[Federal Register Volume 65, Number 78 (Friday, April 21, 2000)]
[Proposed Rules]
[Pages 21506-21546]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 00-4235]
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Part II
Environmental Protection Agency
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40 CFR Part 51
Requirements for Preparation, Adoption, and Submittal of State
Implementation Plans (Guideline on Air Quality Models); Proposed Rule
Federal Register / Vol. 65, No. 78 / Friday, April 21, 2000 /
Proposed Rules
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ENVIRONMENTAL PROTECTION AGENCY
40 CFR Part 51
[AH-FRL-6536-3]
RIN 2060-AF01
Requirements for Preparation, Adoption, and Submittal of State
Implementation Plans (Guideline on Air Quality Models)
AGENCY: Environmental Protection Agency (EPA).
ACTION: Proposed 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 propose to
make several additions and changes to the Guideline. We recommend two
new dispersion models, AERMOD and CALPUFF, for adoption in appendix A
of the Guideline. AERMOD would replace the Industrial Source Complex
(ISC3) model in many assessments that now use it; AERMOD also would
apply to complex terrain. CALPUFF would become a recommended technique
for assessing long-range transport of pollutants and their impacts on
Federal Class I areas. We revise two existing models: ISC3, by
incorporating a new downwash algorithm (PRIME) and renaming the model
ISC-PRIME, and the Emissions Dispersion Modeling System (EDMS), by
incorporating improved emissions and dispersion modules. We make
various editorial changes to update and reorganize information, and
remove obsolete models (CDM, RAM and UAM).
DATES: The period for comment on these proposed changes to the
Guideline closes on July 20, 2000. We plan to hold a public hearing on
the proposed changes in Summer 2000. The specific date and time will be
announced in a separate document published in the Federal Register.
ADDRESSES: We have established an official record for this rulemaking
under docket number A-99-05. You may submit comments pertinent to this
proposal to docket no. A-99-05 at the following address: Air Docket
(6102), Room M-1500, Waterside Mall, U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC. 20460. This docket is
available for public inspection and copying between 8 a.m. and 5:30
p.m., Monday through Friday, at the address above. Please furnish
duplicate comments to Tom Coulter, Air Quality Modeling Group (MD-14),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
You may send electronic versions of comments pertinent to this proposal
to: [email protected]. Alternatively, comments are
acceptable in WordPerfect 6.1 (or higher), preferably zipped (e.g.,
PKware) as an attachment to the e-mail message. You must include the
docket identification (A-99-05) with all electronic submittals. You may
file electronic comments on this proposal online at many Federal
Depository Libraries.
The hearing will be the main agenda for the 7th Conference on Air
Quality Modeling, and the location will be announced in a separate
document published in the Federal Register.
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) 541-5561 or C. Thomas Coulter, telephone (919) 541-
0832.
SUPPLEMENTARY INFORMATION:
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.
Air Quality Modeling Conference
We held the Sixth Conference on Air Quality Modeling (6th
conference) in Washington, DC on August 9-10, 1995. As required by
Section 320 of the Clean Air Act, these conferences take place
approximately every three years to standardize modeling procedures. The
sixth conference featured presentations in several key modeling areas.
One presentation, by the Interagency Workgroup on Air Quality Modeling
(IWAQM \1\), covered long range transport modeling. Another
presentation, by the American Meteorological Society (AMS)/EPA
Regulatory Model Improvement Committee (AERMIC), covered developing an
enhanced Gaussian dispersion model with boundary layer
parameterization: AERMOD \2\. Also at the 6th conference, the Electric
Power Research Institute (EPRI) presented recent research efforts to
better define and characterize dispersion around buildings (downwash
effects). These efforts were part of a program called the Plume RIse
Model Enhancements (PRIME), and PRIME is proposed for integration
within ISC3 (ISC-PRIME).
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\1\ 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.
\2\ AMS/EPA Regulatory MODel
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The presentations were followed by a critical review/discussion of
the CALPUFF and AERMOD modeling systems, 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. For the new and revised models described, we asked the
public to address the following questions:
What is the scientific merit of the models presented?
What is their accuracy?
What should be the regulatory use of individual models for
specific applications?
What implementation issues are apparent and what
additional guidance is needed?
What are the resource requirements of modeling systems
presented?
What additional information or analyses are needed?
We placed a transcript of the 6th conference proceedings and a copy
of all written comments in Docket AQM-95-01. Answers to the above
questions are reflected in the comments, which we reviewed and
summarized (II-G-01). To the extent possible, we believe we have
addressed the main concerns in the refinements proposed today, which
focus on the two new modeling systems, as well as the enhancement of
ISC3 with EPRI's PRIME downwash model (ISC-PRIME).
AERMOD
AERMOD is a state-of-the-practice Gaussian plume dispersion model
whose formulation is based on planetary boundary layer principles. At
the 6th conference, AERMIC members presented interim developmental and
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evaluation results of AERMOD. AERMOD provides better characterization
of plume dispersion than does the ISC3. Comprehensive comments were
submitted on the AERMOD code and formulation document and on the AERMET
draft User's Guide (AERMET is the meteorological preprocessor for
AERMOD). The comments on the AERMET User's Guide were detailed and
generally editorial in nature. Comments on AERMOD identified
inconsistencies in the AERMOD code as well as among variables and
recommended specific default values.
Commenters expressed concern that data bases historically used by
EPA lack the variables required by AERMET and AERMOD. The deficiencies
were thought to obstruct or weaken AERMOD's evaluation. We disagree
that the data bases used for the AERMOD evaluations (Kincaid, Lovett,
Martins Creek, Tracy, etc.) were not of the type used historically by
EPA and furthermore believe that they contain the critical variables
needed by AERMOD. One comment described a perceived ``persistence of
modeling procedures [by EPA] rather than an evolution to other
techniques.'' This tendency, the commenter believes, has been
influenced by testing candidate techniques with the deficient data
bases mentioned earlier. According to the commenter, this leaves the
new candidate technique no way to show its possible superiority over
existing techniques. The commenter argued for a change in this pattern.
We disagree with this criticism in that we believe AERMOD has been
adequately tested and represents, through its formulations, a technical
advancement over its predecessors.
CALPUFF
CALPUFF is a Lagrangian dispersion model that simulates pollutant
releases as a continuous series of puffs. IWAQM carefully studied the
potential regulatory application of CALPUFF in its Phase 1 report.\3\
At the 6th conference, IWAQM recommended that EPA consider CALPUFF as a
preferred technique for long-range air pollution transport assessments
(for example, for federal Class I areas). In its Phase 2 report,\4\
IWAQM has, to the extent possible, attempted to resolve the concern and
criticism over applying the CALPUFF modeling system.
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\3\ 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.
\4\ 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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On the whole, comments appeared to support IWAQM's efforts to
simplify and clarify the modeling methods for addressing long-range
transport and dispersion. The comments endorsed IWAQM's recommendation
to employ one model for all sources and distances. The comments also
endorsed IWAQM's recommendation of an approach whereby a group of
stakeholders is established that, through consensus, defines the
modeling methods, inventories, data bases, and significance criteria to
be applied in assessing impacts for a given Class I area. This activity
would precede an actual regulatory assessment.
Comments suggested that the Level 1 screen described in IWAQM's
Phase I interim recommendations was not working well and needed
improvement. IWAQM has attempted to do this by developing a screening
procedure that uses CALPUFF with ISC-type meteorological input data,
and has shown the results to be conservative for the case(s) tested
(see footnote 4).\5\ However, the screening approach may not give
conservative concentration estimates in all cases (see below).
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\5\ Environmental Protection Agency, 1998. Analyses of the
CALMET/CALPUFF Modeling System in a Screening Mode. EPA Publication
No. EPA-454/R-98-010. Office of Air Quality Planning & Standards,
Research Triangle Park, NC.
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Comments suggested that more comparisons with tracer studies were
needed for transport distances of 50-200km. IWAQM sponsored four such
evaluations.
Commenters also sought clearer guidance on the limits of such
modeling assessments, such as cases with intervening terrain between
the sources and receptors of interest. IWAQM has attempted to make the
modeling community(see footnote 4) aware that conducting a long-range
transport assessment requires competent individuals, expert judgement,
and strong interaction and coordination with the applicable reviewing
authorities.
Comments suggested that comparisons were needed to assess whether
CALPUFF can provide results similar to ISC3 and CTDMPLUS for steady-
state meteorological conditions. We supported this work and examined
CALPUFF for equivalency to ISC3,\6\ both in a steady-state mode as well
as non-steady-state (that is, when meteorological conditions varied
hourly). For steady state conditions, CALPUFF mimicked ISC3 to a
substantial degree. In non-steady state conditions, occurrences of
calms and recirculations resulted in higher source impacts with CALPUFF
than for ISC3 for most comparisons made.
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\6\ Environmental Protection Agency, 1998. A Comparison of
CALPUFF with ISC3. EPA Publication No. EPA-454/R-98-020. Office of
Air Quality Planning & Standards, Reserch Triangle Park, NC.
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ISC-PRIME
The development of PRIME by EPRI featured four key components: a
field effort, laboratory modeling of fluids, developing model codes,
and independently evaluating models.\7\ The field measurements were
made at a combustion turbine site in New Jersey in February and March
1994. Wind tunnel experiments have been done at EPA's Fluid Modeling
Facility and at a facility at Monash University in Australia. PRIME is
modular, it explicitly takes into account stack location and all three
building dimensions, and attempts to model the shape of the ellipsoid
cavity and the flow of the streamline descents over the top of the
cavity. Plume rise calculations are enhanced to treat plumes that are
not neutrally buoyant and have no vertical velocity. Unfortunately, at
the time of the 6th modeling conference, evaluation work was incomplete
and the PRIME code was unavailable for beta testing.
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\7\ Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 1999.
Development and Evaluation of the PRIME Plume Rise and Building
Downwash Model. 34pp. + 10 figures (submitted to Journal of the Air
& Waste Management Association) (A-99-05, II-A-13).
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Comments received at the 6th modeling conference commended EPRI's
development of PRIME as ``a significant improvement over the existing
ISC algorithm'' and one that could ``provide accurate estimates for
idealized building geometries.'' Based on comments, potential problems
were anticipated for proper treatment of the myriad combinations of
building geometry, wind approach angle, upwind roughnesses,
stabilities, etc. Commenters questioned whether all these effects could
be parameterized into a robust algorithm to accurately treat downwash
at actual sites. Another strong concern was the extent to which the
algorithm would work under stable stratification, which is difficult to
simulate in a wind tunnel. One commenter even suggested the application
of a simpler approach, i.e., the original work by Huber and Snyder who
employed a ``building downwash amplification factor'', as careful
parameterization of this factor might
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lead to acceptable accuracy with other benefits. The commenter also
suggested that an integral plume rise model had been shown to yield
good agreement with field and wind tunnel observations for treating
plume trajectories. In terms of PRIME's evaluation, the commenter
suggested using, as a basis for comparison, a version of ISC3 that
excluded the Schulman-Scire downwash algorithm.
Since the 6th modeling conference, EPRI released a beta test
version of PRIME, which was installed within ISC3 (hence, ISC-PRIME).
Beta testing of ISC-PRIME shows significantly improved performance in
comparison to ISC3.\8\ To the extent possible, EPRI has attempted to
address the comments on the PRIME algorithm and its documentation. A
consequence analysis for using ISC-PRIME (versus ISC3) has also been
prepared.\9\
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\8\ Paine, R.J. and F. Lew, 1997. Results of the Independent
Evaluation of ISCST3 and ISC-PRIME. Prepared for the Electric Power
Research Institute, Palo Alto, CA. ENSR Document Number 2460-026-
440. (NTIS No. PB 98-156524)
\9\ Paine, R.J. and F. Lew, 1997. Consequence Analysis for ISC-
PRIME. Prepared for the Electric Power Research Institute, Palo
Alto, CA. ENSR Document Number 2460-026-450. (NTIS No. PB 98-156516)
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Proposed Action
AERMOD
We propose revising section 4 of the Guideline to replace ISC3 by
AERMOD as a state-of-the-practice technique for many air quality impact
assessments. Applications for which AERMOD is suited are stated in
subsequent sections of the Guideline and include assessment of plume
impacts from traditional stationary sources in simple, intermediate,
and complex terrain. In fact, since differentiation of simple versus
complex terrain is unnecessary with AERMOD, we merged pertinent
guidance in section 5 (Model Use in Complex Terrain) with that in
section 4. You will find developmental, evaluation and peer scientific
review references for AERMOD cited as appropriate. A model formulation
document,\10\ as well as a key evaluation reference for the AERMOD
modeling system,\11\ have been placed in the docket. We added a summary
description of AERMOD to appendix A \12\ of the Guideline, where you
are directed to note additional evaluation references and a series of
user's manuals. The essential codes, preprocessors, and test cases have
been uploaded to our website (www.epa.gov/scram001; see 7th
Conference).
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\10\ Cimorelli, A.J., S.G. Perry, A. Venkatram, J.C. Weil, R.J.
Paine, R.B. Wilson, R.F. Lee and W.D. Peters, 1998. AERMOD:
Description of Model Formulation. (12/15/98 Draft Document) Prepared
for Environmental Protection Agency, Research Triangle Park, NC.
113pp. (Docket No. A-99-05; II-A-1)
\11\ Paine, R.J., R.F. Lee, R.W. Brode, R.B. Wilson, A.J.
Cimorelli, S.G., Perry, J.C. Weil, A. Venkatram and W.D. Peters,
1998: Model Evaluation Results for AERMOD (12/17/98 Draft). Prepared
for Environmental Protection Agency, Research Triangle Park, NC.
(Docket No. A-99-05, II-A-5)
\12\ Appendix A of appendix W is a repository for preferred,
refined air quality models recommended for regulatory applications.
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We invite your comment on whether we have reasonably addressed
technical concerns and are on sound footing to recommend AERMOD for its
intended applications. AERMOD lacks a general (all-terrain) screening
tool, so we invite your comment on the practicality of using SCREEN3 as
an interim tool for AERMOD and ISC-PRIME screening in simple terrain.
CALPUFF
In its Phase 2 recommendations, IWAQM recommended the CALPUFF
modeling system 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 endorse its recommendation
and are proposing CALPUFF for addition to appendix A of the Guideline.
We have imposed conforming revisions to section 6 to recommend CALPUFF
for regulatory applications involving long-range transport and have
suggested a possible screening approach. We also propose CALPUFF for
use for all downwind distances for those applications involving complex
wind regimes, with case-by-case justification. Studies that support the
above recommendations are summarized in IWAQM's Phase II Report (op.
cit.).
The essential codes, utilities, preprocessors and test cases have
been uploaded to the developers' Internet website (www.src.com/calpuff/calpuff1.htm). The documentation for CALMET and CALPUFF have been
properly cited in the Guideline and are available from the
aforementioned website. A peer review has also been cited and has been
placed in the docket.
We solicit your comments on our proposal to recommend CALPUFF for
its intended applications.
ISC-PRIME
We have proposed the use of ISC-PRIME \13\ in section 4 of the
Guideline, where we emphasize that if you are interested in treating
aerodynamic downwash or dry deposition, ISC-PRIME is the recommended
model. We have proposed editorial revisions in sections 5-7 of the
Guideline to make it clear when use of ISC-PRIME is appropriate instead
of AERMOD.
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\13\ Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 1997.
Addendum to ISC3 User's Guide, The PRIME Plume Rise and Building
Downwash Model. Prepared for the Electric Power Research Institute,
Palo Alto, CA., Earth Tech Document A287. A-99-05, II-A-12)
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The formulation and evaluation of the PRIME algorithm are described
in open literature (op. cit.) The essential codes, utilities, and test
cases have been uploaded to our website (www.epa.gov/scram001; see 7th
Conference). We invite your comment on whether we are on sound footing
to recommend use of ISC-PRIME as proposed.
We intend to consider AERMOD, ISC-PRIME, and CALPUFF as our
recommended techniques for their intended applications (as specified in
the Guideline) starting one year after we issue the final rule, and
that the models be used in their regulatory default modes. The models
may be used in the interim (i.e., as soon as we issue the final rule).
We invite your comment on the reasonableness of the timing of this
implementation schedule.
We are aware that, where downwash is of concern, some potential
users of AERMOD and ISC-PRIME might find joint application of the two
models burdensome. We invite comment on this matter and seek input on
alternative approaches that ensure that the latest science is used (as
included in both AERMOD and PRIME) for regulatory modeling
applications. One alternative considered by AERMIC is the direct
inclusion of the PRIME algorithm in AERMOD. This effort, including
testing, performance evaluation for the PRIME data bases, and peer
scientific review, could take up to 12 months.
Proposed Editorial Changes
Editorial changes are described by affected sections. For a more
detailed showing of before/after effects, you are referred to a
redline/strikeout version (WordPerfect format) of appendix W that has
been posted on our website (www.epa.gov/scram001; see 7th Conference).
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.
Section 3
We revised section 3 to more accurately reflect current EPA
practice, e.g., functions of the Model
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Clearinghouse and enhanced criteria for the use of alternative models.
Section 4
As mentioned earlier, we revised section 4 to present AERMOD, ISC-
PRIME, and CALPUFF as regulatory modeling techniques for particular
applications. 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.\14\
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\14\ 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.
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Section 5
As mentioned above, we merged pertinent guidance in section 5
(Modeling in Complex Terrain) with that in section 4. With the
anticipated widespread use of AERMOD for all terrain types, there is no
longer any utility in the previous differentiation between simple and
complex terrain for model selection. To further 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), and RTDM remain
available for use in applicable cases where established/accepted
procedures are used. Consultation with the appropriate Regional Office
is still advised for application of these screening models.
Section 6
We revised section 6 (renumbered to section 5) 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). Footnotes have been inserted to
provide caveats pertaining to the recent Court decision to remand or
vacate parts of these new standards. You will note that we inserted
respective subsections for particulate matter and lead from section 7,
so that section 5 now primarily contains modeling guidance for the
criteria pollutants regulated in Part 51 (SO2 analyses are
covered in section 4).
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.\15\ 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. We believe that it will frequently be necessary to consider the
regional scale for such demonstrations and that, since the last
revision to appendix W, newer models have become available. We invite
comment on the need to integrate ozone and fine particle impacts (i.e.,
the ``one atmosphere'' approach). Are modeling tools and air program
policies sufficiently developed to provide guidance on an integrated
approach at this time? We also invite comments on whether specific
validated tools have been sufficiently developed to calculate impacts
of individual point sources of ozone and PM-2.5 precursor pollutants.
Are there any models that can be recommended for source-specific ozone
and PM-2.5 assessments?
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\15\ 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 website, www.epa.gov/scram001, as draft8hr.pdf)
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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 \16\ in section 5. 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 umbrella for possible use
to develop and evaluate national policy and assist State and local
control agencies. For long range transport analyses, we present and
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. Finally, in the
subsection on air pathway analyses, we identify the availability of
AERMOD and removed specific reference to DEGADIS (other heavy gas
models are also available on a case-by-case basis).
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\16\ 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, D.C.
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Section 8
We revised section 8 (renumbered to section 7) to better reflect
our current regulatory practice for the general modeling considerations
addressed.
In subsection 7.2.4, we introduce the atmospheric
stability characterization for AERMOD.
In subsection 7.2.5, we describe the plume rise approaches
used by AERMOD and ISC-PRIME.
We revised subsection 7.2.6 to refer back to subsection
5.2.3 for details on chemical transformation of NOX.
We merged subsection 7.2.8 (Urban/Rural Classification)
with subsection 7.2.3 (Dispersion Coefficients).
We merged discussions in subsections 7.2.9 (Fumigation)
and 7.2.10 (Stagnation) into one new subsection (Complex Winds), and
identify the availability of CALPUFF for certain situations on a case-
by-case basis.
We removed the distinction between short-term and long-
term
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models because when assessing the impacts from criteria air pollutants,
long-term estimates are now practicable using hour-by-hour
meteorological data.
Section 9
We renumbered section 9 as section 8 and made the following
changes:
We revised subsection 8.2.3 (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). This petition, Appalachian Power
Company et al. v. EPA (D.C. Circuit), No. 93-1631, was filed on
November 3, 1993. The plaintiffs challenged the modeling assumptions
required for existing point sources and new (or modified) existing
point source compliance demonstrations as set forth in tables 9-1 and
9-2 of the Guideline. In accordance with the settlement, we are
clarifying the definition of ``nearby sources.'' The ``maximum
allowable emission limit,'' specified in Tables 8-1 and 8-1 (formerly
9-1 and 9-2), is tied in certain circumstances \17\ to the emission
rate representative of a nearby source's maximum physical capacity to
emit. We are also clarifying 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 reviewing
authority that this is, in fact, the case. We added footnotes to tables
8-1 and 8-2 to refer back to applicable paragraphs of subsection 8.2.3
that provide the necessary clarification.
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\17\ See section 8.2.3. of the Guideline.
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We enhanced section 8.3 (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 18, 19, 20 or MM5 \21\ 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 8.3.1.2) for long-range
transport and complex wind situations.
---------------------------------------------------------------------------
\18\ 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-scal data. Monthly Weather Review,
118:1250-1277.
\19\ 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.
\20\ 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 website @
WWW.NNDC.NOAA.GOV/.
\21\ www.mmm.ucar.edu/mm5/mm5-home.html
---------------------------------------------------------------------------
We revised subsection 8.3.2 (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 invite you
to comment on the usefulness of ASOS meteorological data for air
quality modeling. More specifically, we invite comment on whether the
policy of modeling with the most recent 5 years of NWS meteorological
data (section 8.3.1.2) should include ASOS data. We also invite comment
on whether the period of record must be the most recent 5 years--
regardless of whether it contains ASOS data. Similarly, should the
policy to model with the most recent full year of meteorological data
(i.e., section 10.2.3.4) include ASOS data?
We revised subsection 8.3.3.1 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 AERMOD and CTDMPLUS were
moved to their respective 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 8.3.3.2 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 8.3.4 (Treatment of Calms) to
increase accuracy and to include information pertaining to AERMOD.
Section 10
We revised section 10 (renumbered section 9) to include AERMOD,
ISC-PRIME, and CALPUFF.
Section 11
We propose minor revisions for section 11 (renumbered section 10)
to reflect the new ambient air quality standards for fine particles and
ozone. Because EPA has retreated from its emissions trading
(``bubble'') policy for SO2, we have deleted subsection
11.2.3.4.
Section 12 & 13
We redesignated section 13 (Bibliography) as section 11 and
retained section 12 (References). We revised them by adding some
references, deleting obsolete/superseded ones, and resequencing. You
will note that peer scientific reviews for AERMOD, CALPUFF and ISC-
PRIME have 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 intend to
revise the glossary and place it on EPA's Internet SCRAM website.
We invite your comment on any of the changes proposed above
(Proposed editorial changes) for appendix W text, including the merging
of sections 4 and 5.
Appendix A
We updated the introduction to appendix A (section A.0). As
mentioned before, we added AERMOD and CALPUFF to appendix A, and
modified the ISC3 description (now, ISC-PRIME) to include the EPRI
downwash
[[Page 21511]]
algorithm. We propose removing 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.
In the mid-1980s, the Federal Aviation Administration (FAA)
developed the Emissions and Dispersion Modeling System (EDMS) to assess
the air quality of proposed airport development projects by. In
response to the growing needs of the air quality analysis community and
changes in regulations (e.g., conformity requirements from the Clean
Air Act Amendment of 1990), FAA updated EDMS to version 3.1.
Accordingly, we included a revised summary description for EDMS in
appendix A. The emissions module of EDMS 3.1 includes input and
methodology enhancements. The dispersion module of EDMS 3.1 also has
improved and has been refined to incorporate code from two EPA
dispersion models: PAL2 and CALINE3. The dispersion module also has
been revised to allow the user greater flexibility in specifying inputs
such as dispersion settings and coefficients, hourly operational
profiles for aircraft queues, and meteorological data. EDMS 3.1
features provide greater resolution in defining emissions and
dispersion concentrations, and have the potential to increase or
decrease the results, depending on the individual scenario. EDMS has
never been subjected to performance evaluation, and no studies of its
performance have been cited. We invite comment on whether this
compromises its viability as a recommended/preferred model for
assessing airport impacts on air quality. We also invite suggestions as
to how this deficiency can be addressed.
Appendix B: To Be Moved to Website (www.epa.gov/scram001)
Appendix B of the Guideline has been a repository for over 20
alternate models to be used with case-by-case justification. These
models have not necessarily been the subject of any performance
evaluation, and their inclusion in appendix B does not mean the Agency
sanctions their use. They are listed for convenience, and have been
used in few regulatory applications. Production and maintenance of the
appendix B information currently in CFR text presents a real burden to
EPA. Accordingly, we propose to move the appendix B repository of
alternate model summary descriptions to our Internet SCRAM website
(www.epa.gov/scram001). Placement of this material on the website
offers many advantages. In this format, we will be able to maintain the
list and model descriptions more easily and inexpensively. We could,
for example, routinely make revisions on a nominally annual basis,
whereas the current system imposes a nominally 3-year cycle for such
revisions. Model developers could list their own website address for
users to obtain more information. We invite your comments on the
proposed movement of the list of alternative model descriptions to our
website.
Several model developers have submitted new dispersion models for
inclusion in this website repository of alternate models:
Second-Order Closure Integrated Puff Model (SCIPUFF);
Open Burn/Open Detonation Dispersion Model (OBODM);
Atmospheric Dispersion Modeling System (ADMS); and
Comprehensive Air Quality Model with extensions (CAMx).
As described below, codes for these models, as well as applicable
documentation, have been uploaded to our Internet SCRAM website for
your review. We have included summary descriptions in docket no. A-99-
05 for your review and comment. Finally, we propose deleting a model
currently listed in appendix B, MESOPUFF II, which CALPUFF replaces.
Appendix C
We also propose removing appendix C (Example Air Quality Analysis
Checklist) from the CFR. We believe this checklist is outdated, in need
of revision, and would be more practical to maintain if posted on EPA's
Internet SCRAM website (as is our intention for appendix B).
Availability of Related Information
Our Air Quality Modeling Group maintains an Internet website
(Support Center for Regulatory Air Models--SCRAM) at: www.epa.gov/scram001. You may find codes and documentation for models proposed for
adoption in today's action on the SCRAM website. In addition, we have
uploaded various support documents (e.g., evaluation reports) that are
now available for review.
Administrative Requirements
A. Executive Order 12866
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, 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 proposed 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.
For purposes of assessing the impacts of today's rule on small
entities, small entity is 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.
We do not anticipate that today's proposal will have any impacts on
small entities, because existing and new sources of air emissions that
model air
[[Page 21512]]
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 proposal,
the impacts of using updated modeling techniques would be minimal, if
not non-existent. The action proposed today incorporates comments
received at the 6th Conference on Air Quality Modeling in August 1995
in Washington, D.C. The proposal features several new modeling systems
and serves to increase efficiency and accuracy. These systems employ
procedural concepts that are very similar to those currently used,
changing only mathematical formulations and specific data elements. Any
impact on small entities would mainly be ascribed to the proposed use
of AERMOD, which will replace ISC3. Computer run times for AERMOD may
be longer than those for ISC3, owing to AERMOD's increased
sophistication so that more time may be involved in preparing input
data using AERMOD's preprocessors (AERMET and AERMAP) relative to an
ISC3 run. However, this is more than compensated by AERMOD's capability
to treat simple and complex terrain problems in one model, which
actually affords a timesaving advantage. Moreover, we designed AERMOD's
output formats to mimic those of ISC3, thus easing interpretation of
results. Therefore, we do not believe that AERMOD's use poses a
significant or unreasonable burden on any small entities. The proposed
action imposes no new regulatory burdens and, as such, there will be no
additional impact on small entities regarding reporting, recordkeeping,
compliance requirements.
After considering the economic impacts of today's proposed rule on
small entities, I certify that this action will not have a significant
economic impact on a substantial number of small entities.
D. 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
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.''
Under Section 6 of Executive Order 13132, EPA may not issue a
regulation that has federalism implications, that imposes substantial
direct compliance costs, and that is not required by statute, unless
the Federal government provides the funds necessary to pay the direct
compliance costs incurred by State and local governments, or EPA
consults with State and local officials early in the process of
developing the proposed regulation. EPA also may not issue a regulation
that has federalism implications and that preempts State law, unless
the Agency consults with State and local officials early in the process
of developing the proposed regulation.
This proposed 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. The proposal 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, the requirements of
section 6 of the Executive Order do not apply to this rule.
E. Executive Order 13084: Consultation and Coordination With Indian
Tribal Governments
Under Executive Order 13084, EPA may not issue a regulation that is
not required by statute, that significantly or uniquely affects the
communities of Indian tribal governments, and that imposes substantial
direct compliance costs on those communities, unless the Federal
government provides the funds necessary to pay the direct compliance
costs incurred by the tribal governments, or EPA consults with those
governments. If EPA complies by consulting, Executive Order 13084
requires EPA to provide to the Office of Management and Budget, in a
separately identified section of the preamble to the rule, a
description of the extent of EPA's prior consultation with
representatives of affected tribal governments, a summary of the nature
of their concerns, and a statement supporting the need to issue the
regulation. In addition, Executive Order 13084 requires EPA to develop
an effective process permitting elected officials and other
representatives of Indian tribal governments ``to provide meaningful
and timely input in the development of regulatory policies on matters
that significantly or uniquely affect their communities.''
Today's proposed rule does not significantly or uniquely affect the
communities of Indian tribal governments. As stated above with respect
to Executive Order 12875, the proposal does not impose any additional
requirements for the regulated community, including Indian Tribal
Governments. Accordingly, the requirements of section 3(b) of Executive
Order 13084 do not apply to this rule.
F. Executive Order 13045: Protection of Children From Environmental
Health Risks 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 proposed 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 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.
G. Unfunded Mandates Reform Act
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
[[Page 21513]]
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 contains no Federal mandates (under the regulatory
provisions of Title II of the UMRA) for State, local, or tribal
governments or the private sector.
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: February 8, 2000.
Carol M. Browner,
Administrator.
Part 51, chapter I, title 40 of the Code of Federal Regulations is
proposed to be 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: 42 U.S.C. 7410, 7414, 7421, 7470-7479, 7491, 7492,
7601, and 7602.
2. Appendix W to Part 51 is 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 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 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen
Dioxide, and Lead
5.1 Discussion
5.2 Recommendations
5.2.1 Models for Ozone
5.2.2 Models for Particulate Matter
5.2.2.1 PM-2.5
5.2.2.2 PM-10
5.2.3 Models for Carbon Monoxide
5.2.4 Models for Nitrogen Dioxide (Annual Average)
5.2.5 Models for Lead
6.0 Other Model Requirements
6.1 Discussion
6.2 Recommendations
6.2.1 Visibility
6.2.2 Good Engineering Practice Stack Height
6.2.3 Long Range Transport (i.e., beyond 50km)
6.2.4 Modeling Guidance for Other Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
7.2.1 Design Concentrations
7.2.2 Critical Receptor Sites
7.2.3 Dispersion Coefficients
7.2.4 Stability Categories
7.2.5 Plume Rise
7.2.6 Chemical Transformation
7.2.7 Gravitational Settling and Deposition
7.2.8 Complex Winds
7.2.9 Calibration of Models
8.0 Model Input Data
8.1 Source Data
8.1.1 Discussion
8.1.2 Recommendations
8.2 Background Concentrations
8.2.1 Discussion
8.2.2 Recommendations (Isolated Single Source)
8.2.3 Recommendations (Multi-Source Areas)
8.3 Meteorological Input Data
8.3.1 Length of Record of Meteorological Data
8.3.2 National Weather Service Data
8.3.3 Site-Specific Data
8.3.4 Treatment of Calms
9.0 Accuracy and Uncertainty of Models
9.1 Discussion
9.1.1 Overview of Model Uncertainty
9.1.2 Studies of Model Accuracy
9.1.3 Use of Uncertainty in Decision-Making
9.1.4 Evaluation of Models
9.2 Recommendations
10.0 Regulatory Application of Models
10.1 Discussion
10.2 Recommendations
10.2.1 Analysis Requirements
10.2.2 Use of Measured Data in Lieu of Model Estimates
10.2.3 Emission Limits
11.0 Bibliography
12.0 References
[[Page 21514]]
Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality
Models
List of Tables
Table No. and Title
4-1a Neutral/Stable Meteorological Matrix for CTSCREEN
4-1b Unstable/Convective Meteorological Matrix for CTSCREEN
8-1 Model Emission Input Data for Point Sources
8-2 Point Source Model Input Data (Emissions) for PSD NAAQS
Compliance Demonstrations
8-3 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, including prevention of
significant deterioration (PSD).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 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 3.0. 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. Chapter 2 gives an overview of models
and their appropriate use. Chapter 3 provides specific guidance on
the use of ``preferred'' air quality models and on the selection of
alternative techniques. Chapters 4 through 6 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. Chapter 7 discusses issues common to many
modeling analyses, including acceptable model components. Chapter 8
makes recommendations for data inputs to models including source,
meteorological and background air quality data. Chapter 9 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.
[[Page 21515]]
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 website at www.epa.gov/scram001. A list of alternate models that can be used with case-by-
case justification (Section 3.2), a glossary of terms, and an
example air quality analysis checklist are also posted on this
website. This is a site with which modelers should become familiar.
3.0 Recommended Air Quality Models
a. This section recommends refined modeling techniques that are
preferred 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 5 and periodic
Regional/State/Local Modelers workshops.
b. 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
website (Section 2.3), and the preparation of a written protocol
help to keep misunderstandings at a minimum.
c. 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.
d. The 1980 solicitation of new or different models from the
technical community 6 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 16 that include statistical
measures of model performance in comparison with measured air
quality data as suggested by the American Meteorological Society
17 and, where possible, peer scientific
reviews.18 19 20 21
22 23 24
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
Section 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
[[Page 21516]]
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
25 26 are available to assist in developing a
consistent approach when justifying the use of other than the
preferred modeling techniques recommended in the Guideline.
Reference 27 contains advanced statistical techniques for
determining which model performs better than other competing models.
In many cases, this protocol should be considered preferentially to
the material in Chapter 3 of reference 25. 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 below. 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 website (Section 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 3.2.2b, 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 SO2, 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 identified below are satisfied.
d. For condition (2) in paragraph 3.2.2 b, the procedures and
techniques for determining the acceptability of a model for an
individual case based on superior performance are contained in
references 25-27 and 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 3.2.2b, 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 Traditional Stationary Source Models
4.1 Discussion
a. Guidance in this section applies to modeling analyses for
which the predominant meteorological conditions that control the
design concentration are steady state and for which the transport
distances are nominally 50km or less. 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. 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. Complex terrain is defined
as terrain exceeding the height of the stack being modeled.
c. In the early 1980s, model evaluation exercises were conducted
to determine the ``best, most appropriate point source model'' for
use in simple terrain.8 18 No one model was found to be
clearly superior, and, based on past use, public familiarity, and
[[Page 21517]]
availability, ISC (predecessor to ISC3 28) 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).
d. Encouraged by the development of pragmatic methods for better
characterization of plume dispersion 29 30 31 32, the
AMS/EPA Regulatory Model Improvement Committee (AERMIC) developed
AERMOD 33. AERMOD employs state-of-practice
parameterizations for characterizing the meteorological influences
and dispersion. The model utilizes a probability density function
(pdf) and the superposition of several Gaussian plumes to
characterize the distinctly non-Gaussian nature of the vertical
pollutant distribution for elevated plumes during convective
conditions; otherwise the distribution is Gaussian. Also, nighttime
urban boundary layers (and plumes within them) have the turbulence
enhanced by AERMOD to simulate the influence of the urban heat
island. AERMOD has been evaluated using a variety of data sets and
has been found to perform better than ISC3 for many applications,
and as well or better than CTDMPLUS for several complex terrain data
sets (Section A.1; subsection n). Currently, AERMOD does not contain
algorithms for dry deposition.
e. A new building downwash algorithm was developed and tested
within the ISC3 construct, ISC-PRIME,24 which is in
Appendix A. ISC-PRIME has been evaluated using a variety of data
sets and has been found to perform better than ISC3 (Section A.7;
subsection n). ISC-PRIME retains the dry deposition inherent in
ISC3.
4.2 Recommendations
4.2.1 Screening Techniques
4.2.1.1 Simple Terrain
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,34 and a computerized version of the
recommended screening technique, SCREEN, is available.35
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 Section
7.2.3. 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 reviewing authority on the
choice of the screening model for each analysis, and on the input
data as well as the ultimate use of the results.
4.2.1.2 Complex Terrain
a. CTSCREEN 36 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.36 The terrain data must be digitized in the same
manner as for CTDMPLUS and a terrain processor is
available.37 A discussion of the model's performance
characteristics is provided in a technical paper.38
CTSCREEN is designed to execute a fixed matrix of meteorological
values for wind speed (u), standard deviation of horizontal and
vertical wind speeds (v, w),
vertical potential temperature gradient (d/dz), friction
velocity (u*), Monin-Obukhov length (L), mixing height
(zi) as a function of terrain height, and wind directions
for both neutral/stable conditions and unstable convective
conditions. Table 4-1 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 Regional Office 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 \37\ 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 28 35 39 may be
acceptable for complex terrain cases where established procedures
are used. The user is encouraged to confer with the Regional Office
if any unresolvable problems are encountered, e.g., applicability,
meteorological data, receptor siting, or terrain contour processing
issues.
4.2.2 Refined Analytical Techniques
a. A brief description of each preferred model for refined
applications is found in Appendix A. Also listed in that appendix
are availability, the model input requirements, the standard options
that should be selected when running the program, and output
options.
b. For a wide range of regulatory applications in all types of
terrain, the recommended model is AERMOD. This recommendation is
based on extensive developmental and performance evaluation (Section
A.1; subsection n). Differentiation of simple versus complex terrain
is unnecessary with AERMOD. In complex terrain, AERMOD employs the
well-known dividing-streamline concept in a simplified simulation of
the effects of plume-terrain interactions.
c. If dry deposition or aerodynamic building downwash is
important for the modeling analysis, e.g., paragraphs 5.2.2.2(e),
5.2.5(b), 6.2.2(b), and 7.2.7(b), the recommended model is ISC-
PRIME. Line sources can be simulated with ISC-PRIME if point or
volume sources are appropriately combined. If buoyant plume rise
from line sources is important for the modeling analysis, the
recommended model is BLP. For other special modeling applications,
CALINE3 (or CAL3QHCR on a case-by-case basis), OCD, and EDMS are
recommended as described in Sections 5 and 6.
d. If the modeling application involves a well defined hill or
ridge and a detailed dispersion analysis of the spatial pattern of
plume impacts is of interest, CTDMPLUS, listed in Appendix A, is
available. CDTMPLUS provides greater resolution of concentrations
about the contour of the hill feature than does AERMOD through a
different plume-terrain interaction algorithm.
[[Page 21518]]
Table 4-1a.--Neutral/Stable Meteorological Matrix for CTSCREEN
------------------------------------------------------------------------
------------------------------------------------------------------------
Variable: Specific values
U(m/s)............ 1.0 2.0 3.0 4.0 5.0
v(m/s).......... 0.3 0.75 ...... ....... ......
w(m/s).......... 0.08 0.15 0.30 0.75 ......
/ 0.01 0.02 0.035 ....... ......
z (K/m)........
WD....................... (Wind direction is optimized internally
for each meteorological combination.)
------------------------------------------------------------------------
Exceptions:
(1) If U 2 m/s and v > 0.3 m/s, then include w = 0.04 m/s.
(2) If w = 0.75 m/s and U 3.0 m/s, then /z is limited to > 0.01 K/m.
(3) If U 4 m/s, then w 0.15 m/s.
(4) w >V
Table 4-1B.--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 ...... ......
/z (K/m)....................... 0.030 (potential temperature gradient
above Zi)
Zi (m)..................................................... 0.5h 1.0h 1.5h h= terrain
height)
----------------------------------------------------------------------------------------------------------------
5.0 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen
Dioxide, and Lead
5.1 Discussion
a. This section identifies modeling approaches or models
appropriate for addressing ozone (O3),a carbon
monoxide (CO), nitrogen dioxide (NO2), particulates (PM-
2.5 and PM-10),b, c 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.)
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.\40\ \41\ 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 (62 FR 38856). 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 (NOX) 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
[[Page 21519]]
slightly above it. Such a decision needs to be made on a case-by-
case basis in concert with the appropriate 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.\42\ 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 (62 FR 38652), 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 \43\
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.\43\
g. The NAAQS for PM-10 was promulgated in July 1987 (40 CFR
50.6). A SIP development guide \44\ is available to assist in PM-10
analyses and control strategy development. EPA promulgated
regulations for PSD increments measured as PM-10 in a document
published on June 3, 1993 (Sec. 51.166(c)). As an aid to assessing
the impact on ambient air quality of particulate matter generated
from prescribed burning activities, a reference \45\ is available.
h. 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.
i. 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.
j. 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. 46 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.47
5.2 Recommendations
5.2.1 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,48 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.\42\ 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) 49, 50 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. EPA has issued guidance \42\ 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 appropriate Regional Office to determine the most
suitable approach on a case-by-case basis (Section 3.2.2).
5.2.2 Models for Particulate Matter
5.2.2.1 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
\48\ or REMSAD \51\). 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 \43\, 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. Observational 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.\43\
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 appropriate Regional Office to determine the most
suitable approach on a case-by-case basis (Section 3.2.2).
5.2.2.2 PM-10
a. Screening techniques like those identified in Section 4 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.\44\
b. Refined models such as those discussed in Section 4 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 (Section 4.2.2). For
guidance on determination of design concentrations, see paragraph
7.2.1.1(e).
c. Receptor models \52\ \53\ \54\ have proven useful for helping
validate emission inventories and for corroborating source-specific
impacts estimated by dispersion models. In regulatory applications,
dispersion models have been used in conjunction with receptor models
to attribute source (or source category)
[[Page 21520]]
contributions. Guidance is available for PM-10 sampling and analysis
applicable to receptor modeling.\55\
d. Under certain conditions, recommended dispersion models may
not be reliable. In such circumstances, the modeling approach should
be approved by the appropriate 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 (Section
7.2.8).
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. Where
such fugitive emissions can be properly specified, use the
recommended steady-state dispersion model (Section 4.2.2) that
handles gravitational settling and dry deposition. 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 appropriate Regional Office for
each specific situation before the modeling exercise is begun.
5.2.3 Models for Carbon Monoxide
a. Guidance is available for analyzing CO impacts at roadway
intersections.\56\ The recommended screening model for such analyses
is CAL3QHC.\57\ \58\ 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 56; a refined approach may be considered on a case-by-case
basis with CAL3QHCR.\59\ 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.\56\ \60\ \61\
\62\
d. Where point sources of CO are of concern, they should be
treated using the screening and refined techniques described in
Section 4 of the Guideline.
5.2.4 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
5-1 and described in paragraphs 5.2.4 b through d:
b. For Tier 1 (the initial screen), use an appropriate model
from Appendix A to estimate the maximum annual average concentration
and assume a total conversion of NO to NO 2. If the
concentration exceeds the NAAQS and/or PSD increments for NO
2, proceed to the 2nd level screen.
c. For Tier 2 (2nd level) screening analysis, multiply the Tier
1 estimate(s) by an empirically derived NO 2 / NO
x value of 0.75 (annual national default).63
The reviewing agency may establish an alternative default NO
2 / NO x ratio based on ambient annual average
NO 2 and annual average NO x data
representative of area wide quasi-equilibrium conditions Alternative
default NO 2/NO x ratios should be based on
data satisfying quality assurance procedures that ensure data
accuracy for both NO 2 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 NO 2 national ambient
air quality standard may not be adequate. In addition, default NO
2/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
64, may also be considered. Also, a site-specific NO
2/NO x ratio may be used as a detailed
screening method if it meets the same restrictions as described for
alternative default NO 2/NO x ratios. Ambient
NO 2x monitors used to develop a site-specific ratio
should be sited to obtain the NO 2 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 NO 2/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
NO 2 impacts at receptors located within the same
distance of the source as the source-to-monitor distance.
e. In urban areas (Section 7.2.3), 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 NO 2 and to use a
model from Appendix A for nonreactive pollutants to estimate NO
2 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 NO 2 concentrations using an empirically derived
annual NO 2 / NO x ratio. A value of 0.75 is
recommended for this ratio. However, a spatially averaged
alternative default annual NO 2 / 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 NO 2 / 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 NO
2 / 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 NO 2 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 NO 2
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; Section 5.2.3).
g. More refined techniques to handle special circumstances may
be considered on a case-by-case basis and agreement with the
reviewing authority should be obtained. Such techniques should
consider individual quantities of NO and NO 2 emissions,
atmospheric transport and dispersion, and atmospheric transformation
of NO to NO 2. Where they are available, site-specific
data on the conversion of NO to NO 2 may be used.
Photochemical dispersion models, if used for other pollutants in the
area, may also be applied to the NO x problem.
5.2.5 Models for Lead
a. For major lead point sources, such as smelters, which
contribute fugitive emissions and for which deposition is important,
use the appropriate recommended steady-state plume dispersion model
(Section 4.2.2). 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
[[Page 21521]]
carbon monoxide emissions from highways and intersections (Section
5.2.3). However, where deposition is of concern, ISC-PRIME may be
used. Also, 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 (Section 8.2); the
point source and any nearby major roadways should be modeled
separately using the appropriate recommended steady-state plume
dispersion model (Section 4.2.2).
6.0 Other Model Requirements
6.1 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.65 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.4). 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.66
6.2 Recommendations
6.2.1 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--51.307) 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--51.309).
The state of relevant scientific knowledge has expanded
significantly since the Clean Air Act Amendments of 1977. A number
of studies and reports 67 68 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. Guidance and a screening model, VISCREEN, are available.
69 VISCREEN can be used to calculate the potential impact
of a plume of specified emissions for specific transport and
dispersion conditions. If a more comprehensive analysis is required,
any refined model should be selected in consultation with the EPA
Regional Office and the appropriate Federal Land Manager who is
responsible for determining whether there is an adverse effect by a
plume on a Class I area. PLUVUE II, an alternative model listed on
EPA's Internet SCRAM website (Section 2.3), may be applied on a
case-by-case basis when refined plume visibility evaluations are
needed.
e. CALPUFF (Section A.4) may be applied on a case-by-case basis
when assessment is needed of reasonably attributable haze impairment
due to one or a small group of sources. The procedures and analyses
should be determined in consultation with the appropriate Regional
Office, the appropriate regulatory permitting authority, and the
appropriate Federal Land Manager (FLM).
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 \48\ and REMSAD \51\. Model users
should consult with the appropriate Regional Office to determine the
most suitable approach on a case-by-case basis (Section 3.2.2).
6.2.2 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 example
of applying those techniques are found in several references
70, 71 72 73 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 72). Detailed downwash screening procedures \34\ 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 Section 4.2.2 contains algorithms for
building wake calculations and should be used.
6.2.3 Long Range Transport (LRT) (i.e., beyond 50km)
a. Section 165(e) 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
[[Page 21522]]
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 EPA Regional Office, the
appropriate regulatory permitting authority and, for Class I area
analyses, the appropriate Federal Land Manager (FLM). While the
ultimate decision on whether a Class I area is adversely affected is
the responsibility of the permitting authority, the FLM has an
affirmative responsibility to protect air quality related values
that may be affected, and to provide the appropriate procedures and
analysis techniques.
b. If LRT is determined to be important, then refined estimates
utilizing the CALPUFF modeling system should be obtained. A
screening approach \66\ 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 8.3.1.2(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 may be
considered for developing consensus in the methods and procedures to
be followed.
6.2.4 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 Section 7.2.8.
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.
7.0 General Modeling Considerations
7.1 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.
7.2 Recommendations
7.2.1 Design Concentrations (see also Section 10.2.3.1)
7.2.1.1 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 (Section 8.2)
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 Section 8.3.1.2 (e.g., 5
years of National Weather Service (NWS) data or 1 year of site-
specific data; Section 8.3.3), then the design concentration based
on the highest, second-highest short term concentration or 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 (Section 8.3.3), 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.
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.
7.2.1.2 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 4. No definitive
guidance for determining design concentrations for PM-2.5 has been
issued. 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.
7.2.2 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
[[Page 21523]]
resolution is indicated by terrain or source factors.
7.2.3 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 7.2.3 b
through f), 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 74 in rural areas and McElroy-Pooler
75 coefficients in urban areas. A key feature of AERMOD's
formulation is the use of directly observed variables of the
boundary layer to parameterize dispersion.\33\ Research is
continuing toward the development of methods to determine dispersion
coefficients directly from measured or observed
variables.76 77
b. The selection of either rural or urban dispersion
coefficients in a specific application should follow one of the
procedures suggested by Irwin 78 and briefly described
below. 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, Ao, circumscribed by a 3km radius circle
about the source using the meteorological land use typing scheme
proposed by Auer 79; (2) if land use types I1, I2, C1,
R2, and R3 account for 50 percent or more of Ao, 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 Ao 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,
80 is included in the preferred models and should be used
where buoyant sources, e.g., those involving fuel combustion, are
involved.
7.2.4 Stability Categories
a. The Pasquill approach to classifying stability is commonly
used in preferred models (Appendix A). The Pasquill method, as
modified by Turner,81 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 Section 8.3. 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
y and z should be determined
from the same stability category. ``Split sigmas'' in that instance
are not recommended. Sector averaging, which eliminates the
y term, is commonly acceptable in complex
terrain screening methods.
d. AERMOD, also a preferred model in Appendix A, uses a
planetary boundary layer scaling parameter to characterize
stability.33 This approach represents a departure from
the discrete, hourly stability categories estimated under the
Pasquill-Gifford-Turner scheme.
7.2.5 Plume Rise
a. The plume rise methods of Briggs 82, 83 are
incorporated in many of the preferred models and are recommended for
use in many modeling applications. In AERMOD,\33\ for the stable
boundary layer, plume rise is estimated using an iterative approach,
similar to that in the CTDMPLUS model. In the convective boundary
layer, plume rise is superposed on the displacements by random
convective velocities. 84 In ISC-PRIME, plume rise is
computed using the methods of Briggs excepting cases involving
building downwash, in which a numerical solution of the mass,
energy, and momentum conservation laws is performed.24 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. Since there is insufficient information to identify and
quantify dispersion during the transitional plume rise period,
gradual plume rise is not generally recommended for use. There are
two exceptions where the use of gradual plume rise is appropriate:
(1) In complex terrain screening procedures to determine close-in
impacts; (2) when calculating the effects of building wakes. The
building wake algorithm in ISC-PRIME incorporates and automatically
(i.e., internally) exercises the thermodynamically based gradual
plume rise calculations as described in paragraph 7.2.5 a . 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 83 is the
recommended technique for this situation and is found in the point
source preferred models.
7.2.6 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
81 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.
7.2.7 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, use the recommended steady-state plume
dispersion model (Section 4.2.2).
7.2.8 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.85 In general these
geographically 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
[[Page 21524]]
variations of meteorology effects on transport and dispersion. The
setup and application of the model should be determined in
consultation with the Regional Office and the appropriate regulatory
permitting authority 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
8.3.1.2(e).
b. 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
34 that may be used to approximate the concentrations.
Considerable care should be exercised in using the results obtained
from the screening techniques.
c. 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
website (Section 2.3) may be applied on a case-by-case basis when
air quality estimates under shoreline fumigation conditions are
needed.86 Information on the results of EPA's evaluation
of this model together with other coastal fumigation models is
available.87 Selection of the appropriate model for
applications where shoreline fumigation is of concern should be
determined in consultation with the Regional Office.
d. 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. When stagnation periods such as these are found to
occur, they should be addressed in the air quality modeling
analysis. WYNDvalley, listed on EPA's Internet SCRAM website
(Section 2.3), may be applied on a case-by-case basis for stagnation
periods of 24 hours or longer in valley-type situations. Caution
should be exercised when applying WYNDvalley to elevated point
sources. If point sources are of interest, users should note the
guidance provided for CALPUFF in paragraph 7.2.8 a. Users should
consult with the appropriate Regional Office prior to regulatory
application of WYNDvalley.
7.2.9 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.
8.0 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 website (Section 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.
8.1 Source Data
8.1.1 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 88; 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 Regional
Office 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.
8.1.2 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 d 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 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.
---------------------------------------------------------------------------
\d\ 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, 10
\6\ 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.
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
[[Page 21525]]
available for paragraphs 8.1.2b through g 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 8-1. 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 8-1 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 8.0(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 8-2. 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 8-1.--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
federally is greater), or over most recent 2
enforceable permit federally years.3
limit . enforceable permit
condition.
Short term........................ Maximum allowable Actual or design Continuous
emission limit or capacity (whichever operation, i.e.,
federally is greater), or all hours of each
enforceable permit federally time period under
limit. enforceable permit consideration (for
condition 4. all hours of the
meteorological data
base).5
Nearby Source(s) 6, 7--Same input requirements as for stationary point source(s) above
----------------------------------------------------------------------------------------------------------------
Other Source(s) 7--If modeled (Section 8.2.3), input data requirements are defined below
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable Annual level when Actual operating
emission limit or actually operating, factor averaged
federally averaged over the over the most
enforceable permit most recent 2 years recent 2 years.3
limit 6. 3.
Short term........................ Maximum allowable Annual level when Continuous
emission limit or actually operating, operation, i.e.,
federally averaged over the all hours of each
enforceable permit most recent 2 years time period under
limit 6. 3. consideration (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 8.2.3(c).
\7\ See paragraph 8.2.3(d).
Table 8-2.--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 permit hours).\2\
enforceable permit condition.
limit.
[[Page 21526]]
Short term: (24 hours). Maximum allowable Design capacity or Continuous operation
emis sion limit or federally (i.e., all hours of
federally enforceable permit each time period un
enforceable permit condition \3\. der consideration)
limit. (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
federally is greater), or over the most
enforceable permit federally recent 2 years.7, 8
limit \5\. 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 is greater), or each time period un
enforceable permit federally der consideration)
limit \5\. enforceable permit (for all hours of
condition \3\. the 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
federally averaged over the over the most
enforceable permit most recent 2 years recent 2 years.7, 8
limit \5\. \7\.
Short term (24 hours).. Maximum allowable Annual level when Continuous operation
emission limit or actually operating, (i.e., all hours of
federally averaged over the each time period
enforceable permit most recent 2 years under
limit \5\. \7\. consideration) (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 8:00 a.m. to 4:00 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 8.2.3(c).
\6\ See paragraph 8.2.3(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 inpacts from non-nearby (background) sources can be represented by air quality data
unless adequate data do not exist.
8.2 Background Concentrations
8.2.1 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.89 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.
8.2.2 Recommendations (Isolated Single Source)
a. Two options (paragraph 8.2.2b or c) 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.
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.
8.2.3 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.
[[Page 21527]]
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 reviewing authority. 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 8-1 or 8-2. 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 reviewing
authority 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 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 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
Section 8.2.2 or by application of a model using Table 8-1 or 8-2.
8.3 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 8.3c and d.
b. Model input data are normally obtained either from the
National Weather Service or as part of an 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. Regulatory application of AERMOD requires careful
consideration of minimum data for input to AERMET. Data
representativeness, in the case of AERMOD, means utilizing data of
an appropriate type for constructing realistic boundary layer
profiles. Of paramount importance is the requirement that all
meteorological data used as input to AERMOD must be both laterally
and vertically representative of the transport and dispersion within
the analysis domain. The representativeness of data that were
collected off-site should be judged, in part, by comparing the
surface characteristics in the vicinity of the meteorological
monitoring site with the surface characteristics that generally
describe the analysis domain. Furthermore, since the spatial scope
of each variable could be different, representativeness should be
judged for each variable separately. For example, for a variable
such as wind direction, the data may need to be collected very near
plume height to be adequately representative, whereas, for a
variable such as temperature, data from a station several kilometers
away from the source may in some cases be considered to be
adequately representative.
d. For long range transport modeling assessments (as discussed
in Section 6.2.3) or in assessments where the transport winds are
complex and the application involves a non-steady-state dispersion
model (as discussed in Section 7.2.8), use of output from prognostic
mesoscale meteorological models is encouraged. 90
91 92 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.
8.3.1 Length of Record of Meteorological Data
8.3.1.1 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 93 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 94
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.
8.3.1.2 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.
b. The use of 5 years of NWS meteorological data or at least 1
year of site-specific data is required. If one year or more
(including partial years), up to five years, of
[[Page 21528]]
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 Section 8.3.3.2.
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 (as discussed in Section 6.2.3) and for
complex wind situations (as discussed in paragraph 7.2.8(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 years of meteorological data may be used if mesoscale
meteorological fields are available, as discussed in paragraph
8.3(d). 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.
8.3.2 National Weather Service Data
8.3.2.1 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. Direct
measurements of model input parameters have been made for limited
model studies and those methods and techniques are becoming more
widely applied; however, many model 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.
8.3.2.2 Recommendations
a. The preferred models listed in Appendix A all accept as input
the NWS meteorological data preprocessed into model compatible form.
If NWS sata are judged to be adequately representative for a
particular modeling application, they may be used. NCDC makes
available surface 95 96 and upper air 97
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. Note that AERMOD at a minimum requires wind
observations at a height above ground between seven times the local
surface roughness height and 100 meters.
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 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.
8.3.3 Site-Specific Data
8.3.3.1 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 undue local or ``micro'' 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 property does not of itself
guarantee adequate representativeness. For help in determining
representativeness of site-specific measurements, technical guidance
98 is available. Site-specific data should always be
reviewed for consistency by a qualified meteorologist.
8.3.3.2 Recommendations
a. EPA guidance 98 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.89 Detailed
information on quality assurance is also available.99 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 Regional Office will
determine the appropriateness of the measurement locations.
b. All site-specific data should be reduced to hourly averages.
Table 8-3 lists the wind related parameters and the averaging time
requirements.
c. Missing Data Substitution. After valid data retrieval
requirements have been met, 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 98. 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.98 99
e. Temperature Measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
site-specific meteorological guidance.98
f. Temperature Difference Measurements. Temperature difference
(T) measurements should be obtained using matched
thermometers or a reliable thermocouple system to achieve adequate
accuracy. Siting, probe placement, and operation of T
systems should be based on guidance found in Chapter 3 of reference
98, and such guidance should be followed when obtaining vertical
temperature gradient data for use in plume rise estimates or in
determining the critical dividing streamline height.
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. 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 AERMOD and CTDMPLUS, see
Appendix A.) Specifications for wind measuring instruments and
systems are contained in reference 98.
h. Turbulence. There are several dispersion models that are
capable of using direct measurements of turbulence (wind
fluctuation) in the characterization of the vertical and lateral
dispersion (e.g., CTDMPLUS, AERMOD, CALPUFF). For specific
requirements for CTDMPLUS, AERMOD and CALPUFF, see Appendix A. For
technical guidance on measurement and processing of turbulence
parameters, see reference 98. 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 (Table 8-3). There are other dispersion models (e.g., ISC-
PRIME, 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
[[Page 21529]]
are discussed in reference 98. 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., ISC-PRIME), 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.74 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 98. In the absence of requisite data to
implement the Turner method, the SRDT method or wind fluctuation
statistics (i.e., the E and
A methods) may be used.
j. The SRDT method, described in Section 6.4.4.2 of reference
98, is modified slightly from that published from earlier work
100 and has been evaluated with three site-specific data
bases.101 The two methods of stability classification
which use wind fluctuation statistics, the E and
A methods, are also described in detail in
Section 6.4.4 of reference 106 (note applicable tables in Section
6). For additional information on the wind fluctuation methods,
several references are available.102 103 104 105
k. Meteorological Data Preprocessors. The following
meteorological preprocessors are recommended by EPA:
AERMET,106 PCRAMMET,107 MPRM,108
METPRO,109 and CALMET. 110 AERMET, which is
patterned after MPRM, should be used to preprocess all data for use
with AERMOD. Except for applications that employ AERMOD, 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 website (Section 2.3).
Table 8-3.--Averaging Times for Site-Specific Wind and Turbulence
Measurements
------------------------------------------------------------------------
Averaging
Parameter time
(hours)
------------------------------------------------------------------------
Surface wind speed (for use in stability determinations)... 1
Transport direction........................................ 1
Dilution wind speed........................................ 1
Turbulence measurements (E and A) for use (\1\)
in stability determinations...............................
Turbulence Measurements for direct input to dispersion 1
models....................................................
------------------------------------------------------------------------
\1\ To minimize meander effects in A when wind conditions are
light and/or variable, determine the hourly average value
from four sequential 15-minute 's according to the following
formula:
[GRAPHIC] [TIFF OMITTED] TP21AP00.000
8.3.4 Treatment of Calms
8.3.4.1 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.
b. NWS meteorological data preprocessed by PCRAMMET for input to
ISC-PRIME may take one of two formats: ASCII or binary
(unformatted). If the format is ASCII, PCRAMMET does not modify wind
speeds having a value of zero. If the format is binary and PCRAMMET
detects the occurrence of a calm, it sets the wind speed value of
zero to 1.00 m/s and repeats the wind direction from the previous
non-calm hour. Models such as ISC-PRIME identify the original calm
cases by checking for the occurrence of a 1.00 m/s wind speed
coincident with a wind direction equal to that for the previous
hour. ISC-PRIME then treats these calm hours as missing, and no
concentration is calculated.
c. AERMOD, while fundamentally a steady-state Gaussian plume
model, contains improved algorithms for dealing with low wind speed
(near calm) conditions. As a result, AERMOD can produce model
estimates for conditions when the wind speed may be less than 1 m/s,
but still greater than the instrument threshold. Required input to
AERMET, the meteorological processor for AERMOD, includes a
threshold wind speed and a reference wind speed. The threshold wind
speed is typically the threshold of the instrument used to collect
the wind speed data. The reference wind speed is selected by the
model as the lowest level of non-missing wind speed and direction
data where the speed is greater than the wind speed threshold, and
the height of the measurement is between seven times the local
surface roughness and 100 meters. If the only valid observation of
the reference wind speed between these heights is less than the
threshold, the hour is considered calm, and no concentration is
calculated. None of the observed wind speeds in a measured wind
profile that are less than the threshold speed are used in
construction of the modeled wind speed profile in AERMOD.
8.3.4.2 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. ISC-PRIME and AERMOD have been coded to implement these
instructions. For other models listed in Appendix A, a post-
processor computer program, CALMPRO 111 has been
prepared, is available on the SCRAM Internet website (Section 2.3),
and should be used.
[[Page 21530]]
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 Section 7.2.8).
c. When used in steady-state Gaussian plume models except
AERMOD, 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. For
input to AERMOD, no adjustment should be made to the site-specific
wind data. In all cases involving steady-state Gaussian plume
models, calm hours should be treated as missing, and concentrations
should be calculated as in paragraph 8.3.4.2a.
9.0 Accuracy and Uncertainty of Models
9.1 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. EPA recognizes the need for
incorporating such information and has sponsored workshops
112 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 1982,113 was
devoted to that subject.
9.1.1 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. The event is
characterized by measured or ``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.114
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.116 The
statement of accuracy is based on statistical tests or performance
measures such as bias, noise, correlation, etc.\17\ However,
information that allows a distinction between contributions of the
various elements of inherent and reducible uncertainty is only now
beginning to emerge. 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.
9.1.2 Studies of Model Accuracy
a. A number of studies 117 118 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 leading 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,119
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 in paragraph 9.1.2 a, 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
120 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.
9.1.3 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.112 113 115
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
[[Page 21531]]
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.121 122 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.
9.1.4 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, \17\ were generally followed. Third, more
specific information has been provided for justifying the site
specific use of alternative models in previously cited EPA
guidance.25 27 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. In addition to performance evaluation of models, sensitivity
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. 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.
9.2 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 O3, PM-2.5 and
regional haze, additional information and/or procedures may be
appropriate.42 43
10.0 Regulatory Application of Models
10.1 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
g/m3). The 24-hour NAAQS (150 g/
m3) 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).
10.2 Recommendations
10.2.1 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 website (Section 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 should be followed.34
Guidance is also available for mobile sources.56
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 4-7. 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 granted to any 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 and for
all receptor sites used in the previous applications as well as new
sites specific to the new source.
10.2.2 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
[[Page 21532]]
the sole basis for emission limitation. In some instances when the
modeling technique 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 in paragraphs 10.2.2 b.i
through iv 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.25
d. Sources should obtain approval from the Regional Office or
reviewing authority 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.
10.2.3 Emission Limits
10.2.3.1 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.44
10.2.3.2 NAAQS Analyses for New or Modified Sources
a. For new or modified sources predicted to have a significant
ambient impact 89 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 89 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.
10.2.3.3 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 Section 4 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 on-
site or 5 years of off-site NWS data is normally required. 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 Section 6.2.4. As previously noted in Sections 3 and
6, 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.
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85. Stull, R.B., 1988. An Introduction to Boundary Layer
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98. Environmental Protection Agency, 1999. Site Specific
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Environmental Protection Agency, Research Triangle Park, NC. Note:
for copies of this handbook, you may make inquiry to ORD
Publications, 26 West Martin Luther King Dr., Cincinnati, OH 45268.
(513) 569-7562 or (800) 490-9198 (automated request line).
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SO2 and CO Concentrations in St. Louis. Atmospheric
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115. Fox, D.G., 1983. Uncertainty in Air Quality Modeling.
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117. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results,
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Survey of Statistical Measures of Model Performance and Accuracy for
Several Air Quality Models. EPA Publication No. EPA-450/4-83-001.
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119. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff
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the Regulation of an Emission Source. Systems Applications, Inc.,
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Agency, Research Triangle Park, NC. (Docket No. A-80-46, IV-G-1)
Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality
Models
Table of Contents
A.0 Introduction and Availability
A.1 AMS/EPA Regulatory Model-AERMOD
A.2 Buoyant Line and Point Source Dispersion Model (BLP)
A.3 CALINE3
A.4 CALPUFF
A.5 Complex Terrain Dispersion Model Plus Algorithms for Unstable
Situations (CTDMPLUS)
A.6 Emissions and Dispersion Modeling System (EDMS) 3.1
A.7 Industrial Source Complex Model with Prime Downwash Algorithm
(ISC-PRIME)
A.8 Offshore and Coastal Dispersion Model (OCD)
A. REF References
A.0 Introduction and Availability
(1) This appendix summarizes key features of refined air quality
models preferred for specific regulatory applications. For each
model, information is provided on availability, approximate cost
(where applicable), regulatory use, data input, output format and
options, simulation of atmospheric physics, and accuracy. These
models may be used without a formal demonstration of applicability
provided they satisfy the recommendations for regulatory use; not
all options in the models are necessarily recommended for regulatory
use.
(2) Many of these models have been subjected to a performance
evaluation using comparisons with observed air quality data. Where
possible, several of the models contained herein have been subjected
to evaluation exercises, including (1) statistical performance tests
recommended by the American Meteorological Society and (2) peer
scientific reviews. The models in this appendix have been selected
on the basis of the results of the model evaluations, experience
with previous use, familiarity of the model to various air quality
programs, and the costs and resource requirements for use.
(3) 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) website at www.epa.gov/scram001. Documentation is also available from the National
Technical Information Service (NTIS), U.S. Department of Commerce,
Springfield, VA 22161; phone: (800) 553-6847. Where possible,
accession numbers are provided.
A.1 AMS/EPA Regulatory Model--AERMOD
References
Cimorelli, A.J., S.G. Perry, A. Venkatram, J.C. Weil, R.J.
Paine, R.B. Wilson, R.F. Lee and W.D. Peters, 1998. AERMOD:
Description of Model Formulation. (12/15/98 Draft Document) Prepared
for Environmental Protection Agency, Research Triangle Park, NC.
113pp. (Docket No. A-99-05; II-A-1)
Environmental Protection Agency, 1998. User's Guide for the AMS/
EPA Regulatory Model--AERMOD. (11/10/98 Draft) Office of Air Quality
Planning and Standards, Research Triangle Park, NC. (Docket No. A-
99-05, II-A-2)
Environmental Protection Agency, 1998. User's Guide for the
AERMOD Meteorological Preprocessor (AERMET). (November 1998 Draft)
Office of Air Quality Planning and Standards, Research Triangle
Park, NC. (Docket No. A-99-05, II-A-3)
Environmental Protection Agency, 1998. User's Guide for the
AERMOD Terrain Preprocessor (AERMAP). (11/30/98 Draft) Office of Air
Quality Planning and Standards, Research Triangle Park, NC. (Docket
No. A-99-05, II-A-4)
[[Page 21537]]
Availability
The model codes and associated documentation are available on
EPA's Internet SCRAM website (Section A.0).
Abstract
AERMOD is a steady-state plume dispersion model for assessment
of pollutant concentrations from a variety of sources. AERMOD
simulates transport and dispersion from multiple point, area, or
volume sources based on an up-to-date characterization of the
atmospheric boundary layer. Sources may be located in rural or urban
areas, and receptors may be located in simple or complex terrain.
AERMOD accounts for building wake effects (i.e., plume downwash).
The model employs hourly sequential preprocessed meteorological data
to estimate concentrations for averaging times from one hour to one
year. AERMOD is designed to operate in concert with two pre-
processor codes: AERMET processes meteorological data for input to
AERMOD, and AERMAP processes terrain elevation data and generates
receptor information for input to AERMOD.
a. Recommendations for Regulatory Use
(1) AERMOD is appropriate for the following applications:
Point, volume, and area sources;
Surface, near-surface, and elevated releases;
Rural or urban areas;
Simple and complex terrain;
Transport distances over which steady-state assumptions
are appropriate, up to 50 km;
1-hour to annual averaging times; and
Continuous toxic air emissions.
(2) For regulatory applications of AERMOD, the regulatory
default option should be set, i.e., the parameter DFAULT should be
employed in the MODELOPT record in the COntrol Pathway. The DFAULT
option requires the use of terrain elevation data, stack-tip
downwash, sequential date checking, and does not permit the use of
the model in the SCREEN mode. In the regulatory default mode,
pollutant half life or decay options are not employed, except in the
case of an urban source of sulfur dioxide where a four-hour half
life is applied. Terrain elevation data from the U.S. Geological
Survey 7.5-Minute Digital Elevation Model (edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html) or equivalent (approx. 30-meter
resolution) should be used in all applications. In some cases,
exceptions of the terrain data requirement may be made in
consultation with the permit/SIP reviewing authority.
b. Input Requirements
(1) Source data: Required input includes source type, location,
emission rate, stack height, stack inside diameter, stack gas exit
velocity, stack gas temperature, area and volume source dimensions,
and source elevation. Building dimensions and variable emission
rates are optional.
(2) Meteorological data: The AERMET meteorological preprocessor
requires input of surface characteristics, including surface
roughness (zo), Bowen ratio, and albedo by sector and
season or month, as well as, hourly observations of wind speed
between 7zo and 100m (reference wind speed measurement
from which a vertical profile can be developed), wind direction,
cloud cover, and temperature between zo and 100m
(reference temperature measurement from which a vertical profile can
be developed). A morning sounding (in National Weather Service
format) from a representative upper air station, latitude,
longitude, time zone, and wind speed threshold are also required in
AERMET. Additionally, measured profiles of wind, temperature,
vertical and lateral turbulence may be required in certain
applications (e.g., in complex terrain) to adequately represent the
meteorology affecting plume transport and dispersion. Optionally,
measurements of solar, or net radiation may be input to AERMET. Two
files are produced by the AERMET meteorological preprocessor for
input to the AERMOD dispersion model. The surface file contains
observed and calculated surface variables, one record per hour. The
profile file contains the observations made at each level of a
meteorological tower (or remote sensor), or the one-level
observations taken from other representative data (e.g., National
Weather Service surface observations), one record per level per
hour.
(i) Data used as input to AERMET should possess an adequate
degree of representativeness to insure that the wind, temperature
and turbulence profiles derived by AERMOD are both laterally and
vertically representative of the source area. The adequacy of input
data should be judged independently for each variable. The values
for surface roughness, Bowen ratio, and albedo should reflect the
surface characteristics in the vicinity of the meteorological tower,
and should be adequately representative of the modeling domain.
Finally, the primary atmospheric input variables including wind
speed and direction, ambient temperature, cloud cover, and a morning
upper air sounding should also be adequately representative of the
source area.
(ii) For recommendations regarding the length of meteorological
record needed to perform a regulatory analysis with AERMOD, see
Section 8.3.1.
(3) Receptor data: Receptor coordinates, elevations, height
above ground, and height scales are produced by the AERMAP terrain
preprocessor for input to AERMOD. Discrete receptors and/or multiple
receptor grids, Cartesian and/or polar, may be employed in AERMOD.
AERMAP requires input of Digital Elevation Model (DEM) terrain data
produced by the U.S. Geological Survey (USGS), or other equivalent
data. AERMAP can be used optionally to estimate source elevations.
c. Output
Printed output options include input information, high
concentration summary tables by receptor for user-specified
averaging periods, maximum concentration summary tables, and
concurrent values summarized by receptor for each day processed.
Optional output files can be generated for: A listing of occurrences
of exceedances of user-specified threshold value; a listing of
concurrent (raw) results at each receptor for each hour modeled,
suitable for post-processing; a listing of design values that can be
imported into graphics software for plotting contours; an
unformatted listing of raw results above a threshold value with a
special structure for use with the TOXX model component of TOXST; a
listing of concentrations by rank (e.g., for use in quantile-
quantile plots); and, a listing of concentrations, including arc-
maximum normalized concentrations, suitable for model evaluation
studies.
d. Type of Model
AERMOD is a steady-state plume model, using Gaussian
distributions in the vertical and horizontal for stable conditions,
and in the horizontal for convective conditions. The vertical
concentration distribution for convective conditions results from an
assumed bi-Gaussian probability density function of the vertical
velocity.
e. Pollutant Types
AERMOD is applicable to primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Chemical
transformation is treated by simple exponential decay. Settling and
deposition are not yet simulated by AERMOD.
f. Source-Receptor Relationships
AERMOD applies user-specified locations for sources and
receptors. Actual separation between each source-receptor pair is
used. Source and receptor elevations are user input or are
determined by AERMAP using USGS DEM terrain data. Receptors may be
located at user-specified heights above ground level.
g. Plume Behavior
(1) In the convective boundary layer (CBL), the transport and
dispersion of a plume is characterized as the superposition of three
modeled plumes: The direct plume (from the stack), the indirect
plume, and the penetrated plume, where the indirect plume accounts
for the lofting of a buoyant plume near the top of the boundary
layer, and the penetrated plume accounts for the portion of a plume
that, due to its buoyancy, penetrates above the mixed layer, but can
disperse downward and re-enter the mixed layer. In the CBL, plume
rise is superposed on the displacements by random convective
velocities (Weil et al., 1997).
(2) In the stable boundary layer, plume rise is estimated using
an iterative approach, similar to that in the CTDMPLUS model (Perry,
1992; Section 11.0, ref. 33).
(3) Stack-tip downwash and buoyancy induced dispersion effects
are modeled. Building wake effects are simulated for stacks less
than good engineering practice height using the methods contained in
ISCST (Section 11.0, ref. 60). 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 and Hanna, 1986) is used, but stack-tip downwash and
buoyancy-induced dispersion are not used.
(4) For elevated terrain, AERMOD incorporates the concept of the
critical dividing streamline height, in which flow below this height
remains horizontal, and flow above this height tends to rise up and
over terrain (Snyder et al., 1985). Plume
[[Page 21538]]
concentration estimates are the weighted sum of these two limiting
plume states. However, consistent with the steady-state assumption
of uniform horizontal wind direction over the modeling domain,
straight-line plume trajectories are assumed, with adjustment in the
plume/receptor geometry used to account for the terrain effects.
h. Horizontal Winds
Vertical profiles of wind are calculated for each hour based on
measurements and surface-layer similarity (scaling) relationships.
At a given height above ground, for a given hour, winds are assumed
constant over the modeling domain. The effect of the vertical
variation in horizontal wind speed on dispersion is accounted for
through simple averaging over the plume depth.
i. Vertical Wind Speed
In convective conditions, the effects of random vertical updraft
and downdraft velocities are simulated with a bi-Gaussian
probability density function. In both convective and stable
conditions, the mean vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Gaussian horizontal dispersion coefficients are estimated as
continuous functions of the parameterized (or measured) ambient
lateral turbulence and also account for buoyancy-induced and
building wake-induced turbulence. Vertical profiles of lateral
turbulence are developed from measurements and similarity (scaling)
relationships. Effective turbulence values are determined from the
portion of the vertical profile of lateral turbulence between the
plume height and the receptor height. The effective lateral
turbulence is then used to estimate horizontal dispersion.
k. Vertical Dispersion
In the stable boundary layer, Gaussian vertical dispersion
coefficients are estimated as continuous functions of parameterized
vertical turbulence. In the convective boundary layer, vertical
dispersion is characterized by a bi-Gaussian probability density
function, and is also estimated as a continuous function of
parameterized vertical turbulence. Vertical turbulence profiles are
developed from measurements and similarity (scaling) relationships.
These turbulence profiles account for both convective and mechanical
turbulence. Effective turbulence values are determined from the
portion of the vertical profile of vertical turbulence between the
plume height and the receptor height. The effective vertical
turbulence is then used to estimate vertical dispersion.
l. Chemical Transformation
Chemical transformations are generally not treated by AERMOD.
However, AERMOD does contain an option to treat chemical
transformation using simple exponential decay, although this option
is typically not used in regulatory applications, except for sources
of sulfur dioxide in urban areas. Either a decay coefficient or a
half life is input by the user.
m. Physical Removal
Neither wet or dry deposition of particulate or gaseous
pollutants is currently simulated by AERMOD.
n. Evaluation Studies
API, 1998: Evaluation of State of the Science of Air Quality
Dispersion Model, Scientific Evaluation, prepared by Woodward-Clyde
Consultants, Lexington, Massachusetts, for American Petroleum
Institute, Washington, D.C., 20005-4070.
Paine, R.J., R.F. Lee, R.W. Brode, R.B. Wilson, A.J Cimorelli,
S.G. Perry, J.C. Weil, A. Venkatram and W.D. Peters, 1998: Model
Evaluation Results for AERMOD (12/17/98 Draft). Prepared for
Environmental Protection Agency, Research Triangle Park, NC. (Docket
No. A-99-05, II-A-5)
A.2 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 90-500281) 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.
[[Page 21539]]
h. Horizontal Winds
(1) Constant, uniform (steady-state) wind is assumed for an
hour.
(2) Straight line plume transport is assumed to all downwind
distances.
(3) 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.
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.3 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, D.C. (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 website (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.4 CALPUFF
References
Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 1998. 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, 1998. 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 for download
from the model developers' Internet website: 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: [email protected].
Abstract
CALPUFF is a multi-layer, multi-species non-steady-state puff
dispersion modeling 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 50km to 200km) of emissions from point,
volume, area, and line sources. The meteorological input data should
be fully characterized with
[[Page 21540]]
time-and-space-varying three dimensional wind and meteorological
conditions using CALMET, as discussed in paragraphs 8.3(d) and
8.3.1.2(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 7.2.9(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 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 (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
(y, 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.
4. Line sources: source location, release height, base
elevation, average buoyancy parameter, 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 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.
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.
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.
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. A model for aqueous phase chemical transformation of
SO2 to SO4\=\ is included. 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 (MM4, MM5; Section 11.0, ref.
100).
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 or ISC-PRIME data formats.
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.
[[Page 21541]]
j. Horizontal Dispersion
Turbulence-based dispersion coefficients provide estimates of
horizontal plume dispersion based on measured or computed values of
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
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
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=. Aqueous phase oxidation of SO2 to
SO4= by precipitating and non-precipitating clouds is
included. 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. U.S. Environmental Protection
Agency, 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.5 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 website 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 y & 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,
[[Page 21542]]
positions, hill number) at the beginning of the file.
(5) Hourly information provided to these files besides the
concentrations themselves includes the year, month, day, and hour
information as well as the receptor number with the highest
concentration.
d. Type of Model
CTDMPLUS is a refined steady-state, point source plume model for
use in all stability conditions for complex terrain applications.
e. Pollutant Types
CTDMPLUS may be used to model non-reactive, primary pollutants.
f. Source-Receptor Relationship
Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15 deg., so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.
g. Plume Behavior
(1) As in CTDM, the basic plume rise algorithms are based on
Briggs' (1975) recommendations.
(2) A central feature of CTDMPLUS for neutral/stable conditions
is its use of a critical dividing-streamline height (Hc)
to separate the flow in the vicinity of a hill into two separate
layers. The plume component in the upper layer has sufficient
kinetic energy to pass over the top of the hill while streamlines in
the lower portion are constrained to flow in a horizontal plane
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these
flows.
(3) The model calculates on an hourly (or appropriate steady
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly
profiles of wind and temperature measurements are used by CTDMPLUS
to compute plume rise, plume penetration (a formulation is included
to handle penetration into elevated stable layers, based on Briggs
(1984)), convective scaling parameters, the value of Hc,
and the Froude number above Hc.
h. Horizontal Winds
CTDMPLUS does not simulate calm meteorological conditions. Both
scalar and vector wind speed observations can be read by the model.
If vector wind speed is unavailable, it is calculated from the
scalar wind speed. The assignment of wind speed (either vector or
scalar) at plume height is done by either:
Interpolating between observations above and below the
plume height, or
Extrapolating (within the surface layer) from the
nearest measurement height to the plume height.
i. Vertical Wind Speed
Vertical flow is treated for the plume component above the
critical dividing streamline height (Hc); see ``Plume
Behavior''.
j. Horizontal Dispersion
Horizontal dispersion for stable/neutral conditions is related
to the turbulence velocity scale for lateral fluctuations,
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., 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, D.C. (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, D.C. 20591. See Availability below.
(Note: this manual includes supplements that are available on the
EDMS Internet website: 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 $200 from: Federal Aviation
Administration, Attn: Ms. Julie Ann Draper, AEE, 800 Independence
Avenue, S.W., Washington, D.C. 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.
[[Page 21543]]
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.
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
(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.
l. Chemical Transformation
Chemical transformations are not accounted for.
m. Physical Removal
Deposition is not treated.
n. Evaluation Studies
None cited.
A.7 Industrial Source Complex Model With Prime Downwash Algorithm
(ISC-PRIME)
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)
Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 1997. Addendum
to ISC3 User's Guide, The PRIME Plume Rise and Building Downwash
Model. Prepared for the Electric Power Research Institute, Palo
Alto, CA., Earth Tech Document A287. A-99-05, II-A-12)
Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 1998.
Development and Evaluation of the PRIME Plume Rise and Building
Downwash Model. (submitted to Journal of the Air & Waste Management
Association) 34pp. + 10 figures (A-99-05, II-A-13)
Availability
The model code and its documentation are available for download
from EPA's SCRAM Internet website (Section A.0).
Abstract
The ISC-PRIME 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. The model
is based on ISC3, with the PRIME (Plume RIse Model Enhancements)
algorithm added for improved treatment of building downwash. This
model can account for the following: settling and dry deposition of
particles; building downwash; area, line, and volume sources; plume
rise as a function of downwind distance, building dimensions and
stack placement with respect to a building; separation of point
sources; and limited terrain adjustment.
a. Recommendations for Regulatory Use
(1) ISC-PRIME is appropriate for the following applications:
Industrial source complexes where aerodynamic downwash
or deposition is important;
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.
(2) 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, and the appropriate value for pollutant half-life; 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
(1) 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.
(2) Meteorological data: ISC-PRIME requires hourly surface
weather data from the preprocessor program PCRAMMET, which provides
hourly stability class, wind direction, wind speed, temperature, and
mixing height.
(3) 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
ISC-PRIME is a Gaussian plume model. It has been revised to
perform a double integration of the Gaussian plume kernel for area
sources. The PRIME algorithm modifies plume rise and dispersion
during downwash conditions.
e. Pollutant Types
ISC-PRIME 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
(1) ISC-PRIME applies user-specified locations for point, line,
area and volume
[[Page 21544]]
sources, and user-specified receptor locations or receptor rings.
(2) User input topographic evaluation for each receptor is used.
Elevations above stack top are reduced to the stack top elevation,
i.e., ``terrain chopping''.
(3) User input height above ground level may be used when
necessary to simulate impact at elevated or ``flag pole'' receptors,
e.g., on buildings.
(4) Actual separation between each source-receptor pair is used.
g. Plume Behavior
(1) ISC-PRIME uses Briggs (1969, 1971, 1975) plume rise
equations for final rise.
(2) Stack tip downwash equation from Briggs (1974) is used.
(3) For plume rise affected by the presence of a building, the
PRIME downwash algorithm is used. Plume rise is computed using a
numerical solution of the mass, energy and momentum conservation
laws (Zhang and Ghoniem, 1993). Streamline deflection and the
position of the stack relative to the building affect plume
trajectory and dispersion. Enhanced dispersion is based on the
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is re-emitted to
the far wake as a volume source. For GEP height stacks, buildings
downwash is not used.
(4) For rolling terrain (terrain not above stack height), plume
centerline is horizontal at height of final rise above source.
(5) Fumigation is not treated.
h. Horizontal Winds
(1) For each source, a constant, uniform (steady-state) stack-
top wind is assumed for each hour except for PRIME downwash
calculations, which use a power-law speed profile with height and
account for velocity deficits in building wakes.
(2) Straight line plume transport is assumed to all downwind
distances.
(3) Separate wind speed profile exponents (Irwin, 1979; EPA,
1980) for both rural and urban cases are used.
(4) 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
(1) Rural dispersion coefficients from Turner (1969) are used,
with no adjustments for surface roughness or averaging time.
(2) Urban dispersion coefficients from Briggs (Gifford, 1976)
are used.
(3) Buoyancy induced dispersion (Pasquill, 1976) is included.
(4) Six stability classes are used.
(5) Dispersion is enhanced by the presence of a building.
k. Vertical Dispersion
(1) Rural dispersion coefficients from Turner (1969) are used,
with no adjustments for surface roughness.
(2) Urban dispersion coefficients from Briggs (Gifford, 1976)
are used.
(3) Buoyancy induced dispersion (Pasquill, 1976) is included.
(4) Six stability classes are used.
(5) 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.
(6) Perfect reflection is assumed at the ground.
(7) Dispersion is enhanced by the presence of a building.
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. U.S. Environmental Protection
Agency, 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.
U.S. Environmental Protection Agency, 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. U.S.
Environmental Protection Agency, 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. U.S. Environmental Protection Agency, 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. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS No. PB 94-183100)
Paine, R.J. and F. Lew, 1997. Results of the Independent
Evaluation of ISCST3 and ISC-PRIME. Prepared for the Electric Power
Research Institute, Palo Alto, CA. ENSR Document Number 2460-026-
440. (NTIS No. PB 98-156524)
Paine, R.J. and F. Lew, 1997. Consequence Analysis for ISC-
PRIME. Prepared for the Electric Power Research Institute, Palo
Alto, CA. ENSR Document Number 2460-026-450. (NTIS No. PB 98-156516)
Schulman, L.L., D.G. Strimaitis, and J.S. Scire, 1998.
Development and Evaluation of the PRIME Plume Rise and Building
Downwash Model. {submitted to Journal of the Air & Waste Management
Association} 34pp. + figures (A-99-05, II-A-13)
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.
Scire, J.S., L.L. Schulman and D.G. Strimaitis, 1995.
Observations of Plume Descent Downwind of Buildings. 88th Annual
Meeting of the Air & Waste Management Association, Paper 95-
WP75B.01, AWMA, Pittsburgh, PA.
A.8 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 Support Center for
Regulatory Air Models Bulletin Board System and also on diskette (as
PB 91-505230) from the National Technical Information Service (see
Section A.0).
Technical Contact
Minerals Management Service, Attn: Mr. Dirk Herkhof, Parkway
Atrium Building, 381 Elden Street, Herndon, VA 22070-4817, 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 (50 FR
12248; 28 March 1985). OCD is applicable for overwater sources where
onshore receptors are below the lowest source height. Where onshore
receptors are above the lowest source height, offshore plume
transport and dispersion may be modeled on a case-by-case basis in
consultation with the EPA Regional Office.
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,
[[Page 21545]]
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.
(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, D.C.
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, D.C.
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. J. 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.
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,
[[Page 21546]]
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.
U.S. Environmental Protection Agency, 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. U.S.
Environmental Protection Agency, 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
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