EPA-454/R-93-015

INTERAGENCY WORKGROUP ON AIR

QUALITY MODELING (IWAQM)

PHASE 1 REPORT:

INTERIM RECOMMENDATION FOR

MODELING LONG RANGE TRANSPORT

AND IMPACTS ON REGIONAL

VISIBILITY

U.S. Environmental Protection Agency

Technical Support Division (MD-14)

Research Triangle Park, North Carolina 27711

National Park Service

Air Quality Division

Denver, Colorado 80225

USDA Forest Service

Office of Air Quality

Fort Collins, Colorado 80526

U.S. Fish and Wildlife Service

Air Quality Branch

Denver, Colorado 80225

April 1993
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

FORWARD

This document is being released as a publication of the

Environmental Protection Agency (EPA) in response to a request

from members of the Interagency Workgroup on Air Quality

Modeling (IWAQM). Members include representatives from the

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

Park Service, and U.S. Fish and Wildlife Service. The document

includes recommendations on how to estimate air quality impacts

associated with prevention of significant deterioration due to

sources farther than 50 km from a Class I area. Impacts on

visibility and other air quality related values at all downwind

distances are also addressed. IWAQM recommends that the

MESOPUFF-II model be used for these analyses in a somewhat

different mode than previously suggested by EPA.

The recommendations of IWAQM contained in this document

should be considered interim until more suitable techniques can

be developed and tested. Implementation of these

recommendations is a matter for the appropriate regulatory

agencies and should be done in consultation with the applicable

EPA Regional Office.
iii

PREFACE

The Interagency Workgroup on Air Quality Modeling (IWAQM)

was formed to provide a focus for development of technically

sound, regional air quality models for regulatory assessments

of pollutant source impacts on Federal Class I 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 regional 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. Although no States are

signatories, their participation in IWAQM functions is

explicitly noted in the MOU.

This Phase 1 Report is published by the 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 and impacts on regional visibility. The

IWAQM members anticipate issuing additional publications

related to progress toward meeting the IWAQM goals and

objectives, the results of model evaluation studies, proposed

and final recommendations on modeling systems for regulatory

applications, and other topics related to specific objectives

in the MOU.
iv

ACKNOWLEDGEMENTS

The members of the IWAQM acknowledge the special efforts

of John Vimont of the National Park Service for composing the

contents of this document and conducting most of the technical

work. The IWAQM would also like to acknowledge Alan Cimorelli

and John Irwin of the U.S. Environmental Protection Agency;

Richard Fisher 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; Kenneth

McBee and James Browder of the Commonwealth of Virginia,

Department of Air Pollution Control; and Mark Scruggs of the

National Park Service for their input and suggestions on

assembling this document and their subsequent review. IWAQM

also thanks Brenda Cannady for her help in preparing and

proofreading the final text.
v

TABLE OF CONTENTS

EXECUTIVE SUMMARY .................... ES-1

PHASE 1 RECOMMENDATION SUMMARY ........... ES-5

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . 1-1

2. EXISTING MODEL COMPARISONS AND EVALUATIONS . . . . . . 2-1

3. CANDIDATE MODELS . . . . . . . . . . . . . . . . . . . 3-1

3.1 MESOPUFF-II . . . . . . . . . . . . . . . . . . . 3-1

3.2 Acid Rain Mountain Mesoscale Model (ARM3) . . . . 3-2

3.3 Model Comparison and Further Technical

Assessment . . . . . . . . . . . . . . . . . . . 3-4

3.3.1 Initial Air Quality Model Comparisons . 3-4

3.3.2 Meteorological Processor Comparisons . 3-6

3.3.2.1 Wind Fields . . . . . . . . . . . 3-7

3.3.2.2 Mixing Height ......... 3-19

3.3.2.3 Meteorological Field Discussion 3-24

3.3.3 Further Air Quality Model Comparisons 3-26

3.3.3.1 Model Comparison Discussion . . 3-28

4. REGULATORY CONSIDERATIONS FOR THE USE OF LONG RANGE

TRANSPORT MODELS . . . . . . . . . . . . . . . . 4-1

5. INTERIM RECOMMENDATIONS . . . . . . . . . . . . . . . 5-1

5.1 Protocol for Level I Long Range Transport

Analysis . . . . . . . . . . . . . . . . . . . . 5-2

5.1.1 Level I Long Range Transport

Techniques for Analyzing

Increment and NAAQS . . . . . . . 5-4

5.1.2 Level I Analysis Technique for

Evaluating the Effects of Long

Range Transport and Regional

Visibility . . . . . . . . . . . . 5-4

5.1.3 Level I Analysis of Long Range

Transport and Depositional

Impacts . . . . . . . . . . . . . 5-5

5.2 Protocol for Level II Long Range Transport

Analysis . . . . . . . . . . . . . . . . . . . . 5-7

5.2.1 Increment and NAAQS Cumulative Long

Range Transport Analyses .... 5-10

5.2.2 Visibility Analyses ......... 5-12

5.2.3 Analysis of Other AQRVs ....... 5-13

5.2.4 Analysis of Primary Emissions of Fine

Particulates ............ 5-13

6. SUMMARY . . . . . . . . . . . . . . . . . . . . . . . 6-1

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . R-1
vi

IWAQM RECOMMENDATIONS FOR RUNNING THE MESOPUFF-II MODELING

SYSTEM . . . . . . . . . . . . . . . . . . . . . . . . A-1

Spatial Scale . . . . . . . . . . . . . . . . . . . . A-1

Spatial Resolution . . . . . . . . . . . . . . . . . . A-2

Temporal Scale . . . . . . . . . . . . . . . . . . . . A-2

Precipitation and Upper Air Meteorological Processors A-2

User Instructions - Preprocessor Programs . . . . A-2

READ56/READ62 Upper Air Preprocessors . . . A-2

PXTRACT Precipitation Data Extract Program . A-6

PMERGE Precipitation Data Preprocessor . . A-10

MESOPAC Input Fields ................ A-14

MESOPUFF-II Input Fields .............. A-19

METHOD FOR CALCULATING REGIONAL VISIBILITY IMPAIRMENT . . . B-1
vii

LIST OF FIGURES

Figure 1 - Modeling domain used in comparison analysis. 

Surface meteorological stations are indicated by

place name. Rawinsonde stations are indicated by

station number. Elevations contours are in meters. . 3-5

Figure 2a - Wind Vector Plot for July 21, 1984, 1200 LST. 

Level 1 winds are the layer average between the

surface and the mixing depth. ........... 3-10

Figure 2b - Wind Vector Plot for July 21, 1984, 1200 LST. 

Level 2 winds are between the mixing depth and 700

mb. . . . . . . . . . . . . . . . . . . . . . . . . 3-11

Figure 3a - ARM3 wind field for level 1, 10 meters, for

July 21, 1984.................... 3-15

Figure 3b - ARM3 wind field for level 3, 300 meters, for

July 21, 1984.................... 3-16

Figure 3c - ARM3 wind field for level 6, 2400 meters, for

July 21, 1984.................... 3-17

Figure 4a - MESOPUFF-II Mixing Heights for July 21, 1984,

1200 LST...................... 3-21

Figure 4b - MESOPUFF-II Mixing Heights for July 21, 1984,

0000 LST...................... 3-21

Figure 5a - ARM3 Mixing Heights for July 21, 1984 1200

LST. . . . . . . . . . . . . . . . . . . . . . . . . 3-22

Figure 5b - ARM3 Mixing Heights for July 21, 1984 0000

LST. . . . . . . . . . . . . . . . . . . . . . . . . 3-22

Figure 6 - Plot of hourly average mixing depths (m),

plotted every third hour. ............. 3-23

Figure B-1 - Correction factor to adjust for the effects

of relative humidity on light extinction calculations

(Tang et al., 1981). . . . . . . . . . . . . . . . . . B-4

LIST OF INSETS

Inset 1 - Method for calculating concentrations of SO and =

4

NO from SO and NO . . . . . . . . . . . . . . . . . . 5-5 

3 2X

Inset 2 - Description of method to estimate deposition

from SO and NO concentrations. 2 X . . . . . . . . . . . 5-6
ES-1

EXECUTIVE SUMMARY

The need for a consistent, technically credible approach

for evaluating the impacts of sources of air pollution located

more than 50 kilometers from Class I wilderness areas and

national parks, on those areas, has been identified. The

Interagency Workgroup on Air Quality Modeling (IWAQM),

consisting of representatives from the agencies responsible for

managing the wilderness and national park resources [the U.S.

Forest Service (USFS), the National Park Service (NPS), and the

U.S. Fish and Wildlife Service (FWS)], and the Environmental

Protection Agency (EPA), was formed to develop regional

analysis techniques to evaluate such impacts. The major charge

of the IWAQM is to develop a modeling approach for the

permitting of new and modified air pollution sources which

impact these Federal Class I areas. To this end the IWAQM has

developed a multi-year workplan (EPA, 1992) which is to be

implemented in three phases. Recognizing the immediate need

within the permitting community, the first phase of the

workplan called for an interim recommendation, by October of

1992. Given the time constraints and the practical limitations

of resources and hardware, Phase 1 was designed to provide the

best approach from existing "off-the-shelf-techniques." This

report documents the work performed and conclusions reached in

support of the Phase 1 recommendation (stated below). 

Therefore, the IWAQM is proposing a technique, which will

satisfy the above listed need, to be used in the interim, until

a more refined technique is recommended in Phase 2. The

recommended Phase 1 approach is to use the Lagrangian puff

model, MESOPUFF-II (Scire et al., 1984b), 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 of concern are the allowable

Class I increases in pollutants (increments), the National 
ES-2

Ambient Air Quality Standards (NAAQS), and Air Quality Related

Values (AQRVs). AQRV impacts include such effects as

visibility degradation and acidic deposition. The recommended

modeling technique is suitable for conducting single source

impact analyses, as well as, cumulative impact analyses. The

results from this technique will frequently need to be combined

with the results from techniques used to estimate

concentrations from sources closer than 50 kilometers to a

receptor area. 

It is important to note that by restricting the models

considered for Phase 1 to "off-the-shelf" techniques, the IWAQM

recognizes certain limitations in the suggested techniques. 

These include limits in considering the effects of terrain on

the long range transport and dispersion, an underestimation of

the conversion of SO to SO when polluted air interacts with 2 4

=

clouds, and an overestimation of particulate nitrate when a

limited number of sources is considered. Furthermore, the

estimations of the impacts of sources on regional visibility

are simple and do not account for all of the processes

important to regional visibility. Nonetheless, the IWAQM

considers the techniques, suggested herein, to be a significant

improvement over those previously used, in that previous

techniques ignored many of the processes important to the

assessment of air quality impacts in Class I areas. Under some

circumstances, the concentrations of sulfates in the atmosphere

may be underestimated, and hence the impacts on regional

visibility, due to the inability of the model to treat in-cloud

processes. The IWAQM, including the representatives of the

land management agencies, recognize these limitations and

consider the suggested techniques to be technically superior to

simply assuming that there are no impacts on regional

visibility. As the IWAQM work continues, these limitations

will be addressed, to the extent possible.
ES-3

The IWAQM assessed two models for this recommendation, the

MESOPUFF-II model and the Acid Rain Mountain Mesoscale Model

(ARM3). It was believed that the transport and dispersion

portions of both models could be consistent with requirements

outlined in the Guideline on Air Quality Models (Revised) (EPA,

1986). Upon careful examination of both models, however,

coding errors were discovered in the ARM3, which potentially

invalidated its previous evaluations. Therefore, the MESOPUFFII is being recommended, since it satisfies requirements for

Class I area evaluation. 

The meteorological preprocessor which is used by the

MESOPUFF-II model (MESOPAC) does not account for terrain

influences on the wind field. Also, the IWAQM has shown that

MESOPAC produces discontinuities in the mixing height field. 

The IWAQM has reasoned that the possible errors introduced by

the shortcomings of MESOPAC are outweighed by the immediate

need for the Phase 1 recommendation. Therefore, the

recommendation, as stated below, is being made at this time. 

However, the IWAQM has identified an existing meteorological

preprocessor which could be used, in place of MESOPAC. This

preprocessor utilizes a technique for smoothing the mixing

height fields and accounts for terrain through the use of a

diagnostic wind model. It is the IWAQM's intention to revise

the Phase 1 recommendation, to substitute this processor for

MESOPAC, as soon as it has been adequately tested within the

MESOPUFF II structure.

Recommendations for running the MESOPUFF-II model are

provided for increment, NAAQS, and AQRV analyses. Methods are

also provided for combining its results with the results from

steady-state, Gaussian plume models, which are generally used

for calculating impacts from sources closer than 50 kilometers

from receptors. A technique for evaluating regional haze
ES-4

impacts from a single source or from a number of sources is

also provided.
ES-5

PHASE 1 RECOMMENDATION SUMMARY

Until the Phase 2 work of the IWAQM is complete the IWAQM

recommends the following modeling approach be used under

circumstances which require the analysis of Class I area

impacts for sources more than 50 kilometers and up to several

hundred kilometers away. This recommendation is interim in

that certain technical compromises were made in order to

satisfy the immediate need for a workable modeling approach.

I. LEVEL I ANALYSIS (PLUME MODEL)

A. PSD INCREMENT AND STANDARDS

(1) For conditions other than extended

stagnation or known conditions of pollutant

recirculation, a steady-state, Gaussian

plume model may be used for all sources. 

(2) Mass removal model options for either

chemical transformation or deposition

should not be employed.

(3) Where recirculation or stagnation is known

to be important the applicant should use

the Level II analysis only.

(4) If the Level I analysis indicates an

exceedance then a complete Level II

analysis should be performed.

B. VISIBILITY

The applicant should use the same approach as is

described in I.A. with the following additions:

(1) Assume that all of the emitted SO and NO 2 X

has been converted to SO and NO = 

4 3

respectively.

(2) The concentrations of SO and NO should = 

4 3

then be used in conjunction with the

techniques presented in Appendix B to

estimate impacts on Class I area

visibility.

C. OTHER AQRVs

The applicant should use the same approach as is

described in I.A. with the following additions:

(1) Assume that all of the emitted SO remains 2

as SO and that the NO has been converted 2 X

to HNO .3

(2) Use appropriate deposition velocities to

estimate the deposition of the pollutants.

(See Inset 2.)
ES-6

II. LEVEL II ANALYSIS (MESOPUFF-II)

A. PSD INCREMENT AND STANDARDS

(1) For sources > 50 km (and up to several

hundred km) from all Class I area receptors

MESOPUFF-II should be used.

(2) For sources 50 km from all Class I area

receptors, models recommended for use in

the EPA Modeling Guideline should be used.

(3) For those sources located such that some

Class I receptors are 50 km and others

are > 50 km the applicant may either

(a) model all receptors with a Guideline

model, or

 (b) model those receptors which are > 50

km with MESOPUFF-II and those which

are 50 km with a Guideline model.

(4) Concentrations from all sources should be

summed hour-by-hour, receptor-by-receptor

and pollutant-by-pollutant.

B. VISIBILITY

(1) All sources being analyzed, regardless of

their distance from the Class I area,

should be modeled with MESOPUFF-II

following the procedures set forth in

Appendix A.

(2) Using the predicted concentrations of SO=

4

and NO , regional haze calculations should 

3

be made in accordance with the procedures

set forth in Appendix B. 

(3) If it is determined that plume blight

analyses need to be made, the

recommendations regarding use of VISCREEN

and PLUVUE II in the Guideline on Air

Quality Models (Revised) should be

followed.

C. OTHER AQRVs (Depositional Loading)

(1) All sources being analyzed, regardless of

their distance from the Class I area,

should be modeled with MESOPUFF-II

following the procedures set forth in

Appendix A.

(2) Outputs of SO and NO deposition should be = 

4 3

used, as necessary, to quantify the impact

to aquatic and terrestrial ecosystems. 

Close coordination with the Federal Land

Manager will be necessary in determining

the appropriate averaging times for this

analysis.
ES-7

III. MESOPUFF-II

The following applies to all applications of MESOPUFF

II within the context of the Phase 1 recommendation.

A. Follow the recommendations found in Appendix A.

B. The cross over distance for the time dependent

dispersion curves should be set to 10 km.

C. Both wet and dry deposition options should be

employed.

D. The model's chemical transformation algorithms

should be employed.

IV. METEOROLOGY

A. PERIOD OF RECORD (Applies to both MESOPUFF-II

and Guideline models)

(1) A five year National Weather Service (NWS)

meteorological data record should be used

when the applicant source is either > 50 km

from the Class I area or is within 50 km

and does not have at least one year of onsite data.

(2) For an applicant source located within 50

km of a Class I area, all sources being

modeled should use a representative data

record which corresponds to the time period

of the on-site data. On-site data can not

be used unless it covers at least one full

year. Furthermore, if more than one year

of on-site data exists it should be used up

to the most recent 5 years.

B. SELECTION OF DATA BASES

(1) GUIDELINE MODEL: It may be desirable to

divide the analysis domain into

meteorologically similar areas and use area

specific representative meteorological data

to model all sources' impacts in that area. 

The use of multiple meteorological data

bases is not the normal practice with

Guideline models and should be approved on

a case-by-case basis by the appropriate

regulatory authority.

(2) MESOPUFF-II: The number and location of

the NWS meteorological data bases to be

used in the MESOPUFF-II analysis should be

determined on a case-by-case basis,

generally using all available,

representative data.
1-1

1. INTRODUCTION

Under the Clean Air Act, special protection from adverse

air quality impacts is afforded certain national parks and

wilderness areas, through the Prevention of Significant

Deterioration (PSD) program. 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 PSD increments and the AQRVs. Steady-state models

are generally used for PSD analyses, however, 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.
1-2

The IWAQM work plan (EPA, 1992) describes a phased

approach to satisfy the modeling needs described above. 

Phase 1 consists of reviewing EPA guidance and "off-the-shelftechnology" for recommending a modeling approach to meet the

immediate need for a regional scale model for ongoing

permitting activity. It is important to note that in order to

satisfy this immediate need, the IWAQM restricted itself to

"off-the-shelf-technology." Phase 1, described herein, is

based on current EPA guidance and existing models, which have

been further reviewed by the IWAQM. During Phase 2, the

workgroup will augment Phase 1 with a review of other available

models and make a recommendation of the most appropriate

modeling techniques. The Phase 2 recommendation will represent

a compromise between the current modeling state-of-science and

best available operational computer capabilities. The IWAQM

recognizes this later recommendation may change the initial,

first phase, interim recommendation. More advanced modeling

techniques will be considered in Phase 3.

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 the Guideline

on Air Quality Models (Revised) (EPA, 1986). (Hereafter,

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

models, considered generally applicable under a variety of

circumstances, are defined. When a physical situation exists

for which there are no preferred models, criteria are

established in the Guideline to use appropriate methods. These

criteria are:

1. The model can be demonstrated to be applicable

to the problem on a theoretical basis, and

2. the data bases which are necessary to perform

the analysis are available and adequate, and
1-3

3a. performance evaluations of the model in similar

circumstances have shown that the model is not

biased toward underestimates, or

3b. 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 model.

One such situation is long range transport, that is,

transport of pollution beyond distances of 50 km. Therefore,

in order for any Phase 1 recommendation to be viable, from a

regulatory point of view, it will need to satisfy criteria 1.,

2., and 3a. above. It is recognized that justification of an

approach under 3b. is beyond the scope of most projects.

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. 

There are existing long range transport models available which

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

needs, but have not been sufficiently tested. 

One of the primary goals of the 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. Either of

the above approaches will require full testing and evaluation. 

Creating a model with all of the desired features and testing

it and evaluating it requires time. There is, however, an

immediate need for assessing the impacts of long range

transport of pollutants into Class I areas. Therefore, the

IWAQM decided to review a limited set of existing long range
1-4

transport models and recommend a specific model, which meets

the Guideline criteria, for long range transport analysis, in

the interim, until a more comprehensive solution can be

formulated and tested. In addition to the recommendations of a

specific long range transport model the Phase 1 recommendation

also specifies how this model, in conjunction with existing

regulatory models, should be used to provide those analyses

necessary for Class I PSD permitting. 

By restricting the models considered for Phase 1 to "offthe-shelf" techniques, the IWAQM recognizes certain limitations

in the suggested techniques. These include a lack of

consideration of the effects of terrain on the long range

transport and dispersion, an underestimation of the conversion

of SO to SO when polluted air interacts with clouds, and an 2 4

=

overestimation of particulate nitrate when a limited number of

sources are considered. Furthermore, the estimations of the

impacts of sources on regional visibility are simple and do not

account for all of the processes important to regional

visibility. Nonetheless, the IWAQM considers the techniques,

suggested herein, 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. Under some circumstances, the

concentrations of sulfates in the atmosphere may be

underestimated, and hence the impacts on regional visibility,

due to the inability of the model to treat in-cloud processes.

The IWAQM, including the representatives of the land management

agencies, recognize these limitations and consider the

suggested techniques to be technically superior to simply

assuming that there are no impacts on regional visibility. As

the IWAQM work continues, these limitations will be addressed,

to the extent possible.
1-5

Two models were selected for consideration for an interim

approach, the Acid Rain Mountain Mesoscale Model (ARM3) (Morris

et al., 1988) and the MESOPUFF-II model (Scire et al., 1984b). 

The MESOPUFF-II model was considered by the EPA for inclusion

in its list of refined models in the Guideline, but was

subsequently suggested for inclusion only in Appendix B of the

Guideline, the section reserved for models which could be

considered for regulatory use, but not generically preferred. 

The NPS has been evaluating the ARM3 for use in its program of

evaluating the impacts of air pollution in the national parks. 

As part of this evaluation, they chose to compare some of the

ARM3 results against MESOPUFF-II because of MESOPUFF-II's

availability and its consideration by the EPA. These two

models both contain features considered desirable in a model

for use in long range transport to Class I areas, particularly

the ability to consider the chemical transformation of SO and 2

NO to SO and NO and the removal of chemical species through X4 3

= 

deposition. In addition, the ARM3 contains algorithms for

considering the effects of terrain on dispersion and on the

transport flow. Furthermore, both of these models have been

compared against other, similar models and have performed

somewhat better than those other models relative to measured

tracer data (Carhart et al., 1989; Moore et al., 1990).

The IWAQM recognizes that there are certain risks involved

with recommending an interim long range 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 for example, 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 long range transport models under

consideration here, the concentration calculations for a given

hour will be explicitly dependent on the spatially and
1-6

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.
2-1

2. EXISTING MODEL COMPARISONS AND

EVALUATIONS

There have been a number of surveys of long range

transport models which may be suitable for estimating the

concentrations of pollutants which degrade visibility and/or

contribute to acid deposition. One such survey (Thompson et

al., 1987) used a series of screenings and rankings to narrow

the field of Lagrangian, Eulerian, hybrid, and statistical

models which they would consider for application in western

Canada. The model characteristics this study considered

important were:

a. domain - 0 to 500 km and up to one year

b. resolution - 1 to 10 km and event to seasonal

c. predictands - ambient air concentrations of SO , SO , 2 4

=

HF, metals, oxidants and NOX

d. processes - convective and frontal storms, flow in

complex terrain, rain and snow scavenging, influences

of soil particles, cloud physics and chemistry

e. design - modular

f. accuracy - ±30 percent for sulfate concentration and

deposition

g. chemistry - nonlinear.

The desire for a model which exhibits these characteristics is

also shared by the IWAQM. A relatively small number of the

potentially available models were identified through this

process. The MESOPUFF-II model was among the models identified

as meeting the criteria of the survey.

Another review of models was conducted for the EPA as part

of the Rocky Mountain Acid Deposition Model Assessment (Morris

and Kessler, 1987). The conclusion of this study was that

"...no one meteorological or acid deposition model is

significantly superior to the others; all the candidate models
2-2

contained different features that would be desirable attributes

in an acid deposition model for the Rocky Mountain region. 

Hence, the conceptual design of the mesoscale acid deposition

model uses modules selected from various existing

meteorological and acid deposition models." This ultimately

lead to the development of the Acid Rain Mountain Mesoscale

Model (ARM3). 

While there have been a number of reviews and surveys of

models and modeling features which could potentially address

long range transport and visibility and acid deposition

effects, there have been relatively few model evaluation

efforts against field data. One such effort examined eight

short-term, long-range transport models (Carhart et al., 1989). 

The models were tested against two tracer data bases. One of

the data bases was collected in Oklahoma from perfluorocarbon

tracer releases upwind of sampling arcs placed at 100 and 600

km. The second data base was collected at the Savanna River

Plant from the release of Kr gas from a 62 m stack. The 85

samplers from this experiment were at distances from 28-144 km

downwind. The main method used in the evaluation of the

performance of the models was the application of the American

Meteorological Society (AMS) Statistics. Additional statistics

were added to the AMS recommended list in order to assist in

interpreting the results. In addition, graphical analyses were

used to supplement the statistical comparisons in order to shed

light on the causes of model performance trends identified by

the statistics. The data bases both involve an inert tracer,

therefore, the evaluations only deal with the transport and

dispersion algorithms of the models and not the deposition or

chemical conversion algorithms. The field experiments were not

designed to evaluate dispersion in complex terrain. Some of

the important conclusions of this study were: 
2-3

The causes of model/data discrepancies can be largely

traced to inadequate wind field modeling that leads

to an incorrect temporal and spatial positioning of

the plume, and the use of the Turner curves to

downwind distances beyond which they can accurately

represent the scales of atmospheric turbulence. The

use of multilayer wind field models and the use of

the Heffter formula for lateral plume dispersion

close to the source appear to improve model

accuracies.

The above model evaluation study was being conducted about

the time that the Acid Rain Mountain Mesoscale Model (ARM3) was

being completed. As the final portion of the Rocky Mountain

Acid Deposition Model Assessment Project, the ARM3 was

evaluated against the same data bases, using the same

statistics. The results of that evaluation were that the

overall performance of the ARM3 was similar to that of

MESOPUFF-II (Moore et al., 1990). Again, it should be noted

that the data bases used in these analyses were not designed to

stress the models' ability to simulate transport and dispersion

in complex terrain.

The MESOPUFF-II model was also evaluated against data

collected during the Cross-Appalachian Tracer Experiment

(CAPTEX) (Godowitch, 1989). This experiment did include some

transport and dispersion over complex terrain. This study

concluded that any bias in the model estimates was toward overprediction of measured concentrations. 
3-1

3. CANDIDATE MODELS

For the interim recommendation, the IWAQM only considered

models which could meet the Guideline criteria for the use of

alternative models, described above. Due to the results of the

above evaluations, the model features, the availability of the

models, and the relative familiarity of the MESOPUFF-II model

and the ARM3 to the IWAQM, these models seemed the logical

choice to consider for this interim recommendation. The IWAQM

considered that either model was applicable on a theoretical

basis, that the available evaluation data bases, referred to

above, are adequate, although not ideal for the purposes cited

herein, and that the evaluations of the models indicated that

there was not a systematic bias toward underestimation. While

these models meet the Guideline criteria, the IWAQM recognizes

that there are potentially other models which might be better

suited for a particular application, but for general long range

transport modeling, the aforementioned models should be

adequate.

3.1 MESOPUFF-II

The following is an excerpt from the abstract of the

Development of the MESOPUFF-II Dispersion Model, (Scire et al.,

1984a), which provides a good summary of the nature of and

features of the MESOPUFF-II model:

...MESOPUFF-II is a Lagrangian variable-trajectory

puff superposition model suitable for modeling the

transport, diffusion and removal of air pollutants

from multiple point and area sources at transport

distances beyond the range of conventional straightline Gaussian plume models (i.e., beyond  10-50 km). 

It is an extensively modified version of the

MESOscale PUFF (MESOPUFF) model (Benkley and Bass,

1979). Major additions and enhancements include: 

use of hourly surface meteorological data and twice-
3-2

daily rawinsonde data; separate wind fields to represent

flow within and above the boundary layer; parameterization

of vertical dispersion in terms of micrometeorological

turbulence variables; parameterization of SO , to SO and 2 4

=

NO to NO conversion, including the chemical equilibrium X 3



of the HNO /NH /NH NO system; resistance modeling of dry 3 3 43

deposition, including options for source or surface

depletion; time- and space-varying wet removal; and a

computationally efficient puff sampling function...

One of the limitations of the model, with respect to the

calculation of pollutant concentrations in Class I areas, which

are frequently located in complex terrain areas, is the absence

of any complex terrain treatment either on the generation of

the meteorological fields or on the dispersion. The

shortcoming of the meteorological fields is overcome to the

extent that the meteorological observations, which are used to

generate the wind fields in the MESOPAC meteorological

processor, represent the influence of terrain. The lack of

influence of complex terrain on the dispersion is somewhat

obviated by the fact that at the downwind distances of the

receptors from the sources, envisioned by the use of this

model, the puff will generally be uniformly mixed throughout

the depth of the mixed layer. Therefore, in most applications,

it is not expected that these shortcomings will overwhelmingly

bias the results of this model.

3.2 Acid Rain Mountain Mesoscale Model (ARM3)

The following brief description of the ARM3 is taken from

the preface to the ARM3 users guide (Morris et al., 1988):

...The ARM3 model is a Lagrangian trajectory model

with simplified chemistry applied to the discrete

plume parcels...

The ARM3 model consists of mesoscale

meteorological modules and acid deposition/air 
3-3

quality modules applied to the plume parcels transported

with the winds in the Lagrangian frame...

A three-dimensional diagnostic wind model is

used to calculate the spatially and temporally

varying wind fields. Kinematic, blocking and

deflection, and thermodynamic effects are accounted

for through simple parameterizations. The wind model

is designed to generate wind fields within regions

with sparse data; thus, the validity of the wind

field is highly dependent on the quality of the

observations and their applicability to the

interpolation applied between the observations. Each

interpolation of the observed temperature, dew point,

and precipitation amounts contains an orographic

adjustment based on limited climatological data from

the Rocky Mountain region... Mixing height,

stability classification, friction velocity,

convective velocity, Monin-Obukhov length, and

surface pressure are all estimated at each grid cell

using appropriate algorithms with interpolated

observations...

The acid deposition/air quality modules treat

the plume parcels along their trajectories. The

height of the parcel can be set either as terrainfollowing or reduced relative to the difference

between the elevation of the terrain at the parcel

location and elevation at the stack base...

There are three options for determining

dispersion rates. The use of the Pasquill-Gifford

dispersion rates provides the minimum dispersion. 

The other options provide higher dispersion rates

that may be appropriate over regions of complex

terrain. The dry deposition algorithm is based on

the resistance approach. A dry deposition velocity

is calculated based on the land-use type at the plume

parcel location. The algorithm in the ARM3 is

comparable to those in other models that use this

approach. The wet deposition algorithm uses the

scavenging coefficient approach. The precipitation

rate for the grid cell containing the centroid of the

Lagrangian parcel is used...

Chemical transformation of SO and NO to sulfate 2 2

and nitrate can be calculated in the ARM3 using one

of two highly-parameterized options in the ARM3

model. They are the methods adopted from the RIVAD

[Regional Impact in Visibility and Acid Deposition

Model] and the MESOPUFF-II models...
3-4

To treat the aqueous-phase formation of sulfate,

both mechanisms assume a linear oxidation rate that

depends only on the SO concentrations. RIVAD uses a 2

constant rate of 0.2 percent/hr MESOPUFF uses a rate

that ranges from 0.2 to 3 percent/hr depending on the

relative humidity...

The ARM3 is of a similar nature to the MESOPUFF-II model,

except that it has algorithms which explicitly treat complex

terrain. These include a diagnostic wind model which treats

kinematic and blocking effects of terrain on the air flow,

dispersion parameters for complex terrain, and a correction for

plume height as a plume passes over terrain. These

enhancements should, ostensibly, make it more suitable for

calculations in complex terrain.

3.3 Model Comparison and Further Technical Assessment

Rather than proceed with recommending a model strictly

based upon its reported technical merits, a series of

comparison runs were conducted to test the manner in which the

models function under varying input conditions. The workgroup

also examined the results of the meteorological processors of

the two models under consideration, to appraise the credibility

of the fields produced.

3.3.1 Initial Air Quality Model Comparisons

The first step undertaken was to run the ARM3 and

MESOPUFF-II for a hypothetical point source located in southcentral Virginia and calculate the concentrations of pollutants

which might reach Shenandoah National Park. These analyses

used available National Weather Service data for July 1984

(Figure 1). The results of these analyses were unexpected.
3-5

Figure 1 - Modeling domain used in comparison analysis. 

Surface meteorological stations are indicated by place name. 

Rawinsonde stations are indicated by station number. 

Elevations contours are in meters.

The concentrations of pollution calculated by the ARM3

were approximately an order of magnitude higher than those

calculated using MESOPUFF-II. Since this result was not

expected, the IWAQM undertook a series of model runs to

determine whether some of the fundamental differences in the
3-6

air quality model formulations were responsible for the

dramatic concentration differences or whether differences in

the generation of the meteorological fields were responsible.

First, both models were run in an inert mode; that is, the

options for calculation of chemical transformation and

deposition were turned off. The dissimilarities in results,

were again, essentially the same. It was considered that

differences in the results stemmed from the treatment of

complex terrain in the ARM3, either in the wind fields or the

plume dispersion and transport algorithms. Complex terrain

potentially has two effects when considering concentration

calculations from the ARM3 air quality model. First, under the

options selected for this series of tests of ARM3, the

dispersion of pollutants is enhanced by the effects of the

complex terrain. The second effect was the influence terrain

has on bringing the receptor closer to the plume elevation. 

The first effect would have a tendency to lower the

concentration estimates, while the latter could potentially

increase the concentration estimates. Therefore, it was

decided to run the ARM3 without the plume height to receptor

correction included on the original runs. The removal of this

option had little effect on the concentrations calculated by

the model; this was not the expected result. Furthermore,

selecting the option within the ARM3 to use the MESOPUFF-II

dispersion parameters did not bring the modeled concentrations

appreciably closer.

3.3.2 Meteorological Processor Comparisons

Since different options in the air quality models, which

should force them to be nearly the same, could not account for

the discrepancies in the concentrations calculated in the

initial runs, the meteorological fields generated by the

models' respective processors were examined. Each model treats
3-7

the meteorological inputs somewhat differently. The MESOPUFFII uses a two layer representation of the mesoscale winds. The

lower layer (Level 1) is an average wind defined between the

surface and the mixing depth, while the upper level wind (Level

2) is an average between the mixing depth and an arbitrarily

defined upper bound, usually 700 mb. The ARM3 allows the

selection of the number of layers to represent the winds. In

the test cases run, pursuant to this discussion, six vertical

layers were chosen. An average wind for each of these layers

is calculated by the ARM3 meteorological processor. The

methods used to generate the mixing heights in the two models

are somewhat different. This will be discussed further below. 

3.3.2.1 Wind Fields: The wind fields generated by the

MESOPUFF-II processor are spatial and temporal interpolations

of the surface and upper air observations. The method for

calculating the mixed layer wind at each point follows (Scire

et al., 1984a):

(1) A representative rawinsonde sounding (00 or 12

GMT) is selected based upon the stability class

at the nearest surface station to the grid point

and the time of day. Neutral/unstable and

stable conditions are assumed to be represented

by the 00 GMT and 12 GMT sounding, respectively.

(2) Using the sounding selected in Step (1),

vertically averaged u (easterly) and v

(northerly) wind components are computed through

the layer from the surface to the grid point

mixing height.

(3) The ratio, R, of the layer-averaged wind speed

to the surface wind speed at the rawinsonde

station, and the angular difference in the wind

direction, , between the layer averaged and

surface winds are calculated.
(us,vs)ij 



k

s

r

2

s

(uk,vk)



k

s

r

2

s

3-8

(4) The hourly surface wind data are used to

calculate spatially interpolated surface wind

components (u ,v ) at each grid point. Data from s s

all surface stations within a user-specified

'scan-radius' of a grid point are used to

compute (u ,v ) according to s s

where:

u ,v are the easterly and northerly s s

components of the surface wind at grid

point (i,j),

u ,v are the easterly and northerly k k

components of the surface wind at

surface station k,

r is the distance from the surface station to s

grid point (i,j), and

s is an alignment weighting factor

(s ss = 1-0.5 |sin  |, where  is the angle

between the observed wind direction and the

line from the surface station to the grid

point).

For equal values of r , alignment weighting s

causes winds at a station directly upwind or

downwind of a grid point to be weighted twice as

heavily as the winds for a station at right

angles to the grid point.

(5) The mixed layer averaged wind at the grid point

is calculated by multiplying the surface wind

speed at the grid point from Step (4) by the

wind speed ratio, R, at the nearest rawinsonde

site. Similarly, the surface wind direction is

adjusted by the wind direction factor, .

Vertically averaged winds from the mixing height

to the 850 mb, 700 mb, or 500 mb levels are

computed in the following manner. The 00 GMT

and 12 GMT winds at each rawinsonde station are
3-9

first interpolated in time, and then vertically

averaged through the layer from the grid point

mixing height to the selected level (e.g., 700

mb). The winds at grid point (i,j) are obtained

from the previous equation, with the summation

over rawinsonde stations instead of surface

stations. Only rawinsonde stations within a

'scan-radius' of the grid point are considered. 
3-10

Figure 2a - Wind Vector Plot for July 21, 1984, 1200 LST. 

Level 1 winds are the layer average between the surface and the

mixing depth.
3-11

Figure 2b - Wind Vector Plot for July 21, 1984, 1200 LST. 

Level 2 winds are between the mixing depth and 700 mb.

Figures 2a and 2b are examples of the wind fields generated by

the MESOPUFF processor for July 21, 1984 at 1200 LST. The

effects of the 'scan-radius' and the influence of a deviant

surface station on the calculation of mixed layer average

(Level 1) winds can be seen in the vicinity of Richmond. There

are also some aberrant winds generated in the vicinity of
3-12

Philadelphia. It appears, however, that the model is

generating a sea breeze along the coastal areas. The Level 2

winds are generally fairly smooth and uniform.

The ARM3 takes a somewhat different approach to

calculating the wind field for the region of interest. The

ARM3 meteorological processor was designed to account for the

influence of terrain on the wind fields over a data sparse

area. The ARM3 processor computes a three dimensional wind

field. The ARM3 air quality model can use the vertical

velocities, generated by the meteorological processor, to

transport puffs. However, vertical velocities generated by

diagnostic wind models over complex terrain are highly suspect

and are not recommended for use in the air quality model. 

Therefore, the remaining discussion will only pertain to the

formulation of the horizontal components of the wind, although

some of the procedures for calculating the vertical component

can influence the calculation of the horizontal components.

The ARM3 processor uses "domain mean" parameters to

initialize the entire modeling domain, then computes the

effects of terrain on that flow and finally incorporates the

observations into the terrain modified flow in the vicinity of

the observations. The rationale for this is the supposition

that the domain mean winds, modified for the terrain effects,

better represent the flow in data sparse areas than relatively

distant observations. In the ARM3 processor, the domain mean

wind is defined from the sounding at the "central-most"

station. The domain mean horizontal flow is modified to

account for the effects of slope flows and blocking effects. 

The observations are incorporated into the modified mean flow

through a user-specified weighting factor, such that grid cells

near an observation give the observation relatively greater

weight and cells more distant from any observations are

weighted more heavily toward the modified mean flow.
S  S0

dT

dz f1(t)f2()

where:

S0

dT

dz  The slope flow amplitude based on

the domainscale temperature lapse rate

f1(t)  ±1depending on the time of day

(1 for downslope, 1 for upslope)

f2()  variability of the slope speed with slope angle

Fr  S

(Nh)

where:

S  the gridpoint wind speed

N  the BruntVaisala frequency, defined as:

g



d

dz where gacceleration of gravity &

potential temperature

h  the effective terrain height at the gridpoint

3-13

A slope flow vector is calculated for each grid cell and

added to the gridded domain mean (i.e., horizontally uniform)

wind field. The slope flow parameterization does not account

for any nonlinear interaction of slope flow with ambient flow. 

For any slope angle, , the speed, S, of the parameterized

slope flow is defined as:

Blocking effects are calculated from the gridded

horizontal wind field, the available atmospheric stability

information, and the gridded terrain heights. A local Froude

number is calculated at each grid point. The Froude number is

defined as:

If F is less than a critical Froude number, F , usually r rc

equal to 1, and the horizontal wind at a given grid point has

an uphill component, the horizontal wind is adjusted so that
(u,v)' 

k

rn

k (u0,v0)k  Rn

1 (u,v)1

k

rn

k  Rn

1

where:

rk  horizontal distance station k to gridpoint

R1  weighting factor for the diagnostic wind field

n controls the relative influence of the observations

(u0,v0)k  the observed wind at station k

3-14

flow is in a terrain-tangent direction with no change in speed. 

If F is greater than F , then flow is not adjusted. r rc

After the domain mean flow is adjusted for both the

kinematic effects of terrain and thermodynamic blocking

effects, the available observational data are combined with

this field to produce a final gridded wind field. This

involves interpolation, smoothing of the analyzed field,

computation of a vertical velocity field and a minimization of

the three-dimensional divergence. The discussion here will

only focus on the interpolation of the observational data into

the modified domain mean wind field, since this feature will

most affect future considerations of the ARM3 wind fields.

The procedure for interpolating both the surface and upper

air data is a modified inverse weighting scheme. The

interpolation is carried out separately for each model level. 

Unless otherwise specified, all surface wind observations are

incorporated into the lowest model level. Upper-air

observations are first vertically and temporally interpolated

to model levels and desired simulation times. The terrain

adjusted, domain mean horizontal components of the wind at each 

grid point, (u,v) , are modified to yield the observationally 1

interpolated wind, (u,v) as follows: '

This procedure weights the step 1 wind field, (u,v) ,1

heavily in regions far removed from observations. The degree
3-15

Figure 3a - ARM3 wind field for level 1, 10 meters, for July

21, 1984.

of influence exerted by the step 1 wind field is inversely

related to the value of parameter R . The exponent, n, 1

controls the relative influence of observations distant from a

given grid point.
3-16

Figure 3b - ARM3 wind field for level 3, 300 meters, for July

21, 1984.

The wind fields generated by the ARM3 meteorological
3-17

Figure 3c - ARM3 wind field for level 6, 2400 meters, for July

21, 1984.

 processor for July 21, 1984, at 1200 LST (Figures 3a, 3b, and

3c) exhibit markedly different features than those generated by

the MESOPUFF-II processor. First, it must be noted that the

MESOPUFF-II winds are mixed layer averaged winds, whereas the

ARM3 generated winds represent thinner layers of fixed

thickness, above ground level. Although the vector fields of
3-18

Figure 2 represent somewhat different layers than those in

Figure 3, some similarities might be expected. First the 10

meter field of Level 1 exhibits a relatively slow flow field. 

The influence of the interpolation scheme around surface

stations is quite evident, particularly in the vicinity of

Richmond, where the vectors are at about 90 from the mean flow

around Richmond. Other than some of the local station

influences, there is little resemblance between the ARM3 and

MESOPUFF-II Level 1 wind fields. The 300 meter winds generated

by the ARM3 meteorological processor (Figure 3b) might be

expected to be more similar to the MESOPUFF-II Level 1 field

(Figure 2a), since it represents a mixed layer average and

since the ARM3 300 meter winds should be relatively

representative of the mixed layer around 1200 LST; they are,

however, quite disparate. Also, 300 meters might be the

expected plume height of a relatively large source with a

moderately tall stack. The MESOPUFF-II winds reach speeds of

18.9 m/s in the northeast corner of the domain. The ARM3

generated winds only reach 9.4 m/s. Both models have an area

of stronger winds along the coastal area, but the wind

directions are shifted by approximately 90. The inland winds

of the ARM3 Level 3 are relatively uniform from the NE (see

Figure 3b), with the exception of the southwest corner of the

modeling domain where they are light from the SW. The

MESOPUFF-II winds are much more variable. Again the wind

directions are shifted by about 90. The MESOPUFF-II winds

above the mixed layer and the ARM3 2400 meter winds are very

similar. The wind directions are about the same and the

overall magnitude of the winds is about the same. 

As previously noted, the two models handle the generation

of winds above the surface somewhat differently. The MESOPUFFII approach uses the deviation between the surface and upper

air wind speeds and directions at the time of the soundings and

the current hour's surface data to calculate the wind speeds
3-19

and directions within the surface layer. Whereas, the method

used by the ARM3, above the first layer, is to use spatially

and temporally interpolated upper air data to perform its

calculations. Furthermore, the ARM3 interpolations of data are

not performed until after a uniform "first guess" wind field is

modified for the effects of terrain and the modified first

guess field is given higher priority in the interpolations in

areas more removed from observations. Therefore, the general

directional features of the flow fields between the respective

models' lowest levels are similar, since both make use of the

surface station data. However, with the six layer

representation of the atmosphere used in ARM3, the winds at

levels above the first level, and below the height of the

mixing depth, are quite different than those calculated by

MESOPUFF-II. The method for calculating winds above the mixed

layer, however, is similar between the two models in that both

use only spatially and temporally interpolated winds for their

respective calculations. The ARM3 still uses the modified

first guess field, but at higher levels there is generally much

more uniformity to the overall flow field. 

3.3.2.2 Mixing Height: Mixing heights are another parameter

which could potentially result in dramatically different

concentrations calculated by the two air quality models. 

MESOPUFF-II and the ARM3 both calculate a mechanical mixing

depth for the nighttime hours and a mechanical and convective

mixing height during the daytime and use the greater of the two

as the mixing depth. The two models use similar, but somewhat

different, algorithms to calculate the mixing depth, which

yields different results.

The convective mixing depth algorithm in the MESOPUFF-II

meteorological processor assumes that during daylight hours,

solar radiation reaching the ground produces an upward flux of

sensible heat and the development of a well-mixed adiabatic
(zi)t1  (zi)2

t 2H(1E)t

1c

p

2()t(zi)t

1

1

2

 ()t1

1

()t1  21EHt

c

p

1

2

where:

1  the potential temperature lapse rate

in the layer above zi

t  the time step (3600 s)

E  a constant (0.15)

  the temperature discontinuity at the

top of the mixed layer

zi  Bu

(fNB)

1

2

where:

f  the Coriolis parameter

B  a constant ( 2)

u  the friction velocity

NB  the BruntVaisala frequency in the stable

layer aloft

zi  2400u

3

2



3-20

layer. If the hourly sensible heat, H, is known, the mixed

height z at time t+1 can be estimated from time t in a i

stepwise manner. 

The sounding at the nearest rawinsonde station to the grid cell

is used to determine the lapse rate 1.

The daytime mechanical mixing depth is calculated from:

The nighttime mechanical mixed layer is determined from:
3-21

Figure 4a - MESOPUFF-II Mixing

Heights for July 21, 1984,

1200 LST

Figure 4b - MESOPUFF-II

Mixing Heights for July 21,

1984, 0000 LST

Examples of a daytime and nighttime MESOPUFF-II generated

mixing depth field are shown in Figures 4a and 4b. The daytime

mixing depths generated by MESOPUFF-II exhibit some extreme

discontinuities from one grid cell to the next. This is a

result of using only the nearest rawinsonde station sounding to

calculate the convective mixing depth, rather than an

interpolated field. The nighttime mixing depths make a much

smoother transition from one cell to the next. The daytime

mixed depth values range from 552 meters to 1759 meters,

whereas the nighttime mixed depth values range from 10 meters

to 1360 meters. 

In the ARM3 system, the daytime convective mixed depths

are determined as the height of the intersection of the hourly

surface potential temperature and the morning potential

temperature sounding. Cold or warm air advection is accounted

for by adjusting the hourly surface potential temperature

values according to an advection rate. The advection rate is

determined from the difference in potential temperature between
Hmech  53UFree Stream

where: UFree Stream is taken to be the wind speed

at 3000 meters

3-22

Figure 5a - ARM3 Mixing

Heights for July 21, 1984 1200

LST.

Figure 5b - ARM3 Mixing

Heights for July 21, 1984

0000 LST.

the afternoon and morning sounding at a height above the

convective mixing height.

The ARM3 mechanical mixing depth is the same for both

daytime and nighttime conditions.

The ARM3 generated fields (Figures 5a and 5b) show

relatively smooth fields for both daytime and nighttime. The

actual heights, however, are much lower during the daytime than

those calculated for MESOPUFF-II, ranging from 125 meters to

596 meters. During the nighttime, the values range from 185 to

593. So while the ARM3 mixing height fields do not exhibit the

discontinuities from one cell to the next that the MESOPUFF-II
3-23

Figure 6 - Plot of hourly average mixing depths (m), plotted

every third hour.

daytime fields show, they do not show any appreciable diurnal

variation.

To further illustrate this, domain average mixing heights

were calculated for each model and plotted (Figure 6). The

MESOPUFF-II heights show diurnal variation over the entire

time-span plotted, while the ARM3 mixing heights show almost no

relationship to the diurnal cycle. This can be partially

explained by the method used to calculate the mechanical mixing

depth. The choice of the 3000 meter wind speed as an indicator

of the free stream wind will almost always force the wind to be

at least on the order of 5 m/s. If the term U in the ARM3

mechanical mixing equation is set to 5, then the minimum mixing
3-24

depths will generally be on the order of 265. In order for

reasonable nighttime mixing heights to be calculated, the 3000

meter wind would have to be more on the order of 1 m/s. At

that altitude, however, a wind speed of 1 m/s is not likely to

occur very often. The daytime mixing depth calculations,

generated by the ARM3, are generally too low. The reason for

this has not been investigated at this time.

3.3.2.3 Meteorological Field Discussion: The wind fields

generated by the two models are each based on distinctly

different approaches to interpolating surface and upper air

observations. Both methods have some significant problems. 

For the stated purpose of IWAQM, the MESOPUFF-II approach lacks

the ability to treat the effects terrain will have on the mean

flow, except as much as local observations represent the mean

flow in the terrain. The mixed depth average wind speed and

direction used by MESOPUFF-II can be both a strength and a

weakness. The strength of the system is that it provides some

other information to the interpolation scheme between 12 hour

soundings by using the relationship between the surface and

upper air data at the time of the twelve hour soundings. One

problem with this, however, is that it is based upon the

assumption that the surface and upper air data are indeed

coupled. It is quite possible that under some circumstances,

particularly in complex terrain, that the surface and upper air

winds are independent from each other. Hence, the use of the

relationship between the surface and upper air winds may yield

spurious results. Another factor to consider with respect to

the mixed depth average winds is the behavior of the mixed

depth, as illustrated in Figures 4a and 4b. In the northwest

corner of the 1200 LST MESOPUFF-II mixing height field (Figure

4a), the mixing depth jumps from a height of around 500 meters

to approximately 1600 meters in adjoining grid cells. Thus,

the mixed layer average wind will represent very different

quantities between those adjoining cells. 
3-25

The ARM3 wind fields ostensibly meet the IWAQM criteria of

accounting for the effects of terrain on the wind fields. The

method the ARM3 uses to account for terrain effects warrants

some discussion. The wind field generating portion of the

model is called the Diagnostic Wind Model (DWM). The order of

analysis that the DWM uses is to first generate a horizontally

uniform "first-guess wind field." This first-guess field is

defined from the central-most sounding found in the modeling

domain. The effects of terrain, blocking and kinematic

effects, are applied to this first-guess, mean flow field. The

remaining surface and upper air observations are then applied

with the weighted interpolation scheme described earlier. The

strength of this approach is that it can yield a more

reasonable flow field in complex terrain where meteorological

observations are sparse. It does, however, introduce some

problems when generating a regional scale flow field. 

If a wind field is to be generated over a relatively small

air basin, which includes complex terrain, where one may have

only one sounding within the domain, the aforementioned use of

a first-guess wind field is probably valid. When one is

generating a wind field over a larger domain, however, the

assumption of a first guess-field, based on one sounding, is

probably not appropriate. If, for example, a major topographic

barrier runs through the domain, it is quite likely that the

air flow on the opposite sides of the barrier may be very

different. Blocking, for example, will only occur on the

windward side of the barrier. If only one sounding is used,

this blocking and subsequent turning of the wind will only

occur on one side of the barrier, where in reality there may be

upslope flows on both sides of the barrier, with subsequent

terrain modifications to the flow. Thus, while one of the

strengths of the ARM3 wind generation model is its ability to

treat flows in complex terrain, its implementation may

ultimately lead to the generation of spurious winds on the
3-26

sides of barriers opposite the station used to generate the

first-guess field. 

The generation of mixing depths by the two models is

dismal. The implementation of the MESOPUFF-II algorithms

yields large discontinuities in adjoining grid cells, while the

ARM3 implementation of the calculation of mixing depths did not

reproduce any expected diurnal variability. As noted earlier,

the reasons for the behavior of the two algorithms are

generally understood. The MESOPUFF-II algorithms only rely

upon data from the nearest sounding. There has been no attempt

to use a weighted interpolation between soundings. Therefore,

if adjoining grid cells are nearer to different soundings, the

calculated mixing depths may be quite different. The only

independent variable in the ARM3 mechanical mixing depth

calculations is the 3000 meter wind speed at that grid cell. 

At an altitude of 3000 meters, the wind speed will generally be

relatively high, thus leading to spuriously high mixing depths

at night. The daytime mixing depth calculations, generated by

the ARM3, are too low. The reasons for this have not been

investigated at this time; methods which overcome the problems

with both modeling systems' mixing depth algorithms have been

identified and should be implemented in Phase 2.

3.3.3 Further Air Quality Model Comparisons

After reviewing the mixing height fields and the wind

fields, a likely possibility for the higher concentrations

calculated by the ARM3 model was the tendency for the mixed

layer height to stay at relatively low levels over extended

periods. To test this hypothesis, the previously run test case

was run with the MESOPUFF-II generated mixing depth fields and

the ARM3 generated wind fields as input to the ARM3 air quality

model. This resulted in little appreciable change in the

calculated concentrations; the ARM3 maximum concentrations were
3-27

still considerably higher than those generated by MESOPUFF-II. 

Similarly, when the MESOPUFF-II air quality model was run using

the mixing depths generated by the ARM3 meteorological

processor, the calculated maximum concentrations did not change

appreciably.

The wind field from the MESOPUFF-II model was substituted

as input to the ARM3 air quality model. This produced

concentrations quite similar to the MESOPUFF-II air quality

model, using the same wind field. Since this change in the

wind fields generated such different results, it was

anticipated that there might be a region of very light winds

generated by the ARM3 processor. Examination of the plots of

the gridded wind fields of the two models for the time

preceding the maximum concentration calculation, did not elicit

any obvious reason for the vastly different concentration

calculations. The examinations focussed on the Level 2 and 3

fields from ARM3 and the Level 1 field from MESOPUFF-II, since

these were expected to be the respective transport levels for

the hypothetical source being modeled in the test cases. To

check which levels were being selected by the ARM3 to transport

puffs, the code was examined. This lead to the discovery of a

fundamental coding error.

The ARM3 calculates the center of mass of a puff and uses

the wind level at the altitude of the center of mass to

transport the puff. A subroutine is called to extract the wind

data for the appropriate grid cell and level. The algorithm

defined the transport altitude as a fraction of the total model

domain height. This fraction was then compared against the

absolute magnitude of the height of the various wind levels to

select the level of the wind to use. This resulted in a

normalized transport altitude, which would always be less than

or equal to 1, being compared with an array of heights ranging

from 10 to 2400 meters. Thus, the model would always choose
3-28

the 10 meter wind level for all of its calculations. The 10

meter winds did exhibit some very light, almost calm conditions

between the hypothetical source and the receptor area in the

test case. Since the model was always selecting the 10 meter

winds, when the MESOPUFF-II mixed layer average winds were

substituted into appropriate levels in the ARM3 input files,

the ARM3 would always be picking the MESOPUFF-II mixed layer

winds as its 10 meter winds and thus was yielding similar

results.

Subsequent to the discovery of the coding error, the code

was patched, and the ARM3 meteorological and air quality

modeling system was again run. The concentrations from this

run were of the same order as those calculated by the MESOPUFFII. 

3.3.3.1 Model Comparison Discussion: Until a more refined

technique can be evaluated, neither of the models under

consideration by the IWAQM to use in the interim are totally

satisfactory. With respect to the meteorological processors,

the MESOPUFF-II processor should incorporate a weighted

interpolation algorithm in the calculation of mixing depths to

avoid the extreme gradients encountered in the current

formulation. The ARM3 processor, on the other hand, should

employ different algorithms for calculating mixing depths and

should change the order of analysis in the diagnostic wind

field calculations to start out with a field interpolated from

the observations as the first-guess field, upon which the

kinematic and blocking effects of terrain should be applied. 

The MESOPUFF-II model does not explicitly include any

treatment for the effects of terrain on long range transport

and dispersion. This is a relatively major deficiency with

respect to the needs of the IWAQM. While the ARM3 incorporates
3-29

algorithms to treat such effects, the model has been found to

have a number of coding errors which render its use suspect.

One of the rationales for limiting the interim modeling

choices to either the MESOPUFF-II or the ARM3 was their

performance relative to other similar models. After the coding

error, described above, was discovered in the current version

of the ARM3 code, an examination was made of the originally

released version of the code. The code was found to contain

the same fundamental error found in the current version. Thus,

the evaluations of the ARM3 were carried out using only the

surface wind fields. For the case of the Oklahoma data, this

is not necessarily a major problem, since it was based on a

surface tracer release. However, the Savannah River evaluation

involved an elevated release into a wind level above the

surface level. If the surface level and the upper level were

very similar, the ARM3 evaluation results may be valid, but

unless the corrected model is re-run with the Savannah River

data, confidence should not be placed in the Savannah River

comparisons.
4-1

4. REGULATORY CONSIDERATIONS FOR THE USE

OF LONG RANGE TRANSPORT MODELS

The Guideline explicitly identifies certain steady-state,

Gaussian plume models which are preferred for calculating

concentrations of inert pollutants for source-receptor

distances of less than 50 km. The MESOPUFF-II model has been

shown to replicate steady-state, Gaussian plume results, under

many conditions, when it is run with steady-state meteorology

and Pasquill-Gifford dispersion coefficients (Scire et al.,

1984a). When MESOPUFF-II is run in an operational mode,

however, the results could be quite different, since the

meteorology will be time varying and the simulated pollutant

transport can be quite different than the steady-state

approximation. Having two acceptable techniques (i.e.,

MESOPUFF II and a Guideline model) which can give different

results for the same application is untenable for regulatory

purposes. Therefore, while it would be desirable to run only

one model, it will be necessary to run both a preferred,

Guideline model and the recommended long range transport model

for many permitting situations.

For estimating impacts on AQRVs, the approach need not be

as operationally complicated. Since all AQRV analyses, covered

by this recommendation, involve secondary pollutants, the use

of MESOPUFF-II for all distance ranges was felt to be

acceptable. Although the use of MESOPUFF-II for sources

located close to the Class I area is questionable, their impact

should be quite small due to the small travel times involved. 

Also, based on the very stringent Class I increments, any

sources locating close to a Class I area will usually be unable

to emit large quantities of primary pollutants.
4-2

Similarly, there are likely to be occasions when long

range transport is to be considered, when it would be desirable

to run a simpler model, for screening purposes, than the

recommended long range transport model. In past applications,

steady-state, Gaussian plume models have been used. Comparison

runs of the MESOPUFF-II, run in a steady-state mode, and the

ISCST2 model indicate that the ISCST2 will frequently produce

similar or higher concentrations, but not under all

circumstances. Since the MESOPUFF-II will use a spatially and

temporally varying wind field when run in an operational mode,

the concentrations it produces will generally be lower than

those produced by the ISCST2 model. If, however, there are

prolonged periods of near stagnation conditions or if

recirculations occur, the ISCST2 will not necessarily produce

conservative concentrations.
5-1

5. INTERIM RECOMMENDATIONS

From the review of the ARM3 and MESOPUFF-II models, the

IWAQM found neither model to be totally satisfactory. However,

due to the immediate need for a modeling system to estimate

impacts from sources farther than 50 km from a receptor and to

estimate the impacts on visibility and other AQRVs from all

distances, the IWAQM is recommending that the MESOPUFF-II model

be used for these analyses. This recommendation is to be

considered interim, until a more suitable technique can be

developed and tested. The IWAQM is recommending that MESOPUFFII be run in a somewhat different mode than was previously

suggested by EPA (EPA, 1988), and is recommending some

approaches to integrating the long range modeling system with

regulatory modeling requirements. A relatively simple method,

using steady-state, Gaussian plume models, is suggested as a

way to provide a first estimate of concentrations from long

range transport. The recommendations herein are the

suggestions of the IWAQM; implementation of these

recommendations is a matter for the appropriate regulatory

agencies and should be done in consultation with the

appropriate EPA regional office.

The following recommendations are divided into two

categories. The first is referred to as a "Level I" analysis. 

This relatively simple analysis is expected to provide a

conservative estimate of concentrations due to long range

transport. If the Level I estimates indicate that adverse

conditions may be caused in the Class I area of concern, then a

"Level II" analysis, using the MESOPUFF-II modeling system,

should be undertaken.
5-2

5.1 Protocol for Level I Long Range Transport Analysis

The IWAQM is suggesting that a steady-state, Gaussian

plume model may be acceptable as a first level long range

transport analysis technique under many conditions. It is

anticipated that this first level technique will, under most

conditions, yield a conservative estimate of ambient air

quality concentrations. However, under conditions of extended

stagnation or where recirculation patterns or convergence zones

are known to occur, this Level I technique should not be used. 

If a Level I analysis indicates that there are exceedances of

the appropriate parameters, the more refined analyses, using

the Level II long range transport models, should be run for all

meteorological conditions, not just those identified through

the Level I technique. Level I analyses will consist of

running the appropriate steady-state, Gaussian plume models

with five years of meteorological data. Steady-state Gaussian

plume models used for Level I long range transport analyses

should utilize bivariate plume dispersion factors. It is

generally anticipated that plume impaction models, utilizing

uniform horizontal plume dispersion, would not be appropriate

for a long range transport analysis.

While the IWAQM is suggesting a relatively simple, Level

I, technique, it should not be construed as an endorsement of

that approach. With limited testing, the suggested Level I

technique appears to yield higher ambient pollutant estimates

than the Level II procedures. However, if there is any

question about the technical veracity of using the Level I

technique in a given situation, then it is completely

appropriate to proceed directly to a Level II analysis.

In general, the steady-state Gaussian plume models will

yield a high estimate of concentrations at distances beyond 50

kilometers, due to the spatially varying paths of puffs in the
5-3

recommended long range transport model. As mentioned

previously, this is not always the case. Therefore, it is

expected that application of the Level I techniques will need

to be reviewed by those with sound professional judgement. 

Under light wind conditions or under recirculation conditions,

the MESOPUFF-II model may yield higher concentrations than

steady-state, Gaussian plume models. Therefore, if these

conditions occur, the Level I analysis should be adequately

assessed. These conditions are not necessarily generally

defined; the IWAQM is not attempting to define them further. 

It is anticipated that as more experience is gained with the

Level I and II techniques, further resolution on when either

method is appropriate can be better elucidated.

Since many of Class I areas are located in complex

terrain, the issue of the appropriate methods for applying

Level I long range transport modeling techniques to these areas

arises. Each analysis will have unique characteristics which

must be evaluated on a case-by-case basis. In many cases,

however, some of the following considerations will apply. As

noted previously, models incorporating bivariate plume

dispersion parameters are suggested for use in a Level I

analysis. This suggestion is based on the presumption that

after a plume has travelled 50 km or farther, it will have been

affected by a variety of processes, land use, and terrain

intervening along the trajectory of the plume. Therefore, it

may not be appropriate to use a plume impaction model for these

circumstances. Similarly, under many conditions, when long

range transport is involved in moving pollutants to a Class I

area, the plumes will be traversing steadily rising terrain. 

Under the constraints of existing, steady-state, Gaussian plume

models, receptor heights are either restricted to be no 

greater than the height of the stack which emitted the

pollutants, or in the case of some of the plume impaction

models, the concentrations are considered to be zero if the
5-4

elevation of the receptor is sufficiently far above the

calculated plume height. Under conditions of steadily rising

terrain, the air flow, and plumes imbedded in that flow, will

tend to follow the terrain and become well mixed after a

distance of 50 km. Therefore, the various simple terrain

models, outlined in the Guideline, should generally provide a

reasonable approximation of transport and dispersion under

these conditions.

The results of the Level I technique will be analyzed

somewhat differently for increment and NAAQS analyses than for

visibility and other AQRV analyses. The methods for performing

these first level long range transport analyses are discussed

below. 

5.1.1 Level I Long Range Transport Techniques for Analyzing

Increment and NAAQS

If Level I methods are to be utilized for increment and

NAAQS analysis, then all sources, whether closer or farther

than 50 km should be analyzed assuming no conversion of SO to 2

SO and NO to NO and no deposition. If that analysis = 

4 X3

indicates that the increments or NAAQS are being exceeded, then

a full analysis, using the recommended long range transport

model in combination with the steady-state Gaussian model, as

described below, should be employed.

5.1.2 Level I Analysis Technique for Evaluating the Effects

of Long Range Transport and Regional Visibility

If a Level I approach is desired, it should be assumed

that all SO has been converted to SO and that all NO has been 2 4X

=

converted to NO in the analysis. Refer to Inset 1 for the 

3

appropriate method for converting SO and NO to SO and NO . 2 X4 3

= 
5-5

Calculation of SO & NO For Visibility Screening = 

4 3

1. Run appropriate long range transport screening model.

2. Assume no conversion of SO or NO to other species. 2 X

(i.e. assume all NO is emitted as NO and that all SO X 22

remains inert at this step.)

3. Multiply the hourly concentrations of SO and NO by 2 X

the ratios of the molecular weights of the secondary

species to the primary species. 

Note: The molecular weights of SO and SO are 64 2 4

=

and 96 and the molecular weights of NO and 2

NO are 46 and 62. Thus multiplying the 

3

concentration of SO2 by 1.5 will yield the

concentration of SO and multiplying the =

4

concentration of NO2 by 1.35 will yield the

concentration of NO .



3

4. The averaging time of interest is generally 1-hour.

Inset 1 - Method for calculating concentrations of SO and NO = 

4 3

from SO and NO . 2 X

The procedures in Appendix B should be used to estimate the

visibility impacts.

5.1.3 Level I Analysis of Long Range Transport and

Depositional Impacts

If a Level I approach is to be taken for depositional

impacts, it should be assumed that concentrations of SO and NO 2 X

are deposited as SO and HNO (see Inset 2). Since the steady- 2 3

state, Gaussian plume models do not actually remove any mass

from the plume, when run in their recommended modes, this will

provide a conservative deposition estimate. 
5-6

Calculation of Deposition For Level I analysis

1. Run appropriate model for Level I analysis.

2. Assume no conversion of SO or NO to other species. 2 X

(i.e. assume all NO is emitted as NO and that all SO X 22

remains inert at this step.)

3. Multiply the concentrations of NO (μg/m ), if X

3

applicable by the ratio of the molecular weights of

the secondary species (HNO ) to the primary 3

species (NO ). 2

Note: The molecular weights of NO and HNO are 46 2 3

and 63. Thus multiplying the concentration

of NO2 (μg/m ) by 1.37 will yield the 3

concentration of HNO3 (μg/m ). 3

4. The majority of sulfur will be deposited as SO , so no 2

conversion is necessary.

5. The averaging times for deposition will generally

require a long term value (monthly, seasonal, or

annual) and short term value (1, 3 or 24-hour). Since

the Level I models will produce average values, they

must be converted to total rates.

Multiply the concentration of SO or HNO by the number 2 3

of seconds in the averaging time of interest to obtain

a total rate. (3.1536 × 10 seconds/year, 86400 7

seconds/day, 10800 seconds/3-hours, or 3600

seconds/hour)

6. Multiply the result of step 5 by the deposition

velocity for the appropriate pollutant. (0.005 m/s for

SO or 0.05 m/s for HNO ). This will result in a 2 3

deposition value in units of μg/m .2

7. To convert to kg/hectare, multiply the result of step

7 by 10 . -5

Inset 2 - Description of method to estimate deposition from SO2

and NO concentrations. X
5-7

5.2 Protocol for Level II Long Range Transport Analysis

The IWAQM is recommending that the MESOPUFF-II model be

run for source-receptor distances of greater than 50 km, up to

several hundred km, when predicting the concentrations for

criteria pollutants. For impacts on AQRVs, the IWAQM is

recommending the use of MESOPUFF-II for all source-receptor

distances. It is also recommended that the analyst generally

follow the recommendations in A Modeling Protocol for Applying

MESOPUFF-II to Long Range Transport Problems (EPA, 1988); the

primary exceptions to that protocol are the distance at which

time dependent dispersion curves are used, the use of the

chemical transformation and deposition algorithms (both wet and

dry), and the use of five years of meteorological data. The

IWAQM recommendations for model options and input parameters

can be found in Appendix A. 

The two areas where the previous protocol and the IWAQM

protocol diverge are in the specification of the distance where

time dependent dispersion curves are used to calculate puff

dispersion and the use of the chemical transformation and

deposition options. The previously suggested protocol for the

use of MESOPUFF-II (EPA, 1992) stated that the distance at

which the time dependent dispersion curves should be use was

beyond 100 km. It was indicated that the reason for this

choice was that the 100 km distance was used during the

performance evaluations (EPA, 1986), which identified MESOPUFFII as one of the better performing models. The reference to

100 km appears to have been erroneous. A summary article,

describing the performance evaluations (Carhart et al., 1989)

stated:

The Turner curves were established from experiments

carried out over distances of 0-1 km from the source,

and yet some models use the curves out to 100 km. In

the MESOPUFF-II model, the transition from the Turner

to the Heffter formulation is made at 10 km from the
5-8

source (a recommended user-input value adopted in

this study). On the other extreme are MESOPUFF and

MESOPLUME, which specify 100 km as the transition

distance.

Therefore, it seems clear that the performance evaluations

were indeed carried out with the transition between distance

and time dependent formulations set at 10 km. Thus the IWAQM

is recommending that the model be run with the 10 km setting.

The previous protocol also recommended that the model be

run assuming no chemical conversion or deposition of pollutants

(either wet or dry). For the purposes of IWAQM, namely to

calculate visibility impacts, secondary pollutants, such as

SO , are the contributing pollutants. After reviewing the =

4

algorithms used in the MESOPUFF-II code for calculating the

chemical conversion and deposition, the IWAQM considered them

simple, but adequate. Therefore, the IWAQM is recommending

that they be used, recognizing the following limitations. 

First, the treatment of the aqueous phase conversion of SO to 2

SO is likely to be greatly underestimated. Field measurements =

4

have indicated that when a plume passes through a nonprecipitating cloud that the conversion of SO to SO can be as 2 4

=

high as 100% per hour. The assumed conversion of 3% per hour

is, therefore, expected to be an underestimate. The model,

however, does not adequately treat the occurrence of nonprecipitating clouds. Therefore, the tendency will be to

underestimate SO formation when non-precipitating clouds would =

4

be present, with a commensurate overestimation of the primary

SO concentration. 2

Ultimately, when examining Air Quality Related Values

(AQRVs), one of the parameters of interest is frequently

deposition of SO and NO . Also, even when trying to estimate = 

4 3

impacts on visibility, an accurate assessment should include

the removal of these pollutants from the atmosphere. 
5-9

Therefore, the IWAQM is recommending that transformation and

deposition be actively modeled in order to reasonably estimate

the fate of pollutants undergoing long range transport.

The IWAQM recognizes that the chemical and depositional

algorithms have not been rigorously tested against field data. 

Chemical and depositional algorithms do not readily lend

themselves to field evaluation due to their dependence on the

ambient conditions into which they are emitted and the

infeasibility of producing and releasing a unique, chemically

active tracer to assess chemical conversion and deposition on a

source by source basis. Given these limitations, it is not

likely that there will be such data available in the near

future. Therefore, proposing to use a technically credible

method to estimate the formation and deposition of secondary

pollutants, seems appropriate, and is the course recommended by

the IWAQM.

To be consistent with the current modeling guidance and to

attempt to adequately capture year-to-year variation in

meteorological conditions, the IWAQM is recommending that five

years of meteorological data be run with the MESOPUFF-II

analyses. There are some exceptions to this recommendation,

noted below. 

One of the weaknesses of the MESOPUFF-II modeling system,

identified by the IWAQM, is the lack of treatment of terrain on

the air flow and the discontinuities in the mixing height

field. A meteorological processor, which uses a diagnostic

wind model and smooths out the mixing depth fields, has been

identified, but has not yet been thoroughly tested with the

MESOPUFF-II, and thus, is not being recommended at this time. 

When this processor is ready for distribution, Appendix A will

be updated to include these enhancements. Until such time, the

MESOPUFF-II meteorological processor (MESOPAC) should be used.
5-10

5.2.1 Increment and NAAQS Cumulative Long Range Transport

Analyses

Sources at distances greater than 50 km from a given

receptor should be analyzed with the MESOPUFF-II model, run as

described in Appendix A. This includes using the chemical

transformation and deposition algorithms. Sources within 50 km

of a receptor should be modeled using the appropriate model and

model options, as recommended in the Guideline. The

concentrations from these two analyses should be added on an

hour-by-hour, receptor-by-receptor, and pollutant-by-pollutant

basis.

There will be occasions when a source will be both nearer

and farther than 50 km from receptors in an analysis. It is

not intended that such a source be analyzed with both models. 

In general, the nearer receptors will yield the controlling

concentrations for that source, therefore, the appropriate

Guideline model should generally be run for all receptors, for

that source. If, however, it is determined that a particular

source, modeled with a steady-state, Gaussian model, is

possibly causing an exceedance at a receptor more than 50 km

from the source, it is obviously appropriate to model its

impacts at that receptor with the long range transport model,

to more accurately estimate its impacts. Again, it is expected

that a fair degree of professional judgement will need to be

exercised in these analyses.

When analyzing impacts in a large Class I area it may be

appropriate to divide the steady-state, Gaussian plume analyses

into several source groups and use meteorological data

appropriate to each source group. For example, if a Class I

area is 150 km long, it is unlikely that sources located at

opposite ends of the area will experience the same meteorology. 

Therefore, it may be appropriate to use representative
5-11

meteorological data sets for each end of the Class I area,

particularly since the results will be combined with a model

which uses spatially and temporally varying wind fields. The

use of multiple meteorological data sets in a steady-state,

Gaussian plume modeling analysis is not the recommended

approach in the Guideline; therefore, this is a matter for the

appropriate regulatory agencies and should only be considered

on a case-by-case basis in consultation with the appropriate

EPA regional office.

As noted previously, it is recommended that both the long

range transport model and the steady-state Gaussian model be

run with five years of meteorological input data. The

exception to this is when the source being permitted is within

50 km of the Class I area and has collected at least one year

of on-site meteorological data. If a cumulative impact

analysis, which includes sources beyond 50 km, is required, the

meteorological period of analysis for both analyses should

correspond to the period of on-site meteorological data. If,

however, the source being permitted is beyond 50 km from the

affected Class I area, then it is recommended that the full

five years of data should be run for all analyses.

Combining the results of steady-state Gaussian models with

a Lagrangian puff model, such as the MESOPUFF-II, will produce

some contrived results. The steady-state model assumes

instantaneous transport, whereas the Lagrangian puff model

simulates the actual transport time. It may be physically

impossible for the emissions from a source modeled with the

steady-state model to reach a receptor at the predicted time,

given the wind speed. However, steady-state models do not

generally accurately predict the time and location of maximum

concentrations, but are routinely applied as if they do provide

such information. Therefore, while combining the results of

two fundamentally different modeling systems is somewhat
5-12

contrived, it is the only workable approach under the

regulatory framework.

5.2.2 Visibility Analyses

The MESOPUFF-II model will be run for all sources, whether

nearer or farther than 50 km from a receptor, to provide

concentrations of SO and NO for visibility calculations. A = 

4 3

complete description of the visibility calculations is

described in Appendix B.

SO has been identified as the primary constituent of =

4

visibility degradation in the eastern U.S.; organic aerosols

and NO are less important contributors, but a significant 

3

species, nonetheless. Therefore, it is critical to have

estimates of SO in order to estimate visibility impacts. =

4

Estimates of nitrates are desirable and can be obtained from

the long range transport modeling system; estimates of organic

aerosols, while more important to total scattering than

nitrates in most cases, are not readily estimated by the

modeling system. Therefore, organic aerosol concentrations

will only be considered as a background concentration for

determining regional visibility impacts. The MESOPUFF-II model

is capable of producing concentrations of SO and NO , whereas = 

4 3

the preferred models, listed in the Guideline are not. 

Therefore, the IWAQM is recommending that when a cumulative

visibility impact analysis is desired, all sources be modeled

with MESOPUFF-II in order to estimate regional haze impacts. 

It is also important to note that although the application of

MESOPUFF-II for short transport distances is questionable the

contribution for such sources is relatively unimportant. In

many permitting analyses, it is likely that only the source

being permitted will be analyzed. Therefore, the MESOPUFF-II

analysis will be greatly simplified. 
5-13

The primary purpose of this analysis is to identify

regional haze impacts. Plume blight analyses may need to be

performed (EPA, 1988), as well, depending on the location of a

source with respect to the Class I area. For sources located

more than 50 kilometers from a Class I area, the regional haze

analysis will generally be more appropriate. The methods,

outlined in Appendix B, are based upon analysis of ambient,

speciated fine particulate data, correlated with visibility

parameters. Therefore, in order to utilize this method,

concentrations of particulate species need to be obtained. The

MESOPUFF-II model can provide estimates of SO and NO , but = 

4 3

assumptions need to be included with respect to the cations

associated with these radicals in the fine particulate, the

amount of primary carbon particles in the atmosphere, and the

formation of secondary organic particles. These are documented

in Appendix B. 

5.2.3 Analysis of Other AQRVs

The modeling system, described herein, will only provide

estimates of the ambient concentrations of pollutants or the

depositional loadings at given locations. This modeling system

is adequate for providing such estimates, which then may be

used to assess the total or incremental impacts of these

concentrations or depositional loadings on aquatic and

terrestrial ecosystems. The methods for estimating effects on

these other AQRVs are not provided; such methods should be

determined in consultation with the appropriate Federal Land

Manager.

5.2.4 Analysis of Primary Emissions of Fine Particulates

The MESOPUFF-II model is not set up to directly simulate

emissions of primary particulates. It can, however, be used

for such an analysis, although it must be run as an independent
5-14

simulation from other pollutants of interest. The method for

simulating primary emissions of fine particulates follows.

The MESOPUFF-II dispersion model includes the option for

modeling primary emissions of SO . Hence, since these are in =

4

particulate form, SO can be used as a surrogate for the =

4

emissions of other fine particulate species. If the model is

run simulating only SO emissions, with the chemistry options =

4

turned off, an estimate of the ambient concentrations of

primary particulate species can be obtained. The deposition

algorithms for particulate SO can be used for other fine =

4

particulates, as well. Since the MESOPUFF-II does not include

a provision for gravitational settling, ambient concentrations

of the course particle fraction (diameters > 2.5 μm) may be

overestimated over transport distances greater than 50 km.
6-1

6. SUMMARY

The IWAQM considered two Lagrangian puff models for use in

regional scale modeling analyses, the MESOPUFF-II and the ARM3. 

Both models have known limitations. After comparison and

examination of the models, it was determined that the ARM3

contained a number of fundamental coding errors which rendered

its use suspect. Although the errors are correctable, they

invalidate previous evaluation analyses completed with the

ARM3. The MESOPUFF-II has been shown to be applicable, on a

theoretical basis, for long range transport; has been evaluated

with adequate data bases; and has been shown to not be biased

toward underestimation. Therefore, the IWAQM is recommending

the use of the MESOPUFF-II for long range transport analyses,

in the interim, until a more suitable model construct can be

developed. 

It would be desirable to be able to recommend a single

modeling approach for estimating regional impacts on the NAAQS

and PSD increments, that is, have a single model which could

treat source-receptor distances of less than and greater than

50 km. Given the need for regulatory consistency, it was

determined that for calculating impacts on quantities regulated

under the Clean Air Act, techniques identified in the Guideline

on Air Quality Models (Revised) (EPA, 1986) should be used for

source-receptor distances of less than 50 km. The MESOPUFF-II

model should be run for source-receptor distances greater than

50 km and the results of these two techniques should be added

together. 

A technique to estimate visibility impacts, based upon

statistical relationships between observed fine particle

concentrations and visibility parameters, has been provided. 

The most important constituents in these calculations are the
6-2

concentrations of sulfate and the relative humidity. The

modeling techniques, recommended above, will yield

concentration estimates of sulfate and nitrate and the

meteorological fields, used as input to the long range

transport model, contain the relative humidity. Therefore, the

recommended long range transport model can be used to estimate

the impacts of pollutant sources on regional visibility

degradation. 

The recommendations for the use of the long range

transport model include the calculation of the chemical

conversion of SO and NO , and deposition. This is being 2 X

recommended whether the model is being applied to a PSD

increment analysis or to an AQRV analysis. It is recognized

that including these processes will decrease the amount of SO2

calculated by the model, and hence, reduce the calculated

increment concentration. However, these processes are known to

occur over the long range transport distances being modeled

here and their inclusion in the modeling is considered to be

critical for an accurate estimation of impacts. Furthermore,

in an AQRV analysis, the secondary pollutants generated and

removed through these processes are frequently of most concern.

A number of limitations were identified in the recommended

modeling system. As improvements become available, it is

intended that they be incorporated into the system. 

The IWAQM recognizes that by proposing the use of a

Lagrangian puff model for use in the interim, it is not

possible to guarantee that the results will be more restrictive

than more sophisticated techniques to be proposed during Phase

2 of the IWAQM work plan. The IWAQM still asserts, however,

that the interim system is the best of currently available

alternatives for simulating the impacts of long range transport

on the PSD increments, NAAQS, and AQRVs.
R-1

REFERENCES

Benkley, C.W. and A. Bass. 1979. Development of Mesoscale Air

Quality Simulation Model: Vol. 3. User's Guide to

MESOPUFF. EPA-600/7-80-058. U.S. Environmental

Protection Agency, Atmospheric Research and Exposure

Assessment Laboratory, Research Triangle Park, NC.

Carhart, R.A., A.J. Policastro, M. Wastag, and L. Coke. 1989. 

Evaluation of Eight Short-term Long-Range Transport Models

using Field Data. Atmospheric Environment, 23(1):85-105.

EPA. 1986. Guideline on Air Quality Models (Revised). EPA450/2-78-027R. U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards, Research

Triangle Park, NC.

EPA. 1986. Evaluation of Short-term Long-Range Transport

Models, Volumes I and II. EPA-450/4-86-016a and b. U.S.

Environmental Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle Park, NC.

EPA. 1992. A Modeling Protocol for Applying MESOPUFF-II to

Long Range Transport Problems. EPA-454/R-92-021. U.S.

Environmental Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle Park, NC.

EPA. 1992. Workbook for Plume Visual Impact Screening and

Analysis (Revised). EPA-454/R-92-023. U.S. Environmental

Protection Agency, Office of Air Quality Planning and

Standards, Research Triangle Park, NC.

EPA. 1992. Interagency Workgroup on Air Quality Modeling

(IWAQM) Workplan Rationale. EPA-454/R-92-001. U.S.

Environmental Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle Park, NC.

Godowitch, J.M. 1989. Evaluation and Sensitivity Analyses

Results of the MESOPUFF-II Model with CAPTEX Measurements.

EPA-600/3-89-056. U.S. Environmental Protection Agency,

Atmospheric Research and Exposure Assessment Laboratory,

Research Triangle Park, NC.

Moore, G.E., R.E. Morris, S.G. Douglas, and R.E. Kessler. 

1990. Rocky Mountain Acid Deposition Model Assessment: 

ARM3 Model Performance Evaluation. EPA-600/3-90-024. 

U.S. Environmental Protection Agency, Atmospheric Research

and Exposure Assessment Laboratory, Research Triangle

Park, NC. 
R-2

Morris, R.E. and R.C. Kessler. 1987. Rocky Mountain Acid

Deposition Model Assessment: Review of Existing Mesoscale

Models for Use in Complex Terrain. EPA-600/3-87-013. 

U.S. Environmental Protection Agency, Atmospheric Research

and Exposure Assessment Laboratory, Research Triangle

Park, NC.

Morris, R.E., R.C. Kessler, S.G. Douglas, K.R. Styles, and G.E.

Moore. 1988. Rocky Mountain Acid Deposition Model

Assessment: Acid Rain Mountain Mesoscale Model (ARM3).

EPA-600/3-88-042. U.S. Environmental Protection Agency,

Atmospheric Research and Exposure Assessment Laboratory,

Research Triangle Park, NC.

Scire, J.S., F.W. Lurmann, A. Bass, and S.R. Hanna. 1984a. 

Development of the MESOPUFF-II Dispersion Model. EPA600/3-84-057. U.S. Environmental Protection Agency,

Atmospheric Research and Exposure Assessment Laboratory,

Research Triangle Park, NC.

Scire, J.S., F.W. Lurmann, A. Bass, and S.R. Hanna. 1984b. 

User's Guide to the MESOPUFF-II Model and Related

Processor Programs. EPA-600/8-84-013. U.S. Environmental

Protection Agency, Atmospheric Research and Exposure

Assessment Laboratory, Research Triangle Park, NC.

Thomson, R.B., R.P. Angle, and S. Sakiyama. 1987. Selecting

Air Quality-Acid Deposition Models for Mesoscale

Application. J. Air Pollution Cont. Assoc., 37(3):260-

265.
Appendix A

IWAQM Recommendations for

Running The MESOPUFF-II

Modeling System
A-1

IWAQM RECOMMENDATIONS FOR RUNNING THE

MESOPUFF-II MODELING SYSTEM

The MESOPUFF-II modeling system was designed to include

flexibility in its use, so it could be used to simulate a wide

variety of conditions. Therefore, a number of user specifiable

parameters were included as input to the system. Furthermore,

the MESOPUFF-II modeling system is capable of simulating

varying spatial and temporal scales, which also need

specification. The purpose of the recommendations contained in

this appendix is to provide consistent, technically credible

methods for operating the MESOPUFF-II modeling system for

regulatory applications. These recommendations refer to model

specific variables and options without detailed definition of

the options. The user is referred to the MESOPUFF-II user's

manual for further information, where needed.

The primary focus of these recommendations is for the

evaluation of air pollution impacts on Class I areas, both

increment consumption and the impacts on Air Quality Related

Values (AQRVs), from sources located more than 50 kilometers

from the potentially affected area. The general procedures

outlined could also be used for the assessment of pollution

impacts, for source-receptor distances greater than 50 km,

outside of Class I areas. The applicability of such techniques

should, however, be confirmed with the appropriate regulatory

authorities.

Other documents which may be useful to the users of these

recommendations include:

User's Guide to the MESOPUFF-II Model and Related

Processor Programs, (EPA-600/8-84-013).

A Modeling Protocol for Applying MESOPUFF-II to Long Range

Transport Problems, (EPA-454/R-92-021).

Development of the MESOPUFF-II Dispersion Model, (EPA600/3-84-057).

Spatial Scale

The MESOPUFF-II modeling system is generally applicable to

source-receptor distances greater than 50 kilometers. The grid

dimensions used in the MESOPUFF-II system, upon which the

meteorological fields are defined and the puff calculations are

performed, should not exceed 1000 km in the east-west direction

or 600 km in the north-south direction (EPA-454/R-92-021). 

With grid distances greater than this, significant errors can

be introduced through the orthogonal nature of the modeling

grid, superimposed on the curved surface of the earth. The

computational grid size should be such that sources and

receptors of interest are not too close to the edge of the

grid, since once puffs leave the grid, they are eliminated from
A-2

the computations; concentrations may be significantly

underestimated for sources or receptors too close to the edge

of the computational grid.

Spatial Resolution

The various grid systems used in the MESOPUFF-II modeling

system are all relative to the initially defined meteorological

grid. Therefore, the resolution (grid spacing) of the

meteorological grid is of prime importance. Since the

meteorological fields, generated by the MESOPAC processor, are

defined from the interpolation of available observations, the

practical resolution of those fields will depend on the

distance between observation stations. Therefore, the maximum

recommended resolution is the median distance between 1_

3

observation stations. Finer resolutions can be used, but at

the cost of some computation time. If an area in the domain is

considered very important and has relatively dense

meteorological observations, then the resolution should be

based on this area of more refined observations.

In general, all available meteorological stations within

the initially defined grid system should be included in the

analysis. Stations relatively near to the boundaries,

particularly upper air stations, should also be included, as

they will improve the representativeness of the wind fields

generated by the interpolation. 

Temporal Scale

In order to capture year-to-year meteorological

variability and the effect that can have on air pollution

concentrations, five years of meteorological data should be run

with the MESOPUFF-II modeling system.

Precipitation and Upper Air Meteorological Processors

The version of MESOPAC, the meteorological processor for

the MESOPUFF-II modeling system, being distributed, can make

use of upper-air meteorological data in either a TD-5600 format

or the newer TD-6201 format. Processors for both of these data

types are provided with the modeling system. Precipitation

data is now distributed in a TD-3240 format. Descriptions and

information on running these processors are provided below.

User Instructions - Preprocessor Programs

READ56/READ62 Upper Air Preprocessors

 READ56 and READ62 are preprocessing programs which extract

and process upper air wind and temperature data from standard
A-3

NCDC data formats into a form required by the MESOPAC

meteorological model. READ56 operates on the older TD-5600

data format. Although this format is not currently used by

NCDC, many historical data sets contain data in this format. 

READ62 processes data in the current TD-6201 format.

 Although the data inputs are different, the user inputs to

the program are identical as is the processed output file. In

the user input file, the user selects the starting and ending

dates of the data to be extracted and the top pressure level. 

Also selected are processing options determining how missing

data are treated. The programs will flag or eliminate sounding

levels with missing data.

 If the user selects the option to flag (rather than

eliminate) levels with missing data, the data field of the

missing variables are flagged with a series of nines. If the

option to eliminate levels with missing data is chosen, only

sounding levels with all values valid will be included in the

output data file.

 Although MESOPAC allows missing values of wind speed, wind

direction, and temperature at intermediate levels (i.e., levels

other than the surface and model top), the user is cautioned

against using soundings with significant gaps due to missing

data. For example, adequate vertical resolution of the morning

temperature structure near the surface is especially important

to the model for predicting daytime mixing heights. It should

be kept in mind that the model will fill in missing data by

assuming that a straight-line interpolation between valid

levels is appropriate. If this assumption is questionable, the

sounding should not be used with the model.

 Two input files are required by the preprocessor: a user

input control file and the NCDC upper air data file. Two

output files are produced: a list file summarizing the user

option selected and missing data monitored and the processed

data file in MESOPAC format. Table A-1 contains a listing of

the input and output files for READ56 and READ62.

 The READ56/READ62 control file consists of two lines of

data entered in FORTRAN free format. A description of each

input variable is shown in Table A-2. A sample input file is

shown in Table A-3.

 The output data file (UP.DAT) produced by READ56/READ62 is

a formatted file containing the pressure, elevation,

temperature, wind speed, and wind direction at each sounding

level. The contents and format of the UP.DAT file are

discussed in Section A.2.3.
A-4

Table A-1

(a) READ56 Input and Output Files

Unit File_Name Type Format Description

 5 READ56.INP input formatted Control file containing user

inputs

 6 READ56.LST output formatted List file (line printer

output file)

 8 TDF56.DAT input formatted Upper air data in NCDC

TD-5600 format

 9 UP.DAT output formatted Output file containing *

processed upper air data in

format required by MESOPAC

(b) READ62 Input and Output Files

Unit File_Name Type Format Description

 5 READ62.INP input Control file containing user

formatted inputs

 6 READ62.LST output formatted List file (line printer

output file)

 8 TD6201.DAT input Upper air data in NCDC TDformatted 6201 format

 9 UP.DAT output formatted Output file containing *

processed upper air data in

format required by MESOPAC

Should be renamed UP1.DAT (for upper air station #1), UP2.DAT (for station *

#2), etc. for input into the MESOPAC model.
A-5

 Table A-2 

 READ56/READ62 Control File Inputs 

RECORD 1. Starting and ending date/hour, top pressure level to extract. 

Columns Variable Type Description

 * IBYR Starting year of data to extract (two

integer digits)

 * IBDAY Starting Julian day

integer

 * IBHR Starting hour (00 or 12 GMT)

integer

 * IEYR Ending year of data to extract

integer (two digits)

 * IEDAY Ending Julian day

integer

 * IEHR Ending hour (00 or 12 GMT)

integer

 * PSTOP real Top pressure level (mb) for which data is

extracted (possible values are 850 mb, 700

mb, or 500 mb). The output file will

contain data from the surface to the

"PSTOP"-mb pressure level.

* Entered in FORTRAN free format

 Table A-2 (Concluded) 

 READ56/READ62 Control File Inputs 

RECORD 2. Missing data control variables

Columns Variable Type Description

 * LHT logical Height field control variable. If LHT =

T, a sounding level is eliminated if the

height field is missing. If LHT = F, the

sounding level is included in the output

file but the height field is flagged with

a "9999", if missing.

 * LTEMP logical Temperature field control variable. If

LTEMP = T, a sounding level is eliminated

if the temperature field is missing. If

LTEMP = F, the sounding level is included

in the output file but the temperature

field is flagged with a "999.9", if

missing.
A-6

 * LWD logical Wind direction field control variable. If

LWD = T, a sounding level is eliminated if

the wind direction field is missing. If

LWD = F, the sounding level is included in

the output file but the wind direction

field is flagged with a "999", if missing.

 * LWS logical Wind speed field control variable. If LWS

= T, a sounding level is eliminated if the

wind speed field is missing. If LWS = F,

the sounding level is included in the

output file but the wind speed field is

flagged with a "999", if missing.

* Entered in FORTRAN free format

Table A-3

Sample READ56/READ62 Control File (READ56.INP, READ62.INP) 

79, 365, 00, 80, 002, 00, 500. -- Beg. yr, day, hr(GMT), Ending yr, day, hr, top pressure

level

.TRUE., .TRUE., .TRUE., .TRUE. -- Eliminate level if height, temp., wind direction, wind

direction, wind speed missing ?

PXTRACT Precipitation Data Extract Program

 PXTRACT is a preprocessor program which extracts

precipitation data for stations and time periods of interest 

from a fixed length, formatted precipitation data file in NCDC

TD-3240 format. Hourly precipitation data is available from

NCDC in large blocks of data sorted by station. For example, a

typical TD-3240 file for California may contain data from over

100 stations statewide in blocks of time of 30 years or more. 

Modeling applications require the data sorted by time rather

than station, and usually involve limited spatial domains of

tens of kilometers or less and time periods of one year or

less. PXTRACT allows data for a particular model run to be

extracted from the larger data file and creates a set of

station files that are used as input files by the second-stage

precipitation preprocessor, PMERGE (see PMERGE section)

 NOTE: If wet removal is not to be considered by the

MESOPUFF-II dispersion model, no precipitation processing needs

to be done. PXTRACT (and PMERGE) are required only if wet

removal is an important removal mechanism for the modeling

application of interest. In addition, if wet removal is a

factor, the user has the option of creating a free-formatted

precipitation data file that can be read by MESOPAC. This

option eliminates the need to run the precipitation

preprocessing programs for short MESOPAC runs (e.g., screening

runs) for which the input data can easily be input manually.
A-7

 The input files used by PXTRACT include a control file

(PXTRACT.INP) containing user inputs, and a data file

(TD3240.DAT) containing the NCDC data in TD-3240 format. The

precipitation data for stations selected by the user is

extracted from the TD3240.DAT file and stored in separate

output files (one file per station) called xxxxxx.DAT, where

xxxxxx is the station identification code. PXTRACT also

creates an output list file (PXTRACT.LST) which contains the

user options and summarizes the station data extracted. Table

A-12 contains a summary of PXTRACT's input and output files.

 The PXTRACT control file contains the user-specified

variables which determine the method used to extract

precipitation data from the input data file (i.e., by state, by

station, or all stations), the appropriate state or station

codes, and the time period to be extracted. A sample PXTRACT

control file is shown in Table A-13. The format and contents

of the file are described in Table A-14. 

 The PXTRACT output list file (PXTRACT.LST) contains a

listing of the control file inputs and options. It also

summarizes the station data extracted from the input TD-3240

data file, including the starting and ending date of the data

for each station and the number of data records found. The

PXTRACT output data files consist of precipitation data in

TD-3240 format for the time period selected by the user. Each

output data file contains the data for one station. 

Table A-12 

PXTRACT Input and Output Files

Unit File Name Type Format Description

 1 PXTRACT.INP input formatted Control file containing user

inputs

 2 TD3240.DAT input formatted Precipitation data in NCDC

TD-3240 format

 6 PXTRACT.LST output formatted List file (line printer output 

file)

 7 id1.DAT output formatted Precipitation data (in

(id1 is the TD-3240) format for station #1

6-digit for the time period selected

station by the user

code for

station

#1, e.g.,

040001)
A-8

 8 id2.DAT output formatted Precipitation data (in

(id2 is the TD-3240) format for station #2

6-digit for the time period selected

station by the user

code for

station

#2, e.g.,

040002)

.

.

.

(Up to 200 new precipitation data files are allowed by PXTRACT).

 Table A-13 

 Sample PXTRACT Control File (PXTRACT.INP) 

 

2 -- Selection code, ICODE

5 -- Number of states or stations

040001 -- State or station code

040002

040003

040004

040005

80 01 01 01 80 01 02 24 -- Starting yr, month, day, hour(01-24),

ending yr, month, day, hour

 Table A-14 

 PXTRACT Control File Inputs (PXTRACT.INP) 

RECORD 1. Data selection code.

Columns Variable Type Description

 * ICODE Integer Selection Code:

1 = extract all stations within state

or states requested

2 = input a list of station codes of

stations to extract

3 = extract all stations in input file

with data for time period of

interest

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

* Entered in Fortran free format
A-9

 Table A-14 (Continued) 

 PXTRACT Control File Inputs (PXTRACT.INP) 

RECORD 2. Number of state or station codes 

(This record is included only if ICODE = 1 or 2)

Columns Variable Type Description

 * N Integer If ICODE = 1:

 Number of state codes to follow

If ICODE = 2:

 Number of station codes to follow

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

* Entered in Fortran free format

 Table A-14 (Continued) 

 PXTRACT Control File Inputs (PXTRACT.INP) 

RECORD 3, 4, ... 2+N. State or station codes of data to be

 extracted

Columns Format Variable Description

 1-6 I6 IDAT If ICODE=1: State code (two digits)

If ICODE=2: Station code (six digits)

consisting of state code

(two digits) followed by

station ID (four digits) 
A-10

 Table A-14 (Concluded) 

 PXTRACT Control File Inputs (PXTRACT.INP) 

NEXT RECORD. Starting/ending dates and times

Columns Format Variable Description *

 1-2 I2 IBYR Beginning year of data to process (two

digits)

 4-5 I2 IBMO Beginning month

 7-8 I2 IBDAY Beginning day

 10-11 I2 IBHR Beginning hour (01-24 LST)

 13-14 I2 IEYR Ending year of data to process (two

digits)

 16-17 I2 IEMO Ending month

 19-20 I2 IEDAY Ending day

 22-23 I2 IEHR Ending hour (01-24 LST)

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

Record format is (8(i2,1x) *

 Table A-16 

 Sample TD-3240 Format Precipitation Data File (040001.DAT) 

HPD04000100HPCPHI19820100010010100 00002

HPD04000100HPCPHI19820100010010200 00002

HPD04000100HPCPHI19820100010010300 00004

HPD04000100HPCPHI19820100020010700 00000

HPD04000100HPCPHI19820100030011300 00000M

HPD04000100HPCPHI19820100120010400 00000M

HPD04000100HPCPHI19820100120010500 00000

PMERGE Precipitation Data Preprocessor

 PMERGE reads, processes and reformats the precipitation

data files created by the PXTRACT program, and creates an

unformatted data file for input into the MESOPAC meteorological

model. The output file (PRECIP.DAT) contains the precipitation

data sorted by hour, as required by MESOPAC, rather than by

station. The program can also read an existing unformatted

output file and add stations to it, creating a new output file. 

PMERGE also resolves "accumulation periods" and flags missing

or suspicious data.

 Accumulation periods are intervals during which only the

total amount of precipitation is known. The time history of

precipitation within the accumulation period is not available. 

For example, it may be known that within a six-hour

accumulation period, a total of a half inch of precipitation

fell, but information on the hourly precipitation rates within

the period is unavailable. PMERGE resolves accumulation
A-11

periods such as this by assuming a constant precipitation rate

during the accumulation period. For modeling purposes, this

assumption is suitable as long as the accumulation time period

is short (e.g., a few hours). However, for longer accumulation

periods, the use of the poorly time-resolved precipitation data

is not recommended. PMERGE will eliminate and flag as missing

any accumulate periods longer than a user-define maximum

length.

 PMERGE provides an option to "pack" the precipitation data

in the unformatted output in order to reduce the size of the

file. A "zero packing" method is used to pack the

precipitation data. Because many of the precipitation values

are zero, strings of zeros are replaced with a coded integer

identifying the number of consecutive zeros that are being

represented. For example, the following record with data from

20 stations requires 20 unpacked words:

 0.0, 0.0, 0.0, 0.0, 0.0, 1.2, 3.5, 0.0, 0.0, 0.0,

 0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,

These data in packed form would be represented in six

words:

 -5., 1.2, 3.5, -6., 0.7, -6.

where five zero values are replaced by -5., six zero values are

replaced by -6., etc. With many stations and a high frequency

of zeros, very high packing ratios can be obtained with this

simple method. All of the packing and unpacking operations are

performed internally by PMERGE and MESOPAC, and are transparent

to the user. The header records of the data file contain

information flagging the file to MESOPAC as a packed or

unpacked file. If the user selects the unpacked format, each

precipitation value is assigned one full word.

 The input files used by PMERGE include a control file

(PMERGE.INP), an optional unformatted data file (PBIN.DAT)

created in a previous run of PMERGE, and up to 150 TD-3240

precipitation station files (e.g., as created by PXTRACT). The

output file consists of a list file and a new unformatted data

file in MESOPAC format with the data for all stations sorted by

hour. Table A-17 lists the name, type, format, and contents of

PMERGE's input and output data files.

 The PMERGE control file (PMERGE.INP) contains the userspecified input variables indicating the number of stations to

be processed, a flag indicating if data is to be added to an

existing, unformatted data file, the maximum length of an

accumulation period, packing options, station data, and time

zone data. PMERGE allows data from different time zones to be

merged by time-shifting the data to a user-specified base time
A-12

zone. A sample PMERGE control file is shown in Table A-18. 

The format and contents of the file are described in Table

A-19.

 The PMERGE output list file (PMERGE.LST) contains a

listing of the control file inputs and options. It also

summarizes the number of valid and invalid hours for each

station including information on the number of hours with zero

or non-zero precipitation rates and the number of accumulative

period hours. Additional statistics provide information by

station on the frequency and type of missing data in the file

(i.e., data flagged as missing in the original data file, data

which is part of an excessively long accumulation period, or

data missing from the input files before (after) the first

(last) valid record. 

 Table A-17 

PMERGE Input and Output Files

Unit File_Name Type Format Description

3 PBIN.DAT input unformatted Existing PMERGE data file to

which stations are to be added

(Used_only_if_NBF=1)

4 PRECIP.DAT output unformatted Output data file created by

PMERGE (PRECIP.DAT is an input

file to MESOPAC)

5 PMERGE.INP input formatted Control file containing user

inputs

6 PMERGE.LST output formatted List file (line printer output

file)

7 user input input formatted Precipitation data (in TDfile name 3240) format for station #1. 

(Output file of PXTRACT)

8 user input input formatted Precipitation data (in TDfile name 3240) format for station #2. 

(Output file of PXTRACT)

.

.

.

(Up to 150 new precipitation data files are allowed by PMERGE).

 Table A-18 

 Sample PMERGE Control File (PMERGE.INP) 

 5 0 12 5 2 0 -- No. stations,no. binary files,max. accum. period,base time 

zone,ioform(1=binary,2=formatted),pack(0=no, 1=yes) --

(6I4)

040001.dat 5 -- Input file name, time zone -- (A10,I3)

040002.dat 5 " " " " "

040003.dat 5 " " " " "

040004.dat 5 " " " " "

040005.dat 5 " " " " "
A-13

82 01 01 01 82 01 03 01 -- Starting yr, month, day, hour (01-24), ending yr, month, day,

hour (01-24) 

-- (8(I2,1X))

 

 Table A-19 

 PMERGE Control File Inputs (PMERGE.INP) 

RECORD 1. General run information.

Columns Format Variable Description *

 1-4 I4 NFF Number of formatted 80-column NCDC input

files to be processed (up to 150)

 5-8 I4 NBF Flag indicating if data is to be added

to an existing unformatted precip. data

file (0=no, 1=yes)

 9-12 I4 MAXAP Maximum allowed length of an

accumulation period (hours). It is

recommended that MAXAP be set to 24

hours or less.

 13-16 I4 IOTZ Time zone of output data (05=EST,

06=CST, 07=MST, 08=PST) 

 17-20 I4 IOPACK Flag indicating if output data are to be

packed (0=no, 1=yes)

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

Record format is (5i4) *

 Table A-19 (Continued) 

 PMERGE Control File Inputs (PMERGE.INP) 

RECORDS 2, 3, ... 1+NFF. File names and time zone of each station 

 (Each_record_has_the_following_format) 

Columns Format Variable Description *

 1-10 A10 CFFILES Name of file containing formatted

precipitation data (TD-3240 format)

(PXTRACT output file). First six digits

of file name must contain station code

(SSIIII), where SS is the two digit

state code, and IIII is the station ID)

 12-13 I2 ISTZ Time zone of station (08=PST) 

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

 Record format is (a10,1x,i2)
A-14

 Table A-19 (Concluded) 

 PMERGE Control File Inputs (PMERGE.INP)

NEXT RECORD. Starting/ending dates and times

Columns Format Variable Description *

 1-2 I2 IBYR Beginning year of data to process (two

digits)

 4-5 I2 IBMO Beginning month

 7-8 I2 IBDAY Beginning day

 10-11 I2 IBHR Beginning hour (01-24 LST)

 13-14 I2 IEYR Ending year of data to process (two

digits)

 16-17 I2 IEMO Ending month

 19-20 I2 IEDAY Ending day

 22-23 I2 IEHR Ending hour (01-24 LST)

 

 

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

 Record format is (8(i2,1x)) 

MESOPAC Input Fields

The version of MESOPAC discussed herein, includes some

enhancements from earlier versions. These include an expanded

format for including precipitation data (TD-3240) and the

inclusion of site specific data on the wind speed measurements,

specifically the wind speed measurement height and the surface

roughness length appropriate for the surface station site. 

Inclusion of the TD-3240 requires running the precipitation

processors described previously. Some coding enhancements were

implemented to trap artifacts, produced by the model, and treat

them in a consistent manner.

Card Group 1 - TITLE

Columns Typ Variable Description Recommended Value *

 Name

1-80 CA title(20) 80 Character Title Appropriate Choice
A-15

Card Group 2 - GENERAL RUN INFORMATION

Columns Typ Variable Description Recommended Value *

 Name

1-5 I nyr Two Digit Year Appropriate Choice

6-10 I idystr Starting Julian Day Appropriate Choice

11-15 I ihrmax Number of Hours in Run Appropriate Choice

16-20 I nssta # Surface Stations 25 

21-25 I nusta # Rawinsonde Stations 10 

26-30 I ibtz Reference Time Zone 5=EST 6=CST 7=MST 

8=PST 

31-35 I npsta # Precipitation 200 

Stations

Card Group 3 - GRID DATA

Columns Typ Variable Description Recommended Value *

 Name

1-5 I imax # X grid points 40 

(west-east)

6-10 I jmax # Y grid points 40 

(south-north)

11-20 R dgrid grid spacing (m)  median distance

between stations

Card Group 4 - OUTPUT OPTIONS

Columns Typ Variable Description Recommended Value *

 Name

1-5 L lsave Binary To Disk for T 

Post-processing 

6-10 L lprint Print Met fields F (can produce

voluminous output)

11-15 I iprint Print Interval (hours)  1 (used only if

lprint=T)

16-20 L lbd Print Met Input F (can produce

voluminous output)

21-25 I ndy1 Julian day to start (used only if lbd=T)

printing input

26-30 I nhr1 Hour to start printing (used only if lbd=T)

input

31-35 I ndy2 Julian day to stop (used only if lbd=T)

printing input

36-40 I nhr2 Hour to stop printing (used only if lbd=T)

input
A-16

Card Group 5 - GRIDDED LAND USE CATEGORIES

Columns Typ Variable Description Recommended Value *

 Name

1-80 IA Ilandu Land use categories As Appropriate 

Format (40,40) for each grid point

(40i2)

'jmax' cards are required, each card with 'imax' land use categories

(corresponding to X-coordinates 1 to imax). The first card contains

values for Y = jmax, the second card for Y = jmax-1, etc.

Card Group 6 - DEFAULT OVERRIDE OPTIONS

Columns Typ Variable Description Recommended Value *

 Name

1 IAE iopts(1) Use Default Surface 0 

Wind Speed Measurement

Height (Default=10m)

2 IAE iopts(2) Use Default von Karman 0 

Constant (Default=0.4)

3 IAE iopts(3) Use Default Friction 0 

Velocity Constants

(Defaults: =4.7,

A=1100)

4 IAE iopts(4) Use Default Mixing 0 

Height Constants

(Defaults: B=1.41,

E=0.15, z=200m,

/z =0.001 min K/m,

N=2400

5 IAE iopts(5) Use Default Wind Field 0 

Variables (Defaults:

Vertically Averaged

Winds used from Ground

to Mixing Height,

Vertically Averaged

Winds used from Mixing

Height to 700 mb, &

Scan Radius for Wind

Field Interpolation

RADIUS=99.0km)

6 IAE iopts(6) Use Default Surface 0 

Roughness Lengths

(Determined from Land

Use Categories)

7 IAE iopts(7) Use Default Heat Flux 0 

Estimates (Can not be

changed)
A-17

8 IAE iopts(8) Use Default Radiation 0 

Reduction Factors

(Defaults: 1.0, 0.91,

0.84, 0.79, 0.75,

0.72, 0.68, 0.62,

0.53, 0.41, 0.23

9 IAE iopts(9) Use Default Heat Flux 0 

Constants (Default:

RADC=0.3)

10 IAE iopts(10) If iopts(10)=1, 0 or 1 as

starting date of run appropriate

is not the beginning

of the meteorological

file, else set

iopts(10)=0

Card Groups 7-14 - NEW VALUES TO REPLACE DEFAULT PARAMETERS

The default options are recommended for all regulatory uses of the

MESOPUFF-II modeling system. If the defaults are used, these card groups

do not need to be included. See the user's manual for further

information.

Card Group 15 - SURFACE STATION DATA. 'nssta' cards - one for each

CD144/TD9657 surface station

Columns Typ Variable Description Recommended Value *

 Name

1-5 IAE idcd Surface station ID for As Appropriate

CD144 data (5 digits)

6-15 RAE xscoor X-coordinate of As Appropriate

station (in grid

units)

16-25 RAE yscoor Y-coordinate of As Appropriate

station (in grid

units)

26-35 RAE slat Station latitude As Appropriate

(decimal degrees)

36-45 RAE slong Station longitude As Appropriate

(decimal degrees)

46-50 RAE szone Station time zone As Appropriate

(5=EST 6=CST 7=MST

8=PST)

51-55 IAE isunit Logical unit number of As Appropriate

CD144 surface data

56-65 IAE idprcp Surface station ID for TD9657 data not

TD9657 data (6 digits) implemented in this

version
A-18

66-70 IAE ipunit Logical unit number of 999

TD9657 data TD9657 data not

(ipunit=999 if TD9657 implemented in this

data is not available version

for this station)

71-75 R zmsurf Wind measurement As Appropriate

height at station idcd

(m)

76-80 R z0surf Surface Roughness As Appropriate

Length (m) for surface

station site

Card Group 16 - RAWINSONDE STATION DATA. 'nusta' cards - one for each

TDF5600 or TDF6201 rawinsonde station.

Columns Typ Variable Description Recommended Value *

 Name

1-5 IAE idtd Rawinsonde station As Appropriate

identification number

(5 digits)

6-15 RAE xucoor X-coordinate of As Appropriate

station (in grid

units)

16-25 RAE yucoor Y-coordinate of As Appropriate

station (in grid

units)

26-35 RAE ulat Station latitude As Appropriate

(decimal degrees)

36-45 RAE ulong Station longitude As Appropriate

(decimal degrees)

46-50 RAE uzone Station time zone As Appropriate

(5=EST 6=CST 7=MST

8=PST)

51-55 IAE iuunit Logical unit of As Appropriate

processed TDF5600 or

TDF6201 data
A-19

Card Group 17 - PRECIPITATION STATION DATA. 'npsta' cards - one for

each precipitation station.

Columns Typ Variable Description Recommended Value *

 Name

1-5 IAE idp Precipitation station As Appropriate

IDs

6-15 RAE xpcoor X-coordinate (grid As Appropriate

units)

16-25 RAE ypcoor Y-coordinate (grid As Appropriate

units)

The codes under type correspond to the following: *

I Integer Variable

IA Integer Array

IAE Integer Array Element

R Real Variable

RA Real Array

RAE Real Array Element

C Character Variable

CA Character Array

L Logical Variable

MESOPUFF-II Input Fields

The version of the MESOPUFF-II described below contains

some enhancements from earlier versions. These allow for the

initialization of runs from the results of previous runs and

the output of deposition calculations, both wet and dry. 

Some modifications to the modeling code were implemented

to make some of the default values consistent with those

recommended here, and to make some of the calculations, such as

plume rise, consistent with the methods implemented in EPA's

preferred models. 

Card Group 1 - TITLE

Columns Typ Variable Description Recommended Value *

 Name

1-80 CA title(20) 80 Character Title Appropriate Choice
A-20

Card Group 2 - GENERAL RUN INFORMATION

Columns Typ Variable Description Recommended Value *

 Name

1-5 I nsyr Two digit year of run As Appropriate

6-10 I nsday Starting Julian day As Appropriate

11-15 I nshr Starting Hour (00-23)

16-20 I nadvts Number of hours in run As Appropriate

21-25 I npts # point sources (20)

26-30 I nareas # area sources (5)

31-35 I nrec # non-gridded (180)

receptors

36-40 I npec # of chemical species 2 if SO Source

to model (=1 SO | =2 5 if SO and NO 2

SO & SO | =3 SO , source 24 2

=

SO , & NO | =5 SO , =

4X 2

SO , NO , HNO , & NO = 

4X 3 3

2

2 X

41-45 I icont Continuation run As Appropriate

(0=no, 1=yes)

Card Group 3 - COMPUTATIONAL VARIABLES

Columns Typ Variable Description Recommended Value *

 Name

1-5 I iavg Concentration 1

averaging time (hours)

6-10 I npuf Puff Release Rate 4

(puffs/hour)

11-15 I nsamad Minimum Sampling Rate 2

(samples/hour)

16-20 L lvsamp Variable Sampling T

Option (T or F)

21-25 R wsamp Reference wind speed 2.

for variable sampling

26-30 L lsgrid Calculate gridded As Appropriate

concentrations at (T or F)

sampling grid points

31-35 R agemin Minimum age of puffs 900.

to sample (seconds)

Card Group 4 - GRID INFORMATION

Columns Typ Variable Description Recommended Value *

 Name

1-5 I iastar Element # of the (1iastarimax,

meteorological grid where imax=met grid

defining the beginning size from MESOPAC)
A-21

6-10 I iastop Element # of the (1iastopimax)

meteorological grid

defining the end of

the computational grid

in the X-direction

11-15 I jastar Element # of the (1jastarjmax,

meteorological grid where jmax=met grid

defining the beginning size from MESOPAC)

of the computational

grid in the

Y-direction

16-20 I jastop Element # of the (jastarjastopjmax)

meteorological grid

defining the end of

the computational grid

in the Y-direction

21-25 I isastr Element # of the (1isastriastar)

meteorological grid

defining the beginning

of the sampling grid

in the X-direction

26-30 I isastp Element # of the (isastr isastp 

meteorological grid iastop)

defining the end of

the sampling grid in

the X-direction

31-35 I jsastr Element # of the (1jsastrjastar)

meteorological grid

defining the beginning

of the sampling grid

in the Y-direction

36-40 I jsastp Element # of the (jsastr jsastp 

meteorological grid jastop)

defining the end of

the sampling grid in

the Y-direction

41-45 I meshdn Sampling grid spacing The sample grid

factor spacing  dgrid/meshdn, where

dgrid = met grid

spacing (m)
A-22

Card Group 5 - TECHNICAL OPTIONS

Columns Typ Variable Description Recommended Value *

 Name

1-5 L lgauss Vertical Distribution T

control (F=uniform,

T=Gaussian)

6-10 L lchem Chemical T

transformation control

11-15 L ldry Dry deposition control T

16-20 L lwet Wet removal control T

21-25 L l3vl Dry removal from T

surface layer (T) or

throughout mixed layer

(F)

Card Group 6 - OUTPUT OPTIONS

Columns Typ Variable Description Recommended Value *

 Name

1-5 L lsave Disk/tape output Generally true (T)

(allows postanalysis)

6-10 L lprint Print concentrations Generally false (F)

(will usually want

some other averaging

times, so will use

results from postanalysis)

11-15 I iprint Print interval in (Used only if

hours of lprint=T)

concentrations

16-20 L lbd Print puff data (puff Generally false (F)

height, 	y z , 	 , (can produce

location, voluminous output)

transformation rate,

etc.

21-25 I nn1 Time step to begin Generally 0

printing puff data (if lbd=T, then

1 nn1 nadvts)

26-30 I nn2 Time step to stop Generally 0

printing puff data (if lbd=T, then

nn1 nn2 nadvts)

31-35 L lwetg Wet flux at gridded T if gridded

receptors receptors used

36-40 L lwetng Wet flux at non- T if non-gridded

gridded receptors receptors used

41-45 L ldryg Dry flux at gridded T if gridded

receptors receptors used
A-23

46-50 L ldryng Dry flux at non- T if non-gridded

gridded receptors receptors used

51-55 L lsavef Save wet/dry fluxes T (allows for postprocessing)

56-60 L lprflx Print wet/dry fluxes F (can create

voluminous output)

61-65 I ires Save results for 0=no, 1=yes

restart? Generally 1

66-70 I iint Save results every 9999 saves only last

'iint' hours and the hour for restart

last hour of the run

Card Group 7

Columns Typ Variable Description Recommended Value *

 Name

1 IAE iopts(1) Use default dispersion 0

parameters (a , b , a , yyz

b , a as defined by z zt

Turner and Heffter, 

T =10000 m , jsup=5

(T reset in code from m

original default value

of 100000)

2 IAE iopts(2) Use default vertical 0

diffusivity constants

k =0.01, k =0.10 1 2

3 IAE iopts(3) Use default SO canopy 0 2

resistance

4 IAE iopts(4) Use default dry 0

deposition parameters

5 IAE iopts(5) Use default wet 0

removal parameters

6 IAE iopts(6) Use default chemical 0

transformation methods

Card Groups 8-13 - NEW VALUES TO REPLACE DEFAULT PARAMETERS

The default options are recommended for all regulatory uses of the

MESOPUFF-II modeling system. If the defaults are used, these card groups

do not need to be included. See the user's manual for further

information.
A-24

Card Group 14 - POINT SOURCE DATA. 'npts' cards required - one for each

point source.

Columns Typ Variable Description Recommended Value *

 Name

1-5 RAE xstak X-coordinate of point As Appropriate

source (in

meteorological grid

units)

6-10 RAE ystak Y-coordinate of point As Appropriate

source (in met grid

units)

11-15 RAE htstak Stack height (m) As Appropriate

16-20 R d Stack diameter (m) As Appropriate

21-25 R w Stack exit velocity As Appropriate

(m/s)

26-30 R tstak Stack gas temperature As Appropriate

(K)

31-80 RAE emis(1-5) Emission rates (g/s) As Appropriate

for SO , SO , NO , 24X

=

HNO , & NO 3 3



Card Group 15 - AREA SOURCE DATA. 'nareas' cards required - one for

each area source.

Columns Typ Variable Description Recommended Value *

 Name

1-5 RAE xar X-coordinate of area As Appropriate

source center (in

meteorological grid

units)

6-10 RAE yar Y-coordinate of area As Appropriate

source center (in met

grid units)

11-15 RAE htar Effective height of As Appropriate

area source (m)

16-20 RAE sigyar Initial 	y (m) of area As Appropriate

source emissions

21-25 RAE sigzar Initial 	z (m) of area As Appropriate

source emissions

26-75 RAE emisar Emission rates (g/s) As Appropriate

(1-5) for SO , SO , NO , 24X

=

HNO , & NO 3 3


A-25

Card Group 16 - NON-GRIDDED RECEPTOR COORDINATES. 'nrec' cards

required - one for each non-gridded receptor

Columns Typ Variable Description Recommended Value *

 Name

1-10 RAE xrec X-coordinate of non- As Appropriate

gridded receptor (in

meteorological grid

units)

11-20 RAE yrec Y-coordinate of non- As Appropriate

gridded receptor (in

met grid units)

The codes under type correspond to the following: *

I Integer Variable

IA Integer Array

IAE Integer Array Element

R Real Variable

RA Real Array

RAE Real Array Element

C Character Variable

CA Character Array

L Logical Variable
Appendix B

Method for Calculating Regional

Visibility Impairment
B-1

METHOD FOR CALCULATING REGIONAL VISIBILITY

IMPAIRMENT

The primary sources of anthropogenically induced, regional

visibility degradation (also referred to as regional haze),

measured as light extinction, are fine particles (diameters 

2.5 μm) in the atmosphere. In the eastern U.S., these

anthropogenic particles are composed primarily of sulfate (SO )=

4

compounds, organic compounds, and to a much lesser extent,

nitrate (NO ) compounds. These are important constituents in 

3

other areas of the U.S. as well; their relative importance,

however, changes. For example, in some areas of the Pacific

Northwest, organic aerosols are as, or more, important than SO=

4

aerosols. In some parts of Southern California, NO aerosols 

3

are the dominant specie. When examining individual source's or

groups of sources' impacts on regional visibility degradation,

primary emissions of fine particulate should also be

considered. 

The generally observed sulfate compound is ammonium

sulfate {(NH ) SO }, although ammonium bisulfate and un- 42 4

neutralized sulfuric acid particles have also been measured. 

Particles composed of nitrate compounds usually take the form

of ammonium nitrate {NH NO }. These compounds are generally not 4 3

directly emitted from air pollutant sources, but are formed

through a series of chemical reactions in the atmosphere. The

air pollutants, which contribute to the formation of these

particles, are gaseous emissions of oxides of sulfur and

nitrogen (SO and NO ), which eventually oxidize to form SO and X X 4

=

nitric acid (HNO ), as well as other compounds, and 3

utlimately react with natural and anthropogenic emissions of

ammonia. The formation of NH NO is dependent on the 4 3

concentrations of ammonia gas (NH ) and nitric acid (HNO ) as 3 3

well as the concentration of SO . SO competes with HNO for = =

44 3

the available NH . Thus, in the presence of both SO and HNO , 3 4 3

=

(NH ) SO will be formed preferentially to NH NO . Essentially, 42 4 4 3

NH NO will only be formed when there is an excess of NH 4 3 3

available, relative to SO . The MESOPUFF-II modeling system =

4

accounts for the balance between SO , HNO and NH . Therefore, =

43 3

emissions of both SO and NO should be modeled in the same run 2 X

to account for this balance. This balance will not be 

accounted for if Level I methods are used; the contribution of

NO to visibility degradation may be overestimated in Level I 

3

analyses, but this is consistent with the rationale for Level I.

As noted above, fine particles are the major contributor

to anthropogenically produced visibility degradation, and

sulfates and organics constitute the highest contributions to

measured fine particle concentrations in most areas of the

country. Organic aerosols are generally considered to be 
B-2

secondary products of chemical reactions in the atmosphere; the

processes which lead to their formation are not well

understood. The sources of organic aerosols can be both

natural and anthropogenic. Current modeling and analysis

techniques are inadequate for providing an estimate of organic

aerosols.

Thus, for the purposes of calculating regional visibility

degradation due to specific sources of air pollution, the

primary focus will be on the contribution to light extinction

of fine particles of sulfate compounds and nitrate compounds,

expressed as (NH ) SO and NH NO . Once these particles are 42 4 4 3

formed, however, their size can change, and thus their light

scattering efficiency, due to changes in the relative humidity

of the atmosphere. Therefore, in order to adequately account

for the contribution to light extinction of either (NH ) SO or 42 4

NH NO the mass of these constituents and the relative humidity 4 3

of the atmosphere in which these particles reside must be

known. The calculations of the extinction due to primary fine

particulates are assumed to be non-hygroscopic.

Method

1. Apply an appropriate air quality model to obtain hourly

concentrations of SO and/or NO and/or primary fine = 

4 3

particulate.

a. If using MESOPUFF-II concentrations of SO and NO are = 

4 3

obtained as direct model output (refer to

Appendix A).

1) To obtain primary fine particulate

concentrations, The MESOPUFF-II should be run as

an independent run from the SO and NO run, = 

4 3

assuming all of the fine particulate emissions

are SO emissions and that they are the only =

4

emissions in that run, the chemistry options

should be turned off, and the deposition options

should be turned on. The other options should

be set as described in Appendix A.

b. If running a steady-state model, use the methods,

outlined in Inset 1 of the main body of the report,

to convert SO and NO to SO and NO . The primary X X4 3

= 

fine particulate emissions can be directly modeled.

2. As noted above, it is assumed that the compounds of

concern are (NH ) SO and NH NO and primary fine 42 4 4 3

particulate. Therefore the mass concentrations of SO and =

4

NO must be corrected for the presence of NH . (The  +

3 4

primary fine particulate is modeled as SO , as a surrogate =

4

in MESOPUFF-II; this should not be corrected for NH .) 4

+

a. Multiply the mass concentration of SO by 1.375 to =

4

obtain (NH ) SO 42 4
bext.s0.003[part]f(RH)

where

bext.s  The extinction coefficient due to particle scattering (km1

)

0.003  a nominal dry scattering efficiency

[part]  The concentration of (NH4)2SO4 or NH4NO3 in μg/m3

or of primary particulate expressed as SO

4 (μg/m3

)

f(RH)  The RH correction factor (see figure B1)

B-3

b. Multiply the mass concentration of NO by 1.29 to 

3

obtain NH NO 1. Obtain the hourly values of 4 3

relative humidity, appropriate

for each receptor and

corresponding to the hourly

concentrations calculated in step

1.

c. These can be obtained from the MESOPUFF-II

meteorological files.

d. If hourly relative humidity values are not available,

assume that the relative humidity is 95%.

e. Obtain the relative humidity correction factor

(f(RH)) from Figure B-1 (see also Table B-1).

3. Calculate the extinction based on the following equation.

 (The dry efficiency is a consensus value based on Trijonis et

al. (1987) and the relative humidity correction factor is based

on Tang et al. (1981).)

a. It is only appropriate to compute the extinctions

based on hourly values of [part] and relative

humidity. It is not appropriate to use average

values of these quantities.

b. To calculate the extinction due to primary fine

particulate, use the above equation, but set the

relative humidity correction factor (f(RH)) equal to

1.

Example

If one has a sulfate concentration [SO ] of 1.7 μg/m , then = 3

4

this would correspond to an ammonium sulfate concentration

[(NH ) SO ] of 2.34 μg/m . The extinction due to this 42 4

3

concentration would be 0.003×2.34×f(RH) km . From Figure B-1, -1

if the relative humidity is 95%, f(RH) is 11.5. Therefore

b = 0.08 km . ext.s

-1
B-4

Figure B-1 - Correction factor to adjust for the effects of

relative humidity on light extinction calculations (Tang et

al., 1981).
B-5

TABLE B-1. Relative humidity factor values versus humidity

values used to construct Figure B-1.

Relative Relative

Humidity Humidity

Factor

0.0 1

30 1

40 1.2

50 1.35

60 1.65

67 1.95

70 2.3

75 2.6

80 3.5

92 6.5

98 16

References

Tang, I.N., W.T. Wong, and H.R. Munkelwitz. 1981. The

Relative Importance of Atmospheric Sulfates and Nitrates

in Visibility Reduction. Atmospheric Environment,

15(12):2463-2471.

Trijonis, J.C., M. Pitchford, and M. McGown. 1987. 

Preliminary Extinction Budget Results from the RESOLVE

program. In: Visibility Protection Research and Policy

Aspects. P.S. Bhardwaja, ed., Air Pollution Control

Assoc. (Currently Air and Waste Management Assoc.),

Pittsburgh, PA, pp. 872-880.