[Federal Register Volume 68, Number 72 (Tuesday, April 15, 2003)]
[Rules and Regulations]
[Pages 18440-18482]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 03-8542]



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Part III





Environmental Protection Agency





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40 CFR Part 51



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

  Federal Register / Vol. 68 , No. 72 / Tuesday, April 15, 2003 / Rules 
and Regulations  

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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 51

[AH-FRL-7478-3]
RIN 2060-AF01


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

AGENCY: Environmental Protection Agency (EPA).

ACTION: Final rule.

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

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

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

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

SUPPLEMENTARY INFORMATION:

I. General Information

A. How Can I Get Copies of Related Information?

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

II. Background

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

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

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

IV. Discussion of Public Comments and Issues

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

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

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

Other Modeling Systems

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

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

Meteorological Data Issues

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

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

CALPUFF

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

Other Modeling Systems

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

Meteorological Data Issues

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

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

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

[[Page 18445]]

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

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

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

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

    [sbull] 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.
    [sbull] 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).
    [sbull] In the subsection on lead, we deleted references to 40 CFR 
51.83, 51.84, and 51.85, conforming to previous EPA action (51 FR 
40661).
Section 7
    For regional scale modeling, we removed reference to the Regional 
Oxidant Model (ROM) and the Regional Acid Deposition Model (RADM) from 
section 7 because they are outdated and replaced by a reference to 
Models-3 \9\ in section 6. We enhanced the subsection on visibility to 
reflect the provisions of the Clean Air Act, including those for 
reasonable attribution of visibility impairment and regional haze, as 
well as the new NAAQS for PM-2.5. For assessment of reasonably 
attributable haze impairment due to one or a small group of sources, 
CALPUFF is available for use on a case-by-case basis. We identify 
REMSAD and new approaches under the Models-3/CMAQ umbrella for possible 
use to develop and evaluate national policy and assist State and local 
control agencies. For long range transport analyses, we recommend the 
CALPUFF modeling system. To facilitate use of a complex air quality and 
meteorological modeling system like CALPUFF, we stipulate that a 
written protocol may be considered for developing consensus in the 
methods and procedures to be followed.
---------------------------------------------------------------------------

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

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

    \10\ See section 8.2.3 of the Guideline.

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

[[Page 18446]]

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

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

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

Appendix A

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

Appendix B

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

Appendix C

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

Statutory and Executive Order Reviews

A. Executive Order 12866: Regulatory Planning and Review

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

[[Page 18447]]

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

B. Paperwork Reduction Act

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

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

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

D. Unfunded Mandates Reform Act of 1995

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

E. Executive Order 13132: Federalism

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

[[Page 18448]]

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

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

    Executive Order 13175, entitled ``Consultation and Coordination 
with Indian Tribal Governments'' (65 FR 67249, November 9, 2000), 
requires EPA to develop an accountable process to ensure ``meaningful 
and timely input by tribal officials in the development of regulatory 
policies that have tribal implications.'' This final rule does not have 
tribal implications, as specified in Executive Order 13175. As stated 
above (see D. Unfunded Mandates Reform Act of 1995, above), the rule 
does not impose any new requirements for calculating PSD increment 
consumption, and does not impose any additional requirements for the 
regulated community, including Indian Tribal Governments. Thus, 
Executive Order 13175 does not apply to this rule.
    Today's final rule does not significantly or uniquely affect the 
communities of Indian tribal governments. Accordingly, the requirements 
of section 3(b) of Executive Order 13175 do not apply to this rule.

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

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

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

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

I. National Technology Transfer and Advancement Act of 1995

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

J. Congressional Review Act of 1998

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

List of Subjects in 40 CFR Part 51

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

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

0
Part 51, chapter I, title 40 of the Code of Federal Regulations is 
amended as follows:

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

0
1. The authority citation for part 51 continues to read as follows:

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


0
2. Appendix W to Part 51 revised to read as follows:

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

Preface

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

[[Page 18449]]

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

Table of Contents

List of Tables

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

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

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

1.0 Introduction

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

[[Page 18450]]

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

2.0 Overview of Model Use

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

2.1 Suitability of Models

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

2.2 Levels of Sophistication of Models

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

2.3 Availability of Models

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

[[Page 18451]]

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

3.0 Recommended Air Quality Models

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

3.1 Preferred Modeling Techniques

3.1.1 Discussion

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

3.1.2 Recommendations

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

3.2 Use of Alternative Models

3.2.1 Discussion

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

[[Page 18452]]

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

3.2.2 Recommendations

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

3.3 Availability of Supplementary Modeling Guidance

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

4.0 Simple-Terrain Stationary Source Models

4.1 Discussion

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

4.2 Recommendations

4.2.1 Screening Techniques

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

4.2.2 Refined Analytical Techniques

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

5.0 Model Use in Complex Terrain

5.1 Discussion

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

[[Page 18453]]

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

5.2 Recommendations

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

5.2.1 Screening Techniques

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

5.2.2 Refined Analytical Techniques

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

[[Page 18454]]

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

                         Table 5-1a.--Neutral/Stable Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------
 
----------------------------------------------------------------------------------------------------------------
                       Variable                                             Specific values
------------------------------------------------------
U (m/s)..............................................        1.0         2.0        3.0          4.0         5.0
[sigma]v (m/s).......................................        0.3         0.75  ..........  ..........  .........
[sigma]w (m/s).......................................        0.08        0.15       0.30         0.75  .........
[Delta][thetas]/[Delta]z (K/m).......................        0.01        0.02       0.035  ..........  .........
WD...................................................        (Wind direction optimized internally for each
                                                                      meteorological combination)
----------------------------------------------------------------------------------------------------------------
Exceptions:
(1) If U <= 2 m/s and [sigma]v <= 0.3 m/s, then include [sigma]w = 0.04 m/s.
(2) If [sigma]w = 0.75 m/s and U >= 3.0 m/s, then [Delta][thetas]/[Delta]z is limited to <= 0.01 K/m.
(3) If U = 4 m/s, then [sigma]w = 0.15 m/s.
(4) [sigma]w <= [sigma]v


                       Table 5-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------
 
----------------------------------------------------------------------------------------------------------------
                       Variable                                              Specific values
-------------------------------------------------------
U (m/s)...............................................        1.0         2.0         3.0         4.0        5.0
u* (m/s)..............................................        0.1         0.3         0.5   .........  .........
L (m).................................................      -10         -50         -90     .........  .........
[Delta][sigma]/[Delta]z (K/m).........................       0.030 (potential temperature gradient above zi)
zi (m)................................................        0.5h        1.0h        1.5h  .........  .........
                                                            (where h = terrain height)
----------------------------------------------------------------------------------------------------------------


[[Page 18455]]

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

6.1 Discussion

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

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

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

6.2 Recommendations

6.2.1 Models for Ozone

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

[[Page 18456]]

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

6.2.2 Models for Particulate Matter

6.2.2.1 PM-2.5

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

6.2.2.2 PM-10

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

6.2.3 Models for Carbon Monoxide

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

6.2.4 Models for Nitrogen Dioxide (Annual Average)

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

[[Page 18457]]

[GRAPHIC] [TIFF OMITTED] TR15AP03.072

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

[[Page 18458]]

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

6.2.5 Models for Lead

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

7.0 Other Model Requirements

7.1 Discussion

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

7.2 Recommendations

7.2.1 Visibility

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

7.2.2 Good Engineering Practice Stack Height

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

[[Page 18459]]

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

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

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

7.2.4 Modeling Guidance for Other Governmental Programs

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

8.0 General Modeling Considerations

8.1 Discussion

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

8.2 Recommendations

8.2.1 Design Concentrations (see also subsection 11.2.3.1)

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

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

8.2.1.2 Design Concentrations for O3 and PM-2.5

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

[[Page 18460]]

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

8.2.2 Critical Receptor Sites

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

8.2.3 Dispersion Coefficients

    a. Steady-state Gaussian plume models used in most applications 
should employ dispersion coefficients consistent with those 
contained in the preferred models in Appendix A. Factors such as 
averaging time, urban/rural surroundings (see paragraphs (b)-(f) of 
this subsection), and type of source (point vs. line) may dictate 
the selection of specific coefficients. Coefficients used in some 
Appendix A models are identical to, or at least based on, Pasquill-
Gifford coefficients \77\ in rural areas and McElroy-Pooler \78\ 
coefficients in urban areas.\79\
    b. The selection of either rural or urban dispersion 
coefficients in a specific application should follow one of the 
procedures suggested by Irwin \80\ and briefly described in 
paragraphs (c)-(f) of this subsection. These include a land use 
classification procedure or a population based procedure to 
determine whether the character of an area is primarily urban or 
rural.
    c. Land Use Procedure: (1) Classify the land use within the 
total area, Ao, circumscribed by a 3km radius circle 
about the source using the meteorological land use typing scheme 
proposed by Auer \81\; (2) if land use types I1, I2, C1, R2, and R3 
account for 50 percent or more of 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 
\82\, is included in the preferred models and should be used where 
buoyant sources, e.g., those involving fuel combustion, are 
involved.

8.2.4 Stability Categories

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

8.2.5 Plume Rise

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

8.2.6 Chemical Transformation

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

8.2.7 Gravitational Settling and Deposition

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

8.2.8 Complex Winds

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

[[Page 18461]]

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

8.2.9 Calibration of Models

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

9.0 Model Input Data

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

9.1 Source Data

9.1.1 Discussion

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

9.1.2 Recommendations

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

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

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

[[Page 18462]]

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

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


[[Page 18463]]


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

9.2 Background Concentrations

9.2.1 Discussion

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

9.2.2 Recommendations (Isolated Single Source)

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

[[Page 18464]]

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

9.2.3 Recommendations (Multi-Source Areas)

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

9.3 Meteorological Input Data

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

9.3.1 Length of Record of Meteorological Data

9.3.1.1 Discussion

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

9.3.1.2 Recommendations

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

[[Page 18465]]

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

9.3.2 National Weather Service Data

9.3.2.1 Discussion

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

9.3.2.2 Recommendations

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

9.3.3 Site Specific Data

9.3.3.1 Discussion

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

9.3.3.2 Recommendations

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

[[Page 18466]]

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

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

  [GRAPHIC] [TIFF OMITTED] TR15AP03.073
  
9.3.4 Treatment of Near-calms and Calms

9.3.4.1 Discussion

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

9.3.4.2 Recommendations

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

10.0 Accuracy and Uncertainty of Models

10.1 Discussion

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

10.1.1 Overview of Model Uncertainty

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

[[Page 18467]]

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

10.1.2 Studies of Model Accuracy

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

10.1.3 Use of Uncertainty in Decision-Making

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

10.1.4 Evaluation of Models

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

[[Page 18468]]

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

10.2 Recommendations

    a. No specific guidance on the quantification of model 
uncertainty for use in decision-making is being given at this time. 
As procedures for considering uncertainty develop and become 
implementable, this guidance will be changed and expanded. For the 
present, continued use of the ``best estimate'' is acceptable; 
however, in specific circumstances for O3, PM-2.5 and 
regional haze, additional information and/or procedures may be 
appropriate.\41,42\

11.0 Regulatory Application of Models

11.1 Discussion

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

11.2 Recommendations

11.2.1 Analysis Requirements

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

11.2.2 Use of Measured Data in Lieu of Model Estimates

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

[[Page 18469]]

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

11.2.3 Emission Limits

11.2.3.1 Design Concentrations

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

11.2.3.2 NAAQS Analyses for New or Modified Sources

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

11.2.3.3 PSD Air Quality Increments and Impacts

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

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

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

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

[[Page 18470]]

    Fox, D.G., and J.E. Fairobent, 1981. NCAQ Panel Examines Uses 
and Limitations of Air Quality Models. Bulletin of the American 
Meteorological Society, 62(2): 218-221.
    Gifford, F.A., 1976. Turbulent Diffusion Typing Schemes: A 
Review. Nuclear Safety, 17(1): 68-86.
    Gudiksen, P.H., and M.H. Dickerson, Eds., Executive Summary: 
Atmospheric Studies in Complex Terrain Technical Progress Report FY-
1979 Through FY-1983. Lawrence Livermore National Laboratory, 
Livermore, CA. (Docket Reference No. II-I-103).
    Hanna, S.R., G.A. Briggs, J. Deardorff, B.A. Egan, G.A. Gifford 
and F. Pasquill, 1977. AMS Workshop on Stability Classification 
Schemes And Sigma Curves--Summary of Recommendations. Bulletin of 
the American Meteorological Society, 58(12): 1305-1309.
    Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Handbook on 
Atmospheric Diffusion. Technical Information Center, U.S. Department 
of Energy, Washington, DC.
    Haugen, D.A., Workshop Coordinator, 1975. Lectures on Air 
Pollution and Environmental Impact Analyses. Sponsored by the 
American Meteorological Society, Boston, MA.
    Hoffnagle, G.F., M.E. Smith, T.V. Crawford and T.J. Lockhart, 
1981. On-site Meteorological Instrumentation Requirements to 
Characterize Diffusion from Point Sources--A Workshop, 15-17 January 
1980, Raleigh, NC. Bulletin of the American Meteorological Society, 
62(2): 255-261.
    Hunt, J.C.R., R.G. Holroyd, D.J. Carruthers, A.G. Robins, D.D. 
Apsley, F.B. Smith and D.J. Thompson, 1990. Developments in Modeling 
Air Pollution for Regulatory Uses. In Proceedings of the 18th NATO/
CCMS International Technical Meeting on Air Pollution Modeling and 
its Application, Vancouver, Canada. Also In Air Pollution Modeling 
and its Application VIII (1991). H. van Dop and D.G. Steyn, eds. 
Plenum Press, New York, NY. pp. 17-59
    Pasquill, F. and F.B. Smith, 1983. Atmospheric Diffusion, 3rd 
Edition. Ellis Horwood Limited, Chichester, West Sussex, England, 
438pp.
    Randerson, D., Ed., 1984. Atmospheric Science and Power 
Production. DOE/TIC 2760l. Office of Scientific and Technical 
Information, U.S. Department of Energy, Oak Ridge, TN.
    Scire, J.S. and L.L. Schulman, 1980: Modeling plume rise from 
low-level buoyant line and point sources. AMS/APCA Second Joint 
Conference on Applications of Air Pollution Meteorology, March 24-
27, New Orleans, LA.
    Smith, M.E., Ed., 1973. Recommended Guide for the Prediction of 
the Dispersion of Airborne Effluents. The American Society of 
Mechanical Engineers, New York, NY.
    Stern, A.C., Ed., 1976. Air Pollution, Third Edition, Volume I: 
Air Pollutants, Their Transformation and Transport. Academic Press, 
New York, NY.
    Turner, D.B., 1979. Atmospheric Dispersion Modeling: A Critical 
Review. Journal of the Air Pollution Control Association, 29(5): 
502-519.
    Venkatram, A. and J.C. Wyngaard, Editors, 1988. Lectures on Air 
Pollution Modeling. American Meteorological Society, Boston, MA. 
390pp.

13.0 References

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    3. Code of Federal Regulations; Title 40 (Protection of 
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Planning & Standards, Research Triangle Park, NC. (Docket No. A-88-
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[[Page 18471]]

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[[Page 18472]]

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Monitoring Guidance for Regulatory Modeling Applications. EPA 
Publication No. EPA-454/R-99-005. Office of Air Quality Planning & 
Standards, Research Triangle Park, NC. (PB 2001-103606) (http://www.epa.gov/scram001/)
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and Temperature by Acoustic Means. (1994)
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Wind Using Wind Vane and Rotating Anemometer. (1996)
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for Air Pollution Measurement Systems, Volume IV--Meteorological 
Measurements. EPA Publication No. EPA600/R-94/038d. Office of Air 
Quality Planning & Standards, Research Triangle Park, NC. Note: for 
copies of this handbook, you may make inquiry to ORD Publications, 
26 West Martin Luther King Dr., Cincinatti, OH 45268. Phone (513) 
569-7562 or (800) 490-9198 (automated request line)
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Class Determination: A Comparison for One Site. Proceedings, Sixth 
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Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7)
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Solar Radiation/Delta-T (SRDT) Method for Estimating Pasquill-
Gifford (P-G) Stability Categories. EPA Publication No. EPA-454/R-
93-055. Office of Air Quality Planning & Standards, Research 
Triangle Park, NC. (NTIS No. PB 94-113958)
    106. Irwin, J.S., 1980. Dispersion Estimate Suggestion 
8: Estimation of Pasquill Stability Categories. Office of 
Air Quality Planning & Standards, Research Triangle Park, NC (Docket 
No. A-80-46, II-B-10)
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Stability Class from Horizontal Wind Fluctuation. Presented at 72nd 
Annual Meeting of Air Pollution Control Association, Cincinnati, OH; 
June 24-29, 1979. (Docket No. A-80-46, II-P-9)
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Method for Determining Wind Frequency Distributions for the Lowest 
200m from Routine Meteorological Data. J. of Applied Meteorology, 
17(7): 942-954.
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Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena, 
CA. (Docket No. A-80-46, II-P-8)
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Guide. EPA Publication No. EPA-454/R-96-001. Office of Air Quality 
Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 97-
147912)
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Processor for Regulatory Models (MPRM) User's Guide. EPA Publication 
No. EPA-454/B-96-002. Office of Air Quality Planning & Standards, 
Research Triangle Park, NC. (NTIS No. PB 96-180518)
    112. Paine, R.J., 1987. User's Guide to the CTDM Meteorological 
Preprocessor Program. EPA Publication No. EPA-600/8-88-004. Office 
of Research & Development, Research Triangle Park, NC. (NTIS No. PB 
88-162102)
    113. Scire, J.S., F.R. Francoise, M.E. Fernau and R.J. 
Yamartino, 1998. A User's Guide for the CALMET Meteorological Model 
(Version 5.0). Earth Tech, Inc., Concord, MA. (http://www.src.com/calpuff/calpuff1.htm)
    114. Environmental Protection Agency, 1984. Calms Processor 
(CALMPRO) User's Guide. EPA Publication No. EPA-901/9-84-001. Office 
of Air Quality Planning & Standards, Region I, Boston, MA. (NTIS No. 
PB 84-229467)
    115. Fox, D.G., 1984. Uncertainty in air quality modeling. 
Bulletin of the American Meteorological Society, 65(1): 27-36.
    116. Burton, C.S., 1981. The Role of Atmospheric Models in 
Regulatory Decision-Making: Summary Report. Systems Applications, 
Inc., San Rafael, CA. Prepared under contract No. 68-01-5845 for 
U.S. Environmental Protection Agency, Research Triangle Park, NC. 
(Docket No. A-80-46, II-M-6)
    117. Environmental Protection Agency, 1981. Proceedings of the 
Second Conference on Air Quality Modeling, Washington, DC. Office of 
Air Quality Planning & Standards, Research Triangle Park, NC. 
(Docket No. A-80-46, II-M-16)
    118. Hanna, S.R., 1989. Confidence limits for air quality model 
evaluations, as estimated by bootstrap and jackknife resampling 
methods. Atmospheric Environment, 23(6): 1385-1398.
    119. Cox, W.M. and J.A. Tikvart, 1990. A statistical procedure 
for determining the best performing air quality simulation model. 
Atmos. Environ., 24A(9): 2387-2395.
    120. Oreskes, N.K., K. Shrader-Frechette and K. Beliz, 1994. 
Verification, validation and confirmation of numerical models in the 
earth sciences. Science, 263: 641-646.
    121. Dekker, C.M., A. Groenendijk, C.J. Sliggers and G.K. 
Verboom, 1990. Quality Criteria for Models to Calculate Air 
Pollution. Lucht (Air) 90, Ministry of Housing, Physical Planning 
and Environment, Postbus 450, 2260 MB Leidschendam, The Netherlands; 
52pp.
    122. Weil, J.C., R.I. Sykes and A. Venkatram, 1992. Evaluating 
air-quality models: review and outlook. Journal of Applied 
Meteorology, 31: 1121-1145.
    123. Cole, S.T. and P.J. Wicks, Editors (1995): Model Evaluation 
Group: Report of the Second Open Meeting. EUR 15990 EN, European 
Commission, Directorate-General XII, Environmental Research 
Programme, L-2920 Luxembourg; 77pp.
    124. Hanna, S.R., 1982. Natural Variability of Observed Hourly 
SO2 and CO Concentrations in St. Louis. Atmospheric 
Environment, 16(6): 1435-1440.
    125. Bowne, N.E., 1981. Validation and Performance Criteria for 
Air Quality Models. Appendix F in Air Quality Modeling and the Clean 
Air Act: Recommendations to EPA on Dispersion Modeling for 
Regulatory Applications. American Meteorological Society, Boston, 
MA; pp. 159-171. (Docket No. A-80-46, II-A-106)
    126. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, 
and Conclusions for the EPRI Plume Model Validation and Development 
Project: Plains Site. EPRI EA-3074. Electric Power Research 
Institute, Palo Alto, CA.
    127. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A 
Survey of Statistical Measures of Model Performance and Accuracy for 
Several Air Quality Models. EPA Publication No. EPA-450/4-83-001. 
Office of Air Quality Planning & Standards, Research Triangle Park, 
NC. (NTIS No. PB 83-260810)
    128. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff 
Report. Office of Air Quality Planning & Standards, Research 
Triangle Park, NC. (Docket No. A-80-46, II-G-6)
    129. Hanna, S.R., 1993. Uncertainties in air quality model 
predictions. Boundary-Layer Meteorology, 62: 3-20.
    130. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. 
John Wiley and Sons, New York, NY; 479pp.
    131. Morgan, M.G. and M. Henrion, 1990. Uncertainty, A Guide to 
Dealing With Uncertainty in Quantitative Risk and Policy Analysis. 
Cambridge University Press. New York, NY; 332pp.
    132. Irwin, J.S., K. Steinberg, C. Hakkarinen and H. Feldman, 
2001. Uncertainty in Air Quality Modeling for Risk Calculations. 
(CD-ROM) Proceedings of Guideline on Air Quality Models: A New 
Beginning. April 4-6, 2001, Newport, RI, Air & Waste Management 
Association. Pittsburgh, PA; 17pp.
    133. Austin, B.S., T.E. Stoeckenius, M.C. Dudik and T.S. 
Stocking, 1988. User's Guide to the Expected Exceedances System. 
Systems Applications, Inc., San Rafael, CA. Prepared under Contract 
No. 68-02-4352 Option I for the U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-I-3)
    134. Thrall, A.D., T.E. Stoeckenius and C.S. Burton, 1985. A 
Method for Calculating Dispersion Modeling Uncertainty Applied to 
the Regulation of an Emission Source. Systems Applications, Inc., 
San Rafael, CA. Prepared for the U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-80-46, IV-G-1)
    135. ``Ten years of Harmonisation activities: Past, present and 
future'' at http://www.dmu.dk/AtmosphericEnvironment/Harmoni/Conferences/Belgirate/BelgiratePapers.asp
    136. ``A platform for model evaluation'' at http://www.dmu.dk/AtmosphericEnvironment/Harmoni/Conferences/Belgirate/BelgiratePapers.asp

[[Page 18474]]

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

Table of Contents

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

A.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) Web site at http://www.epa.gov/scram001. Documentation is also available from the 
National Technical Information Service (NTIS), http://www.ntis.gov 
or U.S. Department of Commerce, Springfield, VA 22161; phone: (800) 
553-6847. Where possible, accession numbers are provided.

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

Reference

    Schulman, Lloyd L. and Joseph S. Scire, 1980. Buoyant Line and 
Point Source (BLP) Dispersion Model User's Guide. Document P-7304B. 
Environmental Research and Technology, Inc., Concord, MA. (NTIS No. 
PB 81-164642)

Availability

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

Abstract

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

a. Recommendations for Regulatory Use

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

b. Input Requirements

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

c. Output

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

d. Type of Model

    BLP is a gaussian plume model.

e. Pollutant Types

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

f. Source-Receptor Relationship

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

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

[[Page 18475]]

l. Chemical Transformation

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

m. Physical Removal

    Physical removal is not explicitly treated.

n. Evaluation Studies

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

A.2 CALINE3

Reference

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

Availability

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

Abstract

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

a. Recommendations for Regulatory Use

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

b. Input Requirements

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

c. Output

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

d. Type of Model

    CALINE-3 is a Gaussian plume model.

e. Pollutant Types

    CALINE-3 may be used to model primary pollutants.

f. Source-Receptor Relationship

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

g. Plume Behavior

    Plume rise is not treated.

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

    Not treated.

m. Physical Removal

    Optional deposition calculations are included.

n. Evaluation Studies

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

A.3 CALPUFF

References

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

Availability

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

Abstract

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

a. Recommendations for Regulatory Use

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

b. Input Requirements

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

[[Page 18476]]

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

c. Output

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

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationships

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

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

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

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

[[Page 18477]]

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

l. Chemical Transformation

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

m. Physical Removal

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

n. Evaluation Studies

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

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

Reference

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

Availability

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

Abstract

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

a. Recommendation for Regulatory Use

    CTDMPLUS is appropriate for the following applications:
    [sbull] Elevated point sources;
    [sbull] Terrain elevations above stack top;
    [sbull] Rural or urban areas;
    [sbull] Transport distances less than 50 kilometers; and
    [sbull] 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.,
    [sbull] Input meteorological data from ``SURFACE'' and 
``PROFILE''
    [sbull] Stack data for each source
    [sbull] Terrain information
    [sbull] Receptor information
    [sbull] Source-receptor location (line printer map).
    (2) In addition, if the case-study option is selected, the 
listing includes:
    [sbull] Meteorological variables at plume height
    [sbull] Geometrical relationships between the source and the 
hill
    [sbull] Plume characteristics at each receptor, i.e.,

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

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

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationship

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

[[Page 18478]]

any of the hills in the modeling domain, require separate treatment.

g. Plume Behavior

    (1) As in CTDM, the basic plume rise algorithms are based on 
Briggs' (1975) recommendations.
    (2) A central feature of CTDMPLUS for neutral/stable conditions 
is its use of a critical dividing-streamline height (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:
    [sbull] Interpolating between observations above and below the 
plume height, or
    [sbull] Extrapolating (within the surface layer) from the 
nearest measurement height to the plume height.

i. Vertical Wind Speed

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

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

    Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

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

n. Evaluation Studies

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

A.6 Emissions and Dispersion Modeling System (EDMS) 3.1

Reference

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

Availability

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

Abstract

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

a. Recommendations for Regulatory Use

    EDMS is appropriate for the following applications:
    [sbull] Cumulative effect of changes in aircraft operations, 
point source and mobile source emissions at airports or air bases;
    [sbull] Simple terrain;
    [sbull] Non-reactive pollutants;
    [sbull] Transport distances less than 50 kilometers; and
    [sbull] 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:
    [sbull] A summary emission inventory report with pollutant 
totals by source category and detailed emission inventory reports 
for each source category; and
    [sbull] A concentration summary report for up to 8760 hours (one 
year) of meteorological data that lists the number of sources, 
receptors, and the five highest concentrations for applicable 
averaging periods for the respective primary NAAQS.

d. Type of Model

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

[[Page 18479]]

e. Pollutant Types

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

f. Source-Receptor Relationship

    (1) Within hardware and memory constraints, there is no upper 
limit to the number of sources and receptors that can be modeled 
simultaneously.
    (2) The Gaussian point source equation estimates concentrations 
from point sources after determining the effective height of 
emission and the upwind and crosswind distance of the source from 
the receptor. Numerical integration of the Gaussian point source 
equation is used to determine concentrations from line sources 
(runways). Integration over area sources (parking lots), which 
includes edge effects from the source region, is done by considering 
finite line sources perpendicular to the wind at intervals upwind 
from the receptor. The crosswind integration is done analytically; 
integration upwind is done numerically by successive approximations. 
Terrain elevation differences between sources and receptors are 
neglected.
    (3) A reasonable height above ground level may be specified for 
each receptor.

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed to be zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

    (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.5 Industrial Source Complex Model (ISC3)

Reference

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

Availability

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

Abstract

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

a. Recommendations for Regulatory Use

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

b. Input Requirements

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

c. Output

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

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationships

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

g. Plume Behavior

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

[[Page 18480]]

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

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

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

m. Physical Removal

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

n. Evaluation Studies

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

A.7 Offshore and Coastal Dispersion Model (OCD)

Reference

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

Availability

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

Abstract

    (1) OCD is a straight-line Gaussian model developed to determine 
the impact of offshore emissions from point, area or line sources on 
the air quality of coastal regions. OCD incorporates overwater plume 
transport and dispersion as well as changes that occur as the plume 
crosses the shoreline. Hourly meteorological data are needed from 
both offshore and onshore locations. These include water surface 
temperature, overwater air temperature, mixing height, and relative 
humidity.
    (2) Some of the key features include platform building downwash, 
partial plume penetration into elevated inversions, direct use of 
turbulence intensities for plume dispersion, interaction with the 
overland internal boundary layer, and continuous shoreline 
fumigation.

a. Recommendations for Regulatory Use

    OCD has been recommended for use by the Minerals Management 
Service for emissions located on the Outer Continental Shelf. OCD is 
applicable for overwater sources where onshore receptors are below 
the lowest source height. Where onshore receptors are above the 
lowest source height, offshore plume transport and dispersion may be 
modeled on a case-by-case basis in consultation with the appropriate 
reviewing authority (paragraph 3.0(b)).

b. Input Requirements

    (1) Source data: Point, area or line source location, pollutant 
emission rate, building height, stack height, stack gas temperature, 
stack inside diameter, stack gas exit velocity, stack angle from 
vertical, elevation of stack base above water surface and gridded 
specification of the land/water surfaces. As an option, emission 
rate, stack gas exit velocity and temperature can be varied hourly.
    (2) Meteorological data (over water): Wind direction, wind 
speed, mixing height, relative humidity, air temperature, water 
surface temperature, vertical wind direction shear (optional), 
vertical temperature gradient (optional), turbulence intensities 
(optional).
    (3) Meteorological data (over land): Wind direction, wind speed, 
temperature, stability class, mixing height.
    (4) Receptor data: Location, height above local ground-level, 
ground-level elevation above the water surface.

c. Output

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

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationship

    (1) Up to 250 point sources, 5 area sources, or 1 line source 
and 180 receptors may be used.
    (2) Receptors and sources are allowed at any location.

[[Page 18481]]

    (3) The coastal configuration is determined by a grid of up to 
3600 rectangles. Each element of the grid is designated as either 
land or water to identify the coastline.

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

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

m. Physical Removal

    Physical removal is also treated using exponential decay.

n. Evaluation Studies

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

A.REF References

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

[[Page 18482]]

    Weil, J.C. and R.P. Brower, 1984. An Updated Gaussian Plume 
Model for Tall Stacks. Journal of the Air Pollution Control 
Association, 34: 818-827.
    Weil, J.C., 1996. A new dispersion algorithm for stack sources 
in building wakes, Paper 6.6. Ninth Joint Conference on Applications 
of Air Pollution Meteorology with A&WMA, January 28--February 2, 
1996. Atlanta, GA.
    Weil, J.C., L.A. Corio, and R.P. Brower, 1997. A PDF dispersion 
model for buoyant plumes in the convective boundary layer. Journal 
of Applied Meteorology, 36: 982-1003.
    Zhang, X., 1993. A computational analysis of the rise, 
dispersion, and deposition of buoyant plumes. Ph.D. Thesis, 
Massachusetts Institute of Technology, Cambridge, MA.
    Zhang, X. and A.F. Ghoniem, 1993. A computational model for the 
rise and dispersion of wind-blown, buoyancy-driven plumes--I. 
Neutrally stratified atmosphere. Atmospheric Environment, 15: 2295--
2311.

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