18440 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

1AMS/EPA Regulatory MODel.

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.

SUMMARY: EPA’s Guideline on Air 

Quality Models (‘‘Guideline’’) addresses 

the regulatory application of air quality 

models for assessing criteria pollutants 

under the Clean Air Act. In today’s 

action we promulgate several additions 

and changes to the Guideline. We adopt 

a new dispersion model, CALPUFF, in 

appendix A of the Guideline. CALPUFF 

becomes the preferred technique for 

assessing long range transport of 

pollutants and their impacts on Federal 

Class I areas. Action on AERMOD and 

the Emissions and Dispersion Modeling 

System (EDMS) is deferred. We make 

various editorial changes to update and 

reorganize information, and remove 

obsolete models.

DATES: This rule is effective May 15, 

2003. Beginning April 15, 2003 the new 

model (i.e., CALPUFF) should be used 

for its intended purposes, in accordance 

with today’s document. The period 

before required implementation of a 

new model allows user’s sufficient time 

to prepare meteorological data bases and 

to become familiar with model 

operation. The new model may be used 

sooner, if desired.

ADDRESSES: All documents relevant to 

this rule have been placed in Docket No. 

A–99–05 at the following address: EPA 

Docket Center, (EPA/DC) EPA West (MC 

6102T), 1301 Constitution Ave., NW., 

Washington, DC. The EPA Docket 

Center Public Reading Room (B102) is 

open from 8:30 a.m. to 4:30 p.m., 

Monday through Friday, excluding legal 

holidays. The telephone number for the 

Air Docket is (202) 566–1742.

FOR FURTHER INFORMATION CONTACT:

Joseph A. Tikvart, Leader, Air Quality 

Modeling Group (MD–14), Office of Air 

Quality Planning and Standards, U.S. 

Environmental Protection Agency, 

Research Triangle Park, NC 27711; 

telephone (919) 541–5562 

(Tikvart.Joe@epa.gov).

SUPPLEMENTARY INFORMATION: 

I. General Information 

A. How Can I Get Copies of Related 

Information? 

EPA established an official public 

docket for this action under Docket ID 

No. A–99–05. The official public docket 

is the collection of materials that is 

available for public viewing at the Air 

Docket in the EPA Docket Center, (EPA/

DC) EPA West (MC 6102T), 1301 

Constitution Ave., NW., Washington, 

DC. The EPA Docket Center Public 

Reading Room (B102) is open from 8:30 

a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The 

telephone number for the Reading Room 

is (202) 566–1744, and the telephone 

number for the Air Docket is (202) 566–

1742. 

Our Air Quality Modeling Group 

maintains an Internet Web site (Support 

Center for Regulatory Air Models—

SCRAM) at: http://www.epa.gov/

scram001. You may find codes and 

documentation for models referenced in 

today’s action on the SCRAM Web site. 

We have also uploaded various support 

documents (e.g., evaluation reports). 

II. Background 

The Guideline is used by EPA, States, 

and industry to prepare and review new 

source permits and State 

Implementation Plan revisions. The 

Guideline is intended to ensure 

consistent air quality analyses for 

activities regulated at 40 CFR 51.112, 

51.117, 51.150, 51.160, 51.166, and 

52.21. We originally published the 

Guideline in April 1978 and it was 

incorporated by reference in the 

regulations for the Prevention of 

Significant Deterioration (PSD) of Air 

Quality in June 1978. We revised the 

Guideline in 1986, and updated it with 

supplement A in 1987, supplement B in 

July 1993, and supplement C in August 

1995. We published the Guideline as 

appendix W to 40 CFR part 51 when we 

issued supplement B. We republished 

the Guideline in August 1996 (61 FR 

41838) to adopt the CFR system for 

labeling paragraphs. On April 21, 2000 

we published proposed revisions in the 

Federal Register (65 FR 21506), which 

is the basis for today’s promulgation. 

Today’s notice promulgates those 

components of the proposal that were 

clearly supported by public comments 

and that were otherwise not 

controversial, notably: 

• Adoption of CALPUFF in appendix 

A, as proposed, for assessing long range 

transport of pollutants and their impacts 

on Federal Class I areas; 

• Removal of the Climatological 

Dispersion Model (CDM), RAM and the 

Urban Airshed Model (UAM) from 

appendix A, as proposed; 

• Simplification of complex terrain 

screening techniques in section 5; 

• Revision of section 9 to reflect our 

October 1997 settlement with the Utility 

Air Regulatory Group regarding 

specification of emissions from 

background sources, as proposed; 

• Updating information in appendix 

W and reorganizing its structure; and 

• Transfer of appendix B and 

appendix C to our Web site, as 

proposed. 

The proposal also included (1) 

adopting AERMOD 1 to replace the 

Industrial Source Complex (ISC3) model 

in many assessments that now use it, (2) 

revising ISC3 by incorporating a new 

downwash algorithm (PRIME) and 

renaming the model ISC–PRIME, and (3) 

updating the Emissions Dispersion 

Modeling System (EDMS) by 

incorporating improved emissions and 

dispersion modules. Regarding 

AERMOD, nearly every commenter 

urged EPA to integrate aerodynamic 

downwash into AERMOD (i.e., not to 

require two models for some analyses). 

The only cautions were associated with 

the need for documentation, evaluation 

and review of the downwash 

enhancement to AERMOD. As a result 

of AERMIC’s (the American 

Meteorological Society (AMS)/ EPA 

Regulatory Model Improvement 

Committee) efforts to revise AERMOD, 

incorporating the PRIME algorithm and 

making a few other incidental 

modifications and to respond to the 

public’s cautions, we believe that 

AERMOD, as modified for downwash, 

merits another public examination of 

performance results. Also, since the 

April 2000 proposal, the Federal 

Aviation Administration decided to 

configure EDMS3.1 to incorporate the 

AERMOD dispersion model, and results 

of its performance with AERMOD only 

recently became available. 

Consequently, AERMOD and EDMS4.0, 

as well as other conforming changes for 

the Guideline, will be reconsidered in a 

Supplemental Notice of Proposed 

Rulemaking (SNPR) in the near future. 

Note that since AERMOD is not 

included in today’s promulgation, the 

proposed merger of the Guideline’s

sections 4 and 5 will be deferred to 

AERMOD’s adoption in the future.

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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18441

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.

3Summary 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.

4Environmental 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.

5Environmental 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.

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.

We asked the public to address the 

following questions: 

• Has the scientific merit of the 

models presented been established? 

• Are the models’ accuracy 

sufficiently documented? 

• Are the proposed regulatory uses of 

individual models for specific 

applications appropriate and 

reasonable? 

• Do significant implementation 

issues remain or is additional guidance 

needed? 

• Are there serious resource 

constraints imposed by modeling 

systems presented? 

• What additional analyses or 

information are needed? 

We placed a transcript of the 7th 

conference proceedings and a copy of 

all written comments, which embody 

answers to the above questions, in 

Docket No. AQM–95–01. 

IV. Discussion of Public Comments and 

Issues 

All comments submitted to Docket 

No. A–99–05 are filed in Category IV–

D. We summarized these comments, 

developed detailed responses, and drew 

conclusions on appropriate actions for 

today’s action in the summary of public 

comments and EPA responses.3 In this 

document, we considered and discussed 

all significant comments. Whenever the 

comments revealed any new 

information or suggested any alternative 

solutions, we considered such in our 

final action. 

The remainder of this preamble 

section provides an overview of the 

primary issues encountered by the 

Agency during the public comment 

period and summarizes our response-tocomments.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.

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

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

18442 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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-bycase 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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18443

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 caseby-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 ‘‘sitespecific’’ 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

18444 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

6Environmental 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).

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 oneyear 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 stateof-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.

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.

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18445

7Note 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.

8Environmental 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)

9Environmental 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. 10See section 8.2.3 of the Guideline.

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

Section 5

To simplify, the list of acceptable, yet 

equivalent, screening techniques for 

complex terrain was removed. 

CTSCREEN and guidance for its use are 

retained; CTSCREEN remains acceptable 

for all terrain above stack top. The 

screening techniques whose 

descriptions we removed, i.e., Valley (as 

implemented in SCREEN3), COMPLEX I 

(as implemented in ISC3), SHORTZ/

LONGZ, and RTDM remain available for 

use in applicable cases where 

established/accepted procedures are 

used. Consultation with the appropriate 

reviewing authority is still advised for 

application of these screening models. 

Section 6 

As proposed, we revised section 6 to 

reflect the new PM–2.5 and ozone 

ambient air quality standards that were 

issued on July 18, 1997 (62 FR 38652 & 

62 FR 38856). You will note that we 

inserted respective subsections for 

particulate matter and lead from section 

8, so that section 6 now primarily 

contains modeling guidance for the 

criteria pollutants regulated in Part 51 

(SO2 analyses are covered in section 4). 

We also updated information on 

receptor models. 

• We enhanced the subsection on 

particulate matter as much as possible 

to reflect the Agency’s current thinking 

on approaches for fine particulates (PM–

2.5). You will note that we removed the 

references to the Climatological 

Dispersion Model (CDM 2.0) as well as 

to RAM from this section, and also 

deleted CDM and RAM from appendix 

A (see below). 

• We enhanced the subsection on 

ozone to better reflect modeling 

approaches we currently envision, and 

added a reference for current guidance 

on ozone attainment demonstrations.8

You will note that we removed the 

reference to the Urban Airshed Model 

(UAM–IV) from this section, and 

deleted UAM from appendix A. UAM–

IV is no longer the recommended 

photochemical model for attainment 

demonstrations for ozone.

• 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 caseby-case basis. 

• In the subsection on NO2 models, 

we added a third tier for the screening 

approach that allows the use of the 

ozone limiting method on a case-by-case 

basis. You may recall that this approach 

was removed with the Guideline update 

promulgated on August 9, 1995 (60 FR 

40465). 

• In the subsection on lead, we 

deleted references to 40 CFR 51.83, 

51.84, and 51.85, conforming to 

previous EPA action (51 FR 40661). 

Section 7 

For regional scale modeling, we 

removed reference to the Regional 

Oxidant Model (ROM) and the Regional 

Acid Deposition Model (RADM) from 

section 7 because they are outdated and 

replaced by a reference to Models-3 9 in 

section 6. We enhanced the subsection 

on visibility to reflect the provisions of 

the Clean Air Act, including those for 

reasonable attribution of visibility 

impairment and regional haze, as well 

as the new NAAQS for PM–2.5. For 

assessment of reasonably attributable 

haze impairment due to one or a small 

group of sources, CALPUFF is available 

for use on a case-by-case basis. We 

identify REMSAD and new approaches 

under the Models-3/CMAQ umbrella for 

possible use to develop and evaluate 

national policy and assist State and 

local control agencies. For long range 

transport analyses, we recommend the 

CALPUFF modeling system. To 

facilitate use of a complex air quality 

and meteorological modeling system 

like CALPUFF, we stipulate that a 

written protocol may be considered for 

developing consensus in the methods 

and procedures to be followed.

Section 8

As proposed, we revised section 8 to 

better reflect our current regulatory 

practice for the general modeling 

considerations addressed. 

• We revised subsection 8.2.6 to refer 

to subsection 6.2.3 for details on 

chemical transformation of NOX. 

• We merged subsection 8.2.8 (Urban/

Rural Classification) with subsection 

8.2.3 (Dispersion Coefficients), and 

removed reference to WYNDvalley. 

• We merged discussions in 

subsections 8.2.9 (Fumigation) and 

8.2.10 (Stagnation) into one new 

subsection (8.2.8—Complex Winds), 

and specifically identify the availability 

of CALPUFF for certain situations on a 

case-by-case basis. 

• We removed the distinction 

between short-term and long-term 

models because when assessing the 

impacts from criteria air pollutants, 

long-term estimates are now practicable 

using hour-by-hour meteorological data. 

Section 9 

As proposed, 

• We revised subsection 9.2.3 

(recommendations for estimating 

background concentrations from nearby 

sources) to reflect a settlement reached 

on October 16, 1997 in a petition 

brought by the Utility Air Regulatory 

Group (UARG). In accordance with the 

settlement, we are clarifying the 

definition of ‘‘nearby sources.’’ The 

‘‘maximum allowable emission limit,’’

specified in Tables 9–1 and 9–2, is tied 

in certain circumstances 10 to the 

emission rate representative of a nearby 

source’s maximum physical capacity to 

emit. We also clarify that nearby sources 

should be modeled only when they 

operate at the same time as the primary 

source(s) being modeled. Where a 

nearby source does not, by its nature, 

operate at the same time as the primary 

source being modeled, the burden is on 

the primary source to demonstrate to the 

satisfaction of the appropriate reviewing 

authority that this is, in fact, the case. 

We added footnotes to Tables 9–1 and 

9–2 to refer back to applicable 

paragraphs of subsection 9.2.3 that 

provide the necessary clarification.

18446 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

11Stauffer, 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. 

12Stauffer, 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. 

13Hourly 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/.

14http://www.mmm.ucar.edu/mm5/mm5-

home.html

• 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.

• We revised subsection 9.3.2 

(National Weather Service Data) to 

inform users that National Weather 

Service (NWS) surface and upper air 

meteorological data are available on 

CD–ROM from the National Climatic 

Data Center. Recent years of such 

surface data are derived from the NWS’s 

Automated Surface Observing System 

(ASOS). We revised subsection 9.3.1.2 

to address the possible occurrence of 

ASOS data within 5-year sets of 

meteorological data. 

• We revised subsection 9.3.3.1 to 

clarify that, while site-specific 

measurements are frequently made ‘‘onproperty’’ (i.e., on the source’s 

premises), acquisition of adequately 

representative site-specific data does not 

preclude collecting data from a location 

off property. Conversely, collection of 

meteorological data on property does 

not of itself guarantee adequate 

representativeness. The subsection was 

also enhanced by improving the 

discussion of collection of temperature 

difference measurements; a paragraph 

was developed that focuses on 

measurement of aloft winds for 

simulation of plume rise, dispersion and 

transport (some details for CTDMPLUS 

were moved to its appendix A 

descriptions); a paragraph was added to 

address collection and use of direct 

turbulence measurements; and the 

paragraph that discusses meteorological 

data preprocessor has been enhanced. 

• We revised subsection 9.3.3.2 by 

removing reference to the STAR 

processing routine because ISCLT and 

CDM 2.0 (for which STAR formatted 

data were developed) have been 

removed. 

• We revised subsection 9.3.4 

(Treatment of Calms) to increase 

accuracy. 

Section 10 

We updated section 10 to reflect 

current thinking and state-of-thepractice regarding model accuracy and 

uncertainty. 

Section 11 

As proposed, we made minor 

revisions to section 11 to reflect the new 

ambient air quality standards for fine 

particles and ozone. Because EPA has 

revised its emissions trading program 

for SO2, we have deleted subsection 

11.2.3.4. 

Section 12 & 13

We redesignated section 13 

(Bibliography) as section 12 (References) 

and vice-versa. We revised them by 

adding some references, deleting 

obsolete/superseded ones, and 

resequencing. You will note that a peer 

scientific review for CALPUFF has been 

included. 

Section 14 

In a streamlining effort, we removed 

section 14 (Glossary). Given current 

familiarity with modeling terminology, 

we no longer consider that maintenance 

of such a glossary is as necessary as it 

once may have been. For these and 

other reasons relating to Office of 

Federal Register policy (see discussion 

of appendix B below), we have revised 

the glossary and placed it on our 

Internet Web site. 

Appendix A 

We updated the introduction to 

appendix A (section A.0). As mentioned 

before, we added CALPUFF to appendix 

A. We removed the Climatological 

Dispersion Model (CDM 2.0), the 

Gaussian-Plume Multiple Source Air 

Quality Algorithm (RAM), and the 

Urban Airshed Model (UAM) from 

appendix A. These models have been 

superseded and are no longer 

considered preferred techniques. 

Appendix B 

We have moved the appendix B 

repository of alternate model summary 

descriptions to our Internet SCRAM 

Web site (http://www.epa.gov/

scram001). Placement of this material 

on the Web site offers many advantages. 

In this format, we will be able to 

maintain the list and model descriptions 

more easily and inexpensively. 

Several model developers have 

submitted new dispersion models for 

inclusion in this Web site repository of 

alternate models: 

• Second-Order Closure Integrated 

Puff Model (SCIPUFF); 

• Open Burn/Open Detonation 

Dispersion Model (OBODM); 

• Atmospheric Dispersion Modeling 

System (ADMS); 

• Comprehensive Air Quality Model 

with extensions (CAMx); and 

• Urban Airshed Model—V (UAMV). 

As described below, codes 

(executables) for these models, as well 

as applicable documentation, have been 

uploaded to our Internet SCRAM Web 

site. Finally, we deleted a model 

currently listed in appendix B, 

MESOPUFF II, which CALPUFF 

replaces. 

Appendix C 

As proposed, we also moved 

appendix C (Example Air Quality 

Analysis Checklist) from the CFR to our 

Internet SCRAM Web site. We believe 

this checklist is outdated, in need of 

revision, and would be more practical to 

maintain if posted on EPA’s Internet 

SCRAM Web site. 

Statutory and Executive Order Reviews 

A. Executive Order 12866: Regulatory 

Planning and Review 

Under Executive Order 12866 (58 FR 

51735 (October 4, 1993)), the Agency 

must determine whether the regulatory 

action is ‘‘significant’’ and therefore 

subject to review by the Office of 

Management and Budget (OMB) and the 

requirements of the Executive Order. 

The Order defines ‘‘significant 

regulatory action’’ as one that is likely 

to result in a rule that may: 

(1) Have an annual effect on the 

economy of $100 million or more or 

adversely affect in a material way the 

economy, a sector of the economy, 

productivity, competition, jobs, the 

environment, public health or safety, or 

State, local, or tribal governments or 

communities; 

(2) Create a serious inconsistency or 

otherwise interfere with an action taken 

or planned by another agency; 

(3) Materially alter the budgetary 

impact of entitlements, grants, user fees,

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

18448 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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.

■ Part 51, chapter I, title 40 of the Code 

of Federal Regulations is amended as follows:

PART 51—REQUIREMENTS FOR 

PREPARATION, ADOPTION, AND 

SUBMITTAL OF IMPLEMENTATION 

PLANS

■ 1. The authority citation for part 51 

continues to read as follows:

Authority: 23 U.S.C. 100; 42 U.S.C. 7401–

7671q.

■ 2. Appendix W to Part 51 revised to 

read as follows:

Appendix W to Part 51—Guideline on 

Air Quality Models 

Preface 

a. Industry and control agencies have long 

expressed a need for consistency in the 

application of air quality models for 

regulatory purposes. In the 1977 Clean Air 

Act, Congress mandated such consistency 

and encouraged the standardization of model 

applications. The Guideline on Air Quality 

Models (hereafter, Guideline) was first 

published in April 1978 to satisfy these 

requirements by specifying models and 

providing guidance for their use. The 

Guideline provides a common basis for 

estimating the air quality concentrations of 

criteria pollutants used in assessing control 

strategies and developing emission limits. 

b. The continuing development of new air 

quality models in response to regulatory 

requirements and the expanded requirements 

for models to cover even more complex 

problems have emphasized the need for 

periodic review and update of guidance on 

these techniques. Three primary on-going 

activities provide direct input to revisions of 

the Guideline. The first is a series of annual

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

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

18450 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18451

alternate models that can be used with caseby-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 wellestablished 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

18452 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 SO2, i.e.,

the difference in concentrations that is 

judged to be significant. However, 

notwithstanding this demonstration, models 

that are not equivalent may be used when 

one of the two other conditions 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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 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 (sv, sw), vertical potential 

temperature gradient (dq/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 worstcase 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/

LONGZ35, 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

18454 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 sourceplume-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 caseby-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 

sv (m/s) ................................................................................................................ 0.3 0.75 .................. .................. ................

sw (m/s) ................................................................................................................ 0.08 0.15 0.30 0.75 ................

Dq/Dz (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 sv ≤ 0.3 m/s, then include sw = 0.04 m/s. 

(2) If sw = 0.75 m/s and U ≥ 3.0 m/s, then Dq/Dz is limited to ≤ 0.01 K/m. 

(3) If U ≥ 4 m/s, then sw ≥ 0.15 m/s. 

(4) sw ≤ sv

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 ................ ................

Ds/Dz (K/m) ............................................................................................................ 0.030 (potential temperature gradient above zi) 

zi (m) ...................................................................................................................... 0.5h 1.0h 1.5h ................ ................

(where h = terrain height) 

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18455

1Modeling 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/.

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.)

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 reference44 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

18456 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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:

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18457

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 caseby-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


ER15AP03.072</GPH>
18458 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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-bycase 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

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

18460 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 McElroyPooler 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 sy and sz should 

be determined from the same stability 

category. ‘‘Split sigmas’’ in that instance are 

not recommended. Sector averaging, which 

eliminates the sy 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 reemitted 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 steadystate 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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18461

2Malfunctions 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.

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 nonsteady-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 threedimensional 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 shortlived 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 lowlevel 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.

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.

18462 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 startup 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

Averaging time Emission limit

(#/MMBtu) 2 × Operating level(MMBtu/hr) 2 × Operating factor

(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 emission 

limit or federally enforceable 

permit limit. 

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition. 

Actual operating factor averaged over most recent 2 

years.3

Short term .................................. Maximum allowable emission 

limit or federally enforceable 

permit limit. 

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition.4

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of 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 emission 

limit or federally enforceable 

permit limit.6

Annual level when actually operating, averaged over the 

most recent 2 years.3

Actual operating factor averaged over the most recent 2 

years.3

Short term .................................. Maximum allowable emission 

limit or federally enforceable 

permit limit.6

Annual level when actually operating, averaged over the 

most recent 2 years.3

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

data base).5

1The 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. 2Terminology 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. 

4Operating 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.) 6See paragraph 9.2.3(c). 7See paragraph 9.2.3(d). 

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18463

TABLE 9–2.—POINT SOURCE MODEL INPUT DATA (EMISSIONS) FOR PSD NAAQS COMPLIANCE DEMONSTRATIONS

Averaging time Emission limit

(#/MMBtu) 1 × Operating level (MMBtu/hr) 1 × Operating factor (e.g., hr/yr,hr/day) 

Proposed Major New or Modified Source 

Annual & quarterly ..................... Maximum allowable emission 

limit or federally enforceable 

permit limit. 

Design capacity or federally enforceable permit condition. Continuous operation (i.e., 8760 

hours).2

Short term (≤ 24 hours) ............. Maximum allowable emission 

limit or federally enforceable 

permit limit. 

Design capacity or federally enforceable permit condition.3Continuous operation (i.e., all 

hours of each time period 

under consideration) 

(for all hours of the meteorological data base).2

Nearby Source(s) 4,6

Annual & quarterly ..................... Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition. 

Actual operating factor averaged over the most recent 2 

years.7,8

Short term (≤ 24 hours) ............. Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition.3

Continuous operation (i.e., all 

hours of each time period 

under consideration) 

(for all hours of the meteorological data base).2

Other Source(s) 6,9

Annual & quarterly ..................... Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Annual level when actually operating, averaged over the 

most recent 2 years.7

Actual operating factor averaged over the most recent 2 

years.7,8

Short term (≤ 24 hours) ............. Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Annual level when actually operating, averaged over the 

most recent 2 years.7

Continuous operation (i.e., all 

hours of each time period 

under consideration) 

(for all hours of the meteorological data base).2

1Terminology 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. 3Operating 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. 5See paragraph 9.2.3(c). 6See paragraph 9.2.3(d). 7 Unless it is determined that this period is not representative. 

8For those permitted sources not in operation or that have not established an appropriate factor, continuous operation (i.e., 8760) should be 

used. 9Generally, 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° 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.

18464 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 nonsteady-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 NWScollected 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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 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 guidance100 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 (dT) measurements 

should be obtained using matched 

thermometers or a reliable thermocouple 

system to achieve adequate accuracy. Siting, 

probe placement, and operation of dT 

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

18466 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

(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 sE and sA

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 sE and sA 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

Parameter 

Averaging 

time (in 

hours) 

Surface wind speed (for use in 

stability determinations) ........ 1 

Transport direction .................... 1 

Dilution wind speed .................. 1 

Turbulence measurements (sE

and sA) for use in stability 

determinations ....................... 1 1

Turbulence Measurements for 

direct input to dispersion 

models ................................... 1 

1To minimize meander effects in sA when 

wind conditions are light and/or variable, determine the hourly average s value from four 

sequential 15-minute s’s according to the following formula: 

σ

σσσσ

1

15

2

15

2

15

2

15

2

4 -hr = +++

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


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Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 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 studies126,127 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 DecisionMaking 

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 decisionmaking, 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,

18468 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

namely quality assurance planning, 

documentation and scrutiny should be 

consistent with the intended use, and should 

include: 

• Scientific peer review; 

• Supportive analyses (diagnostic 

evaluations, code verification, sensitivity and 

uncertainty analyses); 

• Diagnostic and performance evaluations 

with data obtained in trial locations, and 

• Statistical performance evaluations in 

the circumstances of the intended 

applications. 

Performance evaluations and diagnostic 

evaluations assess different qualities of how 

well a model is performing, and both are 

needed to establish credibility within the 

client and scientific community. Performance 

evaluations allow us to decide how well the 

model simulates the average temporal and 

spatial patterns seen in the observations, and 

employ large spatial/temporal scale data sets 

(e.g., national data sets). Performance 

evaluations also allow determination of 

relative performance of a model in 

comparison with alternative modeling 

systems. Diagnostic evaluations allow 

determination of a model capability to 

simulate individual processes that affect the 

results, and usually employ smaller spatial/

temporal scale date sets (e.g., field studies). 

Diagnostic evaluations allow us to decide if 

we get the right answer for the right reason. 

The objective comparison of modeled 

concentrations with observed field data 

provides only a partial means for assessing 

model performance. Due to the limited 

supply of evaluation data sets, there are 

severe practical limits in assessing model 

performance. For this reason, the conclusions 

reached in the science peer reviews and the 

supportive analyses have particular relevance 

in deciding whether a model will be useful 

for its intended purposes. 

c. To extend information from diagnostic 

and performance evaluations, sensitivity and 

uncertainty analyses are encouraged since 

they can provide additional information on 

the effect of inaccuracies in the data bases 

and on the uncertainty in model estimates. 

Sensitivity analyses can aid in determining 

the effect of inaccuracies of variations or 

uncertainties in the data bases on the range 

of likely concentrations. Uncertainty analyses 

can aid in determining the range of likely 

concentration values, resulting from 

uncertainties in the model inputs, the model 

formulations, and parameterizations. Such 

information may be used to determine source 

impact and to evaluate control strategies. 

Where possible, information from such 

sensitivity analyses should be made available 

to the decision-maker with an appropriate 

interpretation of the effect on the critical 

concentrations. 

10.2 Recommendations 

a. No specific guidance on the 

quantification of model uncertainty for use in 

decision-making is being given at this time. 

As procedures for considering uncertainty 

develop and become implementable, this 

guidance will be changed and expanded. For 

the present, continued use of the ‘‘best 

estimate’’ is acceptable; however, in specific 

circumstances for 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 shortterm (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 μg/m3). The 24-hour NAAQS 

(150 μg/m3) is met if, over a 3-year period, 

there is (on average) no more than one 

exceedance per year. For ozone the short 

term 1-hour standard is expressed in terms of 

an expected exceedance limit while the short 

term 8-hour standard is expressed in terms of 

a three year average of the annual fourth 

highest daily maximum 8-hour value. The 

NAAQS are subjected to extensive review 

and possible revision every 5 years. 

d. This section discusses general 

requirements for concentration estimates and 

identifies the relationship to emission limits. 

The following recommendations apply to: (1) 

Revisions of State Implementation Plans and 

(2) the review of new sources and the 

prevention of significant deterioration (PSD). 

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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18469

cThe documents listed here are major sources of 

supplemental infomation on the theory and 

application of mathematical air quality models.

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 incrementconsuming 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

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18474 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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: 

• Aluminum reduction plants which 

contain buoyant, elevated line sources; 

• Rural areas; 

• Transport distances less than 50 

kilometers; 

• Simple terrain; and 

• One hour to one year averaging times. 

(2) The following options should be 

selected for regulatory applications: 

(i) Rural (IRU=1) mixing height option; 

(ii) Default (no selection) for plume rise 

wind shear (LSHEAR), transitional point 

source plume rise (LTRANS), vertical 

potential temperature gradient (DTHTA), 

vertical wind speed power law profile 

exponents (PEXP), maximum variation in 

number of stability classes per hour (IDELS), 

pollutant decay (DECFAC), the constant in 

Briggs’ stable plume rise equation (CONST2), 

constant in Briggs’ neutral plume rise 

equation (CONST3), convergence criterion 

for the line source calculations (CRIT), and 

maximum iterations allowed for line source 

calculations (MAXIT); and 

(iii) Terrain option (TERAN) set equal to 

0.0, 0.0, 0.0, 0.0, 0.0, 0.0

(3) For other applications, BLP can be used 

if it can be demonstrated to give the same 

estimates as a recommended model for the 

same application, and will subsequently be 

executed in that mode. 

(4) BLP can be used on a case-by-case basis 

with specific options not available in a 

recommended model if it can be 

demonstrated, using the criteria in Section 

3.2, that the model is more appropriate for a 

specific application. 

b. Input Requirements 

(1) Source data: point sources require stack 

location, elevation of stack base, physical 

stack height, stack inside diameter, stack gas 

exit velocity, stack gas exit temperature, and 

pollutant emission rate. Line sources require 

coordinates of the end points of the line, 

release height, emission rate, average line 

source width, average building width, 

average spacing between buildings, and 

average line source buoyancy parameter. 

(2) Meteorological data: Hourly surface 

weather data from punched cards or from the 

preprocessor program PCRAMMET which 

provides hourly stability class, wind 

direction, wind speed, temperature, and 

mixing height. 

(3) Receptor data: Locations and elevations 

of receptors, or location and size of receptor 

grid or request automatically generated 

receptor grid. 

c. Output 

(1) Printed output (from a separate postprocessor 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.

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 18475

l. Chemical Transformation 

Chemical transformations are treated using 

linear decay. Decay rate is input by the user. 

m. Physical Removal 

Physical removal is not explicitly treated. 

n. Evaluation Studies 

Schulman, L.L. and J.S. Scire, 1980. 

Buoyant Line and Point Source (BLP) 

Dispersion Model User’s Guide, P–7304B. 

Environmental Research and Technology, 

Inc., Concord, MA. 

Scire, J.S. and L.L. Schulman, 1981. 

Evaluation of the BLP and ISC Models with 

SF6 Tracer Data and SO2 Measurements at 

Aluminum Reduction Plants. APCA 

Specialty Conference on Dispersion 

Modeling for Complex Sources, St. Louis, 

MO. 

A.2 CALINE3 

Reference 

Benson, Paul E, 1979. CALINE3—A 

Versatile Dispersion Model for Predicting Air 

Pollutant Levels Near Highways and Arterial 

Streets. Interim Report, Report Number 

FHWA/CA/TL–79/23. Federal Highway 

Administration, Washington, DC. (NTIS No. 

PB 80–220841) 

Availability

The CALINE3 model is available on 

diskette (as PB 95–502712) from NTIS. The 

source code and user’s guide are also 

available on EPA’s Internet SCRAM Web site 

( Section A.0). 

Abstract 

CALINE3 can be used to estimate the 

concentrations of nonreactive pollutants from 

highway traffic. This steady-state Gaussian 

model can be applied to determine air 

pollution concentrations at receptor locations 

downwind of ‘‘at-grade,’’ ‘‘fill,’’ ‘‘bridge,’’

and ‘‘cut section’’ highways located in 

relatively uncomplicated terrain. The model 

is applicable for any wind direction, highway 

orientation, and receptor location. The model 

has adjustments for averaging time and 

surface roughness, and can handle up to 20 

links and 20 receptors. It also contains an 

algorithm for deposition and settling velocity 

so that particulate concentrations can be 

predicted. 

a. Recommendations for Regulatory Use 

CALINE–3 is appropriate for the following 

applications: 

• Highway (line) sources; 

• Urban or rural areas; 

• Simple terrain; 

• Transport distances less than 50 

kilometers; and 

• One-hour to 24-hour averaging times. 

b. Input Requirements 

(1) Source data: Up to 20 highway links 

classed as ‘‘at-grade,’’ ‘‘fill’’ ‘‘bridge,’’ or 

‘‘depressed’’; coordinates of link end points; 

traffic volume; emission factor; source height; 

and mixing zone width. 

(2) Meteorological data: Wind speed, wind 

angle (measured in degrees clockwise from 

the Y axis), stability class, mixing height, 

ambient (background to the highway) 

concentration of pollutant. 

(3) Receptor data: Coordinates and height 

above ground for each receptor. 

c. Output 

Printed output includes concentration at 

each receptor for the specified meteorological 

condition. 

d. Type of Model 

CALINE–3 is a Gaussian plume model. 

e. Pollutant Types 

CALINE–3 may be used to model primary 

pollutants. 

f. Source-Receptor Relationship 

(1) Up to 20 highway links are treated. 

(2) CALINE–3 applies user input location 

and emission rate for each link. User-input 

receptor locations are applied. 

g. Plume Behavior 

Plume rise is not treated. 

h. Horizontal Winds 

(1) User-input hourly wind speed and 

direction are applied. 

(2) Constant, uniform (steady-state) wind is 

assumed for an hour. 

i. Vertical Wind Speed 

Vertical wind speed is assumed equal to 

zero. 

j. Horizontal Dispersion 

(1) Six stability classes are used. 

(2) Rural dispersion coefficients from 

Turner (1969) are used, with adjustment for 

roughness length and averaging time. 

(3) Initial traffic-induced dispersion is 

handled implicitly by plume size parameters. 

k. Vertical Dispersion 

(1) Six stability classes are used. 

(2) Empirical dispersion coefficients from 

Benson (1979) are used including an 

adjustment for roughness length.

(3) Initial traffic-induced dispersion is 

handled implicitly by plume size parameters. 

(4) Adjustment for averaging time is 

included. 

l. Chemical Transformation 

Not treated. 

m. Physical Removal 

Optional deposition calculations are 

included. 

n. Evaluation Studies 

Bemis, G.R. et al., 1977. Air Pollution and 

Roadway Location, Design, and Operation—

Project Overview. FHWA–CA–TL–7080–77–

25, Federal Highway Administration, 

Washington, D.C. 

Cadle, S.H. et al., 1976. Results of the 

General Motors Sulfate Dispersion 

Experiment, GMR–2107. General Motors 

Research Laboratories, Warren, MI. 

Dabberdt, W.F., 1975. Studies of Air 

Quality on and Near Highways, Project 2761. 

Stanford Research Institute, Menlo Park, CA. 

A.3 CALPUFF 

References 

Scire, J.S., D.G. Strimaitis and R.J. 

Yamartino, 2000. A User’s Guide for the 

CALPUFF Dispersion Model (Version 5.0). 

Earth Tech, Inc., Concord, MA. 

Scire J.S., F.R. Robe, M.E. Fernau and R.J. 

Yamartino, 2000. A User’s Guide for the 

CALMET Meteorological Model (Version 

5.0). Earth Tech, Inc., Concord, MA. 

Availability 

The model code and its documentation are 

available at no cost for download from the 

model developers’ Internet Web site: http://

www.src.com/calpuff/calpuff1.htm. You may 

also contact Joseph Scire, Earth Tech, Inc., 

196 Baker Avenue, Concord, MA 01742; 

Telephone: (978) 371–4200, Fax: (978) 371–

2468, e-mail: jss@src.com.

Abstract 

CALPUFF is a multi-layer, multi-species 

non-steady-state puff dispersion modeling 

system that simulates the effects of time- and 

space-varying meteorological conditions on 

pollutant transport, transformation, and 

removal. CALPUFF is intended for use on 

scales from tens of meters from a source to 

hundreds of kilometers. It includes 

algorithms for near-field effects such as 

building downwash, transitional buoyant and 

momentum plume rise, partial plume 

penetration, subgrid scale terrain and coastal 

interactions effects, and terrain impingement 

as well as longer range effects such as 

pollutant removal due to wet scavenging and 

dry deposition, chemical transformation, 

vertical wind shear, overwater transport, 

plume fumigation, and visibility effects of 

particulate matter concentrations. 

a. Recommendations for Regulatory Use 

(1) CALPUFF is appropriate for long range 

transport (source-receptor distances of 50 to 

several hundred kilometers) of emissions 

from point, volume, area, and line sources. 

The meteorological input data should be 

fully characterized with time-and-spacevarying 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 caseby-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

18476 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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 (sz) 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. Arbitrarilyvarying 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 (sy, sz) 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 temperaturedependent 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 (sy, sz)—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 stabilitydependent 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 timeand 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 steepwalled 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 sv. 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 sw. 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

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 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 LongRange Transport Impacts. EPA Publication 

No. EPA–454/R–98–019. Office of Air 

Quality Planning & Standards, Research 

Triangle Park, NC. 

Irwin, J.S. 1997. A Comparison of 

CALPUFF Modeling Results with 1997 INEL 

Field Data Results. In Air Pollution Modeling 

and its Application, XII. Edited by S.E. 

Gyrning and N. Chaumerliac. Plenum Press, 

New York, NY. 

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 

1996. A Comparison of CALPUFF Modeling 

Results with CAPTEX Field Data Results. In 

Air Pollution Modeling and its Application, 

XI. Edited by S.E. Gyrning and F.A. 

Schiermeier. Plenum Press, New York, NY. 

Strimaitis, D.G., J.S. Scire and J.C. Chang. 

1998. Evaluation of the CALPUFF Dispersion 

Model with Two Power Plant Data Sets. 

Tenth Joint Conference on the Application of 

Air Pollution Meteorology, Phoenix, Arizona. 

American Meteorological Society, Boston, 

MA. January 11–16, 1998. 

A.4 Complex Terrain Dispersion Model 

Plus Algorithms for Unstable Situations 

(CTDMPLUS) 

Reference 

Perry, S.G., D.J. Burns, L.H. Adams, R.J. 

Paine, M.G. Dennis, M.T. Mills, D.G. 

Strimaitis, R.J. Yamartino and E.M. Insley, 

1989. User’s Guide to the Complex Terrain 

Dispersion Model Plus Algorithms for 

Unstable Situations (CTDMPLUS). Volume 1: 

Model Descriptions and User Instructions. 

EPA Publication No. EPA–600/8–89–041. 

Environmental Protection Agency, Research 

Triangle Park, NC. (NTIS No. PB 89–181424) 

Perry, S.G., 1992. CTDMPLUS: A 

Dispersion Model for Sources near Complex 

Topography. Part I: Technical Formulations. 

Journal of Applied Meteorology, 31(7): 633–

645. 

Availability 

This model code is available on EPA’s 

Internet SCRAM Web site and also on 

diskette (as PB 90–504119) from the National 

Technical Information Service (Section A.0). 

Abstract 

CTDMPLUS is a refined point source 

Gaussian air quality model for use in all 

stability conditions for complex terrain 

applications. The model contains, in its 

entirety, the technology of CTDM for stable 

and neutral conditions. However, 

CTDMPLUS can also simulate daytime, 

unstable conditions, and has a number of 

additional capabilities for improved user 

friendliness. Its use of meteorological data 

and terrain information is different from 

other EPA models; considerable detail for 

both types of input data is required and is 

supplied by preprocessors specifically 

designed for CTDMPLUS. CTDMPLUS 

requires the parameterization of individual 

hill shapes using the terrain preprocessor and 

the association of each model receptor with 

a particular hill. 

a. Recommendation for Regulatory Use 

CTDMPLUS is appropriate for the 

following applications: 

• Elevated point sources; 

• Terrain elevations above stack top; 

• Rural or urban areas; 

• Transport distances less than 50 

kilometers; and 

• One hour to annual averaging times 

when used with a post-processor program 

such as CHAVG. 

b. Input Requirements 

(1) Source data: For each source, user 

supplies source location, height, stack 

diameter, stack exit velocity, stack exit 

temperature, and emission rate; if variable 

emissions are appropriate, the user supplies 

hourly values for emission rate, stack exit 

velocity, and stack exit temperature. 

(2) Meteorological data: For applications of 

CTDMPLUS, multiple level (typically three 

or more) measurements of wind speed and 

direction, temperature and turbulence (wind 

fluctuation statistics) are required to create 

the basic meteorological data file 

(‘‘PROFILE’’). Such measurements should be 

obtained up to the representative plume 

height(s) of interest (i.e., the plume height(s) 

under those conditions important to the 

determination of the design concentration). 

The representative plume height(s) of interest 

should be determined using an appropriate 

complex terrain screening procedure (e.g., 

CTSCREEN) and should be documented in 

the monitoring/modeling protocol. The 

necessary meteorological measurements 

should be obtained from an appropriately 

sited meteorological tower augmented by 

SODAR and/or RASS if the representative 

plume height(s) of interest is above the levels 

represented by the tower measurements. 

Meteorological preprocessors then create a 

SURFACE data file (hourly values of mixed 

layer heights, surface friction velocity, 

Monin-Obukhov length and surface 

roughness length) and a RAWINsonde data 

file (upper air measurements of pressure, 

temperature, wind direction, and wind 

speed). 

(3) Receptor data: Receptor names (up to 

400) and coordinates, and hill number (each 

receptor must have a hill number assigned). 

(4) Terrain data: User inputs digitized 

contour information to the terrain 

preprocessor which creates the TERRAIN 

data file (for up to 25 hills). 

c. Output 

(1) When CTDMPLUS is run, it produces 

a concentration file, in either binary or text 

format (user’s choice), and a list file 

containing a verification of model inputs, i.e., 

• Input meteorological data from 

‘‘SURFACE’’ and ‘‘PROFILE’’

• Stack data for each source 

• Terrain information 

• Receptor information 

• Source-receptor location (line printer 

map). 

(2) In addition, if the case-study option is 

selected, the listing includes: 

• Meteorological variables at plume height

• Geometrical relationships between the 

source and the hill 

• Plume characteristics at each receptor, 

i.e.,

—Distance in along-flow and cross flow 

direction 

—Effective plume-receptor height difference 

—Effective sy sz 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 nonreactive, 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°, so that the 

linearized equation of motion for Boussinesq 

flow are applicable. Receptors upwind of the 

impingement point, or those associated with

18478 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

any of the hills in the modeling domain, 

require separate treatment. 

g. Plume Behavior 

(1) As in CTDM, the basic plume rise 

algorithms are based on Briggs’ (1975) 

recommendations. 

(2) A central feature of CTDMPLUS for 

neutral/stable conditions is its use of a 

critical dividing-streamline height (Hc) to 

separate the flow in the vicinity of a hill into 

two separate layers. The plume component in 

the upper layer has sufficient kinetic energy 

to pass over the top of the hill while 

streamlines in the lower portion are 

constrained to flow in a horizontal plane 

around the hill. Two separate components of 

CTDMPLUS compute ground-level 

concentrations resulting from plume material 

in each of these flows. 

(3) The model calculates on an hourly (or 

appropriate steady averaging period) basis 

how the plume trajectory (and, in stable/

neutral conditions, the shape) is deformed by 

each hill. Hourly profiles of wind and 

temperature measurements are used by 

CTDMPLUS to compute plume rise, plume 

penetration (a formulation is included to 

handle penetration into elevated stable 

layers, based on Briggs (1984)), convective 

scaling parameters, the value of Hc, and the 

Froude number above Hc.

h. Horizontal Winds 

CTDMPLUS does not simulate calm 

meteorological conditions. Both scalar and 

vector wind speed observations can be read 

by the model. If vector wind speed is 

unavailable, it is calculated from the scalar 

wind speed. The assignment of wind speed 

(either vector or scalar) at plume height is 

done by either: 

• Interpolating between observations 

above and below the plume height, or 

• Extrapolating (within the surface layer) 

from the nearest measurement height to the 

plume height. 

i. Vertical Wind Speed 

Vertical flow is treated for the plume 

component above the critical dividing 

streamline height (Hc); see ‘‘Plume 

Behavior’’. 

j. Horizontal Dispersion 

Horizontal dispersion for stable/neutral 

conditions is related to the turbulence 

velocity scale for lateral fluctuations, sv, 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., 

sw (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: 

• Cumulative effect of changes in aircraft 

operations, point source and mobile source 

emissions at airports or air bases; 

• Simple terrain; 

• Non-reactive pollutants; 

• Transport distances less than 50 

kilometers; and 

• 1-hour to annual averaging times. 

b. Input Requirements 

(1) All data are entered through the EDMS 

graphical user interface. Typical entry items 

are annual and hourly source activity, source 

and receptor coordinates, etc. Some point 

sources, such as heating plants, require stack 

height, stack diameter, and effluent 

temperature inputs. 

(2) Wind speed, wind direction, hourly 

temperature, and Pasquill-Gifford stability 

category (P–G) are the meteorological inputs. 

They can be entered manually through the 

EDMS data entry screens or automatically 

through the processing of previously loaded 

NCDC hourly data. 

c. Output 

Printed outputs consist of: 

• A summary emission inventory report 

with pollutant totals by source category and 

detailed emission inventory reports for each 

source category; and 

• A concentration summary report for up 

to 8760 hours (one year) of meteorological 

data that lists the number of sources, 

receptors, and the five highest concentrations 

for applicable averaging periods for the 

respective primary NAAQS. 

d. Type of Model 

For its emissions inventory calculations, 

EDMS uses algorithms consistent with the 

EPA Compilation of Air Pollutant Emission 

Factors, AP–42 (Section 11.0, ref. 96). For its 

dispersion calculations, EDMS uses the Point 

Area & Line (PAL2) model and the 

CALifornia LINE source (CALINE3) model, 

both of which use Gaussian algorithms.

Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 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 (PasquillGifford) 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 (PasquillGifford) 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: 

• Industrial source complexes;

• Rural or urban areas; 

• Flat or rolling terrain; 

• Transport distances less than 50 

kilometers; 

• 1-hour to annual averaging times; and 

• Continuous toxic air emissions. 

The following options should be selected 

for regulatory applications: For short term or 

long term modeling, set the regulatory 

‘‘default option’’; i.e., use the keyword 

DFAULT, which automatically selects stack 

tip downwash, final plume rise, buoyancy 

induced dispersion (BID), the vertical 

potential temperature gradient, a treatment 

for calms, the appropriate wind profile 

exponents, the appropriate value for 

pollutant half-life, and a revised building 

wake effects algorithm; set the ‘‘rural option’’

(use the keyword RURAL) or ‘‘urban option’’

(use the keyword URBAN); and set the 

‘‘concentration option’’ (use the keyword 

CONC). 

b. Input Requirements 

Source data: Location, emission rate, 

physical stack height, stack gas exit velocity, 

stack inside diameter, and stack gas 

temperature. Optional inputs include source 

elevation, building dimensions, particle size 

distribution with corresponding settling 

velocities, and surface reflection coefficients. 

Meteorological data: ISCST3 requires 

hourly surface weather data from the 

preprocessor program RAMMET, which 

provides hourly stability class, wind 

direction, wind speed, temperature, and 

mixing height. For ISCLT3, input includes 

stability wind rose (STAR deck), average 

afternoon mixing height, average morning 

mixing height, and average air temperature. 

Receptor data: Coordinates and optional 

ground elevation for each receptor. 

c. Output 

Printed output options include: 

• Program control parameters, source data, 

and receptor data; 

• Tables of hourly meteorological data for 

each specified day; 

• ‘‘N’’-day average concentration or total 

deposition calculated at each receptor for any 

desired source combinations; 

• Concentration or deposition values 

calculated for any desired source 

combinations at all receptors for any 

specified day or time period within the day; 

• Tables of highest and second highest 

concentration or deposition values calculated 

at each receptor for each specified time 

period during a(n) ‘‘N’’-day period for any 

desired source combinations, and tables of 

the maximum 50 concentration or deposition 

values calculated for any desired source 

combinations for each specified time period. 

d. Type of Model 

ISC3 is a Gaussian plume model. It has 

been revised to perform a double integration 

of the Gaussian plume kernel for area 

sources. 

e. Pollutant Types 

ISC3 may be used to model primary 

pollutants and continuous releases of toxic 

and hazardous waste pollutants. Settling and 

deposition are treated. 

f. Source-Receptor Relationships 

ISC3 applies user-specified locations for 

point, line, area and volume sources, and 

user-specified receptor locations or receptor 

rings. 

User input topographic evaluation for each 

receptor is used. Elevations above stack top 

are reduced to the stack top elevation, i.e., 

‘‘terrain chopping’’. 

User input height above ground level may 

be used when necessary to simulate impact 

at elevated or ‘‘flag pole’’ receptors, e.g., on 

buildings. 

Actual separation between each sourcereceptor 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

18480 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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.

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

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Dispersion Model for Predicting Air 

Pollution Levels Near Highways and Arterial 

Streets. Interim Report, Report Number 

FHWA/CA/TL–79/23. Federal Highway 

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Briggs, G.A., 1969. Plume Rise. U.S. 

Atomic Energy Commission Critical Review 

Series, Oak Ridge National Laboratory, Oak 

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Briggs, G.A., 1971. Some Recent Analyses 

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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. 

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Climate and Applied Meteorology, 24(11): 

1167–1186 

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18482 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations 

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Atmospheric Environment, 15: 2295—2311.

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