68218 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

1http://cascade.epa.gov/RightSite/ 

dk_public_collection_detail.htm?

ObjectType=dk_docket_collection&cid=OAR-2003-

0201&ShowList=items&Action=view. 2AMS/EPA Regulatory MODel. 

ENVIRONMENTAL PROTECTION 

AGENCY 

40 CFR Part 51 

[AH–FRL–7990–9] 

RIN 2060–AK60 

Revision to the Guideline on Air 

Quality Models: Adoption of a 

Preferred General Purpose (Flat and 

Complex Terrain) Dispersion 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 

recommend a new dispersion model— 

AERMOD—for adoption in appendix A 

of the Guideline. AERMOD replaces the 

Industrial Source Complex (ISC3) 

model, applies to complex terrain, and 

incorporates a new downwash 

algorithm—PRIME. We remove an 

existing model—the Emissions 

Dispersion Modeling System (EDMS)— 

from appendix A. We also make various 

editorial changes to update and 

reorganize information. 

DATES: This rule is effective December 9, 

2005. As proposed, beginning November 

9, 2006, the new model—AERMOD— 

should be used for appropriate 

application as replacement for ISC3. 

During the one-year period following 

this promulgation, protocols for 

modeling analyses based on ISC3 which 

are submitted in a timely manner may 

be approved at the discretion of the 

appropriate Reviewing Authority. 

Applicants are therefore encouraged to 

consult with the Reviewing Authority as 

soon as possible to assure acceptance 

during this period. 

ADDRESSES: All documents relevant to 

this rule have been placed in Docket No. 

A–99–05 at the following address: Air 

Docket in the EPA Docket Center, (EPA/ 

DC) EPA West (MC 6102T), 1301 

Constitution Ave., NW., Washington, 

DC 20004. This docket is available for 

public inspection and copying between 

8 a.m. and 5:30 p.m., Monday through 

Friday, at the address above. 

FOR FURTHER INFORMATION CONTACT: 

Tyler J. Fox, Air Quality Modeling 

Group (MD–D243–01), Office of Air 

Quality Planning and Standards, U.S. 

Environmental Protection Agency, 

Research Triangle Park, NC 27711; 

telephone (919) 541–5562. 

(Fox.Tyler@epa.gov). 

SUPPLEMENTARY INFORMATION: 

Outline 

I. General Information 

II. Background 

III. Public Hearing on the April 2000 

proposal 

IV. Discussion of Public Comments and 

Issues from our April 21, 2000 Proposal 

A. AERMOD and PRIME 

B. Appropriate for Proposed Use 

C. Implementation Issues/Additional 

Guidance 

D. AERMOD revision and reanalyses in 

2003 

1. Performance analysis for AERMOD 

(02222) 

a. Non-downwash cases: AERMOD (99351) 

vs. AERMOD (02222) 

b. Downwash cases 

2. Analysis of regulatory design 

concentrations for AERMOD (02222) 

a. Non-downwash cases 

b. Downwash cases 

c. Complex terrain 

E. Emission and Dispersion Modeling 

System (EDMS) 

V. Discussion of Public Comments and Issues 

from our September 8, 2003 Notice of 

Data Availability 

VI. Final action 

VII. Final editorial changes to appendix W 

VIII. Statutory and Executive Order Reviews 

I. General Information 

A. How Can I Get Copies of Related 

Information? 

EPA established an official public 

docket for this action under Docket 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 20004. 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. An electronic image 

of this docket may be accessed via 

Internet at www.epa.gov/eDocket, where 

Docket No. A–99–05 is indexed as 

OAR–2003–0201. Materials related to 

our Notice of Data Availability 

(published September 8, 2003) and 

public comments received pursuant to 

the notice were placed in eDocket OAR– 

2003–0201.1

Our Air Quality Modeling Group 

maintain an Internet website (Support 

Center for Regulatory Air Models— 

SCRAM) at: 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 issued a Notice of Proposed 

Rulemaking (NPR) in the Federal 

Register (65 FR 21506), which was the 

original proposal for today’s 

promulgation. 

III. Public Hearing on the April 2000 

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 

approximately every three years to 

standardize modeling procedures, with 

special attention given to appropriate 

modeling practices for carrying out 

programs PSD (42 U.S.C. 7620). 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 American Meteorological Society 

(AMS)/EPA Regulatory Model 

Improvement Committee (AERMIC) 

covered the enhanced Gaussian 

dispersion model with boundary layer 

parameterization: AERMOD.2 Also at 

the 7th conference, the Electric Power 

Research Institute (EPRI) presented 

evaluation results from the recent 

research efforts to better define and 

characterize dispersion around 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68219 

3Additional comments received since we 

published the final rule on April 15, 2003 

(discussed in the previous section) are filed in 

category IV–E. This category includes comments 

received pursuant to the Notice of Data Availability 

we published in September 2003. 

4Summary of Public Comments and EPA 

Responses: AERMOD; 7th Conference on Air 

Quality Modeling; Washington, DC, June 28–29, 

2000 AND Notice of Data Availability—September 

8, 2003 (Air Docket A–99–05, Item V–C–2). This 

document may also be examined from EPA’s 

SCRAM Web site at www.epa.gov/scram001. 

buildings (downwash effects). These 

efforts were part of a program called the 

Plume RIse Model Enhancements 

(PRIME). At the time, PRIME was 

integrated within ISC3ST (ISC–PRIME) 

and the results presented were within 

the ISC3 context. As discussed in 

today’s rule, the PRIME algorithm has 

now been fully integrated into 

AERMOD. 

We proposed an update to the 

Emissions and Dispersion Modeling 

System (EDMS 3.1), which is used for 

assessing air quality impacts from 

airports. A representative of the Federal 

Aviation Administration (FAA) 

presented a further upgrade to EDMS 

4.0 that would include AERMOD and 

forthcoming performance evaluations 

for two airports. 

The presentations were followed by a 

critical review/discussion of AERMOD 

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. 

For the new models and modeling 

techniques proposed in April 2000, 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, many of which 

address the above questions, in Docket 

No. A–99–05. The comments on 

AERMOD were reviewed and nearly 

every commenter urged us to integrate 

aerodynamic downwash into AERMOD 

(i.e., not to require two models for some 

analyses). The only comments calling 

for further actions were associated with 

the need for documentation, evaluation 

and review of the suggested downwash 

enhancement to AERMOD. 

As a result of American 

Meteorological Society (AMS)/EPA 

Regulatory Model Improvement 

Committee’s (AERMIC) efforts to revise 

AERMOD, incorporating the PRIME 

algorithm and making certain other 

incidental modifications and to respond 

to public concerns, we believed that the 

revised AERMOD merited another 

public examination of performance 

results. Also, since the April 2000 NPR, 

the Federal Aviation Administration 

(FAA) decided to configure EDMS 3.1 to 

incorporate the AERMOD dispersion 

model. FAA presented this strategy at 

the 7th conference and performance 

evaluations at two airports were to be 

available before final promulgation. 

This was in response to public concern 

over lack of EDMS evaluation. 

On April 15, 2003 we published a 

Notice of Final Rulemaking (NFR; 68 FR 

18440) that adopted CALPUFF in 

appendix A of the Guideline. We also 

made various editorial changes to 

update and reorganize information, and 

removed obsolete models. We 

announced that action on AERMOD and 

the Emissions and Dispersion Model 

(EDMS) for assessing airport impacts 

was being deferred, and would be 

reconsidered in a separate action when 

new information became available for 

these models. 

This deferred action took the form of 

a Notice of Data Availability (NDA), 

which was published on September 8, 

2003 (68 FR 52934). In this notice, we 

made clear that the purpose of the NDA 

was to furnish pertinent technical 

details related to model changes since 

the April 2000 NPR. New performance 

data and evaluation of design 

concentration using the revised 

AERMOD are contained in reports cited 

later in this preamble (see section V). In 

our April 2003 NFR, we stated that 

results of EDMS 4.0 performance (with 

AERMOD) had recently become 

available. In the NDA we clarified that 

these results would not be provided 

because of FAA’s decision to withdraw 

EDMS from the Guideline’s appendix A, 

and we affirmed our support for this 

removal. We solicited public comments 

on the new data and information related 

to AERMOD. 

IV. Discussion of Public Comments and 

Issues From Our April 21, 2000 

Proposal 

All comments submitted to Docket 

No. A–99–05 are filed in Category IV–

D.3 We summarized these comments, 

developed detailed responses, and 

documented conclusions on appropriate 

actions in a Response-to-Comments 

document.4 In this document, we 

considered and discussed all significant 

comments. Whenever the comments 

revealed any new information or 

suggested any alternative solutions, we 

considered this prior to taking final 

action. 

The remainder of this preamble 

section discusses the primary issues 

encountered by the Agency during the 

public comment period associated with 

the April 2000 proposal. This overview 

also serves in part to explain the 

changes to the Guideline in today’s 

action, and the main technical and 

policy concerns addressed by the 

Agency. 

A. AERMOD and PRIME 

AERMOD is a best state-of-thepractice Gaussian plume dispersion 

model whose formulation is based on 

planetary boundary layer principles. 

AERMOD provides better 

characterization of plume dispersion 

than does ISC3. At the 7th conference, 

AERMIC members presented 

developmental and evaluation results of 

AERMOD. Comprehensive comments 

were submitted on the AERMOD code 

and formulation document and on the 

AERMET draft User’s Guide (AERMET 

is the meteorological preprocessor for 

AERMOD). 

As identified in the April 2000 

Federal Register proposal, applications 

for which AERMOD was suited include 

assessment of plume impacts from 

stationary sources in simple, 

intermediate, and complex terrain, for 

other than downwash and deposition 

applications. We invited comments on 

whether technical concerns had been 

reasonably addressed and whether 

AERMOD is appropriate for its intended 

applications. Since AERMOD lacks a 

general (all-terrain) screening tool, we 

invited comment on the practicality of 

using SCREEN3 as an interim tool for 

AERMOD. We also sought comments on 

minor changes to the list of acceptable 

screening techniques for complex 

terrain. 

PRIME was designed to incorporate 

the latest scientific algorithms for 

evaluating building downwash. At the 

time of the proposal, the PRIME 

algorithm for simulating aerodynamic 

downwash was not incorporated into 

AERMOD. For testing purposes, PRIME 

was implemented within ISC3ST (shortterm average version of the Industrial 

Source Complex), which AERMOD was 

proposed to replace. This special model, 

called ISC–PRIME, was proposed for 

68220 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

5U.S. Environmental Protection Agency, 2002. 

Compendium of Reports from the Peer Review 

Process for AERMOD. February 2002. Available at 

www.epa.gov/scram001/. 

6Cimorelli, A. et al., 2005. AERMOD: A 

Dispersion Model for Industrial Source 

Applications. Part I: General Model Formulation 

and Boundary Layer Characterization. Journal of 

Applied Meteorology, 44(5): 682–693. 

7Perry, S. et al., 2005. AERMOD: A Dispersion 

Model for Industrial Source Applications. Part II: 

Model Performance against 17 Field Study 

Databases. Journal of Applied Meteorology, 44(5): 

694–708. 

8Paine R. J. et al., 1998. Evaluation Results for 

AERMOD, Draft Report. Docket No. A–99–05; II–A–

05. Available at www.epa.gov./scram001/. 

9Schulman, L.L. et al., 2000. Development and 

Evaluation of the PRIME Plum Rise and Building 

Downwash Model. JAWMA 50: 378–390. 

10Electric Power Research Institute, 1997. Results 

of the Independent Evaluation of ISCST3 and ISC–

PRIME. Final Report, TR–2460026, November 1997. 

Available at www.epa.gov/scram001/. 

11Environmental Protection Agency, 2003. 

AERMOD: Latest Features and Evaluation Results. 

Publication No. EPA–454/R–03–003. Available at 

www.epa.gov/scram001/. 

12Environmental Protection Agency, 2003. 

Comparison of Regulatory Design Concentrations: 

AERMOD versus ISC3ST, CTDMPLUS, and ISC–

PRIME. Final Report. Publication No. EPA–454/R–

03–002. Available at www.epa.gov/scram001/. 

aerodynamic downwash and dry 

deposition. We sought comment on the 

technical viability of AERMOD and 

ISC–PRIME for its intended 

applications. 

Scientific merit and accuracy. 

Regarding the scientific merits of 

AERMOD, substantial support was 

expressed in public comments that 

AERMOD represents sound and 

significant advances over ISC3ST. The 

scientific merits of this approach have 

been documented both through 

scientific peer review and performance 

evaluations. The formulation of 

AERMOD has been subjected to an 

extensive, independent peer review.5

Findings of the peer review panel 

suggest that AERMOD’s scientific basis 

is ‘‘state-of-the-science.’’ Additionally, 

the model formulations used in 

AERMOD and the performance 

evaluations have been accepted for 

publication in two refereed journals.6 7

Finally, the adequacy of AERMOD’s 

complex terrain approach for regulatory 

applications is seen most directly in its 

performance. AERMOD’s complex 

terrain component has been evaluated 

extensively by comparing modelestimated regulatory design values and 

concentration frequency distributions 

with observations. These comparisons 

have demonstrated AERMOD’s 

superiority to ISC3ST and CTDMPLUS 

(Complex Terrain Dispersion Model 

PLUS unstable algorithms) in estimating 

those flat and complex terrain impacts 

of greatest regulatory importance.8 For 

incidental and unique situations 

involving a well-defined hill or ridge 

and where a detailed dispersion 

analysis of the spatial pattern of plume 

impacts is of interest, CTDMPLUS in the 

Guideline’s appendix A remains 

available. 

Public comments also supported our 

conclusion about the scientific merits of 

PRIME. A detailed article in a peerreviewed journal has been published 

which contains all the basic equations 

with clear definitions of the variables, 

and the reasoning and references for the 

model assumptions.9

Although some comments asked for 

more detailed documentation and 

review, there were no comments which 

questioned the technical credibility of 

the PRIME model. In fact, almost every 

commenter asked for PRIME to be 

incorporated into AERMOD. As 

summarized above, we believe that the 

scientific merit of PRIME has been 

established via (1) model evaluation and 

documentation, (2) peer review within 

the submittal process to a technical 

journal, and (3) via the public review 

process. 

Based on the external peer review of 

the evaluation report and the public 

review comments, we have concluded 

that: (1) AERMOD’s accuracy is 

adequately documented; (2) AERMOD’s 

accuracy is an improvement over 

ISC3ST’s ability to predict measured 

concentrations; and (3) AERMOD is an 

acceptable regulatory air dispersion 

model replacement for ISC3ST. 

Some commenters have identified 

what they perceived to be weaknesses in 

the evaluation and performance of ISC–

PRIME,10 and some concerns were 

raised about the scope of the PRIME 

evaluation. However, as shown by the 

overwhelming number of requests for 

the incorporation of PRIME into 

AERMOD, commenters were convinced 

that the accuracy of PRIME, as 

implemented within the ISC3ST 

framework, was reasonably documented 

and found acceptable for regulatory 

applications. Although some 

commenters requested more 

evaluations, practical limitations on the 

number of valid, available data sets 

prevented the inclusion of every source 

type and setting in the evaluation. All 

the data bases that were reasonably 

available were used in the development 

and evaluation of the model, and those 

data bases were sufficient to establish 

the basis for the evaluation. Based on 

our review of the documentation and 

the public comments, we conclude that 

the accuracy of PRIME is sufficiently 

documented and find it acceptable for 

use in a dispersion model recommended 

in the Guideline. 

B. Appropriate for Proposed Use 

Responding to a question posed in our 

April 2000 proposal, the majority of 

commenters questioned the 

reasonableness of requiring 

simultaneous use of two models (ISC–

PRIME and AERMOD) for those sources 

with potential downwash concerns. 

Commenters urged the Agency to 

eliminate the need to use two models 

for evaluating the same source. In 

response to this request, AERMIC 

developed a version of AERMOD that 

incorporates PRIME: AERMOD (02222) 

and initiated an analysis to insure that 

concentration estimates by AERMOD 

(02222) are equivalent to ISC–PRIME 

predictions in areas affected by 

downwash before it replaces ISC–

PRIME. Careful thought was given to the 

way that PRIME was incorporated into 

AERMOD, with the goal of making the 

merge seamless. While discontinuities 

from the concatenation of these two sets 

of algorithms were of concern, we 

mitigated this situation wherever 

possible (see part D of this preamble, 

and the Response to Comments 

document 4). With regard to testing the 

performance of AERMOD (02222), we 

have carefully confirmed that the 

AERMOD (02222)’s air quality 

concentration predictions in the wake 

region reasonably compare to those 

predictions from ISC–PRIME. In fact, 

the results indicate that AERMOD 

(02222)’s performance matches the 

performance of ISC–PRIME, and are 

presented in an updated evaluation 

report 11 and analysis of regulatory 

design concentrations.12 We discuss 

AERMOD (02222) performance in detail 

in part D. 

Because the technical basis for the 

PRIME algorithms and the AERMOD 

formulations have been independently 

peer-reviewed, we believe that further 

peer review of the new model 

(AERMOD 02222) is not necessary. The 

scientific formulation of the PRIME 

algorithms has not been changed. 

However, the coding for the interface 

between PRIME and the accompanying 

dispersion model had to be modified 

somewhat to accommodate the different 

ways that ISC3ST and AERMOD 

simulate the atmosphere. The main 

public concern was the interaction 

between the two models and whether 

the behavior would be appropriate for 

all reasonable source settings. This 

concern was addressed through the 

extensive testing conducted within the 

performance evaluation 11 and analysis 

of design concentrations.12 Both sets of 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68221 

13Peters, W.D. et al., 1999. Comparison of 

Regulatory Design Concentrations: AERMOD vs. 

ISCST3 and CTDMPLUS, Draft Report. Docket No. 

A–99–05; II–A–15. 

analyses indicate that the new model is 

performing acceptably well and the 

results are similar to those obtained 

from the earlier performance 

evaluation 8 10 and analysis of regulatory 

design concentrations (i.e., for AERMOD 

(99351)).13

While dry deposition is treated in 

ISC3ST, time and resources did not 

allow its incorporation in AERMOD 

(99351). Since no recommendation for 

deposition is made for regulatory 

applications, we did not consider that 

the absence of this capability 

compromises the suitability of 

AERMOD for its intended purposes. 

Nevertheless, a number of commenters 

requested that deposition algorithms be 

added to AERMOD, and we developed 

an update to AERMOD (02222) that 

offers dry and wet deposition for both 

gases and particles as an option. 

The version of AERMOD under 

review at the 7th Conference was 

AERMOD (99351) and, as mentioned 

above, AERMIC has made a number of 

changes to AERMOD (99351) following 

this conference. These changes were 

initiated in response to public 

comments and, after the release of a new 

draft version of the model, in response 

to the recommendations from the beta 

testers. Changes made to AERMOD 

include the following: 

• Adding the PRIME algorithms to the 

model (response to public comments); 

• Modifying the complex terrain 

algorithms to make AERMOD less 

sensitive to the selection of the domain 

of the study area (response to public 

comments); 

• Modifying the urban dispersion for 

low-level emission sources, such as area 

sources, to produce a more realistic 

urban dispersion and, as a part of this 

change, changing the minimum layer 

depth used to calculate the effective 

dispersion parameters for all dispersion 

settings (scientific formulation 

correction which was requested by beta 

testers); and 

• Upgrading AERMOD to include all 

the newest features that exist in the 

latest version of ISC3ST such as 

Fortran90 compliance and allocatable 

arrays, EVENTS processing and the 

TOXICS option (response to public 

comments). 

In the follow-up quality control 

checking of the model and the source 

code, additional changes were identified 

as necessary and the following revisions 

were made: 

• Adding meander treatment to: (1) 

Stable and unstable urban cases, and (2) 

the rural unstable dispersion settings 

(only the rural, stable dispersion setting 

considered meander in AERMOD 

(99351)—this change created a 

consistent treatment of air dispersion in 

all dispersion settings); 

• Making some changes to the basic 

meander algorithms (improved 

scientific formulation); and 

• Repairing miscellaneous coding 

errors. 

As we mentioned earlier, the version 

of AERMOD that is being promulgated 

today—AERMOD (02222)—has been 

subjected to further performance 

evaluation 11 and analysis of design 

concentrations.12

C. Implementation Issues/Additional 

Guidance 

Other than miscellaneous suggestions 

for certain enhancements for AERMOD 

(99351) such as a Fortran90 compilation 

of the source code, creation of 

allocatable arrays, and development of a 

Windows graphical user interface, no 

significant implementation obstacles 

were identified in public comments. 

For AERMET (meteorological 

preprocessor for AERMOD), we have 

implemented some enhancements that 

commenters suggested. For site-specific 

applications, several commenters cited 

AERMOD’s requirements for NWS cloud 

cover data. In response, we revised the 

AERMET to incorporate the bulk 

Richardson number methodology. This 

approach uses temperature differences 

near the surface of the earth, which can 

be routinely monitored, and eliminates 

the need for the cloud cover data at 

night. We made a number of other 

revisions in response to public 

comments, enabling AERMET to: (1) 

Use the old and the new Forecasting 

Systems Laboratory formats, (2) use the 

Hourly U.S. Weather Observations/ 

Automated Surface Observing Stations 

(HUSWO/ASOS) data, (3) use sitespecific solar radiation and temperature 

gradient data to eliminate the need for 

cloud cover data, (4) appropriately 

handle meteorological data from above 

the arctic circle, and (5) accept a wider 

range of reasonable friction velocities 

and reduce the number of warning 

messages. As mentioned earlier, we 

added a meander component to the 

treatment of stable and unstable urban 

conditions to consistently treat meander 

phenomena for all cases. 

AERMAP (the terrain preprocessor for 

AERMOD) has been upgraded in 

response to public comments calling for 

it to: (1) Treat complex terrain receptors 

without a dependance on the selected 

domain, (2) accommodate the Spatial 

Data Transfer Standard (SDTS) data 

available from the U.S. Geological 

Survey (USGS), (3) appropriately use 

Digital Elevation Model (DEM) data 

with 2 different datums (NAD27 and 

NAD83); (4) accept all 7 digits of the 

North UTM coordinate, and (5) do more 

error-checking in the raw data (mostly 

checking for missing values, but not for 

harsh terrain changes in adjacent 

points). All of these recommendations 

have been implemented. 

In response to comments about the 

selection of the domain affecting the 

results of the maximum concentrations 

in complex terrain and the way 

AERMAP estimates the effective hill 

height scale (hC), the algorithms within 

AERMAP and AERMOD have been 

adjusted so that the hill height is less 

sensitive to the arbitrary selection of the 

domain. This adjustment has been 

evaluated against the entire set of 

evaluation data. The correction was 

found to substantially reduce the effect 

of the domain size upon the 

computation of controlling hill heights 

for each receptor. Application of this 

change to the evaluation databases did 

not materially affect the evaluation 

results. 

In general, public comments that 

requested additional guidance were 

either obviated by revisions to AERMOD 

(99351) and its related preprocessors or 

deemed unnecessary. In the latter case, 

the reasons were explained in the 

Response-to-Comments document.4

Some public comments suggested 

additional testing of AERMOD (99351). 

In fact, after the model revisions that 

were described earlier were completed, 

AERMOD (02222) was subjected to 

additional testing.11 12 These new 

analyses will be discussed in part D. 

With respect to a screening version of 

AERMOD, a tool called AERSCREEN is 

being developed with a beta version 

expected to be publicly available in Fall 

2005. SCREEN3 is the current screening 

model in the Guideline, and since 

SCREEN3 has been successfully applied 

for a number of years, we believe that 

SCREEN3 produces an acceptable 

degree of conservatism for regulatory 

applications and may be used until 

AERSCREEN or a similar technique 

becomes available and tested for general 

application. 

D. AERMOD Revision and Reanalyses 

Published In 2003 

1. Performance Analysis for AERMOD 

(02222) 

We have tested the performance of 

AERMOD (02222) by applying all of the 

original data sets used to support the 

version proposed in April, 2000: 

AERMOD (99351) 8 and ISC–PRIME.10

These data sets include: 5 complex 

68222 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

terrain data sets, 7 building downwash 

data sets, and 5 simple terrain data sets 

(see appendix A of the Response-toComments document 4). This 

performance analysis, which is a check 

of the model’s maximum concentration 

predictions against observed data, 

includes a comparison of the current 

version of the new model (AERMOD 

02222) with ISC3ST or ISC–PRIME for 

downwash conditions. The results and 

conclusions of the performance analyses 

are presented in 2 sections: Nondownwash and downwash source 

scenarios. 

a. Non-Downwash Cases 

For the user community to obtain a 

full understanding of the impacts of 

today’s proposal for the non-downwash 

source scenarios (flat and complex 

terrain), our performance evaluation of 

AERMOD (02222) must be discussed 

with respect to the old model, ISC3ST, 

and with respect to AERMOD (99351). 

Based on the evaluation, we have 

concluded that AERMOD (02222) 

significantly outperforms ISC3ST and 

that AERMOD (02222)’s performance is 

even better than that of AERMOD 

(99351). 

Evaluation of AERMOD (99351) 

Comparative performance statistics 

were calculated for both ISC3ST and 

AERMOD (99351) using data sets in 

non-downwash conditions. This 

analysis looked at combinations of test 

sites (flat and complex terrain), 

pollutants, and concentration averaging 

times. Comparisons indicated very 

significant improvements in 

performance when applying AERMOD 

(99351). In all but 1 of the total of 20 

cases in which AERMOD (99351) could 

be compared to ISC3ST, AERMOD 

performed as well as (but generally 

better than) ISC3ST, that is, AERMOD 

predicted maximum concentrations that 

were closer to the measured maximum 

concentrations. In the most dramatic 

case (i.e., Lovett; 24-hr) in which 

AERMOD performed better than 

ISC3ST, AERMOD’s maximum 

concentration predictions were about 

the same as the measured 

concentrations while the ISC3ST’s 

predicted maximum concentrations 

were about 9 times higher than the 

measured concentrations. In the one 

case (i.e., Clifty Creek; 3-hr) where 

ISC3ST performed better than AERMOD 

(99351), ISC3ST’s concentration 

predictions matched the observed data 

and the AERMOD concentration 

predictions were about 25% higher than 

the observed data. These results were 

reported in the supporting 

documentation for AERMOD (99351). 

Evaluation of AERMOD (02222) 

With the changes to AERMOD (99351) 

as outlined above, how has the 

performance of the AERMOD been 

affected? The performance of the current 

version of AERMOD is about the same 

or slightly better than the April 2000 

version when a comparison is made 

over all the available data sets. There 

were examples of AERMOD (02222) 

showing better and poorer performance 

when compared to the performance 

results of AERMOD (99351). However, 

for those cases where AERMOD 

(02222)’s performance was degraded, 

the degradation was small. On the other 

side, there were more examples where 

AERMOD (02222) more closely 

predicted measured concentrations. The 

performance improvements were also 

rather small but, in general, were 

somewhat larger than the size of the 

performance degradations. There also 

were a number of cases where the 

performance remained unchanged 

between the 2 models. Thus, overall, 

there was a slight improvement in 

AERMOD’s performance and, 

consequently, we believe that AERMOD 

(02222) significantly outperforms 

ISC3ST for non-downwash source 

scenarios. 

For AERMOD (02222) with the 5 data 

bases examined for simple terrain, the 

ratios of modeled/observed Robust High 

Concentration ranged from 0.77 to 1.11 

(1-hr average), 0.98 to 1.24 (3-hr 

average), 0.94 to 0.97 (24-hr average) 

and 0.30 to 0.97 (annual average). These 

ratios reflect better performance than 

ISC3ST for all cases. 

For AERMOD (02222) with the 5 data 

bases examined for complex terrain, 

these ratios ranged from 1.03 to 1.12 (3- 

hr average), 0.67 to 1.78 (24-hr average) 

and 0.54 to 1.59 (annual average). At 

Tracy—the only site for which there are 

1-hr data—AERMOD performed 

considerably better (ratio = 1.04) than 

either ISC3ST or CTDMPLUS. At three 

of the other four sites, AERMOD 

generally performed much better than 

either ISC3ST or (where applicable) 

alternative models for the 3-hr and 24- 

hr averaging times; results were 

comparable for Clifty Creek (for the 3- 

hr averaging times, AERMOD (02222) 

predictions were only about 5% higher 

than ISC3ST’s—down from 25% for 

AERMOD (99351) as described earlier). 

At the two sites where annual peak 

comparisons are available, AERMOD 

performed much better than either 

ISC3ST or alternative models. 

b. Downwash Cases 

For the downwash data sets, there 

were combinations of test sites, 

pollutants, stack heights and averaging 

times where the proposed (ISC–PRIME) 

model performance could be compared 

to the performance of AERMOD (02222) 

with PRIME incorporated. There was an 

equal number of non-downwash cases 

where AERMOD performed better than 

ISC–PRIME and where ISC–PRIME 

performed better than AERMOD. There 

was only one case where there was a 

significant difference between the two 

models’ performance, and AERMOD 

clearly performed better than ISC–

PRIME in this case. In all other cases, 

the difference in the performance, 

whether an improvement or a 

degradation, was small. This 

comparison indicated that AERMOD 

(02222) performs very similarly, if not 

somewhat better, when compared to 

ISC–PRIME for downwash cases. 

2. Analysis of Regulatory Design 

Concentrations for AERMOD (02222) 

Although not a performance tool, the 

analysis of design concentrations 

(‘‘consequence’’ analysis) is designed to 

test model stability and continuity, and 

to help the user community understand 

the differences to be expected between 

air dispersion models. The 

consequences, or changes in the 

regulatory concentrations predicted 

when using the new model (AERMOD 

02222) versus ISC3ST, cover 96 source 

scenarios and at least 3 averaging 

periods per source scenario, and are 

evaluated and summarized here. The 

purpose is to provide the user 

community with a sense of potential 

changes in their air dispersion analyses 

when applying the new model over a 

broad range of source types and settings. 

The consequence analysis, in which 

AERMOD was run for hundreds of 

source scenarios, also provides a check 

for model stability (abnormal halting of 

model executions when using valid 

control files and input data) and for 

spurious results (unusually high or low 

concentration predictions which are 

unexplained). The results are placed 

into 3 categories: non-downwash source 

scenarios in flat, simple terrain; 

downwash source scenarios in flat 

terrain; and, complex terrain source 

settings. The focus of this discussion is 

on how design concentrations change 

from those predicted by ISC3ST when 

applying the latest version of AERMOD 

versus applying the earlier version of 

AERMOD (99351). 

a. Non-Downwash Cases 

For the non-downwash situations, 

there were 48 cases covering a variety of 

source types (point, area, and volume 

sources), stack heights, terrain types 

(flat and simple), and dispersion 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68223 

14A ratio of 1.00 indicates that the two models 

are predicting the same concentrations. See Table 

4.1 in reference 12. 

15AERMOD uses more complex techniques to 

estimate temperature profiles which, in turn, affect 

the calculation of the plume rise. Plume rise may 

affect the cavity and downwash concentrations. 

16Currently listed in appendix A of the 

Guideline. 

settings (urban and rural). For each case 

in the consequence analysis, we 

calculated the ratio between AERMOD’s 

regulatory concentration predictions 

and ISC3ST’s regulatory concentration 

predictions. The average ratio of 

AERMOD to ISC3ST-predicted 

concentrations changed from 1.14 when 

applying AERMOD (99351) to 0.96 

when applying AERMOD (02222).14

Thus, in general, AERMOD (02222) 

tends to predict concentrations closer to 

ISC3ST than does version 99351 

proposed in April 2000. Also, the 

variation of the differences between 

ISC3ST and AERMOD has decreased 

with AERMOD (02222). Comparing the 

earlier consequence analysis to the 

latest study with AERMOD (02222), we 

saw a 25% reduction in the number of 

cases where the AERMOD-predicted 

concentrations differed by over a factor 

of two from ISC3ST’s predictions. 

b. Downwash Cases 

For the downwash analysis, there 

were 20 cases covering a range of stack 

heights, locations of stacks relative to 

the building, dispersion settings, and 

building shapes. As before, we 

calculated the ratio regulatory 

concentration predictions from 

AERMOD (02222 with PRIME) and 

compared them as ratios to those from 

ISC3ST for each case. For additional 

information, we also included ratios 

with ISC–PRIME that was also proposed 

in April 2000. 

Calculated over all the 20 cases, and 

for all averaging times considered, the 

average ISC–PRIME to ISC3ST 

concentration ratio is about 0.86, 

whereas for AERMOD (PRIME) to 

ISC3ST, it is 0.82. The maximum value 

of the concentration ratios range from 

2.24 for ISC–PRIME/ISC3ST to 3.67 for 

AERMOD (PRIME)/ISC3ST. Similarly, 

the minimum value of the concentration 

ratio range from 0.04 for ISC–PRIME/ 

ISC3ST to 0.08 for AERMOD (PRIME)/ 

ISC3ST. (See Table 4–5 in reference 12.) 

Although results above for the two 

models that use PRIME—AERMOD 

(02222) and ISC–PRIME—show 

differences, we find that building 

downwash is not a significant factor in 

determining the maximum 

concentrations in some of the cases, i.e., 

the PRIME algorithms do not predict a 

building cavity concentration. Of those 

cases where downwash was important, 

the average concentration ratios of ISC–

PRIME/ISC3ST and AERMOD (02222)/ 

ISC3ST are about 1. The maximum 

value of the concentration ratios range 

from 2.24 for ISC–PRIME/ISC3ST to 

1.87 for AERMOD (02222)/ISC3ST and 

the minimum value of the concentration 

ratios range from 0.34 for ISC–PRIME/ 

ISC3ST to 0.38 for AERMOD (02222)/ 

ISC3ST. These results show relatively 

close agreement between the two PRIME 

models. (See Table 4–6 in reference 12.) 

ISC3ST does not predict cavity 

concentrations but comparisons can be 

made between AERMOD and ISC–

PRIME. The average AERMOD (02222) 

predicted 1-hour cavity concentration is 

about the same (112%) as the average 

ISC–PRIME 1-hour cavity concentration. 

In the extremes, the AERMOD (02222)- 

predicted cavity concentrations ranged 

from about 40% higher to 15% lower 

than the corresponding ISC–PRIME 

cavity concentration predictions. Thus, 

in general, where downwash is a 

significant factor, AERMOD (02222) and 

ISC–PRIME predict similar maximum 

concentrations. (See Table 4–8 in 

reference 12.) 

Although the same downwash 

algorithms are used in both models, 

there are differences in the melding of 

PRIME with the core model, and 

differences in the way that these models 

simulate the atmosphere.15 The 

downwash algorithm implementation 

therefore could not be exactly the same. 

c. Complex Terrain 

During the testing of AERMOD after 

modifications were made to the 

complex terrain algorithm (see 

discussion of hill height scale (hC) in B. 

Appropriate for Proposed Use in this 

preamble), a small error was found in 

the original complex terrain code while 

conducting the consequence analysis. 

This error was subsequently repaired. 

Final testing indicated that the revised 

complex terrain code produced 

reasonable results for the consequence 

analysis, as described below. 

The analysis of predicted design 

concentrations included a suite of 

complex terrain settings. There were 28 

cases covering a variety of stack heights, 

stack gas buoyancy values, types of 

hills, and distances between source and 

terrain. The ratios between the 

AERMOD (02222 & 99351)—predicted 

maximum concentrations and the 

ISC3ST maximum concentrations were 

calculated for all cases for a series of 

averaging times. When comparing 

AERMOD (99351) to ISC3ST and then 

AERMOD (02222) to ISC3ST, the 

average maximum concentration ratio, 

the highest ratios and the lowest ratios 

were almost unchanged. There were no 

cases in either consequence analysis 

where AERMOD (02222 & 99351) 

predicted higher concentrations than 

those predicted by ISC3ST. Thus, in 

general, the consequences of moving 

from ISC3ST to AERMOD (02222) rather 

than to AERMOD (99351) in complex 

terrain were essentially the same. (See 

Table 4–9 in reference 12.) 

E. Emission and Dispersion Modeling 

System (EDMS) 

The Emissions and Dispersion 

Modeling System (EDMS) was 

developed jointly by the Federal 

Aviation Administration (FAA) and the 

U.S. Air Force in the late 1970s and first 

released in 1985 to assess the air quality 

of proposed airport development 

projects. EDMS has an emissions 

preprocessor and its dispersion module 

estimates concentrations for various 

averaging times for the following 

pollutants: CO, HC, NOX, SOX, and 

suspended particles (e.g., PM–10). The 

first published application of EDMS was 

in December 1986 for Stapleton 

International Airport (FAA–EE–11–A/ 

REV2). 

In 1988, version 4a4 revised the 

dispersion module to include an 

integral dispersion submodel: GIMM 

(Graphical Input Microcomputer 

Model). This version was proposed for 

adoption in the Guideline’s appendix A 

in February 1991 (56 FR 5900). This 

version was included in appendix A in 

July 1993 (58 FR 38816) and 

recommended for limited applications 

for assessments of localized airport 

impacts on air quality. FAA later 

updated EDMS to Version 3.0. 

In response to the growing needs of 

air quality analysts and changes in 

regulations (e.g., conformity 

requirements from the Clean Air Act 

Amendment of 1990), FAA updated 

EDMS to version 3.1, which is based on 

the CALINE3 16 and PAL2 dispersion 

kernels. In our April 2000 NPR we 

proposed to adopt the version 3.1 

update to EDMS. However, this update 

had not been subjected to performance 

evaluation and no studies of EDMS’

performance have been cited in 

appendix A of the Guideline. Comment 

was invited on whether this 

compromises the viability of EDMS 3.1 

as a recommended or preferred model 

and how this deficiency can be 

corrected. 

Several commenters expressed 

concern about EDMS 3.1 as a 

recommended model in appendix A. 

Indeed, there were concerns that EDMS 

68224 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

17Cambridge Environmental Research 

Consultants; http://www.cerc.co.uk/. 

3.1 had not been as well validated as 

other models, nor subjected to peer 

review, as required by the Guideline’s 

subsection 3.1.1. One of these 

commenters suggested that EDMS 3.1 

should be presented only as one of 

several alternative models. 

At the 7th Conference, FAA proposed 

for appendix A adoption an even newer, 

enhanced version of EDMS—version 

4.0, which incorporates the AERMOD 

dispersion kernel (without alteration). 

In this system, the latest version of 

AERMOD would be employed as a 

standalone component of EDMS. This 

dispersion kernel was to replace PAL2 

and CALINE3 currently in EDMS 3.1. 

There were no public comments specific 

to FAA’s proposed AERMOD-based 

enhancements to EDMS announced after 

our April 2000 NPR. 

In response to written comments on 

our April 2000 NPR, at the 7th 

Conference (transcript) FAA promised a 

complete evaluation process that would 

include sensitivity testing, intermodel 

comparison, and analysis of EDMS 

predictions against field observations. 

The intermodel comparisons were 

proposed for the UK’s Atmospheric 

Dispersion Modeling System (ADMS).17

As we explained in our September 8, 

2003 Notice of Data Availability, FAA 

has decided to withdraw EDMS from 

the Guideline’s appendix A. We stated 

that no new information was therefore 

provided in that notice, and we affirmed 

support for EDMS’ removal from 

appendix A. This removal, which we 

promulgate today, obviates the need for 

EDMS’ documentation and evaluation at 

this time. 

V. Discussion of Public Comments on 

Our September 8, 2003 Notice of Data 

Availability 

As mentioned in section III, after 

AERMOD was revised pursuant to 

comments received on the April 21, 

2000 proposal, a Notice of Data 

Availability (NDA) was issued on 

September 8, 2003 to explain the 

modifications and to reveal AERMOD’s 

new evaluation data. Public comments 

were solicited for 30 days and posted 

electronically in eDocket OAR–2003–

0201.1 (As mentioned in section IV, 

additional comments received since we 

published the final rule on April 15, 

2003 are filed in Docket A–99–05; 

category IV–E.) We summarized these 

comments and developed detailed 

responses; these appear as appendix C 

to the Response-to-Comments 

document.4 In appendix C, we 

considered and discussed all significant 

comments, developed responses, and 

documented conclusions on appropriate 

actions for today’s notice. Whenever the 

comments revealed any new 

information or suggested any alternative 

solutions, we considered them in our 

final action and made corrections or 

enhancements where appropriate. 

In the remainder of this preamble 

section we highlight the main issues 

raised by the commenters who reviewed 

the NDA, and summarize our responses. 

These comments broadly fall into two 

categories: technical/operational, and 

administrative. 

The technical/operational comments 

were varied. One commenter thought 

EPA’s sensitivity studies for simulating 

area sources were too limited, and noted 

that AERMOD, when used to simulate 

an area source adjacent to gently sloping 

terrain, produced ground-level 

concentrations not unlike those from 

ISC3ST. In response we explained 

qualitatively how AERMOD interprets 

this situation and cautioned that 

reviewing authorities should be 

consulted in such scenarios for 

guidance on switch settings. Other 

commenters believed that AERMOD 

exhibited unrealistic treatment of 

complex terrain elements and offered 

supporting data. In response, AERMIC 

concluded that AERMOD does exhibit 

terrain amplification factors on the 

windward side of isolated hills, where 

impacts are expected to be greatest. 

Commenters also presented evidence 

that the PRIME algorithm in AERMOD 

misbehaves in its treatment of building 

wake and wind incidence. Another 

model was cited as having better skill in 

this regard. In response, we 

acknowledged this but established that 

AERMOD’s capability was acceptable 

for handling the majority of building 

geometries encountered (see Responseto-Comments document 4 for more 

details). 

A number of commenters addressed 

administrative or procedural matters. 

Some believed that the transition period 

for implementation—one year—is too 

short. We explained in response that 

one year is consistent with past practice 

and is adequate for most users and 

reviewing authorities given our previous 

experience with new models and the 

fact that AERMOD has been in the 

public domain for several years. Some 

were disappointed that the review 

period (30 days) for the NDA was too 

short. We believe that the period was 

adequate to review the two reports that 

presented updated information on the 

performance and practical consequences 

of the model as revised. Regarding the 

evaluation/comparison regime used for 

AERMOD, others objected to the 

methodology used to evaluate AERMOD 

(one that emphasizes Robust High 

Concentration), claiming it is ill-suited 

to the way dispersion models estimate 

ambient concentrations. We 

acknowledged that other methods are 

available that are designed to reflect the 

underlying physics and formulations of 

dispersion models, and may be more 

robust in their mechanisms to account 

for the stochastic nature of the 

atmosphere. In fact, we cited several 

recent cases from the literature in which 

such methods were applied in 

evaluations that included AERMOD. We 

also explained that the approach taken 

by AERMIC was based on existing 

guidance in section 9 of Appendix W, 

and expressed a commitment to explore 

other methods in the future, including 

an update to section 9. We believe 

however that the evaluation 

methodology used was reasonable for its 

intended purpose—examining a large 

array of concentrations for a wide 

variety of source types—and confers a 

measure of consistency given its past 

use. Other commenters expressed 

disappointment that AERMOD wasn’t 

compared to state-of-the-science models 

as advised in its peer review report. In 

response, we cited a substantial list of 

studies in which AERMOD has, in fact, 

been compared to some of these models, 

e.g., HPDM and ADMS (in various 

combinations). On the whole, as we 

noted in our response, AERMOD 

typically performed as well as HPDM 

and ADMS, and all of them generally 

performed better than ISC3ST. Still 

others expressed disappointment that 

the evaluation input data weren’t posted 

on our Web site until January 22, 2004—

three months after the close of the 

comment period. We acknowledge that 

the input data were not posted when the 

NDA was published. However, the 

actual evaluation input data for 

AERMOD had not been requested 

previously, and we did not believe they 

were required as a basis for reviewing 

the reports we released. Moreover, since 

the posting, we are unaware of any 

belated adverse comments from anyone 

attempting to access and use the data. 

We believe we have carefully 

considered and responded to public 

comments and concerns regarding 

AERMOD. We have also made efforts to 

update appendix W to better reflect 

current practice in model solicitation, 

evaluation and selection. We also have 

made other technical revisions so the 

guidance conforms with the latest form 

of the PM–10 National Ambient Air 

Quality Standard. 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68225 

VI. Final Action 

In this section we explain the changes 

to the Guideline in today’s action in 

terms of the main technical and policy 

concerns addressed by the Agency in its 

response to public comments (sections 

IV & V). Air quality modeling involves 

estimating ambient concentrations using 

scientific methodologies selected from a 

range of possible methods, and should 

utilize the most advanced practical 

technology that is available at a 

reasonable cost to users, keeping in 

mind the intended uses of the modeling 

and ensuring transparency to the public. 

With these changes, we believe that the 

Guideline continues to reflect recent 

advances in the field and balance these 

important considerations. Today’s 

action amends Appendix W of 40 CFR 

part 51 as detailed below: 

AERMOD 

Based on the supporting information 

contained in the docket, and reflected in 

peer review and public comments, we 

find that the AERMOD modeling system 

and PRIME are based on sound 

scientific principles and provide 

significant improvements over the 

current regulatory model, ISC3ST. 

AERMOD characterizes plume 

dispersion better than ISC3ST. The 

accuracy of the AERMOD system is 

generally well-documented and superior 

to that of ISC3ST. We are adopting the 

model based on its performance and 

other factors. 

Public comments on the April 2000 

proposal expressed significant concern 

about the need to use two models 

(AERMOD and ISC–PRIME) to simulate 

just one source when downwash posed 

a potential impact. In response to this 

concern we incorporated PRIME into 

AERMOD and documented satisfactory 

tests of the algorithm. AERMOD, with 

the inclusion of PRIME, is now 

appropriate and practical for regulatory 

applications. 

The state-of-the-science for modeling 

atmospheric deposition continues to 

evolve, the best techniques are currently 

being assessed, and their results are 

being compared with observations. 

Consequently, as we now say in 

Guideline paragraph 4.2.2(c), the 

approach taken for any regulatory 

purpose should be coordinated with the 

appropriate reviewing authority. We 

agreed with the public comments 

calling for the addition of state-of-thescience deposition algorithms, and 

developed a modification to AERMOD 

(02222) for beta testing. This model, 

AERMOD (04079) was posted on our 

Web site http://www.epa.gov/scram001/ 

tt25.htm#aermoddep on March 19, 

2004. The latest version of AERMOD 

may now be used for deposition 

analysis in special situations. 

Since AERMOD treats dispersion in 

complex terrain, we have merged 

sections 4 and 5 of appendix W, as 

proposed in the April 2000 NPR. And 

while AERMOD produces acceptable 

regulatory design concentrations in 

complex terrain, it does not replace 

CTDMPLUS for detailed or receptororiented complex terrain analysis, as we 

have made clear in Guideline section 

4.2.2. CTDMPLUS remains available for 

use in complex terrain. 

We have implemented the majority of 

suggestions to improve the AERMET, 

AERMAP, and AERMOD source code to 

reflect all the latest features that have 

been available in ISC3ST and that are 

available in the latest versions of 

Fortran compilers. Also, the latest 

formats for meteorological and terrain 

input data are now accepted by the new 

versions of AERMET and AERMAP. Our 

guidance, documentation and users’

guides have been modified in response 

to a number of detailed comments. 

With respect to AERMOD (02222)’s 

performance, we have concluded that: 

(1) AERMOD (99351), the version 

proposed in April 2000, performs 

significantly better than ISC3ST, and 

AERMOD (02222) performs slightly 

better than AERMOD (99351) in nondownwash settings in both simple and 

complex terrain; 

(2) The performance evaluation 

indicates that AERMOD (02222) 

performs slightly better than ISC–PRIME 

for downwash cases. 

With respect to changes in AERMOD’s 

regulatory design concentrations 

compared to those for ISC3ST, we have 

concluded that: 

• For non-downwash settings, 

AERMOD (02222), on average, tends to 

predict concentrations closer to ISC3ST, 

and with somewhat smaller variations, 

than the April 2000 proposal of 

AERMOD; 

• Where downwash is a significant 

factor in the air dispersion analysis, 

AERMOD (02222) predicts maximum 

concentrations that are very similar to 

ISC–PRIME’s predictions; 

• For those source scenarios where 

maximum 1-hour cavity concentrations 

are calculated, the average AERMOD 

(02222)-predicted cavity concentration 

tends to be about the same as the 

average ISC–PRIME cavity 

concentrations; and 

• In complex terrain, the 

consequences of using AERMOD 

(02222) instead of ISC3ST remained 

essentially unchanged in general, 

although they varied based on 

individual circumstances. 

Since AERMOD (02222) was released, 

an updated version was posted on our 

Web site on March 22, 2004: AERMOD 

(04079). The version we are releasing 

pursuant to today’s promulgation, 

however, is AERMOD (04300). This 

version, consonant with AERMOD 

(02222) in its formulations, addresses 

the following minor code issues: 

• The area source algorithm in simple 

and complex terrain required a 

correction to the way the dividing 

streamline height is calculated. 

• In PRIME, incorrect turbulence 

parameters were being passed to one of 

the numerical plume rise routines, and 

this has been corrected. 

• A limit has been placed on plume 

cooling within PRIME to avoid 

supercooling, which had been causing 

runtime instability. 

• A correction has been made to 

avoid AERMOD’s termination under 

certain situations with capped stacks 

(i.e., where the routine was attempting 

to take a square root of a negative 

number). Our testing has demonstrated 

only very minor impacts from these 

corrections on the evaluation results or 

the consequence analysis. 

AERMOD (04300) has other draft 

portions of code that represent options 

not required for regulatory applications. 

These include: 

• Dry and wet deposition for both 

gases and particles; 

• The ozone limiting method (OLM), 

referenced in section 5.2.4 (Models for 

Nitrogen Dioxide—Annual Average) of 

the Guideline for treating NOX

conversion; and 

• The Plume Volume Molar Ratio 

Method (PVMRM) for treating NOX

conversion. 

• The bulk Richardson number 

approach (discussed earlier) for using 

near-surface temperature difference has 

been corrected in AERMOD (04300). 

Based on the technical information 

contained in the docket for this rule, 

and with consideration of the 

performance analysis in combination 

with the analysis of design 

concentrations, we believe that 

AERMOD is appropriate for regulatory 

use and we are revising the Guideline to 

adopt it as a refined model today. 

In implementing the changes to the 

Guideline, we recognize that there may 

arise occasions in which the application 

of a new model can result in the 

discovery by a permit applicant of 

previously unknown violations of 

NAAQS or PSD increments due to 

emissions from existing nearby sources. 

This potential has been acknowledged 

previously and is addressed in existing 

EPA guidance (‘‘Air Quality Analysis for 

Prevention of Significant Deterioration 

68226 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

18http://www.epa.gov/ttn/fera/risk

_atra_main.html. 

(PSD),’’ Gerald A. Emison, July 5, 1988). 

To summarize briefly, the guidance 

identifies three possible outcomes of 

modeling by a permit applicant and 

details actions that should be taken in 

response to each: 

1. Where dispersion modeling shows 

no violation of a NAAQS or PSD 

increment in the impact area of the 

proposed source, a permit may be 

issued and no further action is required. 

2. Where dispersion modeling 

predicts a violation of a NAAQS or PSD 

increment within the impact area but it 

is determined that the proposed source 

will not have a significant impact (i.e., 

will not be above de minimis levels) at 

the point and time of the modeled 

violation, then the permit may be issued 

immediately, but the State must take 

appropriate actions to remedy the 

violations within a timely manner. 

3. Where dispersion modeling 

predicts a violation of a NAAQS or PSD 

increment within the impact area and it 

is determined that the proposed source 

will have a significant impact at the 

point and time of the modeled violation, 

then the permit may not be issued until 

the source owner or operator eliminates 

or reduces that impact below 

significance levels through additional 

controls or emissions offsets. Once it 

does so, then the permit may be issued 

even if the violation persists after the 

source owner or operator eliminates its 

contribution, but the State must take 

further appropriate actions at nearby 

sources to eliminate the violations 

within a timely manner. 

In previous promulgations, we have 

traditionally allowed a one-year 

transition (‘‘grandfather’’) period for 

new refined techniques. Accordingly, 

for appropriate applications, AERMOD 

may be substituted for ISC3 during the 

one-year period following the 

promulgation of today’s notice. 

Beginning one year after promulgation 

of today’s notice, (1) applications of 

ISC3 with approved protocols may be 

accepted (see DATES section) and (2) 

AERMOD should be used for 

appropriate applications as a 

replacement for ISC3. 

We separately issue guidance for use 

of modeling for facility-specific and 

community-scale air toxics risk 

assessments through the Air Toxics Risk 

Assessment Reference Library.18 We 

recognize that the tools and approaches 

recommended therein will eventually 

reflect the improved formulations of the 

AERMOD modeling system and we 

expect to appropriately incorporate 

them as expeditiously as practicable. In 

the interim, as appropriate, we will 

consider the use of either ISC3 or 

AERMOD in air toxic risk assessment 

applications. 

EDMS 

FAA has completed development of 

the new EDMS4.0 to incorporate 

AERMOD. The result is a conforming 

enhancement that offers a stronger 

scientific basis for air quality modeling. 

FAA has made this model available on 

its Web site, which we cite in an 

updated Guideline paragraph 7.2.4(c). 

As described earlier in this preamble, 

the summary description for EDMS will 

be removed from appendix A. 

VII. Final Editorial Changes to 

Appendix W 

Today’s update of the Guideline takes 

the form of many revisions, and some of 

the text is unaltered. Therefore, as a 

purely practical matter, we have chosen 

to publish the new version of the entire 

text of appendix W and its appendix A. 

Guidance and editorial changes 

associated with the resolution of the 

issues discussed in the previous section 

are adopted in the appropriate sections 

of the Guideline, as follows: 

Preface 

You will note some minor revisions of 

appendix W to reflect current EPA 

practice. 

Section 4 

As mentioned earlier, we revised 

section 4 to present AERMOD as a 

refined regulatory modeling technique 

for particular applications. 

Section 5 

As mentioned above, we merged 

pertinent guidance in section 5 

(Modeling in Complex Terrain) with 

that in section 4. With the anticipated 

widespread use of AERMOD for all 

terrain types, there is no longer any 

utility in the previous differentiation 

between simple and complex terrain for 

model selection. To further simplify, the 

list of acceptable, yet equivalent, 

screening techniques for complex 

terrain was removed. CTSCREEN and 

guidance for its use are retained; 

CTSCREEN remains acceptable for all 

terrain above stack top. The screening 

techniques whose descriptions we 

removed, i.e., Valley (as implemented in 

SCREEN3), COMPLEX I (as 

implemented in ISC3ST), 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 renumbered this to 

become section 5. In subsection 5.1, we 

reference the Plume Volume Molar 

Ratio Method (PVMRM) for point 

sources of NOX, and mention that it is 

currently being tested to determine 

suitability as a refined method. 

Section 7 

As proposed, we renumbered this to 

become section 6. We updated the 

reference to the Emissions and 

Dispersion Modeling System (EDMS). 

Section 8 

As proposed, we revised section 8 

(renumbered to section 7) to provide 

guidance for using AERMET 

(AERMOD’s meteorological 

preprocessor). 

• In subsection 7.2.4, we introduce 

the atmospheric stability 

characterization for AERMOD. 

• In subsection 7.2.5, we describe the 

plume rise approaches used by 

AERMOD. 

Section 9 

As proposed, we renumbered section 

9 to become section 8. We added 

paragraphs 8.3.1.2(e) and 8.3.1.2(f) to 

clarify use of site specific 

meteorological data for driving 

CALMET in the separate circumstances 

of long range transport and for complex 

terrain applications. 

Section 10 

As proposed, we revised section 10 

(renumbered section 9) to include 

AERMOD. In May 1999, the D.C. Court 

of Appeals vacated the PM–10 standard 

we promulgated in 1997, and this 

standard has since been removed from 

the CFR (69 FR 45592; July 30, 2004). 

Paragraph 10.2.3.2(a) has been corrected 

to be consistent with the current 

(original) PM–10 standard, which is 

based on expected exceedances. 

Section 11 

As proposed, we renumbered section 

11 to become section 10. 

Sections 12 & 13 

We renumbered section 12 to become 

section 11, and section 13 (References) 

to become section 12. We revised 

renumbered section 12 by adding some 

references, deleting obsolete/superseded 

ones, and resequencing. You will note 

that the peer scientific review for 

AERMOD and latest evaluation 

references have been included. 

Appendix A 

We added AERMOD (with the PRIME 

downwash algorithm integrated) to 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68227 

appendix A. We removed EDMS from 

appendix A. We also updated the 

description for CALPUFF, and made 

minor updates to some of the other 

model descriptions. 

Availability of Related Information 

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. 

In addition, we have uploaded various 

support documents (e.g., evaluation 

reports). 

VIII. 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, 

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 Executive Order. 

It has been determined that this rule 

is not a ‘‘significant regulatory action’’

under the terms of Executive Order 

12866 and is therefore not subject to EO 

12866 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. 

Burden means the total time, effort, or 

financial resources expended by persons 

to generate, maintain, retain, or disclose 

or provide information to or for a 

Federal agency. This includes the time 

needed to review instructions; develop, 

acquire, install, and utilize technology 

and systems for the purposes of 

collecting, validating, and verifying 

information, processing and 

maintaining information, and disclosing 

and providing information; adjust the 

existing ways to comply with any 

previously applicable instructions and 

requirements; train personnel to be able 

to respond to a collection of 

information; search data sources; 

complete and review the collection of 

information; and transmit or otherwise 

disclose the information. 

An agency may not conduct or 

sponsor, and a person is not required to 

respond to a collection of information 

unless it displays a currently valid OMB 

control number. The OMB control 

numbers for EPA’s regulations in 40 

CFR are listed in 40 CFR part 9. 

C. Regulatory Flexibility Act (RFA) 

The RFA generally requires an agency 

to prepare a regulatory flexibility 

analysis of any rule subject to notice 

and comment rulemaking requirements 

under the Administrative Procedure Act 

or any other statute unless the agency 

certifies that the rule will not have a 

significant economic impact on a 

substantial number of small entities. 

Small entities include small businesses, 

small organizations, and small 

governmental jurisdictions. 

For purposes of assessing the impact 

of today’s rule on small entities, small 

entities are defined as: (1) A small 

business that meets the RFA default 

definitions for small business (based on 

Small Business Administration size 

standards), as described in 13 CFR 

121.201; (2) a small governmental 

jurisdiction that is a government of a 

city, county, town, school district or 

special district with a population of less 

than 50,000; and (3) a small 

organization that is any not-for-profit 

enterprise which is independently 

owned and operated and is not 

dominant in its field. 

After considering the economic 

impacts of today’s final rule on small 

entities, I certify that this action will not 

have a significant economic impact on 

a substantial number of small entities. 

As this rule merely updates existing 

technical requirements for air quality 

modeling analyses mandated by various 

CAA programs (e.g., prevention of 

significant deterioration, new source 

review, State Implementation Plan 

revisions) and imposes no new 

regulatory burdens, there will be no 

additional impact on small entities 

regarding reporting, recordkeeping, and 

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, AERMOD, to replace 

ISC3ST as an analytical tool for use in 

SIP revisions and for calculating PSD 

increment consumption. AERMOD has 

been used for these purposes on a caseby-case basis (per Guideline subsection 

3.2.2) for several years. Since the two 

modeling systems are comparable in 

scope and purpose, use of AERMOD 

itself does not involve any significant 

increase in costs. 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. 

68228 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 

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: October 21, 2005. 

Stephen L. Johnson, 

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: 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68229 

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. Historically, three primary 

activities have provided direct input to 

revisions of the Guideline. The first is a series 

of annual EPA workshops conducted for the 

purpose of ensuring consistency and 

providing clarification in the application of 

models. The second activity was 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, have been 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 have been 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 Stationary-Source Models 

4.1 Discussion 

4.2 Recommendations 

4.2.1 Screening Techniques 

4.2.1.1 Simple Terrain 

4.2.1.2 Complex Terrain 

4.2.2 Refined Analytical Techniques 

5.0 Models for Ozone, Particulate Matter, 

Carbon Monoxide, Nitrogen Dioxide, and 

Lead 

5.1 Discussion 

5.2 Recommendations 

5.2.1 Models for Ozone 

5.2.2 Models for Particulate Matter 

5.2.2.1 PM–2.5 

5.2.2.2 PM–10 

5.2.3 Models for Carbon Monoxide 

5.2.4 Models for Nitrogen Dioxide 

(Annual Average) 

5.2.5 Models for Lead 

6.0 Other Model Requirements 

6.1 Discussion 

6.2 Recommendations 

6.2.1 Visibility 

6.2.2 Good Engineering Practice Stack 

Height 

6.2.3 Long Range Transport (LRT) (i.e., 

beyond 50 km) 

6.2.4 Modeling Guidance for Other 

Governmental Programs 

7.0 General Modeling Considerations 

7.1 Discussion 

7.2 Recommendations 

7.2.1 Design Concentrations 

7.2.2 Critical Receptor Sites 

7.2.3 Dispersion Coefficients 

7.2.4 Stability Categories 

7.2.5 Plume Rise 

7.2.6 Chemical Transformation 

7.2.7 Gravitational Settling and 

Deposition 

7.2.8 Complex Winds 

7.2.9 Calibration of Models 

8.0 Model Input Data 

8.1 Source Data 

8.1.1 Discussion 

8.1.2 Recommendations 

8.2 Background Concentrations 

8.2.1 Discussion 

8.2.2 Recommendations (Isolated Single 

Source) 

8.2.3 Recommendations (Multi-Source 

Areas) 

8.3 Meteorological Input Data 

8.3.1 Length of Record of Meteorological 

Data 

8.3.2 National Weather Service Data 

8.3.3 Site Specific Data 

8.3.4 Treatment of Near-calms and Calms 

9.0 Accuracy and Uncertainty of Models 

9.1 Discussion 

9.1.1 Overview of Model Uncertainty 

9.1.2 Studies of Model Accuracy 

9.1.3 Use of Uncertainty in DecisionMaking 

9.1.4 Evaluation of Models 

9.2 Recommendations 

10.0 Regulatory Application of Models 

10.1 Discussion 

10.2 Recommendations 

10.2.1 Analysis Requirements 

10.2.2 Use of Measured Data in Lieu of 

Model Estimates 

10.2.3 Emission Limits 

11.0 Bibliography 

12.0 References 

Appendix A to Appendix W of 40 CFR Part 

51—Summaries of Preferred Air Quality 

Models 

LIST OF TABLES

Table No. Title 

4–1a ............ Neutral/Stable Meteorological 

Matrix for CTSCREEN. 

4–1b ............ Unstable/Convective Meteorological Matrix for 

CTSCREEN. 

8–1 .............. Model Emission Input Data for 

Point Sources. 

8–2 .............. Point Source Model Emission 

Input Data for NAAQS Compliance in PSD Demonstrations. 

8–3 .............. Averaging Times for Site Specific Wind and Turbulence 

Measurements. 

1.0 Introduction 

a. The Guideline recommends air quality 

modeling techniques that should be applied 

to State Implementation Plan (SIP) revisions 

for existing sources and to new source 

reviews (NSR), including prevention of 

significant deterioration (PSD).123

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 

judgment. 

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 

68230 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 judgment 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 judgment of 

experienced meteorologists and analysts is 

essential. 

d. The model that most accurately 

estimates concentrations in the area of 

interest is always sought. However, it is clear 

from the needs expressed by the States and 

EPA Regional Offices, by many industries 

and trade associations, and also by the 

deliberations of Congress, that consistency in 

the selection and application of models and 

data bases should also be sought, even in 

case-by-case analyses. Consistency ensures 

that air quality control agencies and the 

general public have a common basis for 

estimating pollutant concentrations, 

assessing control strategies and specifying 

emission limits. Such consistency is not, 

however, promoted at the expense of model 

and data base accuracy. The Guideline 

provides a consistent basis for selection of 

the most accurate models and data bases for 

use in air quality assessments. 

e. Recommendations are made in the 

Guideline concerning air quality models, data 

bases, requirements for concentration 

estimates, the use of measured data in lieu 

of model estimates, and model evaluation 

procedures. Models are identified for some 

specific applications. The guidance provided 

here should be followed in air quality 

analyses relative to State Implementation 

Plans and in supporting analyses required by 

EPA, State and local agency air programs. 

EPA may approve the use of another 

technique that can be demonstrated to be 

more appropriate than those recommended 

in this guide. This is discussed at greater 

length in Section 3. 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 ongoing research programs, new model 

development, 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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68231 

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 

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 5 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 6 and 

the program whereby these models were 

evaluated, established a means by which new 

models are identified, reviewed and made 

available in the Guideline. There is a pressing 

need for the development of models for a 

wide range of regulatory applications. 

Refined models that more realistically 

simulate the physical and chemical process 

in the atmosphere and that more reliably 

estimate pollutant concentrations are needed. 

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. Refined 

models which are preferred and 

recommended by EPA have undergone 

evaluation exercises 7 8 9 10 that include 

statistical measures of model performance in 

comparison with measured air quality data as 

suggested by the American Meteorological 

Society 11 and, where possible, peer scientific 

reviews.12 13 14

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 may be selected on the basis of 

other factors such as past use, public 

familiarity, cost or resource requirements, 

and availability. Accordingly, dispersion 

models listed in Appendix A meet these 

conditions: 

i. The model must be written in a common 

programming language, and the executable(s) 

must run on a common computer platform. 

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 other recommended models in Appendix 

A. 

iii. The model must be accompanied by a 

complete test data set including input 

parameters and output results. The test data 

must be packaged with the model 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 and source code available to users 

at reasonable cost or make them available for 

public access through the Internet or 

National Technical Information Service: The 

model and its code cannot be proprietary. 

c. The evaluation process includes 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. 

d. 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. 

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 

platform 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 

68232 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 Guideline cannot alone 

achieve that consistency nor can it 

necessarily provide the best model for all 

possible situations. An EPA reference 15

provides a statistical technique for evaluating 

model performance for predicting peak 

concentration values, as might be observed at 

individual monitoring locations. This 

protocol is available to assist in developing 

a consistent approach when justifying the use 

of other-than-preferred modeling techniques 

recommended in the Guideline. The 

procedures in this protocol provide a general 

framework for objective decision-making on 

the acceptability of an alternative model for 

a given regulatory application. These 

objective procedures may be used for 

conducting both the technical evaluation of 

the model and the field test or performance 

evaluation. An ASTM reference 16 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. 

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, established procedures and 

techniques 15 16 for determining the 

acceptability of a model for an individual 

case based on superior performance 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 Traditional Stationary Source Models 

4.1 Discussion 

a. Guidance in this section applies to 

modeling analyses for which the 

predominant meteorological conditions that 

control the design concentration are steady 

state and for which the transport distances 

are nominally 50km or less. The models 

recommended in this section are generally 

used in the air quality impact analysis of 

stationary sources for most criteria 

pollutants. The averaging time of the 

concentration estimates produced by these 

models ranges from 1 hour to an annual 

average. 

b. Simple terrain, as used here, is 

considered to be an area where terrain 

features are all lower in elevation than the 

top of the stack of the source(s) in question. 

Complex terrain is defined as terrain 

exceeding the height of the stack being 

modeled. 

c. In the early 1980s, model evaluation 

exercises were conducted to determine the 

‘‘best, most appropriate point source model’’

for use in simple terrain.12 No one model was 

found to be clearly superior and, based on 

past use, public familiarity, and availability, 

ISC (predecessor to ISC3 17) became the 

recommended model for a wide range of 

regulatory applications. Other refined models 

which also employed the same basic 

Gaussian kernel as in ISC, i.e., BLP, CALINE3 

and OCD, were developed for specialized 

applications (Appendix A). Performance 

evaluations were also made for these models, 

which are identified below. 

d. Encouraged by the development of 

pragmatic methods for better characterization 

of plume dispersion 18 19 20 21 the AMS/EPA 

Regulatory Model Improvement Committee 

(AERMIC) developed AERMOD.22 AERMOD 

employs best state-of-practice 

parameterizations for characterizing the 

meteorological influences and dispersion. 

The model utilizes a probability density 

function (pdf) and the superposition of 

several Gaussian plumes to characterize the 

distinctly non-Gaussian nature of the vertical 

pollutant distribution for elevated plumes 

during convective conditions; otherwise the 

distribution is Gaussian. Also, nighttime 

urban boundary layers (and plumes within 

them) have the turbulence enhanced by 

AERMOD to simulate the influence of the 

urban heat island. AERMOD has been 

evaluated using a variety of data sets and has 

been found to perform better than ISC3 for 

many applications, and as well or better than 

CTDMPLUS for several complex terrain data 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68233 

sets (Section A.1; subsection n). The current 

version of AERMOD has been modified to 

include an algorithm for dry and wet 

deposition for both gases and particles. Note 

that when deposition is invoked, mass in the 

plume is depleted. Availability of this 

version is described in Section A.1, and is 

subject to applicable guidance published in 

the Guideline. 

e. A new building downwash algorithm 23

was developed and tested within AERMOD. 

The PRIME algorithm has been evaluated 

using a variety of data sets and has been 

found to perform better than the downwash 

algorithm that is in ISC3, and has been 

shown to perform acceptably in tests within 

AERMOD (Section A.1; subsection n). 

4.2 Recommendations 

4.2.1 Screening Techniques 

4.2.1.1 Simple Terrain 

a. Where a preliminary or conservative 

estimate is desired, point source screening 

techniques are an acceptable approach to air 

quality analyses. EPA has published 

guidance for screening procedures.24 25

b. All screening procedures should be 

adjusted to the site and problem at hand. 

Close attention should be paid to whether the 

area should be classified urban or rural in 

accordance with Section 7.2.3. The 

climatology of the area should be studied to 

help define the worst-case meteorological 

conditions. Agreement should be reached 

between the model user and the appropriate 

reviewing authority on the choice of the 

screening model for each analysis, and on the 

input data as well as the ultimate use of the 

results. 

4.2.1.2 Complex Terrain 

a. CTSCREEN 26 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.26 The terrain data must be digitized in 

the same manner as for CTDMPLUS and a 

terrain processor is available.27 A discussion 

of the model’s performance characteristics is 

provided in a technical paper.28 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 4–1 contains the matrix of 

meteorological variables that is used for each 

CTSCREEN analysis. There are 96 

combinations, including exceptions, for each 

wind direction for the neutral/stable case, 

and 108 combinations for the unstable case. 

The specification of wind direction, however, 

is handled internally, based on the source 

and terrain geometry. Although CTSCREEN 

is designed to address a single source 

scenario, there are a number of options that 

can be selected on a case-by-case basis to 

address multi-source situations. However, 

the appropriate reviewing authority should 

be consulted, and concurrence obtained, on 

the protocol for modeling multiple sources 

with CTSCREEN to ensure that the worst case 

is identified and assessed. The maximum 

concentration output from CTSCREEN 

represents a worst-case 1-hour concentration. 

Time-scaling factors of 0.7 for 3-hour, 0.15 

for 24-hour and 0.03 for annual concentration 

averages are applied internally by 

CTSCREEN to the highest 1-hour 

concentration calculated by the model. 

b. Placement of receptors requires very 

careful attention when modeling in complex 

terrain. Often the highest concentrations are 

predicted to occur under very stable 

conditions, when the plume is near, or 

impinges on, the terrain. The plume under 

such conditions may be quite narrow in the 

vertical, so that even relatively small changes 

in a receptor’s location may substantially 

affect the predicted concentration. Receptors 

within about a kilometer of the source may 

be even more sensitive to location. Thus, a 

dense array of receptors may be required in 

some cases. In order to avoid excessively 

large computer runs due to such a large array 

of receptors, it is often desirable to model the 

area twice. The first model run would use a 

moderate number of receptors carefully 

located over the area of interest. The second 

model run would use a more dense array of 

receptors in areas showing potential for high 

concentrations, as indicated by the results of 

the first model run. 

c. As mentioned above, digitized contour 

data must be preprocessed 27 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 17 25 29 may 

be acceptable for complex terrain cases 

where established procedures are used. The 

user is encouraged to confer with the 

appropriate reviewing authority if any 

unresolvable problems are encountered, e.g., 

applicability, meteorological data, receptor 

siting, or terrain contour processing issues. 

4.2.2 Refined Analytical Techniques 

a. A brief description of each preferred 

model for refined applications is found in 

Appendix A. Also listed in that appendix are 

availability, the model input requirements, 

the standard options that should be selected 

when running the program, and output 

options. 

b. For a wide range of regulatory 

applications in all types of terrain, the 

recommended model is AERMOD. This 

recommendation is based on extensive 

developmental and performance evaluation 

(Section A.1; subsection n). Differentiation of 

simple versus complex terrain is unnecessary 

with AERMOD. In complex terrain, AERMOD 

employs the well-known dividing-streamline 

concept in a simplified simulation of the 

effects of plume-terrain interactions. 

c. If aerodynamic building downwash is 

important for the modeling analysis, e.g., 

paragraph 6.2.2(b), then the recommended 

model is AERMOD. 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, while deposition treatment is 

available in AERMOD, the approach taken for 

any purpose should be coordinated with the 

appropriate reviewing authority. Line sources 

can be simulated with AERMOD if point or 

volume sources are appropriately combined. 

If buoyant plume rise from line sources is 

important for the modeling analysis, the 

recommended model is BLP. For other 

special modeling applications, CALINE3 (or 

CAL3QHCR on a case-by-case basis), OCD, 

and EDMS are available as described in 

Sections 5 and 6. 

d. If the modeling application involves a 

well defined hill or ridge and a detailed 

dispersion analysis of the spatial pattern of 

plume impacts is of interest, CTDMPLUS, 

listed in Appendix A, is available. 

CDTMPLUS provides greater resolution of 

concentrations about the contour of the hill 

feature than does AERMOD through a 

different plume-terrain interaction algorithm. 

TABLE 4–1A.—NEUTRAL/STABLE METEOROLOGICAL MATRIX FOR CTSCREEN 

Variable Specific values 

U (m/s) ............................................................................................. 1 .0 2 .0 3 .0 4 .0 5.0 

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 is optimized internally for each meteorological combination.) 

68234 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

aModeling 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/ 

naags/. 

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 4–1B.—UNSTABLE/CONVECTIVE METEOROLOGICAL MATRIX FOR CTSCREEN 

Variable Specific values 

U (m/s) ............................................................................................... 1 .0 2 .0 3 .0 4.0 5.0 

U* (m/s) .............................................................................................. 0 .1 0 .3 0 .5 

L (m) ................................................................................................... ¥10 ¥50 ¥90 

Dq/Dz (K/m) ........................................................................................ 0 .030 (potential temperature gradient above Zi) 

Zi (m) .................................................................................................. 0 .5h 1 .0h 1 .5h (h = terrain height) 

5.0 Models for Ozone, Particulate Matter, 

Carbon Monoxide, Nitrogen Dioxide, and 

Lead 

5.1 Discussion 

a. This section identifies modeling 

approaches or models appropriate for 

addressing ozone (O3) a, carbon monoxide 

(CO), nitrogen dioxide (NO2), particulates 

(PM–2.5 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.30 31 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.32 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 33 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.33

g. The NAAQS for PM–10 was 

promulgated in July 1987 (40 CFR 50.6). A 

SIP development guide 34 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 (40 CFR 

51.166(c)). As an aid to assessing the impact 

on ambient air quality of particulate matter 

generated from prescribed burning activities, 

a reference 35 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 which does 

not have this limitation and accommodates 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68235 

distance-dependent conversion ratios—the 

Plume Volume Molar Ratio Method 

(PVMRM) 36—is currently being tested to 

determine suitability as a refined method. A 

third (screening) approach is to develop site 

specific (domain-wide) 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.37 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.38

5.2 Recommendations 

5.2.1 Models for Ozone 

a. Choice of Models for Multi-source 

Applications. Simulation of ozone formation 

and transport is a highly complex and 

resource intensive exercise. Control agencies 

with jurisdiction over areas with ozone 

problems are encouraged to use 

photochemical grid models, such as the 

Models-3/Community Multi-scale Air 

Quality (CMAQ) modeling system,39 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.32

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 refined version of the Ozone 

Isopleth Plotting Program (OZIPR) 40 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 to apportion sources of ozone 

precursors (e.g., VOC) in urban domains. EPA 

has issued guidance 32 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). 

5.2.2 Models for Particulate Matter 

5.2.2.1 PM–2.5 

a. Choice of Models for Multi-source 

Applications. Simulation of phenomena 

resulting in high ambient PM–2.5 can be a 

multi-faceted and complex problem resulting 

from PM–2.5’s existence as an aerosol 

mixture. Treating secondary components of 

PM–2.5, such as sulfates and nitrates, can be 

a highly complex and resource-intensive 

exercise. Control agencies with jurisdiction 

over areas with secondary PM–2.5 problems 

are encouraged to use models which integrate 

chemical and physical processes important 

in the formation, decay and transport of these 

species (e.g., Models-3/CMAQ 38 or 

REMSAD 41). 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,33 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–2.5.33

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). 

5.2.2.2 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.34

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). 

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.42 43 44 Other receptor 

models, e.g., the Positive Matrix 

Factorization (PMF) model 45 and Unmix,46

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.47

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 7.2.8). 

e. Fugitive dust usually refers to dust put 

into the atmosphere by the wind blowing 

over plowed fields, dirt roads or desert or 

sandy areas with little or no vegetation. 

Reentrained dust is that which is put into the 

air by reason of vehicles driving over dirt 

roads (or dirty roads) and dusty areas. Such 

sources can be characterized as line, area or 

volume sources. Emission rates may be based 

on site specific data or values from the 

general literature. Fugitive emissions include 

the emissions resulting from the industrial 

process that are not captured and vented 

through a stack but may be released from 

various locations within the complex. 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. 

5.2.3 Models for Carbon Monoxide 

a. Guidance is available for analyzing CO 

impacts at roadway intersections.48 The 

recommended screening model for such 

analyses is CAL3QHC.49 50 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 48; a refined approach may be 

considered on a case-by-case basis with 

CAL3QHCR.51 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. A 

scientific review article for line source 

models is available.52

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.48 53 54 55

d. Where point sources of CO are of 

concern, they should be treated using the 

screening and refined techniques described 

in Section 4. 

5.2.4 Models for Nitrogen Dioxide (Annual 

Average) 

a. A tiered screening approach is 

recommended to obtain annual average 

estimates of NO2 from point sources for New 

Source Review analysis, including PSD, and 

for SIP planning purposes. This multi-tiered 

approach is conceptually shown in Figure 5–

1 and described in paragraphs b through d of 

this subsection: 

Figure 5–1 

Multi-tiered screening approach for 

Estimating Annual NO2 Concentrations from 

Point Sources 

68236 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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).56 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, 

detailed screening techniques such as the 

Ozone Limiting Method 57 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 7.2.3), a 

proportional model may be used as a 

preliminary assessment to evaluate control 

strategies to meet the NAAQS for multiple 

minor sources, i.e., minor point, area and 

mobile sources of 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 5.2.3). 

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 

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. 

5.2.5 Models for Lead 

a. For major lead point sources, such as 

smelters, which contribute fugitive emissions 

and for which deposition is important, 

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 5.2.3). 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 8.2); the point source and any 

nearby major roadways should be modeled 


ER09NO05.001</GPH>
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68237 

separately using the appropriate 

recommended steady-state plume dispersion 

model (subsection 4.2.2). 

6.0 Other Model Requirements 

6.1 Discussion 

a. This section covers those cases where 

specific techniques have been developed for 

special regulatory programs. Most of the 

programs have, or will have when fully 

developed, separate guidance documents that 

cover the program and a discussion of the 

tools that are needed. The following 

paragraphs reference those guidance 

documents, when they are available. No 

attempt has been made to provide a 

comprehensive discussion of each topic since 

the reference documents were designed to do 

that. This section will undergo periodic 

revision as new programs are added and new 

techniques are developed. 

b. Other Federal agencies have also 

developed specific modeling approaches for 

their own regulatory or other requirements.58

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.59

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.60

6.2 Recommendations 

6.2.1 Visibility 

a. Visibility in important natural areas (e.g., 

Federal Class I areas) is protected under a 

number of provisions of the Clean Air Act, 

including Sections 169A and 169B 

(addressing impacts primarily from existing 

sources) and Section 165 (new source 

review). Visibility impairment is caused by 

light scattering and light absorption 

associated with particles and gases in the 

atmosphere. In most areas of the country, 

light scattering by PM–2.5 is the most 

significant component of visibility 

impairment. The key components of PM–2.5 

contributing to visibility impairment include 

sulfates, nitrates, organic carbon, elemental 

carbon, and crustal material. 

b. The visibility regulations as promulgated 

in December 1980 (40 CFR 51.300–307) 

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 61 62 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.63 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 38 and REMSAD.41 Model 

users should consult with the appropriate 

reviewing authority (paragraph 3.0(b)), which 

in this instance would include FLMs. 

6.2.2 Good Engineering Practice Stack 

Height 

a. The use of stack height credit in excess 

of Good Engineering Practice (GEP) stack 

height or credit resulting from any other 

dispersion technique is prohibited in the 

development of emission limitations by 40 

CFR 51.118 and 40 CFR 51.164. The 

definitions of GEP stack height and 

dispersion technique are contained in 40 CFR 

51.100. Methods and procedures for making 

the appropriate stack height calculations, 

determining stack height credits and an 

example of applying those techniques are 

found in several references 64 65 66 67, 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 66). Detailed downwash screening 

procedures 24 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. 

6.2.3 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 

68238 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 60 68 is also available for 

use on a case-by-case basis that generally 

provides concentrations that are higher than 

those obtained using refined 

characterizations of the meteorological 

conditions. The meteorological input data 

requirements for developing the time and 

space varying three-dimensional winds and 

dispersion meteorology for refined analyses 

are discussed in paragraph 8.3.1.2(d). 

Additional information on applying this 

model is contained in Appendix A. To 

facilitate use of complex air quality and 

meteorological modeling systems, a written 

protocol 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. 

6.2.4 Modeling Guidance for Other 

Governmental Programs 

a. When using the models recommended or 

discussed in the Guideline in support of 

programmatic requirements not specifically 

covered by EPA regulations, the model user 

should consult the appropriate Federal or 

State agency to ensure the proper application 

and use of the models. For modeling 

associated with PSD permit applications that 

involve a Class I area, the appropriate Federal 

Land Manager should be consulted on all 

modeling questions. 

b. The Offshore and Coastal Dispersion 

(OCD) model, described in Appendix A, was 

developed by the Minerals Management 

Service and is recommended for estimating 

air quality impact from offshore sources on 

onshore, flat terrain areas. The OCD model is 

not recommended for use in air quality 

impact assessments for onshore sources. 

Sources located on or just inland of a 

shoreline where fumigation is expected 

should be treated in accordance with 

subsection 7.2.8. 

c. The latest version of the Emissions and 

Dispersion Modeling System (EDMS), was 

developed and is supported by the Federal 

Aviation Administration (FAA), and is 

appropriate for air quality assessment of 

primary pollutant impacts at airports or air 

bases. EDMS has adopted AERMOD for 

treating dispersion. Application of EDMS is 

intended for estimating the collective impact 

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 

unrelated to airport operations. If changes in 

other than aircraft operations are associated 

with analyses, a model recommended in 

Chapter 4 or 5 should be used. The latest 

version of EDMS may be obtained from FAA 

at its Web site: http://www.aee.faa.gov/ 

emissions/edms/edmshome.htm. 

7.0 General Modeling Considerations 

7.1 Discussion 

a. This section contains recommendations 

concerning a number of different issues not 

explicitly covered in other sections of this 

guide. The topics covered here are not 

specific to any one program or modeling area 

but are common to nearly all modeling 

analyses for criteria pollutants. 

7.2 Recommendations 

7.2.1 Design Concentrations (See Also 

Subsection 10.2.3.1) 

7.2.1.1 Design Concentrations for SO2, PM–

10, CO, Pb, and NO2

a. An air quality analysis for SO2, PM–10, 

CO, Pb, and NO2 is required to determine if 

the source will (1) cause a violation of the 

NAAQS, or (2) cause or contribute to air 

quality deterioration greater than the 

specified allowable PSD increment. For the 

former, background concentration 

(subsection 8.2) should be added to the 

estimated impact of the source to determine 

the design concentration. For the latter, the 

design concentration includes impact from 

all increment consuming sources. 

b. If the air quality analyses are conducted 

using the period of meteorological input data 

recommended in subsection 8.3.1.2 (e.g., 5 

years of National Weather Service (NWS) 

data or at least 1 year of site specific data; 

subsection 8.3.3), then the design 

concentration based on the highest, secondhighest short term concentration over the 

entire receptor network for each year 

modeled 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. For the 24-hour PM–10 NAAQS 

(which is a probabilistic standard)—when 

multiple years are modeled, they collectively 

represent a single period. Thus, if 5 years of 

NWS data are modeled, then the highest 

sixth highest concentration for the whole 

period becomes the design value. And in 

general, when n years are modeled, the 

(n+1)th highest concentration over the n-year 

period is the design value, since this 

represents an average or expected exceedance 

rate of one per year. 

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 

8.3.3), then the highest concentration 

estimate should be considered the design 

value. This is because the length of the data 

record may be too short to assure that the 

conditions producing worst-case estimates 

have been adequately sampled. The highest 

value is then a surrogate for the 

concentration that is not to be exceeded more 

than once per year (the wording of the 

deterministic standards). Also, the highest 

concentration should be used whenever 

selected worst-case conditions are input to a 

screening technique, as described in EPA 

guidance.24

d. If the controlling concentration is an 

annual average value and multiple years of 

data (site specific or NWS) are used, then the 

design value is the highest of the annual 

averages calculated for the individual years. 

If the controlling concentration is a quarterly 

average and multiple years are used, then the 

highest individual quarterly average should 

be considered the design value. 

e. As long a period of record as possible 

should be used in making estimates to 

determine design values and PSD 

increments. If more than 1 year of site 

specific data is available, it should be used. 

7.2.1.2 Design Concentrations for O3 and 

PM–2.5 

a. Guidance and specific instructions for 

the determination of the 1-hr and 8-hr design 

concentrations for ozone are provided in 

Appendix H and I (respectively) of reference 

4. 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,69 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. 

7.2.2 Critical Receptor Sites 

a. Receptor sites for refined modeling 

should be utilized in sufficient detail to 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68239 

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. 

7.2.3 Dispersion Coefficients 

a. Steady-state Gaussian plume models 

used in most applications should employ 

dispersion coefficients consistent with those 

contained in the preferred models in 

Appendix A. Factors such as averaging time, 

urban/rural surroundings (see paragraphs 

(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 70 in rural areas and McElroyPooler 71 coefficients in urban areas. A key 

feature of AERMOD’s formulation is the use 

of directly observed variables of the 

boundary layer to parameterize dispersion.22

b. The selection of either rural or urban 

dispersion coefficients in a specific 

application should follow one of the 

procedures suggested by Irwin 72 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 73; (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 74, is included in the 

preferred models and should be used where 

buoyant sources, e.g., those involving fuel 

combustion, are involved. 

7.2.4 Stability Categories 

a. The Pasquill approach to classifying 

stability is commonly used in preferred 

models (Appendix A). The Pasquill method, 

as modified by Turner 75, 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 8.3. Any other 

method to determine Pasquill stability 

categories must be justified on a case-by-case 

basis. 

c. For a given model application where 

stability categories are the basis for selecting 

dispersion coefficients, both 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. 

d. AERMOD, also a preferred model in 

Appendix A, uses a planetary boundary layer 

scaling parameter to characterize stability.22

This approach represents a departure from 

the discrete, hourly stability categories 

estimated under the Pasquill-Gifford-Turner 

scheme. 

7.2.5 Plume Rise 

a. The plume rise methods of Briggs 76 77

are incorporated in many of the preferred 

models and are recommended for use in 

many modeling applications. In AERMOD,22

for the stable boundary layer, plume rise is 

estimated using an iterative approach, similar 

to that in the CTDMPLUS model. In the 

convective boundary layer, plume rise is 

superposed on the displacements by random 

convective velocities.78 In AERMOD, plume 

rise is computed using the methods of Briggs 

excepting cases involving building 

downwash, in which a numerical solution of 

the mass, energy, and momentum 

conservation laws is performed.23 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 AERMOD; (2) in complex terrain 

screening procedures to determine close-in 

impacts and (3) when calculating the effects 

of building wakes. The building wake 

algorithm in AERMOD incorporates and 

exercises the thermodynamically based 

gradual plume rise calculations as described 

in (a) above. If the building wake is 

calculated to affect the plume for any hour, 

gradual plume rise is also used in downwind 

dispersion calculations to the distance of 

final plume rise, after which final plume rise 

is used. Plumes captured by the near wake 

are re-emitted to the far wake as a groundlevel 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 77

is the recommended technique for this 

situation and is used in preferred models for 

point sources. 

7.2.6 Chemical Transformation 

a. The chemical transformation of SO2

emitted from point sources or single 

industrial plants in rural areas is generally 

assumed to be relatively unimportant to the 

estimation of maximum concentrations when 

travel time is limited to a few hours. 

However, in urban areas, where synergistic 

effects among pollutants are of considerable 

consequence, chemical transformation rates 

may be of concern. In urban area 

applications, a half-life of 4 hours 75 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. 

7.2.7 Gravitational Settling and Deposition 

a. An ‘‘infinite half-life’’ should be used for 

estimates of particle concentrations when 

steady-state Gaussian plume models 

containing only exponential decay terms for 

treating settling and deposition are used. 

b. Gravitational settling and deposition 

may be directly included in a model if either 

is a significant factor. When particulate 

matter sources can be quantified and settling 

and dry deposition are problems, 

professional judgement should be used, and 

there should be coordination with the 

appropriate reviewing authority (paragraph 

3.0(b)). 

7.2.8 Complex Winds 

a. Inhomogeneous Local Winds. In many 

parts of the United States, the ground is 

neither flat nor is the ground cover (or land 

use) uniform. These geographical variations 

can generate local winds and circulations, 

and modify the prevailing ambient winds 

and circulations. Geographic effects are most 

apparent when the ambient winds are light 

or calm.79 In general these geographically 

induced wind circulation effects are named 

after the source location of the winds, e.g., 

lake and sea breezes, and mountain and 

valley winds. In very rugged hilly or 

mountainous terrain, along coastlines, or 

near large land use variations, the 

characterization of the winds is a balance of 

various forces, such that the assumptions of 

steady-state straight-line transport both in 

time and space are inappropriate. In the 

special cases described, the CALPUFF 

modeling system (described in Appendix A) 

may be applied on a case-by-case basis for air 

quality estimates in such complex nonVerDate Aug<31>2005 16:55 Nov 08, 2005 Jkt 205001 PO 00000 Frm 00023 Fmt 4701 Sfmt 4700 E:\FR\FM\09NOR3.SGM 09NOR3
68240 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

aMalfunctions 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. 

steady-state meteorological conditions. The 

purpose of choosing a modeling system like 

CALPUFF is to fully treat the time and space 

variations of meteorology effects on transport 

and dispersion. The setup and application of 

the model should be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) consistent with 

limitations of paragraph 3.2.2(e). The 

meteorological input data requirements for 

developing the time and space varying threedimensional winds and dispersion 

meteorology for these situations are 

discussed in paragraphs 8.3.1.2(d) and 

8.3.1.2(f). 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 24 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.80

Information on the results of EPA’s 

evaluation of this model together with other 

coastal fumigation models is available.81

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)). 

7.2.9 Calibration of Models 

a. Calibration of models is not common 

practice and is subject to much error and 

misunderstanding. There have been attempts 

by some to compare model estimates and 

measurements on an event-by-event basis 

and then to calibrate a model with results of 

that comparison. This approach is severely 

limited by uncertainties in both source and 

meteorological data and therefore it is 

difficult to precisely estimate the 

concentration at an exact location for a 

specific increment of time. Such 

uncertainties make calibration of models of 

questionable benefit. Therefore, model 

calibration is unacceptable. 

8.0 Model Input Data 

a. Data bases and related procedures for 

estimating input parameters are an integral 

part of the modeling procedure. The most 

appropriate data available should always be 

selected for use in modeling analyses. 

Concentrations can vary widely depending 

on the source data or meteorological data 

used. Input data are a major source of 

uncertainties in any modeling analysis. This 

section attempts to minimize the uncertainty 

associated with data base selection and use 

by identifying requirements for data used in 

modeling. A checklist of input data 

requirements for modeling analyses is posted 

on EPA’s Internet SCRAM 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. 

8.1 Source Data 

8.1.1 Discussion 

a. Sources of pollutants can be classified as 

point, line and area/volume sources. Point 

sources are defined in terms of size and may 

vary between regulatory programs. The line 

sources most frequently considered are 

roadways and streets along which there are 

well-defined movements of motor vehicles, 

but they may be lines of roof vents or stacks 

such as in aluminum refineries. Area and 

volume sources are often collections of a 

multitude of minor sources with individually 

small emissions that are impractical to 

consider as separate point or line sources. 

Large area sources are typically treated as a 

grid network of square areas, with pollutant 

emissions distributed uniformly within each 

grid square. 

b. Emission factors are compiled in an EPA 

publication commonly known as AP–42 82; 

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. 

8.1.2 Recommendations 

a. For point source applications the load or 

operating condition that causes maximum 

ground-level concentrations should be 

established. As a minimum, the source 

should be modeled using the design capacity 

(100 percent load). If a source operates at 

greater than design capacity for periods that 

could result in violations of the standards or 

PSD increments, this load) a 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. 

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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68241 

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 8–1. When using a refined model, 

sources should be modeled sequentially with 

these loads for every hour of the year. To 

evaluate SIPs for compliance with quarterly 

and annual standards, emission input data 

shown in Table 8–1 should again be used. 

Emissions from area sources should generally 

be based on annual average conditions. The 

source input information in each model 

user’s guide should be carefully consulted 

and the checklist (paragraph 8.0(a)) should 

also be consulted for other possible emission 

data that could be helpful. NAAQS 

compliance demonstrations in a PSD analysis 

should follow the emission input data shown 

in Table 8–2. For purposes of emissions 

trading, new source review and 

demonstrations, refer to current EPA policy 

and guidance to establish input data. 

j. Line source modeling of streets and 

highways requires data on the width of the 

roadway and the median strip, the types and 

amounts of pollutant emissions, the number 

of lanes, the emissions from each lane and 

the height of emissions. The location of the 

ends of the straight roadway segments should 

be specified by appropriate grid coordinates. 

Detailed information and data requirements 

for modeling mobile sources of pollution are 

provided in the user’s manuals for each of 

the models applicable to mobile sources. 

k. The impact of growth on emissions 

should be considered in all modeling 

analyses covering existing sources. Increases 

in emissions due to planned expansion or 

planned fuel switches should be identified. 

Increases in emissions at individual sources 

that may be associated with a general 

industrial/commercial/residential expansion 

in multi-source urban areas should also be 

treated. For new sources the impact of 

growth on emissions should generally be 

considered for the period prior to the 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 8–1.—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 Source(s) 7

If modeled (subsection 8.2.3), 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 8.2.3(c). 

7See paragraph 8.2.3(d). 

68242 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

TABLE 8–2.—POINT SOURCE MODEL EMISSION INPUT DATA FOR NAAQS COMPLIANCE IN PSD 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 8.2.3(c). 

6See paragraph 8.2.3(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. 

8.2 Background Concentrations 

8.2.1 Discussion 

a. Background concentrations are an 

essential part of the total air quality 

concentration to be considered in 

determining source impacts. Background air 

quality includes pollutant concentrations due 

to: (1) Natural sources; (2) nearby sources 

other than the one(s) currently under 

consideration; and (3) unidentified sources. 

b. Typically, air quality data should be 

used to establish background concentrations 

in the vicinity of the source(s) under 

consideration. The monitoring network used 

for background determinations should 

conform to the same quality assurance and 

other requirements as those networks 

established for PSD purposes.83 An 

appropriate data validation procedure should 

be applied to the data prior to use. 

c. If the source is not isolated, it may be 

necessary to use a multi-source model to 

establish the impact of nearby sources. Since 

sources don’t typically operate at their 

maximum allowable capacity (which may 

include the use of ‘‘dirtier’’ fuels), modeling 

is necessary to express the potential 

contribution of background sources, and this 

impact would not be captured via 

monitoring. Background concentrations 

should be determined for each critical 

(concentration) averaging time. 

8.2.2 Recommendations (Isolated Single 

Source) 

a. Two options (paragraph (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. 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68243 

c. If there are no monitors located in the 

vicinity of the source, a ‘‘regional site’’ may 

be used to determine background. A 

‘‘regional site’’ is one that is located away 

from the area of interest but is impacted by 

similar natural and distant man-made 

sources. 

8.2.3 Recommendations (Multi-Source 

Areas) 

a. In multi-source areas, two components 

of background should be determined: 

contributions from nearby sources and 

contributions from other sources. 

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 8–1 or 8–2. When modeling a nearby 

source that does not have a permit and the 

emission limit contained in the SIP for a 

particular source category is greater than the 

emissions possible given the source’s 

maximum physical capacity to emit, the 

‘‘maximum allowable emission limit’’ for 

such a nearby source may be calculated as 

the emission rate representative of the nearby 

source’s maximum physical capacity to emit, 

considering its design specifications and 

allowable fuels and process materials. 

However, the burden is on the permit 

applicant to sufficiently document what the 

maximum physical capacity to emit is for 

such a nearby source. 

d. It is appropriate to model nearby sources 

only during those times when they, by their 

nature, operate at the same time as the 

primary source(s) being modeled. Where a 

primary source believes that a nearby source 

does not, by its nature, operate at the same 

time as the primary source being modeled, 

the burden is on the primary source to 

demonstrate to the satisfaction of the 

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 89.2.2 

or by application of a model using Table 8–

1 or 8–2. 

8.3 Meteorological Input Data 

a. The meteorological data used as input to 

a dispersion model should be selected on the 

basis of spatial and climatological (temporal) 

representativeness as well as the ability of 

the individual parameters selected to 

characterize the transport and dispersion 

conditions in the area of concern. The 

representativeness of the data is dependent 

on: (1) The proximity of the meteorological 

monitoring site to the area under 

consideration; (2) the complexity of the 

terrain; (3) the exposure of the meteorological 

monitoring site; and (4) the period of time 

during which data are collected. The spatial 

representativeness of the data can be 

adversely affected by large distances between 

the source and receptors of interest and the 

complex topographic characteristics of the 

area. Temporal representativeness is a 

function of the year-to-year variations in 

weather conditions. Where appropriate, data 

representativeness should be viewed in terms 

of the appropriateness of the data for 

constructing realistic boundary layer profiles 

and three dimensional meteorological fields, 

as described in paragraphs (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. Regulatory application of AERMOD 

requires careful consideration of minimum 

data for input to AERMET. Data 

representativeness, in the case of AERMOD, 

means utilizing data of an appropriate type 

for constructing realistic boundary layer 

profiles. Of paramount importance is the 

requirement that all meteorological data used 

as input to AERMOD must be both laterally 

and vertically representative of the transport 

and dispersion within the analysis domain. 

Where surface conditions vary significantly 

over the analysis domain, the emphasis in 

assessing representativeness should be given 

to adequate characterization of transport and 

dispersion between the source(s) of concern 

and areas where maximum design 

concentrations are anticipated to occur. The 

representativeness of data that were collected 

off-site should be judged, in part, by 

comparing the surface characteristics in the 

vicinity of the meteorological monitoring site 

with the surface characteristics that generally 

describe the analysis domain. The surface 

characteristics input to AERMET should be 

based on the topographic conditions in the 

vicinity of the meteorological tower. 

Furthermore, since the spatial scope of each 

variable could be different, 

representativeness should be judged for each 

variable separately. For example, for a 

variable such as wind direction, the data may 

need to be collected very near plume height 

to be adequately representative, whereas, for 

a variable such as temperature, data from a 

station several kilometers away from the 

source may in some cases be considered to 

be adequately representative. 

d. For long range transport modeling 

assessments (subsection 6.2.3) or for 

assessments where the transport winds are 

complex and the application involves a nonsteady-state dispersion model (subsection 

7.2.8), use of output from prognostic 

mesoscale meteorological models is 

encouraged.84 85 86 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. 

8.3.1 Length of Record of Meteorological 

Data 

8.3.1.1 Discussion 

a. The model user should acquire enough 

meteorological data to ensure that worst-case 

meteorological conditions are adequately 

represented in the model results. The trend 

toward statistically based standards suggests 

a need for all meteorological conditions to be 

adequately represented in the data set 

selected for model input. The number of 

years of record needed to obtain a stable 

distribution of conditions depends on the 

variable being measured and has been 

estimated by Landsberg and Jacobs 87 for 

various parameters. Although that study 

indicates in excess of 10 years may be 

68244 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 88

compared various periods from a 17-year 

data set to determine the minimum number 

of years of data needed to approximate the 

concentrations modeled with a 17-year 

period of meteorological data from one 

station. This study indicated that the 

variability of model estimates due to the 

meteorological data input was adequately 

reduced if a 5-year period of record of 

meteorological input was used. 

8.3.1.2 Recommendations 

a. Five years of representative 

meteorological data should be used when 

estimating concentrations with an air quality 

model. Consecutive years from the most 

recent, readily available 5-year period are 

preferred. The meteorological data should be 

adequately representative, and may be site 

specific or from a nearby NWS station. 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 

(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 8.3.3.2. 

c. For permitted sources whose emission 

limitations are based on a specific year of 

meteorological data, that year should be 

added to any longer period being used (e.g., 

5 years of NWS data) when modeling the 

facility at a later time. 

d. For LRT situations (subsection 6.2.3) 

and for complex wind situations (paragraph 

7.2.8(a)), if only NWS or comparable 

standard meteorological observations are 

employed, five years of meteorological data 

(within and near the modeling domain) 

should be used. Consecutive years from the 

most recent, readily available 5-year period 

are preferred. Less than five, 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 8.3(d). These 

mesoscale meteorological fields should be 

used in conjunction with available standard 

NWS or comparable meteorological 

observations within and near the modeling 

domain. 

e. For solely LRT applications (subsection 

6.2.3), if site specific meteorological data are 

available, these data may be helpful when 

used in conjunction with available standard 

NWS or comparable observations and 

mesoscale meteorological fields as described 

in paragraph 8.3.1.2(d). 

f. For complex wind situations (paragraph 

7.2.8(a)) where site specific meteorological 

data are being relied upon as the basis for 

characterizing the meteorological conditions, 

a data base of at least 1 full-year of 

meteorological data is required. If more data 

are available, they should be used. Site 

specific meteorological data may have to be 

collected at multiple locations. Such data 

should have been subjected to quality 

assurance procedures as described in 

paragraph 8.3.3.2(a), and should be reviewed 

for spatial and temporal representativeness. 

8.3.2 National Weather Service Data 

8.3.2.1 Discussion 

a. The NWS meteorological data are 

routinely available and familiar to most 

model users. Although the NWS does not 

provide direct measurements of all the 

needed dispersion model input variables, 

methods have been developed and 

successfully used to translate the basic NWS 

data to the needed model input. 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. 

8.3.2.2 Recommendations 

a. The preferred models listed in Appendix 

A all accept as input the NWS meteorological 

data preprocessed into model compatible 

form. If NWS data are judged to be 

adequately representative for a particular 

modeling application, they may be used. 

NCDC makes available surface 89 90 and upper 

air 91 meteorological data in CD–ROM format. 

b. Although most NWS measurements are 

made at a standard height of 10 meters, the 

actual anemometer height should be used as 

input to the preferred model. Note that 

AERMOD at a minimum requires wind 

observations at a height above ground 

between seven times the local surface 

roughness height and 100 meters. 

c. Wind directions observed by the 

National Weather Service are reported to the 

nearest 10 degrees. A specific set of randomly 

generated numbers has been developed for 

use with the preferred EPA models and 

should be used 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. 

8.3.3 Site Specific Data 

8.3.3.1 Discussion 

a. Spatial or geographical 

representativeness is best achieved by 

collection of all of the needed model input 

data in close proximity to the actual site of 

the source(s). Site specific measured data are 

therefore preferred as model input, provided 

that appropriate instrumentation and quality 

assurance procedures are followed and that 

the data collected are adequately 

representative (free from 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 92

is available. Site specific data should always 

be reviewed for representativeness and 

consistency by a qualified meteorologist. 

8.3.3.2 Recommendations 

a. EPA guidance 92 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.83 93 94

Detailed information on quality assurance is 

also available.95 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 8–3 lists the wind 

related parameters and the averaging time 

requirements. 

c. Missing Data Substitution. After valid 

data retrieval requirements have been met 92, 

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 92. 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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68245 

with established site specific meteorological 

guidance.92 95

e. Temperature Measurements. 

Temperature measurements should be made 

at standard shelter height (2m) in accordance 

with established site specific meteorological 

guidance.92

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 92, and such 

guidance should be followed when obtaining 

vertical temperature gradient data. AERMET 

employs the Bulk Richardson scheme which 

requires measurements of temperature 

difference. To ensure correct application and 

acceptance, AERMOD users should consult 

with the appropriate Reviewing Authority 

before using the Bulk Richardson scheme for 

their analysis. 

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 AERMOD and CTDMPLUS, 

see Appendix A.) Specifications for wind 

measuring instruments and systems are 

contained in reference 92. 

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, AERMOD, and CALPUFF). For 

specific requirements for CTDMPLUS, 

AERMOD, and CALPUFF, see Appendix A. 

For technical guidance on measurement and 

processing of turbulence parameters, see 

reference 92. When turbulence data are used 

in this manner to directly characterize the 

vertical and lateral dispersion, the averaging 

time for the turbulence measurements should 

be one hour (Table 8–3). There are other 

dispersion models (e.g., 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 92. 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, the P–G stability 

categories, as originally defined, couple nearsurface 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.70

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 92. 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 92, is modified slightly 

from that published from earlier work 96 and 

has been evaluated with three site specific 

data bases.97 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 92 (note applicable tables in 

Section 6). For additional information on the 

wind fluctuation methods, several references 

are available.98 99 100 101

k. Meteorological Data Preprocessors. The 

following meteorological preprocessors are 

recommended by EPA: AERMET,102

PCRAMMET,103 MPRM,104 METPRO,105 and 

CALMET106 AERMET, which is patterned 

after MPRM, should be used to preprocess all 

data for use with AERMOD. Except for 

applications that employ AERMOD, 

PCRAMMET is the recommended 

meteorological preprocessor for use in 

applications employing hourly NWS data. 

MPRM is a general purpose meteorological 

data preprocessor which supports regulatory 

models requiring PCRAMMET formatted 

(NWS) data. MPRM is available for use in 

applications employing site specific 

meteorological data. The latest version 

(MPRM 1.3) has been configured to 

implement the SRDT method for estimating 

P–G stability categories. METPRO is the 

required meteorological data preprocessor for 

use with CTDMPLUS. CALMET is available 

for use with applications of CALPUFF. All of 

the above mentioned data preprocessors are 

available for downloading from EPA’s 

Internet SCRAM Web site (subsection 2.3). 

TABLE 8–3.—AVERAGING TIMES FOR

SITE SPECIFIC WIND AND TURBULENCE MEASUREMENTS

Parameter 

Averaging 

time 

(hour) 

Surface wind speed (for use in 

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

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

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

TABLE 8–3.—AVERAGING TIMES FOR

SITE SPECIFIC WIND AND TURBULENCE MEASUREMENTS—Continued 

Parameter 

Averaging 

time 

(hour) 

Turbulence measurements (sE

and sA) for use in stability 

determinations ....................... 11

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

8.3.4 Treatment of Near-Calms and Calms 

8.3.4.1 Discussion 

a. Treatment of calm or light and variable 

wind poses a special problem in model 

applications since steady-state Gaussian 

plume models assume that concentration is 

inversely proportional to wind speed. 

Furthermore, concentrations may become 

unrealistically large when wind speeds less 

than 1 m/s are input to the model. 

Procedures have been developed to prevent 

the occurrence of overly conservative 

concentration estimates during periods of 

calms. These procedures acknowledge that a 

steady-state Gaussian plume model does not 

apply during calm conditions, and that our 

knowledge of wind patterns and plume 

behavior during these conditions does not, at 

present, permit the development of a better 

technique. Therefore, the procedures 

disregard hours which are identified as calm. 

The hour is treated as missing and a 

convention for handling missing hours is 

recommended. 

b. AERMOD, while fundamentally a 

steady-state Gaussian plume model, contains 

algorithms for dealing with low wind speed 

(near calm) conditions. As a result, AERMOD 

can produce model estimates for conditions 

when the wind speed may be less than 1 m/ 

s, but still greater than the instrument 

threshold. Required input to AERMET, the 

meteorological processor for AERMOD, 

includes a threshold wind speed and a 

reference wind speed. The threshold wind 

speed is typically the threshold of the 

instrument used to collect the wind speed 

data. The reference wind speed is selected by 

the model as the lowest level of non-missing 

wind speed and direction data where the 

speed is greater than the wind speed 

threshold, and the height of the measurement 

is between seven times the local surface 

roughness and 100 meters. If the only valid 

observation of the reference wind speed 

between these heights is less than the 

threshold, the hour is considered calm, and 

no concentration is calculated. None of the 

observed wind speeds in a measured wind 

profile that are less than the threshold speed 


ER09NO05.002</MATH>
68246 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

are used in construction of the modeled wind 

speed profile in AERMOD. 

8.3.4.2 Recommendations 

a. Hourly concentrations calculated with 

steady-state Gaussian plume models using 

calms should not be considered valid; the 

wind and concentration estimates for these 

hours should be disregarded and considered 

to be missing. Critical concentrations for 

3-, 8-, and 24-hour averages should be 

calculated by dividing the sum of the hourly 

concentrations for the period by the number 

of valid or non-missing hours. If the total 

number of valid hours is less than 18 for 24- 

hour averages, less than 6 for 8-hour averages 

or less than 3 for 3-hour averages, the total 

concentration should be divided by 18 for the 

24-hour average, 6 for the 8-hour average and 

3 for the 3-hour average. For annual averages, 

the sum of all valid hourly concentrations is 

divided by the number of non-calm hours 

during the year. AERMOD has been coded to 

implement these instructions. For models 

listed in Appendix A, a post-processor 

computer program, CALMPRO 107 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 7.2.8). 

c. When used in steady-state Gaussian 

plume models, measured site specific wind 

speeds of less than 1 m/s but higher than the 

response threshold of the instrument should 

be input as 1 m/s; the corresponding wind 

direction should also be input. Wind 

observations below the response threshold of 

the instrument should be set to zero, with the 

input file in ASCII format. For input to 

AERMOD, no adjustment should be made to 

the site specific wind data. In all cases 

involving steady-state Gaussian plume 

models, calm hours should be treated as 

missing, and concentrations should be 

calculated as in paragraph (a) of this 

subsection. 

9.0 Accuracy and Uncertainty of Models 

9.1 Discussion 

a. Increasing reliance has been placed on 

concentration estimates from models as the 

primary basis for regulatory decisions 

concerning source permits and emission 

control requirements. In many situations, 

such as review of a proposed source, no 

practical alternative exists. Therefore, there is 

an obvious need to know how accurate 

models really are and how any uncertainty in 

the estimates affects regulatory decisions. 

During the 1980’s, attempts were made to 

encourage development of standardized 

evaluation methods.11 108 EPA recognized the 

need for incorporating such information and 

has sponsored workshops 109 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 110, 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.111 112 Work is 

underway to develop a new generation of 

evaluation metrics 16 that takes into account 

the statistical differences (in error 

distributions) between model predictions and 

observations.113 Even though the procedures 

and measures are still evolving to describe 

performance of models that characterize 

atmospheric fate, transport and 

diffusion 114 115 116, there has been general 

acceptance of a need to address the 

uncertainties inherent in atmospheric 

processes. 

9.1.1 Overview of Model Uncertainty 

a. Dispersion models generally attempt to 

estimate concentrations at specific sites that 

really represent an ensemble average of 

numerous repetitions of the same event.16

The event is characterized by measured or 

‘‘known’’ conditions that are input to the 

models, e.g., wind speed, mixed layer height, 

surface heat flux, emission characteristics, 

etc. However, in addition to the known 

conditions, there are unmeasured or 

unknown variations in the conditions of this 

event, e.g., unresolved details of the 

atmospheric flow such as the turbulent 

velocity field. These unknown conditions, 

may vary among repetitions of the event. As 

a result, deviations in observed 

concentrations from their ensemble average, 

and from the concentrations estimated by the 

model, are likely to occur even though the 

known conditions are fixed. Even with a 

perfect model that predicts the correct 

ensemble average, there are likely to be 

deviations from the observed concentrations 

in individual repetitions of the event, due to 

variations in the unknown conditions. The 

statistics of these concentration residuals are 

termed ‘‘inherent’’ uncertainty. Available 

evidence suggests that this source of 

uncertainty alone may be responsible for a 

typical range of variation in concentrations of 

as much as ±50 percent.117

b. Moreover, there is ‘‘reducible’’

uncertainty 108 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.118 The 

statement of accuracy is based on statistical 

tests or performance measures such as bias, 

noise, correlation, etc.11 However, 

information that allows a distinction between 

contributions of the various elements of 

inherent and reducible uncertainty is only 

now beginning to emerge.16 As a result most 

discussions of the accuracy of models make 

no quantitative distinction between (1) 

limitations of the model versus (2) 

limitations of the data base and of knowledge 

concerning atmospheric variability. The 

reader should be aware that statements on 

model accuracy and uncertainty may imply 

the need for improvements in model 

performance that even the ‘‘perfect’’ model 

could not satisfy. 

9.1.2 Studies of Model Accuracy 

a. A number of studies 119 120 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 121 122, 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 123

estimates that, apart from data input errors, 

maximum ground-level concentrations at a 

given hour for a point source in flat terrain 

could be in error by 50 percent due to these 

uncertainties. Uncertainty of five to 10 

degrees in the measured wind direction, 

which transports the plume, can result in 

concentration errors of 20 to 70 percent for 

a particular time and location, depending on 

stability and station location. Such 

uncertainties do not indicate that an 

estimated concentration does not occur, only 

that the precise time and locations are in 

doubt. 

9.1.3 Use of Uncertainty in 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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68247 

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.108 109 110 124 125

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.126 127

However, work in this area involves 

‘‘breaking new ground’’ with slow and 

sporadic progress likely. As a result, it may 

be necessary to continue using the ‘‘best 

estimate’’ until sufficient technical progress 

has been made to meaningfully implement 

such concepts dealing with uncertainty. 

9.1.4 Evaluation of Models 

a. A number of actions have been taken to 

ensure that the best model is used correctly 

for each regulatory application and that a 

model is not arbitrarily imposed. First, the 

Guideline clearly recommends the most 

appropriate model be used in each case. 

Preferred models, based on a number of 

factors, are identified for many uses. General 

guidance on using alternatives to the 

preferred models is also provided. Second, 

the models have been subjected to a 

systematic performance evaluation and a 

peer scientific review. Statistical 

performance measures, including measures 

of difference (or residuals) such as bias, 

variance of difference and gross variability of 

the difference, and measures of correlation 

such as time, space, and time and space 

combined as recommended by the AMS 

Woods Hole Workshop 11, were generally 

followed. Third, more specific information 

has been provided for justifying the site 

specific use of alternative models in 

previously cited EPA guidance 15, and new 

models are under consideration and 

review.16 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.’’ 128 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 129, 

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. 

9.2 Recommendations 

a. No specific guidance on the 

quantification of model uncertainty for use in 

decision-making is being given at this time. 

As procedures for considering uncertainty 

develop and become implementable, this 

guidance will be changed and expanded. For 

the present, continued use of the ‘‘best 

estimate’’ is acceptable; however, in specific 

circumstances for O3, PM–2.5 and regional 

haze, additional information and/or 

procedures may be appropriate.32 33

10.0 Regulatory Application of Models 

10.1 Discussion 

a. Procedures with respect to the review 

and analysis of air quality modeling and data 

analyses in support of SIP revisions, PSD 

permitting or other regulatory requirements 

need a certain amount of standardization to 

ensure consistency in the depth and 

comprehensiveness of both the review and 

the analysis itself. This section recommends 

procedures that permit some degree of 

standardization while at the same time 

allowing the flexibility needed to assure the 

technically best analysis for each regulatory 

application. 

b. Dispersion model estimates, especially 

with the support of measured air quality 

data, are the preferred basis for air quality 

demonstrations. Nevertheless, there are 

instances where the performance of 

recommended dispersion modeling 

techniques, by comparison with observed air 

quality data, may be shown to be less than 

acceptable. Also, there may be no 

recommended modeling procedure suitable 

for the situation. In these instances, emission 

limitations may be established solely on the 

basis of observed air quality data as would 

be applied to a modeling analysis. The same 

care should be given to the analyses of the 

air quality data as would be applied to a 

modeling analysis. 

c. The current NAAQS for SO2 and CO are 

both stated in terms of a concentration not to 

be exceeded more than once a year. There is 

only an annual standard for NO2 and a 

quarterly standard for Pb. Standards for fine 

particulate matter (PM–2.5) are expressed in 

terms of both long-term (annual) and 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 

68248 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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. As noted in subsection 

7.2.1.1, the modeled compliance for this 

NAAQS is based on the highest 6th highest 

concentration over 5 years. For ozone the 

short term 1-hour standard is expressed in 

terms of an expected exceedance limit while 

the short term 8-hour standard is expressed 

in terms of a three year average of the annual 

fourth highest daily maximum 8-hour value. 

The NAAQS are subjected to extensive 

review and possible revision every 5 years. 

d. This section discusses general 

requirements for concentration estimates and 

identifies the relationship to emission limits. 

The following recommendations apply to: (1) 

Revisions of State Implementation Plans and 

(2) the review of new sources and the 

prevention of significant deterioration (PSD). 

10.2 Recommendations 

10.2.1 Analysis Requirements 

a. Every effort should be made by the 

Regional Office to meet with all parties 

involved in either a SIP revision or a PSD 

permit application prior to the start of any 

work on such a project. During this meeting, 

a protocol should be established between the 

preparing and reviewing parties to define the 

procedures to be followed, the data to be 

collected, the model to be used, and the 

analysis of the source and concentration data. 

An example of requirements for such an 

effort is contained in the Air Quality 

Analysis Checklist posted on EPA’s Internet 

SCRAM 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 24 should be followed. 

Guidance is also available for mobile 

sources.48

c. If the concentration estimates from 

screening techniques indicate a significant 

impact or 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. 

10.2.2 Use of Measured Data in Lieu of 

Model Estimates 

a. Modeling is the preferred method for 

determining emission limitations for both 

new and existing sources. When a preferred 

model is available, model results alone 

(including background) are sufficient. 

Monitoring will normally not be accepted as 

the sole basis for emission limitation. In 

some instances when the modeling technique 

available is only a screening technique, the 

addition of air quality data to the analysis 

may lend credence to model results. 

b. There are circumstances where there is 

no applicable model, and measured data may 

need to be used. However, only in the case 

of a NAAQS assessment for 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? 

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. 

10.2.3 Emission Limits 

10.2.3.1 Design Concentrations 

a. Emission limits should be based on 

concentration estimates for the averaging 

time that results in the most stringent control 

requirements. The concentration used in 

specifying emission limits is called the 

design value or design concentration and is 

a sum of the concentration contributed by the 

primary source, other applicable sources, 

and—for NAAQS assessments—the 

background concentration. 

b. To determine the averaging time for the 

design value, the most restrictive NAAQS or 

PSD increment, as applicable, should be 

identified. For a NAAQS assessment, the 

averaging time for the design value is 

determined 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). For a PSD 

increment assessment, the averaging time for 

the design value is determined by 

calculating, for each averaging time, the ratio 

of the applicable PSD increment (I) and the 

model-predicted concentration (P) (i.e., I/P). 

The averaging time with the lowest ratio 

identifies the most restrictive standard or 

increment. 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.34

10.2.3.2 NAAQS Analyses for New or 

Modified Sources 

a. For new or modified sources predicted 

to have a significant ambient impact 83 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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68249 

aThe documents listed here are major sources of 

supplemental information on the theory and 

application of mathematical air quality models. 

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 83 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 

once per year, on average; (2) the expected 

(i.e., average) annual mean concentration will 

exceed the annual NAAQS; and (3) the 

source contributes significantly, in a 

temporal and spatial sense, to any modeled 

violation. 

10.2.3.3 PSD Air Quality Increments and 

Impacts 

a. The allowable PSD increments for 

criteria pollutants are established by 

regulation and cited in 40 CFR 51.166. These 

maximum allowable increases in pollutant 

concentrations may be exceeded once per 

year at each site, except for the annual 

increment that may not be exceeded. The 

highest, second-highest increase in estimated 

concentrations for the short term averages as 

determined by a model should be less than 

or equal to the permitted increment. The 

modeled annual averages should not exceed 

the increment. 

b. Screening techniques defined in 

subsection 4.2.1 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 8.3.1.2). 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 6.2.3. 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. 

11.0 Bibliography a

American Meteorological Society. 

Symposia on Turbulence, Diffusion, and Air 

Pollution (1st–10th); 1971–1992. Symposia 

on Boundary Layers & Turb. 11th–12th; 

1995–1997. Boston, MA. 

American Meteorological Society, 1977–

1998. Joint Conferences on Applications of 

Air Pollution Meteorology (1st–10th). 

Sponsored by the American Meteorological 

Society and the Air & Waste Management 

Association. Boston, MA. 

American Meteorological Society, 1978. 

Accuracy of Dispersion Models. Bulletin of 

the American Meteorological Society, 59(8): 

1025–1026. 

American Meteorological Society, 1981. 

Air Quality Modeling and the Clean Air Act: 

Recommendations to EPA on Dispersion 

Modeling for Regulatory Applications. 

Boston, MA. 

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

Atomic Energy Commission Critical Review 

Series, Oak Ridge National Laboratory, Oak 

Ridge, TN. 

Drake, R.L. and S.M. Barrager, 1979. 

Mathematical Models for Atmospheric 

Pollutants. EPRI EA–1131. Electric Power 

Research Institute, Palo Alto, CA. 

Environmental Protection Agency, 1978. 

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Analysis. Cambridge University Press. New 

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125. Irwin, J.S., K. Steinberg, C. 

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Calculations. (CD–ROM) Proceedings of 

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Beginning. April 4–6, 2001, Newport, RI, Air 

& Waste Management Association. 

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Dudik and T.S. Stocking, 1988. User’s Guide 

to the Expected Exceedances System. 

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Prepared under Contract No. 68–02–4352 

Option I for the U.S. Environmental 

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NC. (Docket No. A–88–04, II–I–3) 

127. Thrall, A.D., T.E. Stoeckenius and C.S. 

Burton, 1985. A Method for Calculating 

Dispersion Modeling Uncertainty Applied to 

the Regulation of an Emission Source. 

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NC. (Docket No. A–80–46, IV–G–1) 

128. ‘‘Ten years of Harmonisation 

activities: Past, present and future’’ at http:// 

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Harmoni/Conferences/Belgirate/ 

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129. ‘‘A platform for model evaluation’’ at 

http://www.dmu.dk/ 

AtmosphericEnvironment/Harmoni/ 

Conferences/Belgirate/BelgiratePapers.asp. 

APPENDIX A TO APPENDIX W OF 

PART 51—SUMMARIES OF 

PREFERRED AIR QUALITY MODELS 

Table of Contents 

A.0 Introduction and Availability 

A.1 Aermod 

A.2 Buoyant Line and Point Source 

Dispersion Model (BLP) 

A.3 CALINE3 

A.4 CALPUFF 

A.5 Complex Terrain Dispersion Model 

Plus Algorithms for Unstable Situations 

(CTDMPLUS) 

A.6 Offshore and Coastal Dispersion Model 

(OCD) 

A.REF References 

A.0 Introduction and Availability 

(1) This appendix summarizes key features 

of refined air quality models preferred for 

specific regulatory applications. For each 

model, information is provided on 

availability, approximate cost (where 

applicable), regulatory use, data input, 

output format and options, simulation of 

atmospheric physics, and accuracy. These 

models may be used without a formal 

demonstration of applicability provided they 

satisfy the recommendations for regulatory 

use; not all options in the models are 

necessarily recommended for regulatory use. 

(2) Many of these models have been 

subjected to a performance evaluation using 

comparisons with observed air quality data. 

Where possible, several of the models 

contained herein have been subjected to 

evaluation exercises, including (1) statistical 

performance tests recommended by the 

American Meteorological Society and (2) 

peer scientific reviews. The models in this 

appendix have been selected on the basis of 

the results of the model evaluations, 

experience with previous use, familiarity of 

the model to various air quality programs, 

and the costs and resource requirements for 

use. 

(3) 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 AMS/EPA Regulatory Model—

AERMOD 

References 

Environmental Protection Agency, 2004. 

AERMOD: Description of Model 

Formulation. Publication No. EPA–454/R–

03–004. U.S. Environmental Protection 

Agency, Research Triangle Park, NC 27711; 

September 2004. (Available at http:// 

www.epa.gov/scram001/) 

Cimorelli, A. et al., 2005. AERMOD: A 

Dispersion Model for Industrial Source 

Applications. Part I: General Model 

Formulation and Boundary Layer 

Characterization. Journal of Applied 

Meteorology, 44(5): 682–693. 

Perry, S. et al., 2005. AERMOD: A 

Dispersion Model for Industrial Source 

Applications. Part II: Model Performance 

against 17 Field Study Databases. Journal of 

Applied Meteorology, 44(5): 694–708. 

Environmental Protection Agency, 2004. 

User’s Guide for the AMS/EPA Regulatory 

Model—AERMOD. Publication No. EPA–

454/B–03–001. U.S. Environmental 

Protection Agency, Research Triangle Park, 

NC 27711; September 2004. (Available at 

http://www.epa.gov/scram001/) 

Environmental Protection Agency, 2004. 

User’s Guide for the AERMOD 

Meteorological Preprocessor (AERMET). 

Publication No. EPA–454/B–03–002. U.S. 

Environmental Protection Agency, Research 

Triangle Park, NC 27711; November 2004. 

(Available at http://www.epa.gov/scram001/) 

Environmental Protection Agency, 2004. 

User’s Guide for the AERMOD Terrain 

Preprocessor (AERMAP). Publication No. 

EPA–454/B–03–003. U.S. Environmental 

Protection Agency, Research Triangle Park, 

NC 27711; October 2004. (Available at http:// 

www.epa.gov/scram001/) 

Schulman, L.L., D.G. Strimaitis and J.S. 

Scire, 2000. Development and evaluation of 

the PRIME plume rise and building 

downwash model. Journal of the Air and 

Waste Management Association, 50: 378–

390. 

Availability 

The model codes and associated 

documentation are available on EPA’s 

Internet SCRAM Web site (Section A.0). 

Abstract 

AERMOD is a steady-state plume 

dispersion model for assessment of pollutant 

concentrations from a variety of sources. 

AERMOD simulates transport and dispersion 

from multiple point, area, or volume sources 

based on an up-to-date characterization of the 

atmospheric boundary layer. Sources may be 

located in rural or urban areas, and receptors 

may be located in simple or complex terrain. 

AERMOD accounts for building wake effects 

(i.e., plume downwash) based on the PRIME 

building downwash algorithms. The model 

employs hourly sequential preprocessed 

meteorological data to estimate 

concentrations for averaging times from one 

hour to one year (also multiple years). 

AERMOD is designed to operate in concert 

with two pre-processor codes: AERMET 

processes meteorological data for input to 

AERMOD, and AERMAP processes terrain 

elevation data and generates receptor 

information for input to AERMOD. 

a. Recommendations for Regulatory Use 

(1) AERMOD is appropriate for the 

following applications: 

• Point, volume, and area sources; 

• Surface, near-surface, and elevated 

releases; 

• Rural or urban areas; 

• Simple and complex terrain; 

• Transport distances over which steadystate assumptions are appropriate, up to 

50km; 

• 1-hour to annual averaging times; and 

• Continuous toxic air emissions. 

(2) For regulatory applications of 

AERMOD, the regulatory default option 

should be set, i.e., the parameter DFAULT 

should be employed in the MODELOPT 

record in the COntrol Pathway. The DFAULT 

option requires the use of terrain elevation 

data, stack-tip downwash, sequential date 

checking, and does not permit the use of the 

model in the SCREEN mode. In the 

regulatory default mode, pollutant half life or 

68254 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

decay options are not employed, except in 

the case of an urban source of sulfur dioxide 

where a four-hour half life is applied. Terrain 

elevation data from the U.S. Geological 

Survey 7.5-Minute Digital Elevation Model 

(edcwww.cr.usgs.gov/doc/edchome/ndcdb/ 

ndcdb.html) or equivalent (approx. 30-meter 

resolution) should be used in all 

applications. In some cases, exceptions of the 

terrain data requirement may be made in 

consultation with the permit/SIP reviewing 

authority. 

b. Input Requirements 

(1) Source data: Required input includes 

source type, location, emission rate, stack 

height, stack inside diameter, stack gas exit 

velocity, stack gas temperature, area and 

volume source dimensions, and source 

elevation. Building dimensions and variable 

emission rates are optional. 

(2) Meteorological data: The AERMET 

meteorological preprocessor requires input of 

surface characteristics, including surface 

roughness (zo), Bowen ratio, and albedo, as 

well as, hourly observations of wind speed 

between 7zo and 100m (reference wind speed 

measurement from which a vertical profile 

can be developed), wind direction, cloud 

cover, and temperature between zo and 100m 

(reference temperature measurement from 

which a vertical profile can be developed). 

Surface characteristics may be varied by 

wind sector and by season or month. A 

morning sounding (in National Weather 

Service format) from a representative upper 

air station, latitude, longitude, time zone, and 

wind speed threshold are also required in 

AERMET (instrument threshold is only 

required for site specific data). Additionally, 

measured profiles of wind, temperature, 

vertical and lateral turbulence may be 

required in certain applications (e.g., in 

complex terrain) to adequately represent the 

meteorology affecting plume transport and 

dispersion. Optionally, measurements of 

solar, or net radiation may be input to 

AERMET. Two files are produced by the 

AERMET meteorological preprocessor for 

input to the AERMOD dispersion model. The 

surface file contains observed and calculated 

surface variables, one record per hour. The 

profile file contains the observations made at 

each level of a meteorological tower (or 

remote sensor), or the one-level observations 

taken from other representative data (e.g., 

National Weather Service surface 

observations), one record per level per hour. 

(i) Data used as input to AERMET should 

possess an adequate degree of 

representativeness to insure that the wind, 

temperature and turbulence profiles derived 

by AERMOD are both laterally and vertically 

representative of the source area. The 

adequacy of input data should be judged 

independently for each variable. The values 

for surface roughness, Bowen ratio, and 

albedo should reflect the surface 

characteristics in the vicinity of the 

meteorological tower, and should be 

adequately representative of the modeling 

domain. Finally, the primary atmospheric 

input variables including wind speed and 

direction, ambient temperature, cloud cover, 

and a morning upper air sounding should 

also be adequately representative of the 

source area. 

(ii) For recommendations regarding the 

length of meteorological record needed to 

perform a regulatory analysis with AERMOD, 

see Section 8.3.1. 

(3) Receptor data: Receptor coordinates, 

elevations, height above ground, and hill 

height scales are produced by the AERMAP 

terrain preprocessor for input to AERMOD. 

Discrete receptors and/or multiple receptor 

grids, Cartesian and/or polar, may be 

employed in AERMOD. AERMAP requires 

input of Digital Elevation Model (DEM) 

terrain data produced by the U.S. Geological 

Survey (USGS), or other equivalent data. 

AERMAP can be used optionally to estimate 

source elevations. 

c. Output 

Printed output options include input 

information, high concentration summary 

tables by receptor for user-specified 

averaging periods, maximum concentration 

summary tables, and concurrent values 

summarized by receptor for each day 

processed. Optional output files can be 

generated for: a listing of occurrences of 

exceedances of user-specified threshold 

value; a listing of concurrent (raw) results at 

each receptor for each hour modeled, suitable 

for post-processing; a listing of design values 

that can be imported into graphics software 

for plotting contours; an unformatted listing 

of raw results above a threshold value with 

a special structure for use with the TOXX 

model component of TOXST; a listing of 

concentrations by rank (e.g., for use in 

quantile-quantile plots); and, a listing of 

concentrations, including arc-maximum 

normalized concentrations, suitable for 

model evaluation studies. 

d. Type of Model 

AERMOD is a steady-state plume model, 

using Gaussian distributions in the vertical 

and horizontal for stable conditions, and in 

the horizontal for convective conditions. The 

vertical concentration distribution for 

convective conditions results from an 

assumed bi-Gaussian probability density 

function of the vertical velocity. 

e. Pollutant Types 

AERMOD is applicable to primary 

pollutants and continuous releases of toxic 

and hazardous waste pollutants. Chemical 

transformation is treated by simple 

exponential decay. 

f. Source-Receptor Relationships 

AERMOD applies user-specified locations 

for sources and receptors. Actual separation 

between each source-receptor pair is used. 

Source and receptor elevations are user input 

or are determined by AERMAP using USGS 

DEM terrain data. Receptors may be located 

at user-specified heights above ground level. 

g. Plume Behavior 

(1) In the convective boundary layer (CBL), 

the transport and dispersion of a plume is 

characterized as the superposition of three 

modeled plumes: The direct plume (from the 

stack), the indirect plume, and the penetrated 

plume, where the indirect plume accounts 

for the lofting of a buoyant plume near the 

top of the boundary layer, and the penetrated 

plume accounts for the portion of a plume 

that, due to its buoyancy, penetrates above 

the mixed layer, but can disperse downward 

and re-enter the mixed layer. In the CBL, 

plume rise is superposed on the 

displacements by random convective 

velocities (Weil et al., 1997). 

(2) In the stable boundary layer, plume rise 

is estimated using an iterative approach, 

similar to that in the CTDMPLUS model (see 

A.5 in this appendix). 

(3) Stack-tip downwash and buoyancy 

induced dispersion effects are modeled. 

Building wake effects are simulated for stacks 

less than good engineering practice height 

using the methods contained in the PRIME 

downwash algorithms (Schulman, et al., 

2000). For plume rise affected by the 

presence of a building, the PRIME downwash 

algorithm uses a numerical solution of the 

mass, energy and momentum conservation 

laws (Zhang and Ghoniem, 1993). Streamline 

deflection and the position of the stack 

relative to the building affect plume 

trajectory and dispersion. Enhanced 

dispersion is based on the approach of Weil 

(1996). Plume mass captured by the cavity is 

well-mixed within the cavity. The captured 

plume mass is re-emitted to the far wake as 

a volume source. 

(4) For elevated terrain, AERMOD 

incorporates the concept of the critical 

dividing streamline height, in which flow 

below this height remains horizontal, and 

flow above this height tends to rise up and 

over terrain (Snyder et al., 1985). Plume 

concentration estimates are the weighted sum 

of these two limiting plume states. However, 

consistent with the steady-state assumption 

of uniform horizontal wind direction over the 

modeling domain, straight-line plume 

trajectories are assumed, with adjustment in 

the plume/receptor geometry used to account 

for the terrain effects. 

h. Horizontal Winds 

Vertical profiles of wind are calculated for 

each hour based on measurements and 

surface-layer similarity (scaling) 

relationships. At a given height above 

ground, for a given hour, winds are assumed 

constant over the modeling domain. The 

effect of the vertical variation in horizontal 

wind speed on dispersion is accounted for 

through simple averaging over the plume 

depth. 

i. Vertical Wind Speed 

In convective conditions, the effects of 

random vertical updraft and downdraft 

velocities are simulated with a bi-Gaussian 

probability density function. In both 

convective and stable conditions, the mean 

vertical wind speed is assumed equal to zero. 

j. Horizontal Dispersion 

Gaussian horizontal dispersion coefficients 

are estimated as continuous functions of the 

parameterized (or measured) ambient lateral 

turbulence and also account for buoyancyinduced and building wake-induced 

turbulence. Vertical profiles of lateral 

turbulence are developed from measurements 

and similarity (scaling) relationships. 

Effective turbulence values are determined 

from the portion of the vertical profile of 

lateral turbulence between the plume height 

and the receptor height. The effective lateral 

turbulence is then used to estimate 

horizontal dispersion. 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68255 

k. Vertical Dispersion 

In the stable boundary layer, Gaussian 

vertical dispersion coefficients are estimated 

as continuous functions of parameterized 

vertical turbulence. In the convective 

boundary layer, vertical dispersion is 

characterized by a bi-Gaussian probability 

density function, and is also estimated as a 

continuous function of parameterized 

vertical turbulence. Vertical turbulence 

profiles are developed from measurements 

and similarity (scaling) relationships. These 

turbulence profiles account for both 

convective and mechanical turbulence. 

Effective turbulence values are determined 

from the portion of the vertical profile of 

vertical turbulence between the plume height 

and the receptor height. The effective vertical 

turbulence is then used to estimate vertical 

dispersion. 

l. Chemical Transformation 

Chemical transformations are generally not 

treated by AERMOD. However, AERMOD 

does contain an option to treat chemical 

transformation using simple exponential 

decay, although this option is typically not 

used in regulatory applications, except for 

sources of sulfur dioxide in urban areas. 

Either a decay coefficient or a half life is 

input by the user. Note also that the Plume 

Volume Molar Ratio Method (subsection 5.1) 

and the Ozone Limiting Method (subsection 

5.2.4) and for point-source NO2 analyses are 

available as non-regulatory options. 

m. Physical Removal 

AERMOD can be used to treat dry and wet 

deposition for both gases and particles. 

n. Evaluation Studies 

American Petroleum Institute, 1998. 

Evaluation of State of the Science of Air 

Quality Dispersion Model, Scientific 

Evaluation, prepared by Woodward-Clyde 

Consultants, Lexington, Massachusetts, for 

American Petroleum Institute, Washington, 

D.C., 20005–4070. 

Brode, R.W., 2002. Implementation and 

Evaluation of PRIME in AERMOD. Preprints 

of the 12th Joint Conference on Applications 

of Air Pollution Meteorology, May 20–24, 

2002; American Meteorological Society, 

Boston, MA. 

Brode, R.W., 2004. Implementation and 

Evaluation of Bulk Richardson Number 

Scheme in AERMOD. 13th Joint Conference 

on Applications of Air Pollution 

Meteorology, August 23–26, 2004; American 

Meteorological Society, Boston, MA. 

Environmental Protection Agency, 2003. 

AERMOD: Latest Features and Evaluation 

Results. Publication No. EPA–454/R–03–003. 

U.S. Environmental Protection Agency, 

Research Triangle Park, NC. Available at 

http://www.epa.gov/scram001/. 

A.2 Buoyant Line and Point Source 

Dispersion Model (BLP) 

Reference 

Schulman, Lloyd L., and Joseph S. Scire, 

1980. Buoyant Line and Point Source (BLP) 

Dispersion Model User’s Guide. Document 

P–7304B. Environmental Research and 

Technology, Inc., Concord, MA. (NTIS No. 

PB 81–164642; also available at http:// 

www.epa.gov/scram001/) 

Availability 

The computer code is available on EPA’s 

Internet SCRAM Web site 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: surface weather 

data from a preprocessor such as 

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 

68256 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

mixing height; uniform mixing is assumed 

beyond that point. 

(4) Perfect reflection at the ground is 

assumed. 

l. Chemical Transformation 

Chemical transformations are treated using 

linear decay. Decay rate is input by the user. 

m. Physical Removal 

Physical removal is not explicitly treated. 

n. Evaluation Studies 

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

Buoyant Line and Point Source (BLP) 

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

Environmental Research and Technology, 

Inc., Concord, MA. 

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

Evaluation of the BLP and ISC Models with 

SF6 Tracer Data and SO2 Measurements at 

Aluminum Reduction Plants. APCA 

Specialty Conference on Dispersion 

Modeling for Complex Sources, St. Louis, 

MO. 

A.3 CALINE3 

Reference 

Benson, Paul E., 1979. CALINE3—A 

Versatile Dispersion Model for Predicting Air 

Pollutant Levels Near Highways and Arterial 

Streets. Interim Report, Report Number 

FHWA/CA/TL–79/23. Federal Highway 

Administration, Washington, 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, DC. 

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. 

Environmental Protection Agency, 1986. 

Evaluation of Mobile Source Air Quality 

Simulation Models. EPA Publication No. 

EPA–450/4–86–002. Office of Air Quality 

Planning & Standards, Research Triangle 

Park, NC. (NTIS No. PB 86–167293) 

A.4 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) 371–4270; Fax: (978) 371–

2468; e-mail: JScire@alum.mit.edu. 

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 stack 

tip downwash, building downwash, 

transitional buoyant and momentum plume 

rise, rain cap effects, 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 effects, 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 8.3(d) and 8.3.1.2(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 meteorological conditions on transport and 

dispersion, as discussed in paragraph 

7.2.8(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 

judgment and in consultation with the 

appropriate reviewing authorities. 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68257 

b. Input Requirements 

Source Data: 

1. Point sources: Source location, stack 

height, diameter, exit velocity, exit 

temperature, base elevation, wind direction 

specific building dimensions (for building 

downwash calculations), and emission rates 

for each pollutant. Particle size distributions 

may be entered for particulate matter. 

Temporal emission factors (diurnal cycle, 

monthly cycle, hour/season, wind speed/ 

stability class, or temperature-dependent 

emission factors) may also be entered. 

Arbitrarily-varying point source parameters 

may be entered from an external file. 

2. Area sources: Source location and shape, 

release height, base elevation, initial vertical 

distribution (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 (3–

D) meteorological fields generated by 

CALMET. This is the preferred mode for 

running CALPUFF. Data inputs used by 

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) and air-sea 

temperature differences (over water). 

Optional 3–D meteorological prognostic 

model output (e.g., from models such as 

MM5, RUC, Eta and RAMS) can be used by 

CALMET as well (paragraph 8.3.1.2(d)). 

CALMET contains an option to be run in 

‘‘No-observations’’ mode (Robe et al., 2002), 

which allows the 3–D CALMET 

meteorological fields to be based on 

prognostic model output alone, without 

observations. This allows CALMET and 

CALPUFF to be run in prognostic mode for 

forecast applications. 

2. Single station surface and upper air 

meteorological data in CTDMPLUS data file 

formats (SURFACE.DAT and PROFILE.DAT 

files) or AERMOD data file formats. These 

options allow a vertical variation in the 

meteorological parameters but no horizontal 

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 available from various sources such as 

the U.S. Geological Survey (USGS) and the 

National Aeronautics and Space 

Administration (NASA). 

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. Monthly 

concentrations of ammonia concentrations 

can be specified in the CALPUFF input file, 

although higher time-resolution ammonia 

variability can be computed using the 

POSTUTIL program. 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. 

Liquid water content variables including 

cloud water/ice and precipitation water/ice 

can be used as input for visibility analyses 

and other CALPUFF modules. 

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, 

CALPOST, CALSUM, APPEND, and 

POSTUTIL) provide options for summing, 

scaling, analyzing and displaying the 

modeling results. CALPOST contains options 

for computing of light extinction (visibility) 

and POSTUTIL allows the re-partitioning of 

nitric acid and nitrate to account for the 

effects of ammonia limitation (Scire et al., 

2001; Escoffier-Czaja and Scire, 2002). 

CALPUFF contains an options to output 

liquid water concentrations for use in 

computing visible plume lengths and 

frequency of icing and fogging from cooling 

towers and other water vapor sources. The 

CALPRO Graphical User Interface (GUI) 

contains options for creating graphics such as 

contour plots, vector plots and other displays 

when linked to graphics software. 

d. Type of Model 

(1) CALPUFF is a non-steady-state timeand space-dependent Gaussian puff model. 

CALPUFF treats primary pollutants and 

simulates secondary pollutant formation 

using a parameterized, quasi-linear chemical 

conversion mechanism. Pollutants treated 

include SO2, SO4

=, NOX (i.e., NO + NO2), 

HNO3, NO3

-

, NH3, PM–10, PM–2.5, toxic 

pollutants and others pollutant species that 

are either inert or subject to quasi-linear 

chemical reactions. 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 

parameterized treatments of slope flows, 

valley flows, terrain blocking effects, and 

kinematic terrain effects, lake and sea breeze 

circulations, a divergence minimization 

procedure, and objective analysis of 

observational data. 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 12.0, ref. 86), as well as the 

RAMS, Ruc and Eta models. 

e. Pollutant Types 

CALPUFF may be used to model gaseous 

pollutants or particulate matter that are inert 

or which undergo quasi-linear chemical 

reactions, such as SO2, SO4 =, NOX (i.e., NO 

+ NO2), HNO3, NO3-, NH3, PM–10, PM–2.5 

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 

68258 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

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 (1975) for non-downwashing point 

sources, Schulman and Scire (1980) for line 

sources and point sources subject to building 

downwash effects using the Schulman-Scire 

downwash algorithm, and Zhang (1993) for 

buoyant area sources and point sources 

affected by building downwash when using 

the PRIME building downwash method. 

Stack tip downwash effects and partial 

plume penetration into elevated temperature 

inversions are included. An algorithm to treat 

horizontally-oriented vents and stacks with 

rain caps is 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, AERMOD or 

ISCST3 data formats. 

i. Vertical Wind Speed 

Vertical wind speeds are not used 

explicitly by CALPUFF. Vertical winds are 

used in the development of the horizontal 

wind components by CALMET. 

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 

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 

NO3-, HNO3, and NO2. Organic aerosol 

formation is treated. The POSTUTIL program 

contains an option to re-partition HNO3 and 

NO3- in order to treat the effects of ammonia 

limitation. 

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. CALPUFF 

contains an option to evaluate the effects of 

plume tilt resulting from gravitational 

settling. 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. 

Chang, J.C., P. Franzese, K. Chayantrakom 

and S.R. Hanna, 2001. Evaluations of 

CALPUFF, HPAC and VLSTRACK with Two 

Mesoscale Field Datasets. Journal of Applied 

Meteorology, 42(4): 453–466. 

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. 

Morrison, K, Z–X Wu, J.S. Scire, J. Chenier 

and T. Jeffs-Schonewille, 2003. CALPUFFBased Predictive and Reactive Emission 

Control System. 96th A&WMA Annual 

Conference & Exhibition, 22–26 June 2003; 

San Diego, CA. 

Schulman, L.L., D.G. Strimaitis and J.S. 

Scire, 2000. Development and evaluation of 

the PRIME Plume Rise and Building 

Downwash Model. JAWMA, 50: 378–390. 

Scire, J.S., Z–X Wu, D.G. Strimaitis and 

G.E. Moore, 2001. The Southwest Wyoming 

Regional CALPUFF Air Quality Modeling 

Study—Volume I. Prepared for the Wyoming 

Dept. of Environmental Quality. Available 

from Earth Tech at http://www.src.com. 

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

1998. Evaluation of the CALPUFF Dispersion 

Model with Two Power Plant Data Sets. 

Tenth Joint Conference on the Application of 

Air Pollution Meteorology, Phoenix, Arizona. 

American Meteorological Society, Boston, 

MA. January 11–16, 1998. 

A.5 Complex Terrain Dispersion Model 

Plus Algorithms for Unstable Situations 

(CTDMPLUS) 

Reference 

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

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

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

1989. User’s Guide to the Complex Terrain 

Dispersion Model Plus Algorithms for 

Unstable Situations (CTDMPLUS). Volume 1: 

Model Descriptions and User Instructions. 

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

Environmental Protection Agency, Research 

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

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

Dispersion Model for Sources near Complex 

Topography. Part I: Technical Formulations. 

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

645. 

Availability 

This model code is available on EPA’s 

Internet SCRAM 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 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68259 

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 

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 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; also available 

at http://www.epa.gov/scram001/) 

Availability 

This model code is available on 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. 

68260 Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 

a. Recommendations for Regulatory Use 

OCD has been recommended for use by the 

Minerals Management Service for emissions 

located on the Outer Continental Shelf (50 FR 

12248; 28 March 1985). OCD is applicable for 

overwater sources where onshore receptors 

are below the lowest source height. Where 

onshore receptors are above the lowest 

source height, offshore plume transport and 

dispersion may be modeled on a case-by-case 

basis in consultation with the 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). 

(2) Meteorological data: 

Over land: Surface weather data from a 

preprocessor such as PCRAMMET which 

provides hourly stability class, wind 

direction, wind speed, ambient temperature, 

and mixing height are required. 

Over water: Hourly values for mixing 

height, relative humidity, air temperature, 

and water surface temperature are required; 

if wind speed/direction are missing, values 

over land will be used (if available); vertical 

wind direction shear, vertical temperature 

gradient, and turbulence intensities are 

optional. 

(3) Receptor data: Location, height above 

local ground-level, ground-level elevation 

above the water surface. 

c. Output 

(1) All input options, specification of 

sources, receptors and land/water map 

including locations of sources and receptors. 

(2) Summary tables of five highest 

concentrations at each receptor for each 

averaging period, and average concentration 

for entire run period at each receptor. 

(3) Optional case study printout with 

hourly plume and receptor characteristics. 

Optional table of annual impact assessment 

from non-permanent activities. 

(4) Concentration files written to disk or 

tape can be used by ANALYSIS 

postprocessor to produce the highest 

concentrations for each receptor, the 

cumulative frequency distributions for each 

receptor, the tabulation of all concentrations 

exceeding a given threshold, and the 

manipulation of hourly concentration files. 

d. Type of Model 

OCD is a Gaussian plume model 

constructed on the framework of the MPTER 

model. 

e. Pollutant Types 

OCD may be used to model primary 

pollutants. Settling and deposition are not 

treated. 

f. Source-Receptor Relationship 

(1) Up to 250 point sources, 5 area sources, 

or 1 line source and 180 receptors may be 

used. 

(2) Receptors and sources are allowed at 

any location. 

(3) The coastal configuration is determined 

by a grid of up to 3600 rectangles. Each 

element of the grid is designated as either 

land or water to identify the coastline. 

g. Plume Behavior 

(1) As in ISC, the basic plume rise 

algorithms are based on Briggs’

recommendations. 

(2) Momentum rise includes consideration 

of the stack angle from the vertical. 

(3) The effect of drilling platforms, ships, 

or any overwater obstructions near the source 

are used to decrease plume rise using a 

revised platform downwash algorithm based 

on laboratory experiments. 

(4) Partial plume penetration of elevated 

inversions is included using the suggestions 

of Briggs (1975) and Weil and Brower (1984). 

(5) Continuous shoreline fumigation is 

parameterized using the Turner method 

where complete vertical mixing through the 

thermal internal boundary layer (TIBL) 

occurs as soon as the plume intercepts the 

TIBL. 

h. Horizontal Winds 

(1) Constant, uniform wind is assumed for 

each hour. 

(2) Overwater wind speed can be estimated 

from overland wind speed using relationship 

of Hsu (1981). 

(3) Wind speed profiles are estimated using 

similarity theory (Businger, 1973). Surface 

layer fluxes for these formulas are calculated 

from bulk aerodynamic methods. 

i. Vertical Wind Speed 

Vertical wind speed is assumed equal to 

zero. 

j. Horizontal Dispersion 

(1) Lateral turbulence intensity is 

recommended as a direct estimate of 

horizontal dispersion. If lateral turbulence 

intensity is not available, it is estimated from 

boundary layer theory. For wind speeds less 

than 8 m/s, lateral turbulence intensity is 

assumed inversely proportional to wind 

speed. 

(2) Horizontal dispersion may be enhanced 

because of obstructions near the source. A 

virtual source technique is used to simulate 

the initial plume dilution due to downwash. 

(3) Formulas recommended by Pasquill 

(1976) are used to calculate buoyant plume 

enhancement and wind direction shear 

enhancement. 

(4) At the water/land interface, the change 

to overland dispersion rates is modeled using 

a virtual source. The overland dispersion 

rates can be calculated from either lateral 

turbulence intensity or Pasquill-Gifford 

curves. The change is implemented where 

the plume intercepts the rising internal 

boundary layer. 

k. Vertical Dispersion 

(1) Observed vertical turbulence intensity 

is not recommended as a direct estimate of 

vertical dispersion. Turbulence intensity 

should be estimated from boundary layer 

theory as default in the model. For very 

stable conditions, vertical dispersion is also 

a function of lapse rate. 

(2) Vertical dispersion may be enhanced 

because of obstructions near the source. A 

virtual source technique is used to simulate 

the initial plume dilution due to downwash. 

(3) Formulas recommended by Pasquill 

(1976) are used to calculate buoyant plume 

enhancement. 

(4) At the water/land interface, the change 

to overland dispersion rates is modeled using 

a virtual source. The overland dispersion 

rates can be calculated from either vertical 

turbulence intensity or the Pasquill-Gifford 

coefficients. The change is implemented 

where the plume intercepts the rising 

internal boundary layer. 

1. 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. REFERENCES 

Benson, P.E., 1979. CALINE3—A Versatile 

Dispersion Model for Predicting Air 

Pollution Levels Near Highways and Arterial 

Streets. Interim Report, Report Number 

FHWA/CA/TL–79/23. Federal Highway 

Administration, Washington, DC. 

Briggs, G.A., 1975. Plume Rise Predictions. 

Lectures on Air Pollution and Environmental 

Impact Analyses. American Meteorological 

Society, Boston, MA, pp. 59–111. 

Briggs, G.A., 1984. Analytical 

Parameterizations of Diffusion: The 

Convective Boundary Layer. Journal of 

Climate and Applied Meteorology, 24(11): 

1167–1186. 

Environmental Protection Agency, 1980. 

Recommendations on Modeling (October 

1980 Meetings). Appendix G to: Summary of 

Comments and Responses on the October 

1980 Proposed Revisions to the Guideline on 

Air Quality Models. Meteorology and 

Assessment Division, Office of Research and 

Development, Research Triangle Park, NC 

27711. 

Environmental Protection Agency, 1998. 

Interagency Workgroup on Air Quality 

Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations 68261 

Modeling (IWAQM) Phase 2 Summary Report 

and Recommendations for Modeling LongRange Transport Impacts. Publication No. 

EPA–454/R–98–019. (NTIS No. PB 99–

121089). 

Escoffier-Czaja, C. and J.S. Scire, 2002. The 

Effects of Ammonia Limitation on Nitrate 

Aerosol Formation and Visibility Impacts in 

Class I Areas. Twelfth AMS/AWMA 

Conference on the Application of Air 

Pollution Meteorology, 20–24 May 2002; 

Norfolk, VA. 

Gifford, F.A., Jr. 1976. Turbulent Diffusion 

Typing Schemes—A Review. Nuclear Safety, 

17: 68–86. 

Horst, T.W., 1983. A Correction to the 

Gaussian Source-depletion Model. In 

Precipitation Scavenging, Dry Deposition and 

Resuspension. H. R. Pruppacher, R.G. 

Semonin and W.G.N. Slinn, eds., Elsevier, 

NY. 

Hsu, S.A., 1981. Models for Estimating 

Offshore Winds from Onshore Meteorological 

Measurements. Boundary Layer Meteorology, 

20: 341–352. 

Huber, A.H. and W.H. Snyder, 1976. 

Building Wake Effects on Short Stack 

Effluents. Third Symposium on Atmospheric 

Turbulence, Diffusion and Air Quality, 

American Meteorological Society, Boston, 

MA. 

Irwin, J.S., 1979. A Theoretical Variation of 

the Wind Profile Power-Law Exponent as a 

Function of Surface Roughness and Stability. 

Atmospheric Environment, 13: 191–194. 

Liu, M.K. et al., 1976. The Chemistry, 

Dispersion, and Transport of Air Pollutants 

Emitted from Fossil Fuel Power Plants in 

California: Data Analysis and Emission 

Impact Model. Systems Applications, Inc., 

San Rafael, CA. 

Pasquill, F., 1976. Atmospheric Dispersion 

Parameters in Gaussian Plume Modeling Part 

II. Possible Requirements for Change in the 

Turner Workbook Values. Publication No. 

EPA–600/4–76–030b. Office of Air Quality 

Planning & Standards, Research Triangle 

Park, NC 27711. 

Petersen, W.B., 1980. User’s Guide for 

HIWAY–2 A Highway Air Pollution Model. 

Publication No. EPA–600/8–80–018. Office of 

Research & Development, Research Triangle 

Park, NC 27711. (NTIS PB 80–227556) 

Rao, T.R. and M.T. Keenan, 1980. 

Suggestions for Improvement of the EPA–

HIWAY Model. Journal of the Air Pollution 

Control Association, 30: 247–256 (and 

reprinted as Appendix C in Petersen, 1980). 

Robe, F.R., Z–X. Wu and J.S. Scire, 2002: 

Real-time SO2 Forecasting System with 

Combined ETA Analysis and CALPUFF 

Modeling. Proceedings of the 8th 

International Conference on Harmonisation 

within Atmospheric Dispersion Modelling 

for Regulatory Purposes, 14–17 October 2002; 

Sofia, Bulgaria. 

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

Buoyant Line and Point Source (BLP) 

dispersion model user’s guide. The 

Aluminum Association; Washington, DC. 

(See A.2 in this appendix.) 

Schulman, L.L. and S.R. Hanna, 1986. 

Evaluation of Downwash Modification to the 

Industrial Source Complex Model. Journal of 

the Air Pollution Control Association, 36: 

258–264. 

Segal, H.M., 1983. Microcomputer 

Graphics in Atmospheric Dispersion 

Modeling. Journal of the Air Pollution 

Control Association, 23: 598–600. 

Snyder, W.H., R.S. Thompson, R.E. 

Eskridge, R.E. Lawson, I.P. Castro, J.T. Lee, 

J.C.R. Hunt, and Y. Ogawa, 1985. The 

structure of the strongly stratified flow over 

hills: Dividing streamline concept. Journal of 

Fluid Mechanics, 152: 249–288. 

Turner, D.B., 1969. Workbook of 

Atmospheric Dispersion Estimates. PHS 

Publication No. 999–26. U.S. Environmental 

Protection Agency, Research Triangle, Park, 

NC 27711. 

Weil, J.C. and R.P. Brower, 1984. An 

Updated Gaussian Plume Model for Tall 

Stacks. Journal of the Air Pollution Control 

Association, 34: 818–827. 

Weil, J.C., 1996. A new dispersion 

algorithm for stack sources in building 

wakes, Paper 6.6. Ninth Joint Conference on 

Applications of Air Pollution Meteorology 

with A&WMA, January 28–February 2, 1996. 

Atlanta, GA. 

Weil, J.C., L.A. Corio, and R.P. Brower, 

1997. A PDF dispersion model for buoyant 

plumes in the convective boundary layer. 

Journal of Applied Meteorology, 36: 982–

1003. 

Zhang, X., 1993. A computational analysis 

of the rise, dispersion, and deposition of 

buoyant plumes. Ph.D. Thesis, Massachusetts 

Institute of Technology, Cambridge, MA. 

Zhang, X. and A.F. Ghoniem, 1993. A 

computational model for the rise and 

dispersion of wind-blown, buoyancy-driven 

plumes—I. Neutrally stratified atmosphere. 

Atmospheric Environment, 15: 2295–2311. 

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