EPA

Office of Air Quality

Pla nnin g a nd S ta nd a rds

C Air

lean

United States Office of Air Quality EPA-454/R-98-019

Environmental Protection Planning and Standards December 1998

Agency Research Triangle Park, NC 27711

AIR

INTERAGENCY WORKGROUP 

ON AIR QUALITY MODELING (IWAQM) 

PHASE 2 SUMMARY REPORT AND

RECOMMENDATIONS FOR MODELING LONG

RANGE TRANSPORT IMPACTS
EPA-454/R-98-019

INTERAGENCY WORKGROUP ON AIR

QUALITY MODELING (IWAQM)

PHASE 2 SUMMARY REPORT AND

RECOMMENDATIONS FOR MODELING

LONG-RANGE TRANSPORT IMPACTS

U.S. Environmental Protection Agency

Air Quality Modeling Group (MD-14)

Research Triangle Park, North Carolina 27711

National Park Service

Air Resources Division

Denver, Colorado 80225

USDA Forest Service

Air Program

Fort Collins, Colorado 80526

U.S. Fish and Wildlife Service

Air Quality Branch Denver, Colorado 80225

December, 1998
i

NOTICE

The information in this document has been reviewed in its entirety by the U.S.

Environmental Protection Agency (EPA), and approved for publication as an EPA

document. Mention of trade names, products, or services does not convey, and should

not be interpreted as conveying official EPA approval, endorsement, or

recommendation.
ii

PREFACE

The Interagency Workgroup on Air Quality Modeling (IWAQM) was formed to

provide a focus for development of technically sound recommendations regarding

assessment of air pollutant source impacts on Federal Class I and Wilderness areas. 

Meetings were held with personnel from interested Federal agencies, viz. the

Environmental Protection Agency, the U.S. Forest Service, the National Park Service,

and the U.S. Fish and Wildlife Service. The purpose of these meetings was to review

respective modeling programs, to develop an organizational framework, and to

formulate reasonable objectives and plans that could be presented to management for

support and commitment. The members prepared a memorandum of understanding

(MOU) that incorporated the goals and objectives of the workgroup and obtained

signatures of management officials in each participating agency. Even though no

States are signatories, they did participate in IWAQM functions.

This document is being released as a publication of the Environmental

Protection Agency (EPA) in response to a request from the members of IWAQM. 

Members of the workgroup include representatives from the Environmental Protection

Agency, the U.S. Forest Service, the National Park Service, and the U.S. Fish and

Wildlife Service. The document includes IWAQM’s recommendations for modeling

methods that might be used to estimate Prevention of Significant Deterioration air

quality impacts and National Ambient Air Quality Standards (NAAQS) air quality impacts

associated with long-range transport of pollutant emissions to Class I and Wilderness

areas . The IWAQM recommends that the CALPUFF Lagrangian puff dispersion

modeling system be used for characterization of the transport and dispersion.

The recommendations of IWAQM contained in this document is considered

technical guidance tailored for use in assessing air quality impacts associated with

prevention of significant deterioration. 
iii

ACKNOWLEDGMENTS

The members of IWAQM acknowledge the special efforts by Mark Scruggs, John

Notar and John Vimont of the National Park Service; Alan Cimorelli of the U.S.

Environmental Protection Agency (EPA); John Irwin of the National Oceanic

Atmospheric Administration (NOAA) Air Resources Laboratory; Richard Fisher and Bob

Bachman of the U.S. Forest Service; Elwyn Rolofson of the U.S. Fish and Wildlife

Service; Patrick Hanrahan of the State of Oregon, Department of Environmental

Quality; and Kenneth McBee of the Commonwealth of Virginia, Department of Air

Pollution Control for their input and suggestions on assembling this document and their

subsequent review. In compiling the reviews of investigations completed that involved

in some manner the CALMET/CALPUFF modeling system, various authors graciously

provided access to their results and data, and have permitted IWAQM to cite directly

from their reports and papers. The IWAQM gives special recognition and thanks to: 

Andrew Gray of SAI, Inc, John Sherwell of the Maryland Department of Natural

Resources, O. Russ Bullock, Jr., Frank Binkowski, and Robin Dennis of the NOAA Air

Resources Laboratory, David Strimaitis and Joesph Scire of Earth Tech Inc., Jim

Paumier of Pacific Environmental Services Inc., Peter Eckhoff and C. Thomas Coulter

of the EPA Office of Air Quality Planning and Standards.
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TABLE OF CONTENTS

1.0 INTRODUCTION .................................................. 1

2.0 MODELING RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1 Screening Analysis ........................................... 6

2.2 Refined Analysis ............................................. 9

2.2.1 Meteorology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.2 Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2.3 Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Practicalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.1 Screening procedure uncertaintites . . . . . . . . . . . . . . . . . . . . . . . 17

2.3.2 Refined modeling uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3.3 Secondary pollutant uncertainties . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.4 Technical oversight and review . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.0 TRANSFORMATIONS, VISIBILITY, AND DEPOSITION . . . . . . . . . . . . . . . . . . . 20

3.1 Chemical Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Visibility Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3 Deposition Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.4 Assessing Air Quality Related Values - Background . . . . . . . . . . . . . . . . . 31

4.0 STUDIES AND FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.1 MESOPUFF II Implementation Assessment . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Revisions to CALMET and CALPUFF . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3 Trajectory Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.4 Constructing FDDA-MM Data Sets Assessment . . . . . . . . . . . . . . . . . . . . 54

4.5 Regional Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.5.1 An Approach to Implementing a Class I Area Assessments . . . 57

4.6 Comparisons of CALPUFF with Tracer Field Data . . . . . . . . . . . . . . . . . . 58

4.6.1 1975 Savannah River Laboratory Tracer Study . . . . . . . . . . . . . . 58

4.6.2 1977 Idaho Falls Tracer Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.6.3 1980 Great Plains Tracer Study . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.6.4 1992 Project MOHAVE Tracer Study . . . . . . . . . . . . . . . . . . . . . . 73

4.7 Comparisons of CALPUFF With ISC3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.8 CALPUFF SCREEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.8.1 Screening methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.8.2 Year to year variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.8.3 SO concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 2

4.8.4 SO concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4

=

4.8.5 SO deposition 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

4.8.6 SO deposition 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 =

4.8.7 Old screen versus new screen estimates . . . . . . . . . . . . . . . . . . . 94

4.8.8 Findings and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
v

4.9 CALMET/CALPUFF Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

4.9.1 Use of FDDA-MM data with CALMET . . . . . . . . . . . . . . . . . . . . . 97

4.9.2 Use of CALMET to Develop Wind Fields . . . . . . . . . . . . . . . . . . . 99

4.9.3 Kincaid SF and Lovett SO Comparisons 6 2 . . . . . . . . . . . . . . . . . 104

5.0 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

6.0 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

APPENDIX A. CALMET RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

APPENDIX B. CALPUFF RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

APPENDIX C. COMPACT DISK DATA RESOURCES . . . . . . . . . . . . . . . . . . . . . . C-1

APPENDIX D. SIXTH MODELING CONFERENCE . . . . . . . . . . . . . . . . . . . . . . . . D-1
vi

LIST OF FIGURES

Figure 1. Hygroscopic relative humidity adjustment factor f(RH) . . . . . . . . . . . . . . . . 27

Figure 2. Display of MESOPUFF II computational domain . . . . . . . . . . . . . . . . . . . . 35

Figure 3. Display of ring source analysis computational domain . . . . . . . . . . . . . . . . 40

Figure 4. Highest monthly concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 5. 54-km and 18-km MM4 domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Figure 6. Trajectories at 10 m on the 54 km grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Figure 7. Summary of CAPTEX trajectory results . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Figure 8. Savannah River Laboratory field experiment site . . . . . . . . . . . . . . . . . . . . 59

Figure 9. Savannah River Laboratory December 10, 1976 results . . . . . . . . . . . . . . 61

Figure 10. Idaho Falls field experiment site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 11. Summary of Idaho Falls April 19, 1977 results . . . . . . . . . . . . . . . . . . . . . 66

Figure 12. Great Plains field experiment site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Figure 13. Summary of Great Plains 100 and 600 km July 8, 1980 results . . . . . . . . 71

Figure 14. Summary of Great Plains 100 km July 11, 1980 results . . . . . . . . . . . . . . 72

Figure 15. Project MOHAVE experimental site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 16. Project MOHAVE results for Las Vegas Wash site . . . . . . . . . . . . . . . . . 75

Figure 17. Summary for Hour 62 of a CALPUFF simulation . . . . . . . . . . . . . . . . . . . 79

Figure 18. CALPUFF and ISC comparisons for Medford, Oregon . . . . . . . . . . . . . . . 81

Figure 19. Annual average CALPUFF and ISC comparisons . . . . . . . . . . . . . . . . . . 82

Figure 20. Comparison of screening estimates of ambient sulfate concentrations . . 95

Figure 21. Comparison of screening estimates of total sulfur deposition . . . . . . . . . 96

Figure 22. CALMET computational domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Figure 23. Terrain contours for the Wenachee, WA domain . . . . . . . . . . . . . . . . . . 101

Figure 24. Down slope flow components for 1:00 AM July 1, 1994 . . . . . . . . . . . . . 102

Figure 25. Upslope flow components for 3:00 PM July 1, 1994 . . . . . . . . . . . . . . . 103

Figure 26. Q-Q plots for Kincaid comparing CALPUFF with ISC . . . . . . . . . . . . . . . 107

Figure 27. Scatter plot for Kincaid comparing CALPUFF with ISC . . . . . . . . . . . . . 108

Figure 28. Q-Q plots for Lovett comparing CALPUFF with CTDMPLUS . . . . . . . . . 109
vii

LIST OF TABLES

Table 1. Outline of recommendations for screening analysis ................... 8

Table 2. Outline of recommendatinos for refined analysis . . . . . . . . . . . . . . . . . . . . 10

Table 3. Sources modeled for demonstration analysis . . . . . . . . . . . . . . . . . . . . . . . 36

Table 4. PSD and AQRV demonstration analysis results . . . . . . . . . . . . . . . . . . . . . 37

Table 5. Source characteristics for ring source analysis . . . . . . . . . . . . . . . . . . . . . . 39

Table 6. Source ring characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Table 7. Summary of statistical comparisons of wind fields . . . . . . . . . . . . . . . . . . . 47

Table 8. Characteristics for point sources used in CALPUFF/ISC comparisons . . . . 78

Table 9. Year to year variability of SO concentrations 2 . . . . . . . . . . . . . . . . . . . . . . . 88

Table 10. Comparison of CALPUFF(ISC/CALMET) SO concentrations . . . . . . . . . 89 2

Table 11. Comparison of CALPUFF(ISC/CALMET) sulfate concentrations . . . . . . . 91

Table 12. Comparison of CALPUFF(ISC/CALMET) SO deposition 2 . . . . . . . . . . . . . 92

Table 13. Comparison of CALPUFF(ISC/CALMET) sulfate deposition . . . . . . . . . . . 93
1

1.0 INTRODUCTION

Special protection from adverse air quality impacts is afforded certain national

parks and wilderness areas, through the prevention of significant deterioration (PSD)

program (U.S. EPA, 1980). These areas have been designated as Class I areas, and

as such, increases of pollutant levels in these areas are strictly limited. Furthermore,

the Federal Land Manager (FLM) of the Class I area is given an affirmative

responsibility to ensure that Air Quality Related Values (AQRVs) are not adversely

impacted. [The FLMs of the Class I areas are the U.S. Forest Service (USFS), the

National Park Service (NPS), and the U.S. Fish and Wildlife Service (FWS).] Air quality

models are one of the primary tools used to assess the impacts from sources of air

pollution on both the established Prevention of Significant Deterioration (PSD)

increments and the AQRVs. Steady-state models have been generally used for PSD

analyses. As the PSD program has developed, the need for more sophisticated models

to assess air quality impacts in Class I areas, from sources at relatively greater

distances from the Class I areas, has arisen. In some areas, the FLMs have asserted

that Class I areas have been adversely affected by air pollution and that new sources of

pollution over a broad area are further harming the resource. The absence of any

recommended long-range modeling techniques has left permitting authorities without

the means to assess the assertions of the FLMs. The Environmental Protection

Agency (EPA) and the FLMs have undertaken various model development efforts to

address the air quality impacts of pollution transported over relatively long distances. 

The Interagency Workgroup for Air Quality Modeling (IWAQM) was formed to

coordinate the independent modeling efforts of the EPA and the FLMs so that a

consistent, technically credible approach can be recommended and used.

Models used to evaluate the impact of sources of air pollution on the PSD

increments and National Ambient Air Quality Standards (NAAQS), are required to follow

Appendix W to 40 CFR Part 51 (Guideline on Air Quality Models), U.S. EPA (1997). 

(Hereafter, referred to as the Guideline.) For many situations, preferred models,

considered generally applicable under a variety of circumstances, are defined. 

Currently within the Guideline (Section 7.2.6), there is no preferred model listed for

assessing impacts involving transport beyond 50 km. For those situations for which

there are no preferred models, criteria are established in the Guideline to use

appropriate methods on a case-by-case basis. These criteria are:

i. The model can be demonstrated to be applicable to the problem on

a theoretical basis, and

ii. the data bases which are necessary to perform the analysis are

available and adequate, and
2

iii. performance evaluations of the model in similar circumstances

have shown that the model is not biased toward underestimates, or

iv. after consultation with the EPA Regional Office, a second model is

selected as a baseline or reference point for performance and the

interim procedures are then used to demonstrate that the proposed

model performs better than the reference (preferred) model.

The processes which become important in the transport of pollution over long

distances include the spatial and temporal variability of the winds which transport and

disperse air pollutants in the presence of various terrain and water features, the

chemical transformation of the pollutants as they travel, and the deposition of the

pollutants along the way. Cummulative impact assessments likely will need to address

multiple, geographically disperse sources. There are existing long-range transport

models available which meet some, but not, all of these needs and some which meet

these needs but either have not been sufficiently tested or are perceived to need further

development for routine operational use in regulatory assessment analyses. One of the

primary goals of IWAQM is to evaluate existing modeling codes and either recommend

one as an accepted approach or combine the better elements of several of the existing

codes, creating a new modeling construct. 

The IWAQM work plan (U.S. EPA, 1992a) describes a phased approach to

satisfy the modeling needs described above. Phase 1 consists of reviewing EPA

guidance and recommending an interim modeling approach to meet the immediate

need for a long-range transport model for ongoing permitting activity. In developing a

Phase 2 recommendation, the workgroup was to review other available operational

models and make a recommendation of the most appropriate modeling techniques. 

The Phase 2 recommendation was to be a compromise between the current modeling

state-of-science and best available operational computer capabilities. If resources

could be found to pursue a Phase 3 recommendation, the workgroup would add more

advanced modeling techniques to its consideration, probably representing a greater

level of scientific and computer hardware sophistication. 

Given the practical limitations of resources and hardware, the Phase 1 interim

recommendations (U.S. EPA, 1993) were designed to provide the best approach from

existing "off-the-shelf” techniques. Two models were assessed, the MESOPUFF II

model (U.S. EPA, 1994) and the Acid Rain Mountain Mesoscale Model (ARM3), Morris

et al., (1988). Upon careful examination of both models, coding errors were discovered

in the ARM3, which potentially invalidated its previous evaluations. With this in mind

and other considerations as discussed in U.S. EPA (1993), the Phase 1

recommendation was to use on a case-by-case basis the Lagrangian puff model,

MESOPUFF II, to evaluate the impacts of pollutants from sources located more than 50

kilometers from Class I areas, up to several hundred kilometers from Class I areas. 

The impacts are: 1) the allowable Class I increases (increments), 2) the National

Ambient Air Quality Standards (NAAQS), and 3) Air Quality Related Values (AQRVs)
3

associated with emissions of sulfur, nitrogen and particulate matter. The PSD NAAQS

pollutants were sulfur dioxide (SO ) and particulate of size less than or equal to ten 2

microns (PM10). AQRV impacts include such effects as visibility degradation and acidic

deposition. In order to focus this effort, increase the chance for success in at least

certain scenarios, and to meet an articulated immediate need of permitting authorities

and the EPA, IWAQM chose to address only sulfur and nitrogen derived pollutants. It

was acknowledged that there are other pollutants such as photochemical oxidants that

may injure components of the natural ecosystem, but assessment by IWAQM of the

modeling needs and development of oxidant effects modules was postponed until a

later date. The Phase I interim recommendations also provided a screening

methodology, which (as is discussed later) proved to be too concervative to be of much

use.

For the Phase 1 recommendation, MESOPUFF II was deemed suitable for

conducting single source impact analyses, and in some circumstances cumulative

impact analyses. As the dispersion characterizations in MESOPUFF II were not

designed to handle local-scale dispersion effects, it was recognized that the

MESOPUFF II results would frequently need to be combined with the results from other

modeling techniques used to estimate concentrations from sources closer than 50

kilometers to a receptor area. The Phase 1 recommendation was structured to satisfy

case-by-case Guideline criteria i, ii, and iii above. The iv criterion has meaning only

when there is a preferred model, and such is not the case for modeling impacts

involving transport beyond 50 kilometers.

By restricting the models considered for Phase 1 to "off-the-shelf" techniques,

IWAQM recognized certain limitations. These include a lack of consideration of the

effects of terrain on the long-range transport and dispersion, an underestimation of the

conversion of sulfur dioxide, SO to sulfate, SO , when polluted air interacts with 2, 4

=

clouds, and a possible overestimation of particulate nitrate when a limited number of

sources are considered. Nonetheless, IWAQM considered the techniques, suggested

to be a significant improvement to those previously used, in that previous techniques

ignored many of the processes important to the assessment of air quality impacts in

Class I areas. Thus, while under some circumstances the impacts on regional visibility

may be underestimated, because the concentrations of sulfates in the atmosphere may

be underestimated due to the inability of the model to treat in-cloud processes, IWAQM,

including the representatives of the land management agencies, considered the

suggested techniques to be technically superior to simply assuming that there are no

impacts on regional visibility. 

The IWAQM recognized that there were certain risks involved with

recommending an interim modeling approach. From a regulatory perspective, it is

generally desirable to use an interim model which will yield somewhat higher impact

calculations than a more refined, preferred approach. In the case of steady-state air

quality models, this can be relatively easily ensured because of the independence of

the concentration calculations from one hour to the next. In the case of the Lagrangian
4

long-range transport models under consideration here, the concentration calculations

for a given hour will be explicitly dependent on the spatially and temporally varying wind

field from that hour and previous hours. Therefore, the exact behavior of a given

modeling system relative to a similar but different modeling system can not be predicted

with certainty.

Not long after the release of the Phase 1 recommendation, the EPA sponsored

the Sixth Modeling Conference which was held August 9-10, 1995 in Washington, D.C. 

One of the main topics at this two-day event was a review of IWAQM Phase 1

recommendation, a summary of work in progress, with review comments provided by

several groups. At the conference, IWAQM presented long-range trajectory

comparison, that suggested that use of mesoscale meteorological analyses of wind

fields provided a significant improvement in the accord of modeled and observed

trajectories. The IWAQM endorsed specifically the use of mesoscale meteorological

analyses that employ data assimilation. The IWAQM recommended that the Phase 1

recommendation to use the MESOPUFF II modeling system be replaced with a

recommendation to use the CALMET/CALPUFF modeling system. This was a

relatively new Lagrangian puff modeling system, which had additional algorithms to

provide simulation of local-scale short-range dispersion using methods already

endorsed by the EPA. Thus, use of this newer modeling system allowed one model to

be used for all sources in an analysis, regardless of the transport distance involved. 

The IWAQM also endorsed the formation of public/private committees to manage the

myriad of site specific technical decisions that are inherent in assessing mesoscale

PSD and AQRV impacts on Class I areas. Appendix D provides a review of the

information provided at the Sixth Modeling Conference, a summary of comments

received, and IWAQM’s response to these comments. 

Section 2 presents the Phase 2 modeling recommendation, which represents a

compromise between the current modeling state-of-science and best available

operational computer capabilities. The IWAQM Phase 2 recommendations are for

methods and procedures that might be used to estimate air quality Prevention of

Significant Deterioration impacts to increments and AQRVs and National Ambient Air

Quality Standards (NAAQS) impacts from pollutant emissions, that due to the transport

distances and location are best treated using a long-range transport modeling system. 

The Phase 2 recommendations are not limited to recommendations on which modeling

systems to use, as part of IWAQM’s efforts are to assist in fostering best assessment of

impacts. Briefly summarized, the Phase 2 recommendation consist of: 

 a suggested screening technique for modeling worst-case estimates of longrange transport impacts, 

 a recommendation that the CALPUFF modeling system be listed as the

preferred model in Section 7.2.6 in the Guideline, 
5

 recommendations for assessing PSD increment and NAAQS impacts of a new

source’s emissions (or change in an existent source’s emissions) involving longrange transport of emissions, 

 recommendations for assessing the AQRV impacts of a new source’s emissions

(or change in an existent source’s emissions) of sulfur, nitrogen and particulate

matter to regional visibility degradation and deposition, which requires a

knowledge of the current state of the Class I area, and

 a recommendation that consideration be given to forming committees (‘Regional

Approach’) to assist in the resolution of the myriad of decisions associated with

mesoscale modeling of PSD and AQRV impacts for each Class I area (or groups

of areas).

Section 3 discusses the chemistry limitations inherent in CALPUFF, and how

results from CALPUFF can be used to estimate regional visibility impacts and

deposition of sulfur and nitrogen. 

From the comments received at the Sixth Modeling Conference, IWAQM

concluded that in order to provide a firmer basis for a Phase 2 recommendation, more

information was needed comparing mesoscale modeling dispersion results with tracer

field data, and more comparisons were needed to stress test code modifications made

to incorporate local-scale dispersion characterizations. Everyone was in agreement

that the screening analysis, suggested by IWAQM in the Phase 1 recommendation,

was inadequate. It was perceived as providing such large overestimates of SO and 2

sulfate impacts that it was of little use. Section 4 provides a review of the activities that

IWAQM has sponsored in developing the Phase 2 recommendations, as well as,

summaries of investigations that IWAQM is aware of that provide information regarding

lessons learned in using and applying the CALPUFF modeling system.
6

2.0 MODELING RECOMMENDATIONS

For most of the modeling situations discussed in the Guideline where a refined

modeling technique is recommended, a screening analysis is also provided. The

screening analysis is meant to be easy to conduct and to provide a worst-case

maximum impact estimate. If the results of the screening analysis show compliance

with existing regulatory requirements, then no further modeling for compliance with

standards and increments is required. 

Basically, IWAQM’s recommendations for a screening analysis is an approach of

using a simplified set of meteorology with CALPUFF. To encourage the results to be

higher than would be estimated using a fully developed CALMET and CALPUFF

analysis, rings of receptors are used. The maximum concentration value found

anywhere on the receptor rings are used (rather than restricting the analysis to

receptors only located within the Class I area(s) of interest). More discussion of the

steps to be taken and processing requirements for a screening analysis is provided in

Section 2.1.

IWAQM’s recommendations for a refined analysis involve the following

differences from the screening analysis:

 use of a fully developed time and space varying characterization of the

meteorology using CALMET, and

 the receptors are placed within the Class I area(s) of concern, and

 the background concentrations of ozone and ammonia are allowed to vary in

time and space, and

 the concentration and AQRV impacts are computed to more directly correspond

to the standards, increments, and thresholds of concern.

More discussion of the steps to be taken and processing requirements for a refined

analysis is provided in Section 2.2. 

Sections 2.1 and 2.2 focus on how to apply models, specific options and data

sets to be employed, and the processing of the input and output data. Section 2.3

provides more general recommendations on practical issues and limitations of longrange transport modeling assessments.

2.1 Screening Analysis

Section 4.7 presents comparisons of puff and plume model simulation results to

demonstrate what differences might arise in simulated concentration values when the

plume and puff model employ essentially identical meteorology and dispersion

characterizations. The results shown in Section 4.7 show striking evidence that treating

the sequence of meteorological events of all hours (including calms), can result in puff

simulated maxima that are considerably higher than plume simulated maxima for
7

almost any distance downwind or averaging time. This was most evident for the shorter

averaging times that were 24-hours or less. The IWAQM concludes from these results

that use of a plume model as a screen for a puff model’s impacts is unlikely to be

successful. In Section 4.8, a summary is presented of comparisons of results obtained

by using a puff model with single station meteorology (a screening analysis) versus fully

generated wind fields (a refined analysis) for each hour. These results suggest that the

maximum concentration values simulated using the proposed screening approach for a

receptor ring may occasionally underestimate results obtained from a refined model

simulation. To address this tendency, IWAQM recommends use of the maximum

concentration found anywhere on the receptor rings, rather than limiting the analysis to

only receptors within the Class I area(s) of concern (as would be the case in a refined

analysis). These conclusions are for maxima on receptor rings at fixed distances from

isolated point sources where the terrain was relatively flat. 

. 

With these thoughts in mind, the following CALPUFF screening procedure is

suggested by IWAQM (as outlined in Table 1): 

1) generate five years of ISCST3 input meteorology using PCRAMMET,

2) generate an ISCST3 control file (use standard ISC defaults and create

receptor rings as appropriate for the application); use the ISC2PUF

conversion program to create the CALPUFF control file,

3) edit the CALPUFF control file to select MESOPUFF II chemistry, and

specify domain-wide background concentration values for ozone and

ammonia (see Section 2.2.2),

4) run CALPUFF with the ISCMET.DAT data option, and pick the maximum

concentration for each pollutant, for each receptor ring and averaging time

modeled. Perform increment and AQRV comparisons as required. For

haze impact assessment, use the FLM provided “clean” background

extinction coefficient and assume a RH value of 90%.

In Step 1 above, it is anticipated that an update will be made to PCRAMMET and the

Meteorological Processor for Regulatory Modeling (MPRM) so that they will list the

relative humidity for each hour, along with the other parameters needed to compute

deposition. With this update, the MESOPUFF II chemistry can be activated within

CALPUFF. In Step 2 above, it is envisioned that receptor rings would be created that

would pass through the Class I area(s) of interest. The IWAQM recommends that the

placement of the receptor rings, and assumptions to be employed in the CALPUFF

model runs be discussed with the applicable reviewing authorities, prior to actually 
8

Table 1. Outline of recommendations for screening analysis. Given judgement

required in receptor ring placement and background values to be assumed in

CALPUFF and CALPOST analyses, applicants should gain agreement on how

coordination with Federal Land Managers (FLM) and other reviewing authorities will

be addressed, prior to conducting runs and analyses.

Meteorology Use five years of PCRAMMET (extended output for deposition).

Receptors Receptors at least every two degrees on rings that encircle source

and pass through the Class I area(s) of interest.

Dispersion 1. Use ISC2PUF to convert an ISC3 control input file for use by

CALPUFF.

2. Edit control file to use MESOPUFF II chemistry; use wet and dry

deposition (use default setups for these).

3. Use domain average background values for ozone and ammonia

for area.

4. Run CALPUFF using ISC meteorology option (note, define 6 to 10

layers in vertical; top layer should extend above maximum mixing

depth expected); horizontal domain extending 50 to 80 km beyond

outer receptor ring.

Processing 1. For PSD increments: use maximum 3-hr, 24-hr and annual SO2

and PM10 and maximum annual NO for comparison with allowable 2

limits.

2. For haze: use maximum 24-hr SO , NO , and HNO values; 43 3

= -

assume 90% RH for f(RH) for day, calculate extenction coefficients for

each pollutant (see Section 3.2); and compute the precent change in

extinction using the FLM supplied background extinction, as described

in Section 3.

3. For total S or N deposition: convert deposition flux to

kg/(hectaryear) using maximum values of annual SO , SO , NO , 24 3

= -

HNO , and NOx as described in Section 3. 3

conducting any screening analyses. These up front discussions are essential for

defining the AQRV’s of interest, which dictate the averaging times and pollutants of

interest for the AQRV assessments. The maximum 3-hr, 24-hr and annual SO and 2

PM10, and the annual nitrogen dioxide concentrations would be compared with the

current standards and PSD increments as required by the applicable reviewing

authorities. If a haze impact assessment is required, then use the 24-hour maximum

sulfate, nitrate and primary particulate concentrations in equation (8) of Section 3.2. If

total nitrogen or total sulfur deposition impacts are required, then use the maximum

annual concentration values and follow the procedures as described in Section 3.3. 
9

2.2 Refined Analysis

Table 2 outlines the basic IWAQM recommendations for a refined analysis. It is

understood that conducting a CALMET and CALPUFF analysis for any scale of

application will necessarily involve case-specific judgements. It is IWAQM’s conclusion

that it is not possible to prescribe all the decisions needing to be made. We have

attempted to provide suggestions where possible. We have recommended that all

users of CALMET and CALPUFF start with a common set of default conditions and

input data. Then, as the decisions are made to change default settings and discard or

augment the input data, these can be discussed and reviewed with the applicable

authorities, prior to actually committing expensive and time-consuming resources. 

Within this context use of technical review committees, as suggested in the Regional

Approach (Section 4.5), would greatly assist applicants in these endeavors.

2.2.1 Meteorology

Expertise Needed

Currently, developing CALMET meteorological fields is considered a difficult task

just managing the sheer volume of input and output data of CALMET, and excellent

computer skills are needed to manage the operation of the various processors to

CALMET. The software was not written to accept a variety of input data formats. The

software was developed with the assumption that the user is capable of screening the

data for anomalous values. It was assumed that if the data are not in the required

format, the user has the programming skills to write special programs to translate the

data format to the format required. 

Renovating these programs was beyond the resources available to IWAQM. 

The IWAQM focused what resources it had to the issues of testing the technical

aspects of the modeling system (comparisons with tracer experiments, enhancement of

processing controls, etc.) to see if the CALMET/CALPUFF modeling system was

technically sufficient for routine use. It has been assumed that support to renovate

these processors can be found, if and when, the CALMET/CALPUFF modeling system

becomes a recommended modeling approach. 

Currently, adjusting and tailoring the CALMET options to see if the generated

wind fields are reasonable requires strong computer graphics skills to visualize the

results generated by CALMET. This component of the analysis can be simplified by

using existing visualization software.

The control of the CALMET options requires expert understanding of mesoscale

and microscale meteorological effects (such as terrain slope flows) on meteorological 
10

Table 2. Outline of recommendations for refined analysis. Given judgement required

in CALMET, CALPUFF and CALPOST processing, applicants must gain agreement

on how coordination with Federak Land Managers (FLM) and other reviewing

authorities will be addressed, prior to conducting runs and analyses.

Meteorology 1. Use five years of representative hourly NWS surface and

precipitation observations, and twice-daily upper air observations. On

a case-by-case basis, one may use less than five years of FDDA-MM

data with representative NWS data; or use less than five years of onsite meteorological data with available NWS data.

2. CALMET (minimum of 6 to 10 layers in vertical; top layer must

extend above maximum mixing depth expected); horizontal domain

extending 50 to 80 km beyond outer receptors and sources being

modeled; terrain elevation and land-use data resolved for situation

(tailor land-use parameters to situation).

Receptors Within Class I area(s) of concern, provide coverage tailored to

situation (insure FLMs concur ).

Dispersion 1. CALPUFF with default dispersion settings.

2. Use MESOPUFF II chemistry; use wet and dry deposition (use

default setups for these).

3. Define background values for ozone and ammonia for area (tailor

spatial variability to situation needs and data availability).

Processing 1. For PSD increments: use highest second-highest 3-hr, 24-hr

concentration values, and use the maximum annual concentration

values of SO and PM10 and maximum annual for NO for 2 2

comparisons with allowable limits.

2. For haze: process the 24-hr SO , NO , and HNO values adjusted 43 3

= -

using hourly RH as discussed in Section 3; calculate extinction

coefficients for each pollutant (Section 3.2), compute the percent

change in extinction using the FLM supplied background extinction, as

described in Section 3; compare with thresholds as directed by

applicable FLMs and reviewing authorities.

3. For total S or N deposition: convert deposition flux to

kg/(hectaryear) using maximum values of annual SO , SO , NO , 24 3

= -

HNO , and NOx as described in Section 3; compare with threshold as 3

directed by applicable FLMs and reviewing authorities.

conditions, and finesse to adjust the available processing controls within CALMET to

develop the desired effects (e.g., Section 4.9.2). The IWAQM does not anticipate a

lessening in this required expertise in the future. Developing three-dimensional timevarying fields of meteorological conditions is a demanding task, which can not be left to

unskilled or inexperienced staff. The IWAQM does not foresee a time when
11

development of time-varying mesoscale meteorological wind fields (by whatever

means) will become as simplified as the running of the ISC3 modeling software has

become in recent years. 

Appendix A provides a listing of the default settings recommended by IWAQM at

this time. Some of these settings require testing, and we have attempted note these. 

The information provided in Appendix A should not be interpreted as a cookbook

approach to be applied, regardless of results obtained. 

Length of Modeling Assessment

Significant year-to-year variations were seen in the Demonstration Assessment

(Section 4.1) and also in the developmental work towards attempting to build a new

screening methodology (Section 4.8). These results suggest that several years of

refined analysis are needed to address the variation in pollution impacts likely to occur. 

For consistency with other Guideline requirements, a five-year period of analysis using

representative NWS meteorological data is recommended. CALMET employs

diagnostic algorithms to tailor the available meteorological data for slope flow effects,

land-sea circulations, etc. If special meteorological observations are available within

the modeling domain that would assist CALMET’s diagnostic analysis, or if FDDA-MM

data are to be employed in the CALMET analysis, then less than a five-year period of

analysis can be accepted, on a case-by-case basis by the applicable reviewing

authorities. This accommodates use of refined meteorological data for those locations

where refined meteorological data have been suitably processed, or where mesoscale

meteorological campaigns have been accomplished. 

Geophysical Data

Terrain heights and land-use are needed for input to CALMET. The IWAQM has

provided one set of these values for the contiguous United States. The grid resolution

is approximately 900 m for the terrain data and 1/12 degree latitude (9.25 km) by 1/8

degree longitude (9.8 km at 45latitude) for the land-use data (CALMET, CALPUFF,

And CALPOST Modeling System (Version 1) CD-ROM; see Appendix C). Default

values characterizing the surface parameters associated with each land-use class are

also provided. The resolution of this data set is considered adequate for assessments

involving transport distances of 50 km and greater, although the land-use categorization

should be examined carefully. The resolution of this data set may also be adequate for

many local-scale assessments. 

The IWAQM recommends use of this data set, as a first choice. The IWAQM

recognizes that there are instances when these terrain heights and land-use data must

be considered inappropriate, such as the situation discussed in Section 4.9.2. It is

anticipated that rejection of this data set will most often be for treatment of sources

located in very rugged terrain, which is anticipated to significantly affect the transport

trajectory of the plume. Using this data set, as a first choice with assignment of the
12

default surface parameters as listed on the distribution CD, should provide consistency

in future analyses and helps to standardize the input requirements for software

development.

Another source of terrain heights and land-use data is from the United States

Geological Survey (USGS). Some of these data can be accessed through the World

Wide Web. The general USGS site is at http://www.usgs.gov. At the time of this

writing, the data was available at http://edcwww.cr.usgs.gov/doc/edchome/

ndcdb/ndcdb.html. 

Precipitation Data

Precipitation is notoriously spotty, with many localized maxima and minima. It is

recommended by IWAQM that all precipitation reports reasonably available be used. 

Precipitation data are available for the United States from the National Climatic

Data Center (NCDC) in a format called TD-3240. No data filling is necessary for these

data sets for use by CALMET. But as discussed in EPA (1995a), the formatting of

these data for a large domain is not trivial. The software available is not robust and

could stand to be improved.

Another source of precipitation data is from private firms that purchase the data

from NCDC, repackage it, and provide software for extracting the repackaged data. 

This may be an attractive alternative to some users. 

National Weather Service Data

The number of surface and upper air sites will be determined by the size of the

modeling domain and the availability of meteorological data. It is recommeded that for

refined analyses one would use all sites that are within and near the modeling domain,

so as to provide as good a characterization as possible of the spatial variation of the

meteorological conditions.

The CD-ROM data sets (Appendix C) are made available by the National

Climatic Data Center. These hourly surface weather observations (Solar and

Meteorological Surface Observation Network (SAMSON) and Hourly United State

Weather Observations (HUSWO) 1990-1995), and twice-daily upper air observations

(Radiosonde Data for North America) are recommended by IWAQM as first choice for

acquiring access to these data. Using these data sets, as a first choice, should provide

consistency in future analyses and helps to standardize the input requirements for

software development. 

The current CALMET software requires the user to closely inspect the upper-air

data prior to use. Since the program does not fill in missing levels or time periods

(which sometimes occur in the observations), the user should inspect the data to insure
13

that complete data is available at least for all levels below and just above the

anticipated maximum mixing depth for the modeling domain. Furthermore, CALMET

can not process if the time difference between successive upper-air soundings is

greater than 12 hours. If this occurs, the user must use judgement to fill in the requisite

data. When multiple NWS sites are used for characterizing the hourly surface weather

conditions, missing observations from one station can be handled simply by filling in a

missing data indictor for that site for that hour. But the user should review the hourly

surface observations to insure that the data coverage in space and time is adequate. 

This requires judgement and finesse and given the complexities of long-range transport

analysis, is not amenable to cookbook cures.

FDDA-MM Data

As mentioned in Section 4.4 there are various groups capable of developing

FDDA-MM data suitable for use as input to the CALMET/CALPUFF modeling system. 

Alternatively, consideration could be given to using the 1990 MM4 data set (NCDC,

1995) or other data sets which might be completed for other years. The problem with

these data sets is that the they are costly to construct and advances are currently being

made at such a rapid pace, they are becoming dated almost as soon as they are

constructed. Furthermore, there are situations defined in the Guideline when on-site

meteorological data are to be collected and used in the modeling assessments (e.g.,

when using CTDMPLUS for a complex terrain impact assessment, where the transport

distances are less than 50 km). Hence, there appears to be a never-ending need to

construct additional years of data. The IWAQM recommends finding a link to some

operational source of FDDA-MM data, as the optimal solution.

Towards this end, IWAQM recommends that links to the operational model

outputs that are produced by the NOAA Mesoscale Modeling Branch (MMB) (see

discussion by Schulze and Turner, 1998). This group is developing data suitable for

use in the CALMET/CALPUFF modeling system. They have an active plan to refine

and improve the modeling science and to reduce the grid size in future years. These

model outputs are being developed to directly support operational needs of the National

Weather Service, and should prove to be a stable source of such data in the future.

The major obstacle is access to these data. An operational means for gaining

easy access to comprehensive mesoscale meteorological data sets has yet to be

developed. Therefore, IWAQM recommends that a solution be developed that will

provide the public routine, inexpensive access to such data. The data can be

depended on being available in future years. Users who either are required to collect

and use on-site data, or need such data for best characterization of their situations, can

use CALMET to manage the blending of their on-site data, with routine NWS

observations and available FDDA-MM data.
14

Once an operational link is established to an operational source of FDDA-MM

data and a sufficiently long record of the data is available, IWAQM recommends the

use of these data. 

2.2.2 Chemistry

The MESOPUFF II chemistry option, currently available in CALPUFF, is

adequate for representing gas phase oxidation of sulfur dioxide to sulfate and for the

nitrate chemistry. The algorithms currently do not adequately account for the aqueous

phase oxidation of sulfur dioxide to sulfate. The aqueous phase chemistry can

dominate the formation of sulfate. Therefore, in many applications sulfate is likely to be

underestimated.

The IWAQM recommends use of the MESOPUFF II chemistry option, although it

is recognized that this module may grossly underestimate the conversion of sulfur

dioxide to sulfate when the pollutants interact with clouds or fog. 

Ozone Background 

CALPUFF provides two options for providing the ozone background data: (1) a

single, typical background value appropriate for the modeling region, or (2) hourly

ozone data from one or more ozone monitoring stations. The second and preferred

option requires the creation of the OZONE.DAT file containing the necessary data. For

the Demonstration Assessment, the domain was large (700 km by 1000 km) such that

the second option was necessary. The IWAQM does not anticipate such large domains

as being the typical application. Rather, it is anticipated that the more typical

application will involve domains of order 400 km by 400 km or smaller. But even for

smaller domains, the ability to provide at least monthly background values of ozone is

deemed desirable. The problem in developing time (and perhaps spatial) varying

background ozone values is having access to representative background ozone data.

Ozone data are available from EPA’s Aerometric Information Retrieval System

(AIRS); however, AIRS data must be used with caution. Many ozone sites are located

in urban and suburban centers and are not representative of oxidant levels experienced

by plumes undergoing long range transport.

Ammonia Background

 A further complication is that the formation of particulate nitrate is dependent on

the ambient concentration of ammonia, which preferentially reacts with sulfate. The

ambient ammonia concentration is an input to the model. Accurate specification of this

parameter is critical to the accurate estimation of particulate nitrate concentrations. 

Based on a review of available data, Langford et al. (1992) suggest that typical (within a

factor of 2) background values of ammonia are: 10 ppb for grasslands, 0.5 ppb for

forest, and 1 ppb for arid lands at 20 C. Langford et al. (1992) provide strong evidence o
15

that background levels of ammonia show strong dependence with ambient temperature

(variations of a factor of 3 or 4) and a strong dependence on the soil pH. However,

given all the uncertainties in ammonia data, IWAQM recommends use of the

background levels provided above, unless specific data are available for the modeling

domain that would discredit the values cited. It should be noted, however, that in areas

where there are high ambient levels of sulfate, values such as 10 ppb might

overestimate the formation of particulate nitrate from a given source, for these polluted

conditions. Furthermore, areas in the vicinity of strong point sources of ammonia, such

as feed lots or other agricultural areas, may experience locally high levels of

background ammonia.

2.2.3 Dispersion

Expertise Needed

The control of the CALPUFF options requires expert understanding of terrain

affects on meteorological conditions and some finesse to adjust the available

processing controls. Appendix B provides a listing of the default settings for CALPUFF

recommended by IWAQM at this time. Some of these settings require testing, and

IWAQM has attempted note these. The information provided in Appendix B should not

be interpreted as a cookbook approach to be applied, regardless of results obtained. 

Emissions

Developing an inventory with agreed upon emission rates is not trivial. The

inventory could differ depending on whether the analysis is addressing NAAQS

assessments (which typically address maximum allowable emission rates from only

PSD sources) versus AQRV assessments (which typically address actual current

emission rates from all existing sources). The IWAQM recommends that the manner in

which the sources and emissions are to be characterized be agreed upon in the initial

up-front discussions with the reviewing authorities.

Receptors

For some Class I areas the FLMs have developed receptor networks for use in

the modeling analyses, which facilitates assessment of cumulative impacts from

successive applicants. In such cases, the applicants should use these predefined

receptor networks. In other situations, IWAQM suggests use of judgement. The more

rugged the terrain and the presence of local sources being included in the analysis will

necessitate use of more closely spaced receptor networks in order to adequately

characterize the concentration patterns to be simulated for the Class I area(s). This is

another area where judgement and review by others will be needed.
16

Local-scale

The IWAQM is recommending use of the CALPUFF modeling system for the

characterization of all sources being explicitly modeled. This eliminates the need to

simulate the long-range impacts (those involving transport greater than 50-km)

separately, and then combine these results with those obtained using some other

model for the local-scale impacts (those involving transport of less than 50-km). We

have CALPUFF and ISC comparison results using both steady-state and non-steadystate hourly meteorology. It was the conclusion of IWAQM that CALPUFF could

reproduce the steady-state results of the ISC plume dispersion model. A benefit of

using one model for all sources is that CALPUFF has the MESOPUFF II chemistry,

which provides characterization of pollutant species that are not treated by currently

available local-scale models (such as ISC and CTDMPLUS). 

Comprehensive tests results are not available comparing CALPUFF with

CTDMPLUS for steady-state impacts on isolated hills and ridges. The IWAQM

assumes that once these have been accomplished and the results have been shown to

be similar, that long-range impact assessment with CALPUFF (using fully developed

CALMET meteorology) can also include more explicit hill impaction assessments, as

necessary, for all sources regardless of the transport distances involved.

2.3 Practicalities

The IWAQM recommendations for conducting a long-range transport screening

analysis are presented in Section 2.1, and the IWAQM recommendations for

conducting a long-range transport refined analysis are presented in Section 2.2. The

focus of these discussions was how to run the simulations and the management of the

input and output data. There are other practical concerns that applicants and reviewing

authorities should be mindful of, namely: the uncertainties associated with the

screening analysis, the uncertainties associated with the refined analysis, differences

between plume and puff simulation results, and commensurate difficulties in providing

technical oversight.

2.3.1 Screening procedures uncertainties

In Section 4.8 comparisons are presented of CALPUFF simulation results

generated either through the use of ISC or CALMET meteorology. Anticipating that

most analyses will involve a moderate to tall stack, of order 35-m to 200-m in height, it

is seen that the screening estimates of sulfur-dioxide and sulfate concentration maxima

obtained using ISC meteorology, typically range within ±70% of that simulated using

CALMET meteorology. The sulfur dioxide and sulfate deposition fluxes obtained using

ISC meteorology, typically range within ±60% of that simulated using CALMET

meteorology. This suggest that the screening analysis as proposed is not providing a

biased (overestimate) of these impacts. It was for this reason that IWAQM

recommended that all receptors on the ring be included in the screening assessment. It
17

was hoped that this would provide a measure of conservatism to the screening

analysis. Adding a measure of conservatism is deemed reasonable, as the proposed

screening analysis completely disregards the terrain and land-use induced wind effects,

that would arise if fully-developed three-dimensional wind fields were developed using

available surface and upper-air observations. The IWAQM concludes that the impacts

estimated by the screening procedure proposed are conservative and yet less onerous

than results as would be obtained by the Phase 1 Level 1 screening, and addresses

concerns raised at the Sixth Modeling Conference

2.3.2 Refined modeling uncertainties

In Section 4.6 comparison results of CALPUFF simulations with tracer field data

are provided. The studies presented are considered representative of a few of the

better tracer field studies that involve comprehensive sampling along arcs at downwind

distances of order 50- to 100-km. As summarized in Section 4.6, in general the

CALPUFF simulated concentration values were within a factor of two of that observed. 

Little differences in overall performance was seen, whether Pasquill or similarity

dispersion curves were employed. This is understandable since as transport times and

distances increase, the dispersing material is becoming well-mixed in the vertical, and

the horizontal extent of the dispersing material is more related to the wind field

variations than is the rate of relative dispersion about simulated puff centroids. This is

dramatically made apparent by the results shown in Section 4.7, where direct

comparisons were made of the ISC plume model and CALPUFF. In these comparisons

it was shown that for steady-state assumptions, CALPUFF could suitably mimic results

as would be obtained by ISC. And yet with identical specification of the dispersion and

meteorology, once the puff model was allowed to simulate the ‘causality’ of hour-byhour variations, the puff model’s results no longer were similar to that obtained by ISC. 

In fact, as a result of explicitly treating calms and wind reversal effects by the puff

model, the simulated maximum concentrations by CALPUFF were generally greater

than that simulated by ISC for all transport distances and averaging times.

The quality and uncertainties associated with long-range transport simulations is

driven more by the characterization of the mixing depth and by the characterization of

the transport winds. Better characterization of the mixing depth may be possible in the

future, but it should be understood that the mixing in the vertical reflects both local and

mesoscale effects. The influence of local variations in land-use (forests, lakes,

farmland, cities) can be significant. Hence uncertainties of order ±40% in mixing depth

are likely (Irwin and Paumier, 1990) and will be difficult to avoid. Improvement in the

characterization of the transport winds is possible through the use of FDDA-MM

meteorological data, as discussed in Section 4.3. Both the comparisons of simulated

trajectories and the comparisons of trajectories derived using the CAPTEX tracer field

show improvement through the use of sophisticated mesoscale meteorological data

employing FDDA. 
18

Based on the tracer comparison results presented in Section 4.6, it appears that

CALPUFF provides reasonable correspondence with observations for transport

distances of order 100 km. Most of these comparisons involved concentration values

averaged over 5 to 12 hours. The CAPTEX comparisons, which involved comparisons

at receptors that were 300 km to 1000 km from the release, suggest that CALPUFF

tends to overestimate surface concentrations by a factor of 3 to 4. Use of the puff

splitting option in CALPUFF might have improved these comparisons, but there are

serious conceptual concerns with the use of puff dispersion for very long-range

transport (300 km and beyond). As the puffs enlarge due to dispersion, it becomes

problematic to characterize the transport by a single wind vector, as significant wind

direction shear may well exist over the puff dimensions.

With the above thoughts in mind, IWAQM recommends use of CALPUFF for

transport distances of order 200 km and less. Use of CALPUFF for characterizing

transport beyond 200 to 300 km should be done cautiously with an awareness of the

likely problems involved. Since the long-range transport results appear to be relatively

insensitive to the exact selection made for characterizing the puff dispersion

parameters, IWAQM recommends use of dispersion parameters that provide results

most similar to the local-scale model of choice (which currently is ISC). It would appear

that CALMET is capable of treatment of highly complex wind fields that are strongly

influenced by terrain slope flows (Section 4.9.2). However, as mentioned at the Sixth

Modeling Conference, there are situations that one can imagine involving highly rugged

terrain, that any model simulation’s results must be viewed as uncertain. 

2.3.3 Secondary pollutant uncertainties

The CALPUFF simulation for gas phase oxidation of sulfur dioxide to sulfate and

for the nitrate chemistry is considered adequate. The algorithms currently do not

adequately account for the aqueous phase oxidation of sulfur-dioxide to sulfate, which

can become dominant in the presence of fog or clouds. Even if planned updates to

CALPUFF include consideration of the aqueous phase chemistry, the input data may

preclude routine use of this enhancement. Finally, it must be mentioned that evaluation

studies have not been conducted regarding CALPUFF’s simulations of secondary

formed sulfate and nitrate. The IWAQM is not aware of comprehensive tracer-fielddata studies that would lend themselves to such evaluation studies. Given that

aqueous phase chemistry is not treated, it is likely that CALPUFF simulations of sulfate

would be less than observed. Mention is made in Section 4.9.1 that the total nitrogen

deposition estimates by CALPUFF were about a factor of 3 less than estimates

provided by a more physically complete model (RADM). Notwithstanding these obvious

deficiencies, IWAQM recommends use of CALPUFF’s estimates of sulfate and nitrate

for purposes of addressing the need to assess AQRV impacts in Class I areas (in lieu

of assuming that there are no impacts at all). 
19

2.3.4 Technical oversight and review

It would be convenient if objective criteria and cookbook procedures could be

constructed that would preclude inappropriate application of air dispersion models. 

This has proved to be troublesome for local-scale modeling, and likely is impossible for

mesoscale and long-range transport modeling. As in any air quality simulation, the

usefulness of the results obtained depends mostly on the expertise brought to the

analysis in characterizing the situation, and on the experience applied in interpreting the

results obtained. In response to these considerations, IWAQM has attempted to warn

the modeling community that conducting a long-range transport assessment requires

esperts. We have also tried to warn the modeling community that application of the

CALPUFF modeling system to any situation will require expert judgment, it will likely

involve site-specific decisions, and it will require strong interaction and coordination with

the applicable reviewing authorities. 

In this regard, the use of technical review committees, as suggested in the

Regional Approach (Section 4.5) would likely prove useful to both the applicants and to

the reviewing authorities. These technical committees could assist in sorting through

the site-specific decisions, and they could provide a forum for reaching consensus. 

Having a standing technical committee would provide applicants with some assurance

of being treated equitably, and could provide data sets for use to facilitate comparability

between individual analyses. Whether such a committee would prove useful is

dependent on availability of experts and on the ability to obtain long-term commitments

of service. Furthermore, not all Class I areas will require use of such committees. 

Hence, while IWAQM endorses the use of such committees, IWAQM does not

recommend Federal agencies mandate or require their use.
20

3.0 TRANSFORMATIONS, VISIBILITY, AND DEPOSITION

In principle, estimating the impact of emissions from an individual source is best

accomplished using a Lagrangian dispersion model, as a Lagrangian model ‘follows’

the emissions as they are transported downwind. This provides within the model the

ability to directly assess how much each source’s emissions are impacting each

receptor in the analysis. However, there are some physical processes (like nonlinear

chemical transformations) that can be more explicitly and completely characterized

using Eulerian grid modeling techniques. To treat nonlinear chemical transformations

within a Lagrangian modeling framework is presently so computationally demanding as

to be impractical for routine use.

 The IWAQM recognized these limitations and tradeoffs between Lagrangian and

Eulerian grid modeling techniques. The purpose of the Phase 2 recommendations was

to recommend techniques useful for permitting individual PSD sources. For individual

source impacts involving complicated terrain and transport distances of order 50 to 250

km, a Lagrangian puff model like CALPUFF is an optimal choice. In this section we

summarize the approximations made in the CALPUFF chemical transformations and

associated inherent limitations. We then summarize the methodology recommended

for computing haze and deposition impacts in Class I areas. Finally, we discuss

conceptually how assessment of adverse impact of Air Quality Related Values is

somewhat different than traditional NAAQS assessments.

While drafting this report, the National Park Service, the U.S. Fish and Wildlife

Service and the U.S. Forest Service have been holding intensive meetings to promote a

greater consistency in the procedures Federal Land Managers use in identifying and

evaluating AQRV impacts. We have discussed in this report the assessment of

regional visibility impacts using the deciview, which at the time of the drafting of this

report was the preferred metric. As time progresses, it is looking more like the change

of extinction may become the preferred metric. Hence, although the information

provided here is useful, the details and implementation may be somewhat different as a

consequence of the ongoing discussions. For the latest information on procedures and

metrics, we suggest visiting the web site: http://www.nature.nps.gov/ard/flagfree/index.html. 

3.1 Chemical Transformations

The MESOPUFF II chemistry option, currently available in CALPUFF(4.0), is

adequate for representing gas phase oxidation of SO to SO and for the nitrate 2 4

=

chemistry. The algorithms currently do not adequately account for the aqueous phase

oxidation of SO to SO . The aqueous phase chemistry can dominate the formation of 2 4

=

sulfate. Therefore, in many applications sulfate is likely to be underestimated. A further

complication is that the formation of particulate nitrate (NO ) is dependent on the 3

-

ambient concentration of ammonia, which preferentially reactswith SO . The ambient 4

=

ammonia concentration is an input to the model. Appropriate specification of this
NH4NO3  HNO3  NH3

21

parameter is critical to the estimation of realistic values of particulate nitrate

concentrations. 

For instance, if we were simulating effects of emissions coming from an as yet to

be built new single source, then the ammonia available to react with these new

emissions could be estimated from monitored background levels of ammonia. If there

are large major sources near the proposed new source included in the analysis, then

the background ammonia should in principle be adjusted to a value somewhat above

that monitored to reflect ammonia available that these existing sources typically

scavenge from the atmosphere. By way of this example, we see that CALPUFF is most

easily applied for isolated new emissions, and becomes more problematic as the

number of sources increases. This supports limiting application of CALPUFF to a

relatively few sources of emissions, so that the ‘background’ levels of ozone and

ammonia can be derived using appropriate monitoring data.

The spatial and temporal scales, where CALPUFF might be used, are long

 enough so that the chemical conversion of SO to SO and NO to HNO are of 24 X 3

=

interest. The oxidation of SO and NO may occur by gas and aqueous phase X X

reactions. The gas phase reactions for both SO and NO involve free radical X X

photochemistry and, therefore, are coupled to the oxidation of reactive organic gases

(ROG). Homogeneous gas phase reaction is the dominant SO oxidation pathway 2

during clear, dry conditions. Ozone and hydrogen peroxide are believed to be the

principal oxidants for aqueous phase oxidation of SO . Homogeneous gas phase 2

reactions may convert SO at most a few percent per hour, whereas aqueous phase 2

reactions can convert SO up to 100% per hour. 2

The oxidation of NO is dependent on gas phase ROG/NO /O photochemistry. It X X3

is generally more rapid than SO gas phase oxidation. NO can be oxidized to nitric 2 X

acid (HNO ) and organic nitrates (RNO ) such as peroxyacetylnitrate (PAN). HNO 33 3

combines with ammonia gas to form solid or aqueous ammonium nitrate (NH NO ). 4 3

Unlike sulfate formation, the nitrate process is reversible. Equilibrium is established

between nitric acid, ammonia, and ammonium nitrate:

The equilibrium constant for this reaction is a nonlinear function of temperature

and relative humidity. The equilibrium constant can vary several orders of magnitude

over a typical diurnal cycle. Given fixed amounts of total nitrate, ammonia, and water

vapor, higher NH NO concentrations are expected at night due to lower nighttime 4 3

temperatures and higher relative humidities. Thus, the nitrate aerosol can not be

considered a stable product like sulfate. 

The transformation rate expressions, used in the MESOPUFF II algorithm, were

developed statistically analyzing hourly transformation rates produced by a
k1 36R 0.55[O3]

0.71S 1.29  k1(aq)

k2 1206[O3]

1.5S 1.41[NOx

]

0.33

k3 1261[O3]

1.45S 1.34[NOx

]

0.12

22

photochemical model. Plume SO /NO dispersing into background air containing ozone X X

and reactive hydrocarbons was simulated over a wide range of conditions representing

different solar radiation intensities, temperatures, dispersion conditions, background

ozone concentration and surface-level relative humidity, RH, plume NO concentrations X

and emissions times. The following transformation rate expressions, representing

curve fits to the daytime hourly conversion rates predicted by the photochemical model,

were determined:

Where, k is the SO to SO transformation rate (percent/hour), 1 24

=

k is the NO to HNO + RNO transformation rate (percent/hour), 2 X 33

k is the NO to HNO (only) transformation rate (percent/hour), 3 X3

R is the total solar radiation intensity (kw/m ), 2

S is a stability index from 2 to 6 (PG stability A&B = 2, C=3, etc.,

RH is the surface-level relative humidity (percent),

[O ] is the background ozone concentration (ppm), 3

[NO ] is the plume NO concentration (ppm), and, X X

k is the aqueous phase SO oxidation term. 1(aq) 2

The term, k peaks at a value of 3 percent/hour at 100% relative humidity. This 1(aq)

is much less than might be expected if the plume interacted with clouds or fog. These

transformation rate expressions only apply during the day. At night oxidation rates of

0.2% and 2.0% for SO and NO , respectively, are suggested as default values in the 2 X

model.

3.2 Visibility Analysis

In the context of the Phase 2 recommendation, the focus of the visibility analysis

is on haze. These techniques are applicable in the range of thirty to fifty kilometers and

beyond from a source. At source-receptor distances less than thirty to fifty kilometers,

the techniques for analyzing visual plumes (sometimes referred to as ‘plume blight’)

should be applied. 

There are two approaches to determining visibility effects (NAPAP Report to

Congress). One is a technically rigorous, complex, and situation-specific method, while

the other is a more generalized approach. The more rigorous approach requires the

calculation of aerosol growth dynamics and the application of Mie theory to determine

the optical characteristics of the aerosol distribution. Sophisticated radiative transfer

models are then applied, using the aerosol optical characteristics, the lighting and

scene characteristics and the spatial distribution of the pollutants to calculate the path
VR(km) 3.912

bext(km 1

)

23

(1)

and wavelength of image-forming and non-image-forming light that reaches a specific

observer at a specific time and date from all points in the scene being viewed. While

this detailed analysis may be useful for assessing specific cases, it is impractical for

addressing haze issues where visibility is experienced in a nearly infinite variety of

situations and where detailed characteristics of the pollution, lighting, and scene

conditions are rarely known.

The generalized approach uses aerosol species extinction efficiencies, with water

growth functions, to determine the light extinction coefficient of the aerosol, from its

composition and from the relative humidity of the atmosphere. The extinction

efficiencies and relative humidity dependence of the aerosol are based upon typical

results from the more rigorous analyses mentioned above. 

A generalized approach is recommended by IWAQM for Class I area analyses. 

Under this approach, the concentrations of pollutants (in this case calculated by an air

quality model) are used to calculate the extinction coefficient due to these pollutants. 

This is then compared against the light extinction coefficient of the background air. A

constant fractional change in the extinction coefficient produces a similar perceptual

change for a scene regardless of baseline conditions. Therefore, under cleaner

visibility conditions, relatively less pollution will cause a perceptible change than under

more polluted background conditions. 

Visibility is an instantaneous phenomenon. When an observer looks at a scene,

the view is what is seen at that moment. Many of our measurements and modeling

techniques deal with averaged values from one hour and longer. Therefore, some

consideration is needed to accommodate this dichotomy. 

Visibility Parameters

Visibility is usually characterized by either visual range (VR) (the greatest distance

that a large dark object can be seen) or by the light-extinction coefficient (bext) (the

attenuation of light per unit distance due to scattering and absorption by gases and

particles in the atmosphere) (Sisler,1996). Under certain assumed conditions, these

parameters are inversely related to each other by Equation 1. 

The dimensions of VR are length and the dimensions of bext are 1/length. Visual range

is usually expressed in kilometers. The extinction coefficient is sometimes expressed

as “inverse kilometers” (km ) or as “inverse megameters” (Mm ) (the reciprocal of 1 -1 -1

million meters). If bext is expressed in Mm , the coefficient 3.912 in Equation 1 -1

becomes 3912. The value 3.912 = -ln(0.02), which assumes a two percent contrast
dv 10 ln

bext(km 1)

0.01(km 1

)

24

(2)

threshold for the viewer. Other researcher have assumed a five percent threshold,

which would change the value to 2.995.

A constant fractional change in the extinction coefficient produces a similar

perceptual change for a scene regardless of baseline conditions. Using the relationship

of a constant fractional change in the extinction coefficient resulting in a similar

perceived visual change, an alternate visibility index, the deciview (dv) has been

defined (Equation 2). 

This index was specifically designed so that anywhere along its scale, haziness

changes that are equally perceptible correspond to the same deciview difference. For

example, a 3dv difference caused by a change in air quality should result in about the

same perceived change in haziness, whether under clean or highly polluted conditions. 

This characteristic of the deciview scale requires that the scene being viewed has

sufficient sensitive scenic features to detect changes in visibility from the baseline haze

level. No one scene is likely to have such scenic features for all conceivable haze

levels. However, the nearly infinite variety of scenes available, where hazes are

concerned, ensures that many will have the desired characteristic for any haze level

(NAPAP report to congress).

Calculating the Extinction Coefficient 

Visibility is degraded by light scattered into and out of the line of sight and by light

absorbed along the line of sight. Light extinction is the sum of light scattering and

absorption, and is usually quantified using the light extinction coefficient (bext). Using

the generalized approach to estimating visibility effects, outlined above, one can

calculate the extinction coefficient as the sum of its parts, i.e., bext scat abs = b + b , where

b scat abs and b are the light scattering and absorption coefficients. The light scattering

and absorption coefficients can be further broken down by their respective components. 

The scattering coefficient is affected by Rayleigh scattering (bRay) from air molecules

and from particle scattering (b ); the particles can be natural aerosol or result from air sp

pollutants. The absorption coefficient is affected by gaseous absorption (bag) and

particulate absorption (bap). Nitrogen dioxide is the only major light-absorbing gas in the

lower atmosphere; it generally does not affect hazes, although it can be an important

element in a coherent plume assessment. Therefore, only particle absorption is

considered in the suggested haze analyses. 

 

Particle scattering, b , can be broken down by the contributions of different sp

particulate species. It has been convenient to consider the scattering coefficients of
b

sp bSO4  bNO3  bOC  bSoil  bCoarse

bSO4 3 [(NH4)

2

SO4] f(RH)

bNO3 3 [NH4NO3] f(RH)

bOC 4 [OC]

bSoil 1 [Soil Mass]

bCoarse 0.6 [Coarse Mass]

b

ap 10 [EC]

bext bSO4  bNO3  bOC  bSoil  bCoarse  b

ap  bRay

25

(3)

(4)

(5)

(6)

fine particles (PM2.5) (particles with mass mean diameters less than or equal to 2.5 μm)

and coarse particles (mass mean diameters greater than 2.5μm but less than or equal

to 10μm). The fine particle scattering coefficient can be further defined by the sum of

the scattering coefficient due to sulfates (b ), nitrates(b ), organic aerosols (b ), SO4 NO3 OC

and soil (bSoil Coarse ); the coarse scattering coefficient (b ) is typically not refined any

further. Thus the particle scattering coefficient (b ) can be expressed as in Equation 3. sp

Each of the particle scattering coefficients can be related to the mass of the

components using the relationships in Equation 4. 

The quantities in brackets are the masses expressed in μg/m . (It is assumed that the 3

forms of the SO and NO are ammonium sulfate [(NH ) SO ] and ammonium nitrate 4 3 42 4

= -

[NH NO ].) The numeric coefficients are the dry scattering efficiencies (m /g). The term 4 3

2

f(RH) is the relative humidity adjustment factor. The extinction coefficients are in Mm . -1

If the dry scattering efficiencies are divided by 1000 (i.e., 0.003 instead of 3) the

resultant extinction coefficients will be in km . -1

Particle absorption (bap) is primarily due to elemental carbon (soot). For purposes

of analyzing the effects of soot on visibility in a modeling analysis, the relationship in

Equation 5 should be used. Again, the quantity in brackets is the mass of elemental

carbon in μg/m and 10 is the extinction efficiency. 3

The total atmospheric extinction can be expressed as in Equation 6. To the

extent that a source contributes to the formation of some of these constituents, those
bext bSN f(RH)  bdry

bSN 3 [(NH4)2SO4  NH4NO3]

bdry bOC  bSoil  bCoarse  b

ap  bRay

26

(7)

contributions can be summed to yield the source’s contribution to extinction. This will

be discussed in more detail below. 

Examination of Equation 4 reveals that the sulfate and nitrate components of the

extinction coefficient are dependent upon relative humidity. These aerosols are

hygroscopic and the presence of water enhances their scattering efficiency. It is

sometimes convenient to consider the sulfate and nitrate components of extinction

separately from the remaining components of Equation 6 and to keep the relative

humidity adjustment factor (f(RH)) separate. Equation 6 can then be rewritten as in

Equation 7, 

where

where bSN is the combined extinction coefficient of sulfate and nitrate, excluding the

relative humidity adjustment factor, and b is the sum of b , b , b , b , and b . dry OC Soil Coarse ap Ray

The relative humidity adjustment factor requires some further explanation. The

variation of the effect of relative humidity on the extinction efficiency of sulfates and

nitrates is shown in Figure 1. As can be seen, the effect of relative humidity on the

extinction efficiency of these aerosols is non-linear, and is several times greater at

higher relative humidities. These factors are applicable on a short-term basis. If the

particulate concentrations are only available over a longer averaging time (i.e., a 24-

hour sample or a seasonal average) then the average relative humidity adjustment

factor for that time period must be applied, not the average relative humidity. 

(Alternately, short-term extinction coefficients (i.e., 1-hour) may be averaged to yield a

longer-term average.)
0 20 40 60 80 100

Relative Humidity, RH, (percent)

0

5

10

15

20

25

f(RH)

27

Figure 1. Variation of f(RH) (see Equation 7).

Background visibility conditions

As noted previously, visibility analyses are compared against a background

condition. The estimates of background visibility conditions at Class I areas are derived

from the IMPROVE (Interagency Monitoring of PRotected Visual Environments)

network. There are several methods of obtaining estimates of the background visibility.

These include reconstructed extinction from speciated measurements of particulate

matter, direct measurement of extinction with a transmissometer, and estimates of

extinction from photographs (Malm et al., 1996). Reconstructed extinction is usually

used to estimate background conditions, since this can be directly related to pollutant

loadings. It should be noted that reconstructed extinction values from the IMPROVE

network are based on 24-hour average particulate concentrations. The temporal

average at a point is used to represent a short-term spatial average.

The background conditions provided for a Class I visibility analysis will be

representative of clean conditions. Changes in visibility are most sensitive under clean

conditions. By using clean conditions for all comparisons in a Class I analysis, it

ensures that already clean conditions will not be impaired. Additionally, the Clean Air

Act states as a national goal that the visibility in Class I areas is to be unimpaired by

man-made air pollutants and that any such impairment is to be remedied. To represent

clean conditions, the average of the cleanest 20% of the data from IMPROVE, at that

site, is generally used. Even the data from the cleanest days usually exhibit some

made-made influence. This average of 24-hour values for the 20% cleanest conditions

is used as representative of a clean background condition. 
dv 10 ln

bback  bsource

bback

28

(8)

Background conditions may be provided using any of the visibility parameters,

defined above, although the preferred method is to use the extinction coefficient, with

the hygroscopic components and other components dissociated, as in Equation 7. 

Using the form of Equation 7 allows the effects of relative humidity to be applied

consistently to both the background aerosol and the aerosol attributable to a new

source. If only one of the other visibility parameters is available, without the

dissociation of the hygroscopic components, then that value would be used. However,

the relative humidity conditions that represent the average background will likely be

different than the condition being analyzed. The usual effect of this is that the relative

effect of sulfate and nitrate emissions is overstated under high relative humidity

conditions.

Calculating a change in extinction

The modeling techniques outlined in this recommendation will provide ground

level concentrations of visibility impairing pollutants. These concentrations are then

used to calculate the extinction coefficient due to these pollutants, using the

relationships outlined in Equations 4 and 5. The results of this are then compared

against the background extinction from Equation 7. The metric used for this

comparison is usually the change in deciview (dv) from a “clean” background

condition. Thus, for a given background extinction, bback, and a source or sources

contribution to extinction of b , dv is given by Equation 8. source

These methods are embodied in the CALPOST program which post-processes

the concentrations from the CALPUFF air quality model. However, if another model is

used it is necessary to be able to perform these calculations separately. Even if the

CALPUFF system is used, it is sometimes more convenient to calculate changes in

visibility outside of the post-processor. 

Example Problem

This example assumes that a dispersion model has been run and yielded

concentrations of SO and soot (elemental carbon). From these concentrations the 4

=

analyst wishes to calculate a change in visibility. 

First, we will consider the background visibility condition. If the background 24-

hour average visibility at the Class I area of interest has a combined sulfate and nitrate

extinction coefficient (bSN) of 1.8 Mm (neglecting the effects of relative humidity) and -1

an extinction coefficient from the other components (bdry) of 19.6 Mm , then the -1

background extinction (b ), expressed in the form of Equation 7 would be: back
bback 1.8 f(RH)  19.6

bsource 0.9 f(RH)  1.0

29

In a typical analysis, the model will be run for an extended period, such as a

month, a season, annually, or for multiple years. This will produce a corresponding

number of 24-hour averaging periods, which will each need to be analyzed against the

background condition. In our example we are only considering one 24-hour averaging

period. For this example we will assume that the sources in the analysis contributed

0.218 μg/m of sulfate (SO ) and 0.05 μg/m of soot (elemental carbon). The first step 3= 3

4

is to convert the mass of SO to ammonium sulfate ((NH ) SO ), which is accomplished 4 42 4

=

by multiplying by the ratio of the molecular weights of (NH ) SO to SO , which is 42 4 4

=

1.375. This yields a concentration of (NH ) SO of 0.30 μg/m . This result is then 42 4

3

multiplied by the dry scattering efficiency of (NH ) SO (which is 3, from Equation 4), 42 4

yielding an extinction coefficient for the sulfate of 0.9 Mm ; the relative humidity -1

adjustment has not yet been applied. In this example, our modeling does not require

any conversion of the mass of soot, so we need only to multiply the soot concentration

(0.05 μg/m ) by the extinction efficiency of elemental carbon (which is 20, from Equation 3

5). This yields an extinction coefficient of 1.0 Mm . Therefore, following the form of -1

Equation 7, the source contribution would be:

The relative humidity adjustment factor for this averaging period has not yet been

applied. Our example is based on a 24-hour average. The representative hourly RH

values for this day would need to be obtained. For each hour, the corresponding f(RH)

must be obtained from Figure 28 (or a corresponding table). These values are then

averaged together. Let us assume that for this day the average f(RH) is 3.4. With the

day-average relative humidity adjustment factor (f(RH)) of 3.4, bback would be 25.72

Mm (corresponding to a visual range of 152 km from Equation 1) and b would be -1

source

4.06 Mm . Using these values in Equation 8 would yield a dv of 1.46. -1

These calculations would be repeated for each 24-hour average concentration in

the analysis, using the corresponding day-average f(RH). Background visibility

conditions may be given for seasons or months. The corresponding background values

should be used. A spread sheet program is suggested if the results are not processed

with the CALPOST processor. 

Visibility Summary

The following list summarizes the steps necessary for conducting a visibility

analysis under this recommendation.
30

 Consult with the appropriate regulatory agency and with the appropriate FLM

 Run an air quality model which yields ambient concentrations of visibility impairing

pollutants

 Obtain data on the background visibility conditions for use in the form of Equation

7, b = b f(RH) + ext SN dry b

 Calculate the extinction coefficient for the source or sources being analyzed using

Equations 4 and 5.

 Apply appropriate and consistent relative humidity adjustment factors to both the

source contribution to light-extinction and the background light-extinction.

 Calculate a change in deciview (dv) using equation 8.

3.3 Deposition Calculations

Estimates of atmospheric deposition are obtained by selecting the options in

CALPUFF to calculate and output the wet and dry fluxes of the pollutants modeled. 

The units of the fluxes are in g/m /s of the pollutant modeled (i.e., g/m /s of HNO ). 2 2

3

Generally AQRV analyses require values of total deposition (background plus modeled

impact) to be given in units of kg/ha/yr of an element, such as nitrogen (N) or sulfur (S). 

Therefore, the modeled deposition flux of each of the oxides of sulfur or nitrogen from

CALPUFF must be adjusted for the difference of the molecular weight of their oxides

and the element and the various forms must be summed to yield a total deposition of

sulfur or nitrogen. This can be accomplished using a multiplier in CALPOST to do all of

the conversions. The CALPOST program will produce an average flux (i.e., annual

average), therefore, the average value must be multiplied by the number of seconds in

an hour and the total number of hours used in the averaging period for the total

deposition. 

The wet and dry fluxes of SO , SO , NO , HNO , and NO need to be calculated 24 X 3 3

= -

and saved in a CALPUFF run. It is necessary to make a separate CALPOST run of the

wet and dry fluxes for each species modeled, normalizing each species by the

molecular weight of a common compound or element (usually S or N), converting the

units, and adjusting for the total number of averaging periods used in the CALPOST run

(i.e., 8760 for 1 year). Then the results of the sulfur CALPOST runs are summed and

the results of the nitrogen CALPOST runs are summed to yield a total deposition value

for sulfur and nitrogen, respectively. The following table indicates the multipliers to use

to correct for molecular weight differences and unit changes, as well as the correction to

go from a short-term flux to annual deposition. 

 
D a p

b  b 

31

Deposition of: Ratio Mol wt. of g to kg m2 to ha sec to hours Number Multiplier (a in

Oxidant to S or N of Hours CALPOST) x

in N

S from SO2 0.50000 10 10 3600 N 1.800000E+04 -3 4

S from SO4 0.33333 10 10 3600 N 1.200000E+04 -3 4

N from NOX 0.30435 10 10 3600 N 1.095652E+04 -3 4

N from HNO3 0.22222 10 10 3600 N 8.000000E+03 -3 4

N from NO3 0.22581 10 10 3600 N 8.129032E+03 -3 4

For example, if CALPUFF was run for SO and SO , for one year, it would be 2 4

=

necessary to run four different CALPOST runs for wet and dry deposition of SO and 2

SO . For the SO runs, the multiplier in CALPOST would be set to 1.8x10 x8760 = 4 2

= 4

1.576x10 , assuming a non-leap year with 8760 hours and to 1.2x10 x8760 = 8 4

1.0512x10 for the SO runs. If one was interested in the deposition over the month of 8 =

4

January, assuming a run length of 744 hours, one would use 744 in place of the 8760

to calculate the multiplier in CALPOST.

3.4 Assessing Air Quality Related Values - Background

It usually is not possible to assess Air Quality Related Values (AQRVs) in

isolation of the existing background stress. To illustrate this point, we consider two

examples. The first using a leaf injury model and the second discusses discerning

differences in visibility.

 Larsen et al., (1983) presented a model of leaf injury, in which leaf injury was

seen to be a function of the hourly sulfur-dioxide concentration raised to the 1.845

power times the ozone concentration raised to the 1.271 power. The impact for a

series of hours was to be computed as a summation over each hour’s computed effect. 

What should be noted is the fact that the injury was not linear, but was proportional to a

power, in this case, greater than 1. We can simplify this to an equation of the form:

where D = damage, a is some constant, p is a power (like those cited above), is the

total concentration (or deposition, etc.), which is composed of a background

concentration, b, and an additional concentration, b’, associated with one or more

sources. If p is not equal to 1, then it is not possible to assess the effects of the

background concentration, b, separately from the effects associated with one or more

sources, b’.

Example: Let p = 2, b = 10 and b’ = 1, then D = 121a. If one separately computes the

damage as a summation, as D = D(b) + D(b’), the result will underestimate the total

damage, as D = 100a + a = 101a.
dv10 ln[ (b  b 

)

b ]

32

The deciview, dv change associated with adding some new source’s effects onto

an existing background haze effect can be computed as:

where b is the extinction coefficient associated with the background and b’ is the

extinction coefficient associated with the additional source or sources. It is worth noting

in the above equation that the human perception of a change in visibility is

fundamentally a function of the existing background condition. A one dv change is

computed whenever the ratio (b+b’)/b = 1.11. Or stated in other terms, when b’ is 11%

of b, a one dV change in the visibility will be computed. 

These two examples were provided to illustrate that assessing some Air Quality

Related Vales (e.g., as might be related to crop injury, or visibility effects) is

fundamentally tied to knowing the current stress (background) being exerted on the

system. Assessing the response of a resource (plant health, visibility) is related to the

cumulative effects of all the current existing stresses. Whether a model is used to

estimate the existing condition, or whether existing monitoring measurements can be

used to define the current stress (background), is decided for each case based on

available information.
33

4.0 STUDIES AND FINDINGS

Sections 4.1 through 4.5 provide extended summaries of the investigations

conducted prior to the Sixth Modeling Conference. Following the Sixth Modeling

Conference, a series of investigations were conducted in response to the comments

received, and these results are summarized in Sections 4.6 through 4.9. In Section 4.6,

summaries are presented of several investigations in which surface concentration

values estimated by the CALPUFF puff dispersion model were compared with tracer

field data. These are presented to provide further information, as requested at the Sixth

Modeling Conference, for deriving conclusions on the performance of the

CALMET/CALPUFF modeling system in characterizing long-range transport. In Section

4.7, a summary is presented in which direct model-to-model comparisons are made

between CALPUFF and ISC simulations of point source release dispersion. These are

presented to provide evidence on the ability of CALPUFF to replicate the dispersion

results of ISC for steady-state meteorological conditions. Some coding deficincies were

detected in compariing the steady-state CALPUFF results with ISC, but the primary

lessons learned were what differences could be expected due to the fact that puff

models treat the sequence of meteorological events (‘causality’). This information is

valuable since CALPUFF was to be used to simulate all sources (even though for

some, the transport may be less than 50 km) in a long-range transport assessment. 

Section 4.8 summarizes results available towards developing a new screening

technique that might be used to see if it is worthwhile or needful to develop a full windfield puff-dispersioin analysis using the CALMET/ CALPUFF modeling system. It was

evident at the Sixth Modeling Conference that use of the ISC plume dispersion model to

develop screening estimates of long-range transport impacts was not providing much

help. Section 4.9 summarizes studies that have recently been completed of further

enhancements and refinements, some of which are implemented in Version 5.0 of the

CALMET/CALPUFF modeling system. Not all of these enhancements have been fully

tested, but it was felt desirable to summarize that which was known.

4.1 MESOPUFF II Implementation Assessment

A case study was conducted to apply the MESOPUFF II air quality modeling

system following IWAQM Phase 1 interim recommendations (U.S. EPA, 1993). This

study would identify and summarize the decisions made, would record and summarize

the resolution process for these decisions, and would provide a written record of the

resources used to complete the effort. The objective was to learn by experience where

the difficulties are in the process of conducting such an analysis, and when possible, to

provide a means for resolving these difficulties. It was not an objective to provide a

meaningful assessment of PSD, NAAQS or AQRV impacts for the Class I areas

considered in the study. A complete description of the study results is presented in

U.S. EPA (1995a). As part of this study the following tasks were carried out:

• The MESOPUFF II model and associated processors were tested using the

example problem intended for Support Center for Regulatory Air Models
34

bulletin board (SCRAM BBS) distribution. The SCRAM BBS example

problem computer files were evaluated and some suggested improvements

were implemented.

• A five-year meteorological data set suitable for input to the MESOPUFF II

model was developed for a multi-state area surrounding Shenandoah

National Park (SNP), including the James River Face (JRF) wilderness area. 

Demonstration model simulations were performed using three years of the

developed five-year meteorological data set for the assessment of visibility,

acidic deposition, and PSD increments for a set of real sources in the states

surrounding Shenandoah National Park. 

• Model simulations were performed to test the sensitivity of concentrations to

the distance between sources and receptors using a set of "pseudo" sources

placed in successive rings around Shenandoah National Park.

As discussed in the summary report for this project (U.S. EPA, 1995a), on

several occasions significant departures were made in conducting this study from that

which would be expected if a realistic assessment were to be developed. For instance,

the source inventory considered only some of the states surrounding the Shenandoah

National Park and the James River Face wilderness area, and thus is incomplete. To

conserve resources, the sources were consolidated into ten surrogate sources for the

purposes of this study. These departures allowed the emphasis of the project to be

focused on a critique of the process and resource needs, which were the primary study

objectives.

A realistic assessment, would require that all important sources be modeled

(without consolidation). If the modeling objective is to determine PSD impacts, then all

relevant sources that consume PSD increment must be considered. If one desires to

determine the impact of a single new (or modified) source, then the PSD increment

from the new source must be added to all pre-existing PSD sources. It would be

possible to model the impacts from a single source and then add those impacts to prior

MESOPUFF II results, assuming the prior results were available. If not, it would be

necessary to model all relevant PSD sources to assess the total PSD increment

consumed.
35

Figure 2. Display of the MESOPUFF II computational domain, showing positions of

all PSD sources compiled for the demonstration assessment. Crosses represent all

27 original PSD sources, while circles denote the ten final aggregated sources used

for the demonstration analyses. The source characteristics for the ten aggregate

sources (numbered) are given in Table 2. One unit along left and bottom axes

equals 20 km. Top and right axes are the UTM coordinates in km.

Demonstration Results

In the MESOPUFF II modeling for this project, only one run was performed (for

three years) using sources beyond 50 km of Shenandoah National Park. The

MESOPUFF II results (for one month) were then added to ISCST2 results to

demonstrate the integration process. For the demonstration study, sources were to be

located as far as 200 km from SNP. All sources and receptors to be modeled must be

contained within the computational domain. Puffs are not tracked after they leave this

grid. To avoid underestimating concentrations by immediately losing puffs from nearboundary sources or missing short-term recirculation events at near-boundary

receptors, all sources and receptors should be at least 20-50 km from the boundaries of
36

the computational grid. Provision for a 80 km buffer zone on all sides of this source

region yielded a computational domain of 30 by 30 grid points (Figure 2), with a grid

spacing of 20 km. 

For the demonstration study, existing sources that began operation since the

beginning of the PSD program would be modeled. For practical considerations and

ease of communications between affected states, the sources were limited to those

within EPA Region III, comprising the states of Delaware, Maryland, Pennsylvania,

Virginia and West Virginia, and the District of Columbia. Small PSD sources, with

emissions of both SO and NO less than 5 g/s, were excluded. This resulted in a set of 2 x

27 sources (indicated by crosses in Figure 2). To reduce run times the set of 27

sources were consolidated into 10 sources. Table 3 shows the final set of 10

consolidated sources, the facilities that are included in each, and the location and

emission parameters. Figure 2 also displays the locations of these ten sources

(indicated by circles) in relationship to all 27 original PSD sources. 

Table 3. Final condensed set of sources modeled with MESOPUFF II for the

demonstration assessment. HS, DS, VS, and TS are the source’s stack height, stack

diameter, effluent exit velocity, and effluent exit temperature, respectively.

Sources UTM X UTM Y HS DS VSl TS SO SO NO

(km) (km) (m) (m) (m/s) (K) (g/s) (g/s) (g/s) 2

=

4 x

 1 PEPCO II, Patawmack 803.6 4341.0 48 8.77 36.6 557 348.2 15.7 230.4

 2 Warrior Run 693.6 4385.0 82 3.75 23.6 398 54.8 2.5 26.0

 3 Ogden-Martin, SEO Birch 823.6 4265.0 106 3.24 10.0 372 48.0 2.2 151.9

 4 N. Branch, Mettike 639.6 4349.0 76 4.69 21.3 389 112.1 5.0 83.6

 5 LG&E Altavista, Multitrade 653.6 4109.0 60 4.44 21.4 407 24.7 1.1 42.6

 6 Mecklenburg, Old Dominion 711.6 4067.0 109 7.11 18.6 331 194.6 8.8 425.1

 7 Doswell, Cogentrix-Richmond, Cogentrix- 819.6 349.6 70 5.35 14.0 352 252.1 11.3 426.4

Dinwiddie, LG&E Hopewell

 8 Brandon 885.2 4345.0 187 6.71 27.3 413. 1893.6 85.2 630.8

 9 Cambria Cogen, Colver PP, Ebensburg 689.6 683.6 86 5.08 21.3 418 287.8 13.0 155.8

10 P.H. Glalter, Harrisburg, Lancaster, Solar 861.6 4439.0 67 6.60 15.4 438 14.1 0.6 212.3

Turbine, York Co.

For this study, PM10 increments were calculated as described in the Phase 1

recommendations. Modeled concentrations of sulfate and aerosol nitrate were

converted to ammonium sulfate and ammonium nitrate, and summed to estimate PM10

increments. The PSD increments were calculated for each receptor, and the highest

values within SNP and JRF were identified. Since there are no standards or mandated

increments for AQRVs, it was necessary to define AQRV criteria before postprocessing

could begin. For visibility, incremental extinction coefficients were computed from 3-

hour average modeled sulfate and nitrate concentrations using relative humidity data
37

and the equation provided in Appendix B of the Phase 1 recommendations. The

maximum 3-hour extinction resulting from modeled emissions over all receptors was

compared to measured total extinction from the SNP IMPROVE monitoring site. The

number (and percentage) of 3-hour periods for which the maximum extinction

represents 10 percent or more of the measured extinction for the 90th percentile

cleanest day was reported. Deposition impacts were calculated for each receptor for

total sulfur (SO plus sulfate, expressed as S) and total nitrogen (NO plus HNO plus 2 x 3

nitrate, expressed as N). Deposition impacts were expressed as the cumulative annual

sum of wet and dry deposition, in units of kg/hectare. 

Table 4. PSD and AQRV parameters calculated from MESOPUFF II demonstration

assessment.

Shenandoah NP James River Face Allowable Class I

Increment

Parameter Averaging Period 1988 1989 1990 1988 1989 1990

PSD Increments

SO Annual (ug/m3) 0.23 0.32 0.21 0.08 0.10 0.06 2 2

24-hour (ug/m3) 3.92 5.03 3.15 1.39 2.93 1.61 5

3-hour (ug/m3) 16.56 19.62 9.55 5.38 7.35 3.65 25

NO Annual (ug/m3) 0.12 0.15 0.13 0.05 0.07 0.04 2.5 x

PM10 Annual (ug/m3) 0.08 0.12 0.07 0.05 0.07 0.04 4

24-hour (ug/m3) 1.24 3.32 1.31 0.85 1.25 1.31 8

Visibility

Extinction 3-hour (% of year) 19.9 27.9 22.4 7.3 8.2 6.8

Deposition

Total S Annual (kg./Ha) 0.38 0.36 0.28 0.11 0.15 0.08

Total N Annual (km/Ha) 0.09 0.12 0.07 0.04 0.16 0.03

Extinction = percent of 3-hour periods for which incremental extinction is greater than 10 percent of clear-day extinction levels.

Table 4 displays a summary of calculations from three years of MESOPUFF II

output (1988-1990). Results are given for receptor groups in both SNP and JRF. 

There is considerable year-to-year variations in all of the simulated impacts, typically of

order 20 to 25%. As for concentrations of criteria pollutants, annual averages of SO ,2

NO , and modeled PM10 in both Class I areas were predicted to be small fractions of x

the total allowable Class I increments for all years modeled. Short-term concentration

increments were predicted to approach the allowable Class I increments, particularly at

SNP. The second highest 3-hour and 24-hour average SO concentrations approach or 2

exceed the allowable Class I increments, while the second highest 24-hour average

modeled PM10 is between an eighth and one-half the allowable limit. Also note, that
38

whereas the summer 1988 was characterized by widespread high pollution levels

throughout the eastern U.S. (particularly for ozone), all modeled concentration,

extinction, and deposition predictions were distinctly higher during 1989 than during the

other two modeled years at both Class I areas.

The extinction measure reported in Table 4 corresponds to the percent of each

year in which calculated sulfate plus nitrate concentrations lead to extinction levels

more than 10 percent above the defined “clean” background levels for SNP. According

to these calculations, it is predicted that secondary particulate matter from the PSD

sources modeled in this analysis lead to such conditions between 20 and 28 percent of

the year for SNP, and about 7 to 8 percent of the year at JRF. At SNP, maximum 3-

hour extinction for the years 1988, 1989, and 1990, were 0.0507, 0.3348, and 0.1345

km , respectively. Using a simple standard estimation procedure for clear-sky visual -1

range (3.0 divided by extinction in km ), the extinction levels computed for SNP -1

translate to minimum visual ranges of 59, 9, and 22 km, respectively, for each year

modeled. The maximum extinction levels are quite high when one considers that these

estimates were calculated solely as a result of particulate matter generated by these

emission sources alone, and that other SO and NO sources, and natural and 2 x

anthropogenic sources of organics and dust, were not taken into account.

Source-Receptor Separation Distance Sensitivity Study

A set of 24 MESOPUFF II simulations were performed with sources at varying

distances from Shenandoah National Park (SNP) to provide some insight into the

relationship between distance from SNP and potential PSD and AQRV impacts (U.S.

EPA, 1995a). Four months of one year (representing each season of a year) were

modeled under six different source scenarios. Each source scenario included a

number of identical hypothetical point sources placed on a ring at a constant distance

from the “spine” of the SNP. The source rings were established at 50, 100, 125, 150,

175, and 200 km from the spine (Figure 3).

The distribution of hypothetical point sources around each ring was determined

such that the linear density of sources was held as constant as possible. The emission

rates and source emission characteristics were set to be similar to those listed in Table

3. The emission rate and stack parameters used for the hypothetical sources are

summarized in Table 5. The stack parameters are average values from the PSD

source data in Table 3. Emission rates of primary sulfate were specified at 3 percent of

SO rates, with an additional 1.5 factor to account for the larger sulfate molecular 2

weight. Source characteristics of each ring are summarized in Table 6, and the

locations of the sources are shown in Figure 3.
39

Table 5. Emission rates and stack parameters for the idealized sources.

Stack Parameters Value for Idealized Sources

SO Emission Rate (g/s) 181.0 2

SO Emission Rate (g/s) 8.1 4

NO Emission Rate (g/s) 135.0 x

Stack Height Im) 81.0

Stack Diameter (m) 3.97

Stack Gas Exit Velocity (m/s) 20.7

Stack Gas Exit Temperature (K) 425.0

A total of 24 MESOPUFF II runs were made, corresponding to six distances for

each of the four months. MESOPUFF II output for the ring source analysis consisted of

3-hour average concentrations, and wet and dry fluxes of all species. 24-hour averages

and monthly averages were produced for NO , nitric acid, sulfate (SO ), nitrate (NO ) x 43

= -

and modeled PM10, SO and NO concentrations output by MESOPUFF II were 4 3

= -

multiplied by factors of 1.38 and 1.29 to convert to ammonium sulfate, (NH ) SO , and 42 4

ammonium nitrate, NH NO , respectively (these conversions are discussed further in 4 3

Section 3). Following the Phase I recommendations, the sum of ammonium sulfate and

ammonium nitrate was reported as modeled PM10. Total sulfur deposition was

calculated by summing wet and dry deposition fluxes for SO and SO over the month. 2 4

=

Conversion factors were applied to convert SO and SO to a sulfur basis, and to 2 4

=

convert 3-hour average fluxes to 3-hour cumulative deposition. Total nitrogen

deposition was calculated by summing wet and dry deposition fluxes for NO , HNO , x 3

and NO over each month. Conversion factors were applied to convert NO , HNO , 3 x 3

-

and NO to a nitrogen basis, and to convert 3-hour average fluxes to 3-hour cumulative 3

-

deposition. (For further discussion of the computations of total S and N deposition, see

Section 3.3). An important AQRV associated with PM10 is visibility. MESOFILE was

not capable of producing visibility estimates as outlined in the Phase I

recommendations, where extinction is a function of relative humidity. Thus, visibility

impacts were not assessed for the ring source analysis.
40

Figure 3. MESOPUFF II computation domain for ring source analyses, showing the

location of the ring sources. One unit along left and bottom axes equals 20 km. Top

and right axes are the UTM coordinates in km.

Figure 4a shows the results obtained for the monthly average SO concentration 2

for each of the four months as a function of source distance from SNP. The nearly

linear decrease as a function of distance from SNP seen for these results is typical of

that seen in the highest 24-hour average SO results. As might be expected for a 2

primary pollutant, the highest impacts occur for the 50-km source ring. The highest

monthly average concentration is 0.45 μg/m , and decreases by a little over a factor of 3

2 for sources located on the 200 km ring. The maximum 3-hour and 24-hour SO2

concentrations were also for the sources on the 50 km ring and were 20 μg/m and 2.8 3

μg/m , respectively. The computed maximum 3-hour SO concentration for sources on 3

2

the 50 km ring is close to the allowable 3-hour increment of 25 μg/m . The computed 3

maximum 24-hour SO concentration for sources on the 50 km ring is slightly more than 2

half the allowable Class I PSD increment of 5 μg/m . 3
41

Table 6. Source ring characteristics.

Total Emissions (g/s)

Distance from Number of Distance Between

SNP (km) Sources Sources (km) SO NO 2 x

50 4 138 724 540

100 6 144 1086 810

125 7 146 1267 945

150 8 148 1448 1080

175 9 149 1629 1215

200 10 150 1810 1350

Figure 4b shows the highest monthly average concentrations for NO . (Plots of x

shorter-term NO concentrations were not prepared because there are no short-term x

PSD standards for NO .) Comparison of Figure 4a to Figure 4b for monthly average x

SO illustrates the faster rate of chemical decay for NO . For sources on the 50 km 2 x

ring, NO concentrations are generally on the order of 60 percent of the SO x 2

concentration for January, April, and October, reflecting the ratio of the emission

strengths of SO and NO in the ring source input files. NO concentrations at 50 km 2x x

are 40 percent of SO concentrations for July because NO reacts much more rapidly 2 x

than SO . For sources on the 200 km ring, NO concentrations are on the order of 50 2 x

percent of the SO concentration for January, April, and October, and 30 percent of SO 2 2

concentrations for July. The highest monthly average NO concentration was slightly x

less than 0.3 μg/m , or about 11 percent of the allowable increment of 2.5 μg/m for NO 3 3

x

annual average in Class I areas. The annual average will be lower than the highest

monthly average. Thus, much greater NO source strengths than those used in this x

analysis would be needed for NO concentrations to approach the allowable increment. x

Figure 4c presents the highest monthly average modeled PM10 concentrations as

a function of source distance from SNP. These impacts show a completely different

pattern than SO or NO , in that there is no clear decrease with source ring distance. 2 x

This pattern is expected as, unlike SO or NO , sulfate and nitrate are actively being 2 x

formed during transport downwind. The seasonal effect is very pronounced for the

monthly average modeled PM10, with July concentrations more than twice those in

January, and April and October falling somewhere between the two. Modeled sulfate

and nitrate
42

(A)

(B)

(C)

Figure 4. Highest monthly concentrations of (A) SO , (B) NO , and (C) PM10 (μg/m3) 2 x

from ring sources for January, April, July and October, 1988.
43

 concentrations were nearly equal for all months but July. This is contrary to measured

PM10 data for SNP and other locations in the eastern U.S., where sulfate

concentrations are typically three times as large as nitrate concentrations. Two factors

are likely to be responsible for this apparent inconsistency. The high background

ammonia concentration of 10 ppb used as a default value in the MESOPUFF II analysis

will result in an overestimate of aerosol nitrate concentrations. However, the lack of a

parameterization of rapid in-cloud sulfate formation in MESOPUFF II may lead to an

underestimate of sulfate concentrations.

The highest monthly total S deposition of nearly 0.05 kg/hectare occurred for

July for the 50 km ring. Modeled S deposition decreased with source distance from

SNP, although the decreasing trend was very weak for October. For all sources, the

highest S deposition occurred in July, and the lowest in January. Dry SO deposition 2

accounted for most of the modeled S deposition in all months. Deposition of SO was 4

=

small for all months except July. The main reason for this is that most of the total sulfur

remained in the form SO at all distances modeled. Wet deposition shows greater 2

random variability than dry deposition. This makes sense, as wet deposition requires

both a puff and precipitation to be present at the same time at a given receptor. Thus,

trends in wet deposition with source distance are somewhat obscured by random

variability.

The highest monthly total N deposition of 0.013 kg/hectare occurred for the 50

km ring for July. Modeled N deposition decreased slowly with source distance from

SNP. For all sources, the highest N deposition occurred in July, and the lowest in

January. Total N deposition was less than half the modeled total S deposition for all

months and distances. MESOPUFF II assumes zero wet deposition for NO . The x

majority of modeled N deposition in July is due to dry deposition of HNO . In the other 3

months, NO dry deposition is important, and NO deposition can be important as well. x 3

-

Considering the high solubility of HNO , the modeled wet deposition of HNO appears 3 3

surprisingly low.

The equilibrium between HNO and NO affects the results presented here, both 3 3

-

for the modeled PM10 concentrations and the nitrogen deposition. The ring source

analysis utilized the default value for background ammonia of 10 ppb. This value is

likely to be too high, especially for winter. When ammonia concentrations are high, the

nitrate equilibrium favors the formation of aerosol nitrate. As a result, modeled aerosol

nitrate values may be too high. For N deposition, it is less clear what the effect of high

background ammonia would be. Dry deposition is faster for HNO than it is for NO , 3 3

-

but wet deposition is faster for NO . Snow is assumed in MESOPUFF II to scavenge 3

-

particles, but not gases. If background ammonia is high, NO deposition will be 3

-

overestimated and HNO deposition will be underestimated. The net effect on N 3

deposition may be small.
44

4.2 Revisions to CALMET and CALPUFF

In the course of completing the Phase 1 recommendations, IWAQM had become

aware of the CALMET/CALPUFF modeling system (Scire et al., 1990ab), which was

actively under development. Building from lessons learned from the

MESOPAC/MESOPUFF II modeling system, the CALMET/CALPUFF modeling system

was a Lagrangian puff model designed to include: 1) the capability to treat time-varying

point and area sources, 2) suitability for modeling domains from tens of meters to

hundreds of kilometers from a source, 3) predictions for averaging times ranging from

one-hour to one year, 4) applicability to inert pollutants and those subject to linear

removal and chemical conversion mechanisms, and 5) applicability for rough or

complex terrain situations. The CALMET meteorological processor was designed to be

compatible with both CALPUFF and a photochemical grid model, called CALGRID

(Scire et al., 1989). Even though the current focus of IWAQM was on puff model

simulations, the Phase 3 work was anticipated to include grid modeling. Hence the

compatibility of CALMET to a grid model was considered advantageous. 

There were two areas where IWAQM felt further enhancements were needed. 

With a view towards allowing one model to be used for all sources (which might include

sources-receptor distances of less than 50 km), the first area for enhancement was to

include within CALPUFF dispersion additional algorithms, so that CALPUFF simulation

results would be consistent with ISC and CTDMPLUS (Perry et al., 1989) modeling

results for steady-state meteorological conditions. The ISC plume dispersion model is

recommended in the Guideline for use in gently-rolling terrain, and the CTDMPLUS

plume dispersion model is recommend in the Guideline for use in complex terrain

where plume impaction on elevated isolated terrain features is likely. Both ISC and

CTDMPLUS are limited to source-receptor distances of less than 50 km. The IWAQM

has concluded that one of the most challenging issues for long-range transport is the

characterization of the time-varying three-dimensional wind field. Therefore, the second

area for enhancement to the CALMET/CALPUFF modeling system was to include

provisions within CALMET to allow use of mesoscale meteorological modeling results

created using data assimilation techniques, for example Stauffer and Seaman (1989)

and Stauffer et al., (1990).

At the time of the Sixth Modeling Conference, there were only preliminary

sensitivity testing results to show that the modifications to CALPUFF would be

successful in replicating ISC. There were no comparison results available showing

consistency between CALPUFF and CTDMPLUS. Comparisons of CALPUFF with ISC

are presented in Section 4.7. In following discussion, we summarize the modifications

that were made to CALMET to expand its use to long-range transport applications.

The wind field module in CALMET is based on the Diagnostic Wind Model

(DWM). In anticipation of using CALMET and CALPUFF for long-range transport

distances, a series of modifications were made (U.S. EPA, 1995b). These included

options to use a spatially variable initial guess field based on observations or results
45

from coarse-grid mesoscale meteorological modeling analyses, optional use of Lambert

conformal coordinates for sources and receptors versus Universal Transverse Mercator

(UTM) Cartesian coordinates, and (as mentioned before) provisions to allow processing

of mesoscale meteorological modeling results created using data assimilation

techniques.

The DWM in CALMET uses a two step procedure in developing the final wind

fields. An initial guess field is developed based on a domain-average wind profile, and

this domain-average profile of winds is adjusted for terrain effects and divergence

minimization to produce a “Step 1" wind field. The second step in the processing of the

wind field is the introduction of the observational data into the terrain-adjusted Step 1

wind field. As originally configured, the initial guess domain-average profile was

constant over the domain. For long-range transport analyses, it is easily conceivable

that terrain features (such as a ridge, or a deeply cut river gorge that turns sharply

within the computational domain) would invalidate the appropriateness of using an initial

guess wind profile that is everywhere the same over the domain (i.e., homogeneous). 

To address this, options were added to CALMET that allow spatially variable winds as

an initial guess field. The spatially variable winds are computed using an inversedistance interpolation from the available wind profile observations. The provision to

allow use of a Lambert conformal projection versus a flat Cartesian mapping of

receptors and sources was perfunctory, but necessary, in anticipation of analyses that

might include transport distances where the curvature of the earth might become a

significant factor in the analyses (say computational domains larger than 200 km by 200

km).

The adaptations needed to allow use of meteorological wind fields as analyzed

by sophisticated mesoscale meteorological models (hereafter referred to as FDDA-MM

data) involved more than simply providing a new data input option. Four Dimensional

Data Assimilation (FDDA) and the development of FDDA-MM data are discussed

further in Section 4.4. The representativeness of the fine-scale observations (which

can be viewed as point-value observations) as compared with winds derived from

FDDA-MM analyses (which can be viewed as grid-average observations) was expected

to depend on such factors as the height above the surface, subgrid-scale terrain

variations, and the ratio of the input FDDA-MM data grid size to the output grid size of

the CALMET analyses. For example, the FDDA-MM results having a grid spacing of

80-km will not reflect potentially important local features of the surface flow field

induced by terrain variations (e.g., in vicinity of the Shenandoah National Park or the

Columbia river gorge) which can not be resolved by a grid resolution of 80 km. On the

other hand, the point-value observations in such areas do not necessarily represent

larger-scale flow fields as well as the FDDA-MM data fields. Therefore, IWAQM

investigated development of a weighting factor based on the subgrid-scale terrain

variations, that could be employed to blend the FDDA-MM data fields into the network

of available surface and upper air observations. The developmental work to define the

blending weights is described in the summary project report U.S. EPA (1995b). The
46

detailed description of how to compute the weights is provided in the current CALMET

user’s guide.

The decision as to where to introduce the FDDA-MM winds in the CALMET

processing involves judgment. Basically, the finer the grid resolution used in developing

the FDDA-MM winds the more reasonable it is to bring these data directly in as

observations in developing the Step 2 winds. The coarser the grid resolution used in

developing the FDDA-MM winds, the more reasonable it is to bring in these data to

initialize the Step 1 analyses, and allow the diagnostic wind model of CALMET to

develop the local terrain effects.

Figure 5. The MM4 domains for the 54-km grid (outer box) and the 18-km grid (inner

box).
47

Table 7. Summary of statistical comparisons of wind fields.

 54-km Results 18-km Results

 R RMSE R RMSE 2 2

80-km MM4 Interpolated 0.961 0.992 0.958 0.949

CALMET-L Observations only 0.789 2.211 0.858 1.774

CALMET-S Observations only 0.542 3.220 0.814 2.039

CALMET-L MM4 as initial guess field 0.926 1.378 0.920 1.328

CALMET-S MM4 as initial guess field 0.932 1.316 0.922 1.300

CALMET-L MM4 as Step 1 field 0.962 0.992 0.959 0.932

CALMET-S MM4 as Step 1 field 0.962 0.993 0.959 0.938

CALMET-L MM4 as observations 0.960 1.020 0.952 1.011

CALMET-S MM4 as observations 0.959 1.021 0.952 1.011

R = correlation coefficient, and RMSE = root mean squared error. 2

L = large radius of influence settings, and S = small radius of influence settings.

R and RMSE are computed on a concatenation of the time series of the east-west (u) and north-south (v) 2

components of the wind (each of length N) into one time series (of length 2N).

The radii used were defined as: Small Large

 R1 50 km 500 km

 R2 100 km 1000 km

 Rmax1 100 km 500 km

 Rmax2 200 km 1000 km

 Rmax3 1000 km 1000 km

where R1, R2 = the distances at which the Step 1 wind and observations have equal weight in the surface layer

(R1) and the upper layers (R2), Rmax1, Rmax2 = the maximum radius of influence of observations in the surface

(Rmax1) and upper layers (Rmax2), and Rmax3 = the over-water radius of influence.

To investigate the effectiveness of the terrain weights developed, the CALMET

diagnostic wind field model was used to analyze two episodes, one summer episode

(August 1-6, 1988) and one winter episode (December 3-10, 1988). The summer

episode was characterized by light wind, stagnating conditions. The winter episode was

characterized as an active period that included the passage of a front and low-pressure

system through the domain. Penn State Mesoscale Meteorological (MM4) results were

available employing four dimensional data assimilation (Stauffer and Seaman, 1989) for

both episodes, for three different grid resolutions, 18-, 54- and 80-km. Figure 5 depicts

the domains over which comparisons were made of CALMET simulated wind fields,

developed for comparison with the MM4 54-km winds (outer box in Figure 5) and with

the MM4 18-km winds (inner box in Figure 5). The CALMET winds were developed

using the MM4 80-km winds as input (at various stages in the CALMET processing),
48

and CALMET winds were developed using only the hourly surface weather

observations and twice-daily upper air observations from the National Weather Service. 

Table 7 summarizes the comparison results obtained by using the available 80-

km MM4 winds in various ways with CALMET to develop either 54- or 18-km gridded

wind fields. As a further comparison, the hourly 80-km MM4 winds were linearly

interpolated directly to the 54- or 18-km grid resolution. In general, the introduction of

the 80-km MM4 winds improves the ability of CALMET to reproduce the reference 54-

and 18-km MM4 wind fields. Slightly better agreement was achieved when the 80-km

MM4 winds were brought in after the diagnostic terrain adjustment procedures (i.e., as

the Step 1 wind fields or as “observations”). This is conjectured to occur due to the fact

that 1) the 80-km MM4 are already close to the 54- and 18-km MM4 results, and 2) the

CALMET diagnostic adjustments may duplicate terrain effects that were already

accounted for in the development of the 80-km MM4 winds. The similarity of the

interpolated 80-km MM4 winds to the 54- and 18-km MM4 winds (as evidenced by the

close agreement achieved by simple linear interpolation) suggest that there might not

be significant new terrain effects between the 80-, 54-, and 18-km scales for this region

of the United States.

4.3 Trajectory Comparisons

As discussed in the previous section, modifications were made to CALMET in

anticipation of using CALMET and CALPUFF for long-range transport distances (U.S.

EPA, 1995b). It was anticipated that use of FDDA-MM winds would improve

CALPUFF’s characterization of trajectories of dispersing pollutants. To investigate this,

two trajectory studies were conducted: 1) a numerical simulation study, and 2) a

comparison with regional-scale observations of trajectories.

Numerical simulation study

Trajectories were computed from four release locations at three levels (10 m,

200 m, and 400 m) for each of the wind fields discussed in Section 4.2 for the summer

episode (U.S. EPA, 1995b). Trajectories were generated at each location every 4, 6

and 12 hours from the beginning of the simulation, for up to 24 hours before the end of

the simulation. A statistical analysis was conducted on the trajectories to assess the

effect of the different wind fields. 

Figures 6a and 6b show the trajectories at an elevation of 10 m on the 54 km

grid for each of the four locations. The trajectories developed using the MM4 54 km

gridded meteorology directly are shown in both figures with small “x’s.” The trajectories

developed using the CALMET generated 54 km winds are shown in both figures with 
49

(A)

(B)

Figure 6. Trajectories at 10 m on the 54 km grid produced by CALMET (A) using the

large radius of influence and observations only, and (B) using the large radius of

influence and introducing the MM4 80 km winds in the Step 1 initial guess. CALMET

results are shown with “o” and trajectories developed directly from MM4 54 km winds

are shown with “x”. The starting date was August 1, 1988 at 7 AM. The small

numbers indicate hours after release.

small “o’s.” Figure 6a illustrates the results obtained by CALMET with observations

only, and Figure 6b illustrates the results obtained by CALMET by introducing the MM4
50

80 km winds in the development of the Step 1 initial guess wind fields. The most

striking differences between the two figures is for the trajectory developed from the

Knoxville release location. The CALMET winds (based on observation only) show a

light southwest flow, whereas the winds resulting from introducing 80 km MM4 winds to

CALMET show the corrent movement towards the north and northeast. 

Trajectory statistics were computed from each release time for each site and

three levels. In general, the introduction of the 80 km MM4 winds into CALMET to

develop either 54 km or 18 km gridded wind fields significantly improved the

comparisons with the trajectories developed from the 54 km and 18 km MM4 wind fields

directly, versus using only the routine hourly weather observations and twice-daily

upper air observations as input to CALMET. There was a slight difference to be seen in

using a large or small radius of influence in developing the trajectories with CALMET

when only observations were input to CALMET. When the MM4 data are used as input

to CALMET, the choice of the radius of influence appears statistically to have no effect

on the trajectory comparisons. 

CAPTEX comparisons

One of the objectives of the CAPTEX comparisons (Irwin et al., 1996) was to

assess whether use of mesoscale dynamic wind fields developed using FourDimensional Data Assimilation (FDDA), exhibiting improved spatial and temporal

resolution versus typical mesoscale wind fields determined diagnostically from the

available hourly surface and twice-daily upper air observations, would improve the

quality of the characterization of the transport and dispersion. Results were generated

for CAPTEX releases 3, 5 and 7. 

The Cross-APpalachian Tracer EXperiment (CAPTEX) is a unique series of

tracer releases, which besides testing a particular tracer technology, was conducted for

the purpose of providing data to evaluate and improve computer models of pollutant

dispersion and to provide insight into the mechanisms involved in long-range transport

and dispersion (Ferber et al., 1986). A three-hour ground-level release of

perfluoromonomethylcyclohexan (C H , PMCH) was made five times near Dayton, 7 14

Ohio and twice from near Sudbury, Ontario when winds were expected to transport the

tracer over the ground-level sampling network. Samplers were operated at 86 sites in

Ohio, Pennsylvania, New Jersey, New York, New England and southern Canada at

distances from 300 to 1100 km from the release site. Air concentrations were collected

for 3- and 6-hour durations for several days following each release.

Meteorological data available for use in developing the CAPTEX wind fields

consisted of 122 National Weather Service (NWS) surface locations reporting hourly

and 13 upper-air locations reporting twice-daily (0000 GMT and 1200 GMT) throughout

the region. Furthermore, mesoscale wind fields developed using FDDA were available

on an 80-km grid. Three wind field models were used to obtain a gridded field of

meteorological data with a horizontal resolution of 18-km: MESOPAC II, CALMET, and
51

CALMET using the mesoscale wind fields as STEP-1 inputs. In the discussion to

follow, the latter modeled wind field is referred to as CALMET/MM4. 

Version 8 of the Penn State/NCAR Mesoscale Model - Generation 4 (MM4) was

used to develop profiles of atmospheric wind, temperature, and moisture. The MM4 is

a primitive equation, mesoscale, hydrostatic Eulerian modeling system of the

atmosphere (Anthes et al., 1987). The vertical layers are terrain following. FDDA is

incorporated as Newtonian nudging by adding to the general momentum and

thermodynamic equation a "Force-Restore" term, to effectively nudge the numerical

solution towards the observed data (Stauffer and Seaman, 1989; Stauffer et al., 1990). 

Nudging was applied for the east-west and north-south wind components, temperature

and mixing ratio at all levels, except for temperature at the surface and at levels aloft

within the lowest 6 layers of the analyses (top of sixth layer is typically 1500m). The

horizontal grid spacing of the MM4 simulation was 80 km in both dimensions, with a grid

array size of 84 by 55 centered on 90W longitude and 40N latitude to cover the

domain of the CAPTEX releases. 

MESOPUFF II was driven by each of the modeled wind fields to produce a set of

three simulations of ground-level concentrations for each of the three CAPTEX releases

(9 simulations in all). CALPUFF was driven by two of the modeled wind fields,

CALMET and CALMET/MM4, to produce a set of two simulations of ground-level

concentrations for each of the three CAPTEX releases (6 simulations in all). 

In both the MESOPUFF II and CALPUFF simulations, chemical transformations,

dry deposition and wet removal were not modeled because PMCH was assumed to be

inert and non-depositing. No attempts were made to optimize the choice of model

options within CALPUFF, such as the dispersion coefficients, mode of incorporation of

MM4 data, and meteorological vertical layer structure. In effect, CALPUFF was run in a

mode designed to make it most like MESOPUFF II, in order that the effects of different

wind fields and transport characterization could be identified. MESOPUFF II and

CALPUFF differ in the way transport winds are computed for each puff. MESOPUFF

II uses a two-layer wind field, the lower layer for the transport of puffs within the mixed

layer, and an upper layer field for puffs above the mixing height. For a surface release,

as in CAPTEX, MESOPUFF II will always use the lower layer (mixed-layer averaged)

wind field. CALPUFF internally computes for each sampling step, a transport wind

averaged over the depth of the puff from the multi-layer winds provided to it from 

CALMET. As the puff grows in the vertical, the depth through which the wind is

averaged is increased.

The distance from the nearest to furthest receptor was approximately 800 km

and there where 86 receptor locations. The average distance between the receptors

was roughly 86 km = [(800) /86] , which meant that typically there were 4 to 6 2 1/2

receptors with nonzero concentration values for analysis for the shorter travel times

(300 to 600 km transport) and from 8 to 16 receptors with nonzero concentration values

for analysis for the larger travel times (600 to 900 km transport). This suggests that the
52

maximum concentration was likely not well characterized and is uncertain both in the

observations and in the simulations.

For each 6-hour period, the centroid position was computed for the observed and

simulated tracer puff as it was transported downwind over time. The results for each

release are shown in Figure 7. It was concluded that the simulated trajectories are

sensitive to the manner in which the wind field is characterized, but are insensitive to

the model employed. This can be seen by the fact that the simulated trajectories are

nearly identical for MESOPUFF and CALPUFF when the models have the same input

wind fields.

Ratios were formed by dividing the simulated and observed maximum

concentration values for each six-hour period. There was no noticeable trend seen as

a function of travel time (hours after release), but clearly there was a tendency to

overestimate the maximum concentration value by roughly a factor of 3.7. To further

investigate the tendency to overestimate the surface concentration maxima, ratios were

formed by dividing the simulated and observed lateral dispersion (as determined from

the second moment of the concentration values about the centroid positions) for each

six-hour period. There was no apparent trend in the ratio values with travel time

following release, however there was a clear trend to underestimate the 

horizontal extent of the tracer puff for each six-hour period. There were only slight

differences seen for the different wind fields employed. The overall geometric average

was 0.54 and the geometric standard deviation was 1.81. If the only difficulty or “bias”

in the simulation was to underestimate the horizontal extent of the puffs, the effect on

the simulated maximum concentration values would be proportional to the inverse

square of the bias in the horizontal dispersion, which would be 3.43 = (1/0.54) . It is 2

concluded that the underestimation seen in the simulated horizontal dispersion is able

to explain the overestimates seen in the simulated maximum concentration values. The

large scatter seen in concentration and lateral dispersion ratio values in part may relate

to the stochastic nature of atmospheric dispersion, but also is traceable to poor

definition of the maximum concentration values and lateral dispersion.
30.00 40.00 50.00 60.00 70.00 80.00 90.00

X-GRID (18 km)

30.00

40.00

50.00

60.00

70.00

80.00

90.00

Y -G R ID (km )

Release 5

October 26, 1983

0345-0645 GMT

OB SER VATION S

MESOPUFF-MESOPAC

MESOPUFF-CALMET

CALPUFF-CALMET

MESOPUFF-MM4

CALPUFF-MM4

(B)

30.00 40.00 50.00 60.00 70.00 80.00 90.00

X-GRID (18 km)

30.00

40.00

50.00

60.00

70.00

80.00

90.00

Y-G R ID (km )

Release 7

October 29, 1983

0600-0900 GMT

OB SER VATION S

MESOPUFF-MESOPAC

MESOPUFF-CALMET

CALPUFF-CALMET

MESOPUFF-MM4

CALPUFF-MM4

(C)

30.00 40.00 50.00 60.00 70.00 80.00 90.00

X-GRID (18 km)

30.00

40.00

50.00

60.00

70.00

80.00

90.00

Y -G R ID (km )

Release 3

October 2, 1983

1900-2200 GMT

(A)

OBSERVATIONS

MESOPUFF-MESOPAC

MESOPUFF-CALMET

CALPUFF-CALMET

MESOPUFF-MM4

CALPUFF-MM4

200.00 400.00 600.00 800.00 1000.00

Travel Distance (km )

0.00

0.10

0.20

0.30

0.40

0.50

F ractiona l D iffere nce

Legend

MESOPAC

CALMET

MM4

Summary Over All Releases (D)

53

Figure 7. Summary of trajectory results for each of the releases are shown in parts (a), (b) and (c). In

part (d) is shown the fractional difference computed between the observed and simulated trajectories,

summarized over all releases for each of the three wind fields. The open smaller circles indicate

sampling locations. The X and Y coordinates have been specified in terms of the meteorological grid,

which had an 18-km size.
54

The purpose of this investigation was to assess whether different methods of

characterizing the wind fields affect the performance of the simulated trajectory. The

comparison results presented were conducted with simplified puff dispersion model

assumptions, hence the model-to-model differences were minimized. The results

shown in Figure 7 suggest a noticeable improvement in the puff simulation results of

the centroid trajectory when mesoscale dynamic wind fields developed using FDDA

were used to characterize the time and space varying wind field, versus using

mesoscale wind fields determined diagnostically from the available hourly surface and

twice-daily upper air observations. The favorable comparison of the CALMET wind field

results with the MM4 results is influenced by Release 3 results, where CALMET

happened to well characterize the average transport direction. The trajectory

simulations for Releases 5 and 7 derived using MM4 winds are as good or better than

the results obtained with CALMET wind fields derived solely from routine National

Weather Service observations. It was concluded that use of MM4 is preferred and that

both CALMET and MM4 wind fields provide better simulations of the trajectories than

MESOPAC wind fields.

The analysis of the concentration maxima and lateral dispersion values suggest

that the simulation assumptions employed in these results consistently underestimate

the horizontal extent of the tracer puff as it is transported downwind. The centroid

maximum surface concentration was found to be correspondingly overestimated and

relatively insensitive to the mesoscale wind characterization. In these simulations, no

provisions were made to address delayed shear enhancement of the dispersion as

described by Moran and Pielke (1994) and Shi et al. (1990). Inclusion of some sort of

puff splitting is obviously warranted, but the computational demands are not trivial if one

is attempting to develop an operational model for routine use. Furthermore, in those

cases where the puff model dynamics have been enhanced, for example Draxler (1987)

and Davis et al. (1986), there was a tendency to underestimate the surface maximum

concentrations. 

4.4 Constructing FDDA-MM Data Sets Assessment

To foster interest in the use of mesoscale meteorological (MM) data processed

using Four Dimensional Data Assimilation (FDDA) in routine air pollution modeling

assessments and to learn what problems might be associated with such a project,

IWAQM sponsored the development of a one-year meteorological data set for 1990

that spans the contiguous United States, southern Canada and northern Mexico. 

Hourly profiles of wind, temperature and moisture were provided at 23 levels in the

atmosphere on an 80-km gird. The Penn State mesoscale meteorological model

(MM4) with FDDA was used in developing these data. The horizontal grid spacing of

the MM4 simulation was 80 km in both dimensions, with a grid array size of 85 by 56

centered on 90 W longitude and 40 N latitude to cover most of the North American

continent and adjacent oceanic areas (Bullock, 1993). The model simulation variables

from the outer 2 columns and rows of grid points were not included in the published

data set to avoid the boundary effects typical of fixed-grid numerical models. The
55

domain of the model's vertical coordinate system extended from the earth's surface to

the 100 millibar pressure level (approximately 15 km above sea level). The 15-level

structure provides height-resolved information similar to that routinely obtained by the

National Weather Service at 12-hour intervals from approximately 80 rawinsonde

balloon sounding locations across North America. However, the data set obtained from

the MM4 simulation provides synthetic soundings at 1-hour intervals for 4080 model

grid-point locations, or about 600 times more information than is available from routine

observational networks. The 1990 data set contains over 20 billion bytes of

information.

The annual data set was developed from the MM4 output of 80 separate

simulations. Each simulation was 5 days in length with a 12-hour overlap with

chronologically adjacent simulations. This 12-hour overlap was used to allow a model

"spin-up" so each simulation's hour 12 conditions would match the previous simulation's

ending conditions without the detrimental effects of model initialization. The first 6

hours of each simulation were not used in the definition of the final data set. Hour 7

thru 11 results were blended with the previous simulation results with a time-linear

weighting function to produce temporally continuous fields in the final data set. 

Beginning at hour 12, the simulation results were copied directly in the production of the

annual data set. Differences between adjacent simulations for these overlap periods

were monitored and no significant discontinuities were detected. At hour 12, the

differences were often zero at the floating-point numerical precision of the CRAY Y/MP

at EPA's National Environmental Supercomputing Center where the 80 MM4

simulations were performed. Each of these simulations required about 2.5 hours of

CPU time to compute on the Cray Y/MP. The MM4 output files were then transferred to

a DEC 3500 AXP workstation where the data were chronologically blended and chained

and various QA checks were performed. The final product was then re-partitioned into

36 sequential files of about 600 Mbytes each. Three files were produced for each

month, the first containing days 1 thru 10, the second containing days 11-20, and the

third containing day 21 through the end of each month. These files were then

processed by the National Climagic Data Center to produce a 12 Compact Diskette

data set (NCDC, 1995). 

The science of mesoscale analysis using data assimilation is rapidly developing. 

Major advancements have been occurring every several months during the period from

1995 through 1997. There are various research groups who have active development

programs investigating mesoscale meteorological modeling employing data

assimilation, e.g., Pielke et al., (1997), Turner and DeToro, (1998). 

A major obstacle is access to these data. The 1990 MM4 data set in a

compressed format, and providing only profiles of wind, temperature and moisture,

requires 12 Compact Diskettes. An operational means for gaining easy access to

comprehensive mesoscale meteorological data sets, as alluded to here, has yet to be

developed.
56

4.5 Regional Approach

Federal and State modelers have struggled with the issues related to Class I

area analyses and have found their resolutions to be elusive. Currently the consensus

opinion within IWAQM is that air quality impact assessements for Class I areas are

fundamentally different from local-scale assessements, typically associated with Class

II assessments and State Implementation Plans (SIP's). Unfortunately, the Federal

Agencies have attempted to implement the assessment of Class I impacts as if the

assessment is similar to that associated with Class II and SIP impacts. This has lead to

a significant mis-match between the analysis process and the inherent needs of a Class

I impact assessment.

Local-scale assessments require that analyses be performed within a domain on

the order of 50 km or less and centered on the source (the domain changes from permit

to permit). Given the small domain, Class II analyses lend themselves to individual

state implementation. Class I analyses, on the other hand, are centered on specific

land areas. Therefore, the modeling domain does not change from permit to permit. 

Furthermore, these analyses involve a modeling domain on the order of hundreds of

km, thus requiring multi-state coordination. Additionally, Class I area analyses for

AQRV's may require estimating the deposition of secondary pollutants and their impact

on visibility. Finally, the affirmative responsibilities of the FLM's inherantly adds to the

coordination difficulties.

As an alternative to the current permit-by-permit practice, Class I air quality

modeling assessments could be designed for each Class I area (or cluster of Class I

areas). The cornerstone of this approach is an up-front comprehensive increment and

AQRV analysis of the area. We envision an "initialization" study being accomplished

outside the context of a permit application. An up-front study is preferred since many of

the decisions which need to be made (e.g., inventory, AQRV's criteria, etc.) are specific

to the Class I area, not the applicant's source. Further, it avoids having these decisions

colored by the negotiations which occur for a single source. If desired, the

"initialization" can involve technical experts from private and public groups. Finally (and

perhaps most importantly), it provides future applicants with up-front information

needed for planning and assurance of what is expected for the given situation.

In large part, the emissions inventory and meteorological data which are

developed during the initializing process remain fixed in subsequent analyses. 

Therefore, once "initialization" is complete, each additional new source need only

determine its additive contribution; as such, increment and AQRV's are directly tracked. 

This approach has the benefit of removing the burden, from each applicant, of

developing an emissions inventory specific for their application. Updates may be

necessary to account for changes in actual emissions from other sources. Within this

approach, provisions can be made for updating the initial analysis under certain

circumstances. For instance, a re-initialization analyses could result from any one of
 An issue yet to be resolved is developing an effective means to assist those 1

States faced with assessing impacts on Class I areas not within their respective

borders. Will coordination problems arise between States on roles and responsibilities?

57

the following: 1) periodic State audit as contemplated by the PSD regulations, 2) by

choice of the permit applicant, or 3) as the scientific understanding changes. Once the

system is "re-initialized", new meteorological data and grids may result. 

4.5.1 A Regional Approach to Implementing a Class I Area Assessments

The following outline presents a conceptual approach how the air quality

modeling could be accomplished for Class I areas. This approach was designed to

provide a framework in which many of the issues could be resolved. 

I. INITIALIZING ANALYSIS: Use the recommendations of the Interagency

Workgroup on Air Quality Modeling (IWAQM) to initialize each Class I area

outside the context of a permit application. 

A. Preprocess a fixed 5 year data base. All subsequent analyses will use this

data set. That is, the meteorological grid will be fixed for all future analyses.

B. Define both the computational and receptor grids. These grids are also fixed

for all future analyses (unless a reanalysis warrants changes).

C. Decide on the measures to be used in the Air Quality Related Values

(AQRV's) analyses, and determine the significance (de minimis) criteria for

the AQRV's (this should be done specific to the Class I area)

D. Develop an Inventory:

1. Source inventory for PSD increment analyses.

2. Source inventory for AQRV analyses.

E. Run IWAQM recommended approach to produce appropriate

concentration/impact fields. 

F. Archive these fields for use by future Prevention of Significant Deterioration

(PSD) applicants. 

II. INITIALIZATION STUDY PARTICIPANTS: The initialization work should be a

cooperative effort among the FLM for the area and the EPA regions and states

who have or could have sources which affect the Class I area. The initialization

study participants could include technical experts from industry and academia.

III. PERMITTING:

A. The State within which the Class I area resides could be the ultimate

caretaker of all data bases . The data bases would include the 1

meteorological data, the computation and receptor grid definitions, the Class

I area specific emissions inventories, the various topographical and other

data and the concentration/impact fields.
58

B. Using the data provided, an applicant need only determine its own

incremental impacts. These impacts will then be added to the archived

concentration/impact fields for comparison against appropriate increments

and AQRV's. 

IV. DATABASE UPDATES: It is envisioned that re-initialization analyses could

result from the following:

A. Periodic state audit as contemplated by the PSD regulations.

B. By choice of the permit applicant. An applicant will be permitted to use a

more recent 5 year set of meteorological data with the requirement that they

perform a complete re-initialization analysis. Such an analysis would update

the comprehensive data base for future analyses. 

C. As scientific understanding changes.

4.6 Comparisons of CALPUFF with Tracer Field Data

There are very few intensive tracer field experiments available for investigating

model simulations of mesoscale transport and dispersion. The IWAQM is aware that

there are several other expriments for which the CALMET/CALPUFF modeling system

has not been exercised (mostly in Europe). The results that are summarized here

represent those that IWAQM was aware of and for which the data could be obtained.

4.6.1 1975 Savannah River Laboratory Tracer Study

In this study (U.S. EPA, 1998a), concentration estimates from the CALPUFF

dispersion model were compared to observed tracer concentrations from a short-term

field experiment conducted at the Savannah River Laboratory (SRL) in South Carolina

on December 10, 1975 (U.S. DOE, 1978). This experiment was designed to examine

long-range transport of inert tracer materials to demonstrate the feasibility of using

other tracers as alternatives to the more commonly used SF . Several tracers were 6

released for a short duration (3-4 hours) and the resulting plume concentrations were

recorded at an array of monitors downwind from the source. 

For the CALMET/CALPUFF simulations, a meteorological grid extending from

32N to 34N latitude and from 80W to 82W longitude was used. Figure 8 shows

the region of the SRL field experiment. The SRL facility is near the west edge of the

domain and the sampling monitors are located along Interstate 95. A 24-by-24

horizontal grid with a 10-kilometer resolution was used for the SRL modeling. To

adequately characterize the vertical structure of the atmosphere, six layers were

defined: surface-20, 20-50, 50-100, 100-500, 500-2000, and 2000-3300 meters. 
59

Figure 8. Savannah River Laboratory field experiment site.

The CALMET preprocessor utilizes National Weather Service (NWS)

meteorological data and on-site data to produce temporally and spatially varying three

dimensional wind fields for CALPUFF. Only NWS data were used for this effort and

were extracted from two compact disc (CD) data sets (see Appendix C). The first was

the Solar and Meteorological Surface Observation Network (SAMSON) compact discs,

which were used to obtain the hourly surface observations. The following surface

stations were used: 

 Georgia: Athens, Atlanta, Augusta, Macon, Savannah

 North Carolina: Asheville, Charlotte, Greensboro, Raleigh-Durham, 

 Wilmington

 South Carolina: Charleston, Columbia, Greer-Spartanburg

Twice daily soundings came from the second set of compact discs, the Radiosonde

Data for North America. The following stations were used:

 Georgia: Athens, Waycross

 South Carolina: Charleston

 North Carolina: Greensboro, Cape Hatteras
2

60

The geophysical parameters were derived from geographical information system

(GIS) land-use categories. Terrain and land-use data were available on the CALMET,

CALPUFF, and CALPOST Modeling System (version 1.0) CD (hereafter referred to as

the CALPUFF CD, see Appendix C). The terrain and GIS land-use data on the

CALPUFF CD were used to define gridded land-use data for each field experiment. 

These data are defined with a resolution of 1/6latitude and 1/4longitude. 

SF and two heavy methanes were released. For this analysis, the SF tracer 6 6

emission rates were used. The source parameters for this analysis were, a release

height of 62 m, with 154 kg of SF tracer released over a 4 hour period (10.69 g/s) with 6

no buoyant plume rise.

The distance to the monitoring arc was approximately 100 kilometers. The

monitors were located along I-95 (Figure 8) from MP76 on I-95 near St. George south

to Hwy 336 west of Tillman, SC and along SC 336. The monitors subtended an arc of

about 70. Receptors for modeling were placed along an arc every 1/4degree from

MP76 to MP22 near Ridgeland, resulting in 261 receptor locations. The distance

between receptors was about 450 meters.

Two separate CALPUFF model runs were made: 1) using Pasquill-Gifford (PG)

dispersion parameters, and 2) using dispersion coefficients from internally-calculated v

and  from the micrometeorological variables calculated in CALMET (hereafter w

referred to as similarity dispersion). The central maximum concentration is estimated

from a Gaussian fit to the modeled and observed data (Cmax) and computed from the

crosswind integrated concentration (CWIC) and the lateral dispersion, y, as Cmax =

CWIC/(  ). The CWIC was computed by trapezoidal integration. The program that y

computed these measures utilized only those values that were 1% or greater of the

maximum.

The observed concentrations are the cumulative concentration from bag samples

located along Interstate 95 from about St. George south to Ridgeland (Figure 9). 

Background concentration was estimated to be 0.5 ppt (DOE, 1978). The tracer

release started at 10:25 Local Standard Time (LST) and continued until 14:25 LST. 

The bag samplers were started at different times, ranging from about 10:40 to 12:30

LST, and the duration of the sampling ranged from 7.0 to 7.5 hours. Since the release

started at 10:25 LST, it seems likely that sampling at the monitors would have begun

prior to the arrival of the plume. The arrival time of the modeled plume was the hour

ending at 13:00 LST for both PG and similarity dispersion. The simulated plume

required seven hours to pass the arc with the PG dispersion coefficients, but only six

hours with similarity dispersion coefficients. Therefore, seven-hour-average modeled

and observed concentrations were computed for comparison with the measurements. 

Since the first monitors were turned on prior to 11:00 LST and only cumulative

concentration is reported for the observed data, the simulated concentrations were

summed over the seven-hour period from 11:00 LST through 1800 LST.
100 110 120 130 140 150 160

Azimuth (degrees)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

P-G Dispersion

Similarity Dispersion

Observations

Savannah River

Dec. 10, 1975

100 km arc

76 70 64 58

54

50

46

42

38

34

30 26

22

(a)

61

Figure 9. Simulated and observed seven-hour average concentration values along

sampling arc for the Savannah River Laboratory December 10, 1975 tracer field

experiment.

 Figure 9 shows the plots of the concentration estimates at the receptors

(continuous curves) and the observed concentrations at the receptors (labeled points). 

The modeled peaks are 10to 20further to the south than the observed peak. It

appears that the CALMET meteorology derived using routine NWS was not able to

characterize this initial difference in wind direction sufficiently to transport the plume

more toward the north. 

Clearly, there is general agreement in the shape and magnitude of the

distributions. Note that there are two local maxima in the observations near 135and

145. The winds were more northerly shortly after the release and may have resulted

in the observed local peaks (DOE, 1978) that were not captured in the modeled

meteorology. The observed lateral dispersion is 50-100% larger than the modeled

dispersion due to these local peaks. If these two secondary peaks are omitted from the

analysis, then the statistical measures of the simulated plumes are in better agreement

with the measures of the observed plume. Without these secondary peaks, the fitted

central maximum to the observations increases by 37% to 3.8 ppt (modeled: PG 7.2 ppt

and Similarity 5.1 ppt), The observed computed lateral dispersion is reduced by 33% to

7.77 kilometers (modeled: PG 6.9 km and Similarity 5.0 km). The observed CWIC is

reduced only slightly to 0.732 ppt-m (modeled: PG 1.29 ppt-m and Similarity 0.8 ppt-m). 
62

With only one realization for comparison, general conclusions regarding model

performance are not possible. But the simulation results are in reasonable accord and

do not suggest there are severe problems in the modeling system. It is encouraging

that the correspondence is as close as it is given that only routine NWS observations

were employed in developing the meteorological fields.

4.6.2 1977 Idaho FallsTracer Study

CALPUFF dispersion modeling results (Irwin, 1997) were compared with data

obtained following a single 3-hour late afternoon tracer release, lasting from 1240 to

1540 Mountain Standard Time (MST), conducted on April 19, 1977 near Idaho Falls,

Idaho. The tracer release results (Clements, 1979) were obtained as a consequence of

an investigation into the feasibility of using certain perfluorocarbons and heavy

methanes as alternative tracers in place of sulfur hexafluoride (SF ). Hence, although 6

the results have found use for testing alternative characterizations of dispersion and

transport, this was not a primary purpose in the original design of the investigation. 

Draxler (1979) included this experiment in an assessment of the effects of alternative

methods of processing wind data for characterization of the mesoscale trajectory and

dispersion. He concluded that a network of wind observations having a spacing on the

order of 25 kilometers might be needed to simulate mesoscale transport associated

with variable-flow situations, and that spacing of order 100 kilometers might prove

adequate for stationary and homogeneous flow situations.

The design for meteorological data collection and sampling locations relative to

the release location is shown in Figure 10. Since locations of towers and sites were

extracted from data volume figures, the relative positions are likely accurate but the

absolute positions are no better than 0.5 km. The receptor arcs at 48 and 90 km

downwind from the release are shown in Figure 10. Meteorological data were available

from eleven sites providing hourly-averaged winds; four sites providing hourly-averaged

winds and temperatures, three sites providing hourly pibal observations of winds aloft

(CFA, MTV, DBS). Two of the pibal sites (CFA and DBS) also provided hourlyaveraged winds and temperatures. Hourly rawindsonde observations were taken at

about 600 m northwest of the release location. The meteorological masts ranged in

height above ground with two at 6.1 m, eleven at 15.2 m, three at 22.8 m, and two at 30

m. The pibal observations taken at Billings, Montana (well past the farthest sampling

arc downwind) were not used in this investigation. The skies were clear of clouds and

no precipitation occurred during the experiment. The National Weather Service

observations taken at Pocatello, Idaho (approximately 75 km southeast of the release

location) were included to provide station pressure (required input for CALMET).
-40.00 -20.00 0.00 20.00 40.00 60.00 80.00 100.00

East-West (km)

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

N orth-South (km )

Winds and Temperature

Winds

PIBAL

Receptors

Release

CFA

MTV

DBS

63

Figure 10. The Idaho tracer experiment sampling arcs and meteorological data

collection network. The sampling arcs at 48 km and 90 km are shown. The receptor

arc at 3.2 km downwind of the release is omitted for clarity.

To estimate the effects of drainage flow on the near-surface wind field, gridded

values of land-use and terrain heights are needed. The land-use data are used as

surrogates for typical values of surface roughness, albedo, soil heat flux, anthropogenic

heat flux and leaf area index. These surface parameters are used in estimating the

surface energy balance. For this analysis, U.S. Geological Service land-use and terrain

height data were extracted from data bases included in U.S. EPA (1996). The basic

grid size for these data is approximately 900 m. They were processed into a 20 by 20

grid with a grid resolution of 10 km. Default values, as defined in U.S. EPA (1996), for

the surface parameters to be associated with the land-use data were used. The

southwest corner of this grid was approximately 50 km southwest of the release. The

area depicted in Figure 10 is fairly flat, but the terrain sharply increases in height to the

west and north of the area depicted. The dominant land-use was rangeland; and the

surface roughness was estimated based on land-use to be on the order of 10

centimeters. 

Hourly-averaged winds and temperature were available from midnight April 18

through midnight April 19. To mitigate the effects of not having surface data beyond

midnight of April 19, the surface meteorological tower data were duplicated to form two
64

24-hour periods, having identical meteorology. The assumption being made is that

conditions were steady-state. The pibal and rawindsonde data, which were available

from 0700 MST to 1900 MST, were treated in a similar manner. CALMET assumes all

upper-air observations are from rawindsondes, and thus expects upper-air observations

to provide winds, dry-bulb temperature and pressure with height. CALMET interpolates

in height for missing data values at intermediate heights in an observation, but CALMET

will not extrapolate upper air data. Thus observations are rejected that fail to reach the

user-prescribed top of the modeling domain (3300 m for this analysis), or have missing

data values at the surface. To make use of the hourly pibal observed winds,

temperature and pressure values were added by linearly interpolating in time and height

between available rawindsonde observations, which were available every 1 to 3 hours. 

The pibal wind directions were consistent with those from the one rawindsonde, but the

wind speeds were generally less in magnitude.

A purpose of this investigation was to assess the effects of having different

amounts of meteorological data for use in the development of the time varying field of

meteorological data. For this purpose four separate runs were made: Case 1 using all

available upper-air and surface mast observations, Case 2 using all surface mast

observations but only the one on-site rawindsonde upper-air observation, Case 3 using

only the CFA wind and temperature observations with the one on-site rawindsonde

upper-air observations, and Case 4 using only the CFA wind and temperature 

observations with all upper-air observations. In Cases 1 and 2, all the on-site hourly

wind and temperature data are employed but different amounts of upper-air

observations are used. In Cases 3 and 4, hourly winds and temperatures taken close

to the release are used with different amounts of upper-air observations. For all the

CALMET simulations, winds and temperatures were computed for six layers in the

vertical, the midpoints of which were: 10 m, 35 m, 75 m, 300 m, 1250 m, and 2650 m.

The winds at CFA were higher than those generally seen throughout the

network. Hence in Cases 1 and 2 when all the on-site winds were employed the lowlevel winds were lower than when only CFA data were used. In Cases 1 and 2, the

afternoon stability was Pasquill category B/C (Monin Obukhov lengths of order -30 m). 

As a consequence of higher winds in Cases 3 and 4, the surface friction velocities were

higher, and the Monin Obukhov lengths were larger (in magnitude), thus closer to

neutral stability. The afternoon mixing heights are similar regardless of data used. This

results because the “upper-air” temperatures all have a common source, namely the

rawindsonde observations taken 600 m northwest of the release. The nighttime mixing

heights are mostly a function of the magnitude of the friction velocity. Hence, where

estimated friction velocities were largest and differ most among the various processing

methods, differences were seen in the nighttime mixing height values.

Each of the four analyses of meteorology was used to produce two CALPUFF

simulations of ground-level concentrations for each of the three sampling arcs. In the

first simulation, the dispersion was described using Pasquill-Gifford dispersion

parameters. In the second simulation, the dispersion was described using dispersion
2

65

parameters suggested by Draxler (1976), which require values of the standard deviation

of the vertical and lateral wind fluctuations (referred to hereafter as “similarity

dispersion”). The wind fluctuation standard deviations estimated within CALMET are

primarily dependent on the surface friction velocity. The surface friction velocity is a

strong function of stability (largest during unstable conditions), roughness length and

wind speed (increases as roughness length or wind speed increase). 

For each sampling step, CALPUFF internally computes a transport wind

averaged over the depth of the puff from the multi-layer winds provided to it from

CALMET. As a surface release puff grows in the vertical, the depth through which the

wind is averaged increases. The SF tracer emission was reported to be steady at 6

25.37 g/s over the three hour period, and was simulated within CALPUFF as a 3-hour

point-source release at 10-m starting at 1300 MST. The release height was set at the

midpoint of the lowest CALMET layer, to insure that the internally computed standard

deviations of lateral and vertical velocity fluctuations (for use in the similarity dispersion

parameter characterizations) at the specified release height, were in accord with the

wind speed used by CALPUFF for the lowest layer. 

For each 6-hour period, the second moment (lateral dispersion, y 6 ) of SF

concentration values about its centroid position along the arc was computed. The

crosswind integrated concentration, CWIC, was computed by trapezoidal integration. 

By assuming the concentration profile along the arc is Gaussian, the central maximum, 

Cmax, was computed as, Cmax = CWIC/(  ). y

A goal of this investigation was to assess the sensitivity of the modeling results to

different treatments of processing the meteorology, as well as to assess the

performance of CALPUFF in characterizing dispersion for transport distances beyond

50 km. Figure 11 depicts the observed SF concentrations with the simulation results 6

where all the surface and upper-air observations were used to generate the hourly wind

fields. For the observed values, there were from 14 to 17 receptors along each arc with

valid data for analysis. For analysis of the simulation results, receptors were spaced at

each arc distance at 2 degree intervals, over the 90 degree sector northeast of the
0.00 20.00 40.00 60.00 80.00

Arc Azim uth (degrees)

0.00

200.00

400.00

600.00

800.00

1000.00

S F6 C oncentration (ppt)

calpuff\inel\run\arc032.grf

Observations

Pasquill D ispersion

Sim ilarity Dispersion

102 103

104 105

107 108 109

110 111 112 113 114

115 116 118 119 120

0.00 20.00 40.00 60.00 80.00

Arc Azim uth (de grees)

0.00

5.00

10.00

15.00

20.00

25.00

SF6 C oncentration (ppt)

calpuff\inel\run\arc048.grf

Observations

Pasquill D is persion

Sim ilarity D ispers ion

305

307

315

316

317

319320

321

323

324

326

327

329

330

0.00 20.00 40.00 60.00 80.00

Arc Azim uth (de grees)

0.00

2.00

4.00

6.00

8.00

10.00

S F6 C oncentration (ppt)

calpuff\inel\run\arc090.grf

O bservations

P asquill D ispersion

S im ilarity D ispersion

410

411

412413

414

415

416

417418

419

420

421 DBS422423

424

425

12.00 14.00 16.00 18.00 20.00

Hour of Day (LST)

0.00

10.00

20.00

30.00

40.00

50.00

S F 6 (P D C H ) C o n cen tra tio n (p p t)

PDCH Two-Trap

PD CH C assette

PG D ispersion

SM Dispersion

calpuff\inel\run\arctim e.grf

66

(A) (B)

(C) (D)

Figure 11. Six-hour average SF concentration values observed and 6

estimated for April 19, 1977, (A) 3.2-km arc, 1300-1900 MST; (B) 48-km arc,

1400-2000 MST; (C) 90-km arc, 1600-2200 MST. Azimuth is defined as

viewed from the release position with 0 due North and 90 due East (see

Figure 9). Receptor numbers are shown just above each observed

concentration value. (D) Time history of observed PDCH and estimated SF6

concentrations along the 48-km arc for April 19, 1977. Observed PDCH

values were multiplied by 3.16 for comparison with estimated SF values 6

(volume of SF divided by volume of PDCH released equals 3.16). 6
67

release location. The second moment,  , represents a measure of the puff horizontal y

dispersion. For these 6-hour periods, the observed lateral dispersion ranged from

roughly 22% to 15% of the travel distance downwind. The crosswind integrated

concentration (CWIC) value characterizes the amount of pollutant mass seen at the

surface. The observed CWIC values at all of the arcs is close to what one would

expect if the tracer had become well mixed in the vertical. As shown in Figure 11

(which is typical for all of the simulations), the simulated transport was somewhat south

of the observed position along the first two arcs. It is also apparent that the

concentrations simulated for the first arc are at least a factor of 5 higher than observed.

Figure 11d provides a comparison of the time history of the puff, as it passed by

the 48-km arc. Sampling results are shown for the two-trap sampler which provided 5-

minute samples, and a cassette sampler which provided approximately 15-minute

samples. These samplers were quite close to the observed position of the 6-hour SF .6

maximum along this arc. The dispersion results are for the simulated position of the

maximum, which was somewhat displaced from that observed. The Pasquill dispersion

results are in remarkable accord with the tracer results. The similarity results arrive and

depart slightly later than observed. The slower transport for the similarity dispersion

occurs because the vertical dispersion was less than that simulated by Pasquill

dispersion, hence the transport speed was computed over a more shallow layer for the

similarity results. These results and those discussed above suggest that the similarity

dispersion was underestimating the vertical dispersion for this case.

The primary purpose of this investigation was to assess whether the CALPUFF

simulations were in reasonable accord with the observed concentrations. The

comparison results presented reveal as yet unexplained differences for the nearest arc,

3.2 km downwind from the release. The simulated pattern of dispersion was displaced

as much as 40 degrees from that observed, regardless of how the wind fields were

characterized. For all arcs, the lateral dispersion along the sampling arcs was best 

characterized by both dispersion characterizations when all the surface tower winds

were used. Except for the first sampling arc, the simulated maxima along the arcs were

typically within a factor of 2 of that observed. The Pasquill simulations were most

sensitive to how the wind fields were characterized, showing the most variability

between the various wind field results. Having but one puff release limits conclusions to

be reached. For this one realization, it would appear that simulations by both

dispersion characterizations were in best accord overall with observations when all the

low-level winds and upper-air observations were used. And for this case, the similarity

dispersion simulations may have underestimated the vertical dispersion.

4.6.3 1980 Great Plains Tracer Study

In this study (U.S. EPA, 1998a), concentration estimates from the CALPUFF

dispersion model were compared to observed tracer concentrations from a short-term

field experiment (the Great Plains experiment) near Norman, Oklahoma (Ferber et al.,

1981) in July 1980. This experiment examined long-range transport of inert tracer
68

materials to demonstrate the feasibility of using other tracers as alternatives to the more

commonly used SF . Several tracers were released for a short duration (3-4 hours) and 6

the resulting plume concentrations were recorded at an array of monitors downwind

from the source. For the Great Plains experiment, arcs of monitors were located 100

and 600 kilometers from the source. 

Figure 12. Great Plains field experiment site.

Previous studies have compared the results from the Great Plains experiment to

dispersion model results. Carhart et al. (1989) intercompared the results from eight

short-term, long-range dispersion models to the Great Plains results and to a longerterm study at the Savannah River Laboratory (not the study discussed in Section 4.6.1). 

The primary method for evaluating model performance was the use of the American

Meteorological Society (AMS) statistical measures (Fox, 1981) and graphical

techniques. They concluded that model results compared in space and in time to

observations were generally poor and that predictions for a specific location and time

for averaging periods less than one day were not reliable. They also noted that

unpairing decreases the scatter. They concluded that “model improvement can be

made by better representing the wind field. The use of multiple layers seems to

improve results substantially.”

The transport and diffusion of a tracer gas was simulated by Moran and Pielke

(1995a,b) using the Colorado State University mesoscale atmospheric dispersion

modeling (CSU MAD) system, which consists of a prognostic meteorological model

coupled to a mesoscale Lagrangian particle dispersion model. Results from several

simulations with the model were compared to observations from the Great Plains

experiment. Their baseline simulation generally compared favorably to observations for
69

both arcs although directional errors of up to 20were apparent. The results also

suggest that the nocturnal low-level jet plays an important role in transport and

deformation of the tracer plume and that some flow regimes require better temporal

resolution of boundary layer winds than is available from the National Weather Service

(NWS) twice-daily rawinsondes.

The Great Plains site is shown in Figure 12. Two arcs of monitors were

deployed during the field experiment -- 100 and 600 kilometers. For this analysis, two

separate grids were defined. For the 100-kilometer arc, a grid extending approximately

from 35N to 36.5N latitude and from 96W to 98.5W longitude was defined. A 42-

by-40 horizontal grid with a 10-kilometer resolution was used for this arc. For the 600-

kilometer arc, the grid extended from approximately 35N to 42N latitude and from

89W to 100W longitude. A 44-by-40 horizontal grid with a 20-kilometer resolution

was used for this arc.

To adequately characterize the vertical structure of the atmosphere, six layers

were defined: surface-20, 20-50, 50-100, 100-500, 500-2000, and 2000-3300 meters. 

The CALMET preprocessor utilizes NWS meteorological data and on-site data to

produce temporally and spatially varying three dimensional wind fields for CALPUFF. 

Only NWS data were used for this effort and came from two compact disc (CD) data

sets (see Appendix C). The first was the Solar and Meteorological Surface Observation

Network (SAMSON) compact discs, which were used to obtain the hourly surface

observations. The following surface stations were used:

 Arkansas: Fort Smith

 Iowa: Des Moines

 Illinois: Springfield 

 Kansas: Dodge City, Topeka, Wichita

 Missouri: Columbia, Kansas City, Springfield, St. Louis

 Nebraska: Grand Island, Omaha, North Platte

 Oklahoma: Oklahoma City, Tulsa

 Texas: Amarillo, Dallas-Fort Worth, Lubbock, Wichita Falls

Twice daily soundings came from the second set of compact discs, the

Radiosonde Data for North America. The following stations were used:

 Arkansas: Little Rock Nebraska: North Platte, Omaha

 Illinois: Peoria Oklahoma: Oklahoma City

 Kansas: Dodge City, Topeka Texas: Amarillo

 Missouri: Monett

CALMET requires a file of terrain elevations and geophysical parameters in order

to prepare the wind fields and other meteorological parameters. The geophysical

parameters were derived from geographical information system (GIS) land-use

categories. Terrain and land-use data were available on the CALMET, CALPUFF, and
70

CALPOST Modeling System (version 1.0) CD (see Appendix C). These data are

defined with a resolution of 1/6latitude and 1/4longitude. 

The primary purpose of the Great Plains field study was to demonstrate the

efficacy of perfluorocarbons as tracers in atmospheric dispersion field studies. 

Perfluoromonomethylcyclohexane (PMCH), perfluorodimethylcyclohexane (PDCH),

SF , and two heavy methanes were released during this experiment. For this analysis, 6

the PDCH emission rates were used since the monitoring data appeared to have a

more complete record of PDCH concentrations. The following source parameters were

used for this analysis:

Source (m) (m) (m s ) (K) (kg) (hr) tracer

Release Stack Exit Exit Total tracer Length of Emission rate

height diameter velocity temp. released release (g s ) and -1

-1

Oklahoma 10.0 1.0 0.001 ambient 186 3.0 17.22

(July 8) PDCH

*

Oklahoma 10.0 1.0 0.001 ambient 26 3.0 2.41

(July 11) PDCH

*

 * The stack diameter for each study is the same as was used for the study with the INEL data

For both experiments, the emission rate was assumed to be constant over the

entire period of the release, and the release was assumed to be nonbuoyant. 

For the July 8 Great Plains experiment, sampling was conducted using two arcs

of monitors: 100 kilometers and 600 kilometers as shown in Figure 12. Two separate

CALPUFF model runs were made: 1) using Pasquill-Gifford (PG) dispersion

parameters, and 2) using dispersion coefficients from internally-calculated v w and 

from the micrometeorological variables calculated in CALMET (hereafter referred to as

similarity dispersion). 

Beginning at 1300 LST on July 8, the PDCH and PMCH tracer gases were

released at a constant rate for a three-hour period from an open field at the National

Severe Storms Laboratory in Norman, Oklahoma. A background concentration for

PDCH of 26 ppt (Ferber et al., 1981) was removed from the observed concentrations. 

The five-hour average modeled and observed concentrations for the 100-kilometer arc

on July 8 of the Great Plains field experiment are shown in Figure 13a along with Moran

and Pielke’s baseline simulation (experiment 4b). Two things are immediately

apparent: the monitoring did not capture the entire plume and the observed maximum

concentration is very likely less than the simulated maxima. Given the incomplete

sampling of the observed plume at 100 kilometers for this release, the statistical

measures of the observed plume likely are suspect and are not sufficient to draw

conclusions regarding model performance. In comparing the CALPUFF results to

Moran and Pielke’s simulation, the CSU MAD model placed the maximum about 25-20 -10 0 10 20 30

Azimuth (degrees)

0.0

1.0

2.0

3.0

Great Plains

July 8, 1980

100 km arc

P-G Dispersion

Similarity Dispersion

Observations

Moran 4b

12 13

14

15 16

18

19 2021 22

(a)

-10 0 10 20 30 40 50

Azimuth (degrees)

0.00

0.10

0.20

0.30

0.40 P-G Dispersion

Similarity Dispersion

Observations

Moran 4b

Great Plains

July 8, 1980

600 km arc

Moran 4b

A 2 4 7

9

10

11

12

13

1516 19

(b)

71

west of the actual plume. Moran and Pielke’s result for the 100 kilometer arc are very

similar to the CALPUFF simulations.

(A) (B)

Figure 13. Simulated and observed concentrations for the Great Plains tracer field

study for July 8, 1980 for A) 5-hour average concentrations along the 100-km arc,

and B) 12-hour average concentrations along the 600-km arc.

The 12-hour average modeled and observed concentrations for the 600-

kilometer arc on July 8 are shown in Figure 13b. Two things are apparent: 1) the

observed maximum concentration is about three times higher than the simulated

concentrations and 2) the maxima of the simulations are in relatively good agreement

with each other. As noted above, the tracer arrived at the sampling arc earlier than

anticipated and the sampling likely missed some of the tracer material. Ferber et al.

(1981) speculate that the plume probably arrived just before the samplers were

activated and a small amount of plume material was not collected. As described by

Moran and Pielke (1995a), the most likely reason for the earlier-than-expected arrival

was the formation of a low-level nocturnal jet. Hoecker (1963), in detailed studies of the

low-level jet over the Midwestern plains (from Amarillo, TX to Little Rock, AR) using a

series of pibal stations, found that jet speed maxima occur between 300 and 800

meters above local ground. In examining available data for the 1980 Great Plains field

experiment, Moran and Pielke (1994) note an approximate doubling of the average

nocturnal wind speeds from their daytime values. Examination of the upper air wind

profiles for Oklahoma City through the period indicate the presence of a jet between

500 and 1000 meters for the 1200 Greenwich Mean Time (GMT) soundings. 
-20 0 20 40

Azimuth (degrees)

0.00

0.10

0.20

0.30

PDCH Concentration (ppt)

P-G dispersion

Similarity dispersion

O bservations

G reat Plains

July 11, 1980

100 km arc

14 16

18

20 21 22

23

24

26

72

Figure 14. Simulated and observed 6-hour average concentration for the Great

Plains tracer field study on July 11, 1980 along the 100-km arc.

Beginning at 1300 LST on July 11, tracer gases were released for a three-hour

period using the same system as on July 8. The PDCH was released as an aerosol

spray at an average rate of 2.41 g/s, about 1/7 the release rate on July 8. A

background concentration for PDCH of 26 ppt was removed from the observed

concentrations. The transit time for the observed plume was six hours. The transit time

of the simulated plume in CALPUFF using both P-G and similarity dispersion

coefficients also was six hours. Therefore, six-hour average concentrations were used

in this part of the analysis.

The six-hour average modeled and observed concentrations for the 100-

kilometer arc are shown in Figure 14. As with the July 8 study, the monitoring did not

capture the entire plume at 100 kilometers, although the peak appears to be a little

better defined, with an observed maximum at receptor 18. There were no aircraft flights

to assist in determining the western extent of the plume. The simulated plumes using

PG and similarity dispersion agree with each other very well, but, as with the July 8

results for the 100-kilometer arc, the peaks are more than twice the magnitude of the

observed plume and the simulated lateral dispersion is less than the observed plume. .

As with the 100 kilometer arc for the July 8 study, the question remains - why do

the simulated plumes have higher central maxima and narrower dispersion. With a

more sophisticated modeling system, Moran and Pielke (1995a,b) encountered similar

differences in their examination of the July 8 simulation at 100 kilometers, and they

could not explain to their satisfaction why their dispersion model was not able to more

closely represent the observed dispersion patterns at the receptor arcs.
73

4.6.4 1992 Project MOHAVE Tracer Study

In this investigation (Vimont, 1998), the CALMET/CALPUFF modeling system’s

simulation of tracer gas dispersion was compared with field measurements collected

during the 1992 summer intensive monitoring period of Project MOHAVE. The Project

MOHAVE tracer monitoring sites used in this analysis were at Dolan Springs (DOSP),

Las Vegas Wash (LVWA), Meadview (MEAD), and Overton Beach (OVBE), (Figure

15). The tracer was released from the stack of the Mohave Power Plant (MOPP) in

direct proportion to the time-varying sulfur dioxide emissions. The tracer was collected

in 24-hour samples starting at 7 AM Mountain Standard Time (MST) at each of the

sites, except at MEAD which had two 12-hour samples with start times of 7 AM and 7

PM MST. The monitoring network is not well suited for rigorous model evaluation as it

is too sparse. Ideally, a network designed for model evaluation would have arcs of

monitors placed at distances of interest (as in the studies mentioned in previous

sections of this report). Also, it would be of interest to have sufficiently short sampling

intervals so that passage of tracer past and along an arc could be analyzed.

The meteorology is strongly influenced by the east-west river canyon that runs

from Las Vegas Wash and past Meadview. The Colorado River sharply turns south at

Las Vegas Wash along a north-south transect that connects Las Vegas Wash with the

location of the Mohave Power station. Upper air wind measurements were available

from three radar profiles located at the Mohave Power Plant (MOPP), Meadview

(MEAD), and Truxton (TRUX); RASS temperature profiles were available at MOPP,

(Figure 14). Twice-daily balloon soundings (winds and temperature) were available

from Dolan Springs (DOSP). Since CALMET requires both winds and temperatures at

each upper air station, it was necessary to generate temperature profiles for the radar

wind profiles collected at MEAD and TRUX. These were constructed by linear

interpolation in time using the temperature profiles at DOSP (DOSP is closer in

elevation to MEAD and TRUX, whereas MOPP is 600 to 800 m lower in elevation). The

radar winds were reported in both a high resolution mode, and a low resolution mode. 

The high-resolution data does not extend as high, but provides more details. Thus the

high-resolution data were combined with the low resolution data before processing by

CALMET.
74

Figure 15. The Project MOHAVE tracer field study collected tracer samples at Dolan

Springs (DOSP), Las Vegas Wash (LVWA), Meadview (MEAD), and Overton Beach

(OVBE). The tracer was released from the stack of the Mohave Power Plant

(MOPP). The bold solid lines are the CALMET “barriers” that were employed in

developing the CALMET analyses.

A number of diagnostic model runs were made, and some general conclusions

were reached. The initial CALPUFF runs with no complex terrain treatment grossly

underestimated concentrations, especially at the nearest monitoring site at DOSP. The

inclusion of the partial plume path adjustment terrain treatment option in CALPUFF

improved the correspondence of the calculated concentration with the measurements,

which is not surprising since several of the monitoring sites are more than 600 m higher

than the base of the Mohave Power Plant stack. It was also concluded that the

monitoring sites were too sparse to be used in a conventional manner. The narrowness

of the dispersing plume meant that even a very small shift of a few kilometers in the

plume position would result in major (in some cases orders of
75

(A)

(B)

Figure 16 Tracer concentrations (femtoliters/liter) predicted and observed for the Las

Vegas Wash (LVW) monitoring site, (A) are results obtained without use of barriers,

and (B) are results obtained using barriers in the CALMET processing.

 magnitude) changes in the simulated concentration values. To counter this deficiency,

eight receptors were placed 15 km around each monitor’s location. The estimated

concentration in closest correspondence to that measured was selected for use in

assessing model performance. 

When CALMET was initialized using the wind and temperature profiles from all

three sites, the CALPUFF tracer simulation results were in poor agreement with the

observations. It was determined that the 1/(distance) initialization used in CALMET 2

was not capturing the fact that each of these sites were located in very unique settings. 

The TRUX profiler is located at a high elevation in a valley with a southwest to
76

northeast orientation. The MOPP profiler is located in the Colorado River Valley, which

encompasses the DOSP location and continues up to the MEAD monitoring site. The

MEAD site is located near the west entrance of the Grand Canyon, which runs eastwest. CALMET was modified to allow ‘barriers’ to be placed in the Step 1 initialization

process, so that the influence of observations could be limited to better reflect the

unique settings of the profiler locations. The bold solid lines in Figure 15 show the

placement of the barriers. Figures 16a and 16b illustrate the differences seen in the

simulated concentrations without and with the barriers in the CALMET processing. The

correspondence of the simulated tracer concentrations with those monitored seems to

have been improved by allowing the barriers to influence the development of the

CALMET Step 1 wind fields.

4.7 Comparisons of CALPUFF With ISC3

A sensitivity study comparing the ground level concentration values of CALPUFF

(Scire et al, 1995b) with those of the Industrial Source Complex Short Term (ISC3)

model (U.S. EPA, 1995c) for steady state and nonsteady state conditions was

performed. The study (U.S. EPA, 1998; Eckhoff and Coulter, 1998) was divided into

two parts. First, specific CALPUFF model input settings were tested for the best setting

for emulating ISC3 under steady-state conditions. In the second part, the same input

settings were then used to compare CALPUFF to ISC3 results under nonsteady state

conditions. 

For the first part of the study, CALPUFF (4.0) was compared with the latest

version of ISC3 (Version 96113). ISC3 was implemented in the 'Regulatory Default'

mode and the input file for CALPUFF was configured so as to emulate ISC3 as closely

as possible. Point sources were simulated for rural environments free of obstacles and

with stack heights of 2 m, 35 m, 100 m and 200 m above ground level. Meteorological

data sets were synthesized with fixed meteorological conditions (Pasquill-Gifford

stability category, wind speed, and mixing height) and were of duration estimated to be

sufficient to advect CALPUFF's puffs to the edge of the domain. A line of 62 receptors

out to 100 km was placed along the 360radial, aligned with the transport wind flow,

and spaced at increasing intervals from the source.

For each pair of model runs (CALPUFF and ISC3), a residual concentration was

computed at each of the 62 receptors. From the 62 residuals (one for each receptor), a

mean, standard deviation (R), and sum of residuals squared were computed. The

mean provides an indication (sign) of bias along the receptor radial. The variance of

the residuals provides general indication of the variance along the receptor radial. 

Because many of the absolute residuals were quite small, the sum of the residuals

squared was also computed to provide a relatively robust indicator of accord along the

receptor radial. A Fractional Bias equation, FB = 2*(CALPUFF-ISC)/(CALPUFF+ISC)

was also computed. The steady state agreement between CALPUFF and ISC3 was

very good. Minimum and maximum fractional bias values of -0.18 and 0.06 are an
77

indication of how well CALPUFF, with the slug model invoked, can emulate ISC3 in a

steady-state environment using point source input data. 

Having confirmed that the dispersion algorithms within CALPUFF do emulate

quite well those within ISC3, the second part of the study was to compare modeling

results when the meteorological conditions were allowed to varying each hour. Hourly

meteorological data processed for input into ISC3 were selected from three stations to

simulate three climatically different regions of the United States: 1991 Boise, Idaho;

1990 Medford, Oregon; and 1964 Pittsburgh, Pennsylvania. Each set contains a year

of hourly averaged values of wind speed, wind direction, ambient temperature, stability

class, and mixing heights. In these simulations, wind fields were not generated for the

CALPUFF simulations. Instead, the ISC option was used, which allows CALPUFF to

operate using meteorological data processed for input to ISC3. This means that the

meteorology for these simulations is assumed to be everywhere the same, over the

entire CALPUFF computational domain. This admittedly artificial assumption for the

CALPUFF meteorology at least insured that the only reason for differences in the

modeling results, resulted from the fundamental differences in the treatment of

transport between a plume and a puff model. 

The Boise data were selected because they were obtained in a river valley which

has highly directional wind flows. More than 33% of the recorded winds have a

northwesterly component and more than 33% of the winds have a southeasterly

component with the majority of those winds having speeds greater than two m/s. Some

of the modeled puffs were expected to be transported directly to the most distant

receptors. The Medford data were selected because they were obtained in an area

surrounded by mountains with a high number of calm wind hours. In 1990, 22% of the

Medford Oregon winds were calm winds (<1 m/s). This compares to the average of

6.5% occurrence of calm winds for the other two sites. During calm wind events, 

CALPUFF calculates concentrations while ISC3 does not calculate concentrations. 

ISC3 treats the hour as missing when determining concentration averages. There was

an expectation that there would be large differences in concentration averages because

of the high number of calm winds. The Pittsburgh data were selected because the

recording site is located on an open plain above a river valley and that the data have

been used as a standard test set for a number of years. The wind directions and wind

speeds are fairly well distributed, although there is a tendency for southwesterly winds.

The main receptor placement consisted of 15 rings of 36 receptors each, with

receptors spaced every 10 degrees starting at 360 degrees. The rings were spaced at

distances of 0.5, 1, 2, 3, 5, 10, 15, 20, 30, 50, 100, 150, 200, 250, and 300 km from the
78

Table 8. Characteristics for point sources used in the

CALPUFF/ISC3 comparisons.

Stack Emission Exit Stack Diameter Exit

Height Rate Velocity (m) Temperatur

(m) (g/s) (m/s) e (K)

2 100 10.0 0.5 300

35 100 11.7 2.4 432

100 100 18.8 4.6 416

200 100 26.5 5.6 425

source. Four point sources (Table 8) were used in these ISC3/CALPUFF comparisons

with hourly varying meteorology. For each year of hourly meteorology, and for each of

the 15 rings of receptors, the highest and second-highest concentration was

determined for four averaging times (1-hour, 3-hour, 24-hour, and annual) for each

model. It was anticipated that some differences would be seen in the simulated

maxima, since ISC3 ignores hours with wind speed less than or equal to 1 m/s (calms),

for which CALPUFF continues to process, and because ISC3 can not treat the

consequences of a flow reversal between hours, a situation easily interpreted by a puff

model. Even though such differences were anticipated, the magnitude of these

differences, and sensitivity to release height was not known. 

As a case study, a ten-hour period was examined to determine the cause of

large differences detected in the concentrations simulated by the two models using the

Boise meteorological data. The large differences were detected 5 to 15 km downwind

from the 2 -m point source for Hour 62 of the simulation, following a 4 hour period of

calm winds and then a wind reversal. Figure 17a illustrates the distribution of the

pollutant mass, in which emissions from particular hours, preceding Hour 62 have been

depicted so that it is possible to see how the sequence of events preceding Hour 62

has affected the results seen. Starting with Hour 52 there was a 5-hour period for

which the winds were from the east-southeast. Then there was a period of 4 hours of

calm winds, which was followed by a 180-degree wind shift for 2 hours. Thus the

pollutant mass which had started out moving towards the west-northwest, for the last

two hours is seen moving back towards the southwest. As illustrated in the figure, the

result is that a broad area upwind (in terms of Hour 62) is being affected by emissions

that were released during Hours 52-60. The subsequent superposition of all of the

puffs is shown in Figure 17b. For receptors 5 to 12 km southwest of the stack, the

superposition of all the puffs results in concentration values that are more than a factor

of two greater than that simulated by ISC3 for Hour 62. During one of the calm hours,

Hour 57, the emissions were simulated to penetrate a low-level inversion. CALPUFF
-40 -30 -20 -10 0 10 20 30 40

Distance (km)

-30

-20

-10

0

10

20

Distance (km)

Centerline at Hour 62

Calpuff Emissions

From Hours 52-56 Source

ISCST Emissions

From Hour 62 Only

Calpuff Emissions

From Hours 61-62

Calpuff Emissions

From Hours 57-60

-40 -20 0 20 40

Centerline Distance Downwind From Source (km )

1E-1

1E+0

1E+1

1E+2

1E+3

1E+4

Concentrations (ug/m**3)

Concentration C ontributions

Calpuff, Hours 52-56

Calpuff, Hours 57-60

Calpuff, Hours 61-62

Calpuff Total

ISC ST3, H our 62 only

Hours 61-62

Hours 57-60

Calpuff Total

ISCST3

Hours 52-56

Calpuff Total

ISCST3

Hours 61-62

Hours 61-62 Hours 57-60

Hours 52-56

79

(A) (B)

Figure 17. Case study of a ten-hour period of simulated dispersion by CALPUFF. 

(A) depicts impacts for Hour 62, showing contributions from emissions released prior

to Hour 62, (B) depicts centerline concentrations from CALPUFF and ISC.

tracked the puffs and the subsequent fumigation to the ground once the inversion grew

to encompass the puffs which were aloft. 

The ten-hour example just described is illustrative of how differences can arise. 

It also serves notice that large differences can arise, and that these differences may

arise from a rather complicated history of events. Both the sequence of events, as well

as the dispersiveness of the atmosphere are important in understanding the puff

simulation results. Unlike in plume simulations, the concentration results obtained for a

given hour are unlikely to be understood simply by knowing the meteorology for the

given hour. The sequence of the meteorological conditions leading up to the hour in

question may be all important.

For each of the three sites, the results for each averaging time were summarized

by plotting on a common graph the percent difference, PD = 100(C-I)/I, where C equals

the highest (or second-highest) CALPUFF concentration along a receptor ring, and I

equals the highest (or second-highest) ISC3 concentration along the same receptor ring

for the given averaging time. Figure 18 is an example of some of the CALPUFF/ISC3

comparison results obtained by this sensitivity study. Illustrated in the figure are results

for the Medford meteorology, and the results shown are for the 1-, 3-, 24-hour and

annual average comparisons of the second-highest concentration values for each of the

15 receptor rings. As each receptor ring is at a prescribed distance, the results

illustrate as a function of downwind distance and source-type whether CALPUFF is

providing higher (PD>0) or lower (PD<0) concentrations than ISC3. The Medford
80

meteorological data had the highest occurrence of calm wind conditions, and some of

the largest differences. CALPUFF was seen to provide concentration values greater

than ISC3, for this site for all averaging times. It is instructive to note that CALPUFF

can produce nonzero concentration values at the base of an elevated point source

release (when flow reverses and pollutants previously released are transported back

towards the source). ISC3 can never produce such an effect. It is also instructive to

note that CALPUFF can yield much greater concentrations than ISC3 at most any

distance downwind and for most any averaging time. The exact timing and distance

downwind of such occurrences is dependent on the sequence of meteorological events,

but typically can occur whenever there is a period of calm winds of extended length

(say more than three hours in duration).

Figure 19 provides a sense of the differences to be seen between sites. Here

annual averages are compared for each site. Typically, the greatest differences seen

between the two models are seen in the shorter averaging times. The comparisons of

the annual averages most clearly illustrate that as release height increases, CALPUFF

tends to increasingly provided higher concentration impacts in comparison to ISC3. 

Also the comparisons of the annual averages best illustrate the occurrences of nonzero

concentration values being simulated by CALPUFF and the lack thereof by ISC3. The

effect on the model simulations and resulting comparison of concentration values of the

high incidence of calm wind conditions (~23%) at Medford is apparent. The Pittsburgh

and Boise meteorological data had similar incidences of calm wind conditions (7% and

6% respectively), but the Boise data was highly directional, with 33% of the wind

towards the southeast and 33% of the winds towards the northwest (suggestive of high

incidences of flow reversals). Thus it is that the Boise comparisons show CALPUFF

yielding higher percentage difference in the annual averages than those seen using the

Pittsburgh meteorological data.

In the above comparisons discussed, it is important to remember that no terrain

effects on the meteorology were modeled (other than that which is inherent in the

climatology of the surface weather observations). Hence, the percentage differences

seen could have occurred at any of the sites, given the correct sequence of

meteorological events. In this sensitivity analysis, one year of data was used from each

site. The site-to-site differences is seen to be large. In the comparisons to be

discussed in the next section, several years of data are used from one site. 
1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

Stack Height Key

 2 Meter

 35 Meter

100 Meter

200 Meter

Prcnt Dif = 100*(Calpuff-ISC3)/ISC3

1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

81

(A) (B)

(C) (D)

Figure 18. Percent differences (CALPUFF versus ISC3) as a function of downwind

distance for the second-highest concentrations; (A) 1-hr, (B) 3-hr, (C) 24-hr, and (D)

annual averages. Data are for Medford, Oregon.
1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

Stack Height Key

 2 Meter

 35 Meter

100 Meter

200 Meter

Prnt Dif = 100*(Calpuff-ISC3)/ISC3

1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

Stack Height Key

 2 Meter

 35 Meter

100 Meter

200 Meter

Prcnt Dif = 100*(Calpuff-ISC3)/ISC3

1 10 100 1000

Distance Downwind (km)

-200

0

200

400

600

Percent Difference

Stack Height Key

 2 Meter

 35 Meter

100 Meter

200 Meter

Prcnt Dif = 100*(Calpuff-ISC3)/ISC3

82

(A) (B)

(C)

Figure 19. Percent differences (CALPUFF versus ISC3) as a function of downwind

distance for the annual average: (A) Boise, (B) Medford, and (C) Pittsburgh.
83

4.8 CALPUFF SCREEN

By far, one of the most demanding tasks in performing refined puff model

simulations is to successfully develop a valid time and space varying characterization of

the meteorological conditions for use by the CALPUFF puff dispersion model. The

processors that format and organize the input data to CALMET are not presently userfriendly and demand strong computer skills. Often special custom routines are needed

to format available data into the acceptable formats for use by CALMET. Even if the

logistics of manipulating the data were simplified, developing realistic characterizations

of the time-varying three-dimensional wind fields will always require specialized skills. 

Developing mesoscale and microscale meteorological characterizations requires having

not only the specialized understanding of micrometeorological wind effects, but also the

experience and expert judgement to know when a characterization developed by the

meteorological processor is unreasonable. Furthermore, to review and critique the

CALMET results requires strong computer skills for visualization of the CALMET

results, or for listing out for special inspection portions of the CALMET results.

In this section, we summarize the results from a study in which a methodology

was tested whereby CALPUFF could be used with a simplified set of meteorological

data, for the purpose of providing screening estimates of concentration and deposition

impacts (U.S. EPA, 1998b). The methodology was tested in two ways: 1) five years of

hourly meteorology were used to develop data for assessing the year-to-year variability,

and 2) one year of hourly meteorology was fully processed through CALMET to assess

whether the screening methodology devised did indeed provide concentration impacts

greater than would be developed using a fully developed set of meteorology. 

4.8.1 Screening methodology

As a design goal, it was our intention to minimize the effort needed to create the

meteorological input for use by CALPUFF. CALPUFF has a built-in mode whereby it

can use the meteorological data file generated by PCRAMMET for the Industrial Source

Complex Short Term (ISCST3) model, thus bypassing the need to run CALMET.

The following approach was devised for running CALPUFF in a screening mode

to estimate ground-level concentrations over a large area:

1) generate five years of ISCST3 input meteorology using PCRAMMET,

2) generate an ISCST3 control file and use the ISC2PUF conversion program

to create the CALPUFF control file,

3) use the CALPUFF Graphical User Interface (GUI) to finalize the CALPUFF

control file before running the CALPUFF model,
84

4) run CALPUFF with the ISCMET.DAT data option, and

5) pick the maximum concentration for each pollutant, for each distance and

averaging time modeled (see discussion below on receptor placement).

Generating ISC3 input meteorology

PCRAMMET can be used to generate the meteorological data files for

CALPUFF. Using hourly surface observations and twice-daily mixing heights,

PCRAMMET computes atmospheric stability in the form of Pasquill-Gifford (PG)

categories and rural and urban mixing heights. These data, along with the wind

direction, wind speed, and temperature, are read directly by CALPUFF without any

modification to the data file. It is recognized that this characterization of the

meteorology will not vary spatially as in a refined modeling effort, and the consequent

differences will be significant as the terrain becomes more rugged and complicated. As

is known by those experienced in puff dispersion modeling, one can make conservative

choices and assumptions, but it is difficult to guarantee that the results obtained will

always be more conservative (but not onerously so) than that derived using fully

developed time and space varying meteorological input data. In order to encourage the

resulting concentration estimates to be higher than would be obtained using a refined

set of meteorology, we have made conservative assumptions as to how the receptors

will be placed, and which concentration values will be selected for use. 

To perform dry deposition calculations, the surface roughness, friction velocity,

and Monin-Obukhov length are required. These parameters can be computed by

PCRAMMET and written to an output file for use. In order to estimate these additional

parameters, several additional input values are required by PCRAMMET, including:

surface roughness at the site where the wind measurements are taken (usually an

airport), surface roughness at the site where the model is to be applied, noon-time

albedo, Bowen ratio, and fraction of the net radiation absorbed by the ground. 

For the test results to be discussed, we applied the screening methodology to

the region about Oklahoma City depicted in Figure 12. We had previously developed

the CALMET terrain and land-use files as discussed in Section 4.6.3. A surface

roughness length at the measurement site (Oklahoma City airport) of 0.10 meters was

assumed. An average roughness length of 0.34 meters was computed from the

GEO.DAT file created for the entire modeling domain, as well as, an average noon-time

albedo of 0.15 and Bowen ratio of 1.00. A value of 0.15 was assumed for the fraction

of net radiation absorbed by the ground. 
85

ISC3 control file settings

Using this proposed methodology, the control file and meteorology input can be

created as if preparing for an application of the ISCST3 dispersion model. This

approach has the advantage that many dispersion modelers are familiar with both the

meteorological data and control file structures. The rural dispersion coefficients with

the regulatory default settings were selected, which include use of stack-tip downwash,

buoyancy induced dispersion, final plume rise, default wind speed profile exponents,

and default vertical potential temperature gradient. Averaging times for the model runs

were 1-hr, 3-hr, 24-hr, and annual averages.

A polar grid was used to define the receptor locations. CALPUFF does not

accept a polar grid directly. Receptors must be a subset of the CALPUFF

computational domain (which is a subset of the meteorological domain), i.e., a

Cartesian grid, or they must be discrete receptors. The ISC2PUF program can convert

a polar grid and associated terrain to discrete receptors with terrain. The ISC2PUF

limits the number of receptors that can be processed to 1200. Hence very large

applications, may require two or more runs of this program followed by merging the

results into a single CALPUFF control file.

For the test results to be discussed, the following polar receptor networks were

used:

 Receptor Spacing Number of Distance Along Ring

Distance (km) Along Each Ring Receptors Between Receptors

1, 2, 3, 5, 10, 15, 20, 30, 50 every 10324 0.2 - 8.7 km

75, 100, 150, 200, 250, 300 every 5432 6.5 - 26.2 km

These two polar networks result in 756 receptors, well below the limit of about 1200.

CALPUFF control file settings

 The CALPUFF system includes a program (ISC2PUF) that translates an

ISCST3 control file to a CALPUFF control file. The converted control file must be

edited prior to running CALPUFF. This can be accomplished in one of two ways: using

a text editor to modify the control file directly, or using the CALPUFF graphical user

interface (GUI) to guide the user through the options.

The CALPUFF control parameters that must be set by editing the ISC2PUF

generated CALPUFF control file include: the use of puffs or slugs, and the type of

dispersion - Pasquill-Gifford or internally-computed ’s. Also to be considered are the

pollutants of interest, modeling of chemical transformations, and modeling dry

deposition processes.
86

Pollutants: The transformation pathways for five active pollutants are treated by

the MESOPUFF II scheme in CALPUFF: SO , SO , NO , HNO , and NO . Since haze 24 x 3 3

= -

and visibility are of concern in areas such as national parks, CALPUFF is most likely to

be applied to model sulfates and nitrates. For the testing results to be presented, the

focus was on SO and SO . 2 4

=

Slug model versus puff model: The slug model was used, with the set of

default options for slugs.

Dispersion coefficients: Pasquill-Gifford dispersion coefficients for both the

screening and refined CALPUFF modeling were used.

Concentration estimates: CALPUFF was run initially for both the refined (one

year) and screening modes (five years) without any chemistry or deposition. This

provided ‘baseline’ concentration estimates without the effects of chemistry and

deposition. CALPUFF was then run for one year with chemistry and deposition

activated. 

Chemical transformations: There are two transformation options in CALPUFF:

1) MESOPUFF II mechanisms and 2) a file with a diurnal cycle of transformation rates. 

The MESOPUFF II option requires relative humidity as one of the input variables for

chemical transformations. However, this variable was not present in the current

ISCST3 meteorological data file. The second mode requires a file of diurnal

transformation rates specified by the user. In this mode, transformation rates are

spatially uniform but provides for some temporal variability. The second method, with

the file of transformation rates, was used in this modeling effort.

For the test results to be discussed, we used a 3.0 %/hr transformation rate for

daylight hours and 0.2 %/hr at night for the SO to SO transformation. These rates 2 4

=

were used for both CALPUFF refined and screening modes. The daytime period was

defined as 0700 to 2000 LST, which biases the daylight period towards a summer day

and should result in production of more sulfates.

Dry deposition: In CALPUFF, deposition can be modeled as either particle or

gas, depending on the pollutant. To estimate deposition, the surface roughness length,

surface friction velocity, and Monin-Obukhov length are estimated by CALMET and vary

temporally and spatially when CALPUFF is run in a refined mode. In the screening

mode, though, these variables are specified for each hour on the ‘extended’ data record

in the meteorological file. PCRAMMET was run to generate these extended data

records for the CALPUFF screening mode.

To compute dry deposition of particles or gases, CALPUFF requires one of the

following: 1) a file of the diurnal variation of deposition velocities for each pollutant

modeled, or 2) specification of the mass mean diameter, geometric standard deviation,
87

and number of particle size intervals to evaluate the effective particle deposition

velocity. For the latter option, CALPUFF has default parameters for several pollutants,

including SO and SO . For this modeling effort, we used the second option with the 2 4

=

gaseous SO default parameters and the particulate SO default parameters. 2 4

=

4.8.2 Year to year variability

Five years of meteorological data were processed through PCRAMMET to create

the necessary input meteorology for the CALPUFF screening model runs. Oklahoma

City was used for the hourly surface observations and Oklahoma City/Norman for the

upper air data. Data from the Solar and Meteorological Surface Observation Network

(SAMSON) compact disc (see Appendix C) were used to obtain the hourly surface data. 

The twice-daily mixing heights were retrieved from EPA’s Support Center for Regulatory

Air Models (SCRAM) system. The mixing height data on SCRAM restricted the period

of choice from 1984 through 1991. Oklahoma City for 1985-1988 were used for this

modeling effort. There were no mixing height data for 1989 on SCRAM. The upper air

station changed from Oklahoma City to Norman in 1989. Since Norman is only 25-30

kilometers from Oklahoma City, data from Norman for 1990-1991 was used. There

were no periods of missing data for Oklahoma City that required filling. There were five

2-hour periods of unfilled mixing heights for the two years of data at Norman. Following

EPA guidance, the mixing heights were filled by linearly interpolating between the hours

before and after the missing periods to fill in the mixing heights.

The 2-m, 35-m and 200-m point sources listed in Table 8 were used in this

assessment. The rings of receptors (as listed earlier) were placed around each source,

and the five years of ISC-type meteorology were processed by CALPUFF. For this

assessment of year to year variations, inspection was made of the variations seen in

the highest 1-hr, 3-hr, 24-hr and annual simulated SO concentration, along each 2

receptor ring. Table 9 summarizes the maximum and minimum value seen for C/Cavg,

where C is the maximum concentration seen in any one year along a receptor ring for a

particular source and averaging time, and Cavg is the average of the five values seen

for this source along this ring for the five years simulated. Rather than list the results

separately for each of the 15 receptor rings, we have compiled the results for three

groups, with group 1 having four receptor rings at distances 1-, 2-, 3-, and 5-km; group

2 having five receptor rings at distances 10-, 15-, 20-, 30-, and 50-km; and group 3

having six receptor rings at distances 75-, 100-, 150-, 200-, 250-, and 300-km. These

three groups subjectively relate to near-field impacts (group 1), mesoscale impacts

(group 2), and longer-range impacts (group 3).
88

Table 9. Summary for three hypothetical sources and for four averaging times in the

variation seen in the ratio C/Cavg, where Cavg is the average of the 5 maximum SO2

concentrations for a receptor ring and averaging time, and C is the maximum SO2

concentration simulate during one of the five years. The variations listed in C/Cavg

reflect both variations seen with the distance range and variations over the five year

period of the analysis.

2-m Source 35-m Source 200-m Source

1-Hour Range Min Max Min Max Min Max

1-5 km 0.79 1.52 0.89 1.14 0.68 2.05

10-50 km 0.65 1.86 0.76 1.47 0.47 2.32

75-300 km 0.56 1.66 0.72 1.23 0.44 1.74

3-Hour 1-5 km 0.83 1.26 0.89 1.21 0.80 1.49

10-50 km 0.58 1.43 0.80 1.48 0.58 2.19

75-300 km 0.59 1.87 0.60 1.52 0.71 1.51

24-Hour 1-5 km 0.85 1.16 0.80 1.26 0.75 1.29

10-50 km 0.64 1.55 0.79 1.45 0.77 1.60

75-300 km 0.61 2.13 0.59 1.52 0.68 1.41

Annual 1-5 km 0.90 1.11 0.88 1.13 0.83 1.26

10-50 km 0.88 1.13 0.90 1.12 0.88 1.17

75-300 km 0.81 1.29 0.89 1.25 0.86 1.17

As averaging time increased, there was less variation between years. For

instance, for the 75 to 300 km receptor rings, the ratio of the one-hour maxima to the

five-year average one-hour maxima varied from 0.45 to 2.30. Whereas the ratios for

the annual-averages ranged from 0.80 to 1.30. For these results it is concluded that we

might expect variation in the maximum concentrations of at least 20 to 30 percent.

4.8.3 SO concentrations 2

One of the comments received at the Sixth Modeling Conference was the

perception that the Phase 1 (Level I) screening procedure (U.S. EPA, 1993) was too

conservative to be of practical use. Thus a second part of developing a new screening

methodology was to test the results obtained with the new methodology with those that

would be obtained in a refined modeling analysis. Table 10 summarizes the maximum 
89

Table 10. Summary for three hypothetical sources and for four averaging times in

the variation seen in the ratio Cs/Cr, where Cs is the SO maximum concentration for 2

a receptor ring and averaging time using CALPUFF with ISC meteorology, and Cr is

the second highest SO concentration simulated for the same ring using CALPUFF 2

with CALMET meteorology. Results were generated for only 1990, so the minimum

and maximum values for Cs/Cr, reflect variations seen over the receptor rings

included in the range.

2-m Source 35-m Source 200-m Source

1-Hour Range Min Max Min Max Min Max

1-5 km 0.59 1.31 1.03 1.51 0.86 3.07

10-50 km 0.74 1.30 0.87 1.86 1.27 3.29

75-300 km 0.99 3.20 1.55 2.09 0.68 1.09

3-Hour 1-5 km 0.82 1.42 1.02 1.08 0.77 1.56

10-50 km 0.88 1.43 0.80 1.53 0.72 2.71

75-300 km 0.96 3.89 1.17 3.35 0.73 1.04

24-Hour 1-5 km 0.95 1.00 0.86 0.98 0.73 0.99

10-50 km 0.88 1.21 0.86 1.28 0.92 1.54

75-300 km 1.57 4.48 1.11 2.55 0.96 1.50

Annual 1-5 km 1.28 1.37 1.41 1.47 0.58 1.13

10-50 km 1.49 2.28 1.59 2.29 1.16 1.30

75-300 km 1.82 3.47 1.85 2.89 1.07 1.27

and minimum value seen for Cs/Cr, where Cs is the maximum SO concentration seen 2

for 1990 along a receptor ring for a particular source and averaging time using the

CALPUFF with ISC meteorology (a screening estimate), and Cr is the second-highest

SO concentration seen along the same receptor ring resulting from using the 2

CALPUFF with fully developed CALMET meteorology (a refined estimate). Comparing

the CALPUFF screening estimate of the maximum concentration along a ring versus

the second-highest concentration from a refined model run, provides an assessment of

whether the new screening methodology is a conservative screening estimate. 

In each of the three ranges, instances can be found where the maximum SO2

concentration obtained using ISC meteorology as input to CALPUFF (screening

estimate), Cs, was not as high as the second-highest concentration obtained from using

CALMET meteorology as input to CALPUFF (refined analysis), Cr. It is important to
90

remember that these comparisons are between the maximum screening estimate found

anywhere along the receptor ring and the refined modeling results for the maximum of

the second-highest concentrations found anywhere along the ring. If an actual source

were being modeled for a particular Class I area, we would anticipate that it is quite

likely that the refined model’s estimate of the highest of the second-high concentrations

for the segment of a receptor ring passing through the Class I area would be lower than

what would be found anywhere around the receptor ring. 

For the receptor rings at 75-km to 300-km, the Cs/Cr ratios for the 1-hour

comparisons range from 1.00 to 3.0 for the 2-m source, and from 0.70 to 1.10 for the

200-m source. As averaging time increases, the screening estimate of the maximum

concentration tends to be greater than the second-highest concentration obtained in the

refined analysis. The IWAQM concludes the new screening method (ISC meteorology

as input to the CALPUFF model) provided conservative estimates of maximum SO2

concentration values, that were not overly conservative for practical use. Although

there is a finite possibility for the refined analyses to develop higher concentration

impacts, the likelihood of this result is low for specific source-receptor pairings.

4.8.4 SO concentrations 4

=

Table 11 summarizes the maximum and minimum value seen for Cs/Cr, where

Cs is the maximum sulfate concentration seen for 1990 along a receptor ring for a

particular source and averaging time using the CALPUFF with ISC meteorology, and Cr

is the maximum sulfate concentration seen along the same receptor ring resulting from

using the CALPUFF with fully developed CALMET meteorology. In each of the three

ranges in Table 11, instances can be found where the screening estimate for sulfate

concentration, Cs, is not as high as that obtained from the refined analysis, Cr. The 24-

hour ambient sulfate concentrations are typically used in long-range haze impact

assessments. For the 24-hour averaging time, the 2-m source maximum sulfate

concentrations obtained with CALPUFF using ISC meteorology are typically greater

than were obtained for this site in 1990 using CALPUFF with CALMET meteorological

data. For the 200-m release, the CALPUFF screening estimates are less than that

obtained using CALPUFF with CALMET meteorology.
91

Table 11. Summary for three hypothetical sources and for four averaging times in

the variation seen in the ratio Cs/Cr, where Cs is the maximum sulfate

concentration for a receptor ring and averaging time using CALPUFF with ISC

meteorology, and Cr is the maximum sulfate concentration simulated for the same

ring using CALPUFF with CALMET meteorology. Results were generated for only

1990, so the minimum and maximum values for Cs/Cr, reflect variations seen over

the receptor rings included in the range.

2-m Source 35-m Source 200-m Source

1-Hour Range Min Max Min Max Min Max

1-5 km 0.52 0.89 0.80 2.12 0.56 1.72

10-50 km 0.44 0.73 0.65 1.74 0.72 1.16

75-300 km 0.76 2.57 0.58 2.11 0.33 0.51

3-Hour 1-5 km 0.42 0.76 0.91 1.02 0.42 1.27

10-50 km 0.50 0.72 0.38 1.15 0.65 1.55

75-300 km 0.56 3.25 0.54 2.16 0.25 0.50

24-Hour 1-5 km 0.62 1.04 0.55 0.83 0.30 0.70

10-50 km 0.61 0.91 0.36 0.50 0.32 1.39

75-300 km 0.58 3.07 0.38 1.51 0.16 0.33

Annual 1-5 km 1.10 1.17 1.23 1.43 0.47 0.89

10-50 km 1.20 2.02 1.16 1.52 0.82 0.93

75-300 km 2.00 3.20 1.50 1.88 0.68 0.80

4.8.5 SO deposition 2

Table 12 summarizes the maximum and minimum value seen for Cs/Cr, where

Cs is the maximum SO deposition seen for 1990 along a receptor ring for a particular 2

source and averaging time using the CALPUFF with ISC meteorology, and Cr is the

maximum SO deposition seen along the same arc resulting from using the CALPUFF 2
92

Table 12. Summary for three hypothetical sources and for four averaging times in

the variation seen in the ratio Cs/Cr, where Cs is the maximum SO deposition for a 2

receptor ring and averaging time using CALPUFF with ISC meteorology, and Cr is the

maximum SO deposition simulated for the same ring using CALPUFF with CALMET 2

meteorology. Results were generated for only 1990, so the minimum and maximum

values for Cs/Cr, reflect variations seen over the receptor rings included in the range.

2-m Source 35-m Source 200-m Source

1-Hour Range Min Max Min Max Min Max

1-5 km 0.95 1.29 0.81 0.96 0.33 0.99

10-50 km 0.69 1.47 0.48 1.16 0.28 1.03

75-300 km 0.70 1.18 0.85 1.27 0.23 0.62

3-Hour 1-5 km 0.73 0.81 0.73 0.78 0.26 0.60

10-50 km 0.52 1.06 0.59 0.91 0.27 0.87

75-300 km 0.70 1.05 0.71 1.47 0.27 0.47

24-Hour 1-5 km 0.65 0.70 0.50 0.55 0.16 0.55

10-50 km 0.80 1.14 0.55 0.63 0.44 0.62

75-300 km 1.04 1.25 0.58 1.11 0.41 0.77

Annual 1-5 km 1.16 1.00 0.90 0.99 0.30 0.64

10-50 km 1.32 1.76 1.14 1.71 0.70 0.91

75-300 km 1.50 2.54 1.48 2.13 0.89 1.02

with fully developed CALMET meteorology. In each of the three ranges in Table 12,

instances can be found where the screening estimate of SO deposition, Cs, is not as 2

high as that obtained from the refined analysis, Cr. For SO deposition, the annual or 2

seasonal average deposition is of most interest. For the lower release heights of 2-m

and 35-m, the maximum annual SO deposition obtained with CALPUFF using ISC 2

meteorology are consistently greater than were obtained for this site in 1990 using

CALPUFF with CALMET meteorological data. For the 200-m release, the CALPUFF

screening estimates of SO deposition are similar to those obtained using CALPUFF 2

with CALMET meteorology.
93

Table 13. Summary for three hypothetical sources and for four averaging times in

the variation seen in the ratio Cs/Cr, where Cs is the maximum sulfate deposition for

a receptor ring and averaging time using CALPUFF with ISC meteorology, and Cr is

the maximum sulfate deposition simulated for the same ring using CALPUFF with

CALMET meteorology. Results were generated for only 1990, so the minimum and

maximum values for Cs/Cr, reflect variations seen over the receptor rings included in

the range.

2-m Source 35-m Source 200-m Source

1-Hour Range Min Max Min Max Min Max

1-5 km 3.60 6.79 2.66 4.09 1.20 2.20

10-50 km 1.39 3.90 1.21 3.43 1.01 1.67

75-300 km 1.32 2.76 0.94 2.63 0.45 1.04

3-Hour 1-5 km 2.05 3.68 1.54 1.97 0.59 1.31

10-50 km 1.63 3.77 0.96 1.72 0.96 2.06

75-300 km 1.21 3.47 0.83 2.26 0.34 0.62

24-Hour 1-5 km 0.93 2.38 0.98 1.06 0.30 0.74

10-50 km 2.32 2.79 0.78 1.35 0.72 1.38

75-300 km 1.50 4.50 0.90 1.58 0.31 0.68

Annual 1-5 km 1.41 1.51 1.54 1.90 0.80 1.39

10-50 km 1.61 2.05 1.54 1.87 1.11 1.37

75-300 km 1.98 4.07 1.38 2.63 0.81 1.27

4.8.6 SO deposition 4 

=

Table 13 summarizes the maximum and minimum value seen for Cs/Cr, where

Cs is the maximum sulfate deposition seen for 1990 along a receptor ring for a

particular source and averaging time using the CALPUFF with ISC meteorology, and Cr

is the maximum sulfate deposition seen along the same arc resulting from using the

CALPUFF with fully developed CALMET meteorology. In each of the three ranges in

Table 13, instances can be found where the screening estimate of sulfate deposition,

Cs, is not as high as that obtained from the refined analysis, Cr. For sulfate deposition,

the annual or seasonal average deposition is of most interest. For the lower release

heights of 2-m and 35-m, the maximum annual sulfate deposition obtained with

CALPUFF using ISC meteorology are consistently greater than were obtained for this

site in 1990 using CALPUFF with CALMET meteorological data. For the 200-m
94

release, the CALPUFF screening estimates of sulfate deposition are generally higher

than those obtained using CALPUFF with CALMET meteorology.

4.8.7 Old screen versus new screen estimates

Ambient SO2 concentrations

In Section 4.7, it was shown that when both CALPUFF and ISC use the ISC

meteorology with assumed flat terrain, there was for these comparisons a clear

tendency for CALPUFF to develop higher maximum concentration estimates the ISC for

all averaging times and for all distances downwind. Whether this will happen for a

particular source-receptor pairing, is dependent on whether calms and wind reversal

are existent in the proper sequence. The more frequently calm conditions occur for a

site, the more likely CALPUFF can develop higher concentration impacts. It is

concluded from comparisons results as summarized in Section 4.7 that it would be a

misconception to consider the Level I screening estimates for ambient SO2

concentration values as being overly conservative (i.e., too high). CALPUFF is capable

of providing higher SO concentration values than ISC simply due to the fact that calms 2

and wind reversals are not ignored. 

Ambient sulfate concentrations

In the IWAQM Phase 1 and Phase 2 recommendations, ambient sulfate

concentrations are used in assessing long-range transport haze impacts. And in this

context, the 24-hour averages are used (as explained in Section 3) to address the

‘regional’ nature of the haze assessment. In the Phase 1 recommendations, the

screening estimate (Level 1) of sulfate concentration was obtained by multiplying the

SO concentration by 1.5 (to account for the difference in molecular weight between 2

SO and SO ). 4 2

=

Shown in Figure 20 is a comparison of the 24-hr maximum sulfate

concentrations derived using the Phase 1 screening procedure and using CALMET

meteorology as input to CALPUFF. The Phase 1 screening procedure is seen to be

considerably higher (on the order of 30 times higher) than that derived using CALPUFF. 

For the 200-m source, The Level 1 screening estimates are seen to be higher than that

derived using CALPUFF for receptor rings of 50-km or less. Beyond 50-km, the Level 1

24-hr sulfate concentration estimates are similar to that derived using CALPUFF. 
1 10 100 1000

Downwind Distance (km)

0.10

1.00

10.00

100.00

1000.00

10000.00 24-hr Sulfate (ug/m3)

24-hr Sulfate

Level 1, 2-m Source

Level 1, 35-m Source

Level 1, 200-m Source

CALPUFF, 2-m Source

CALPUFF, 35-m Source

CALPUFF, 200-m Source

95

Figure 20. Comparison of Phase 1 Level 1 screening estimates of maximum 24-hour

ambient sulfate concentrations versus maximum 24-hour ambient sulfate

concentration derived using CALMET meteorology as input to CALPUFF.

Total sulfur deposition

In the IWAQM Phase 1 and Phase 2 recommendations, sulfur deposition is used

in assessing air quality related values associated with forest health. In this context, the

seasonal or annual averages are used (as explained in Section 3). In the Phase 1

recommendations, the screening estimate (Level 1) of total sulfur deposition flux was

obtained by multiplying the SO concentration by an assumed deposition velocity of 2

0.005 m/s, which was then multiplied by 0.5, since each gram of SO deposited 2

contributes 0.5 grams of sulfur. In the Phase 1 screening estimates, the sulfur

deposition was assumed to be mostly from SO gaseous deposition. In CALPUFF we 2

can simulate the dry deposition of both SO and sulfate. The total sulfur flux is then 2

computed as 0.5 times the SO deposition flux plus 0.33 times the SO deposition flux 2 4

=

(to account for amount of sulfur provided by SO and SO ). 4 2

=
1 10 100 1000

Distance Downwind (km)

0.10

1.00

10.00

100.00

Total Sulfur (ISC)/Total Sulfur (CALPUFF)

2-m Source

35-m Source

200-m Source

Annual Average

Total Sulfur

96

Figure 21. Comparison of annual average total sulfur flux as derived using the Phase

1 Level screening procedure and using CALMET as input to CALPUFF. Shown in

the figure is the ratio formed by dividing the Level 1 result by the CALPUFF result.

Shown in Figure 21 is a comparison of the annual average total sulfur flux,

derived using the Phase 1 Level 1 screening procedure and using CALMET as input to

CALPUFF. In Figure 21, the comparison is shown by dividing the Phase 1 screening

estimate by the CALPUFF result. For the more distant receptor rings, the Phase 1

screening procedure is seen to provide higher (by a factor of 2 to more than a factor of

10 times higher) than that derived using CALPUFF. 

4.8.8 Findings and conclusions

In this section, we have summarized the results from a study in which a

methodology was tested whereby CALPUFF was used with a simplified set of

meteorological data, for the purpose of providing screening estimates of concentration

and deposition impacts. It was seen that there were reasonably large variations in the

SO concentration maxima from one year to the next. There are limitations to the 2

conclusions that can be reached, due to the limited nature of the testing that has been

thus far accomplished. Comparisons of results obtained using the new screening

methodology versus results obtained using fully developed CALMET meteorology has

only been conducted for one location and for one year. In all cases examined, cases

could be found where the CALPUFF screening results underestimated the maximum

impacts simulated using more fully developed (CALMET) meteorology as input to

CALPUFF. Thus IWAQM concludes that the screening method that has been tested
97

does not guarantee that the pollutant impacts will always be greater than that obtained

using refined meteorology. Whether this precludes its use is a judgement decision. 

There is a certain degree of conservatism inherent in the screening procedure tested. 

This arises because the screening procedure requires use of receptor rings that

completely surround the source being assessed, and it requires use of the maximum

impact found anywhere along the receptor ring. In an actual situation, it is unlikely that

the Class I area will completely surround the source being analyzed. It is more likely

that the actual Class I area is limited to a small segment of a receptor ring. Thus if

actual refined (fully developed) meteorology were developed and used, with actual

source locations and receptors limited to the Class I area, one is likely to find the

impacts simulated within the Class I may be considerably lower than that derived from

the screening procedure for receptors that encircle the source.

4.9 CALMET/CALPUFF Enhancements

4.9.1 Use of FDDA-MM data with CALMET

Results of an ongoing investigation were reported by Sherwell and Garrison

(1997). The study was being conducted by the Maryland Department of Natural

Resources Power Plant Research Program (PPRP), in cooperation with EPA, to

investigate the results that might be obtained using the CALMET/CALPUFF modeling

system to simulate the magnitudes, sources, and possible reductions of NOx deposition

to the Chesapeake Bay. In this study, the Penn State MM4 gridded 1990 meteorology

data (discussed in Section 4.4) were employed. The EPA 1990 National Emissions

Inventory for NOx has been used to derive source inputs. In the presentation by

Sherwell and Garrison (1997), an overview was presented of the experiences gained in

preparing and running the CALMET/CALPUFF system, and on the preliminary results of

the analysis of NOx deposition to the Chesapeake Bay. A large part of their effort was

developing the NOx inventory, due to its sheer size, as the raw inventory contained

almost 90,000 entries for point sources and over 1100 counties in the defined domain.

The selection of their modeling domain was guided in part by a desire to

compare results obtained using the CALMET/CALPUFF modeling system with those

available from modeling runs from the Regional Acid Deposition Model (RADM). The

RADM contains a more detailed treatment of the atmospheric chemical transformations

and removal processes, and thus provides an interesting ‘check’ of the

CALMET/CALPUFF modeling results. The final domain selected extended west

through Illinois, north almost to the northern border of New York, south through most of
         

















BIRMINGHAM

HUNTSVILLE

BRIDGEPORT

HARTFORD

WILMINGTON

ATHENS

ATLANTA

AUGUSTA

MACON

COVINGTON

LEXINGTON

LOUISVILLE

BOSTON

WORCHESTER

BALTIMORE

PORTLAND

ASHEVILLE CAPE HATTERAS

CHARLOTTE

GREENSBORO

RALEIGH

WILMINGTON

CONCORD

ATLANTIC CITY

NEWARK

ALBANY

BINGHAMTON

BUFFALO

NEW YORK CITY

ROCHESTER SYRACUSE

AKRON

CLEVELAND

COLUMBUS DAYTON

MANSFIELD

TOLEDO

YOUNGSTOWN

ALLENTOWN

BRADFORD

ERIE

HARRISBURG

PHILADELPHIA

PITTSBURGH

WILKES-BARRE WILLIAMSPORT

PROVIDENCE

CHARLESTON

COLUMBIA

GREENVILLE

BRISTOL

CHATTANOOGA

KNOXVILLE

NASHVILLE

LYNCHBURG

NORFOLK

RICHMOND

ROANOKE

STERLING

CHARLESTON

ELKINS

HUNTINGTON

CHICAGO

PEORIA

ROCKFORD

SPRINGFIELD

EVANSVILLE

FORT WAYNE

INDIANAPOLIS

SOUTH BEND

DETROIT

GRAND RAPIDS FLINT

LANSING

MUSKEGON

MADISON

MILWAUKEE

Contour elevations shown are in feet;

Plot coordinates are in meters

Figure 1: Calpuff Modeling Domain

98

Figure 22. CALMET computational domain. 

 South Carolina, and covers most of southern New England (Figure 22). The MM4 data

has a grid interval of 80 km. CALMET was used to produce 40 km gridded wind and

meteorological fields. The fields had seven layers in the vertical: 10, 50, 150, 300, 750,

1500 and 3000 meters. Precipitation data were derived from the stations available on

the Samson CD-ROM obtained from the National Climatic Data Center. Given the large

computational domain, a lambert conformal projection scheme was selected. Figure 22

shows the location of the MM4 profile points throughout the domain, contours of terrain

elevations based on the MM4 grid points, and the locations of the surface stations from

which precipitation measurements were obtained and used.

To reduce the CALPUFF simulation times, a series of overlapping receptor grids

were constructed, with the density of receptor increasing in the Chesapeake Bay area. 

The meteorological grid is displayed in Figure 22. The computation domain was sized

to be somewhat larger than the domain of the sources being modeled. They had

divided the source inventory into four ‘zones’, with zone 1 including sources within 50

km of the Chesapeake Bay, zone 2 including all sources out to 210 km, zone 3

including all sources out to 500, and zone 4 including all sources from 500 out to the

edge of the domain shown in Figure 22. The CALPUFF simulations where configured

to use the MESOPUFF II fine species chemical transformation scheme, and wet and
99

dry fluxes for HNO and NO were calculated and stored. Constant background 3 3

-

concentrations of ozone (80 ppb) and ammonia (10 ppb) were specified.

CALPUFF was run in one-month segments on a 200 MHZ Pentium Pro

computer. Storage requirement per month were approximately 220 MB for the raw

MM4 data for the selected domain, 75 MB for the CALMET output file used to drive

CALPUFF, and 40 MB for the output files of wet and dry fluxes for two species (HNO3

and NO ). Each month took approximately 10 hours to run. 3

-

Total deposition due to dry and wet processes were roughly equal, with predicted

wet deposition dominated by deposition of HNO and dry deposition dominated by NO . 3 3

-

On a domain-wide basis , the average total Nitrogen deposition rate was calculated to

be about 3.64 kg/(hectareyear). This can be compared to the RADM results for 1990

(Dennis, 1997), which estimated the total Nitrogen deposition rate to be approximately

10 kg/hectare/year in the Chesapeake Bay area. In the most recent RADM simulations

(R. Dennis, personal communication), the observations in the vicinity of the

Chesapeake Bay and the current RADM seem to be in accord, with most of the wet and

dry deposition resulting from HNO . In the Demonstration Assessment (Section 4.1), 3

the low amount of wet deposition of HNO estimated by CALPUFF was considered a 3

surprise, given the high aqueous phase solubility of HNO . Given these more recent 3

results and comparisons of RADM with observations, there is further cause to suspect

the wet deposition results for HNO to be underestimated by CALPUFF. 3

Of interest is that this analysis has in fact used the FDDA-MM meteorological

data with the CALMET/CALPUFF modeling system successfully. They provide an

independent check of the resources needed. The results are consistent with those

found in performing the demonstration analyses, discussed in Section 4.1.

4.9.2 Use of CALMET to Develop Wind Fields

Scire and Robe (1997) reported on a series of enhancements to CALMET for

improving the characterization of wind fields in the presence of topographical features

that might be anticipated to induce strong upslope and downslope winds. Many

industrial facilities are located in river valleys where the terrain effects can have a

dominant influence on pollutant transport and dispersion. Furthermore, it is quite likely

that the surface wind observations available may not be representative of the flow in the

near vicinity of the facility (or conversely may only be representative in the near vicinity

and not representative of flow conditions downwind). Attempting to resolve the finescale wind effects prognostic meteorological modeling is not only computationally

demanding, but often beyond the state-of-the-art. CALMET uses diagnostic (empirical)

models of these local-scale effects (upslope and downslope winds), which is

computationally more cost-effective and provides a pragmatic solution to a complex

problem.
100

The original wind field module in CALMET, was based on the parameterizations

in the Diagnostic Wind Model (DWM) of Douglas and Kessler (1988). This module

computed a slope flow (a vector oriented in the drainage direction) which was added to

the initialized wind field components (Step 1 initialization). Time of day was used to

deduce whether the slope flow was upwind or downwind. The magnitude of the slope

flow was based on the local steepness of the terrain, the terrain elevation, the vertical

temperature lapse rate, and the maximum terrain height within a radius of influence

about the local point under consideration. An undesirable trait of this algorithm was that

it would yield larger slope flows for higher terrain elevations, all other factors being

equal. The original module used the ambient temperature lapse rate, whereas the

potential temperature lapse rate provides the proper characterization of the static

stability of the atmosphere. To address these concerns, a new downslope flow module

was developed based on the shooting flow parameterization of Mahrt (1982) and a new

upslope module was develop based on surface drag concepts. These new modules

are no longer restricted to the first layer within CALMET, but instead can affect flow in

upper layers.

The local sensible heat flux is an important parameter in deducing the magnitude

and direction of the slope flows. This module in CALMET was upgraded to account for

the angle of the terrain relative to the sun. For the sensible heat flux computations, the

original algorithms assumed the terrain was horizontal to the earth. An east facing

terrain slope will “feel” the effects of the sun’s heating in the morning and cooling in the

afternoon sooner than a west facing terrain slope. To address this concern, algorithms

by Whiteman and Allwine (1986 ) were added to CALMET that compute the solar

radiation for a surface of arbitrary inclination and azimuth.

No matter how much skill is added in the computation of the slope flows, if the

initial guess field in the Step 1 initialization is too far off, CALMET will poorly

characterize the local wind flows. To address this concern, two enhancements were

added to CALMET. First, instead of relying on only the upper-air observations to

characterize the local vertical temperature profiles, an option was added to CALMET

that allows the vertical temperature profiles using similarity theory (van Ulden and

Holtslag, 1985). Second, a modification was added to CALMET that allows the user to

control the influence of the upper-air observations and local surface observations in the

construction of the Step 1 initialization wind fields. The user is allowed to specify height

dependent factors, B, ranging from -1 (no influence by upper air observations) to +1 (no

influence by surface observations). If the local surface observations are within the

valley, they might be restricted to influencing the Step 1 initialization only for heights

below the top of the valley, above which the upper-air observations could be given

dominance.

Scire and Robe (1997) illustrated the enhancements discussed above by

calculating wind fields in the Columbia River Valley. The region of interest included the

City of Wenatchee, WA and the region to the southeast of the city. Wenatchee is

located on both sides of the river and includes a relatively flat elevated plain. Figure 23
101

shows the CALMET modeling domain. The terrain variation is substantial, extending

from under 200 meters elevations above mean sea level (MSL) to over 1700 meters

MSL at the higher peaks in the southwest part of the domain. The Columbia River runs

to the southeast, turns roughly east, and then turns again to the south over a distance

of 20 kilometers. The high terrain features change orientation from east-west to northsouth in the southern part of the domain.

Figure 23. Terrain contours for the Wenachee, WA domain. Contour internal is 100

m, and the location of the Wenatchee (Pangborn) Airport is indicated by the filled

circle. The box inset shows the subdomain for which slope flows are presented.

To characterize the terrain, a grid resolution of 250 meters was used in

CALMET. The modeling domain consisted of 108 by 84 horizontal grid cells. Nine

levels were used in the vertical: 10, 50, 120, 230, 450, 800, 1250 and 2600 meters

above ground. The lower four levels are within the valley, the upper three levels are

above most of the terrain features. The local wind observations were available at the

location shown with a filled circle in Figure 23. This location is within the valley, and the

wind rose for the location shows the influence for the local winds to channel and align

with the local orientation of the valley. The height dependent factors, B, were set at -1,

-1, -1, -1, +0.5, +0.8, +1, +1, +1. The nearest upper air observation station is Spokane,

WA, which is over 200 km to the east of the CALMET domain.
102

Figure 24. Down slope flow components for 1:00 AM July 1, 1994

for Wenatchee, WA . Wind vectors are plotted every 500 meters

(at every second grid point).
103

Figure 25. Upslope flow components for 3:00 PM July 1, 1994 for

Wenatchee, WA . Wind vectors are plotted every 500 meters (at

every second grid point).

Figure 24 shows the computed CALMET layer 1 downslope flows and Figure 25

shows the computed CALMET layer 1 upslope flows. The region shown in these

figures is the box inset of Figure 23. The moderately strong nighttime drainage flows

(1:00 AM, July 1, 1994) are shown in Figure 24. Figure 25 shows the computed

upslope flows, which are (as expected) weaker than the downslope flows. Upslope

flows are not expected to accelerate as rapidly as downslope flows.
104

The pattern and magnitude of the CALMET winds were considered consistent

with expectations. These winds were developed using a single surface station in the

valley and an upper air station located well outside of the valley. The computed wind

fields show the expected diurnal cycle of nighttime drainage followed by daytime

upslope flows. The patterns are consistent with the strong terrain channeling of the

main valley and within the smaller side canyons. These wind fields would be expected

to better characterize the transport of the pollutants. It was anticipated that the proper

characterization of the local terrain influences on the wind field would provide a more

realistic characterization of the transport around major terrain features rather than

directly impacting apon such features.

These results are relevant to the Phase 2 recommendations. They demonstrate

that realistic wind fields can be produced using diagnostic techniques. They illustrate

that in some situations, the proper characterization of the terrain’s influence on the wind

fields will require gridded terrain heights, with a grid resolution of order 250 m. This

would not be expected for resolving transport and dispersion of plumes that have

already traveled large distances and are already quite broad relative to the local terrain

features. But it is reasonable to expect that sources within such terrain would require

more care to be taken in the characterization of the wind fields, otherwise the transport

and resulting dispersion would be poorly characterized. These results also illustrate the

feasibility of performing local-scale impacts assessments of the transport and

dispersion of emissions from sources located in severe terrain, even when available

meteorological observations are sparse.

4.9.3 Kincaid SF and Lovett SO Comparisons 6 2

The goal of this study (Strimaitis et al., 1997) was to conduct an evaluation of

CALPUFF in comparison with ISCST3 (U.S. EPA, 1995c) for the Kincaid data set, and

CTDMPLUS (Perry et al., 1989) for the Lovett data set. Also, enhancements were

tested in a special version of CALPUFF (4.07), in which convective scaling

parameterizations and concepts were implemented to better characterize dispersion

from tall stacks during unstable conditions. Earlier versions of CALPUFF use a simple

Gaussian distribution to characterize the vertical distribution of puff material within the

convective boundary layer (CBL). As the puff grew, it soon filled the layer between the

ground and the mixing height, resulting in a uniform vertical distribution. Within this

framework, the primary effect of the convective motions in the mixed layer was to cause

a rapid growth in the vertical size of a puff. Depending on the CALPUFF dispersion

option selected, this growth rate is either parameterized by the stability class, scaled by

measured turbulence intensity, or scaled by a turbulence intensity computed from the

surface layer parameters.

In the last decade, modeling techniques that recognize the asymmetry of the

vertical dispersion process in the CBL have matured. These techniques explicitly

account for the differences between the distribution and strength of updrafts and

downdrafts in the layer as they relate to the ensemble-mean concentration distribution. 
105

One such technique that is simple yet effective is the “p.d.f.” approach that relates the

probability density function of the vertical position of puff mass in the layer to the

skewed probability density function of vertical velocity. Using the superposition of two

Gaussian distributions to characterize the skewed p.d.f of the vertical velocity, the p.d.f.

model produces a “dual plume” formulation that maps the evolution of one plume that is

initially carried toward the ground in a (mean) downdraft, and a second plume that

initially rises toward the top of the mixed layer in a (mean) updraft. Subsequent

“reflections” from both the ground and the lid are simulated using image sources. 

Because each of these plumes has its own mean vertical velocity and rate of spread,

the resulting vertical distribution of mass is skewed in much the same way as the

observed distributions. The initial downdraft plume is called the direct source because

it travels directly from an elevated source (accounting for the plume rise velocity) to the

ground, while the initial updraft plume is called the indirect source because it reaches

the ground only after traversing the full depth of the mixed layer.

U.S. EPA (1995d) extended the p.d.f. CBL formulation to include a simple way of

simulating the tendency of highly buoyant plumes to “loft” at the top of the mixed layer,

remaining there for some time before the convective eddies are able to overcome the

buoyancy and mix their mass to the surface. This formulation forms the basis of the

CBL component of AERMOD, U.S. EPA (1995d), and has been adapted for use in

CALPUFF (4.07). Strimaitis et al. (1997) adopted AERMOD’s CBL parameterizations

for obtaining the mean updraft and downdraft properties, and also their novel simulation

of the lofting plume by means of the effective rise of the indirect source. However, the

CALPUFF algorithms for treating partial penetration and subsequent entrainment into

the mixed layer as the layer grows remain unchanged.

The Kincaid Generating Station is a coal-fired electric generating station with two

606 megawatt (MW) units vented through a single 187 meter stack. It is located in

Kincaid, Illinois, approximately 25 kilometers southeast of Springfield. The power plant

is in an area of relatively flat terrain surrounded by farmland. As part of an intensive

monitoring program sponsored by the Electric Power Research Institute (EPRI), SF6

tracer was released as a gas continuously through the stack for approximately 30

experiments of 6-9 hours in length during 1980 and 1981. A network of 200 samplers

located from 0.5 to 50 km from the stack measured hourly SF concentrations at the 6

ground level. The SF samplers were located approximately in arcs at distances of 0.5, 6

1, 2, 3, 5, 7, 10, 15, 20, 30, 40, and 50 km from the stack. In addition to

measurements of winds and turbulence made at four levels on a 100-m tower, other

meteorological data collected include vertical profile data from balloons, solar and net

radiation, and cloud cover. 

In this study the meteorological data files were developed by emphasizing the

tower winds and temperatures, the “observed” mixing heights, and by computing the

surface layer parameters using the RAMMET (U.S. EPA, 1995e) preprocessor. One

major element in preparing the data files for CALPUFF relates to the transport time

from the stack to the outermost receptor arc, which is frequently greater than 1 hour. 
106

While plume models neglect this feature of the study, puff models do not. The tracer

gas was typically started 1 to 4 hours before the sampling network, so emissions during

the initial transport period are known. Therefore, the full meteorological data set was

used (not just the meteorological hours for when tracer data were available) to extract

data for complete days (24 hours) for each of the tracer experiments. This allowed

development of all of the data needed to properly simulate each period.

The Lovett Power Plant is located in the Hudson River Valley approximately 70

km north of New York City. The terrain in the area rises to approximately 330 meters

above the stack base elevation along a ridge 2.5 km north of the stack. The highest

point in the area is 340 meters above stack base at about 3 km north of the facility. 

SO emissions were released from a 145 meter stack containing two flues. Hourly 2

stack parameters (temperatures and flows) were derived from continuously reported

load data. The monitoring period is one full year. Twelve continuous SO monitors 2

were located to the west, south, and north of the facility. Ten of the twelve monitors

were located in complex terrain from 2 km to 3.5 km from the stack. These ten

monitors were all located at or above the elevation of the stack top. Two monitors were

located south of the facility at distances of 2 km and 8.5 km for purposes of estimating

background concentrations.

Meteorological data were collected on a 100 meter tower located in the valley

south-southwest of the stack. The tower was instrumented at three levels (10 m, 50 m,

and 100 m). Wind speed, wind direction, and horizontal turbulence () were measured

at all three levels. In addition, four 10 meter meteorological towers collecting wind

speed, wind direction and  were located in an arc on the high terrain to the west, 

west-northwest, north-northwest, and north of the stack. Much of the data needed to

apply CALPUFF to this site was prepared by Paumier et al. (1992) for their evaluation

of CTDMPLUS. These data files were used in this evaluation, and all but the hourly

emissions data could be applied directly to CALPUFF.

For the Kincaid data, quality indicators of 1, 2, and 3 had been assigned to the

data. A quality indicator of 1 meant that sampling was very incomplete and no

maximum concentration could reliably be determined for the arc. A quality indicator of

3 meant that a distinct pattern could be seen in the data for the arc, and a maximum

concentration could be determined reliably. Model performance results for the Kincaid

data set were presented for two subsets of the data. The first were comprised of all

arc-hours for which the peak concentration on the arc was given a quality indicator (QI)

of 2 or 3; the second was the subset of these arc-hours with a QI of 3.
0 100 200 300 400

Observed SF6 (g/m 3) x109

0

100

200

300

400

Estim ated SF6 (g/m 3 ) x10 9

Legend

ISC3

CALPUFF(4.0)

CALPUFF(4.07)

107

Figure 26. Q-Q plots for Kincaid, comparing observed surface concentration values

of SF with simulation results by CALPUFF(4.0), CALPUFF(4.07) and ISC3 for 6

Quality 2 and 3 data.

Figure 26 shows the Quantile-Quantile (Q-Q) plots for CALPUFF(4.07),

CALPUFF(4.0) and ISCST3 compared to the observations, for data of quality 2 and 3. 

Although all models display a tendency to predict more zero-impacts (see inset) than is

observed, both versions of CALPUFF predict fewer zeros than ISCST3. This is

traceable to the fact that for all CALPUFF model runs, the option for partial plume

penetration was enabled. ISC3 sets all surface concentrations to zero whenever the

computed centerline of the plume is detected to be above the mixing height. CALPUFF

would do the same, except when the partial plume penetration option is enabled. 

When this option is enabled, a computation is made based on the size of the dispersing

plume, and any mass below the mixing height is allowed to continue to disperse and

possibly impact surface receptors. While fewer observed concentrations are

underpredicted by both versions of CALPUFF (106 out of 586 values) than are
0 50 100 150 200 250

ISC3 Estim ated SF6 (g/m 3) x109

0

5 0

100

150

200

250

CALPUFF(4.0) Estim ated SF6 (g/m 3 ) x10 9

108

underpredicted by ISCST3 (236 out of 586 values), there remains a substantial number

of large observed concentrations for which both models miss (zero is predicted)

Figure 27. Scatter plot for Kincaid, comparing CALPUFF(4.0) and ISC3 simulation

results for surface concentration values of SF for Quality 2 and 3 data. 6

The CALPUFF(4.0) results in Figure 26 appear to be similar to the ISC3 results. 

This is true in the sense that the computed maxima are of a similar value. But the

CALPUFF(4.0) runs were conducted using dispersion parameters based on similarity

theory, and a scatter plot of CALPUFF(4.0) versus ISC3, Figure 27, reveals that the two

models are not providing as similar of results as might be deduced from inspection of

Figure 26. 

CALPUFF(4.07) is seen to overpredict the upper range of observed

concentrations, while CALPUFF(4.0) and ISCST3 underpredict throughout, and the

ranked distribution for CALPUFF(4.07) lies nearer the 1:1 line in Figure 26. How much

of this improved performance might be attributed to the p.d.f. algorithms for the CBL? 
0 200 400 600 800

Observed SO2 (g/m 3) x106

0

200

400

600

800

Estim ated SO 2 (g/m 3 ) x10 6

CALPUFF(4.07)

CTDMPLUS

109

As can be seen in Figure 26, the lower end of the ranked distributions for both versions

of CALPUFF appear to coincide, which implies that reasons for the elevated frequency

of zero-impact predictions are not related to the treatment of dispersion in the CBL. 

The character of the higher end is quite different, with CALPUFF(4.0) producing

concentrations more like ISCST3 than CALPUFF(4.07). This suggests that the p.d.f. is

responsible for the improvements at the high end noted in Figure 26. 

Figure 28. Q-Q plots for Lovett, comparing observed surface concentration values of

SO with simulation results CALPUFF(4.07) and CTDMPLUS 2

For the Lovett data set, Paumier et al. (1992) found that CTDMPLUS and RTDM

predictions were poorly correlated with the observed peak hourly concentrations. Given

that CALPUFF was applied with the same meteorological data, similar results were

expected and found. Figure 28 shows the Q-Q plot for CALPUFF(4.07) and

CTDMPLUS compared to the observations. Although both models tend to overpredict

the observed ranked distribution, CALPUFF predictions lie nearer the 1:1 line. The
110

dashed line in Figure 28 indicates an overprediction of two, which is close to the results

depicted for CTDMPLUS.

The evaluation results presented suggests that the p.d.f. formulations tested in

CALPUFF(4.07) eliminates the tendency seen in the CALPUFF(4.0) and ISC3 to

underestimate the surface concentration values for the Kincaid data set, and

CALPUFF(4.07) overpredicts surface concentration values less than CTDMPLUS for

the Lovett data set. Correlation of hourly predicted and observed concentrations for the

Lovett data set are poor for both CALPUFF(4.07) and CTDMPLUS, suggesting that

more diagnostic analyses of this data set may be fruitful. For the Kincaid data, 

CALPUFF (4.0) performance is similar to ISC3's. And for the Kincaid evaluation,

performance during periods with observed mixing heights less that about 600m

suggests that more mass remains in the mixed layer than is predicted by the models,

even with partial penetration enabled.
111

5.0 CONCLUSIONS

This report provides a summary and status report of the activities sponsored and

initiated by the Interagency Workgroup on Air Quality Modeling (IWAQM). The IWAQM

was formed to provide a focus for development of air quality models for assessment of

pollutant source impacts on Federal Class I areas and wilderness areas. In particular,

IWAQM has focused attention on providing modeling techniques for assessing possible

adverse air quality impacts resulting from long-range transport of pollutants, as required

by the prevention of significant deterioration (PSD) program.

In 1993, IWAQM provided interim Phase 1 recommendations that provided the

best approach from existing “off-the-shelf-techniques.” The MESOPUFF II puff

modeling system was recommended for use. This model provided the ability to

simulate the influence of time and space varying meteorological conditions on transport

and dispersion. It provided a first-order approximate characterization of the formation

of sulfate and nitrate during transport downwind. It provided characterizations for the

removal of pollutants by dry and wet deposition. The meteorology processing had

limited capabilities, as it was not able to characterize geographical terrain slope flows. 

The puff dispersion model had no capabilities to address effects associated with

variation of terrain heights. Furthermore, due to limitations in the dispersion treatments,

the puff model was not considered appropriate for use in characterizing impacts

associated with transport distances of less than 50 km.

As discussed in Appendix D, IWAQM provided a status report of ongoing

activities at the Sixth Modeling Conference, which was held in Washington, D.C. August

9-10, 1995. As a result of comments received, a series of investigations were

undertaken. Comparisons were made of CALPUFF simulated dispersion with nearsurface concentrations collected during several tracer field studies, where the transport

distances were of the order 50 to 300 km. Comparisons were made of CALPUFF

simulated dispersion with simulation results obtained using the Industrial Source

Complex (ISC3) plume dispersion model. Initially, the focus was on whether CALPUFF

and ISC3 gave similar results for steady-state meteorological conditions. Once this was

confirmed, the focus shifted to investigating what difference might result between a puff

and plume model, having identical meteorology and dispersion. It became clear that

wind reversals and buildup of pollutant mass during calm wind conditions could result in

large differences in simulation results. As a consequence of comments received and of

the puff/plume model comparisons, a new screening analysis was tested that relied on

use of the CALPUFF dispersion model with a greatly simplified characterization of the

hourly meteorological conditions. 

Based on the findings of the various investigations summarized in Section 4,

IWAQM is providing a Phase 2 recommendation to replace the interim Phase 1

recommendation. The CALPUFF modeling system is recommended in place of the

MESOPUFF II modeling system for a number of reasons. A primary consideration is

that the CALMET meteorological processor is capable of diagnostically characterizing
112

geographic terrain slope flows, and has been updated to allow use of sophisticated

output from modern mesoscale meteorological processors. Another important

consideration is that the CALPUFF puff dispersion algorithms have been fashioned to

allow characterization of both local-scale and long-range transport and dispersion. This

allows use of one model for all sources with a consistent treatment of the chemistry and

fate of the pollutants. Using CALPUFF within the new screening analysis provides

consistent treatment of the chemistry and fate, and some of the ‘causality’ effects of

hourly varying meteorology that a puff dispersion model can treat more directly and

easily than a standard plume dispersion model. 

This Phase 2 recommendation provides a major improvement in the treatment

and characterization of the meteorological conditions and the mesoscale transport. 

There is yet room for improvement in the characterization of the chemistry and fate of

the pollutants. The CALPUFF dispersion model represents a major improvement over

the MESOPUFF II model in its flexibility for treating a variety of source types, time

varying emission rates, and of dispersion situations. The IWAQM recommends the

CALPUFF modeling system for use as a refined long-range transport and dispersion

modeling technique for characterizing reasonably attributable pollutant impacts from

one or a few sources.
113

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U.S. EPA document dated July, 1995. U.S. EPA, Research Triangle Park, NC.

U.S. Environmental Protection Agency, 1996: CALMET, CALPUFF and CALPOST

Modeling System, PB 96-502-083INC (CD ROM and diskette), National Technical

Information Service, U.S. Department of Commerce, Springfield, VA, 22161.

U.S. Environmental Protection Agency, 1997. Appendix W to 40 CFR Part 51:

Guideline on Air Quality Models. Code of Federal Regulations, Office of the Federal
119

Register National Archives and Records Adminsitration, U.S. Government Printing

Office, Washington, DC. Pg 346-438

U.S. Environmental Protection Agency, 1998a: A Comparison of CALPUFF Modeling

Results to Two Tracer Field Experiments. EPA-454/R-98-009, U.S. EPA, Research

Triangle Park, NC.

U.S. Environmental Protection Agency, 1998b: Analysis of the CALMET/CALPUFF

Modeling System in a Screening Mode. EPA-454/R-98-030. U.S. EPA, Research

Triangle Park, NC.

Van Ulden, A.P., and A.A.M. Holtslag, 1985: Estimation of Atmospheric Boundary Layer

Paramters for Diffusion Applications. Journal of Climate and Applied Meteorology,

(24):1196-1207.

Vimont, J.C., 1998: Evaluation of the CALMET/CALPUFF Modeling System in the

Southwestern U.S. 10th Joint Conference on the Applications of Air Pollution

Meteorology with the A&WMA, American Meteorological Society, Phoenix, AZ, 30-34.

Whiteman, C.D., and K.J. Allwine, 1986: Extraterrestiral Solar Radiation on Included

Surfaces. Environmental Software. Vol. 1(3):164-169.
A-1

APPENDIX A

CALMET RECOMMENDATIONS

In the following, a listing is provided of the defaults currently assumed in

CALMET for long-range transport analyses in involving assessments of not on

concentration impacts, but also deposition flux impacts and visibility impacts. Some of

the variables have the ‘Value’ is listed in bold. This is meant to indicate that these likely

will need to be tailored for a given application. 
A-2

Variable Description Value

GEO.DAT Name of Geophysical data file GEO.DAT

SURF.DAT Name of Surface data file SURF.DAT

PRECIP.DAT Name of Precipitation data file PRECIP.DAT

NUSTA Number of upper air data sites User Defined

UPn.DAT Names of NUSTA upper air data files UPn.DAT

IBYR Beginning year User Defines

IBMO Beginning month User Defines

IBDY Beginning day User Defines

IBHR Beginning hour User Defines

IBTZ Base time zone User Defines

IRLG Number of hours to simulate User Defines

IRTYPE Output file type to create (must be 1 for 1

CALPUFF)

LCALGRD Are w-components and temperature needed? T

NX Number of east-west grid cells User Defines

NY Number of north-south grid cells User Defines

DGRIDKM Grid spacing User Defines

XORIGKM Southwest grid cell X coordinate User Defines

YORIGKM Southwest grid cell Y coordinate User Defines

XLAT0 Southwest grid cell latitude User Defines

YLON0 Southwest grid cell longitude User Defines

IUTMZN UTM Zone User Defines

LLCONF When using Lambert Conformal map F

coordinates, rotate winds from true north to map

north?

XLAT1 Latitude of 1st standard parallel 30

XLAT2 Latitude of 2nd standard parallel 60
A-3

Variable Description Value

RLON0 Longitude used if LLCONF = T 90

RLAT0 Latitude used if LLCONF = T 40

NZ Number of vertical layers User Defines

ZFACE Vertical cell face heights (NZ+1 values) User Defines

LSAVE Save met. data fields in an unformatted file? T

IFORMO Format of unformatted file (1 for CALPUFF) 1

NSSTA Number of stations in SURF.DAT file User Defines

NPSTA Number of stations in PRECIP.DAT User Defines

ICLOUD Is cloud data to be input as gridded fields? (0 = 0

No)

IFORMS Format of surface data (2 = formatted) 2

IFORMP Format of precipitation data (2 = formatted) 2

IFORMC Format of cloud data (2 = formatted) 2

IWFCOD Generate winds by diagnostic wind module? (1 = 1

Yes)

IFRADJ Adjust winds using Froude number effects? (1 = 1

Yes)

IKINE Adjust winds using kinematic effects? (1 = Yes) 0

IOBR Use O’Brien procedure for vertical winds? (0 = 0

No)

ISLOPE Compute slope flows? (1 = Yes) 1

IEXTRP Extrapolate surface winds to upper layers? (-4 = -4

use similarity theory and ignore layer 1 of upper

air station data) 

ICALM Extrapolate surface calms to upper layers? (0 = 0

No)

BIAS Surface/upper-air weighting factors (NZ values) NZ*0
A-4

Variable Description Value

IPROG Using prognostic or MM-FDDA data? (0 = No) 0

LVARY Use varying radius to develop surface winds? F

RMAX1 Max surface over-land extrapolation radius (km) User Defines

RMAX2 Max aloft over-land extrapolation radius (km) User Defines

RMAX3 Maximum over-water extrapolation radius (km) User Defines

RMIN Minimum extrapolation radius (km) 0.1

RMIN2 Distance (km) around an upper air site where 4

vertical extrapolation is excluded (Set to -1 if

IEXTRP = ±4)

TERRAD Radius of influence of terrain features (km) User Defined

R1 Relative weight at surface of Step 1 field and obs User Defines

R2 Relative weight aloft of Step 1 field and obs User Defines

DIVLIM Maximum acceptable divergence 5.E-6

NITER Max number of passes in divergence 50

minimization

NSMTH Number of passes in smoothing (NZ values) 2, 4*(NZ-1)

NINTR2 Max number of stations for interpolations (NA

values)

99

CRITFN Critical Froude number 1

ALPHA Empirical factor triggering kinematic effects 0.1

IDIOPT1 Compute temperatures from observations (0 = 0

True)

ISURFT Surface station to use for surface temperature

(between 1 and NSSTA)

User Defines

IDIOPT2 Compute domain-average lapse rates? (0 = 0

True)

IUPT Station for lapse rates (between 1 and NUSTA) User Defines

ZUPT Depth of domain-average lapse rate (m) 200
A-5

Variable Description Value

IDIOPT3 Compute internally inital guess winds? (0 = True) 0

IUPWND Upper air station for domain winds (-1 = 1/r**2 -1

interpolation of all stations)

ZUPWND Bottom and top of layer for 1st guess winds (m) 1, 1000

IDIOPT4 Read surface winds from SURF.DAT? ( 0 = 0

True)

IDIOPT5 Read aloft winds from UPn.DAT? (0 = True) 0

CONSTB Neutral mixing height B constant 1.41

CONSTE Convective mixing height E constant 0.15

CONSTN Stable mixing height N constant 2400

CONSTW Over-water mixing height W constant 0.16

FCORIOL Absolute value of Coriolis parameter 1.E-4

IAVEXZI Spatial averaging of mixing heights? (1 = True) 1

MNMDAV Max averaging radius (number of grid cells) 1

HAFANG Half-angle for looking upwind (degrees) 30

ILEVZI Layer to use in upwind averaging (between 1

and NZ)

1

DPTMIN Minimum capping potential temperature lapse 0.001

rate

DZZI Depth for computing capping lapse rate (m) 200

ZIMIN Minimum over-land mixing height (m) 50

ZIMAX Maximum over-land mixing height (m) 3000

ZIMINW Minimum over-water mixing height (m) 50

ZIMAXW Maximum over-water mixing heigh (m) 3000

IRAD Form of temperature interpolation (1 = 1/r) 1

TRADKM Radius of temperature interpolation (km) 500
A-6

Variable Description Value

NUMTS Max number of stations in temperature 5

interpolations

IAVET Conduct spatial averaging of temperature? (1 = 1

True)

TGDEFB Default over-water mixed layer lapse rate (K/m) -0.0098

TGDEFA Default over-water capping lapse rate (K/m) -0.0045

JWAT1 Beginning landuse type defining water 999

JWAT2 Ending landuse type defining water 999

NFLAGP Method for precipitation interpolation (2 = 1/r**2) 2

SIGMAP Precip radius for interpolations (km) 100

CUTP Minimum cut off precip rate (mm/hr) 0.01

SSn NSSTA input records for surface stations User Defines

USn NUSTA input records for upper-air stations User Defines

PSn NPSTA input records for precipitation stations User Defines
B-1

APPENDIX B

CALPUFF RECOMMENDATIONS

In the following, a listing is provided of the defaults currently assumed in

CALPUFF for long-range transport analyses in involving assessments of not on

concentration impacts, but also deposition flux impacts and visibility impacts. Some of

the variables have the ‘Value’ is listed in bold. This is meant to indicate that these likely

will need to be tailored for a given application. 
B-2

Variable Description Value

METDAT CALMET input data filename CALMET.DAT

PUFLST Filename for general output from CALPUFF CALPUFF.LST

CONDAT Filename for output concentration data CONC.DAT

DFDAT Filename for output dry deposition fluxes DFLX.DAT

WFDAT Filename for output wet deposition fluxes WFLX.DAT

VISDAT Filename for output relative humidities (for VISB.DAT

visibility)

METRUN Do we run all periods (1) or a subset (0)? 0

IBYR Beginning year User Defined

IBMO Beginning month User Defined

IBDY Beginning day User Defined

IBHR Beginning hour User Defined

IRLG Length of run (hours) User Defined

NSPEC Number of species modeled (for MESOPUFF II 5

chemistry)

NSE Number of species emitted 3

MRESTART Restart options (0 = no restart), allows splitting

runs into smaller segments

0

METFM Format of input meteorology (1 = CALMET) 1

AVET Averaging time lateral dispersion parameters 60

(minutes)

MGAUSS Near-field vertical distribution (1 = Gaussian) 1

MCTADJ Terrain adjustments to plume path (3 = Plume

path)

3

MCTSG Do we have subgrid hills? (0 = No), allows

CTDM-like treatment for subgrid scale hills

0

MSLUG Near-field puff treatment (0 = No slugs) 0
B-3

Variable Description Value

MTRANS Model transitional plume rise? (1 = Yes) 1

MTIP Treat stack tip downwash? (1 = Yes) 1

MSHEAR Treat vertical wind shear? (0 = No) 0

MSPLIT Allow puffs to split? (0 = No) 0

MCHEM MESOPUFF-II Chemistry? (1 = Yes) 1

MWET Model wet deposition? (1 = Yes) 1

MDRY Model dry deposition? (1 = Yes) 1

MDISP Method for dispersion coefficients (3 = PG & MP) 3

MTURBVW Turbulence characterization? (Only if MDISP = 1 3

or 5)

MDISP2 Backup coefficients (Only if MDISP = 1 or 5) 3

MROUGH Adjust PG for surface roughness? (0 = No) 0

MPARTL Model partial plume penetration? (0 = No) 1

MTINV Elevated inversion strength (0 = compute from 0

data)

MPDF Use PDF for convective dispersion? (0 = No) 0

MSGTIBL Use TIBL module? (0 = No) allows treatment of 0

subgrid scale coastal areas

MREG Regulatory default checks? (1 = Yes) 1

CSPECn Names of species modeled (for MESOPUFF II,

must be SO2, SO4, NOX, HNO3, NO3)

User Defined

Specie Manner species will be modeled

Names

User Defined

Specie Grouping of species, if any.

Groups

User Defined

NX Number of east-west grids of input meteorology User Defined

NY Number of north-south grids of input meteorology User Defined

NZ Number of vertical layers of input meteorology User Defined
B-4

Variable Description Value

DGRIDKM Meteorology grid spacing (km) User Defined

ZFACE Vertical cell face heights of input meteorology User Defined

XORIGKM Southwest corner (east-west) of input User Defined

meteorology

YORIGIM Southwest corner (north-south) of input User Defined

meteorology

IUTMZN UTM zone User Defined

XLAT Latitude of center of meteorology domain User Defined

XLONG Longitude of center of meteorology domain User Defined

XTZ Base time zone of input meteorology User Defined

IBCOMP Southwest X-index of computational domain User Defined

JBCOMP Southwest Y-index of computational domain User Defined

IECOMP Northeast X-index of computational domain User Defined

JECOMP Northeast Y-index of computational domain User Defined

LSAMP Use gridded receptors? (T = Yes) F

IBSAMP Southwest X-index of receptor grid User Defined

JBSAMP Southwest Y-index of receptor grid User Defined

IESAMP Northeast X-index of receptor grid User Defined

JESAMP Northeast Y-index of receptor grid User Defined

MESHDN Gridded recpetor spacing = DGRIDKM/MESHDN 1

ICON Output concentrations? (1 = Yes) 1

IDRY Output dry deposition flux? (1 = Yes) 1

IWET Output west deposition flux? (1 = Yes) 1

IVIS Output RH for visibility calculations (1 = Yes) 1

LCOMPRS Use compression option in output? (T = Yes) T
B-5

Variable Description Value

ICPRT Print concentrations? (0 = No) 0

IDPRT Print dry deposition fluxes (0 = No) 0

IWPRT Print wet deposition fluxes (0 = No) 0

ICFRQ Concentration print interval (1 = hourly) 1

IDFRQ Dry deposition flux print interval (1 = hourly) 1

IWFRQ West deposition flux print interval (1 = hourly) 1

IPRTU Print output units (1 = g/m**3; g/m**2/s) 1

IMESG Status messages to screen? (1 = Yes) 1

Output Where to output various species

Species

User Defined

LDEBUG Turn on debug tracking? (F = No) F

Dry Gas Dep Chemical parameters of gaseous deposition

species

User Defined

Dry Part. Dep Chemical parameters of particulate deposition

species

User Defined

RCUTR Reference cuticle resistance (s/cm) 30.

RGR Reference ground resistance (s/cm) 10.

REACTR Reference reactivity 8

NINT Number of particle-size intervals 9

IVEG Vegetative state (1 = active and unstressed) 1

Wet Dep Wet deposition parameters User Defined

MOZ Ozone background? (1 = read from ozone.dat) 1

BCKO3 Ozone default (ppb) (Use only for missing data) 80

BCKNH3 Ammonia background (ppb) 10

RNITE1 Nighttime SO2 loss rate (%/hr) 0.2

RNITE2 Nighttime NOx loss rate (%/hr) 2

RNITE3 Nighttime HNO3 loss rate (%/hr) 2
B-6

Variable Description Value

SYTDEP Horizontal size (m) to switch to time dependence 550.

MHFTSE Use Heffter for vertical dispersion? (0 = No) 0

JSUP PG Stability class above mixed layer 5

CONK1 Stable dispersion constant (Eq 2.7-3) 0.01

CONK2 Neutral dispersion constant (Eq 2.7-4) 0.1

TBD Transition for downwash algorithms (0.5 = ISC) 0.5

IURB1 Beginning urban landuse type 10

IURB2 Ending urban landuse type 19

Use Following Only For Single-Point Meteorological Input (CALPUFF Screen)

ILANDUIN Land use type (20 = Unirrigated agricultural land) 20

ZOIN Roughness length (m) 0.25 

XLAIIN Leaf area index 3

ELEVIN Met. Station elevation (m above MSL) 0

XLATIN Met. Station North latitude (degrees) User Defined

XLONIN Met. Station West longitude (degrees) User Defined

ANEMHT Anemometer height of ISC meteorological data 10.0

(m)

ISIGMAV Lateral turbulence (Not used with ISC 1

meteorology)

IMIXCTDM Mixing heights (Not used with ISC meteorology) 0

End of Single Point Meteorology Input Variables

XMXLEN Maximum slug length in units of DGRIDKM 1

XSAMLEN Maximum puff travel distance per sampling step 1

(units of DGRIDKM)
B-7

Variable Description Value

MXNEW Maximum number of puffs per hour 99

MXSAM Maximum sampling steps per hour 99

SL2PF Maximum Sy/puff length 10

PLX0 Wind speed power-law exponents 0.07,0.07,0.10,0.

15,0.35,0.55

WSCAT Upper bounds 1st 5 wind speed classes (m/s) 1.54,3.09,5.14,8.

23.10.8

PGGO Potential temperature gradients PG E and F 0.020, 0.035

(deg/km)

SYMIN Minimum lateral dispersion of new puff (m) 1.0

SZMIN Minimum vertical dispersion of new puff (m) 1.0

SVMIN Array of minimum lateral turbulence (m/s) 6*0.50

SWMIN Array of minimum vertical turbulence (m/s) 0.20, 0.12, 0.08,

0.06, 0.03, 0.016

CDIV Divergence criterion for dw/dz (1/s) 0.01

WSCALM Minimum non-calm wind speed (m/s) 0.5

XMAXZI Maximum mixing height (m) 3000

XMINZI Minimum mixing height (m) 50

PPC Plume path coefficients (only if MCTADJ = 3) 0.5,0.5,0.5,0.5,0.

35,0.35

NSPLIT Number of puffs when puffs split 3

IRESPLIT Hours when puff are eligible to split User Defined

ZISPLIT Previous hour’s mixing height (minimum), (m) 100

ROLDMAX Previous Max mixing height/current mixing 0.25

height ratio, must be less then this value to

allow puff split
B-8

Variable Description Value

EPSSLUG Convergence criterion for slug sampling 1.0E-04

integration

PESAREA Convergence criterion for area source 1.0E-06

integration

NPT1 Number of point sources User Defined

IPTU Units of emission rates (1 = g/s) 1

NSPT1 Number of point source-species combinations 0

NPT2 Number of point sources with fully variable 0

emission rates

Point Sources Point sources characteristics User Defined

Area Sources Area sources characteristics User Defined

Line Sources Buoyant lines source characteristics User Defined

Volume Volume sources characteristics User Defined

Sources

NREC Number of user defined receptors User Defined

Receptor Data Location and elevation (MSL) of receptors User Defined
C-1

APPENDIX C

COMPACT DISK DATA RESOURCES

Solar and Meteorological Surface Observation Network (SAMSON), 1961 - 1990

Version 1.0, September 1993

 Available from U.S. Department of Commerce, National Climatic Data Center,

Federal Building, 37 Battery Park Ave., Asheville, NC 28801

Radiosonde Data of North America, 1946 - 1992

Version 1.0, August 1993

Available from U.S. Department of Commerce, National Climatic Data Center,

Federal Building, 37 Battery Park Ave., Asheville, NC 28801

CALMET, CALPUFF, and CALPOST Modeling System

Version 1.0

Available from U.S. Department of Commerce, National Technical Information

Service, 5285 Port Royal Rd., Springfield, VA 22161. NTIS PB 96-502 083.

Hourly United States Weather Observations (HUSWO) 1990-1995

Available from U.S. Department of Commerce, National Climatic Data Center,

Federal Building, 37 Battery Park Ave., Asheville, NC 28801

MM4 - 1990 Meteorological Data, Volumes 1-12

Available from U.S. Department of Commerce, National Climatic Data Center,

Federal Building, 37 Battery Park Ave., Asheville, NC 28801

NCDC Precipitation data CD’s 

Available from U.S. Department of Commerce, National Climatic Data Center,

Federal Building, 37 Battery Park Ave., Asheville, NC 28801
D-1

APPENDIX D

SIXTH MODELING CONFERENCE

Section 320 of the 1990 CAA amendments requires the EPA to conduct public

conferences on air quality modeling at least every three years. These conferences are

to give special attention to appropriate modeling necessary for addressing prevention of

significant deterioration of air quality. The sixth of these modeling conferences was

held August 9-10, 1995 in Washington, D.C. One of the main topics discussed at the

sixth conference was a review of the status of work by IWAQM in the development of a

Phase 2 recommendation. At the conference comments were presented by several

attendees regarding their experiences in applying the Phase 1 interim

recommendations, and thoughts and recommendations were presented on future

needs. The presentation results, comments received and conclusions reached at the

Sixth Modeling Conference are fundamental to the development of the Phase 2

recommendations, and will be discussed in the following sections.

D.1 Presentation Summary

The IWAQM presentation at the Sixth Modeling Conference began with a review

of the work plan (U.S. EPA, 1992a) and the Phase 1 interim recommendations (U.S.

EPA, 1993), which have briefly been discussed above. The presentation then provided

a summary of work accomplishments in five areas: 1) results from a case study

conducted using the Phase 1 interim recommendations; 2) adaptations made to a

Lagrangian puff modeling system called CALPUFF; 3) MESOPUFF II and CALPUFF

simulation results for three of the 1983 Cross APpalachian Tracer EXperiment

(CAPTEX) tracer releases; 4) conclusions reached regarding use of sophisticated

mesoscale meteorological analyses; 5) a proposed process for modeling specific Class

I areas. In the following five subsections, brief summaries are presented for each of

the topics listed. More extensive summaries are presented in Section 4.

D.1.1 MESOPUFF II Implementation Assessment

The objective was to learn by experience where the difficulties are in the process

of applying the MESOPUFF II air quality modeling system following IWAQM Phase 1

interim recommendations (U.S. EPA, 1993) and when possible, to provide a means for

resolving these difficulties. It was not an objective to provide a meaningful assessment

of PSD, NAAQS or AQRV impacts for the Class I areas considered in the study. As

part of this study the following tasks were carried out:

• The MESOPUFF II model and associated processors were tested using the

example problem intended for Support Center for Regulatory Air Models

bulletin board (SCRAM BBS) distribution. The SCRAM BBS example

problem computer files were evaluated and some suggested improvements

were implemented.
D-2

• A five-year meteorological data set suitable for input to the MESOPUFF II

model was developed for a multi-state area surrounding Shenandoah

National Park (SNP), including the James River Face (JRF) wilderness area. 

Model simulations were performed using three years of the developed fiveyear meteorological data set to demonstrate the assessment of visibility,

acidic deposition, and PSD increments for a set of real sources in the states

surrounding Shenandoah National Park. 

• Model simulations were performed to test the sensitivity of concentrations to

the distance between sources and receptors using a set of "pseudo" sources

placed in successive rings around Shenandoah National Park.

Several conclusions were reached as a result of the demonstration assessment. 

First, tailoring a puff simulation analysis to assess PSD and AQRV impacts at SNP and

JRF was a difficult task. A primary goal is to develop a reasonably good

characterization of the spatially-varying, time-varying three dimensional wind field. This

goal alone requires an air pollution modeler with strong dispersion meteorology

experience, having expert judgement and finesse. The differing goals between PSD

permitting and AQRV assessment required frequent consultation with FLMs. Since the

modeling necessarily required case-specific judgement decisions, strong collaboration

and review was required by the various regulatory reviewing authorities. All these

considerations have caused IWAQM to believe in and recommend a Regional

Approach, as discussed in Section 4.5.

Even with anticipated improvements in the software, it is likely that the analyses

will require programming special routines to convert data into appropriate formats, or to

assist in the analysis and summarization of the data. Also even though the modeling

software can be executed on personal computers, it is likely that a workstation would

prove more convenient and useful. Thus it is concluded that mesoscale analyses using

Lagrangian puff dispersion models must be viewed as more involved and difficult than

using conventional plume dispersion models. This is especially true in consideration

that most plume dispersion modeling assessments use a single-station’s hourly

meteorological observation, whereas puff dispersion modeling is founded on using a

three-dimensional wind field, that is consistent with the terrain and land-use.

The PSD and AQRV impacts developed in the demonstration assessment from

the ten aggregated sources exhibited strong year-to-year variations. Furthermore,

some of the impacts were close to the PSD increments. These results suggest that at

least three years of modeling is needed in order to assess the likely maximum impacts. 

Part of the reason for recommending a Regional Approach is to address concerns that 

more attention needs to be given to comparing PSD and AQRV impacts generated by

all the sources as a the cumulative impact assessment.

The ring source analysis illustrates some of the effects of source-receptor

distance on air quality and deposition impacts. For the primary species, SO and NO , 2 x
D-3

peak impacts declined rapidly with distance. MESOPUFF II results suggest that

sources of the size used for this analysis (183 g/s SO ), located 50 km from SNP, are 2

capable of producing 3-hour SO impacts close to the allowable PSD Class I increment. 2

For the secondary species, SO and NO , impacts did not show a decreasing trend for 4 3

= -

sources between 50 km and 200 km from SNP. Although modeled PM10

concentrations were well below allowable PSD Class I increments for all rings, the lack

of a clear trend suggests that sources beyond 200 km may need to be considered in

some cases in order to assess the impact upon PM10 and related parameters, such as

visibility.

D.1.2 Revisions to CALMET and CALPUFF

In the course of completing the Phase 1 recommendations, IWAQM became

aware of the CALMET/CALPUFF modeling system (Scire et al., 1990ab), which was

actively under development. Building from lessons learned from the

MESOPAC/MESOPUFF II modeling system, IWAQM felt further enhancements were

needed in two areas. With a view towards allowing one model to be used for all

sources (which might include source-receptor distances of less than 50 km), the first

area for enhancement was to include within CALPUFF dispersion additional algorithms,

so that CALPUFF simulation results would be consistent with the Industrial Source

Complex (ISC) model and the Complex Terrain Dispersion Model (CTDMPLUS) (Perry

et al., 1989) modeling results for steady-state meteorological conditions. Both ISC and

CTDMPLUS are recommended in the Guideline for use for source-receptor distances of

less than 50 km. The second area for enhancement to the CALMET/CALPUFF

modeling system was to include provisions within CALMET to allow use of mesoscale

meteorological modeling results created using data assimilation techniques, for

example Stauffer and Seaman (1989) and Stauffer et al. (1990). The characterization

of the time-varying three-dimensional wind field is one of the most challenging issues

for long-range transport. 

Consistency with local-scale plume dispersion models.

At the Sixth Modeling Conference, IWAQM reported on the inclusion within

CALPUFF of dispersion algorithms to provide results consistent with ISC and

CTDMPLUS. At the time of the conference, there were only preliminary sensitivity

testing results to show that the modifications to CALPUFF would be successful in

mimicking ISC. There were no comparison results available showing consistency

between CALPUFF and CTDMPLUS. It was recognized and mentioned by IWAQM at

the Sixth Modeling Conference that more testing was needed to test whether the code

modifications implemented in CALPUFF would replicate dispersion results as would be

simulated by ISC and CTDMPLUS.
D-4

Enhancement of CALMET for mesoscale applications.

The wind field module in CALMET is based on the Diagnostic Wind Model

(DWM). In anticipation of using CALMET and CALPUFF for long-range transport

distances, a series of modifications were made (U.S. EPA, 1995b). The DWM in

CALMET uses a two step procedure in developing the final wind fields. An initial guess

field is developed based on a domain-average wind profile, and this domain-average

profile of winds is adjusted for terrain effects and divergence minimization to produce a

“Step 1" wind field. The second step in the processing of the wind field is the

introduction of the observational data into the terrain adjusted Step 1 wind field.

The adaptations needed to allow use of meteorological wind fields as analyzed

by sophisticated mesoscale meteorological models (hereafter referred to as FDDA-MM

data) involved more than simply providing a new data input option. (For further

discussion of Four Dimensional Data Assimilation (FDDA) and how such data sets are

constructed, see Section 4.4.) IWAQM was confident that since the surface and upperair observations are included as part of the data assimilation process, the mesoscale

meteorological analyses from such analyses could be treated as data. But IWAQM was

aware that inherently such data are representative of a certain scale, as defined by the

physics included in the numerical equations and by the grid scale of the results. 

Therefore, CALMET was modified to allow introduction of the FDDA-MM data 1) as

input in the creation of the CALMET Step 1 wind fields; 2) as the CALMET Step 1 wind

fields; or 3) as input in the creation of the CALMET Step 2 wind fields. 

At the Sixth Conference, results from a series of sensitivity analyses were

reported investigating how to combine the FDDA-MM data with observations, and

investigating the impact of FDDA-MM data on simulated trajectory results. The

sensitivity analyses were conducted for two episodes in eastern United States, one

summer episode (August 1-6, 1988) and one winter episode (December 3-10, 1988). 

The summer episode was characterized by light wind, stagnant conditions. The winter

episode was characterized as an active period that included the passage of a front and

low-pressure system through the domain. Penn State Mesoscale Meteorological

(version 4) modeling results (MM4) were available employing four dimensional data

assimilation (Stauffer and Seaman, 1989) for both episodes and for three different grid

resolutions, 18-, 54- and 80-km. The CALMET model was run using hourly weather

observations from 119 surface stations for both episodes, and used twice-daily

observations from 25 upper-air stations for the summer episode and 23 upper-air

stations for the winter episode. In general, the introduction of the 80-km MM4 winds

improved the ability of CALMET to reproduce the reference 54- and 18-km MM4 wind

fields. Slightly better agreement was achieved when the 80-km MM4 winds were

brought in after the diagnostic terrain adjustment procedures (i.e., as the Step 1 wind

fields or as “observations”). This was conjectured to occur due to the fact that 1) the

80-km MM4 are already close to the 54- and 18-km MM4 results, and 2) the CALMET

diagnostic adjustments may duplicate terrain effects that were already accounted for in

the development of the 80-km MM4 winds. From the numerical sensitivity tests,
D-5

IWAQM concluded that using FDDA-MM wind fields adds a noticeable and significant

improvement in the characterization of trajectories of the dispersing material within the

atmosphere. 

D.1.3 Trajectory Comparisons

As discussed in the previous section, modifications were made to CALMET in

anticipation of using CALMET and CALPUFF for long-range transport distances (U.S.

EPA, 1995b). It was anticipated that use of FDDA-MM winds would improve

CALPUFF’s characterization of trajectories of dispersing pollutants. To further

investigate this, two trajectory studies were conducted: 1) comparisons with simulated

trajectories, and 2) comparisons with trajectories derived from surface tracer

monitoring.

CALMET trajectory comparisons.

Trajectories were computed from four release locations at three levels (10 m,

200 m, and 400 m) for each of the wind fields discussed in Section 4.1.2 for the

summer episode (U.S. EPA, 1995b). Trajectories were generated at each location

every 4, 6 and 12 hours from the beginning of the simulation, for up to 24 hours before

the end of the simulation. A statistical analysis was conducted on the trajectories to

assess the effect of the different wind fields. 

Trajectory statistics were computed from each release time and for all three

levels. In general, the introduction of the 80 km MM4 winds into CALMET to develop

either 54 km or 18 km gridded wind fields significantly improved the comparisons with

the trajectories developed from the 54 km and 18 km MM4 wind fields directly, versus

using only the routine hourly weather observations and twice-daily upper air

observations as input to CALMET. 

CAPTEX comparisons

One of the primary objectives of this study was to assess whether use of

mesoscale dynamic wind fields developed using FDDA-MM data exhibited improved

spatial and temporal resolution versus typical mesoscale wind fields determined

diagnostically from the available hourly surface and twice-daily upper air observations,

would improve the quality of the characterization of the transport and dispersion. 

Results were presented for CAPTEX releases 3, 5 and 7 (Irwin et al., 1996). The

Cross-APpalachian Tracer EXperiment (CAPTEX) is a unique series of tracer releases

which, besides testing a particular tracer technology, was conducted for the purpose of

providing insight into the mechanisms involved in long-range transport and dispersion

(Ferber et al., 1986). A three-hour ground-level release of perfluoromonomethylcyclohexan (C H , PMCH) was made five times near Dayton, Ohio and twice from near 7 14

Sudbury, Ontario when winds were expected to transport the tracer over the groundlevel sampling network. Samplers were operated at 86 sites in Ohio, Pennsylvania,
D-6

New Jersey, New York, New England and southern Canada at distances from 300 to

1100 km from the release site. Air concentrations were collected for 3 and 6 hour

durations for several days following each release.

Meteorological data available for use in developing the CAPTEX wind fields

consisted of 122 National Weather Service (NWS) surface locations reporting hourly

and 13 upper-air locations reporting twice-daily (0000 GMT and 1200 GMT) throughout

the region. Furthermore, mesoscale wind fields developed using Version 8 of the Penn

State/NCAR Mesoscale Model - Generation 4 (MM4) were available on an 80-km grid. 

Three wind field models were used to obtain a gridded field of meteorological data with

a horizontal resolution of 18-km: MESOPAC II, CALMET, and CALMET using the

mesoscale wind fields as STEP-1 inputs. The latter modeled wind fields were

developed using FDDA and are referred to as CALMET/MM4. 

The comparison results presented were conducted with simplified puff dispersion

model assumptions, hence the model-to-model differences were minimized. It was

concluded there was a noticeable improvement in the simulation of the puff centroid

trajectories, when the wind fields were developed using FDDA-MM data, versus

developing the wind fields diagnostically from the available hourly surface and twicedaily upper air observations. The analysis of the concentration maxima and lateral

dispersion values suggest that the simulation assumptions employed in these results

consistently underestimate the horizontal extent of the tracer puff as it is transported

downwind. The centroid maximum surface concentration was found to be

correspondingly overestimated and relatively insensitive to the mesoscale wind

characterization. In these simulations, no provisions were made to address delayed

shear enhancement of the dispersion as described by Moran and Pielke (1994) and Shi

et al. (1990). Inclusion of some sort of puff splitting is obviously warranted, but the

computational demands are not trivial if one is attempting to develop an operational

model for routine use. Furthermore, in those cases where the puff model dynamics

have been enhanced, for example Draxler (1987) and Davis et al. (1986), there was a

tendency to underestimate the surface maximum concentrations. 

D.1.4 Constructing FDDA-MM Data Sets Assessment

To foster use of mesoscale meteorological (MM) data processed using Four

Dimensional Data Assimilation (FDDA) in routine air pollution modeling assessments

and to learn what problems might be associated with such a project, a one-year

meteorological data set for 1990 was fabricated that spans the contiguous United

States, southern Canada and northern Mexico. The 1990 data set (NCDC, 1995) was

somewhat dated when it was discussed at the Sixth Modeling Conference. Yet it still

represents a significant advancement to meteorological characterizations employed in

many current routine air pollution assessments. These data were made available to

allow investigators to have access to this type of data that they might otherwise not

have access to (NCDC, 1995), with the hope that this would stimulate investigations,

exploring the strengths and weaknesses of these data in a variety of ways. 
D-7

The 1990 data set consist of hourly profiles of wind, temperature and moisture at

23 levels in the atmosphere on an 80-km grid spacing. The Penn State mesoscale

meteorological model (MM4) with FDDA was used in developing these data. The

horizontal grid spacing of the MM4 simulation was 80 km in both dimensions, with a grid

array size of 85 by 56 centered on 90 W longitude and 40 N latitude to cover most of

the North American continent and adjacent oceanic areas (Bullock, 1993). The domain

of the model's vertical coordinate system extended from the earth's surface to the 100

millibar pressure level (approximately 15 km above sea level). It's 15-level structure

provides height-resolved information similar to that routinely obtained by the National

Weather Service at 12-hour intervals from approximately 80 rawinsonde balloon

sounding locations across North America. However, the data set obtained from the

MM4 simulation provides synthetic soundings at 1-hour intervals for 4080 model

grid-point locations, or about 600 times more information than is available from routine

observational networks. The 1990 data set is comprized of over 20 billion bytes of

information.

One of the lessons learned in developing the 1990 data set was that the science

of mesoscale analysis using data assimilation is rapidly developing. Major

advancements have been occurring every several months during the period from 1995

through 1997. There are various research groups with active development programs

investigating mesoscale meteorological modeling employing data assimilation, e.g.

Pielke et al., (1997). This suggests that developing and maintaining a multi-year data

set would likely involve a substantial committment in resources. An alternative to

commissioning special one-year runs would be to use results from an existing

operational ongoing activity (Pielke and Uliasz, 1997). One such activity is a product

that currently is under development and refinement by the NOAA Environmental

Modeling Center Mesoscale Modeling Branch. They are employing a mesoscale

numerical weather prediction model, known as the NCEP ETA Model, with data

assimilation. This mesoscale meteorological model produces analyses of the vertical

profiles of wind, temperature, pressure and moisture on a 48 km grid resolution that

covers a very large domain (e.g., as far west as the Hawaiian Islands, all of the

contiguous United States, as far north as all of Alaska). The current operational output

provides information for 38 layers in the vertical. The model employs a surface

moisture balance model (Chen et al., 1997), and hence is capable of reporting the

surface heat and moisture fluxes, soil moisture, and precipitation (amounts for both wet,

frozen and snow). 

The Environmental Modeling Center (EMC) Home Page is: http://nic.fb4.noaa.

gov:8000/. The ETA model outputs are produced by the Mesoscale Modeling Branch

(MMB). The Mesoscale Modeling Branch Home Page is: http://nic.fb4.noaa.gov:8000

/research/mesoscale2.html.

A major obstacle is access to these data. The 1990 MM4 data set in a

compressed format (providing only profiles of wind, temperature and moisture) requires

12 Compact Diskettes. An operational means for gaining easy access to
D-8

comprehensive mesoscale meteorological data sets, as alluded to here, has yet to be

developed. But IWAQM believes that the future of air quality modeling requires a

solution being found to gain routine inexpensive access to such data. And IWAQM

believes that it is only a matter of time (perhaps less than three years) that such a

solution could be found.

D.1.5 Regional Approach

The effective treatment of mesoscale transport of pollutants requires treatment of

the time and space variations of the three-dimensional meteorological conditions. This

in turn involves consideration of the variations in terrain heights and land-use, and their

consequent effects on the dispersive characteristics of the atmosphere. A major issue

in assessing mesoscale impacts to specific Class I areas from specific sources is giving

due consideration to the proper characterization of the transport (trajectory) of the

pollutants. As alluded to in the discussion of Section 4.1, where MESOPUFF II was

used to assess impacts on the Shenandoah National Park, and in Section 4.3, where

the characterization of the transport trajectories was investigated, this is not a process

that lends itself to a cookbook approach. Tailoring the wind field analysis will require

technical judgement and discretion, and IWAQM has come to the opinion that these

case-specific decisions are best achieved using a team of experts and development of

a consensus. 

The discretion and case-specific decision making does not apply soley to the

implementation of the dispersion modeling. Not all wilderness areas have the same

flora and fauna. The AQRVs of interest will be specific to each wilderness area. 

Developing an inventory with agreed upon emission rates is not trivial. The inventory

could differ depending on whether the analysis is addressing NAAQS assessments

(which typically address maximum allowable emission rates from PSD sources) versus

AQRV assessments (which typically address actual current emission rates from all

sources).

Therefore at the Sixth Modeling Conference, IWAQM suggested that Class I air

quality modeling assessments be designed for each Class I area (or cluster of Class I

areas), rather than for each permit as is the case for the Class II program. The

cornerstone of this approach is an up-front comprehensive increment and AQRV

analysis of the area. This "initialization" study would be accomplished outside the

context of a permit application, and could involve technical experts from private and

public groups. Perhaps most importantly, it provides future applicants with up-front

information needed for planning and assurance of what is expected for the given

situation. 

D.1.6 Sixth Modeling Conference IWAQM Recommendations

At the Sixth Modeling Conference, IWAQM recommended that the CALPUFF

modeling system (Scire et al., 1995a,b) replace the MESOPUFF II modeling system. 
D-9

Based on sensitivity studies and the CAPTEX comparisons of results obtained by the

two modeling systems, IWAQM had concluded that the CALPUFF modeling system

was at least as good as the MESOPUFF II modeling system, and offered several

improvements. For one, the CALPUFF modeling system code was better documented

and was believed to be better engineered. Graphical User Interface (GUI) applications

were under development to assist and make easier the use of the CALPUFF modeling

system. CALMET had been enhanced to allow use of FDDA-MM mesoscale

meteorology. CALPUFF had been enhanced to allow better correspondence with localscale (less than 50 km transport) models recommended for use in the Guideline. 

These latter enhancements provided the basis for allowing one model to be used for all

sources in a mesoscale assessment, even though for some of the sources the transport

might be less than 50 km. At the time of the Sixth Modeling Conference, the CALPUFF

modeling system was not available from the EPA Support Center for Regulatory Models

(SCRAM) electronic Bulletin Board Service (BBS), but it was anticipated that it would

soon be available in an area designated “Topics for Review and Comment.”

Even though such data were not available, nor was it likely that IWAQM would

be able to develop such data, IWAQM recommended that use of FDDA-MM mesoscale

meteorological data be approved in regulatory assessments. The comparisons

between alternative methods for developing CALMET wind fields, and the comparisons

of the resulting trajectories, suggested to IWAQM that the added skill in the assessment

was dramatic and desirable. There were (and still remain) serious logistical issues to

development and use of such data. But in each case where such data have been used,

the characterization of the trajectories has improved.

Finally, given the myriad of decisions needing to be made in most mesoscale

assessments of transport and dispersion impacting Class I areas, IWAQM

recommended that a Regional Approach be used to resolve the various decisions. This

involves assembling a committee of the various public and private stakeholders, and

gaining consensus that the committee will be proactive in developing a plan to chart a

course through the specific decisions needing to be made for the Class I area they are

concerned with, and following the developed plan. For those seeking permits, being

able to go to such a committee for information is of great benefit. It insures that all

applicants are treated equitably, and it allows packaging of the results from each

applicant in a manner that supports cumulative impact tracking and assessment. If the

committee sponsors development of meteorological data sets, these can be made

available to the applicants to save costs and time.

D.2 Summary Of Sixth Modeling Conference Comments

One commenter pointed out the practical deficiencies with straight line plume

transport models and strongly urged the development and adoption of a trajectory

model. Another commenter pointed out the technical superiority of CALPUFF over the

best Gaussian models EPA supports. The commenter urged that the advantages and

applicability of CALPUFF be discussed among state and regional modeling contacts,
D-10

that evaluation and comparison data be amassed, and that early consideration be given

to case-by-case regulatory applications of CALPUFF.

Another commenter noted that "[The IWAQM] apparently did not address the

question of whether or not [an air quality analysis beyond 50 km] can be done with the

hope of reaching any reasonable answer." The commenter argued that there is no

accurate way to do this. The commenter also noted that IWAQM did not address the

accuracy of the Phase I screening analysis (Level I) or refined analysis (Level II). Also,

the results were not qualified (i.e., level of conservatism was not stipulated). It was

recommended that IWAQM qualify the accuracy of the results. For Level I analyses, it

was recommended that IWAQM remove the stagnation and recirculation wording and

that IWAQM specify with "reasonable particularity" the Guideline models to be used,

that some reasonable yet conservative and consistent conversion rates for both visibility

and deposition be assumed, and that the Level I analysis be a true screen. For Level II

analyses, the commenter recommended that, if a preapproved protocol is required, the

regulatory and FLM agencies must agree to abide by the results and that on-site

meteorological data not be required. The commenter believes that in some cases the

regulatory need to analyze long-range impacts is beyond the state-of-the-practice, and

that IWAQM failed to portray how conservative such calculations really may be. 

Several figures were included to illustrate the commenter's contention that CALPUFF

would have severe difficulty simulating accurate concentrations on the back side of

terrain along certain transects near the Rockies.

Aside from the more specific comments mentioned above, the commenters all

seemed in agreement that the Regional Approach outlined by IWAQM was desirous. 

There seemed general agreement that the Level I screen suggested in the Phase I

interim recommendations was not working well and needed to be improved. There was

also general agreement that the CAPTEX comparisons were for transport distances of

300 to 1000 km, well beyond the anticipated range of PSD Class I impact assessments. 

Therefore, more comparisons with tracer data studies for transport distances of order

50 to 200 km were needed. Finally, although only mentioned by the last commenter,

there was a sense that mesoscale transport assessments may be very difficult

analyses, involving discretion and expert judgment. In some instances, the modeling

results would be so uncertain as to preclude them of being any use.

D.3 Response to Sixth Modeling Conference Comments

The comments received at the Sixth Modeling Conference can be summarized

into several general areas: 1) there seemed to be agreement that the Regional

Approach outlined by IWAQM was desirous; 2) there was general agreement that as

the CAPTEX comparisons were for transport distances of 300 to 1000 km, well beyond

the anticipated range of PSD Class I impact assessments, that more comparisons with

tracer data studies for transport distances of order 50 to 200 km were needed; 3) there

was general agreement that the Level I screen suggested in the Phase I interim

recommendations was not working well and needed to be improved; 4) there was
D-11

agreement that comparisons were needed to assess whether CALPUFF can provide

results similar to ISC and CTDMPLUS for steady-state meteorological conditions; and

5) finally there was a sense that mesoscale transport assessments may be difficult

analyses (perhaps impossible in some cases), requiring discretion and expert judgment. 

Regional Approach

It has been IWAQM’s experience that most long-range transport dispersion

assessments involve expert judgement and finesse in order to provide a reasonable

characterization of the transport and dispersion with the information and resources

available. The process has yet to be successfully accomplished using “cookbook”

procedures. To further complicate matters, the AQRV’s of interest are typically specific

to the Class I area and climate, and thus the modeling endpoints need to be

customized to address the site-specific issues of concern. It is therefore

understandable that most people concur with the recommendation by IWAQM to

employ committees of technical experts, whenever possible, to sort through the various

decisions and tradeoffs. It also understandable that most people would endorse having

a panel of experts dedicated to a particular Class I area (or areas) that could provide

some consistency in the modeling assessments and assist in addressing site-specific

questions as they arise. But just as some of the options in an air quality modeling

system can not be automatically prescribed, the same can be said for use of technical

committees. Hence, while IWAQM endorses use of such committees, IWAQM does

not recommend Federal agencies mandate or require their use.

Model evaluations for the 50 to 200 km range

There are very few tracer dispersion field studies with sufficient sampling to

depict with some certainty the relative location of the receptors to the puff of dispersing

tracer as it was transported downwind. In fact, the number of studies is so small that

comparison results that can be developed only provide anecdotal evidence regarding

model performance. In response to the suggestion that comparison results be

developed for transport of order 50 to 200 km, IWAQM commissioned studies for four

tracer dispersion experiments. A summary of the findings for each of the four tracer

studies is provided in Section 4.6. Three of these field studies involved 3 to 4 hour

releases that were then sampled along arcs of receptors downwind. In some cases,

shorter-term concentration values were available, such that the transport of the puff

past an arc could be seen. Differences on the order of 10 to 20 degrees were seen

between the simulated and observed center of mass of the puff as it passed the

receptor arc. Most of the simulated centerline concentration maxima along each arc

were within a factor of two of those observed. In those instances when large over- or

underpredictions occurred, there was insufficient information available to ascertain the

physical processes that resulted in the observed concentration values. Without a better

understanding of the physical dispersion processes affecting these instances, no

simulation has (or likely will) show skill. 
D-12

It was concluded from these admittedly anecdotal case studies, that CALPUFF is

performing in a reasonable manner, and has no apparent bias towards over- or underprediction. This is in contrast to the CALPUFF comparison results with the CAPTEX

tracer field data, where CALPUFF consistently underestimated the ‘footprint’ of the puff

in contact with the surface, and correspondingly overestimated the puff concentration

maxima by a factor of 2 or more. This suggests CALPUFF (as configured for the

CAPTEX comparisons) was poorly characterizing dispersion processes (such as

delayed shear enhancement) which become important for transport distances of order

300 km or more. It is concluded that CALPUFF will need to be further developed

before it can more realistically characterize transport and dispersion for more than one

diurnal cycle. 

The IWAQM concludes that CALPUFF can be recommended as providing

unbiased estimates of concentration impacts for transport distances of order 200 km or

less, and for transport times of order 12 hours or less. For larger transport times and

distances, our experience thus far is that CALPUFF tends to underestimate the

horizontal extent of the dispersion and hence tends to overestimate the surface-level

concentration maxima. This does not preclude the use of CALPUFF for transport

beyond 300 km, but it does suggest that results in such instances be used cautiously

and with some understanding.

Comparisons with ISC and CTDMPLUS

At the time of the Sixth Modeling Conference, the algorithms installed in

CALPUFF to allow it to mimic ISC and CTDMPLUS had not been thoroughly tested. In

response to the need for further comparisons, a thorough investigation and comparison

with ISC was completed. A similar investigation is needed but has yet to be

accomplished with CTDMPLUS. A summary of the comparison results with ISC is

presented in Section 4.7. 

As expected, when the meteorological conditions were steady-state (fixed wind

direction, wind speed, mixing height, and stability), there were only minor differences

seen between ISC and CALPUFF. For all of the point source and volume source

comparisons, the differences seen between ISC and CALPUFF were less than one

percent. The largest differences were seen for receptors within and near the downwind

edge of a simulated area source. For the area source comparisons, the differences on

average were less than four percent, but individual cases were seen with larger

differences. These larger differences seen with the area source are traceable to the

difficulty of efficiently replicating the plume model’s characterization of an area source. 

The IWAQM concluded that, for all practical purposes, although improvements might be

made, the algorithms within CALPUFF are capable of replicating the results of ISC for

steady-state meteorological conditions. 

When the meteorology was allowed to vary from one hour to the next, but not in

space, large differences were seen in the simulated highest and second-highest
D-13

concentrations for all averaging times examined (1-hour, 3-hour, 24-hour, and annual)

and for all distances downwind out to 300 km. In general, CALPUFF was seen to

provide higher surface-level concentration impacts than ISC in those special situations

where the preceding hours had several hours of calm winds, or involved a dramatic

wind reversal and caused previously released material to be simulated to combine with

newly released material. Given that ISC ignores impacts during calm wind conditions,

and can not characterize the ‘causality’ resulting from a buildup of material during an

extended period of calm winds or a reversal in the transport winds, such differences

were expected. 

The IWAQM concludes that CALPUFF does reproduce, for all practical

purposes, the results that would be obtained using the ISC plume dispersion model,

when the meteorological conditions are steady-state. For situations involving complex

winds conditions (frequent periods of extended calms, routine and periodic wind

reversals, complex topographical wind effects and channeling, etc.), where a local-scale

plume dispersion model (such as ISC) is the recommended modeling approach,

IWAQM recommends acceptance on a case-by-case basis the results obtained using

the CALPUFF modeling system. In these situations, the three-dimensional time-varying

wind field and the ‘causality’ from one hour to the next are most important in deriving an

assessment of the impacts associated with the transport and dispersion of pollutants.

In conducting the comparisons of CALPUFF with ISC, minor inconsistencies

were found and corrections were made to the CALPUFF algorithms, so that the

CALPUFF results would replicate (to the extent a puff model can) the results that would

be obtained by a plume model for steady-state meteorological conditions. A similar

intensive inspection of the CTDMPLUS algorithms within CALPUFF has yet to be

accomplished. Once such an investigation has been completed (assuming that

CALPUFF can be made to provide results acceptably similar to those obtained with

CTDMPLUS for steady-state meteorological conditions), then IWAQM would endorse

use of results from the CALPUFF modeling system on a case-by-case basis, in lieu of

those obtained from the CTDMPLUS model, for complex wind situations.

Develop a better screen technique

At the Sixth Modeling Conference, the ‘Level I’ technique suggested in IWAQM

Phase I interim recommendations (EPA, 1993), of using as a screening model results

for the ISC dispersion model, was generally considered inadequate. The Level I

screening estimates of the AQRV impacts were considered so conservative that only

sources with very small emissions were ‘screened’ from further consideration. In

response to these comments, a study was conducted in an attempt to construct a more

meaningful and useful screening technique, a summary of which is presented in

Section 4.8. 

By far the most demanding task to successfully accomplish is to develop a valid

time and space varying characterization of the meteorological conditions for use by the
D-14

CALPUFF puff dispersion model. Thus IWAQM concluded that if a simplification could

be made in specifying the meteorological conditions, perhaps CALPUFF could be used

to develop the screening estimates of pollutant impacts. This would allow the chemistry

and fate to be appropriately characterized, and at least some of the ‘causality’ effects

would be captured. It was with this background and understanding that IWAQM

investigated developing a screening estimate using CALPUFF with a highly simplified

characterization of the meteorological conditions.

In the course of the development of the CALPUFF modeling system, options

were implemented that allow the meteorological conditions to be specified using either

the meteorological input required for the ISC model, or the meteorological input

required for the CTDMPLUS model. These optional formats for specifying the

meteorology to CALPUFF were implemented initially to facilitate testing of ISC and

CTDMPLUS algorithms in CALPUFF. The IWAQM decided to pursue development of a

screening estimate that could be made using the meteorological input for the ISC

model, as input to CALPUFF. 

One of the consequences of simplifying the meteorological input to that of the

ISC model, is all terrain effects (channeling, slope flows, etc.) are lost. The

consequence of these effects can become important in assessing pollutant impacts for

specific source-receptor combinations. To counter for the loss of properly

characterizing the spatial variations in the meteorological conditions, it was decided

that rings of receptors would be used that completely surround the source under

analysis. The rings would be placed to pass through the Class I area(s) of concern,

and would be spaced to provide suitable coverage, using expert judgement and in

agreement with applicable reviewing authorities. The maximum pollutant impacts

(increment, AQRV, etc.) found anywhere on any of the rings of receptors would be used

as the screening impact estimate of the pollutant impact. Selecting only receptors

within the particular Class I area(s) is not appropriate, as the screening analysis as

designed does not address the spatial variations in the meteorological conditions.

As summarized in Section 4.7, use of ISC meteorological input does not

guarantee that the maximum SO concentration found on a ring of receptors is 2

consistently estimated to be larger than what would be found if a complete threedimensional time-varying wind field and meteorological conditions were used, all other

factors being equal. In the sensitivity tests conducted, the longer the averaging time of

the SO concentration, the more likely the maximum for a ring of receptors will be 2

similar, whether ISC or CALMET meteorology input is used. The CALPUFF screening

estimates of sulfate concentrations and deposition were consistently much less than

would be estimated using IWAQM Phase 1 Level 1 ISC procedures. The CALPUFF

screening estimates of sulfate concentrations and deposition were sometimes higher

and sometimes lower than that seen using full CALMET input to CALPUFF. 

It is concluded by IWAQM that using five years of ISC meteorology as input to

CALPUFF, and selecting the maximum concentration from rings of receptors that
D-15

completely surround the source under analysis, provides a reasonable basis for

developing screening estimates. There is a finite possibility that using fully developed

CALMET input to CALPUFF, somewhat higher maxima might be simulated, but whether

these would be at receptors of interest in the Class I area(s) is considered less likely. In

fact, it is considered highly likely that lower pollutant impacts would be simulated if use

was made of fully developed CALMET input to CALPUFF, and only impacts to

receptors in the Class I area(s) were considered. The IWAQM recommends use of the

new screening technique, as it provides a more realistic assessment of the fate and

transport of the pollutants than the Phase 1 screening (Level 1) technique. The new

screening technique provides estimates of maximum impacts, that are similar to those

derived using fully developed CALMET input. Although, the air dispersion modeling

community has less experience using the CALPUFF puff dispersion model than the ISC

plume dispersion model, the operation of CALPUFF (with ISC meteorological input) is

not anticipated to be unduly difficult or onerous.

Realistic expectations

At the Sixth Modeling Conference, some of the concerns expressed were that

estimating pollutant impacts is so uncertain in some circumstances to perhaps preclude

meaningful use of the estimated impacts. An example that was used to illustrate this

point was described where the source emissions being characterized were separated

from the receptors of interest by two mountain ridges, whose tops exceeded the

effective release height of the example source. The situation described was neither

impossible nor unrealistic. The IWAQM agrees with the commenter’s conclusion that in

such a situation highly uncertain pollutant impacts would be generated, regardless of

the air quality model employed. Whether such estimates would be of little practical use

would depend on the exact nature of the assessment being attempted. Even though

the estimates might be uncertain and depend on the physics incorporated in the air

dispersion model simulations, inspection of the simulation results might suggest that

impacts from the source in question could reasonably be argued to be inconsequential

or highly unlikely. As in any air quality simulation, the usefulness of the results obtained

depends mostly on the expertise brought to the analysis in characterizing the situation,

and on the experience applied in interpreting the results obtained. The IWAQM agrees

with the sentiments of the commenters at the Sixth Modeling Conference, that as the

terrain and land-use induced mesoscale circulations become more dominant, the

expertise and integrity of the modeler will define the usefulness of the results. 

It would be convenient if objective criteria could be developed that would identify

when air quality simulation results are of questionable integrity. It would be convenient

if objective criteria and “cookbook” procedures could be constructed that would

preclude inappropriate application of air dispersion models. This has proved to be

troublesome for local-scale modeling, and even more problematic for mesoscale and

long-range transport modeling. 
D-16

In response to the comments received, IWAQM has attempted to warn the

modeling community in the summary of its Phase 2 recommendations (Section 2), that

conducting a long-range transport assessment requires competent individuals. The

IWAQM have also tried to warn the modeling community that application of the

CALPUFF modeling system to any situation will require expert judgment, it will likely

involve site-specific decisions, and it will require strong interaction and coordination with

the applicable reviewing authorities. 
TECHNICAL REPORT DATA

(Please read Instructions on reverse before completing)

1. REPORT NO. 2. 3. RECIPIENT'S ACCESSION NO.

 EPA-454/R-98-019

4. TITLE AND SUBTITLE 5. REPORT DATE

 Interagency Workgroup on Air Quality Modeling

(IWAQM) Phase 2 Summary Report and

Recommendations for Modeling Long Range Transport

Impacts

 December 1998

6. PERFORMING ORGANIZATION CODE

7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO.

 John S. Irwin

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT NO.

Office of Air Quality Planning and Standards

Emissions, Monitoring, and Analysis Division

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

11. CONTRACT/GRANT NO.

 

12. SPONSORING AGENCY NAME AND ADDRESS 13. TYPE OF REPORT AND PERIOD COVERED

14. SPONSORING AGENCY CODE

15. SUPPLEMENTARY NOTES

 

16. ABSTRACT

This Phase 2 Report is published by the Interagency Workgroup on Air Quality Modeling

(IWAQM) in an effort to provide the sponsoring agencies and other interested parties

information on appropriate “off-the-shelf” methods for estimating long range transport

impacts of air pollutants on Federal Class I areas.

17. KEY WORDS AND DOCUMENT ANALYSIS

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

 Air Pollution New Source Review

 Meteorological Data Air Pollution Control

 Air Dispersion Models

18. DISTRIBUTION STATEMENT 19. SECURITY CLASS (Report) 21. NO. OF PAGES

 Release Unlimited

 Unclassified 151

20. SECURITY CLASS (Page) 22. PRICE

 Unclassified

EPA Form 2220-1 (Rev. 4-77)

PREVIOUS EDITION IS OBSOLETE