed in
Docket No. A-99-05 at the following address: EPA Docket Center, (EPA/
DC) EPA West (MC 6102T), 1301 Constitution Ave., NW., Washington, DC.
The EPA Docket Center Public Reading Room (B102) is open from 8:30 a.m.
to 4:30 p.m., Monday through Friday, excluding legal holidays. The
telephone number for the Air Docket is (202) 566-1742.

FOR FURTHER INFORMATION CONTACT: Joseph A. Tikvart, Leader, Air Quality
Modeling Group (MD-14), Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711;
telephone (919) 541-5562 (Tikvart.Joe@epa.gov).

SUPPLEMENTARY INFORMATION:

I. General Information

A. How Can I Get Copies of Related Information?

EPA established an official public docket for this action under
Docket ID No. A-99-05. The official public docket is the collection of
materials that is available for public viewing at the Air Docket in the
EPA Docket Center, (EPA/DC) EPA West (MC 6102T), 1301 Constitution
Ave., NW., Washington, DC. The EPA Docket Center Public Reading Room
(B102) is open from 8:30 a.m. to 4:30 p.m., Monday through Friday,
excluding legal holidays. The telephone number for the Reading Room is
(202) 566-1744, and the telephone number for the Air Docket is (202)
566-1742.
Our Air Quality Modeling Group maintains an Internet Web site
(Support Center for Regulatory Air Models--SCRAM) at: http://www.epa.gov/
scram001. You may find codes and documentation for models
referenced in today's action on the SCRAM Web site. We have also
uploaded various support documents (e.g., evaluation reports).

II. Background

The Guideline is used by EPA, States, and industry to prepare and
review new source permits and State Implementation Plan revisions. The
Guideline is intended to ensure consistent air quality analyses for
activities regulated at 40 CFR 51.112, 51.117, 51.150, 51.160, 51.166,
and 52.21. We originally published the Guideline in April 1978 and it
was incorporated by reference in the regulations for the Prevention of
Significant Deterioration (PSD) of Air Quality in June 1978. We revised
the Guideline in 1986, and updated it with supplement A in 1987,
supplement B in July 1993, and supplement C in August 1995. We
published the Guideline as appendix W to 40 CFR part 51 when we issued
supplement B. We republished the Guideline in August 1996 (61 FR 41838)
to adopt the CFR system for labeling paragraphs. On April 21, 2000 we
published proposed revisions in the Federal Register (65 FR 21506),
which is the basis for today's promulgation.
Today's notice promulgates those components of the proposal that
were clearly supported by public comments and that were otherwise not
controversial, notably:
? Adoption of CALPUFF in appendix A, as proposed, for
assessing long range transport of pollutants and their impacts on
Federal Class I areas;
? Removal of the Climatological Dispersion Model (CDM), RAM
and the Urban Airshed Model (UAM) from appendix A, as proposed;
? Simplification of complex terrain screening techniques in
section 5;
? Revision of section 9 to reflect our October 1997
settlement with the Utility Air Regulatory Group regarding
specification of emissions from background sources, as proposed;
? Updating information in appendix W and reorganizing its
structure; and
? Transfer of appendix B and appendix C to our Web site, as
proposed.
The proposal also included (1) adopting AERMOD \1\ to replace the
Industrial Source Complex (ISC3) model in many assessments that now use
it, (2) revising ISC3 by incorporating a new downwash algorithm (PRIME)
and renaming the model ISC-PRIME, and (3) updating the Emissions
Dispersion Modeling System (EDMS) by incorporating improved emissions
and dispersion modules. Regarding AERMOD, nearly every commenter urged
EPA to integrate aerodynamic downwash into AERMOD (i.e., not to require
two models for some analyses). The only cautions were associated with
the need for documentation, evaluation and review of the downwash
enhancement to AERMOD. As a result of AERMIC's (the American
Meteorological Society (AMS)/ EPA Regulatory Model Improvement
Committee) efforts to revise AERMOD, incorporating the PRIME algorithm
and making a few other incidental modifications and to respond to the
public's cautions, we believe that AERMOD, as modified for downwash,
merits another public examination of performance results. Also, since
the April 2000 proposal, the Federal Aviation Administration decided to
configure EDMS3.1 to incorporate the AERMOD dispersion model, and
results of its performance with AERMOD only recently became available.
Consequently, AERMOD and EDMS4.0, as well as other conforming changes
for the Guideline, will be reconsidered in a Supplemental Notice of
Proposed Rulemaking (SNPR) in the near future. Note that since AERMOD
is not included in today's promulgation, the proposed merger of the
Guideline's sections 4 and 5 will be deferred to AERMOD's adoption in
the future.
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\1\ AMS/EPA Regulatory MODel.
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III. Public Hearing on the Proposal

We held the 7th Conference on Air Quality Modeling (7th conference)
in Washington, DC on June 28-29, 2000. As required by section 320 of
the Clean Air Act, these conferences take place

[[Page 18441]]

approximately every three years to standardize modeling procedures.
This conference served as the forum for receiving public comments on
the Guideline revisions proposed in April 2000. The 7th conference
featured presentations in several key modeling areas that support the
revisions promulgated today. A presentation by the Interagency
Workgroup on Air Quality Modeling (IWAQM \2\) covered long range
transport modeling for point sources. This presentation was followed by
a critical review/discussion of the CALPUFF modeling system and
available performance evaluations, facilitated jointly by the Air &
Waste Management Association's AB-3 Committee and the American
Meteorological Society's Committee of Meteorological Aspects of Air
Pollution.
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\2\ IWAQM was formed in 1991 to provide a focus for development
of technically sound air quality models for regulatory assessments
of long range transport of pollutant source impacts on federal Class
I areas. IWAQM is an interagency collaboration that includes efforts
by EPA, U.S. Forest Service, National Park Service, and Fish and
Wildlife Service.
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We asked the public to address the following questions:
? Has the scientific merit of the models presented been
established?
? Are the models' accuracy sufficiently documented?
? Are the proposed regulatory uses of individual models for
specific applications appropriate and reasonable?
? Do significant implementation issues remain or is
additional guidance needed?
? Are there serious resource constraints imposed by modeling
systems presented?
? What additional analyses or information are needed?
We placed a transcript of the 7th conference proceedings and a copy
of all written comments, which embody answers to the above questions,
in Docket No. AQM-95-01.

IV. Discussion of Public Comments and Issues

All comments submitted to Docket No. A-99-05 are filed in Category
IV-D. We summarized these comments, developed detailed responses, and
drew conclusions on appropriate actions for today's action in the
summary of public comments and EPA responses.\3\ In this document, we
considered and discussed all significant comments. Whenever the
comments revealed any new information or suggested any alternative
solutions, we considered such in our final action.
The remainder of this preamble section provides an overview of the
primary issues encountered by the Agency during the public comment
period and summarizes our response-to-comments.\3\ This overview also
serves to explain the changes to the Guideline in today's action, and
the main technical and policy concerns addressed by the Agency.
Guidance and editorial changes associated with the resolution of these
issues are adopted in the appropriate sections of the Guideline. While
modeling by its nature involves approximation based on scientific
methodology, and entails utilization of advanced technology as it
evolves, we believe these changes respond to recent advances in the
area so that the Guideline continues to reflect the best and most
proven of the publicly available models and analytical techniques, as
well as to reflect reasonable policy choices.
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\3\ Summary of Public Comments and EPA Responses 7th Conference
on Air Quality Modeling, Washington, D.C., June 2000 (Air Docket A-
99-05, Item V-C-1). This document may also be examined from EPA's
SCRAM Web site (http://www.epa.gov/scram001). Note that comments/
responses re: AERMOD & EDMS are deferred to a companion document to
be released when the SNPR is published.
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CALPUFF

CALPUFF is a Lagrangian dispersion model that simulates pollutant
releases as a continuous series of puffs. Preceding our proposal to
adopt CALPUFF in the Guideline, IWAQM carefully studied the potential
regulatory application of CALPUFF in its Phase 1 report \4\ and in its
Phase 2 report.\5\
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\4\ Environmental Protection Agency, 1993. Interagency Workgroup
on Air Quality Modeling (IWAQM) Phase I report: Interim
Recommendation for Modeling Long range Transport and Impacts on
Regional Visibility; EPA Publication No. EPA-454/R-93-015.
\5\ Environmental Protection Agency, 1998. Interagency Workgroup
on Air Quality Modeling (IWAQM) Phase 2 Summary Report and
Recommendations for Modeling Long-Range Transport Impacts. EPA
Publication No. EPA-454/R-98-019.
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In our April 2000 Federal Register notice, we proposed adoption of
the CALPUFF modeling system, developed by Earth Tech, Inc., for refined
use in modeling long range transport and dispersion to characterize
reasonably attributable impacts from one or a few sources for PSD Class
I impacts. We also proposed use of CALPUFF for those applications
involving complex wind regimes, with case-by-case justification. We
sought comments on the use of CALPUFF for these applications, as well
as on related uses of meteorological information, e.g., on use of
prognostic mesoscale meteorological models and the length of record for
meteorological data.
Scientific merits and accuracy. In public comments there was a
general consensus that the technical basis of the CALPUFF modeling
system has merit and provides substantial capabilities to not only
address long range transport, but to address transport and dispersion
effects in some complex wind situations.
Commenters generally agreed that the CALPUFF modeling system has
adequate accuracy for use in the 50-200km range, with some studies
showing that acceptable results can be achieved at least out to 200 to
300km. Since the 7th Modeling Conference, enhancements were made to
CALPUFF that allow puffs to be split both horizontally (to address wind
direction shear) and vertically (to address spatial variation in
meteorological conditions). These enhancements likely will extend the
system's ability to treat transport and dispersion beyond 300km.
With respect to accuracy for complex wind situations, we believe
that the commenters agreed with our proposal to promote use of CALPUFF
for complex winds with prior approval by the reviewing authority.
CALPUFF has been demonstrated to perform as well as, or better than,
other short-range plume dispersion models for a few cases involving
complex winds, several with wind fields that are dominated by terrain
effects. Some suggested a need for more testing of CALPUFF, prior to
accepting its results in all cases involving complex wind situations.
We intend to post on our Web site citations to investigations for any
cases involving complex winds as they become available, and to build a
knowledge base from which determinations can be made on the use of
CALPUFF for various complex wind situations. This will support
consideration of new field study comparisons as they become available.
For the reasons stated above, it is apparent that CALPUFF contains the
scientific basis for more appropriately addressing long range transport
and dispersion effects in complex wind situations than do standard
plume models.
We conclude that, although the scientific advancements will
continue to emerge, CALPUFF in its current configuration is suitable
for regulatory use for long range transport, and on a case-by-case
basis for complex wind situations. We will require approval to be
obtained prior to accepting CALPUFF for complex wind situations, as
this will ensure that a protocol is agreed to between the parties
involved, and that all are willing to accept the results as binding. As
experience is gained in using CALPUFF for complex wind

[[Page 18442]]

situations, acceptance will become clear and those cases that are
problematic will be better identified. As suggested by comments, we
have removed reference to WYNDvalley from the Guideline.
Implementation issues/additional guidance. Some comments suggested
that the CALMET (meteorological preprocessor for CALPUFF) and CALPUFF
options should be defined for a variety of specific situations. We
believe that more experience is needed before specific guidance can be
offered for the variety of applications envisioned that might use the
CALPUFF modeling system. We placed emphasis on (1) amplifying the
available guidance information, (2) expanding the data formats for
meteorological input data, and (3) making the code more robust to
various choices in compilers. When sufficient experience has been
attained, and it has become obvious what settings should be employed
for best results for certain situations, we will promulgate expanded
guidance after allowing opportunity for public review and comment. In
the meantime, we will release interim guidance as it becomes available
to assist users in tailoring CALPUFF for application. We have created a
series of frequently asked questions (FAQ) with answers which the
public can access via Earth Tech's Internet Web site: (
http://www.src.com/calpuff/calpuff1.htm). This interim FAQ
list will be extended as resources permit.
For long range transport and complex winds applications, we
proposed that if only National Weather Service (NWS) or comparable
standard meteorological observations are employed, then five
consecutive years of data should be used. We further proposed that less
than five years of data were acceptable if appropriate NWS data are
merged with available mesoscale meteorological fields. These proposals
were generally supported by public comments,\3\ but the commenters did
provide a variety of opinions about how many years of data should be
minimally acceptable, ranging from 1 to 5 years. As we explained in our
response-to-comments, we sought to strike a balance between the need
for a sufficiently robust meteorological record to ensure results of
reliable integrity, while maintaining administrative and computational
burdens at a practical level. In consultation with the Regional
Offices, we therefore have agreed to allow use of less than five, but
at least three, years of assimilated mesoscale meteorological data.
More than 3 years may lead to the objectionable computations burdens
noted here, whereas less than 3 provides insufficient variation in
meteorological conditions to capture the range of possible
concentrations. We have also clarified that when merging NWS data with
mesoscale meteorological fields, the NWS data should be shown to be
relevant and appropriate.
For long range transport, we proposed use of a CALPUFF screening
approach on a case-by-case basis that was first outlined in the IWAQM
Phase 2 report (op. cit.) and was generally supported by commenters.
The full scope of public comments is presented and addressed in our
response-to-comments document.\3\ We agree with the comments suggesting
use of terrain heights for each receptor ring to be representative of
the Class I areas of interest. Furthermore, to ensure an appropriate
degree of flexibility, we will allow the permitting agency to decide
whether it will accept the CALPUFF screening results as proposed, and
in that decision process will defer to the appropriate reviewing
authority to decide on the details of how the CALPUFF screen is to be
implemented.
Resource constraints. The full scope of public comments is
presented and addressed in our response-to-comments document.\3\ There
was a general sense from commenters that a skilled person having
experience with CALMET can perform the required processing steps. Still
some commenters encouraged us to find and promote a simplification to
the CALMET meteorological processing steps. We did not support the
suggestion to use screening level (ISC-like) meteorological data until
such time as packaged data sets are made available. This would negate
the benefits of using the system to simulate trajectories over large
downwind distances, thereby undermining the purpose for which CALPUFF
is intended. Although the processing steps are numerous and complex,
they can be managed by competent staff.
Long range transport and complex wind situations are not trivial
modeling problems. All commenters were aware that to address these
situations requires more information (e.g., terrain heights, land use
mosaic, time and space variations in meteorological conditions) than is
typical when using standard plume models. Processing the input data is
a necessary but demanding task. The complexity of these situations
requires a selection of options to provide the flexibility to tailor
the model to specific situations. The CALPUFF system is currently
configured to support a specific applied approach for long range
transport, while at the same time, it has the flexibility for case-by-
case applications involving complex winds.
Additional analyses. Some commenters questioned whether CALPUFF has
undergone sufficient testing to secure its accuracy for assessing
impacts on air quality related values (AQRVs). We believe the available
testing for assessing AQRVs addresses many of these concerns. In
addition, it should be recognized that the FLMs are responsible for
defining the relevant AQRV's of interest and the procedures to employ
to assess whether there is an adverse impact. When CALPUFF is used for
a visibility impact assessment, this would likely be for a Class I AQRV
assessment, and the reviewing authorities are the FLMs responsible for
the management and protection of the resources for the particular Class
I areas involved. The Federal Land Managers' Air Quality Related Values
Work Group (FLAG) was formed in 1997 to provide a more consistent
approach for FLMs to evaluate air pollution effects on their resources.
In IWAQM's Phase 2 report, we indicated that EPA would use the
procedures specified by the FLMs as a consequence of their
deliberations (e.g., in their FLAG report: http://www2.nature.nps.gov/air/
Permits/flag/htm/index.html). To assist permit applicants, the FLMs
have provided procedures in the December 2000 (Phase I) FLAG report for
performing such analyses as may be required. Included in these
instructions, they have identified significance thresholds for
potential adverse impacts, and methodologies for computing a visibility
impact. The commenters are in fact addressing the FLAG procedures which
are not the subject of today's action. To the extent that they were
addressed in the response to comments developed by the FLMs in the FLAG
Phase I report, we refer commenters to that document.
Criticism was also directed at CALPUFF's treatment of chemical
transformations, which affect AQRVs. Specific concern was expressed
about the sulfate and aqueous phase chemistry algorithms. As chronicled
on the FLAG Web site (above), these procedures and criteria have been
published and received review and comment. However, today's rule
addresses the suitability of CALPUFF for PSD increment consumption and
for complex wind situations (with case-by-case approval), not AQRV
analyses.

Other Modeling Systems

Our proposal to remove UAM-IV from appendix A as a recommended
model for ozone and to remove reference to ROM and RADM for

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regional scale applications was supported by some commenters who
understood that these models were no longer state-of-the-science. Those
who objected to removal of UAM-IV were concerned that the Models-3/CMAQ
(Community Multi-scale Air Quality) model, as a replacement for UAM-IV,
was not sufficiently tested. In fact, Models-3/CMAQ is identified as
only one option among currently available models that are appropriate
in simulating the highly complex ozone/PM-2.5 formation and transport
processes. It is the responsibility of the appropriate control
agency(ies) with jurisdiction for the model application to exercise
discretion in the choice of models. Alternately, criteria for using
models not in appendix A are clearly delineated in revised wording that
we proposed for subsection 3.2.2 of appendix W. These options should
more than mitigate concerns expressed by the commenters.
We generally agree that Models-3/CMAQ and REMSAD will continue to
benefit from further evaluation and testing for use in urban/regional
scale assessments of ozone and PM-2.5, and are not the only models
available for these applications. The same is true of all similar
regional scale models. However, CMAQ and REMSAD have been successfully
subjected to peer scientific reviews and are currently undergoing
performance evaluations that will extend over several years as data
bases become more extensive and complete for both ozone and PM-2.5.
While comment was solicited on the need to integrate ozone and fine
particle impacts (i.e., the ``one atmosphere'' approach) for regional
scale assessments, we did not receive substantial comment. Comments on
integrating analyses were supportive and comments on source-specific
analyses indicated that more work was needed in this area. It is clear
that further developmental efforts on estimating the impact of
individual sources is necessary before specific modeling requirements
are identified for such applications.
Comments \3\ were generally supportive of our proposal to remove
appendix B (Summaries of Alternative Air Quality Models) from appendix
W and maintaining it as a PDF file on our SCRAM Internet Web site. As
we stated in the preamble to the notice of proposed rulemaking for this
action, appendix B of the Guideline was created solely for the
convenience of those seeking information about alternatives to the
models adopted in appendix A. The models described in appendix B may or
may not have not been the subject of performance evaluations and their
inclusion in appendix B does not confer special status or EPA sanction
on their use. Conversely, the fact that a model has not been listed in
appendix B carries no implication that its performance or acceptability
for use is any poorer than appendix B listed models. Whether or not a
model is listed, potential users will be subject to the same
requirements, i.e., to demonstrate that the model performs acceptably
for its intended regulatory application. Because production and
maintenance of appendix B information in the Code of Federal
Regulations presents a substantial administrative burden for EPA and is
not updated frequently enough to provide current information to
potential users, we are moving the appendix B repository of alternative
model summary descriptions to our Internet SCRAM Web site. This action
offers the advantages of easier and less expensive maintenance, as well
as more frequent updating, and is thus more likely to contain a
comprehensive description of alternative models which have been brought
to our attention. Similarly, the air quality checklist (formerly
appendix C of the Guideline) will be available on the Web site as a PDF
file.
The appendix B listing will therefore now appear as a list of
Alternative Models (PDF file) on our Web site. We have clarified in its
Introduction and Availability section that new models added to the list
were/are not necessarily the subject of review upon their addition. On
the other hand, it should be noted that the models identified in our
proposal (i.e., ADMS, SCIPUFF, OBODM, and CAMx) were included in the
review process for today's action concerning the list of alternative
models. At the request of the developer, we will remove MESOPUFF from
appendix B since its function is replaced by CALPUFF.
Comments on the dispersion model ADMS argued that proprietary
limitations on the availability of ADMS should not preclude it from
having equal status with other Appendix A models and that it should be
recommended in appendix A. However, as specified by Guideline paragraph
3.1.1(c)(vi), air quality models used in U.S. regulatory programs must
be in the public domain at reasonable cost. This is because the source
code needs to be open for public access and scrutiny to enable
meaningful opportunity for public comment on new source permits, PSD
increment consumption and SIPs. These criteria have been in place in
U.S. regulatory programs since the inception of the Guideline and are
needed to meet EPA's obligations under the CAA and the Administrative
Procedure Act. Until the joint issues of availability (source code) and
cost are addressed by the authors of ADMS, it is most appropriately
listed as an alternative model for use on a case-by-case basis. Even if
the model is justified on a case-by-case basis, users are responsible
for making the model available for public review and comment for
specific applications.
A similar comment regarding the puff model SCIPUFF did not consider
that the model has not gone through the same extensive testing and
regulatory evaluation as has CALPUFF, nor has it been as widely used as
CALPUFF for regulatory applications. As has been done by CALPUFF's
developers, a commitment to support public availability of SCIPUFF
would have to be made by its supporter before it could be considered
for adoption in appendix A.
Developers of neither ADMS nor SCIPUFF have addressed conflicts
associated with multiple models for the same application in such a way
as to assist EPA in resolving this issue. Moreover, we believe that
neither ADMS nor SCIPUFF technically fill a particular technical need
that is different from that occupied by the suite of refined dispersion
models that EPA has promulgated for regulatory purposes after public
review and comment.
Based on public comments and the rationale provided in our notice
of proposed rulemaking, our decision to reference the ozone limiting
method (OLM) and CAL3QHC for use in specific circumstances is
justified.

Meteorological Data Issues

In our proposal we solicited comment on terminology and meaning of
``site-specific'' data and on use of surface meteorological data
derived from the NWS's Automated Surface Observing System (ASOS). More
specifically, we invited comment on whether the policy of modeling with
the most recent 5 years of NWS meteorological data should include ASOS
data and whether the period of record must be the most recent 5 years,
regardless of whether it contains ASOS data.
No one provided negative comments on the use of the term ``site-
specific'' or associated definitions as used in the proposed revisions.
Thus, for the reasons discussed in the proposal, we will retain this
terminology.
The majority of commenters who addressed the topic of ASOS data
felt that the ASOS data were inferior for use with Gaussian models,
though not all commenters agreed. With respect to the

[[Page 18444]]

use of the most recent 5 years of meteorological data, there was some
concern about the reliability of ASOS data. We revised guidance to
specifically address this concern by allowing flexibility in the choice
of ASOS or observer-based observations depending on which provided the
most representative meteorological information.
Final Action
Today's action amends appendix W of 40 CFR part 51 as detailed
below:

CALPUFF

The public comments provided constructive suggestions but did not
suggest altering promulgation of the CALPUFF modeling system. We will
therefore promulgate use of the CALPUFF modeling system as follows:
(A) Long Range Transport
CALPUFF will be adopted as a refined model for use in sulfur
dioxide and particulate matter ambient air quality standards and PSD
increment impact analyses involving (1) transport greater than 50km
from one or several closely spaced sources, and (2) analyses involving
a mixture of both long range and short-range source-receptor
relationships in a large modeling domain (e.g., several industrialized
areas located along a river or valley). The screening approach outlined
in the IWAQM Phase 2 report is available for use on a case-by-case
basis that generally provides concentrations that are higher than those
obtained using refined characterizations of the meteorological
conditions.
Given the judgement and refinement involved, conducting a long
range transport modeling assessment will require significant
consultation with the appropriate reviewing authority, and for Class I
analyses the appropriate FLM. To facilitate use of complex air quality
and meteorological modeling systems, a written protocol may be
considered for developing consensus in the methods and procedures to be
followed.
(B) Complex Winds
(1) On a case-by-case basis, the CALPUFF modeling system may be
applied for air quality estimates involving complex meteorological
conditions, where the assumptions of steady-state straight-line
transport both in time and space are inappropriate.
(2) In such situations, where the otherwise preferred dispersion
model is found to be less appropriate, use of the CALPUFF modeling
system will be in accordance with the procedures and requirements
outlined in paragraph 3.2.2(e) of the Guideline.
The public comments provided constructive suggestions, but did not
suggest altering the meteorological data requirements for refined
modeling assessments using the CALPUFF modeling system. Therefore, we
will promulgate use of the CALPUFF modeling system with the following
meteorological data requirements. For long range transport and for
complex winds situations, there are two possibilities:
(A) If only NWS or comparable standard meteorological observations
are employed, then five years of meteorological data should be used.
(B) If mesoscale meteorological fields are employed with
appropriate NWS observations, then less than five years but at least
three years of meteorological data may be used. Following the
suggestions provided in public comments, we revised the Guideline to
emphasize that appropriate NWS observations should be used in
conjunction with mesoscale meteorological data.
In response to the suggestions provided in public comments, we: (1)
Created a series of frequently asked questions to provide additional
technical information to users, which will be made publicly available
via Earth Tech's Internet Web site, (2) expanded the meteorological and
precipitation data formats that can be processed, (3) have tested and
made changes as necessary that allow the modeling software to be
compiled by several Fortran compilers, thus making the code more robust
to various choices in compilers, and (4) will maintain and make
publicly available via our Web site, a list of technical papers and
reports that describe testing and evaluation of the CALPUFF modeling
system in a variety of situations and thus provide a basis for wider
use of the CALPUFF modeling system.
For appropriate applications, CALPUFF may be used during the one-
year period following the promulgation of today's notice. After one
year following promulgation of today's notice, CALPUFF should be used
for appropriate applications.

Other Modeling Systems

We have removed UAM-IV from appendix A for urban ozone applications
and removed reference to ROM and RADM for regional scale applications
to reflect the current state-of-science. Similarly, we have identified
Models-3/CMAQ and REMSAD as example modeling systems that have been
evaluated and peer reviewed for regional scale applications, and make
clear that this does not preclude the use of other models.
We have removed appendix B and appendix C from appendix W and
placed equivalent counterparts on our SCRAM Internet Web site. Former
appendix B will simply become a list of alternative model summaries,
and should be readily updated as new models in the proper format are
submitted and not on a restrictive schedule. Given the current status
of ADMS and SCIPUFF, as well as OBODM, CAMx and UAMV (an update to UAM-
IV), all have now been included in the web-based Alternative Models
list.
As proposed, we have referenced OLM and CAL3QHC for use in specific
circumstances, and removed RAM and CDM from appendix A.

Meteorological Data Issues

The terminology for ``site-specific'' has been implemented as
proposed since there was a lack of negative comment. The prevailing
concept is, as commenters recognized, representativeness, and this is
now emphasized in our guidance.
Due to limitations of ASOS data for use with standard dispersion
models, paragraph 8.3.1.2(a) of appendix W has been revised to indicate
that where the latest 5 years of data includes ASOS data (now the
typical situation) discretion should be used. Where judgment indicates
ASOS data are inadequate for cloud cover observations, the most recent
5 years of NWS data that are observer-based may be considered for use.
In response to public comment, we have updated our meteorological
data processors (i.e., MPRM and CALMET) to allow processing of
meteorological data formats from the National Climatic Data Center
necessary to operate associated air quality models; no further updates
to MPRM are necessary at this time. The meteorological monitoring
guidance \6\ has been updated.
------------------------------------------------------------------------

\6\ Environmental Protection Agency, 2000. Meteorological
Monitoring Guidance for Regulatory Modeling Applications. EPA
Publication No. EPA-454/R-99-005. U.S. Environmental Protection
Agency, Research Triangle Park, NC. (www.epa.gov/scram001).
------------------------------------------------------------------------

Final Editorial Changes to Appendix W
Preface
You will note some minor revisions to reflect current EPA practice.
Section 2
In a streamlining effort, we removed section 2.2 and added a new
section 2.3 to address model availability.

[[Page 18445]]

Section 3
As proposed, we revised section 3 to more accurately reflect
current EPA practice, e.g., functions of the Model Clearinghouse and
enhanced criteria for the use of alternative models. Requirements for
alternative models when preferred models are less appropriate for
specific applications have been clarified. These requirements include
scientific peer review and the establishment of an acceptable protocol
prior to the model's use.
Section 4
We revised section 4.2.2 to reflect the widespread use of short-
term models for all averaging periods. Hence, we no longer reference
long-term models (e.g., ISCLT) in the Guideline.\7\
------------------------------------------------------------------------

\7\ Note that because appendix W is designed to guide
assessments for criteria pollutants, the proposed discontinuation of
ISCLT for purposes herein does not preclude its use for other
pollutant assessments, as applicable. For example, the ASPEN model
(Assessment System for Population Exposure Nationwide) uses the
capabilities of ISCLT to estimate ambient concentrations of toxic
pollutants nationwide by census tract. Such applications require the
abbreviated computing possible with ISCLT.
------------------------------------------------------------------------

Section 5
To simplify, the list of acceptable, yet equivalent, screening
techniques for complex terrain was removed. CTSCREEN and guidance for
its use are retained; CTSCREEN remains acceptable for all terrain above
stack top. The screening techniques whose descriptions we removed,
i.e., Valley (as implemented in SCREEN3), COMPLEX I (as implemented in
ISC3), SHORTZ/LONGZ, and RTDM remain available for use in applicable
cases where established/accepted procedures are used. Consultation with
the appropriate reviewing authority is still advised for application of
these screening models.
Section 6
As proposed, we revised section 6 to reflect the new PM-2.5 and
ozone ambient air quality standards that were issued on July 18, 1997
(62 FR 38652 & 62 FR 38856). You will note that we inserted respective
subsections for particulate matter and lead from section 8, so that
section 6 now primarily contains modeling guidance for the criteria
pollutants regulated in Part 51 (SO2 analyses are covered in section
4). We also updated information on receptor models.
? We enhanced the subsection on particulate matter as much as
possible to reflect the Agency's current thinking on approaches for
fine particulates (PM-2.5). You will note that we removed the
references to the Climatological Dispersion Model (CDM 2.0) as well as
to RAM from this section, and also deleted CDM and RAM from appendix A
(see below).
? We enhanced the subsection on ozone to better reflect
modeling approaches we currently envision, and added a reference for
current guidance on ozone attainment demonstrations.\8\ You will note
that we removed the reference to the Urban Airshed Model (UAM-IV) from
this section, and deleted UAM from appendix A. UAM-IV is no longer the
recommended photochemical model for attainment demonstrations for
ozone.
------------------------------------------------------------------------

\8\ Environmental Protection Agency, 1998. Use of Models and
Other Analyses in Attainment Demonstrations for the 8-hr Ozone NAAQS
(Draft). Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (Docket No. A-99-05, II-A-14) (Also available on
SCRAM Web site, http://www.epa.gov/scram001, as draft8hr.pdf)
------------------------------------------------------------------------

? We updated the subsection on carbon monoxide by removing
reference to RAM. While UAM-IV is deleted from appendix A, reference to
areawide analyses is retained. For refined intersection modeling,
CAL3QHCR is specifically mentioned for use on a case-by-case basis.
? In the subsection on NO₂ models, we added a
third tier for the screening approach that allows the use of the ozone
limiting method on a case-by-case basis. You may recall that this
approach was removed with the Guideline update promulgated on August 9,
1995 (60 FR 40465).
? In the subsection on lead, we deleted references to 40 CFR
51.83, 51.84, and 51.85, conforming to previous EPA action (51 FR
40661).
Section 7
For regional scale modeling, we removed reference to the Regional
Oxidant Model (ROM) and the Regional Acid Deposition Model (RADM) from
section 7 because they are outdated and replaced by a reference to
Models-3 \9\ in section 6. We enhanced the subsection on visibility to
reflect the provisions of the Clean Air Act, including those for
reasonable attribution of visibility impairment and regional haze, as
well as the new NAAQS for PM-2.5. For assessment of reasonably
attributable haze impairment due to one or a small group of sources,
CALPUFF is available for use on a case-by-case basis. We identify
REMSAD and new approaches under the Models-3/CMAQ umbrella for possible
use to develop and evaluate national policy and assist State and local
control agencies. For long range transport analyses, we recommend the
CALPUFF modeling system. To facilitate use of a complex air quality and
meteorological modeling system like CALPUFF, we stipulate that a
written protocol may be considered for developing consensus in the
methods and procedures to be followed.
------------------------------------------------------------------------

\9\ Environmental Protection Agency, 1998. EPA Third-Generation
Air Quality Modeling System. Models-3, Volume 9b: User Manual. EPA
Publication No. EPA-600/R-98/069(b). Office of Research and
Development, Washington, DC.
------------------------------------------------------------------------

Section 8
As proposed, we revised section 8 to better reflect our current
regulatory practice for the general modeling considerations addressed.
? We revised subsection 8.2.6 to refer to subsection 6.2.3
for details on chemical transformation of NO_X.
? We merged subsection 8.2.8 (Urban/Rural Classification)
with subsection 8.2.3 (Dispersion Coefficients), and removed reference
to WYNDvalley.
? We merged discussions in subsections 8.2.9 (Fumigation) and
8.2.10 (Stagnation) into one new subsection (8.2.8--Complex Winds), and
specifically identify the availability of CALPUFF for certain
situations on a case-by-case basis.
? We removed the distinction between short-term and long-term
models because when assessing the impacts from criteria air pollutants,
long-term estimates are now practicable using hour-by-hour
meteorological data.
Section 9
As proposed,
? We revised subsection 9.2.3 (recommendations for estimating
background concentrations from nearby sources) to reflect a settlement
reached on October 16, 1997 in a petition brought by the Utility Air
Regulatory Group (UARG). In accordance with the settlement, we are
clarifying the definition of ``nearby sources.'' The ``maximum
allowable emission limit,'' specified in Tables 9-1 and 9-2, is tied in
certain circumstances \10\ to the emission rate representative of a
nearby source's maximum physical capacity to emit. We also clarify that
nearby sources should be modeled only when they operate at the same
time as the primary source(s) being modeled. Where a nearby source does
not, by its nature, operate at the same time as the primary source
being modeled, the burden is on the primary source to demonstrate to
the satisfaction of the appropriate reviewing authority that this is,
in fact, the case. We added footnotes to Tables 9-1 and 9-2 to refer
back to applicable paragraphs of subsection 9.2.3 that provide the
necessary clarification.
---------------------------------------------------------------------------

\10\ See section 8.2.3 of the Guideline.

------------------------------------------------------------------------

[[Page 18446]]

? We enhanced section 9.3 (Meteorological Input Data) to
develop concepts of meteorological data representativeness, minimum
meteorological data requirements, and the use of prognostic mesoscale
meteorological models in certain situations. These models (e.g., the
Penn State/NCAR MM4 11,12,13 or MM5 \14\ model) assimilate
meteorological data from several surface and upper air stations in or
near a domain and generate a 3-dimensional field of wind, temperature
and relative humidity profiles. We revised recommendations for length
of record for meteorological data (subsection 9.3.1.2) for long range
transport and complex wind situations. In paragraph 9.3.1.2(d) we
specifically allow the use of at least three years (need not be
consecutive) of assimilated mesoscale meteorological data.
------------------------------------------------------------------------

\11\ Stauffer, D.R. and Seaman, N.L., 1990. Use of four-
dimensional data assimilation in a limited-area mesoscale model.
Part I: Experiments with synoptic-scale data. Monthly Weather
Review, 118: 1250-1277.
\12\ Stauffer, D.R., Seaman, N.L., and Binkowski, F.S., 1991.
Use of four-dimensional data assimilation in a limited-area
mesoscale model. Part II: Effect of data assimilation within the
planetary boundary layer. Monthly Weather Review, 119: 734-754.
\13\ Hourly Modeled Sounding Data. MM4--1990 Meteorological
Data, 12-volume CD-ROM. Jointly produced by NOAA's National Climatic
Data Center and Atmospheric Sciences Modeling Division. August 1995.
Can be ordered from NOAA National Data Center's Internet Web site @
www.nndc.noaa.gov/.
\14\ http://www.mmm.ucar.edu/mm5/mm5-home.html

? We revised subsection 9.3.2 (National Weather Service Data)
to inform users that National Weather Service (NWS) surface and upper
air meteorological data are available on CD-ROM from the National
Climatic Data Center. Recent years of such surface data are derived
from the NWS's Automated Surface Observing System (ASOS). We revised
subsection 9.3.1.2 to address the possible occurrence of ASOS data
within 5-year sets of meteorological data.
? We revised subsection 9.3.3.1 to clarify that, while site-
specific measurements are frequently made ``on-property'' (i.e., on the
source's premises), acquisition of adequately representative site-
specific data does not preclude collecting data from a location off
property. Conversely, collection of meteorological data on property
does not of itself guarantee adequate representativeness. The
subsection was also enhanced by improving the discussion of collection
of temperature difference measurements; a paragraph was developed that
focuses on measurement of aloft winds for simulation of plume rise,
dispersion and transport (some details for CTDMPLUS were moved to its
appendix A descriptions); a paragraph was added to address collection
and use of direct turbulence measurements; and the paragraph that
discusses meteorological data preprocessor has been enhanced.
? We revised subsection 9.3.3.2 by removing reference to the
STAR processing routine because ISCLT and CDM 2.0 (for which STAR
formatted data were developed) have been removed.
? We revised subsection 9.3.4 (Treatment of Calms) to
increase accuracy.
Section 10
We updated section 10 to reflect current thinking and state-of-the-
practice regarding model accuracy and uncertainty.
Section 11
As proposed, we made minor revisions to section 11 to reflect the
new ambient air quality standards for fine particles and ozone. Because
EPA has revised its emissions trading program for SO₂, we
have deleted subsection 11.2.3.4.
Section 12 & 13
We redesignated section 13 (Bibliography) as section 12
(References) and vice-versa. We revised them by adding some references,
deleting obsolete/superseded ones, and resequencing. You will note that
a peer scientific review for CALPUFF has been included.
Section 14
In a streamlining effort, we removed section 14 (Glossary). Given
current familiarity with modeling terminology, we no longer consider
that maintenance of such a glossary is as necessary as it once may have
been. For these and other reasons relating to Office of Federal
Register policy (see discussion of appendix B below), we have revised
the glossary and placed it on our Internet Web site.

Appendix A

We updated the introduction to appendix A (section A.0). As
mentioned before, we added CALPUFF to appendix A. We removed the
Climatological Dispersion Model (CDM 2.0), the Gaussian-Plume Multiple
Source Air Quality Algorithm (RAM), and the Urban Airshed Model (UAM)
from appendix A. These models have been superseded and are no longer
considered preferred techniques.

Appendix B

We have moved the appendix B repository of alternate model summary
descriptions to our Internet SCRAM Web site (http://www.epa.gov/scram001).
Placement of this material on the Web site offers many
advantages. In this format, we will be able to maintain the list and
model descriptions more easily and inexpensively.
Several model developers have submitted new dispersion models for
inclusion in this Web site repository of alternate models:
? Second-Order Closure Integrated Puff Model (SCIPUFF);
? Open Burn/Open Detonation Dispersion Model (OBODM);
? Atmospheric Dispersion Modeling System (ADMS);
? Comprehensive Air Quality Model with extensions (CAMx); and
? Urban Airshed Model--V (UAMV).
As described below, codes (executables) for these models, as well
as applicable documentation, have been uploaded to our Internet SCRAM
Web site. Finally, we deleted a model currently listed in appendix B,
MESOPUFF II, which CALPUFF replaces.

Appendix C

As proposed, we also moved appendix C (Example Air Quality Analysis
Checklist) from the CFR to our Internet SCRAM Web site. We believe this
checklist is outdated, in need of revision, and would be more practical
to maintain if posted on EPA's Internet SCRAM Web site.

Statutory and Executive Order Reviews

A. Executive Order 12866: Regulatory Planning and Review

Under Executive Order 12866 (58 FR 51735 (October 4, 1993)), the
Agency must determine whether the regulatory action is ``significant''
and therefore subject to review by the Office of Management and Budget
(OMB) and the requirements of the Executive Order. The Order defines
``significant regulatory action'' as one that is likely to result in a
rule that may:
(1) Have an annual effect on the economy of $100 million or more or
adversely affect in a material way the economy, a sector of the
economy, productivity, competition, jobs, the environment, public
health or safety, or State, local, or tribal governments or
communities;
(2) Create a serious inconsistency or otherwise interfere with an
action taken or planned by another agency;
(3) Materially alter the budgetary impact of entitlements, grants,
user fees,

[[Page 18447]]

or loan programs of the rights and obligations of recipients thereof;
or
(4) Raise novel legal or policy issues arising out of legal
mandates, the President's priorities, or the principles set forth in
the Order.
This rule is not a ``significant regulatory action'' under the
terms of Executive Order 12866 and is therefore not subject to OMB
review.

B. Paperwork Reduction Act

This final rule does not contain any information collection
requirements subject to review by OMB under the Paperwork Reduction
Act, 44 U.S.C. 3501 et seq.

C. Regulatory Flexibility Act (RFA), as amended by the Small Business
Regulatory Enforcement Fairness Act of 1996 (SBREFA), 5 U.S.C. 601 et
seq.

The RFA generally requires an agency to prepare a regulatory
flexibility analysis of any rule subject to notice and comment
rulemaking requirements under the Administrative Procedure Act or any
other statute unless the agency certifies that the rule will not have a
significant economic impact on a substantial number of small entities.
Small entities include small businesses, small organizations, and small
governmental jurisdictions.
EPA has determined that it is not necessary to prepare a regulatory
flexibility analysis in connection with this final rule. EPA has also
determined that this rule will not have a significant economic impact
on a substantial number of small entities. For purposes of assessing
the impact of today's rule on small entities, small entities are
defined as: (1) A small business that meets the RFA default definitions
for small business (based on Small Business Administration size
standards), as described in 13 CFR 121.201; (2) a small governmental
jurisdiction that is a government of a city, county, town, school
district or special district with a population of less than 50,000; and
(3) a small organization that is any not-for-profit enterprise which is
independently owned and operated and is not dominant in its field.
After considering the economic impacts of today's final rule on
small entities, EPA has concluded that this action will not have a
significant economic impact on a substantial number of small entities.
This final rule will not impose any requirements on small entities.
Today's rule will not have any impacts on small entities because
existing and new sources of air emissions that model air quality for
State Implementation Plans and the prevention of significant
deterioration are typically not small entities. The modeling techniques
described today are primarily used by state air control agencies and by
industry.
To the extent that any small entities would ever have to model air
quality using the modeling techniques described in today's rule, the
impacts of using updated modeling techniques would be minimal, if not
non-existent. The action promulgated today incorporates comments
received at the 7th Conference on Air Quality Modeling in June 2000 in
Washington, DC. The rule features a new modeling system for calculating
PSD increment consumption--CALPUFF--and serves to increase efficiency
and accuracy. This system employs procedural concepts that are very
similar to those currently used, changing only mathematical
formulations and specific data elements. No impacts on small entities
in the use of CALPUFF are anticipated. We do not believe that CALPUFF's
use poses a significant or unreasonable burden on any small entities.
This final action imposes no new regulatory burdens and, as such, there
will be no additional impact on small entities regarding reporting,
recordkeeping, compliance requirements.

D. Unfunded Mandates Reform Act of 1995

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public
Law 104-4, establishes requirements for Federal agencies to assess the
effects of their regulatory actions on State, local, and tribal
governments and the private sector. Under section 202 of the UMRA, EPA
generally must prepare a written statement, including a cost-benefit
analysis, for proposed and final rules with ``Federal mandates'' that
may result in expenditures to State, local, and tribal governments, in
the aggregate, or to the private sector, of $100 million or more in any
one year. Before promulgating an EPA rule for which a written statement
is needed, section 205 of the UMRA generally requires EPA to identify
and consider a reasonable number of regulatory alternatives and adopt
the least costly, most cost-effective or least burdensome alternative
that achieves the objectives of the rule. The provisions of section 205
do not apply when they are inconsistent with applicable law. Moreover,
section 205 allows EPA to adopt an alternative other than the least
costly, most cost-effective or least burdensome alternative if the
Administrator publishes with the final rule an explanation why that
alternative was not adopted. Before EPA establishes any regulatory
requirements that may significantly or uniquely affect small
governments, including tribal governments, it must have developed under
section 203 of the UMRA a small government agency plan.
The plan must provide for notifying potentially affected small
governments, enabling officials of affected small governments to have
meaningful and timely input in the development of EPA regulatory
proposals with significant Federal intergovernmental mandates, and
informing, educating, and advising small governments on compliance with
the regulatory requirements.
Today's rule recommends a new modeling system for calculating PSD
increment consumption--CALPUFF--that increases efficiency and accuracy.
CALPUFF has been used for these purposes on a case-by-case basis (per
Guideline subsection 3.2.2) for several years, as has its predecessor--
MESOPUFF II. While Guideline subsection 3.2.2 still allows for
alternative models to be used, EPA is now sufficiently confident in
CALPUFF's technical formulation and performance to adopt it in appendix
A of the Guideline. Since the two modeling systems are comparable in
scope and purpose, use of CALPUFF itself does not involve any increase
in costs. The optional use of prognostic meteorological data (e.g.,
MM5) input files, however, may result in a small incremental cost
increase. To the extent that the use of more refined models with
comprehensive input data bases reduces the potential for over-or
underprediction of air quality impacts, air quality management programs
become more economically efficient. Moreover, modeling costs (which
include those for input data acquisition) are typically among the
implementation costs that are considered as part of the programs (i.e.,
PSD) that establish and periodically revise requirements for
compliance. Any incremental modeling costs attributable to today's rule
do not approach the $100 million threshold prescribed by UMRA. EPA has
determined that this rule contains no regulatory requirements that
might significantly or uniquely affect small governments. This rule
therefore contains no Federal mandates (under the regulatory provisions
of Title II of the UMRA) for State, local, or tribal governments or the
private sector.

E. Executive Order 13132: Federalism

Executive Order 13132, entitled ``Federalism `` (64 FR 43255,
August 10, 1999), requires EPA to develop an accountable process to
ensure ``meaningful and timely input by State and local officials in
the development of regulatory policies that have federalism

[[Page 18448]]

implications.'' ``Policies that have federalism implications `` is
defined in the Executive Order to include regulations that have
``substantial direct effects on the States, on the relationship between
the national government and the States, or on the distribution of power
and responsibilities among the various levels of government.''
This final rule does not have federalism implications. It will not
have substantial direct effects on the States, on the relationship
between the national government and the States, or on the distribution
of power and responsibilities among the various levels of government,
as specified in Executive Order 13132. This rule does not create a
mandate on State, local or tribal governments. The rule does not impose
any enforceable duties on these entities (see D. Unfunded Mandates
Reform Act of 1995, above). The rule would add better, more accurate
techniques for air dispersion modeling analyses and does not impose any
additional requirements for any of the affected parties covered under
Executive Order 13132. Thus, Executive Order 13132 does not apply to
this rule.

F. Executive Order 13175: Consultation and Coordination With Indian
Tribal Governments

Executive Order 13175, entitled ``Consultation and Coordination
with Indian Tribal Governments'' (59 FR 22951, November 9, 2000),
requires EPA to develop an accountable process to ensure ``meaningful
and timely input by tribal officials in the development of regulatory
policies that have tribal implications.'' This final rule does not have
tribal implications, as specified in Executive Order 13175. As stated
above (see D. Unfunded Mandates Reform Act of 1995, above), the rule
does not impose any new requirements for calculating PSD increment
consumption, and does not impose any additional requirements for the
regulated community, including Indian Tribal Governments. Thus,
Executive Order 13175 does not apply to this rule.
Today's final rule does not significantly or uniquely affect the
communities of Indian tribal governments. Accordingly, the requirements
of section 3(b) of Executive Order 13175 do not apply to this rule.

G. Executive Order 13045: Protection of Children From Environmental
Health and Safety Risks

Executive Order 13045 applies to any rule that EPA determines (1)
to be ``economically significant '' as defined under Executive Order
12866, and (2) the environmental health or safety risk addressed by the
rule has a disproportionate effect on children. If the regulatory
action meets both the criteria, the Agency must evaluate the
environmental health or safety effects of the planned rule on children;
and explain why the planned regulation is preferable to other
potentially effective and reasonably feasible alternatives considered
by the Agency.
This final rule is not subject to Executive Order 13045, entitled
``Protection of Children from Environmental Health Risks and Safety
Risks '' (62 FR 19885, April 23, 1997) because it does not impose an
economically significant regulatory action as defined by Executive
Order 12866 and the action does not involve decisions on environmental
health or safety risks that may disproportionately affect children.

H. Executive Order 13211: Actions that Significantly Affect Energy
Supply, Distribution, or Use

This rule is not subject to Executive Order 13211, ``Actions
Concerning Regulations That Significantly Affect Energy Supply,
Distribution, or Use'' (66 FR 28355 (May 22, 2001)) because it is not a
significant regulatory action under Executive Order 12866.

I. National Technology Transfer and Advancement Act of 1995

Section 12(d) of the National Technology Transfer and Advancement
Act of 1995 (``NTTAA''), Public Law 104-113, section 12(d) (15 U.S.C.
272 note) directs EPA to use voluntary consensus standards in its
regulatory activities unless to do so would be inconsistent with
applicable law or otherwise impractical. Voluntary consensus standards
are technical standards (e.g., materials specifications, test methods,
sampling procedures, and business practices) that are developed or
adopted by voluntary consensus standards bodies. The NTTAA directs EPA
to provide Congress, through OMB, explanations when the Agency decides
not to use available and applicable voluntary consensus standards.
This action does not involve technical standards. Therefore, EPA
did not consider the use of any voluntary consensus standards.

J. Congressional Review Act of 1998

The Congressional Review Act, 5 U.S.C. 801 et seq., as added by the
Small Business Regulatory Enforcement Fairness Act of 1996, generally
provides that before a rule may take effect, the agency promulgating
the rule must submit a rule report, which includes a copy of the rule,
to each House of the Congress and to the Comptroller General of the
United States. EPA will submit a report containing this rule and other
required information to the U.S. Senate, the U.S. House of
Representatives, and the Comptroller General of the United States prior
to publication of the rule in the Federal Register. A Major rule cannot
take effect until 60 days after it is published in the Federal
Register. This action is not a ``major rule'' as defined by 5 U.S.C.
804(2), and will be effective 30 days from the publication date of this
notice.

List of Subjects in 40 CFR Part 51

Environmental protection, Administrative practice and procedure,
Air pollution control, Carbon monoxide, Intergovernmental relations,
Nitrogen oxides, Ozone, Particulate matter, Reporting and recordkeeping
requirements, Sulfur oxides.

Dated: April 2, 2003.
Christine Todd Whitman,
Administrator.

? Part 51, chapter I, title 40 of the Code of Federal Regulations is
amended as follows:

PART 51--REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF
IMPLEMENTATION PLANS

? 1. The authority citation for part 51 continues to read as follows:

Authority: 23 U.S.C. 100; 42 U.S.C. 7401-7671q.

? 2. Appendix W to Part 51 revised to read as follows:

Appendix W to Part 51--Guideline on Air Quality Models

Preface

a. Industry and control agencies have long expressed a need for
consistency in the application of air quality models for regulatory
purposes. In the 1977 Clean Air Act, Congress mandated such
consistency and encouraged the standardization of model
applications. The Guideline on Air Quality Models (hereafter,
Guideline) was first published in April 1978 to satisfy these
requirements by specifying models and providing guidance for their
use. The Guideline provides a common basis for estimating the air
quality concentrations of criteria pollutants used in assessing
control strategies and developing emission limits.
b. The continuing development of new air quality models in
response to regulatory requirements and the expanded requirements
for models to cover even more complex problems have emphasized the
need for periodic review and update of guidance on these techniques.
Three primary on-going activities provide direct input to revisions
of the Guideline. The first is a series of annual

[[Page 18449]]

EPA workshops conducted for the purpose of ensuring consistency and
providing clarification in the application of models. The second
activity is the solicitation and review of new models from the
technical and user community. In the March 27, 1980 Federal
Register, a procedure was outlined for the submittal to EPA of
privately developed models. After extensive evaluation and
scientific review, these models, as well as those made available by
EPA, are considered for recognition in the Guideline. The third
activity is the extensive on-going research efforts by EPA and
others in air quality and meteorological modeling.
c. Based primarily on these three activities, new sections and
topics are included as needed. EPA does not make changes to the
guidance on a predetermined schedule, but rather on an as needed
basis. EPA believes that revisions of the Guideline should be timely
and responsive to user needs and should involve public participation
to the greatest possible extent. All future changes to the guidance
will be proposed and finalized in the Federal Register. Information
on the current status of modeling guidance can always be obtained
from EPA's Regional Offices.

Table of Contents

List of Tables

1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
2.2 Levels of Sophistication of Models
2.3 Availability of Models
3.0 Recommended Air Quality Models
3.1 Preferred Modeling Techniques
3.1.1 Discussion
3.1.2 Recommendations
3.2 Use of Alternative Models
3.2.1 Discussion
3.2.2 Recommendations
3.3 Availability of Supplementary Modeling Guidance
4.0 Traditional Stationary-Source Models
4.1 Discussion
4.2 Recommendations
4.2.1 Screening Techniques
4.2.1.1 Simple Terrain
4.2.1.2 Complex Terrain
4.2.2 Refined Analytical Techniques
5.0 Model Use in Complex Terrain
5.1 Discussion
5.2 Recommendations
5.2.1 Screening Techniques
5.2.2 Refined Analytical Techniques
6.0 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen
Dioxide, and Lead
6.1 Discussion
6.2 Recommendations
6.2.1 Models for Ozone
6.2.1 Models for Particulate Matter
6.2.2.1 PM-2.5
6.2.2.2 PM-10
6.2.3 Models for Carbon Monoxide
6.2.4 Models for Nitrogen Dioxide (Annual Average)
6.2.5 Models for Lead
7.0 Other Model Requirements
7.1 Discussion
7.2 Recommendations
7.2.1 Visibility
7.2.2 Good Engineering Practice Stack Height
7.2.3 Long Range Transport (i.e., beyond 50km)
7.2.4 Modeling Guidance for Other Governmental Programs
8.0 General Modeling Considerations
8.1 Discussion
8.2 Recommendations
8.2.1 Design Concentrations
8.2.2 Critical Receptor Sites
8.2.3 Dispersion Coefficients
8.2.4 Stability Categories
8.2.5 Plume Rise
8.2.6 Chemical Transformation
8.2.7 Gravitational Settling and Deposition
8.2.8 Complex Winds
8.2.9 Calibration of Models
9.0 Model Input Data
9.1 Source Data
9.1.1 Discussion
9.1.2 Recommendations
9.2 Background Concentrations
9.2.1 Discussion
9.2.2 Recommendations (Isolated Single Source)
9.2.3 Recommendations (Multi-Source Areas)
9.3 Meteorological Input Data
9.3.1 Length of Record of Meteorological Data
9.3.2 National Weather Service Data
9.3.3 Site Specific Data
9.3.4 Treatment of Calms
10.0 Accuracy and Uncertainty of Models
10.1 Discussion
10.1.1 Overview of Model Uncertainty
10.1.2 Studies of Model Accuracy
10.1.3 Use of Uncertainty in Decision-Making
10.1.4 Evaluation of Models
10.2 Recommendations
11.0 Regulatory Application of Models
11.1 Discussion
11.2 Recommendations
11.2.1 Analysis Requirements
11.2.2 Use of Measured Data in Lieu of Model Estimates
11.2.3 Emission Limits
12.0 Bibliography
13.0 References

Appendix A to Appendix W of 40 CFR Part 51--Summaries of Preferred Air
Quality Models

List of Tables
------------------------------------------------------------------------
Table No. Title
------------------------------------------------------------------------
5-1............................... Neutral/Stable Meteorological Matrix
for CTSCREEN.
5-1............................... Unstable/Convective Meteorological
Matrix for CTSCREEN.
9-1............................... Model Emission Input Data for Point
Sources.
9-2............................... Point Source Model Input Data
(Emissions) for PSD NAAQS
Compliance Demonstrations.
9-3............................... Averaging Times for Site Specific
Wind and Turbulence Measurements.
------------------------------------------------------------------------

1.0 Introduction

a. The Guideline recommends air quality modeling techniques that
should be applied to State Implementation Plan (SIP) revisions for
existing sources and to new source reviews (NSR), including
prevention of significant deterioration (PSD). (See Ref. 1, 2, 3).
Applicable only to criteria air pollutants, it is intended for use
by EPA Regional Offices in judging the adequacy of modeling analyses
performed by EPA, State and local agencies and by industry. The
guidance is appropriate for use by other Federal agencies and by
State agencies with air quality and land management
responsibilities. The Guideline serves to identify, for all
interested parties, those techniques and data bases EPA considers
acceptable. The Guideline is not intended to be a compendium of
modeling techniques. Rather, it should serve as a common measure of
acceptable technical analysis when supported by sound scientific
judgement.
b. Due to limitations in the spatial and temporal coverage of
air quality measurements, monitoring data normally are not
sufficient as the sole basis for demonstrating the adequacy of
emission limits for existing sources. Also, the impacts of new
sources that do not yet exist can only be determined through
modeling. Thus, models, while uniquely filling one program need,
have become a primary analytical tool in most air quality
assessments. Air quality measurements can be used in a complementary
manner to dispersion models, with due regard for the strengths and
weaknesses of both analysis techniques. Measurements are
particularly useful in assessing the accuracy of model estimates.
The use of air quality measurements alone however could be
preferable, as detailed in a later section of this document, when
models are found to be unacceptable and monitoring data with
sufficient spatial and temporal coverage are available.
c. It would be advantageous to categorize the various regulatory
programs and to apply a designated model to each proposed source
needing analysis under a given program. However, the diversity of
the nation's topography and climate, and variations in source
configurations and operating characteristics dictate against a
strict modeling ``cookbook''. There is no one model capable of
properly addressing all conceivable situations even within a broad
category such as point sources. Meteorological phenomena associated
with threats to air quality standards are rarely amenable to a
single mathematical treatment; thus, case-by-case analysis and
judgement are frequently required. As modeling efforts become more
complex, it is increasingly important that they be directed by
highly competent individuals with a broad range of experience and
knowledge in air quality meteorology. Further, they should be
coordinated closely with specialists in emissions characteristics,
air monitoring and data processing. The judgement of experienced
meteorologists and analysts is essential.
d. The model that most accurately estimates concentrations in
the area of interest is always sought. However, it is clear from the
needs expressed by the States and EPA Regional Offices, by many
industries and trade associations, and also by the deliberations of
Congress, that consistency in

[[Page 18450]]

the selection and application of models and data bases should also
be sought, even in case-by-case analyses. Consistency ensures that
air quality control agencies and the general public have a common
basis for estimating pollutant concentrations, assessing control
strategies and specifying emission limits. Such consistency is not,
however, promoted at the expense of model and data base accuracy.
The Guideline provides a consistent basis for selection of the most
accurate models and data bases for use in air quality assessments.
e. Recommendations are made in the Guideline concerning air
quality models, data bases, requirements for concentration
estimates, the use of measured data in lieu of model estimates, and
model evaluation procedures. Models are identified for some specific
applications. The guidance provided here should be followed in air
quality analyses relative to State Implementation Plans and in
supporting analyses required by EPA, State and local agency air
programs. EPA may approve the use of another technique that can be
demonstrated to be more appropriate than those recommended in this
guide. This is discussed at greater length in Section 3. In all
cases, the model applied to a given situation should be the one that
provides the most accurate representation of atmospheric transport,
dispersion, and chemical transformations in the area of interest.
However, to ensure consistency, deviations from this guide should be
carefully documented and fully supported.
f. From time to time situations arise requiring clarification of
the intent of the guidance on a specific topic. Periodic workshops
are held with the headquarters, Regional Office, State, and local
agency modeling representatives to ensure consistency in modeling
guidance and to promote the use of more accurate air quality models
and data bases. The workshops serve to provide further explanations
of Guideline requirements to the Regional Offices and workshop
reports are issued with this clarifying information. In addition,
findings from on-going research programs, new model submittals, or
results from model evaluations and applications are continuously
evaluated. Based on this information changes in the guidance may be
indicated.
g. All changes to the Guideline must follow rulemaking
requirements since the Guideline is codified in Appendix W of Part
51. EPA will promulgate proposed and final rules in the Federal
Register to amend this Appendix. Ample opportunity for public
comment will be provided for each proposed change and public
hearings scheduled if requested.
h. A wide range of topics on modeling and data bases are
discussed in the Guideline. Section 2 gives an overview of models
and their appropriate use. Section 3 provides specific guidance on
the use of ``preferred'' air quality models and on the selection of
alternative techniques. Sections 4 through 7 provide recommendations
on modeling techniques for application to simple-terrain stationary
source problems, complex terrain problems, and mobile source
problems. Specific modeling requirements for selected regulatory
issues are also addressed. Section 8 discusses issues common to many
modeling analyses, including acceptable model components. Section 9
makes recommendations for data inputs to models including source,
meteorological and background air quality data. Section 10 covers
the uncertainty in model estimates and how that information can be
useful to the regulatory decision-maker. The last chapter summarizes
how estimates and measurements of air quality are used in assessing
source impact and in evaluating control strategies.
i. Appendix W to 40 CFR Part 51 itself contains an appendix:
Appendix A. Thus, when reference is made to ``Appendix A'' in this
document, it refers to Appendix A to Appendix W to 40 CFR Part 51.
Appendix A contains summaries of refined air quality models that are
``preferred'' for specific applications; both EPA models and models
developed by others are included.

2.0 Overview of Model Use

a. Before attempting to implement the guidance contained in this
document, the reader should be aware of certain general information
concerning air quality models and their use. Such information is
provided in this section.

2.1 Suitability of Models

a. The extent to which a specific air quality model is suitable
for the evaluation of source impact depends upon several factors.
These include: (1) The meteorological and topographic complexities
of the area; (2) the level of detail and accuracy needed for the
analysis; (3) the technical competence of those undertaking such
simulation modeling; (4) the resources available; and (5) the detail
and accuracy of the data base, i.e., emissions inventory,
meteorological data, and air quality data. Appropriate data should
be available before any attempt is made to apply a model. A model
that requires detailed, precise, input data should not be used when
such data are unavailable. However, assuming the data are adequate,
the greater the detail with which a model considers the spatial and
temporal variations in emissions and meteorological conditions, the
greater the ability to evaluate the source impact and to distinguish
the effects of various control strategies.
b. Air quality models have been applied with the most accuracy,
or the least degree of uncertainty, to simulations of long term
averages in areas with relatively simple topography. Areas subject
to major topographic influences experience meteorological
complexities that are extremely difficult to simulate. Although
models are available for such circumstances, they are frequently
site specific and resource intensive. In the absence of a model
capable of simulating such complexities, only a preliminary
approximation may be feasible until such time as better models and
data bases become available.
c. Models are highly specialized tools. Competent and
experienced personnel are an essential prerequisite to the
successful application of simulation models. The need for
specialists is critical when the more sophisticated models are used
or the area being investigated has complicated meteorological or
topographic features. A model applied improperly, or with
inappropriate data, can lead to serious misjudgements regarding the
source impact or the effectiveness of a control strategy.
d. The resource demands generated by use of air quality models
vary widely depending on the specific application. The resources
required depend on the nature of the model and its complexity, the
detail of the data base, the difficulty of the application, and the
amount and level of expertise required. The costs of manpower and
computational facilities may also be important factors in the
selection and use of a model for a specific analysis. However, it
should be recognized that under some sets of physical circumstances
and accuracy requirements, no present model may be appropriate.
Thus, consideration of these factors should lead to selection of an
appropriate model.

2.2 Levels of Sophistication of Models

a. There are two levels of sophistication of models. The first
level consists of relatively simple estimation techniques that
generally use preset, worst-case meteorological conditions to
provide conservative estimates of the air quality impact of a
specific source, or source category. These are called screening
techniques or screening models. The purpose of such techniques is to
eliminate the need of more detailed modeling for those sources that
clearly will not cause or contribute to ambient concentrations in
excess of either the National Ambient Air Quality Standards
(NAAQS)\4\ or the allowable prevention of significant deterioration
(PSD) concentration increments.2,3 If a screening
technique indicates that the concentration contributed by the source
exceeds the PSD increment or the increment remaining to just meet
the NAAQS, then the second level of more sophisticated models should
be applied.
b. The second level consists of those analytical techniques that
provide more detailed treatment of physical and chemical atmospheric
processes, require more detailed and precise input data, and provide
more specialized concentration estimates. As a result they provide a
more refined and, at least theoretically, a more accurate estimate
of source impact and the effectiveness of control strategies. These
are referred to as refined models.
c. The use of screening techniques followed, as appropriate, by
a more refined analysis is always desirable, however there are
situations where the screening techniques are practically and
technically the only viable option for estimating source impact. In
such cases, an attempt should be made to acquire or improve the
necessary data bases and to develop appropriate analytical
techniques.

2.3 Availability of Models

a. For most of the screening and refined models discussed in the
Guideline, codes, associated documentation and other useful
information are available for download from EPA's Support Center for
Regulatory Air Modeling (SCRAM) Internet Web site at
http://www.epa.gov/scram001. A list of

[[Page 18451]]

alternate models that can be used with case-by-case justification
(subsection 3.2) and an example air quality analysis checklist are
also posted on this Web site. This is a site with which modelers
should become familiar.

3.0 Recommended Air Quality Models

a. This section recommends the approach to be taken in
determining refined modeling techniques for use in regulatory air
quality programs. The status of models developed by EPA, as well as
those submitted to EPA for review and possible inclusion in this
guidance, is discussed. The section also addresses the selection of
models for individual cases and provides recommendations for
situations where the preferred models are not applicable. Two
additional sources of modeling guidance are the Model Clearinghouse
and periodic Regional/State/Local Modelers workshops.
b. In this guidance, when approval is required for a particular
modeling technique or analytical procedure, we often refer to the
``appropriate reviewing authority''. In some EPA regions, authority
for NSR and PSD permitting and related activities has been delegated
to State and even local agencies. In these cases, such agencies are
``representatives'' of the respective regions. Even in these
circumstances, the Regional Office retains the ultimate authority in
decisions and approvals. Therefore, as discussed above and depending
on the circumstances, the appropriate reviewing authority may be the
Regional Office, Federal Land Manager(s), State agency(ies), or
perhaps local agency(ies). In cases where review and approval comes
solely from the Regional Office (sometimes stated as ``Regional
Administrator''), this will be stipulated. If there is any question
as to the appropriate reviewing authority, you should contact the
Regional modeling contact (http://www.epa.gov/scram001/tt28.htm
#regionalmodelingcontacts) in the appropriate EPA Regional
Office, whose jurisdiction generally includes the physical location
of the source in question and its expected impacts.
c. In all regulatory analyses, especially if other than
preferred models are selected for use, early discussions among
Regional Office staff, State and local control agencies, industry
representatives, and where appropriate, the Federal Land Manager,
are invaluable and are encouraged. Agreement on the data base(s) to
be used, modeling techniques to be applied and the overall technical
approach, prior to the actual analyses, helps avoid
misunderstandings concerning the final results and may reduce the
later need for additional analyses. The use of an air quality
analysis checklist, such as is posted on EPA's Internet SCRAM Web
site (subsection 2.3), and the preparation of a written protocol
help to keep misunderstandings at a minimum.
d. It should not be construed that the preferred models
identified here are to be permanently used to the exclusion of all
others or that they are the only models available for relating
emissions to air quality. The model that most accurately estimates
concentrations in the area of interest is always sought. However,
designation of specific models is needed to promote consistency in
model selection and application.
e. The 1980 solicitation of new or different models from the
technical community and the program whereby these models were
evaluated, established a means by which new models are identified,
reviewed and made available in the Guideline. There is a pressing
need for the development of models for a wide range of regulatory
applications. Refined models that more realistically simulate the
physical and chemical process in the atmosphere and that more
reliably estimate pollutant concentrations are needed. Thus, the
solicitation of models is considered to be continuous.

3.1 Preferred Modeling Techniques

3.1.1 Discussion

a. EPA has developed models suitable for regulatory application.
Other models have been submitted by private developers for possible
inclusion in the Guideline. These refined models have undergone
evaluation exercises 7,8,9,10,11,12,13,14,15 that include
statistical measures of model performance in comparison with
measured air quality data as suggested by the American
Meteorological Society \16\ and, where possible, peer scientific
reviews. \17,18,19,20,21\
b. When a single model is found to perform better than others,
it is recommended for application as a preferred model and listed in
Appendix A. If no one model is found to clearly perform better
through the evaluation exercise, then the preferred model listed in
Appendix A is selected on the basis of other factors such as past
use, public familiarity, cost or resource requirements, and
availability. No further evaluation of a preferred model is required
for a particular application if the EPA recommendations for
regulatory use specified for the model in the Guideline are
followed. Alternative models to those listed in Appendix A should
generally be compared with measured air quality data when they are
used for regulatory applications consistent with recommendations in
subsection 3.2.
c. The solicitation of new refined models which are based on
sounder scientific principles and which more reliably estimate
pollutant concentrations is considered by EPA to be continuous.
Models that are submitted in accordance with the established
provisions will be evaluated as submitted. These requirements are:
i. The model must be computerized and functioning in a common
computer code suitable for use on a variety of computer systems.
ii. The model must be documented in a user's guide which
identifies the mathematics of the model, data requirements and
program operating characteristics at a level of detail comparable to
that available for currently recommended models.
iii. The model must be accompanied by a complete test data set
including input parameters and output results. The test data must be
included in the user's guide as well as provided in computer-
readable form.
iv. The model must be useful to typical users, e.g., State air
pollution control agencies, for specific air quality control
problems. Such users should be able to operate the computer
program(s) from available documentation.
v. The model documentation must include a comparison with air
quality data (and/or tracer measurements) or with other well-
established analytical techniques.
vi. The developer must be willing to make the model available to
users at reasonable cost or make it available for public access
through the Internet or National Technical Information Service: the
model cannot be proprietary.
d. The evaluation process will include a determination of
technical merit, in accordance with the above six items including
the practicality of the model for use in ongoing regulatory
programs. Each model will also be subjected to a performance
evaluation for an appropriate data base and to a peer scientific
review. Models for wide use (not just an isolated case) that are
found to perform better will be proposed for inclusion as preferred
models in future Guideline revisions.

3.1.2 Recommendations

a. Appendix A identifies refined models that are preferred for
use in regulatory applications. If a model is required for a
particular application, the user should select a model from that
appendix. These models may be used without a formal demonstration of
applicability as long as they are used as indicated in each model
summary of Appendix A. Further recommendations for the application
of these models to specific source problems are found in subsequent
sections of the Guideline.
b. If changes are made to a preferred model without affecting
the concentration estimates, the preferred status of the model is
unchanged. Examples of modifications that do not affect
concentrations are those made to enable use of a different computer
or those that affect only the format or averaging time of the model
results. However, when any changes are made, the Regional
Administrator should require a test case example to demonstrate that
the concentration estimates are not affected.
c. A preferred model should be operated with the options listed
in Appendix A as ``Recommendations for Regulatory Use.'' If other
options are exercised, the model is no longer ``preferred.'' Any
other modification to a preferred model that would result in a
change in the concentration estimates likewise alters its status as
a preferred model. Use of the model must then be justified on a
case-by-case basis.

3.2 Use of Alternative Models

3.2.1 Discussion

a. Selection of the best techniques for each individual air
quality analysis is always encouraged, but the selection should be
done in a consistent manner. A simple listing of models in this
guide cannot alone achieve that consistency nor can it necessarily
provide the best model for all possible situations. EPA reports
22,23 are available to assist in developing a consistent
approach when justifying the use of other than the preferred
modeling techniques recommended

[[Page 18452]]

in the Guideline. An ASTM reference 24 provides a general
philosophy for developing and implementing advanced statistical
evaluations of atmospheric dispersion models, and provides an
example statistical technique to illustrate the application of this
philosophy. An EPA reference 25 provides a statistical
technique for evaluating model performance for predicting peak
concentration values, as might be observed at individual monitoring
locations. In many cases, this protocol should be considered
preferentially to the material in Chapter 3 of reference 22. The
procedures in these documents provide a general framework for
objective decision-making on the acceptability of an alternative
model for a given regulatory application. The documents contain
procedures for conducting both the technical evaluation of the model
and the field test or performance evaluation.
b. This section discusses the use of alternate modeling
techniques and defines three situations when alternative models may
be used.

3.2.2 Recommendations

a. Determination of acceptability of a model is a Regional
Office responsibility. Where the Regional Administrator finds that
an alternative model is more appropriate than a preferred model,
that model may be used subject to the recommendations of this
subsection. This finding will normally result from a determination
that (1) a preferred air quality model is not appropriate for the
particular application; or (2) a more appropriate model or
analytical procedure is available and applicable.
b. An alternative model should be evaluated from both a
theoretical and a performance perspective before it is selected for
use. There are three separate conditions under which such a model
may normally be approved for use: (1) If a demonstration can be made
that the model produces concentration estimates equivalent to the
estimates obtained using a preferred model; (2) if a statistical
performance evaluation has been conducted using measured air quality
data and the results of that evaluation indicate the alternative
model performs better for the given application than a comparable
model in Appendix A; or (3) if the preferred model is less
appropriate for the specific application, or there is no preferred
model. Any one of these three separate conditions may make use of an
alternative model acceptable. Some known alternative models that are
applicable for selected situations are listed on EPA's SCRAM
Internet Web site (subsection 2.3). However, inclusion there does
not confer any unique status relative to other alternative models
that are being or will be developed in the future.
c. Equivalency, condition (1) in paragraph (b) of this
subsection, is established by demonstrating that the maximum or
highest, second highest concentrations are within 2 percent of the
estimates obtained from the preferred model. The option to show
equivalency is intended as a simple demonstration of acceptability
for an alternative model that is so nearly identical (or contains
options that can make it identical) to a preferred model that it can
be treated for practical purposes as the preferred model. Two
percent was selected as the basis for equivalency since it is a
rough approximation of the fraction that PSD Class I increments are
of the NAAQS for SO\2\, i.e., the difference in concentrations that
is judged to be significant. However, notwithstanding this
demonstration, models that are not equivalent may be used when one
of the two other conditions described in paragraphs (d) and (e) of
this subsection are satisfied.
d. For condition (2) in paragraph (b) of this subsection, the
procedures and techniques for determining the acceptability of a
model for an individual case based on superior performance are
contained in references 22-25 should be followed, as appropriate.
Preparation and implementation of an evaluation protocol which is
acceptable to both control agencies and regulated industry is an
important element in such an evaluation.
e. Finally, for condition (3) in paragraph (b) of this
subsection, an alternative refined model may be used provided that:
i. The model has received a scientific peer review;
ii. The model can be demonstrated to be applicable to the
problem on a theoretical basis;
iii. The data bases which are necessary to perform the analysis
are available and adequate;
iv. Appropriate performance evaluations of the model have shown
that the model is not biased toward underestimates; and
v. A protocol on methods and procedures to be followed has been
established.

3.3 Availability of Supplementary Modeling Guidance

a. The Regional Administrator has the authority to select models
that are appropriate for use in a given situation. However, there is
a need for assistance and guidance in the selection process so that
fairness and consistency in modeling decisions is fostered among the
various Regional Offices and the States. To satisfy that need, EPA
established the Model Clearinghouse \5\ and also holds periodic
workshops with headquarters, Regional Office, State, and local
agency modeling representatives.
b. The Regional Office should always be consulted for
information and guidance concerning modeling methods and
interpretations of modeling guidance, and to ensure that the air
quality model user has available the latest most up-to-date policy
and procedures. As appropriate, the Regional Office may request
assistance from the Model Clearinghouse after an initial evaluation
and decision has been reached concerning the application of a model,
analytical technique or data base in a particular regulatory action.

4.0 Simple-Terrain Stationary Source Models

4.1 Discussion

a. Simple terrain, as used here, is considered to be an area
where terrain features are all lower in elevation than the top of
the stack of the source(s) in question. The models recommended in
this section are generally used in the air quality impact analysis
of stationary sources for most criteria pollutants. The averaging
time of the concentration estimates produced by these models ranges
from 1 hour to an annual average.
b. In the early 1980s, model evaluation exercises were conducted
to determine the ``best, most appropriate point source model'' for
use in simple terrain.8,17 No one model was found to be
clearly superior and, based on past use, public familiarity, and
availability, ISC (predecessor to ISC3 \26\) became the recommended
model for a wide range of regulatory applications. Other refined
models which also employed the basic Gaussian kernel, i.e., BLP,
CALINE3, OCD, and EDMS, were developed for specialized applications
(Appendix A). Performance evaluations were also made for these
models, which are identified in Appendix A.

4.2 Recommendations

4.2.1 Screening Techniques

a. Where a preliminary or conservative estimate is desired,
point source screening techniques are an acceptable approach to air
quality analyses. EPA has published guidance for screening
procedures,\27\ and a computerized version of the recommended
screening technique, SCREEN3, is available.\28\
b. All screening procedures should be adjusted to the site and
problem at hand. Close attention should be paid to whether the area
should be classified urban or rural in accordance with subsection
8.2.3. The climatology of the area should be studied to help define
the worst-case meteorological conditions. Agreement should be
reached between the model user and the appropriate reviewing
authority (paragraph 3.0(b)) on the choice of the screening model
for each analysis, and on the input data as well as the ultimate use
of the results.

4.2.2 Refined Analytical Techniques

a. A brief description of preferred models for refined
applications is found in Appendix A. Also listed in that appendix
are the model input requirements, the standard options that should
be selected when running the program, and output options.
b. When modeling for compliance with short term NAAQS and PSD
increments is of primary concern, a short term model may be used to
provide long term concentration estimates. The conversion from long
term to short term concentration averages by any transformation
technique is not acceptable in regulatory applications.
c. The state-of-the-science for modeling atmospheric deposition
is evolving and the best techniques are currently being assessed and
their results are being compared with observations. Consequently,
the approach taken for any purpose should be coordinated with the
appropriate reviewing authority (paragraph 3.0(b)).

5.0 Model Use in Complex Terrain

5.1 Discussion

a. For the purpose of the Guideline, complex terrain is defined
as terrain exceeding the height of the stack being

[[Page 18453]]

modeled. Complex terrain dispersion models are normally applied to
stationary sources of pollutants such as SO₂ and
particulates.
b. A major outcome from the EPA Complex Terrain Model
Development project has been the publication of a refined dispersion
model (CTDM) suitable for regulatory application to plume impaction
assessments in complex terrain.\29\ Although CTDM as originally
produced was only applicable to those hours characterized as neutral
or stable, a computer code for all stability conditions--CTDMPLUS--
together with a user's guide,\30\ and site specific meteorological
and terrain data processors \31,32\ is available. Moreover,
CTSCREEN,\33\ a version of CTDMPLUS that does not require site
specific meteorological data inputs, is also available as a
screening technique.
c. The methods discussed in this section should be considered in
two categories: (1) Screening techniques, and (2) the refined
dispersion model, CTDMPLUS, discussed in this subsection and listed
in Appendix A.
d. Continued improvements in ability to accurately model plume
dispersion in complex terrain situations can be expected, e.g., from
research on lee side effects due to terrain obstacles. New
approaches to improve the ability of models to realistically
simulate atmospheric physics, e.g., hybrid models which incorporate
an accurate wind field analysis, will ultimately provide more
appropriate tools for analyses. Such hybrid modeling techniques are
also acceptable for regulatory applications after the appropriate
demonstration and evaluation.\22\

5.2 Recommendations

a. Recommendations in this section apply primarily to those
situations where the impaction of plumes on terrain at elevations
equal to or greater than the plume centerline during stable
atmospheric conditions are determined to be the problem. If a
violation of any NAAQS or the controlling increment is indicated by
using any of the preferred screening techniques, then a refined
complex terrain model may be used. Phenomena such as fumigation,
wind direction shear, lee-side effects, building wake- or terrain-
induced downwash, deposition, chemical transformation, variable
plume trajectories, and long range transport are not addressed by
the recommendations in this section.
b. Where site specific data are used for either screening or
refined complex terrain models, a data base of at least 1 full-year
of meteorological data is preferred. If more data are available,
they should be used. Meteorological data used in the analysis should
be reviewed for both spatial and temporal representativeness.
c. Placement of receptors requires very careful attention when
modeling in complex terrain. Often the highest concentrations are
predicted to occur under very stable conditions, when the plume is
near, or impinges on, the terrain. The plume under such conditions
may be quite narrow in the vertical, so that even relatively small
changes in a receptor's location may substantially affect the
predicted concentration. Receptors within about a kilometer of the
source may be even more sensitive to location. Thus, a dense array
of receptors may be required in some cases. In order to avoid
excessively large computer runs due to such a large array of
receptors, it is often desirable to model the area twice. The first
model run would use a moderate number of receptors carefully located
over the area of interest. The second model run would use a more
dense array of receptors in areas showing potential for high
concentrations, as indicated by the results of the first model run.
d. When CTSCREEN or CTDMPLUS is used, digitized contour data
must be first processed by the CTDM Terrain Processor \32\ to
provide hill shape parameters in a format suitable for direct input
to CTDMPLUS. Then the user supplies receptors either through an
interactive program that is part of the model or directly, by using
a text editor; using both methods to select receptors will generally
be necessary to assure that the maximum concentrations are estimated
by either model. In cases where a terrain feature may ``appear to
the plume'' as smaller, multiple hills, it may be necessary to model
the terrain both as a single feature and as multiple hills to
determine design concentrations.
e. The user is encouraged to confer with the Regional Office if
any unresolvable problems are encountered with any screening or
refined analytical procedures, e.g., meteorological data, receptor
siting, or terrain contour processing issues.

5.2.1 Screening Techniques

a. CTSCREEN \33\ can be used to obtain conservative, yet
realistic, worst-case estimates for receptors located on terrain
above stack height. CTSCREEN accounts for the three-dimensional
nature of plume and terrain interaction and requires detailed
terrain data representative of the modeling domain. The model
description and user's instructions are contained in the user's
guide.\33\ The terrain data must be digitized in the same manner as
for CTDMPLUS and a terrain processor is available.\32\ A discussion
of the model's performance characteristics is provided in a
technical paper.\34\ CTSCREEN is designed to execute a fixed matrix
of meteorological values for wind speed (u), standard deviation of
horizontal and vertical wind speeds ([sigma]_v,
[sigma]_w), vertical potential temperature gradient
(d[thetas]/dz), friction velocity (u_*), Monin-Obukhov
length (L), mixing height (z_i) as a function of terrain
height, and wind directions for both neutral/stable conditions and
unstable convective conditions. Table 5-1 contains the matrix of
meteorological variables that is used for each CTSCREEN analysis.
There are 96 combinations, including exceptions, for each wind
direction for the neutral/stable case, and 108 combinations for the
unstable case. The specification of wind direction, however, is
handled internally, based on the source and terrain geometry.
Although CTSCREEN is designed to address a single source scenario,
there are a number of options that can be selected on a case-by-case
basis to address multi-source situations. However, the appropriate
reviewing authority (paragraph 3.0(b)) should be consulted, and
concurrence obtained, on the protocol for modeling multiple sources
with CTSCREEN to ensure that the worst case is identified and
assessed. The maximum concentration output from CTSCREEN represents
a worst-case 1-hour concentration. Time-scaling factors of 0.7 for
3-hour, 0.15 for 24-hour and 0.03 for annual concentration averages
are applied internally by CTSCREEN to the highest 1-hour
concentration calculated by the model.
b. Placement of receptors requires very careful attention when
modeling in complex terrain. Often the highest concentrations are
predicted to occur under very stable conditions, when the plume is
near, or impinges on, the terrain. The plume under such conditions
may be quite narrow in the vertical, so that even relatively small
changes in a receptor's location may substantially affect the
predicted concentration. Receptors within about a kilometer of the
source may be even more sensitive to location. Thus, a dense array
of receptors may be required in some cases. In order to avoid
excessively large computer runs due to such a large array of
receptors, it is often desirable to model the area twice. The first
model run would use a moderate number of receptors carefully located
over the area of interest. The second model run would use a more
dense array of receptors in areas showing potential for high
concentrations, as indicated by the results of the first model run.
c. As mentioned above, digitized contour data must be
preprocessed \32\ to provide hill shape parameters in suitable input
format. The user then supplies receptors either through an
interactive program that is part of the model or directly, by using
a text editor; using both methods to select receptors will generally
be necessary to assure that the maximum concentrations are estimated
by either model. In cases where a terrain feature may ``appear to
the plume'' as smaller, multiple hills, it may be necessary to model
the terrain both as a single feature and as multiple hills to
determine design concentrations.
d. Other screening techniques, e.g., Valley (as implemented in
SCREEN3 \28\), COMPLEX I (as implemented in ISC3 \26\), SHORTZ/LONGZ
\35\, and RTDM \36\ may be acceptable for complex terrain cases
where established procedures are used. The user is encouraged to
confer with the appropriate reviewing authority (paragraph 3.0(b))
if any unresolvable problems are encountered, e.g., applicability,
meteorological data, receptor siting, or terrain contour processing
issues.

5.2.2 Refined Analytical Techniques

a. When the results of the screening analysis demonstrate a
possible violation of NAAQS or the controlling PSD increments, a
more refined analysis may need to be conducted.
b. The Complex Terrain Dispersion Model PLus Algorithms for
Unstable Situations (CTDMPLUS) is a refined air quality model that
is preferred for use in all stability conditions for complex terrain
applications. CTDMPLUS is a sequential model that requires five
input files: (1) General program specifications; (2) a terrain data
file; (3) a receptor file; (4) a surface meteorological data file;
and (5) a user created meteorological profile data file. Two
optional input files consist of hourly emissions parameters and a
file containing upper air data from rawinsonde data files, e.g., a
National Climatic Data Center TD-6201 file, unless

[[Page 18454]]

there are no hours categorized as unstable in the record. The model
description and user instructions are contained in Volume 1 of the
User's Guide.\30\ Separate publications 32,31 describe
the terrain preprocessor system and the meteorological preprocessor
program. In Part I of a technical article \37\ is a discussion of
the model and its preprocessors; the model's performance
characteristics are discussed in Part II of the same article.\38\
The size of the CTDMPLUS executable file on a personal computer is
approximately 360K bytes. The model produces hourly average
concentrations of stable pollutants, i.e., chemical transformation
or decay of species and settling/deposition are not simulated. To
obtain concentration averages corresponding to the NAAQS, e.g., 3-
or 24-hour, or annual averages, the user must execute a
postprocessor program such as CHAVG. CTDMPLUS is applicable to all
receptors on terrain elevations above stack top. However, the model
contains no algorithms for simulating building downwash or the
mixing or recirculation found in cavity zones in the lee of a hill.
The path taken by a plume through an array of hills cannot be
simulated. CTDMPLUS does not explicitly simulate calm meteorological
periods, and for those situations the user should follow the
guidance in subsection 9.3.4. The user should follow the
recommendations in the User's Guide under General Program
Specifications for: (1) Selecting mixed layer heights, (2) setting
minimum scalar wind speed to 1 m/s, and (3) scaling wind direction
with height. Close coordination with the Regional Office is
essential to insure a consistent, technically sound application of
this model.
c. The performance of CTDMPLUS is greatly improved by the use of
meteorological data from several levels up to plume height. However,
due to the vast range of source-plume-hill geometries possible in
complex terrain, detailed requirements for meteorological monitoring
in support of refined analyses using CTDMPLUS should be determined
on a case-by-case basis. The following general guidance should be
considered in the development of a meteorological monitoring
protocol for regulatory applications of CTDMPLUS and reviewed in
detail by the Regional Office before initiating any monitoring. As
appropriate, EPA guidance (see reference 100) should be consulted
for specific guidance on siting requirements for meteorological
towers, selection and exposure of sensors, etc. As more experience
is gained with the model in a variety of circumstances, more
specific guidance may be developed.
d. Site specific meteorological data are critical to dispersion
modeling in complex terrain and, consequently, the meteorological
requirements are more demanding than for simple terrain. Generally,
three different meteorological files (referred to as surface,
profile, and rawin files) are needed to run CTDMPLUS in a regulatory
mode.
e. The surface file is created by the meteorological
preprocessor (METPRO) \31\ based on site specific measurements or
estimates of solar and/or net radiation, cloud cover and ceiling,
and the mixed layer height. These data are used in METPRO to
calculate the various surface layer scaling parameters (roughness
length, friction velocity, and Monin-Obukhov length) which are
needed to run the model. All of the user inputs required for the
surface file are based either on surface observations or on
measurements at or below 10m.
f. The profile data file is prepared by the user with site
specific measurements (from at least three levels) of wind speed,
wind direction, turbulence, and potential temperature. These
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 if the representative plume
height(s) of interest exceed 100m. The meteorological tower need not
exceed the lesser of the representative plume height of interest
(the highest plume height if there is more than one plume height of
interest) or 100m.
g. Locating towers on nearby terrain to obtain stack height or
plume height measurements for use in profiles by CTDMPLUS should be
avoided unless it can clearly be demonstrated that such measurements
would be representative of conditions affecting the plume.
h. The rawin file is created by a second meteorological
preprocessor (READ62) \31\ based on NWS (National Weather Service)
upper air data. The rawin file is used in CTDMPLUS to calculate
vertical potential temperature gradients for use in estimating plume
penetration in unstable conditions. The representativeness of the
off-site NWS upper air data should be evaluated on a case-by-case
basis.
i. In the absence of an appropriate refined model, screening
results may need to be used to determine air quality impact and/or
emission limits.

Table 5-1a.--Neutral/Stable Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Variable Specific values
------------------------------------------------------
U (m/s).............................................. 1.0 2.0 3.0 4.0 5.0
[sigma]_v (m/s)....................................... 0.3 0.75 .......... .......... .........
[sigma]_w (m/s)....................................... 0.08 0.15 0.30 0.75 .........
[Delta][thetas]/[Delta]z (K/m)....................... 0.01 0.02 0.035 .......... .........
WD................................................... (Wind direction optimized internally for each
meteorological combination)
----------------------------------------------------------------------------------------------------------------
Exceptions:
(1) If U <= 2 m/s and [sigma]_v <= 0.3 m/s, then include [sigma]_w = 0.04 m/s.
(2) If [sigma]_w = 0.75 m/s and U >= 3.0 m/s, then [Delta][thetas]/[Delta]z is limited to <= 0.01 K/m.
(3) If U £= 4 m/s, then [sigma]w £= 0.15 m/s.
(4) [sigma]_w <= [sigma]_v

Table 5-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Variable Specific values
-------------------------------------------------------
U (m/s)............................................... 1.0 2.0 3.0 4.0 5.0
u_* (m/s).............................................. 0.1 0.3 0.5 ......... .........
L (m)................................................. -10 -50 -90 ......... .........
[Delta][sigma]/[Delta]z (K/m)......................... 0.030 (potential temperature gradient above z_i)
z_i (m)................................................ 0.5h 1.0h 1.5h ......... .........
(where h = terrain height)
----------------------------------------------------------------------------------------------------------------

[[Page 18455]]

6.0 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen
Dioxide, and Lead

6.1 Discussion

a. This section identifies modeling approaches or models
appropriate for addressing ozone (O₃) \1\, carbon
monoxide (CO), nitrogen dioxide (NO₂), particulates (PM-
2.5 \a\ and PM-10), and lead. These pollutants are often associated
with emissions from numerous sources. Generally, mobile sources
contribute significantly to emissions of these pollutants or their
precursors. For cases where it is of interest to estimate
concentrations of CO or NO₂ near a single or small group
of stationary sources, refer to Section 4. (Modeling approaches for
SO₂ are discussed in Section 4.)
---------------------------------------------------------------------------

\1\ Modeling for attainment demonstrations for O₃ and
PM-2.5 should be conducted in time to meet required SIP submission
dates as provided for in the respective implementation rules.
Information on implementation of the 8-hr O₃ and PM-2.5
standards is available at: http://www.epa.gov/ttn/naaqs/.
---------------------------------------------------------------------------

b. Several of the pollutants mentioned in the preceding
paragraph are closely related to each other in that they share
common sources of emissions and/or are subject to chemical
transformations of similar precursors.\39, 40\ For example,
strategies designed to reduce ozone could have an effect on the
secondary component of PM-2.5 and vice versa. Thus, it makes sense
to use models which take into account the chemical coupling between
O₃ and PM-2.5, when feasible. This should promote
consistency among methods used to evaluate strategies for reducing
different pollutants as well as consistency among the strategies
themselves. Regulatory requirements for the different pollutants are
likely to be due at different times. Thus, the following paragraphs
identify appropriate modeling approaches for pollutants
individually.
c. The NAAQS for ozone was revised on July 18, 1997 and is now
based on an 8-hour averaging period. Models for ozone are needed
primarily to guide choice of strategies to correct an observed ozone
problem in an area not attaining the NAAQS for ozone. Use of
photochemical grid models is the recommended means for identifying
strategies needed to correct high ozone concentrations in such
areas. Such models need to consider emissions of volatile organic
compounds (VOC), nitrogen oxides (NO_X) and carbon
monoxide (CO), as well as means for generating meteorological data
governing transport and dispersion of ozone and its precursors.
Other approaches, such as Lagrangian or observational models may be
used to guide choice of appropriate strategies to consider with a
photochemical grid model. These other approaches may be sufficient
to address ozone in an area where observed concentrations are near
the NAAQS or only slightly above it. Such a decision needs to be
made on a case-by-case basis in concert with the Regional Office.
d. A control agency with jurisdiction over one or more areas
with significant ozone problems should review available ambient air
quality data to assess whether the problem is likely to be
significantly impacted by regional transport.\41\ Choice of a
modeling approach depends on the outcome of this review. In cases
where transport is considered significant, use of a nested regional
model may be the preferred approach. If the observed problem is
believed to be primarily of local origin, use of a model with a
single horizontal grid resolution and geographical coverage that is
less than that of a regional model may suffice.
e. The fine particulate matter NAAQS, promulgated on July 18,
1997, includes particles with an aerodynamic diameter nominally less
than or equal to 2.5 micrometers (PM-2.5). Models for PM-2.5 are
needed to assess adequacy of a proposed strategy for meeting annual
and/or 24-hour NAAQS for PM-2.5. PM-2.5 is a mixture consisting of
several diverse components. Because chemical/physical properties and
origins of each component differ, it may be appropriate to use
either a single model capable of addressing several of the important
components or to model primary and secondary components using
different models. Effects of a control strategy on PM-2.5 is
estimated from the sum of the effects on the components composing
PM-2.5. Model users may refer to guidance \42\ for further details
concerning appropriate modeling approaches.
f. A control agency with jurisdiction over one or more areas
with PM-2.5 problems should review available ambient air quality
data to assess which components of PM-2.5 are likely to be major
contributors to the problem. If it is determined that regional
transport of secondary particulates, such as sulfates or nitrates,
is likely to contribute significantly to the problem, use of a
regional model may be the preferred approach. Otherwise, coverage
may be limited to a domain that is urban scale or less. Special care
should be taken to select appropriate geographical coverage for a
modeling application.\42\
g. The NAAQS for PM-10 was promulgated in July 1987. A SIP
development guide \43\ is available to assist in PM-10 analyses and
control strategy development. EPA promulgated regulations for PSD
increments measured as PM-10 in a notice published on June 3, 1993.
As an aid to assessing the impact on ambient air quality of
particulate matter generated from prescribed burning activities, a
reference\44\ is available.
h. Models for assessing the impacts of particulate matter may
involve dispersion models or receptor models, or a combination
(depending on the circumstances). Receptor models focus on the
behavior of the ambient environment at the point of impact as
opposed to source-oriented dispersion models, which focus on the
transport, diffusion, and transformation that begin at the source
and continue to the receptor site. Receptor models attempt to
identify and apportion sources by relating known sample compositions
at receptors to measured or inferred compositions of source
emissions. When complete and accurate emission inventories or
meteorological characterization are unavailable, or unknown
pollutant sources exist, receptor modeling may be necessary.
i. Models for assessing the impact of CO emissions are needed
for a number of different purposes. Examples include evaluating
effects of point sources, congested intersections and highways, as
well as the cumulative effect of numerous sources of CO in an urban
area.
j. Models for assessing the impact of sources on ambient
NO₂ concentrations are primarily needed to meet new
source review requirements, such as addressing the effect of a
proposed source on PSD increments for annual concentrations of
NO₂. Impact of an individual source on ambient
NO₂ depends, in part, on the chemical environment into
which the source's plume is to be emitted. There are several
approaches for estimating effects of an individual source on ambient
NO₂. One approach is through use of a plume-in-grid
algorithm imbedded within a photochemical grid model. However,
because of the rigor and complexity involved, and because this
approach may not be capable of defining sub-grid concentration
gradients, the plume-in-grid approach may be impractical for
estimating effects on an annual PSD increment. A second approach is
to develop site specific conversion factors based on measurements.
If it is not possible to develop site specific conversion factors
and use of the plume-in-grid algorithm is also not feasible, other
screening procedures may be considered.
k. In January 1999 (40 CFR part 58, Appendix D), EPA gave notice
that concern about ambient lead impacts was being shifted away from
roadways and toward a focus on stationary point sources. EPA has
also issued guidance on siting ambient monitors in the vicinity of
such sources.\45\ For lead, the SIP should contain an air quality
analysis to determine the maximum quarterly lead concentration
resulting from major lead point sources, such as smelters, gasoline
additive plants, etc. General guidance for lead SIP development is
also available.\46\

6.2 Recommendations

6.2.1 Models for Ozone

a. Choice of Models for Multi-source Applications. Simulation of
ozone formation and transport is a highly complex and resource
intensive exercise. Control agencies with jurisdiction over areas
with ozone problems are encouraged to use photochemical grid models,
such as the Models-3/Community Multi-scale Air Quality (CMAQ)
modeling system \47\, to evaluate the relationship between precursor
species and ozone. Judgement on the suitability of a model for a
given application should consider factors that include use of the
model in an attainment test, development of emissions and
meteorological inputs to the model and choice of episodes to
model.\41\ Similar models for the 8-hour NAAQS and for the 1-hour
NAAQS are appropriate.
b. Choice of Models to Complement Photochemical Grid Models. As
previously noted, observational models, Lagrangian models, or the
Empirical Kinetics Modeling Approach (EKMA) \48, 49\ may be used to
help guide choice of strategies to simulate with a photochemical
grid model and to corroborate results obtained with a grid model.
Receptor models have also been used

[[Page 18456]]

to apportion sources of ozone precursors (e.g., VOC) in urban
domains. EPA has issued guidance \41\ in selecting appropriate
techniques.
c. Estimating the Impact of Individual Sources. Choice of
methods used to assess the impact of an individual source depends on
the nature of the source and its emissions. Thus, model users should
consult with the Regional Office to determine the most suitable
approach on a case-by-case basis (subsection 3.2.2).

6.2.2 Models for Particulate Matter

6.2.2.1 PM-2.5

a. Choice of Models for Multi-source Applications. Simulation of
phenomena resulting in high ambient PM-2.5 can be a multi-faceted
and complex problem resulting from PM-2.5's existence as an aerosol
mixture. Treating secondary components of PM-2.5, such as sulfates
and nitrates, can be a highly complex and resource-intensive
exercise. Control agencies with jurisdiction over areas with
secondary PM-2.5 problems are encouraged to use models which
integrate chemical and physical processes important in the
formation, decay and transport of these species (e.g., Models-3/CMAQ
\47\ or REMSAD \50\). Primary components can be simulated using less
resource-intensive techniques. Suitability of a modeling approach or
mix of modeling approaches for a given application requires
technical judgement \42\, as well as professional experience in
choice of models, use of the model(s) in an attainment test,
development of emissions and meteorological inputs to the model and
selection of days to model.
b. Choice of Analysis Techniques to Complement Air Quality
Simulation Models. Receptor models may be used to corroborate
predictions obtained with one or more air quality simulation models.
They may also be potentially useful in helping to define specific
source categories contributing to major components of PM-2.5.\42\
c. Estimating the Impact of Individual Sources. Choice of
methods used to assess the impact of an individual source depends on
the nature of the source and its emissions. Thus, model users should
consult with the Regional Office to determine the most suitable
approach on a case-by-case basis (subsection 3.2.2).

6.2.2.2 PM-10

a. Screening techniques like those identified in subsection
4.2.1 are applicable to PM-10. Conservative assumptions which do not
allow removal or transformation are suggested for screening. Thus,
it is recommended that subjectively determined values for ``half-
life'' or pollutant decay not be used as a surrogate for particle
removal. Proportional models (rollback/forward) may not be applied
for screening analysis, unless such techniques are used in
conjunction with receptor modeling.\43\
b. Refined models such as those discussed in subsection 4.2.2
are recommended for PM-10. However, where possible, particle size,
gas-to-particle formation, and their effect on ambient
concentrations may be considered. For point sources of small
particles and for source-specific analyses of complicated sources,
use the appropriate recommended steady-state plume dispersion model
(subsection 4.2.2). For guidance on determination of design
concentrations, see paragraph 8.2.1.1(e).
c. Receptor models have proven useful for helping validate
emission inventories and for corroborating source-specific impacts
estimated by dispersion models. The Chemical Mass Balance (CMB)
model is useful for apportioning impacts from localized
sources.\51,52,53\ Other receptor models, e.g., the Positive Matrix
Factorization (PMF) model \54\ and Unmix \55\, which don't share
some of CMB's constraints, have also been applied. In regulatory
applications, dispersion models have been used in conjunction with
receptor models to attribute source (or source category)
contributions. Guidance is available for PM-10 sampling and analysis
applicable to receptor modeling.\56\
d. Under certain conditions, recommended dispersion models may
not be reliable. In such circumstances, the modeling approach should
be approved by the Regional Office on a case-by-case basis. Analyses
involving model calculations for stagnation conditions should also
be justified on a case-by-case basis (subsection 8.2.8).
e. Fugitive dust usually refers to dust put into the atmosphere
by the wind blowing over plowed fields, dirt roads or desert or
sandy areas with little or no vegetation. Reentrained dust is that
which is put into the air by reason of vehicles driving over dirt
roads (or dirty roads) and dusty areas. Such sources can be
characterized as line, area or volume sources. Emission rates may be
based on site specific data or values from the general literature.
Fugitive emissions include the emissions resulting from the
industrial process that are not captured and vented through a stack
but may be released from various locations within the complex. In
some unique cases a model developed specifically for the situation
may be needed. Due to the difficult nature of characterizing and
modeling fugitive dust and fugitive emissions, it is recommended
that the proposed procedure be cleared by the Regional Office for
each specific situation before the modeling exercise is begun.

6.2.3 Models for Carbon Monoxide

a. Guidance is available for analyzing CO impacts at roadway
intersections.\57\ The recommended screening model for such analyses
is CAL3QHC.\58,59\ This model combines CALINE3 (listed in Appendix
A) with a traffic model to calculate delays and queues that occur at
signalized intersections. The screening approach is described in
reference 57; a refined approach may be considered on a case-by-case
basis with CAL3QHCR.\60\ The latest version of the MOBILE (mobile
source emission factor) model should be used for emissions input to
intersection models.
b. For analyses of highways characterized by uninterrupted
traffic flows, CALINE3 is recommended, with emissions input from the
latest version of the MOBILE model.
c. For urban area wide analyses of CO, an Eulerian grid model
should be used. Information on SIP development and requirements for
using such models can be found in several
references.57,61,62,63
d. Where point sources of CO are of concern, they should be
treated using the screening and refined techniques described in
Section 4.

6.2.4 Models for Nitrogen Dioxide (Annual Average)

a. A tiered screening approach is recommended to obtain annual
average estimates of NO₂ from point sources for New
Source Review analysis, including PSD, and for SIP planning
purposes. This multi-tiered approach is conceptually shown in Figure
6-1 and described in paragraphs b through d of this subsection:

[[Page 18457]]

[GRAPHIC]
[TIFF OMITTED]
TR15AP03.072

b. For Tier 1 (the initial screen), use an appropriate model in
subsection 4.2.2 to estimate the maximum annual average
concentration and assume a total conversion of NO to NO₂.
If the concentration exceeds the NAAQS and/or PSD increments for
NO₂, proceed to the 2nd level screen.
c. For Tier 2 (2nd level) screening analysis, multiply the Tier
1 estimate(s) by an empirically derived NO₂/
NO_X value of 0.75 (annual national default).\64\ The
reviewing agency may establish an alternative default
NO₂/NO_X ratio based on ambient annual average
NO₂ and annual average NO_X data representative
of area wide quasi-equilibrium conditions. Alternative default
NO₂/NO_X ratios should be based on data
satisfying quality assurance procedures that ensure data accuracy
for both NO₂ and NO_X within the typical range
of measured values. In areas with relatively low NO_X
concentrations, the quality assurance procedures used to determine
compliance with the NO₂ national ambient air quality
standard may not be adequate. In addition, default NO₂/
NO_X ratios, including the 0.75 national default value,
can underestimate long range NO₂ impacts and should be
used with caution in long range transport scenarios.
d. For Tier 3 (3rd level) analysis, a detailed screening method
may be selected on a case-by-case basis. For point source modeling,
other refined screening methods, such as the ozone limiting
method,\65\ may also be considered. Also, a site specific
NO₂/NO_X ratio may be used as a detailed
screening method if it meets the same restrictions as described for
alternative default NO₂/NO_X ratios. Ambient
NO_X monitors used to develop a site specific ratio should
be sited to obtain the NO₂ and NO_X
concentrations under quasi-equilibrium conditions. Data obtained
from monitors sited at the maximum NO_X impact site, as
may be required in a PSD pre-construction monitoring program, likely
reflect transitional NO_X conditions. Therefore,
NO_X data from maximum impact sites may not be suitable
for determining a site specific NO₂/NO_X ratio
that is applicable for the entire modeling analysis. A site specific
ratio derived from maximum impact data can only be used to estimate
NO₂ impacts at receptors located within the same distance
of the source as the source-to-monitor distance.
e. In urban areas (subsection 8.2.3), a proportional model may
be used as a preliminary assessment to evaluate control strategies
to meet the NAAQS for multiple minor sources, i.e., minor point,
area and mobile sources of NO_X; concentrations resulting
from major point sources should be estimated separately as discussed
above, then added to the impact of the minor sources. An acceptable
screening technique for urban complexes is to assume that all
NO_X is emitted in the form of NO₂ and to use a
model from Appendix A for nonreactive pollutants to estimate
NO₂ concentrations. A more accurate estimate can be
obtained by: (1) Calculating the annual average concentrations of
NO_X with an urban model, and (2) converting these
estimates to NO₂ concentrations using an empirically
derived annual NO₂/NO_X ratio. A value of 0.75
is recommended for this ratio. However, a spatially averaged
alternative default annual NO₂/NO_X ratio may
be determined from an existing air quality monitoring network and
used in lieu of the 0.75 value if it is determined to be
representative of prevailing ratios in the urban area by the
reviewing agency. To ensure use of appropriate locally derived
annual average NO₂ / NO_X ratios, monitoring
data under consideration should be limited to those collected at
monitors meeting siting criteria defined in 40 CFR Part 58, Appendix
D as representative of ``neighborhood'', ``urban'', or ``regional''
scales. Furthermore, the highest annual spatially averaged
NO₂/NO_X ratio from the most recent 3 years of
complete data should be used to foster conservatism in estimated
impacts.
f. To demonstrate compliance with NO₂ PSD increments
in urban areas, emissions from major and minor sources should be
included in the modeling analysis. Point and area source emissions
should be modeled as discussed above. If mobile source emissions do
not contribute to localized areas of high ambient NO₂
concentrations, they should be modeled as area sources. When modeled
as area sources, mobile source emissions should be assumed uniform
over the entire highway link and allocated to each area source grid
square based on the portion of highway link within each grid square.
If localized areas of high concentrations are likely, then mobile
sources should be modeled as line sources using an appropriate
steady-state plume dispersion model (e.g., CAL3QHCR; subsection
6.2.3).
g. More refined techniques to handle special circumstances may
be considered on a case-by-case basis and agreement with the
appropriate reviewing authority (paragraph 3.0(b)) should be
obtained. Such techniques should consider individual quantities of
NO and NO₂ emissions, atmospheric transport

[[Page 18458]]

and dispersion, and atmospheric transformation of NO to
NO₂. Where they are available, site specific data on the
conversion of NO to NO₂ may be used. Photochemical
dispersion models, if used for other pollutants in the area, may
also be applied to the NO_X problem.

6.2.5 Models for Lead

a. For major lead point sources, such as smelters, which
contribute fugitive emissions and for which deposition is important,
professional judgement should be used, and there should be
coordination with the appropriate reviewing authority (paragraph
3.0(b)). To model an entire major urban area or to model areas
without significant sources of lead emissions, as a minimum a
proportional (rollback) model may be used for air quality analysis.
The rollback philosophy assumes that measured pollutant
concentrations are proportional to emissions. However, urban or
other dispersion models are encouraged in these circumstances where
the use of such models is feasible.
b. In modeling the effect of traditional line sources (such as a
specific roadway or highway) on lead air quality, dispersion models
applied for other pollutants can be used. Dispersion models such as
CALINE3 and CAL3QHCR have been used for modeling carbon monoxide
emissions from highways and intersections (subsection 6.2.3). Where
there is a point source in the middle of a substantial road network,
the lead concentrations that result from the road network should be
treated as background (subsection 9.2); the point source and any
nearby major roadways should be modeled separately using the
appropriate recommended steady-state plume dispersion model
(subsection 4.2.2).

7.0 Other Model Requirements

7.1 Discussion

a. This section covers those cases where specific techniques
have been developed for special regulatory programs. Most of the
programs have, or will have when fully developed, separate guidance
documents that cover the program and a discussion of the tools that
are needed. The following paragraphs reference those guidance
documents, when they are available. No attempt has been made to
provide a comprehensive discussion of each topic since the reference
documents were designed to do that. This section will undergo
periodic revision as new programs are added and new techniques are
developed.
b. Other Federal agencies have also developed specific modeling
approaches for their own regulatory or other
requirements.66 Although such regulatory requirements and
manuals may have come about because of EPA rules or standards, the
implementation of such regulations and the use of the modeling
techniques is under the jurisdiction of the agency issuing the
manual or directive.
c. The need to estimate impacts at distances greater than 50km
(the nominal distance to which EPA considers most steady-state
Gaussian plume models are applicable) is an important one especially
when considering the effects from secondary pollutants.
Unfortunately, models originally available to EPA had not undergone
sufficient field evaluation to be recommended for general use. Data
bases from field studies at mesoscale and long range transport
distances were limited in detail. This limitation was a result of
the expense to perform the field studies required to verify and
improve mesoscale and long range transport models. Meteorological
data adequate for generating three-dimensional wind fields were
particularly sparse. Application of models to complicated terrain
compounds the difficulty of making good assessments of long range
transport impacts. EPA completed limited evaluation of several long
range transport (LRT) models against two sets of field data and
evaluated results.\13\ Based on the results, EPA concluded that long
range and mesoscale transport models were limited for regulatory use
to a case-by-case basis. However a more recent series of comparisons
has been completed for a new model, CALPUFF (Section A.3). Several
of these field studies involved three-to-four hour releases of
tracer gas sampled along arcs of receptors at distances greater than
50km downwind. In some cases, short-term concentration sampling was
available, such that the transport of the tracer puff as it passed
the arc could be monitored. Differences on the order of 10 to 20
degrees were found between the location of the simulated and
observed center of mass of the tracer puff. Most of the simulated
centerline concentration maxima along each arc were within a factor
of two of those observed. It was concluded from these case studies
that the CALPUFF dispersion model had performed in a reasonable
manner, and had no apparent bias toward over or under prediction, so
long as the transport distance was limited to less than
300km.67

7.2 Recommendations

7.2.1 Visibility

a. Visibility in important natural areas (e.g., Federal Class I
areas) is protected under a number of provisions of the Clean Air
Act, including Sections 169A and 169B (addressing impacts primarily
from existing sources) and Section 165 (new source review).
Visibility impairment is caused by light scattering and light
absorption associated with particles and gases in the atmosphere. In
most areas of the country, light scattering by PM-2.5 is the most
significant component of visibility impairment. The key components
of PM-2.5 contributing to visibility impairment include sulfates,
nitrates, organic carbon, elemental carbon, and crustal material.
b. The visibility regulations as promulgated in December 1980
(40 CFR 51.300-307) require States to mitigate visibility
impairment, in any of the 156 mandatory Federal Class I areas, that
is found to be ``reasonably attributable'' to a single source or a
small group of sources. In 1985, EPA promulgated Federal
Implementation Plans (FIPs) for several States without approved
visibility provisions in their SIPs. The IMPROVE (Interagency
Monitoring for Protected Visual Environments) monitoring network, a
cooperative effort between EPA, the States, and Federal land
management agencies, was established to implement the monitoring
requirements in these FIPs. Data has been collected by the IMPROVE
network since 1988.
c. In 1999, EPA issued revisions to the 1980 regulations to
address visibility impairment in the form of regional haze, which is
caused by numerous, diverse sources (e.g., stationary, mobile, and
area sources) located across a broad region (40 CFR 51.308-309). The
state of relevant scientific knowledge has expanded significantly
since the Clean Air Act Amendments of 1977. A number of studies and
reports 68,69 have concluded that long range
transport (e.g., up to hundreds of kilometers) of fine particulate
matter plays a significant role in visibility impairment across the
country. Section 169A of the Act requires states to develop SIPs
containing long-term strategies for remedying existing and
preventing future visibility impairment in 156 mandatory Class I
federal areas. In order to develop long-term strategies to address
regional haze, many States will need to conduct regional-scale
modeling of fine particulate concentrations and associated
visibility impairment (e.g., light extinction and deciview metrics).
d. To calculate the potential impact of a plume of specified
emissions for specific transport and dispersion conditions (``plume
blight''), a screening model, VISCREEN, and guidance are
available.70 If a more comprehensive analysis is
required, a refined model should be selected . The model selection
(VISCREEN vs. PLUVUE II or some other refined model), procedures,
and analyses should be determined in consultation with the
appropriate reviewing authority (paragraph 3.0(b)) and the affected
Federal Land Manager (FLM). FLMs are responsible for determining
whether there is an adverse effect by a plume on a Class I area.
e. CALPUFF (Section A.3) may be applied when assessment is
needed of reasonably attributable haze impairment or atmospheric
deposition due to one or a small group of sources. This situation
may involve more sources and larger modeling domains than that to
which VISCREEN ideally may be applied. The procedures and analyses
should be determined in consultation with the appropriate reviewing
authority (paragraph 3.0(b)) and the affected FLM(s).
f. Regional scale models are used by EPA to develop and evaluate
national policy and assist State and local control agencies. Two
such models which can be used to assess visibility impacts from
source emissions are Models-3/CMAQ 47 and
REMSAD.50 Model users should consult with the appropriate
reviewing authority (paragraph 3.0(b)), which in this instance would
include FLMs.

7.2.2 Good Engineering Practice Stack Height

a. The use of stack height credit in excess of Good Engineering
Practice (GEP) stack height or credit resulting from any other
dispersion technique is prohibited in the development of emission
limitations by 40 CFR 51.118 and 40 CFR 51.164. The definitions of
GEP stack height and dispersion technique are contained in 40 CFR
51.100. Methods and procedures for making the appropriate stack
height calculations, determining stack height credits and an

[[Page 18459]]

example of applying those techniques are found in several references
71, 72, 73, 74, which
provide a great deal of additional information for evaluating and
describing building cavity and wake effects.
b. If stacks for new or existing major sources are found to be
less than the height defined by EPA's refined formula for
determining GEP height, then air quality impacts associated with
cavity or wake effects due to the nearby building structures should
be determined. The EPA refined formula height is defined as H + 1.5L
(see reference 73). Detailed downwash screening procedures
27 for both the cavity and wake regions should be
followed. If more refined concentration estimates are required, the
recommended steady-state plume dispersion model in subsection 4.2.2
contains algorithms for building wake calculations and should be
used.

7.2.3 Long Range Transport (LRT) (i.e., Beyond 50km)

a. Section 165(d) of the Clean Air Act requires that suspected
adverse impacts on PSD Class I areas be determined. However, 50km is
the useful distance to which most steady-state Gaussian plume models
are considered accurate for setting emission limits. Since in many
cases PSD analyses show that Class I areas may be threatened at
distances greater than 50km from new sources, some procedure is
needed to (1) determine if an adverse impact will occur, and (2)
identify the model to be used in setting an emission limit if the
Class I increments are threatened. In addition to the situations
just described, there are certain applications containing a mixture
of both long range and short range source-receptor relationships in
a large modeled domain (e.g., several industrialized areas located
along a river or valley). Historically, these applications have
presented considerable difficulty to an analyst if impacts from
sources having transport distances greater than 50km significantly
contributed to the design concentrations. To properly analyze
applications of this type, a modeling approach is needed which has
the capability of combining, in a consistent manner, impacts
involving both short and long range transport. The CALPUFF modeling
system, listed in Appendix A, has been designed to accommodate both
the Class I area LRT situation and the large modeling domain
situation. Given the judgement and refinement involved, conducting a
LRT modeling assessment will require significant consultation with
the appropriate reviewing authority (paragraph 3.0(b)) and the
affected FLM(s). The FLM has an affirmative responsibility to
protect air quality related values (AQRVs) that may be affected, and
to provide the appropriate procedures and analysis techniques. Where
there is no increment violation, the ultimate decision on whether a
Class I area is adversely affected is the responsibility of the
appropriate reviewing authority (Section 165(d)(2)(C)(ii) of the
Clean Air Act), taking into consideration any information on the
impacts on AQRVs provided by the FLM. According to Section
165(d)(2)(C)(iii) of the Clean Air Act, if there is a Class I
increment violation, the source must demonstrate to the satisfaction
of the FLM that the emissions from the source will have no adverse
impact on the AQRVs.
b. If LRT is determined to be important, then refined estimates
utilizing the CALPUFF modeling system should be obtained. A
screening approach 67, 75 is also available
for use on a case-by-case basis that generally provides
concentrations that are higher than those obtained using refined
characterizations of the meteorological conditions. The
meteorological input data requirements for developing the time and
space varying three-dimensional winds and dispersion meteorology for
refined analyses are discussed in paragraph 9.3.1.2(d). Additional
information on applying this model is contained in Appendix A. To
facilitate use of complex air quality and meteorological modeling
systems, a written protocol approved by the appropriate reviewing
authority (paragraph 3.0(b)) and the affected FLM(s) may be
considered for developing consensus in the methods and procedures to
be followed.

7.2.4 Modeling Guidance for Other Governmental Programs

a. When using the models recommended or discussed in the
Guideline in support of programmatic requirements not specifically
covered by EPA regulations, the model user should consult the
appropriate Federal or State agency to ensure the proper application
and use of the models. For modeling associated with PSD permit
applications that involve a Class I area, the appropriate Federal
Land Manager should be consulted on all modeling questions.
b. The Offshore and Coastal Dispersion (OCD) model, described in
Appendix A, was developed by the Minerals Management Service and is
recommended for estimating air quality impact from offshore sources
on onshore, flat terrain areas. The OCD model is not recommended for
use in air quality impact assessments for onshore sources. Sources
located on or just inland of a shoreline where fumigation is
expected should be treated in accordance with subsection 8.2.8.
c. The Emissions and Dispersion Modeling System (EDMS),
described in Appendix A, was developed by the Federal Aviation
Administration and the United States Air Force and is recommended
for air quality assessment of primary pollutant impacts at airports
or air bases. Regulatory application of EDMS is intended for
estimating the cumulative effect of changes in aircraft operations,
point source, and mobile source emissions on pollutant
concentrations. It is not intended for PSD, SIP, or other regulatory
air quality analyses of point or mobile sources at or peripheral to
airport property that are independent of changes in aircraft
operations. If changes in other than aircraft operations are
associated with analyses, a model recommended in Chapter 4 or 5
should be used.

8.0 General Modeling Considerations

8.1 Discussion

a. This section contains recommendations concerning a number of
different issues not explicitly covered in other sections of this
guide. The topics covered here are not specific to any one program
or modeling area but are common to nearly all modeling analyses for
criteria pollutants.

8.2 Recommendations

8.2.1 Design Concentrations (see also subsection 11.2.3.1)

8.2.1.1 Design Concentrations for SO₂, PM-10, CO, Pb, and
NO₂

a. An air quality analysis for SO₂, PM-10, CO, Pb,
and NO₂ is required to determine if the source will (1)
cause a violation of the NAAQS, or (2) cause or contribute to air
quality deterioration greater than the specified allowable PSD
increment. For the former, background concentration (subsection 9.2)
should be added to the estimated impact of the source to determine
the design concentration. For the latter, the design concentration
includes impact from all increment consuming sources.
b. If the air quality analyses are conducted using the period of
meteorological input data recommended in subsection 9.3.1.2 (e.g., 5
years of National Weather Service (NWS) data or at least 1 year of
site specific data; subsection 9.3.3), then the design concentration
based on the highest, second-highest short term concentration or the
highest long term average, whichever is controlling, should be used
to determine emission limitations to assess compliance with the
NAAQS and PSD increments.
c. When sufficient and representative data exist for less than a
5-year period from a nearby NWS site, or when site specific data
have been collected for less than a full continuous year, or when it
has been determined that the site specific data may not be
temporally representative (subsection 9.3.3), then the highest
concentration estimate should be considered the design value. This
is because the length of the data record may be too short to assure
that the conditions producing worst-case estimates have been
adequately sampled. The highest value is then a surrogate for the
concentration that is not to be exceeded more than once per year
(the wording of the deterministic standards). Also, the highest
concentration should be used whenever selected worst-case conditions
are input to a screening technique, as described in EPA
guidance.27
d. If the controlling concentration is an annual average value
and multiple years of data (site specific or NWS) are used, then the
design value is the highest of the annual averages calculated for
the individual years. If the controlling concentration is a
quarterly average and multiple years are used, then the highest
individual quarterly average should be considered the design value.
e. As long a period of record as possible should be used in
making estimates to determine design values and PSD increments. If
more than 1 year of site specific data is available, it should be
used.

8.2.1.2 Design Concentrations for O₃ and PM-2.5

a. Guidance and specific instructions for the determination of
the 1-hr and 8-hr design concentrations for ozone are provided in
Appendix H and I (respectively) of reference

[[Page 18460]]

4. Appendix H explains how to determine when the expected number of
days per calendar year with maximum hourly concentrations above the
NAAQS is equal to or less than 1. Appendix I explains the data
handling conventions and computations necessary for determining
whether the 8-hour primary and secondary NAAQS are met at an ambient
monitoring site. For PM-2.5, Appendix N of reference 4, and
supplementary guidance 76, explain the data handling
conventions and computations necessary for determining when the
annual and 24-hour primary and secondary NAAQS are met. For all SIP
revisions the user should check with the Regional Office to obtain
the most recent guidance documents and policy memoranda concerning
the pollutant in question. There are currently no PSD increments for
O₃ and PM-2.5.

8.2.2 Critical Receptor Sites

a. Receptor sites for refined modeling should be utilized in
sufficient detail to estimate the highest concentrations and
possible violations of a NAAQS or a PSD increment. In designing a
receptor network, the emphasis should be placed on receptor
resolution and location, not total number of receptors. The
selection of receptor sites should be a case-by-case determination
taking into consideration the topography, the climatology, monitor
sites, and the results of the initial screening procedure. For large
sources (those equivalent to a 500MW power plant) and where
violations of the NAAQS or PSD increment are likely, 360 receptors
for a polar coordinate grid system and 400 receptors for a
rectangular grid system, where the distance from the source to the
farthest receptor is 10km, are usually adequate to identify areas of
high concentration. Additional receptors may be needed in the high
concentration location if greater resolution is indicated by terrain
or source factors.

8.2.3 Dispersion Coefficients

a. Steady-state Gaussian plume models used in most applications
should employ dispersion coefficients consistent with those
contained in the preferred models in Appendix A. Factors such as
averaging time, urban/rural surroundings (see paragraphs (b)-(f) of
this subsection), and type of source (point vs. line) may dictate
the selection of specific coefficients. Coefficients used in some
Appendix A models are identical to, or at least based on, Pasquill-
Gifford coefficients \77\ in rural areas and McElroy-Pooler \78\
coefficients in urban areas.\79\
b. The selection of either rural or urban dispersion
coefficients in a specific application should follow one of the
procedures suggested by Irwin \80\ and briefly described in
paragraphs (c)-(f) of this subsection. These include a land use
classification procedure or a population based procedure to
determine whether the character of an area is primarily urban or
rural.
c. Land Use Procedure: (1) Classify the land use within the
total area, A_o, circumscribed by a 3km radius circle
about the source using the meteorological land use typing scheme
proposed by Auer \81\; (2) if land use types I1, I2, C1, R2, and R3
account for 50 percent or more of A_o, use urban
dispersion coefficients; otherwise, use appropriate rural dispersion
coefficients.
d. Population Density Procedure: (1) Compute the average
population density, p per square kilometer with A_o as
defined above; (2) If p is greater than 750 people/km2,
use urban dispersion coefficients; otherwise use appropriate rural
dispersion coefficients.
e. Of the two methods, the land use procedure is considered more
definitive. Population density should be used with caution and
should not be applied to highly industrialized areas where the
population density may be low and thus a rural classification would
be indicated, but the area is sufficiently built-up so that the
urban land use criteria would be satisfied. In this case, the
classification should already be ``urban'' and urban dispersion
parameters should be used.
f. Sources located in an area defined as urban should be modeled
using urban dispersion parameters. Sources located in areas defined
as rural should be modeled using the rural dispersion parameters.
For analyses of whole urban complexes, the entire area should be
modeled as an urban region if most of the sources are located in
areas classified as urban.
g. Buoyancy-induced dispersion (BID), as identified by Pasquill
\82\, is included in the preferred models and should be used where
buoyant sources, e.g., those involving fuel combustion, are
involved.

8.2.4 Stability Categories

a. The Pasquill approach to classifying stability is commonly
used in preferred models (Appendix A). The Pasquill method, as
modified by Turner \83\, was developed for use with commonly
observed meteorological data from the National Weather Service and
is based on cloud cover, insolation and wind speed.
b. Procedures to determine Pasquill stability categories from
other than NWS data are found in subsection 9.3. Any other method to
determine Pasquill stability categories must be justified on a case-
by-case basis.
c. For a given model application where stability categories are
the basis for selecting dispersion coefficients, both
[sigma]_y and [sigma]_z should be determined
from the same stability category. ``Split sigmas'' in that instance
are not recommended. Sector averaging, which eliminates the
[sigma]_y term, is commonly acceptable in complex terrain
screening methods.

8.2.5 Plume Rise

a. The plume rise methods of Briggs 84, 85
are incorporated in many of the preferred models and are recommended
for use in many modeling applications. In the convective boundary
layer, plume rise is superposed on the displacements by random
convective velocities.\86\ No explicit provisions in these models
are made for multistack plume rise enhancement or the handling of
such special plumes as flares; these problems should be considered
on a case-by-case basis.
b. Gradual plume rise is generally recommended where its use is
appropriate: (1) In complex terrain screening procedures to
determine close-in impacts and (2) when calculating the effects of
building wakes. If the building wake is calculated to affect the
plume for any hour, gradual plume rise is also used in downwind
dispersion calculations to the distance of final plume rise, after
which final plume rise is used. Plumes captured by the near wake are
re-emitted to the far wake as a ground-level volume source.
c. Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs \85\ is the recommended
technique for this situation and is found in the point source
preferred models.

8.2.6 Chemical Transformation

a. The chemical transformation of SO₂ emitted from
point sources or single industrial plants in rural areas is
generally assumed to be relatively unimportant to the estimation of
maximum concentrations when travel time is limited to a few hours.
However, in urban areas, where synergistic effects among pollutants
are of considerable consequence, chemical transformation rates may
be of concern. In urban area applications, a half-life of 4 hours
\83\ may be applied to the analysis of SO₂ emissions.
Calculations of transformation coefficients from site specific
studies can be used to define a ``half-life'' to be used in a
steady-state Gaussian plume model with any travel time, or in any
application, if appropriate documentation is provided. Such
conversion factors for pollutant half-life should not be used with
screening analyses.
b. Use of models incorporating complex chemical mechanisms
should be considered only on a case-by-case basis with proper
demonstration of applicability. These are generally regional models
not designed for the evaluation of individual sources but used
primarily for region-wide evaluations. Visibility models also
incorporate chemical transformation mechanisms which are an integral
part of the visibility model itself and should be used in visibility
assessments.

8.2.7 Gravitational Settling and Deposition

a. An ``infinite half-life'' should be used for estimates of
particle concentrations when steady-state Gaussian plume models
containing only exponential decay terms for treating settling and
deposition are used.
b. Gravitational settling and deposition may be directly
included in a model if either is a significant factor. When
particulate matter sources can be quantified and settling and dry
deposition are problems, professional judgement should be used, and
there should be coordination with the appropriate reviewing
authority (paragraph 3.0(b)).

8.2.8 Complex Winds

a. Inhomogeneous Local Winds. In many parts of the United
States, the ground is neither flat nor is the ground cover (or land
use) uniform. These geographical variations can generate local winds
and circulations, and modify the prevailing ambient winds and
circulations. Geographic effects are most apparent when the ambient
winds are light or calm.\87\ In general these geographically

[[Page 18461]]

induced wind circulation effects are named after the source location
of the winds, e.g., lake and sea breezes, and mountain and valley
winds. In very rugged hilly or mountainous terrain, along
coastlines, or near large land use variations, the characterization
of the winds is a balance of various forces, such that the
assumptions of steady-state straight-line transport both in time and
space are inappropriate. In the special cases described, the CALPUFF
modeling system (described in Appendix A) may be applied on a case-
by-case basis for air quality estimates in such complex non-steady-
state meteorological conditions. The purpose of choosing a modeling
system like CALPUFF is to fully treat the time and space variations
of meteorology effects on transport and dispersion. The setup and
application of the model should be determined in consultation with
the appropriate reviewing authority (paragraph 3.0(b)) consistent
with limitations of paragraph 3.2.2(e). The meteorological input
data requirements for developing the time and space varying three-
dimensional winds and dispersion meteorology for these situations
are discussed in paragraph 9.3.1.2(d). Examples of inhomogeneous
winds include, but aren't limited to, situations described in the
following paragraphs (i)-(iii):
i. Inversion Breakup Fumigation. Inversion breakup fumigation
occurs when a plume (or multiple plumes) is emitted into a stable
layer of air and that layer is subsequently mixed to the ground
through convective transfer of heat from the surface or because of
advection to less stable surroundings. Fumigation may cause
excessively high concentrations but is usually rather short-lived at
a given receptor. There are no recommended refined techniques to
model this phenomenon. There are, however, screening procedures \27\
that may be used to approximate the concentrations. Considerable
care should be exercised in using the results obtained from the
screening techniques.
ii. Shoreline Fumigation. Fumigation can be an important
phenomenon on and near the shoreline of bodies of water. This can
affect both individual plumes and area-wide emissions. When
fumigation conditions are expected to occur from a source or sources
with tall stacks located on or just inland of a shoreline, this
should be addressed in the air quality modeling analysis. The
Shoreline Dispersion Model (SDM) listed on EPA's Internet SCRAM Web
site (subsection 2.3) may be applied on a case-by-case basis when
air quality estimates under shoreline fumigation conditions are
needed.\88\ Information on the results of EPA's evaluation of this
model together with other coastal fumigation models is
available.\89\ Selection of the appropriate model for applications
where shoreline fumigation is of concern should be determined in
consultation with the appropriate reviewing authority (paragraph
3.0(b)).
iii. Stagnation. Stagnation conditions are characterized by calm
or very low wind speeds, and variable wind directions. These
stagnant meteorological conditions may persist for several hours to
several days. During stagnation conditions, the dispersion of air
pollutants, especially those from low-level emissions sources, tends
to be minimized, potentially leading to relatively high ground-level
concentrations. If point sources are of interest, users should note
the guidance provided for CALPUFF in paragraph (a) of this
subsection. Selection of the appropriate model for applications
where stagnation is of concern should be determined in consultation
with the appropriate reviewing authority (paragraph 3.0(b)).

8.2.9 Calibration of Models

a. Calibration of models is not common practice and is subject
to much error and misunderstanding. There have been attempts by some
to compare model estimates and measurements on an event-by-event
basis and then to calibrate a model with results of that comparison.
This approach is severely limited by uncertainties in both source
and meteorological data and therefore it is difficult to precisely
estimate the concentration at an exact location for a specific
increment of time. Such uncertainties make calibration of models of
questionable benefit. Therefore, model calibration is unacceptable.

9.0 Model Input Data

a. Data bases and related procedures for estimating input
parameters are an integral part of the modeling procedure. The most
appropriate data available should always be selected for use in
modeling analyses. Concentrations can vary widely depending on the
source data or meteorological data used. Input data are a major
source of uncertainties in any modeling analysis. This section
attempts to minimize the uncertainty associated with data base
selection and use by identifying requirements for data used in
modeling. A checklist of input data requirements for modeling
analyses is posted on EPA's Internet SCRAM Web site (subsection
2.3). More specific data requirements and the format required for
the individual models are described in detail in the users' guide
for each model.

9.1 Source Data

9.1.1 Discussion

a. Sources of pollutants can be classified as point, line and
area/volume sources. Point sources are defined in terms of size and
may vary between regulatory programs. The line sources most
frequently considered are roadways and streets along which there are
well-defined movements of motor vehicles, but they may be lines of
roof vents or stacks such as in aluminum refineries. Area and volume
sources are often collections of a multitude of minor sources with
individually small emissions that are impractical to consider as
separate point or line sources. Large area sources are typically
treated as a grid network of square areas, with pollutant emissions
distributed uniformly within each grid square.
b. Emission factors are compiled in an EPA publication commonly
known as AP-42 \90\; an indication of the quality and amount of data
on which many of the factors are based is also provided. Other
information concerning emissions is available in EPA publications
relating to specific source categories. The appropriate reviewing
authority (paragraph 3.0(b)) should be consulted to determine
appropriate source definitions and for guidance concerning the
determination of emissions from and techniques for modeling the
various source types.

9.1.2 Recommendations

a. For point source applications the load or operating condition
that causes maximum ground-level concentrations should be
established. As a minimum, the source should be modeled using the
design capacity (100 percent load). If a source operates at greater
than design capacity for periods that could result in violations of
the standards or PSD increments, this load \2\ should be modeled.
Where the source operates at substantially less than design
capacity, and the changes in the stack parameters associated with
the operating conditions could lead to higher ground level
concentrations, loads such as 50 percent and 75 percent of capacity
should also be modeled. A range of operating conditions should be
considered in screening analyses; the load causing the highest
concentration, in addition to the design load, should be included in
refined modeling. For a steam power plant, the following (b-h) is
typical of the kind of data on source characteristics and operating
conditions that may be needed. Generally, input data requirements
for air quality models necessitate the use of metric units; where
English units are common for engineering usage, a conversion to
metric is required.
---------------------------------------------------------------------------

\2\ Malfunctions which may result in excess emissions are not
considered to be a normal operating condition. They generally should
not be considered in determining allowable emissions. However, if
the excess emissions are the result of poor maintenance, careless
operation, or other preventable conditions, it may be necessary to
consider them in determining source impact.
---------------------------------------------------------------------------

b. Plant layout. The connection scheme between boilers and
stacks, and the distance and direction between stacks, building
parameters (length, width, height, location and orientation relative
to stacks) for plant structures which house boilers, control
equipment, and surrounding buildings within a distance of
approximately five stack heights.
c. Stack parameters. For all stacks, the stack height and inside
diameter (meters), and the temperature (K) and volume flow rate
(actual cubic meters per second) or exit gas velocity (meters per
second) for operation at 100 percent, 75 percent and 50 percent
load.
d. Boiler size. For all boilers, the associated megawatts,
106 BTU/hr, and pounds of steam per hour, and the design
and/or actual fuel consumption rate for 100 percent load for coal
(tons/hour), oil (barrels/hour), and natural gas (thousand cubic
feet/hour).
e. Boiler parameters. For all boilers, the percent excess air
used, the boiler type (e.g., wet bottom, cyclone, etc.), and the
type of firing (e.g., pulverized coal, front firing, etc.).
f. Operating conditions. For all boilers, the type, amount and
pollutant contents of fuel, the total hours of boiler operation and
the boiler capacity factor during the year, and the percent load for
peak conditions.

[[Page 18462]]

g. Pollution control equipment parameters. For each boiler
served and each pollutant affected, the type of emission control
equipment, the year of its installation, its design efficiency and
mass emission rate, the date of the last test and the tested
efficiency, the number of hours of operation during the latest year,
and the best engineering estimate of its projected efficiency if
used in conjunction with coal combustion; data for any anticipated
modifications or additions.
h. Data for new boilers or stacks. For all new boilers and
stacks under construction and for all planned modifications to
existing boilers or stacks, the scheduled date of completion, and
the data or best estimates available for items (b) through (g) of
this subsection following completion of construction or
modification.
i. In stationary point source applications for compliance with
short term ambient standards, SIP control strategies should be
tested using the emission input shown on Table 9-1. When using a
refined model, sources should be modeled sequentially with these
loads for every hour of the year. To evaluate SIPs for compliance
with quarterly and annual standards, emission input data shown in
Table 9-1 should again be used. Emissions from area sources should
generally be based on annual average conditions. The source input
information in each model user's guide should be carefully consulted
and the checklist (paragraph 9.0(a)) should also be consulted for
other possible emission data that could be helpful. PSD and NAAQS
compliance demonstrations should follow the emission input data
shown in Table 9-2. For purposes of emissions trading, new source
review and demonstrations, refer to current EPA policy and guidance
to establish input data.
j. Line source modeling of streets and highways requires data on
the width of the roadway and the median strip, the types and amounts
of pollutant emissions, the number of lanes, the emissions from each
lane and the height of emissions. The location of the ends of the
straight roadway segments should be specified by appropriate grid
coordinates. Detailed information and data requirements for modeling
mobile sources of pollution are provided in the user's manuals for
each of the models applicable to mobile sources.
k. The impact of growth on emissions should be considered in all
modeling analyses covering existing sources. Increases in emissions
due to planned expansion or planned fuel switches should be
identified. Increases in emissions at individual sources that may be
associated with a general industrial/commercial/residential
expansion in multi-source urban areas should also be treated. For
new sources the impact of growth on emissions should generally be
considered for the period prior to the start-up date for the source.
Such changes in emissions should treat increased area source
emissions, changes in existing point source emissions which were not
subject to preconstruction review, and emissions due to sources with
permits to construct that have not yet started operation.

Table 9-1.--Model Emission Input Data for Point Sources \1\
----------------------------------------------------------------------------------------------------------------
Emission limit Operating level Operating factor
Averaging time (#/MMBtu) \2\ x (MMBtu/hr) \2\ x (e.g., hr/yr, hr/day)
----------------------------------------------------------------------------------------------------------------
Stationary Point Source(s) Subject to SIP Emission Limit(s) Evaluation for Compliance With Ambient Standards
(Including Areawide Demonstrations)
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Actual or design .. Actual operating
emission limit or capacity (whichever factor averaged over
federally enforceable is greater), or most recent 2
permit limit. federally years.\3\
enforceable permit
condition.
Short term........................ Maximum allowable .. Actual or design .. Continuous operation,
emission limit or capacity (whichever i.e., all hours of
federally enforceable is greater), or each time period
permit limit. federally under consideration
enforceable permit (for all hours of
condition.\4\ the meteorological
data base).\5\
-----------------------------------
Nearby Source(s) 6, 7
Same input requirements as for stationary point source(s) above.
----------------------------------------------------------------------------------------------------------------
Other Sources \7\
If modeled (subsection 9.2.3), input data requirements are defined below.
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Annual level when .. Actual operating
emission limit or actually operating, factor averaged over
federally enforceable averaged over the the most recent 2
permit limit.\6\ most recent 2 years.\3\
years.\3\
Short term........................ Maximum allowable .. Annual level when .. Continuous operation,
emission limit or actually operating, i.e., all hours of
federally enforceable averaged over the each time period
permit limit.\6\ most recent 2 under consideration
years.\3\ (for all hours of
the meteorological
data base).\5\
----------------------------------------------------------------------------------------------------------------
\1\ The model input data requirements shown on this table apply to stationary source control strategies for
STATE IMPLEMENTATION PLANS. For purposes of emissions trading, new source review, or prevention of significant
deterioration, other model input criteria may apply. Refer to the policy and guidance for these programs to
establish the input data.
\2\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be
used for other types of sources.
\3\ Unless it is determined that this period is not representative.
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\5\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating time
periods.)
\6\ See paragraph 9.2.3(c).
\7\ See paragraph 9.2.3(d).

[[Page 18463]]

Table 9-2.--Point Source Model Input Data (Emissions) for PSD NAAQS Compliance Demonstrations
----------------------------------------------------------------------------------------------------------------
Emission limit (# Operating level Operating factor
Averaging time /MMBtu) \1\ x (MMBtu/hr) \1\ x (e.g., hr/yr,hr/day)
----------------------------------------------------------------------------------------------------------------
Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Design capacity or .. Continuous operation
emission limit or federally (i.e., 8760
federally enforceable enforceable permit hours).\2\
permit limit. condition.
Short term (<= 24 hours).......... Maximum allowable .. Design capacity or .. Continuous operation
emission limit or federally (i.e., all hours of
federally enforceable enforceable permit each time period
permit limit. condition.\3\ under consideration)
(for all hours of the
meteorological data
base).\2\
-----------------------------------
Nearby Source(s) 4,6
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Actual or design .. Actual operating
emission limit or capacity (whichever factor averaged over
federally enforceable is greater), or the most recent 2
permit limit.\5\ federally years.7,8
enforceable permit
condition.
Short term (<= 24 hours).......... Maximum allowable .. Actual or design .. Continuous operation
emission limit or capacity (whichever (i.e., all hours of
federally enforceable is greater), or each time period
permit limit.\5\ federally under consideration)
enforceable permit (for all hours of the
condition.\3\ meteorological data
base).\2\
-----------------------------------
Other Source(s) 6,9
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Annual level when .. Actual operating
emission limit or actually operating, factor averaged over
federally enforceable averaged over the the most recent 2
permit limit.\5\ most recent 2 years.7,8
years.\7\
Short term (<= 24 hours).......... Maximum allowable .. Annual level when .. Continuous operation
emission limit or actually operating, (i.e., all hours of
federally enforceable averaged over the each time period
permit limit.\5\ most recent 2 under consideration)
years.\7\ (for all hours of the
meteorological data
base).\2\
----------------------------------------------------------------------------------------------------------------
\1\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be
used for other types of sources.
\2\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating time
periods.
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\4\ Includes existing facility to which modification is proposed if the emissions from the existing facility
will not be affected by the modification. Otherwise use the same parameters as for major modification.
\5\ See paragraph 9.2.3(c).
\6\ See paragraph 9.2.3(d).
\7\ Unless it is determined that this period is not representative.
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
operation (i.e., 8760) should be used.
\9\ Generally, the ambient impacts from non-nearby (background) sources can be represented by air quality data
unless adequate data do not exist.

9.2 Background Concentrations

9.2.1 Discussion

a. Background concentrations are an essential part of the total
air quality concentration to be considered in determining source
impacts. Background air quality includes pollutant concentrations
due to: (1) Natural sources; (2) nearby sources other than the
one(s) currently under consideration; and (3) unidentified sources.
b. Typically, air quality data should be used to establish
background concentrations in the vicinity of the source(s) under
consideration. The monitoring network used for background
determinations should conform to the same quality assurance and
other requirements as those networks established for PSD
purposes.\91\ An appropriate data validation procedure should be
applied to the data prior to use.
c. If the source is not isolated, it may be necessary to use a
multi-source model to establish the impact of nearby sources. Since
sources don't typically operate at their maximum allowable capacity
(which may include the use of ``dirtier'' fuels), modeling is
necessary to express the potential contribution of background
sources, and this impact would not be captured via monitoring.
Background concentrations should be determined for each critical
(concentration) averaging time.

9.2.2 Recommendations (Isolated Single Source)

a. Two options (paragraph (b) or (c) of this section) are
available to determine the background concentration near isolated
sources.
b. Use air quality data collected in the vicinity of the source
to determine the background concentration for the averaging times of
concern. Determine the mean background concentration at each monitor
by excluding values when the source in question is impacting the
monitor. The mean annual background is the average of the annual
concentrations so determined at each monitor. For shorter averaging
periods, the meteorological conditions accompanying the
concentrations of concern should be identified. Concentrations for
meteorological conditions of concern, at monitors not impacted by
the source in question, should be averaged for each separate
averaging time to determine the average background value. Monitoring
sites inside a 90[deg]
sector downwind of the source may be used to
determine the area of impact. One hour concentrations may be added
and averaged to determine longer averaging periods.

[[Page 18464]]

c. If there are no monitors located in the vicinity of the
source, a ``regional site'' may be used to determine background. A
``regional site'' is one that is located away from the area of
interest but is impacted by similar natural and distant man-made
sources.

9.2.3 Recommendations (Multi-Source Areas)

a. In multi-source areas, two components of background should be
determined: Contributions from nearby sources and contributions from
other sources.
b. Nearby Sources: All sources expected to cause a significant
concentration gradient in the vicinity of the source or sources
under consideration for emission limit(s) should be explicitly
modeled. The number of such sources is expected to be small except
in unusual situations. Owing to both the uniqueness of each modeling
situation and the large number of variables involved in identifying
nearby sources, no attempt is made here to comprehensively define
this term. Rather, identification of nearby sources calls for the
exercise of professional judgement by the appropriate reviewing
authority (paragraph 3.0(b)). This guidance is not intended to alter
the exercise of that judgement or to comprehensively define which
sources are nearby sources.
c. For compliance with the short-term and annual ambient
standards, the nearby sources as well as the primary source(s)
should be evaluated using an appropriate Appendix A model with the
emission input data shown in Table 9-1 or 9-2. When modeling a
nearby source that does not have a permit and the emission limit
contained in the SIP for a particular source category is greater
than the emissions possible given the source's maximum physical
capacity to emit, the ``maximum allowable emission limit'' for such
a nearby source may be calculated as the emission rate
representative of the nearby source's maximum physical capacity to
emit, considering its design specifications and allowable fuels and
process materials. However, the burden is on the permit applicant to
sufficiently document what the maximum physical capacity to emit is
for such a nearby source.
d. It is appropriate to model nearby sources only during those
times when they, by their nature, operate at the same time as the
primary source(s) being modeled. Where a primary source believes
that a nearby source does not, by its nature, operate at the same
time as the primary source being modeled, the burden is on the
primary source to demonstrate to the satisfaction of the appropriate
reviewing authority (paragraph 3.0(b)) that this is, in fact, the
case. Whether or not the primary source has adequately demonstrated
that fact is a matter of professional judgement left to the
discretion of the appropriate reviewing authority. The following
examples illustrate two cases in which a nearby source may be shown
not to operate at the same time as the primary source(s) being
modeled. Some sources are only used during certain seasons of the
year. Those sources would not be modeled as nearby sources during
times in which they do not operate. Similarly, emergency backup
generators that never operate simultaneously with the sources that
they back up would not be modeled as nearby sources. To reiterate,
in these examples and other appropriate cases, the burden is on the
primary source being modeled to make the appropriate demonstration
to the satisfaction of the appropriate reviewing authority.
e. The impact of the nearby sources should be examined at
locations where interactions between the plume of the point source
under consideration and those of nearby sources (plus natural
background) can occur. Significant locations include: (1) The area
of maximum impact of the point source; (2) the area of maximum
impact of nearby sources; and (3) the area where all sources combine
to cause maximum impact. These locations may be identified through
trial and error analyses.
f. Other Sources: That portion of the background attributable to
all other sources (e.g., natural sources, minor sources and distant
major sources) should be determined by the procedures found in
subsection 9.2.2 or by application of a model using Table 9-1 or 9-
2.

9.3 Meteorological Input Data

a. The meteorological data used as input to a dispersion model
should be selected on the basis of spatial and climatological
(temporal) representativeness as well as the ability of the
individual parameters selected to characterize the transport and
dispersion conditions in the area of concern. The representativeness
of the data is dependent on: (1) The proximity of the meteorological
monitoring site to the area under consideration; (2) the complexity
of the terrain; (3) the exposure of the meteorological monitoring
site; and (4) the period of time during which data are collected.
The spatial representativeness of the data can be adversely affected
by large distances between the source and receptors of interest and
the complex topographic characteristics of the area. Temporal
representativeness is a function of the year-to-year variations in
weather conditions. Where appropriate, data representativeness
should be viewed in terms of the appropriateness of the data for
constructing realistic boundary layer profiles and three dimensional
meteorological fields, as described in paragraphs (c) and (d) below.
b. Model input data are normally obtained either from the
National Weather Service or as part of a site specific measurement
program. Local universities, Federal Aviation Administration (FAA),
military stations, industry and pollution control agencies may also
be sources of such data. Some recommendations for the use of each
type of data are included in this subsection.
c. For long range transport modeling assessments (subsection
7.2.3) or for assessments where the transport winds are complex and
the application involves a non-steady-state dispersion model
(subsection 8.2.8), use of output from prognostic mesoscale
meteorological models is encouraged.92, 93,
94 Some diagnostic meteorological processors are designed
to appropriately blend available NWS comparable meteorological
observations, local site specific meteorological observations, and
prognostic mesoscale meteorological data, using empirical
relationships, to diagnostically adjust the wind field for mesoscale
and local-scale effects. These diagnostic adjustments can sometimes
be improved through the use of strategically placed site specific
meteorological observations. The placement of these special
meteorological observations (often more than one location is needed)
involves expert judgement, and is specific to the terrain and land
use of the modeling domain. Acceptance for use of output from
prognostic mesoscale meteorological models is contingent on
concurrence by the appropriate reviewing authorities (paragraph
3.0(b)) that the data are of acceptable quality, which can be
demonstrated through statistical comparisons with observations of
winds aloft and at the surface at several appropriate locations.

9.3.1 Length of Record of Meteorological Data

9.3.1.1 Discussion

a. The model user should acquire enough meteorological data to
ensure that worst-case meteorological conditions are adequately
represented in the model results. The trend toward statistically
based standards suggests a need for all meteorological conditions to
be adequately represented in the data set selected for model input.
The number of years of record needed to obtain a stable distribution
of conditions depends on the variable being measured and has been
estimated by Landsberg and Jacobs \95\ for various parameters.
Although that study indicates in excess of 10 years may be required
to achieve stability in the frequency distributions of some
meteorological variables, such long periods are not reasonable for
model input data. This is due in part to the fact that hourly data
in model input format are frequently not available for such periods
and that hourly calculations of concentration for long periods may
be prohibitively expensive. Another study \96\ compared various
periods from a 17-year data set to determine the minimum number of
years of data needed to approximate the concentrations modeled with
a 17-year period of meteorological data from one station. This study
indicated that the variability of model estimates due to the
meteorological data input was adequately reduced if a 5-year period
of record of meteorological input was used.

9.3.1.2 Recommendations

a. Five years of representative meteorological data should be
used when estimating concentrations with an air quality model.
Consecutive years from the most recent, readily available 5-year
period are preferred. The meteorological data should be adequately
representative, and may be site specific or from a nearby NWS
station. Where professional judgment indicates NWS-collected ASOS
(automated surface observing stations) data are inadequate {for
cloud cover observations, the most recent 5 years of NWS data that
are observer-based may be considered for use.
b. The use of 5 years of NWS meteorological data or at least l
year of site specific data is required. If one year or more

[[Page 18465]]

(including partial years), up to five years, of site specific data
is available, these data are preferred for use in air quality
analyses. Such data should have been subjected to quality assurance
procedures as described in subsection 9.3.3.2.
c. For permitted sources whose emission limitations are based on
a specific year of meteorological data, that year should be added to
any longer period being used (e.g., 5 years of NWS data) when
modeling the facility at a later time.
d. For LRT situations (subsection 7.2.3) and for complex wind
situations (paragraph 8.2.8(a)), if only NWS or comparable standard
meteorological observations are employed, five years of
meteorological data (within and near the modeling domain) should be
used. Consecutive years from the most recent, readily available 5-
year period are preferred. Less than five, but at least three, years
of meteorological data (need not be consecutive) may be used if
mesoscale meteorological fields are available, as discussed in
paragraph 9.3(c). These mesoscale meteorological fields should be
used in conjunction with available standard NWS or comparable
meteorological observations within and near the modeling domain. If
site specific meteorological data are available, these data may be
especially helpful for local-scale complex wind situations, when
appropriately blended together with standard NWS or comparable
observations and mesoscale meteorological fields.

9.3.2 National Weather Service Data

9.3.2.1 Discussion

a. The NWS meteorological data are routinely available and
familiar to most model users. Although the NWS does not provide
direct measurements of all the needed dispersion model input
variables, methods have been developed and successfully used to
translate the basic NWS data to the needed model input. Site
specific measurements of model input parameters have been made for
many modeling studies, and those methods and techniques are becoming
more widely applied, especially in situations such as complex
terrain applications, where available NWS data are not adequately
representative. However, there are many model applications where NWS
data are adequately representative, and the applications still rely
heavily on the NWS data.
b. Many models use the standard hourly weather observations
available from the National Climatic Data Center (NCDC). These
observations are then preprocessed before they can be used in the
models.

9.3.2.2 Recommendations

a. The preferred models listed in Appendix A all accept as input
the NWS meteorological data preprocessed into model compatible form.
If NWS data are judged to be adequately representative for a
particular modeling application, they may be used. NCDC makes
available surface \97,98\ and upper air \99\ meteorological data in
CD-ROM format.
b. Although most NWS measurements are made at a standard height
of 10 meters, the actual anemometer height should be used as input
to the preferred model.
c. Wind directions observed by the National Weather Service are
reported to the nearest 10 degrees. A specific set of randomly
generated numbers has been developed for use with the preferred EPA
models and should be used with NWS data to ensure a lack of bias in
wind direction assignments within the models.
d. Data from universities, FAA, military stations, industry and
pollution control agencies may be used if such data are equivalent
in accuracy and detail to the NWS data, and they are judged to be
adequately representative for the particular application.

9.3.3 Site Specific Data

9.3.3.1 Discussion

a. Spatial or geographical representativeness is best achieved
by collection of all of the needed model input data in close
proximity to the actual site of the source(s). Site specific
measured data are therefore preferred as model input, provided that
appropriate instrumentation and quality assurance procedures are
followed and that the data collected are adequately representative
(free from inappropriate local or microscale influences) and
compatible with the input requirements of the model to be used. It
should be noted that, while site specific measurements are
frequently made ``on-property'' (i.e., on the source's premises),
acquisition of adequately representative site specific data does not
preclude collection of data from a location off property.
Conversely, collection of meteorological data on a source's property
does not of itself guarantee adequate representativeness. For help
in determining representativeness of site specific measurements,
technical guidance \100\ is available. Site specific data should
always be reviewed for representativeness and consistency by a
qualified meteorologist.

9.3.3.2 Recommendations

a. EPA guidance\100\ provides recommendations on the collection
and use of site specific meteorological data. Recommendations on
characteristics, siting, and exposure of meteorological instruments
and on data recording, processing, completeness requirements,
reporting, and archiving are also included. This publication should
be used as a supplement to other limited guidance on these
subjects.\91,101,102\ Detailed information on quality assurance is
also available.\103\ As a minimum, site specific measurements of
ambient air temperature, transport wind speed and direction, and the
variables necessary to estimate atmospheric dispersion should be
available in meteorological data sets to be used in modeling. Care
should be taken to ensure that meteorological instruments are
located to provide representative characterization of pollutant
transport between sources and receptors of interest. The appropriate
reviewing authority (paragraph 3.0(b)) is available to help
determine the appropriateness of the measurement locations.
b. All site specific data should be reduced to hourly averages.
Table 9-3 lists the wind related parameters and the averaging time
requirements.
c. Missing Data Substitution. After valid data retrieval
requirements have been met \100\, hours in the record having missing
data should be treated according to an established data substitution
protocol provided that data from an adequately representative
alternative site are available. Such protocols are usually part of
the approved monitoring program plan. Data substitution guidance is
provided in Section 5.3 of reference 100. If no representative
alternative data are available for substitution, the absent data
should be coded as missing using missing data codes appropriate to
the applicable meteorological pre-processor. Appropriate model
options for treating missing data, if available in the model, should
be employed.
d. Solar Radiation Measurements. Total solar radiation or net
radiation should be measured with a reliable pyranometer or net
radiometer, sited and operated in accordance with established site
specific meteorological guidance.\100,103\
e. Temperature Measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
site specific meteorological guidance.\100\
f. Temperature Difference Measurements. Temperature difference
([delta]T) measurements should be obtained using matched
thermometers or a reliable thermocouple system to achieve adequate
accuracy. Siting, probe placement, and operation of [delta]T systems
should be based on guidance found in Chapter 3 of reference 100, and
such guidance should be followed when obtaining vertical temperature
gradient data.
g. Winds Aloft. For simulation of plume rise and dispersion of a
plume emitted from a stack, characterization of the wind profile up
through the layer in which the plume disperses is required. This is
especially important in complex terrain and/or complex wind
situations where wind measurements at heights up to hundreds of
meters above stack base may be required in some circumstances. For
tall stacks when site specific data are needed, these winds have
been obtained traditionally using meteorological sensors mounted on
tall towers. A feasible alternative to tall towers is the use of
meteorological remote sensing instruments (e.g., acoustic sounders
or radar wind profilers) to provide winds aloft, coupled with 10-
meter towers to provide the near-surface winds. (For specific
requirements for CTDMPLUS, see Appendix A.) Specifications for wind
measuring instruments and systems are contained in reference 100.
h. Turbulence. There are several dispersion models that are
capable of using direct measurements of turbulence (wind
fluctuations) in the characterization of the vertical and lateral
dispersion (e.g., CTDMPLUS and CALPUFF). For specific requirements
for CTDMPLUS and CALPUFF, see Appendix A. For technical guidance on
measurement and processing of turbulence parameters, see reference
100. When turbulence data are used in this manner to directly
characterize the vertical and lateral dispersion, the averaging time
for the turbulence measurements should be one hour

[[Page 18466]]

(Table 9-3). There are other dispersion models (e.g., BLP, and
CALINE3) that employ P-G stability categories for the
characterization of the vertical and lateral dispersion. Methods for
using site specific turbulence data for the characterization of P-G
stability categories are discussed in reference 100. When turbulence
data are used in this manner to determine the P-G stability
category, the averaging time for the turbulence measurements should
be 15 minutes.
i. Stability Categories. For dispersion models that employ P-G
stability categories for the characterization of the vertical and
lateral dispersion (e.g., ISC3), the P-G stability categories, as
originally defined, couple near-surface measurements of wind speed
with subjectively determined insolation assessments based on hourly
cloud cover and ceiling height observations. The wind speed
measurements are made at or near 10m. The insolation rate is
typically assessed using observations of cloud cover and ceiling
height based on criteria outlined by Turner.\77\ It is recommended
that the P-G stability category be estimated using the Turner method
with site specific wind speed measured at or near 10m and
representative cloud cover and ceiling height. Implementation of the
Turner method, as well as considerations in determining
representativeness of cloud cover and ceiling height in cases for
which site specific cloud observations are unavailable, may be found
in Section 6 of reference 100. In the absence of requisite data to
implement the Turner method, the SRDT method or wind fluctuation
statistics (i.e., the [sigma]_E and [sigma]_A
methods) may be used.
j. The SRDT method, described in Section 6.4.4.2 of reference
100, is modified slightly from that published from earlier work
\104\ and has been evaluated with three site specific data
bases.105 The two methods of stability classification
which use wind fluctuation statistics, the [sigma]_E and
[sigma]_A methods, are also described in detail in Section
6.4.4 of reference 100 (note applicable tables in Section 6). For
additional information on the wind fluctuation methods, several
references are available.\106,\\107,\\108,\\109,\
k. Meteorological Data Preprocessors. The following
meteorological preprocessors are recommended by EPA: PCRAMMET,\110\
MPRM,\111\ METPRO,\112\ and CALMET.\113\ PCRAMMET is the recommended
meteorological preprocessor for use in applications employing hourly
NWS data. MPRM is a general purpose meteorological data preprocessor
which supports regulatory models requiring PCRAMMET formatted (NWS)
data. MPRM is available for use in applications employing site
specific meteorological data. The latest version (MPRM 1.3) has been
configured to implement the SRDT method for estimating P-G stability
categories. METPRO is the required meteorological data preprocessor
for use with CTDMPLUS. CALMET is available for use with applications
of CALPUFF. All of the above mentioned data preprocessors are
available for downloading from EPA's Internet SCRAM Web site
(subsection 2.3).

Table 9-3.--Averaging Times for Site Specific Wind and Turbulence
Measurements
------------------------------------------------------------------------
Averaging
Parameter time (in
hours)
------------------------------------------------------------------------
Surface wind speed (for use in stability determinations)... 1
Transport direction........................................ 1
Dilution wind speed........................................ 1
Turbulence measurements ([sigma]_E and [sigma]_A) for use in \1\ 1
stability determinations..................................
Turbulence Measurements for direct input to dispersion 1
models....................................................
------------------------------------------------------------------------
\1\ To minimize meander effects in [sigma]_A when wind conditions are
light and/or variable, determine the hourly average [sigma]
value from
four sequential 15-minute [sigma]'s according to the following
formula:

[GRAPHIC]
[TIFF OMITTED]
TR15AP03.073

9.3.4 Treatment of Near-calms and Calms

9.3.4.1 Discussion

a. Treatment of calm or light and variable wind poses a special
problem in model applications since steady-state Gaussian plume
models assume that concentration is inversely proportional to wind
speed. Furthermore, concentrations may become unrealistically large
when wind speeds less than l m/s are input to the model. Procedures
have been developed to prevent the occurrence of overly conservative
concentration estimates during periods of calms. These procedures
acknowledge that a steady-state Gaussian plume model does not apply
during calm conditions, and that our knowledge of wind patterns and
plume behavior during these conditions does not, at present, permit
the development of a better technique. Therefore, the procedures
disregard hours which are identified as calm. The hour is treated as
missing and a convention for handling missing hours is recommended.

9.3.4.2 Recommendations

a. Hourly concentrations calculated with steady-state Gaussian
plume models using calms should not be considered valid; the wind
and concentration estimates for these hours should be disregarded
and considered to be missing. Critical concentrations for 3-, 8-,
and 24-hour averages should be calculated by dividing the sum of the
hourly concentrations for the period by the number of valid or non-
missing hours. If the total number of valid hours is less than 18
for 24-hour averages, less than 6 for 8-hour averages or less than 3
for 3-hour averages, the total concentration should be divided by 18
for the 24-hour average, 6 for the 8-hour average and 3 for the 3-
hour average. For annual averages, the sum of all valid hourly
concentrations is divided by the number of non-calm hours during the
year. For models listed in Appendix A, a post-processor computer
program, CALMPRO \114\ has been prepared, is available on the SCRAM
Internet Web site (subsection 2.3), and should be used.
b. Stagnant conditions that include extended periods of calms
often produce high concentrations over wide areas for relatively
long averaging periods. The standard steady-state Gaussian plume
models are often not applicable to such situations. When stagnation
conditions are of concern, other modeling techniques should be
considered on a case-by-case basis (see also subsection 8.2.8).
c. When used in steady-state Gaussian plume models, measured
site specific wind speeds of less than l m/s but higher than the
response threshold of the instrument should be input as l m/s; the
corresponding wind direction should also be input. Wind observations
below the response threshold of the instrument should be set to
zero, with the input file in ASCII format. In all cases involving
steady-state Gaussian plume models, calm hours should be treated as
missing, and concentrations should be calculated as in paragraph (a)
of this subsection.

10.0 Accuracy and Uncertainty of Models

10.1 Discussion

a. Increasing reliance has been placed on concentration
estimates from models as the primary basis for regulatory decisions
concerning source permits and emission control requirements. In many
situations, such as review of a proposed source, no practical
alternative exists. Therefore, there is an obvious need to know how
accurate models really are and how any uncertainty in the estimates
affects regulatory decisions. During the 1980's, attempts were made
to encourage development of standardized evaluation
methods.\16\,\115\ EPA recognized the need for incorporating such
information and has sponsored workshops \116\ on model accuracy, the
possible ways to quantify accuracy, and on considerations in the
incorporation of model accuracy and uncertainty in the regulatory
process. The Second (EPA) Conference on Air Quality Modeling, August
1982,\117\ was devoted to that subject.
b. To better deduce the statistical significance of differences
seen in model performance in the face of unaccounted for
uncertainties and variations, investigators have more recently
explored the use of bootstrap
techniques.118,119 Work is underway to develop
a new generation of evaluation metrics \24\ that takes into account
the statistical differences (in error distributions) between model
predictions and observations.\120\ Even though the procedures and
measures are still evolving to describe performance of models that
characterize atmospheric fate, transport and diffusion
121, 122, 123 there has been
general acceptance of a need to address the uncertainties inherent
in atmospheric processes.

10.1.1 Overview of Model Uncertainty

a. Dispersion models generally attempt to estimate
concentrations at specific sites that really represent an ensemble
average of numerous repetitions of the same event.\24\ The event is
characterized by measured or

[[Page 18467]]

``known'' conditions that are input to the models, e.g., wind speed,
mixed layer height, surface heat flux, emission characteristics,
etc. However, in addition to the known conditions, there are
unmeasured or unknown variations in the conditions of this event,
e.g., unresolved details of the atmospheric flow such as the
turbulent velocity field. These unknown conditions, may vary among
repetitions of the event. As a result, deviations in observed
concentrations from their ensemble average, and from the
concentrations estimated by the model, are likely to occur even
though the known conditions are fixed. Even with a perfect model
that predicts the correct ensemble average, there are likely to be
deviations from the observed concentrations in individual
repetitions of the event, due to variations in the unknown
conditions. The statistics of these concentration residuals are
termed ``inherent'' uncertainty. Available evidence suggests that
this source of uncertainty alone may be responsible for a typical
range of variation in concentrations of as much as +/-50
percent.\124\
b. Moreover, there is ``reducible'' uncertainty \115\ associated
with the model and its input conditions; neither models nor data
bases are perfect. Reducible uncertainties are caused by: (1)
Uncertainties in the input values of the known conditions (i.e.,
emission characteristics and meteorological data); (2) errors in the
measured concentrations which are used to compute the concentration
residuals; and (3) inadequate model physics and formulation. The
``reducible'' uncertainties can be minimized through better (more
accurate and more representative) measurements and better model
physics.
c. To use the terminology correctly, reference to model accuracy
should be limited to that portion of reducible uncertainty which
deals with the physics and the formulation of the model. The
accuracy of the model is normally determined by an evaluation
procedure which involves the comparison of model concentration
estimates with measured air quality data.\125\ The statement of
accuracy is based on statistical tests or performance measures such
as bias, noise, correlation, etc.\16\ However, information that
allows a distinction between contributions of the various elements
of inherent and reducible uncertainty is only now beginning to
emerge.\24\ As a result most discussions of the accuracy of models
make no quantitative distinction between (1) limitations of the
model versus (2) limitations of the data base and of knowledge
concerning atmospheric variability. The reader should be aware that
statements on model accuracy and uncertainty may imply the need for
improvements in model performance that even the ``perfect'' model
could not satisfy.

10.1.2 Studies of Model Accuracy

a. A number of studies\126,127\ have been conducted to examine
model accuracy, particularly with respect to the reliability of
short-term concentrations required for ambient standard and
increment evaluations. The results of these studies are not
surprising. Basically, they confirm what expert atmospheric
scientists have said for some time: (1) Models are more reliable for
estimating longer time-averaged concentrations than for estimating
short-term concentrations at specific locations; and (2) the models
are reasonably reliable in estimating the magnitude of highest
concentrations occurring sometime, somewhere within an area. For
example, errors in highest estimated concentrations of +/-10 to 40
percent are found to be typical \128,129\, i.e., certainly well
within the often quoted factor-of-two accuracy that has long been
recognized for these models. However, estimates of concentrations
that occur at a specific time and site, are poorly correlated with
actually observed concentrations and are much less reliable.
b. As noted above, poor correlations between paired
concentrations at fixed stations may be due to ``reducible''
uncertainties in knowledge of the precise plume location and to
unquantified inherent uncertainties. For example, Pasquill \130\
estimates that, apart from data input errors, maximum ground-level
concentrations at a given hour for a point source in flat terrain
could be in error by 50 percent due to these uncertainties.
Uncertainty of five to 10 degrees in the measured wind direction,
which transports the plume, can result in concentration errors of 20
to 70 percent for a particular time and location, depending on
stability and station location. Such uncertainties do not indicate
that an estimated concentration does not occur, only that the
precise time and locations are in doubt.

10.1.3 Use of Uncertainty in Decision-Making

a. The accuracy of model estimates varies with the model used,
the type of application, and site specific characteristics. Thus, it
is desirable to quantify the accuracy or uncertainty associated with
concentration estimates used in decision-making. Communications
between modelers and decision-makers must be fostered and further
developed. Communications concerning concentration estimates
currently exist in most cases, but the communications dealing with
the accuracy of models and its meaning to the decision-maker are
limited by the lack of a technical basis for quantifying and
directly including uncertainty in decisions. Procedures for
quantifying and interpreting uncertainty in the practical
application of such concepts are only beginning to evolve; much
study is still required.115,116,117,131,132
b. In all applications of models an effort is encouraged to
identify the reliability of the model estimates for that particular
area and to determine the magnitude and sources of error associated
with the use of the model. The analyst is responsible for
recognizing and quantifying limitations in the accuracy, precision
and sensitivity of the procedure. Information that might be useful
to the decision-maker in recognizing the seriousness of potential
air quality violations includes such model accuracy estimates as
accuracy of peak predictions, bias, noise, correlation, frequency
distribution, spatial extent of high concentration, etc. Both space/
time pairing of estimates and measurements and unpaired comparisons
are recommended. Emphasis should be on the highest concentrations
and the averaging times of the standards or increments of concern.
Where possible, confidence intervals about the statistical values
should be provided. However, while such information can be provided
by the modeler to the decision-maker, it is unclear how this
information should be used to make an air pollution control
decision. Given a range of possible outcomes, it is easiest and
tends to ensure consistency if the decision-maker confines his
judgement to use of the ``best estimate'' provided by the modeler
(i.e., the design concentration estimated by a model recommended in
the Guideline or an alternate model of known accuracy). This is an
indication of the practical limitations imposed by current abilities
of the technical community.
c. To improve the basis for decision-making, EPA has developed
and is continuing to study procedures for determining the accuracy
of models, quantifying the uncertainty, and expressing confidence
levels in decisions that are made concerning emissions
controls.\133,134\ However, work in this area involves ``breaking
new ground'' with slow and sporadic progress likely. As a result, it
may be necessary to continue using the ``best estimate'' until
sufficient technical progress has been made to meaningfully
implement such concepts dealing with uncertainty.

10.1.4 Evaluation of Models

a. A number of actions have been taken to ensure that the best
model is used correctly for each regulatory application and that a
model is not arbitrarily imposed. First, the Guideline clearly
recommends the most appropriate model be used in each case.
Preferred models, based on a number of factors, are identified for
many uses. General guidance on using alternatives to the preferred
models is also provided. Second, the models have been subjected to a
systematic performance evaluation and a peer scientific review.
Statistical performance measures, including measures of difference
(or residuals) such as bias, variance of difference and gross
variability of the difference, and measures of correlation such as
time, space, and time and space combined as recommended by the AMS
Woods Hole Workshop \16\, were generally followed. Third, more
specific information has been provided for justifying the site
specific use of alternative models in previously cited EPA guidance
\22,25\, and new models are under consideration and review.\24\
Together these documents provide methods that allow a judgement to
be made as to what models are most appropriate for a specific
application. For the present, performance and the theoretical
evaluation of models are being used as an indirect means to quantify
one element of uncertainty in air pollution regulatory decisions.
b. EPA has participated in a series of conferences entitled,
``Harmonisation within Atmospheric Dispersion Modelling for
Regulatory Purposes.'' \135\ for the purpose of promoting the
development of improved methods for the characterization of model
performance. There is a consensus developing on what should be
considered in the evaluation of air quality models \136\,

[[Page 18468]]

namely quality assurance planning, documentation and scrutiny should
be consistent with the intended use, and should include:
? Scientific peer review;
? Supportive analyses (diagnostic evaluations, code
verification, sensitivity and uncertainty analyses);
? Diagnostic and performance evaluations with data
obtained in trial locations, and
? Statistical performance evaluations in the circumstances
of the intended applications.
Performance evaluations and diagnostic evaluations assess
different qualities of how well a model is performing, and both are
needed to establish credibility within the client and scientific
community. Performance evaluations allow us to decide how well the
model simulates the average temporal and spatial patterns seen in
the observations, and employ large spatial/temporal scale data sets
(e.g., national data sets). Performance evaluations also allow
determination of relative performance of a model in comparison with
alternative modeling systems. Diagnostic evaluations allow
determination of a model capability to simulate individual processes
that affect the results, and usually employ smaller spatial/temporal
scale date sets (e.g., field studies). Diagnostic evaluations allow
us to decide if we get the right answer for the right reason. The
objective comparison of modeled concentrations with observed field
data provides only a partial means for assessing model performance.
Due to the limited supply of evaluation data sets, there are severe
practical limits in assessing model performance. For this reason,
the conclusions reached in the science peer reviews and the
supportive analyses have particular relevance in deciding whether a
model will be useful for its intended purposes.
c. To extend information from diagnostic and performance
evaluations, sensitivity and uncertainty analyses are encouraged
since they can provide additional information on the effect of
inaccuracies in the data bases and on the uncertainty in model
estimates. Sensitivity analyses can aid in determining the effect of
inaccuracies of variations or uncertainties in the data bases on the
range of likely concentrations. Uncertainty analyses can aid in
determining the range of likely concentration values, resulting from
uncertainties in the model inputs, the model formulations, and
parameterizations. Such information may be used to determine source
impact and to evaluate control strategies. Where possible,
information from such sensitivity analyses should be made available
to the decision-maker with an appropriate interpretation of the
effect on the critical concentrations.

10.2 Recommendations

a. No specific guidance on the quantification of model
uncertainty for use in decision-making is being given at this time.
As procedures for considering uncertainty develop and become
implementable, this guidance will be changed and expanded. For the
present, continued use of the ``best estimate'' is acceptable;
however, in specific circumstances for O_3, PM-2.5 and
regional haze, additional information and/or procedures may be
appropriate.\41,42\

11.0 Regulatory Application of Models

11.1 Discussion

a. Procedures with respect to the review and analysis of air
quality modeling and data analyses in support of SIP revisions, PSD
permitting or other regulatory requirements need a certain amount of
standardization to ensure consistency in the depth and
comprehensiveness of both the review and the analysis itself. This
section recommends procedures that permit some degree of
standardization while at the same time allowing the flexibility
needed to assure the technically best analysis for each regulatory
application.
b. Dispersion model estimates, especially with the support of
measured air quality data, are the preferred basis for air quality
demonstrations. Nevertheless, there are instances where the
performance of recommended dispersion modeling techniques, by
comparison with observed air quality data, may be shown to be less
than acceptable. Also, there may be no recommended modeling
procedure suitable for the situation. In these instances, emission
limitations may be established solely on the basis of observed air
quality data as would be applied to a modeling analysis. The same
care should be given to the analyses of the air quality data as
would be applied to a modeling analysis.
c. The current NAAQS for SO₂ and CO are both stated
in terms of a concentration not to be exceeded more than once a
year. There is only an annual standard for NO₂ and a
quarterly standard for Pb. Standards for fine particulate matter
(PM-2.5) are expressed in terms of both long-term (annual) and
short-term (daily) averages. The long-term standard is calculated
using the three year average of the annual averages while the short-
term standard is calculated using the three year average of the 98th
percentile of the daily average concentration. For PM-10, the
convention is to compare the arithmetic mean, averaged over 3
consecutive years, with the concentration specified in the NAAQS (50
[mu]g/m\3\). The 24-hour NAAQS (150 [mu]g/m\3\) is met if, over a 3-
year period, there is (on average) no more than one exceedance per
year. For ozone the short term 1-hour standard is expressed in terms
of an expected exceedance limit while the short term 8-hour standard
is expressed in terms of a three year average of the annual fourth
highest daily maximum 8-hour value. The NAAQS are subjected to
extensive review and possible revision every 5 years.
d. This section discusses general requirements for concentration
estimates and identifies the relationship to emission limits. The
following recommendations apply to: (1) Revisions of State
Implementation Plans and (2) the review of new sources and the
prevention of significant deterioration (PSD).

11.2 Recommendations

11.2.1 Analysis Requirements

a. Every effort should be made by the Regional Office to meet
with all parties involved in either a SIP revision or a PSD permit
application prior to the start of any work on such a project. During
this meeting, a protocol should be established between the preparing
and reviewing parties to define the procedures to be followed, the
data to be collected, the model to be used, and the analysis of the
source and concentration data. An example of requirements for such
an effort is contained in the Air Quality Analysis Checklist posted
on EPA's Internet SCRAM Web site (subsection 2.3). This checklist
suggests the level of detail required to assess the air quality
resulting from the proposed action. Special cases may require
additional data collection or analysis and this should be determined
and agreed upon at this preapplication meeting. The protocol should
be written and agreed upon by the parties concerned, although a
formal legal document is not intended. Changes in such a protocol
are often required as the data collection and analysis progresses.
However, the protocol establishes a common understanding of the
requirements.
b. An air quality analysis should begin with a screening model
to determine the potential of the proposed source or control
strategy to violate the PSD increment or NAAQS. For traditional
stationary sources, EPA guidance \27\ should be followed. Guidance
is also available for mobile sources.\57\
c. If the concentration estimates from screening techniques
indicate that the PSD increment or NAAQS may be approached or
exceeded, then a more refined modeling analysis is appropriate and
the model user should select a model according to recommendations in
Sections 4-8. In some instances, no refined technique may be
specified in this guide for the situation. The model user is then
encouraged to submit a model developed specifically for the case at
hand. If that is not possible, a screening technique may supply the
needed results.
d. Regional Offices should require permit applicants to
incorporate the pollutant contributions of all sources into their
analysis. Where necessary this may include emissions associated with
growth in the area of impact of the new or modified source. PSD air
quality assessments should consider the amount of the allowable air
quality increment that has already been consumed by other sources.
Therefore, the most recent source applicant should model the
existing or permitted sources in addition to the one currently under
consideration. This would permit the use of newly acquired data or
improved modeling techniques if such have become available since the
last source was permitted. When remodeling, the worst case used in
the previous modeling analysis should be one set of conditions
modeled in the new analysis. All sources should be modeled for each
set of meteorological conditions selected.

11.2.2 Use of Measured Data in Lieu of Model Estimates

a. Modeling is the preferred method for determining emission
limitations for both new and existing sources. When a preferred
model is available, model results alone (including background) are
sufficient. Monitoring will normally not be accepted as the sole
basis for emission limitation. In some instances when the modeling
technique

[[Page 18469]]

available is only a screening technique, the addition of air quality
data to the analysis may lend credence to model results.
b. There are circumstances where there is no applicable model,
and measured data may need to be used. However, only in the case of
an existing source should monitoring data alone be a basis for
emission limits. In addition, the following items (i-vi) should be
considered prior to the acceptance of the measured data:
i. Does a monitoring network exist for the pollutants and
averaging times of concern?
ii. Has the monitoring network been designed to locate points of
maximum concentration?
iii. Do the monitoring network and the data reduction and
storage procedures meet EPA monitoring and quality assurance
requirements?
iv. Do the data set and the analysis allow impact of the most
important individual sources to be identified if more than one
source or emission point is involved?
v. Is at least one full year of valid ambient data available?
vi. Can it be demonstrated through the comparison of monitored
data with model results that available models are not applicable?
c. The number of monitors required is a function of the problem
being considered. The source configuration, terrain configuration,
and meteorological variations all have an impact on number and
placement of monitors. Decisions can only be made on a case-by-case
basis. Guidance is available for establishing criteria for
demonstrating that a model is not applicable.\22\
d. Sources should obtain approval from the appropriate reviewing
authority (paragraph 3.0(b)) for the monitoring network prior to the
start of monitoring. A monitoring protocol agreed to by all
concerned parties is highly desirable. The design of the network,
the number, type and location of the monitors, the sampling period,
averaging time as well as the need for meteorological monitoring or
the use of mobile sampling or plume tracking techniques, should all
be specified in the protocol and agreed upon prior to start-up of
the network.

11.2.3 Emission Limits

11.2.3.1 Design Concentrations

a. Emission limits should be based on concentration estimates
for the averaging time that results in the most stringent control
requirements. The concentration used in specifying emission limits
is called the design value or design concentration and is a sum of
the concentration contributed by the source and the background
concentration.
b. To determine the averaging time for the design value, the
most restrictive NAAQS should be identified by calculating, for each
averaging time, the ratio of the difference between the applicable
NAAQS (S) and the background concentration (B) to the (model)
predicted concentration (P) (i.e., (S-B)/P). The averaging time with
the lowest ratio identifies the most restrictive standard. If the
annual average is the most restrictive, the highest estimated annual
average concentration from one or a number of years of data is the
design value. When short term standards are most restrictive, it may
be necessary to consider a broader range of concentrations than the
highest value. For example, for pollutants such as SO₂,
the highest, second-highest concentration is the design value. For
pollutants with statistically based NAAQS, the design value is found
by determining the more restrictive of: (1) The short-term
concentration over the period specified in the standard, or (2) the
long-term concentration that is not expected to exceed the long-term
NAAQS. Determination of design values for PM-10 is presented in more
detail in EPA guidance.\43\

11.2.3.2 NAAQS Analyses for New or Modified Sources

a. For new or modified sources predicted to have a significant
ambient impact \91\ and to be located in areas designated attainment
or unclassifiable for the SO₂, Pb, NO₂, or CO
NAAQS, the demonstration as to whether the source will cause or
contribute to an air quality violation should be based on: (1) The
highest estimated annual average concentration determined from
annual averages of individual years; or (2) the highest, second-
highest estimated concentration for averaging times of 24-hours or
less; and (3) the significance of the spatial and temporal
contribution to any modeled violation. For Pb, the highest estimated
concentration based on an individual calendar quarter averaging
period should be used. Background concentrations should be added to
the estimated impact of the source. The most restrictive standard
should be used in all cases to assess the threat of an air quality
violation. For new or modified sources predicted to have a
significant ambient impact \91\ in areas designated attainment or
unclassifiable for the PM-10 NAAQS, the demonstration of whether or
not the source will cause or contribute to an air quality violation
should be based on sufficient data to show whether: (1) The
projected 24-hour average concentrations will exceed the 24-hour
NAAQS more than 1 percent of the time, on average ; (2) the expected
(i.e., average) annual mean concentration will exceed the annual
NAAQS; and (3) the source contributes significantly, in a temporal
and spatial sense, to any modeled violation.

11.2.3.3 PSD Air Quality Increments and Impacts

a. The allowable PSD increments for criteria pollutants are
established by regulation and cited in 40 CFR 51.166. These maximum
allowable increases in pollutant concentrations may be exceeded once
per year at each site, except for the annual increment that may not
be exceeded. The highest, second-highest increase in estimated
concentrations for the short term averages as determined by a model
should be less than or equal to the permitted increment. The modeled
annual averages should not exceed the increment.
b. Screening techniques defined in subsection 4.1 can sometimes
be used to estimate short term incremental concentrations for the
first new source that triggers the baseline in a given area.
However, when multiple increment-consuming sources are involved in
the calculation, the use of a refined model with at least 1 year of
site specific or 5 years of (off-site) NWS data is normally required
(subsection 9.3.1.2). In such cases, sequential modeling must
demonstrate that the allowable increments are not exceeded
temporally and spatially, i.e., for all receptors for each time
period throughout the year(s) (time period means the appropriate PSD
averaging time, e.g., 3-hour, 24-hour, etc.).
c. The PSD regulations require an estimation of the
SO₂, particulate matter (PM-10), and NO₂
impact on any Class I area. Normally, steady-state Gaussian plume
models should not be applied at distances greater than can be
accommodated by the steady state assumptions inherent in such
models. The maximum distance for refined steady-state Gaussian plume
model application for regulatory purposes is generally considered to
be 50km. Beyond the 50km range, screening techniques may be used to
determine if more refined modeling is needed. If refined models are
needed, long range transport models should be considered in
accordance with subsection 7.2.3. As previously noted in Sections 3
and 7, the need to involve the Federal Land Manager in decisions on
potential air quality impacts, particularly in relation to PSD Class
I areas, cannot be overemphasized.

12.0 Bibliography \c\
---------------------------------------------------------------------------

\c\ The documents listed here are major sources of supplemental
infomation on the theory and application of mathematical air quality
models.
---------------------------------------------------------------------------

American Meteorological Society. Symposia on Turbulence,
Diffusion, and Air Pollution (1st-10th); 1971-1992. Symposia on
Boundary Layers & Turb. 11th-12th; 1995-1997. Boston, MA.
American Meteorological Society, 1977-1998. Joint Conferences on
Applications of Air Pollution Meteorology (1st-10th). Sponsored by
the American Meteorological Society and the Air & Waste Management
Association. Boston, MA.
American Meteorological Society, 1978. Accuracy of Dispersion
Models. Bulletin of the American Meteorological Society, 59(8):
1025-1026.
American Meteorological Society, 1981. Air Quality Modeling and
the Clean Air Act: Recommendations to EPA on Dispersion Modeling for
Regulatory Applications. Boston, MA.
Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge,
TN.
Drake, R.L. and S.M. Barrager, 1979. Mathematical Models for
Atmospheric Pollutants. EPRI EA-1131. Electric Power Research
Institute, Palo Alto, CA.
Environmental Protection Agency, 1978. Workbook for Comparison
of Air Quality Models. EPA Publication No. EPA-450/2-78-028a and b.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC.
Erisman J.W., Van Pul A. and Wyers P. (1994) Parameterization of
surface resistance for the quantification of atmospheric deposition
of acidifying pollutants and ozone. Atmos. Environ., 28: 2595-2607.

[[Page 18470]]

Fox, D.G., and J.E. Fairobent, 1981. NCAQ Panel Examines Uses
and Limitations of Air Quality Models. Bulletin of the American
Meteorological Society, 62(2): 218-221.
Gifford, F.A., 1976. Turbulent Diffusion Typing Schemes: A
Review. Nuclear Safety, 17(1): 68-86.
Gudiksen, P.H., and M.H. Dickerson, Eds., Executive Summary:
Atmospheric Studies in Complex Terrain Technical Progress Report FY-
1979 Through FY-1983. Lawrence Livermore National Laboratory,
Livermore, CA. (Docket Reference No. II-I-103).
Hanna, S.R., G.A. Briggs, J. Deardorff, B.A. Egan, G.A. Gifford
and F. Pasquill, 1977. AMS Workshop on Stability Classification
Schemes And Sigma Curves--Summary of Recommendations. Bulletin of
the American Meteorological Society, 58(12): 1305-1309.
Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Handbook on
Atmospheric Diffusion. Technical Information Center, U.S. Department
of Energy, Washington, DC.
Haugen, D.A., Workshop Coordinator, 1975. Lectures on Air
Pollution and Environmental Impact Analyses. Sponsored by the
American Meteorological Society, Boston, MA.
Hoffnagle, G.F., M.E. Smith, T.V. Crawford and T.J. Lockhart,
1981. On-site Meteorological Instrumentation Requirements to
Characterize Diffusion from Point Sources--A Workshop, 15-17 January
1980, Raleigh, NC. Bulletin of the American Meteorological Society,
62(2): 255-261.
Hunt, J.C.R., R.G. Holroyd, D.J. Carruthers, A.G. Robins, D.D.
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Wind Using Wind Vane and Rotating Anemometer. (1996)
103. Environmental Protection Agency, 1995. Quality Assurance
for Air Pollution Measurement Systems, Volume IV--Meteorological
Measurements. EPA Publication No. EPA600/R-94/038d. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. Note: for
copies of this handbook, you may make inquiry to ORD Publications,
26 West Martin Luther King Dr., Cincinatti, OH 45268. Phone (513)
569-7562 or (800) 490-9198 (automated request line)
104. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability
Class Determination: A Comparison for One Site. Proceedings, Sixth
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Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7)
105. Environmental Protection Agency, 1993. An Evaluation of a
Solar Radiation/Delta-T (SRDT) Method for Estimating Pasquill-
Gifford (P-G) Stability Categories. EPA Publication No. EPA-454/R-
93-055. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 94-113958)
106. Irwin, J.S., 1980. Dispersion Estimate Suggestion
#8: Estimation of Pasquill Stability Categories. Office of
Air Quality Planning & Standards, Research Triangle Park, NC (Docket
No. A-80-46, II-B-10)
107. 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)
108. Smedman--Hogstrom, A. and V. Hogstrom, 1978. A Practical
Method for Determining Wind Frequency Distributions for the Lowest
200m from Routine Meteorological Data. J. of Applied Meteorology,
17(7): 942-954.
109. 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)
110. Environmental Protection Agency, 1993. PCRAMMET User's
Guide. EPA Publication No. EPA-454/R-96-001. Office of Air Quality
Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 97-
147912)
111. Environmental Protection Agency, 1996. Meteorological
Processor for Regulatory Models (MPRM) User's Guide. EPA Publication
No. EPA-454/B-96-002. Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB 96-180518)
112. Paine, R.J., 1987. User's Guide to the CTDM Meteorological
Preprocessor Program. EPA Publication No. EPA-600/8-88-004. Office
of Research & Development, Research Triangle Park, NC. (NTIS No. PB
88-162102)
113. Scire, J.S., F.R. Francoise, M.E. Fernau and R.J.
Yamartino, 1998. A User's Guide for the CALMET Meteorological Model
(Version 5.0). Earth Tech, Inc., Concord, MA. (http://www.src.com/
calpuff/calpuff1.htm)
114. Environmental Protection Agency, 1984. Calms Processor
(CALMPRO) User's Guide. EPA Publication No. EPA-901/9-84-001. Office
of Air Quality Planning & Standards, Region I, Boston, MA. (NTIS No.
PB 84-229467)
115. Fox, D.G., 1984. Uncertainty in air quality modeling.
Bulletin of the American Meteorological Society, 65(1): 27-36.
116. Burton, C.S., 1981. The Role of Atmospheric Models in
Regulatory Decision-Making: Summary Report. Systems Applications,
Inc., San Rafael, CA. Prepared under contract No. 68-01-5845 for
U.S. Environmental Protection Agency, Research Triangle Park, NC.
(Docket No. A-80-46, II-M-6)
117. Environmental Protection Agency, 1981. Proceedings of the
Second Conference on Air Quality Modeling, Washington, DC. Office of
Air Quality Planning & Standards, Research Triangle Park, NC.
(Docket No. A-80-46, II-M-16)
118. Hanna, S.R., 1989. Confidence limits for air quality model
evaluations, as estimated by bootstrap and jackknife resampling
methods. Atmospheric Environment, 23(6): 1385-1398.
119. Cox, W.M. and J.A. Tikvart, 1990. A statistical procedure
for determining the best performing air quality simulation model.
Atmos. Environ., 24A(9): 2387-2395.
120. Oreskes, N.K., K. Shrader-Frechette and K. Beliz, 1994.
Verification, validation and confirmation of numerical models in the
earth sciences. Science, 263: 641-646.
121. Dekker, C.M., A. Groenendijk, C.J. Sliggers and G.K.
Verboom, 1990. Quality Criteria for Models to Calculate Air
Pollution. Lucht (Air) 90, Ministry of Housing, Physical Planning
and Environment, Postbus 450, 2260 MB Leidschendam, The Netherlands;
52pp.
122. Weil, J.C., R.I. Sykes and A. Venkatram, 1992. Evaluating
air-quality models: review and outlook. Journal of Applied
Meteorology, 31: 1121-1145.
123. Cole, S.T. and P.J. Wicks, Editors (1995): Model Evaluation
Group: Report of the Second Open Meeting. EUR 15990 EN, European
Commission, Directorate-General XII, Environmental Research
Programme, L-2920 Luxembourg; 77pp.
124. Hanna, S.R., 1982. Natural Variability of Observed Hourly
SO₂ and CO Concentrations in St. Louis. Atmospheric
Environment, 16(6): 1435-1440.
125. Bowne, N.E., 1981. Validation and Performance Criteria for
Air Quality Models. Appendix F in Air Quality Modeling and the Clean
Air Act: Recommendations to EPA on Dispersion Modeling for
Regulatory Applications. American Meteorological Society, Boston,
MA; pp. 159-171. (Docket No. A-80-46, II-A-106)
126. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results,
and Conclusions for the EPRI Plume Model Validation and Development
Project: Plains Site. EPRI EA-3074. Electric Power Research
Institute, Palo Alto, CA.
127. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A
Survey of Statistical Measures of Model Performance and Accuracy for
Several Air Quality Models. EPA Publication No. EPA-450/4-83-001.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. (NTIS No. PB 83-260810)
128. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff
Report. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (Docket No. A-80-46, II-G-6)
129. Hanna, S.R., 1993. Uncertainties in air quality model
predictions. Boundary-Layer Meteorology, 62: 3-20.
130. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition.
John Wiley and Sons, New York, NY; 479pp.
131. Morgan, M.G. and M. Henrion, 1990. Uncertainty, A Guide to
Dealing With Uncertainty in Quantitative Risk and Policy Analysis.
Cambridge University Press. New York, NY; 332pp.
132. Irwin, J.S., K. Steinberg, C. Hakkarinen and H. Feldman,
2001. Uncertainty in Air Quality Modeling for Risk Calculations.
(CD-ROM) Proceedings of Guideline on Air Quality Models: A New
Beginning. April 4-6, 2001, Newport, RI, Air & Waste Management
Association. Pittsburgh, PA; 17pp.
133. Austin, B.S., T.E. Stoeckenius, M.C. Dudik and T.S.
Stocking, 1988. User's Guide to the Expected Exceedances System.
Systems Applications, Inc., San Rafael, CA. Prepared under Contract
No. 68-02-4352 Option I for the U.S. Environmental Protection
Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-I-3)
134. Thrall, A.D., T.E. Stoeckenius and C.S. Burton, 1985. A
Method for Calculating Dispersion Modeling Uncertainty Applied to
the Regulation of an Emission Source. Systems Applications, Inc.,
San Rafael, CA. Prepared for the U.S. Environmental Protection
Agency, Research Triangle Park, NC. (Docket No. A-80-46, IV-G-1)
135. ``Ten years of Harmonisation activities: Past, present and
future'' at http://www.dmu.dk/AtmosphericEnvironment/Harmoni/
Conferences/Belgirate/BelgiratePapers.asp
136. ``A platform for model evaluation'' at http://www.dmu.dk/
AtmosphericEnvironment/Harmoni/Conferences/Belgirate/BelgiratePapers.asp

[[Page 18474]]

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

Table of Contents

A.0 Introduction and Availability
A.1 Buoyant Line and Point Source Dispersion Model (BLP)
A.2 Caline3
A.3 Calpuff
A.4 Complex Terrain Dispersion Model Plus Algorithms for Unstable
Situations (CTDMPLUS)
A.5 Emissions and Dispersion Modeling System (EDMS) 3.1
A.6 Industrial Source Complex Model (ISC3)
A.7 Offshore and Coastal Dispersion (OCD)
A. Ref References

A. 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) With the exception of EDMS, codes and documentation for all
models listed in this appendix are available from EPA's Support
Center for Regulatory Air Models (SCRAM) Web site at http://www.epa.gov/
scram001. Documentation is also available from the
National Technical Information Service (NTIS), http://www.ntis.gov
or U.S. Department of Commerce, Springfield, VA 22161; phone: (800)
553-6847. Where possible, accession numbers are provided.

A.1 Buoyant Line and Point Source Dispersion Model (BLP)

Reference

Schulman, Lloyd 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 computer code is available on EPA's Internet SCRAM website
and also on diskette (as PB 2002-500051) from the National Technical
Information Service (see Section A.0).

Abstract

BLP is a Gaussian plume dispersion model designed to handle
unique modeling problems associated with aluminum reduction plants,
and other industrial sources where plume rise and downwash effects
from stationary line sources are important.

a. Recommendations for Regulatory Use

(1) The BLP model is appropriate for the following applications:
? Aluminum reduction plants which contain buoyant,
elevated line sources;
? Rural areas;
? Transport distances less than 50 kilometers;
? Simple terrain; and
? One hour to one year averaging times.
(2) The following options should be selected for regulatory
applications:
(i) Rural (IRU=1) mixing height option;
(ii) Default (no selection) for plume rise wind shear (LSHEAR),
transitional point source plume rise (LTRANS), vertical potential
temperature gradient (DTHTA), vertical wind speed power law profile
exponents (PEXP), maximum variation in number of stability classes
per hour (IDELS), pollutant decay (DECFAC), the constant in Briggs'
stable plume rise equation (CONST2), constant in Briggs' neutral
plume rise equation (CONST3), convergence criterion for the line
source calculations (CRIT), and maximum iterations allowed for line
source calculations (MAXIT); and
(iii) Terrain option (TERAN) set equal to 0.0, 0.0, 0.0, 0.0,
0.0, 0.0
(3) For other applications, BLP can be used if it can be
demonstrated to give the same estimates as a recommended model for
the same application, and will subsequently be executed in that
mode.
(4) BLP can be used on a case-by-case basis with specific
options not available in a recommended model if it can be
demonstrated, using the criteria in Section 3.2, that the model is
more appropriate for a specific application.

b. Input Requirements

(1) Source data: point sources require stack location, elevation
of stack base, physical stack height, stack inside diameter, stack
gas exit velocity, stack gas exit temperature, and pollutant
emission rate. 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.
(2) Meteorological data: Hourly surface weather data from
punched cards or from the preprocessor program PCRAMMET which
provides hourly stability class, wind direction, wind speed,
temperature, and mixing height.
(3) Receptor data: Locations and elevations of receptors, or
location and size of receptor grid or request automatically
generated receptor grid.

c. Output

(1) Printed output (from a separate post-processor program)
includes:
(2) Total concentration or, optionally, source contribution
analysis; monthly and annual frequency distributions for 1-, 3-, and
24-hour average concentrations; tables of 1-, 3-, and 24-hour
average concentrations at each receptor; table of the annual (or
length of run) average concentrations at each receptor;
(3) Five highest 1-, 3-, and 24-hour average concentrations at
each receptor; and
(4) Fifty highest 1-, 3-, and 24-hour concentrations over the
receptor field.

d. Type of Model

BLP is a gaussian plume model.

e. Pollutant Types

BLP may be used to model primary pollutants. This model does not
treat settling and deposition.

f. Source-Receptor Relationship

(1) BLP treats up to 50 point sources, 10 parallel line sources,
and 100 receptors arbitrarily located.
(2) User-input topographic elevation is applied for each stack
and each receptor.

g. Plume Behavior

(1) BLP uses plume rise formulas of Schulman and Scire (1980).
(2) Vertical potential temperature gradients of 0.02 Kelvin per
meter for E stability and 0.035 Kelvin per meter are used for stable
plume rise calculations. An option for user input values is
included.
(3) Transitional rise is used for line sources.
(4) Option to suppress the use of transitional plume rise for
point sources is included.
(5) The building downwash algorithm of Schulman and Scire (1980)
is used.

h. Horizontal Winds

(1) Constant, uniform (steady-state) wind is assumed for an
hour.
Straight line plume transport is assumed to all downwind
distances.
(2) Wind speeds profile exponents of 0.10, 0.15, 0.20, 0.25,
0.30, and 0.30 are used for stability classes A through F,
respectively. An option for user--defined values and an option to
suppress the use of the wind speed profile feature are included.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

(1) Rural dispersion coefficients are from Turner (1969), with
no adjustment made for variations in surface roughness or averaging
time.
(2) Six stability classes are used.

k. Vertical Dispersion

(1) Rural dispersion coefficients are from Turner (1969), with
no adjustment made for variations in surface roughness.
(2) Six stability classes are used.
(3) Mixing height is accounted for with multiple reflections
until the vertical plume standard deviation equals 1.6 times the
mixing height; uniform mixing is assumed beyond that point.
(4) Perfect reflection at the ground is assumed.

[[Page 18475]]

l. Chemical Transformation

Chemical transformations are treated using linear decay. Decay
rate is input by the user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Schulman, L.L. and J.S. Scire, 1980. Buoyant Line and Point
Source (BLP) Dispersion Model User's Guide, P-7304B. Environmental
Research and Technology, Inc., Concord, MA.
Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and
ISC Models with SF₆ Tracer Data and SO₂
Measurements at Aluminum Reduction Plants. APCA Specialty Conference
on Dispersion Modeling for Complex Sources, St. Louis, MO.

A.2 CALINE3

Reference

Benson, Paul E, 1979. CALINE3--A Versatile Dispersion Model for
Predicting Air Pollutant Levels Near Highways and Arterial Streets.
Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway
Administration, Washington, DC. (NTIS No. PB 80-220841)

Availability

The CALINE3 model is available on diskette (as PB 95-502712)
from NTIS. The source code and user's guide are also available on
EPA's Internet SCRAM Web site ( Section A.0).

Abstract

CALINE3 can be used to estimate the concentrations of
nonreactive pollutants from highway traffic. This steady-state
Gaussian model can be applied to determine air pollution
concentrations at receptor locations downwind of ``at-grade,''
``fill,'' ``bridge,'' and ``cut section'' highways located in
relatively uncomplicated terrain. The model is applicable for any
wind direction, highway orientation, and receptor location. The
model has adjustments for averaging time and surface roughness, and
can handle up to 20 links and 20 receptors. It also contains an
algorithm for deposition and settling velocity so that particulate
concentrations can be predicted.

a. Recommendations for Regulatory Use

CALINE-3 is appropriate for the following applications:
? Highway (line) sources;
? Urban or rural areas;
? Simple terrain;
? Transport distances less than 50 kilometers; and
? One-hour to 24-hour averaging times.

b. Input Requirements

(1) Source data: Up to 20 highway links classed as ``at-grade,''
``fill'' ``bridge,'' or ``depressed''; coordinates of link end
points; traffic volume; emission factor; source height; and mixing
zone width.
(2) Meteorological data: Wind speed, wind angle (measured in
degrees clockwise from the Y axis), stability class, mixing height,
ambient (background to the highway) concentration of pollutant.
(3) Receptor data: Coordinates and height above ground for each
receptor.

c. Output

Printed output includes concentration at each receptor for the
specified meteorological condition.

d. Type of Model

CALINE-3 is a Gaussian plume model.

e. Pollutant Types

CALINE-3 may be used to model primary pollutants.

f. Source-Receptor Relationship

(1) Up to 20 highway links are treated.
(2) CALINE-3 applies user input location and emission rate for
each link. User-input receptor locations are applied.

g. Plume Behavior

Plume rise is not treated.

h. Horizontal Winds

(1) User-input hourly wind speed and direction are applied.
(2) Constant, uniform (steady-state) wind is assumed for an
hour.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

(1) Six stability classes are used.
(2) Rural dispersion coefficients from Turner (1969) are used,
with adjustment for roughness length and averaging time.
(3) Initial traffic-induced dispersion is handled implicitly by
plume size parameters.

k. Vertical Dispersion

(1) Six stability classes are used.
(2) Empirical dispersion coefficients from Benson (1979) are
used including an adjustment for roughness length.
(3) Initial traffic-induced dispersion is handled implicitly by
plume size parameters.
(4) Adjustment for averaging time is included.

l. Chemical Transformation

Not treated.

m. Physical Removal

Optional deposition calculations are included.

n. Evaluation Studies

Bemis, G.R. et al., 1977. Air Pollution and Roadway Location,
Design, and Operation--Project Overview. FHWA-CA-TL-7080-77-25,
Federal Highway Administration, Washington, D.C.
Cadle, S.H. et al., 1976. Results of the General Motors Sulfate
Dispersion Experiment, GMR-2107. General Motors Research
Laboratories, Warren, MI.
Dabberdt, W.F., 1975. Studies of Air Quality on and Near
Highways, Project 2761. Stanford Research Institute, Menlo Park, CA.

A.3 CALPUFF

References

Scire, J.S., D.G. Strimaitis and R.J. Yamartino, 2000. A User's
Guide for the CALPUFF Dispersion Model (Version 5.0). Earth Tech,
Inc., Concord, MA.
Scire J.S., F.R. Robe, M.E. Fernau and R.J. Yamartino, 2000. A
User's Guide for the CALMET Meteorological Model (Version 5.0).
Earth Tech, Inc., Concord, MA.

Availability

The model code and its documentation are available at no cost
for download from the model developers' Internet Web site:
http://www.src.com/calpuff/calpuff1.htm. You may also
contact Joseph Scire, Earth Tech, Inc., 196 Baker Avenue, Concord,
MA 01742; Telephone: (978) 371-4200, Fax: (978) 371-2468, e-mail:
jss@src.com.

Abstract

CALPUFF is a multi-layer, multi-species non-steady-state puff
dispersion modeling system that simulates the effects of time- and
space-varying meteorological conditions on pollutant transport,
transformation, and removal. CALPUFF is intended for use on scales
from tens of meters from a source to hundreds of kilometers. It
includes algorithms for near-field effects such as building
downwash, transitional buoyant and momentum plume rise, partial
plume penetration, subgrid scale terrain and coastal interactions
effects, and terrain impingement as well as longer range effects
such as pollutant removal due to wet scavenging and dry deposition,
chemical transformation, vertical wind shear, overwater transport,
plume fumigation, and visibility effects of particulate matter
concentrations.

a. Recommendations for Regulatory Use

(1) CALPUFF is appropriate for long range transport (source-
receptor distances of 50 to several hundred kilometers) of emissions
from point, volume, area, and line sources. The meteorological input
data should be fully characterized with time-and-space-varying three
dimensional wind and meteorological conditions using CALMET, as
discussed in paragraphs 9.3(c) and 9.3.1.2(d) of Appendix W.
(2) CALPUFF may also be used on a case-by-case basis if it can
be demonstrated using the criteria in Section 3.2 that the model is
more appropriate for the specific application. The purpose of
choosing a modeling system like CALPUFF is to fully treat
stagnation, wind reversals, and time and space variations of
meteorology effects on transport and dispersion, as discussed in
paragraph 8.2.8(a).
(3) For regulatory applications of CALMET and CALPUFF, the
regulatory default option should be used. Inevitably, some of the
model control options will have to be set specific for the
application using expert judgement and in consultation with the
relevant reviewing authorities.

b. Input Requirements

Source Data:
1. Point sources: Source location, stack height, diameter, exit
velocity, exit temperature, base elevation, wind direction specific
building dimensions (for building downwash calculations), and
emission rates for each pollutant. Particle size distributions may
be entered for particulate matter. Temporal emission factors
(diurnal cycle, monthly cycle, hour/season, wind speed/stability
class, or temperature-dependent

[[Page 18476]]

emission factors) may also be entered. Arbitrarily-varying point
source parameters may be entered from an external file.
2. Area sources: Source location and shape, release height, base
elevation, initial vertical distribution ([sigma]_z) and
emission rates for each pollutant. Particle size distributions may
be entered for particulate matter. Temporal emission factors
(diurnal cycle, monthly cycle, hour/season, wind speed/stability
class, or temperature-dependent emission factors) may also be
entered. Arbitrarily-varying area source parameters may be entered
from an external file. Area sources specified in the external file
are allowed to be buoyant and their location, size, shape, and other
source characteristics are allowed to change in time.
3. Volume sources: Source location, release height, base
elevation, initial horizontal and vertical distributions
([sigma]_y, [sigma]_z) and emission rates for
each pollutant. Particle size distributions may be entered for
particulate matter. Temporal emission factors (diurnal cycle,
monthly cycle, hour/season, wind speed/stability class, or
temperature-dependent emission factors) may also be entered.
Arbitrarily-varying volume source parameters may be entered from an
external file. Volume sources with buoyancy can be simulated by
treating the source as a point source and entering initial plume
size parameters--initial ([sigma]_y,
[sigma]_z)--to define the initial size of the volume
source.
4. Line sources: Source location, release height, base
elevation, average buoyancy parameter, and emission rates for each
pollutant. Building data may be entered for line source emissions
experiencing building downwash effects. Particle size distributions
may be entered for particulate matter. Temporal emission factors
(diurnal cycle, monthly cycle, hour/season, wind speed/stability
class, or temperature-dependent emission factors) may also be
entered. Arbitrarily-varying line source parameters may be entered
from an external file.
Meteorological Data (different forms of meteorological input can
be used by CALPUFF):
1. Time-dependent three-dimensional meteorological fields
generated by CALMET. This is the preferred mode for running CALPUFF.
Inputs into CALMET include surface observations of wind speed, wind
direction, temperature, cloud cover, ceiling height, relative
humidity, surface pressure, and precipitation (type and amount), and
upper air sounding data (wind speed, wind direction, temperature,
and height). Optional large-scale model output (e.g., from MM5) can
be used by CALMET as well (paragraph 9.3.1.2(d)).
2. Single station surface and upper air meteorological data in
CTDMPLUS data file formats (SURFACE.DAT and PROFILE.DAT files). This
allows a vertical variation in the meteorological parameters but no
spatial variability.
3. Single station meteorological data in ISCST3 data file
format. This option does not account for variability of the
meteorological parameters in the horizontal or vertical, except as
provided for by the use of stability-dependent wind shear exponents
and average temperature lapse rates.
Gridded terrain and land use data are required as input into
CALMET when Option 1 is used. Geophysical processor programs are
provided that interface the modeling system to standard terrain and
land use data bases provided by the U.S. Geological Survey (USGS).
Receptor Data:
CALPUFF includes options for gridded and non-gridded (discrete)
receptors. Special subgrid-scale receptors are used with the
subgrid-scale complex terrain option. An option is provided for
discrete receptors to be placed at ground-level or above the local
ground level (i.e., flagpole receptors). Gridded and subgrid-scale
receptors are placed at the local ground level only.
Other Input:
CALPUFF accepts hourly observations of ozone concentrations for
use in its chemical transformation algorithm. Subgrid-scale
coastlines can be specified in its coastal boundary file. Optional,
user-specified deposition velocities and chemical transformation
rates can also be entered. CALPUFF accepts the CTDMPLUS terrain and
receptor files for use in its subgrid-scale terrain algorithm.
Inflow boundary conditions of modeled pollutants can be specified in
a boundary condition file.

c. Output

CALPUFF produces files of hourly concentrations of ambient
concentrations for each modeled species, wet deposition fluxes, dry
deposition fluxes, and for visibility applications, extinction
coefficients. Postprocessing programs (PRTMET and CALPOST) provide
options for analysis and display of the modeling results.

d. Type of Model

(1) CALPUFF is a non-steady-state time- and space-dependent
Gaussian puff model. CALPUFF includes parameterized gas phase
chemical transformation of SO₂,
SO₄=, NO, NO₂, HNO₃,
NO₃-, and organic aerosols. CALPUFF can treat
primary pollutants such as PM-10, toxic pollutants, ammonia, and
other passive pollutants. The model includes a resistance-based dry
deposition model for both gaseous pollutants and particulate matter.
Wet deposition is treated using a scavenging coefficient approach.
The model has detailed parameterizations of complex terrain effects,
including terrain impingement, side-wall scrapping, and steep-walled
terrain influences on lateral plume growth. A subgrid-scale complex
terrain module based on a dividing streamline concept divides the
flow into a lift component traveling over the obstacle and a wrap
component deflected around the obstacle.
(2) The meteorological fields used by CALPUFF are produced by
the CALMET meteorological model. CALMET includes a diagnostic wind
field model containing objective analysis and parameterized
treatments of slope flows, valley flows, terrain blocking effects,
and kinematic terrain effects, lake and sea breeze circulations, and
a divergence minimization procedure. An energy-balance scheme is
used to compute sensible and latent heat fluxes and turbulence
parameters over land surfaces. A profile method is used over water.
CALMET contains interfaces to prognostic meteorological models such
as the Penn State/NCAR Mesoscale Model (e.g., MM5; Section 13.0,
ref. 94), as well as the RAMS and Eta models.

e. Pollutant Types

CALPUFF may be used to model gaseous pollutants or particulate
matter that are inert or undergo linear chemical reactions, such as
SO₂, SO₄=, NO, NO₂,
HNO₃, NO₃-, NH₃, PM-10,
and toxic pollutants. For regional haze analyses, sulfate and
nitrate particulate components are explicitly treated.

f. Source-Receptor Relationships

CALPUFF contains no fundamental limitations on the number of
sources or receptors. Parameter files are provided that allow the
user to specify the maximum number of sources, receptors, puffs,
species, grid cells, vertical layers, and other model parameters.
Its algorithms are designed to be suitable for source-receptor
distances from tens of meters to hundreds of kilometers.

g. Plume Behavior

Momentum and buoyant plume rise is treated according to the
plume rise equations of Briggs (1974, 1975) for non-downwashing
point sources, Schulman and Scire (1980) for line sources and point
sources subject to building downwash effects, and Zhang (1993) for
buoyant area sources. Stack tip downwash effects and partial plume
penetration into elevated temperature inversions are included.

h. Horizontal Winds

A three-dimensional wind field is computed by the CALMET
meteorological model. CALMET combines an objective analysis
procedure using wind observations with parameterized treatments of
slope flows, valley flows, terrain kinematic effects, terrain
blocking effects, and sea/lake breeze circulations. CALPUFF may
optionally use single station (horizontally-constant) wind fields in
the CTDMPLUS data format.

i. Vertical Wind Speed

Vertical wind speeds are not used explicitly by CALPUFF.
Vertical winds are used in the development of the horizontal wind
components by CALMET.

j. Horizontal Dispersion

Turbulence-based dispersion coefficients provide estimates of
horizontal plume dispersion based on measured or computed values of
[sigma]_v. The effects of building downwash and buoyancy-
induced dispersion are included. The effects of vertical wind shear
are included through the puff splitting algorithm. Options are
provided to use Pasquill-Gifford (rural) and McElroy-Pooler (urban)
dispersion coefficients. Initial plume size from area or volume
sources is allowed.

k. Vertical Dispersion

Turbulence-based dispersion coefficients provide estimates of
vertical plume dispersion based on measured or computed values of
[sigma]_w. The effects of building downwash and buoyancy-
induced dispersion are included. Vertical dispersion during
convective conditions is simulated with a probability density
function (pdf) model based on Weil et al. (1997). Options are

[[Page 18477]]

provided to use Pasquill-Gifford (rural) and McElroy-Pooler (urban)
dispersion coefficients. Initial plume size from area or volume
sources is allowed.

l. Chemical Transformation

Gas phase chemical transformations are treated using
parameterized models of SO₂ conversion to
SO₄= and NO conversion to NO₂,
HNO₃, and SO₄=. Organic aerosol
formation is treated.

m. Physical Removal

Dry deposition of gaseous pollutants and particulate matter is
parameterized in terms of a resistance-based deposition model.
Gravitational settling, inertial impaction, and Brownian motion
effects on deposition of particulate matter is included. Wet
deposition of gases and particulate matter is parameterized in terms
of a scavenging coefficient approach.

n. Evaluation Studies

Berman, S., J.Y. Ku, J. Zhang and S.T. Rao, 1977: Uncertainties
in estimating the mixing depth--Comparing three mixing depth models
with profiler measurements, Atmospheric Environment, 31: 3023-3039.
Environmental Protection Agency, 1998. Interagency Workgroup on
Air Quality Modeling (IWAQM) Phase 2 Summary Report and
Recommendations for Modeling Long-Range Transport Impacts. EPA
Publication No. EPA-454/R-98-019. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
Irwin, J.S. 1997. A Comparison of CALPUFF Modeling Results with
1997 INEL Field Data Results. In Air Pollution Modeling and its
Application, XII. Edited by S.E. Gyrning and N. Chaumerliac. Plenum
Press, New York, NY.
Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996. A Comparison
of CALPUFF Modeling Results with CAPTEX Field Data Results. In Air
Pollution Modeling and its Application, XI. Edited by S.E. Gyrning
and F.A. Schiermeier. Plenum Press, New York, NY.
Strimaitis, D.G., J.S. Scire and J.C. Chang. 1998. Evaluation of
the CALPUFF Dispersion Model with Two Power Plant Data Sets. Tenth
Joint Conference on the Application of Air Pollution Meteorology,
Phoenix, Arizona. American Meteorological Society, Boston, MA.
January 11-16, 1998.

A.4 Complex Terrain Dispersion Model Plus Algorithms for Unstable
Situations (CTDMPLUS)

Reference

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.
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

This model code is available on EPA's Internet SCRAM Web site
and also on diskette (as PB 90-504119) from the National Technical
Information Service (Section A.0).

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. Recommendation for 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
? One 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
? 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
? 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 4 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 disk 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.
(3) 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

[[Page 18478]]

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 (H_c)
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 H_c,
and the Froude number above H_c.

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 (H_c); 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. 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.
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.6 Emissions and Dispersion Modeling System (EDMS) 3.1

Reference

Benson, Paul E., 1979. CALINE3--A Versatile Dispersion Model for
Predicting Air Pollutant Levels Near Highways and Arterial Streets.
Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway
Administration, Washington, DC. (NTIS No. PB 80-220841)
Federal Aviation Administration, 1997. Emissions and Dispersion
Modeling System (EDMS) Reference Manual. FAA Report No. FAA-AEE-97-
01, USAF Report No. AL/EQ-TR-1997-0010, Federal Aviation
Administration, Washington, DC 20591. SEE Availability below. (Note:
this manual includes supplements that are available on the EDMS
Internet Web site: http://www.aee.faa.gov/aee-100/aee-120/edms/banner.htm)
Petersen, W.B. and E.D. Rumsey, 1987. User's Guide for PAL 2.0--
A Gaussian-Plume Algorithm for Point, Area, and Line Sources. EPA
Publication No. EPA-600/8-87-009. Office of Research and
Development, Research Triangle Park, NC. (NTIS No. PB 87-168 787/AS)

Availability

EDMS is available for $45 ($55 for users outside of the United
States). The order form is available from: http://www.faa.gov/about/
office_org/headquarters_offices/aep/models/edms_model/.
Click the EDMS button on the left side of the page, and then click
on the ``EDMS Order Form'' link. The $45 cost covers the
distribution of the EDMS package: A CD ROM containing the executable
installation file, the user manual, and the model changes document.
This EDMS package does not include the source code, which is
available only through special request and FAA approval. Upon
installation the user will have on their computer an executable file
for the model and supporting data and program files. Official
contact at Federal Aviation Administration: Ms. Julie Draper, AEE,
800 Independence Avenue, SW., Washington, DC 20591, Phone: (202)
267-3494.

Abstract

EDMS is a combined emissions/dispersion model for assessing
pollution at civilian airports and military air bases. This model,
which was jointly developed by the Federal Aviation Administration
(FAA) and the United States Air Force (USAF), produces an emission
inventory of all airport sources and calculates concentrations
produced by these sources at specified receptors. The system stores
emission factors for fixed sources such as fuel storage tanks and
incinerators and also for mobile sources such as aircraft or
automobiles. The EDMS emissions inventory module incorporates
methodologies described in AP-42 for calculating aircraft emissions,
on-road and off-road vehicle emissions, and stationary source
emissions. The dispersion modeling module incorporates PAL2 and
CALINE3 (Section A.3) for the various emission source types. Both of
these components interact with the database to retrieve and store
data. The dispersion module, which processes point, area, and line
sources, also incorporates a special meteorological preprocessor for
processing up to one year of National Climatic Data Center (NCDC)
hourly data.

a. Recommendations for Regulatory Use

EDMS is appropriate for the following applications:
? Cumulative effect of changes in aircraft operations,
point source and mobile source emissions at airports or air bases;
? Simple terrain;
? Non-reactive pollutants;
? Transport distances less than 50 kilometers; and
? 1-hour to annual averaging times.

b. Input Requirements

(1) All data are entered through the EDMS graphical user
interface. Typical entry items are annual and hourly source
activity, source and receptor coordinates, etc. Some point sources,
such as heating plants, require stack height, stack diameter, and
effluent temperature inputs.
(2) Wind speed, wind direction, hourly temperature, and
Pasquill-Gifford stability category (P-G) are the meteorological
inputs. They can be entered manually through the EDMS data entry
screens or automatically through the processing of previously loaded
NCDC hourly data.

c. Output

Printed outputs consist of:
? A summary emission inventory report with pollutant
totals by source category and detailed emission inventory reports
for each source category; and
? A concentration summary report for up to 8760 hours (one
year) of meteorological data that lists the number of sources,
receptors, and the five highest concentrations for applicable
averaging periods for the respective primary NAAQS.

d. Type of Model

For its emissions inventory calculations, EDMS uses algorithms
consistent with the EPA Compilation of Air Pollutant Emission
Factors, AP-42 (Section 11.0, ref. 96). For its dispersion
calculations, EDMS uses the Point Area & Line (PAL2) model and the
CALifornia LINE source (CALINE3) model, both of which use Gaussian
algorithms.

[[Page 18479]]

e. Pollutant Types

EDMS includes emission factors for carbon monoxide, nitrogen
oxides, sulfur oxides, hydrocarbons, and suspended particles and
calculates the dispersion for all except hydrocarbons.

f. Source-Receptor Relationship

(1) Within hardware and memory constraints, there is no upper
limit to the number of sources and receptors that can be modeled
simultaneously.
(2) The Gaussian point source equation estimates concentrations
from point sources after determining the effective height of
emission and the upwind and crosswind distance of the source from
the receptor. Numerical integration of the Gaussian point source
equation is used to determine concentrations from line sources
(runways). Integration over area sources (parking lots), 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.
Terrain elevation differences between sources and receptors are
neglected.
(3) A reasonable height above ground level may be specified for
each receptor.

g. Plume Behavior

(1) Briggs final plume rise equations are used. If plume height
exceeds mixing height, concentrations are assumed equal to zero.
Surface concentrations are set to zero when the plume centerline
exceeds mixing height.
(2) For roadways, plume rise is not treated.
(3) Building and stack tip downwash effects are not treated.

h. Horizontal Winds

(1) Steady state winds are assumed for each hour. Winds are
assumed to be constant with altitude.
(2) Winds are entered manually by the user or automatically by
reading previously loaded NCDC annual data files.

i. Vertical Wind Speed

Vertical wind speed is assumed to be zero.

j. Horizontal Dispersion

(1) Six stability classes are used (P-G classes A through F).
(2) Aircraft runways, vehicle parking lots, stationary sources,
and training fires are modeled using PAL2. Either rural (Pasquill-
Gifford) or urban (Briggs) dispersion settings may be specified
globally for these sources.
(3) Vehicle roadways, aircraft taxiways, and aircraft queues are
modeled using CALINE3. CALINE3 assumes urban dispersion curves. The
user specifies terrain roughness.

k. Vertical Dispersion

l. Chemical Transformation

Chemical transformations are not accounted for.

m. Physical Removal

Deposition is not treated.

n. Evaluation Studies

None cited.

A.5 Industrial Source Complex Model (ISC3)

Reference

Environmental Protection Agency, 1995. User's Guide for the
Industrial Source Complex (ISC3) Dispersion Models, Volumes 1 and 2.
EPA Publication Nos. EPA-454/B-95-003a & b. Environmental Protection
Agency, Research Triangle Park, NC. (NTIS Nos. PB 95-222741 and PB
95-222758, respectively)

Availability

The model code is available on the EPA's Internet SCRAM website.
ISCST3 (as PB 2002-500055) is also available on diskette from the
National Technical Information Service (see Section A.0).

Abstract

The ISC3 model is a steady-state Gaussian plume model which can
be used to assess pollutant concentrations from a wide variety of
sources associated with an industrial source complex. This model can
account for the following: Settling and dry deposition of particles;
downwash; area, line and volume sources; plume rise as a function of
downwind distance; separation of point sources; and limited terrain
adjustment. ISC3 operates in both long-term and short-term modes.

a. Recommendations for Regulatory Use

ISC3 is appropriate for the following applications:
? Industrial source complexes;
? Rural or urban areas;
? Flat or rolling terrain;
? Transport distances less than 50 kilometers;
? 1-hour to annual averaging times; and
? Continuous toxic air emissions.
The following options should be selected for regulatory
applications: For short term or long term modeling, set the
regulatory ``default option''; i.e., use the keyword DFAULT, which
automatically selects stack tip downwash, final plume rise, buoyancy
induced dispersion (BID), the vertical potential temperature
gradient, a treatment for calms, the appropriate wind profile
exponents, the appropriate value for pollutant half-life, and a
revised building wake effects algorithm; set the ``rural option''
(use the keyword RURAL) or ``urban option'' (use the keyword URBAN);
and set the ``concentration option'' (use the keyword CONC).

b. Input Requirements

Source data: Location, emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, and stack gas
temperature. Optional inputs include source elevation, building
dimensions, particle size distribution with corresponding settling
velocities, and surface reflection coefficients.
Meteorological data: ISCST3 requires hourly surface weather data
from the preprocessor program RAMMET, which provides hourly
stability class, wind direction, wind speed, temperature, and mixing
height. For ISCLT3, input includes stability wind rose (STAR deck),
average afternoon mixing height, average morning mixing height, and
average air temperature.
Receptor data: Coordinates and optional ground elevation for
each receptor.

c. Output

Printed output options include:
? Program control parameters, source data, and receptor
data;
? Tables of hourly meteorological data for each specified
day;
? ``N''-day average concentration or total deposition
calculated at each receptor for any desired source combinations;
? Concentration or deposition values calculated for any
desired source combinations at all receptors for any specified day
or time period within the day;
? Tables of highest and second highest concentration or
deposition values calculated at each receptor for each specified
time period during a(n) ``N''-day period for any desired source
combinations, and tables of the maximum 50 concentration or
deposition values calculated for any desired source combinations for
each specified time period.

d. Type of Model

ISC3 is a Gaussian plume model. It has been revised to perform a
double integration of the Gaussian plume kernel for area sources.

e. Pollutant Types

ISC3 may be used to model primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Settling and
deposition are treated.

f. Source-Receptor Relationships

ISC3 applies user-specified locations for point, line, area and
volume sources, and user-specified receptor locations or receptor
rings.
User input topographic evaluation for each receptor is used.
Elevations above stack top are reduced to the stack top elevation,
i.e., ``terrain chopping''.
User input height above ground level may be used when necessary
to simulate impact at elevated or ``flag pole'' receptors, e.g., on
buildings.
Actual separation between each source-receptor pair is used.

g. Plume Behavior

ISC3 uses Briggs (1969, 1971, 1975) plume rise equations for
final rise.
Stack tip downwash equation from Briggs (1974) is used.
Revised building wake effects algorithm is used. For stacks
higher than building height plus one-half the lesser of the building
height or building width, the building wake algorithm of Huber and
Snyder (1976) is used. For lower stacks, the building wake algorithm
of Schulman and Scire (Schulman

[[Page 18480]]

and Hanna, 1986) is used, but stack tip downwash and BID are not
used.
For rolling terrain (terrain not above stack height), plume
centerline is horizontal at height of final rise above source.
Fumigation is not treated.

h. Horizontal Winds

Constant, uniform (steady-state) wind is assumed for each hour.
Straight line plume transport is assumed to all downwind
distances.
Separate wind speed profile exponents (Irwin, 1979; EPA, 1980)
for both rural and urban cases are used.
An optional treatment for calm winds is included for short term
modeling.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used, with
no adjustments for surface roughness or averaging time.
Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used, with
no adjustments for surface roughness.
Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.
Mixing height is accounted for with multiple reflections until
the vertical plume standard deviation equals 1.6 times the mixing
height; uniform vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Time constant is input by the user.

m. Physical Removal

Dry deposition effects for particles are treated using a
resistance formulation in which the deposition velocity is the sum
of the resistances to pollutant transfer within the surface layer of
the atmosphere, plus a gravitational settling term (EPA, 1994),
based on the modified surface depletion scheme of Horst (1983).

n. Evaluation Studies

Bowers, J.F. and A.J. Anderson, 1981. An Evaluation Study for
the Industrial Source Complex (ISC) Dispersion Model, EPA
Publication No. EPA-450/4-81-002. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
Bowers, J.F., A.J. Anderson and W.R. Hargraves, 1982. Tests of
the Industrial Source Complex (ISC) Dispersion Model at the Armco
Middletown, Ohio Steel Mill. EPA Publication No. EPA-450/4-82-006.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC.
Environmental Protection Agency, 1992. Comparison of a Revised
Area Source Algorithm for the Industrial Source Complex Short Term
Model and Wind Tunnel Data. EPA Publication No. EPA-454/R-92-014.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. (NTIS No. PB 93-226751)
Environmental Protection Agency, 1992. Sensitivity Analysis of a
Revised Area Source Algorithm for the Industrial Source Complex
Short Term Model. EPA Publication No. EPA-454/R-92-015. Office of
Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS
No. PB 93-226769)
Environmental Protection Agency, 1992. Development and
Evaluation of a Revised Area Source Algorithm for the Industrial
Source Complex Long Term Model. EPA Publication No. EPA-454/R-92-
016. Office of Air Quality Planning & Standards, Research Triangle
Park, NC. (NTIS No. PB 93-226777)
Environmental Protection Agency, 1994. Development and Testing
of a Dry Deposition Algorithm (Revised). EPA Publication No. EPA-
454/R-94-015. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 94-183100)
Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and
ISC Models with SF₆ Tracer Data and SO₂
Measurements at Aluminum Reduction Plants. Air Pollution Control
Association Specialty Conference on Dispersion Modeling for Complex
Sources, St. Louis, MO.
Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash
Modification to the Industrial Source Complex Model. Journal of the
Air Pollution Control Association, 36: 258-264.

A.7 Offshore and Coastal Dispersion Model (OCD)

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

This model code is available on the EPA's Internet SCRAM Web
site and also on diskette (as PB 91-505230) from the National
Technical Information Service (see Section A.0). Official contact at
Minerals Management Service: Mr. Dirk Herkhof, Parkway Atrium
Building, 381 Elden Street, Herndon, VA 20170, Phone: (703) 787-
1735.

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. Recommendations for Regulatory Use

OCD has been recommended for use by the Minerals Management
Service for emissions located on the Outer Continental Shelf. 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 (over water): Wind direction, wind
speed, mixing height, relative humidity, air temperature, water
surface temperature, vertical wind direction shear (optional),
vertical temperature gradient (optional), turbulence intensities
(optional).
(3) Meteorological data (over land): Wind direction, wind speed,
temperature, stability class, mixing height.
(4) 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 files written to disk or tape 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.

[[Page 18481]]

(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) As in ISC, 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.

A.REF References

Benson, P.E., 1979. CALINE3--A Versatile Dispersion Model for
Predicting Air Pollution Levels Near Highways and Arterial Streets.
Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway
Administration, Washington, DC.
Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge,
TN. (NTIS No. TID-25075)
Briggs, G.A., 1971. Some Recent Analyses of Plume Rise
Observations. Proceedings of the Second International Clean Air
Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New
York, NY.
Briggs, G.A., 1974. Diffusion Estimation for Small Emissions.
USAEC Report ATDL-106. U.S. Atomic Energy Commission, Oak Ridge, TN.
Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air
Pollution and Environmental Impact Analyses. American Meteorological
Society, Boston, MA, pp. 59-111.
Briggs, G.A., 1984. Analytical Parameterizations of Diffusion:
The Convective Boundary Layer. Journal of Climate and Applied
Meteorology, 24(11): 1167-1186
Environmental Protection Agency, 1980. Recommendations on
Modeling (October 1980 Meetings). Appendix G to: Summary of Comments
and Responses on the October 1980 Proposed Revisions to the
Guideline on Air Quality Models. Meteorology and Assessment
Division, Office of Research and Development, Research Triangle
Park, NC.
Environmental Protection Agency, 1998. Interagency Workgroup on
Air Quality Modeling (IWAQM) Phase 2 Summary Report and
Recommendations for Modeling Long-Range Transport Impacts. EPA
Publication No. EPA-454/R-98-019. (NTIS No. PB 99-121089)
Gifford, F.A., Jr. 1976. Turbulent Diffusion Typing Schemes--A
Review. Nuclear Safety, 17: 68-86.
Horst, T.W., 1983. A Correction to the Gaussian Source-depletion
Model. In Precipitation Scavenging, Dry Deposition and Resuspension.
H.R. Pruppacher, R.G. Semonin and W.G.N. Slinn, eds., Elsevier, NY.
Hsu, S.A., 1981. Models for Estimating Offshore Winds from
Onshore Meteorological Measurements. Boundary Layer Meteorology, 20:
341-352.
Huber, A.H. and W.H. Snyder, 1976. Building Wake Effects on
Short Stack Effluents. Third Symposium on Atmospheric Turbulence,
Diffusion and Air Quality, American Meteorological Society, Boston,
MA.
Irwin, J.S., 1979. A Theoretical Variation of the Wind Profile
Power-Law Exponent as a Function of Surface Roughness and Stability.
Atmospheric Environment, 13: 191-194.
Liu, M.K. et al., 1976. The Chemistry, Dispersion, and Transport
of Air Pollutants Emitted from Fossil Fuel Power Plants in
California: Data Analysis and Emission Impact Model. Systems
Applications, Inc., San Rafael, CA.
Pasquill, F., 1976. Atmospheric Dispersion Parameters in
Gaussian Plume Modeling Part II. Possible Requirements for Change in
the Turner Workbook Values. EPA Publication No. EPA-600/4-76-030b.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC.
Petersen, W.B., 1980. User's Guide for HIWAY-2 A Highway Air
Pollution Model. EPA Publication No. EPA-600/8-80-018. Office of
Research & Development, Research Triangle Park, NC. (NTIS PB 80-
227556)
Rao, T.R. and M.T. Keenan, 1980. Suggestions for Improvement of
the EPA-HIWAY Model. Journal of the Air Pollution Control
Association, 30: 247-256 (and reprinted as Appendix C in Petersen,
1980).
Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash
Modification to the Industrial Source Complex Model. Journal of the
Air Pollution Control Association, 36: 258-264.
Segal, H.M., 1983. Microcomputer Graphics in Atmospheric
Dispersion Modeling. Journal of the Air Pollution Control
Association, 23: 598-600.
Snyder, W. H., R.S. Thompson, R. E. Eskridge, R. E. Lawson, I.
P. Castro, J. T. Lee, J. C. R. Hunt, and Y. Ogawa, 1985. The
structure of the strongly stratified flow over hills: Dividing
streamline concept. Journal of Fluid Mechanics, 152: 249-288.
Turner, D.B., 1969. Workbook of Atmospheric Dispersion
Estimates. PHS Publication No. 999-26. U.S. Environmental Protection
Agency, Research Triangle, Park, NC.

[[Page 18482]]

Weil, J.C. and R.P. Brower, 1984. An Updated Gaussian Plume
Model for Tall Stacks. Journal of the Air Pollution Control
Association, 34: 818-827.
Weil, J.C., 1996. A new dispersion algorithm for stack sources
in building wakes, Paper 6.6. Ninth Joint Conference on Applications
of Air Pollution Meteorology with A&WMA, January 28--February 2,
1996. Atlanta, GA.
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.
Zhang, X., 1993. A computational analysis of the rise,
dispersion, and deposition of buoyant plumes. Ph.D. Thesis,
Massachusetts Institute of Technology, Cambridge, MA.
Zhang, X. and A.F. Ghoniem, 1993. A computational model for the
rise and dispersion of wind-blown, buoyancy-driven plumes--I.
Neutrally stratified atmosphere. Atmospheric Environment, 15: 2295--
2311.

[FR Doc. 03-8542 Filed 4-14-03; 8:45 am]
BILLING CODE 6560-50-P

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