[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

[[Page 21510]]

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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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

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2311.

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