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

1 A MS/ E PA Regulatory MOD el. 

### ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 51

[AH–FRL–7478–3]

RIN 2060–AF01



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

AGENCY 

### : Environmental Protection Agency (EPA).

ACTION 

### : Final rule.

SUMMARY 

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

DATES 

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

ADDRESSES 

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

FOR FURTHER INFORMATION CONTACT 

### : Joseph A. Tikvart, Leader, Air Quality Modeling Group (MD–14), Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; telephone (919) 541–5562 ( Tikvart.Joe@epa.gov ).

SUPPLEMENTARY INFORMATION 

### : I. General Information A. How Can I Get Copies of Related Information?

EPA established an official public docket for this action under Docket ID No. A–99–05. The official public docket is the collection of materials that is available for public viewing at the Air Docket in the EPA Docket Center, (EPA/ DC) EPA West (MC 6102T), 1301 Constitution Ave., NW., Washington, DC. The EPA Docket Center Public Reading Room (B102) is open from 8:30 a.m. to 4:30 p.m., Monday through Friday, excluding legal holidays. The telephone number for the Reading Room is (202) 566–1744, and the telephone number for the Air Docket is (202) 566–
1742.

Our Air Quality Modeling Group maintains an Internet Web site (Support Center for Regulatory Air Models— SCRAM) at: http://www.epa.gov/
scram001. You may find codes and documentation for models referenced in today’s action on the SCRAM Web site. We have also uploaded various support documents ( e.g. , evaluation reports). II. Background

The Guideline is used by EPA, States, and industry to prepare and review new source permits and State Implementation Plan revisions. The Guideline is intended to ensure consistent air quality analyses for activities regulated at 40 CFR 51.112, 51.117, 51.150, 51.160, 51.166, and 52.21. We originally published the Guideline in April 1978 and it was incorporated by reference in the regulations for the Prevention of Significant Deterioration (PSD) of Air Quality in June 1978. We revised the Guideline in 1986, and updated it with supplement A in 1987, supplement B in July 1993, and supplement C in August 1995. We published the Guideline as appendix W to 40 CFR part 51 when we issued supplement B. We republished the Guideline in August 1996 (61 FR 41838) to adopt the CFR system for labeling paragraphs. On April 21, 2000 we published proposed revisions in the Federal Register (65 FR 21506), which is the basis for today’s promulgation.

Today’s notice promulgates those components of the proposal that were clearly supported by public comments and that were otherwise not controversial, notably:

• Adoption of CALPUFF in appendix A, as proposed, for assessing long range transport of pollutants and their impacts on Federal Class I areas;

• Removal of the Climatological Dispersion Model (CDM), RAM and the Urban Airshed Model (UAM) from appendix A, as proposed;

• Simplification of complex terrain screening techniques in section 5;

• Revision of section 9 to reflect our October 1997 settlement with the Utility Air Regulatory Group regarding specification of emissions from background sources, as proposed;

• Updating information in appendix W and reorganizing its structure; and

• Transfer of appendix B and appendix C to our Web site, as proposed.

The proposal also included (1) adopting AERMOD 1 

### to replace the Industrial Source Complex (ISC3) model in many assessments that now use it, (2) revising ISC3 by incorporating a new downwash algorithm (PRIME) and renaming the model ISC–PRIME, and (3) updating the Emissions Dispersion Modeling System (EDMS) by incorporating improved emissions and dispersion modules. Regarding AERMOD, nearly every commenter urged EPA to integrate aerodynamic downwash into AERMOD ( i.e. , not to require two models for some analyses). The only cautions were associated with the need for documentation, evaluation and review of the downwash enhancement to AERMOD. As a result of AERMIC’s (the American Meteorological Society (AMS)/ EPA Regulatory Model Improvement Committee) efforts to revise AERMOD, incorporating the PRIME algorithm and making a few other incidental modifications and to respond to the public’s cautions, we believe that AERMOD, as modified for downwash, merits another public examination of performance results. Also, since the April 2000 proposal, the Federal Aviation Administration decided to configure EDMS3.1 to incorporate the AERMOD dispersion model, and results of its performance with AERMOD only recently became available. Consequently, AERMOD and EDMS4.0, as well as other conforming changes for the Guideline, will be reconsidered in a Supplemental Notice of Proposed Rulemaking (SNPR) in the near future. Note that since AERMOD is not included in today’s promulgation, the proposed merger of the Guideline’s sections 4 and 5 will be deferred to AERMOD’s adoption in the future. III. Public Hearing on the Proposal

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


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

2 IWAQM was formed in 1991 to provide a focus for development of technically sound air quality models for regulatory assessments of long range transport of pollutant source impacts on federal Class I areas. IWAQM is an interagency collaboration that includes efforts by EPA, U.S. Forest Service, National Park Service, and Fish and Wildlife Service. 3 Summary of Public Comments and EPA Responses 7th Conference on Air Quality Modeling, Washington, D.C., June 2000 (Air Docket A

– 99

– 05, Item V

– C

– 1). This document may also be examined from EPA ’ s SCRAM Web site ( http://www.epa.gov/
scram001 ). Note that comments/responses re: AERMOD & EDMS are deferred to a companion document to be released when the SNPR is published.

4 Environmental Protection Agency, 1993. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase I report: Interim Recommendation for Modeling Long range Transport and Impacts on Regional Visibility; EPA Publication No. EPA

– 454/ R

– 93

– 015.

5 Environmental Protection Agency, 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long-Range Transport Impacts. EPA Publication No. EPA

– 454/ R

– 98

– 019. 

### approximately every three years to standardize modeling procedures. This conference served as the forum for receiving public comments on the Guideline revisions proposed in April 2000. The 7th conference featured presentations in several key modeling areas that support the revisions promulgated today. A presentation by the Interagency Workgroup on Air Quality Modeling (IWAQM 2 

### ) covered long range transport modeling for point sources. This presentation was followed by a critical review/discussion of the CALPUFF modeling system and available performance evaluations, facilitated jointly by the Air & Waste Management Association ’ s AB

– 3 Committee and the American Meteorological Society ’ s Committee of Meteorological Aspects of Air Pollution.

We asked the public to address the following questions:

• Has the scientific merit of the models presented been established?

• Are the models ’ accuracy sufficiently documented?

• Are the proposed regulatory uses of individual models for specific applications appropriate and reasonable?

• Do significant implementation issues remain or is additional guidance needed?

• Are there serious resource constraints imposed by modeling systems presented?

• What additional analyses or information are needed?

We placed a transcript of the 7th conference proceedings and a copy of all written comments, which embody answers to the above questions, in Docket No. AQM

– 95

– 01.

IV. Discussion of Public Comments and Issues

All comments submitted to Docket No. A

– 99

– 05 are filed in Category IV

– D. We summarized these comments, developed detailed responses, and drew conclusions on appropriate actions for today ’ s action in the summary of public comments and EPA responses. 3 

### In this document, we considered and discussed all significant comments. Whenever the comments revealed any new information or suggested any alternative solutions, we considered such in our final action.

The remainder of this preamble section provides an overview of the primary issues encountered by the Agency during the public commentperiod and summarizes our response-tocomments. 3 

### This overview also serves to explain the changes to the Guideline in today ’ s action, and the main technical and policy concerns addressed by the Agency. Guidance and editorial changes associated with the resolution of these issues are adopted in the appropriate sections of the Guideline. While modeling by its nature involves approximation based on scientific methodology, and entails utilization of advanced technology as it evolves, we believe these changes respond to recent advances in the area so that the Guideline continues to reflect the best and most proven of the publicly available models and analytical techniques, as well as to reflect reasonable policy choices.

CALPUFF

CALPUFF is a Lagrangian dispersion model that simulates pollutant releases as a continuous series of puffs

. Preceding our proposal to adopt CALPUFF in the Guideline , IWAQM carefully studied the potential regulatory application of CALPUFF in its Phase 1 report 4 

### and in its Phase 2 report. 5



### In our April 2000 Federal Register notice, we proposed adoption of the CALPUFF modeling system, developed by Earth Tech, Inc., for refined use in modeling long range transport and dispersion to characterize reasonably attributable impacts from one or a few sources for PSD Class I impacts. We also proposed use of CALPUFF for those applications involving complex wind regimes, with case-by-case justification. We sought comments on the use of CALPUFF for these applications, as well as on related uses of meteorological information, e.g. , on use of prognostic mesoscale meteorological models and the length of record for meteorological data. Scientific merits and accuracy. In public comments there was a general consensus that the technical basis of the CALPUFF modeling system has merit and provides substantial capabilities to not only address long range transport, but to address transport and dispersion effects in some complex wind situations.

Commenters generally agreed that the CALPUFF modeling system has adequate accuracy for use in the 50

– 200km range, with some studies showing that acceptable results can be achieved at least out to 200 to 300km. Since the 7th Modeling Conference, enhancements were made to CALPUFF that allow puffs to be split both horizontally (to address wind direction shear) and vertically (to address spatial variation in meteorological conditions). These enhancements likely will extend the system ’ s ability to treat transport and dispersion beyond 300km.

With respect to accuracy for complex wind situations, we believe that the commenters agreed with our proposal to promote use of CALPUFF for complex winds with prior approval by the reviewing authority. CALPUFF has been demonstrated to perform as well as, or better than, other short-range plume dispersion models for a few cases involving complex winds, several with wind fields that are dominated by terrain effects. Some suggested a need for more testing of CALPUFF, prior to accepting its results in all cases involving complex wind situations. We intend to post on our Web site citations to investigations for any cases involving complex winds as they become available, and to build a knowledge base from which determinations can be made on the use of CALPUFF for various complex wind situations. This will support consideration of new field study comparisons as they become available. For the reasons stated above, it is apparent that CALPUFF contains the scientific basis for more appropriately addressing long range transport and dispersion effects in complex wind situations than do standard plume models.

We conclude that, although the scientific advancements will continue to emerge, CALPUFF in its current configuration is suitable for regulatory use for long range transport, and on a case-by-case basis for complex wind situations. We will require approval to be obtained prior to accepting CALPUFF for complex wind situations, as this will ensure that a protocol is agreed to between the parties involved, and that all are willing to accept the results as binding. As experience is gained in using CALPUFF for complex wind


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

situations, acceptance will become clear and those cases that are problematic will be better identified. As suggested by comments, we have removed reference to WYNDvalley from the Guideline

.

Implementation issues/additional guidance. Some comments suggested that the CALMET (meteorological preprocessor for CALPUFF) and CALPUFF options should be defined for a variety of specific situations. We believe that more experience is needed before specific guidance can be offered for the variety of applications envisioned that might use the CALPUFF modeling system. We placed emphasis on (1) amplifying the available guidance information, (2) expanding the data formats for meteorological input data, and (3) making the code more robust to various choices in compilers. When sufficient experience has been attained, and it has become obvious what settings should be employed for best results for certain situations, we will promulgate expanded guidance after allowing opportunity for public review and comment. In the meantime, we will release interim guidance as it becomes available to assist users in tailoring CALPUFF for application. We have created a series of frequently asked questions (FAQ) with answers which the public can access via Earth Tech ’ s Internet Web site: ( http://www.src.com/ calpuff/calpuff1.htm ). This interim FAQ list will be extended as resources permit.

For long range transport and complex winds applications, we proposed that if only National Weather Service (NWS) or comparable standard meteorological observations are employed, then five consecutive years of data should be used. We further proposed that less than five years of data were acceptable if appropriate NWS data are merged with available mesoscale meteorological fields. These proposals were generally supported by public comments, 3 

### but the commenters did provide a variety of opinions about how many years of data should be minimally acceptable, ranging from 1 to 5 years. As we explained in our response-to-comments, we sought to strike a balance between the need for a sufficiently robust meteorological record to ensure results of reliable integrity, while maintaining administrative and computational burdens at a practical level. In consultation with the Regional Offices, we therefore have agreed to allow use of less than five, but at least three, years of assimilated mesoscale meteorological data. More than 3 years may lead to the objectionable computations burdens noted here, whereas less than 3 provides insufficient variation in meteorological conditions to capture the range of possible concentrations. We have also clarified that when merging NWS data with mesoscale meteorological fields, the NWS data should be shown to be relevant and appropriate.

For long range transport, we proposed use of a CALPUFF screening approach on a case-by-case basis that was first outlined in the IWAQM Phase 2 report ( op. cit. ) and was generally supported by commenters. The full scope of public comments is presented and addressed in our response-to-comments document. 3 

### We agree with the comments suggesting use of terrain heights for each receptor ring to be representative of the Class I areas of interest. Furthermore, to ensure an appropriate degree of flexibility, we will allow the permitting agency to decide whether it will accept the CALPUFF screening results as proposed, and in that decision process will defer to the appropriate reviewing authority to decide on the details of how the CALPUFF screen is to be implemented.

Resource constraints. The full scope of public comments is presented and addressed in our response-to-comments document. 3 

### There was a general sense from commenters that a skilled person having experience with CALMET can perform the required processing steps. Still some commenters encouraged us to find and promote a simplification to the CALMET meteorological processing steps. We did not support the suggestion to use screening level (ISC-like) meteorological data until such time as packaged data sets are made available. This would negate the benefits of using the system to simulate trajectories over large downwind distances, thereby undermining the purpose for which CALPUFF is intended. Although the processing steps are numerous and complex, they can be managed by competent staff.

Long range transport and complex wind situations are not trivial modeling problems. All commenters were aware that to address these situations requires more information ( e.g. , terrain heights, land use mosaic, time and space variations in meteorological conditions) than is typical when using standard plume models. Processing the input data is a necessary but demanding task. The complexity of these situations requires a selection of options to provide the flexibility to tailor the model to specific situations. The CALPUFF system is currently configured to support a specific applied approach for long range transport, while at the same time, it has the flexibility for case-by-case applications involving complex winds.

Additional analyses. Some commenters questioned whether CALPUFF has undergone sufficient testing to secure its accuracy for assessing impacts on air quality related values (AQRVs). We believe the available testing for assessing AQRVs addresses many of these concerns. In addition, it should be recognized that the FLMs are responsible for defining the relevant AQRV ’ s of interest and the procedures to employ to assess whether there is an adverse impact. When CALPUFF is used for a visibility impact assessment, this would likely be for a Class I AQRV assessment, and the reviewing authorities are the FLMs responsible for the management and protection of the resources for the particular Class I areas involved. The Federal Land Managers ’ Air Quality Related Values Work Group (FLAG) was formed in 1997 to provide a more consistent approach for FLMs to evaluate air pollution effects on their resources. In IWAQM ’ s Phase 2 report, we indicated that EPA would use the procedures specified by the FLMs as a consequence of their deliberations ( e.g. , in their FLAG report: http:// www.aqd.nps.gov/ard/flagfree/ index.htm ). To assist permit applicants, the FLMs have provided procedures in the December 2000 (Phase I) FLAG report for performing such analyses as may be required. Included in these instructions, they have identified significance thresholds for potential adverse impacts, and methodologies for computing a visibility impact. The commenters are in fact addressing the FLAG procedures which are not the subject of today ’ s action. To the extent that they were addressed in the response to comments developed by the FLMs in the FLAG Phase I report, we refer commenters to that document.

Criticism was also directed at CALPUFF ’ s treatment of chemical transformations, which affect AQRVs. Specific concern was expressed about the sulfate and aqueous phase chemistry algorithms. As chronicled on the FLAG Web site (above), these procedures and criteria have been published and received review and comment. However, today ’ s rule addresses the suitability of CALPUFF for PSD increment consumption and forcomplex wind situations (with case-bycase approval), not AQRV analyses. Other Modeling Systems

Our proposal to remove UAM

– IV from appendix A as a recommended model for ozone and to remove reference to ROM and RADM for


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

regional scale applications was supported by some commenters who understood that these models were no longer state-of-the-science. Those who objected to removal of UAM

– IV were concerned that the Models-3/CMAQ (Community Multi-scale Air Quality) model, as a replacement for UAM

– IV, was not sufficiently tested. In fact, Models-3/CMAQ is identified as only one option among currently available models that are appropriate in simulating the highly complex ozone/ PM

– 2.5 formation and transport processes. It is the responsibility of the appropriate control agency(ies) with jurisdiction for the model application to exercise discretion in the choice of models. Alternately, criteria for using models not in appendix A are clearly delineated in revised wording that we proposed for subsection 3.2.2 of appendix W. These options should more than mitigate concerns expressed by the
commenters.

We generally agree that Models-3/ CMAQ and REMSAD will continue to benefit from further evaluation and testing for use in urban/regional scale assessments of ozone and PM

– 2.5, and are not the only models available for these applications. The same is true of all similar regional scale models. However, CMAQ and REMSAD have been successfully subjected to peer scientific reviews and are currently undergoing performance evaluations that will extend over several years as data bases become more extensive and complete for both ozone and PM

– 2.5.

While comment was solicited on the need to integrate ozone and fine particle impacts ( i.e. , the ‘‘ one atmosphere ’’ approach) for regional scale assessments, we did not receive substantial comment. Comments on integrating analyses were supportive and comments on source-specific analyses indicated that more work was needed in this area. It is clear that further developmental efforts on estimating the impact of individual sources is necessary before specific modeling requirements are identified for such applications.

Comments 3 

### were generally supportive of our proposal to remove appendix B (Summaries of Alternative Air Quality Models) from appendix W and maintaining it as a PDF file on our SCRAM Internet Web site. As we stated in the preamble to the notice of proposed rulemaking for this action, appendix B of the Guideline was created solely for the convenience of those seeking information about alternatives to the models adopted in appendix A. The models described in appendix B may or may not have not been the subject of performance evaluations and their inclusion in appendix B does not confer special status or EPA sanction on their use. Conversely, the fact that a model has not been listed in appendix B carries no implication that its performance or acceptability for use is any poorer than appendix B listed models. Whether or not a model is listed, potential users will be subject to the same requirements, i.e. , to demonstrate that the model performs acceptably for its intended regulatory application. Because production and maintenance of appendix B information in the Code of Federal Regulations presents a substantial administrative burden for EPA and is not updated frequently enough to provide current information to potential users, we are moving the appendix B repository of alternative model summary descriptions to our Internet SCRAM Web site. This action offers the advantages of easier and less expensive maintenance, as well as more frequent updating, and is thus more likely to contain a comprehensive description of alternative models which have been brought to our attention. Similarly, the air quality checklist (formerly appendix C of the Guideline ) will be available on the Web site as a PDF file.

The appendix B listing will therefore now appear as a list of Alternative Models (PDF file) on our Web site. We have clarified in its Introduction and Availability section that new models added to the list were/are not necessarily the subject of review upon their addition. On the other hand, it should be noted that the models identified in our proposal ( i.e. , ADMS, SCIPUFF, OBODM, and CAMx) were included in the review process for today ’ s action concerning the list of alternative models. At the request of the developer, we will remove MESOPUFF from appendix B since its function is replaced by CALPUFF.

Comments on the dispersion model ADMS argued that proprietary limitations on the availability of ADMS should not preclude it from having equal status with other Appendix A models and that it should be recommended in appendix A. However, as specified by Guideline paragraph 3.1.1(c)(vi), air quality models used in U.S. regulatory programs must be in the public domain at reasonable cost. This is because the source code needs to be open for public access and scrutiny to enable meaningful opportunity for public comment on new source permits, PSD increment consumption and SIPs. These criteria have been in place in U.S. regulatory programs since the inception of the Guideline and are needed to meet EPA ’ s obligations under the CAA and the Administrative Procedure Act. Until the joint issues of availability (source code) and cost are addressed by the authors of ADMS, it is most appropriately listed as an alternative model for use on a case-by-case basis.Even if the model is justified on a caseby-case basis, users are responsible for making the model available for public review and comment for specific applications.

A similar comment regarding the puff model SCIPUFF did not consider that the model has not gone through the same extensive testing and regulatory evaluation as has CALPUFF, nor has it been as widely used as CALPUFF for regulatory applications. As has been done by CALPUFF ’ s developers, a commitment to support public availability of SCIPUFF would have to be made by its supporter before it could be considered for adoption in appendix A.

Developers of neither ADMS nor SCIPUFF have addressed conflicts associated with multiple models for the same application in such a way as to assist EPA in resolving this issue. Moreover, we believe that neither ADMS nor SCIPUFF technically fill a particular technical need that is different from that occupied by the suite of refined dispersion models that EPA has promulgated for regulatory purposes after public review and comment.

Based on public comments and the rationale provided in our notice of proposed rulemaking, our decision to reference the ozone limiting method (OLM) and CAL3QHC for use in specific circumstances is justified. Meteorological Data Issues

In our proposal we solicited comment on terminology and meaning of ‘‘ sitespecific ’’ data and on use of surface meteorological data derived from the NWS ’ s Automated Surface Observing System (ASOS). More specifically, we invited comment on whether the policy of modeling with the most recent 5 years of NWS meteorological data should include ASOS data and whether the period of record must be the most recent 5 years, regardless of whether it contains ASOS data.

No one provided negative comments on the use of the term ‘‘ site-specific ’’ or associated definitions as used in the proposed revisions. Thus, for the reasons discussed in the proposal, we will retain this terminology.

The majority of commenters who addressed the topic of ASOS data felt that the ASOS data were inferior for use with Gaussian models, though not all commenters agreed. With respect to the


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6 Environmental Protection Agency, 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications. EPA Publication No. EPA

– 454/R

– 99

– 005. U.S. Environmental Protection Agency, Research Triangle Park, NC. ( www.epa.gov/
scram001 ). 

### use of the most recent 5 years of meteorological data, there was some concern about the reliability of ASOS data. We revised guidance to specifically address this concern by allowing flexibility in the choice of ASOS or observer-based observations depending on which provided the most representative meteorological information.

Final Action

Today ’ s action amends appendix W of 40 CFR part 51 as detailed below:

CALPUFF

The public comments provided constructive suggestions but did not suggest altering promulgation of the CALPUFF modeling system. We will therefore promulgate use of the CALPUFF modeling system as follows: (A) Long Range Transport

CALPUFF will be adopted as a refined model for use in sulfur dioxide and particulate matter ambient air quality standards and PSD increment impact analyses involving (1) transport greater than 50km from one or several closely spaced sources, and (2) analyses involving a mixture of both long range and short-range source-receptor relationships in a large modeling domain ( e.g. , several industrialized areas located along a river or valley). The screening approach outlined in the IWAQM Phase 2 report is available for use on a case-by-case basis that generally provides concentrations that are higher than those obtained using refined characterizations of the meteorological conditions.

Given the judgement and refinement involved, conducting a long range transport modeling assessment will require significant consultation with the appropriate reviewing authority, and for Class I analyses the appropriate FLM. To facilitate use of complex air quality and meteorological modeling systems, a written protocol may be considered for developing consensus in the methods and procedures to be followed. (B) Complex Winds

(1) On a case-by-case basis, the CALPUFF modeling system may be applied for air quality estimates involving complex meteorological conditions, where the assumptions of steady-state straight-line transport both in time and space are inappropriate.

(2) In such situations, where the otherwise preferred dispersion model is found to be less appropriate, use of the CALPUFF modeling system will be in accordance with the procedures and requirements outlined in paragraph 3.2.2(e) of the Guideline.

The public comments provided constructive suggestions, but did not suggest altering the meteorological data requirements for refined modeling assessments using the CALPUFF modeling system. Therefore, we will promulgate use of the CALPUFF modeling system with the following meteorological data requirements. For long range transport and for complex winds situations, there are two possibilities:

(A) If only NWS or comparable standard meteorological observations are employed, then five years of meteorological data should be used.

(B) If mesoscale meteorological fields are employed with appropriate NWS observations, then less than five years but at least three years of meteorological data may be used. Following the suggestions provided in public comments, we revised the Guideline to emphasize that appropriate NWS observations should be used in conjunction with mesoscale meteorological data.

In response to the suggestions provided in public comments, we: (1) Created a series of frequently asked questions to provide additional technical information to users, which will be made publicly available via Earth Tech ’ s Internet Web site, (2) expanded the meteorological and precipitation data formats that can be processed, (3) have tested and made changes as necessary that allow the modeling software to be compiled by several Fortran compilers, thus making the code more robust to various choices in compilers, and (4) will maintain and make publicly available via our Web site, a list of technical papers and reports that describe testing and evaluation of the CALPUFF modeling system in a variety of situations and thus provide a basis for wider use of the CALPUFF modeling system.

For appropriate applications, CALPUFF may be used during the oneyear period following the promulgation of today ’ s notice. After one year following promulgation of today ’ s notice, CALPUFF should be used for appropriate applications. Other Modeling Systems

We have removed UAM

– IV from appendix A for urban ozone applications and removed reference to ROM and RADM for regional scaleapplications to reflect the current stateof-science. Similarly, we have identified Models-3/CMAQ and REMSAD as example modeling systems that have been evaluated and peer reviewed for regional scale applications, and make clear that this does not preclude the use of other models.

We have removed appendix B and appendix C from appendix W and placed equivalent counterparts on our SCRAM Internet Web site. Former appendix B will simply become a list of alternative model summaries, and should be readily updated as new models in the proper format are submitted and not on a restrictive schedule. Given the current status of ADMS and SCIPUFF, as well as OBODM, CAMx and UAMV (an update to UAM

– IV), all have now been included in the web-based Alternative Models list.

As proposed, we have referenced OLM and CAL3QHC for use in specific circumstances, and removed RAM and CDM from appendix A. Meteorological Data Issues

The terminology for ‘‘ site-specific ’’ has been implemented as proposed since there was a lack of negative comment. The prevailing concept is, as commenters recognized, representativeness , and this is now emphasized in our guidance.

Due to limitations of ASOS data for use with standard dispersion models, paragraph 8.3.1.2(a) of appendix W has been revised to indicate that where the latest 5 years of data includes ASOS data (now the typical situation) discretion should be used. Where judgment indicates ASOS data are inadequate for cloud cover observations, the most recent 5 years of NWS data that are observer-based may be considered for use.

In response to public comment, we have updated our meteorological data processors ( i.e. , MPRM and CALMET) to allow processing of meteorological data formats from the National Climatic Data Center necessary to operate associated air quality models; no further updates to MPRM are necessary at this time. The meteorological monitoring guidance 6 

### has been updated. Final Editorial Changes to Appendix W

Preface

You will note some minor revisions to reflect current EPA practice.

Section 2

In a streamlining effort, we removed section 2.2 and added a new section 2.3 to address model availability.


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

7 Note that because appendix W is designed to guide assessments for criteria pollutants, the proposed discontinuation of ISCLT for purposes herein does not preclude its use for other pollutant assessments, as applicable. For example, the ASPEN model (Assessment System for Population Exposure Nationwide) uses the capabilities of ISCLT to estimate ambient concentrations of toxic pollutants nationwide by census tract. Such applications require the abbreviated computing possible with ISCLT. 8 Environmental Protection Agency, 1998. Use of Models and Other Analyses in Attainment Demonstrations for the 8-hr Ozone NAAQS (Draft). Office of Air Quality Planning & Standards, Research Triangle Park, NC. (Docket No. A

– 99

– 05, II

–A

– 14) (Also available on SCRAM Web site, http://www.epa.gov/scram001, as draft8hr.pdf )

9 Environmental Protection Agency, 1998. EPA Third-Generation Air Quality Modeling System. Models-3, Volume 9b: User Manual. EPA Publication No. EPA

– 600/R

– 98/069(b). Office of Research and Development, Washington, DC. 10 See section 8.2.3 of the Guideline

. 

### Section 3

As proposed, we revised section 3 to more accurately reflect current EPA practice, e.g., functions of the Model Clearinghouse and enhanced criteria for the use of alternative models. Requirements for alternative models when preferred models are less appropriate for specific applications have been clarified. These requirements include scientific peer review and the establishment of an acceptable protocol prior to the model ’ s use.

Section 4

We revised section 4.2.2 to reflect the widespread use of short-term models for all averaging periods. Hence, we no longer reference long-term models ( e.g., ISCLT) in the Guideline

. 7



### Section 5

To simplify, the list of acceptable, yet equivalent, screening techniques for complex terrain was removed. CTSCREEN and guidance for its use are retained; CTSCREEN remains acceptable for all terrain above stack top. The screening techniques whose descriptions we removed, i.e., Valley (as implemented in SCREEN3), COMPLEX I (as implemented in ISC3), SHORTZ/ LONGZ, and RTDM remain available for use in applicable cases where established/accepted procedures are used. Consultation with the appropriate reviewing authority is still advised for application of these screening models.

Section 6

As proposed, we revised section 6 to reflect the new PM

– 2.5 and ozone ambient air quality standards that were issued on July 18, 1997 (62 FR 38652 & 62 FR 38856). You will note that we inserted respective subsections for particulate matter and lead from section 8, so that section 6 now primarily contains modeling guidance for the criteria pollutants regulated in Part 51 (SO2 analyses are covered in section 4). We also updated information on receptor models.

• We enhanced the subsection on particulate matter as much as possible to reflect the Agency ’ s current thinking on approaches for fine particulates (PM

– 2.5). You will note that we removed the references to the Climatological Dispersion Model (CDM 2.0) as well as to RAM from this section, and also deleted CDM and RAM from appendix A ( see below).

• We enhanced the subsection on ozone to better reflect modeling approaches we currently envision, and added a reference for current guidance on ozone attainment demonstrations. 8 

### You will note that we removed the reference to the Urban Airshed Model (UAM

– IV) from this section, and deleted UAM from appendix A. UAM

– IV is no longer the recommended photochemical model for attainment demonstrations for ozone.

• We updated the subsection on carbon monoxide by removing reference to RAM. While UAM

– IV is deleted from appendix A, reference to areawide analyses is retained. For refined intersection modeling, CAL3QHCR isspecifically mentioned for use on a caseby-case basis.

• In the subsection on NO 2 

### models, we added a third tier for the screening approach that allows the use of the ozone limiting method on a case-by-case basis. You may recall that this approach was removed with the Guideline update promulgated on August 9, 1995 (60 FR 40465).

• In the subsection on lead, we deleted references to 40 CFR 51.83, 51.84, and 51.85, conforming to previous EPA action (51 FR 40661).

Section 7

For regional scale modeling, we removed reference to the Regional Oxidant Model (ROM) and the Regional Acid Deposition Model (RADM) from section 7 because they are outdated and replaced by a reference to Models-3 9 

### in section 6. We enhanced the subsection on visibility to reflect the provisions of the Clean Air Act, including those for reasonable attribution of visibility impairment and regional haze, as well as the new NAAQS for PM

– 2.5. For assessment of reasonably attributable haze impairment due to one or a small group of sources, CALPUFF is available for use on a case-by-case basis. We identify REMSAD and new approaches under the Models-3/CMAQ umbrella for possible use to develop and evaluate national policy and assist State and local control agencies. For long range transport analyses, we recommend the CALPUFF modeling system. To facilitate use of a complex air quality and meteorological modeling system like CALPUFF, we stipulate that a written protocol may be considered for developing consensus in the methods and procedures to be followed.

Section 8

As proposed, we revised section 8 to better reflect our current regulatory practice for the general modeling considerations addressed.

• We revised subsection 8.2.6 to refer to subsection 6.2.3 for details on chemical transformation of NO X 

### .

• We merged subsection 8.2.8 (Urban/ Rural Classification) with subsection 8.2.3 (Dispersion Coefficients), and removed reference to WYNDvalley.

• We merged discussions in subsections 8.2.9 (Fumigation) and 8.2.10 (Stagnation) into one new subsection (8.2.8

— Complex Winds), and specifically identify the availability of CALPUFF for certain situations on a case-by-case basis.

• We removed the distinction between short-term and long-term models because when assessing the impacts from criteria air pollutants, long-term estimates are now practicable using hour-by-hour meteorological data.

Section 9

As proposed,

• We revised subsection 9.2.3 (recommendations for estimating background concentrations from nearby sources) to reflect a settlement reached on October 16, 1997 in a petition brought by the Utility Air Regulatory Group (UARG). In accordance with the settlement, we are clarifying the definition of ‘‘ nearby sources. ’’ The

‘‘ maximum allowable emission limit, ’’ specified in Tables 9

– 1 and 9

– 2, is tied in certain circumstances 10 

### to the emission rate representative of a nearby

source ’ s maximum physical capacity to emit. We also clarify that nearby sources should be modeled only when they operate at the same time as the primary source(s) being modeled. Where a nearby source does not, by its nature, operate at the same time as the primary source being modeled, the burden is on the primary source to demonstrate to the satisfaction of the appropriate reviewing authority that this is, in fact, the case. We added footnotes to Tables 9

– 1 and
9

– 2 to refer back to applicable paragraphs of subsection 9.2.3 that provide the necessary clarification.


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

11 Stauffer, D.R. and Seaman, N.L., 1990. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Monthly Weather Review, 118:
1250

– 1277.

12 Stauffer, D.R., Seaman, N.L., and Binkowski, F.S., 1991. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effect of data assimilation within the planetary boundary layer. Monthly Weather Review, 119:
734

– 754.

13 Hourly Modeled Sounding Data. MM4

— 1990 Meteorological Data, 12-volume CD

–ROM. Jointly produced by NOAA ’ s National Climatic Data Center and Atmospheric Sciences Modeling Division. August 1995. Can be ordered from NOAA National Data Center ’ s Internet Web site @ www.nndc.noaa.gov/

.

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



### • We enhanced section 9.3 (Meteorological Input Data) to develop concepts of meteorological data representativeness, minimum meteorological data requirements, and the use of prognostic mesoscale meteorological models in certain situations. These models ( e.g. , the Penn State/NCAR MM4 11,12,13 

### or MM5 14 

### model) assimilate meteorological data from several surface and upper air stations in or near a domain and generate a 3-dimensional field of wind, temperature and relative humidity profiles. We revised recommendations for length of record for meteorological data (subsection 9.3.1.2) for long range transport and complex wind situations. In paragraph 9.3.1.2(d) we specifically allow the use of at least three years (need not be consecutive) of assimilated mesoscale meteorological data.

• We revised subsection 9.3.2 (National Weather Service Data) to inform users that National Weather Service (NWS) surface and upper air meteorological data are available on CD

–ROM from the National Climatic Data Center. Recent years of such surface data are derived from the NWS ’ s Automated Surface Observing System (ASOS). We revised subsection 9.3.1.2 to address the possible occurrence of ASOS data within 5-year sets of meteorological data.

• We revised subsection 9.3.3.1 to clarify that, while site-specific measurements are frequently made ‘‘ onproperty ’’ ( i.e. , on the source ’ s premises), acquisition of adequately representative site-specific data does not preclude collecting data from a location off property. Conversely, collection of meteorological data on property does not of itself guarantee adequate representativeness. The subsection was also enhanced by improving the discussion of collection of temperature difference measurements; a paragraph was developed that focuses on measurement of aloft winds for simulation of plume rise, dispersion and transport (some details for CTDMPLUS were moved to its appendix A descriptions); a paragraph was added to address collection and use of direct turbulence measurements; and the paragraph that discusses meteorological data preprocessor has been enhanced.

• We revised subsection 9.3.3.2 by removing reference to the STAR processing routine because ISCLT and CDM 2.0 (for which STAR formatted data were developed) have been removed.

• We revised subsection 9.3.4 (Treatment of Calms) to increase
accuracy.

Section 10

We updated section 10 to reflectcurrent thinking and state-of-thepractice regarding model accuracy and uncertainty.

Section 11

As proposed, we made minor revisions to section 11 to reflect the new ambient air quality standards for fine particles and ozone. Because EPA has revised its emissions trading program for SO 2 

### , we have deleted subsection
11.2.3.4.

Section 12 & 13

We redesignated section 13 (Bibliography) as section 12 (References) and vice-versa . We revised them by adding some references, deleting obsolete/superseded ones, and resequencing. You will note that a peer scientific review for CALPUFF has been included.

Section 14

In a streamlining effort, we removed section 14 (Glossary). Given current familiarity with modeling terminology, we no longer consider that maintenance of such a glossary is as necessary as it once may have been. For these and other reasons relating to Office of Federal Register policy (see discussion of appendix B below), we have revised the glossary and placed it on our Internet Web site. Appendix A

We updated the introduction to appendix A (section A.0). As mentioned before, we added CALPUFF to appendix A. We removed the Climatological Dispersion Model (CDM 2.0), the Gaussian-Plume Multiple Source Air Quality Algorithm (RAM), and the Urban Airshed Model (UAM) from appendix A. These models have been superseded and are no longer considered preferred techniques. Appendix B We have moved the appendix B repository of alternate model summary descriptions to our Internet SCRAM Web site ( http://www.epa.gov/
scram001 ). Placement of this material on the Web site offers many advantages. In this format, we will be able to maintain the list and model descriptions more easily and inexpensively.

Several model developers have submitted new dispersion models for inclusion in this Web site repository of alternate models:

• Second-Order Closure Integrated Puff Model (SCIPUFF);

• Open Burn/Open Detonation Dispersion Model (OBODM);

• Atmospheric Dispersion Modeling System (ADMS);

• Comprehensive Air Quality Model with extensions (CAMx); and

• Urban Airshed Model

—V (UAMV). As described below, codes (executables) for these models, as well as applicable documentation, have been uploaded to our Internet SCRAM Web site. Finally, we deleted a model currently listed in appendix B, MESOPUFF II, which CALPUFF replaces. Appendix C

As proposed, we also moved appendix C (Example Air Quality Analysis Checklist) from the CFR to our Internet SCRAM Web site. We believe this checklist is outdated, in need of revision, and would be more practical to maintain if posted on EPA ’ s Internet SCRAM Web site. Statutory and Executive Order Reviews A. Executive Order 12866: Regulatory Planning and Review

Under Executive Order 12866 (58 FR 51735 (October 4, 1993)), the Agency must determine whether the regulatory action is ‘‘ significant ’’ and therefore subject to review by the Office of Management and Budget (OMB) and the requirements of the Executive Order. The Order defines ‘‘ significant regulatory action ’’ as one that is likely to result in a rule that may:

(1) Have an annual effect on the economy of $100 million or more or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities;

(2) Create a serious inconsistency or otherwise interfere with an action taken or planned by another agency;

(3) Materially alter the budgetary impact of entitlements, grants, user fees,


## 18447 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

or loan programs of the rights and obligations of recipients thereof; or

(4) Raise novel legal or policy issues arising out of legal mandates, the President ’ s priorities, or the principles set forth in the Order.

This rule is not a ‘‘ significant regulatory action ’’ under the terms of Executive Order 12866 and is therefore not subject to OMB review. B. Paperwork Reduction Act

This final rule does not contain any information collection requirements subject to review by OMB under the Paperwork Reduction Act, 44 U.S.C.
3501 et seq. C. Regulatory Flexibility Act (RFA), as amended by the Small Business Regulatory Enforcement Fairness Act of 1996 (SBREFA), 5 U.S.C. 601 et seq.

The RFA generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entities include small businesses, small organizations, and small governmental jurisdictions.

EPA has determined that it is not necessary to prepare a regulatory flexibility analysis in connection with this final rule. EPA has also determined that this rule will not have a significant economic impact on a substantial number of small entities. For purposes of assessing the impact of today ’ s rule on small entities, small entities are defined as: (1) A small business that meets the RFA default definitions for small business (based on Small Business Administration size standards), as described in 13 CFR 121.201; (2) a small governmental jurisdiction that is a government of a city, county, town, school district or special district with a population of less than 50,000; and (3)a small organization that is any not-forprofit enterprise which is independently owned and operated and is not dominant in its field.

After considering the economic impacts of today ’ s final rule on small entities, EPA has concluded that this action will not have a significant economic impact on a substantial number of small entities. This final rule will not impose any requirements on small entities. Today ’ s rule will not have any impacts on small entities because existing and new sources of air emissions that model air quality for State Implementation Plans and the prevention of significant deterioration are typically not small entities. The modeling techniques described today are primarily used by state air control agencies and by industry.

To the extent that any small entities would ever have to model air quality using the modeling techniques described in today ’ s rule, the impacts of using updated modeling techniques would be minimal, if not non-existent. The action promulgated today incorporates comments received at the 7th Conference on Air Quality Modeling in June 2000 in Washington, DC. The rule features a new modeling system for calculating PSD increment consumption

— CALPUFF

— and serves to increase efficiency and accuracy. This system employs procedural concepts that are very similar to those currently used, changing only mathematical formulations and specific data elements. No impacts on small entities in the use of CALPUFF are anticipated. We do not believe that CALPUFF ’ s use poses a significant or unreasonable burden on any small entities. This final action imposes no new regulatory burdens and, as such, there will be no additional impact on small entities regarding reporting, recordkeeping, compliance requirements. D. Unfunded Mandates Reform Act of
1995

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104

– 4, establishes requirements for Federal agencies to assess the effects of their regulatory actions on State, local, and tribal governments and the private sector. Under section 202 of the UMRA, EPA generally must prepare a written statement, including a cost-benefit analysis, for proposed and final rules with ‘‘ Federal mandates ’’ that may result in expenditures to State, local, and tribal governments, in the aggregate, or to the private sector, of $100 million or more in any one year. Before promulgating an EPA rule for which a written statement is needed, section 205 of the UMRA generally requires EPA to identify and consider a reasonable number of regulatory alternatives andadopt the least costly, most costeffective or least burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do not apply when they are inconsistent with applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the least costly, most cost-effective or least burdensome alternative if the Administrator publishes with the final rule an explanation why that alternative was not adopted. Before EPA establishes any regulatory requirements that may significantly or uniquely affect small governments, including tribal governments, it must have developed under section 203 of the UMRA a small government agency plan.

The plan must provide for notifying potentially affected small governments, enabling officials of affected small governments to have meaningful and timely input in the development of EPA regulatory proposals with significant Federal intergovernmental mandates, and informing, educating, and advising small governments on compliance with the regulatory requirements.

Today ’ s rule recommends a new modeling system for calculating PSD increment consumption

— CALPUFF

— that increases efficiency and accuracy. CALPUFF has been used for these purposes on a case-by-case basis (per Guideline subsection 3.2.2) for several years, as has its predecessor

— MESOPUFF II. While Guideline subsection 3.2.2 still allows for alternative models to be used, EPA is now sufficiently confident in CALPUFF ’ s technical formulation and performance to adopt it in appendix A of the Guideline . Since the two modeling systems are comparable in scope and purpose, use of CALPUFF itself does not involve any increase in costs. The optional use of prognostic meteorological data ( e.g. , MM5) input files, however, may result in a small incremental cost increase. To the extent that the use of more refined models with comprehensive input data bases reduces the potential for over-or underprediction of air quality impacts, air quality management programs become more economically efficient. Moreover, modeling costs (which include those for input data acquisition) are typically among the implementation costs that are considered as part of the programs ( i.e. , PSD) that establish and periodically revise requirements for compliance. Any incremental modeling costs attributable to today ’ s rule do not approach the $100 million threshold prescribed by UMRA. EPA has determined that this rule contains no regulatory requirements that might significantly or uniquely affect small governments. This rule therefore contains no Federal mandates (under the regulatory provisions of Title II of the UMRA) for State, local, or tribal governments or the private sector.

E. Executive Order 13132: Federalism

Executive Order 13132, entitled

‘‘ Federalism ‘‘ (64 FR 43255, August 10, 1999), requires EPA to develop an accountable process to ensure

‘‘ meaningful and timely input by State and local officials in the development of regulatory policies that have federalism


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

implications. ’’ ‘‘ Policies that have federalism implications ‘‘ is defined in the Executive Order to include regulations that have ‘‘ substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government. ’’

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

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

Executive Order 13175, entitled

‘‘ Consultation and Coordination with Indian Tribal Governments ’’ (65 FR 67249, November 9, 2000), requires EPA to develop an accountable process to

ensure ‘‘ meaningful and timely input by tribal officials in the development of regulatory policies that have tribal implications. ’’ This final rule does not have tribal implications, as specified in Executive Order 13175. As stated above (see D. Unfunded Mandates Reform Act of 1995, above), the rule does not impose any new requirements for calculating PSD increment consumption, and does not impose any additional requirements for the regulated community, including Indian Tribal Governments. Thus, Executive Order 13175 does not apply to this rule.

Today ’ s final rule does not significantly or uniquely affect the communities of Indian tribal governments. Accordingly, the requirements of section 3(b) of Executive Order 13175 do not apply to this rule. G. Executive Order 13045: Protection of Children From Environmental Health and Safety Risks

Executive Order 13045 applies to any rule that EPA determines (1) to be

‘‘ economically significant ’’ as defined under Executive Order 12866, and (2) the environmental health or safety risk addressed by the rule has a disproportionate effect on children. If the regulatory action meets both the criteria, the Agency must evaluate the environmental health or safety effects of the planned rule on children; and explain why the planned regulation is preferable to other potentially effective and reasonably feasible alternatives considered by the Agency.

This final rule is not subject to Executive Order 13045, entitled

‘‘ Protection of Children from Environmental Health Risks and Safety Risks ’’ (62 FR 19885, April 23, 1997) because it does not impose an economically significant regulatory action as defined by Executive Order 12866 and the action does not involve decisions on environmental health or safety risks that may disproportionately affect children.

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

This rule is not subject to Executive Order 13211, ‘‘ Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use ’’ (66 FR 28355 (May 22, 2001)) because it is not a significant regulatory action under Executive Order 12866. I. National Technology Transfer and Advancement Act of 1995

Section 12(d) of the National Technology Transfer and Advancement Act of 1995 ( ‘‘ NTTAA ’’ ), Public Law
104

– 113, section 12(d) (15 U.S.C. 272 note) directs EPA to use voluntary consensus standards in its regulatory activities unless to do so would be inconsistent with applicable law or otherwise impractical. Voluntary consensus standards are technical standards ( e.g. , materials specifications, test methods, sampling procedures, and business practices) that are developed or adopted by voluntary consensus standards bodies. The NTTAA directs EPA to provide Congress, through OMB, explanations when the Agency decides not to use available and applicable voluntary consensus standards.

This action does not involve technical standards. Therefore, EPA did not consider the use of any voluntary consensus standards. J. Congressional Review Act of 1998

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

. This action is not a ‘‘ major rule ’’ as defined by 5 U.S.C. 804(2), and will be effective 30 days from the publication date of this notice. List of Subjects in 40 CFR Part 51

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

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

■ 

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

PART 51

— REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS

■ 

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

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

– 7671q.

■ 

### 2. Appendix W to Part 51 revised to read as follows: Appendix W to Part 51—Guideline on Air Quality Models

Preface

a. Industry and control agencies have long expressed a need for consistency in the application of air quality models for regulatory purposes. In the 1977 Clean Air Act, Congress mandated such consistency and encouraged the standardization of model applications. The Guideline on Air Quality Models (hereafter, Guideline ) was first published in April 1978 to satisfy these requirements by specifying models and providing guidance for their use. The Guideline provides a common basis for estimating the air quality concentrations of criteria pollutants used in assessing control strategies and developing emission limits.

b. The continuing development of new air quality models in response to regulatory requirements and the expanded requirements for models to cover even more complex problems have emphasized the need for periodic review and update of guidance on these techniques. Three primary on-going activities provide direct input to revisions of the Guideline . The first is a series of annual


## 18449 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

EPA workshops conducted for the purpose of ensuring consistency and providing clarification in the application of models. The second activity is the solicitation and review of new models from the technical and user community. In the March 27, 1980 Federal Register , a procedure was outlined for the submittal to EPA of privately developed models. After extensive evaluation and scientific review, these models, as well as those made available by EPA, are considered for recognition in the Guideline

. The third activity is the extensive on-going research efforts by EPA and others in air quality and meteorological modeling.

c. Based primarily on these three activities, new sections and topics are included as needed. EPA does not make changes to the guidance on a predetermined schedule, but rather on an as needed basis. EPA believes that revisions of the Guideline should be timely and responsive to user needs and should involve public participation to the greatest possible extent. All future changes to the guidance will be proposed and finalized in the Federal Register . Information on the current status of modeling guidance can always be obtained from EPA ’ s Regional Offices.

Table of Contents List of Tables

1.0 Introduction 2.0 Overview of Model Use

2.1 Suitability of Models 2.2 Levels of Sophistication of Models 2.3 Availability of Models 3.0 Recommended Air Quality Models

3.1 Preferred Modeling Techniques 3.1.1 Discussion 3.1.2 Recommendations 3.2 Use of Alternative Models 3.2.1 Discussion 3.2.2 Recommendations 3.3 Availability of Supplementary

Modeling Guidance 4.0 Traditional Stationary-Source Models

4.1 Discussion 4.2 Recommendations 4.2.1 Screening Techniques 4.2.1.1 Simple Terrain 4.2.1.2 Complex Terrain 4.2.2 Refined Analytical Techniques 5.0 Model Use in Complex Terrain

5.1 Discussion 5.2 Recommendations 5.2.1 Screening Techniques 5.2.2 Refined Analytical Techniques 6.0 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen Dioxide, and Lead 6.1 Discussion 6.2 Recommendations 6.2.1 Models for Ozone 6.2.1 Models for Particulate Matter 6.2.2.1 PM

– 2.5 6.2.2.2 PM

– 10 6.2.3 Models for Carbon Monoxide 6.2.4 Models for Nitrogen Dioxide

(Annual Average) 6.2.5 Models for Lead 7.0 Other Model Requirements

7.1 Discussion 7.2 Recommendations 7.2.1 Visibility 7.2.2 Good Engineering Practice Stack

Height 7.2.3 Long Range Transport (i.e., beyond 50km) 7.2.4 Modeling Guidance for Other

Governmental Programs 8.0 General Modeling Considerations

8.1 Discussion 8.2 Recommendations 8.2.1 Design Concentrations 8.2.2 Critical Receptor Sites 8.2.3 Dispersion Coefficients 8.2.4 Stability Categories 8.2.5 Plume Rise 8.2.6 Chemical Transformation 8.2.7 Gravitational Settling and

Deposition 8.2.8 Complex Winds 8.2.9 Calibration of Models 9.0 Model Input Data

9.1 Source Data 9.1.1 Discussion 9.1.2 Recommendations 9.2 Background Concentrations 9.2.1 Discussion 9.2.2 Recommendations (Isolated Single

Source) 9.2.3 Recommendations (Multi-Source

Areas) 9.3 Meteorological Input Data 9.3.1 Length of Record of Meteorological

Data 9.3.2 National Weather Service Data 9.3.3 Site Specific Data 9.3.4 Treatment of Calms 10.0 Accuracy and Uncertainty of Models

10.1 Discussion 10.1.1 Overview of Model Uncertainty 10.1.2 Studies of Model Accuracy10.1.3 Use of Uncertainty in DecisionMaking 10.1.4 Evaluation of Models 10.2 Recommendations 11.0 Regulatory Application of Models

11.1 Discussion 11.2 Recommendations 11.2.1 Analysis Requirements 11.2.2 Use of Measured Data in Lieu of

Model Estimates 11.2.3 Emission Limits 12.0 Bibliography 13.0 References 

### Appendix A to Appendix W of 40 CFR Part 51

— Summaries of Preferred Air Quality Models



### L IST OF 

### T ABLES

Table No. Title

5–1 ........... Neutral/Stable Meteorological Matrix for CTSCREEN.5–1 ........... Unstable/Convective Meteorological Matrix for CTSCREEN. 9–1 ........... Model Emission Input Data for Point Sources. 9–2 ........... Point Source Model Input Data (Emissions) for PSD NAAQS Compliance Demonstrations.9–3 ........... Averaging Times for Site Specific Wind and Turbulence Measurements.

1.0 Introduction

a. The Guideline recommends air quality modeling techniques that should be applied to State Implementation Plan (SIP) revisions for existing sources and to new source reviews (NSR), including prevention of significant deterioration (PSD). ( See Ref. 1, 2, 3). Applicable only to criteria air pollutants, it is intended for use by EPA Regional Offices in judging the adequacy of modeling analyses performed by EPA, State and local agencies and by industry. The guidance is appropriate for use by other Federal agencies and by State agencies with air quality and land management responsibilities. The Guideline serves to identify, for all interested parties, those techniques and data bases EPA considers acceptable. The Guideline is not intended to be a compendium of modeling techniques. Rather, it should serve as a common measure of acceptable technical analysis when supported by sound scientific judgement.

b. Due to limitations in the spatial and temporal coverage of air quality measurements, monitoring data normally are not sufficient as the sole basis for demonstrating the adequacy of emission limits for existing sources. Also, the impacts of new sources that do not yet exist can only be determined through modeling. Thus, models, while uniquely filling one program need, have become a primary analytical tool in most air quality assessments. Air quality measurements can be used in a complementary manner to dispersion models, with due regard for the strengths and weaknesses of both analysis techniques. Measurements are particularly useful in assessing the accuracy of model estimates. The use of air quality measurements alone however could be preferable, as detailed in a later section of this document, when models are found to be unacceptable and monitoring data with sufficient spatial and temporal coverage are available.

c. It would be advantageous to categorize the various regulatory programs and to apply a designated model to each proposed source needing analysis under a given program. However, the diversity of the nation ’ s topography and climate, and variations in source configurations and operating characteristics dictate against a strict modeling ‘‘ cookbook ’’ . There is no one model capable of properly addressing all conceivable situations even within a broad category such as point sources. Meteorological phenomena associated with threats to air quality standards are rarely amenable to a single mathematical treatment; thus, case-by-case analysis and judgement are frequently required. As modeling efforts become more complex, it is increasingly important that they be directed by highly competent individuals with a broad range of experience and knowledge in air quality meteorology. Further, they should be coordinated closely with specialists in emissions characteristics, air monitoring and data processing. The judgement of experienced meteorologists and analysts is essential.

d. The model that most accurately estimates concentrations in the area of interest is always sought. However, it is clear from the needs expressed by the States and EPA Regional Offices, by many industries and trade associations, and also by the deliberations of Congress, that consistency in


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

the selection and application of models and data bases should also be sought, even in case-by-case analyses. Consistency ensures that air quality control agencies and the general public have a common basis for estimating pollutant concentrations, assessing control strategies and specifying emission limits. Such consistency is not, however, promoted at the expense of model and data base accuracy. The Guideline provides a consistent basis for selection of the most accurate models and data bases for use in air quality assessments.

e. Recommendations are made in the Guideline concerning air quality models, data bases, requirements for concentration estimates, the use of measured data in lieu of model estimates, and model evaluation procedures. Models are identified for some specific applications. The guidance provided here should be followed in air quality analyses relative to State Implementation Plans and in supporting analyses required by EPA, State and local agency air programs. EPA may approve the use of another technique that can be demonstrated to be more appropriate than those recommended in this guide. This is discussed at greater length in Section 3. In all cases, the model applied to a given situation should be the one that provides the most accurate representation of atmospheric transport, dispersion, and chemical transformations in the area of interest. However, to ensure consistency, deviations from this guide should be carefully documented and fully supported.

f. From time to time situations arise requiring clarification of the intent of the guidance on a specific topic. Periodic workshops are held with the headquarters, Regional Office, State, and local agency modeling representatives to ensure consistency in modeling guidance and to promote the use of more accurate air quality models and data bases. The workshops serve to provide further explanations of Guideline requirements to the Regional Offices and workshop reports are issued with this clarifying information. In addition, findings from on-going research programs, new model submittals, or results from model evaluations and applications are continuously evaluated. Based on this information changes in the guidance may be indicated.

g. All changes to the Guideline must follow rulemaking requirements since the Guideline is codified in Appendix W of Part 51. EPA will promulgate proposed and final rules in the Federal Register to amend this Appendix. Ample opportunity for public comment will be provided for each proposed change and public hearings scheduled if requested.

h. A wide range of topics on modeling and data bases are discussed in the Guideline. Section 2 gives an overview of models and their appropriate use. Section 3 provides specific guidance on the use of ‘‘ preferred ’’ air quality models and on the selection of alternative techniques. Sections 4 through 7 provide recommendations on modeling techniques for application to simple-terrain stationary source problems, complex terrain problems, and mobile source problems. Specific modeling requirements for selected regulatory issues are also addressed. Section 8 discusses issues common to many modeling analyses, including acceptable model components. Section 9 makes recommendations for data inputs to models including source, meteorological and background air quality data. Section 10 covers the uncertainty in model estimates and how that information can be useful to the regulatory decision-maker. The last chapter summarizes how estimates and measurements of air quality are used in assessing source impact and in evaluating control strategies.

i. Appendix W to 40 CFR Part 51 itself contains an appendix: Appendix A. Thus, when reference is made to ‘‘ Appendix A ’’ in this document, it refers to Appendix A to Appendix W to 40 CFR Part 51. Appendix A contains summaries of refined air quality models that are ‘‘ preferred ’’ for specific applications; both EPA models and models developed by others are included.

2.0 Overview of Model Use

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

a. The extent to which a specific air quality model is suitable for the evaluation of source impact depends upon several factors. These include: (1) The meteorological and topographic complexities of the area; (2) the level of detail and accuracy needed for the analysis; (3) the technical competence of those undertaking such simulation modeling; (4) the resources available; and (5) the detail and accuracy of the data base, i.e., emissions inventory, meteorological data, and air quality data. Appropriate data should be available before any attempt is made to apply a model. A model that requires detailed, precise, input data should not be used when such data are unavailable. However, assuming the data are adequate, the greater the detail with which a model considers the spatial and temporal variations in emissions and meteorological conditions, the greater the ability to evaluate the source impact and to distinguish the effects of various control strategies.

b. Air quality models have been applied with the most accuracy, or the least degree of uncertainty, to simulations of long term averages in areas with relatively simple topography. Areas subject to major topographic influences experience meteorological complexities that are extremely difficult to simulate. Although models are available for such circumstances, they are frequently site specific and resource intensive. In the absence of a model capable of simulating such complexities, only a preliminary approximation may be feasible until such time as better models and data bases become available.

c. Models are highly specialized tools. Competent and experienced personnel are an essential prerequisite to the successful application of simulation models. The need for specialists is critical when the more sophisticated models are used or the area being investigated has complicated meteorological or topographic features. A model applied improperly, or with inappropriate data, can lead to serious misjudgements regarding the source impact or the effectiveness of a control strategy.

d. The resource demands generated by use of air quality models vary widely depending on the specific application. The resources required depend on the nature of the model and its complexity, the detail of the data base, the difficulty of the application, and the amount and level of expertise required. The costs of manpower and computational facilities may also be important factors in the selection and use of a model for a specific analysis. However, it should be recognized that under some sets of physical circumstances and accuracy requirements, no present model may be appropriate. Thus, consideration of these factors should lead to selection of an appropriate model.
2.2 Levels of Sophistication of Models

a. There are two levels of sophistication of models. The first level consists of relatively simple estimation techniques that generally use preset, worst-case meteorological conditions to provide conservative estimates of the air quality impact of a specific source, or source category. These are called screening techniques or screening models. The purpose of such techniques is to eliminate the need of more detailed modeling for those sources that clearly will not cause or contribute to ambient concentrations in excess of either the National Ambient Air Quality Standards (NAAQS) 4 or the allowable prevention of significant deterioration (PSD) concentration increments. 2,3 If a screening technique indicates that the concentration contributed by the source exceeds the PSD increment or the increment remaining to just meet the NAAQS, then the second level of more sophisticated models should be applied.

b. The second level consists of those analytical techniques that provide more detailed treatment of physical and chemical atmospheric processes, require more detailed and precise input data, and provide more specialized concentration estimates. As a result they provide a more refined and, at least theoretically, a more accurate estimate of source impact and the effectiveness of control strategies. These are referred to as refined models.

c. The use of screening techniques followed, as appropriate, by a more refined analysis is always desirable, however there are situations where the screening techniques are practically and technically the only viable option for estimating source impact. In such cases, an attempt should be made to acquire or improve the necessary data bases and to develop appropriate analytical techniques.
2.3 Availability of Models

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


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

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

a. This section recommends the approach to be taken in determining refined modeling techniques for use in regulatory air quality programs. The status of models developed by EPA, as well as those submitted to EPA for review and possible inclusion in this guidance, is discussed. The section also addresses the selection of models for individual cases and provides recommendations for situations where the preferred models are not applicable. Two additional sources of modeling guidance are the Model Clearinghouse and periodic Regional/State/Local Modelers workshops.

b. In this guidance, when approval is required for a particular modeling technique or analytical procedure, we often refer to the

‘‘ appropriate reviewing authority ’’ . In some EPA regions, authority for NSR and PSD permitting and related activities has been delegated to State and even local agencies. In these cases, such agencies are

‘‘ representatives ’’ of the respective regions. Even in these circumstances, the Regional Office retains the ultimate authority in decisions and approvals. Therefore, as discussed above and depending on the circumstances, the appropriate reviewing authority may be the Regional Office, Federal Land Manager(s), State agency(ies), or perhaps local agency(ies). In cases where review and approval comes solely from the Regional Office (sometimes stated as

‘‘ Regional Administrator’’ ), this will be stipulated. If there is any question as to the appropriate reviewing authority, you should contact the Regional modeling contact ( http://www.epa.gov/scram001/ tt28.htm#regionalmodelingcontacts ) in the appropriate EPA Regional Office, whose jurisdiction generally includes the physical location of the source in question and its expected impacts.

c. In all regulatory analyses, especially if other than preferred models are selected for use, early discussions among Regional Office staff, State and local control agencies, industry representatives, and where appropriate, the Federal Land Manager, are invaluable and are encouraged. Agreement on the data base(s) to be used, modeling techniques to be applied and the overall technical approach, prior to the actual analyses, helps avoid misunderstandings concerning the final results and may reduce the later need for additional analyses. The use of an air quality analysis checklist, such as is posted on EPA ’ s Internet SCRAM Web site (subsection 2.3), and the preparation of a written protocol help to keep misunderstandings at a minimum.

d. It should not be construed that the preferred models identified here are to be permanently used to the exclusion of all others or that they are the only models available for relating emissions to air quality. The model that most accurately estimates concentrations in the area of interest is always sought. However, designation of specific models is needed to promote consistency in model selection and application.

e. The 1980 solicitation of new or different models from the technical community and the program whereby these models were evaluated, established a means by which new models are identified, reviewed and made available in the Guideline. There is a pressing need for the development of models for a wide range of regulatory applications. Refined models that more realistically simulate the physical and chemical process in the atmosphere and that more reliably estimate pollutant concentrations are needed. Thus, the solicitation of models is considered to be continuous.
3.1 Preferred Modeling Techniques

3.1.1 Discussion

a. EPA has developed models suitable for regulatory application. Other models have been submitted by private developers for possible inclusion in the Guideline. These refined models have undergone evaluation exercises 7,8,9,10,11,12,13,14,15 that include statistical measures of model performance in comparison with measured air quality data as suggested by the American Meteorological Society 16 and, where possible, peer scientific reviews. 17,18,19,20,21

b. When a single model is found to perform better than others, it is recommended for application as a preferred model and listed in Appendix A. If no one model is found to clearly perform better through the evaluation exercise, then the preferred model listed in Appendix A is selected on the basis of other factors such as past use, public familiarity, cost or resource requirements, and availability. No further evaluation of a preferred model is required for a particular application if the EPA recommendations for regulatory use specified for the model in the Guideline are followed. Alternative models to those listed in Appendix A should generally be compared with measured air quality data when they are used for regulatory applications consistent with recommendations in subsection 3.2.

c. The solicitation of new refined models which are based on sounder scientific principles and which more reliably estimate pollutant concentrations is considered by EPA to be continuous. Models that are submitted in accordance with the established provisions will be evaluated as submitted. These requirements are:

i. The model must be computerized and functioning in a common computer code suitable for use on a variety of computer systems.

ii. The model must be documented in a

user ’ s guide which identifies the mathematics of the model, data requirements and program operating characteristics at a level of detail comparable to that available for currently recommended models.

iii. The model must be accompanied by a complete test data set including input parameters and output results. The test data must be included in the user ’ s guide as well as provided in computer-readable form.

iv. The model must be useful to typical

users, e.g., State air pollution control agencies, for specific air quality control problems. Such users should be able to operate the computer program(s) from available documentation.

v. The model documentation must include a comparison with air quality data (and/ortracer measurements) or with other wellestablished analytical techniques.

vi. The developer must be willing to make the model available to users at reasonable cost or make it available for public access through the Internet or National Technical Information Service: the model cannot be proprietary.

d. The evaluation process will include a determination of technical merit, in accordance with the above six items including the practicality of the model for use in ongoing regulatory programs. Each model will also be subjected to a performance evaluation for an appropriate data base and to a peer scientific review. Models for wide use (not just an isolated case) that are found to perform better will be proposed for inclusion as preferred models in future Guideline revisions.
3.1.2 Recommendations

a. Appendix A identifies refined models that are preferred for use in regulatory applications. If a model is required for a particular application, the user should select a model from that appendix. These models may be used without a formal demonstration of applicability as long as they are used as indicated in each model summary of Appendix A. Further recommendations for the application of these models to specific source problems are found in subsequent sections of the Guideline.

b. If changes are made to a preferred model without affecting the concentration estimates, the preferred status of the model is unchanged. Examples of modifications that do not affect concentrations are those made to enable use of a different computer or those that affect only the format or averaging time of the model results. However, when any changes are made, the Regional Administrator should require a test case example to demonstrate that the concentration estimates are not affected.

c. A preferred model should be operated with the options listed in Appendix A as

‘‘ Recommendations for Regulatory Use. ’’ If other options are exercised, the model is no longer ‘‘ preferred. ’’ Any other modification to a preferred model that would result in a change in the concentration estimates likewise alters its status as a preferred model. Use of the model must then be justified on a case-by-case basis.
3.2 Use of Alternative Models

3.2.1 Discussion

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


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

b. This section discusses the use of alternate modeling techniques and defines three situations when alternative models may be used.

3.2.2 Recommendations

a. Determination of acceptability of a model is a Regional Office responsibility. Where the Regional Administrator finds that an alternative model is more appropriate than a preferred model, that model may be used subject to the recommendations of this subsection. This finding will normally result from a determination that (1) a preferred air quality model is not appropriate for the particular application; or (2) a more appropriate model or analytical procedure is available and applicable.

b. An alternative model should be evaluated from both a theoretical and a performance perspective before it is selected for use. There are three separate conditions under which such a model may normally be approved for use: (1) If a demonstration can be made that the model produces concentration estimates equivalent to the estimates obtained using a preferred model; (2) if a statistical performance evaluation has been conducted using measured air quality data and the results of that evaluation indicate the alternative model performs better for the given application than a comparable model in Appendix A; or (3) if the preferred model is less appropriate for the specific application, or there is no preferred model. Any one of these three separate conditions may make use of an alternative model acceptable. Some known alternative models that are applicable for selected situations are listed on EPA ’ s SCRAM Internet Web site (subsection 2.3). However, inclusion there does not confer any unique status relative to other alternative models that are being or will be developed in the future.

c. Equivalency, condition (1) in paragraph (b) of this subsection, is established by demonstrating that the maximum or highest, second highest concentrations are within 2 percent of the estimates obtained from the preferred model. The option to show equivalency is intended as a simple demonstration of acceptability for an alternative model that is so nearly identical (or contains options that can make it identical) to a preferred model that it can be treated for practical purposes as the preferred model. Two percent was selected as the basis for equivalency since it is a rough approximation of the fraction that PSD Class I increments are of the NAAQS for SO 2

, i.e., the difference in concentrations that is judged to be significant. However, notwithstanding this demonstration, models that are not equivalent may be used when one of the two other conditions described in paragraphs (d) and (e) of this subsection are satisfied.

d. For condition (2) in paragraph (b) of this subsection, the procedures and techniques for determining the acceptability of a model for an individual case based on superior performance are contained in references 22

– 25 should be followed, as appropriate. Preparation and implementation of an evaluation protocol which is acceptable to both control agencies and regulated industry is an important element in such an evaluation.

e. Finally, for condition (3) in paragraph (b) of this subsection, an alternative refined model may be used provided that:

i. The model has received a scientific peer review;

ii. The model can be demonstrated to be applicable to the problem on a theoretical basis;

iii. The data bases which are necessary to perform the analysis are available and adequate;

iv. Appropriate performance evaluations of the model have shown that the model is not biased toward underestimates; and

v. A protocol on methods and procedures to be followed has been established.
3.3 Availability of Supplementary Modeling Guidance

a. The Regional Administrator has the authority to select models that are appropriate for use in a given situation. However, there is a need for assistance and guidance in the selection process so that fairness and consistency in modeling decisions is fostered among the various Regional Offices and the States. To satisfy that need, EPA established the Model Clearinghouse 5 and also holds periodic workshops with headquarters, Regional Office, State, and local agency modeling representatives.

b. The Regional Office should always be consulted for information and guidance concerning modeling methods and interpretations of modeling guidance, and to ensure that the air quality model user has available the latest most up-to-date policy and procedures. As appropriate, the Regional Office may request assistance from the Model Clearinghouse after an initial evaluation and decision has been reached concerning the application of a model, analytical technique or data base in a particular regulatory action.
4.0 Simple-Terrain Stationary Source Models

4.1 Discussion

a. Simple terrain, as used here, is considered to be an area where terrain features are all lower in elevation than the top of the stack of the source(s) in question. The models recommended in this section are generally used in the air quality impact analysis of stationary sources for most criteria pollutants. The averaging time of the concentration estimates produced by these models ranges from 1 hour to an annual
average.

b. In the early 1980s, model evaluation exercises were conducted to determine the

‘‘ best, most appropriate point source model ’’ for use in simple terrain. 8,17 No one model was found to be clearly superior and, based on past use, public familiarity, and availability, ISC (predecessor to ISC3 26 ) became the recommended model for a wide range of regulatory applications. Other refined models which also employed the basic Gaussian kernel, i.e. , BLP, CALINE3, OCD, and EDMS, were developed for specialized applications (Appendix A). Performance evaluations were also made for these models, which are identified in Appendix A.

4.2 Recommendations 4.2.1 Screening Techniques

a. Where a preliminary or conservative estimate is desired, point source screening techniques are an acceptable approach to air quality analyses. EPA has published guidance for screening procedures, 27 and a computerized version of the recommended screening technique, SCREEN3, is available. 28

b. All screening procedures should be adjusted to the site and problem at hand. Close attention should be paid to whether the area should be classified urban or rural in accordance with subsection 8.2.3. The climatology of the area should be studied to help define the worst-case meteorological conditions. Agreement should be reached between the model user and the appropriate reviewing authority (paragraph 3.0(b)) on the choice of the screening model for each analysis, and on the input data as well as the ultimate use of the results. 4.2.2 Refined Analytical Techniques

a. A brief description of preferred models for refined applications is found in Appendix A. Also listed in that appendix are the model input requirements, the standard options that should be selected when running the program, and output options.

b. When modeling for compliance with short term NAAQS and PSD increments is of primary concern, a short term model may be used to provide long term concentration estimates. The conversion from long term to short term concentration averages by any transformation technique is not acceptable in regulatory applications.

c. The state-of-the-science for modeling atmospheric deposition is evolving and the best techniques are currently being assessed and their results are being compared with observations. Consequently, the approach taken for any purpose should be coordinated with the appropriate reviewing authority (paragraph 3.0(b)).
5.0 Model Use in Complex Terrain

5.1 Discussion

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


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modeled. Complex terrain dispersion models are normally applied to stationary sources of pollutants such as SO 2 and particulates.

b. A major outcome from the EPA Complex Terrain Model Development project has been the publication of a refined dispersion model (CTDM) suitable for regulatory application to plume impaction assessments in complex terrain. 29 Although CTDM as originally produced was only applicable to those hours characterized as neutral or stable, a computer code for all stability conditions

— CTDMPLUS

—together with a user ’ s guide, 30 and site specific meteorological and terrain data processors 31,32 is available. Moreover, CTSCREEN, 33 a version of CTDMPLUS that does not require site specific meteorological data inputs, is also available as a screening technique.

c. The methods discussed in this section should be considered in two categories: (1) Screening techniques, and (2) the refined dispersion model, CTDMPLUS, discussed in this subsection and listed in Appendix A.

d. Continued improvements in ability to accurately model plume dispersion in complex terrain situations can be expected,
e.g. , from research on lee side effects due to terrain obstacles. New approaches to improve the ability of models to realistically simulate atmospheric physics, e.g. , hybrid models which incorporate an accurate wind field analysis, will ultimately provide more appropriate tools for analyses. Such hybrid modeling techniques are also acceptable for regulatory applications after the appropriate demonstration and evaluation. 22

5.2 Recommendations

a. Recommendations in this section apply primarily to those situations where the impaction of plumes on terrain at elevations equal to or greater than the plume centerline during stable atmospheric conditions are determined to be the problem. If a violation of any NAAQS or the controlling increment is indicated by using any of the preferred screening techniques, then a refined complex terrain model may be used. Phenomena such as fumigation, wind direction shear, lee-side effects, building wake- or terrain-induced downwash, deposition, chemical transformation, variable plume trajectories, and long range transport are not addressed by the recommendations in this section.

b. Where site specific data are used for either screening or refined complex terrain models, a data base of at least 1 full-year of meteorological data is preferred. If more data are available, they should be used. Meteorological data used in the analysis should be reviewed for both spatial and temporal representativeness.

c. Placement of receptors requires very careful attention when modeling in complex terrain. Often the highest concentrations are predicted to occur under very stable conditions, when the plume is near, or impinges on, the terrain. The plume under such conditions may be quite narrow in the vertical, so that even relatively small changes in a receptor ’ s location may substantially affect the predicted concentration. Receptors within about a kilometer of the source may be even more sensitive to location. Thus, a dense array of receptors may be required in some cases. In order to avoid excessively large computer runs due to such a large array of receptors, it is often desirable to model the area twice. The first model run would use a moderate number of receptors carefully located over the area of interest. The second model run would use a more dense array of receptors in areas showing potential for high concentrations, as indicated by the results of the first model run.

d. When CTSCREEN or CTDMPLUS is used, digitized contour data must be first processed by the CTDM Terrain Processor 32 to provide hill shape parameters in a format suitable for direct input to CTDMPLUS. Then the user supplies receptors either through an interactive program that is part of the model or directly, by using a text editor; using both methods to select receptors will generally be necessary to assure that the maximum concentrations are estimated by either model. In cases where a terrain feature may ‘‘ appear to the plume ’’ as smaller, multiple hills, it may be necessary to model the terrain both as a single feature and as multiple hills to determine design concentrations.

e. The user is encouraged to confer with the Regional Office if any unresolvable problems are encountered with any screening or refined analytical procedures, e.g. , meteorological data, receptor siting, or terrain contour processing issues. 5.2.1 Screening Techniques

a. CTSCREEN 33 can be used to obtain conservative, yet realistic, worst-case estimates for receptors located on terrain above stack height. CTSCREEN accounts for the three-dimensional nature of plume and terrain interaction and requires detailed terrain data representative of the modeling domain. The model description and user ’ s instructions are contained in the user ’ s guide. 33 The terrain data must be digitized in the same manner as for CTDMPLUS and a terrain processor is available. 32 A discussion of the model ’ s performance characteristics is provided in a technical paper. 34 CTSCREEN is designed to execute a fixed matrix of meteorological values for wind speed (u), standard deviation of horizontal and vertical wind speeds ( s v , s w ), vertical potential temperature gradient (d q /dz), friction velocity (u * ), Monin-Obukhov length (L), mixing height (z i ) as a function of terrain height, and wind directions for both neutral/ stable conditions and unstable convective conditions. Table 5

– 1 contains the matrix of meteorological variables that is used for each CTSCREEN analysis. There are 96 combinations, including exceptions, for each wind direction for the neutral/stable case, and 108 combinations for the unstable case. The specification of wind direction, however, is handled internally, based on the source and terrain geometry. Although CTSCREEN is designed to address a single source scenario, there are a number of options that can be selected on a case-by-case basis to address multi-source situations. However, the appropriate reviewing authority (paragraph 3.0(b)) should be consulted, and concurrence obtained, on the protocol for modeling multiple sources with CTSCREEN to ensure that the worst case is identified and assessed. The maximum concentrationoutput from CTSCREEN represents a worstcase 1-hour concentration. Time-scaling factors of 0.7 for 3-hour, 0.15 for 24-hour and 0.03 for annual concentration averages are applied internally by CTSCREEN to the highest 1-hour concentration calculated by the model.

b. Placement of receptors requires very careful attention when modeling in complex terrain. Often the highest concentrations are predicted to occur under very stable conditions, when the plume is near, or impinges on, the terrain. The plume under such conditions may be quite narrow in the vertical, so that even relatively small changes in a receptor ’ s location may substantially affect the predicted concentration. Receptors within about a kilometer of the source may be even more sensitive to location. Thus, a dense array of receptors may be required in some cases. In order to avoid excessively large computer runs due to such a large array of receptors, it is often desirable to model the area twice. The first model run would use a moderate number of receptors carefully located over the area of interest. The second model run would use a more dense array of receptors in areas showing potential for high concentrations, as indicated by the results of the first model run.

c. As mentioned above, digitized contour data must be preprocessed 32 to provide hill shape parameters in suitable input format. The user then supplies receptors either through an interactive program that is part of the model or directly, by using a text editor; using both methods to select receptors will generally be necessary to assure that the maximum concentrations are estimated by either model. In cases where a terrain feature
may ‘‘ appear to the plume ’’ as smaller, multiple hills, it may be necessary to model the terrain both as a single feature and as multiple hills to determine design concentrations.

d. Other screening techniques, e.g. , Valley (as implemented in SCREEN3 28 ), COMPLEX I (as implemented in ISC3 26 ), SHORTZ/ LONGZ 35 , and RTDM 36 may be acceptable for complex terrain cases where established procedures are used. The user is encouraged to confer with the appropriate reviewing authority (paragraph 3.0(b)) if any unresolvable problems are encountered, e.g. , applicability, meteorological data, receptor siting, or terrain contour processing issues. 5.2.2 Refined Analytical Techniques

a. When the results of the screening analysis demonstrate a possible violation of NAAQS or the controlling PSD increments, a more refined analysis may need to be conducted.

b. The Complex Terrain Dispersion Model PLus Algorithms for Unstable Situations (CTDMPLUS) is a refined air quality model that is preferred for use in all stability conditions for complex terrain applications. CTDMPLUS is a sequential model that requires five input files: (1) General program specifications; (2) a terrain data file; (3) a receptor file; (4) a surface meteorological data file; and (5) a user created meteorological profile data file. Two optional input files consist of hourly emissions parameters and a file containing upper air data from rawinsonde data files, e.g. , a National Climatic Data Center TD

– 6201 file, unless


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there are no hours categorized as unstable in the record. The model description and user instructions are contained in Volume 1 of the User ’ s Guide. 30 Separate publications 32,31 describe the terrain preprocessor system and the meteorological preprocessor program. In Part I of a technical article 37 is a discussion of the model and its preprocessors; the model ’ s performance characteristics are discussed in Part II of the same article. 38 The size of the CTDMPLUS executable file on a personal computer is approximately 360K bytes. The model produces hourly average concentrations of stable pollutants, i.e.

, chemical transformation or decay of species and settling/deposition are not simulated. To obtain concentration averages corresponding to the NAAQS, e.g. , 3- or 24-hour, or annual averages, the user must execute a postprocessor program such as CHAVG. CTDMPLUS is applicable to all receptors on terrain elevations above stack top. However, the model contains no algorithms for simulating building downwash or the mixing or recirculation found in cavity zones in the lee of a hill. The path taken by a plume through an array of hills cannot be simulated. CTDMPLUS does not explicitly simulate calm meteorological periods, and for those situations the user should follow the guidance in subsection 9.3.4. The user should follow the recommendations in the User ’ s Guide under General Program Specifications for: (1) Selecting mixed layer heights, (2) setting minimum scalar wind speed to 1 m/s, and (3) scaling wind direction with height. Close coordination with the Regional Office is essential to insure a consistent, technically sound application of this model.

c. The performance of CTDMPLUS is greatly improved by the use of meteorological data from several levels up to plume height. However, due to the vast range of sourceplume-hill geometries possible in complex terrain, detailed requirements for meteorological monitoring in support of refined analyses using CTDMPLUS should be determined on a case-by-case basis. The following general guidance should be considered in the development of a meteorological monitoring protocol for regulatory applications of CTDMPLUS and reviewed in detail by the Regional Office before initiating any monitoring. As appropriate, EPA guidance (see reference 100) should be consulted for specific guidance on siting requirements for meteorological towers, selection and exposure of sensors, etc. As more experience is gained with the model in a variety of circumstances, more specific guidance may be developed.

d. Site specific meteorological data are critical to dispersion modeling in complex terrain and, consequently, the meteorological requirements are more demanding than for simple terrain. Generally, three different meteorological files (referred to as surface, profile, and rawin files) are needed to run CTDMPLUS in a regulatory mode.

e. The surface file is created by the meteorological preprocessor (METPRO) 31 based on site specific measurements or estimates of solar and/or net radiation, cloud cover and ceiling, and the mixed layer height. These data are used in METPRO to calculate the various surface layer scaling parameters (roughness length, friction velocity, and Monin-Obukhov length) which are needed to run the model. All of the user inputs required for the surface file are based either on surface observations or on measurements at or below 10m.

f. The profile data file is prepared by the user with site specific measurements (from at least three levels) of wind speed, wind direction, turbulence, and potential temperature. These measurements should be obtained up to the representative plume height(s) of interest ( i.e. , the plume height(s) under those conditions important to the determination of the design concentration). The representative plume height(s) of interest should be determined using an appropriate complex terrain screening procedure ( e.g. , CTSCREEN) and should be documented in the monitoring/modeling protocol. The necessary meteorological measurements should be obtained from an appropriately sited meteorological tower augmented by SODAR if the representative plume height(s) of interest exceed 100m. The meteorological tower need not exceed the lesser of the representative plume height of interest (the highest plume height if there is more than one plume height of interest) or 100m.

g. Locating towers on nearby terrain to obtain stack height or plume height measurements for use in profiles by CTDMPLUS should be avoided unless it can clearly be demonstrated that such measurements would be representative of conditions affecting the plume.

h. The rawin file is created by a second meteorological preprocessor (READ62) 31 based on NWS (National Weather Service) upper air data. The rawin file is used in CTDMPLUS to calculate vertical potential temperature gradients for use in estimating plume penetration in unstable conditions. The representativeness of the off-site NWSupper air data should be evaluated on a caseby-case basis.

i. In the absence of an appropriate refined model, screening results may need to be used to determine air quality impact and/or emission limits.



### T ABLE 

### 5

– 1 A



### . — N EUTRAL 

### /S TABLE 

### M ETEOROLOGICAL 

### M ATRIX FOR 

### CTSCREEN

Variable Specific values

U (m/s) ................................................................................................................. 1.0 2.0 3.0 4.0 5.0

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

................. .................. ................ s w (m/s) ................................................................................................................ 0.08 0.15 0.30 0.75 ................
Dq/ D z (K/m) .......................................................................................................... 0.01 0.02 0.03 5 .................. ................ WD ....................................................................................................................... (Wind direction optimized internally for each meteorological combination)

Exceptions: (1) If U ≤ 2 m/s and s v ≤ 0.3 m/s, then include s w = 0.04 m/s. (2) If s w = 0.75 m/s and U ≥ 3.0 m/s, then Dq/ D z is limited to ≤ 0.01 K/m. (3) If U ≥ 4 m/s, then s w ≥ 0.15 m/s. (4) s w ≤ s v



### T ABLE 

### 5

– 1 B



### . — U NSTABLE 

### /C ONVECTIVE 

### M ETEOROLOGICAL 

### M ATRIX FOR 

### CTSCREEN

Variable Specific values

U (m/s) ................................................................................................................... 1.0 2 .0 3.0 4.0 5.0

u * (m/s) .................................................................................................................. 0.1 0.3 0.5 ................ ................ L (m) .......................................................................................................................

¥ 10

¥ 50

¥ 90 ................ ................
Ds

/ D z (K/m) ............................................................................................................ 0.030 (poten tial temperature gradient above z i )

z i (m) ...................................................................................................................... 0.5h 1 .0h 1.5h ................ ................ (where h = terrain height)


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

1 Modeling for attainment demonstrations for O 3 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 O 3 and PM

– 2.5 standards is available at: http://www.epa.gov/ttn/ naaqs/. 6.0 Models for Ozone, Particulate Matter, Carbon Monoxide, Nitrogen Dioxide, and Lead

6.1 Discussion

a. This section identifies modeling approaches or models appropriate for addressing ozone (O 3 ) 1 , carbon monoxide (CO), nitrogen dioxide (NO 2 ), 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 NO 2 near a single or small group of stationary sources, refer to Section 4. (Modeling approaches for SO 2 are discussed in Section 4.)

b. Several of the pollutants mentioned in the preceding paragraph are closely related to each other in that they share common sources of emissions and/or are subject to chemical transformations of similar
precursors. 39, 40 For example, strategies designed to reduce ozone could have an effect on the secondary component of PM

– 2.5 and vice versa. Thus, it makes sense to use models which take into account the chemical coupling between O 3 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 (NO X ) and carbon monoxide (CO), as well as means for generating meteorological data governing transport and dispersion of ozone and its precursors. Other approaches, such as Lagrangian or observational models may be used to guide choice of appropriate strategies to consider with a photochemical grid model. These other approaches may be sufficient to address ozone in an area where observed concentrations are near the NAAQS or only slightly above it. Such a decision needs to be made on a case-by-case basis in concert with the Regional Office.

d. A control agency with jurisdiction over one or more areas with significant ozone problems should review available ambient air quality data to assess whether the problem is likely to be significantly impacted by regional transport. 41 Choice of a modeling approach depends on the outcome of this review. In cases where transport is considered significant, use of a nested regional model may be the preferred approach. If the observed problem is believed to be primarily of local origin, use of a model with a single horizontal grid resolution and geographical coverage that is less than that of a regional model may suffice.

e. The fine particulate matter NAAQS, promulgated on July 18, 1997, includes particles with an aerodynamic diameter nominally less than or equal to 2.5 micrometers (PM

– 2.5). Models for PM

– 2.5 are needed to assess adequacy of a proposed strategy for meeting annual and/or 24-hour NAAQS for PM

– 2.5. PM

– 2.5 is a mixture consisting of several diverse components. Because chemical/physical properties and origins of each component differ, it may be appropriate to use either a single model capable of addressing several of the important components or to model primary and secondary components using different models. Effects of a control strategy on PM

– 2.5 is estimated from the sum of the effects on the components composing PM

– 2.5. Model users may refer to guidance 42 for further details concerning appropriate modeling approaches.

f. A control agency with jurisdiction over one or more areas with PM

– 2.5 problems should review available ambient air quality data to assess which components of PM

– 2.5 are likely to be major contributors to the problem. If it is determined that regional transport of secondary particulates, such as sulfates or nitrates, is likely to contribute significantly to the problem, use of a regional model may be the preferred approach. Otherwise, coverage may be limited to a domain that is urban scale or less. Special care should be taken to select appropriate geographical coverage for a modeling application. 42

g. The NAAQS for PM

– 10 was promulgated in July 1987. A SIP development guide 43 is available to assist in PM

– 10 analyses and control strategy development. EPA promulgated regulations for PSD increments measured as PM

– 10 in a notice published on June 3, 1993. As an aid to assessing the impact on ambient air quality of particulate matter generated from prescribed burning activities, a reference 44 is available.

h. Models for assessing the impacts of particulate matter may involve dispersion models or receptor models, or a combination (depending on the circumstances). Receptor models focus on the behavior of the ambient environment at the point of impact as opposed to source-oriented dispersion models, which focus on the transport, diffusion, and transformation that begin at the source and continue to the receptor site. Receptor models attempt to identify and apportion sources by relating known sample compositions at receptors to measured or inferred compositions of source emissions. When complete and accurate emission inventories or meteorological characterization are unavailable, or unknown pollutant sources exist, receptor modeling may be necessary. i. Models for assessing the impact of CO emissions are needed for a number of different purposes. Examples include evaluating effects of point sources, congested intersections and highways, as well as the cumulative effect of numerous sources of CO in an urban area.

j. Models for assessing the impact of sources on ambient NO 2 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 NO 2 . Impact of an individual source on ambient NO 2 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 NO 2 . One approach is through use of a plume-in-grid algorithm imbedded within a photochemical grid model. However, because of the rigor and complexity involved, and because this approach may not be capable of defining sub-grid concentration gradients, the plume-in-grid approach may be impractical for estimating effects on an annual PSD increment. A second approach is to develop site specific conversion factors based on measurements. If it is not possible to develop site specific conversion factors and use of the plume-in-grid algorithm is also not feasible, other screening procedures may be considered.

k. In January 1999 (40 CFR part 58, Appendix D), EPA gave notice that concern about ambient lead impacts was being shifted away from roadways and toward a focus on stationary point sources. EPA has also issued guidance on siting ambient monitors in the vicinity of such sources. 45 For lead, the SIP should contain an air quality analysis to determine the maximum quarterly lead concentration resulting from major lead point sources, such as smelters, gasoline additive plants, etc. General guidance for lead SIP development is also available. 46

6.2 Recommendations

6.2.1 Models for Ozone

a. Choice of Models for Multi-source Applications. Simulation of ozone formation and transport is a highly complex and resource intensive exercise. Control agencies with jurisdiction over areas with ozone problems are encouraged to use photochemical grid models, such as the Models-3/Community Multi-scale Air Quality (CMAQ) modeling system 47 , to evaluate the relationship between precursor species and ozone. Judgement on the suitability of a model for a given application should consider factors that include use of the model in an attainment test, development of emissions and meteorological inputs to the model and choice of episodes to model. 41 Similar models for the 8-hour NAAQS and for the 1-hour NAAQS are appropriate.

b. Choice of Models to Complement Photochemical Grid Models. As previously noted, observational models, Lagrangian models, or the Empirical Kinetics Modeling Approach (EKMA) 48, 49 may be used to help guide choice of strategies to simulate with a photochemical grid model and to corroborate results obtained with a grid model. Receptor models have also been used


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to apportion sources of ozone precursors ( e.g. , VOC) in urban domains. EPA has issued guidance 41 in selecting appropriate techniques.

c. Estimating the Impact of Individual
Sources. Choice of methods used to assess the impact of an individual source depends on the nature of the source and its emissions. Thus, model users should consult with the Regional Office to determine the most suitable approach on a case-by-case basis (subsection 3.2.2).

6.2.2 Models for Particulate Matter

6.2.2.1 PM

– 2.5

a. Choice of Models for Multi-source Applications. Simulation of phenomena resulting in high ambient PM

– 2.5 can be a multi-faceted and complex problem resulting from PM

– 2.5 ’ s existence as an aerosol mixture. Treating secondary components of
PM

– 2.5, such as sulfates and nitrates, can be a highly complex and resource-intensive exercise. Control agencies with jurisdiction over areas with secondary PM

– 2.5 problems are encouraged to use models which integrate chemical and physical processes important in the formation, decay and transport of these species ( e.g. , Models-3/CMAQ 47 or REMSAD 50 ). Primary components can be simulated using less resource-intensive techniques. Suitability of a modeling approach or mix of modeling approaches for a given application requires technical judgement 42 , as well as professional experience in choice of models, use of the model(s) in an attainment test, development of emissions and meteorological inputs to the model and selection of days to model.

b. Choice of Analysis Techniques to Complement Air Quality Simulation Models. Receptor models may be used to corroborate predictions obtained with one or more air quality simulation models. They may also be potentially useful in helping to define specific source categories contributing to major components of PM

– 2.5. 42

c. Estimating the Impact of Individual
Sources. Choice of methods used to assess the impact of an individual source depends on the nature of the source and its emissions. Thus, model users should consult with the Regional Office to determine the most suitable approach on a case-by-case basis (subsection 3.2.2). 6.2.2.2 PM

– 10 a. Screening techniques like those identified in subsection 4.2.1 are applicable to PM

– 10. Conservative assumptions which do not allow removal or transformation are suggested for screening. Thus, it is recommended that subjectively determined values for ‘‘ half-life ’’ or pollutant decay not be used as a surrogate for particle removal. Proportional models (rollback/forward) may not be applied for screening analysis, unless such techniques are used in conjunction with receptor modeling. 43

b. Refined models such as those discussed in subsection 4.2.2 are recommended for PM

– 10. However, where possible, particle size, gas-to-particle formation, and their effect on ambient concentrations may be considered. For point sources of small particles and for source-specific analyses of complicated sources, use the appropriate recommended steady-state plume dispersion model (subsection 4.2.2). For guidance on determination of design concentrations, see paragraph 8.2.1.1(e).

c. Receptor models have proven useful for helping validate emission inventories and for corroborating source-specific impacts estimated by dispersion models. The Chemical Mass Balance (CMB) model is useful for apportioning impacts from localized sources. 51,52,53 Other receptor models, e.g. , the Positive Matrix Factorization (PMF) model 54 and Unmix 55

, which don ’ t share some of CMB ’ s constraints, have also been applied. In regulatory applications, dispersion models have been used in conjunction with receptor models to attribute source (or source category) contributions. Guidance is available for PM

– 10 sampling and analysis applicable to receptor modeling. 56

d. Under certain conditions, recommended dispersion models may not be reliable. In such circumstances, the modeling approach should be approved by the Regional Office on a case-by-case basis. Analyses involving model calculations for stagnation conditions should also be justified on a case-by-case basis (subsection 8.2.8).

e. Fugitive dust usually refers to dust put into the atmosphere by the wind blowing over plowed fields, dirt roads or desert or sandy areas with little or no vegetation. Reentrained dust is that which is put into the air by reason of vehicles driving over dirt roads (or dirty roads) and dusty areas. Such sources can be characterized as line, area or volume sources. Emission rates may be based on site specific data or values from the general literature. Fugitive emissions include the emissions resulting from the industrial process that are not captured and vented through a stack but may be released from various locations within the complex. In some unique cases a model developed specifically for the situation may be needed. Due to the difficult nature of characterizing and modeling fugitive dust and fugitive emissions, it is recommended that the proposed procedure be cleared by the Regional Office for each specific situation before the modeling exercise is begun.

6.2.3 Models for Carbon Monoxide

a. Guidance is available for analyzing CO impacts at roadway intersections. 57 The recommended screening model for such analyses is CAL3QHC. 58,59 This model combines CALINE3 (listed in Appendix A) with a traffic model to calculate delays and queues that occur at signalized intersections. The screening approach is described in reference 57; a refined approach may be considered on a case-by-case basis with CAL3QHCR. 60 The latest version of the MOBILE (mobile source emission factor) model should be used for emissions input to intersection models.

b. For analyses of highways characterized by uninterrupted traffic flows, CALINE3 is recommended, with emissions input from the latest version of the MOBILE model.

c. For urban area wide analyses of CO, an Eulerian grid model should be used. Information on SIP development and requirements for using such models can be found in several references. 57,61,62,63

d. Where point sources of CO are of concern, they should be treated using the screening and refined techniques described
in Section 4. 6.2.4 Models for Nitrogen Dioxide (Annual Average)

a. A tiered screening approach is recommended to obtain annual average estimates of NO 2 from point sources for New Source Review analysis, including PSD, and for SIP planning purposes. This multi-tiered approach is conceptually shown in Figure 6

– 1 and described in paragraphs b through d of this subsection:


## 18457 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / 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 NO 2 . If the concentration exceeds the NAAQS and/or PSD increments for NO 2 , proceed to the 2nd level screen.

c. For Tier 2 (2nd level) screening analysis, multiply the Tier 1 estimate(s) by an empirically derived NO 2 /NO X value of 0.75 (annual national default). 64 The reviewing agency may establish an alternative default NO 2 /NO X ratio based on ambient annual average NO 2 and annual average NO X data representative of area wide quasi-equilibrium conditions. Alternative default NO 2 /NO X ratios should be based on data satisfying quality assurance procedures that ensure data accuracy for both NO 2 and NO X within the typical range of measured values. In areas with relatively low NO X concentrations, the quality assurance procedures used to determine compliance with the NO 2 national ambient air quality standard may not be adequate. In addition, default NO 2 /NO X ratios, including the 0.75 national default value, can underestimate long range NO 2 impacts and should be used with caution in long range transport scenarios.

d. For Tier 3 (3rd level) analysis, a detailedscreening method may be selected on a caseby-case basis. For point source modeling, other refined screening methods, such as the ozone limiting method, 65 may also be considered. Also, a site specific NO 2 /NO X ratio may be used as a detailed screening method if it meets the same restrictions as described for alternative default NO 2 /NO X ratios. Ambient NO X monitors used to develop a site specific ratio should be sited to obtain the NO 2 and NO X concentrations under quasi-equilibrium conditions. Data obtained from monitors sited at the maximum NO X impact site, as may be required in a PSD pre-construction monitoring program, likely reflect transitional NO X conditions. Therefore, NO X data from maximum impact sites may not be suitable for determining a site specific NO 2 / NO X ratio that is applicable for the entire modeling analysis. A site specific ratio derived from maximum impact data can only be used to estimate NO 2 impacts at receptors located within the same distance of the source as the source-to-monitor distance.

e. In urban areas (subsection 8.2.3), a proportional model may be used as a preliminary assessment to evaluate control strategies to meet the NAAQS for multiple minor sources, i.e. , minor point, area and mobile sources of NO X ; concentrations resulting from major point sources should be estimated separately as discussed above, then added to the impact of the minor sources. An acceptable screening technique for urban complexes is to assume that all NO X is emitted in the form of NO 2 and to use a model from Appendix A for nonreactive pollutants to estimate NO 2 concentrations. A more accurate estimate can be obtained by: (1) Calculating the annual average concentrations of NO X with an urban model, and (2) converting these estimates to NO 2 concentrations using an empirically derived annual NO 2 /NO X ratio. A value of 0.75 is recommended for this ratio. However, a spatially averaged alternative default annual NO 2 /NO X ratio may be determined from an existing air quality monitoring network and used in lieu of the 0.75 value if it is determined to be representative of prevailing ratios in the urban area by the reviewing agency. To ensure use of appropriate locally derived annual average NO 2 / NO X ratios, monitoring data under consideration should be limited to those collected at monitors meeting siting criteria defined in 40 CFR Part 58, Appendix D as representative of

‘‘ neighborhood ’’

, ‘‘ urban ’’ , or ‘‘ regional ’’ scales. Furthermore, the highest annual spatially averaged NO 2 /NO X ratio from the most recent 3 years of complete data should be used to foster conservatism in estimated impacts.

f. To demonstrate compliance with NO 2 PSD increments in urban areas, emissions from major and minor sources should be included in the modeling analysis. Point and area source emissions should be modeled as discussed above. If mobile source emissions do not contribute to localized areas of high ambient NO 2 concentrations, they should be modeled as area sources. When modeled as area sources, mobile source emissions should be assumed uniform over the entire highway link and allocated to each area source grid square based on the portion of highway link within each grid square. If localized areas of high concentrations are likely, then mobile sources should be modeled as line sources using an appropriate steady-state plume dispersion model ( e.g., CAL3QHCR; subsection 6.2.3).

g. More refined techniques to handle special circumstances may be considered on a case-by-case basis and agreement with the appropriate reviewing authority (paragraph 3.0(b)) should be obtained. Such techniques should consider individual quantities of NO and NO 2 emissions, atmospheric transport


## 18458 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

and dispersion, and atmospheric transformation of NO to NO 2 . Where they are available, site specific data on the conversion of NO to NO 2 may be used. Photochemical dispersion models, if used for other pollutants in the area, may also be applied to the NO X problem.

6.2.5 Models for Lead

a. For major lead point sources, such as smelters, which contribute fugitive emissions and for which deposition is important, professional judgement should be used, and there should be coordination with the appropriate reviewing authority (paragraph 3.0(b)). To model an entire major urban area or to model areas without significant sources of lead emissions, as a minimum a proportional (rollback) model may be used for air quality analysis. The rollback philosophy assumes that measured pollutant concentrations are proportional to emissions. However, urban or other dispersion models are encouraged in these circumstances where the use of such models is feasible.

b. In modeling the effect of traditional line sources (such as a specific roadway or highway) on lead air quality, dispersion models applied for other pollutants can be used. Dispersion models such as CALINE3 and CAL3QHCR have been used for modeling carbon monoxide emissions from highways and intersections (subsection 6.2.3). Where there is a point source in the middle of a substantial road network, the lead concentrations that result from the road network should be treated as background (subsection 9.2); the point source and any nearby major roadways should be modeled separately using the appropriate recommended steady-state plume dispersion model (subsection 4.2.2).
7.0 Other Model Requirements

7.1 Discussion

a. This section covers those cases where specific techniques have been developed for special regulatory programs. Most of the programs have, or will have when fully developed, separate guidance documents that cover the program and a discussion of the tools that are needed. The following paragraphs reference those guidance documents, when they are available. No attempt has been made to provide a comprehensive discussion of each topic since the reference documents were designed to do that. This section will undergo periodic revision as new programs are added and new techniques are developed.

b. Other Federal agencies have also developed specific modeling approaches for their own regulatory or other requirements. 66 Although such regulatory requirements and manuals may have come about because of EPA rules or standards, the implementation of such regulations and the use of the modeling techniques is under the jurisdiction of the agency issuing the manual or directive.

c. The need to estimate impacts at distances greater than 50km (the nominal distance to which EPA considers most steady-state Gaussian plume models are applicable) is an important one especially when considering the effects from secondary pollutants. Unfortunately, models originally available to EPA had not undergone sufficient field evaluation to be recommended for general use. Data bases from field studies at mesoscale and long range transport distances were limited in detail. This limitation was a result of the expense to perform the field studies required to verify and improve mesoscale and long range transport models. Meteorological data adequate for generating three-dimensional wind fields were particularly sparse. Application of models to complicated terrain compounds the difficulty of making good assessments of long range transport impacts. EPA completed limited evaluation of several long range transport (LRT) models against two sets of field data and evaluated results. 13 Based on the results, EPA concluded that long range and mesoscale transport modelswere limited for regulatory use to a case-bycase basis. However a more recent series of comparisons has been completed for a new model, CALPUFF (Section A.3). Several of these field studies involved three-to-four hour releases of tracer gas sampled along arcs of receptors at distances greater than 50km downwind. In some cases, short-term concentration sampling was available, such that the transport of the tracer puff as it passed the arc could be monitored. Differences on the order of 10 to 20 degrees were found between the location of the simulated and observed center of mass of the tracer puff. Most of the simulated centerline concentration maxima along each arc were within a factor of two of those observed. It was concluded from these case studies that the CALPUFF dispersion model had performed in a reasonable manner, and had no apparent bias toward over or under prediction, so long as the transport distance was limited to less than 300km. 67

7.2 Recommendations 7.2.1 Visibility

a. Visibility in important natural areas ( e.g., Federal Class I areas) is protected under a number of provisions of the Clean Air Act, including Sections 169A and 169B (addressing impacts primarily from existing sources) and Section 165 (new source review). Visibility impairment is caused by light scattering and light absorption associated with particles and gases in the atmosphere. In most areas of the country, light scattering by PM

– 2.5 is the most significant component of visibility impairment. The key components of PM

– 2.5 contributing to visibility impairment include sulfates, nitrates, organic carbon, elemental carbon, and crustal material.

b. The visibility regulations as promulgated in December 1980 (40 CFR 51.300

– 307) require States to mitigate visibility impairment, in any of the 156 mandatory Federal Class I areas, that is found to be

‘‘ reasonably attributable ’’ to a single source or a small group of sources. In 1985, EPA promulgated Federal Implementation Plans (FIPs) for several States without approved visibility provisions in their SIPs. The IMPROVE (Interagency Monitoring for Protected Visual Environments) monitoring network, a cooperative effort between EPA, the States, and Federal land management agencies, was established to implement the monitoring requirements in these FIPs. Data has been collected by the IMPROVE network since 1988.

c. In 1999, EPA issued revisions to the 1980 regulations to address visibility impairment in the form of regional haze, which is caused by numerous, diverse sources ( e.g., stationary, mobile, and area sources) located across a broad region (40 CFR 51.308

– 309). The state of relevant scientific knowledge has expanded significantly since the Clean Air Act Amendments of 1977. A number of studies and reports 68,69 have concluded that long range transport ( e.g., up to hundreds of kilometers) of fine particulate matter plays a significant role in visibility impairment across the country. Section 169A of the Act requires states to develop SIPs containing long-term strategies for remedying existing and preventing future visibility impairment in 156 mandatory Class I federal areas. In order to develop long-term strategies to address regional haze, many States will need to conduct regional-scale modeling of fine particulate concentrations and associated visibility impairment ( e.g., light extinction and deciview metrics).

d. To calculate the potential impact of a plume of specified emissions for specific transport and dispersion conditions ( ‘‘ plume blight ’’ ), a screening model, VISCREEN, and guidance are available. 70 If a more comprehensive analysis is required, a refined model should be selected . The model selection (VISCREEN vs. PLUVUE II or some other refined model), procedures, and analyses should be determined in consultation with the appropriate reviewing authority (paragraph 3.0(b)) and the affected Federal Land Manager (FLM). FLMs are responsible for determining whether there is an adverse effect by a plume on a Class I area.

e. CALPUFF (Section A.3) may be applied when assessment is needed of reasonably attributable haze impairment or atmospheric deposition due to one or a small group of sources. This situation may involve more sources and larger modeling domains than that to which VISCREEN ideally may be applied. The procedures and analyses should be determined in consultation with the appropriate reviewing authority (paragraph 3.0(b)) and the affected FLM(s).

f. Regional scale models are used by EPA to develop and evaluate national policy and assist State and local control agencies. Two such models which can be used to assess visibility impacts from source emissions are Models-3/CMAQ 47 and REMSAD. 50 Model users should consult with the appropriate reviewing authority (paragraph 3.0(b)), which in this instance would include FLMs. 7.2.2 Good Engineering Practice Stack Height

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


## 18459 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

example of applying those techniques are found in several references 71, 72, 73, 74 , which provide a great deal of additional information for evaluating and describing building cavity and wake effects.

b. If stacks for new or existing major sources are found to be less than the height defined by EPA ’ s refined formula for determining GEP height, then air quality impacts associated with cavity or wake effects due to the nearby building structures should be determined. The EPA refined formula height is defined as H + 1.5L (see reference 73). Detailed downwash screening procedures 27 for both the cavity and wake regions should be followed. If more refined concentration estimates are required, the recommended steady-state plume dispersion model in subsection 4.2.2 contains algorithms for building wake calculations and should be used. 7.2.3 Long Range Transport (LRT) ( i.e., Beyond 50km)

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

b. If LRT is determined to be important, then refined estimates utilizing the CALPUFF modeling system should be obtained. A screening approach 67, 75 is also available for use on a case-by-case basis that generally provides concentrations that are higher than those obtained using refined characterizations of the meteorological conditions. The meteorological input data requirements for developing the time and space varying three-dimensional winds and dispersion meteorology for refined analyses are discussed in paragraph 9.3.1.2(d). Additional information on applying this model is contained in Appendix A. To facilitate use of complex air quality and meteorological modeling systems, a written protocol approved by the appropriate reviewing authority (paragraph 3.0(b)) and the affected FLM(s) may be considered for developing consensus in the methods and procedures to be followed. 7.2.4 Modeling Guidance for Other Governmental Programs

a. When using the models recommended or discussed in the Guideline in support of programmatic requirements not specifically covered by EPA regulations, the model user should consult the appropriate Federal or State agency to ensure the proper application and use of the models. For modeling associated with PSD permit applications that involve a Class I area, the appropriate Federal Land Manager should be consulted on all modeling questions.

b. The Offshore and Coastal Dispersion (OCD) model, described in Appendix A, was developed by the Minerals Management Service and is recommended for estimating air quality impact from offshore sources on onshore, flat terrain areas. The OCD model is not recommended for use in air quality impact assessments for onshore sources. Sources located on or just inland of a shoreline where fumigation is expected should be treated in accordance with subsection 8.2.8.

c. The Emissions and Dispersion Modeling System (EDMS), described in Appendix A, was developed by the Federal Aviation Administration and the United States Air Force and is recommended for air quality assessment of primary pollutant impacts at airports or air bases. Regulatory application of EDMS is intended for estimating the cumulative effect of changes in aircraft operations, point source, and mobile source emissions on pollutant concentrations. It is not intended for PSD, SIP, or other regulatory air quality analyses of point or mobile sources at or peripheral to airport property that are independent of changes in aircraft operations. If changes in other than aircraft operations are associated with analyses, a model recommended in Chapter 4 or 5 should be used.
8.0 General Modeling Considerations

8.1 Discussion

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

8.2 Recommendations 8.2.1 Design Concentrations (see also subsection 11.2.3.1)
8.2.1.1 Design Concentrations for SO 2 , PM

– 10, CO, Pb, and NO 2

a. An air quality analysis for SO 2 , PM

– 10, CO, Pb, and NO 2 is required to determine if the source will (1) cause a violation of the NAAQS, or (2) cause or contribute to air quality deterioration greater than the specified allowable PSD increment. For the former, background concentration (subsection 9.2) should be added to the estimated impact of the source to determine the design concentration. For the latter, the design concentration includes impact from all increment consuming sources.

b. If the air quality analyses are conducted using the period of meteorological input data recommended in subsection 9.3.1.2 ( e.g., 5 years of National Weather Service (NWS) data or at least 1 year of site specific data; subsection 9.3.3), then the designconcentration based on the highest, secondhighest short term concentration or the highest long term average, whichever is controlling, should be used to determine emission limitations to assess compliance with the NAAQS and PSD increments.

c. When sufficient and representative data exist for less than a 5-year period from a nearby NWS site, or when site specific data have been collected for less than a full continuous year, or when it has been determined that the site specific data may not be temporally representative (subsection 9.3.3), then the highest concentration estimate should be considered the design value. This is because the length of the data record may be too short to assure that the conditions producing worst-case estimates have been adequately sampled. The highest value is then a surrogate for the concentration that is not to be exceeded more than once per year (the wording of the deterministic standards). Also, the highest concentration should be used whenever selected worst-case conditions are input to a screening technique, as described in EPA guidance. 27

d. If the controlling concentration is an annual average value and multiple years of data (site specific or NWS) are used, then the design value is the highest of the annual averages calculated for the individual years. If the controlling concentration is a quarterly average and multiple years are used, then the highest individual quarterly average should be considered the design value.

e. As long a period of record as possible should be used in making estimates to determine design values and PSD increments. If more than 1 year of site specific data is available, it should be used. 8.2.1.2 Design Concentrations for O 3 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


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4. Appendix H explains how to determine when the expected number of days per calendar year with maximum hourly concentrations above the NAAQS is equal to or less than 1. Appendix I explains the data handling conventions and computations necessary for determining whether the 8-hour primary and secondary NAAQS are met at an ambient monitoring site. For PM

– 2.5, Appendix N of reference 4, and supplementary guidance 76 , explain the data handling conventions and computations necessary for determining when the annual and 24-hour primary and secondary NAAQS are met. For all SIP revisions the user should check with the Regional Office to obtain the most recent guidance documents and policy memoranda concerning the pollutant in question. There are currently no PSD increments for O 3 and PM

– 2.5. 8.2.2 Critical Receptor Sites

a. Receptor sites for refined modeling should be utilized in sufficient detail to estimate the highest concentrations and possible violations of a NAAQS or a PSD increment. In designing a receptor network, the emphasis should be placed on receptor resolution and location, not total number of receptors. The selection of receptor sites should be a case-by-case determination taking into consideration the topography, the climatology, monitor sites, and the results of the initial screening procedure. For large sources (those equivalent to a 500MW power plant) and where violations of the NAAQS or PSD increment are likely, 360 receptors for a polar coordinate grid system and 400 receptors for a rectangular grid system, where the distance from the source to the farthest receptor is 10km, are usually adequate to identify areas of high concentration. Additional receptors may be needed in the high concentration location if greater resolution is indicated by terrain or source factors. 8.2.3 Dispersion Coefficients

a. Steady-state Gaussian plume models used in most applications should employ dispersion coefficients consistent with those contained in the preferred models in Appendix A. Factors such as averaging time, urban/rural surroundings (see paragraphs (b)

– (f) of this subsection), and type of source (point vs. line) may dictate the selection of specific coefficients. Coefficients used in some Appendix A models are identical to, or at least based on, Pasquill-Gifford coefficients 77 in rural areas and McElroyPooler 78 coefficients in urban areas. 79

b. The selection of either rural or urban dispersion coefficients in a specific application should follow one of the procedures suggested by Irwin 80 and briefly described in paragraphs (c)

– (f) of this subsection. These include a land use classification procedure or a population based procedure to determine whether the character of an area is primarily urban or rural.

c. Land Use Procedure: (1) Classify the land use within the total area, A
o , circumscribed by a 3km radius circle about the source using the meteorological land use typing scheme proposed by Auer 81 ; (2) if land use types I1, I2, C1, R2, and R3 account for 50 percent or more of A
o , 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 A
o as defined above; (2) If p  ̄ is greater than 750 people/km 2

, use urban dispersion coefficients; otherwise use appropriate rural dispersion coefficients.

e. Of the two methods, the land use procedure is considered more definitive. Population density should be used with caution and should not be applied to highly industrialized areas where the population density may be low and thus a rural classification would be indicated, but the area is sufficiently built-up so that the urban land use criteria would be satisfied. In this case, the classification should already be

‘‘ urban ’’ and urban dispersion parameters should be used.

f. Sources located in an area defined as urban should be modeled using urban dispersion parameters. Sources located in areas defined as rural should be modeled using the rural dispersion parameters. For analyses of whole urban complexes, the entire area should be modeled as an urban region if most of the sources are located in areas classified as urban.

g. Buoyancy-induced dispersion (BID), as identified by Pasquill 82 , is included in the preferred models and should be used where buoyant sources, e.g. , those involving fuel combustion, are involved. 8.2.4 Stability Categories

a. The Pasquill approach to classifying stability is commonly used in preferred models (Appendix A). The Pasquill method, as modified by Turner 83 , was developed for use with commonly observed meteorological data from the National Weather Service and is based on cloud cover, insolation and wind speed.

b. Procedures to determine Pasquill stability categories from other than NWS data are found in subsection 9.3. Any other method to determine Pasquill stability categories must be justified on a case-by-case basis.

c. For a given model application where stability categories are the basis for selecting dispersion coefficients, both s y and s z should be determined from the same stability category. ‘‘ Split sigmas ’’ in that instance are not recommended. Sector averaging, which eliminates the s y term, is commonly acceptable in complex terrain screening methods.

8.2.5 Plume Rise

a. The plume rise methods of Briggs 84, 85 are incorporated in many of the preferred models and are recommended for use in many modeling applications. In the convective boundary layer, plume rise is superposed on the displacements by random convective velocities. 86 No explicit provisions in these models are made for multistack plume rise enhancement or the handling of such special plumes as flares; these problems should be considered on a case-by-case basis.

b. Gradual plume rise is generally recommended where its use is appropriate: (1) In complex terrain screening procedures to determine close-in impacts and (2) when calculating the effects of building wakes. If the building wake is calculated to affect the plume for any hour, gradual plume rise is also used in downwind dispersion calculations to the distance of final plume rise, after which final plume rise is used.Plumes captured by the near wake are reemitted to the far wake as a ground-level volume source.

c. Stack tip downwash generally occurs with poorly constructed stacks and when the ratio of the stack exit velocity to wind speed is small. An algorithm developed by Briggs 85 is the recommended technique for this situation and is found in the point source preferred models.

8.2.6 Chemical Transformation

a. The chemical transformation of SO 2 emitted from point sources or single industrial plants in rural areas is generally assumed to be relatively unimportant to the estimation of maximum concentrations when travel time is limited to a few hours. However, in urban areas, where synergistic effects among pollutants are of considerable consequence, chemical transformation rates may be of concern. In urban area applications, a half-life of 4 hours 83 may be applied to the analysis of SO 2 emissions. Calculations of transformation coefficients from site specific studies can be used to define a ‘‘ half-life ’’ to be used in a steadystate Gaussian plume model with any travel time, or in any application, if appropriate documentation is provided. Such conversion factors for pollutant half-life should not be used with screening analyses.

b. Use of models incorporating complex chemical mechanisms should be considered only on a case-by-case basis with proper demonstration of applicability. These are generally regional models not designed for the evaluation of individual sources but used primarily for region-wide evaluations. Visibility models also incorporate chemical transformation mechanisms which are an integral part of the visibility model itself and should be used in visibility assessments. 8.2.7 Gravitational Settling and Deposition

a. An ‘‘ infinite half-life ’’ should be used for estimates of particle concentrations when steady-state Gaussian plume models containing only exponential decay terms for treating settling and deposition are used.

b. Gravitational settling and deposition may be directly included in a model if either is a significant factor. When particulate matter sources can be quantified and settling and dry deposition are problems, professional judgement should be used, and there should be coordination with the appropriate reviewing authority (paragraph 3.0(b)). 8.2.8 Complex Winds

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


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2 Malfunctions which may result in excess emissions are not considered to be a normal operating condition. They generally should not be considered in determining allowable emissions. However, if the excess emissions are the result of poor maintenance, careless operation, or other preventable conditions, it may be necessary to consider them in determining source impact. 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 airquality estimates in such complex nonsteady-state meteorological conditions. The purpose of choosing a modeling system like CALPUFF is to fully treat the time and space variations of meteorology effects on transport and dispersion. The setup and application of the model should be determined in consultation with the appropriate reviewing authority (paragraph 3.0(b)) consistent with limitations of paragraph 3.2.2(e). The meteorological input data requirements fordeveloping the time and space varying threedimensional winds and dispersion meteorology for these situations are discussed in paragraph 9.3.1.2(d). Examples of inhomogeneous winds include, but aren ’ t limited to, situations described in the following paragraphs (i)

– (iii):

i. Inversion Breakup Fumigation. Inversion breakup fumigation occurs when a plume (or multiple plumes) is emitted into a stable layer of air and that layer is subsequently mixed to the ground through convective transfer of heat from the surface or because of advection to less stable surroundings. Fumigation may cause excessively highconcentrations but is usually rather shortlived at a given receptor. There are no recommended refined techniques to model this phenomenon. There are, however, screening procedures 27 that may be used to approximate the concentrations. Considerable care should be exercised in using the results obtained from the screening techniques.

ii. Shoreline Fumigation. Fumigation can be an important phenomenon on and near the shoreline of bodies of water. This can affect both individual plumes and area-wide emissions. When fumigation conditions are expected to occur from a source or sources with tall stacks located on or just inland of a shoreline, this should be addressed in the air quality modeling analysis. The Shoreline Dispersion Model (SDM) listed on EPA ’ s Internet SCRAM Web site (subsection 2.3) may be applied on a case-by-case basis when air quality estimates under shoreline fumigation conditions are needed. 88 Information on the results of EPA ’ s evaluation of this model together with other coastal fumigation models is available. 89 Selection of the appropriate model for applications where shoreline fumigation is of concern should be determined in consultation with the appropriate reviewing authority (paragraph 3.0(b)).

iii. Stagnation. Stagnation conditions are characterized by calm or very low wind speeds, and variable wind directions. These stagnant meteorological conditions may persist for several hours to several days. During stagnation conditions, the dispersion of air pollutants, especially those from lowlevel emissions sources, tends to be minimized, potentially leading to relatively high ground-level concentrations. If point sources are of interest, users should note the guidance provided for CALPUFF in paragraph (a) of this subsection. Selection of the appropriate model for applications where stagnation is of concern should be determined in consultation with the appropriate reviewing authority (paragraph 3.0(b)).

8.2.9 Calibration of Models

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

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

9.1 Source Data

9.1.1 Discussion

a. Sources of pollutants can be classified as point, line and area/volume sources. Point sources are defined in terms of size and may vary between regulatory programs. The line sources most frequently considered are roadways and streets along which there are well-defined movements of motor vehicles, but they may be lines of roof vents or stacks such as in aluminum refineries. Area and volume sources are often collections of a multitude of minor sources with individually small emissions that are impractical to consider as separate point or line sources. Large area sources are typically treated as a grid network of square areas, with pollutant emissions distributed uniformly within each grid square.

b. Emission factors are compiled in an EPA publication commonly known as AP

– 42 90 ; an indication of the quality and amount of data on which many of the factors are based is also provided. Other information concerning emissions is available in EPA publications relating to specific source categories. The appropriate reviewing authority (paragraph 3.0(b)) should be consulted to determine appropriate source definitions and for guidance concerning the determination of emissions from and techniques for modeling the various source types.
9.1.2 Recommendations

a. For point source applications the load or operating condition that causes maximum ground-level concentrations should be established. As a minimum, the source should be modeled using the design capacity (100 percent load). If a source operates at greater than design capacity for periods that could result in violations of the standards or PSD increments, this load 2 should be modeled. Where the source operates at substantially less than design capacity, and the changes in the stack parameters associated with the operating conditions could lead to higher ground level concentrations, loads such as 50 percent and 75 percent of capacity should also be modeled. A range of operating conditions should be considered in screening analyses; the load causing the highest concentration, in addition to the design load, should be included in refined modeling. For a steam power plant, the following (b

–h) is typical of the kind of data on source characteristics and operating conditions that may be needed. Generally, input data requirements for air quality models necessitate the use of metric units; where English units are common for engineering usage, a conversion to metric is required.

b. Plant layout. The connection scheme between boilers and stacks, and the distance and direction between stacks, building parameters (length, width, height, location and orientation relative to stacks) for plant structures which house boilers, control equipment, and surrounding buildings within a distance of approximately five stack heights.

c. Stack parameters. For all stacks, the stack height and inside diameter (meters), and the temperature (K) and volume flow rate (actual cubic meters per second) or exit gas velocity (meters per second) for operation at 100 percent, 75 percent and 50 percent load.

d. Boiler size. For all boilers, the associated megawatts, 10 6 BTU/hr, and pounds of steam per hour, and the design and/or actual fuel consumption rate for 100 percent load for coal (tons/hour), oil (barrels/hour), and natural gas (thousand cubic feet/hour).

e. Boiler parameters. For all boilers, the percent excess air used, the boiler type ( e.g. , wet bottom, cyclone, etc.), and the type of firing ( e.g. , pulverized coal, front firing, etc.).

f. Operating conditions. For all boilers, the type, amount and pollutant contents of fuel, the total hours of boiler operation and the boiler capacity factor during the year, and the percent load for peak conditions.


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g. Pollution control equipment parameters. For each boiler served and each pollutant affected, the type of emission control equipment, the year of its installation, its design efficiency and mass emission rate, the date of the last test and the tested efficiency, the number of hours of operation during the latest year, and the best engineering estimate of its projected efficiency if used in conjunction with coal combustion; data for any anticipated modifications or additions.

h. Data for new boilers or stacks. For all new boilers and stacks under construction and for all planned modifications to existing boilers or stacks, the scheduled date of completion, and the data or best estimates available for items (b) through (g) of this subsection following completion of construction or modification.

i. In stationary point source applications for compliance with short term ambient standards, SIP control strategies should be tested using the emission input shown on Table 9

– 1. When using a refined model, sources should be modeled sequentially with these loads for every hour of the year. To evaluate SIPs for compliance with quarterly and annual standards, emission input data shown in Table 9

– 1 should again be used. Emissions from area sources should generally be based on annual average conditions. The source input information in each model

user ’ s guide should be carefully consulted and the checklist (paragraph 9.0(a)) should also be consulted for other possible emission data that could be helpful. PSD and NAAQS compliance demonstrations should follow the emission input data shown in Table 9

– 2. For purposes of emissions trading, new source review and demonstrations, refer to current EPA policy and guidance to establish input data.

j. Line source modeling of streets and highways requires data on the width of the roadway and the median strip, the types and amounts of pollutant emissions, the number of lanes, the emissions from each lane and the height of emissions. The location of the ends of the straight roadway segments should be specified by appropriate grid coordinates. Detailed information and data requirements for modeling mobile sources of pollution are provided in the user ’ s manuals for each of the models applicable to mobile sources.

k. The impact of growth on emissions should be considered in all modeling analyses covering existing sources. Increases in emissions due to planned expansion or planned fuel switches should be identified. Increases in emissions at individual sources that may be associated with a general industrial/commercial/residential expansion in multi-source urban areas should also be treated. For new sources the impact of growth on emissions should generally beconsidered 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.



### T ABLE 

### 9

– 1.

— M ODEL 

### E MISSION 

### I NPUT 

### D ATA FOR 

### P OINT 

### S OURCES 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 Areawid e Demonstrations)

Annual & quarterly ..................... Maximum allowable emission limit or federally enforceable permit limit. Actual or design capacity(whichever is greater), or federally enforceable permit condition. Actual operating factor averaged over most recent 2
years. 3

Short term .................................. Maximum allowable emission limit or federally enforceable permit limit. Actual or design capacity(whichever is greater), or federally enforceable permit condition. 4 Continuous operation, i.e. , all hours of each time period under consideration (for all hours of the meteorological data base). 5

Nearby Source(s) 6, 7 Same input requirements as for stationary point source(s) above.

Other Sources 7 If modeled (subsection 9.2.3), input data requirements are defined below.

Annual & quarterly ..................... Maximum allowable emission limit or federally enforceable permit limit. 6 Annual level when actually operating, averaged over the most recent 2 years. 3 Actual operating factor averaged over the most recent 2
years. 3 Short term .................................. Maximum allowable emission limit or federally enforceable permit limit. 6 Annual level when actually operating, averaged over the most recent 2 years. 3 Continuous operation, i.e. , all hours of each time period under consideration (for all hours of the meteorological data base). 5

1 The model input data requirements shown on this table apply to stationary source control strategies for STATE IMPLEMENTATION PL ANS. For purposes of emissions trading, new source review, or prevention of significant deterioration, other model input criteria ma y apply. Refer to the policy and guidance for these programs to establish the input data.

2 Terminology applicable to fuel burning sources; analogous terminology ( e.g. , #/throughput) may be used for other types of sources.

3 Unless it is determined that this period is not representative.

4 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highestconcentration.

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 acro ss non-operating time periods.)

6 See paragraph 9.2.3(c).

7 See paragraph 9.2.3(d).


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T ABLE 

### 9

– 2.

— P OINT 

### S OURCE 

### M ODEL 

### I NPUT 

### D ATA 

### (E MISSIONS 

### ) FOR 

### PSD NAAQS C OMPLIANCE 

### D EMONSTRATIONS

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. 3 Continuous 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

1 Terminology applicable to fuel burning sources; analogous terminology ( e.g. , #/throughput) may be used for other types of sources.

2 If operation does not occur for all hours of the time period of consideration ( e.g. , 3 or 24 hours) and the source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made ( e.g. , if operation is only 8 a.m. to 4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged acro ss non-operating time periods.

3 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highestconcentration.

4 Includes existing facility to which modification is proposed if the emissions from the existing facility will not be affected b y the modification. Otherwise use the same parameters as for major modification.

5 See paragraph 9.2.3(c).

6 See paragraph 9.2.3(d).

7 Unless it is determined that this period is not representative.

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

9 Generally, the ambient impacts from non-nearby (background) sources can be represented by air quality data unless adequate data do not exist.

9.2 Background Concentrations

9.2.1 Discussion

a. Background concentrations are an essential part of the total air quality concentration to be considered in determining source impacts. Background air quality includes pollutant concentrations due to: (1) Natural sources; (2) nearby sources other than the one(s) currently under consideration; and (3) unidentified sources.

b. Typically, air quality data should be used to establish background concentrations in the vicinity of the source(s) under consideration. The monitoring network used for background determinations should conform to the same quality assurance and other requirements as those networks established for PSD purposes. 91 An appropriate data validation procedure should be applied to the data prior to use. c. If the source is not isolated, it may be necessary to use a multi-source model to establish the impact of nearby sources. Since sources don ’ t typically operate at their maximum allowable capacity (which may include the use of ‘‘ dirtier ’’ fuels), modeling is necessary to express the potential contribution of background sources, and this impact would not be captured via monitoring. Background concentrations should be determined for each critical (concentration) averaging time. 9.2.2 Recommendations (Isolated Single Source)

a. Two options (paragraph (b) or (c) of this section) are available to determine the background concentration near isolated

sources.

b. Use air quality data collected in the vicinity of the source to determine the background concentration for the averaging times of concern. Determine the mean background concentration at each monitor by excluding values when the source in question is impacting the monitor. The mean annual background is the average of the annual concentrations so determined at each monitor. For shorter averaging periods, the meteorological conditions accompanying the concentrations of concern should be identified. Concentrations for meteorological conditions of concern, at monitors not impacted by the source in question, should be averaged for each separate averaging time to determine the average background value. Monitoring sites inside a 90 ° 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.


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c. If there are no monitors located in the vicinity of the source, a ‘‘ regional site ’’ may be used to determine background. A

‘‘ regional site ’’ is one that is located away from the area of interest but is impacted by similar natural and distant man-made

sources. 9.2.3 Recommendations (Multi-Source Areas)

a. In multi-source areas, two components of background should be determined: Contributions from nearby sources and contributions from other sources.

b. Nearby Sources: All sources expected to cause a significant concentration gradient in the vicinity of the source or sources under consideration for emission limit(s) should be explicitly modeled. The number of such sources is expected to be small except in unusual situations. Owing to both the uniqueness of each modeling situation and the large number of variables involved in identifying nearby sources, no attempt is made here to comprehensively define this term. Rather, identification of nearby sources calls for the exercise of professional judgement by the appropriate reviewing authority (paragraph 3.0(b)). This guidance is not intended to alter the exercise of that judgement or to comprehensively define which sources are nearby sources.

c. For compliance with the short-term and annual ambient standards, the nearby sources as well as the primary source(s) should be evaluated using an appropriate Appendix A model with the emission input data shown in Table 9

– 1 or 9

– 2. When modeling a nearby source that does not have a permit and the emission limit contained in the SIP for a particular source category is greater than the emissions possible given the source ’ s maximum physical capacity to emit, the

‘‘ maximum allowable emission limit ’’ for such a nearby source may be calculated as the emission rate representative of the nearby

source ’ s maximum physical capacity to emit, considering its design specifications and allowable fuels and process materials. However, the burden is on the permit applicant to sufficiently document what the maximum physical capacity to emit is for such a nearby source.

d. It is appropriate to model nearby sources only during those times when they, by their nature, operate at the same time as the primary source(s) being modeled. Where a primary source believes that a nearby source does not, by its nature, operate at the same time as the primary source being modeled, the burden is on the primary source to demonstrate to the satisfaction of the appropriate reviewing authority (paragraph 3.0(b)) that this is, in fact, the case. Whether or not the primary source has adequately demonstrated that fact is a matter of professional judgement left to the discretion of the appropriate reviewing authority. The following examples illustrate two cases in which a nearby source may be shown not to operate at the same time as the primary source(s) being modeled. Some sources are only used during certain seasons of the year. Those sources would not be modeled as nearby sources during times in which they do not operate. Similarly, emergency backup generators that never operate simultaneously with the sources that they back up would not be modeled as nearby sources. To reiterate, in these examples and other appropriate cases, the burden is on the primary source being modeled to make the appropriate demonstration to the satisfaction of the appropriate reviewing authority.

e. The impact of the nearby sources should be examined at locations where interactions between the plume of the point source under consideration and those of nearby sources (plus natural background) can occur. Significant locations include: (1) The area of maximum impact of the point source; (2) the area of maximum impact of nearby sources; and (3) the area where all sources combine to cause maximum impact. These locations may be identified through trial and error analyses.

f. Other Sources: That portion of the background attributable to all other sources ( e.g. , natural sources, minor sources and distant major sources) should be determined by the procedures found in subsection 9.2.2 or by application of a model using Table 9

– 1 or 9

– 2. 9.3 Meteorological Input Data

a. The meteorological data used as input to a dispersion model should be selected on the basis of spatial and climatological (temporal) representativeness as well as the ability of the individual parameters selected to characterize the transport and dispersion conditions in the area of concern. The representativeness of the data is dependent on: (1) The proximity of the meteorological monitoring site to the area under consideration; (2) the complexity of the terrain; (3) the exposure of the meteorological monitoring site; and (4) the period of time during which data are collected. The spatial representativeness of the data can be adversely affected by large distances between the source and receptors of interest and the complex topographic characteristics of the area. Temporal representativeness is a function of the year-to-year variations in weather conditions. Where appropriate, data representativeness should be viewed in terms of the appropriateness of the data for constructing realistic boundary layer profiles and three dimensional meteorological fields, as described in paragraphs (c) and (d) below.

b. Model input data are normally obtained either from the National Weather Service or as part of a site specific measurement program. Local universities, Federal Aviation Administration (FAA), military stations, industry and pollution control agencies may also be sources of such data. Some recommendations for the use of each type of data are included in this subsection.

c. For long range transport modeling assessments (subsection 7.2.3) or for assessments where the transport winds arecomplex and the application involves a nonsteady-state dispersion model (subsection 8.2.8), use of output from prognostic mesoscale meteorological models is encouraged. 92, 93, 94 Some diagnostic meteorological processors are designed to appropriately blend available NWS comparable meteorological observations, local site specific meteorological observations, and prognostic mesoscale meteorological data, using empirical relationships, to diagnostically adjust the wind field for mesoscale and local-scale effects. These diagnostic adjustments can sometimes be improved through the use of strategically placed site specific meteorological observations. The placement of these special meteorological observations (often more than one location is needed) involves expert judgement, and is specific to the terrain and land use of the modeling domain. Acceptance for use of output from prognostic mesoscale meteorological models is contingent on concurrence by the appropriate reviewing authorities (paragraph 3.0(b)) that the data are of acceptable quality, which can be demonstrated through statistical comparisons with observations of winds aloft and at the surface at several appropriate locations. 9.3.1 Length of Record of Meteorological Data

9.3.1.1 Discussion

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

9.3.1.2 Recommendations

a. Five years of representative meteorological data should be used when estimating concentrations with an air quality model. Consecutive years from the most recent, readily available 5-year period are preferred. The meteorological data should be adequately representative, and may be site specific or from a nearby NWS station. Whereprofessional 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


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(including partial years), up to five years, of site specific data is available, these data are preferred for use in air quality analyses. Such data should have been subjected to quality assurance procedures as described in subsection 9.3.3.2.

c. For permitted sources whose emission limitations are based on a specific year of meteorological data, that year should be added to any longer period being used ( e.g. , 5 years of NWS data) when modeling the facility at a later time.

d. For LRT situations (subsection 7.2.3) and for complex wind situations (paragraph 8.2.8(a)), if only NWS or comparable standard meteorological observations are employed, five years of meteorological data (within and near the modeling domain) should be used. Consecutive years from the most recent, readily available 5-year period are preferred. Less than five, but at least three, years of meteorological data (need not be consecutive) may be used if mesoscale meteorological fields are available, as discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used in conjunction with available standard NWS or comparable meteorological observations within and near the modeling domain. If site specific meteorological data are available, these data may be especially helpful for local-scale complex wind situations, when appropriately blended together with standard NWS or comparable observations and mesoscale meteorological fields.

9.3.2 National Weather Service Data

9.3.2.1 Discussion

a. The NWS meteorological data are routinely available and familiar to most model users. Although the NWS does not provide direct measurements of all the needed dispersion model input variables, methods have been developed and successfully used to translate the basic NWS data to the needed model input. Site specific measurements of model input parameters have been made for many modeling studies, and those methods and techniques are becoming more widely applied, especially in situations such as complex terrain applications, where available NWS data are not adequately representative. However, there are many model applications where NWS data are adequately representative, and the applications still rely heavily on the NWS data.

b. Many models use the standard hourly weather observations available from the National Climatic Data Center (NCDC). These observations are then preprocessed before they can be used in the models.

9.3.2.2 Recommendations

a. The preferred models listed in Appendix A all accept as input the NWS meteorological data preprocessed into model compatible form. If NWS data are judged to be adequately representative for a particular modeling application, they may be used. NCDC makes available surface 97,98 and upper air 99 meteorological data in CD

–ROM format.

b. Although most NWS measurements are made at a standard height of 10 meters, the actual anemometer height should be used as input to the preferred model.

c. Wind directions observed by the National Weather Service are reported to the nearest 10 degrees. A specific set of randomly generated numbers has been developed for use with the preferred EPA models and should be used with NWS data to ensure a lack of bias in wind direction assignments within the models.

d. Data from universities, FAA, military stations, industry and pollution control agencies may be used if such data are equivalent in accuracy and detail to the NWS data, and they are judged to be adequately representative for the particular application. 9.3.3 Site Specific Data

9.3.3.1 Discussion

a. Spatial or geographical representativeness is best achieved by collection of all of the needed model input data in close proximity to the actual site of the source(s). Site specific measured data are therefore preferred as model input, provided that appropriate instrumentation and quality assurance procedures are followed and that the data collected are adequately representative (free from inappropriate local or microscale influences) and compatible with the input requirements of the model to be used. It should be noted that, while site specific measurements are frequently made

‘‘ on-property ’’ ( i.e. , on the source ’ s premises), acquisition of adequately representative site specific data does not preclude collection of data from a location off property. Conversely, collection of meteorological data on a

source ’ s property does not of itself guarantee adequate representativeness. For help in determining representativeness of site specific measurements, technical guidance 100 is available. Site specific data should always be reviewed for representativeness and consistency by a qualified meteorologist.

9.3.3.2 Recommendations

a. EPA guidance 100 provides recommendations on the collection and use of site specific meteorological data. Recommendations on characteristics, siting, and exposure of meteorological instruments and on data recording, processing, completeness requirements, reporting, and archiving are also included. This publication should be used as a supplement to other limited guidance on these subjects. 91,101,102 Detailed information on quality assurance is also available. 103 As a minimum, site specific measurements of ambient air temperature, transport wind speed and direction, and the variables necessary to estimate atmospheric dispersion should be available in meteorological data sets to be used in modeling. Care should be taken to ensure that meteorological instruments are located to provide representative characterization of pollutant transport between sources and receptors of interest. The appropriate reviewing authority (paragraph 3.0(b)) is available to help determine the appropriateness of the measurement locations.

b. All site specific data should be reduced to hourly averages. Table 9

– 3 lists the wind related parameters and the averaging time requirements.

c. Missing Data Substitution. After valid data retrieval requirements have been met 100

, hours in the record having missing data should be treated according to an established data substitution protocol provided that data from an adequately representative alternative site are available. Such protocols are usually part of the approved monitoring program plan. Data substitution guidance is provided in Section 5.3 of reference 100. If no representative alternative data are available for substitution, the absent data should be coded as missing using missing data codes appropriate to the applicable meteorological pre-processor. Appropriate model options for treating missing data, if available in the model, should be employed.

d. Solar Radiation Measurements. Total solar radiation or net radiation should be measured with a reliable pyranometer or net radiometer, sited and operated in accordance with established site specific meteorological guidance. 100,103

e. Temperature Measurements. Temperature measurements should be made at standard shelter height (2m) in accordance with established site specific meteorological guidance. 100

f. Temperature Difference Measurements. Temperature difference ( dT) measurements should be obtained using matched thermometers or a reliable thermocouple system to achieve adequate accuracy. Siting, probe placement, and operation of dT systems should be based on guidance found in Chapter 3 of reference 100, and such guidance should be followed when obtaining vertical temperature gradient data.

g. Winds Aloft. For simulation of plume rise and dispersion of a plume emitted from a stack, characterization of the wind profile up through the layer in which the plume disperses is required. This is especially important in complex terrain and/or complex wind situations where wind measurements at heights up to hundreds of meters above stack base may be required in some circumstances. For tall stacks when site specific data are needed, these winds have been obtained traditionally using meteorological sensors mounted on tall towers. A feasible alternative to tall towers is the use of meteorological remote sensing instruments ( e.g. , acoustic sounders or radar wind profilers) to provide winds aloft, coupled with 10-meter towers to provide the near-surface winds. (For specific requirements for CTDMPLUS, see Appendix A.) Specifications for wind measuring instruments and systems are contained in reference 100.

h. Turbulence. There are several dispersion models that are capable of using direct measurements of turbulence (wind fluctuations) in the characterization of the vertical and lateral dispersion ( e.g. , CTDMPLUS and CALPUFF). For specific requirements for CTDMPLUS and CALPUFF, see Appendix A. For technical guidance on measurement and processing of turbulence parameters, see reference 100. When turbulence data are used in this manner to directly characterize the vertical and lateral dispersion, the averaging time for the turbulence measurements should be one hour


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(Table 9

– 3). There are other dispersion models ( e.g. , BLP, and CALINE3) that employ
P

– G stability categories for the characterization of the vertical and lateral dispersion. Methods for using site specific turbulence data for the characterization of P

– G stability categories are discussed in reference 100. When turbulence data are used in this manner to determine the P

– G stability category, the averaging time for the turbulence measurements should be 15 minutes.

i. Stability Categories. For dispersion models that employ P

– G stability categories for the characterization of the vertical and lateral dispersion ( e.g. , ISC3), the P

– G stability categories, as originally defined, couple near-surface measurements of wind speed with subjectively determined insolation assessments based on hourly cloud cover and ceiling height observations. The wind speed measurements are made at or near 10m. The insolation rate is typically assessed using observations of cloud cover and ceiling height based on criteria outlined by Turner. 77 It is recommended that the P

– G stability category be estimated using the Turner method with site specific wind speed measured at or near 10m and representative cloud cover and ceiling height. Implementation of the Turner method, as well as considerations in determining representativeness of cloud cover and ceiling height in cases for which site specific cloud observations are unavailable, may be found in Section 6 of reference 100. In the absence of requisite data to implement the Turner method, the SRDT method or wind fluctuation statistics ( i.e. , the s E and s A methods) may be used.

j. The SRDT method, described in Section 6.4.4.2 of reference 100, is modified slightly from that published from earlier work 104 and has been evaluated with three site specific data bases. 105 The two methods of stability classification which use wind fluctuation statistics, the s E and s A methods, are also described in detail in Section 6.4.4 of reference 100 (note applicable tables in Section 6). For additional information on the wind fluctuation methods, several references are available. 106,107,108,109,

k. Meteorological Data Preprocessors. The following meteorological preprocessors are recommended by EPA: PCRAMMET, 110 MPRM, 111 METPRO, 112 and CALMET. 113 PCRAMMET is the recommended meteorological preprocessor for use in applications employing hourly NWS data. MPRM is a general purpose meteorological data preprocessor which supports regulatory models requiring PCRAMMET formatted (NWS) data. MPRM is available for use in applications employing site specific meteorological data. The latest version (MPRM 1.3) has been configured to implement the SRDT method for estimating
P

– G stability categories. METPRO is the required meteorological data preprocessor for use with CTDMPLUS. CALMET is available for use with applications of CALPUFF. All of the above mentioned data preprocessors are available for downloading from EPA ’ s Internet SCRAM Web site (subsection 2.3). 

### T ABLE 

### 9

– 3.

—A VERAGING 

### T IMES FOR 

### S ITE 

### S PECIFIC 

### W IND AND 

### T URBU



### LENCE 

### M EASUREMENTS

Parameter Averaging time (in hours)

Surface wind speed (for use in

stability determinations) ........ 1 Transport direction .................... 1 Dilution wind speed .................. 1 Turbulence measurements ( s E

and s A ) for use in stability determinations ....................... 1 1 Turbulence Measurements for

direct input to dispersion models ................................... 1

1 To minimize meander effects in s A whenwind conditions are light and/or variable, determine the hourly average s value from four sequential 15-minute s ’ s according to the following formula:



### σ σσσσ

1 15 2 15 2 15 2 15 2



### 4 -hr 

### = +++

9.3.4 Treatment of Near-calms and Calms

9.3.4.1 Discussion

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

9.3.4.2 Recommendations

a. Hourly concentrations calculated with steady-state Gaussian plume models using calms should not be considered valid; the wind and concentration estimates for these hours should be disregarded and considered to be missing. Critical concentrations for 3- , 8-, and 24-hour averages should be calculated by dividing the sum of the hourly concentrations for the period by the number of valid or non-missing hours. If the total number of valid hours is less than 18 for 24- hour averages, less than 6 for 8-hour averages or less than 3 for 3-hour averages, the total concentration should be divided by 18 for the 24-hour average, 6 for the 8-hour average and 3 for the 3-hour average. For annual averages, the sum of all valid hourly concentrations is divided by the number of non-calm hours during the year. For models listed in Appendix A, a post-processor computer program, CALMPRO 114 has been prepared, is available on the SCRAM Internet Web site (subsection 2.3), and should be used.

b. Stagnant conditions that include extended periods of calms often produce high concentrations over wide areas for relatively long averaging periods. The standard steady-state Gaussian plume models are often not applicable to such situations. When stagnation conditions are of concern, other modeling techniques should be considered on a case-by-case basis (see also subsection 8.2.8).

c. When used in steady-state Gaussian plume models, measured site specific wind speeds of less than l m/s but higher than the response threshold of the instrument should be input as l m/s; the corresponding wind direction should also be input. Wind observations below the response threshold of the instrument should be set to zero, with the input file in ASCII format. In all cases involving steady-state Gaussian plume models, calm hours should be treated as missing, and concentrations should be calculated as in paragraph (a) of this subsection.
10.0 Accuracy and Uncertainty of Models

10.1 Discussion

a. Increasing reliance has been placed on concentration estimates from models as the primary basis for regulatory decisions concerning source permits and emission control requirements. In many situations, such as review of a proposed source, no practical alternative exists. Therefore, there is an obvious need to know how accurate models really are and how any uncertainty in the estimates affects regulatory decisions. During the 1980 ’ s, attempts were made to encourage development of standardized evaluation methods. 16

, 115 EPA recognized the need for incorporating such information and has sponsored workshops 116 on model accuracy, the possible ways to quantify accuracy, and on considerations in the incorporation of model accuracy and uncertainty in the regulatory process. The Second (EPA) Conference on Air Quality Modeling, August 1982, 117 was devoted to that subject.

b. To better deduce the statistical significance of differences seen in model performance in the face of unaccounted for uncertainties and variations, investigators have more recently explored the use of bootstrap techniques. 118,119 Work is underway to develop a new generation of evaluation metrics 24 that takes into account the statistical differences (in error distributions) between model predictions and observations. 120 Even though the procedures and measures are still evolving to describe performance of models that characterize atmospheric fate, transport and diffusion 121, 122, 123 there has been general acceptance of a need to address the uncertainties inherent in atmospheric
processes. 10.1.1 Overview of Model Uncertainty

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


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‘‘ known ’’ conditions that are input to the models, e.g. , wind speed, mixed layer height, surface heat flux, emission characteristics, etc. However, in addition to the known conditions, there are unmeasured or unknown variations in the conditions of this event, e.g. , unresolved details of the atmospheric flow such as the turbulent velocity field. These unknown conditions, may vary among repetitions of the event. As a result, deviations in observed concentrations from their ensemble average, and from the concentrations estimated by the model, are likely to occur even though the known conditions are fixed. Even with a perfect model that predicts the correct ensemble average, there are likely to be deviations from the observed concentrations in individual repetitions of the event, due to variations in the unknown conditions. The statistics of these concentration residuals are termed ‘‘ inherent ’’ uncertainty. Available evidence suggests that this source of uncertainty alone may be responsible for a typical range of variation in concentrations of as much as ± 50 percent. 124

b. Moreover, there is ‘‘ reducible ’’ uncertainty 115 associated with the model and its input conditions; neither models nor data bases are perfect. Reducible uncertainties are caused by: (1) Uncertainties in the input values of the known conditions ( i.e.

, emission characteristics and meteorological data); (2) errors in the measured concentrations which are used to compute the concentration residuals; and (3) inadequate model physics and formulation. The ‘‘ reducible ’’ uncertainties can be minimized through better (more accurate and more representative) measurements and better model physics.

c. To use the terminology correctly, reference to model accuracy should be limited to that portion of reducible uncertainty which deals with the physics and the formulation of the model. The accuracy of the model is normally determined by an evaluation procedure which involves the comparison of model concentration estimates with measured air quality data. 125 The statement of accuracy is based on statistical tests or performance measures such as bias, noise, correlation, etc. 16 However, information that allows a distinction between contributions of the various elements of inherent and reducible uncertainty is only now beginning to emerge. 24 As a result most discussions of the accuracy of models make no quantitative distinction between (1) limitations of the model versus (2) limitations of the data base and of knowledge concerning atmospheric variability. The reader should be aware that statements on model accuracy and uncertainty may imply the need for improvements in model performance that even the ‘‘ perfect ’’ model could not satisfy. 10.1.2 Studies of Model Accuracy

a. A number of studies 126,127 have been conducted to examine model accuracy, particularly with respect to the reliability of short-term concentrations required for ambient standard and increment evaluations. The results of these studies are not surprising. Basically, they confirm what expert atmospheric scientists have said for some time: (1) Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and (2) the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of
± 10 to 40 percent are found to be typical 128,129

, i.e. , certainly well within the often quoted factor-of-two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable.

b. As noted above, poor correlations between paired concentrations at fixed stations may be due to ‘‘ reducible ’’ uncertainties in knowledge of the precise plume location and to unquantified inherent uncertainties. For example, Pasquill 130 estimates that, apart from data input errors, maximum ground-level concentrations at a given hour for a point source in flat terrain could be in error by 50 percent due to these uncertainties. Uncertainty of five to 10 degrees in the measured wind direction, which transports the plume, can result in concentration errors of 20 to 70 percent for a particular time and location, depending on stability and station location. Such uncertainties do not indicate that an estimated concentration does not occur, only that the precise time and locations are in doubt.10.1.3 Use of Uncertainty in DecisionMaking

a. The accuracy of model estimates varies with the model used, the type of application, and site specific characteristics. Thus, it is desirable to quantify the accuracy or uncertainty associated with concentration estimates used in decision-making. Communications between modelers and decision-makers must be fostered and further developed. Communications concerning concentration estimates currently exist in most cases, but the communications dealing with the accuracy of models and its meaning to the decision-maker are limited by the lack of a technical basis for quantifying and directly including uncertainty in decisions. Procedures for quantifying and interpreting uncertainty in the practical application of such concepts are only beginning to evolve; much study is still required. 115,116,117,131,132

b. In all applications of models an effort is encouraged to identify the reliability of the model estimates for that particular area and to determine the magnitude and sources of error associated with the use of the model. The analyst is responsible for recognizing and quantifying limitations in the accuracy, precision and sensitivity of the procedure. Information that might be useful to the decision-maker in recognizing the seriousness of potential air quality violations includes such model accuracy estimates as accuracy of peak predictions, bias, noise, correlation, frequency distribution, spatial extent of high concentration, etc. Both space/ time pairing of estimates and measurements and unpaired comparisons are recommended. Emphasis should be on the highest concentrations and the averaging times of the standards or increments of concern. Where possible, confidence intervals about the statistical values should be provided. However, while such information can be provided by the modeler to the decision-maker, it is unclear how this information should be used to make an air pollution control decision. Given a range of possible outcomes, it is easiest and tends to ensure consistency if the decision-maker confines his judgement to use of the ‘‘ best estimate ’’ provided by the modeler ( i.e. , the design concentration estimated by a model recommended in the Guideline or an alternate model of known accuracy). This is an indication of the practical limitations imposed by current abilities of the technical community.

c. To improve the basis for decisionmaking, EPA has developed and is continuing to study procedures for determining the accuracy of models, quantifying the uncertainty, and expressing confidence levels in decisions that are made concerning emissions controls. 133,134 However, work in this area involves

‘‘ breaking new ground ’’ with slow and sporadic progress likely. As a result, it may be necessary to continue using the ‘‘ best estimate ’’ until sufficient technical progress has been made to meaningfully implement such concepts dealing with uncertainty.

10.1.4 Evaluation of Models

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

b. EPA has participated in a series of conferences entitled, ‘‘ Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. ’’ 135 for the purpose of promoting the development of improved methods for the characterization of model performance. There is a consensus developing on what should be considered in the evaluation of air quality models 136

,


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namely quality assurance planning, documentation and scrutiny should be consistent with the intended use, and should include:

• Scientific peer review;

• Supportive analyses (diagnostic evaluations, code verification, sensitivity and uncertainty analyses);

• Diagnostic and performance evaluations with data obtained in trial locations, and

• Statistical performance evaluations in the circumstances of the intended applications.

Performance evaluations and diagnostic evaluations assess different qualities of how well a model is performing, and both are needed to establish credibility within the client and scientific community. Performance evaluations allow us to decide how well the model simulates the average temporal and spatial patterns seen in the observations, and employ large spatial/temporal scale data sets ( e.g. , national data sets). Performance evaluations also allow determination of relative performance of a model in comparison with alternative modeling systems. Diagnostic evaluations allow determination of a model capability to simulate individual processes that affect the results, and usually employ smaller spatial/ temporal scale date sets ( e.g. , field studies). Diagnostic evaluations allow us to decide if we get the right answer for the right reason. The objective comparison of modeled concentrations with observed field data provides only a partial means for assessing model performance. Due to the limited supply of evaluation data sets, there are severe practical limits in assessing model performance. For this reason, the conclusions reached in the science peer reviews and the supportive analyses have particular relevance in deciding whether a model will be useful for its intended purposes.

c. To extend information from diagnostic and performance evaluations, sensitivity and uncertainty analyses are encouraged since they can provide additional information on the effect of inaccuracies in the data bases and on the uncertainty in model estimates. Sensitivity analyses can aid in determining the effect of inaccuracies of variations or uncertainties in the data bases on the range of likely concentrations. Uncertainty analyses can aid in determining the range of likely concentration values, resulting from uncertainties in the model inputs, the model formulations, and parameterizations. Such information may be used to determine source impact and to evaluate control strategies. Where possible, information from such sensitivity analyses should be made available to the decision-maker with an appropriate interpretation of the effect on the critical concentrations.

10.2 Recommendations

a. No specific guidance on the quantification of model uncertainty for use in decision-making is being given at this time. As procedures for considering uncertainty develop and become implementable, this guidance will be changed and expanded. For the present, continued use of the ‘‘ best estimate ’’ is acceptable; however, in specific circumstances for O 3, PM

– 2.5 and regional haze, additional information and/or procedures may be appropriate. 41,42 11.0 Regulatory Application of Models

11.1 Discussion

a. Procedures with respect to the review and analysis of air quality modeling and data analyses in support of SIP revisions, PSD permitting or other regulatory requirements need a certain amount of standardization to ensure consistency in the depth and comprehensiveness of both the review and the analysis itself. This section recommends procedures that permit some degree of standardization while at the same time allowing the flexibility needed to assure the technically best analysis for each regulatory application.

b. Dispersion model estimates, especially with the support of measured air quality data, are the preferred basis for air quality demonstrations. Nevertheless, there are instances where the performance of recommended dispersion modeling techniques, by comparison with observed air quality data, may be shown to be less than acceptable. Also, there may be no recommended modeling procedure suitable for the situation. In these instances, emission limitations may be established solely on the basis of observed air quality data as would be applied to a modeling analysis. The same care should be given to the analyses of the air quality data as would be applied to a modeling analysis.

c. The current NAAQS for SO 2 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 NO 2 and a quarterly standard for Pb. Standards for fine particulate matter (PM

– 2.5) are expressed interms of both long-term (annual) and shortterm (daily) averages. The long-term standard is calculated using the three year average of the annual averages while the short-term standard is calculated using the three year average of the 98th percentile of the daily average concentration. For PM

– 10, the convention is to compare the arithmetic mean, averaged over 3 consecutive years, with the concentration specified in the NAAQS (50 μ g/m 3 ). The 24-hour NAAQS (150 μ g/m 3 ) is met if, over a 3-year period, there is (on average) no more than one exceedance per year. For ozone the short term 1-hour standard is expressed in terms of an expected exceedance limit while the short term 8-hour standard is expressed in terms of a three year average of the annual fourth highest daily maximum 8-hour value. The NAAQS are subjected to extensive review and possible revision every 5 years.

d. This section discusses general requirements for concentration estimates and identifies the relationship to emission limits. The following recommendations apply to: (1) Revisions of State Implementation Plans and (2) the review of new sources and the prevention of significant deterioration (PSD).

11.2 Recommendations 11.2.1 Analysis Requirements

a. Every effort should be made by the Regional Office to meet with all parties involved in either a SIP revision or a PSD permit application prior to the start of any work on such a project. During this meeting, a protocol should be established between the preparing and reviewing parties to define the procedures to be followed, the data to be collected, the model to be used, and the analysis of the source and concentration data. An example of requirements for such an effort is contained in the Air Quality Analysis Checklist posted on EPA ’ s Internet SCRAM Web site (subsection 2.3). This checklist suggests the level of detail required to assess the air quality resulting from the proposed action. Special cases may require additional data collection or analysis and this should be determined and agreed upon at this preapplication meeting. The protocol should be written and agreed upon by the parties concerned, although a formal legal document is not intended. Changes in such a protocol are often required as the data collection and analysis progresses. However, the protocol establishes a common understanding of the requirements.

b. An air quality analysis should begin with a screening model to determine the potential of the proposed source or control strategy to violate the PSD increment or NAAQS. For traditional stationary sources, EPA guidance 27 should be followed. Guidance is also available for mobile

sources. 57

c. If the concentration estimates from screening techniques indicate that the PSD increment or NAAQS may be approached or exceeded, then a more refined modeling analysis is appropriate and the model user should select a model according to recommendations in Sections 4

– 8. In some instances, no refined technique may be specified in this guide for the situation. The model user is then encouraged to submit a model developed specifically for the case at hand. If that is not possible, a screening technique may supply the needed results.

d. Regional Offices should require permit applicants to incorporate the pollutant contributions of all sources into their analysis. Where necessary this may include emissions associated with growth in the area of impact of the new or modified source. PSD air quality assessments should consider the amount of the allowable air quality increment that has already been consumed by other sources. Therefore, the most recent source applicant should model the existing or permitted sources in addition to the one currently under consideration. This would permit the use of newly acquired data or improved modeling techniques if such have become available since the last source was permitted. When remodeling, the worst case used in the previous modeling analysis should be one set of conditions modeled in the new analysis. All sources should be modeled for each set of meteorological conditions selected.

11.2.2 Use of Measured Data in Lieu of Model Estimates

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


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c The documents listed here are major sources of supplemental infomation on the theory and application of mathematical air quality models. available is only a screening technique, the addition of air quality data to the analysis may lend credence to model results.

b. There are circumstances where there is no applicable model, and measured data may need to be used. However, only in the case of an existing source should monitoring data alone be a basis for emission limits. In addition, the following items (i

–vi) should be considered prior to the acceptance of the measured data:

i. Does a monitoring network exist for the pollutants and averaging times of concern?

ii. Has the monitoring network been designed to locate points of maximum concentration?

iii. Do the monitoring network and the data reduction and storage procedures meet EPA monitoring and quality assurance requirements?

iv. Do the data set and the analysis allow impact of the most important individual sources to be identified if more than one source or emission point is involved?

v. Is at least one full year of valid ambient data available?

vi. Can it be demonstrated through the comparison of monitored data with model results that available models are not applicable?

c. The number of monitors required is a function of the problem being considered. The source configuration, terrain configuration, and meteorological variations all have an impact on number and placement of monitors. Decisions can only be made on a case-by-case basis. Guidance is available for establishing criteria for demonstrating that a model is not applicable. 22

d. Sources should obtain approval from the appropriate reviewing authority (paragraph 3.0(b)) for the monitoring network prior to the start of monitoring. A monitoring protocol agreed to by all concerned parties is highly desirable. The design of the network, the number, type and location of the monitors, the sampling period, averaging time as well as the need for meteorological monitoring or the use of mobile sampling or plume tracking techniques, should all be specified in the protocol and agreed upon prior to start-up of the network.

11.2.3 Emission Limits
11.2.3.1 Design Concentrations

a. Emission limits should be based on concentration estimates for the averaging time that results in the most stringent control requirements. The concentration used in specifying emission limits is called the design value or design concentration and is a sum of the concentration contributed by the source and the background concentration.

b. To determine the averaging time for the design value, the most restrictive NAAQS should be identified by calculating, for each averaging time, the ratio of the difference between the applicable NAAQS (S) and the background concentration (B) to the (model) predicted concentration (P) ( i.e. , (S

– B)/P). The averaging time with the lowest ratio identifies the most restrictive standard. If the annual average is the most restrictive, the highest estimated annual average concentration from one or a number of years of data is the design value. When short term standards are most restrictive, it may be necessary to consider a broader range of concentrations than the highest value. For example, for pollutants such as SO 2 , the highest, second-highest concentration is the design value. For pollutants with statistically based NAAQS, the design value is found by determining the more restrictive of: (1) The short-term concentration over the period specified in the standard, or (2) the long-term concentration that is not expected to exceed the long-term NAAQS. Determination of design values for PM

– 10 is presented in more detail in EPA guidance. 43 11.2.3.2 NAAQS Analyses for New or Modified Sources

a. For new or modified sources predicted to have a significant ambient impact 91 and to be located in areas designated attainment or unclassifiable for the SO 2 , Pb, NO 2 , or CO NAAQS, the demonstration as to whether the source will cause or contribute to an air quality violation should be based on: (1) The highest estimated annual average concentration determined from annual averages of individual years; or (2) the highest, second-highest estimated concentration for averaging times of 24-hours or less; and (3) the significance of the spatial and temporal contribution to any modeled violation. For Pb, the highest estimated concentration based on an individual calendar quarter averaging period should be used. Background concentrations should be added to the estimated impact of the source. The most restrictive standard should be used in all cases to assess the threat of an air quality violation. For new or modified sources predicted to have a significant ambient impact 91 in areas designated attainment or unclassifiable for the PM

– 10 NAAQS, the demonstration of whether or not the source will cause or contribute to an air quality violation should be based on sufficient data to show whether: (1) The projected 24-hour average concentrations will exceed the 24-hour NAAQS more than 1 percent of the time, on average ; (2) the expected ( i.e. , average) annual mean concentration will exceed the annual NAAQS; and (3) the source contributes significantly, in a temporal and spatial sense, to any modeled violation. 11.2.3.3 PSD Air Quality Increments and Impacts

a. The allowable PSD increments for criteria pollutants are established by regulation and cited in 40 CFR 51.166. These maximum allowable increases in pollutant concentrations may be exceeded once per year at each site, except for the annual increment that may not be exceeded. The highest, second-highest increase in estimated concentrations for the short term averages as determined by a model should be less than or equal to the permitted increment. The modeled annual averages should not exceed the increment.

b. Screening techniques defined in subsection 4.1 can sometimes be used to estimate short term incremental concentrations for the first new source that triggers the baseline in a given area.However, when multiple incrementconsuming sources are involved in the calculation, the use of a refined model with at least 1 year of site specific or 5 years of (off-site) NWS data is normally required (subsection 9.3.1.2). In such cases, sequential modeling must demonstrate that the allowable increments are not exceeded temporally and spatially, i.e. , for all receptors for each time period throughout the year(s) (time period means the appropriate PSD averaging time, e.g. , 3-hour, 24-hour, etc.).

c. The PSD regulations require an estimation of the SO 2 , particulate matter (PM

– 10), and NO 2 impact on any Class I area. Normally, steady-state Gaussian plume models should not be applied at distances greater than can be accommodated by the steady state assumptions inherent in such models. The maximum distance for refined steady-state Gaussian plume model application for regulatory purposes is generally considered to be 50km. Beyond the 50km range, screening techniques may be used to determine if more refined modeling is needed. If refined models are needed, long range transport models should be considered in accordance with subsection 7.2.3. As previously noted in Sections 3 and 7, the need to involve the Federal Land Manager in decisions on potential air quality impacts, particularly in relation to PSD Class I areas, cannot be overemphasized.
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72. Lawson, Jr., R.E. and W.H. Snyder, 1983. Determination of Good Engineering Practice Stack Height: A Demonstration Study for a Power Plant. EPA Publication No. EPA

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73. Environmental Protection Agency, 1985. Guideline for Determination of Good Engineering Practice Stack Height (Technical Support Document for the Stack Height Regulations), Revised. EPA Publication No. EPA

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74. Snyder, W.H. and R.E. Lawson, Jr., 1985. Fluid Modeling Demonstration of Good Engineering-Practice Stack Height in Complex Terrain. EPA Publication No. EPA

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75. Bennett, M.J., M.E. Yansura, I.G. Hornyik, J.M. Nall, D.G. Caniparoli and C.G. Ashmore, 2002. Evaluation of the CALPUFF Long-range Transport Screening Technique by Comparison to Refined CALPUFF Results for Several Power Plants in Both the Eastern and Western United States. Proceedings of the Air & Waste Management Association ’ s 95th Annual Conference, June 23

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77. Turner, D.B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999

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88. Environmental Protection Agency, 1988. User ’ s Guide to SDM

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91. Environmental Protection Agency, 1987. Ambient Air Monitoring Guidelines for Prevention of Significant Deterioration (PSD). EPA Publication No. EPA

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95. Landsberg, H.E. and W.C. Jacobs, 1951. Compendium of Meteorology. American Meteorological Society, Boston, MA; pp.
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97. Solar and Meteorological Surface Observation Network, 1961

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## 18473 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

Center and National Renewable Energy Laboratory. Can be ordered from NOAA National Data Center ’ s Internet Web site @ http://www.nndc.noaa.gov/.

98. Hourly United States Weather Observations, 1990

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99. Radiosonde Data of North America,
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100. Environmental Protection Agency, 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications. EPA Publication No. EPA

– 454/R

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– 103606) ( http://www.epa.gov/scram001/ ) 101. ASTM D5527: Standard Practice for Measuring Surface Winds and Temperature by Acoustic Means. (1994)

102. ASTM D5741: Standard Practice for Characterizing Surface Wind Using Wind Vane and Rotating Anemometer. (1996)

103. Environmental Protection Agency, 1995. Quality Assurance for Air Pollution Measurement Systems, Volume IV

— Meteorological Measurements. EPA Publication No. EPA600/R

– 94/038d. Office of Air Quality Planning & Standards, Research Triangle Park, NC. Note: for copies of this handbook, you may make inquiry to ORD Publications, 26 West Martin Luther King Dr., Cincinatti, OH 45268. Phone (513) 569

– 7562 or (800) 490

– 9198 (automated request line)

104. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability Class Determination: A Comparison for One Site. Proceedings, Sixth Symposium on Turbulence and Diffusion. American Meteorological Society, Boston, MA; pp. 211

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105. Environmental Protection Agency, 1993. An Evaluation of a Solar Radiation/ Delta

– T (SRDT) Method for Estimating Pasquill-Gifford (P

– G) Stability Categories. EPA Publication No. EPA

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– 055. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB
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– 113958)

106. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8: Estimation of Pasquill Stability Categories. Office of Air Quality Planning & Standards, Research Triangle Park, NC (Docket No. A

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107. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric Stability Class from Horizontal Wind Fluctuation. Presented at 72nd Annual Meeting of Air Pollution Control Association, Cincinnati, OH; June
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108. Smedman

—Hogstrom, A. and V. Hogstrom, 1978. A Practical Method for Determining Wind Frequency Distributions for the Lowest 200m from Routine Meteorological Data. J. of Applied Meteorology, 17(7): 942

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109. Smith, T.B. and S.M. Howard, 1972. Methodology for Treating Diffusivity. MRI 72 FR

– 1030. Meteorology Research, Inc., Altadena, CA. (Docket No. A

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110. Environmental Protection Agency, 1993. PCRAMMET User ’ s Guide. EPA Publication No. EPA

– 454/R

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– 147912)

111. Environmental Protection Agency, 1996. Meteorological Processor for Regulatory Models (MPRM) User ’ s Guide. EPA Publication No. EPA

– 454/B

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– 002. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB
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– 180518)

112. Paine, R.J., 1987. User ’ s Guide to the CTDM Meteorological Preprocessor Program. EPA Publication No. EPA

– 600/8

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– 004. Office of Research & Development, Research Triangle Park, NC. (NTIS No. PB 88

– 162102)

113. Scire, J.S., F.R. Francoise, M.E. Fernau and R.J. Yamartino, 1998. A User ’ s Guide for the CALMET Meteorological Model (Version 5.0). Earth Tech, Inc., Concord, MA. ( http:/ /www.src.com/calpuff/calpuff1.htm )

114. Environmental Protection Agency, 1984. Calms Processor (CALMPRO) User ’ s Guide. EPA Publication No. EPA

– 901/9

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– 001. Office of Air Quality Planning & Standards, Region I, Boston, MA. (NTIS No. PB 84

– 229467)

115. Fox, D.G., 1984. Uncertainty in air quality modeling. Bulletin of the American Meteorological Society, 65(1): 27

– 36.

116. Burton, C.S., 1981. The Role ofAtmospheric Models in Regulatory DecisionMaking: Summary Report. Systems Applications, Inc., San Rafael, CA. Prepared under contract No. 68

– 01

– 5845 for U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A

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117. Environmental Protection Agency, 1981. Proceedings of the Second Conference on Air Quality Modeling, Washington, DC. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (Docket No. A

– 80

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–M

– 16)

118. Hanna, S.R., 1989. Confidence limits for air quality model evaluations, as estimated by bootstrap and jackknife resampling methods. Atmospheric Environment, 23(6): 1385

– 1398.

119. Cox, W.M. and J.A. Tikvart, 1990. A statistical procedure for determining the best performing air quality simulation model. Atmos. Environ., 24A(9): 2387

– 2395.

120. Oreskes, N.K., K. Shrader-Frechette and K. Beliz, 1994. Verification, validation and confirmation of numerical models in the earth sciences. Science, 263: 641

– 646.

121. Dekker, C.M., A. Groenendijk, C.J. Sliggers and G.K. Verboom, 1990. Quality Criteria for Models to Calculate Air Pollution. Lucht (Air) 90, Ministry of Housing, Physical Planning and Environment, Postbus 450, 2260 MB Leidschendam, The Netherlands; 52pp.

122. Weil, J.C., R.I. Sykes and A. Venkatram, 1992. Evaluating air-quality models: review and outlook. Journal of Applied Meteorology, 31: 1121

– 1145.

123. Cole, S.T. and P.J. Wicks, Editors (1995): Model Evaluation Group: Report of the Second Open Meeting. EUR 15990 EN, European Commission, Directorate-General XII, Environmental Research Programme, L

– 2920 Luxembourg; 77pp.

124. Hanna, S.R., 1982. Natural Variability of Observed Hourly SO 2 and CO Concentrations in St. Louis. Atmospheric Environment, 16(6): 1435

– 1440.

125. Bowne, N.E., 1981. Validation and Performance Criteria for Air Quality Models. Appendix F in Air Quality Modeling and the Clean Air Act: Recommendations to EPA on Dispersion Modeling for Regulatory Applications. American Meteorological Society, Boston, MA; pp. 159

– 171. (Docket No. A

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126. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, and Conclusions for the EPRI Plume Model Validation and Development Project: Plains Site. EPRI EA

– 3074. Electric Power Research Institute, Palo Alto, CA.

127. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models. EPA Publication No. EPA

– 450/4

– 83

– 001. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB
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– 260810)

128. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff Report. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (Docket No. A

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129. Hanna, S.R., 1993. Uncertainties in air quality model predictions. Boundary-Layer Meteorology, 62: 3

– 20.

130. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John Wiley and Sons, New York, NY; 479pp.

131. Morgan, M.G. and M. Henrion, 1990. Uncertainty, A Guide to Dealing With Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press. New York, NY; 332pp.

132. Irwin, J.S., K. Steinberg, C. Hakkarinen and H. Feldman, 2001. Uncertainty in Air Quality Modeling for Risk Calculations. (CD

–ROM) Proceedings of Guideline on Air Quality Models: A New Beginning. April 4

– 6, 2001, Newport, RI, Air & Waste Management Association. Pittsburgh, PA; 17pp.

133. Austin, B.S., T.E. Stoeckenius, M.C. Dudik and T.S. Stocking, 1988. User ’ s Guide to the Expected Exceedances System. Systems Applications, Inc., San Rafael, CA. Prepared under Contract No. 68

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134. Thrall, A.D., T.E. Stoeckenius and C.S. Burton, 1985. A Method for Calculating Dispersion Modeling Uncertainty Applied to the Regulation of an Emission Source. Systems Applications, Inc., San Rafael, CA. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A

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135. ‘‘ Ten years of Harmonisation activities: Past, present and future ’’ at http:/ /www.dmu.dk/AtmosphericEnvironment/ Harmoni/Conferences/Belgirate/ BelgiratePapers.asp

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

Appendix A to Appendix W of Part
51

— Summaries of Preferred Air Quality Models

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

(1) This appendix summarizes key features of refined air quality models preferred for specific regulatory applications. For each model, information is provided on availability, approximate cost (where applicable), regulatory use, data input, output format and options, simulation of atmospheric physics, and accuracy. These models may be used without a formal demonstration of applicability provided they satisfy the recommendations for regulatory use; not all options in the models are necessarily recommended for regulatory use.

(2) Many of these models have been subjected to a performance evaluation using comparisons with observed air quality data. Where possible, several of the models contained herein have been subjected to evaluation exercises, including (1) statistical performance tests recommended by the American Meteorological Society and (2) peer scientific reviews. The models in this appendix have been selected on the basis of the results of the model evaluations, experience with previous use, familiarity of the model to various air quality programs, and the costs and resource requirements for

use.

(3) With the exception of EDMS, codes and documentation for all models listed in this appendix are available from EPA ’ s Support Center for Regulatory Air Models (SCRAM) Web site at http://www.epa.gov/scram001. Documentation is also available from the National Technical Information Service (NTIS), http://www.ntis.gov or U.S. Department of Commerce, Springfield, VA 22161; phone: (800) 553

– 6847. Where possible, accession numbers are provided.
A.1 Buoyant Line and Point Source Dispersion Model (BLP)
Reference

Schulman, Lloyd L. and Joseph S. Scire, 1980. Buoyant Line and Point Source (BLP) Dispersion Model User ’ s Guide. Document P

– 7304B. Environmental Research and Technology, Inc., Concord, MA. (NTIS No. PB 81

– 164642)
Availability

The computer code is available on EPA ’ s Internet SCRAM website and also on diskette (as PB 2002

– 500051) from the National Technical Information Service (see Section A.0).

Abstract

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

(1) The BLP model is appropriate for the following applications:

• Aluminum reduction plants which contain buoyant, elevated line sources;

• Rural areas;

• Transport distances less than 50 kilometers;

• Simple terrain; and

• One hour to one year averaging times. (2) The following options should be selected for regulatory applications:

(i) Rural (IRU=1) mixing height option; (ii) Default (no selection) for plume rise wind shear (LSHEAR), transitional point source plume rise (LTRANS), vertical potential temperature gradient (DTHTA), vertical wind speed power law profile exponents (PEXP), maximum variation in number of stability classes per hour (IDELS), pollutant decay (DECFAC), the constant in Briggs ’ stable plume rise equation (CONST2), constant in Briggs ’ neutral plume rise equation (CONST3), convergence criterion for the line source calculations (CRIT), and maximum iterations allowed for line source calculations (MAXIT); and

(iii) Terrain option (TERAN) set equal to 0.0, 0.0, 0.0, 0.0, 0.0, 0.0

(3) For other applications, BLP can be used if it can be demonstrated to give the same estimates as a recommended model for the same application, and will subsequently be executed in that mode.

(4) BLP can be used on a case-by-case basis with specific options not available in a recommended model if it can be demonstrated, using the criteria in Section 3.2, that the model is more appropriate for a specific application. b. Input Requirements

(1) Source data: point sources require stack location, elevation of stack base, physical stack height, stack inside diameter, stack gas exit velocity, stack gas exit temperature, and pollutant emission rate. Line sources require coordinates of the end points of the line, release height, emission rate, average line source width, average building width, average spacing between buildings, and average line source buoyancy parameter.

(2) Meteorological data: Hourly surface weather data from punched cards or from the preprocessor program PCRAMMET which provides hourly stability class, wind direction, wind speed, temperature, and mixing height.

(3) Receptor data: Locations and elevations of receptors, or location and size of receptor grid or request automatically generated receptor grid. c. Output

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

(3) Five highest 1-, 3-, and 24-hour average concentrations at each receptor; and

(4) Fifty highest 1-, 3-, and 24-hour concentrations over the receptor field. d. Type of Model

BLP is a gaussian plume model. e. Pollutant Types

BLP may be used to model primary pollutants. This model does not treat settling and deposition. f. Source-Receptor Relationship

(1) BLP treats up to 50 point sources, 10 parallel line sources, and 100 receptors arbitrarily located.

(2) User-input topographic elevation is applied for each stack and each receptor. g. Plume Behavior

(1) BLP uses plume rise formulas of Schulman and Scire (1980).

(2) Vertical potential temperature gradients of 0.02 Kelvin per meter for E stability and 0.035 Kelvin per meter are used for stable plume rise calculations. An option for user input values is included.

(3) Transitional rise is used for line

sources.

(4) Option to suppress the use of transitional plume rise for point sources is included.

(5) The building downwash algorithm of Schulman and Scire (1980) is used.

h. Horizontal Winds

(1) Constant, uniform (steady-state) wind is assumed for an hour.

Straight line plume transport is assumed to all downwind distances.

(2) Wind speeds profile exponents of 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 are used for stability classes A through F, respectively. An option for user

— defined values and an option to suppress the use of the wind speed profile feature are included. i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero. j. Horizontal Dispersion

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

(2) Six stability classes are used. k. Vertical Dispersion

(1) Rural dispersion coefficients are from Turner (1969), with no adjustment made for variations in surface roughness.

(2) Six stability classes are used. (3) Mixing height is accounted for with multiple reflections until the vertical plume standard deviation equals 1.6 times the mixing height; uniform mixing is assumed beyond that point.

(4) Perfect reflection at the ground is assumed.


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

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 SF 6 Tracer Data and SO 2 Measurements at Aluminum Reduction Plants. APCA Specialty Conference on Dispersion Modeling for Complex Sources, St. Louis, MO.

A.2 CALINE3 Reference

Benson, Paul E, 1979. CALINE3

—A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL

– 79/23. Federal Highway Administration, Washington, DC. (NTIS No. PB 80

– 220841) Availability

The CALINE3 model is available on diskette (as PB 95

– 502712) from NTIS. The source code and user ’ s guide are also available on EPA ’ s Internet SCRAM Web site ( Section A.0).

Abstract

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

CALINE

– 3 is appropriate for the following applications:

• Highway (line) sources;

• Urban or rural areas;

• Simple terrain;

• Transport distances less than 50 kilometers; and

• One-hour to 24-hour averaging times. b. Input Requirements

(1) Source data: Up to 20 highway links classed as ‘‘ at-grade, ’’ ‘‘ fill ’’ ‘‘ bridge, ’’ or

‘‘ depressed ’’ ; coordinates of link end points; traffic volume; emission factor; source height; and mixing zone width.

(2) Meteorological data: Wind speed, wind angle (measured in degrees clockwise from the Y axis), stability class, mixing height, ambient (background to the highway) concentration of pollutant. (3) Receptor data: Coordinates and height above ground for each receptor. c. Output

Printed output includes concentration at each receptor for the specified meteorological condition. d. Type of Model

CALINE

– 3 is a Gaussian plume model. e. Pollutant Types

CALINE

– 3 may be used to model primary pollutants. f. Source-Receptor Relationship

(1) Up to 20 highway links are treated. (2) CALINE

– 3 applies user input location and emission rate for each link. User-input receptor locations are applied. g. Plume Behavior

Plume rise is not treated.

h. Horizontal Winds

(1) User-input hourly wind speed and direction are applied.

(2) Constant, uniform (steady-state) wind is assumed for an hour. i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero. j. Horizontal Dispersion

(1) Six stability classes are used. (2) Rural dispersion coefficients from Turner (1969) are used, with adjustment for roughness length and averaging time.

(3) Initial traffic-induced dispersion is handled implicitly by plume size parameters. k. Vertical Dispersion

(1) Six stability classes are used. (2) Empirical dispersion coefficients from Benson (1979) are used including an adjustment for roughness length.

(3) Initial traffic-induced dispersion is handled implicitly by plume size parameters.

(4) Adjustment for averaging time is included.

l. Chemical Transformation

Not treated. m. Physical Removal

Optional deposition calculations are included.

n. Evaluation Studies

Bemis, G.R. et al. , 1977. Air Pollution and Roadway Location, Design, and Operation

— Project Overview. FHWA

– CA

– TL

– 7080

– 77

– 25, Federal Highway Administration, Washington, D.C.

Cadle, S.H. et al. , 1976. Results of the General Motors Sulfate Dispersion Experiment, GMR

– 2107. General Motors Research Laboratories, Warren, MI.

Dabberdt, W.F., 1975. Studies of Air Quality on and Near Highways, Project 2761. Stanford Research Institute, Menlo Park, CA.

A.3 CALPUFF References

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

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

– 4200, Fax: (978) 371

– 2468, e-mail: jss@src.com.

Abstract

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

(1) CALPUFF is appropriate for long range transport (source-receptor distances of 50 to several hundred kilometers) of emissions from point, volume, area, and line sources. The meteorological input data should befully characterized with time-and-spacevarying three dimensional wind and meteorological conditions using CALMET, as discussed in paragraphs 9.3(c) and 9.3.1.2(d) of Appendix W.

(2) CALPUFF may also be used on a caseby-case basis if it can be demonstrated using the criteria in Section 3.2 that the model is more appropriate for the specific application. The purpose of choosing a modeling system like CALPUFF is to fully treat stagnation, wind reversals, and time and space variations of meteorology effects on transport and dispersion, as discussed in paragraph 8.2.8(a).

(3) For regulatory applications of CALMET and CALPUFF, the regulatory default option should be used. Inevitably, some of the model control options will have to be set specific for the application using expert judgement and in consultation with the relevant reviewing authorities. b. Input Requirements

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


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

emission factors) may also be entered. Arbitrarily-varying point source parameters may be entered from an external file.

2. Area sources: Source location and shape, release height, base elevation, initial vertical distribution ( s z ) and emission rates for each pollutant. Particle size distributions may be entered for particulate matter. Temporal emission factors (diurnal cycle, monthly cycle, hour/season, wind speed/stability class, or temperature-dependent emissionfactors) 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 ( s y , s z ) and emission rates for each pollutant. Particle size distributions may be entered for particulate matter. Temporal emission factors (diurnal cycle, monthly cycle, hour/season, windspeed/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 ( s y , s z )

—to define the initial size of the volume source.

4. Line sources: Source location, release height, base elevation, average buoyancy parameter, and emission rates for each pollutant. Building data may be entered for line source emissions experiencing building downwash effects. Particle size distributions may be entered for particulate matter. Temporal emission factors (diurnal cycle, monthly cycle, hour/season, wind speed/ stability class, or temperature-dependent emission factors) may also be entered. Arbitrarily-varying line source parameters may be entered from an external file.

Meteorological Data (different forms of meteorological input can be used by CALPUFF):

1. Time-dependent three-dimensional meteorological fields generated by CALMET. This is the preferred mode for running CALPUFF. Inputs into CALMET include surface observations of wind speed, wind direction, temperature, cloud cover, ceiling height, relative humidity, surface pressure, and precipitation (type and amount), and upper air sounding data (wind speed, wind direction, temperature, and height). Optional large-scale model output ( e.g. , from MM5) can be used by CALMET as well (paragraph 9.3.1.2(d)).

2. Single station surface and upper air meteorological data in CTDMPLUS data file formats (SURFACE.DAT and PROFILE.DAT files). This allows a vertical variation in the meteorological parameters but no spatial variability.

3. Single station meteorological data in ISCST3 data file format. This option does not account for variability of the meteorological parameters in the horizontal or vertical,except as provided for by the use of stabilitydependent wind shear exponents and average temperature lapse rates. Gridded terrain and land use data are required as input into CALMET when Option 1 is used. Geophysical processor programs are provided that interface the modeling system to standard terrain and land use data bases provided by the U.S. Geological Survey (USGS).

Receptor Data: CALPUFF includes options for gridded and non-gridded (discrete) receptors. Special subgrid-scale receptors are used with the subgrid-scale complex terrain option. An option is provided for discrete receptors to be placed at ground-level or above the local ground level ( i.e. , flagpole receptors). Gridded and subgrid-scale receptors are placed at the local ground level only.

Other Input: CALPUFF accepts hourly observations of ozone concentrations for use in its chemical transformation algorithm. Subgrid-scale coastlines can be specified in its coastal boundary file. Optional, user-specified deposition velocities and chemical transformation rates can also be entered. CALPUFF accepts the CTDMPLUS terrain and receptor files for use in its subgrid-scale terrain algorithm. Inflow boundary conditions of modeled pollutants can be specified in a boundary condition file. c. Output

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

(1) CALPUFF is a non-steady-state timeand space-dependent Gaussian puff model. CALPUFF includes parameterized gas phase chemical transformation of SO 2 , SO 4 = , NO, NO 2 , HNO 3 , NO 3 - , and organic aerosols. CALPUFF can treat primary pollutants such as PM

– 10, toxic pollutants, ammonia, and other passive pollutants. The model includes a resistance-based dry deposition model for both gaseous pollutants and particulate matter. Wet deposition is treated using a scavenging coefficient approach. The model has detailed parameterizations of complex terrain effects, including terrainimpingement, side-wall scrapping, and steepwalled terrain influences on lateral plume growth. A subgrid-scale complex terrain module based on a dividing streamline concept divides the flow into a lift component traveling over the obstacle and a wrap component deflected around the obstacle.

(2) The meteorological fields used by CALPUFF are produced by the CALMET meteorological model. CALMET includes a diagnostic wind field model containing objective analysis and parameterized treatments of slope flows, valley flows, terrain blocking effects, and kinematic terrain effects, lake and sea breeze circulations, and a divergence minimization procedure. An energy-balance scheme is used to compute sensible and latent heat fluxes and turbulence parameters over land surfaces. A profile method is used over water. CALMET contains interfaces to prognostic meteorological models such as the Penn State/NCAR Mesoscale Model ( e.g. , MM5; Section 13.0, ref. 94), as well as the RAMS and Eta models. e. Pollutant Types

CALPUFF may be used to model gaseous pollutants or particulate matter that are inert or undergo linear chemical reactions, such as SO 2 , SO 4 = , NO, NO 2 , HNO 3 , NO 3 - , NH 3 , PM

– 10, and toxic pollutants. For regional haze analyses, sulfate and nitrate particulate components are explicitly treated. f. Source-Receptor Relationships

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

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

h. Horizontal Winds

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

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

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

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


## 18477 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

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 SO 2 conversion to SO 4 = and NO conversion to NO 2 , HNO 3 , and SO 4 = . Organic aerosol formation is treated. m. Physical Removal

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

n. Evaluation Studies

Berman, S., J.Y. Ku, J. Zhang and S.T. Rao, 1977: Uncertainties in estimating the mixing depth

— Comparing three mixing depth models with profiler measurements, Atmospheric Environment, 31: 3023

– 3039.

Environmental Protection Agency, 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Reportand Recommendations for Modeling LongRange Transport Impacts. EPA Publication No. EPA

– 454/R

– 98

– 019. Office of Air Quality Planning & Standards, Research Triangle Park, NC.

Irwin, J.S. 1997. A Comparison of CALPUFF Modeling Results with 1997 INEL Field Data Results. In Air Pollution Modeling and its Application, XII. Edited by S.E. Gyrning and N. Chaumerliac. Plenum Press, New York, NY.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996. A Comparison of CALPUFF Modeling Results with CAPTEX Field Data Results. In Air Pollution Modeling and its Application, XI. Edited by S.E. Gyrning and F.A. Schiermeier. Plenum Press, New York, NY.

Strimaitis, D.G., J.S. Scire and J.C. Chang. 1998. Evaluation of the CALPUFF Dispersion Model with Two Power Plant Data Sets. Tenth Joint Conference on the Application of Air Pollution Meteorology, Phoenix, Arizona. American Meteorological Society, Boston, MA. January 11

– 16, 1998. A.4 Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS)
Reference

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User ’ s Guide to the Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and User Instructions. EPA Publication No. EPA

– 600/8

– 89

– 041. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 89

– 181424)

Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near Complex Topography. Part I: Technical Formulations. Journal of Applied Meteorology, 31(7): 633

– 645. Availability

This model code is available on EPA ’ s Internet SCRAM Web site and also on diskette (as PB 90

– 504119) from the National Technical Information Service (Section A.0).

Abstract

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

CTDMPLUS is appropriate for the following applications:

• Elevated point sources;

• Terrain elevations above stack top;

• Rural or urban areas;

• Transport distances less than 50 kilometers; and

• One hour to annual averaging times when used with a post-processor program such as CHAVG. b. Input Requirements

(1) Source data: For each source, user supplies source location, height, stack diameter, stack exit velocity, stack exit temperature, and emission rate; if variable emissions are appropriate, the user supplies hourly values for emission rate, stack exit velocity, and stack exit temperature.

(2) Meteorological data: For applications of CTDMPLUS, multiple level (typically three or more) measurements of wind speed and direction, temperature and turbulence (wind fluctuation statistics) are required to create the basic meteorological data file ( ‘‘ PROFILE ’’ ). Such measurements should be obtained up to the representative plume height(s) of interest ( i.e. , the plume height(s) under those conditions important to the determination of the design concentration). The representative plume height(s) of interest should be determined using an appropriate complex terrain screening procedure ( e.g. , CTSCREEN) and should be documented in the monitoring/modeling protocol. The necessary meteorological measurements should be obtained from an appropriately sited meteorological tower augmented by SODAR and/or RASS if the representative plume height(s) of interest is above the levels represented by the tower measurements. Meteorological preprocessors then create a SURFACE data file (hourly values of mixed layer heights, surface friction velocity, Monin-Obukhov length and surface roughness length) and a RAWINsonde data file (upper air measurements of pressure, temperature, wind direction, and wind speed). (3) Receptor data: Receptor names (up to 400) and coordinates, and hill number (each receptor must have a hill number assigned).

(4) Terrain data: User inputs digitized contour information to the terrain preprocessor which creates the TERRAIN data file (for up to 25 hills). c. Output

(1) When CTDMPLUS is run, it produces a concentration file, in either binary or text format (user ’ s choice), and a list file containing a verification of model inputs, i.e.

,

• Input meteorological data from

‘‘ SURFACE ’’ and ‘‘ PROFILE ’’

• Stack data for each source

• Terrain information

• Receptor information

• Source-receptor location (line printer map).

(2) In addition, if the case-study option is selected, the listing includes:

• Meteorological variables at plume height

• Geometrical relationships between the source and the hill

• Plume characteristics at each receptor, i.e.,

—Distance in along-flow and cross flow

direction

—Effective plume-receptor height difference

—Effective s y s z values, both flat terrain and

hill induced (the difference shows the effect of the hill)

— Concentration components due to WRAP,

LIFT and FLAT. (3) If the user selects the TOPN option, a summary table of the top 4 concentrations at each receptor is given. If the ISOR option is selected, a source contribution table for every hour will be printed.

(4) A separate disk file of predicted (1-hour only) concentrations ( ‘‘ CONC ’’ ) is written if the user chooses this option. Three forms of output are possible:

(i) A binary file of concentrations, one value for each receptor in the hourly
sequence as run;

(ii) A text file of concentrations, one value for each receptor in the hourly sequence as

run; or

(iii) A text file as described above, but with a listing of receptor information (names, positions, hill number) at the beginning of the file.

(3) Hourly information provided to these files besides the concentrations themselves includes the year, month, day, and hour information as well as the receptor number with the highest concentration. d. Type of Model

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

CTDMPLUS may be used to model nonreactive, primary pollutants. f. Source-Receptor Relationship

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


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

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

(1) As in CTDM, the basic plume rise algorithms are based on Briggs ’ (1975) recommendations.

(2) A central feature of CTDMPLUS for neutral/stable conditions is its use of a critical dividing-streamline height (H
c ) 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 H c, and the Froude number above H
c. 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 (H
c ); see ‘‘ Plume Behavior ’’

. j. Horizontal Dispersion

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

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

l. Chemical Transformation

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

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation of the CTDMPLUS Dispersion Model: Daytime Convective Conditions. Environmental Protection Agency, Research Triangle Park, NC.

Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of CTDMPLUS Model Predictions with the Lovett Power Plant Data Base. Environmental Protection Agency, Research Triangle Park, NC.

Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A Dispersion Model for Sources near Complex Topography. Part II: Performance Characteristics. Journal of Applied Meteorology, 31(7): 646

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

Benson, Paul E., 1979. CALINE3

—A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL

– 79/23. Federal Highway Administration, Washington, DC. (NTIS No. PB 80

– 220841)

Federal Aviation Administration, 1997. Emissions and Dispersion Modeling System (EDMS) Reference Manual. FAA Report No. FAA

–AEE

– 97

– 01, USAF Report No. AL/EQ

– TR

– 1997

– 0010, Federal Aviation Administration, Washington, DC 20591. SEE Availability below. ( Note: this manual includes supplements that are available on the EDMS Internet Web site: http:// www.aee.faa.gov/aee

– 100/aee

– 120/edms/ banner.htm )

Petersen, W.B. and E.D. Rumsey, 1987. User ’ s Guide for PAL 2.0

—A Gaussian-Plume Algorithm for Point, Area, and Line Sources. EPA Publication No. EPA

– 600/8

– 87

– 009. Office of Research and Development, Research Triangle Park, NC. (NTIS No. PB
87

– 168 787/AS)
Availability

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

– 3494.

Abstract

EDMS is a combined emissions/dispersion model for assessing pollution at civilian airports and military air bases. This model, which was jointly developed by the Federal Aviation Administration (FAA) and the United States Air Force (USAF), produces an emission inventory of all airport sources and calculates concentrations produced by these sources at specified receptors. The system stores emission factors for fixed sources such as fuel storage tanks and incinerators and also for mobile sources such as aircraft or automobiles. The EDMS emissions inventory module incorporates methodologies described in AP

– 42 for calculating aircraft emissions, on-road and off-road vehicle emissions, and stationary source emissions. The dispersion modeling module incorporates PAL2 and CALINE3 (Section A.3) for the various emission source types. Both of these components interact with the database to retrieve and store data. The dispersion module, which processes point, area, and line sources, also incorporates a special meteorological preprocessor for processing up to one year of National Climatic Data Center (NCDC) hourly data. a. Recommendations for Regulatory Use

EDMS is appropriate for the following applications:

• Cumulative effect of changes in aircraft operations, point source and mobile source emissions at airports or air bases;

• Simple terrain;

• Non-reactive pollutants;

• Transport distances less than 50 kilometers; and

• 1-hour to annual averaging times. b. Input Requirements

(1) All data are entered through the EDMS graphical user interface. Typical entry items are annual and hourly source activity, source and receptor coordinates, etc. Some point sources, such as heating plants, require stack height, stack diameter, and effluent temperature inputs.

(2) Wind speed, wind direction, hourly temperature, and Pasquill-Gifford stability category (P

– G) are the meteorological inputs. They can be entered manually through the EDMS data entry screens or automatically through the processing of previously loaded NCDC hourly data. c. Output

Printed outputs consist of:

• A summary emission inventory report with pollutant totals by source category and detailed emission inventory reports for each source category; and

• A concentration summary report for up to 8760 hours (one year) of meteorological data that lists the number of sources, receptors, and the five highest concentrations for applicable averaging periods for the respective primary NAAQS. d. Type of Model

For its emissions inventory calculations, EDMS uses algorithms consistent with the EPA Compilation of Air Pollutant Emission Factors, AP

– 42 (Section 11.0, ref. 96). For its dispersion calculations, EDMS uses the Point Area & Line (PAL2) model and the CALifornia LINE source (CALINE3) model, both of which use Gaussian algorithms.


## 18479 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

e. Pollutant Types

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

(1) Within hardware and memory constraints, there is no upper limit to the number of sources and receptors that can be modeled simultaneously.

(2) The Gaussian point source equation estimates concentrations from point sources after determining the effective height of emission and the upwind and crosswind distance of the source from the receptor. Numerical integration of the Gaussian point source equation is used to determine concentrations from line sources (runways). Integration over area sources (parking lots), which includes edge effects from the source region, is done by considering finite line sources perpendicular to the wind at intervals upwind from the receptor. The crosswind integration is done analytically; integration upwind is done numerically by successive approximations. Terrain elevation differences between sources and receptors are neglected.

(3) A reasonable height above ground level may be specified for each receptor. g. Plume Behavior

(1) Briggs final plume rise equations are used. If plume height exceeds mixing height, concentrations are assumed equal to zero. Surface concentrations are set to zero when the plume centerline exceeds mixing height.

(2) For roadways, plume rise is not treated. (3) Building and stack tip downwash effects are not treated.

h. Horizontal Winds

(1) Steady state winds are assumed for each hour. Winds are assumed to be constant with altitude.

(2) Winds are entered manually by the user or automatically by reading previously loaded NCDC annual data files. i. Vertical Wind Speed

Vertical wind speed is assumed to be zero. j. Horizontal Dispersion

(1) Six stability classes are used (P

– G classes A through F).

(2) Aircraft runways, vehicle parking lots, stationary sources, and training fires aremodeled using PAL2. Either rural (PasquillGifford) or urban (Briggs) dispersion settings may be specified globally for these sources.

(3) Vehicle roadways, aircraft taxiways, and aircraft queues are modeled using CALINE3. CALINE3 assumes urban dispersion curves. The user specifies terrain roughness. k. Vertical Dispersion

(1) Six stability classes are used (P

– G classes A through F).

(2) Aircraft runways, vehicle parking lots, stationary sources, and training fires aremodeled using PAL2. Either rural (PasquillGifford) or urban (Briggs) dispersion settings may be specified globally for these sources.

(3) Vehicle roadways, aircraft taxiways, and aircraft queues are modeled using CALINE3. CALINE3 assumes urban dispersion curves. The user specifies terrain roughness.

l. Chemical Transformation

Chemical transformations are not accounted for. m. Physical Removal

Deposition is not treated.

n. Evaluation Studies

None cited.
A.5 Industrial Source Complex Model (ISC3)
Reference

Environmental Protection Agency, 1995. User ’ s Guide for the Industrial Source Complex (ISC3) Dispersion Models, Volumes 1 and 2. EPA Publication Nos. EPA

– 454/B

– 95

– 003a & b. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 95

– 222741 and PB 95

– 222758, respectively)
Availability

The model code is available on the EPA ’ s Internet SCRAM website. ISCST3 (as PB
2002

– 500055) is also available on diskette from the National Technical Information Service (see Section A.0).

Abstract

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

ISC3 is appropriate for the following applications:

• Industrial source complexes;

• Rural or urban areas;

• Flat or rolling terrain;

• Transport distances less than 50 kilometers;

• 1-hour to annual averaging times; and

• Continuous toxic air emissions. The following options should be selected for regulatory applications: For short term or long term modeling, set the regulatory

‘‘ default option ’’ ; i.e. , use the keyword DFAULT, which automatically selects stack tip downwash, final plume rise, buoyancy induced dispersion (BID), the vertical potential temperature gradient, a treatment for calms, the appropriate wind profile exponents, the appropriate value for pollutant half-life, and a revised building wake effects algorithm; set the ‘‘ rural option ’’ (use the keyword RURAL ) or ‘‘ urban option ’’ (use the keyword URBAN ); and set the

‘‘ concentration option ’’ (use the keyword CONC ). b. Input Requirements

Source data: Location, emission rate, physical stack height, stack gas exit velocity, stack inside diameter, and stack gas temperature. Optional inputs include source elevation, building dimensions, particle size distribution with corresponding settling velocities, and surface reflection coefficients.

Meteorological data: ISCST3 requires hourly surface weather data from the preprocessor program RAMMET, which provides hourly stability class, wind direction, wind speed, temperature, and mixing height. For ISCLT3, input includes stability wind rose (STAR deck), average afternoon mixing height, average morning mixing height, and average air temperature.

Receptor data: Coordinates and optional ground elevation for each receptor. c. Output

Printed output options include:

• Program control parameters, source data, and receptor data;

• Tables of hourly meteorological data for each specified day;

• ‘‘ N ’’ -day average concentration or total deposition calculated at each receptor for any desired source combinations;

• Concentration or deposition values calculated for any desired source combinations at all receptors for any specified day or time period within the day;

• Tables of highest and second highest concentration or deposition values calculated at each receptor for each specified time period during a(n) ‘‘ N ’’ -day period for any desired source combinations, and tables of the maximum 50 concentration or deposition values calculated for any desired source combinations for each specified time period. d. Type of Model

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

sources. e. Pollutant Types

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

ISC3 applies user-specified locations for point, line, area and volume sources, and user-specified receptor locations or receptor rings.

User input topographic evaluation for each receptor is used. Elevations above stack top are reduced to the stack top elevation, i.e.

,

‘‘ terrain chopping ’’

.

User input height above ground level may be used when necessary to simulate impact at elevated or ‘‘ flag pole ’’ receptors, e.g. , on buildings.

Actual separation between each sourcereceptor pair is used. g. Plume Behavior

ISC3 uses Briggs (1969, 1971, 1975) plume rise equations for final rise.

Stack tip downwash equation from Briggs (1974) is used.

Revised building wake effects algorithm is used. For stacks higher than building height plus one-half the lesser of the building height or building width, the building wake algorithm of Huber and Snyder (1976) is used. For lower stacks, the building wake algorithm of Schulman and Scire (Schulman


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

and Hanna, 1986) is used, but stack tip downwash and BID are not used.

For rolling terrain (terrain not above stack height), plume centerline is horizontal at height of final rise above source.

Fumigation is not treated.

h. Horizontal Winds

Constant, uniform (steady-state) wind is assumed for each hour.

Straight line plume transport is assumed to all downwind distances.

Separate wind speed profile exponents (Irwin, 1979; EPA, 1980) for both rural and urban cases are used.

An optional treatment for calm winds is included for short term modeling. i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero. j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used, with no adjustments for surface roughness or averaging time.

Urban dispersion coefficients from Briggs (Gifford, 1976) are used.

Buoyancy induced dispersion (Pasquill, 1976) is included.

Six stability classes are used. k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used, with no adjustments for surface roughness.

Urban dispersion coefficients from Briggs (Gifford, 1976) are used.

Buoyancy induced dispersion (Pasquill, 1976) is included.

Six stability classes are used. Mixing height is accounted for with multiple reflections until the vertical plume standard deviation equals 1.6 times the mixing height; uniform vertical mixing is assumed beyond that point.

Perfect reflection is assumed at the ground.

l. Chemical Transformation

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

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

n. Evaluation Studies

Bowers, J.F. and A.J. Anderson, 1981. An Evaluation Study for the Industrial Source Complex (ISC) Dispersion Model, EPA Publication No. EPA

– 450/4

– 81

– 002. Office of Air Quality Planning & Standards, Research Triangle Park, NC.

Bowers, J.F., A.J. Anderson and W.R. Hargraves, 1982. Tests of the Industrial Source Complex (ISC) Dispersion Model at the Armco Middletown, Ohio Steel Mill. EPA Publication No. EPA

– 450/4

– 82

– 006. Office of Air Quality Planning & Standards, Research Triangle Park, NC.

Environmental Protection Agency, 1992. Comparison of a Revised Area Source Algorithm for the Industrial Source Complex Short Term Model and Wind Tunnel Data. EPA Publication No. EPA

– 454/R

– 92

– 014. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB
93

– 226751)

Environmental Protection Agency, 1992. Sensitivity Analysis of a Revised Area Source Algorithm for the Industrial Source Complex Short Term Model. EPA Publication No. EPA

– 454/R

– 92

– 015. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 93

– 226769)

Environmental Protection Agency, 1992. Development and Evaluation of a Revised Area Source Algorithm for the Industrial Source Complex Long Term Model. EPA Publication No. EPA

– 454/R

– 92

– 016. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB
93

– 226777)

Environmental Protection Agency, 1994. Development and Testing of a Dry Deposition Algorithm (Revised). EPA Publication No. EPA

– 454/R

– 94

– 015. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 94

– 183100)

Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and ISC Models with SF 6 Tracer Data and SO 2 Measurements at Aluminum Reduction Plants. Air Pollution Control Association Specialty Conference on Dispersion Modeling for Complex Sources, St. Louis, MO.

Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash Modification to the Industrial Source Complex Model. Journal of the Air Pollution Control Association, 36:
258

– 264. A.7 Offshore and Coastal Dispersion Model (OCD)
Reference

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

– 144384 and PB 93

– 144392)
Availability

This model code is available on the EPA ’ s Internet SCRAM Web site and also on diskette (as PB 91

– 505230) from the National Technical Information Service (see Section A.0). Official contact at Minerals Management Service: Mr. Dirk Herkhof, Parkway Atrium Building, 381 Elden Street, Herndon, VA 20170, Phone: (703) 787

– 1735.

Abstract

(1) OCD is a straight-line Gaussian model developed to determine the impact of offshore emissions from point, area or line sources on the air quality of coastal regions. OCD incorporates overwater plume transport and dispersion as well as changes that occur as the plume crosses the shoreline. Hourly meteorological data are needed from both offshore and onshore locations. These include water surface temperature, overwater air temperature, mixing height, and relative humidity.

(2) Some of the key features include platform building downwash, partial plume penetration into elevated inversions, direct use of turbulence intensities for plume dispersion, interaction with the overland internal boundary layer, and continuous shoreline fumigation. a. Recommendations for Regulatory Use

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

(1) Source data: Point, area or line source location, pollutant emission rate, building height, stack height, stack gas temperature, stack inside diameter, stack gas exit velocity, stack angle from vertical, elevation of stack base above water surface and gridded specification of the land/water surfaces. As an option, emission rate, stack gas exit velocity and temperature can be varied hourly.

(2) Meteorological data (over water): Wind direction, wind speed, mixing height, relative humidity, air temperature, water surface temperature, vertical wind direction shear (optional), vertical temperature gradient (optional), turbulence intensities (optional).

(3) Meteorological data (over land): Wind direction, wind speed, temperature, stability class, mixing height.

(4) Receptor data: Location, height above local ground-level, ground-level elevation above the water surface. c. Output

(1) All input options, specification of sources, receptors and land/water map including locations of sources and receptors.

(2) Summary tables of five highest concentrations at each receptor for each averaging period, and average concentration for entire run period at each receptor.

(3) Optional case study printout with hourly plume and receptor characteristics. Optional table of annual impact assessment from non-permanent activities.

(4) Concentration files written to disk or tape can be used by ANALYSIS postprocessor to produce the highest concentrations for each receptor, the cumulative frequency distributions for each receptor, the tabulation of all concentrations exceeding a given threshold, and the manipulation of hourly concentration files. d. Type of Model

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

OCD may be used to model primary pollutants. Settling and deposition are not treated. f. Source-Receptor Relationship

(1) Up to 250 point sources, 5 area sources, or 1 line source and 180 receptors may be used.

(2) Receptors and sources are allowed at any location.


## 18481 Federal Register / Vol. 68, No. 72 / Tuesday, April 15, 2003 / Rules and Regulations

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

(1) As in ISC, the basic plume rise algorithms are based on Briggs ’ recommendations.

(2) Momentum rise includes consideration of the stack angle from the vertical.

(3) The effect of drilling platforms, ships, or any overwater obstructions near the source are used to decrease plume rise using a revised platform downwash algorithm based on laboratory experiments.

(4) Partial plume penetration of elevated inversions is included using the suggestions of Briggs (1975) and Weil and Brower (1984).

(5) Continuous shoreline fumigation is parameterized using the Turner method where complete vertical mixing through the thermal internal boundary layer (TIBL) occurs as soon as the plume intercepts the TIBL.
h. Horizontal Winds

(1) Constant, uniform wind is assumed for each hour.

(2) Overwater wind speed can be estimated from overland wind speed using relationship of Hsu (1981).

(3) Wind speed profiles are estimated using similarity theory (Businger, 1973). Surface layer fluxes for these formulas are calculated from bulk aerodynamic methods. i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero. j. Horizontal Dispersion

(1) Lateral turbulence intensity is recommended as a direct estimate of horizontal dispersion. If lateral turbulence intensity is not available, it is estimated from boundary layer theory. For wind speeds less than 8 m/s, lateral turbulence intensity is assumed inversely proportional to wind speed.

(2) Horizontal dispersion may be enhanced because of obstructions near the source. A virtual source technique is used to simulate the initial plume dilution due to downwash.

(3) Formulas recommended by Pasquill (1976) are used to calculate buoyant plume enhancement and wind direction shear enhancement.

(4) At the water/land interface, the change to overland dispersion rates is modeled using a virtual source. The overland dispersion rates can be calculated from either lateral turbulence intensity or Pasquill-Gifford curves. The change is implemented where the plume intercepts the rising internal boundary layer. k. Vertical Dispersion

(1) Observed vertical turbulence intensity is not recommended as a direct estimate of vertical dispersion. Turbulence intensity should be estimated from boundary layer theory as default in the model. For very stable conditions, vertical dispersion is also a function of lapse rate.

(2) Vertical dispersion may be enhanced because of obstructions near the source. A virtual source technique is used to simulate the initial plume dilution due to downwash. (3) Formulas recommended by Pasquill (1976) are used to calculate buoyant plume enhancement.

(4) At the water/land interface, the change to overland dispersion rates is modeled using a virtual source. The overland dispersion rates can be calculated from either vertical turbulence intensity or the Pasquill-Gifford coefficients. The change is implemented where the plume intercepts the rising internal boundary layer.

l. Chemical Transformation

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

Physical removal is also treated using exponential decay.

n. Evaluation Studies

DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and Coastal Dispersion Model. Volume I: User ’ s Guide. Sigma Research Corporation, Westford, MA.

Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The Offshore and Coastal Dispersion (OCD) Model User ’ s Guide, Revised. OCS Study, MMS 84

– 0069. Environmental Research & Technology, Inc., Concord, MA. (NTIS No. PB 86

– 159803)

Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer, 1985. Development and Evaluation of the Offshore and Coastal Dispersion (OCD) Model. Journal of the Air Pollution Control Association, 35: 1039

– 1047.

Hanna, S.R. and D.C. DiCristofaro, 1988. Development and Evaluation of the OCD/API Model. Final Report, API Pub. 4461, American Petroleum Institute, Washington, DC.

A.REF References

Benson, P.E., 1979. CALINE3

—A Versatile Dispersion Model for Predicting Air Pollution Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL

– 79/23. Federal Highway Administration, Washington, DC.

Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN. (NTIS No. TID

– 25075)

Briggs, G.A., 1971. Some Recent Analyses of Plume Rise Observations. Proceedings of the Second International Clean Air Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New York, NY.

Briggs, G.A., 1974. Diffusion Estimation for Small Emissions. USAEC Report ATDL

– 106. U.S. Atomic Energy Commission, Oak Ridge, TN.

Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air Pollution and Environmental Impact Analyses. American Meteorological Society, Boston, MA, pp. 59

– 111.

Briggs, G.A., 1984. Analytical Parameterizations of Diffusion: The Convective Boundary Layer. Journal of Climate and Applied Meteorology, 24(11):
1167

– 1186

Environmental Protection Agency, 1980. Recommendations on Modeling (October 1980 Meetings). Appendix G to: Summary of Comments and Responses on the October 1980 Proposed Revisions to the Guideline on Air Quality Models. Meteorology and Assessment Division, Office of Research and Development, Research Triangle Park, NC.

Environmental Protection Agency, 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Reportand Recommendations for Modeling LongRange Transport Impacts. EPA Publication No. EPA

– 454/R

– 98

– 019. (NTIS No. PB 99

– 121089)

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

—A Review. Nuclear Safety,
17: 68

– 86.

Horst, T.W., 1983. A Correction to the Gaussian Source-depletion Model. In Precipitation Scavenging, Dry Deposition and Resuspension. H.R. Pruppacher, R.G. Semonin and W.G.N. Slinn, eds., Elsevier, NY.

Hsu, S.A., 1981. Models for Estimating Offshore Winds from Onshore Meteorological Measurements. Boundary Layer Meteorology,
20: 341

– 352.

Huber, A.H. and W.H. Snyder, 1976. Building Wake Effects on Short Stack Effluents. Third Symposium on Atmospheric Turbulence, Diffusion and Air Quality, American Meteorological Society, Boston, MA.

Irwin, J.S., 1979. A Theoretical Variation of the Wind Profile Power-Law Exponent as a Function of Surface Roughness and Stability. Atmospheric Environment, 13: 191

– 194.

Liu, M.K. et al. , 1976. The Chemistry, Dispersion, and Transport of Air Pollutants Emitted from Fossil Fuel Power Plants in California: Data Analysis and Emission Impact Model. Systems Applications, Inc., San Rafael, CA.

Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume Modeling Part II. Possible Requirements for Change in the Turner Workbook Values. EPA Publication No. EPA

– 600/4

– 76

– 030b. Office of Air Quality Planning & Standards, Research Triangle Park, NC.

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

– 2 A Highway Air Pollution Model. EPA Publication No. EPA

– 600/8

– 80

– 018. Office of Research & Development, Research Triangle Park, NC. (NTIS PB 80

– 227556)

Rao, T.R. and M.T. Keenan, 1980.Suggestions for Improvement of the EPAHIWAY Model. Journal of the Air Pollution Control Association, 30: 247

– 256 (and reprinted as Appendix C in Petersen, 1980).

Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash Modification to the Industrial Source Complex Model. Journal of the Air Pollution Control Association, 36:
258

– 264.

Segal, H.M., 1983. Microcomputer Graphics in Atmospheric Dispersion Modeling. Journal of the Air Pollution Control Association, 23: 598

– 600.

Snyder, W. H., R.S. Thompson, R. E. Eskridge, R. E. Lawson, I. P. Castro, J. T. Lee, J. C. R. Hunt, and Y. Ogawa, 1985. The structure of the strongly stratified flow over hills: Dividing streamline concept. Journal of Fluid Mechanics, 152: 249

– 288.

Turner, D.B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999

– 26. U.S. Environmental Protection Agency, Research Triangle, Park, NC.


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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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