SC553-37FR
REGIONAL AIR POLLUTION STUDY (RAPS)
EMISSION INVENTORY HANDBOOK
TASK ORDER NO. 37
CONTRACT NO. 68-02-1081
Prepared for
Environmental Protection Agency
Office of Air Quality Planning and Standards
National Air Data Branch
Research Triangle Park
North Carolina 27711
Edited by
Fred E. Littman
Air Monitoring Center
Rockwell International
Newbury Park, California 91320
Science Center
Rockwell International
1049 CAM I NO DOS ftlOS
THOUSAND OAKS, CALIF 91360
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APRIL 1977 AMC7010.T0108H
REGIONAL AIR POLLUTION STUDY
(RAPS)
EMISSION INVENTORY HANDBOOK
SECOND EDITION
TASK ORDER 108
SUBTASK H
CONTRACT NO. 68-02-2093
Prepared for:
Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Prepared by:
F. E. Littman
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RAPS EMISSION INVENTORY HANDBOOK
TABLE OF CONTENTS
VOLUME 1
SECTION A INTRODUCTION
Purpose and Content of Handbook
SECTION B AN OVERVIEW OF THE REGIONAL AIR POLLUTION STUDY
1. RAPS: A Prospectus (Summary)
2. RAPS: Expeditionary Research Program
SECTION C SCOPE OF THE RAPS INVENTORY
1. A Regional Air Pollution Study Preliminary Emission
Inventory
2. RAPS Participants
3. Special Emission Inventories for Field Studies
VOLUME 2
SECTION D POINT SOURCE EMISSIONS
1. RAPS Point Source Methodology and Inventory
2. Emission Source Testing Program
3. Methodology for Inventorying Hydrocarbons
4. Hydrocarbon Emission Inventory
5. Point and Area Source Organic Emissions Inventory
6. Non-Criteria Pollutant Emission Inventory
7. Heat Emission Inventory
8. Sulfur Compounds and Particulate Size Distribution
Inventory
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TABLE OF CONTENTS (continued)
VOLUME 3
SECTION E AREA SOURCE EMISSION
1. The RAPS Grid System
2. Residential and Commercial Area Source Emission Inventory
3. Industrial Area Source Emission Inventory
4. Methodology for the Determination of Line Emission Sources
5. Methodology for Line and Area Emissions from Motor Vehicles
VOLUME 4
SECTION E (continued)
6. An Estimation of River Towboat Air Pollution
7. Airport Emission Inventory Methodology
8. Methodology and Emissions from Rail Operations
9. Methodology for Emissions from Off Highway Mobile Sources
10. Off Highway Mobile Emission Inventory
11. Methodology and Emission Inventory for Fugitive Dust
VOLUME 5
SECTION F RAPS EMISSION INVENTORY DATA HANDLING SYSTEM
Users Manual and System Documentation
SECTION G EVALUATION AND VALIDATION OF RAPS EMISSION MODELS
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TO: Recipients of RAPS Emission Inventory Handbook
As the Regional Air Pollution Study (RAPS) continued, many new reports
were generated. This necessitated an expansion and revision of the ^-volume
Handbook you received in 1975.
The revised Handbook now consists of five binders. We are enclosing
binders No. 4 as well as all currently available reports. Several
additional reports will be sent to you in the near future; they are alre-ady
included in the Table of Contents.
The organization of the Handbook has been changed to reflect more
closely the results of the RAPS study. For this reason we have substituted
newer versions of some reports, as well as eliminated some.
Observe that your original handbook contents were divided into sections
A, B, C, etc., and further divisions into subsection reports were made by
pink separation sheets. We will first proceed through each of the three
original volumes and eliminate outdated information; please do the following:
Please remove:
From Volume 1:
1. The Table of Contents
2. The Introduction which is section A
3. All introductory pages for sections B, C and D
4. Fran Section B:
RAPS Series I Study Plan
RAPS Experimental Design Plan, Task Order #9 of
Contract 68-02-1081
Budget Summary
5. From Section C:
Purpose of inventory
RAPS Participants
RAPS Inventory Users & Uses
Presentation of NEDS Emission Data
6» From Section D: All
From Volume 2:
All introductory pages for sections E, F and G
7. From Section E: All
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8. From Section F:
Survey of Existing Inventory Data
Emission Factor Development (one page)
9. From Section G:
Preliminary Information
Gridding Study (Computer Assisted Area Source Emission
Gridding Procedure)
Highway Area Source Methodology
River Vessel Methodology
From Volume 3
All introductory pages for sections H, I, J, K and L
10. From Section H:
Rail Operations Air Pollution Emission Study Plan
for St. Louis
11. From Section I: All
12. From Section J: All
13. From Section K: All
14. From Section L: All
After removing these materials, please reorganize the remaining reports
in accordance with the new Table of Contents into the binders and add the
enclosed reports in their appropriate places:
VOLUME 1
TABLE OF CONTENTS (new)
SECTION A Introduction (new)
Purpose and Content of Handbook
SECTION B An Overview of the Regional Air Pollution Study
1. RAPS: A Prospectus (Summary) (original from section B)
2. RAPS: Expeditionary Research Program (new)
SECTION C Scope of the RAPS Inventory (new)
1. A Regional Air Pollution Study Preliminary Emission
Inventory (new)
2. RAPS Participants (new)
3. Special Emission Inventories for Field Studies
(original from section C)
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VOLUME 2
SECTION D
Point Source Emissions (new)
1. RAPS Point Source Methodology and Inventory
(original from section F)
2. Emission Source Testing Program (not yet available)
3. Methodology for Inventorying Hydrocarbons (original
from section F)
4. Hydrocarbon Emission Inventory (new)
5. Point and Area Source Organic Emissions Inventory
(not yet available)
6. Non-Criteria Pollutant Emission Inventory (new)
7. Heat Emission Inventory (not yet available)
8. Sulfur Compounds and Particulate Size Distribution
Inventory (new)
VOLUME 3
SECTION E Area Source Emission (new)
1. The RAPS Grid System (new)
2. Residential and Commercial Area Source Emission
Inventory (new)
Industrial Area Source Emission Inventory (new)
3.
4.
5.
Methodology for the Determination of Line Emission
Sources (original from section H)
Methodology for Line and Area Emissions from Motor
Vehicles (not yet available)
VOLUME 4
SECTION E Area Sources (continued) (new one page)
6. An Estimation of River Towboat Air Pollution (new)
7. Airport Emission Inventory Methodology (original
from section 6)
8. Methodology and Emission from Rail Operations
(not yet available)
9. Methodology for Emission from Off Highway Mobile
Sources (original from section G)
10. Off Highway Mobile Emission Inventory (new)
11. Methodology and Emission Inventory for Fugitive Dust
(new)
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VOLUME 5
SECTION F RAPS Emission Inventory Data Handling System
Users Manual and System Documentation (not yet available)
SECTION G Evaluation and Validation of RAPS Emission Models
(not yet available)
When the last of the RAPS reports become available, they will be sent
to you along with the Volume 5 binder.
Also included are new cover sheets for the following reports:
Methodology for Inventorying Hydrocarbons
Methodology for Estimating Emissions from Off-Highway Mobile Sources
Airport Emission Inventory Methodology
Please use these cover sheets because they have EPA report numbers.
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SECTION A: INTRODUCTION
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Purpose and Content of Handbook
The Regional Air Pollution Study (RAPS), which encompasses the St. Louis
Air Quality Control Region (AQCR), is the largest and most comprehensive attempt
to date to obtain a quantitative understanding of urban air pollution. Its aim
is to describe the complex relationships between emissions to the atmosphere,
atmospheric dispersions and transformation processes, and ambient concentra-
tions of pollutants. In addition, numerous corollary studies were contemplated
under the RAPS "umbrella" dealing with land use, transportation planning, health
effects and others.
An accurate, detailed and comprehensive inventory of emissions to the
atmosphere constitutes a basic input to this understanding. The RAPS emission
inventory was planned to provide far more detailed information than has been
available anywhere in the past; its aim was to obtain hourly data for key pol-
lutants based insofar as possible, on measured values. Thus, emission data
are available for a base period of two years, (1975 and 1976) commensurate in
detail and accuracy with data on ambient concentrations and micrometeorological
information gathered by the Regional Air Monitoring Stations (RAMS).
The ultimate value of the RAPS Study will, it is hoped, not be confined
to the St. Louis area, but rather will provide a model for future studies of
this type in other areas. For this reason it was decided to provide complete
documentation of all efforts connected with the assembly of the regional emission
inventory in the hope that the techniques and methodology developed for this
project will be applicable to future studies. The RAPS Emission Inventory
Handbook is the result of this effort.
Air Management Technology Branch
MDAD, OAQPS, EPA
Second Edition, Revised 1977
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SECTION B: AN OVERVIEW OF THE REGIONAL AIR POLLUTION STUDY
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An Overview of the Regional Air Pollution Study
The initial planning document for RAPS was a Prospectus prepared by
Stanford Research Institute for EPA-NERC (1). It consisted of four volumes:
Part I - Summary, Part II - Research Plan, Part III - Research Facility and
Part IV - Management Plan. The Summary is included in this section.
Following the general outlines of the Prospectus, plans were developed
by EPA both for continuous monitoring efforts and for special studies con-
ducted on an expeditionary basis. The former includes the Regional Air
Monitoring Stations (RAMS) network, the Upper Air Sounding Network, and the
RAPS Emission Inventory. The latter are outlined in the "Expeditionary
Research Program", which is also contained in this section.
1. Regional Air Pollution Study: A Prospectus, Stanford Research
Institute, Contract No. 68-02-0207, 1972.
2. RAPS Expeditionary Research Program, Ryckman/Edgerly/Tomlinson and
Associates, St. Louis, EPA-600/3-76-016, 1976.
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Report
January 1972
REGIONAL AIR POLLUTION STUDY:
A PROSPECTUS
Part I — Summary
Prepared for:
THE ENVIRONMENTAL PROTECTION AGENCY
NATIONAL ENVIRONMENTAL RESEARCH CENTER
RESEARCH TRIANGLE PARK, NORTH CAROLINA
CONTRACT 68-02-0207
SRI Project 1365
R. T. H. COLLIS, Director
Atmospheric Sciences Laboratory (Project Director)
DON R. SCHEUCH, Vice President,
Office of Research Operations
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FOREWORD
This Prospectus was prepared by Stanford Research Institute for the
Environmental Protection Agency under Contract No. 68-02-0207. While
this Prospectus has been reviewed by the Environmental Protection Agency
and approved for publication, approval does not signify that the contents
necessarily reflect the views and policies of the Environmental Protec-
tion Agency, nor is it intended to describe the Agency's program.
The complete Prospectus for the Regional Air Pollution Study is pre-
sented in four parts.
Part I Summary
Part II Research Plan
Part III Research Facility
Part IV Management Plan
A table of contents for all parts is provided in each of the four
parts to facilitate the use of the Prospectus.
111
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ACKNOWLEDGMENT
This Prospectus was prepared at the Institute by a project team
representing the full range of disciplines necessary for the comprehensive
analysis of problems of air pollution. Research team members were drawn
from four of the eight Institute Research Divisions, including the fol-
lowing:
Electronic and Radio Sciences
Physical Sciences
Information Science and Engineering
Engineering Systems
Because of the interdisciplinary nature of the effort, the contributions
and research findings of many team members are distributed throughout this
Prospectus rather than concentrated in one or more specific chapters. Ac-
cordingly, contributions are acknowledged below by general areas associ-
ated with the study of air pollution problems.
This Prospectus was prepared under the supervision of R.T.H. Collis,
Project Director. The Project Leader was Elmer Robinson (now of Washing-
ton State University) until 15 January, when Richard B. Bothun, who had
been Deputy Project Leader, succeeded him.
The main contributions were as follows:
• Elmer Robinson—Project leadership and the formulation of the
Research Plan
• Richard B. Bothun—Project leadership and administrative man-
agement and the formulation of the Management Plan.
Technically, the principal contributions were:
• Richard B. Bothun—Management, scheduling, costing, planning
• Leonard A. Cavanagh—Air quality instruments, atmospheric
chemistry
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• Ronald T. H. Collis—Meteorology, remote sensing, research
planning
• Walter F. Dabberdt—Transport and diffusion modeling, mete-
orology, instrumentation
• Paul A. Davis—Solar radiation, tracer studies
• Roy M. Endlich—Meteorological models, satellite systems
• James L. Mackin—Helicopter and aircraft systems
• Elmer Robinson—Meteorology, instrumentation, atmospheric
chemistry, research planning
• Sylvin Rubin—Data processing systems
• Konrad T. Semrau--Source inventory and emissions
• Elmer B. Shapiro—Communication systems
• James H. Smith—Atmospheric chemical transformation processes
• Eldon J. Wiegman--Synoptic climatology
Valuable contributions were made in the latter stages of the project
by Dr. W. A. Perkins and Mr. J. S. Sandberg, consultants.
The Institute wishes to express its appreciation for the assistance
and provision of information by many staff members of the Environmental
Protection Agency, especially Charles R. Hosier, Contracting Officer's
Technical Representative; Dr. Warren B. Johnson, Jr., Chief, Model Devel-
opment Branch; Robert A. McCormick, Director, Division of Meteorology;
and Dr. A. P. Altshuller, Director, Division of Chemistry and Physics.
Additionally, the constructive criticism and comment provided by
members of the Meteorology Advisory Committee of the Environmental Pro-
tection Agency during the preparation of the Prospectus were of signifi-
cant value, and our indebtedness is hereby acknowledged.
vi
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CONTENTS
PART I - SUMMARY
FOREWORD iii
ACKNOWLEDGMENT v
I THE BASIC PREMISE 1
II SCIENTIFIC AIR QUALITY MANAGEMENT 3
The Basic Tool—The Mathematical Model 3
The Processes To Be Modeled 3
Accuracy 4
Current Limitations of Modeling 4
Steps To Improve Models 5
The Regional Scale 7
III THE REGIONAL AIR POLLUTION STUDY (RAPS) 9
Concept 9
Purpose 10
Organization 10
Objectives 14
IV SITE SELECTION 17
V THE RESEARCH PLAN 19
Introduction 19
Model Evaluation and Verification Program 21
Meteorological Factors 24
Pollutant Source Estimates . 25
Air Quality Measurements 25
Atmospheric, Chemical, and Biological Processes 26
Human Social and Economic Factors 28
RAPS Technology Transfer 28
vn
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CONTENTS
- V Continued
Schedules and Task Specifications for the Research Plan . . 30
Introduction 30
100 Model Verification 45
101 Boundary Layer Meteorology Program 46
102 Emission Inventory 46
103 Air Quality Measurements 47
104 Model Calculation and Verification 47
200 Atmospheric, Chemical, and Biological Processes ... 48
201 Gaseous Chemical Processes 49
202 Atmospheric Aerosol Processes 49
203 Other Pollutant Related Atmospheric Processes .... 51
204 Atmospheric Scavenging by Precipitation 51
205 Air Pollutant Scavenging by the Biosphere 52
206 Atmospheric Processes 53
300 Human, Social, and Economic Factors 54
301 Human and Social Factors 54
302 Economic Factors 55
400 Transfer of RAPS Technology for Control Agency
Applications and the Formulation of Control Strategies . 55
401 Source Inventory Procedures 57
402 Atmospheric Monitoring 57
403 Data Handling 58
404 Modeling Technology 59
405 Other Significant Factors in Control Strategy
Formulation 60
VI THE FACILITY 63
Rationale 63
Basic Operations 63
Basis for Monitoring Network 63
The St. Louis Regional Monitoring Network 66
viii
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CONTENTS
VII MANAGEMENT AND SCHEDULING 71
Introduction 71
Facility Activation Schedule 71
Permanent Management and Staffing 73
VIII COST SUMMARY 77
Permanent Facilities and Staff 77
Helicopter and Mixing Layer Observational Program 80
Research Plan 80
Personnel 81
Instrumentation and Equipment 83
Operations 85
Total Cost of Research Plan 86
Total Costs of RAPS 86
ix
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ILLUSTRATIONS
PART I - SUMMARY
1 Steps in the Air Quality Model
2 RAPS Role in the Overall EPA Function of Formulating
Control Strategies ..." 11
3 General Scheme of the RAPS Research Tasks 13
4 Model Verification Program 22
5 Summary Organization of the Regional Study 74
xix
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TABLES
PART I - SUMMARY
1 Classification of the Regional Study Instrument Stations . . 67
2 Estimated Initial Costs of the St. Louis Facility by
Principal Installation 77
3 Estimated Initial Costs of the St. Louis Facility by
System Components 78
4 Estimated Total Annual Operating Costs of the St. Louis
Facility and Permanent Staff 79
5 Estimated Initial and Operating Costs During Implementation
of the St. Louis Facility 79
6 Estimated Costs of Helicopter Operation by Quarter 80
7 Summary of Personnel Requirements for the Research Plan . . 81
8 Estimated Cost of Personnel Required by the Research Plan . 83
9 Estimated Costs of Specialized Equipment for the Research
Plan 84
10 Estimated Operational Costs of the METRAC System 85
11 Total Estimated Costs of the Research Plan 87
12 Estimated Total Quarterly Costs of the Regional Study ... 88
xx i
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THE BASIC PREMISE
Both implicitly and explicitly, the Air Quality Act as Amended (1970)
accepts the premise that air quality improvement can be planned scientif-
ically. Specifically, it presupposes that emission standards can be set
by reference to the desired ambient air quality standards, taking into
account the manner in which the combined products of various sources in
any particular area are dispersed or concentrated by physical, chemical,
or meteorological processes. Following its announced policy of emphasis
on enforcement, the Environmental Protection Agency is aiding state and
local agencies to develop strategies to ensure compliance with such air
quality standards as have been set. EPA is also following a policy to
extend control procedures to achieve improved air quality standards as
they are specified.
This overall concept will fail or succeed to the extent that the
basic premise is true. Can air quality improvement be planned scientif-
ically, at least to a useful degree, with existing knowledge and capa-
bilities? If not, what is lacking? By what time can any shortcomings
be rectified? The procedures, such as developing Implementation Plans
called for under the Air Quality Act (or of filing Environmental Impact
Statements under the National Environmental Policy Act), take for granted
that existing knowledge and capability are at least minimally adequate
for planning. Those who challenge this belief feel that all that can
usefully be done at the present time and in the near future is to reduce
all emissions to the minimum possible within the state of the art, re-
gardless of any postulated requirements to meet what they may consider
to be artificial air quality standards.
In either case, it is clear that room exists for considerable im-
provement in the knowledge and capability that are necessary for confi-
dently relating cause and effect both between emissions and air quality
and between control strategies and air quality.
There also can be no doubt as to the value of such a capability.
Without such a basis, intelligent direction cannot be given to improving
the state of the art of emission control; nor can correct decisions be
made between alternatives in control strategies that do not depend on
emission suppression and in allocating priorities and assessing cost-
effectiveness in either case.
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II SCIENTIFIC AIR QUALITY MANAGEMENT
The Basic Tool—The Mathematical Model
The central element of the concept of scientific air quality manage-
ment is the mathematical simulation model. With this tool, the result
of certain actions or inaction can be predicted. If the models are
accurate and the data input correctly known or forecast, their output
will show the effects of change, whether it occurs without specific in-
tervention or as the result of such intervention. The accuracy of the
models will depend on the degree to which the processes used are under-
stood and can be described within the constraints of the mathematical
technique. Accuracy will also reflect the inherent uncertainties of
the statistical approach. In addition, since the atmosphere is the
medium in which pollutants are dispersed, the only too well known dif-
ficulties in describing and predicting meteorological conditions must
be recognized.
The Processes to be Modeled
However, before a simulation model can be formulated, it is neces-
sary that the physical, chemical, and meteorological processes that are
entailed be understood. In the very simplest form, models will consider
only the gross relationships between emissions and air quality and will
be correspondingly imprecise. More detailed and comprehensive models
can be developed for certain processes, but they may be limited in their
application by difficulties in providing adequate input data. The latter,
particularly in regard to emissions, are often difficult to obtain at any
price, and certainly their collection poses critical questions as to
costs. Such factors become pertinent when the usefulness or value of
any given model is considered. Will more timely and voluminous input
data improve the accuracy of the models' output? Will more highly devel-
oped formulations of complex physical, chemical or meteorological pro-
cesses more accurately describe and predict what is happening in the
real world? How accurate are such descriptions and predictions anyway?
These questions must be faced and satisfactorily answered if the models
are really to play a key role in air quality management.
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Accuracy
A most important question is the accuracy of the models. If con-
trol strategies with far-reaching economic and social effects are to be
imposed on the basis of a simulation model, it is more than desirable
that the model's output be accurate and sufficiently precise to enable
the effects of the strategies to be evaluated. In dealing with control
and abatement procedures that require large increases of investment in
plant or of costs of operation, especially where the relationship between
such procedures and the hoped for improvement in air quality is not
linearly related, it is not enough to know that the model is accurate
in general terms. It must be capable of providing more precise informa-
tion on the benefits in air quality to be derived from the costs of the
control and abatement procedures proposed. Without such a capability,
the procedures could be very uneconomical and absorb effort and resources
that could be used more profitably elsewhere. These aspects become in-
creasingly important as the more obvious control and abatement proce-
dures are adopted and attention is turned to the more marginally produc-
tive strategies.
Above all, in the use of modeling techniques in air quality manage-
ment, the need is most urgent to ensure that all concerned have confi-
dence in the approach. With large financial involvements, with the
livelihood of whole communities at stake, with the tremendous pressures
on limited resources to plan, implement, and enforce control strategies,
it is imperative that decisions are not only wisely taken but also that
they are readily seen and recognized as being wisely taken. Sound model-
ing capability serves all aspects of this obligation and provides the
evidence on which sound enforcement strategies can be sustained.
Current Limitations of Modeling
As noted above, the position regarding air quality modeling at the
present time is far from satisfactory. A wide diversity of such models
exists or is being developed, and these models cover a range from quite
limited problems—such as diffusion from a single point source—to the
extended scale covering large urban complexes. Only in a few cases
that are concerned with fairly simple problems are the models well
tried and sufficiently verified to warrant confidence in their use.
There are many reasons for this state of affairs. The major reason,
quite simply, is that solving the problem has just not been undertaken
on an adequate scale. Progress made with the limited funds and resources
available to date has lagged far behind the need, which itself has been
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recognized as urgent only in the Last year or so. As indicated, .the
development and verification of suitable models are very difficult,
especially on a major scale, and depend strongly on the availability
of adequate data on which to base the formulation and to develop its
application. It is possibly even more difficult to assess its capability
and measure its precision accurately, especially where long time periods
are needed to provide an adequate basis of comparison.
Steps to Improve Models
The steps taken in conceiving, developing, and proving an air
quality model are shown diagramatically in Figure 1. To the present,
most progress has been made in understanding the processes and in for-
mulating the model, even though the item in the diagram—"Observations
of Various Conditions"—has been limited if not thoroughly inadequate.
For the latter reasons, operation of the model both for description
and prediction has generally been constrained, and the comparison and
evaluation of its results against observations have frequently been
inconclusive or inadequate, if indeed they have been attempted at all.
The decisive limitation in the whole process lies in the first
area noted in the diagram—"Observation of Various Conditions." This
area is fundamental to all aspects of the problem, and its shortcomings
cripple every subsequent endeavor. Thus, many existing models are
based on the very limited data that have been acquired routinely for
quite different purposes, e.g., airway weather forecasts, and are woe-
fully inadequate in both extent and spatial and temporal resolution
for the development and verification of models or even, in some cases,
for developing an adequate understanding of the processes involved.
The limitation of basic data results from the fact that the collec-
tion of such data on a sufficiently continuous scale in space and time
and over a large enough area for long enough periods is extremely
costly, even if suitable sensing devices exist. (The situation is
much worse when there is no practical or reasonably economical way to
make even the individual observations—as is the case with certain
pollutants.) Even when attempts have been made in certain circum-
stances to set up special networks of observational facilities and
carry out intensive data collection programs, the results have often
left much to be desired and reflect the shortcuts that have been taken
because of cost in instrumentation and in collecting and handling the
data. Particularly in the past, when much reliance had to he placed
on manual procedures of data collection and analysis, the data ac-
quired under such programs were limited and somewhat piece .il—serving
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OBSERVATION
OF VARIOUS
CONDITIONS
UNDERSTANDING
THE PROCESSES
INVOLVED
t
FORMULATION
OF MODEL
t t
I
|
I
Feedback
for
Improvement
OPERATION OF
MODEL
PREDICTION
COMPARISON
AND
EVALUATION
DESCRIPTION
SA-1365-49
FIGURE 1 STEPS IN THE AIR QUALITY MODEL
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only the immediate purpose for which they were collected—and remaining
of little value for use in other investigations.
Even with the emergence of relatively moderately priced automatic
data processing facilities, the acquisition of basic data remains a con-
siderable and costly undertaking, especially on a major and extended
scale. But to those considering the major and pressing problems of air
quality management, it has become increasingly clear that little progress
can be made in the solution of such problems unless they are treated on
a major and extended scale. Only by fully recognizing the magnitude of
the technical problems in the way in which current legislation and EPA
policies have recognized the whole question of air quality, can adequate
tools be developed for scientific air quality management.
The Regional Scale
The difficulties of air quality management are apparent in the con-
text of Air Quality Regions, where emission standards within the region
are to be related to air quality standards. For these purposes it is
necessary that at all scales, from the local to the extended regional
scale, there is an adequate understanding of how emission from the sev-
eral sources combine and react to produce concentrations of specific
pollutants throughout the area. This is particularly the case where
the air quality of large rural and suburban areas is greatly affected
by emissions from remote urban centers that may be up to 100 miles away.
Control and abatement procedures must take these indirect effects into
account. To formulate economical and realistic strategies for such
procedures, however, it is necessary also to consider the less direct
aspects of health, economics, land use, and community planning in the
urban/rural complex.
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Ill THE REGIONAL AIR POLLUTION STUDY (RAPS)
Concept
The problem of effectively managing air quality within the framework
of current legislation _on _a regional basis is thus seen to be extensive
and complex. The need to evaluate and improve control strategies already
adopted or to identify new approaches must be considered on this basis.
Realization of this, together with the recognition that much of the exist-
ing understanding is based on limited and piecemeal data, has led to the
concept that it is necessary to make a new large scale integrated attack
on the total problem. By effectively coordinating efforts on a number of
interrelated problems, a combination of resources can be brought to bear
in an economical and productive manner. This concept is particularly
applicable to the problems of air quality management on the regional
scale, where it is necessary to relate the effects of the urban center
on the suburban and rural environs.
Only within such a framework can the necessary basic data on emission
sources, meteorological factors, and other physical and chemical effects
be studied in appropriate relationships and used to improve the basic
understanding and formulation of the appropriate air quality models. Only
on such a basis can the results of such models be evaluated by comparison
with adequate data on the true nature of conditions. Only with such a
background of relevant data can the ramifications of air pollution in the
urban/rural complex be adequately understood in terms of health, economics,
land use, and community planning so that control strategies can be formu-
lated and ordered with maximum effect.
It is the purpose of this Prospectus to identify the separate ele-
ments constituting such an undertaking and to describe how it can be
carried out.
In this Part I—General Summary—the major concept of RAPS is pre-
sented, with an outline description of the Research Plan, the Facility,
and the Management Plan (which includes budgetary information). Subsequent
sections, following the same pattern, provide fully detailed descriptions.
Where appropriate, the rationale for the approach proposed is discussed.
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Purpose
The initial purpose of the Regional Air Pollution Study is to
evaluate and demonstrate how well the effectiveness of air pollution con-
trol strategies on all scales appropriate to air quality within a region
can be assessed and predicted. Its further purpose is to serve as the
basis for developing improved control strategies that can be applied
generally.
Both purposes require the development of a better understanding of
the chemical, physical, and biological processes that are entailed in
determining the concentration of pollutants and the modification of air
quality. They also require a better understanding of certain human,
social, and economic factors that are significant in formulating control
strategies. Above all, however, they require the testing, verification,
evaluation, and improvement of mathematical simulation models that are the
basic tools of scientific air quality management and a knowledge of how
such models can be used the most effectively.
It should be noted that the overall purpose of RAPS is to provide
the basis necessary for the formulation of control strategies rather
than to develop control and abatement procedures as such. Its relation-
ship to the analytical and decision-making processes in formulating con-
trol strategies is illustrated diagramatically in Figure 2.
Organization
Since the organiztion of the RAPS program by design is an integration
and combination of endeavors, it cannot be described briefly. However,
its essential structure follows from the purposes noted. Within this
structure, key tasks with well defined objectives and a series of sub-tasks
that are often interrelated and interdependent can be identified. Some
of the activities constituting these tasks are in fact part of ongoing
programs within the existing research organization of EPA. Following
basic management principles, it is proposed that the work of the overall
study be identified and organized as separate tasks, for each of which the
objectives are specified, the purposes described, and the responsibility
clearly defined. These taks will be arranged in a series of levels that
will reflect the dependence of one activity on another and will establish
the chain of responsibility of the task leaders. This responsibility is
functional rather than administrative and relates to the technical accomp-
lishment of the research task.
10
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r
RAPS
MODELS FOR ASSESSING
AND PREDICTING THE
EFFECTS OF CONTROL
STRATEGIES ON AIR
QUALITY
IMPROVED KNOWLEDGE
OF POLLUTION
TRANSFORMATION
PROCESSES
INFORMATION ON
AND HUMAN FACTORS
AIR QUALITY
STANDARDS
POLICY
*
COMPARATIVE
ANALYSES OF
PROPOSED
STRATEGIES
FORMULATION OF
CONTROL
STRATEGIES
t
OTHER ECONOMIC,
HUMAN, SOCIAL,
LEGAL. AND
POLITICAL FACTORS
SA-1365-50
FIGURE 2 RAPS ROLE IN THE OVERALL EPA FUNCTION OF FORMULATING CONTROL
STRATEGIES
11
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The material to be used in these research tasks will be provided by
the facilities and support elements of RAPS, some of which will have a
general role to play only, while others will be substantially engaged in
specific research tasks. Again. EPA groups outside the RAPS organization
will provide some support and facilities.
The various facility and support elements will be the responsibility
of designated leaders, but here the responsibility is administrative as
well as functional. Thus, the manager, say, of a data acquisition net-
work will be responsible administratively for the performance of the
personnel and equipment assigned for this purpose and for the quality of
the product of the network. Functionally, his responsibility is to pro-
vide data as required for the various research tasks, of one or more of
which he may be the leader. The general scheme is indicated in Figure 3.
The organization of the research tasks and the unctional roles of
the support and facilities elements are set out in the Research Plan.
The organization and the administrative structure of the support and
facilities elements are set out in the Management Plan. In both cases,
only the roles and responsibilities of the participants in relation to
RAPS are considered. Thus, the contribution of other EPA research programs
to the RAPS must be identified in terms of specific functional contribu-
tions as described in the research tasks.
The costs of such, participation by personnel of other EPA research
programs, as would also be the case with facilities and support provided
from outside the RAPS organization, are included in the RAPS budget for the
purposes of this Prospectus (interdepartmental adjustments can readily
be effected by transfer procedures as necessary).
The general data base acquired under RAPS and, subject to availability
after meeting primary responsibilities, the resources and facilities of
RAPS will be available to other research programs of EPA, or for that
matter, to other agencies. Any additional costs incurred would naturally
be a charge on such other programs and budgeted accordingly.
By design, this Prospectus is based on the principle that RAPS should
be an independent, self-sufficient activity of EPA, certainly for planning
purposes. The possibility that other research programs and operational
activities could make valuable contributions has been considered, espe-
cially in the selection of the site. In the data collection and obser-
vational programs, however, such contributions have been considered as
supplementary rather than complementary, otherwise the tasks of specifying
facilities and assessing costs could not have been accomplished at this
time.
12
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in
-------
In any case, although some economies and improvements might result
from integrating the resources and research efforts within RAPS and
with those of other programs planned for the St. Louis area, it is
considered that the scope and importance of the RAPS program require
that it be carried out as a principal endeavor. Any proposals to share
resources with other programs should be carefully evaluated to ensure
that efforts are not deflected from the main goals of RAPS.
In this context, it should be stressed that in the other programs
noted the emphasis is primarily on the scientific aspects of the problems
attacked or on operational problems quite different from those of RAPS.
RAPS is concerned with the development of improved air pollution control
strategies—and this concept must dominate all its research tasks.
Objectives
The overall objectives of the RAPS are to:
(l) Demonstrate and evaluate how well the.effectiveness of air
pollution control strategies may be assessed and predicted
within an air quality region.
(2) Provide a basis for developing improved control strategies.
The specific objectives of the four principal tasks under which the
overall objectives will be accomplished are described below. Each princi-
pal task is divided further into a structure of subordinate tasks as dis-
cussed later in Section V in which are presented the objectives of each
subordinate task, together with details of the problems to be solved,
the approach to be followed, the schedule, budget, and interrelation to
other tasks.
The objectives of the four principal tasks are to:
• Test, verify, and evaluate the capability of mathematical simu-
lation models to describe and predict the transport, diffusion,
and concentration of both inert and reactive pollutants over a
regional area. (100 series*)
These refer to a numerical classification system of tasks within the
Research Plan.
14
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Develop an improved understanding of the chemical, physical, and
biological processes that are entailed in determining the con-
centration (the dispersal) of polltants and the modification of
air quality. (200 series)
Develop a better understanding of factors of significance to the
design of improved control strategies in the urban/rural complex,
including health and economic effects and the role of land use
and community planning. (300 series)
Develop improved technology that can be applied in local and
regional control agency operations, including techniques for
emission inventories, air quality and meteorological measurement,
data handling and analysis, and the objective assessment of
effectiveness. (400 series)
15
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IV SITE SELECTION
The best site for the RAPS program is centered in St. Louis, Missouri.
This selection was based on the need to find a large city lying within the
central United States, which was away from oceans and mountains and which
typified the coal-burning industrial nature of many urban areas yet which
lay in an extended region of suburban or rural country. Some 33 Standard
Metropolitan Statistical Areas (SMSAs) larger than 400,000 population were
considered in terms of the following criteria:
• Surrounding area—This criterion includes measures of the isola-
tion of the SMSA from other SMSAs to gauge interarea pollutant
tendencies, measures of the rural fringe to be anticipated, and
similar location factors. Clear-cut relationships and interactive
mechanisms between the urban and rural areas are essential. The
absence of a surrounding area with a low level of development,
e.g., agricultural,eliminated an area from further consideration,
as did the proximity of large bodies of water.
• Heterogeneous emissions--Several tests were applied for this
criterion, including fuel used, types of industry in the region,
and the current pollution mix. An important specific factor was
the extent of coal usage because of its relation to both sulfur
oxides and particle emissions. A lack of sulfur oxide emissions
eliminated an area.
• Area size--This criterion gave a general measure of the expected
scope and magnitude of the Regional Study for each candidate site.
• Pollution control program—The various candidate areas had con-
siderable variation in their existing control programs. It ap-
peared desirable that the Regional Study be carried out at a site
where the control program is generally well developed. Such a
site would provide a background of data and general experience
that could be used to establish the Regional Study. In addition,
a Central District program could provide initial contacts with
industrial sources for the gathering of source inventory data.
• Historical information—Historical data, including meteorological
and pollution, applicable to a site also varied widely. In
17
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general, a site tended to be more attractive as the quantity of
historical data increases. Care was exercised to distinguish
between quantity and quality.
• Climate—The site should possess a climatic representative of a
large section of the nation or other potential sites. Moreover
the climate should permit experimental work throughout the great-
est possible portion of the year.
The comparison of the characteristics of the 33 candidate areas with
these criteria reduced the group to four most appropriate possible areas.
These are Birmingham, Cincinnati, Pittsburgh, and St. Louis.
A final review of the respective merits of the four candiate sites
led to the following comparative assessment
Criterion
Surrounding area
Heterogeneous emissions
Area size
Pollution control program
Historical information
Climate
Bir-
mingham
Cincin-
nati
Pitts-
burgh
St.
Louis
Fair
Fair
Good
Poor
Poor
Good
Poor
Fair
Good
Good
Good
Fair
Good
Fair
Good
Good
Fair
Fair
Good
Good
Good
Good
Good
Good
On this basis, St. Louis emerged as the obvious prime choice.
18
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V THE RESEARCH PLAN
Introduction
The Research Plan and its rationale are outlined in broad terms, fol-
lowed by a detailed specification of the task structure that will be set
up to accomplish the objectives of the plan.
The scope of research efforts in the field of boundary layer simula-
tion modeling centers in the need to understand, describe, predict, and
ultimately control air quality in the lowermost stratum of the atmosphere.
Simulation modeling provides the necessary link between inferences gleaned
from air quality data obtained at isolated, single-point monitoring sta-
tions and the broad, yet detailed, picture of air quality that is required
over an entire urban region; it also permits assessment of the ramifica-
tions of actual or projected growth (zoning) patterns and emission con-
trol (proportional versus selective) prodecures.
Simulation modeling of the boundary layer refers precisely to phys-
ical or mathematical modeling of the atmospheric planetary boundary layer—
the lowermost stratum of the atmosphere (on the order of 1 km in depth) in
which the effect of surface friction on the wind field is manifested.
From a more practical standpoint, it is also the layer in which atmospheric
pollutants are generally emitted, transported, diffused, and transformed.
Mathematical models include, generally, gradient-transfer, similarity,
Gaussian plume and puff, and statistical formulations, while physical
modeling is done with the aid of wind or water tunnels. The utility of
the mathematical models lies in their ability to describe and parameterize
the physics of the problem over a large region and to predict meteorolog-
ical and air quality changes that may occur either in time (as a result
of the progression or development of weather systems) or from the altera-
tion of emission patterns. Ideally, mathematical models are capable of
achieving arbi-trary degrees of temporal and spatial resolution to solve
specific problems. High resolution, for example, may be necessary when
considering pollutant concentration in urban core areas, whereas relatively
low spatial resolution may be required for the study on the mesoclimatic
scale. Physical models perhaps are less flexible yet are ideal for evalu-
ating the gross features of, for example, pollutant distributions in ex-
tremely homogeneous or complex locations.
19
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It is highly desirable that the RAPS program evaluate the ability of
the more promising models to simulate the atmospheric environment on both
the micro- and mesoscales. In this regard, the models should be evaluated
according to the specific function that they may serve. Specifically,
evaluation programs are recommended for the following three functional
model types: (1) diagnostic, (2) predictive, and (3) climatic. The pri-
mary emphasis at this time should be placed on the diagnostic model types,
because the current or steady-state distribution of pollutants on the meso-
scale must be described before detailed prognostic models can be developed.
Prognostic or predictive models in this sense do not include diagnostic
models, which may be input with anticipated meteorological and emissions
data to simulate an expected condition. Rather, predictive models use
current initial conditions to predict (on the order, say, of one or two
days) meteorological and perhaps emission fields, thereby predicting the
level of air quality for some time in the future. Moreover, emphasis
must be given to the development and evaluation of dynamic climatological
models that have the ability to describe the mesoclimate and changes that
may result from mesoscale urbanization.
Toward achievement of these goals, it is essential that the various
models be evaluated (and refined as appropriate) with "real" data collected
in the field. Such data collection will further one's ability to under-
stand and simulate the lower atmosphere on the mesoscale (on the order of
250 km). But unless one is able to observe the process he is attempting
to describe, simulation modeling may be little more than an esoteric ex-
ercise. A vast amount of effort has been expended in the development of
the various mathematical models and in their subsequent evaluation. In
almost no case have the observed data been obtained on a scale compatible
with the resolution of the model computation. Virtually all meteorological
and air quality data collected on a routine basis for purposes other than
model verification are the result of single point measurements (or, at the
very best, several adjacent points). As such, the observation is a repre-
sentation of very localized conditions and, in perspective, is a measure
of the integrated effect of various scales of motion: micro, meso, and
macro. In many cases, it is the microscale phenomena that predominate
and there can be little surprise at the inability of numerical models to
simulate the observation when the model, in fact, may predict only average
conditions over a broad area, say a one kilometer square or larger. There-
fore, another objective of the program should be the definition of the
spatial variability of ambient air quality, as well as the spatial resolu-
tion of the simulation models. In practice, a feedback between observa-
tions and computations should result whereby the observations define the
appropriate time and space scales that the models need to achieve, and
eventually the models are used to describe spatial and temporal variations
on the basis of a few representative measurements.
20
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Federal air quality guidelines define levels of air quality on several
time scales: hourly, eight-hourly, daily, and annual. On the basis of
results from the observation-'modeling programs, spatial criteria also may
need to be established. It appears equally necessary to define criteria
where the air quality is evaluated in parallel over various lengths (or
areas or volumes) and time scales. In this regard and in consideration
of the requirements of model verification, the data collection program
must also concern itself with the potential of remote (long-path) observa-
tion techniques where a particular contaminant can be measured on the ap-
propriate spatial scale.
In summary, an extensive air monitoring network is required to define
the scope of the problem and to evaluate and refine the mathematical models
that are to simulate the level of air quality over a broad region. As
such, the network should be not only extensive but also flexible so that
it will serve the many purposes of the simulation program. It must be
capable of providing data from the substreet microscale to the regional
mesoscale. The observations will also be used to evaluate these models
on a variety of temporal scales: simulation of existing conditions (diag-
nostic requirement), prediction of short term changes (prognostic require-
ment), and evaluation of potential mesoclimatic alterations (extended
prognosis). (Physical modeling techniques could be used to supplement
the field observation program in urban core areas where the complexities
of building structures may severely limit routine in-situ measurements on
a practical basis, but ,this approach is more appropriate for studying
specific and linked problems).
Model Evaluation and Verification Program (100 Series)
The various simulation models can be evaluated most efficiently by
considering the models in terms of the functions that they may be expected
to serve. For convenience, these functions may be divided into three
parallel types: (1) the time frame and resolution of the model, (2) the
spatial frame and resolution, and (3) the class of contaminants to be con-
sidered. Therefore, the models may be classified according to whether
they simulate the quality of the air in terms of the concentration dis-
tribution of inert, reactive, or particulate contaminants over either
localized or expansive regions for current or forecasted conditions. Ac-
cordingly, there are about a dozen functions that a model or models may
be expected to fulfill; that is, there may be localized, diagnostic models
for inert pollutants that have application in planning and evaluation
studies or regional, prognostic models of reactive contaminants required
for emissions control procedures. Figure 4 illustrates this concept, as
well as the subsequent steps in a model evaluation and verification program,
21
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MODEL FUNCTION
I
2.
3
SCALE.
agnostic
agnostic
mat ic
SPATIAL SCALE
1 I ocal (micro)
2 Regional (meso)
1
1
POLLl
1
2.
3
JTAf
Ine
Hee
Par
'
ELEMENTARY METEOROLOGICAL STRATIFICATION
1. Wind speed—stagnation versus dilution
2. Stationanty—steady-state versus time variant
3. Insolation—strong versus weak
4. Etc
MODEL(S)
INPUT DATA
INITIAL MODEL
EVALUATION
OBSERVATIONS
OF AIR QUALITY
T
I
AIR QUALITY
SIMULATION,
"SIMPLE" MODEL
MODEL
PERFORMANCE
1
r
UNACCEPTABLE
ACCEPTABLE
T
DETAILED MODEL
EVALUATION
ROUTINE, DETAILED
OBSERVATIONS
SPECIAL STUDIES
METEOROLOGY
EMISSIONS
FINAL VERSION
OF MODEL
TRANSFORMATION
PROCESSES
REVISION TO
MODEL
SA-1365-52
FIGURE 4 MODEL VERIFICATION PROGRAM
22
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It may be further desirable to test the various models under a variety of
distinct meteorological conditions. One such stratification could be the
separation of low and moderate-to-high wind speed cases (isolation of
stagnation conditions); other distinctions may be made between near steady-
state and strong advective conditions or strong versus weak insolation.
At this point, the purpose of the simulation will have been defined
and certain forcing physical criteria established; specific models falling
within this framework can then be introduced for evaluation and subsequent
verification. It is strongly recommended that the performance of the
models initially be evaluated against both observed measures of air quality
and the predictions of a simple standard (or reference model or models).
The simple model used by Hanna (1971)* or a relatively simple box or Gaus-
sian formulation (see Chapter III in Part II) should be considered. Quan-
titative, statistical techniques then should be applied to test the model
against the reference and the observations. If the test model cannot show
significant superiority to the reference, it can be safely excluded from
the later, detailed evaluation program. Emphasis should also be placed
on a qualitative assessment of the extent and nature of the required input
data. If a given model performs well but requires input data that may not
be readily available at the present or in the foreseeable future, then
alternative formulations or parameterization may be necessary for further
consideration.
Having initially demonstrated its feasibility, the model should be
evaluated in detail to define both its strong and weak points so that re-
finements may be made. This detailed model evaluation program should in-
clude both fundamental and applied research tasks for the testing of the
basic components of the model, namely, metorological, emissions, and
transformation processes. The applied tasks are, in effect, individual
evaluation programs for the three process areas (or submodels). Submodel
predictions of wind, turbulence, stability, emissions, plume rise, reaction
rates, and so forth would be compared with routine observations from the
research network (which may not be strictly routine for nonresearch pro-
grams). The sensitivity of each model and submodel should be evaluated
with regard to the response of the output (model prediction) to variations
of the input parameters. In addition to these direct or applied tasks,
there is an acknowledged need for complementary research programs of a
more fundamental nature. These would be directed toward providing a
See Hanna, S. R., "Simple Methods of Calculating Dispersion from Urban
Area Sources," paper presented at the Conference on Air Polluation
Meteorology, Raleigh, N.C., Sponsored by Amer. Met. Soc., April 1971.
23
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better understanding of the physical nature of the various physical pro-
cesses so that the models may be revised accordingly and in conjunction
with the requirements resulting from the applied program. Many of these
fundamental programs can be anticipated; however, others, will result
only as output requirements of the initial, detailed evaluations. The
fundamental programs or special studies are given in detail in Part II.
Meteorological Factors
The transport and diffusion of pollutants in the atmospheric boundary
layer is a basic aspect. The RAPS program will include a comprehensive
network of observing stations at the surface, throughout the regional
area, that will provide the data from which trajectories and stream line
flow patterns can be calculated and used to determine the transport of
pollutants. Practical models usually will be based on less sophisticated
wind transport data, and thus the availability of this detailed informa-
tion will permit a candidate meteorological submodel to be evaluated in-
dependently and, if necessary, modified and retested.
Since total atmospheric transport of air pollutants is not always
adequately depicted by surface meteorological measurements, it will be
necessary to define the important transport weather parameters above
the surface to altitudes between 1000 m and 1500 m. The RAPS program
proposes to do this by.extensive use of instrumented aircraft with supple-
mental use of balloon sounding systems.
Regionwide measurements are not totally adequate to interpret the
meteorological conditions within the meteorological boundary layer and
several specific research studies are incorporated in the RAPS program
to define in more detail a number of important boundary layer transport
processes. It is generally recognized that mixing and transport conditions
depend strongly on the nature of the underlying surface and that built-up
urban areas and relatively smooth rural and agricultural areas can affect
an air mass differently as it moves across these areas of different rough-
ness. To a certain degree, these effects can be predicted, but how to
account for changing conditions within an air mass as it moves from one
roughness regime to another is relatively unknown.
Specific experiments using specially dispersed tracer materials,
constant-altitude balloons, and special aircraft instrumentation will be
carried out to develop techniques for including changes in surface rough-
ness in the meteorological boundary layer submodel. Within an urban area,
building temperature, as well as the changes in building height and den-
sity, affect the meteorological transport processes, and thus special care
24
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will be given to experiments that will greatly improve understanding of the
interactions between urban surface characteristics and the pollutant trans-
port processes.
The RAPS program will also include studies of meteorological disper-
sion of effluents from specific single sources to characterize the inter-
actions between these effluents and environmental factors. One very sig-
nificant study topic is that entailing the dispersion of pollutants from
tall stacks across very rough areas characteristic of an urban area. From
this type of study, a better understanding can be obtained of pollutant
transport in an urban area and this information can be used as a "feedback
loop" in the evaluation and further development of diffusion models.
Pollutant Source Estimates
Pollutant emissions from both moving and stationary sources are an
obvious ingredient of any air pollution situation. The RAPS program must
have as an integral part a detailed, ongoing air pollutant inventory pro-
gram. This program must include, in addition to data on source emission
rates, the development of techniques by which these emission rates can be
estimated for specific periods of time. These time periods may be as
specific as a given hour for a particular day and will be necessary to
support the short time simulation modeling efforts. Thus the emission
estimation submodel will have two major components: the inventory of
source emissions and the development of techniques by which this inventory
can be used to estimate emissions over specific time periods.
It is expected that the RAPS emission estimation techniques, including
the programs for inventorying, data handling, and data storage, will also
have wide applicability to regional air pollution control programs because
of the general importance of emission assessment in a control operation.
Thus the techniques for emission assessment will be a significant and
relatively early area of RAPS technology development that can be applied
to the nation's air pollution control programs.
Air Quality Measurements
The acquisition of air quality data is another major RAPS activity.
Air quality data are incorporated in model verification studies through
the development of either average concentrations at a given set of points
or average concentration patterns over a given test area. The RAPS pro-
gram will include model verification studies of a variety of pollutants
but especially for major pollutants for which quality criteria have been
25
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or will be published. These pollutants currently include S02, CO, N0/N02,
photochemical oxidants, hydrocarbons, and suspended particles. Air quality
criteria are expected to be published for lead aerosols and fluorides.
There is also considerable air pollution control interest in mercury, H_S,
nonspecific odors, asbestos, toxic heavy metals, and polynuclear aromatic
compounds. Over the five-year research period estimated for the RAPS
program, it can be expected that the simulation model verification program
will require regionwide data on ambient air concentrations for each of
these materials. To meet these needs for air concentration data, the
RAPS design provides for an extensive program of air quality monitoring,
including a network of continuously operating telemetering stations.
Atmospheric, Chemical, and Biological Processes (200 Series)
In pollutant dispersion modeling studies, the transformation or loss
of pollutants in the atmosphere after emission from the source and before
arrival at the receptor point has been either ignored, i.e., pollutants
are assumed to be stable, nonreactive compounds, or treated in a very
simple manner, i.e., application of a simple "half-life" term. It is
known from both theoretical and experimental studies that pollutant com-
pounds in an ambient atmospheric environment are affected by a variety
of complex reaction processes. These processes can rapidly reduce the
concentrations observed in the atmosphere, i.e., precipitation will scrub
contaminants out of the air at a rate dependent on precipitation charac-
teristics and on the nature of the pollutant. There are also situations
in which pollutants are formed in the atmosphere by reactions containing
one or several other pollutant compounds. Notable in this category are
the photochemical processes used in the formation of photochemical smog
where both gases and aerosols are formed in the atmosphere by chemical
reactions.
The RAPS program will carry out specific research studies to deter-
mine how pollutants are transformed or scavenged in the atmosphere with
special reference to the major pollutants—S02, NO, N02, CO, hydrocarbons,
and particulate materials. These transformation studies will include
reactions with other atmospheric constituents and the formation of aerosols
in the cases of S02, NO2, and hydrocarbons. Photochemical reactions are
involved with S02, NO, N02, and hydrocarbons. For CO, the scavenging
mechanisms appear to be centered in the biosphere, although atmospheric
chemical reactions may also occur.
A typical research study in this transformation task program will be
the "mass balance" design in which the pollutant, e.g., SO will be fol-
lowed through the atmospheric reaction processes—S09 to H SO or (NH^) SO
26
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to sulfate in rain—and the various reaction rates and other parameters
determined. Surface reactions and vegetation pickup can also be important.
The result will be a set of transformation submodels that provides a
technique to predict the atmospheric transformation of the major pollutants.
Precipitation is generally considered to be a major process bringing
about the removal of both gaseous and particulate material from the at-
mosphere. As such, it is a major scavenging process. However, this pol-
lutant scavenging can also have a major effect on the chemical content
of precipitation and through this on the regional environment. Recently
"acid rain" in Sweden received wide publicity, and it is likely that im-
portant changes in precipitation chemistry as a result of air pollutant
emissions also could occur in the United States. The RAPS program will
include a significant study of precipitation chemistry and its relation
to regional air pollution emissions, with the goal of obtaining quantita-
tive measures of the interaction between precipitation processes and pol-
lutant emissions.
Within an urban area that is adversely affected by air pollution,
visible pollutants from sources and as a general urban haze cloud are
probably a major public complaint. The particles that constitute this
visible urban pollution can come from two pollutant sources—from the
direct emission of solid and liquid particles such as dust, fly ash, fumes,
or smoke and from the formation of particles in the atmosphere as a re-
sult of reactions among various gaseous pollutant emissions. Photochem-
ical smog reactions in. the atmosphere constitute one source of the haze
that can afflict urban areas. Transformation of SCX, into a sulfate aerosol
is considered to be another common source of urban haze.
In the RAPS program, a major effort will be directed toward finding
the reasons for the formation and dispersion of urban haze. The ultimate
goal beyond the RAPS program would be the development of means by which the
formation processes could be abated. While such an abatement goal could be
expected to require the reduction of source emissions, until the formation
process can be described, the specification of a source control program can
be little more than educated guess work.
The RAPS aerosol study program will include extensive sampling of
aerosol constituents and the characterization of size distribution. De-
tailed emission data will be available as will comprehensive information
on atmospheric chemical composition. Translation of these observational
results into a haze formation submodel will depend on relating these field
data to laboratory and theoretical studies of aerosol formation and the
development of rational hypotheses for urban haze formation. The solution
of urban pollution haze problem is probably the most difficult of the
several RAPS tasks. By contrast, it seems quite probable that the general
27
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public will consider it vital to find solutions to visible pollution and
urban haze formation before success can be claimed for an urban air pol-
lution control operation.
Human Social and Economic Factors (300 Series)
In the decision-making processes used in formulating air pollution
control strategies, it is necessary to consider factors other than the
basic cause and effect relationship between sources and air quality. Hu-
man, social, and economic factors are involved, either directly, as when
they are manipulated to effect reduction of pollution, or indirectly, in
terms of the costs and benefits of control strategies. To provide a better
understanding of such factors, it is intended to take advantage of the
unique facility provided by the RAPS organization to collect relevant
data in an economical, well focussed manner.
Data would be acquired on both human and social factors (e.g., health,
population distribution, land use, and labor force characteristics) and
the economic aspects (i.e., the cost of air pollution and of control and
abatement procedures).
Particularly in connection with the source inventory surveys, it is
hoped to collect information (subject to legal and other constraints re-
garding privacy) that may contribute to a better assessment of the costs
entailed in staffing and operating control and abatement devices or in
the costs that result from modifications of the productivity of the plants
in question.
This element of the research plan would relate to the specific con-
ditions in a given study area and would be made available for studies
directly related to this area. The material acquired and the lessons
learned in collecting it also would be used to develop more generally
applicable information and methodologies in Task Area 400 described below,
which is concerned with the transfer of technology acquired in RAPS for
use in wider contexts.
RAPS Technology Transfer (400 Series)
It clearly is desirable that the knowledge and technology developed
in the RAPS program be made available for air pollution control purposes
as early and as effectively as possible.
28
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This requires passing on improvements and innovations in the tech-
niques of measurement, data handling, and utilization in a direct form.
It also entails conversion and distillation of the knowledge and tech-
nology developed in RAPS in the specific test region to a generalized form
so that it can be applied in other regions and for other problems with
minimum difficulty. Above all, this task provides the basis for the de-
velopment of improved control strategies, by national, state, and local
agencies.
The approach follows four main lines. The first approach is the
development and description of techniques and criteria by which the basic
air pollution factors can be assessed and monitored on an operational (as
distinct from research) basis. Particular attention will be given to
identifying and developing new techniques of monitoring atmospheric and
air quality conditions on extended scales appropriate to regional and sub-
regional control strategies so that the costs may be minimized. The use
of aircraft and remote probing techniques either from such aircraft or from
the surface are especially suited to this purpose, and every attempt should
be made to advance their applicability.
The second approach is the provision of tested, effective simulation
models suitable for other areas and conditions. The third approach is
the development of a methodology for assessing the validity (in terms of
confidence, accuracy, precision) of the preferred models for varying de-
grees of input data quality. The fourth approach is the provision of
methodologies for determining and assessing other factors such as health
effects and economic costs and benefits relevant to the formulation of
improved control strategies.
First priority will be to consider current data resources, deficient
though these may be, with emphasis on the optimum methods of providing
more complete data for input to models and for air quality monitoring
and model verification purposes. The needs of the states and local
authorities (and EPA) to improve and extend Implementation Plans will be
treated first, with concurrent though subordinate attention to environ-
mental impact statement requirements. The first milestone will be the
advancement of interim capabilities for these purposes. Thereafter, a
more complete and detailed facility will be developed, covering all as-
pects of control strategies formulation then current and capable of ex-
tension to other pollutants and so forth as the need arises.
29
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Schedules and Task Specifications for the Research Plan
Introduction
The following schedules and specifications show how the Research
Plan would be accomplished within the proposed 5-year period. They follow
a structure reflecting the principal objectives of the study. Thus, each
principal objective is approached within principal tasks numbered 100,
200, 300, and 400, respectively. Separate research tasks are assigned
numbers within the 100, 200, 300, and 400 series, and a further division
into subtasks is provided by adding numbers after the decimal point. For
example, Task 101 Boundary Layer Meteorology is a task within the 100
series which is concerned with Model Verification. (Task No. 100.)
Task 101.1 Area Climatology is an element of Task 101.
A task specification is given for each task, which states the objec-
tive and purposes and scope of the research element concerned. It is
intended that professional responsibility for each task be assigned to an
individual, who would be charged with accomplishing his task in terms of
its objective. The Principal Tasks 100, 200, 300, and 400 in fact would
be carried out by the senior personnel of the project as part of their
assigned responsibilities. However, the leaders of the subordinate tasks
and subtasks (e.g., 101 and 101.l) would be engaged in the day-to-day
accomplishment of the work specified, each task leader of a separate
task (e.g., 101) being.responsible for the product of the subtask leaders
(e.g., 101.l) and responsible to the principal task leader (e.g., 100).
Specifications for these tasks follow. Also presented are schedules
showing the general timing of the activity within the five-year period.
In both cases, the amount of effort that has been used as a basis for
planning and costing is indicated in terms of man-years of professional
and subprofessional effort.
30
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100 MODEL VERIFICATION
1972 1973 1974 1975 1976 1977
101 Boundary Layer Meteorology
(2.5 p)
101.1 Area climatology
(.5 p, .5 s)
XX XXXX XXXX XXXX XXXX XX
XX XX.
101.2 Prior diffusion data
(.5 p, .5 s)
XX XX.
101.3 Compilation and analysis
of upper air data
(2.1 p, 2.1 s)
.XXX XXXX XXXX XXXX XX
101.4 Compilation and analysis
of near-surface data
(2.1 p, 2.1 s)
.XXX XXXX XXXX XXXX XX
101.5 Balloon-tracking experi-
ment
(.5 p, 1.5 s)
.XX.
101.6 Diffusion tracer experi-
ments
(.5 p, 1.5 s)
.xx.
101.7 Weather satellite appli-
cations
(1.5 p, .75 s)
101.8 Forecast models
(2 p, 2 s)
XXXX XX. .
. XX XXXX XX
Total effort 12.25 man-years—professional (p)
11.00 man-years—subprofessional (s)
31
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100 MODEL VERIFICATION (Continued)
102 Emission Inventory
_____
1972 1973 1974 1975 1976 1977
XX XXXX XXXX XXXX XXXX XX
102.1 Emission inventory design .x x
(1 p, 1 s)
102.2 Collection of emission data .. xxxx xxxx (xxxx xxxx xx)
for stationary sources
(5.25 p, 5.25 s)
102.3 Collection of emission data .. .xx(x xxxx xxxx xxxx xx)
for mobile sources
(.5 p, 5.25 s)
102.4 Emission model for station- .. .xxx xxxx x
ary sources
(1 p, 1 s)
102.5 Emission model for mobile .. . .xx xxxx xx
sources
(1 p, 1 s)
102.6 Emission source test .. ..xx xxxx xx(xx xxxx xx)
(6 p, 6 s)
102.7 Analysis of status of .. .xxx
source controls
(1.25 p)
102.8 Emissions inventory of agri- .. .xx
cultural data sources
(.25 p, .25 s)
Total effort 20.25 man-years—professional
19.75 man-years—subprofessional
32
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100 MODEL VERIFICATION (Continued)
1972 1973 1974 1975 1976 1977
103 Air Quality Measurement
(4.25 p)
XX XXXX XXXX XXXX XXXX
XX
103.1 Data base
(.25 p)
103.2 Air quality data from local
agencies
(1.18 p, 4.75 s)
.x x.
.X XXXX XXXX XXXX XXXX
XX
103.3 Analysis of air quality and
meteorological data acquired
by RAPS
(4.5 p, 8.5 s)
103.4 Analysis of air quality
data acquired by aircraft
(1.06 p, 2.1 s)
.X XXXX XXXX XXXX XXXX
XX
.(xxx xxxx xxxx xxxx xx
103.5 Fine scale spatial variation
of air quality
(.75 p)
XXXX XX.
Total effort 12 man-years—professional
15.35 man years—subprofessional
33
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100 MODEL VERIFICATION (Concluded)
1972 1973 1974 1975 1976 1977
104 Model Calculation and
Verification
(2.5 p)
104.1 Evaluation of selected
models
(30 p, 70 s)
XX XXXX XXXX XXXX XXXX
XX
XX XXXX XXXX XXXX XXXX
XX
104.2 Model modification and
improvement
(2 p, 2 s)
104.3 Methodology for determining
model accuracy
(1.88 p)
104.4 On-site computation and data
display
(2 P)
.XX XXXX XXXX XXXX
XX
, X XXXX XXXX XXXX
XX
.XX XXXX XXXX XXXX
XX
Total effort 38.35 man-years—professional
72 man-years—subprofessional
34
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200 ATMOSPHERIC, CHEMICAL, AND BIOLOGICAL PROCESSES
1972 1973 1974 1975 1976 1977
201 Gaseous Chemical Processes
(5 p shared with 202)
XX XXXX XXXX XXXX XXXX
XX
201.1 Hydrocarbon analyses and xxxx xxxx xxxx
monitoring
(2.06 p, 5.63 s)
201.2 Development of hydrocarbon xx xxxx
classifier instrumentation
(1.38 p)
201.3 Total aldehyde-formaldehyde xxxx xxxx xxxx
monitoring program
(.19 p, .31 s)
201.4 Determination of peroxy- xxxx xxxx
acetyl nitrate
(.44 p, .75 s)
201.5 Ammonia monitoring program x xxxx xxxx xxxx xx
(1 p, 3.12 s)
201.6 CO, S02, and N02 mass flux xx xxxx xxxx xx
measurements
(.81 p, .88 s)
201.7 Origin of atmospheric CO xx xxxx xx
(106 p, .75 s)
201.8 Atmospheric odor identifi- x xx
cation
(.75 p, 1 s)
Total effort 11.69 man-years—professional
12.45 man-years—subprofessional
35
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200 ATMOSPHERIC, CHEMICAL, AND BIOLOGICAL PROCESSES (Continued)
1972 1973 1974 1975 1976 1977
202 Atmospheric Aerosol Processes
(Shared with 201)
202.1 Determination of total
nitrate in aerosol samples
(.31 p, 1.5 s)
XX XXXX XXXX XXXX XXXX
XX
XXXX
202.2 Determination of total sul-
fate in aerosol samples
(.31 p, 1.38 s)
XXXX
202.3 Determination of aerosol xxxx xx
size-distribution
(.69 p, 2.75 s)
202.4 The M^NaCl reaction in xxx xxxx xxxx xx
aerosol
(.25 p, .13 s)
202.5 Isotope ratios of sulfate xx xxxx
aerosols
(.56 p, .75 s)
202.6 Organic compounds in partic- xxxx xx. .
ulate material
(.75 p, .88 s)
202.7 Experimental measurements xxxx xx
of deposition velocity
(.56 p, 2.5 s)
Total effort 3.44 man-years—professional
9.87 man-years—subprofessional
36
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200 ATMOSPHERIC, CHEMICAL, AND BIOLOGICAL PROCESSES (Continued
1972 1973 1974 1975 1976 1977
203 Other Pollutant Related Processes xx xxxx xxxx xxxx xxxx
xx
203.1 Radiation balance modifica-
tion
(.69 p, 1 s)
.XX XXXX XXXX
203.2 Visibility reduction in
urban and rural areas
(.69 p, 1 s)
xxxx xxxx
203.3 Transport of atmospheric
odors
(.25 p, .25 s)
203.4 Trace metals and toxic trace
materials
(.69 p, 2.5 s)
203.5 Agricultural chemical dis-
tribution
(.25 p, .75 s)
203.6 Natural sources of air pol-
luted compounds
(1.13 p, .19 s)
.XX
XX
xxxx xxxx
.XX XXXX
XX
xxxx xxxx xxxx
Total effort 3.69 man-years—professional
5.69 man-years—subprofessional
37
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200 ATMOSPHERIC, CHEMICAL, AND BIOLOGICAL PROCESSES (Continued)
1972 1973 1974 1975 1976 1977
204 Atmospheric Scavenging by Pre-
cipitation
204.1 Instrument development for
pH and chemical sampling
(.50 p, .5 s)
204.2 Rainfall pH measurement
(.53 p)
XX XXXX
XXXX XXXX XXXX XX
204.3 Measurements of rainfall
chemistry
(.88 p, 5.25 s)
.XXX XXXX XXXX XX
Total effort 1.90 man-years—professional
5.75 man-years—subprofessional
205 Air Pollutant Scavenging by the
Biosphere
205.1 Chemical content of vege-
tation
(.5 p, 2 s)
,XX. ..XX XXXX
205.2 Atmospheric pollutant con-
centrations related to vege-
tation absorption
(.5 p, 1.5 s)
. XX XXXX XX
Total effort
1 man-year—professional
3.5 man year—subprofessional
38
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200 ATMOSPHERIC, CHEMICAL, AND BIOLOGICAL PROCESSES (Concluded)
1972 1973 1974 1975 1976 1977
206 Atmospheric Processes
(1.5 p, 3 s)
206.1 Source factors in pollutant .. ..xx xxxx xxxx xx..
dispersal
(.5 p)
206.2 Terrain and surface rough- xxx
ness effects
(.38 p)
206.3 Extraregional and synoptic x xxxx xx.
scale circulation
(.75 p)
Total effort 3.12 man-years—professional
3 man-years—subprofessional
39
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300 HUMAN, SOCIAL, AND ECONOMIC FACTORS
1972 1973 1974 1975 1976 1977
301 Human and Social Factors
301.1 Data on epidemiology and
health effects
(4.5 p, 4.5 s)
Continuing low scale effort
301.2 Data on population and land-
use characteristics
301.3 Data on labor force utili-
zation
Total effort 4.5 man-year—professional
4.5 man-year—subprofessional
302 Economic Factors
302.1 Costs of inferior air
quality to industrial and
general population
(4.5 p, 4.5 s)
Continuing low scale effort
302.2 Costs of control strategies
303.3 Data collection surveys of
specific effects
Total effort 4.5 man-years—professional
4.5 man-years—subprofessional
40
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400 TRANSFER OF RAPS TECHNOLOGY FOR CONTROL AGENCY APPLICATIONS AND THE
FORMULATION OF CONTROL STRATEGIES
1972 1973 1974 1975 1976 1977
401 Source Inventory Procedures
(l p, 1 s)
401.1 Techniques for making
source inventory procedures
401.2 Techniques for inventory
storage and retrieval
.... XX XX
401.3 Techniques for updating
the source inventory
401.4 Relating source inventory
to control strategy
Total effort 1 man-year—professional
1 man-year-subprofessional
41
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400 TRANSFER OF RAPS TECHNOLOGY FOR CONTROL AGENCY APPLICATIONS AND THE
FORMULATION OF CONTROL STRATEGIES (Continued)
1972 1973 1974 1975 1976 1977
402 Atmospheric Monitoring
402.1 Basic network principles
(.5 p)
402.2 Criteria for organization
and maintenance of ob-
servational networks
(.5 p)
402.3 Station siting and instru-
ment exposure criteria
(.25 p)
. .xx
XX.
XX
XX
XXXX XXXX XX
402.4 Methodology for modern-
ization of monitoring
networks
(-25 p)
402.5 Evaluation of new aircraft
measurement techniques
(1.63 p)
xx
,XX I XXXX XXXX XXXX XX,
402.6 Evaluation of remote measur-
ing techniques
(2.75 p, 3 s)
.XX (XX XXXX XXXX XX .
Total effort
5.89 man-years—professional
3 man-years-subprofessional
42
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400 TRANSFER OF RAPS TECHNOLOGY FOR CONTROL AGENCY APPLICATIONS AND THE
FORMULATION OF CONTROL STRATEGIES (Continued)
1972 1973 1974 1975 1976 1977
403 Data Handling
403.1 Optimize techniques for xx xx
data acquisition, storage,
and retrieval
(.5 p)
Total effort .5 man-year—professional
404 Modeling Technology
404.1 Significance of modeling to xx xxxx xxxx xxxx xxxx xx
the formation of control
strategies and their
implementation
(2.25 p)
404.2 Implementation Plan appli- xx xx xx
cations
(.75 p, .75 s)
404.3 Environmental Impact State- xx xx xx
ment applications
(.75 p, .75 s)
404.4 Methodology for assessing
model validity in control
agency operations
(.75 p, .75 s)
Total effort 4.5 man-years—professional
2.25 man-years—subprofessional
43
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400 TRANSFER OF RAPS TECHNOLOGY FOR CONTROL AGENCY APPLICATIONS AND THE
FORMULATION OF CONTROL STRATEGIES (Concluded)
1972 1973 1974 1975 1976 1977
405 Other Significant Factors in
Control Strategy Formulation
405.1 Liaison and interaction
with other environmental
research programs
405.2 Techniques of assessing
social and economic
factors
405.3 Methodology of assessing
operational costs of con-
trol strategies
405.4 Methodology of assessing
resultant costs of con-
trol strategies
405.5 Institutional aspects
Continuing low scale effort
Total effort
4.5 man-year—professional
4.5 man-year—subprofessional
44
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100 Model Verification
Objective
The objective is to test, evaluate, and verify the capability
of mathematical simulation models to describe and predict the transport,
diffusion, and concentration of both inert and reactive pollutants over
a regional area.
Purpose and Scope
The purpose of this series is to investigate the performance of
a series of selected mathematical simulation models in circumstances such
that as many variables as possible are known. In this way the validity
of the model can be determined, or its deficiencies identified, and
methods of measuring input or verification data can be optimized for use
in subsequent operational applications. The program will investigate
emission source assessment, the modeling of admospheric physical and
chemical factors, and the methods of verifying and evaluating the opera-
tion of the models.
The approach follows two major lines. The first approach is
development of the most complete description possible of the meteorolog-
ical conditions, emissions, and air quality. The second approach applies
the appropriate input data to a series of selected models and examines
their product against observed data.
The selection of models will follow an evolutionary program,
starting with the models ready for immediate testing (FN). Where possible,
competing models will be applied to the same data sets. Subsequent tests
will be made of improvements of such models on an iterative basis, or in
combinations of the most successful features of such models. Major em-
phasis will be on models relating to the regional scale, but subscale
models will be treated in time. First priority will be given to the
development of useful tools for reviewing and assessing Implementation
Plans. The similar need for Environmental Impact Statements will be
given concurrent but subordinate attention. Subsequently, the aim will
be to provide fully tested optimum models, the performance of which can
be objectively assessed, at each scale for each main class of pollutants.
Consideration will also be given to the role of modeling in
connection with air pollution episode prediction.
45
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101 Boundary Layer Meteorology Program
Objective
The objective is to collect meteorological data on the parameters
that affect the dispersion of atmospheric pollutants in the St. Louis
region.
Purpose and Scope
The purpose of this task is to:
• Provide the meteorological data that are required as inputs
to air quality simulation models.
• Obtain data with which to evaluate model computations of
meteorological parameters.
• Provide supplemental measurements of meteorological param-
eters for study of fundamental meteorological processes
for subsequent use in the revision of various model com-
ponents .
The program will commence with the collection of historical
climatological and experimental diffusion data for the St. Louis area.
Later, data from the various routine RAPS meteorological facilities will
be assembled and compiled in a meaningful format using standard metric
units; the routine facilities include the basic surface network and the
upper air (aircraft and balloon) systems. The program will also include
collection and compilation of data from special research programs.
102 Emission Inventory
Objective
The objective of this task is to develop and maintain a compre-
hensive source inventory.
Purpose and Scope
Numerous components of the Research Plan will require a complete
inventory of all emission sources in the study area. This will include
46
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both fixed and mobile sources of all major pollutants and perhaps selected
minor materials. Emission levels must be described for selected times
ranging from yearly to hourly intervals. The inventory should serve in
the validation of models, analysis of control strategies, and the investi-
gation of air quality impacts on the human, social, and economic systems
of the area.
103 Air Quality Measurement
Objective
The objective of this task is to provide as complete and detailed
a description as possible of the distribution (both in space and time) and
concentration of air pollutants in the St. Louis region.
Purpose and Scope
The purpose of this task is to provide the data on pollutant
distribution necessary for the verification of simulation models of
various scales (in space and time) and for various types of pollutant
(i.e., both reactive and nonreactive) and various types of source.
Data will be. obtained from past records, from routine measure-
ments already being made in the St. Louis area, and from the special
measurement networks of the RAPS facility.
104 Model Calculation and Verification
Objective
The objective of this task is to operate, evaluate, and modify
a series of air quality simulation models and identify the models that
are most suitable for use in formulating air pollution control strategies.
Purpose and Scope
Purposes of this task are to:
• Provide evidence and information as to the effectiveness of
a series of selected candidate models so that optimum models
can be selected for each aspect of air pollution control.
47
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• Develop techniques of categorizing models in terms of their
applicability in respect to both the temporal and spatial
resolution and the type or cJass of pollutants for which
they are appropriate.
200 Atmospheric, Chemical, and BJological Processes
Objective
The objective of this series of tasks is to develop an improved
understanding of the chemical, physical, and biological processes that
are entailed in determining the concentration (the dispersal) of pollu-
tants and the modification of air quality.
Purpose and Scope
Purposes of these tasks are to:
• Investigate in a number of parallel programs the various
mechanisms entailed in the transport, transformation, and
removal of pollutants riot now well understood.
• Develop techniques of describing (or better describing) such
mechanisms so that they can be accounted for in existing
models or models to be developed to accommodate them.
• Identify conditions or processes that are significant in
formulating control and abatement strategies to provide air
quality amelioration.
The approach follows three major lines. The first approach is
the acquisition of a better quantitative knowledge of processes already
recognized as significant but that have not been adequately described
for modeling purposes. The second approach is development of a better
understanding of the significance of various processes in terms of ade-
quately modeling complex air quality factors. The third approach is
investigation of pollutants and pollutant processes that are not yet con-
sidered in control strategies, and assessment of their importance so that
appropriate strategies can be formulated.
48
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201 Gaseous Chemical Processes
Objective
The objective of this task is to develop an improved understand-
ing of the gaseous chemical processes that are important in determining
the concentrations of air pollutants and in the design and specifications
of simulation models dealing with the transport of gaseous pollutants.
Purpose and Scope
Purposes of these tasks are to:
• Investigate through a number of discrete but interrelated
projects various mechanisms that are important in the trans-
formation and scavenging of gaseous air pollutants.
• Carry out specific sampling programs designed to better
describe the concentration field of various important pol-
lutants that are not covered by the regular monitoring
system.
• Develop special'measurement techniques and automatic instru-
mentation that can be used to describe in more detail the
atmospheric concentration fields of specific air pollutants,
• Relate the processes and conditions observed in the field
program to existing or potential simulation models and to
abatement strategies.
202 Atmospheric Aerosol Processes
Objective
The objective of this area of the program is to develop improved
understanding of, and expanded data on, the nature of atmospheric urban
aerosols and the processes that are important in determining (l) the
chemistry and concentrations of these materials and (2) the application
of this information in the design and specifications of simulation models
dealing with the transport and transformation of atmospheric aerosols.
49
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Purpose and Scope
The purposes of this task are to:
• Investigate through a number of individual but interrelated
projects the various mechanisms that are important in the
formation, transformation, and scavenging of atmospheric
aerosol particles.
• Carry out specific sampling programs designed to better
describe both the chemistry and concentration fields of
various atmospheric aerosols and to relate these measure-
ments to the routine measurements obtained by the regular
monitoring system.
• Conduct a coordinated program of gaseous and particulate
sampling experiments with the goal of describing the specific
mechanisms by which atmospheric aerosol particles are formed
from gaseous contaminants.
• Develop special measurement and instrumentation techniques
that through automatic operation can be used to describe in
more detail the atmospheric concentration fields of specific
particulate materials.
• Relate the processes and conditions relative to the formation
and transport of atmospheric aerosols to existing and future
simulation models and to abatement strategies.
The individual projects within this program area fall into three
general categories: (l) special monitoring programs for specific aerosol
materials, including size distribution studies, aerosol chemistry, and
studies of the interaction of aerosols and gaseous contaminants; (2) de-
velopment of instrumentation and analytical techniques to improve the
effectiveness of aerosol monitoring programs and to provide the data
necessary for simulation modeling applications; and (3) special transport
and scavenging research studies aimed specifically at delineating atmo-
spheric processes that serve to determine the characteristics of atmospheric
aerosols in urban and rural areas.
For the most part, research in atmospheric aerosols is hampered
by the fact that relatively little automatic instrumentation can be used
to obtain detailed data on the chemical constituents that make up the
atmospheric mass. As a result, most of the programs are based on a
gathering of specific field samples followed by a period of laboratory
50
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analysis to determine the nature of the collected samples. This means
that the samples generally have poor ti.me resolution because of the
length of time necessary to collect enough material for adequate analysis,
and because the number of samples that can be collected practically is
limited because of lack of available manpower and laboratory facilities.
If suggested instrumentation development is successful, some of these
problems may be ameliorated; however, since the suggested instrumentation
is not new but has been recognized for a number of years, a higher degree
of hope cannot be expressed for the successful resolution of this instru-
mentation design problem.
203 Other Pollutant Related Atmospheric Processes
Objective
The objective of this task is to develop an improved understand-
ing of a wide range of atmospheric processes that are important in under-
standing the transport, transformation, and final removal processes of
atmospheric pollutants.
Purpose and Scope
The research, effort will investigate through a number of indi-
vidual research projects various atmospheric processes and mechanisms that
are related to the understanding of pollutant distribution over an urban
and rural area. These atmospheric processes cover a range of applicable
conditions and have not been readily classifiable into other areas of
this research prospectus.
The individual research projects carried out within this
section, while dealing with specific and identifiable objectives, will
generally relate in some detail to one or more of the other research
projects described in other parts of this Prospectus.
204 Atmospheric Scavenging by Precipitation
Objective
The objective of this task is to develop an improved understand-
ing of the nature of precipitation scavenging of atmospheric pollutants
and to relate these processes to other environmental factors such as
water quality.
51
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Purpose and Scope
Purposes of this task are to:
• Investigate through sampling and analytical projects the
extent and processes of precipitation scavenging of atmo-
spheric pollutants.
• Relate the observed precipitation scavenging processes to
pollutant emissions and downwind pollutant concentration
patterns and to develop models by which the impact of pre-
cipitation scavenging can be included in simulation model-
ing of the transport and dispersion of atmospheric
pollutants.
• Develop special instrumentation where necessary to sample
and analyze precipitation for scavenged air pollutants.
The individual projects within this program area include instru-
ment development and sampling programs directed toward the measurement
of precipitation chemistry. After analytical results are available, the
data will be related to air quality concentration patterns and to meteo-
rological conditions to develop a better understanding of the nature of
the precipitation scavenging process.
205 Air Pollutant Scavenging by the Biosphere
Objective
The objective of this task is to develop an improved understand-
ing of the relationship of the biosphere to air pollutant dispersion and
transport and in particular the effect that the biosphere has on the
scavenging of air pollutants from the atmosphere.
Purpose and Scope
Atmospheric transport and dispersion processes serve to bring
air pollutants in contact with large amounts of biological material, and
it is known that plants are effective absorbers of trace chemical materials
from the atmosphere. This program will conduct projects designed to pro-
vide quantitative estimates of the scavenging mechanisms that are effec-
tive within the biosphere in removing air pollutants from the atmosphere.
52
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The research effort also will relate the scavenging processes
observed in the field to existing or potentential simulation models and
to an evaluation of the interaction between air pollution and the bio-
sphere.
Purpose and Scope
During its life cycle, vegetation has a large intake of atmo-
spheric air and this forms a very large part of the plant life cycle.
During the intake of atmospheric air, pollutants are also brought into
the plant where they are retained; in addition, plant surfaces are ex-
posed to the flow of air, and as such they provide areas where pollutants
may be deposited or absorbed even though not being brought directly into
the plant tissues. These are loss mechanisms that in some cases have
been shown to be significant and to cause measurable changes in the
ground level concentrations of specific air pollutants. This research
project will attempt to quantify these absorption or removal mechanisms.
206 Atmospheric Processes
Objective
The objective of this task is to provide an additional under-
standing and description of physical atmospheric processes not already
accounted for in generalized boundary layer meteorological modeling.
Purpose and Scope
The research for this task will extend the capability of general
boundary layer theory to include anomalous physical factors of signif-
icance in the dispersal of pollutants. These factors include smaller
scale phenomena, such as the effects of special emission conditions
(i.e., at the stack) on the behavior of effluent plumes, or the local
variation of surface roughness (i.e., different terrain or land use con-
ditions) on air flow—or larger scale phenomena, such as the influence
of extraregional features or synoptic scale circulations on the air flow
characteristics within the Region.
Inputs will be obtained from other ongoing research programs
or from special studies conducted within the RAPS program. Consideration
will be given to conducting physical modeling studies in the laboratory
where appropriate.
53
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300 Human, Social, and Economic Factors
Objective
The objective of the tasks in this series is to develop a better
understanding of factors of significance to the design of improved con-
trol strategies in the urban/rural complex, including health and economic
effects and the role of land use and community planning.
Purpose and Scope
Purposes of this research program are to take advantage of the
unique facility the RAPS organization provides to collect data on human,
social, and economic factors in an economical and well focused manner to
complement the purely physical aspects of RAPS, for the subsequent for-
mulation of improved control strategies. The data will provide the addi-
tional basis that will be needed to apply the lessons learned in the RAPS
in improved control strategies that are effective and acceptable in terms
of priorities, costs of implementation, and value of results.
The resources of field teams, data analysts, and data processing
facilities will be made available to collect human, social, and economic
data identified as significant to the purpose noted. Data will be col-
lected either by adding elements to other data collection surveys or by
initiating special surveys. Steps will be taken to ensure that the data
are compatible with the physical data collected so that interpretation
and evaluation of the data on effects will be facilitated.
301 Human and Social Factors
Objective
The objective of this task is to provide a data base of relevant
information on human and social factors that can be used in RAPS and in
developing methodologies for using such data in other areas.
Purpose and Scope
Purposes of the task are to:
• Take advantage of the data handling and analysis capabilities
of a RAPS organization to gather data on epidemiology, mor-
tality, and the like.
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• Determine population concentrations and land use character-
istics.
• Provide information on the utilization of the labor force
and its skills and mobility.
302 Economic Factors
Objective
The objective of this economic research is to provide a data
base of relevant information on economic factors that can be used in RAPS
and in developing methodologies for using such data in other areas.
Purpose and Scope
The purpose of the task is to take advantage of the data handling
and analysis capabilities of the RAPS organization to gather data on the
various costs of air pollution to the industrial and general population
(e.g., depression of property values, damage to property, loss of produc-
tivity due to sickness) and also the cost of air pollution control strat-
egies, in terms of both plant modification and increased costs of pro-
duction.
400 Transfer of RAPS Technology for Control Agency Applica-
tions and the Formulation of Control Strategies
Objective
The objective of this series of tasks and supplies is to develop
improved technology that can be applied in local and regional control
agency operations, including techniques for emission inventories, air
quality and meteorological measurement, data handling and analysis, and
the objective assessment of control strategy effectiveness.
Purpose and Scope
The purpose of this research area is to ensure that the knowl-
edge and technology developed in the RAPS is transferred widely to the
air pollution control community at large as early and effectively as
possible. This requires passing on improvements and innovations in the
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techniques of measurement, data handling, utilization in a direct form.
It also includes the conversion and distillation of the knowledge and
technology developed in RAPS in the specific test region to a generalized
form, so that it can be applied in other regions and for other problems
with minimum difficulty. Above all, however, this task provides the
basis for the development of improved control strategies, by national,
state and local agencies.
The approach follows four major lines. The first approach is
development and description of techniques and criteria by which the basic
air pollution factors can be assessed and monitored on an operational
(as distinct from research) basis. Particular attention will be given
to identifying and developing new techniques of monitoring atmospheric
and air quality conditions on extended scales appropriate to regional
and subregional control strategies so that the costs may be minimized.
The use of aircraft and remote probing techniques either from such air-
craft, or from the surface are especially suited to this purpose and
every attempt should be made to advance their applicability.
The second approach is the provision of tested, effective
simulation models, suitable for operational use on a generalized basis
(that can be readily modified and adapted), for other areas and conditions.
The third approach is development of a methodology for assessing the
validity (in terms of confidence, accuracy, and precision) of the pre-
ferred models, for varying degrees of input data quality. The fourth
approach is to provide methodologies for determining and assessing other
factors such as health effects and economic costs and benefits relevant
to the formulation of improved control strategies.
First priority will be to provide data applicable to current
data resources, deficient though these may be, with emphasis on the optimum
methods of providing more complete data for input to models and for air
quality monitoring and model verification purposes. The needs of the
states and local authorities (and EPA) to improve and extend Implementa-
tion Plans will be treated first, with concurrent although subordinate
attention to Environmental Impact Statement requirements. First mile-
stone will be the achievement of interim capabilities for these purposes.
Thereafter, a more complete and detailed facility will be developed,
covering all aspects of control strategies formulation then current and
capable of extension to other pollutants and the like as the need arises.
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401 Source Inventory Procedures
Objective
The objective of the task is to provide guidelines for the
development and maintenance of source inventories as derived from the
experience in the Regional Study.
Purpose and Scope
The source inventory for the Regional Study is likely to be
the most comprehensive such inventory yet developed. The methods by which
the source data are acquired and updated, and the manner in which the in-
ventory is organized, stored, and retrieved should prove to be of consider-
able value to others faced with the task of preparing sources inventories.
Accordingly, this component of the Research Plan includes efforts required
to prepare guidelines and reports for the benefit of others, covering the
techniques developed in the Regional Study for the management and use of
source inventories.
402 Atmospheric Monitoring
Objective
The objective of this task is to improve the technology and
technique of monitoring atmospheric conditions, particularly on extended
scales, so that control strategies can be better implemented.
Purpose and Scope
The purpose of this task is to make available for use in all
types of air pollution control operations as early as possible, the
lessons learned and the techniques developed in the RAPS.
Basic principles for designing measurement networks for control
agency operation and criteria for the siting of monitoring stations and
instrument exposure, will be developed on the basis of experience with
the RAPS data collection network.
Criteria for the organization and maintenance of extended net-
works of measuring instruments, with special reference to calibration
and standardization, will be established. This research effort also
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will develop a methodology by which newly acquired data, using new tech-
niques, can be related to older data so that the value of the latter is
fully realized, even if strict continuity is not maintained.
Remote probing systems will be tested and evaluated, in com-
parative trials as candidate systems become available. Such tests will
be conducted in conjunction with both the standard RAPS data collection
system and special programs that provide especially detailed knowledge
of meteorological conditions or the concentration of pollutants.
In particular, use will be made of helicopter soundings and
other aircraft acquired data. The application of remote probing tech-
niques from aircraft, as well as of aircraft in situ measuring techniques,
is an additional and important study subject.
403 Data Handling
Objective
The objective of the data output task is to develop optimum
techniques for acquiring, storing, and retrieving data on an extended
scale for use in air pollution control agency operations.
Purpose and Scope
The purposes of the task are to:
• Make available the lessons learned and techniques developed
in RAPS regarding the handling of all types of data collected
and used in an extensive monitoring network and emission
inventory.
• Develop and publish Standard formats as used in RAPS that
are suitable for general use.
• Develop and publish manuals and computer programs for all
major types of data collection and initial processing, suit-
able for use in air pollution control agency use.
• Provide guidelines on quality control procedures for use
in collecting data.
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404 Modeling Technology
Objective
The objective of this task is to provide the best available
modeling capability for use in operation in air quality management.
Purpose and Scope
The purpose of this task is to extract from the research and
experience of RAPS a number of models that have been tried and demonstrated
and to show how these can be adapted and used for a range of specific
operational requirements in an optimum fashion.
A most important major task is to evaluate the significance of
modeling techniques to the formulation of control strategies and their
implementation. This entails an assessment of the accuracy and precision
of the models output as a function of the degree of completeness of the
input. Given that the resources for data collection and monitoring in
the general case will be far less complete than those for RAPSf it will
be necessary to analyze the way in which limitations of the input can
compromise the output of the models. Any shortcomings or uncertainties
of the predictions derived from the models must be fully assessed and
understood in terms of, the control strategies based on them—especially
where such strategies have significant economic or social impacts.
In addition to models suited to regional areas in general for
the range of significant pollutants, special attention must be paid to
providing suitable models for use in formulating or checking Implementa-
tion Plans, as well as for use in Environmental Impact Statements, and
the requirements of air pollution episode prediction.
For all these purposes it will be necessary to:
(l) Select and publish a series of models relating to appro-
priate scales or pollutants in a form in which they can
be readily applied in an operational role.
(2) Develop and provide a methodology for assessing the
sensitivity of such models to practical limitations—
such as the quantity or quality of input data, and
qualifying topographical features.
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(3) Develop and provide a methodology for measuring the
accuracy of predictions based on such models, using either
existing or specially provided (but limited) additional
measurement facilities.
In general the detailed tasks, covered within the 404 series
are scheduled well along in the Research Plan at a relatively low level
of effort. Accordingly, their ultimate detailed content tends to be
somewhat more speculative than tasks presented elsewhere in the Research
Plan, so that presentation of their content in detail does not appear
warranted at this early time.
405 Other Significant Factors in Control Strategy
Formulation
Objective
The objective of the other tasks concerned with strategy formu-
lation is to ensure that all knowledge and experience acquired under the
RAPS program is made available for use by the air pollution control com-
munity in general and those concerned with formulating improved control
strategies in particular.
Purpose
The purpose of these other efforts is to take care that both
during the RAPS program and at its conclusion, fullest advantage is taken
of other research in progress (both by EPA and other agencies) and also
that the products of RAPS not directly connected with its principal ob-
jectives nevertheless are made available in appropriate form to potential
users. Five principal components are involved.
(l) Liaison and interaction will be required with other re-
search programs, both inside EPA and in other agencies,
particularly those being carried out in the St. Louis
area, such as METROMEX.
(2) Techniques should be developed to assess and evaluate
social and economic factors in control strategy formula-
tion, using as a base the data collected in 302 and 302.
(3) A methodology for assessing operational costs of control
strate'gies should be developed for use in areas where avail-
able data are less complete than in the RAPS area.
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(4) Similarly, the development of a methodology for assessing
the resultant costs in terms of lost production and in-
creased production costs of proposed strategies will be
appropriate.
(5) Investigations on the basis of study and experience in
the St. Louis region should be carried forward to identify
the interaction of local government and other institutional
aspects that are relevant to the formulation of effective
control strategies and their enforcement.
In general the detailed tasks, covered within the 405 series
are scheduled well along in the Research Plan at a relatively low level
of effort. Accordingly, their ultimate detailed content tends to be
somewhat more speculative than tasks presented elsewhere in the Research
Plan so that presentation of their content in detail does not appear
warranted.
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VI THE FACILITY
Rationale
Basic Operations
Since the RAPS research and development program is planned as a
five year integrated operation, it is discussed in this chapter in terms
of a general time sequence of project initiation. As a point of departure,
it is assumed that the total RAPS program will be initiated in July 1972
and that research operations can be started at this time.
The RAPS program is such that two types of field operations are
necessary. One type of field operation is the research expedition, char-
acterized by the fact that it has a limited or single objective and re-
quires relatively short periods of field operation for data acquisition.
The second type of field operation is the ongoing research study, charact-
erized by a need for routine data acquisition over an extended period of
time. The four basic RAPS task areas include projects in both field
operational categories; however, since the provision of a data acquisition
network for routine, ongoing research operations results in a major in-
vestment of both funds and personnel, the needs for such an investment
will be examined.
Basis for Monitoring Network
A major objective of the RAPS program is the verification and evalu-
ation of simulation modeling techniques. To fulfill the RAPS goals, this
verification process must include much more than a statistical comparison
of observed and model-predicted pollutant concentrations in the area
around a source region. The RAPS program must also provide the opportunity
to evaluate separately the several submodel routines that are component
parts of all simulation modeling systems. The comparison of the submodel
routines, e.g., the transport wind direction field, with the actual con-
ditions prevailing during the verification test operation, will permit the
identification of specific weaknesses in the simulation programs and
procedures. Follow-up research leading to modifications in the simulation
procedures can then be conducted to improve and extend the candidate
simulation techniques*
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This necessity to be able to understand why a candidate simulation
model produces a given result requires detailed knowledge of the metero-
logical, air quality, and emission fields in the test area. One way to
obtain detailed meteorological and air quality data is through establish-
ment of a comprehensive network of observational stations throughout the
verification test area. Verification and data acquisition studies carried
out to date usually have been limited either by having to use existing
monitoring network data where the network had not been established to
meet the needs of a verification program or by establishment of a special
test program where funding and other practical problems limited the amount
and time-span of the data acquisition program.
Since the objective of the RAPS program is the verification of a
variety of current and future candidate model systems, it cannot be
adequately carried out if the verification data acquisition system is not
geared specifically to the range of verification problems that the RAPS
program is expected to solve. Thus the RAPS verification data acquisition
system must: (l) include detailed meteorological and air quality measure-
ments, (2) give adequate coverage over distances of up to 150 km from the
source area, and (3) provide these data on average ambient conditions for
periods as short as one hour and as long as one year. In this context,
detailed air quality data include ambient air concentration information
for pollutants that are covered by current and proposed air quality
criteria, including but not limited to S02, Co, No, NC>2, oxidants, hydro-
carbons, suspended particles, lead aerosols, and fluorides, because model
verification studies are expected to be concerned with these individual
pollutants.
When all these factors are considered, the installation of a sophis-
ticated monitoring system is the optimum way to meet the needs of the
RAPS model verification program for regionwide air quality and meteoro-
logical data.
The RAPS program also is directed toward providing new data and an
improved understanding of atmospheric chemical reactions, pollutant
scavenging, and other atmospheric processes. Such a goal also requires
a basic foundation of detailed data on atmospheric pollutant concentrations
that a monitoring system can provide most effectively. The needs of this
program are generally parallel to the basic components needed for the
model verification program. The planned monitoring system design incor-
porates some components and design features that are specifically directed
toward supporting chemical transformation studies and scavenging projects.
The RAPS program is also specifically charged with improving avail-
able technology in the area of air monitoring networks through the develop-
ment of optimum designs and operational techniques.
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Thus establishment of an air quality and meteorological monitoring
network is justified on the basis of the requirements that have been
placed on the RAPS program in the areas of air pollution simulation model
verification, atmospheric transformation processes, and improved control
agency technolgoy. Because of the area to be covered and the number of
variables that are to be monitored, a telemetering, computer-controlled
network has been designed for this RAPS program. Instrumentation com-
ponents are, in general, the newer designs now being specified for local
and regional use, although they have not generally been placed into
general service by local control agencies. The proposed research program
is based on the monitoring network being operational about 18 months
after the RAPS program is initiated.
The special objective, expedition-type, data acquisition program can
be fairly closely specified—both in terms of the type and nature of the
data collected and the periods over which such data are needed. The more
general, comprehensive data collection program, designed to meet the
known, anticipated, or potential requirements of a number of studies,
cannot be so closely specified. In proposing to maintain a continuous
data collection facility to meet the general need, it is recognized that
a danger exists accumulating large masses of data that could have little
or no use; therefore, great care must be taken in setting up the general
data acquisition program to avoid wasted costs and dissipated effort.
However, some redundancy is both inevitable and desirable in a data
acquisition program of this type.
To ensure that adequate data are available for investigations that
cannot be defined a priori or to be sure that no data of a specific type
are missed or imperfectly collected, it will be necessary to make extensive
and continuous collections that will include some material that may be used
quite infrequently; their importance in achieving the program objective
may nonetheless be great. This principle is well recognized in many
observational activities—especially where weather is involved. With
modern data acquisition and processing systems, however, the unit cost of
collecting additional data decreases rapidly once the initial investment
in equipment and in setting up procedures has been made. Indeed, if data
are to be collected at successive times throughout the year, even if
considerable intervals can be anticipated, it is almost certain that
little or no saving in overall costs could be made by operating inter-
mittently, since the costs of shutting down and opening up would outweigh
any minor savings by not running continuously on a routine basis.
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The St. Louis Regional Monitoring Network
The St. Louis facility is conceived as consisting of a system of
air quality and meteorological instrument stations established within an
area roughly enclosed by a circle of 100-km radius with the St. Louis
arch as its center. A central support facility is also planned that in-
cludes data-handling and processing equipment, office and laboratory space
and repair and maintenance shops. Most instrument stations are expected
to be linked to the central facility by telephone circuits to permit
automated remote data recording at the central facility.
The St. Louis facility is planned as the basic instrument system of
the Regional Study. It could be operated in a fully continuous or
part-time mode to develop a comprehensive data base of air quality and
meteorological conditions. Detailed statistical and other analyses then
could be performed as required by the various elements of the Research
Plan. During the field studies and data-acquisition efforts covered in
the Research Plan, the facility would be operated to support these efforts
most effectively. Support could include equipping the instrument stations
with additional instruments, locating transportable stations as specified
by the research group, preparing and operating specialized data processing
programs, and providing instrument and experimental technician support.
Six types or classes of instrument stations are included in the
facility. These range, from the permanently installed Class A-^ stations
with 30-meter instrument towers equipped with a full complement of air
quality and meteorological instruments to the trailer-mounted Class €2
stations having no air quality instruments and a single meteorological
instrument. The principal characteristics of the stations are summarized
in Table 1.
Stations of Classes AI, &2> and Bl are visualized throughout the
Regional Study as permanently sited, although this is certainly not a
fixed requirement. These stations are considered the basic units for the
long term observational program. The Class V>2, stations have the same
instrument complement as the Class B^ stations, but they are transportable
units housed in trailers. The Class C^ stations are denoted as trans-
portable units since a trailer is used for instrument installation.
However, the fact that the station is equipped with a 30-meter tower, sug-
gests less frequent movement than the other transportable stations.
The Class C2 stations are a hybrid unit. As part of the central
facility they include the trailer unit, tower, and digital data terminal
equipment. They will be used by the various groups carrying out field
experiments and data-gathering efforts associated with the various research
efforts presented in Part II. Additional instrumentation required at a
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Table 1
CLASSIFICATION OF THE REGIONAL STUDY
INSTRUMENT STATIONS
Class of Station
Air quality instruments
Carbon monoxide - methane - hydrocarbon
Hydrogen sulfide-sulfur dioxide
Total sulfur
Ozone
Nitrous oxide - oxides of nitrogen
Nephelometer
Carbon monoxide (NDIR)
Hi-vol sampler
Meteorological instruments
Temperature
Wind direction and speed
Pyranometer
Pressure transducer
Mercury barometer
Net. radiometer
Dew point hygrometer
Rain - snow gauge
Tower height
30-meter
10-meter
Data recording
Remote
Local
Mobility
Fixed
Transportable
Total quantities
—
1
1
1
1
1
1
1
2
1
3
1
1
1
1
1
1
A B B C C
21212
Number of Instruments
1 1 1 --
1 1 1 —
1
111----
1 1 1
1 1 1 — —
1
222----
1 i
311 31
1
I
1
1
I
1
Station Characteristics
24
x
4
x
24
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Class C2 station in support of these field efforts would be consids. j . d
a part of the particular research effort rather than of the St. Louis
facility. Accordingly, the instrumentation of the Class C2 stations
would be expected to vary widely during the Regional Study. This same
concept would also apply to instruments added to other classes of stations
set up in support of field activities.
Data acquisition and handling in the St. Louis facility are expected
to be automated to the greatest possible extent. Instrument observations
at all but the Class C^ and C2 stations are planned to be transmitted by
telephone circuits to the central facility for automatic computer-
controlled recording. The Class C-^ and C2 stations are currently planned
to have local data-recording facilities, but further analysis might in-
deed indicate these also could use a remote reporting capability efficiently
The digital data terminal equipment of the remotely reporting in-
strument stations interrogates each air quality and meteorological instru-
ment at a predetermined frequency, converts the analog instrument output
to its digital equivalent, and stores the digital data in a relatively
small-capacity magnetic core memory. On command from the central facility,
expected to occur at about 15-minute intervals for each station, the
stored data are automatically transmitted to the central facility.
Calibration curves of all instruments are stored in the data-processing
system at the central facility so that immediate conversion is made from
the digital data format to engineering units for archiving.
Each air quality and meteorological instrument would be equipped
with a solid-state, nonerasable memory unit to serve as an identifier of
each instrument. A unique serial number, or equivalent, of each instru-
ment would be coded into the identifier with the identifier then mounted
on the instrument. At each interrogation of each instrument, the identi-
fier would respond immediately before or after the instrument reading was
acquired so that the instrument reading and its identification would always
be together. This procedure should result in an absolute minimum of data
ambiguity and erroneous interpretation and is judged to be far superior
to customary procedures using instrument log books and other manual methods.
The principal data-handling and processing function at- the central
facility would include the recording and archiving of all instrument
station data and other field and experiment information. The archival
tapes would be forwarded to Research Triangle Park or other EPA installa-
tions as appropriate or to contractors for use in their analyses on a
particular research project. Minimal detailed data analysis is expected
to be carried out at the St. Louis facility, and t:ie electronic data-
processing equipment is sized accordingly. Selected research experiments
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may require limited data processing during their execution, and the
St. Louis facility should have the required capability. But large scale
data processing covering data acquired over a period, say, of the spring
and summer seasons would be expected to be undertaken on the larger computer
systems existing at the Research Triangle Park and elsewhere.
The instrument stations constituting the St. Louis facility are
planned to be located within a circle of about a 100-km radius centered
generally on the St. Louis arch. Eight Class A stations are symmetrically
located around the 100-km circle, while an additional eight are symmetrically
deployed on a 40-km square with the arch as its center. The final Class
A station is planned at the arch itself. Depending on actual conditions
at the arch site, advantage might well be taken of nearby taller television
or other towers.
The Class B^ stations are planned for installation on a uniform
square grid about the arch with station spacings of about 12 km.
The remaining stations are considered to be transportable and would
be deployed as required to support a given field data-acquisition program.
This is particularly true for the Class C2 stations. The stations other
than the Class C2 would generally be expected to provide the overall or
ambient observations of air quality, meteorological, and other parameters
of interest on an areawide basis. Observations in detail within a specific
smaller area would be carried out by deployment of the Class C2 stations
within the area of interest.
Since the precise station locations will depend on the availability
of suitable sites, the pattern presented here must be regarded as tenta-
tive. Instrument station sites must be selected with respect to several
important factors, including freedom from unique or overriding micro-
meteorological effects, general absence of nearby significant pollutant
sources, convenient access to electric power and communication utility
services, and free access at all times. Only a detailed field survey will
reveal sites possessing these and other necessary characteristics.
In addition to the routine surface-based data acquisition network,
upper air measurements of meteorological and air quality parameters will
be undertaken both on a routine and special task basis. The primary
meteorological system will use METRAC or an equivalent precision automated
balloon-tracking system. With METRAC, it is proposed that vertical sound-
ings of wind, temperature, and possibly humidity, pressure, and/or net
radiation be made at a four- to six-hour interval at a minimum of two
stations (typically urban and rural). The system has the capability to
track simultaneously up to six different balloons (both vertically-rising
and constant-level) providing detailed wind information and data from four
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different sensors on each balloon. As such, it can also support a variety
of special purpose programs. Air quality data will be obtained on a
regular basis through the use of a helicopter monitoring system. Flights
will be made on two or three days per week with two (three-hour) flights
each day. In addition to the air quality observations, supplemental
meteorological data, such as radiation measurements, will also be made.
Fixed-wing aircraft are recommended for use only on a nonroutine basis
and in support of special-task programs.
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VII MANAGEMENT AND SCHEDULING
Introduction
The Regional Study will constitute the largest and most comprehensive
scientific investigation and analysis of the phenomenology of air quality
and pollution yet undertaken. Field data describing air quality, mete-
orology, and other pertinent factors will be obtained by an instrument
and data processing system unprecedented in the study of air pollutants.
This critically important effort will require the most careful planning
and management both before and during its execution to ensure effective
utilization of the facilities and personnel assigned to the Regional
Study and the most appropriate expenditure of funds.
This chapter of Part I presents findings largely applicable to
scheduling, management, and staffing of the St. Louis facility. It is
certain that the scope of the Regional Study is such that continual
review and modification will be required of all the estimated schedules,
costs, and other factors presented in this Prospectus. This tends to be
of particular importance in regard to the estimated activation schedule,
since many policy and design considerations are present, not all of which
can be anticipated or evaluated at this time. Moreover, several important
aspects of the schedule and perhaps certain costs will depend on the
actual conditions found to exist in St. Louis after authorization of the
Regional Study. Accordingly, the planning factors presented are regarded
as having an accuracy and reliability suitable for the planning purposes
of this Prospectus and for the purpose of providing a working format for
additional and more detailed planning efforts.
Facility Activation Schedule
The activation schedule of the St. Louis facility is viewed as
having three principal components. The first covers the design, instal-
lation, and shakedown and acceptance of two prototype instrument stations,
The second includes the activation of all Class A and B stations. The
third provides for completion of the Class C stations.
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The overall schedule adopted for the St. Louis facility activation
will depend on a number of critical factors. These include the urgency
for initiation of the research experiments requiring the full instrument
system and the magnitude of funds allocated for the Regional Study.
Also, a number of alternative methods or rationales may be used to de-
velop the geographical pattern of station location. In one case all
stations could be installed in a continuous sequential schedule on the
basis of currently available emission source data and air quality and
meteorological information. In the other case, stations might be in-
stalled at a far lower rate, so that the first group of stations would
be allowed to acquire significant air quality and meteorogical data from
which possible guidance would be derived for the second group, and so on.
The basic concepts of scheduling under each procedure should be essentially
the same; the continuous schedule has been selected for purposes here.
The design installation, and operational acceptance tests of the
instrument prototype stations is estimated to require on balance about
44 weeks. The critical path through the scheduling network consists
almost exclusively of the digial data terminal equipment. This situation
is caused primarily by the fact that all air quality and meteorological
instruments are considered to be standard catalogue items with relatively
short procurement times, whereas the digital data terminal equipment con-
sists of a combination of standard and specially designed equipment. The
latter group of digital data equipment causes much of the length of the
critical path, especially when combined with the design decisions asso-
ciated with the telephone communication system design.
Two alternatives have been identified for scheduling the activation
of all Class A and B stations. One alternative is to delay all activa-
tion until the prototype station has been thoroughly tested and all com-
ponents have been accepted. Scheduling on this basis is estimated to
require an additional 33 weeks for final station completion, or 77 weeks
for full activation of the St. Louis facility.
The other alternative is to initiate activation before prototype
station acceptance. In this case, activation could be started at the
end of the acceptance tests of the prototype station air quality instru-
ments, which is estimated to occur 23 weeks after authorization of the
Regional Study. Since, as noted above, the prototype station critical
path is estimated at 44 weeks, initiation of system activation after
prototype operation of the air quality instruments is likely to achieve
considerable economies in time. Such overlapping is estimated to bring
full system operation 18 weeks earlier than the former schedule, with
completion at 59 weeks.
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Moderate risk is estimated to exist in continuing station activation
without full completion of the prototype stations. This risk arises with
the design of the equipment linking the meteorological and air quality
instruments with the bulk of the digital data equipment. However, in
view of the inherent flexibility of digital data circuitry designs and
equipment, any incompatibilities revealed in prototype station design
undoubtedly can be corrected in the digital data equipment before instal-
lation in the remaining stations.
The Class C station essentially can be scheduled independently of
the other stations, since their digital data terminal equipment provides
for local rather than remote recording. The Research Plan indicates a
need for approximately ten Class C stations about eight months after
authorization of the Regional Study with the remainder following soon
thereafter. Accordingly, initiation of the digital data equipment pro-
curement cycle can be initiated ten weeks after authorization with station
activation beginning 12 weeks later. At an activation rate of one per
week, all stations will be completed in 51 weeks after authorization with
the first ten available 33 weeks after authorization.
These activation schedules are based on the assumption that the
central facility and all instrument stations sites have been acquired
before the time of scheduled station activation. This is regarded as a
most critical assumption , and the lack of instrument sites could indeed
cause serious delay in system activation. Immediate field survey ini-
tiation following authorization of the Regional Study and preferably
before, appears essential to permit station activation to proceed on
schedule.
Permanent Management and Staffing
Of the 54 permanent personnel assigned to the Regional Study, nine
are estimated to be located at Research Triangle Park and 45 in St. Louis.
The organizational structure is summarized in Figure 5.
The significance of the Regional Study is such that the establishment
of the position of Deputy Director for Regional Studies appears appropriate,
The Deputy Director will report directly to the Director, National En-
vironmental Research Center, Research Triangle Park. The Deputy Director
will be supported by three staff groups as follows.
• Office of Programs—This Office will provide EPA coordination,
budgeting, and planning support throughout the study. A minimum
of two professionals are estimated to be required.
73
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• Office of Interagency Coordination and Technology Transfer—This
Office will be charged with providing full coordination among all
agencies dealing with problems of air pollution. The broad scope
of the Regional Study is such that programs of other organizations
and agencies will be continually monitored to determine possible
interfacing points, cooperative ventures, and other modes of joint
operation. Conversely, the Office will have the principal task
of advising other agencies of the programs planned for the Regional
Study to again promote full cooperation. The Office will have the
additional responsibility of continual review of findings developed
in the Regional Study for application to other areas and experi-
mental efforts. The Office is estimated to require one profes-
sional in the early phase of RAPS, increasing to three professionals
after the first year.
• Office of Research Operations—This Office is expected to provide
the very important technical link between the research divisions
at Research Triangle Park and the St. Louis facility. The Office
would consist of at least one representative from each division
but would remain administratively within the division. The chief
responsibility of each representative would be to organize and
supervise the research programs within his division that will
make use of the St. Louis facility. He will be responsible for
data acquisition quality control, as well as for execution of the
subsequent analysis. Three professionals are estimated to be re-
quired in this Office in addition to the Division representatives.
The St. Louis staff will be largely responsibile for the operation
of the facility and support of the field research experimental effort.
The staff consists of nine professionals and 36 nonprofessionals with 17
of the nonprofessionals engaged in instrument station maintenance and
calibration. The professional staff includes the following:
• Facility Director—The facility director will be responsible for
all St. Louis operations, reporting to the Deputy Director for
Regional Studies.
• Research Coordinator—The research coordinator' provides all
logistic and facility support to special research groups carrying
out field data gathering programs.
• Instrument Engineer—Two engineers are estimated for system
operation, maintenance, and modification during the five-year
program.
75
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Meteorologist—Two meteorologists are estimated to be required to
provide sustained analysis of meteorological conditions in the
St. Louis area and direct support to field groups for specific
purposes.
Computer System Engineer—One engineer is estimated to be re-
quired for supervision of data handling and recording procedures,
special computer program preparation, and related duties.
Control Engineer—The control engineer will be responsible for
development and maintenance of the St. Louis emission inventory.
Effects Research—An on-site professional is estimated to be
required to provide direct support for all effects research in
the St. Louis area; he will arrange for acquisition of all per-
tinent local data necessary for the program.
76
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VIII COST SUMMARY
Permanent Facilities and Staff
Initial costs of the St. Louis facility have been estimated at about
$3.94 million. This includes all instrument stations, the central facil-
ity, and other equipment estimated to be required. These initial costs
are summarized in Table 2.
Table 2
ESTIMATED INITIAL COSTS OF THE ST. LOUIS
FACILITY BY PRINCIPAL INSTALLATION
(Thousands of Dollars)
Instrument Stations
A $ 771.3
A 625.6
B 1,370.4
B 455.2
C 134.0
C 367.2
Subtotal $3,723.7
Central facility 121.0
Vehicles 98.9
Total $3,943.6
The estimated initial costs can also be summarized in terms of the
principal system components as shown in Table 3.
77
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Table 3
ESTIMATED INITIAL COSTS OF THE ST. LOUIS
FACILITY BY SYSTEM COMPONENTS
(Thousands of Dollars)
Cost
Air quality instruments $1,606.0
Calibration equipment and accessories 392.2
Meteorological instruments 234.2
Instrument spare parts 220.5
Site preparation, housing, fixtures 501.5
Digital data terminal equipment 769.2
Data processing and communication 81.0
General facilities 40.0
Support vehicles 98 .9
Total $3,943.6
The air quality instruments account for almost one-half the initial
costs at $1.6 million, with the digital data terminals at somewhat less
than 18% of the total. One of the lowest cost elements is attributable
to the data processing and communication facilities and accounts for
slightly more than 2% of the total costs. Significant advances in the
state of the art and high volume production of computers and peripheral
equipment have combined to create dramatic reductions in cost over the
past two to three years.
The annual operating cost once full operational status has been
achieved is estimated at about $1.5 million. This cost includes the
staff at both Research Triangle Park and St. Louis and all standard
operating supplies at St. Louis. These costs are summarized in Table 4.
The estimated personnel costs clearly constitute the chief element
of the annual costs, accounting for almost 80fo of the total. The remain-
ing elements stand at 6% or less. A particularly uncertain cost is that
for rental of the central facility and especially land for the instru-
ment stations. A cost for instrument station sites was taken nominally
at $1000 per site. Undoubtedly, large variations will be found in this
unit estimate which can only be known with certainty following the actual
field survey.
78
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Table 4
ESTIMATED TOTAL ANNUAL OPERATING COSTS OF THE
ST. LOUIS FACILITY" AND PERMANENT STAFF
(Thousands of Dollars)
Personnel
Research Triangle Park
St. Louis
Subtotal
Instrument replacement and parts
Motor vehicle operation
Telephone communication system
Building and land rental
Calibration gases
Electric power
Total
225.0
990.0
$1,215.0
91.8
14.5
38.4
78.4
98.0
12.7
$1,548.8
The activation schedule of the St. Louis facility is estimated to
span about five calendar quarters following authorization of the Regional
Study. The overall expenditure schedule for both the initial and oper-
ating costs by quarter is summarized in Table 5. The operating costs in
the fifth quarter are judged to typify all subsequent quarters.
Table 5
ESTIMATED INITIAL AND OPERATING COSTS DURING
IMPLEMENTATION OF THE ST. LOUIS FACILITY
(Thousands of Dollars)
Initial costs
Operating costs
Total
Quarter
1
$48.8
99.1
2
$347.1
163.3
3
$2,770.2
288.7
4
$470.9
349.0
5
$306.6
387.2
$147.9 $510.4 $3,058.9 $819.9 $693.8
79
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Helicopter and Mixing Layer Observational Program
The estimated costs of the mixing layer observational program are
presented in detail in Chapter XIV of Part III. The costs cover the
acquisition and operation of one helicopter and a balloon-borne instrument
system known as METRAC. Operational costs ol the helicopter are based on
18 hours of operation during the period March through November and 12
hours per week for the balance of the year. Total helicopter costs on
a quarterly basis for this operational schedule are shown in Table 6.
Table 6
ESTIMATED COSTS OF HELICOPTER
OPERATION BY QUARTER
(Thousands of Dollars)
Quarter Cost
January-March $19.3
April-June 24.1
July-September 24.1
October-December 16.9
Total $84.4
Research Plan
The following sections present the estimated requirements for per-
sonnel, major equipment, and the costs of these programs. Although
these estimates are judged to be suitable for the planning purposes of
this Prospectus, they will require continual review and modification in
further planning of the Regional Study and during its execution. This
is especially important for the estimates in the later time periods.
The estimates are intended to cover the requirements of each particular
program component, and all are considered a part of the Regional Study.
Fur-'icr consideration of the Research Plan and perhaps the EPA policy
co, ' derations may result in the transfer of part or all of certain
program components from the Regional Study to other components of the
ongoing EPA research program.
80
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The estimated costs of the Research Plan as presented in Chapter XXI
of Part IV total $9.7 million and are summarized later in Table 11. By
far the bulk of the costs are attributable to personnel, accounting for
85% of the total. About $900,000 is estimated to be required for spe-
cialized instruments for selected components of the Research Plan. Be-
cause of their somewhat specialized nature or because they require
additional development to achieve operational status they were not
considered as part of the permanent facility.
Personnel
Requirements
Requirements estimated for personnel stemming from the Research
Plan are summarized in Table 7. Scheduling is shown on the assumption
that the Regional Study is authorized by July 1, 1972. More than 311
man-years of professional and technical support personnel are estimated
to be required to carry out the Research Plan. Almost one-half the
personnel requirements stem from the 100-series tasks—Model Verifica-
tion—alone. Within this series about one-half of the personnel are
associated with the critical 104 component which covers the specific
efforts associated with model verification. The 100 and 200 series have
a ratio in the range of two-thirds to three-fourths between professional
and technical support personnel which tends to be appropriate in view of
the extensive laboratory and field efforts expected. The 300 to 400
series tend to require considerably fewer technical support personnel
compared with the number of professionals, because far lower field
efforts are expected.
Table 7
SUMMARY OF PERSONNEL REQUIREMENTS FOR THE RESEARCH PLAN
(Man-Years)
Program
Element Professional Support Clerical Total
100 83 118 14 215
200 25 40 4 69
300 9 9 2 20
400 16 5 _3 24
Total 133 172 23 328
81
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Each of the four major program elements is expected to be
coordinated by the various Research Division representatives in the
Office of Research Operations. Major program components, especially
those continuing throughout the life of the Regional Study, would neces-
sarily have full-time supervisors within the respective interested
Research Divisions. The extent to which contractor participation will
be necessary and appropriate is difficult to state and would likely de-
pend on the balance between program requirements in terms of scheduling
and capability and the available resources within EPA. Total personnel
requirements, however, should remain substantially identical regardless
of the manner in which the effort is conducted.
Professional and technical support personnel were estimated
on a task-by-task basis from their descriptions in Part II. An addi-
tional component of the staffing would include clerical support. For
planning purposes, clerical personnel requirements are estimated on the
basis of one per six professionals, bringing the total requirements to
328 man-years.
Costs
The total estimated costs of personnel associated directly
with the tasks included in the Research Plan are presented in Table 8,
based on the requirements shown in Table 7. The estimated costs, as
discussed in Chapter XIX of Part IV are based on a unit cost of $25,000
per year per staff member, regardless of his labor or job classification,
With the mix of classifications estimated to be required, the aggregated
estimates should be valid. Estimates for the smaller components of the
Research Plan that do not have a balanced staffing pattern would tend
to be less reliable. The unit cost includes direct salary, benefits,
travel, and all other funds necessary for support.
Total personnel costs directly associated with the Research
Plan are estimated at about $8.3 million.
82
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Table 8
ESTIMATE! 'OST OF PERSONNEL REQUIRED BY THE RESEARCH PLAN
(Thousands of Dollars)
Calendar
Year
19,72
1973
1974
1975
1976
1977
$
1
1
1
1
100
383
,112
,202
,128
,035
507
.6
.7
.0
.5
.2
.1
200
$ 24
87
445
490
525
157
.9
.3
.0
.5
.6
.4
300
„
$208.
108.
108.
108.
54.
—
4
4
4
4
2
400
—
$105.
180.
149.
156.
154.
Total
—
0
3
9
3
7
$
1
1
1
1
408.5
,413.4
,935.7
,877.3
,825.5
873.4
Total $5,369.1 $1,730.7 $487.8 $746.2 $8,333.8
Instrumentation and Equipment
The bulk of the instrumentation and equipment necessary for execu-
tion of the Research Plan is included in the St. Louis facility as dis-
cussed in Chapters XI-XII of Part III and Chapter XVIII of Part IV.
These items are generally expected to function throughout the life of
the Regional Study. However, several major items of equipment are in-
cluded more appropriately in the costs of the Research Plan rather than
in the St. Louis facility. The first includes the METRAC balloon-borne
instrument system discussed in Chapter III of Part II and Chapter XIV
of Part III for observations in the mixing layer.
Costs for additional research and development were estimated at
$100,000 in the first year after authorization of the Regional Study.
If the development is successful, an additional cost of $376,000 was
estimated for full implementation of the system having a capability to
simultaneously track six balloons. The estimated costs by quarter are
shown in Table 9. Program element 200—Atmospheric Chemical and Bio-
logical Processes—is estimated to require certain additional instrumen-
tation and equipment not included in the St. Louis facility. Their costs
are included within the costs of the Research Plan rather than the St.
Louis facility. Table 9 presents the estimated costs of these instru-
ments and equipment by program component and date of acquisition. Com-
pared with personnel costs, these expenditures tend to be modest except
perhaps for the gas chromatograph-mass spectrometer estimated at $100,000.
83
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Table 9
ESTIMATED COSTS OF SPECIALIZED EQUIPMENT FOR THE RESEARCH PLAN
(Thousands of dollars)
Program
Component
103
201
202
203
20-4
406
Equipment Doscriotion
METRAC system development
METRAC procurement and
installation
Gas chromatograph
Electron capture gas chromato-
graph
G. C. mass spectrometer
Correlation spectrometer
Recorders for gas chromntographs
Sample vessels, valving, stan-
dard units
Total
Electron mobility counter
Royco photometer counter
Anderson impactor
Total
Atomic absorber
Transmi ssometer
Radiative balance instruments
Total
Digital pH meter
Tipping bucket rain-gage
Fabrication of precipitation pH
measurement and calibration
pH meter
Chemical electrodes
Total
Thermosonde
Acoustic sounder
Total
Quantity Cost
SI 00.0
376.0
3 18.0
3 14.0
1 100.0
1 10.0
6 6.0
10.0
SI 58.0
2 40.4
5 41.3
5 5.6
S 87.3
1 4.0
3 27.0
50.0
S 81 .0
5 5.0
10 3.2
5 2. 5
1 1.8
7 1.4
S 13.9
3 120.0
2 40.0
Acquisition
Date
Year/Quarter
1972/4
1974/1
1975/1
1975/1
1975/1
1975/1
1975/1
1975/1
1974/1
1974 '1
1974/1
1975/1
1975/1
1973/3
1974 /2
1974 /2
1974/2
1974 /2
1974/2
1973/2
1973 '2
S160.0
84
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This unit would be installed at the central facility with the bulk of
the remaining items installed mainly at selected Class A and B stations
as discussed in the Research Plan.
Finally the research effort under program element 402—Atmospheric
Modeling—will require the use of two atmospheric sounders and three
thermosondes early in 1973. The estimated costs of these units are
also presented in Table 9.
Operations
Execution of the Research Plan will entail certain direct operating
costs in both the 100 and 200 series. In the 100 series, significant costs
are estimated to be associated with the 101 component for the operation
of the METRAC system during wind transport and tracer studies. The
Research Plan indicates the execution of the wind-tracking experiment
during the second and third quarters of 1974 and tracer studies in the
same quarters in 1975.
As noted in Chapter XIV of Part III the estimated operating costs
of the METRAC system are S8000 per month per balloon launch point for an
intensive experimental effort. Thus, if the METRAC system is taken as
having four launch points, the total operating costs would be $32,000
per month. Under the research schedule shown above, the quarterly METRAC
operational costs expected are shown in Table 10.
Table 10
ESTIMATED OPERATIONAL COSTS
OF THE METRAC SYSTEM
(Thousands of Dollars)
Year—Quarter Cost
1974—2 $ 92
1974—3 92
1975—2 92
1975—3 92
Total $368
85
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Operating costs of the efforts in the 200 series are expected to
cover consumable and expendable laboratory supplies and equipment. The
costs of these items should be insignificant in comparison to personnel
costs, for example, so that a detailed estimate here does not appear
warranted. Accordingly, an average cost of S4000 per quarter will be
taken as the cost of these consumable and expendable items.
Total Cost of Research Plan
The total estimated cost of the effort covered by the Research Plan
is summarized in Table 11 by quarter. A total of $9.7 million is esti-
mated, with about 85% attributed to personnel. On an annual basis, costs
tend to peak in 1974 at $2.6 million, caused primarily by higher costs
of equipment acquisition and operations.
Total Costs of RAPS
The total estimated cost of the Regional Study is summarized in
Table 12 by quarter and is almost $21.2 million. The schedule is based
on the assumption that the Regional Study would be authorized on
July 1, 1972, and that activities•are initiated immediately. The
greatest part of the total costs are attributable to personnel, with
about two-thirds of the total costs. Except for the quarter in which
the St. Louis facility is largely completed, the cost within any cate-
gory does not exceed personnel costs. The research staff costs tend to
lie in the range of 1.5 times the permanent staff. Combined instrument
costs of the St. Louis facility and the Research Plan are close to $5.0
million, or almost 25% of the total estimated cost.
86
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Table 11
TOTAL ESTIMATED COSTS OF THE RESEARCH PLAN
(Thousands of Dollars)
Year-Quarter Personnel Instruments Operations
Total
1972-3
-4
Subtotal
1973-1
-2
-3
-4
Subtotal
1974-1
-2
-3
-4
Subtotal
1975-1
-2
-3
-4
Subtotal
1976-1
-2
-3
-4
Subtotal
1977-1
-2
Subtotal
$ 175.6
232.9 $100.0
$ 408.5 $100.0
304.2
338.9 160.0
395.9 50.0
374.4
$1,413.4 $210.0
480.1 463.3
504.5 13.9
476.9
474.2
$1,935.7' $477.2
479.1 189.0
470.8
467.7
459.7
$1,877.3 $189.0
465.7
444.2
448.6
467.0
$1,825.5
442.9
430.5
$ 873.4
$ 4.0
4.0
$ 8.0
4.0
4.0
4.0
4.0
$ 16.0
4.0
96.0
96.0
4.0
$200.0
4.0
96.0
96.0
4.0
$200.0
4.0
4.0
4.0
4.0
$ 16.0
4.0
4.0
$ 8.0
$ 179.6
336.9
$ 516.5
308.2
502.9
449.9
378.4
$1,639.4
947.4
614.4
572.9
478.2
$2,612.9
672.1
566.8
563.7
463.7
$2,266.3
469.7
448.2
452.6
471.0
$1,841.5
446.9
434.5
$ 881.4
Total $8,333.8
$976.2
$448.0 $9,758.0
87
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Table 12
ESTIMATED TOTAL QUARTERLY COSTS OF T}ffi REGIONAL STUDY
(Thousands of Dollars1!
Operating Costs
Initial Costs
Year- St. Louis Research
Quarter Facility Instruments
1972-3 $ 48.8
-4 347.1 $100.0
Subtotal $ 395.9 $100.0
1973-1 2,770.2 160.0
-2 470.9 50.0
-3 306.6
-4
Subtotal 53,547.7 $210.0
1974-1 463.1
-2 13.9
-3
-4
Subtotal $477.0
1975-1 189.0
-2
-3
-4
Subtotal $189.0
197C-1
-2
-3
-4
Subtotal
1977-1
-2
Subtotal
Equipment
St. Louis
Helicopter
? 16.
* 16.
19.
24.
24.
16.
* 84.
19.
24.
24.
16.
* 84.
19.
24.
24.
16.
S 84.
19.
24.
24.
16.
* 84.
19.
24.
* -13.
9
9
3
1
1
9
4
3
1
1
9
4
3
1
1
9
4
3
1
1
9
4
3
1
4
Facili
$ 2.
12.
* 14.
51.
57.
83.
83.
* 275.
83.
83.
83.
83.
S 334.
83.
83.
83.
83.
* 334.
83.
83.
83.
83.
- 334.
83.
83.
$ 167.
tv
0
1
1
0
0
5
5
0
5
5
5
5
0
5
5
5
5
0
5
5
5
5
0
5
5
0
Research
Plan
S 4.
4.
$ 8.
4.
4.
4.
4.
$ 16.
4.
96.
96.
4.
$200.
4.
96.
96.
4.
$200.
4.
4.
4.
4.
$ 16.
4.
4.
S 8.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Personnel
Permanent
Staff
* 97.
151.
S 248.
237.
292.
303.
303.
$1,137.
303.
303.
303.
303.
$1,214.
303.
303.
303.
303.
$1,214.
303.
303.
303.
303.
$1,214.
303.
303.
$ 607.
0
2
2
7
0
7
7
1
7
7
7
7
8
7
7
7
7
8
7
7
7
7
8
7
7
4
Researcli
Staff
$ 175.
232.
$ 408.
304.
338.
395.
374.
$1,413.
480.
501.
476.
471.
$1 ,935.
479.
470.
467.
459.
$1,877.
465.
444.
448.
467.
$1,825.
442.
430.
$ 873.
6
9
5
2
9
9
4
4
1
5
9
2
7
1
8
7
7
3
7
2
6
0
5
9
o
4
Total
* 327.4
864.2
* 1,191.6
3,546.4
1,236.9
1,117.8
782.5
$ 6.G83.6
1,353.7
1,025.7
984.2
882. 3
$ 4.245.9
1,078.6
978. 1
975.0
867.8
S 3,899.5
876.2
859.5
863.9
875.1
* 3,474.7
853. 4
845.8
$ 1 .699.2
Total $3,943.6
$976.0
$397.9 $1,458.1 $448.0 $5,637.1 $8,333.8 $21,194.5
88
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SC553,T050FR
July 1975
REGIONAL AIR POLLUTION STUDY
FINAL REPORT
EXPEDITIONARY RESEARCH PROGRAM
SUMMER 1975
TASK ORDER NO. 50
Prepared for
Environmental Protection Agency
Environmental Sciences Research Laboratory
Research Triangle Park, North Carolina
by
William C. Zegel
Ryckman/Edgerly/Tomlinson and Associates
-------
TABLE OF CONTENTS
1.
2.
3.
4.
PAGE NO.
INTRODUCTION
1 . 1 Background
1.2 Goals and Objectives of the Regional Air
Pollution Study
1.3 Products of the Regional Air Pollution Study
1.4 Selection of Study Area
1.5 Report Organization
REGIONAL AIR POLLUTION STUDY OVERVIEW
2.1 Introduction
2.2 Model Evaluation and Development
2.3 Data Management
2.4 Data Gathering
2.4.1 General
2.4.2 Emission Inventories
2.4.3 Atmospheric Monitoring Network
2.4.4 Expeditionary Research Program
EXPEDITIONARY RESEARCH PROGRAM, SUMMER 1975
3.1 Pollutant Transport and Dispersion Studies
3.1.1 Boundary Layer Measurement Program
3.1.2 Boundary Layer Tracer Studies
3.1.3 Radiation Studies
3.1.4 Heat Flux Studies
3.2 Pollutant Transformation and Removal Studies
3.2.1 Point Source Plume Studies
3.2.2 Urban Plume Studies
3.2.3 Photochemical Reaction Studies
3.2.4 Aerosol Characterization
3.2.5 Dry Removal Processes
3.3 Pollutant Measurement Program
3.3.1 Gas Monitoring Instrument Evaluation
3.3.2 Aerosol Monitoring Instrument Evaluation
3.3.3 Variability Studies
3.4 Pollutant Effects Studies
3.4.1 Damage to Health
3.4.2 Damage to Materials
RAPS STATUS
4 . 1 Status of Model Evaluation and Development
4.2 Status of RAPS Data Bank
4.3 Status of Emission Inventory
1
1
2
3
4
4
5
5
6
6
8
8
9
13
19
22
22
24
30
31
35
40
40
48
51
55
60
62
63
69
78
81
81
82
83
83
83
86
-------
LIST OF TABLES
TABLE NO. PAGE NO.
1 Classification of Sources for Emission Inventory 11
2 RAMS Instrumentation 15
3 Aerosol Instrumentation, Sampling Equipment and _n
Analysis Techniques
4 Details of the Manual and Automated Dichotomous 72
Sampler (MDS and ADS)
5 Utilization of Filter Media in MDS and Hi Vol 73
Samplers
6 Summary of Measurements for Determining Mass 74
Balance
7 Analysis of Sulfur and Sulfur Compounds During 75
Summer, 1975
8 Overall Status of RAPS Emission Inventory 87
9 Status of RAPS Inventory Projects 88
-------
LIST OF FIGURES
FIGURE NO. PAGE NO.
1 Work Breakdown for the Regional Air Pollution 5
Study Showing Interrelationships Between Sub-
Projects
2 Work Breakdown of Model Evaluation and Devel- 7
opment Sub-Project Showing Interrelationships
Between Activities
3 Work Breakdown for Data Management Sub-Project 7
4 Work Breakdown of Data Gathering Sub-Project 8
5 Work Breakdown of the Emission Inventories 12
Activity Showing Interrelationships Between
Sub-Activities
6 Components of the RAPS Atmospheric Monitoring 13
Network
7 RAPS Stations 14
8 Work Breakdown for the RAPS Expeditionary 20
Research Program
9 Work Breakdown for RAPS Pollutant Transport and 23
Dispersion Studies
10 Work Breakdown of Pollutant Transformation and 41
Removal Research Program
11 Work Breakdown of Pollutant Measurement Program 62
12 Availability of RAMS Data Tapes at Level I in 85
St. Louis (STL) and Level II in Research Triangle
Park (RTP)
-------
1. INTRODUCTION
1.1 Background
The Congress of the United States has recognized that air
pollution control is one of the most important problems facing our large
urban and industrial centers. Through the Clean Air Act*, Congress has
charged the EPA with:
1. Protecting and enhancing the quality of the Nation's air
resources so as to promote public health and welfare and
the productive capacity of its population.
2. Initiating and accelerating a national research and develop-
ment program to achieve the prevention and control of air
pollution.
3. Providing technical and financial assistance to State and Local
governments in connection with the development and execution
of their air pollution prevention and control programs.
4.
Encouraging and assisting the development and operation of
regional air pollution control programs.
Based upon these Congressional charges, the EPA has developed a
series of programs implemented through various offices of the Agency. The
Office of Research and Development is responsible for providing the scienti-
fic and technological bases for the establishment of criteria and standards,
and the pollution control technologies to alleviate or deter adverse effects,
primarily upon human health. The programs of the Office of Research and
Development place emphasis on four major areas of activity.
1. The development and standardization of techniques for the
measurement of pollutants, both at their source and in the
ambient environment.
2. The quantification of the effect of human exposare to air
pollutants on both health and welfare.
3. The development of cost-effective control technologies.
4. The development of relationships between sources of pollution
and ambient air quality through an understanding of pollutant emission,
transport, transformation, and removal processes.
*Clean Air Act and its amendments, particularly the Clean Air
Amendments of 1970 and the Air Quality Act of 1967.
-------
The Regional Air Pollution Study (RAPS), a major program of the
Office of Research and Development, is focused primarily on the fourth area
of activity, the verification and development of relationships between
sources of pollution and ambient air quality measurements on the scale of
an air quality control region. Material advancements in technology and
methodology of air quality monitoring and other aspects of air pollution
control, particularly improvement in emission inventory procedures, are also
expected.
The verification and development of these relationships will allow
control actions to become more sophisticated and selective. General control
actions can be confidently tested through these relationships to develop
strategies for a region which provide the desired level of control for the
lowest cost. The verification and development of such relationships will
also allow impact on air quality to become a factor in community and industrial
planning for future growth. They can also be utilized to optimize the size
of a monitoring network needed to define a region's air quality.
1.2 Goals and Objectives of the Regional Air Pollution Study
The goals of the RAPS are:
1. Verification of relationships between sources of pollution
and ambient air quality for all criteria pollutants on the
scale of an air quality control region.
2. Development of improved relationships for source, transport,
dispersion, transformation, and removal processes for all
criteria pollutants (sulfur dioxide, particulates, carbon
monoxide, nitrogen oxides, oxidants and hydrocarbons), but
particularly for sulfur oxides.
The attainment of these broad goals requires the achievement of
several major objectives:
1. Development of improved emission inventory procedures to supply
emission data for the study region with unprecedented high
spatial and temporal resolution.
2. Development of an atmospheric monitoring system capable of
reporting pollutant and meteorological characteristics of the
atmosphere over the study region with very high accuracy and
temporal resolution.
3. Creation of an extensive validated data bank containing emission,
air quality and meteorological data, as well as other relevant
information, for the study region, with appropriate data handling
procedures, to be used in verification of existing and improved
relationships.
4. Improvement in our understanding of the pollutant transport and
-------
dispersion processes of the atmosphere through experimental
studies of energy and momentum fluxes over the study region.
5. Improvement in our understanding of the pollutant transforma-
tions occurring in the atmosphere through experimental studies
of the role of sulfur dioxide, carbon monoxide, nitric oxide,
and organics in producing sulfates, nitrogen dioxide, nitrates,
ozone, organic aerosols and other finely divided particulate
materials in the atmosphere over the study region.
6. Improvement in our understanding of pollutant removal processes,
particularly experimental determination of dry deposition veloci-
ties of sulfur dioxide for various types of land surfaces in the
study region.
7. Improvement in our understanding of local-scale phenomena which
complement regional-scale relationships.
1.3 Products of the Regional Air Pollution Study
As a. result of achieving these objectives, several products can be
expected from the RAPS. The primary product is a group of relationships
between sources of pollution and ambient air quality which are available to
other EPA Offices, air pollution control and planning agencies of State,
Regional, County and Local governments, and industry. These relationships
will be in an appropriate form for use; they will have been tested and veri-
fied, and their best use identified in consideration of their accuracy and
required input data, as well as their spatial and temporal resolution.
A second major product is improved methodologies for emission in-
ventories. Because of the stringent demands of the RAPS, new approaches to
emission inventories must be developed. This will result in methodologies
offering air pollution control agencies an opportunity to improve their
inventories and thereby their understanding of pollution sources in their
areas and control of these sources.
Another important product is a data bank with unprecedented resolu-
tion of air quality, meteorological and emission information. This data bank,
with its associated data management system, will be invaluable in the testing
and verification of relationships between pollution sources and ambient air
quality. This extensive description of a region may also suggest new forms
for these relationships and assist in the development of new relationships.
The RAPS will also provide an opportunity for new instruments and
instrument systems to be tested under field conditions and compared with a
state-of-the-art monitoring system. This will allow verification of their
measurements and a demonstration of their utility in monitoring systems of the
future.
A very important product of the RAPS, and perhaps one with the
greatest implications for future control of air pollution, is an improved
-------
understanding of the processes of pollutant transport and dispersion, and
pollutant transformation and removal in the atmosphere. These may be expressed
as improvements in the overall relationships between sources and ambient air
quality.
1.4 Selection of Study Area
Thirty-three Standard Metropolitan Statistical Areas larger than
400,000 population were evaluated by the Stanford Research Institute (SRI),
with regard to:
1. Surrounding area - Isolation from other large areas containing
sources of considerable air pollution, presence of a clear-cut
gradient of emissions around the edge of the urban area, and
absence of large bodies of water.
2. Heterogeneous emissions - Presence of a satisfactory mixture
of emissions and types of sources within the area.
3. Area size - An indication of the scope and magnitude of the
study for each site.
4. Pollution control program - Existence of a well-developed con-
trol program as a source of background data, experience, and
industrial cooperation.
5. Historical information - Adequate meteorological, air quality,
economic, and other forms of information for the study.
6. Climate - Relatively uncomplicated meteorological patterns
and a climate suitable for year-round outside work.
The SRI recommendation that St. Louis be selected as the study site
was accepted and approved by the EPA.
1.5 Report Organization
This report contains, in addition to this introductory section,
three sections dealing with the RAPS.
Section 2 presents an overview of the RAPS to show the role of
the Expeditionary Research Program in achieving the goals and
objectives of the RAPS.
Section 3 presents the details of the Expeditionary Research
Program, focusing on the Summer 1975 exercise.
Section 4 contains a status report on model evaluation, model
development, RAPS data bank, and emission inventory.
-------
2.
REGIONAL AIR POLLUTION STUDY OVERVIEW
2.1
Introduction
For maximum utility, relationships for a region between pollution
sources and ambient air quality generally take the form of a system of mathe-
matical simulation models, which may include models for pollutant source
characteristics, models for pollutant transport and dispersion processes, models
for transformation and removal processes, and models for various local phenomena.
These systems of models are the focus of the RAPS. The project of verifying and
developing these models consists of three fundamental sub-projects, as shown in
Figure 1:
1. Model Evaluation and Development - Testing and verification
of existing systems of models, and the development of improved
models with subsequent testing and verification.
2. Data Management - A bridge between the sub-projects of model
evaluation and development and data gathering made necessary
by the complexity of available systems of models and the sheer
volume of data needed for model development, testing and veri-
fication.
3. Data Gathering - Providing the values that the attributes of
the various models can have and defining the relationships
involved in the component models.
These three sub-projects are intimately related, as shown in
Figure 1. Assumptions concerning the source and atmospheric processes in the
various models direct, through the data management sub-project, the gathering
of data; analysis of the data gathered through the data management sub-project,
which will confirm or refute those assumptions, and may, in fact, disclose an
unsuspected relationship that changes the model structure.
REGIONAL
AIR POLLUTION
STUDY
MODEL
EVALUATION AND
DEVELOPMENT
DATA
MANAGEMENT
DATA
GATHERING
FIGURE 1 - WORK BREAKDOWN FOR THE REGIONAL AIR POLLUTION
STUDY SHOWING INTERRELATIONSHIPS BETWEEN SUB-
• PROJECTS
-------
This section of the report examines each of these sub-projects in
turn, showing their component activities and interrelationships.
2.2 Model Evaluation and Development
Functionally there are three types of models:
1. Diagnostic - Use meteorological and emission inputs to
compute pollutant distribution.
2. Predictive - Use current initial conditions to predict
meteorological and emission fields and hence future
pollutant distribution.
3. Climatic - Use long-term data to describe changes in the
mesoclimate as a result of mesoscale urbanization.
Considering the needs of air pollution control agencies, the
primary emphasis in the RAPS has been placed on diagnostic models. Fur-
ther, the emphasis has been placed upon deterministic, physically-based
relationships between emissions and ambient air quality.
While there are presently a large number of such models in use,
none except the most simple have demonstrated a quantitative capability to
predict the air quality of a region within a specified degree of accuracy.
A major thrust of the RAPS is the evaluation of existing models using a
significant set of regional atmospheric and emission data. Model develop-
ment, while important, particularly in the areas of pollutant transforma-
tion and removal, is given a lower priority.
As the name of the sub-project implies, in the evaluation and
development of models, two distinct, but related, activities are needed,
as shown in Figure 2:
1. Evaluation of models utilizing the data gathered in
the RAPS.
2. Development of improved models by synergistic combinations
of existing models, or new approaches suggested by the
experiments associated with the RAPS.
The primary objective of model evaluation and development is a
group of models in a form useable to various control and planning agencies.
These models will have been tested, verified and their best use determined.
2.3 Data Management
The RAPS involves an average data collection in excess of one-million
observations per day over a period of two years. The objectives of the data
management sub-project are to develop and maintain a data management system
-------
MODEL
EVALUATION AND
DEVELOPMENT
EVALUATION
OF
MODELS
DEVELOPMENT OF
IMPROVED
MODELS
FIGURE 2 - WORK BREAKDOWN OF MODEL EVALUATION AND
DEVELOPMENT SUB-PROJECT SHOWING INTER-
RELATIONSHIPS BETWEEN ACTIVITIES
responsive to user requirements within the framework of the RAPS objectives.
It is also to act as the interface between the data-gathering and the model
evaluation and development sub-projects. In addition, data integrity must be
kept as high as possible through validation programs while minimizing the
computer requirements. All of this required efficient storage and retrieval
software, simple on-line display and analysis capability, time distribution
of data in user specified formats, periodic data base summary reports and
adaptability to changing needs and schedules.
The work breakdown diagram for this sub-project is shown in Figure
3 and reveals two major activities:
1. Develop and Maintain the RAPS Data Bank - The archiving of the data
gathered, together with appropriate insertion and access software.
2. Fulfill User Requirements- Develop systems to respond to the re-
quirements of the Model Evaluation and Development Sub-Project
and the Data Gathering Sub-project.
DATA
MANAGEMENT
DEVELOP AND
MAINTAIN
DATA BANK
FULFILL USER
REQUIREMENTS
FIGURE 3 - WORK BREAKDOWN FOR DATA MANAGEMENT SUB-PROJECT
-------
2.4 Data Gathering
2.4.1 General
Data gathering on both a routine and special basis is a fundamental
sub-project carried-out in the program of the RAPS. The data gathered includes
detailed information concerning pollution sources, meteorological conditions and
air quality throughout the region. The data gathering sub-project can be visua-
lized in terms of three activities as shown in Figure 4:
1. Emission Inventories - to identify, locate and quantify sources
of pollutants in the St. Louis region.
2. Continuous Atmospheric Monitoring - to produce a data base of
sufficient scope to support the RAPS objectives and extend the
understanding of atmospheric phenomena.
3. Expeditionary Research Program - supply detailed data to better
understand selected pollutant and atmospheric phenomena.
DATA
GATHERING
EMISSION
INVENTORIES
ATMOSPHERIC
MONITORING
NETWORK
EXPEDITIONARY
RESEARCH
PROGRAM
FIGURE 4 - WORK BREAKDOWN OF DATA GATHERING SUB-PROJECT
Each of these activities fills an essential role in achieving the RAPS goals
and objectives.
-------
Another activity includes efforts to ensure the quality and
integrity of data obtained by the RAPS. This is accomplished through a
two-pronged effort:
1. The computer checks the values of each received datum for
reasonableness. Such a check compares the datum, its rate
of change with time, and variation from site to site, with
an upper and lower bound associated with that measurement
and, if appropriate, generates an error code so that an
investigation, and possibly corrective action, can be under-
taken.
2. An independent audit and study of all data gathering activities
is performed. This independent audit includes:
A. A systematic, on-site qualitative review of the existing
data handling procedures for the Regional Air Monitoring
System (RAMS) and Upper Air Sounding Network (UASN).
B. A systematic, on-site/off-site qualitative review of docu-
mentation, data collection, retrieval and validation tech-
niques employed by the various RAPS field investigators
during any of the intensive exercises; and
C. A systematic, on-site quantitative audit to collect informa-
tion on the precision and accuracy of the air quality and
meteorological measurements obtained from and generated by
the RAMS.
2.4.2 Emission Inventories
Emission inventories are an essential part of any attempt to predict
air quality through a regional air quality simulation model. The accuracy of
such predictions is directly proportional to the overall accuracy of the
inventories. In consideration of the various air quality model input require-
ments, the RAPS inventories are based on the following criteria:
1. Pollutants - Sulfur dioxide, carbon monoxide, nitrogen oxides,
hydrocarbons (by types), particulates, and heat emissions.
2. Resolution - Temporal, hourly, for each hour; Spatial, 0.01
kilometer point sources, 1 square kilometer grid squares.
3. Area Covered - The St. Louis Air Quality Control Region.
4. Period Covered - Hourly throughout the period of the RAPS data
acquisition.
5. Units - Emissions by metric weight; distance in kilometers;
location in Universal Transverse Mercator (UTM) coordinates;
elevation in meters above sea level.
-------
6. Other information - Data on sources (e.g., stack height, exit
temperature, velocity) where appropriate; data for mobile
sources, such as traffic flow, and aircraft movements, as
required for use in appropriate emission models.
The sources to be inventoried can be classified according to the
nature of the source. Such a classification system is presented in Table 1.
This scheme is intended to accommodate all possible sources and pollutants
in a format structured according to the methodology that must be used to
gather the data and/or according to the method that the information must
be applied in diffusion modeling.
The primary division of sources into categories separates station-
ary from mobile sources, since these present radically different problems with
respect to both emission inventpries and modeling. In the secondary division,
stationary sources are divided into area sources and point sources, whereas
mobile sources are composed of area and line sources. Dividing the mobile
sources into area or line sources is a matter of expediency. Well-defined
and heavily traveled traffic arteries, such as freeways, can be treated as
individual line sources. The more diffuse traffic on city streets can best be
handled on an area basis.
The division of stationary sources into point and area sources is
necessarily arbitrary. The point sources, or source units, are those large
enough to warrant individual consideration. Area source units are, by con-
trast, units having relatively small emissions, and they cannot for practical
purposes be treated individually. The emissions from those small units exist-
ing in a given area are therefore aggregated and estimated from some facts,
such as the consumption of fuel within the specified area.
The criterion of size for the definition of point source units is
relative, and is related mainly to the precision desired for the inventory
and for the diffusion estimates derived from it. A unit emitting a small
absolute quantity of pollutant material may in fact be an important point
source if it nevertheless contributes an appreciable fraction of the total
emission of that specific pollutant into the region. A given source unit
may be relatively insignificant with respect to one pollutant of interest,
and at the same time be a very large emitter of another pollutant.
The source processes are conveniently classified as either combustion
or noncombustion processes. Combustion processes are defined as those in
which the pollutants are produced exclusively by the burning of fuels or
of solid or liquid wastes. They include all those processes in which there
is indirect transfer of the heat produced (e.g., boilers, indirect-fired air
heaters) as well as incinerators, internal combustion engines, and gas turbines.
Noncombustion processes comprise all other pollutant sources not falling under
the specific definition of combustion processes. They include operations in
which combustion takes place, but in which part or all of the pollutants
emitted arise from operations other than the burning of fuel or wastes.
Examples are those processes in which the products of fuel combustion come
into direct contact with materials being processed, such as calcining of
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materials in kilns.
By far the largest number of pollutant sources, stationary and
mobile, are combustion sources. In particular, combustion processes comprise
the most important area source units, and estimates of emissions from these
numerous contributors can be made from estimates of fuel consumption. The
most important noncombustion sources are industrial, and are generally point
sources.
The emission inventory activity consists of three basic sub-
activities, as shown in Figure 5:
1.
Establishing Emission Inventory Methodologies - Developing
methodologies for each of the inventories needed in the RAPS
in light of the characteristics of each category of sources,
and the state-of-the-art for such inventories, and the RAPS
needs.
Developing the Emission Inventory Data System - Design and
implementation of a system capable of recording, storing,
retrieving, editing, and updating all data required for the
computation of emissions consistent with the RAPS require-
ments.
Gathering Emission Inventory Data - According to methodologies
and data system.
EMISSION
INVENTORIES
ACTIVITY
DEVELOP
DATA
SYSTEM
ESTABLISH
INVENTORY
METHODOLOGIES
GATHER
INVENTORY
DATA
FIGURE 5 - WORK BREAKDOWN OF THE EMISSION INVENTORIES ACTIVITY
SHOWING INTERRELATIONSHIPS BETWEEN SUB-ACTIVITIES
12
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2.4.3
Atmospheric Monitoring Network
The operation of the atmospheric monitoring network in support of the
RAPS constitutes the longest and most concentrated effort ever undertaken to
define and describe an urban atmosphere. It far surpasses previous data collection
efforts in terms of volume and diversity. The majority of this data will be
routinely gathered by the various components of the atmospheric monitoring network.
This network consists of an extensive ground-based Regional Air Monitoring System.
(RAMS), an Upper Air Sounding Network (UASN), and an aerial monitoring system as
shown in Figure 6.
ATMOSPHERIC
MONITORING
NETWORK
REGIONAL AIR
MONITORING
SYSTEM
UPPER AIR
SOUNDING
NETWORK
AERIAL
MONITORING
SYSTEM
FIGURE 6 - COMPONENTS OF THE RAPS ATMOSPHERIC MONITORING NETWORK
The RAMS consists of 25 remotely-operated, automated stations con-
trolled and polled via telemetry by a central data acquisition system. The
locations of these stations are shown in Figure 7. The stations are indi-
vidually "managed" by mini-computers which provide for automatic calibration
of the pollutant gas instruments. It is the objective of this network to
provide a long term, uniform, verified data base of ground-based measurements
of various air pollutants, as well as solar radiation and meterological
variables. The instrumentation included in the RAMS are summarized in Table
2.
13
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FIGURE 7
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The UASN consists of four stations, one in an urban area and three
in rural areas. The locations of these stations are also shown in Figure 7.
Two of the stations (Sites 143 and 144) operate only during intensive experi-
ment periods. The RAMS network provides a relatively dense data base of sur-
face winds, temperature and relative humidity. The combination of these data
with those obtained from the UASN allows the determination of changes in winds
and stability throughout the area, particularly as they relate to terrain features
and synoptic scale meteorology. It is the objective of the UASN to provide a
data base of the upper air structure over the St. Louis region. This data base
consists of winds, temperature, dew point and relative humidity aloft. This
provides the basis for more extensive definition of the urban boundary layer as
part of the Expeditionary Research Program.
The RAMS and UASN are augmented by the use of instrumented helicopters
which act as vertical extensions of the RAMS. This aerial monitoring system
functions only during selected periods to coincide with data gathering by the
various research investigators. The aerial system consists of three Sikorsky-
58 helicopters modified to carry two complete aerial monitoring systems; the
third helicopter serves as back-up. Data collected consists of vertical distri-
bution of pollutants and meteorological variables above the surface. In addition
to providing data for model validation, they are also able to obtain data on con-
ditions at the lateral boundaries of the St. Louis region.
2.4.4 Expeditionary Research Program
A basic objective of the RAPS is to improve our understanding of
fundamental atmospheric processes. The relatively extensive characterization
of the St. Louis region resulting from the emission inventories and continuous
atmospheric monitoring provides an excellent background for research programs
investigating various atmospheric pollutant processes. Several expeditionary
investigations are planned to be carried out during the full scale operation
of the Continuous Atmospheric Monitoring Network. These field expeditions
supply short-term, detailed atmospheric observations in support of the develop-
ment and validation of source-ambient air quality relationships. They extend
and augment the data from the RAMS and UASN and concentrate on improving our
understanding of particular atmospheric processes.
These expeditionary investigations are carried out during three periods
of intensive research each year, at about February, August and November. The
periods selected represent the two extremes of climate, fuel utilization, and
seasonal variation in sources, with a transitional period between. The fall
transitional period was selected over the spring due to the higher frequencies
of violent weather in the spring and stagnation in the fall.
19
-------
The investigation to be conducted during the Expeditionary Research
Program can be categorized, as shown in Figure 8:
EXPEDITIONARY
RESEARCH
PROGRAM
POLLUTANT
TRANSPORT
DISPERSION
POLLUTANT
TRANSFORMATION
$ REMOVAL
POLLUTANT
EFFECTS
STUDIES
POLLUTANT
MEASUREMENT
PROGRAM
FIGURE 8 - WORK BREAKDOWN FOR THE RAPS EXPEDITIONARY
RESEARCH PROGRAM
Pollutant Transport and Dispersion Studies - Investigations
aimed at improving our understanding of the transport and dis-
persion of pollutants after they are released to the atmosphere.
Pollutant Transformation and Removal Studies - Studies designed
to uncover the basic mechanisms for transformation of one pol-
lutant to another, or to a non-pollutant, in the atmosphere
and at the solid-atmosphere and water-atmosphere interfaces.
20
-------
3. Pollutant Measurement Program - Conduct field measurements
utilizing new and/or different instrumentation methods to
determine how representative the RAMS measurements are for
the St. Louis atmosphere.
4. Pollutant Effects Studies - Measurement of selected effects
of pollutants on living and non-living systems.
21
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3. EXPEDITIONARY RESEARCH PROGRAM, SUMMER 1975
The principal objectives of the field expeditions are to supply
short-term, detailed atmospheric observations in support of the validation
and development of source-ambient air quality relationships, and to extend
and augment the data from the RAMS and UASN and to concentrate on improving our
understanding of particular atmospheric processes. As previously indicated, the
investigations to be conducted during the RAPS Expeditionary Research Program (ERP)
can be categorized into four areas:
1. Pollutant Transport and Dispersion Studies
2. Pollutant Transformation and Removal Studies
3. Pollutant Measurement Program
4. Pollutant Effects Studies
3.1 Pollutant Transport and Dispersion Studies
The transport and dispersion of pollutants in the atmosphere occurs
principally in the planetary boundary layer. These series of experiments
are directed toward understanding and subsequently describing the relationships
between atmospheric dynamic, kinematic, and energetic processes which occur in
this boundary layer and the resultant transport and diffusion of effluents.
Of particular interest is the impact on the boundary layer of the widely
varying thermal and mechanical properties of the urban surface. Knowledge of
this impact in terms of the temporal and spatial structure of the boundary
layer over the urban area is limited since few measurements have been made,
and even fewer studies have been carried out to relate the sparse observations
to underlying physical causes.
Since mathematical dispersion formulations are inherently limited in
accuracy by definition of the boundary layer structure, including its turbulent
properties as implicitly assumed in these formulations, better description of
this structure is vital for model validation studies. In addition, a better
knowledge of the influence of the urban surface on the boundary layer structure
will permit application of results obtained in the St. Louis region to other
areas, whether existent or hypothetical.
The experiments associated with the Pollutant Transport and Disper-
sion Studies may be segregated into two areas of emphasis, those primarily
dealing with describing the effect of the urban area on the boundary layer and
those associated with understanding the mechanism that produce this urban effect.
The former refers to the spatial and temporal definition of the boundary layer
over the St. Louis region, whereas the latter refers to the investigations into
various components of the energy budget for the St. Louis region. The breakdown
of the experiments associated with the Pollutant Transport and Dispersion
Studies is shown in Figure 9.
22
-------
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3.1.1 Boundary Layer Measurement Program
The objective of the measurement program is to spatially and temporally
describe the boundary layer over the St. Louis region. The techniques to be used
can be categorized as direct, in the sense that the measurement is made of a
sample of the air representative of the area around the measurement system, or
remote, where measurements are made of atmospheric properties some distance from
the measurement system. For direct measurements, a group of specially instru-
mented aircraft and ground vehicles with special pibal teams are used to obtain
detailed information concerning structure and turbulent properties of the atmos-
phere.
The data from the Upper Air Sounding Network and the Aerial Monitor-
ing System will also be used in the definition of the boundary layer, although
the helicopters generally cannot descend to low enough altitudes and cannot
adequately cover the center of the urban area. These constitute the other data
available for boundary layer definition.
The remote measurements consist of lidars and an acoustic echo sounder.
The lidars will be used to scan for the aerosol loading both day and night to
obtain a measure of mixing depth and aerosol structure above the mixing depth.
Temporal sequences from a stationary (but movable) lidar van and spatial patterns
from this van and the NERC-LV C-45 lidar aircraft will be obtained. The sounder
determines the height of the mixing layer by a return of an acoustic echo at the
level of a sharp change in air density. It is permanently implanted at the
Upper Air Sounding Network Station located in downtown St. Louis to constantly
monitor the mixing depth over the urban area and cross check the UASN data.
Experiment descriptions for the Summer of 1975 are presented in the
following pages.
24
-------
Measurement of Boundary Layer Structure
Summer 1975
Key Personnel:
J. McElroy, Monitoring Systems Research and Development
Laboratory - EPA
Research Goal:
Determine the detailed temporal and spatial variability
in the urban boundary layer structure under a wide spec-
trum of weather and wind conditions
General Experiment Design:
In past intensive studies, emphasis was placed on the rapid tran-
sitional periods around sunset and sunrise. This will continue to the
extent that "gaps" in the data base will be filled. Experiments will also
be conducted to ascertain effects of local land-use features such as Forest
Park or the Mississippi River. Intercomparisons of various techniques for
determining mixing layer depth which began during the Winter '75 exercises
will be continued.
A small helicopter, a panel van, a second surface vehicle, a
mobile ground-based lidar, and a downward-pointing lidar flown in a fixed-
wing aircraft will be operated in coordinated fashion during boundary layer
structure experiments. Coordination will be achieved using communications
radios. Personnel from Meteorology Laboratory and a contractor will partic-
ipate in this program. The helicopter will be obtained through the selected
contractor.
The helicopter will make vertical soundings from near ground level
through the extent of the boundary layer across the metropolitan area roughly
in the direction of the mean low-level wind. From these soundings vertical
profiles of temperature, dew point temperature, total light scatter from
aerosols (nephelometer), and sulfur dioxide concentration will be obtained.
Locations and frequencies of soundings will be determined in real time,
based on information by observers in the various vehicles via radio. Level
traverses will also be made where appropriate. Pollutant data is primarily
collected to aid in real time and post analysis of weather information to
the extent that such data serves as a tracer for physical processes.
The panel van and second surface vehicle will be utilized to map
the near-surface features of the structure. The panel van has identical
instrumentation and will generally travel in line with the helicopter. The
other surface vehicle has sensors for measuring temperature and dew point
temperature and will provide detailed information concerning these para-
meters in areas selected in real time.
25
-------
The lidars will be used to furnish details of boundary layer
structure, chiefly mixing depth, which cannot effectively be obtained utilizing
the helicopter. The mode of operation for the ground-based lidar will depend
on the anticipated degree of spatial and temporal variability during sampling
periods. The airborne lidar (NERC-LV) will generally be flown when extreme
variability in structure, mainly temporal, is anticipated. This will probably
only be used for a two-week interval during the intensive. The ground-based
lidar will be operated over extended periods at the downtown upper air site
in coordination with the acoustic sounder to compare these different methods
of measuring mixing depth and data on boundary layer structure.
Quality Assurance Plans:
Extensive calibrations will be performed on all equipment at
the NERC's prior to and following the intensives. Limited calibrations will
be performed before and after each experiment. All such calibrations will be
placed into the logs for the respective equipment.
Field Schedule:
Field experiments will be conducted over a one month period
beginning about July 14, 1975.
Data Management Information:
Data obtained by the helicopter, panel van, and ground-based
and airborne lidars will be collected on magnetic tape. That obtained by
the second surface vehicle will be collected on analog strip charts. In
addition, temperature and pressure-altitude are collected on analog strip
charts in the helicopter and panel van for real time decision-making. Lidar
data for the ground-based vehicle is also collected on a video disc, thus
permitting polaroid prints of signal returns from single or composite firings
if a cathode ray tube display system is available.
The facilities at the RAMS Central Facility will be used
for reading magnetic tapes for the lidar. Additionally, the digitizer at
this facility will be used for the processing of data collected on analog
strip charts. At present, it is planned that this digitized data and other
data collected on magnetic tapes will be processed by R. Browning's group at
NERC-RTP.
*
Rawinsonde and pibal data from all RAPS upper air stations
and hourly-averaged data from all RAMS stations will be required for post
analysis. Unchecked data will be required during the intensive for preli-
minary analyses. In addition, limited rawinsonde, pibal, and "instantaneous"
RAMS data from selected stations will be required in real time for decision
making in the field. Parameters of primary interest at the RAMS stations
include wind speed, wind direction, temperature, dew point temperature,
total light scatter from aerosols, and carbon monoxide and sulfur dioxide
26
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concentrations.
Logistics and Services Required from RAPS/STL:
1. Office space and radio facilities to direct
various units participating in the experiments.
2. Weather forecasting for day-to-day planning of
experiments.
3. May require additional personnel to assist in opera-
ting equipment and as observer in helicopter.
Power Requirements:
No RAMS power required.
27
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Boundary Layer Studies
Summer 1975
Key Personnel:
A. Auer, University of Wyoming
Research Goals:
1. Determination of the space-time variability of atmos-
pheric structure over St. Louis region in terms of
temperature, moisture, wind and turbulence.
2. Determination of the boundaries of the urban plume
to approximately 55 km of downtown St. Louis.
General Experimental Design:
The University of Wyoming Queen Air aircraft will be the princi-
pal instrument platform. This will be supported by two mobile meteorological
units and a mobile radiosonde unit. The experiments will be conducted as a
series of concentrated case studies which take advantage of existing weather
conditions. Of particular interest are effects of aerosols on radiational
properties and, hence, on the boundary layer, and gross estimates of heat,
moisture and Aitkennuclei budgets over several land use and areas under spe-
cific conditions.
Quality Assurance Plans:
Equipment is calibrated prior to and following the experiment per-
iod at the University of Wyoming. During experiments, fly-bys will be made
with other RAPS aircraft.
Field Schedule:
July 15 to August 20
Data Management Information:
The aircraft has an on-board computer which keeps track of and re-
cords on magnetic tape, temperature, potential temperature, dew point,
specific humidity, doppler winds, Aitken nuclei, turbulence intensity, and
equivalent potential temperature. These data tapes will be processed by the
University of Wyoming and made available in the form of reports and/or papers.
28
-------
Power Requirements:
None
Poteatial Problem Areas:
Coordination with J. McElroy and B. Ackerman
29
-------
3.1.2 Boundary Layer Tracer Studies
Tracer studies are utilized to determine the patterns resulting from
the transport and dispersion of airborne material over the St. Louis region.
Tracer studies are one technique in which known amounts of identifiable materials
(gases or particulates) are released and sampled downstream at different posi-
tions and times. The release of balloons, floating at constant altitude
(tetroons) serves to track the transport wind and is another technique for
tracing. The introduction of tracers enables the analyst to start with a known
source at a specific location and to avoid confusion with other sources in
subsequent measurements downstream. During the tracer studies for the RAPS,
measures of the tracer dispersion and its variations are related to measures of
atmospheric turbulence and transport; atmospheric indices in turn are related to
meteorological analysis based on observations feasible for routine acquisition.
Tracer experiments are designed to supplement data concerning the
dispersion of airborne materials. Of particular interest is the standard
deviation of the vertical distribution of material in a plume, since this is
most likely to be influenced by the urban environment. Also of interest is
'the variation of the standard deviation of the horizontal distribution with
range and height. These apply to both short scale experiments of a few kilo-
meters and long scale tests in excess of 100 kilometers.
For the purpose of examining the transport and dispersion character-
istics of the atmosphere, four types of experiments can be performed:
1. Simultaneous release of several tracers at various heights
or at various crosswind or along-the-wind distances.
2. Injection of a tracer into an actual source of pollution.
3. Study of tracers incorporated in the urban plume a distance
from the urban area.
4. Tetroon studies over extended areas around the city.
No boundary layer tracer studies are planned for the Summer of 1975.
30
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3.1.3 Radiation Studies
Existing observational data have revealed differences in the radiation
budget of an urban area with respect to a neighboring rural area. As land use
varies, differences in radiation budgets also vary. To establish this radia-
tion budget variation, a series of experiments has been designed involving
selected RAMS stations and a specially instrumented aircraft from Pennsylvania
State University. The most important component of the surface radiation budget,
and in fact the total surface heat budget, is the available total solar radia-
tion. This includes both the direct radiation and the diffuse or sky radiation.
Sensors are installed at selected RAMS stations to measure the UV direct
and sky visible, IR and long-wave radiations. These are supplemented by normal
incidence pyrheliometers at some of the sites with the ability to place differ-
ent types of filters in the light path and thereby measure different spectral
components of the direct radiation. These include a portion of the visible
spectrum free of molecular absorbers and a portion of the infrared spectrum
including water vapor absorption.
At several key solar hour angles, aircraft observations of downward
and upward solar fluxes and downward and upward total radiation are made both
at low level and just above the boundary layer. Sufficient flights are con-
ducted under various meteorological conditions to:
1. Provide direct information on solar heating and infrared cooling
rates for the boundary layers over urban and rural areas.
2. Provide a relative measure of the spatial distribution of the
surface albedo and the thermal emission.
The flights are over several representative land use types so that
appropriate radiation budgets can be prepared.
Experiments planned for the Summer of 1975 are described on the
following pages.
31
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Radiation Measurements
Summer 1975
Key Personnel:
J. Peterson, E. Flowers - MIL, EPA
D. Thompson - Pennsylvania State University
Research Goals:
1. Measure surface albedo of representative land use areas
throughout St. Louis.
2. Measure vertical variation of solar radiation as a study of
the effect of pollutants on atmospheric heating and cooling
rates.
3. Validate solar radiation ground monitoring network.
4. Evaluate urban-rural variability in solar radiation.
General Experiment Design:
Pennsylvania State University's Aerocommander 680E meteorological
research aircraft will be utilized as the platform for the airborne radia-
tion measurements. This aircraft has six Eppley Precision Spectral Pyrano-
meters, three "looking up" and three "looking down." Each set of three
measures the incident global solar irradiance in three broad spectral bands:
ultraviolet, visible and infrared. Similar sets of pyranometers are also
installed on the roofs of RAMS stations 103, 104, 108, 114, 118 and 122,
which are in urban and rural areas and on the roof of the MTL LIDAR van.
The aircraft also has upward and downward "looking" Eppley pyrgeometers
to measure long wave radiation.
A flight path will be designed over selected land use types, such
as rural (field and forest), new residential, old residential, commercial,
and industrial. To determine the effect of sun angle and building shadows,
albedo measurements will be taken at 8-9 a.m., noon, and 3-4 p.m. To mini-
mize atmospheric effects, low-level (about 1000 ft.) flights will also be
made to determine the gross urban-rural effects.
During each two to three hour flight, two vertical profiles will
be made, one in relatively clean air upwind of the city and one in the down-
wind conditions. For each profile, the aircraft will climb to approximately
10,000 ft. and in descending, level-off for about one minute of measurements
at 1,000 ft. intervals above the haze and 500 ft. intervals within the haze,
to 500 ft. above the ground.
32
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In the aircraft, three atmospheric aerosol sensors will be avail-
able: nephelometer, Royco counter for size distribution and Hnvironmental-
One cloud condensation nuclei counter. A number of supporting parameters
will also be measured and recorded: temperature, dew point, pressure, indi-
cated speed, ground speed, drift angle, pitch, roll, heading, VOR-DME, radio
altitude, date and time. A Barnes PRT-5 radiation thermometer is also avail-
able for measurements of ground-surface temperature.
Lidar data would be valuable in the analysis of the radiation data,
and the Lidar van will be utilized during mid-day. The sensors installed
on the roof of the van will be used to support the Lidar observations. The
van will, whenever possible, be positioned beneath the aircraft during the
vertical flux profiles to provide a ground check on the aircraft measurements.
Quality Assurance Plans:
The calibration factors provided by Eppley for the sensors on the
aircraft are checked with a Link-Fuessner pyrheliometer. On a clear day the
Link-Fuessner is used to determine the solar beam flux density in a plane
perpendicular to the direction of the sun. The Eppley units are then com-
pared with these results.
RAMS sensors and some of the aircraft sensors are checked by com-
parison with a reference pyronometer.
Field Schedule:
Aircraft measurements for three weeks starting July 13. Additional
time may be required for RAMS checks.
Data Management Information:
The Penn State aircraft has a digital data recording system which
will be utilized to record all measurements (radiation, aerosol and support-
ing) on magnetic tape. The data system has 35 channels of input plus fixed
information (ID, date, time). All channels are scanned twice per second.
Following each flight the output tape will be taken to the RAMS
computer for an initial view of the data. This includes a listing and plots
of parts of the data. Calibration factors will be applied and a tape of all
measurements in engineering units (not voltages) will be brought to Penn
State for further processing and then to RTP for analysis. A one or two
day turn-around time is sufficient. We estimate that during the three week
experiment, ten tapes will be generated, each with some 14,000 scans of 35
channels of data. Rockwell currently possesses the computer program for
this task.
Computer time is required at RTP for analysis of tapes.
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During the experimental period, RAMS radiation data and UASN data are
required. Following this period, the validated data, with calibration and
other applicable inputs applied, will be required.
Logistics and Services Required from RAPS/STL:
Access to RAMS stations 103, 104, 108, 114, 118 and 122 is necessary
for checks and cleaning with space on the counter to locate a recording system.
Central computer time and software for a "quick look" at the data and a
preliminary processing of the data is required. During the investigation period
a print-out of the one-minute data from all radiation sensors at the six sites
from 0300 to 2100 daily is desired. At one line/minute, 60 lines/page this will
amount to only 504 pages for 28 days.
Operational data from the UASN is required. On selected days copies of
the rawinsonde measurements will be required.
Use of the Las Vegas helicopters for as many as ten vertical sampling
profiles is requested. Profiles should be made during cloud free conditions,
between 0900 and 1500 CST from near ground level to as high as 10,000 feet. The
top of the profile should be at least 1000 feet above the top of the mixing layer.
The profiles should be made over RAMS sites having a pyrheliometer ( 103, 114, 118,
122). At any single site the profiles should be separated by approximately 1/2
hour or more in time. It is requested that both helicopters be equipped with
operating ROYCO counters.
Weather forecasting service - data cannot be taken on cloudy days, thus
forecasting for the next day is extremely important in scheduling personnel and
equipment. Primary time for forecasting cloud conditions is 0700 - 1200.
Power Requirements:
Only at aircraft hanger
Potential Problems:
Weather
Coordination of Lidar unit with J. McElroy.
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3.1.4 Heat Flux Studies
The measurement of vertical sensible heat flux through the boundary
layer is approached using four different measurement platforms due to limita-
tions involved with each. At the surface, a 4m array of fast response ther-
mistor and vertical velocity measurements units called a Fluxatron is used.
Above 300 meters a specially instrumented NCAR aircraft is used. This aircraft
can detect the sensible heat flux as well as the latent heat flux. Between
these two may be a specially developed tethered balloon. This will serve as
a platform for sensible heat flux instrumentation, such as that in the Fluxatron,
at several altitudes in the boundary layer. Below the surface are arrays of
thermistors imbedded in typical urban and rural surfaces.
Descriptions of experiments planned for the Summer of 1975 are pre-
sented on the following pages.
35
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Heat Flux Studies
Summer 1975
Key Personnel:
J. McElroy
Research Goal:
Determine the sensible heat flux near the surface and below
the surface.
General Experiment Design:
The measurement of vertical sensible heat flux is approached using
three different measurement platforms. The high level platform is described
under "High Level Vertical Flux Studies" as the National Center for Atmospheric
Research (NCAR) aircraft. At the surface a 4 meter array of fast response
thermistor and vertical velocity measurement units called a Fluxatron is used.
Sub-surface heat flux is based upon arrays of thermistors imbedded in typical
urban and rural surfaces.
Three fluxatrons will be utilized to measure sensible heat flux near
the surface and within the "building canopy" over a variety of land-use types
and under various ambient weather situations. These devices will primarily be
utilized in support of the NCAR aircraft operation. According to present plans,
one fluxatron will be set up near the surface in a rural area, and two on urban
rooftops.
The sub-surface heat flux array will be installed this year and its
data recorded on a magnetic tape system. It is planned to operate these for
at least one, and possibly two, annual cycles.
Quality Assurance Plans:
The fluxatrons will be calibrated against standards operated by
B. Hicks of Argonne National Laboratory.
Field Schedule:
Fluxatrons will be operated over a one month interval beginning
July 14. The thermistor arrays will be operated from date of installation for
1 or 2 years. Installation should occur prior to July 14.
Data Management Information:
Data from the Fluxatrons will be collected on analog strip charts
36
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and extracted manually. The data will then be made available in tabular form
to the RAPS Data Bank.
The data from the thermistor arrays will be processed by R. Browning's
group at Research Triangle Park.
Logistics and Services Required from RAPS/STL:
1. Installation of sub-surface thermistor arrays.
2. Routine maintenance of Fluxatrons.
3. Use of digitizer to reduce analog Fluxatron data.
4. Site selection and local coordination for installation of
Fluxatrons.
Power Requirements:
110V, 0.5a service at Fluxatron sites.
Potential Problem Areas:
Finding satisfactory location for thermistor arrays.
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High Level Vertical Flux Studies
Summer 1975
Key Personnel:
B. Ackerman, Illinois State Water Survey
Research Goals:
1. To determine the vertical fluxes of momentum, sensible
heat and moisture in upper boundary layer over St. Louis
and surrounding rural areas.
2. To relate the eddy fluxes in the upper boundary layer,
calculated directly from the products of the turbulent
quantities, to the profile approximations.
3. To study morphology of boundary layer during transition
periods.
General Experiment Design: ,
This experiment is based upon a specially instrumented aircraft
from the National Center for Atmospheric Research (NCAR). This aircraft is
capable of measuring the mean and fluctuation components of the three-
dimensional air velocity, temperature and humidity. The aircraft will be
operated 4 to 5 hours at a time, mostly raid-day, but also during some noc-
turnal and transition periods. At the same time, data on upper air winds
in appropriate area will be taken by double theodolite pibal teams at
5 to 6 sites with balloon launches at 20 minute intervals. The sites have
not been selected as yet.
Two tethered balloons with NCAR instrument packages will also be
utilized at two locations to obtain boundary layer profiles of temperature
and humidity up to 600 m. They will be located at one urban site and one
rural site. The sites have not been selected as yet.
Quality Assurance Plans:
Instrumentation is calibrated and checked by NCAR using bench tests
and comparison equipment. As the data is processed, it is scanned for
unusual variations, etc.
Field Schedule:
July 1-31, 1975
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Data Management Information:
Experimental data is recorded on magnetic tape archived at NCAR.
Pibal and tethered balloon data are recorded on data sheets, keypunched and
computer processed at Illinois State Water Survey. These are then used to
generate experiment reports which are available to the RAPS.
Post-Operational Data Requirements:
A. Unchecked soundings and the RAMS meteorological data for oper-
ations summary.
B. Checked data at a later date for analyses.
Logistics and Services Required from the RAPS/STL:
1. Assistance in siting boundary layer profilers. This equipment
involves an instrument package, tethered balloon (about 14 ft.
long) and receiver and recorder equipment. Would like to lo-
cate at or near RAMS sites. Require 110-volt 60 cps electric
power, small amount of inside operational space for small re-
ceiver and recorder (less than desk top), open space for tent
to house balloon when not is use. Site has to be secure.
Help in locating site would be appreciated.
2. Unchecked data from sounding sites on request for operational
purposes.
3. Operational forecast by 9 a.m. Second forecast late afternoon.
Will be operating nominally on a 7-day week, but only in suit-
able weather conditions.
Power Requirements:
110V, 60 hz at tethered balloon sites
Potential Problem Areas:
1. Weather
2. Coordination with Dr. McElroy and Dr. Auer.
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3.2 Pollutant Transformation and Removal Studies
At the present time, much more is known concerning sources of atmos-
pheric pollutants than about their ultimate fate. Studies of the nature and
role of pollutant transformation and removal processes represent some of the
most important and meaningful studies in the field of air pollution research
today. The RAPS provides a detailed description of the St. Louis region's
atmosphere and pollution, which can provide a base for experiments dealing with
pollutant transformation and removal.
The formation rate and mechanism of particulate sulfur compounds in
the atmosphere is one of the outstanding problems of current environmental
research. Sulfur compound aerosol particles can contribute to a reduction in
visibility and, more importantly, have been linked to adverse health effects.
Recognizing these adverse effects, the research needs, and the pervasiveness
of sulfur compounds in a large proportion of the community atmospheres across
the country, this problem has been selected as the focus of the RAPS pollutant
transformation and removal process studies.
The study of gas to particle conversion and removal must proceed
along five broad fronts as shown in Figure 10:
1. Point source plume study to identify sulfur oxides and nitrogen
oxides transformations in plumes.
2. Urban plume study to determine urban plume size and composition
under a variety of meteorological conditions in order to identify
major rate processes which take place in the urban air mixture.
3. Photochemical reaction study to ascertain the photochemically
stimulated transformations which occur in the atmosphere of the
St. Louis region.
4. Characterization of aerosols sampled in the St. Louis region in
terms of their physical and chemical properties and their probable
origins and evolution.
5. Study of dry removal processes to determine the dry deposition
rate for SCL as a function of different land classes.
3.2.1 Point Source Plume Studies
Plume transformations are an integral part of atmospheric chemistry
and become part of the overall set of transformations occurring in an urban
atmosphere. The emission inventory can predict the types and amounts of
pollutants released from stationary sources in a given area, but without know-
ledge of the transformations during plume dilution, one cannot properly use
the emissions data in a regional source-ambient air quality relationship.
Of particular interest are:
1. A determination of diurnal variation in and the extent to which
40
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2.
sulfur dioxide is converted to sulfate in plumes and the role
of humidity in the conversion.
A determination of the rate of oxidation of nitric oxide to
nitrogen dioxide and the extent to which nitric acid is formed,
particularly in power plant plumes.
POLLUTANT
TRANSFORMATION
AND REMOVAL
POINT SOURCE
PLUME STUDIES
URBAN
PLUME STUDIES
DRY REMOVAL
PROCESSES
PHOTOCHEMICAL
REACTION STUDIES
AEROSOL
CHARACTERIZATION
FIGURE 10 - WORK BREAKDOWN OF POLLUTANT TRANSFORMATION AND
REMOVAL RESEARCH PROGRAM
These are attained through a coordinated effort by aircraft and
surface vechicles using sophisticated monitoring and sampling equipment.
Point source plume studies planned for the Summer of 1975 are de-
scribed on the following pages.
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Plume Mapping Program
Summer 1975
Key Personnel:
W. Wilson, F. Durham - MRS - EPA
R. Husar - Washington University
D. Blumenthal - Meteorology Research, Inc.
K. Whitby - University of Minnesota
R. Paur - MRS - EPA
W. Vaughn - Environmental Measurements, Inc. (EMI)
Research Goals:
1. Elucidate rate processes acting on aerosols and
aerosol precursor gases in large energy plant plumes,
focusing on sulfates and the sulfate precursor, S0?.
2. Testing and tune-up of existing dispersion (aerosol)
growth (dry) removal models for single plumes.
General Experiment Design:
It is anticipated that the Meteorology Research, Inc. instrumented
aircraft will be available in St. Louis from July 15 to August 12. This air-
craft is capable of continuously monitoring bscat condensation nuclei, 63, NOX,
CO, S02, temperature, RH, turbulence and altitude. In addition, a miniature
University of Minnesota system for monitoring aerosol size distribution, and
a Hi Vol type of filter system will also be installed. The filter from the
later system will be subsequently analyzed for particulate mass and various
chemical species.
This aircraft will be utilized in mapping the pollutant concentra-
tions, aerosol properties, etc., of the plume over a 100 tan path. Measurements
will be made while flying across the plume at preselected traverse points at
different altitudes. The altitude increments will range from 200 to 1000 ft.,
depending on meteorological conditions, plume configuration, etc. A scout
aircraft will be used to locate the plume for the MRI aircraft.
A team of three pibal operators in mobile units will be synchronized
with the aircraft to enable calculation of pollutant fluxes at each given plume
cross section.
Also coordinated with the aircraft, a surface mobile unit from EMI
will be utilized to measure plume sulfur dioxide flux overhead and total sulfur
levels at ground level.
The downward looking Lidar aircraft from NERC/LV will also be used
to define the plume under study and examine the particulate matter in the plume.
These data will be combined and analyzed to determine rates of dis-
appearance and formation of various pollutants and intermediates. This information
will be utilized to test and improve an existing model for sulfur dioxide sulfate
transformation.
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Quality Assurance Plans:
Calibration cross-check with RAPS, helicopters, AARS trailer, MRI
Aircraft, EMI truck.
Schedule:
Between July 15 and August 15
Data Management Information:
Data will be gathered using Metro-Data Model 620 analog/digital
data collection units. These units record the data on a special magnetic
tape cassette. The cassette reader is interfaced with a PDF 11/15, which
is hard-wired to the Washington University IBM 370. The data will be pre-
processed on the PDP-11 and the output dumped on the 370's line printer and
9 track IBM compatible magnetic tapes. These tapes will be distributed to
the appropriate groups for analysis, with a copy sent to the RAPS data manage-
ment for incorporation in the RAPS Data Bank.
Logistics and Services Required from RAPS/STL:
1. Meteorological forecasts are most important in planning the
experiments, since they cannot be conducted with precipitation
or when the plume contacts the ground too quickly. RAPS/STL
will be relied upon to prepare these forecasts.
2. Support will also be needed in supplying the mobile pibal team
for the experiments.
3. UASN data will be needed to project plume behavior.
Power Requirements:
None required from RAMS.
Potential Problem Areas:
1. Weather
2. The experiments involve several delicate instrument and sampling
systems in different vehicles requiring close coordination and
teamwork. In such a complex arrangement, the possibility of
human and mechanical malfunctions exists.
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Power Plant Plume Mapping
Summer 1975
Key Personnel:
L. Newman - Brookhaven National Laboratory
Research Goals:
1. Investigate rates of conversion of S02 to particulate
aulfate, using the isotopic ratio technique in a coal-
fired power plant plume.
2. Investigate the rate of conversion of NO to N02 in a coal-
fired power plant plume.
General Experimental Design:
Samples of the power plant plume for isotopic ratio and concentration
measurements will be obtained through the use of a single engine Cessna 182
outfitted with a high volume filter assembly. The essential features of the
sampling system are a glass fiber prefilter for particulate sulfur removal,
followed by alkaline impregnated papers to remove S02. Samples will be pro-
cessed in the laboratory (Brookhaven) in a manner suitable for isotopic ratio
measurements. A Sign - X Laboratories electroconductivity analyzer will be
used to record S02 concentrations for purposes of locating the plume.
Another aircraft, possibly a Cessna 206, will be utilized to monitor
nitrogen oxides and ozone, using chemiluminescent instrumentation.
A typical experiment will consist of obtaining a background measure-
ment upwind of the plant and at plume altitude. When feasible, the plume will
then be sampled at a minimum of five distances downwind to a maximum of about
100 km. Sufficient sampling will be conducted at each distance to collect a
minimum of 1 mg of S02 on the filter (amount needed for isotopic ratio analysis).
The samples and data are returned to Brookhaven National Laboratory
for analysis.
Measurements for particle size will also be made utilizing a diffusion
battery in the EPA van and compared with the Whitby aerosol analyzer. Measure-
ments will also be made for sulfate levels and acidity at the EPA van and two
RAMS sites to be selected based on a daily evaluation of the predicted wind
fields.
Quality Assurance Plans:
Field equipment will be calibrated prior to and following each data
gathering mission using a controlled ozone source and a standard cylinder of
nitric oxide in nitrogen.
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Laboratory analysis will be subject to existing laboratory quality
assurance plans.
Field Schedule:
July 21 to August 4
and a two week period around September 1
Data Management Information:
Data will be reduced at Brookhaven National Laboratory and produced
as a series of tables. Plans to incorporate these data in the RAPS Data Bank
are not complete.
Logistics and Services Required from RAPS/STL:
1. Weather forecasts
2. Assistance in case of instrument failure
3. Pibal support in vicinity of power plant(s)
Power Requirements:
Facilities for samplers at selected RAMS sites
Potential Problem Areas:
1. Weather
2. Equipment Failure
3. Coordination with Dr. W. Wilson's experiments
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High and Low Level Plume Tracer Study
Summer 1975
Key Personnel:
W. Wilson, MRS - EPA
F. Shair, California Institute of Technology
R. Husar, Washington University
C. Chetlynne, Control Systems Laboratory - EPA
Research Goals:
1. Determine relative contribution of high vs. low level
sources to sulfur oxides and nitrogen oxides.
2. Determine pattern and concentration of single plumes for
model evaluation.
General Experiment Design:
Sulfur hexafluoride and another Freon type tracer will be used as a
conservative tracer in selected plumes to establish both plume location and
pollutant dilution. The plumes will be a high plume such as that from the
Labadie power plant, and a low plume as that from an industrial boiler with a
relatively short stack.
Samples will be collected at the RAMS stations, in the NERC/LV
helicoptors, and possibly in the MRI aircraft, using a specially designed
plastic syringe device developed by Shair. At the same time, the instruments
in each of these units will be monitoring the levels of sulfur oxides, nitrogen
oxides and particulates. The syringes will be returned to the RAMS Central
Facility for analysis by frontal chromatography, with electron capture detection,
using gas chromatographs installed in the Aerosol Laboratory.
Normalization of the sulfur oxides and nitrogen oxides data by the
tracer concentrations for various distances downwind yield sulfur dioxide
and nitric oxide loss rates, as well as sulfate, nitrogen dioxide, and nitrate
formation rates.
Quality Assurance Plans:
Cross-check tracer analyses with other groups operating in area.
Schedule:
Ten days between July 15 and August 15. Selection depends on equipment
status and weather outlook.
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Data Management Information:
Data management plans are not complete. After the experiment is com-
pleted, all UASN data and RAMS data for sulfur dioxide, nitric oxide, nitrogen
dioxide, sulfate and nitrate will be required. A complete set of data from
NERC/LV helicopters for the time they were utilized in support of this experiment
will also be required.
Logistics and Services Required from RAPS/STL:
1. Shair automated syringe samplers to be placed at each RAMS site.
2. Electron capture gas chromatographs to be placed in aerosol
laboratory at central facility.
3. Operational forecasts seven days a week for wind speed and
direction, atmospheric stability, inversion heights and
percipitation.
4. Use of NERC/LV helicopters on five selected days to collect
samples and monitor pollutant levels.
Power Requirements:
110 volt receptacle at each RAMS site for sampler.
Potential Problem Areas:
1. Weather.
2. May need support in collecting samples at RAMS sites.
3. Availability of NERC/LV helicopters on short notice for the
experiments.
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3.2.2 Urban Plume Studies
The urban plume study is closely related to the point source plume
study in that similar equipment is required, and as one follows a large plume
over sufficient distance, it becomes incorporated in the urban plume. The
primary difference is that a determination of the urban plume size and composi-
tion under a variety of meteorological conditions helps to identify the major
rate processes, such as chemical reactions, gas-particle conversion and dry
removal, which take place in the urban air mixture.
With wind field data, the total flux of pollutants is determined and
compared with the corresponding emission flux to assess the importance of
conversion processes and/or the quality of emission inventories. By comparing
sulfur dioxide to total sulfur at various distances downwind, the importance
of chemical conversion can be determined. A comparison of changes in the
total mass of the aerosol and size distribution permits estimation of gas-
particle conversion rates. These determinations, when combined with emission
data and vertical pollutant profiles, afford a method of determining the
importance of dry deposition.
A description of the urban plume study for Summer of 1975 is presented
in the following pages.
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Urban Plume Study
Summer 1975
Key Personnel:
W. Wilson, AARS - EPA
A. Waggoner, R. Charlson, University of Washington
K. Whitby, University of Minnesota
R. Husar, Washington University
P. Frenzen, Argonne Nat. Lab
D. Blumenthal, Meteorology Research Incorporated
Research Goals:
1. Characterize background pollutant levels in air entering
the St. Louis region.
2. Characterize changes in gas and aerosol pollutants during
long-range transport.
General Experiment Design:
Dr. Charlson and the University of Washington mobile laboratory will
initially be located at Tyson Valley, WSW of St. Louis. Following a two week
period, this equipment will be moved to northwestern Arkansas near Table Rock
Lake. At the same time, the EPA mobile aerosol laboratory will be located
approximately 100 km NNE of St. Louis. By monitoring air entering the St. Louis
region, characteristic background pollutant levels for the region can be developed.
As the urban plume passes over the downwind laboratory, a determination of its
composition will help to identify the major rate processes, such as chemical
reactions, gas-particle conversion and dry removal, which take place in the urban
air mixture.
Quality Assurance Plans:
Not completed
Schedule:
North of St. Louis, July 15 to August 15.
South of St. Louis, August 1 to September 1.
Data Management Information:
No RAMS/RAPS requirements, data plans not complete.
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Logistics and Service Required from RAPS/STL:
None
Power Requirements:
None
Potential Problem Areas:
None defined
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3.2.3 Photochemical Reaction Studies
Photochemical oxidant formation, while not nearly as important in
St. Louis as in an area such as Los Angeles, has an important potential impact
on the sulfur dioxide to sulfate conversion. For this reason, three groups of
related experiments are used to explore photochemical reactions. First is the
Bag Irradiation Experiment. Its purpose is to ascertain the photochemically
stimulated transformations in the sulfur cycle in order to develop appropriate
chemical kinetic models for the St. Louis region.
The approach taken in the bag irradiation study is to isolate
chemical effects from meteorological and variable emissions effects by irradia-
ting representative atmospheric samples in irradiation chambers. Two identical
samples are collected in Teflon or Tedlar bags, and the contents of one bag are
modified by the addition of particular materials. During the simultaneous
irradiation of the two bags, the results are compared and related to known and/or
postulated chemical and physical phenomena.
The details of photochemical oxidant formation in the St. Louis area
will be provided by a combination of two experiments. One is an ongoing series
of smog chamber studies being conducted by Dr. Pitts of the University of
California at Riverside; the other is a hydrocarbon characterization of the
St. Louis atmosphere by gas chromatography. The chamber studies investigate
the reaction rates for individual hydrocarbons under various conditions of
concentration and presence of other reactants. These kinetic results can be
applied to the St. Louis region after determination of which hydrocarbons
(between CL and Cin) are typically present and to what concentration in the
St. Louis atmosphere. This is accomplished by fuel analysis, particularly
gasoline, and analysis of gas bag samples collected from all across the St. Louis
region.
Plans for the photochemical reaction study for the Summer of 1975 are
presented on the following pages.
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Photochemical Studies
Hydrocarbon Characterization
Summer 1975
Key Personnel:
S. Kopczynski, RAPS/STL
R. Mindrup, Rockwell International
Research Goals:
1. Evaluate the contribution of various sources, particularly
automotive, to the hydrocarbon levels in the St. Louis
atmosphere.
2. Assess the photochemical smog potential of the St. Louis
atmosphere on a regular basis.
General Experimental Design:
Selected RAMS stations will be sampled to relate morning hydrocarbon
composition and loadings to smog levels developed later in the day and to other
pollutant species measured (viz. total particulates, chemical elements, visi-
bility) . This sampling will be a continuation of the present sampling schedule
and will be conducted throughout the summer.
The Las Vegas helicopters will be utilized to collect bag samples
as part of the vertical extension of RAMS and pollutant transport. The
helicopters will also be utilized to collect samples from the vicinity of
various emission sources. Samples will be analyzed to characterize the
composition of such emissions (viz. refineries, auto plants, power plant
plumes, chemical plants, etc.)
Samples will be taken in Teflon bags and returned to the RAMS
Central Facility for gas chromatographic analysis for C1 to CIQ hydrocarbons,
as well as total hydrocarbon, carbon monoxide, and nitrogen oxides analyses.
Quality Assurance Plans:
The instrumentation utilized in the Gas Chromatograph Laboratory was
selected to provide the latest, most accurate, and dependable systems possible.
Operational procedures have been developed for all instruments to insure maxi-
mum performance. To insure that high-quality data are generated by the labora-
tory, all instruments are subjected to preventative maintenance and repair, both
on a routine and an "as needed" basis. A detailed description of all maintenance
performed, both routine and unscheduled, is entered in the Maintenance Log Book,
which is indexed for each instrument in the laboratory.
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Data accuracy is assured by performing both daily detailed and
monthly general instrument calibration with quality control standards.
The results of calibrations are entered in the Operational Log Book for each
instrument, along with the sample analysis for that day. To check repeata-
bility of the instrumentation, one of the bag samples will be analyzed in
duplicate before and after each set of samples. This verification, along
with daily calibration, will provide a check for variations in instrument
parameters such as, temperature, pressure, flow rate, etc. A periodic
cross-check between different instruments is frequently made using the
quality control gas standards. An independent auditing check of the sample
analysis is conducted weekly by the EPA Task Coordinator to spot-check the
data reported.
Schedule:
Hydrocarbon characterization is designed to be a routine series
of analyses conducted by the Gas Chromatograph Laboratory at the RAMS Central
Facility. It should be in full operation prior to July 15, 1975 and continue
for at least one year from this date.
Data Management Information:
The data is initially recorded in the form of strip chart chromato-
grams, punch tape and/or teletype printouts. Next, the data is given one of
its first quality reviews by manually inspecting the data for general chromato-
graph form factors and quantitative values for each gas component. Following
review and approval, the data is tabulated on a special pre-printed form
for keypunching.
At the end of approximately a ten-day collection period, the data
forms are sent to Research Triangle Park for keypunching and keypunching vali-
dation. The cards are then shipped to the RAMS Central Computer Facility,
St. Louis, for processing and further validation. Keypunching errors are
normally corrected by computer operators at the RAMS Computer Facility, pro-
vided they are not excessive. Should a significant quantity of keypunch
errors develop that the RAMS computer operators cannot process in their normal
schedule, the card decks and data sheets are returned to RTP for repunching.
Data processing entails checking the cards for index number consistency,
as provided for by the form, and producing a triple-copy printout of labeling
information, and for each component the name, code number, concentration (PPB),
ratio relative to CO, and flags if the concentration or ratio is outside an
upper and lower set of limits. Four quantities, aggregated by software, are
treated as components in all respects: sum of non-methane paraffins, olefins,
aromatics, and non-methane hydrocarbons. Validation of the data concludes
upon successful visual inspection and comparison of the data with the chromato-
gram and original tabulated data. Also, special attention will be directed to
flagged data for validity and proper annotation.
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Upon completion of data validation, a 600 foot, 9 track, 800 BPI,
odd-parity magnetic data tape is prepared and sent to RTF, along with a copy
of the printout. One copy of the remaining two printouts is sent to the EPA
RAPS Task Order Coordinator (St. Louis) and the third copy retained by the
RAMS Central Computer Facility.
Post operational data requirements include concurrent helicopter
analyses as well as wind trajectory analysis on sampling days (both aloft
and surface), hourly RAMS data for stations being sampled (up to 5) with
spiraled stations requiring 1 minute average data during each hour interval
of the spiral.
Preliminary data processing of gas chromatography analyses and RAMS
data are required within 48 hours.
Logistics and Services Required from RAPS/STL:
1. Helicopter sampling is required on 10 days, 2 flights/day,
5 bag samples/flight. Meteorological conditions required
are distinct, persistent winds, surface and aloft (7-10 mph)
low mixing heights, and both sunny and overcast days. Both
early morning and afternoon flights are required. Actual
dates are flexible and can be specified as flight plans are
developed. Forecasting, including mixing height and sur-
face winds and winds aloft, are required prior to helicopter
take off.
2. Vertical profiles are required at 2 stations, 1 spiral per
day, 10 days per station. Meteorology and forecasting simi-
lar to above, as well as stagnant conditions, are required.
3. Source sampling require helicopter samples on overcast days,
stagnant or light, persistent winds, 4 samples per source/
flight (2 upwind-2 downwind), 5 sources, 2 days per source.
4. Support of the Chromatographic Laboratory at the RAMS Central
Facility.
5. Collection and replacement of sampling bags at selected
RAMS stations.
Power Requirements:
None over present system.
Potential Problems:
None Identified.
54
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3.2.4 Aerosol Characterization
Fine particle pollutants are the most obvious, as well as the least
understood, component of air pollution. The most easily noted effect of these
particulates is visibility loss. Furthermore, the particulate matter plays
an important role in the removal of gaseous pollutants, apparently acting
as a sink for nitrogen oxides, sulfur dioxide and organics. The fine particu-
lates have also been indicated as having an adverse impact on human health.
The purpose of this series of experiments is the characterization
of the aerosols sampled in the St. Louis region in terms of their physical
and chemical properties and their probable origins and evolution. An exten-
sive array of instruments and devices has been assembled into two moveable
laboratories, each with a computer compatible data acquisition system, to
measure the chemical and physical properties of the St. Louis aerosol. In
addition, several other aerosol sampling and analysis apparatus are expected
to be installed as part of this study. Measurements are made in either real
time or as aerosol samples which are collected for subsequent chemical analy-
sis using various state-of-the-art and traditional techniques.
Experiments dealing with aerosol characterization for the Summer
1975 are described on the following pages.
55
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Aerosol Characterization I
Summer 1975
Key Personnel:
S. Friedlander, California Inst. of Tech.
R. Draftz, Illinois Inst. of Tech.
A. Waggoner, University of Washington
J. Husar, Washington University
W. Wilson, AARS-EPA
Research Goals:
1. Determine from ambient measurements, the contribution of various
sources to the ambient aerosol.
2. Determine an aerosol emission inventory with size and composition
information by combining aerosol data with wind field, inversion
height, and trajectory measurements.
General Experiment Design:
Install four wind direction controlled aerosol samples, a Battelle
impactor, a Sierrahead Hi-Vol and a manual dichotomous sampler at each of our
local agency hi-vol sites. The aerosol samples thus obtained will be analyzed
for size-resolved aerosol composition and examined by optical and electron
microscopy.
Quality Assurance Plans:
Not Completed.
Schedule:
July 15 to August 15.
Data Management Information:
No data needed in a near real time mode; however, historical data on
wind speed and direction, inversion heights, and emissions together with Hi-Vol
and dichotomous sampler data are required. Data reduction plans have not been
completed.
Logistics and Services Required from RAPS/STL:
1. Routine weather forecasts with particular emphasis on wind speed,
direction and inversion forecasts.
56
-------
2. Use of Aerosol Laboratory at the Central Facility for sample
analysis, instrument checks and minor repairs.
Power Requirements:
None identified.
Potential Problem Areas:
1. Cooperation with local agencies for use of their sampling
sites.
2. Obtaining appropriate emission data.
57
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Aerosol Characterization II
Summer 1975
Key Personnel:
J. Winchester, W. Nelson - Florida State University
W. Wilson - AARS-EPA
Research Goals:
1. Continuous sampling and elemental analysis of particulate ma-
terial in the atmosphere of the St. Louis region to identify
sources of these particulates.
2. Test new sampler design.
3. Obtain limited data on element distribution with particle size
by impactor collection.
General Experimental Design:
Operation of "Streaker" samplers on the RAMS meteorological
towers by replacing the Nucleopore filter once a week at each
station for the duration of the intensive experiment period.
The accumulated filters are analyzed on an hour-by-hour basis
at Florida State for elements from Lithuim to Lead in Atomic
number. The accumulated elemental analyses are tested for
correlation with wind trajectories and various pollutant con-
centrations.
Two to four eight-stage impactors may also be operated to
explore the variation of elemental analysis with particle size.
A new design "Streaker" will also be mechanically tested by
running under field conditions.
Quality Assurance Plans:
EPA will operate comparison devices, including Hi-Vols, Lundgren
impactors, LBL samplers and streakers in a side-by-side test in
vicinity of portions of streaker network. Also check flows
regularly before and after changing filter using flow meter.
Analytical methods are checked through intercomparisons with other
laboratories using similar and other methods.
58
-------
Field Schedule:
Collect samples July 14-August 11.
Data Management Information:
Parameters measured: Elemental analysis of particulate samples,
time of sample, place of sample.
Data volume: Between 600 and 700 analyses for each of 20 to
25 locations. Each analysis is as two sets of data, the first
from Lithium to Chlorine, the second from Sulfur to Lead.
Data reduction: Because of complexity of analysis equipment,
samples must be analyzed and data reduced at Florida State.
Data reduction will be completed as facility utilization allows.
Data form: Magnetic tape and as a report.
Data needs: Require wind trajectories for each hour of sampling
period, upper air data, gaseous pollutant concentrations for
each RAMS station and precipitation data. These should be
available before analysis of samples is begun.
Logistics and Services Required from RAPS/STL:
1. Require personnel to operate system - need to change
filter once per week at each RAMS station, and check flow rate.
2. Require personnel to service samplers prior to July 15 to put sam-
plers in running order.
Power Requirements:
Place at each RAMS station to plug in a standard 110 extension
cord to carry about 2 amps.
Potential Problem Areas:
Replacement parts may be needed at some locations to make samplers
operational.
Vacuum tubing will run from pump at base of mast to sampler at
30 ft. Caution must be exercised in other operations on the mast.
59
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3.2.5 Dry Removal Processes
The removal of pollutants from the atmosphere under dry conditions
(no precipitation) occurs as a result of gravitational settling for sufficient-
ly large particles and turbulent transport, impaction, Browniari diffusion, and
molecular diffusion for successively smaller particle sized and gaseous
substances. The rate of removal for gases depends upon the chemical and phy-
sical nature of the ground surface, the presence of vegetation and the growth
stage of the vegetation.
Of particular interest is the dry deposition velocity for sulfur
dioxide. One approach is to use the gradient method for its determination.
The deposition velocity is defined through the mass transfer relationship
and experimentally determined via direct measurements of the S02 vertical
concentration gradient near the surface and the turbulent dispersion coef-
ficient. Measurements should be made over several homogeneous surface types.
A mobile laboratory can also be utilized in removal experiments by
comparing the flux of the pollutant of interest upwind and downwind of selec-
ted homogeneous surface types.
Efforts to measure the dry deposition velocity of sulfur dioxide
for the summer of 1975 are presented on the following pages.
60
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FIXED-SITE AND MOBILE-MODE
S02 DEPOSITION FLUX MEASUREMENTS
SUMMER 1975
Key Personnel:
W. Wilson, AARS-EPA
W. Dannevik, Environmental Quality Research, Inc.
R. Husar, Washington University, St. Louis
Research Goals:
Fixed-site: Assessment of reproducibility and representativeness
of experimental technique and characterization of diurnal variability
in dry deposition flux and velocity, under conditions of fixed site
characteristics.
Mobile-mode: Characterization of absorption phenomena agricultural
canopies and larger vegetation groupings.
General Experimental Design:
Experimental technique based on the flux/gradient (i.e., profile)
method, in which vertical concentration profiles of S02 and tur-
bulence parameters are combined to estimate vertical turbulent flux
of S02 within the surface boundary layer.
This program represents a continuation of the Summer 1974 and
Winter 1975 measurement series. A total of approximately 65
S02 deposition velocity measurements are planned.
Quality Assurance Plans:
Daily zeroing of Theta-Sensor S02 monitor utilizing charcoal filters
is planned. It is hoped that the RAPS Winnebago mobile calibration
laboratory can be used at least twice. Primary source for S02 vertical
profiles is eight bubbles spaced in the lowest 6 meters above canopy and
within canopy. Flow rate checks from mobile calibration laboratory
would be desirable.
Field Schedule:
Fixed-Site Measurements: June 1 - July 12
Mobile Measurements: July 15 - August 31
Logistics and Service Required from RAPS/STL:
Access to weather facsimile and teletype service C data for operational
planning purposes, and near-real time RAMS S02 data for siting of
mobile measurements and go/no go for Fixed-site measurements would
be very useful.
61
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3.3
Pollutant Measurement Program
The Regional Air Pollution Study, being based upon the best available
monitoring instrumentation at the time of its design, provides an excellent
opportunity to test new instruments and techniques to monitor air quality. By
use of some of these newly developed instruments, the RAMS approach to monitor-
ing by selection of a point to represent a 1 km grid area can also be evaluated.
Thus two types of experiments will be conducted, as shown in Figure 11:
1. Instrument evaluation studies-determining the strengths and
weaknesses of newly developed instruments and instrument systems.
These can be segregated as evaluation of gaseous pollutant moni-
tors and evaluation of aerosol monitors because of the unique
problems associates with each.
2. Variability studies - evaluating the variability of pollutant
levels over a 1 km area around selected RAMS stations
POLLUTANT
MEASUREMENT
PROGRAM
INSTRUMENT
EVAULATION
STUDIES
VARIABILITY
STUDIES
GASEOUS
INSTRUMENTS
AEROSOL
INSTRUMENTS
FIGURE 11 WORK BREAKDOWN OF POLLUTANT MEASUREMENT PROGRAM
62
-------
3.3.1 Gas Monitoring Instrument Evaluation
New instrumentation for monitoring ambient levels of various gases
will be evaluated under field conditions by comparison with RAMS results
or other appropriate standards. The currently envisioned evaluations include
the nitrogen dioxide laser-induced fluorescent monitor, the MIT laser system
for carbon monoxide, ozone, nitric oxide and possibly ammonia, and the GE ILAMS
for ozone, ammonia and ethylene. Others may be added as the programs develops.
The nitrogen dioxide monitor test is accomplished by running the
unit in series with a RAMS chemiluminescent instrument and comparing the
results. It is expected that the laser fluorescence monitor is more accurate
than the RAMS instrument since it is a "non-destructive" method using the
physical properties of nitrogen dioxide. The chemiluminescent unit requires
the air stream to be reduced over a catalyst to form nitric oxide from the
nitrogen dioxide. The nitric oxide is then detected by its chemiluminescence
as it reacts with ozone to form nitrogen dioxide. The complexity of this
process presents potential problems in accuracy. During the test periods,
independent checks are made by wet chemical procedures.
In the evaluation of the open path monitors, portable monitors
traverse the monitoring path and the averages thus obtained are compared with
the laser system results. In this way the time and spatial averages determined
by the open path monitors can be validated. Additional operational evaluation
can be run as part of the pollutant variability experiments.
63
-------
Long Path Monitoring
Summer 1975
Key Personnel:
E. Hinkley MIT Lincoln Labs
R. Ku MIT Lincoln Labs
J. Sample MIT Lincoln Labs
J. Gormley MIT Lincoln Labs
W. McClenny EPA, Research Triangle Park, N.C.
L. Chancy University of Michigan
G. Russervine Northrup Corporation
Research Goals:
1. To compare real-time, path-averaged readings with point
monitors at selected RAMS sites.
2. To conduct validation tests for a long path monitor,
using portable point monitors.
3. To compare typical path-averaged readings at one rural and
one urban RAMS site.
4. To provide a data base from which long path data can be used
to check atmospheric model predictions.
General Experimental Design:
Trace gas concentrations are determined by measuring wavelength
dependent attenuation of radiation over long atmospheric paths. The
measurement system is located in a mobile van, the measurement path being
defined by a steerable mirror which directs a laser beam to a remotely placed
reflector. The mobile van will be set up at RAMS sites 108, 105 and one other
location yet to be selected.
Quality Assurance Plans:
Calibrations established hourly by recording response due to
standards. NBS certified standard reference materials (SRM's) will be
used for calibration of NO and CO. Ultraviolet absorption will be used
for established 03 concentrations. Large concentrations of the target
gas (NO, CO or 03) with over-shoot paths are used to duplicate total gas
burdens (average concentration times path distance) over the measurement
path and thereby to establish a system calibration.
Pre-mix bottles of calibration gases will be checked by arranging
a calibration check visit from the RAPS mobile calibration facility.
64
-------
Field Schedule:
July 1 to July 28-Site 108
July 28 to August 15-Site 105
August 15 to November 1-not yet scheduled
Data Management Information:
Operational Data Needs.
RAMS readings for CO, NO, 03 and total hydrocarbons plus wind
direction, wind speed, dew point, barometric pressure and ambient tempera-
ture will be needed. Request key to RAMS stations 105 and 108 so that
computer can be interrogated and specific pollutants can be measured on
a recorder.
Post-Operational Data.
Written records of RAMS results giving one minute averages of
specific pollutants during limited time spans (hours). Data request will
be called in as information is required. This data will be requested fre-
quently throughout the field exercises and will be used for day-after com-
parison with long path monitor.
Written record of RAMS results giving half-hourly averages for
all pollutants and all meteorological conditions on all week days during
which long path monitoring occurred. Needed only at end of each two week
interval.
Data Plans:
Data reduction will take place at Lincoln Labs unless a HP 9830
is available in the mobile van in St. Louis.
Data formats will be suitable for input to the RAPS data base.
Logistics and Services Required from RAPS/STL:
1. Telephone-installed for period and at location as indicated
under Schedule and Location.
2. Parking Space-30 ft. by 10 ft. required adjacent to the
RAMS station.
3. Information-expected power shortage or voltage reduction,
extreme weather conditions, and operational status of the
monitors inside the closest RAMS station.
65
-------
4. No requirements for on-site data processing at the central
facility.
Power Requirements:
Electrical - 10 kw at sites 105 and 108 (these sites were used for
the same purpose last summer so that no new installation should be required).
Potential Problem Area:
Weather.
66
-------
Ammonia Measurements
Summer 1975
Key Personnel:
W.A. McClenny, G. Russiwurm-FMDS, CPL, NERC / RTF
L. Hrubesh-Lawrence Livermore Laboratory
C. Russovini-Northrup Corporation
Research Goals:
Determine ammonia levels characteristic of the St. Louis atmosphere.
Provide basic tests to determine sample integrity under typical
ambient air conditions.
General Experimental Design:
A field test of several ammonia monitors is planned. This test
is not necessarily connected with the RAPS, but it may be convenient to
perform the tests at St. Louis University. Dr. L. Hrubesh of Lawrence
Livermore Laboratory will provide a monitor based on microwave absorption,
and a prototype chemiluminescence ammonia monitor will be used. A labora-
tory prototype of a new type of NH3 monitor, known as an optoarvistic detector,
will also be available for comparison.
Quality Assurance Plans:
Depend on monitoring unit selected but rely upon frequent calibra-
tion with appropriate reference materials.
Field Schedule:
August 15 to September 30 operation of unit.
Data Management Information:
Data will be reduced at RTP or Lawrence Livermore Laboratory and
supplied as a report.
Logistics and Services Required from RAPS/STL:
None at this time.
67
-------
Power Requirements:
Arrangements made at St. Louis University.
Potential Problem Areas:
None identified.
68
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3.3.2 Aerosol Monitoring Instrument Evaluation
Aerosols, due to the diversity of their sources, physical proper-
ties, and chemical properties, present special challenges to air monitoring
technology. As part of the pollutant measurement program, the Field Methods
Development Section of the Chemistry Physics Lab is supervising an extensive
array of instruments, devices and analytical methods used for aerosol measure-
ments in the St. Louis atmosphere. These include both newly developed in-
strumentation and standard sampling devices. These are each teamed with
established and new techniques for sample analysis. Some of the equipment
and analysis methods being used are shown in Table 3. Newly developed instru-
ments and methods will be added as they become available.
Experiment description for the summer of 1975 is presented on the
pages following Table 3.
69
-------
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70
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Aerosol Mc;isumneiit:.
Summer 1975
Key Personnel:
R. Baumgardner - FMDS/EPA
T. Dzubay - FMDS/EPA
L. Hines - FMDS/EPA
P. Lamothe - FMDS/EPA
C. Sawicki - FMDS/EPA
R. Stevens - FMDS/EPA
T. Novakov - Lawrence Venkley Laboratory
P. Cunningham - Argonne National Laboratory
J. Moyers - University of Arizona
R. Husar - Washington University
Research Goals:
1. To field test and evaluate several methods for aerosol sampling
and analysis which are being developed by FMDS and other con-
tractors and participants.
2. To conduct the tests at one rural RAMS site in order to test
the methods with a wide range of concentrations and chemical
species.
3. To test the comparability between samples collected in manual
dichotomous samplers which we will provide and samples collect-
ed by the automated dichotomous samplers within the RAMS sta-
tions.
4. To test ability of methods for determining the mass balance of
aerosol in two size ranges by measuring mass, carbon, nitrate,
ammonium ions, oxidation states of nitrogen and sulfur, total
sulfur, sulfate, sulfuric acid, strong acid and elemental com-
position.
General Experimental Design:
A mobile laboratory will be set up at both an urban site (RAMS 106)
and a rural site (RAMS 124). This van will contain a Sulfuric Acid Aerosol
Analyzer, a prototype Ultrasensitive Sulfur Dioxide Monitor, a prototype
Ammonia Monitor, and a beta gauge mass monitor. Also operated in conjunction
with the van are a High Volume Sampler (Hi Vol), five Manual Dichotomous Sam-
plers (MDS), a Lundgren Impactor, a Sulfuric Acid Sampler, one Automated
Dichotomous Sampler (ADS), and a Florida State University "Streaker."
Some relevant details of the MDS and ADS are shown in Table 4. The
utilization of filter media in the samplers is shown in Table 5. A summary of
71
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measurements for determining mass balance is presented in Table 6, and Table
7 contracts various techniques for the analysis of sulfur and sulfur-related
materials.
TABLE 4 - DETAILS OF THE MANUAL AND AUTOMATED DICHOTOMOUS
SAMPLER CMOS AND ADS)
MDS
Fine
Coarse
Total
Size
Range
Size
Code
Filter
dia., mm
0-3.5 F 37
3.5-20 C 37
0-20 T 126
Sample
dia., mm
29
29
114
Flow Rate
1/min.
14
14
200
ADS
Fine
Coarse
0-24
2.4-20
F
C
37
37
28
28
50
50
72
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TABLE 5 - UTILIZATION OF FILTER MEDIA IN MDS AND HI VOL SAMPLER
Sampler
MDS 1
MDS 2
MDS 3
MDS 4
MDS 5
Hi Vol
ADS
Fine
Fluoropore
Fluoropore
Fluoropore
Fluoropore
Quartz
Quartz
Cellulosic
Coarse
Fluoropore
Fluoropore
Fluoropore
Fluoropore
Quartz
Quartz
Cellulosic
Total
Quartz
Glass Fiber
Quartz
Quartz
Glass Fiber
Glass Fiber
Glass Fiber
*Hi Vol Flow Rate: 1000 1/min (35 dfra)
In addition, an infra-red analysis of aerosols collected in the
Lundgren impactor will be performed by Cunningham of Argonne National Lab-
oratory, and sulfuric acid analysis by a low temperature volatilization
technique will be made by Lamothe of FMDS/EPA.
73
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TABLE 6
SUMMARY OF MEASUREMENTS FOR DETERMINING MASS BALANCE
Parameter
Mass
Mass
Mass
Elemental (Z>12)
Elemental (Z>20)
Ammonium
Strong acid
Sulfate
Nitrate
Carbon
Sampler
MDS, HiVol
ADS
Two Mass
ADS
Streaker
MDS
MDS
MDS
MDS
Size
Code
F,C,T
F,C
F,T
F,C
T
F,C, T
F,C, T
F,C, T
F,C, T
Method
Gravimetric
Beta Gauge
Beta Gauge
XRF
Proton
Scattering
Ion Selective
Electrode
Gran titration
See Table 7
Electrode
Colorimetric
Combustion
Lab
EPA
LBL
WU
LBL
FSU
Northrup
Northrup
Ariz. U
EPA
74
-------
Parameter
S
S
S
S°4
S0=
so!
Soluble S
Oxides of S
NH4+
H+
TABLE 7
ANALYSIS OF SULFUR AND SULFUR COMPOUNDS
DURING SUMMKR, 1975
Method
XRF
Proton Scattering
XRF
Thorin Titration
NASN
AIHL
Barium Chioranilate
Flash Volatitization
ESCA (S, S=,S03, S04)
Ion Selective Electrode
Gran Titration
Investigator
Loo (LBL)
Nelson (FSU)
Northrup
Northrup
AIHL
AIHL
Husar
Novakov
Northrup
Northrup
Sampler
ADS
Streaker
MDS 1,2,3, 4
MDS 1
MDS 5
MDS 5
MDS 3
MDS 4
MDS 1
MDS 1
75
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Quality Assurance Plans:
Standard laboratory quality assurance checks will be made for the
laboratory analyses. The other equipment will be checked for proper flow-
rates and results compared between equipment. Calibration standards trace-
able to NBS will be used as appropriate for the monitoring equipment.
Field Schedule:
o Manual Dichotomous Sampler
August 10-31
12:15 PM to 11:45 AM (23% hours)
o Automated Dichotomous Sampler
Urban site (106) 2 hours August 10 - 25
12 hours August 25-31
Rural site (124) 2 hours August 10 - 25
12 hours August 25 - 31
o RTI Van
Urban site August 10-20
Rural site August 20-31
Data Management Information:
Plans not complete.
Logistics and Services Required from RAPS/STL:
1. Permission to operate the specified equipment at the requested
urban and rural sites. At the urban site it will be necessary
to operate all of the samplers and the van within the fenced-in
security area. At the rural site, at least the samplers must
be within the fenced-in area.
2. Four (4) keys for unlocking the gates and doors.
3. Six (6) 20A, 115 V, 60 Hz circuits. Two outdoor outlets are
needed for each circuit.
4. For the automated dichotomous samplers operated within RAMS
at the two sites, please change the sampling interval from 2
hours to 12 hours at Noon, August 25.
76
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5. Power drop for the instrumented van at the urban and rural
sites; a fused switch box, 100 A, 220 V is needed.
6. Parking place for the 32-foot long instrumented van is needed
at both the urban and rural sites.
7. The above power drop must be located within 45 feet of the
parking place for the van.
Power Requirements:
At the two selected RAMS stations, six 20 A, 115 V, 60 Hz circuits
and one 100 A, 220 V, 60 Hz circuit are needed.
Potential Problem Area:
None identified.
77
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3.3.3. Variability Studies
The siting criteria employed for the RAMS sitings and various quality
assurance and data validation procedures used in the RAMS measurements were
instituted to minimize instrument-related errors and unrepresentative siting
effects. However, natural fluctuations over the one kilometer square grid
employed by most mathematical simulation models are still expected. These
variability studies quantitatively assess the representativeness of the RAMS
air quality and meteorological observations and/or the degree of ambient
variability with which the computations of mathematical simulation models may
be verified effectively and their dependency upon emission field and land use
variations.
Pollutant variability is measured utilizing several different systems.
These include open path monitors, such as the MIT tunable laser system, the
GE ILAMS gas laser system, and the Barringer COSPEC III operated by Environ-
mental Measurements, Inc.; portable monitors than can be carried by a man or
small vehicle; a mobile unit carrying and array of air quality instrumentation
similar to the RAMS instrumentation; and bag samplers. The initial effort is
to develop a coordinated methodology leading to more extensive subsequent field
expeditions. In these experiments, measurements of sulfur dioxide, ozone and
carbon monoxide are made with the portable units and later by the open path
units. The mobile unit is also able to monitor these pollutants and nitrogen
oxides, hydrocarbons and aerosols (by Nephelometry).
The portable and mobile units are deployed to determine the spatial
and temporal pollutant variability by traveling along prescribed paths or by
being stationed along prescribed paths. In either case, spatial and temporal
variabilities are obtained from which averages and variances are calculated.
The open path units measure the average values of the various pollu-
tants over a prescribed area around a RAMS station by setting up various
measurement paths centered at the station. These values are compared with the
RAMS output to determine how representative the RAMS results are of the area
and the relationships between the results.
Bag samples allow an independent check on the average values of
relatively non-reactive pollutants by continuous sampling along prescribed
paths and subsequent analysis of the contents of the bag.
The meteorological variability experiments determine, for specified
areas, the meteorological heterogeneity in order to parameterize the relation-
ship between point (station) measurements and grid-averaged measurements. The
basic approach is to use a mobile van traversing specified one square
kilometer areas around selected RAMS stations. The selected areas have land
use patterns which result in relatively homogeneous aerodynamic roughness.
The van makes temperature and humidity measurements along a prescribed travel
pattern in the area of interest, with extra measurements being made during
transitional periods.
The variability study planned for the summer of 1975 is described on
the following pages.
78
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Pollutant Variability
Summer 1975
Key Personnel:
W.A. McClenny, R.J. Paur-FMDS, CPL, NERC/RTP
L.W. Chancy-University of Michigan
R.T. Ku, E.D. Hinkley, P.O. Sample-MIT Lincoln Labs
Research Goals:
1. To compare readings at the RAMS stations with readings taken
in the area which the station represents.
2. To determine subgrid pollutant variability at selected sites
and under various macro-meteorological conditions.
3. To determine any siting bias inherent in the placement of the
RAMS station at selected sites.
General Experimental Design:
MIT open path monitor located near RAMS sites 108 and 105 to
measure concentrations of carbon monoxide, nitric oxide and ozone over
four paths placed in quadrants surrounding the RAMS station. Path length
will be between 0.5 and 1 km.
Point monitors are carried across the site area or located in
selected sites around RAMS station. Point monitors are used to check
experimental values obtained with path monitors. Both point monitors and
path monitors are then compared with RAMS readings. Subsequent analysis
establishes degree of correlation and nature of relationship between grid-
average and RAMS. Representativeness of RAMS data is also obtained by com-
parison with field instrumentation results.
Quality Assurance Plans:
Calibration of point monitors will be accomplished using 1%
neutral buffered potassium iodide for ozone and standard cylinders for
carbon monoxide and nitric oxide. These will be checked using gas phase
titration and ultraviolet absorption for ozone, and referencing nitric
oxide levels to ozone by gas titration.
Field Schedule:
July 1 to July 28 - Site 108
July 28 to August 15 - Site 105
79
-------
Data Management Information:
Parameters measured: Concentrations of CO, NO and 0^ as analog
signals on chart recorders.
Data Volume: Approximately 45 days (at six hours per day) of data
will be recorded.
Data Reduction: L. Chaney at University of Michigan will reduce
variability data by 1 December 1975. Data will be reduced in St.
Louis or at University of Michigan.
E.D. Hinkley, et al, will process long path data to incorporate
calibration sequences. Tapes of data will be made available to
RAPS data manager with approximately one month's lag time.
Data Form: Tapes with data averaged over time periods compatible
with needs of Data Management for long path experiments. Reports
on pollutant variability and RAMS representativeness.
Logistics and Services Required From RAPS/STL
Two copies of a Daily Report of CO, NO, 03 and all meteorological
data for stations 105 and 108 for 9 A.M. to 5 P.M., June 23 and September 30
as 10 min. averages and as 1 min. averages for selected time periods.
Request four periods of pollutant variability data at site 108 be-
tween June 23 and August 1, using Las Vegas helicopters.
Access to the RAMS stations 105 and 108 to make comparisons with the
station readings for S02, NO, NOx, CO and 03.
Permission to use a recorder at sites 105 and 108.
Power Requirements:
Existing station outlets for van at 105 and 108.
Potential Problem Areas:
If computer in van develops a problem, may wish long path tapes
to be processed at Central Facility.
No pollutant variability studies when raining; long path studies
can continue.
80
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3.4 Pollutant Effects Studies
Air pollution problems are concerned not only with engineering,
chemistry, physics and meteorology, but also have large-scale impacts on
the economic and social life of a community and a nation. The Regional Air
Pollution Study does not have as a goal the quantification of these impacts;
however, it provides an unparalleled opportunity for other studies to be
conducted toward accumulating information on these community air pollution
impacts. If these additional studies collect the necessary economic and
social data for the St. Louis area, they can be related to the RAPS data on
detailed air pollution concentrations and exposure. The resulting correlations
between detailed physical and social information on air pollution within the
community should lead to an increased understanding and more accurate assessment
of the actual impact of air pollution on the life of a community.
There are several obvious costs of air pollution to the community.
These include damage to health, damage to property through corrosion and
staining, and reduced property values due to the unwillingness of buyers to
accept obvious impacts of air pollution, such as increased dust, odors, etc.
It is presently planned to investigate the first two of these in conjunction
with the RAPS as ongoing programs not necessarily related to the Summer 1975
experimental period. Information on the studies is included to make the RAPS
participants aware of these programs.
3.4.1 Damage to Health
The Human Studies Laboratory of the Environmental Protection Agency
has arranged with St. Louis University Medical School to investigate some of
the relationships between human health and air pollution. This study will be
carried-out in two phases. Phase one, which will be complete prior to July 1,
1975, is a study of between 10 and 20 letter carriers from the Benton Park
Postal Station. These will all be male non-smokers who work outside all day
in an area with relatively high air pollution. All will be given a thorough
physical, including pulmonary function and chest x-rays, and an extensive
medical history taken. Then for an eight-week period they will receive an
examination each work-day after they return from their rounds. This includes
pulmonary function, chest examination, blood examination for carbon monoxide
effects, and appropriate cultures and other blood analysis. At the same time,
air quality data will be acquired from the local RAMS station (Station 105)
and correlations with the health data sought.
Phase two will start in October 1975, and consists of a study of about
ten asthmatics and ten healthy non-smokers. These people will live in the
general area of the Benton Park Postal Station. The asthmatics will be selected
from regular patients of the St. Louis University Hospital Clinic. These
people will be visited daily by a physician to examine the person, including
pulmonary function, etc., as with the postal workers. Any respiratory problems
will be reported, and these reports will be compared with the RAMS air quality
data (Station 105) to evaluate the role of air pollution in aggravating res-
piratory problems.
81
-------
In Phase two, an instrument designed to continuously monitor
aero-allergens will be installed and operated in Station 105 and its readings
included with the air quality data to preclude interference by unusual levels
of pollens, molds, etc., during the season when this may be a problem.
3.4.2 Damage to Materials
The Materials Section of the Environmental Protection Agency has been
conducting controlled environment chamber studies to assess the damaging
effects of air pollutants - SCL, NC" , 0_ - on various materials. From the
data, dose-response relationships and damage predictive equations are being
developed. Field studies are now being conducted to complement the laboratory
work. These field studies attempt to evaluate the overall agreement between
the laboratory results and real world damage observed and measured under am-
bient environmental conditions. The RAPS offers an ideal opportunity to make
these comparisons, in terms of the nature of the pollutants, high degree of
monitoring network sophistication, and sufficient number and placement of sites.
Nine exposure sites at existing RAMS network monitoring sites in
greater St. Louis have been selected for these tests. They are sites 103,
105, 106, 108, 112, 115, 118, 120 and 122. The sites reflect a concentration
gradient of atmospheric sulfur pollutants, primarily from steam power generating
facilities.
The materials to be exposed in this study are:
1. Galvanized Steel.
2. Weathering Steel - Corten A, U. S. Steel Corp.
3. Aluminum.
4. Household Paints - Oil Base and Latex.
5. White Cherokee Marble.
6. Silver.
7. Textiles - Nylon Hose.
The materials effects data is evaluated and attempts made to establish
cause - effect relationships by statistically analyzing the effects data and
corresponding air quality data.
82
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4. RAPS STATUS
4.1 Status of Model Evaluation and Development
Several models are in the process of being adapted to St. Louis so
that evaluation can be made utilizing the RAPS data base. Potential candidate
models which may be considered include:
1. IBM sulfur dioxide model.
2. Lawrence Livermore Laboratory's LIRAC-1 and LIRAC-2.
3. Environmental Research and Technology Model.
4. Model by the Center for the Environment and Man.
5. Xonics CAPSE, MADCAP, and others.
6. System Application Inc.
7. General Research Corporation photochemical model.
8. Systems, Science and Software's PICK.
9. Several Gaussian models for inert species.
10. Hanna's model and other approaches to photochemical modeling.
At present the major problem is a lack of inventory data. Presently
available are the NEDS point source yearly averages for 1970 and the NEDS
area source yearly averages for 1970 on a non-RAPS grid system. In July,
it is expected that the RAPS sulfur dioxide point source hourly average inven-
tory will be available. However, the RAPS sulfur dioxide area source hourly
averages will not be available until spring of 1976. Work is therefore
proceeding on adapting the 1973 NEDS area source inventory to the RAPS grid
system for use with the RAPS point source data. This should be completed by
fall of 1975 so that evaluation efforts can proceed.
4.2 Status of RAPS Data Bank
The RAPS data bank consists primarily of the RAMS data.
Data from the UASN is being incorporated as software allows, and the data from
expeditionary research programs and the emission inventory is included as it
becomes available. The RAMS data is compiled from the stations onto Level I
tapes (voltages). This is then processed in St. Louis to convert to engineering
units in appropriate format, apply calibration factors, and perform status checks
to eliminate data from improperly operating instruments. This generates a
83
-------
Level II tape. The data from each instrument is then compared with allowable
"windows" for the data, and data beyond the limits are flagged. This pro-
cessing is presently done at Research Triangle Park, but will be shifted to
St. Louis in the near future. Presently, these Level II tapes are placed in
an archive. As software is developed later this year, all these tapes will
undergo Level III and Level IV checks. At Level III, each station's data
will be statistically checked for consistency over time and that which is
found inconsistent will be flagged. At Level IV the data from different sta-
tions will be statistically compared for consistency over space (network
consistency) and that which does not meet criteria for acceptability will be
identified in a like manner.
Figure 12 summarized the present status of the data bank. Level I
tapes have been accumulated since the startup of the RAMS in July, 1974.
Only a portion of these have been processed to Level II and placed in the
archive. The remaining tapes will be processed as soon as software can be
adapted to other computer facilities. New tapes will be processed as they
are received.
84
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4.3 Status of Emission Inventory
The RAPS emission inventory efforts consist of twenty-one separate but
related projects. The overall status of the inventory is shown in Table 8.
The status of each individual project is presented in Table 9. Of particular
interest is the RAPS Emission Inventory Handbook which summarized the method-
ologies being used for the evaluation of each type of source. The Table of
Contents for this report is attached to this section as Exhibit 1. Also of
interest is the design of the RAPS emission inventory data handling system. A
copy of the design is included in this section as Exhibit 2. It presents
examples of the output format for data from the inventory.
86
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87
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TABLE 9 - STATUS OF RAPS INVENTORY PROJECTS
PROJECT
1. RAPS PRELIMINARY EMISSION INVENTORY
2. VESSEL METHODOLOGY $ INVENTORY (DOT/TSC)
3. RAIL METHODOLOGY § INVENTORY (DOT/TSC)
4. AIRPORT METHODOLOGY $ INVENTORY
5. STATIONARY AREA SOURCE METHODOLOGY
AND INVENTORY
6. OFF-HIGHWAY MOBILE SOURCE METHODOLOGY
7. DATA HANDLING SYSTEM
8. FIELD EXPEDITION SUPPORT METHODOLOGY
9. HIGHWAY LINE SOURCE METHODOLOGY
10. HIGHWAY LINE SOURCE MEASUREMENTS (DOT/FHWA)
11. POINT § AREA HC COMPONENT METHODOLOGY
12. EMISSION INVENTORY HANDBOOK
13. POINT SOURCE METHODOLOGY AND INVENTORY
14. HEAT METHODOLOGY § INVENTORY
15. PRECISION ANALYSIS
16. STATIONARY INVENTORY OF S02 COMPONENTS
AND PARTICLE SIZE DISTRIBUTION
17. NON-CRITERIA POLLUTANT INVENTORY
18. AUTOMOTIVE AREA SOURCE METHODOLOGY
§ INVENTORY
19. FUGITIVE DUST METHODOLOGY AND INVENTORY
20. SOURCE TESTING FOR EMISSION FACTORS
21. EVALUATION § FIELD VALIDATION METHODOLOGY
OF EMISSION MODELS
ANTICIPATED
COMPLETION DATE
COMPLETE
JAN- 7 5
JULY- 7 5
COMPLETE
JUNE-75
COMPLETE
APR- 7 5
COMPLETE
COMPLETE
MAR- 7 5
COMPLETE
MAY-75
COMPLETE
JULY-75
AUG-75
NOV-75
NOV-75
NOV-75
OCT-75
JUNE-76
MAR-76
REPORT
AVAILABLE
YES
BEING PRINTED
NO
YES
NO
YES
NO
BEING PRINTED
BEING PRINTED
NO
YES
BEING PRINTED
BEING PRINTED
NO
NO
NO
NO
NO
NO
NO
NO
88
-------
EXHIBIT 1
TABLE OF CONTENTS
RAPS
EMISSION INVENTORY HANDBOOK
-------
RAPS Emission Inventory Handbook
TABLE OF CONTENTS
A. _ Introduction
Purpose and Content of Handbook
B_. _ An Overview of the Regional Air Pollution Study
RAPS Prospectus
RAPS Series I Study Plan
RAPS Experimental Design Plan
Budget Summary
C_. _ The RAPS Emission Inventory
Purpose of Inventory
RAPS Participants
RAPS Inventory Users and Uses
Special Emission Inventories for Field Studies
Presentation of NEDS Emission Data
D_. _ Scope of the Inventory
Pollutants of Importance to RAPS
Weighted Sensitivity Analysis Program
Precision Analysis
Emissions Projections
E_. _ Classification of Sources
K Point Sources
Survey of Existing Emission Inventory Data
Point Source Methodology
Hydrocarbon Inventory Methodology
Heat Emission Methodology
Emission Factor Development
G. Area Sources
Preliminary Information
Gridding Study
Stationary Area Source Methodology
Highway Area Source Methodology
River Vessel Methodology and Inventory
Fugitive Dust Methodology
Airport Emission Methodology and Inventory
Off-highway Mobile Sources Methodology
-------
JL Line Sources
Highway (Line Source) Methodology
Railroad Methodology and Inventory
_!_. _ Emission Models
£. _ Validation of Area and Line Emission Models
Validation Methodology
Field Measurements
JC _ RAPS Emission Inventory Data Handling System
Purpose and Scope
Hardware System
Software Development
L. Information Transfer
-------
EXHIBIT 2
RAPS
EMISSION INVENTORY DATA HANDLING SYSTEM
RETRIEVAL DESIGN
-------
SC553.T020CR March 5, 1975
REGIONAL AIR POLLUTION STUDY
100% Completion Report for Task Order No. 20
Step III - Retrieval Design
RAPS Emission Inventory Data Handling System
General Order No. 553
Contract No. 68-02-1081
Prepared For
ENVIRONMENTAL PROTECTION AGENCY
Research Triangle Park, N. C. 27711
by
Anne Duke
Principal Investigator
-------
STEP III
Retrieval Design
1.0 Introduction
2.0 Summary Formats
3.0 Modeling Tape Formats
4.0 Retrieval Keys
5.0 Financial
-------
1.0 INTRODUCTION
This report details the results of Rockwell's efforts in design of
retrieval formats for the RAPS Emission Inventory. Summary reports similar
to those commonly used in NEDS have been formatted with provision for
hourly or other time interval data in tabular form. For consistency, data
for input to the modeling program will be formatted in a manner similar to
the input formats; the modeler will select the fields of interest. Retriev-
al may be made on any element of the data base which was defined as key
(in Step IV report), by a user accessing the data base directly through
System 2000. The retrieval keys available to the user through the RAPS
Emission Inventory Data Handling System were initially defined in the Step
II report and will be detailed in Section 4.
-------
2.0 SUMMARY FORMATS
Four formatted summaries will be available to the user through the
RAPS Emission Inventory Data Handling System:
1. Complete Point Source Listing
2. A Condensed Point Source Listing
3. A Daily Summary
4. A Complete Data Base Dump Selectable by Plant or Area
2.1 Point Source Listing
An example of the format for the RAPS Emission Inventory Point
Source Listing appears as Figure I. Tabulation of the data continues for as
many pages as required. For a given plant, the descriptive information ap-
pears on the first page only; the main heading reflecting data and page
number and the column headings are repeated on each page. Data may be tabu-
lated for any of the time intervals allowed as retrieval keys.
2.2 Condensed Point Source Listing
A sample of the format for the Condensed Point Source Listing ap-
pears as Figure II. As for the Point Source Listing all time interval data
is in tabular form with descriptive information on the first page only; date
and page identifier and column headings appear on each page.
-------
FI CURE I
MM/UU/YY
RAPS ST. LOUIS EMISSION INVENTORY
POINT SOURCE LISTING
PAGE 999
NAME: ILL. POWER COMPANY
ADDRESS: WOOD RVR STA EAST ALTON 92024
CITY(OOO): UNKNOWN
COUNTY(4680): MADISON CO
STATE(14): ILLINOIS
PLANT ID: 0001
POINT ID: 01
AQCR(70): METROPOLITAN ST. LOUIS
SIC(4911): ELECTRIC SERVICES
OWNERSHIP: UNKNOWN
SCC(1-01-002-02): EXTCOMB -ELECTRIC GENERATN-BITUMINOUS COAL -MOOMMBTU PULVDRY
UTM GRID COORDINATES
********************
STACK PARAMETERS
****************
EMISSION FACTORS
*****************
FUEL CONTENT
************
UTM ZONE: 0015
HORIZONTAL: 748.70 KM
VERTICAL: 4305.40 KM
STACK HEIGHT: 0250 S02: AP-42
STACK DIAMETER: 015.5 CO: AP-42
GAS TEMPERATURE: 0329 NOX: AP-42
GAS FLOW RATE: 0154649 HC: AP-42
PLUME HT(NO STACK):0000 PART: .AP-42
SULFUR: 2.90% '
ASH: 10.6%
HEAT: 22 MBTU/TON
CONTROL EQUIPMENT
*****************
POLLUTANT: ***** 502 *****
PRIMARY: NONE
SECONDARY: NONE
EFFICIENCIES: 00.0%
***** QQ *****
NONE
NONE
00.0%
NOX
NONE
NONE
00.0%
NONE
HONE
00.0%
**** PARTICULATE *
GRAVITY COLLECTOR
NONE
15.0%
**************************************************
COMPUTER CALCULATED EMISSIONS
DATE HOUR
*****-*-** ****
10/03/74
01
10/03/74 02
10/03/74 03
S02
***********
104.0 KG
105.7 KG
106.4 KG
CO
**********
8.5 KG
8.1 KG
7.7 KG
NOX
***********
HC
*********** -
PART
********
35.0 GM
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2.3 Point Source Daily Summary
An example of a daily summary is included as Figure III. Daily
totals for the five pollutants of interest are tabulated by SCC sub-fields I,
II, and III. All categories relative to the St. Louis inventories will be
included in the summary.
2.4 Data Base Dump
To ensure data base integrity or allow correction of misplaced
data, the List Command in the Immediate Access mode may be invoked to produce
an indented listing of data base contents selectable by level 0 entry in the
data base. (A level $ entry for Point Source is a Plant). This listing
should allow detection of incorrect insertions into the data base, and enable
deletion and reinsertion of the entries.
* The dump feature necessitates inclusion of the Report Type
DUMP as an acceptable entry on Card 8, columns 1-4.
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3.0 MODELING TAPE FORMATS
Upon request from the user ** a tape containing the requested sub-
set of the data base will be produced; the tape will contain the data in card
image format. Should the requested time interval for the data differ from
that stored in the data base, the appropriate model will be applied and the
requested time interval data will be calculated.
Only the data card format will differ from the input formats (see
Step II Report); cards 1-4 will contain the same information as on input but
with columns 72-80 left blank. To avoid redundancy in the data descriptors
for card type 5, all 80 columns of the card image will be utilized; data will
be present in 16 fields of width 5. The start and stop times determine the
quantity of data present in the card type 5 images.
-------
4.0 RETRIEVAL KEYS
Retrieval may be made directly on any element of the data base
defined as key. (See Appendix A, Step II Report; or, Step IV Report, Section
4)
Card type 7 contains fields for each of the RAPS Emission Inven-
tory Data Handling System retrieval keys. These keys include those available
through NEDS. Retrieval is made by inserting the acceptable entry on which
the data is to be selected in the appropriate field as defined in Step II.
The following are the component keys for retrieval under the data handling
system.
1. State
2. UTM Zone
3. County
4. City
5. Area ID
6. Plant ID
7. Stack ID
8. Point ID
9. Pollutant
10. Ownership
11. Time
12. SCC Code Fields
-------
THE RAPS EMISSION INVENTORY
-------
SECTION C: SCOPE OF THE RAPS EMISSION INVENTORY
-------
Scope of the RAPS Emission Inventory
This section of the Handbook deals with background information relating
to the RAPS emission inventory. The scope and purpose of the emission in-
ventory is discussed in relation to the overall program in Section 1. A
list of the RAPS emission inventory participants is presented in Section 2
along with a description of the portion of the emission inventory for which
they were responsible. The third section addresses the problem of how to
obtain additional emission inventory information which is not gathered by the
Office of Air Quality Planning and Standards for the RAPS program.
1. RAPS Preliminary Emission Inventory, Stanford Research Institute,
EPA-450/3-74-030, January 1974.
2. RAPS Emission Inventory Participants, prepared by Charles C. Masser,
of the Air Management Technology Branch for this Handbook, February
1977.
3. Special Emission Inventories for Field Studies, Rockwell International,
Science Center, Contract No. 68-02-1081, Task Order No. 17, 1974.
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2. RAPS Emission Inventory Participants
Since the National Air Data Branch, Monitoring and Data Analysis Division,
Office of Air Quality Planning and Standards, Office of Air and Waste Manage-
ment, is the organization within the Environmental Protection Agency charged
with maintenance of emission inventories as well as emission factors, NADB was
assigned lead responsibility for the emission inventory in the St. Louis AQCR.
Within NADB, Mr. Charles C. Masser was given the responsibility to develop the
needed methodologies, data handling system, and data gathering efforts. In
November 1976 Mr. Masser, along with this responsibility, was transferred to the
Air Management Technology Branch, a newly organized branch within MDAD.
The National Air Data Branch maintains the NEDS (National Emission Data
Systems) inventory, which provides uniform, nationwide computerized coverage
of emission data, obtained originally by local air pollution control agencies.
In the St. Louis area, this comprised the following agencies:
Missouri Air Conservation Commission
Air Pollution Control Div., St. Louis County Health Dept.
Air Pollution Control, City of St. Louis
Illinois Environmental Protection Agency
This inventory is based on annual figures; since the requirements of
RAPS dictated a more detailed resolution, MDAD contracted with a number of
companies to provide the necessary inputs. The following is an alphabetized
list of the contractors and their principal contributions:
Environmental Science and Engineering - Area Sources Methodology
GCA/Technology
Midwest Research Institute
Research Triangle Institute
- Hydrocarbon Emissions Methodology
- Airport Emission Methodology and
Inventory
- Methodology and Emission Inventory
for Fugitive Dust
- RAPS Grid System
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Rockwell International
Atomics International
Air Monitoring Center
Southwest Research Institute
Stanford Research Institute
Washington University
Wai den Research
- Point Source Methodology
- Special Inventories for Field Studies
- Emission Inventory Data Handling System
- Heat Emissions
- Non-Criteria Pollutant Inventory
- Emission Factor Verification
- Hydrocarbon Emission Inventory
- Industrial Area Source Emission
Inventory
- Off-Highway Mobile Source Inventory
- Sulfur Compounds and Particulate
Size Inventory
- Methodology for Off-Highway Mobile
Sources
- RAPS Preliminary Emission Inventory
- Automotive Line and Area Source
Methodology
- Rail Methodology and Inventory
In addition, the Department of Transportaiton has contributed resources
and manpower into helping the Monitoring and Data Analysis Division develop
the RAPS Emission Inventory.
Department of Transportation
Transportation Systems Center
Cambridge, Massachusetts
Federal Highway Administration
Washington, D.C.
River Vessel Methodology and Inventory
Rail Inventory Data
Highway Vehicle Line Source Driving
Patterns
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EPA-450/3-74-030
A REGIONAL AIR POLLUTION
STUDY (RAPS) PRELIMINARY
EMISSION INVENTORY
by
Fred E. Littman, Sylvan Rubin,
Konrad T. Semrau, Walter F. Dabberdt
Stanford Research Institute
Menlo Park, California 94025
Contract No. 68-02-1026
EPA Project Officer: Charles Masser
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality .Planning and Standards
Research Triangle Park, N. C. 27711
January 1974
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This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees, current contractors and
grantees, and nonprofit organizations - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, or from the
National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22151.
This report was furnished to the Environmental Protection Agency by
Stanford Research Institute, Menlo Park, California 94025, in fulfillment
of Contract No. 68-02-1026. The contents of this report are reproduced
herein as received from Stanford Research Institute. The opinions,
findings, and conclusions expressed are those of the author and not
necessarily those of the Environmental Protection Agency. Mention of
company or product names is not to be considered as an endorsement
by the Environmental Protection Agency.
Publication No. EPA-450/3-74-030
11
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CONTENTS
LIST OF ILLUSTRATIONS vii
LIST OF TABLES ix
ACKNOWLEDGMENTS xi
I INTRODUCTION 1
A. Saint Louis Regional Air Pollution Study (RAPS) .... 1
B. The Emission Inventory 2
C. Organization of the Report 3
II SUMMARY 5
A. Basic Concepts 5
B. Results of Specific Tasks 8
1. Task A 8
2. Task B 9
3. Task C 12
4. Task D 13
5. Task E 14
C. Recommendations 18
III TASK A: DEFINITION OF POTENTIAL USERS AND USES 21
A. General 21
B. Summary 21
C. RAPS Requirements 22
1. Atmospheric Structure Models 24
2. Transformation Processes Models 25
3. Mobile Sources 27
4. Other Studies 29
D. State and Local Control Agencies 30
1. City of Saint Louis 30
2. Saint Louis County 32
111
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Ill TASK A: DEFINITION OF POTENTIAL USERS AND USES (Continued)
3. Missouri Air Conservation Commission 32
4. Illinois Environmental Protection Agency 33
5. Federal Highway Department DOT 34
E. The Planning Agencies 35
1. East-West Gateway Coordinating Council 35
2. Industrial-Waste Control Council 35
F. Research Programs 36
1. Metromex 36
2. Community Health and Environmental Surveillance
System (CHESS) 36
3. University Research 37
4. Plant Damage 38
G. Conclusions 39
IV TASK B: EMISSION INVENTORY CONTENT 41
A. General Principles 41
1. Introduction 41
2. The Emission Inventory System 43
B. Precision of Emission Estimates 46
C. Inventory Resolution 48
1. Temporal Resolution 48
2. Spatial Resolution 48
D. Pollutants 50
1. General Discussion 50
2. Nonreactive Gases 50
3. Reactive Gases 51
4. Particulate Matter 53
5. Other Pollutants 55
6. Heat and Water Vapor Releases 56
E. Source Categories 56
1. Classification Scheme 56
2. Stationary Sources 58
3. Mobile Sources 66
F. Emission or Data Conversion Factors 67
G. Physical Aspects of Emission Sources 68
iv
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IV TASK B: EMISSION INVENTORY CONTENT (Continued)
H. Units of Measurement 68
I, Specification of the RAPS Emission Inventory 69
1. General 69
2. Point Sources 70
3. Point Combustion Sources 75
4. Point Noncombustion Sources 77
J. Stationary Area Sources 77
1. General 77
2. Area Combustion Sources 77
3. Area Noncombustion Sources 79
K. Mobile Source Procedures 79
L. Natural Background Emissions 80
M. Particulates 80
N. Scheduling and Scale of Effort 81
V TASK C: EMISSION INVENTORY FILE SYSTEM 83
A. General 83
B. System Environment 84
C. Inventory Operation 87
D. Data Structure and Formats 93
1. Source Categories 93
2. Data Item Formats 94
3. Other Data 97
4. Data Element Formats 97
5. Summary Table 100
E. Format Extensions for Batch Data Entry 102
F. Implementation 104
G. Specifications for Computer Programs 105
1. Data Management System 105
2. Input and Output Subroutines for Emission Model
Programs 106
3. Verification Program 106
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VI TASK D: SURVEY AND EVALUATION OF EXISTING EMISSION
INVENTORY DATA 107
A. Introduction 107
B. Existing Emission Inventories for the Metropolitan
Saint Louis Interstate Air Quality Control Region . . . 108
1. IPP Inventory—1968 108
2. IBM Inventory—1970 109
3. DAQED Emission Inventory—1971 109
4. NATO Emission Inventory —1971 109
5. NEDS Inventory—1973 113
C. Traffic and Transportation Inventories 127
1. Streets and Highways 127
2. Railways and Vessels 129
D. Summary 133
VII TASK E: EMISSIONS MODELING 135
A. Introduction 135
B. Modeling 137
1. Review of Existing Models 137
2. Specification of Emissions 146
3. Emissions Model Verification—Mobile Sources . . . 153
4. Resolution of Source Location 158
C. Consideration of Natural Emissions 170
D. Summary of Conclusions and Recommendations 172
REFERENCES 175
APPENDICES
A SAMPLE EMISSION INVENTORY PRINTOUTS A-l
B SUMMARY OF EMISSION MODEL REPORTS B-l
VI
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ILLUSTRATIONS
1 Metropolitan Saint Louis Interstate Air Quality Control
Region 31
2 Emission Inventory System 44
3 Schematic of Inventory System 92
4 Sample Point Source Item 95
5 Sample Area Source Item 96
6 Emission Inventory System Concept 115
7 Number of Point Sources and Emissions 120
8 Particulate Emissions for Saint Louis Air Quality Control
Region 122
9 SOg Emissions for the Saint Louis Air Quality Control
Region 123
10 NOX Emissions for the Saint Louis Air Quality Control
Region 124
11 Hydrocarbon Emissions for the Saint Louis Air Quality
Control Region 125
12 CO Emissions for the Saint Louis Air Quality Control
Region 126
13 1969 Traffic Map of Saint Louis Metropolitan Area
Interstate and Freeway System 130
14 Schematic Illustration of Classification Matrix for
Emission Models 136
15 Performance Curves for Cooling Towers Giving the Exit
Temperature as a Function of the Ambient Wet Bulb
Temperatures for Various Exit Relative Humidities 154
16 Performance Curves for Cooling Towers Giving the
Moisture Discharge as a Function of the Ambient Wet Bulb
Temperature for Various Exit Relative Humidites 155
17 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height is at the
Surface and Atmospheric Conditions Are Stable 162
vii
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18 Variation f i \ntegrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is
Five Meters and Atmospheric Conditions Are Stable 163
19 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is 30 Meters
and Atmospheric Conditions Are Stable 164
20 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is 100 Meters
and Atmospheric Conditions Are Stable 165
21 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is at the
Surface and Atmospheric Conditions Are Slightly
Unstable 166
22 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is Five Meters
and Atmospheric Conditions Are Slightly Unstable 167
23 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is 30 Meters
and Atmospheric Conditions Are Slightly Unstable 168
24 Variation of Integrated Normalized Error with Longitudinal
Distance from the Source When Source Height Is 100 Meters
and Atmospheric Conditions Are Slightly Unstable 169
viii
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TABLES
1 Emission Inventory Requirements 23
2 Emission Inventory Input Requirements for
Atmospheric Structure (WATPIC) Model 25
3 Emission Inventory Input Requirements for the
Transformation Processes Model 26
4 Emission Inventory Input Requirements for
Vehicular Sources Model 27
5 Emission Inventory Input Requirements for
Mobile Sources (Airports) Model 28
6 Emission Inventory Input Requirements of the
Air Pollution Control Office, City of Saint Louis 32
7 Emission Inventory Input Requirements of the
Air Pollution Control Division, Saint Louis County
Health Department 33
8 Emission Inventory Input Requirements of the
Illinois Environmental Protection Agency 34
9 Emission Inventory Input Requirements for CHESS Studies ... 37
10 Classification of Sources for Emission Inventory 57
11 Major Noncombustion Sources of Pollutants 64
12 Pollutant Sources by Class 72
13 Tentative Workload for the Monitoring of
SO and NO Sources 73
2 x
14 Development Schedule 82
15 Sample of DAQED Inventory SO Emission Summary 110
x
16 Sample of DAQED Inventory Current and Trend Data,
Ambient Concentrations Ill
17 Comparison of Emission Inventories for the
Metropolitan Saint Louis Interstate Air Quality Region . . . 118
18 Number of Point Sources ; 119
IX
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19 Number of Sources Monitored 121
20 Breakdown of Area Sources 128
21 Classification of Possible Emission Inventories 131
22 1967 Emissions from Rail Operations Within
100 Miles of Saint Louis 132
23 Summary of Content of Emission Models Reviewed 138
24 Emissions Models Input/Output Specifications 139
25 Ratio of Cold-Start Emissions to Hot-Start Emissions .... 150
26 Cold-Start Emissions from Light-Duty Vehicles 151
27 Natural Pollutant Emissions in the Saint Louis Area 172
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ACKNOWLEDGMENTS
This study was carried out by a project team led by Dr. F. Littman
of the Operations Evaluation Department of the Engineering Systems Divi-
sion at Stanford Research Institute. The project supervisor was Mr. R.
Rodden, Assistant Director of that department. R.T.H. Collis, Director
of the Atmospheric Sciences Laboratory of the Electronics and Radio Sci-
ences Division, acted as project advisor and assisted in the preparation
of the final report.
The principal authors were as follows:
Section III (Task A)
Section IV (Task B)
Section V (Task C)
Section VI (Task D)
Section VII (Task E)
Dr. F. E. Littman
K. T. Semrau, Chemical Engineer,
Physical Science Division
Dr. S. Rubin, Information Science
Laboratory, Information Science
and Engineering Division
Dr. F. E. Littman
Dr. W. F. Dabberdt, Atmospheric
Sciences Laboratory
Other contributors were as follows:
P. A. Buder
R. C. Robbins
P. J. Martin
A. E. Moon
G. I. Thompson
xi
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INTRODUCTION
This report presents an operational plan for providing emissions
data for the Saint Louis Regional Air Pollution Study (RAPS). It also
describes existing emission inventories for the Saint Louis area and
reviews in detail emission models that have in the past been used to
provide emissions data.
A. Saint Louis Regional Air Pollution Study (RAPS)
The Saint Louis Regional Air Pollution Study is the most ambitious
study of its type ever attempted. Focused principally upon the verifi-
cation and development of air quality models, it is also designed to de-
velop a better understanding of atmospheric transformation processes and
to provide a basis for studying various air pollution control strategies.
Material advancements in the technology and methodology of air quality
monitoring and other aspects of air pollution control, including partic-
ularly the improvement of emissions inventory procedures, are expected
also.
Considerable attention has already been paid to measuring meteoro-
logical conditions and air quality in the test area, and a network of
25 automated data collection stations will be installed, stations which
will continuously telemeter their readings to the data center. Data of
this quality will be valuable to RAPS, particularly for diffusion model-
ing on an extended scale.
For the full realization of such purposes, however, especially for
the modeling and prediction of air quality, it is essential to know the
emissions from the air-polluting sources. Without effective assessment
of such emissions to an appropriate degree of resolution in time and
space, the experiments and studies of the RAPS program will be severely
handicapped no matter how well other measurements are made or other fac-
tors assessed. To collect data on critical emissions for the multitude
of different sources in an area the size of Saint Louis is a very tall
order. It can only be met by an effort of considerable magnitude and
cost for exceeding any previous undertakings of a similar nature. Indeed,
it must be recognized that the requirement demands and warrants the appli-
cation of detailed data collection procedures considerably different from
-------
those appropriate to a less rigorous determination of high resolution
data and that many of the estimation or modeling techniques used hitherto
cannot be used.
B. The Emission Inventory
To determine how to meet RAPS' needs for emission data, the problem
was considered under five tasks. These logically follow each other se-
quentially, and they were addressed, at least initially, in that order.
As the study proceeded, however, the interrelationship of the problems
addressed under each task became ever more apparent, and, although the
separate submission of the task reports in draft form served the useful
purpose of checking and correcting progress, it was agreed that the re-
sults of the project were most useful when integrated as they are in this
final report.
The tasks and their objectives were
• Task A. To ascertain the uses and users of emission data in
the RAPS program (and elsewhere).
• Task B. To specify the content and nature of the emission
inventory for RAPS.
• Task C. To specify the data formats and a data handling
system needed for the inventory.
• Task D. To locate and assess existing sources of data suitable
for incorporation in the RAPS inventory.
• Task E. To review the emission models available for developing
emission data for studies of the type to be carried
out in RAPS.
As noted, the full value of these tasks is only realized if they
are allowed to interact, and a failure to recognize this until late in
the project led to difficulties in formulating and presenting our conclu-
sions .
In the final form, summarized below, the findings of the studies
made under Tasks D and E have determined, to a certain degree, the con-
tent of Task B, which in turn reflects certain constraints and opportu-
nities of the data handling system described in Task C.
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C. Organization of the Report
The first part of this report, the Summary, is an overview of the
project that summarizes the results. The second and largest part includes
sections on the five separate tasks, each section reporting the appropri-
ate detailed results. Appendix A is an assembly of data in printout form
that pertains to Task D, and Appendix B is a lengthy summary of emission
model reports.
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11 SUMMARY
A. Basic Concepts
The requirements of the RAPS program are wide ranging, diverse, and,
in many cases, stipulate degrees of spatial and temporal resolution that
have not hitherto been achieved. For the multiple purposes of the RAPS
program, it is thus more appropriate to consider data on pollutant emis-
sions in terms of a system of data resources than in terms of a single
all-serving emission inventory. Alternatively, one can look upon an
emission inventory for RAPS as being a multifaceted data source that
comprises a number of elements, which are capable of providing needed
information as required. In either case, we are led to a system in
which varying criteria are applied to determining the nature and extent
of the inventory content.
A major distinction can be drawn between the requirements of RAPS
for high resolution data (in space and time), for such purposes as the
verification and development of air quality models, and the more general
needs of the program, such as, for example, investigations of economic,
social, or political processes, or the investigation of control or abate-
ment strategies not immediately served by the air quality modeling studies.
This distinction leads immediately to a concept in which a special high
resolution inventory (for pollutants of significance) is provided to the
air quality modeling studies and other investigations (such as transpor-
tation processes or improved inventory methodology) where such high res-
olution data is appropriate. Those pollutants are S02, NOX, CO, and
hydrocarbons (HCs). For other pollutants, the high costs and difficul-
ties of providing such high resolution emission data on a routine basis
are not justified. For such other pollutants, we recommend that the
National Emissions Data System (NEDS) Inventory be used.
When special information on emissions is required—for example, for
extra precise high resolution data for limited "maximum effort" periods
of model validation or for intensive transformation process investiga-
tions—we recommend that each such emission data collected be treated
as a special case. This will avoid placing excessive requirements on
the basic emission inventories and save cost and effort.
-------
It is convenient, therefore, in speaking of the RAPS emission inven-
tory, to consider it primarily as the specially developed, high resolution
data source concerned initially with SOV, NO , CO, and HC. This inventory,
x x
as noted, must be flexible and capable of serving many purposes.
To ensure this, it is necessary to retain the basic or prime data
from which it is built in its compilations in order to make revisions or
extensions readily possible. Data should not be lumped in a form that
applies to a single, or narrowly perceived, requirement.
The most satisfactory approach, wherever possible, is to provide
files of emissions data for each pollutant in terms of direct statements
of weight of pollutant emitted by each source or class of sources as a
function of point or area location for every hour. Although this is a
substantial undertaking, as described below, in most cases it can be ac-
complished to a useful level of precision within the RAPS program. This
is mainly because the bulk of the emissions is a result of a limited num-
ber of sources. In a program of the scope of RAPS, it is possible to
treat such sources individually and to use measured data providing direct
or closely related information on emissions as the basis for hourly emis-
sion estimates. The frequency and quality of the measurements will be
proportional to the magnitude of each source. This approach differs
from that commonly used in developing emission data for purposes similar
to the RAPS main objectives, which has perforce relied heavily upon emis-
sion modeling techniques to interpolate from gross annual data to provide
information with high nominal temporal resolution. In the proposed RAPS
inventory, such modeling techniques would only be applied to estimating
hourly emissions from minor point sources or from area sources, although
in certain of these cases it is recommended that the required precision
demands the development of improved modeling methods for providing hourly
data. In the case of mobile sources, however, where considerable progress
has been made in assessing emissions with high spatial and temporal reso-
lution, the current modeling approach is suitable for the RAPS inventory;
the records of mobile sources to be incorporated in the inventory have a
special form.
The term "emission modeling" sometimes leads to confusion, and it
is necessary to clarify its significance. Strictly speaking, any compu-
tational manipulation of raw data can be described as modeling, for ex-
ample, the combination of pollutant concentrations and flow rates to pro-
vide emission estimates. More often, modeling applies to the derivation
or estimations of emissions from limited prime data, such as the estima-
tion of emissions from residential space heating on an area basis (from
a consideration of population density and daily temperature). In partic-
ular, the apportionment of emissions from any source or class of sources
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on, say, an hourly basis, from data such as gross annual tonnage of luel
burnt, is commonly performed by modeling using expressions (which can be
more or less arbitrary) to describe the patterns of variability that oc-
cur by season, by day of the week, or by hour of the day.
In a more comprehensive sense, various emission models have been
developed for specific purposes. These may consist of series of sub-
elements integrated into a single system for deriving required emissions
data. Such models are generally intended to provide emissions data in
a specified form suitable for a particular use, deriving their estimates
from relatively limited prime data (usually that relating to gross annual
fuel consumption). A typical example is the Argonne Hourly SC>2 Model.1
This model, described in detail in Section VII, provides hourly estimates
of SC>2 and thermal emissions from power plants, industrial point sources,
and one-square-mile areas of residential, commercial, and institutional
sources. It derives such estimates from a range of input parameters
based largely upon fuel consumption statistics as well as upon other
factors such as temperature and time.
In considering such comprehensive emission models, it is important
to emphasize their limitations for the purposes of RAPS. Adequate though
they may be for the uses for which they were developed, they reflect the
expediency and economy that was appropriate to such use. They seek to
provide the best emissions estimates possible from available (i.e., mini-
mal) data. They are not the best approach to developing emission inven-
tories if better data can be specially acquired—as it can in the case
of RAPS. Another aspect of emission models is the way that the specific
output requirements determine the manner of developing the emission esti-
mates from the prime data. This generally results in a lack of the flex-
ibility needed to meet the multiple and varied demands of RAPS. The RAPS
Emission Inventory should make emissions information available in such a
manner that any desired selection of data may be extracted and presented
in any desired form. This requirement is met by providing emission data
in the form noted above, i.e., as direct statements of weight of pollutant
emitted by each source, or class of sources as a function of location for
every hour. In this approach, the emission modeling techniques, where
used, are limited to the production of a common database, i.e., the gen-
eral purpose high resolution emission inventory. The task of extracting
emissions data for any purpose and of presenting it in any appropriate
form then becomes a separate function.
It is recommended, therefore, that the RAPS inventory be developed
upon the basis of specially collected data, rather than upon current
emission models (with the exception of mobile source data). Further, it
is recommended that the inventory provides flexibility and universality
-------
by being a simpl- compilation of all required a^^a in the lowest common
denominator foim, i.e., hourly emissions by weight of each pollutant
(SO , NO , HC, and CO).
2 x
For other pollutants, it is recommended that the National Emissions
Data System (NEDS) be used.
B. Results of Specific Tasks
1. Task A
To determine the potential users and uses of the emission in-
ventory for RAPS, a survey was made. Users range from the research groups
directly involved in the RAPS program to city, county, and other planning
organizations and air pollution control agencies in the area. Research
programs other than RAPS were also considered. The principal uses of
emissions data will be in the verification and development of transport
and diffusion models and in studies of chemical transformation processes,
the main interest of the RAPS program. Other uses will be in the analysis
of economic, social, and political impact of air pollution and its control
and abatement and in the development of such control and abatement strat-
egies (particularly episode control). The RAPS emissions data could also
be useful to programs such as the Community Health and Environmental Sur-
veillance System (CHESS) or research studies such as METROMEX (a joint
investigation of meteorological effects of air pollution) or other locally
based investigations requiring detailed knowledge of pollutant emissions.
Two factors emerge from this survey. First, the requirements
for these purposes are multiple and diverse, covering a wide variety of
pollutants and very different degrees of temporal and spatial resolution.
Second, the needs of the air quality modelers are the most demanding, and
they dominate the requirements for high resolution data in space and time.
The needs for emissions data in the RAPS program can, thus,
best be met routinely by
• Relying on NEDS for data on the broad range of pollutants
for which high resolution information is not essential.
• Developing a special RAPS Emission Inventory capable of
providing the high resolution data needed by the modelers
and of serving other purposes.
For special purposes, such as particular experiments to inves-
tigate chemical transformation processes or studies of the dispersion of
8
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specific pollutants for which the above mentioned inventories are inade-
quate, appropriate special data collections should be made to determine
emissions on an experimental basis. (Such data may be collected, pro-
cessed, and stored within the RAPS Emission Inventory data handling sys-
tem, but are not an integral part of this inventory.)
The specification of various air-quality model input require-
ments that act as common denominators thus determines what is required
of the RAPS Emission Inventory. Specifically, the requirements are
• Pollutants: SO , CO, NO , hydrocarbons (bv types), and
2 x '
particulates.
• Resolution: Temporal, hourly, for each hour; spatial,
r>
0.01 km point sources, 1 km grid squares [area sources
using Universal Transverse Mercator (UTM) coordinates
in each case].
• Area covered: The Saint Louis Air Quality Control
Region (AQCR).
• Period covered: Continuous hour by hour throughout the
years of full RAPS operations.
• Units: Emissions by metric weight; distance in kilo-
meters, in UTM coordinates.
• Output format: Variable, but in FORTRAN" compatible form.
• Other information: Data on sources (e.g., stack height,
exit temperature, velocity) where appropriate; data for
mobile sources, traffic flow data, and aircraft movements
as required for use in appropriate emission models.
2. Task B
In this task (specification of the content of the RAPS Emission
Inventory) an extensive analysis was made of the problems of developing
emission data with the necessary spatial and temporal resolution and ac-
curacy to meet the requirements of RAPS.
After an initial review of the basic form the inventory system
should take to meet the multiple purposes of RAPS, we discussed each
aspect of such a system in detail.
Because of the need for precision in estimating the emission
and the requirements for spatial and temporal resolution, a quantitative
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basis can only be provided by analyses made for specific air quality or
dispersion models (as discussed in Section VII). The emission inventory
can potentially provide the most complete data input to such models,
depending upon the cost and effort applied, although even in the most
careful direct assessments of emissions at individual sites, uncertain-
ties of ±10 percent must be expected. We described the pollutants of
interest, distinguishing between the reactive gases, nonreactive gases,
and particulates. The role of the emission inventory of such pollutants
is discussed in the context of RAPS. While data can fairly readily be
obtained for the key gaseous pollutants, particulate emissions are ex-
tremely difficult to monitor. Existing data on such emissions are often
inaccurate and give no information on the size distribution of the parti-
cles, which is so important in the modeling context. In the circumstances,
it is proposed that particulates not be included in the high resolution
inventory on an hourly basis, and that the needs of RAPS for particulate
data for dispersion model studies or research on transformation processes
be dealt with on a special experimental or project basis—including, spe-
cifically, the use of tracer techniques.
The study team described and analyzed sources categorized ac-
cording to identity, function, and pollutant emitted. Each category is
discussed in detail, necessary for the data collection procedures to be
recommended later.
The importance of emission and data conversion factors is then
noted. Such factors are critical to any inventory and especially to an
inventory of the type needed for RAPS. The economics of the problem,
however, are such that, while it would be desirable to test every size-
able source in the inventory on an individual basis, wherever reasonable
confidence can be placed in standard emission factors, these should be
used in practice. Finally, the physical characteristics of the sources
needed for the inventory are noted, and recommendations are made regard-
ing the metric units to be used.
The second part of Task B was to specify the RAPS Emission In-
ventory system outlined below.
While NEDS is proposed as a source of information on annual or
long-term emissions from a broad range of pollutants, it is recommended
that a special, high-resolution RAPS Emission Inventory be developed to
provide hourly data emissions of S02, CO, N0x, and EC in the form of
direct statements of weight of pollutant emitted in kilograms each hour
from each point or line source or area element for the Saint Louis AQCR.
Locational data will be provided in UTM coordinates with a resolution of
at least 0.1 km for point sources. Mobile sources will be specified in
terms of traffic links (with a resolution of approximately 0.2 km) for
major routes and in areas for secondary routes. Both for stationary and
10
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mobile sources, the area elements will reflect (-:f;hrr the spatial resolu-
tion of the basic data from which the emissions are estimated (.e.g., bous-
ing developments) or one kilometer grid squares, whichever is the smaller.
Information on the physical characteristics of point sources and the prime
data used in compiling the inventory would also be provided. It is not
considered practicable to develop comparable data for particulates.
For initial planning purposes we propose that only point sources
emitting over 100 tons per year of each pollutant be considered individu-
ally. The estimation of emissions from such sources will be accomplished
in classes according to the magnitude of each source. In general, we rec-
ommend that emission data be based upon direct monitoring of emissions
for the largest sources. For other major sources, direct monitoring of
hourly fuel consumption, operating, or process data should be used as the
basis for estimating emissions. For smaller point sources, we propose
that emissions be estimated on the basis of generalized patterns of tem-
poral variation of emission as a function of source type applied to the
best available data on fuel consumption, operating, or process data (per
shift, daily, or weekly). This approach approximates current practice
in which annual totals of such data are used as a basis for modeling.
For area sources, we recommend the use of modifications and adaptions of
already available emission models. Similarly, for a first approach to
assessing mobile source emissions, we recommend the use of available
models. Improved mobile source models should be employed, as they become
available, to provide higher quality data. A simple new model should be
developed to estimate natural emissions of nonmethane hydrocarbons on
summer days. In all cases, we propose that special testing efforts be
carried out to ensure that the emission and data conversion factors used
to derive the emission estimates are as reliable as possible, and have
regard for the high temporal resolution required. Standard emission fac-
tors should only be employed if a review of the source on a case-by-case
basis indicates that such a factor may be used with confidence.
The concluding portion of Task B is a description of methodology
and detailed procedures for developing the inventory in the form proposed.
Point sources using the NEDS data are classified according to size to
facilitate the description of how emission data would be derived in four
broad groups according to the basic records used to estimate emissions—
Group I, continuous emissions monitoring records; Group II, continuous
records of fuel consumption, operating, or process data; Group III, short
term, periodic records of fuel consumption, operating, or process data
(per shift, daily, or weekly); Group IV, long-term (semiannual or annual)
records or estimates of fuel consumption, operating, or process data.
Within Groups II and III, a further distinction would be made between
sources for which separate conversion or emission factors are established
11
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and those for which representative factors will be established. Data
from Groups I, II, and III will be collected on an ongoing basis.
In principle, it would obviously be desirable to deal with
all sources under Group I. In practice, sources should be treated in
the highest ranking group possible within limits of cost and difficulty.
Some 95 percent of the S00 emissions and 70 percent of the NC> emissions
& X
can be derived from direct data (Groups I, II, and III) on the basis of
routine data inputs from 101 sources with a high confidence based upon
the testing of 65 individual sources. Similarly, in the case of CO
sources, over 90 percent of the emissions can be estimated on an hourly
basis by the treatment of direct data from only six sources. On the
other hand, it would be necessary to obtain data from and test some ten
or more sources to achieve hourly estimates from direct data for only
50 percent of the HC emissions. The proposed approach, however, is in-
tended to provide considerable flexibility, so that optimum results can
be achieved within budgetary and other limitations. Thus, the number of
sources to be treated on an individual basis and the numbers of tests to
be made should be decided upon the basis of an initial survey on a case-
by-case basis.
In the case of area sources, the shortcomings of existing emis-
sion models require that special efforts be made to improve and verify
these, for, although such sources contribute only a small proportion of
the total burden, their local effect is significant. Similarly, in the
case of mobile sources, while a simple form of existing models will suf-
fice initially (with improved traffic data inputs), the products of more
sophisticated models should be used as they become available. Finally,
it is noted that a method for estimating emissions of nonmethane hydro-
carbons from natural sources on,summer days is needed.
Task B includes a tentative schedule aimed at providing initial
data on S02 and NOX by the first quarter of the second year of full activ-
ities, complete data by the third quarter of that year; effective initial
data on CO and HC would become available by the end of the second year,
and complete data would be available by the third quarter of the second
year.
3. Task C
Task C is the specification of a system for handling, storing,
and processing the relevant data. Specification is based upon certain
assumptions regarding the nature and capability of the data management
system that will be available as part of the RAPS overall data handling
12
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system. These assumptions either indicate facilities that are normally
provided by such a system or could be readily provided by appropriate
adaptation or additional programming of a conventional type.
In order to specify the data formats and structures, it is
necessary to determine how the inventory files will be used as a system
for source data preparation and entry, for processing data through emis-
sion models, and finally, for production of the data files for the vari-
ous user activities. The system is designed to minimize manual transfor-
mations of the raw data to the form in which it is entered into the
computer. Input data are thus based upon the sources, and all pertinent
data (e.g., pollutants, fuels, consumption) are entered in direct form
as reported. Rather than using integer codes such as the SAROAD manual,
we propose the use of direct, mnemonic identifiers. We also specify the
use of a hierarchical, list type structure to enter such data and similar
necessary information. This leads to data structures that comprise ex-
tendable data items within an appropriate data reference system allowing
maximum flexibility both to accommodate disparate types of data and to
allow ready extension to cope with new or additional data forms that might
need to be added after the establishment of basic lists or thesauri.
The master inventory file will consist of data items, each of
which describes a source or a set of identical sources. Each item will
include source identification data, source operating parameters, the set
of primary measurements from which emissions will be computed, and the
set of computed hourly emission values for each pertinent pollutant.
This inventory file is designed as the core of the whole inventory sys-
tem. It will be supported and maintained by a general-purpose data
management system and by such special-purpose programs as may be required
for verification, emission computations, and production of data files
properly formatted for use as input to air quality models.
4. Task D
Task D, the review of the existing emission data sources in the
Saint Louis area, has been disappointing. Despite the fact that Saint
Louis has been the site of a number of advanced air quality studies and
has active and well-organized local air pollution control agencies, the
available emissions inventory data is quite inadequate for the purposes
of RAPS. Although the emissions data that have been collected in the
agea place it in the forefront of urban areas in the availability of such
*>*-
information and the existing data fairly well meets (or met) the require-
ments for which it was collected, it falls far short of the new and highly
demanding requirements of RAPS. Specifically, the data do not provide
for hourly resolution.
13
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The fi~
-------
gross, and much reliance is placed upon extensive interpolation procedures.
Since these modeling procedures are clearly of limited precision for use
in the context of RAPS requirements and since, in the absence of extensive
verification, confidence in their use must be guarded, it was not possible
to recommend the use of such models unreservedly for the RAPS program.
Certain models appear to hax^e potential utility for the purposes of RAPS
depending upon the degree to which interpolation procedures will be needed
(as distinct from the preferable procedure of deriving emissions directly
from time resolved related data). These models are noted below.
In addition to the review of existing models, a brief analysis
is also made of the nature of and need for verification procedures. In
particular, a procedure is set out for evaluating the validity of mobile
source models by combining specially collected experimental data on traf-
fic volume with basic statistical data.
Another point addressed in detail is the degree of resolution
required for the specification of location of point sources. The inter-
play of source strength, stack height, lateral and longitudinal receptor
position, and atmospheric stability makes the definition of locational
precision complicated and complex. Accordingly, a new methodology is
developed to enable the varying interrelated factors to be considered
for any given circumstances, enabling the effects of positional error
to be assessed for any downwind distance or distance interval, and pro-
viding a recommended spatial resolution for each source specification
based upon stack height and emission rate. Finally, the role of natural
mechanisms in adding significantly to the man-made pollutant burden of
the Saint Louis region is considered.
The specific conclusions that result from the studies carried
out under Task E are listed here.
• No existing models adequately meet all the requirements
of RAPS.
• To provide emissions data with the necessary high resolu-
tion in space and time, it is recommended that
- Direct information of emissions or of factors deter-
mining emissions be acquired to the extent possible.
- Where this is not possible (e.g., for small point
sources and area sources) the most suitable of the
existing models should be used in adapted and improved
form.
15
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• Specifically, for stationary sources
- Direct data from emissions monitoring should be used
wherever available (probably only for the largest
sources, such as power stations).
Information on hourly fuel consumption rates, process
operations, and so forth should be obtained and used
to derive emissions for other large sources.
Modeling procedures for small point sources and area
sources, based upon the models noted below, should be
used, but, with refinement of the input data wherever
possible.
o
- Recommended models are the Argonne model (for hourly
SC>2 from distributed residential, commercial, and in-
stitutional sources; and hourly SO2 and heat from major
point sources) and the Systems Applications, Inc. (SAI)
model1'3 (for hourly NO and HC from stationary point
X
and area sources).
- For automotive mobile sources, we recommend that an
average route speed model be used with a link or line
source geometry supplemented with measured data of traf-
fic flow from fixed sensors on high volume freeways and
on selected arterials.
The recommended models are the SAI model1'0 (for hourly
CO, NO and HC with modified inputs derived from Stanford
Research Institute's model4 for spatial and temporal dis-
tribution of vehicle number and speed on a link basis for
primary traffic and area basis for secondary traffic).
- For other mobile sources, the recommended models are the
Geomet model (for diurnal emissions from river vessels
and railroads) and the Northern Research and Engineering
model6 as revised by Geomet (in preparation, for aircraft
emissions).
- For both stationary and mobile sources, the Ontario De-
partment of the Environment 1971 Model for Toronto should
be referred to for overall guidance and planning method-
ology.
• For microscale studies under the RAPS program, the basic
inventory will need to be supplemented with special emis-
sion inputs. These should be obtained on an experimental
or project basis (e.g., the acquisition of detailed data
16
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from small stationary sources or for mobile sources, con-
tinuous and detailed monitoring of vehicle behavior in a
given area with the application of a multimodal emission
model to forecast emissions).
• In view of the limitations of existing mobile emission
source models and the impossibility of resorting to direct
information, it is recommended that special steps be taken
to improve and verify such mobile emission models. Spe-
cifically,
- An experimental evaluation should be made of mobile
emissions models to refine the specification of process
input parameters and the accuracy of postulated input-
output relationships.
Parameterization of inputs for mobile emissions (statis-
tical database plus continuous measurement of vehicle
volume, speed, and mix on key links to generate a dy-
namic link-by-link inventory) should be verified on a
selective basis via aerial photography and/or side-
looking aerial radar.
- Parameterization of outputs from mobile sources should
be evaluated in various models to verify representation
of time-averaged emissions on a per-link basis taking
into account types of link and the impacts of deteriora-
tion, mix, road grade, drag, and so forth.
• Of the pollutant emissions (CO, NOV, S00, H0S, nonmethane
.X £ £
HC, and particulates) from natural phenomena in the Saint
Louis area, nonmethane hydrocarbons may contribute a sig-
nificant background during summer daylight hours and should
be included in the RAPS emission inventory. Suitable proce-
dures to estimate these should be developed early in the
RAPS program.
• In the context of the RAPS program, the accuracy of point
source locations is best considered in terms of the effect
of errors throughout the study area. Accordingly, we pro-
pose that a measure of error in source location be defined
as the integral value of the error over both the lateral
(crosswind) and longitudinal (downwind) directions.
17
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C. Recommendations
The following recommendations provide an outline of an operational
plan to derive the RAPS Emission Inventory.
• An initial survey should be made of point sources emitting over
100 tons per year of SOQ, NO , CO, or HC. The survey should be
£ X
based upon the information of the NEDS inventory, and as many
as possible of the sources should be inspected. An initial
assessment, based upon the nature and quality of the records
available, should be made on how each source should be treated
by group (according to the data collection procedure).
• With regard for budget and the results of the survey, detailed
plans should be made as to the number of sources to be treated
in each group, especially the number of individual source tests
to be made.
• Meanwhile, the proposed data handling system should be developed
and coordinated with the specific data management system actually
procured for the RAPS project.
• As soon as possible, effort should be applied to the development
and refinement of emission modeling procedures to meet the needs
of the RAPS inventory. Specifically, improved techniques of
handling shorter term input data for both point source and area
sources should be applied to the Argonne model and the SAI model.
For mobile sources, similar improvements should be made on the
basis of the SAI model with modified inputs derived from the
SRI model. A model must be developed to estimate summer day
emissions of nonmethane hydrocarbons from natural sources.
• Following the detailed plan developed from the initial survey,
an intensive program of source testing should be undertaken to
develop a reliable basis of emission factors or conversion fac-
tors to relate the routinely collected input data to the emis-
sions of specified pollutant on an hourly basis.
• Arrangements must be made for the routine periodic collection
of input data from the sources to be treated on that basis or
additional data for modeling purposes not acquired in the source
of the initial survey. Arrangements should also be made to in-
stall any necessary monitoring equipment provided under the RAPS
program for point sources or measuring traffic flow on selected
high volume freeways or arterials.
18
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Finally, the system should be operated on a routine basis folBow-
ing the sequence noted in Task B to provide data first on SO
^
and NO and later on CO and HC.
x
In support of the inventory development effort, the following
inputs should be sought from other elements of RAPS as early as
possible:
Tests of specific air quality or dispersion models should
be made to ascertain sensitivity to the precision and reso-
lution of emissions data in the Saint Louis area (using NEDS
data as a basis).
- The vicinity of air monitoring stations should be surveyed
and any significant point sources within a radius of say one
kilometer of each station should be identified so that consid-
eration can be given to treating them on an individual basis
even though they do not emit over 100 tons of pollutants per
year.
19
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Ill TASK A: DEFINITION OF POTENTIAL USERS AND USES
A. General
An emission inventory is a fundamental part of any air pollution
study. The Regional Air Pollution Study (RAPS) in Saint Louis is ex-
pected to be unique in scope and depth. Consequently, the emission
inventory contemplated for this study needs to be far more detailed and
accurate than those currently available.
The actual scope, content, and format of the RAPS inventory is de-
termined by its proposed use, and, in order to ensure that the inventory
will correspond as closely as possible to the demands placed on it by
future users, a survey was made to determine the characteristics desired
by potential users both within EPA and elsewhere. Primary consideration
was given to RAPS requirements. To complete the picture, the needs of
other potential users were then considered.
B. Summary
Although an emission inventory is a key information base for all
air pollution studies and control strategies, it is of direct use to
RAPS investigators concerned with the relationship between ambient
concentrations of pollutants and their source, that is, the modelers.
Other groups directly concerned with emission inventories are (1) the
regulatory agencies charged with the achievement and maintenance of the
National Air Standards (this group has the responsibility and authority
to collect and store emission data) and (2) other workers in the field
concerned with transportation problems, health effects, plant damage,
and the like (these groups are primarily concerned with ambient concen-
trations of pollutants of interest).
The requirements of these users are quite different. The modelers
are concerned primarily with those pollutants for which air quality
standards have been promulgated, that is, sulfur dioxide, carbon monox-
ide, hydrocarbons, nitrogen dioxide, and suspended particulates. The
information they require is very detailed (accurate location of the
sources, detailed information on emissions as a function of time, and
precise estimates of quantities emitted are all essential). The period
21
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for which such high resolution data is needed will vary considerably.
In many cases, model verifications will be limited to relatively short
periods, of, say, 48 to 72 hours, selected for representative meteoro-
logical conditions. Since such periods may not be readily determinable
until a later time, when the best combination of circumstances is deter-
mined, post facto, it is necessary to collect appropriate emissions data
on a continuing basis, parallel with the even more comprehensive contin-
uous measurements of air quality and meteorological factors.
The requirements of the people working with health effects or plant
damage, on the other hand, are quite different. The range of pollutants
of interest is very broad, including not only compounds of known toxic
effects such as mercury or cadmium but also an almost infinite variety
of compounds suspected of having an effect on plants or people. For
these purposes, detailed time resolution of emission is seldom required
(except in cases of accidents or unusual meteorological events) since
most effects are cumulative over long periods of time. Therefore, long-
time historical records on many substances are of main importance in de-
termining causative relationships of air pollution to health or vegeta-
tion effects.
State and local regulatory agencies are in a sense caught between
these two requirements. They have to maintain inventories adequate for
the enforcement of legal standards, and they would like to have records
of emissions that are potentially hazardous.
As RAPS may eventually include a number of investigations that cur-
rently are not well defined, it is essential to include in the inventory
design the flexibility required for the addition of other pollutants.
For the moment, however, the primary users of the RAPS emission inventory
are the modelers, and it is their requirements that have to be met. In
the following section the details of these requirements are delineated.
Table 1 summarizes the requirements.
C. RAPS Requirements
The Saint Louis emission inventory is of prime concern to the RAPS
research groups engaged in the development, evaluation, and refinement
of air pollution models. Several types of models are currently under
consideration or active study as part of one RAPS study plan;7 in this
plan, the four basic model types are emission, atmospheric structure,
transformation, and removal process models. Also being considered are
existing models that encompass one or more of the basic models, such as
some models that treat the generation and dispersion of pollutants from
certain transportation sources.
22
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Upon examination of the four basic models, it can be seen that only
the emission model is self-contained. The others exhibit various degrees
of interdependence. For example, a model of boundary layer structure
will need to account for the meteorological effects that result from
emissions of, say, heat and water vapor. Similarly, transformation
models must consider both emissions and atmospheric structure models,
while removal process models consider all three models. Thus, in assess-
ing the requirements of the various specific models, we need to recognize
that, while the primary emphasis of the particular model may be one thing,
it is quite likely and indeed probable that many of its input require-
ments derive from the structure of the other model components.
Up to now little effort has been devoted to the development of com-
prehensive or nonspecialized emission models. In fact, the development
of a framework for a RAPS emission model program is an objective of this
study. In the absence of an established emission model that the emis-
sion inventory would serve, we have surveyed the various model require-
ments for emission inputs so as to specify criteria and objectives for
such an inventory. These models and their requirements are discussed
below.
1. Atmospheric Structure Models
Atmospheric structure models describing transport and diffu-
sion within the planetary layer on a regional scale (£30 km) are being
developed at the Model Development Branch, Meteorology Laboratory, NERC,
Research Triangle Park, North Carolina. The specific requirements re-
lated to emissions are shown in Table 2. The model is currently under
development, and, hence, these requirements reflect our best judgment
based on available information. Three additional models used at the
Meteorology Laboratory of NERC are
• PTMAX, an interactive program that performs an analysis
of the maximum, short-term concentration from a point
source as a function of stability and wind speed.
• PTDIS, an interactive program that computes short-term
concentrations downwind from a point source at distances
specified by the user.
• PTMTP, an interactive program that computes, at multiple
receptors, short-term concentrations resulting from mul-
tiple point sources.
24
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Table 2
EMISSION INVENTORY INPUT REQUIREMENTS
FOR ATMOSPHERIC STRUCTURE (WATPIC) MODEL
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
SO , CO, particulates, heat, water vapor
£
1 hour
Point sources on UTM grid (±0.01 km)
2
Area sources on variable grid from 1 km
Tape
Grams/second, watts
Stack height (meters), exit velocity
(meters/second), temperature (°K), stack
diameter (meters) '
Diurnal, daily, seasonal
Depends on source of data (stack sampling,
questionnaires)
We believe that the emission inventory input requirements of these
models are met by the specifications in Table 2. However, when the
manuals for these models become available, we will review those require-
ments.
2.
Transformation Processes Models
Many materials emitted into the atmosphere undergo not only
physical dispersion but chemical changes as well. We need to know the
whole life history of a pollutant, from emission through chemical and
physical transformation to eventual removal, before accurately predict-
ing the relationship of emission to air quality.
At present, the fate of six major pollutants is under investi-
gation by modelers at Research Triangle Park and outside contractors under
the guidance of the Chemistry and Physics Laboratory. The pollutants are
25
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sulfur oxides, nitrogen oxides, hydrocarbons, oxidant, carbon monoxide,
and particulates. The photochemical transformations have received the
most attention so far. The emission inventory inputs into this model
are listed in Table 3.
Table 3
EMISSION INVENTORY INPUT REQUIREMENTS
FOR THE TRANSFORMATION PROCESSES* MODEL
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
S0r
NO . Oo, CO, HC, particulates (MX, by com-
X O A
pounds; HC by categories, e.g., paraffins, ole-
fins, aromatics)
1 hour
Point sources on UTM grid. Line sources (end
points) in UTM coordinates. Area sources on
2
variable-size grid from 1 km . Eulerian models
utilize a fixed rectilinear grid, while La-
grangian models may require a flexible grid
aligned along a given wind trajectory.
Tape
Grams, meters, seconds
See Table 1
Diurnal, daily, seasonal
Depends on source data
T^
Principal candidate-model EPA contractors:
1. Systems Applications, Inc.
Beverly Hills, California 90202
2. General Research Corporation
Santa Barbara, California 93105
3. Pacific Environmental Services, Inc.
Santa Monica, California 90403
4. Systems, Science & Software, Inc.
La Jolla, California 92037
26
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3.
Mobile Sources
a.
Surface Vehicles
Emissions from mobile sources such as automobiles, trucks,
railroads, and vessels constitute an important segment of urban pollution.
They account for virtually all of the carbon monoxide, the bulk of hydro-
carbons, and a significant share of nitrogen oxides.
Since emissions from these sources are a function of the
speed, number, and type of vehicles and the traffic patterns for given
highway segments, they are best represented as line sources for heavily
traveled streets. Lightly traveled areas can be considered area sources.
Emission inventory inputs into transportation-related
models will require items indicated in Table 4. Emission requirements
of models that treat vehicular sources have been limited to the require-
ments of mesoscale transportation modes. Microscale models—those treat-
ing near-source effects—may have more stringent emission requirements
both in quantity, types, and precision of the data. Consequently, these
Table 4
EMISSION INVENTORY INPUT REQUIREMENTS
FOR VEHICULAR SOURCES MODEL
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
CO, hydrocarbons (by types such as olefins,
paraffins, aromatics), NO , particulates
X
1 hour
Street segments. Endpoints (in UTM coordi-
nates), width (meters), center strip width
(meters). Type of roadway at grade, elevated,
1 km (minimum)
cut section, street canyon.
blocks for area sources.
Tape
Grams/second, meter
Mean speed (meter/second), traffic volume by
mix
Diurnal, daily, seasonal
Depends on source data (sampling, traffic
counts, simulated traffic patterns, and the
like)
27
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types of highly specialized requirements are not within the scope of the
basic inventory and will need to be obtained as a special task in conjunc-
tion with the particular microscale model experiments and evaluations.
b. Aircraft and Airports
Models dealing with pollutant emissions from planes on
the ground level and above airports are also being developed. These
are essentially microscale models dealing with the movement of individual
aircraft during landings, taxiing, and takeoff. The degree of detail of
emission data required for such a model (Table 5) is probably outside the
scope of a regional inventory.
This work is coordinated by the Model Applications Branch
of the Meteorology Laboratory, NERC, Research Triangle Park.
Table 5
EMISSION INVENTORY INPUT REQUIREMENTS
FOR MOBILE SOURCES (AIRPORTS) MODEL
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
CO, hydrocarbons, NO , particulates
x
Every flight: taxiing time, takeoff and
landing
Each runway, taxiway, gate, direction of
takeoff
Tape
Grams/second, meter
Type of plane
c. Railroads and Vessels
The contribution of railroads and vessels to the air pol-
lution burden of the Saint Louis area is under investigation by the Trans-
portation Center, Department of Transportation, Cambridge, Massachusetts.
28
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The Center is conducting a study that will include a sur-
vey, an experimental program to obtain emission data and some dispersion
modeling studies. In the course of this work, the Center will be both a
user of and a contributor to the RAPS emission inventory.
The main pollutants of concern are SO and particulates.
4. Other Studies
a. Particulate Emissions
Visibility loss caused by suspended fine particles is one
of the most obvious manifestations of air pollution. Particles also may
be involved in gas phase reactions and provide one of the mechanisms for
the removal of nitrogen and sulfur oxides. Thus, the formation and re-
moval of fine particles is part of our understanding of atmospheric re-
actions and will ultimately have to be accounted for in models describing
such reactions.
Particles also play an important role in health studies,
since those fine enough to remain suspended for indefinite periods (2 to
3 microns) are breathed in and can deposit in the lungs. Thus, there is
a particular interest in this fraction of particulate emissions.
Studies are under way under the auspices of the Chemistry
and Physics Laboratory, NERC-RTP, in the Saint Louis area to measure par-
ticle size distribution and their chemical analysis. Data on the emission
of particles are needed, including data on size distribution of major
sources. At the very least, data on the coarse (greater than 3p,) and
fine (less than 3|-i) fractions should be obtained.
b. Various Analyses
In addition to the modeling studies dealing with the phys-
ical characteristics of air pollution, such as emissions, air movement,
or ambient concentrations of pollutants, the RAPS data are expected to
be used for a number of studies and analyses dealing with the social,
economic, and political impacts of air pollution and its control. Most
of these studies will be based on long-term averages, and they do not
require the fine resolution the RAPS inventory will provide. Still,
they will profit from the increased accuracy of the new inventory.
29
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The development of an inventory 01 tr±e scope and detail
of the Saint Louis study is in itself no mean achievement. On the basis
of the experience gained in this study, guidelines for improved emission
inventory procedures will no doubt be developed.
D. State and Local Control Agencies
The Metropolitan Saint Louis Interstate Air Quality Control Region
comprises two states—Missouri and Illinois—and 12 counties. Five
counties are in Missouri (Saint Louis City, Saint Louis, Saint Charles,
Jefferson, and Franklin); seven are in Illinois (Bond, Clinton, Madison,
Randolph, Saint Clair, and Washington). The Missouri counties are under
the jurisdiction of the EPA Region VII office at Kansas City, Missouri.
The Illinois counties are the responsibility of the EPA Region V office
at Chicago, Illinois. Saint Louis City and Saint Louis County have sepa-
rate air pollution control offices; the other Missouri counties are ad-
ministered directly by the Missouri Air Conservation Commission at Jef-
ferson City. The seven Illinois counties are under the jurisdiction of
the Illinois Environmental Protection Agency. The area is shown in Fig-
ure 1. The crosshatched areas are those covered more completely by mon-
itoring stations.
The ultimate purpose of the state and local control agencies is to
achieve and maintain the air quality that corresponds to at least the
national primary and secondary air standards, although states may estab-
lish more stringent standards. Thus, control agencies are concerned not
only with pollution episodes (emergency conditions), but also with long-
term pollution control programs aimed at a gradual reduction of air pol-
lution levels and with community growth planning as it relates to added
pollution. Ultimately, they are responsible for the effect of pollution
on the health of the community, its vegetation, and perhaps even weather
modifications.
1. City of Saint Louis
The Air Pollution Control Office, Department of Public Safety,
City of Saint Louis, has a source evaluation section in charge of the
emission inventory; it has identified some 16,000 point sources, but
reliable data are available primarily for the large sources. The sec-
tion's primary mission is enforcement rather than data collection.
The emission inventory is in the form of reports gathered by
inspectors. The data were not computerized. See Table 6.
30
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100 km
STE. GENEVIEVE
FRANCOIS \
0 10 20 30 40 50
SCALE — km
EMISSION RATE — tons/day
• 10 - 100 of any single
> 100 pollutant
SA-1365-33
FIGURE 1 METROPOLITAN SAINT LOUIS INTERSTATE AIR QUALITY CONTROL REGION
31
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Table 6
EMISSION INVENTORY INPUT REQUIREMENTS
OF THE AIR POLLUTION CONTROL OFFICE,
CITY OF SAINT LOUIS
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
SO (HC, CO), NO , particulates
4j x
Yearly average output
Point sources, UTM coordinates
Reports
Engineering
Stack height, exit velocity, temperature
Inspectors' reports
2. Saint Louis County
The Air Pollution Control Division, Saint Louis County Health
Department, like its Saint Louis City counterpart, is primarily an enforce-
ment agency. It has identified the major pollution sources (about five
that are emitting more than 100 tons per day, about 20 with emissions in
excess of 25 tons per day). Some modeling of sulfur dioxide concentra-
tions has been done, and some CO modeling is contemplated. The inventory
(Table 7) is based on inspectors' reports. The control of additional
pollution resulting from the growth of the area through proper land use
is under consideration.
3. Missouri Air Conservation Commission, Jefferson City,
Missouri
This agency has responsibility for the air quality of the state
of Missouri. It operates through county or local air pollution control
boards where such agencies exist, and it operates directly in other areas.
Its function is to promulgate and enforce air pollution standards for the
state of Missouri.
32
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Table 7
EMISSION INVENTORY INPUT REQUIREMENTS
OF THE AIR POLLUTION CONTROL DIVISION,
SAINT LOUIS COUNTY HEALTH DEPARTMENT
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
SO , NO , CO, HC, particulates
£ x
Yearly averages
Point sources on UTM coordinates
Printout or tape
Engineering
Stack height, exit velocity, temperature
None
Major sources have been updated and some
stack sampling is performed every six
months
At present, the commission maintains data on particulates, SO ,
XOX, total hydrocarbons, and CO. The data are based on an original sur-
vey carried out in 1968 (for particulates and SOg only), which has been
updated in 1970 by IBM to include the other three pollutants. It was
further updated in 1972 by Radian, Inc., and is now in National Emissions
Data System (NEDS) format.
Emission input requirements are similar to those shown in Table
7 for Saint Louis County.
4. Illinois Environmental Protection Agency, Division of
Air Pollution Control, Springfield, Illinois
The Illinois side of the Saint Louis Air Quality Control Region,
which is heavily industrialized, is under the jurisdiction of the Illinois
Environmental Protection Agency through its regional office (State Region
IV, Collinsville) and the central office at Springfield. The Surveillance
Section is in charge of the emission inventory, which is continually re-
vised and enlarged. Currently it contains information on about 2,500
sources, but it is expected to reach 14,000 sources. The inventory is
in the form of a computer printout, and it is used constantly as a guide
33
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for issuance of permits (surveillance), as a buiH'cn of information to
other agencies and interested parties, and as an aid in modeling. Tb-
modeling is contracted to Argonne National Laboratories, the State Im-
plementation Plan is based on the Argonne work. Emission inputs are
shown in Table 8.
Table 8
EMISSION INVENTORY INPUT REQUIREMENTS
OF THE ILLINOIS ENVIRONMENTAL PROTECTION AGENCY,
AIR POLLUTION CONTROL DIVISION
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
SO , CO, NO , HC, particulates
^ X
Quarterly, seasonal
Daily during air pollution episodes
Point sources: UTM coordinates ±0.1 km
Area sources by counties
Printout, tape
Engineering
Stack height, exit velocity, temperature
5. Federal Highway Department, Department of Transportation,
400 - 7th Street, N.W., Washington, D.C.
The Federal Highway Department, in cooperation with the Air
Data Office of EPA, will conduct a study designed to provide information
on driving patterns on highways in the Saint Louis area. This informa-
tion, in combination with emission data based on different driving modes,
will permit an accurate assignment of the contribution of automotive
traffic to air pollution in Saint Louis. The emission inputs necessary
for this study will have to be developed from dynamometer runs under
simulated driving conditions for the pollutants of interest (CO, hydro-
carbons. NO ) .
x
34
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E. The Planning Agencies
1. East-West Gateway Coordinating Council
The East-West Gateway Coordinating Council (Laclede Gas Build-
ing, Saint Louis, Missouri) is a coordinating agency created to facili-
tate the land use and transportation planning for the Saint Louis metro-
politan area. It helps to coordinate the planning efforts of the Missouri
State Highway Commission, the Illinois Department of Transportation, and
various county and city planning departments. It has also received sup-
port from the U.S. Department of Housing and Urban Development and the
Federal Urban Mass Transportation Administration.
The council has produced a comprehensive Transportation Study
Report analyzing present problems and suggesting long- and short-range
solutions to transportation needs based on socioeconomic projections,
analyses of present traffic patterns, and estimates of future travel
demands. The council has generated an "Average Daily Traffic Volume
Summary" for the Saint Louis metropolitan area (1970), which is updated
yearly and constitutes a valuable source of information on mobile sources.
The council, thus, is both a source and a user of data on mo-
bile sources. Additional traffic data are available from
• The Illinois Department of Transportation, 930 St. Clair
Avenue, Fairview Heights, Illinois (Mr. Robert Kronst,
District Engineer).
• The Missouri State Highway Department, State Highway
Building, Jefferson City, Missouri (Mr. Carl Klam,
Highway Engineer).
• The Saint Louis County Department of Highways, 120 N. Gay
Street, Clayton, Missouri (Mr. Richard Daykin, Director).
• The Saint Louis City Traffic Department, City Hall,
Saint Louis, Missouri (Mr. Jim Bauman).
2. Industrial Waste Control Council
The Industrial Waste Control Council is an association of pol-
lution control officers from the major industrial firms in the Saint
Louis area. As the problems they face are to some extent common to all
of them, they share technical information and develop public relations.
Their members are probably in the best position to develop emission
35
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inventories for their respective companies, inventories which they need
to satisfy legal requirements. The data thus developed are generally
confidential and available only to organizations empowered to receive
them.
F. Research Programs
1. Metromex
Metromex is a joint program involving groups from Argonne
National Laboratory, Battelle Pacific Northwest Laboratories, University
of Chicago, Illinois State Water Survey, Stanford Research Institute,
and the University of Wyoming. It is basically a federation of individual
scientists with diverse sources of funding, but common or at least compli-
mentary research interests in the inadvertent weather modifications pro-
duced by large urban complexes through the interaction of their emission
of pollutants and their contribution to the energy balance of the area.
The study is centered in the Saint Louis area. Pollutants of interest
to Metromex are primarily suspended particulate matter. Thus, major
sources of particles, their physical properties and chemical analysis,
and their size distribution are all parameters of interest.
2. Community Health and Environmental Surveillance System
The Community Health and Environmental Surveillance System
(CHESS) is a national program that relates community health to changing
environmental quality. It consists of a series of standardized epidemi-
ologic studies designed to measure simultaneously environmental quality
and sensitive health indicators in sets of communities representing ex-
posure gradients for common air pollutants. The program is conducted
by EPA in cooperation with local public health agencies, universities,
and private research institutes. The purpose of the CHESS program is
• To evaluate existing environmental standards
• To obtain health intelligence for new standards
• To document health benefits of air pollution control.
One of the areas under study is Saint Louis.
As mentioned earlier, CHESS, like other health effect studies,
requires primarily ambient concentration data rather than emission data.
At present, it obtains such data from its own network of air monitoring
36
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stations sited within each CHESS community. Eventually, RAPS data may
be used. Since historical data on ambient concentrations are generally
not available, estimates of past pollutant concentrations have to be
based on past emission data from which estimated ambient concentrations
are derived via meteorological dispersion models. Thus, CHESS has some
need for emission data. The CHESS program is administered by the Human
Studies Laboratory at Research Triangle Park. The Bioenvironmental Mea-
surements Branch is responsible for obtaining exposure data for the use
of epidemiologists. Compounds of interest to these studies are shown
in Table 9.
Table 9
EMISSION INVENTORY INPUT REQUIREMENTS
FOR CHESS STUDIES
Content of inventory
Time resolution
Spatial resolution
Preferred medium
Units
Source descriptors
Temporal variations
Level of confidence
S09, NO , particulates, especially sulfates,
s-i X
nitrates,and organics (size fractionated, if
possible), hydrocarbons, Oo, CO, trace metals,
e.g., Cd, Zn, Pb, Hg, Cr, V, Ni, Cu, Mn, as-
bestos, benzo-a-pyrene, PCB, pollens
For acute studies: 1 to 4 hours
For chronic studies: cumulative exposures
School districts
Printout
Gram/cent imeter/second
Seasonal
3. University Research
Individual researchers at several of the universities in the
study area are engaged in work requiring knowledge of pollutant levels.
For example, at Washington University, Dr. Richard Gardener is conducting
37
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work in the area of thermal pollution—"heat islands"—and coat; exacting
an aerosol submodel in conjunction with other agencies. Dr. Christopher
Hill is studying the total (mining, processing, use, and disposal) envi-
ronmental impact of certain industries. At the Center for the Biology
of Natural Systems, Dr. Charles Lee is establishing pollution burdens
in rain and soil samples and in human tissues.
At Saint Louis University, Dr. Slavin is studying the relation-
ship of air pollution to health problems. There seem to be strong cor-
relations between airborne sulfates and asthma attacks, between SO^ and
emphysema, and between carbon monoxide and heart attacks. His department
is collecting a daily pollen count and could make it available to the
RAPS inventory.
At Southern Illinois University, Dr. Al Kahn is carrying out
studies of the carbon monoxide and hemoglobin reactions.
As mentioned earlier in this report, these activities have
only a peripheral requirement for emission data at this point. However,
to a certain extent, the format of the inventory should be designed with
the potential to accommodate such data if they should become necessary.
4. Plant Damage
There are a number of compounds in the atmosphere that cause
vegetation damage either by reducing yield and growth or by affecting
the quality of the crop. Well over 90 percent of the losses are caused
by the following pollutants:8
• Ozone
• Peroxyacetyl nitrate (PAN)
• Oxides of nitrogen
• Sulfur dioxide
• Fluorides
Out of 65 standard metropolitan statistical areas, the Saint Louis metro-
politan area is rated by Benedict8 16th with respect to potential damage
due to hydrocarbons, 24th with respect to oxides of nitrogen, and 18th
with respect to sulfur dioxide on a scale that takes into consideration
total emissions, meteorological factors, area, and stagnation periods.
Thus, plant damage constitutes a significant aspect of air
pollution in Saint Louis, something that should be taken into account
in setting up the emission inventory. Data on oxidants—ozone, PAN, and
38
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oxides of nitrogen—and sulfur dioxide would normally be covered by the
inventory. It may be advisable to make provisions for a fluoride inven-
tory as well. The main known sources of fluorides are aluminum, phosphate,
and ceramic plants.
G. Conclusions
The emission requirements data for RAPS are such that inventories
taken for other purposes—such as the Implementation Planning Program
(IPP) or air pollution control agencies—are inadequate mainly in the
degree of detail required for modeling studies. Since the verification
of atmospheric dispersion models is an early and major goal of RAPS, the
requirements of the modelers constitute the most important criterion of
the adequacy of the inventory.
The most important requirements for modeling are
• Pollutants
- From stationary sources: SO , CO, particulates, NO .
^ X
From mobile sources: CO, NO . particulates, hydrocarbons
X
(by types),
• Resolution
Temporal: 1 hour
Spatial: Point sources—0.01 km.
Area sources—variable size grid from
1 to 16 (km)2.
• Units. Depends on model, but always metric.
• Output programs. Depends on model. Inventory should provide
FORTRAN interface. Output to include source descriptors such
as location (UTM coordinates), stack height, exit temperature,
and velocity. For traffic models, description to include de-
scriptors of roadway.
• Area. The area to be covered coincides with the boundaries
of the Metropolitan Saint Louis Interstate Air Quality Region.
Alternatively, it could be resolved to a smaller, more central
area covering only the City of Saint Louis and the Counties
of Saint Charles, Jefferson, Saint Louis, Monroe, Madison, and
Saint Clair (Figure 1).
39
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• Time. The period will vary considerably. In some cases, model
verifications will be limited to relatively short periods (48
to 72 hours).
The requirements of other study groups, such as the health study
workers, are more extensive, but generally less intensive. They require
the system to store and retrieve information on many compounds, but the
demands for spatial and particularly temporal resolution are much less
critical.
Special programs, such as Metromex, have specific requirements for
certain categories of pollutants, which may or may not be satisfied by
a general emission inventory.
40
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IV TASK B: EMISSION INVENTORY CONTENT
A. General Principles
1. Introduction
As is shown in Section III, there are numerous agencies and
groups—both inside and outside the RAPS program—that have actual or
potential needs for emission inventory data from the Saint Louis Air
Quality Control Region (AQCR). Wide differences exist in the specific
data requirements of these agencies, particularly with respect to the
time resolution of the estimated emissions. However, the most demanding
requirements are set by the need to verify experimentally various multi-
source atmospheric dispersion (air quality) models. In particular, the
emission inventories used for this purpose must provide a high degree of
time resolution, giving emission estimates at one-hour intervals. A high
degree of spatial resolution is also required.
Other possible users of emission inventories surveyed under
Task A have generally far less demanding requirements. In particular,
they do not commonly need the refinements of time resolution required for
short-interval dispersion modeling. If they use the emission data for
air quality modeling at all, it would probably be to estimate relatively
long-term, average pollutant concentrations. Others may use inventory
data only for surveillance and for determining the priorities for regu-
latory programs. In any case, the high-resolution emission inventory
designed for use in dispersion model verification will be more than ade-
quate for any of the other uses.
Attempts have been made previously to verify experimentally
predictions of short-term air quality made from dispersion models. How-
ever, the source emission data fed into the dispersion models have been
derived primarily from long-term average emissions (e.g., annual emis-
sions), at least for stationary sources. Furthermore, most of the annual
emission data have been derived by applying emission factors to survey
information on fuel consumption and materials flow, rather than by making
direct measurements. Estimates of hourly emmissions have been derived or
extrapolated from the annual rates by more or less elaborate calculation
procedures (see Section VII). The resulting compilation of extrapolation
procedures, algorithms, and correlations for simulation of short-term
41
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emissions may be described, for lack of a better term, as an emission
model. Unfortunately, the errors and uncertainties, both in the basic
data and in the assumptions required for their extrapolation, are so
great (or so indefinite) that estimates of hourly emissions derived from
gross annual emissions cannot be relied upon for the critical tests of
short-term air quality predictions to be carried out in RAPS.
At best, the predictions of pollutant concentrations from an
air quality model can be no more precise than the source emission data
used as input to the model. In addition to errors in emission data, other
sources of error in the predictions, besides the inherent limitations of
the dispersion model itself, include the description and measurement of
the atmospheric structure, the measurement of the actual pollutant con-
centrations, and the representativeness of the pollutant concentrations
actually measured. Assuming that sufficient care is exercised, we can
estimate source emissions more accurately than any of the other input
data to the air quality model. The level of effort needed to ensure the
accuracy of the emission estimates rises rapidly as the required accuracy
is increased, particularly as the time resolution is extended to shorter
and shorter periods. Nevertheless, the emission estimates, being the
least equivocal of the inputs to the air quality model, should be made
accurately enough to ensure that they do not become the limiting factor
in the testing of the model.
There is no practical way by which the emissions from all sources
can actually be measured during the particular hours in which the test of
an air quality model is carried out. Consequently, it is necessary to
employ simulation techniques to estimate the hourly emissions from most
sources, a process that also can be described as emission modeling. How-
ever, such emission modeling should involve as little extrapolation of data
as possible. In particular, the emission model for a particular source
should, where possible, contain an independent variable to be evaluated
from data collected in real time at the source. Such data can, as in an
extreme case, indicate whether the source was actually in operation during
the period of time under consideration. In the general case, the data
should indicate the value of some variable having a functional relationship
to the emission. Most directly, in sources having continuous monitoring
systems, the variable may be the concentration of the specified pollutant
in the waste gas. In other cases, it may be a quantity such as the rate
of firing of fuel. At all important sources, the relationship between the
emission and the chosen independent variable should be established from
actual measurements. Such measurements essentially constitute a calibra-
tion of the emission model. It will, of course, be necessary to redeter-
mine the relationship between the independent variable and the emission
(recalibrate the model) whenever there are significant changes in the mode
of operation of the source.
42
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It is feasible to carry out an emission inventory of the fore-
going type for the Saint Louis AQCR and to store hourly values of the
chosen independent variables and of the corresponding emission estimates
for an appropriate period (for two or three years if desired). However,
the process is necessarily difficult, time consuming, and expensive. It
should therefore be carried out only for the minimum number of pollutants
necessary for verification of atmospheric dispersion models; as discussed
in Task A, these are considered to be SO , NO , HC, and CO. The inventory,
X X
which should be adequate for use in verification of mesoscale atmospheric
dispersion models, may be designated as the RAPS Emission Inventory.
It should be appreciated that the RAPS Emission Inventory will
be essentially separate and distinct from the existing National Emissions
Data System (NEDS) Inventory, which was compiled under far less demanding
criteria. Some information from the NEDS Inventory will be useful in the
RAPS Inventor}^; for example, the NEDS Inventory locates and identifies
stationary point emission sources and indicates their relative magnitudes.
Some of the data in the NEDS Inventory relative to area sources and some
small point sources may be, if carefully evaluated, adequate for inclusion
in the RAPS Inventory. In addition, NEDS will provide the needed data
on emissions for purposes that do not require high resolution data of the
principle pollutants covered by the RAPS Emission Inventory.
For some anticipated microscale atmospheric dispersion studies,
very high degrees of temporal and spatial resolution of emissions may be
required for specific, small areas. Again, for particular studies, higher
resolution data may be required for a pollutant and included in the RAPS
Emission Inventory. The precise requirements for emissions data in such
cases will vary considerably, and cannot be anticipated at this time.
Specification and execution of these special emission data collections are
basically research tasks that will be best handled by the research project
staff carrying on the dispersion studies.
2. The Emission Inventory System
The following discussion is intended to provide an understanding
of the general process by which information is generated and flows through
the proposed RAPS Emission Inventory system and is intended to present
definitions of terms (see Figure 2).
It should be appreciated that, whereas the eventual emission
inventory is organized primarily according to pollutants, the execution
of the inventory is necessarily organized primarily according to sources
(the manner in which the original data must be obtained). The process
43
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DATA
SURVEYS
INTERMITTENT
EMISSION
MEASUREMENTS
CONTINUOUS
EMISSION
MONITORING
PROCESSED EXPERIMENTAL
AND ANALYTICAL DATA
ON EMISSIONS
EMISSION SOURCE
OPERATING
VARIABLES
BASIC SOURCE
DATA FILE
CONTINUOUS
EMISSION
MONITORING
EMISSION
MODEL
EMISSION
ESTIMATES
EMISSION
INVENTORY
EMISSION SOURCE
OPERATING
VARIABLES
Output
FIGURE 2 EMISSION INVENTORY SYSTEM
SA-2579-42
44
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by which the RAPS inventory is to be carried out is essentially conven-
tional; however, the special emphasis on and treatment of certain steps
is determined by the high degree of temporal and spatial resolution
required to meet the specific requirements of RAPS such as verification
of atmospheric dispersion models.
Original information from emission sources is to be compiled
from data surveys and, as far as possible, directly from intermittent
measurements or continuous monitoring of the sources themselves. Sub-
sidiary data on the operating variables and parameters of the sources
must be collected so that correlations can be made between the variables
and parameters and the actual measured emissions. The source variables
are to be quantities that are--or can be—routinely measured as part of
the source operation. Hence, once a correlation is established, the
measurement of the variable can be substituted for the more difficult
operation of measuring the actual pollutant emission.
For a relatively small number of the largest point sources, it
is anticipated that continuous analyzers will be installed to monitor the
concentration of key pollutants (SC>2 and NOX). For the remaining point
sources as far as practicable, reliance must be placed on intermittent
manual measurements of emissions correlated with continuously measured
process variables as described above, rather than upon the use of exist-
ing emission factors, although the latter can be used in certain cases.
For the smaller point sources, and for area and line sources
(stationary and mobile), individual emission measurements will not be
made, but more accurate emission factors than are currently available for
typical sources must be derived from a sampling program.
Processing procedures for the raw data will depend on the nature
of the information. At least initially, computation of the emissions from
raw experimental data will probably be most conveniently carried out
manually. Once the processed data is suitably organized, it is to be
stored in the Basic Source Data File, which then is the source of infor-
mation required for creation of the Emission Model for each source cate-
gory of sources.
The emission model in this context is essentially a collection
of correlations or algorithms that permit calculation of estimated emis-
sions from readily or routinely measured operating variables and parameters
of the various emission sources (as discussed above). The relationships
must be worked out from the basic source data; they may range from simple
to complex, depending on the nature of the emission sources.
45
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To generate specific emission estimates, it will be necessary
to supply to the emission model particular values of the independent
emission source variables pertinent to the desired time interval. These
input data must be collected from the sources on a continuing basis over
whatever time period is required. The Emission Estimates thus generated
provide the input to the Emission Inventory.
The Emission Inventory will consist of the compiled and stored
estimates of the hourly emissions of the specified pollutants on an
hourly basis for the time period (two or more years) of interest to the
RAPS program. The emission estimates corresponding to any chosen sequence
of one-hour intervals will then be withdrawn from the Emission Inventory
to form input for the air quality models and other purposes.
B. Precision of Emission Estimates
Since the requirements for the emission inventory are to be set by
the needs of dispersion modeling, it is necessary to specify, within some
limits, what degree of agreement between predicted and measured pollutant
concentrations will be considered to constitute a verification of a dis-
persion model. While the work of Hilst9 discussed below approaches this
issue, no such specification of model precision has been made. Modelers
have thus far simply striven to obtain the best results that they could
get. In tests of models made to date, it has been common to encounter
deviations of ±50 to 100 percent or more between predicted short-term
pollutant concentrations and the measured values. Better results are
reported for prediction of long-term average concentrations.
An ideal multisource atmospheric dispersion model would permit
accurate prediction of the concentration of a specified pollutant at any
desired point in the geographical region of interest over as short a time
interval as might be of practical interest. In practice, it is necessary
to compromise the requirements for the model with respect to both temporal
and spatial resolution. In addition to the inherent limitations of a
model itself, the precision of measurement of emissions and meteorologi-
cal variables and the practicability and precision of measuring atmospheric
pollutants in the field set practical lower limits on the time intervals
and the receptor areas that can be considered.
The predicted pollutant concentration is assumed to represent the
average value within one square kilometer, which is the smallest element
of the standard geographical coordinate grid. The pollutant concentra-
tion measured by a single monitor will bear a relation to the average
concentration that will be determined by the distribution of the pollutant
46
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over the specified area. The distribution of the pollui,ar' -x-j 1J depend
on the geographical and topographical characteristics of the area. The
distribution will therefore vary not only from one grid area to another,
but also within any one grid area under different circumstances and at
different times.
Other experimental studies in the RAPS program are expected to address
the problem of the spatial variation of pollution concentrations within the
receptor area. However, where it is necessary to employ a single monitor
for one square kilometer area, the relationship of the measured concentra-
tion to the true average concentration for the area will necessarily be
statistical and subject to a greater or lesser degree of uncertainty.
For such studies, or the development of microscale submodels designed
to couple the regional scale models to specific localities or points where
measurements are made or air pollution effects are significant, special
efforts must be made to collect detailed emission data on an experimental
basis. The routinely collected data of the RAPS Emission Inventory should
be designed to serve the broader scale needs of the modelers on a con-
tinuing basis.
It will be important, however, to make a special check in the vicinity
of each permanent monitoring station (say within a radius of one kilometer)
to ensure that any point or other source that could significantly effect
the representativeness of readings made at the monitoring station, are
taken into account. For example, it may be found necessary to treat an
office building heater system efflux as a specially considered point source,
rather than as part of a nonspecified area source. Alternatively, if such
consideration cannot be made in connection with the emission inventory
system, steps should be taken by those responsible for air quality moni-
toring to develop weighting or calibration factors for the station in
question, or even to set its readings aside for certain purposes under
particular wind conditions
As stated above, it is essential that the precision of the pollutant
emission data not limit the precision of the dispersion model. The dif-
ficulty of attaining highly precise measurements or estimations of pollut-
ant emissions increases rapidly as time intervals are shortened. Hence,
it is necessary to make compromises between precision and the cost and
difficulty of measurement and estimation.
The question of the significance of errors in specifying the strength
or location of sources has been addressed in Section VII, especially in
terms of the precision needed in defining position. The sensitivity of air
quality models to such errors is a complicated question and specific to
47
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each model and each area. The work of Hilst9 in a case study of the
TRC Model in Connecticut is indicative of what can and should be done
for the major models to be tested in the RAPS program. Depending upon
the resolution in space and time of the predictions made by an air
quality model, Hilst's findings in New England are encouraging because
the cancelling effect of random uncertainties minimizes the errors due
to inaccurate point source data. Most serious errors can result from
systematic inaccuracies, such as those introduced by incorrectly esti-
mating area sources, and the smaller the proportion of the total inven-
tory reported in this way the better. It is recommended that as early
as possible, specific studies (based initially on NEDS data) of the type
reported by Hilst be performed to provide information on the effects of
varying the degree of precision of emissions data in Saint Louis, espe-
cially in terms of hourly resolution. In the meantime, it is highly
desirable to develop the most detailed and accurate emissions database
economically feasible in Saint Louis, both to ensure the best support
of the air quality model studies and to provide the base data for re-
search in emission modeling methodology.
C. Inventory Resolution
1. Temporal Resolution
As is indicated above, the difficulties both of formulating
an adequate dispersion model and of testing it increase rapidly as the
time interval is shortened. Ideally, a model should be capable of pre-
dicting the atmospheric concentration of a pollutant over the shortest
interval for which an air quality standard is specified. In fact, it is
probably impractical to specify an interval shorter than one hour. One
limitation is that it is practically impossible to specify significant
values for the pollutant emissions from most sources for periods of less
than one hour. Indeed, it is difficult even to make precise estimates of
emissions for periods as short as one hour, at least without making direct
measurements in the particular time interval of concern. In view of these
considerations, the minimum time interval to be considered may be set at
one hour. This degree of time resolution has been accepted by most
modelers.
2. Spatial Resolution
Stationary sources fall into two categories: point sources
and area sources. The distinction between the two is generally arbitrary;
point sources are those having emissions of a specified pollutant in excess
of some selected minimum value, and they are treated individually. Smaller
48
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sources within a selected region are aggregated, and their total emission
is assumed to arise uniformly from the area.
Choice of the minimum emission rate for specification of a point
source obviously depends on the nature of the pollutant and the ratio of
the emission from the particular source to the total emission from all
sources.
Specification of the location of a source, whether a point or
an area, is to be made by reference to UTM coordinates. The precision
required in specifying the position of a source is also partly related to
the position of the receptor. Some dispersion modelers have asked that
the position of point sources be determined within ±10 meters. From the
standpoint of a nearby receptor, the physical size of the stack or vent
nay be appreciable; large stacks may be 10 meters or more in diameter,
and some building vents may be still larger in their principal dimension.
An objective method for determining the spatial resolution required is
presented in Section VII.
The method enables one to estimate the allowable error Ay, in
the cross wind position of a source (for any specified stack height,
source strength, wind velocity, and stability conditions), as it affects
pollutant concentrations at given downwind distances. ''Allowable error"
is defined arbitrarily by comparison with a reference source strength—
e.g., the 100 tons/year commonly used as the lowest value for point
sources, and a reference positional error—e.g., 100 m, the resolution
of most current inventories.
Although such a high degree of precision in specifying location
r.ay only be necessary in a limited number of applications, it is recom-
nended that wherever possible the location of point sources be reported
•*ith a resolution of ±10 meters. Although existing data (in NEDS) is only
specified to ±100 meters, additional surveys of significant sources will
have to be made in any case, and it would therefore be readily possible
to obtain the further locational data at that time.
As noted above, verification of microscale dispersion models
•»ill generally be special exercises. They will require finer spatial
resolution and more detailed specification for local sources than will be
required for tests or use of the mesoscale models. This finer degree of
resolution can be incorporated into the general emission inventory for the
entire area covered by the major dispersion models. However, the addi-
tional time and cost required may not be justified, since the increased
resolution will probably never be required except in relatively few small
areas being treated in the microscale dispersion studies. It is more
practical to make special, fine-resolution inventories for the particular
limited areas of the microscale studies.
49
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D. Pollutants
1. General Discussion
Initially, inventories must be carried out for those pollutants
that are to be used in model verification. In principle, it would be
desirable to carry out the inventories simultaneously for as many of the
pollutants as possible, particularly where surveys of the same sources
are involved. But in practice, it may not be entirely feasible to do this,
because the basic data may simply not be available for all of the pollu-
tants at the same time. Acquisition of adequate data on emissions of
nitrogen oxides and hydrocarbons will probably take much longer than will
the gathering of equivalent information for sulfur oxides.
2. Nonreactive Gases
Although none of the major pollutant gases that may be used in
tests of dispersion models are actually nonreactive, both sulfur dioxide
and carbon monoxide are relatively unreactive in relation to the time of
dispersion that is of primary interest to dispersion model verification.
Carbon monoxide is the more inert of the two. However, sulfur dioxide
has the advantage that its reaction products (sulfuric acid mist and
sulfates) can be traced by chemical analysis, whereas the identity of
carbon monoxide is lost after reaction.
Carbon monoxide is best suited to testing models of dispersion
from automotive sources, since these are by far the major sources of this
pollutant. It will be necessary, nevertheless, to inventory stationary
sources, since their contribution to the ambient concentration of carbon
monoxide may be appreciable in relation to the model testing.
It has been found that large quantities of carbon monoxide are
produced in nature by the oxidation of methane in the atmosphere. Carbon
monoxide is removed from the atmosphere by microorganisms in the soil.
The background concentration of carbon monoxide in the atmosphere is
determined by the balance of the natural removal processes and of the
natural and anthropogenic formation of the compound. It is unlikely that
either natural formation or natural removal is important on the time scale
of concern to the dispersion studies and within urban areas, where high
concentrations of carbon monoxide may be generated. Task E presents a
more detailed discussion of natural emissions.
Sulfur dioxide is best suited to use in testing of dispersion
models for pollutants from stationary sources, since relatively little is
50
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emitted from mobile sources. It is more reactive than carbon monoxide
arid more subject in the atmosphere to the action of various removal
processes. The oxidation of the sulfur dioxide to sulfur trioxide re-
sults in conversion to an aerosol (sulfuric acid mist), and subsequent
or coincident reaction of the sulfuric acid with ammonia or other basic
materials in the atmosphere results in formation of solid aerosols. If
the time interval of interest to the dispersion study is no more than a
few hours, these reactions will probably not be of great consequence.
Fortunately, it is possible to sample for both sulfur dioxide and the
sulfate aerosols and, thus, to deduce the probable history and dispersion
pattern of the total sulfur originally being discharged to the atmosphere
as sulfur dioxide. (About two to three percent of the sulfur emitted
from combustion processes is usually already in the form of sulfur tri-
oxide. )
Sulfur dioxide and its reaction products may be removed, or
scavenged, from the atmosphere by a variety of mechanisms. The sulfur
dioxide may be absorbed by vegetation or reactive solid surfaces or be
adsorbed by a variety of solid surfaces. It may also be absorbed by rain
drops. The sulfuric acid or sulfate aerosols may be rained out or washed
out or may be mechanically deposited by inertial, diffusional, or gravita-
tional mechanisms. In time, the aerosols will also coagulate, increasing
the particle size and accentuating particle deposition by the inertial and
gravitational mechanisms. The coagulation mechanism will be strongly
affected by the total concentration of particulate matter present in the
atmosphere
In general, the influence of the various removal or scavenging
processes on sulfur dioxide should be much greater than the corresponding
influence on carbon monoxide. Allowances must nevertheless be made for
the depletion of both pollutants by the various removal processes, but
determining these adjustments is, of course, a task separate from the
emission inventory. Estimating the depletion of the emitted pollutants
is, in fact, an inherent part of the formulation and development of the
dispersion models. It will call for an extensive supporting experimental
program.
3. Reactive Gases
The most important transformation process involving major air
pollutants is the photochemical reaction of the nitrogen oxides with hydro-
carbons to form PAN and ozone. The nitrogen oxides are contributed in
roughly comparable amounts by both stationary and mobile sources, which
51
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also contribute tiie hydrocarbons. Consequently, it is necessary to
devote comparable attention to an inventory of emissions of both of these
types of pollutants from both classes of sources.
As they are present in waste gases at elevated temperatures, the
nitrogen oxides are primarily in the form of nitric oxide (NO). Partial
conversion of the NO to nitrogen dioxide (N02) takes place primarily after
the waste gases are cooled and mixed with ambient air. There appears to
be no compelling reason why the distribution of NO and N02 in the hot
waste gases need be determined or specified as part of the emission inven-
tory. The subsequent distribution of the two species in the atmosphere
is, of course, of critical concern in the transformation models, but it
will have to be determined from separate experimental and analytical
studies.
Developing an inventory for nitrogen oxides presents special
problems. Most of the nitrogen oxides are formed by fixation of atmo-
spheric nitrogen at elevated temperatures in the combustion processes, and
the quantity formed is dependent on the temperature reached, the residence
time at high temperature, the size and configuration of furnace or other
combustion devices, the amount of excess air present, and any other fac-
tors that may influence the time and temperature history of the combustion
gases. The nature of the fuel also influences the formation of nitrogen
oxides, primarily through those fuel properties that affect the time and
temperature relationships—flame temperature and flame emissivity. Any
nitrogen compounds in the fuel may also be partly oxidized. Hence, the
quantity of nitrogen oxides emitted from a given piece of combustion
equipment cannot be estimated from first principles by any currently
available methods. Strictly, it must be determined experimentally for each
device and each operating condition.
Fortunately, the range of variation in nitrogen oxides formation
is not so great that reasonably representative values cannot be obtained
by testing specific types of devices over appropriate ranges of operating
conditions. Nevertheless, extensive source sampling will be required to
obtain adequate information for a precise emissions inventory.
Inventory of the hydrocarbon gases and vapors presents a poten-
tially complicated case because of the very large number of different
organic compounds that may be emitted and that may also vary widely in
photochemical reactivity. It is completely impractical even to attempt
to inventory emissions compound by compound, except in instances where,
for example, a relatively pure, known compound is lost by vaporization.
Probably the most that can be done in a practical way is to identify the
organic compounds by major groups or classes (e.g., paraffins, olefins,
52
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diolefins, aromatics, alcohols, ketones, and so on). For the most part,
even this will require a major source-sampling program. Identification
and quantitative determination of the classes of compounds, alone, will
call for a large analytical effort. Hence, it is essential to keep the
scale of the sampling and analysis to a minimum consistent with attain-
ment of the program objectives.
4. Particulate Matter
The development of dispersion models for particulate matter, as
well as efforts at verification, are well behind comparable developments
for dispersion models for gases. The dispersion of particulate matter is
more complex than that of gases, since the dynamics of the particles are
strongly affected by the particle size, as are the removal processes that
result in a decay of the particulate cloud. In addition, the nonspecific
nature of most particulate matter makes it difficult, if not impossible,
to identify the source of the material, trace its path from source to
receptor, and allow for losses resulting from removal processes.
In using simple dispersion models, it has been common for workers
to assume that all dust emitted from emission sources remains airborne and
contributes to the resulting atmospheric concentration of suspended par-
ticulate matter at the receptor. Such an assumption is highly unrealistic,
since relatively coarse particles will, in fact, tend to settle out. Rel-
atively fine particles (about 10 microns in diameter and smaller) are
frequently assumed to behave more or less like a gas; this is a fairly
safe assumption, but one still subject to somewhat indeterminate errors.
Making a realistic test of models concerned with particulate
formation or dispersion would require the following information:
• The quantity of particulate material emitted from each
source and the distribution of effective particle sizes
of the particulates. It should be noted that the effec-
tive particle size distribution, which may reflect
particle flocculation, is not necessarily the same as
the distribution that may be indicated by the measuring
techniques commonly employed.
The particle size should, of course, be expressed in
'^errns of the Stokes diameter (i.e., the diameter of
the equivalent spherical particle of the same density
that would have the same terminal settling velocity as
the particle in question).
53
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• The extent to which particles of each size are de-
posited by removal processes during passage over the
terrain between the source and the receptor. No
essential quantitative information is available on
these phenomena. The technical problem is similar
to that of determining the efficiency of dust col-
lection equipment in the collection of particles of
various sizes. However, the studies of particle
deposition from the open atmosphere are necessarily
less controllable than experiments with dust collec-
tors, and work in even the latter field is not well
advanced from the standpoint of basic science and
technology.
Determination of the mass emissions and particle-size distri-
butions of particulates emitted from even the major sources alone would
require a major experimental program. The experimental methods and
procedures for determination of the effective particle size of the mate-
rials as emitted are still poorly developed. In addition, the study of
particle deposition from the atmosphere calls for a far greater experi-
mental effort. If a comprehensive effort is to be made at confirmation
of particulate dispersion models using particulates normally discharged
from various sources, a lengthy and costly preliminary experimental and
theoretical investigation will be required. Although information on
particulates is noted as a requirement by a number of users identified
in Task A, there is no evidence that the implications of including such
data have been seriously considered.
It is therefore open to question whether an attempt to verify
particulate dispersion models should be made using the actual particu-
lates emitted from various sources. An alternative procedure using
particulate tracers may be usable with less preliminary work and less
expense and at an earlier date.
Use of particulate as well as gaseous tracers has already been
proposed as a part of the RAPS program for tracing wind movements and
plume dispersion. Conversely, it is possible to use synthetic particu-
lates prepared from tracer materials (e.g., dyestuffs) to test predictions
of particulate concentrations at various receptor areas, and also to get
a relatively direct measurement of the depletion of the particulate cloud
in its movement from source to receptor. The synthetic particulate matter
can be produced within reasonable ranges of desired particle size, and it
can be discriminated from other particulate matter in the atmosphere by
its chemical or physical characteristics. The most convenient materials
of this kind are those that can be measured by colorimetric or fluoro-
metric techniques.
54
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If the synthetic particulates can be used 101 the testing of
the dispersion models, there will probably be no need to develop a par-
ticulate emission inventory that has a high degree of resolution with
respect to time intervals and particle-size distributions. This will
result in major savings of both cost and time.
For the other purposes of RAPS, e.g., research on transformation
processes, there may be a need for detailed information on particulate con-
centrations with high temporal and spatial resolution. Because of the
difficulties noted above, however, it is suggested that research studies
for these purposes be planned to avoid, as far as possible, dependence
upon general source emissions data. Better results can be achieved by
special monitoring of only the critical sources in a carefully selected
area. The alternative is an extremely costly and difficult inventory
development program.
In summary, it is proposed that data on particulates should
not be included in the high-resolution RAPS inventory emission, since the
means are not currently at hand either to acquire the full range of data
that may in principle be required or to employ the information effectively
if it were obtained.
5. Other Pollutants
In the RAPS area, only a few agencies have indicated an interest
in obtaining inventories of pollutants other than the major ones discussed
above, and none of the agencies that did were concerned with short-term
dispersion modeling. Local and state regulatory agencies were interested
in emission inventories for designated hazardous air pollutants, including
mercury, beryllium, cadmium, lead, and asbestos. They called only for
quarterly or yearly average emission rates or, at most, for daily average
emissions during air pollution episodes.
The CHESS program includes consideration of a wider number of
trace materials as pollutants including cadmium, zinc, lead, mercury,
chromium, vanadium, nickel, copper, manganese, asbestos, benzo-a-pyrene,
and polychlorinated biphenyls. Although CHESS relies mainly on air con-
centration measurements obtained from its monitoring network, it will
probably require estimates of relatively long-term average concentration?
in the years before the air monitoring network was established. Thus, it
may be necessary to prepare estimates of emission rates for these earlier
periods, so that the atmospheric concentrations at those times can be
calculated from use of dispersion models.
55
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In general, there is little point in attempting to carry out
emission inventories for pollutants other than the major ones discussed
above until such time as the precise requirements are specified by the
potential users. Variations in the required precision and the time and
space resolution can greatly change the costs and time required for the
inventory.
6. Heat and Water Vapor Releases
Although not pollutants, sensible heat and water vapor releases
are of interest to meteorology and dispersion modeling because of their
effects on atmospheric stability. Studies are now being made of the
effects of the urban "heat island. The sensible heat and water vapor
content in waste gases can generally be calculated readily from data that
will be collected in the course of the emission inventories for sulfur
oxides, nitrogen oxides, and particulate matter. The heat transferred to
the atmosphere by convection and radiation from the surfaces of buildings,
paved areas, and the earth must, of course, be estimated by separate pro-
cedures not related to the emissions inventory.
The temperatures and volumetric flow rates of the waste gases
will be part of the information collected during the inventories. Hence,
it will be possible to calculate the sensible heat content of the gases
above the ambient temperature level. For combustion processes, it will
generally be possible to calculate the water vapor emission from the
quantity and the hydrogen content of the fuel burned. For some other
fuel-burning operations, such as direct-fired drying, the water vapor
present in the waste gas will greatly exceed that formed by burning of
the hydrogen in the fuel. However, the water vapor content of the gas
may be obtained during the sampling for various pollutants. Sometimes
data on the process may permit calculation of the water content of the
gas.
E. Source Categories
1. Classification Scheme
A general classification scheme for emission sources is pre-
sented in Table 10. It is broad enough to encompass all emissions of
pollutants from all sources. The source categories (stationary and
mobile) and subcategories (area, line, and point) are convenient from
the standpoint of dispersion modeling as well as from inventory orga-
nization. The division of source processes into combustion and
56
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noncombustion processes is a convenient one that is not subject to
serious ambiguities. Combustion processes are here defined as those in
which the release of pollutants comes solely from the burning of fuels
or wastes. They include all processes in which there is indirect trans-
fer of the heat produced (e.g., water heaters and boilers, indirect-fired
air heaters) as well as incinerators, internal combustion engines, and
gas turbines.
Noncombustion processes comprise all other pollutant sources
not falling under the specific definition of combustion processes given
above. In many of them, combustion of fuels actually does take place,
but the products of combustion come into contact with other process
materials that may give rise to pollutant emissions; examples are direct-
fired driers, calcining kilns, cement kilns, and paint-baking ovens. In
other cases, the process emitting pollutants may have no direct contact
at all with products of fuel combustion; many processes for manufacturing
organic chemicals are in this class. Heating of such processes is by
indirect transfer from combustion processes that they themselves consti-
tute separate combustion sources. Still other processes involve only
venting or ventilation, with consequent release of pollutants to the
atmosphere; examples include dust emissions from flour mills and solvent
emissions from paint spray booths.
2. Stationary Sources
a. Point Sources
Combustion Sources—The major point combustion sources
are utilities and industrial plants. The largest industrial sources are
cov.'.r.on ly the power and steam plants. However, there are other types of
indirect-fired process heaters (e.g., pipe stills in petroleum refineries)
that are used for heating various kinds of process fluids and heat trans-
fer ~.edia other than water.
Utility power plants handle a fluctuating generating load
that usually follows a fairly well-defined cycle. The typical load
passes through a minimum in the early morning hours. The absolute levels
_--r.d the hours at which the minimum and maximum loads are reached are
functions of the season of the year. Summer and winter loads reflect
the effects of air conditioning and space heating loads. The patterns
for different power plants may vary widely, however, depending on whether
a plant is handling a base load or is being used for peaking.
58
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Industrial power and steam plants will usuai]y d-i •: p la y
load patterns substantially different from those of the public utility
plants serving the general area. Where the manufacturing plants are
operated on a 24-hour basis, both the electrical power and the steam
required for process use should in general represent fairly steady loads.
On the other hand, the power and steam plants will be essentially if not
completely shut down when the manufacturing plant is out of operation.
The power and steam required for heating and air conditioning the plant
will superimpose a fluctuating load on that resulting from process re-
quirements .
The nature of the load cycle in an industrial power plant
will also be affected if the plant is tied into the public utility power
grid. In such cases the plant will usually send power into the public
system when power demand is low in the manufacturing plant and draw power
from the public system when demand is high.
Depending on the type of manufacturing, electrical loads
may exhibit cyclic behavior, sometimes over relatively short intervals
(e.g., electrical power loads in a steel rolling mill). Batch chemical
processes may have a cyclic steam demand.
To obtain a significant emission inventory from such
sources, particularly an inventory from which it is possible to simulate
one-hour emissions, it is necessary to acquire detailed knowledge of
the operating cycle of each installation as well as of the characteris-
tics of the installation itself, the fuel or fuels used, and so on. A
sampling program to acquire information for derivation of representative
emission factors will have to be carried on.
Although emissions from such sources are commonly esti-
mated by emission modeling techniques (as described in Section VII)
using information on load and operating patterns discussed above, we
recommend that, wherever possible, emission data from the point sources
responsible for the major proportion of emissions be acquired by direct
methods using information on actual emissions, loads or fuel consumption.
Details of this approach are given below.
Incinerators constitute another point combustion source.
The sulfur contents of common wastes are low, so that incinerators are
only rarely significant sources of sulfur dioxide. The variability of
the composition of wastes will, however, make it much more difficult
to correlate the measured emissions with operating variables than with
fuel-burning combustion sources.
59
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The design of most existing incinerators has not been
favorable to efficient combustion, so that emissions of carbon monoxide
and hydrocarbons tend to be much higher than in fuel-burning operations.
This problem is frequently aggravated by improper operating conditions.
The formation of nitrogen oxides can vary widely depending on incinerator
operating temperatures as they are affected by the amount of excess air
used in combustion.
Since the variations between different incinerators may be
extreme, emissions are relatively unpredictable. Hence, any large units
(municipal incinerators and possibly some industrial incinerators) should
be tested individually, i.e., on the basis of operating data, such as
daily tonnage of throughput. Smaller industrial and commercial inciner-
ators may be treated by modeling with only representative units being
sampled, since their emissions will be small enough to be estimated with
adequate precision from such data.
Noncombustion Sources—Noncombustion point sources of
pollutants are practically exclusively industrial in nature. They com-
prise such a diverse collection of manufacturing operations that no
generalizations can be offered concerning either their emissions of
pollutants or the factors affecting them. Most noncombustion point
sources will emit one or more of the pollutants of concern to the dis-
persion model studies, as is shown in the following discussion under
each pollutant.
The principal point noncombustion sources of S02 are found
in petroleum refineries, nonferrous smelters, sulfuric acid plants, and--
possibly—integrated steel mills.
In petroleum refineries, the process sources are the Claus
sulfur plants, the regenerators of catalytic cracking units, and flares
burning waste sour refinery gases. Refinery process heaters or boiler
furnaces burning sulfur-containing fuels (including sour refinery gases)
fall in the category of point combustion sources discussed above. The
flare represents only a combustion source, but it is one of such special-
ized nature that it is best classified with the process sources. Because
of its irregular operation, the flare presents special problems of emis-
sion estimation, which must be worked out in reference to process measure-
ments available and with consideration of the actual level of S02 emmissions
for which the source is responsible. Claus sulfur plants are likely to
be fitted with continuous monitors for measurement of S02 in the tail gases.
In the event that they are not, manual measurements of SC>2 emissions must
be made and correlated with an available measured process variable, such
60
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as sulfur production rate. Measurements of NO emission, also required,
X
will probably have to be made manually. The regenerator of a catalytic
cracking unit is not only a source of SOg but (at least potentially) a
major point stationary source of CO. The actual emission of CO depends
on whether the regenerator is fitted with a CO boiler and on the combus-
tion efficiency of the CO boiler. The S02 emission is related primarily
to the sulfur content of the petroleum feedstock being processed in the
catalytic cracking unit. Measurements must be made of the S02, CO, and
NOX concentrations in the waste gases and of the waste gas flow. The
quantities can probably be correlated with the throughput rate of catalyst
being regenerated. The sulfur content of the cracker feedstock will be
an added parameter of the SO,, emission.
In secondary nonferrous smelters, the principal source of
S02 emission is likely to be the fuel used, and the measurements and
correlations of the emissions will be similar to those employed in com-
bustion sources. However, primary smelters operating on sulfide ores
are potentially major point sources of S02 emissions. Apparently, there
are no primary nonferrous smelters operating in the Saint Louis area
completely without SO2 recovery (by manufacture of sulfuric acid). How-
ever, the tail gases from the sulfuric acid plants operating on smelter
gases can themselves be major sources of SOg emission, as noted below.
Contact sulfuric acid plants, whether sulfur-burning units
or by-product acid plants, are at least potentially major sources of S02
emissions. In the future, at least, they will probably be equipped with
continuous monitoring devices for measurement of the S02 concentration in
the tail gases partly for process control. Whether manual or automated,
determinations will have to be made of SO2 and NOX concentrations and
waste gas flow rates. The results must then be correlated with operating
variables, such as the acid production rate.
Integrated steel mills have a common major source of actual
or potential SO2 emissions—the production of coke oven gas—which has a
substantial sulfur content. If the coke oven gas is not desulfurized
before use, the sulfur will be emitted as SO2 from the various combustion
sources (e.g., power houses, underfiring of coke ovens) or process sources
(e.g., open hearth furnaces, soaking pits) where the gas is used as fuel,
and it will be accounted for in the emissions inventories of those sources.
If the coke oven gas is desulfurized before burning, a Claus plant or a
contact sulfuric acid plant will be used to convert the recovered hydrogen
sulfide to elemental sulfur or sulfuric acid. Whichever may be used, the
tail gas will constitute a S02 emission source that must be investigated
as discussed above.
61
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In addition to the principal noncombustion sources of SOQ,
£j
there are a variety of direct-fired processes in which S02 emissions can
arise from the fuel burned or, possibly also in some instances, from the
material being processed. Important examples are drying and calcining
kilns and cement kilns. These sources are probably more important for
their emissions of other pollutants, such as particulates and NO , than
for their S02 emissions- Cement kilns, because of their high operating
temperatures, are probably relatively important sources of NC> , but there
X
are few reliable data on their emissions of SOU. Measurements of both
NOX and SOg emissions and their correlation with cement kiln operating
conditions are particularly important because cement kilns are relatively
large consumers of fuel.
Major point noncombustion sources of CO, actual or poten-
tial, are relatively rare. Catalytic cracking unit regenerators have
been listed above because they are also sources of SO,,. The blast fur-
naces in steel mills are probably the only other important examples.
Safety considerations alone normally dictate that blast furnace gas not
used as fuel be burned in a flare before discharge to the atmosphere.
Venting of some unburned gas from relief valves at the tops of blast fur-
naces does take place intermittently as the result of "slips" in the
furnace that produce surges in gas pressure. However, the fraction of
the total gas generated that is lost in this manner is relatively small
under modern operating practices. The total gas generated is computed
from the volume of air supplied to the furnace, and the amounts used as
fuel or flared are commonly metered. The amount lost by venting during
slips is obtained by difference. The average daily loss is thus obtain-
able with reasonable precision, although the actual releases are commonly
of short duration—a matter of minutes. Establishing the actual hourly
emission rates of CO from this source is probably impractical, but it is
also unlikely that the amounts are large enough to seriously affect CO
dispersion studies except possibly in the locality of a steel plant.
Apart from the types of point noncombustion sources of NOX
already mentioned, nitric acid plants constitute the major process source
of NOV. The NO,, concentrations in the tail gases are likely to be moni-
X •"•
tored continuously as a process control measure. If they are not, manual
sampling and flow measurements must be used to develop a correlation
between the NO emission and the measured production rate of nitric acid
X
(or some other convenient operating variable).
The NEDS inventory data indicate that petroleum refineries
constitute the major point process emission sources of hydrocarbon gases
and vapors. A substantial amount of the total emissions from refineries
is known to often result from a multiplicity of small leaks and vents,
62
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so that the source location is rather diffuse. Control of such emissions
has been made difficult by the small sizes of many of the individual leaks.
However, vaporization losses from storage tanks has been a large and spe-
cific source of hydrocarbon emissions. Losses from evaporation occur not
only in refineries, but also in subsequent handling and storage of gasoline
and other hydrocarbons liquids; estimates of such emissions should be made
wherever possible.
Other point sources of emissions of hydrocarbons and hydro-
carbon derivatives will include large surface coating operations, such
as the spray painting of automobiles at assembly plants. Data will have
to be developed on the actual consumption of coatings and solvent in these
operations and on the rate of consumption. Even the operators may not
currently have reliable data on anything but the gross long-term con-
sumption.
Chemical analyses should be made to classify the hydrocar-
bons and hydrocarbon derivatives emitted according the major groups
(paraffins, olefins, diolefins, aromatics, alcohols, ketones, and alde-
hydes). Where possible, the actual material vaporized, not the liquid
stocks, should be sampled.
To summarize, Table 11 shows the major noncombustion
sources and the basis of the principal pollutants emitted.
b. Area Sources
Combustion Sources—The stationary area sources are com-
posed of:
• Residences.
• Commercial establishments.
• Institutional establishments (e.g., schools,
hospitals, public buildings).
• Some small industrial (manufacturing) estab-
lishments .
Most of the pollutants arise from the combustion of fuels used for cook-
ing, space and water heating, and steam generation. Some may come from
combustion of wastes in small incinerators. Industrial establishments
in this category are composed mostly of small manufacturing operations
that employ fuels primarily for space heating or that burn small quanti-
ties of waste.
63
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Table 11
MAJOR NONCOMBUSTION SOURCES OF POLLUTANTS
Pollutants
Sources
S°2' S°3
NO
Particulate matter
Hydrocarbons and derivatives
Contact sulfuric acid plants
Claus sulfur plants
Coke oven gas production
Copper, lead, and zinc smelters
Sulfonation of organic compounds
Nitric acid plants
Nitration of organic compounds
EOF steel furnaces
Secondary lead smelters
Grey iron foundries
Flour mills
Cement plants
Coke quenchers
Organic chemicals manufacture
Paint and varnish manufacture
Coating of fabrics with rubber
or plastics
Venting of storage tanks for
petroleum products and organic
chemicals
The rate of fuel consumption by the area combustion
sources exhibits pronounced diurnal and seasonal variations as well as
variations related to ambient temperatures. Fuel consumption peaks
appear over periods of one to three hours. The consumption patterns of
residences differ from those of commercial, institutional, and small
industrial operations, which may operate at night and over weekends as
well as during the day. The different types of sources may also use
different kinds of fuel. Individual residences and small apartments
tend to use natural gas, whereas large apartment houses and commercial,
institutional, and industrial operations are relatively more likely to
use oil or coal.
64
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The amounts and nature of the emissions from fuel combus-
tion are related not only to the type of fuel burned but also to the type
and the size of the combustion equipment. If waste incineration is used,
the practices employed in incineration may vary widely.
The only feasible way to develop an emission inventory for
the area combustion sources is to apply emission factors characteristic
of the combustion equipment to the quantities of fuels consumed. Unfor-
tunately, there will seldom be data that will give directly the quantities
of various fuels consumed by particular types of equipment and by specific
types of users. The flow of natural gas to various sections of a city may
be measured at metering stations. However, the measurements will indicate
only the temporal pattern of the total flow to all users. Data on sales
of oil and coal indicate only the average rates of consumption over rela-
tively extended periods of time.
For significant area sources, it will be necessary to deter-
mine, by survey and experiment, the amounts and the patterns of fuel usage
by typical components of area sources. For instance, the consumption of
fuel by a typical apartment for cooking, water heating, and space heating
could be measured and fitted to a model incorporating diurnal and seasonal
influences and the effects of ambient temperature. Pollutant emission
factors should be determined experimentally for the combustion devices
unless they are already available. Similar determinations should be made
for other representative source units: individual homes, offices or office
buildings, retail stores, and such.
Noncombustion Sources—The most important noncorabustion
area sources are those that emit organic vapors and may make appreciable
contributions to the total emission of hydrocarbons. Prime examples
include vapors from (1) gasoline storage and handling by distributors
and service stations, (2) dry cleaners, (3) painting, (4) degreasing,
and (5) application of asphalt to roofing and pavement.
The total quantity of vapors released can be estimated
fairly readily by applying appropriate emission factors to the quantity
of gasoline handled, the amount of cleaning fluids consumed, the amount
of paint applied, and so on. However, attainment of adequate spatial and
temporal resolution will require a fairly extensive survey of the sources
and a detailed study of operations at a representative sample of estab-
lishments or activities of each type.
The emissions of gasoline vapors from automobile service
stations will vary with the day of the week and the hour of the day, as
65
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the volume of business fluctuates. The emissions of cleaning fluid
vapors from dry-cleaning establishments are probably relatively constant
during actual working hours. Solvent vapor emissions from the painting
of products in small manufacturing establishments and from solvent de-
greasing operations should have a similar temporal pattern. However,
the emissions of solvent vapors from the painting of buildings (interior
and exterior) are irregular with respect to quantity, time, and the loca-
tion of the activity. Nonmethane hydrocarbons are also emitted in sig-
nificant quantities in summer daylight hours. (See Section VII.)
Specific estimates for the Saint Louis area are needed before that con-
tribution can be properly evaluated.
Again, for the major contributing sources of this type we
recommend the direct approach to determining emissions data described
later.
3. Mobile Sources
The determination of emissions from mobile sources presents
special difficulties. The problem has received considerable attention
in connection with air quality modeling specifically related to the
nature and variability of traffic sources. In studies of this type (as
described in detail in Section VII) a considerable degree of sophistica-
tion has been achieved in some cases. In these however, it has been con-
venient and appropriate to consider the relationships between the behavior
of the sources and the ultimate air quality conditions in models which
integrate and combine the various parameters involved in ways specific to
the purpose for which the model is designed. In many respects, current
modeling capability in this area is inadequate, and its improvement will
be an important aspect of the RAPS program. The basic RAPS Emission
Inventory may properly be expected to provide basic data for the use of
such modeling development, and thus data on traffic flow and traffic
links will be part of the data collected and stored. For the primary
needs of the principle users of RAPS, however, the inventory must provide
data on the emissions of the key pollutants from mobile sources, which
contribute to overall input of emission data to air quality modeling or
transformation process studies. Accordingly, we recommend that, until
more refined inputs are available as a result of progress in mobile
source emissions modeling studies, the basic traffic data be used with
simple models based upon average speed to develop the initial informa-
tion on emissions of the key pollutants.
66
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F. Emission or Data Conversion Factors
The derivation of emission estimates from continuously monitored
operating variables is a critical step that will determine the validity
of the high resolution inventory data. Emission factors as currently
used in developing emission inventories are generalized factors that are
most valid when applied to a large number of sources or processes espe-
cially over extended time periods. They have limited applicability for
use with single sources, certainly in the estimation of hourly emissions,
and cannot therefore be used indiscriminately in deriving an inventory
for RAPS in the manner proposed. The whole point of this inventory is
to have high precision in terms of space and time, and for this reason,
the best possible conversion factors must be used to develop the emissions
data from the monitored variables. In general, this means that specific
factors must be developed on an individual basis for each source for
which the critical relationships are not already known with a high degree
of confidence. (Even in the latter cases, some testing and verification
would be desirable.) On the other hand, such specific source testing is
a costly and time-consuming activity to be performed only when necessary.
It is recommended, therefore, that consideration be given to using pub-
lished, established emission factors for each source,* where the type of
equipment or activity is sufficiently typical that the published emission
factors can be identified as being truly representative of the case in
question. It should be noted that, in general, the published factors
have the most credibility for the major combustion sources (particularly
in the case of SCv, emissions). It is important to recognize, however,
that in the case of the RAPS inventory, the role of minor sources is of
great importance in local effect, i.e., at near-surface levels, with the
spatial resolution required for RAPS. Lower confidence factors are
assigned to emission factors for such lesser sources (quality factors
B and lower in the categorization of Reference 11). Considerable caution
must therefore be exercised in treating such lesser sources. This is
especially the case for area sources, which, although minor contributors
to the total burden, are important on the local scale. Special care is
necssary to improve the accuracy of emission factors from such sources
for use in refined modeling techniques.
*
For example, Reference 11.
67
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G. Physical Aspects of Emission Sources
For purposes of dispersion modeling, it is essential to know not
only the quantity of a pollutant emitted to the atmosphere but also the
manner in which it is emitted. It is necessary to know the effective
height from which the emitted pollutant can be assumed to start dispers-
ing. Therefore, there must be sufficient information from which it will
be possible to estimate the effective height of the plume of gas. The
effective height of the plume is the sum of the physical height of the
stack and of the plume rise, which is the distance that the plume ascends
under the influence of its initial momentum and its buoyancy.
For small source units, such as those that constitute area sources,
the water gases are usually discharged effectively at ground level. Even
where the gases are discharged a short distance above the roof of a build-
ing, they will generally be carried quickly down to ground level. Hence,
it is commonly assumed in dispersion modeling that the waste gases are
discharged at ground level and that neither their initial temperature nor
velocity influences subsequent dispersion.
For point sources, and particularly for the larger ones, the emission
inventory data must be supplemented by data on the gas flow rate, tempera-
ture, pressure, and specific gravity, and on the physical height and
diameter of the stack. Calculations of the plume rise require knowledge
of the gas exit velocity and the stack inside diameter at the plane of
discharge, and of the temperature and specific gravity of the gas. Cal-
culation of the specific gravity of the gas requires knowledge of the
composition of the gas stream including the amount of water vapor.
In addition to the data noted above, the inventory should include
information on the layout of the source stack with respect to other
structures. As a rule of thumb, a stack should be at least 2.5 times
the height of the nearest structure; otherwise, the proximity of the
other structure may lead to "downwash of the gas from the stack and a
consequent rapid descent of the plume to the ground. Short stacks pro-
jecting from the roof of a building are particularly subject to this kind
of problem, and the role of the stack in securing dilution and dispersion
of the gas is largely lost.
H. Units of Measurement
Many agencies continue to use English engineering units, and it is
virtually certain that many of the data coming into the inventory will
continue to be expressed in engineering units despite a growing preference
68
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for the metric system. It is undesirable to convert such data into
metric units manually before storage in the inventory because of the
large amount of time it would take plus the time required for checking
for conversion errors.
It is recommended that the data be entered into computer storage in
the same units in which they are originally received. The computer should
then be programmed to convert the data to metric units^when they are re-
called and printed out. This procedure will diminish the amount of manual
processing that the data must undergo and still retain flexibility.
I. Specification of the RAPS Emission Inventory
1. Genera 1
While NEDS as a source of information on annual or long-term
emissions from a broad range of pollutants, it is recommended that a spe-
cial high resolution RAPS Emission Inventory be developed to provide
hourly data emissions of S02, CO, NOX, and HC in the form of direct state-
ments of weight of pollutant emitted in kilograms each hour from each
point or line source or area element, for the Saint Louis (AQCR). Loca-
tional data will be provided in UTM coordinates with a resolution of at
least 0.1 kilometers for point sources. Mobile sources will be specified
in terms of traffic links (with a resolution of approximately 0.02 kilo-
meters) for major routes and of areas for secondary routes. Both for
stationary and mobile sources, the area elements will reflect either
the spatial resolution of the basic data from which the emissions are
estimated (e.g., housing developments) or one kilometer grid squares,
whichever is the smaller.
Information on the physical characteristics of point sources
and the prime data used in compiling the inventory would also be provided.
Although it is not considered practicable to develop comparable data for
particulates, it is recommended that additional data on particulate
emissions be collected to supplement the NEDS inventory, specifically
data to distinguish between coarse and fine particulate emissions from
the major sources.
For initial planning purposes we propose that only point sources
emitting over 100 tons per year of each pollutant be considered individ-
ually. The estimation of emissions from such sources will be accomplished
in classes according to the magnitude of each source as specified in
detail below. We recommend that emissions data be based upon direct
monitoring of emissions for the largest sources. For other major sources,
69
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direct monitoring of hourly fuel consumption operating &r process data
should be used as the basis for estimating emissions. For smaller point
sources, we propose that emissions be estimated on the basis of general-
ized patterns of temporal variation of emission as a function of source
type and using the best available data on fuel consumption, operating or
process data (i.e., per shift, daily, weekly). In the limit, this ap-
proach approximates current practice in which annual totals of such data
are used as a basis for modeling. For area sources, we recommend the use
of modifications and adaptions of already available emission models.
Similarly, for a first approach to assessing mobile course emissions, we
recommend the use of available models. Improved mobile source models
should be employed as they become available to provide higher quality
data. A simple new model should be developed to estimate natural emis-
sions of nonmethane hydrocarbons on summer days. In all cases, we pro-
pose that special testing efforts be carried out to ensure that the
emission and conversion factors used to derive the emission estimates
are as reliable as possible and have regard for the high temporal reso-
lution required. Standard emission factors should be employed only if
a review of the source on a case-by-case basis indicates that such a
factor may be used with confidence in the particular case.
2. Point Sources
a. Classification by Size
It is convenient to classify point sources by size accord-
ing to the amount of pollutant emitted annually.
• Class I, over 100,000 tons per year.
• Class II, over 10,000 but less than 100,000 tons
per year.
• Class III, over 5,000 but less than 10,000 tons
per year.
• Class IV, over 1,000 but less than 5,000 tons
per year.
• Class V, over 100 but less than 1,000 tons per
year.
• Class VI, over 1 but less than 100 tons per year.
The numbers of sources comprising these classes for SO , CO, NO and
-------
Table 12 based upon NEDS data that is discussed in greater detail in
Section VI.
As indicated by these classifications, a relatively few
sources account for the bulk of the emissions.
The annual emissions, obtained from the NEDS inventory,
although adequate for initial source classification, are not entirely
representative of the probable contribution of the various sources to
air quality at specific time periods. For example, units supplying
steam for space heating will be operated only in periods of cold weather,
and the actual emissions at that time will be higher than indicated by
the annual average. Furthermore, the emission rate will follow a diurnal
cycle even within a single day.
The foregoing considerations are important because reports
from atmospheric dispersion studies have indicated that ground-level pol-
lutant concentrations are disproportionately affected by local low-level
pollutant emissions, even though the latter are individually small and
contribute—individually or even collectively—only a minor percentage
of the total pollutant emission from all sources. Thus, the smaller emis-
sion sources cannot be ignored even as part of the general RAPS inventory
that is intended to supply emission estimates for use in mesoscale atmo-
spheric dispersion models. .They must be appropriately evaluated even
though the effort required is more than proportionate to their relative
contribution to the total emission. Within these reservations, the sta-
tistics noted in Table 13 provide a useful basis for assessing the gen-
eral magnitude of the problems involved in developing a program of point
source emission estimates and describing the approach recommended.
b. Data Acquisition Groups
We propose that point source emission data be derived in
four broad groups according to the basic records used to estimate emis-
sions.
Group I. Continuous emissions monitoring records
Group II. Continuous records of fuel consumption,
operating, or process data
Group III. Short term, periodic records of fuel con-
sumption, operating, or process data (per
shift, day, or week)
71
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Group IV. Long-terra (semiannual or annual) records
or estimates of fuel consumption, operating,
or process data.
Within Groups II and III, a further distinction should be made between
sources for which separate conversion or emission factors are established
and those for which representative factors will be established. Data
from Groups I, II, and III will be collected on an ongoing basis.
In principle, it would be desirable to deal with all
sources under Group I. In practice, sources should be treated in the
highest ranking group possible while maintaining regard for the costs
and difficulties of so doing. A tentative work load is indicated in
Table 13 for S00 and NO sources to show how the scope of effort can be
£ X
analyzed and assessed. This shows that, by collecting data routinely
from some 101 sources (of which 65 have been individually tested), ap-
proximately 95 percent of the hourly SOg emissions and 70 percent of the
hourly NO emissions can be estimated on a direct basis. Similarly, over
90 percent of the emissions of CO sources can be estimated on an hourly
basis by the treatment of direct data from only six sources. Still, it
would be necessary to obtain data from and test some 10 or more sources
to achieve hourly estimates from direct data for only 50 percent of the
HC emissions.
It is recommended that the method of grouping sources for
this purpose be determined after a survey has been made of all the sources
over 100 tons per year. This survey will produce a better understanding
of the data available and the difficulty of collecting useful direct data
on fuel consumption or operating or process data from which hourly emis-
sions may be derived. Such a survey should preferably be made in site
visits to obtain more details of the source and operations than are avail-
able from the NEDS review. It should ascertain the availability of direct
monitoring data on emissions or fuel consumption and of operating or pro-
cess data that can provide direct information on hourly emissions. Alter-
natively it should reveal the nature of short-term data from which hourly
emission estimates can be derived. The survey should pay particular at-
tention to the form of the available records, and the optimum frequency
and method of collecting them.
The survey will provide a basis for determining how much
special source testing is necessary to provide reliable conversion or
emission factors for individual sources and possible categories of source.
This survey would also reveal the needs and opportunities for installing
appropriate monitoring devices at selected sources, an approach that could
usefully upgrade the inputs from certain sources and reduce data collec-
tion effort.
74
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It will be appreciated that the methodology proposed allows
considerable flexibility in the manner in which sources are treated. In
this way, optimum benefit may be derived from the available RAPS inventory
budget on a case-by-case basis. In any limitation of effort for budgetary
reasons, it would be preferable to reduce the numbers of sources treated
individually, rather than to restrict the source testing program, since
operating patterns may be more confidently determinable at the present
time than emissions or conversion factors.
3, Point Combustion Sources
Combustion point sources burning oil and coal are responsible
for most of the SO^ emissions in the region, and are also major sources
of NOX. Those burning natural gas produce N70 , but do not produce SOg.
Neither type of point source is a major source of CO or HC, particularly
when compared with automotive sources, but their emissions of these pol-
lutants are significant and must be considered in the context of RAPS.
The procedure for collecting emission data within the groups
described in Table 13 is described here.
For each source, appropriate reference data, such as type of
unit, nominal ratings, control equipment, and so forth, together with
physical characteristics of the emissions point (i.e., stack height,
diameter at the discharge, location of stack in UTM coordinates) must
be compiled. In addition, critical fuel consumption, operating, or
process data must be acquired in routine collections. These constitute
data as a function of time, ascertained in the initial survey to be basic
for the determination of emissions.
Typical of such data are
• Type of fuel (e.g., coal, oil, natural gas).
• Characteristics of fuel (e.g., sulfur and ash contents,
higher heating value).
• Fuel-firing rate (e.g., weight or volume rate, thermal
rate if available).
• For boiler furnaces, the steam generation rate at
specified temperature and pressure.
• For power boiler furnaces, the steam generation rate
and electric power output.
Then, the following procedures should be applied.
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Group I; Directly Monitored Emission Sourcos—Most of the
sources in Class I (over 10,000 tons per year of pollutants)
are expected to be equipped with continuous monitoring devices
for the measurement of SO and NO and possibly with a Pitot
2 x
element to measure flue gas flow continuously. Exit flue gas
temperatures are likely to be recorded continuously at such
large sources. The calibration of the permanently installed
recorders will have to be verified experimentally unless data
on such verification are available at the source. Similarly,
flow measurements have to be obtained or verified. If no per-
manently installed Pitot element is on hand, such flow measure-
ments will have to be correlated with a measured furnace oper-
ating parameter or variable. It may be advantageous to under-
take the determination of other pollutants at the time of
calibration. For example, from flue gas analysis, measurements
of CO and hydrocarbons should be made, if the source is a sig-
nificant (more than 100 tons per year) emitter of these pollu-
tants.
Group II: Directly Monitored Fuel Consumption, Process, or
Operating Data—Typically these are sources emitting less than
10,000 tons per year of SOQ or NO but more than 5,000 tons per
^ X
year and any larger sources not equipped with continuous moni-
toring devices. These units are not expected to have continu-
ous monitoring systems for measurements of pollutant concentra-
tions, but they should have good records of fuel consumption
or other operating data with hourly resolution. In Group IIA,
conversion factors are derived linking the continuously moni-
tored parameter with the emission rates are derived from manual
sampling at each source. This is carried out initially in the
program and thereafter repeated intermittently to ensure the
continuing validity of the results. In Group IIB, standard
emission factors may be assigned to categories of similar sources
after they are determined by testing representative stacks.
Group III: Short-Term, Measured Fuel Consumption, Processing,
or Operating Data Available—In this group, in which both Class
IV (1,000 to 5,000 tons per year) and Class V (100 to 1,000
tons per year) sources will be treated, records will be avail-
able on a shift, daily, or weekly basis for fuel consumption,
process, or operating data. (Direct hourly data must be inter-
polated.) Again two possibilities are open. In Group IIIA,
each plant is considered individually, and tests are made to
establish both the time dependence and the amount of pollutant
emissions that can be deduced from the recorded data. Appro-
priate conversion or emission factors are thus assigned to each
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source tested. In Group IIIB, factors are established for ap-
propriate categories of similar sources on the basis of tests
of representative samples.
Group IV: Long-Term Fuel Consumption, Processing, or Operating
Data—For sources over 100 tons per year, for which suitable
specific data are not available (or which must be treated in
this fashion for budgetary reasons), we recommend the use of
modeling procedures of the type described in Section VII. A
suggested approach for SOg is to employ the Argonne2 model, and
for NOX and HC the SAI model,1'0 but with adaptation and modi-
fication to ensure that the best possible input data on fuel con-
sumption are used and that conversion factors are verified by
testing.
4. Point Xoncombustion Sources
Because of the extreme diversity in point noncombustion sources,
no general and detailed procedure for correlating emissions with source
operating variables can be offered; appropriate procedures will have to
be worked out, case by case, for each type of source, following the gen-
eral principles outlined in Table 13 and in Section IV-E-2.
For the purpose of organizing the inventory, such sources should
also be considered according to the most important pollutant that they
emit. In this grouping, particulate emissions will not be used as a basis
even though they may be the principal pollutant emitted by some sources.
J. Stationary Area Sources
1. General
Stationary area sources contribute minor proportions of the
total burden of S00, XO , CO, or HC as described here. Their importance
^ X
may be significant, however, in the context of RAPS, since they may con-
tribute significantly to the concentrations of the key pollutants at the
near-surface level on a local basis.
2. Area Combustion Sources
The NEDS inventory data indicate broadly that the emission
sources in this category are primarily those using natural gas or dis-
tillate oils. Consequently, the estimated S09 emissions are minor (only
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about 1 percent of the estimated total). The relative contribution of
NOX from these sources is greater, but still a minor part of the total.
The NEDS inventory indicates that 28 percent of the total NOX emitted
comes from area sources, but these include vehicles that are known to
be, in the aggregate, a large contributor. The contributions of CO and
hydrocarbons from the stationary area combustion sources are likely to
be minimal.
As discussed, the only practical way to estimate short-term
emission rates from the area sources is the use of simulation techniques
such as are discussed in Section VII. The two most advanced models to
date are the Argonne model2 for SO2 and the SAI model1'0 for CO and NO .
They are alone in their class in that they have received significant
field verification. As discussed in Section VII, the models have limited
precision for the estimation of area combustion source emissions for the
RAPS inventory. Accordingly, it is suggested that to improve emissions
estimates by such modeling techniques, more accurate and precise survey
data on fuel consumptions as well as more accurate emission factors be
obtained. Both will require that new experimental studies be made at
typical sources.
Data on fuel consumptions in specific, small areas of cities
are not readily available. Commonly, continuous metering of natural gas
is carried on only at a few points on lines supplying major sections of
the city. The flows metered at these points give no indication of the
breakdown of usage by type of source, and the temporal variations in
flow reflect only the summation of the demand patterns of different
users. The consumption of gas by individual customers can, of course,
be obtained from meter records. However, these will, at best, give only
the monthly totals of gas consumed; they will not show the different uses
by a given customer.
Data on fuel oil consumption by residential and commercial
users are largely restricted to data on fuel oil sales. Even users will
generally have no more than a qualitative idea of the actual rates of
consumption over short periods.
To obtain a high degree of both temporal and spatial resolu-
tion in the estimates of emissions from area sources, it will be neces-
sary to conduct extensive surveys of the patterns and amounts of fuel
usage by representative sources. Sufficient sources must be surveyed
to constitute statistically valid samples. It will probably be necessary
to install meters at these sources that will record both the rates of
fuel consumption at various times and the integrated total.
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Residential and commercial installations, in particular, will
show marked diurnal variations in their fuel consumption patterns as well
as variations in usage between week days and weekends or holidays. There
will also be seasonal variations in the magnitude and distribution of fuel
consumption. For example, the consumption of fuel for water heating may
be relatively constant throughout the year, but the consumption for space
heating will be highly seasonal. Because space heating will be the most
highly variable as well as the largest consumer of fuel, the rate of fuel,
consumption for this purpose should be correlated with ambient temperature.
Better emission factors must be obtained for the typical fuel-
burning units used in residences and commercial establishments. Some re-
quired data may be derived from other studies currently in progress, but
others will probably have to be made specifically to meet the needs of
the RAPS inventory.
Once emissions are determined for the representative sources,
the emissions from specific areas can be estimated from the number and
sizes of residences and commercial establishments in the areas.
3. Area Noncombustion Sources
The only area noncombustion emission sources that appear to be
significant are those emitting hydrocarbons and hydrocarbon derivatives
(gasoline and solvents). The nature of the sources and their emissions
was already discussed.
The release of solvent vapors from the painting of buildings
probably cannot be incorporated in a practical way into models for pre-
diction of hourly emission rates; the emissions are too irregular in
quantity, time, and location. Probably also, the solvent emissions from
this source are too small to be of concern in mesoscale dispersion studies.
However, a painting operation might be of concern to a microscale disper-
sion study being conducted nearby. If so, making allowances for the emis-
sion is best handled as a project task of the dispersion stud}-.
The area hydrocarbon sources of consistent importance will con-
sist primarily of the smaller gasoline service stations and dry cleaning
establishments. It will be necessary to survey the typical operating
cycles of such establishments to establish the pattern of their emissions.
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K. Mobile Source Procedures
For automotive mobile sources, we recommend that an average route
speed model with a link or line source geometry supplemented with mea-
sured data of traffic flow (via fixed sensors on high volume freeways
and selected arterials) be used.
The SAI (Systems Applications, Inc.) model1'3 for hourly CO, NO ,
and HC emissions is recommended. It can have modified inputs derived
from Stanford Research Institute's model4 for spatial and temporal dis-
tribution of vehicle number and speed on a link basis for primary traffic
and area basis for secondary traffic. As a basis for this approach, data
should be assembled on traffic volumn and the location of the primary
traffic links and of the areas of secondary traffic. Additionally, ar-
rangements should be made to install and collect data from fixed sensors
on selected routes, as noted above.
As soon as improved methodology is developed for refining the inputs
to such models, it should be used to update the inventory, and, in due
course, the simple average speed approach could be replaced by more com-
prehensive modeling techniques as they become available.
In any case, prime data relating to route links and such would be
available from the RAPS Emission Inventory records for use in such model
development or other purposes.
For other mobile sources the following models should be used to the
extent possible:
• Geomet model" for diurnal emissions from river vessels and
railroads.
• Northern Research and Engineering Model,6 as revised by Geomet
(in preparation), for aircraft emissions.
L. Natural Background Emissions
Techniques of estimating the natural emissions of nonmethane hydro-
carbons in summer daytime should be developed so that such emissions may
be added to the inventory in time for studies in which hydrocarbon emis-
sions are considered.
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M. Particulates
As noted, it is impracticable to develop a high resolution inventory
of particulate emissions on a routine basis. For modeling studies it
would appear that such emissions must be treated on a project basis with
special collections of data (possibly limited in space and time) as re-
quired. In addition, it is suggested that the tracer technique be em-
ployed to investigate the dispersion of particulates under various con-
ditions.
N. Scheduling and Scale of Effort
The development of a complete RAPS Emission Inventory is scheduled
to take up to three years.
It would be desirable, however, to aim for an earlier provision of
emissions data, especially for the first critical tests of air quality
and dispersion models. These will use SOg (emphasizing stationary
sources). Accordingly, the development of the inventory of these pol-
lutants should be given priority, particularly in the source testing and
modeling phases. (Since the majority of point sources of S09 also emit
NOX, the collection of data on this pollutant can conveniently be carried
on in parallel.)
A possible schedule is shown in Table 14. The aim is to provide
initial data on S00 and NO by the first quarter of the second year of
£ A
full activities and complete data by the end of the third quarter of the
second year. For CO and HC, effective initial data should become avail-
able by the end of the second year and complete data by the third quarter
of that year.
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Table 14
DEVELOPMENT SCHEDULE
Inventory Operations
First
Years
Second
Third
Stationary sources
Survey of point sources and
assimilation of NEDS data
Development and adaptation of modeling
procedures
Source sampling
Organization of routine data collection
Routine data collection
Mobile sources
Survey of mobile sources and routes
and assimilation of existing data
Development and adaptation of modeling
procedure
Organization of routine data collection
Routine data collection
Data handling system and system operation
Development of programs for use with
data management system selected
Operation of system
Completed acquisition of data
S02 and N0x
Initial
Full-scale
CO and HC
Initial
Full-scale
Additional effort if interface facilities of basic system need major extension.
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V. TASK C: EMISSION INVENTORY FILE SYSTEM
A. General
This section concerns the specifications for data formats in the
emission inventory. Because this inventory is intended primarily for
air quality modeling in the RAPS program, the data elements were chosen
to provide input data and supplementary documentation for that purpose.
The data structures specified below are extensible data items in a
data reference system that provides the facilities to manage such data
types. Data systems of this kind have been developed and are available.
We describe in this report the main functional features of the inventory
system, and the computer system interfaces needed to provide data to
modeling programs.
Although most of the expected categories of data are used to
demonstrate the data structures and formats, this chapter is not
intended to be a complete listing of the content of the emission
inventory. Because the data structures are specified to be extensible,
any data not explicitly shown can be attached to the structure.
Section B describes the computer system environment in which we are
assuming this inventory will be implemented. Section C discusses the
main operational aspects of the inventory. Both of these discussions
provide the basis for the design of the data structures described in
detail in Section D. Section E suggests a possible alternative format
for implementing these data structures in the absence of a supporting
data reference system. Section F lists the steps required for
implementation of the inventory system, and Section G is a tabulation of
the principal specifications of the computer programs needed.
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B. System Environment
In this section, we discuss the key features of the system and
software environment in which the emission inventory will be
implemented. A data management system will be needed for the operation
of the RAPS Emission Inventory, although it may be possible to perform
the inventory operations with systems not having exactly the features
described here. Data management software is usually the responsibility
of the central computing group of an organization, however, because this
system must meet the needs of the Air Data Branch for the inventory
functions, specifications for the system are given in this report.
Either a general-purpose or a custom-designed data management
software system must be obtained or constructed that will run on the
computer facility at EPA-RTCC. This data management system should
provide the basic functions of file definition; addition, revision, and
deletion of data items; retrieval of selected items using selection
criteria on data content; hierarchic sorting by content; and output of
specified data components of selected items in specified formats to
construct files for use in other programs.
The system should provide for defining hierarchically structured
data items, with components consisting either of single data records or
lists of records, subordinate to other components of the data item. We
assume that the system will provide means for assigning names to each of
the components of a data item. The system should provide functional
records of any desired length to contain character strings.
The system should provide for data entry from punch cards, tapes,
and terminals (if it functions in an on-line or interactive mode). It
should also provide for merging of preliminary files from disk. Data
should be readable as ASCII character strings. Conversion of declared
numeric data to numeric internal representation may be provided, but is
not necessary. The system should not pad card input with blanks and
should provide for continuation of a data record to successive cards
until the record is terminated by an end-of-record character.
For the purpose of this emission inventory, all operations can be
carried out in batch operating mode. However, the RAPS Data Manager has
indicated that the RAPS program will have other data base requirements,
and that compatibility among these different data bases would be opera-
tionally and economically beneficial. One level of compatibility that
can be obtained is to use the same data management software to implement
all of the RAPS data bases. Some of the data base applications may
require on-line use of the data system for retrieval and perhaps even
for data entry. Therefore, it would be desirable for the data manage-
ment system to have the potential capability of being used in an on-line
mode, perhaps by means of additional software options.
All of the presently existing air quality models are written in
Fortran. The primary purpose of the emission inventory file is to
provide input data to these models. Therefore, a system that has an
interface for Fortran programs would be advantageous, because the input
data files could be constructed by means of Fortran programs that could
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access the inventory files through the system interface and write data
in the formats and data representations required by the models. Since
the inventory file formats include data embedded in character strings,
using various separator and operator characters, these records must be
decomposed by the Fortran interface programs, the numerical values
extracted as character strings, and converted by the interface into
proper internal number representation for arithmetic operations. This
translation of the internal formats will have to be added to any
interface routines that a data management system may already have.
The RAPS Data Manager has stated that EPA-RTCC will obtain the
Univac DMS-1100 data management system when their Univac 1100 computer
is installed. DMS-1100 is described in a catalog as a host language
system: it is based on Cobol and can be utilized only by Cobol programs
that issue calls to the data management system for file operations.
DMS-1100 is not in itself a complete data management system. It must
be implemented by writing Cobol programs to perform the functions of
defining and labeling data structures, entering data, sorting and
indexing, and formatting output data. There is no Fortran interface
to DMS-1100, so additional system programming work would be needed to
provide a facility for generating input files to models if the inventory
is based on DMS-1100. (DMS-2000 is also being acquired.)
At this time we do not have sufficiently detailed information about
the facilities and file structures provided by DMS-1100 to be able to
make any positive recommendation concerning its applicability to the
RAPS Emission Inventory.
It is estimated in Section IV on Task B that the total number of
point sources that should be included in the RAPS inventory will
probably not exceed 1000. The number of area sources will depend on how
the areas are defined. If each area is only 1 square kilometer, then
there may be several thousand area sources to be described. However,
the format described here allows for any size and shape of area source
and also for including sets of equivalent area sources in one item, so
the number of inventory items needed to describe the area sources can be
reduced to a few hundred if this feature is used. The line sources that
need to be described will be mostly major traffic routes, and they
should be relatively few in number.
For the purpose of specifying the capacity requirements of a data
management system for the RAPS inventory, we can probably put an upper
bound of about 2000 data items per file. For some point sources, the
data elements may be fairly detailed, but most source items will
describe only one or two products or pollutants, and only a few typical
patterns of time dependence can be predicted by emission models, so that
most of the data items will contain from 200 to 500 characters for a
total of about one million characters. The longest elements in the file
will be the hourly data on consumption and emission for about 50 point
sources, each of which will require up to 75,000 characters to describe
hourly consumption to 1% precision for a year. Then, if hourly emission
values are also stored for an average of three pollutants for each such
source, the total size of each of these point source items will be about
300,000 characters, or 15 million characters for 50 sources.
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The master inventory files will be earily accomodated on a single
large disk pack, such as the IBM 3330, or the Univac Fastrand drum,
which holds over 100 million characters per drum. Therefore, the
storage of emission values in the inventory file will not significantly
affect the computer facility requirements.
The accumulation of historical inventory data, over a period of
years, will most conveniently be accomplished by making a separate file
for each calendar year with the possible exception of the first one,
which will have to average several prior years experience. If this
system is eventually extended to maintain emission inventories for other
regions, each Air Quality Control Region (AQCR) should be handled as a
separate set of data files.
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C. Inventory Operations
To define the data formats for a system file, it is necessary to
have a view of how the file will be used as a part of an overall system
operation. In the case of this emission inventory, there are three
major operational activities: source data preparation and entry,
computation of emission values of sources and production of data file
inputs to air quality model programs.
A principle we have followed in designing these data formats is
that source data should be organized and filed with a minimum of manual
transformations from the form in which it is gathered to the form in
which it is entered into the computer. Thus we have chosen to organize
the file with sources as the primary items, and we are making provision
for entering the names of fuels, pollutants, and other materials in
readable standard nomenclature. Consumption data should be entered in
the units in which it is normally described to avoid manual computations
and errors. The emission modelling programs can compute emission values
in standard system units.
The SAROAD parameter coding manual of EPA has assigned integer code
numbers to many hundreds of compounds of interest in air pollution.
However, only a relatively few pollutants will be included in the high
resolution emission inventory for air quality model verification.
Furthermore, the SAROAD codes do not include materials other than air
pollutants, such as consumables that will be included in the emission
inventory as a basis for computing emissions. Finally, writing 'S02'
instead of '42401' to describe sulfur dioxide is more brief, more
readable, requires no catalog look-up by an inspector or data clerk, and
is far less error prone.
We suggest, in this report, that compounds and other materials be
labeled mnemonically and concisely. Of course, it is essential that the
labels be absolutely consistent. Since the number of materials is
expected to be relatively small, a standard thesaurus of material
identifiers should be easily maintained.
A considerable amount of data is needed to identify and describe
each source in addition to the set of values for emission or consumption
of a variety of possible pollutants or materials and a specification of
the time dependence of those values. The number of different kinds of
sources and different materials indicates that a hierarchic list type of
data structure is most applicable to this purpose.
In connection with model sensitivity studies or studies of control
strategies, it will be desirable to be able to include the effects of
simulated sources, or real sources operating under simulated conditions
or under actual but very unusual conditions. All such sources can be
entered into the Master Inventory in the standard way, but identified by
an appropriate category name so that they can be selectively included
with or substituted for real sources in data files for model runs.
It is expected that most of the data will be entered in a batch
mode via punched cards or using key-to-tape machines. In either case,
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the hierarchic structure of the data items must be represented in the
character stream by additional delimiter characters. An extended format
for this purpose is described in Section E on extensions for batch data
entry; however, any existing system will have its own format for batch
entry. This format is sufficiently complicated that it is recommended
that the data be written by a skilled data clerk, who can transcribe
original data from an inspector's notes or from data collection forms.
This clerk should also be qualified to proof read data entries both for
syntactic correctness and for data sense. Data preparation forms can be
used to aid in the structuring of the data and to make it easier to use
the correct syntax and punctuation characters. However, it is not
possible to depend only on well-designed data preparation forms for data
quality, because the content as well as the form of the data must be
verified.
A data management system generally will check its own data entry
syntax, and should be used for this purpose. However, the internal
formats of some of the data elements, such as consumption data, will be
transparent to the system and will need to be tested by a separate
emission inventory verification program. The same program should also
verify whether material names are included in the material name
thesaurus. This thesaurus therefore should be kept in the form of a
computer reference file available to the data entry program as well as
in paper reference copies in the data preparation rooms. For example,
the substitution of the digit-'0' for the letter-'O' in S02 by a typist
can be detected this way. The same kind of verification should be
applied to all other data descriptors for which there is only a small
number of correct choices. The verification program should make output
listings of all items in which it detects errors in a form in which the
erroneous data component or syntax is identified. Although the
verification program could include facilities for user-entered
corrections, it will be more practical to use the editing facilities of
the data management system for this purpose, and this is recommended as
the preferred approach.
Data should be entered first into a preliminary data system file,
so that they can more easily be further verified by printing out the
entire content of a small data set in an expanded easily readable format
for proof reading and further correction prior to adding these items to
the main inventory file. Another reason for this preliminary stage is
that most data management systems perform a significant amount of
behind-the-scenes processing on new entries into a directly accessible
file, such as building address tables, hash coding computations, making
inverted lists of key data elements. The small preliminary file, on the
other hand, can be a simple sequential file with none of this extra
processing until after the data manager has determined that it is
accurate enough to merge it into the main file.
The retrieval facility of the data management system will be used
for the selection of groups of sources for which emission values can be
computed by the same model or are selected for production of a
particular data file for model validation or for editing and updating of
selected items. The retrieval of source items should be accomplished on
the basis of any appropriate data in the item, such as source category,
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pollutant, location, or status of previous computation of emissions.
The retrieval facility can also be used to produce special files
organized in terms of any user-specified parameters, such as a
pollutant, sources using fuel at greater than some rate, location. This
can provide for a variety of special applications without requiring
additional file programming. A sorting capability in the data
management system should be provided to order and index these files by
their content.
In very few cases will source data include measured values of
emission. For most actual sources, the emission must be calculated from
consumption or other source data. Formats are provided in this
inventory for both emission data and consumption data, using the same
data syntax and structure for both. For those sources for which there
are no direct emission data, emission must be calculated using emission
factors or emission models applied to consumption or activity data. The
emission factors have been compiled by EPA into a table as a function of
the Source Classification Codes (SCC) for a wide variety of industrial
processes and consumables.
After a set of new data source items has been entered, the next
logical operation is to calculate the emission values for those sources
that have no pollutant data entered. The results of these computations
can be appended to each source item as the content of a pollutant
component using the same time interval description for each source as
was used for consumption if only the emission factor is applied. If a
specific emission model is used, the time interval description also must
be generated by the model program.
The emission inventory software system will require emission model
programs that can access the consumption data element for each item,
refer to the SCC code for that material, look up the appropriate
emission factor in an auxiliary Emission Factor File maintained as a
direct access file on the system, and compute the emission values for
each of a prespecified set of pollutants. If the programs have access
to the data system for file writing as well as reading, they can then
update each item by adding a pollutant list component with an entry for
each pollutant to be calculated. One of these will be a "no model"
program that will only multiply consumption data by emission factors to
obtain emission values. These emission models will generally also
require input of ambient atmospheric data, which can be obtained from
meteorological data files based on the RAPS data network. These files
are not considered in this report, because they are not part of the
Emission Inventory system.
In this manner, the master emission inventory file will serve both
as a file of input source data and also as the repository for derived
emission values with no duplication of source information.
For the principal objective of the RAPS program the final function
is that of production of input data files for air quality models. In
general, the required emission data will be that for a specified
pollutant for a specified time interval or series of time intervals and
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also for specified positional coordinates. Many data management systems
have search and retrieval features for selecting data items that satisfy
specified data constraints or specifications. Some preliminary
selections may be made using the system facilities, such as selection of
a particular type of source, certain UTM coordinate ranges, and the
like. However, the time dependence data are coded in the emission data
element format in a special way, and a general system feature will not
be able to decode it.
If the general data management system includes the capability for
calling procedural language subroutines, such a subroutine can be
written to decode the time and emission data in the emission data
element and can be linked to the data system so that it will
automatically be invoked whenever the system is asked to search for
emission data within some particular time range. If that capability is
not available, then the data file production program must have a
subroutine included within it to perform the time interval selection
function.
After the items containing the desired data have been identified,
it is necessary to abstract the emission data for the particular
pollutant and write an input data file for the model program. The input
file must be written in exactly the format expected by the model; the
values must be in the units defined for that model program. Since the
model programs are written in Fortran, the data file must also be
written with the appropriate integer or floating point representation
for each quantity.
The generation of these input data files will require interface
programs designed to perform this task. These programs (or maybe one
general-purpose file-building program) must read the emission data
elements that are character-string data, decode them, compute new time
sequences if the model requires them, transform the string
representation of the emission value to internal numeric form, read the
name of the unit used and multiply the values by the proper conversion
factor, and write the file for the model.
Most of the air quality models operate on the basis of allocation
of emission values to a fixed geographical grid system. The data
interface programs will also have the function of translating the
geographical coordinates of point, area, and line sources in the
inventory into the appropriate grid units for the model.
The data file derived from the RAPS Emission Inventory can be
combined with similar data files derived from other source inventories,
such as NEDS, to obtain the most economical use of existing systems. It
is assumed that programs to generate input data files for atmospheric
models already exist in the NEDS system. If such programs do not exist,
then it may be more economical to transfer all the NEDS data relating to
the RAPS area into this RAPS inventory system.
The only sources that may be duplicated in both systems should be
those for which time-resolved data can be obtained. Since there are
expected to be not more than one or two hundred such sources, it will
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not be efficient to provide a program for computer translation of NEDS
to RAPS data. It will be cheaper to manually copy these source data,
especially since much of the data for each source will be new. If,
eventually, the use of the RAPS inventory is extended to cover much more
data from other areas, then the possibility of computer transfer of data
between the two systems should be re-examined.
Figure 3 shows a schematic of the inventory system.
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INPUT AND
OUTPUT
ORIGINAL
SOURCE DATA
ERROR
LISTING
COMMANDS
ERROR
MESSAGES
PROOF COPY
f
RETRIEVAL
SPECIFICATIONS
EDITING \
CORRECTIONS /
FILE INDEXES
f
SELECTED
DATA LISTINGS
MODEL
PARAMETERS
DATA FORMAT
SPECIFICATIONS •
PROGRAMS
DATA FILES
REFERENCE FILES
DATA
ENTRY
PRELIM.
SOURCE
DATA
VERIFICATION
THESAURUS OF
PERMITTED
IDENTIFIERS
DATA
MANAGEMENT
SYSTEM
PROGRAMS:
MERGE,
EDIT,
SELECT,
DELETE,
SORT,
OUTPUT
MASTER
EMISSION
INVENTORY
SELECTED
SUBFILES
EMISSION
MODELS:
"NO MODEL"
"A" - "Z"
EMISSION FACTORS
AND SCC CODES
\
DATA FILE
GENERATORS
INPUT DATA FILES
FOR AIR QUALITY
MODELS
SA-2579-8
FIGURE 3 SCHEMATIC OF INVENTORY SYSTEM
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D. Data Structures and Formats
1. Source Categories
The three principal categories of emission sources are point, line,
and area. Because they require sufficiently different identification
parameters, they should be filed by using different item data
structures. They can be stored in separate computer files or in the
same file in different item categories.
Point sources can be identified by the use of a group of
identification parameters selected from those given in the manual for
the NEDS inventory. Although this is a RAPS emission inventory, some of
the NEDS identifiers are included primarily to facilitate data
interchange between the two systems. The primary identifiers should be:
* Assigned source item number (sequence number assigned by
inventory system);
* Source category (e.g., power station, foundry, refinery);
* Source name (name of company, organization, or generic name
if it has no specific name);
* Plant No., Point No.;
* Street address (or nearest street identification);
* City, County, State, AQCR No.
The UTM coordinates are not included in the source identifiers of
point sources, because each stack of a multistack source will have its
UTM coordinate position in the data item. If some stacks at a plant
have different emission values or time sequences than others, more than
one data item will be needed to inventory that plant. The Point No. is
intended to identify such different sources within a plant.
The NEDS Source Category Code (SCC) is included as part of the
identification of each consumed or manufactured product, rather than as
a single-valued source identifier, because a source may utilize more
than one product. The code includes both the material and process
identification.
Location, source category, and sequence number identify area
sources. Predetermined area sizes or shapes need not be allocated. Any
closed simple area bounded by straight lines can be uniquely specified
by the coordinates of the intersections of these lines taken in order.
Thus the areas can be selected on the basis of convenience and
appropriate internal contents without size or shape restrictions. Area
sources can overlap; for example, an area identified for automotive
emissions may include or overlap an apartment house area. The
recommended identifiers for area sources are:
* Item number;
* Source category (e.g., automobiles, residential, dump);
* Area number (assigned sequence number);
* List of UTM coordinates of corners of boundary (starting
with most westerly or southwest corner and going clockwise);
* City, County, State, AQCR No.;
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* English language paragraph for general descriptive purposes.
This should always include a street or locality description
to supplement the UTM coordinate specification.
Identification of line sources will be similar to that for area
sources except for the significance of the UTM coordinate list. The
sequence of the UTM coordinates of the end points of a connected
sequence of straight lines approximating the position of the source will
locate each line source.
2. Data Item Formats
The items of the file will consist of point sources, area sources,
or line sources.
Many sources will have fuel-burning data or process rate data
instead of direct emission data. For some plants, the materials being
produced may be significant sources of air pollutants, and these
products can be included in the process category. Or, if data are
lacking on either, an activity may have to be identified to which an
average emission model can be applied (for example, automobile traffic).
An activity might be included for some point sources (like a freight
terminal), if the fuel or emission rates cannot be measured directly.
Thus, three different components of the source data that can be used in
combination for some sources are:
* Emission data
* Consumption and process data
* Activities.
In the case of consumables, the data element would describe rates
of consumption. For activities, the possibilities are so varied that no
single standard format for descriptive data is likely to be usable. One
example of an activity, automobile traffic, is discussed in the line
source category.
Except where direct emission data are available for a source, the
emission can be calculated from the consumption or activity data by
application of emission factors and emission models, and the result
entered into that source item in the inventory file as calculated
emission data.
a. Point Sources
Any point source may emit one or more of several pollutants of
interest. If data capacity is provided in each item for all possible
pollutants, the file structure will be inefficiently used (mostly
empty). A more appropriate structure would use list elements
implemented such that each pollutant or consumable actually included in
the item list is associated with a pointer to an associated data
element, each of which is a logical record of variable length. This
will permit efficient use of computer storage and efficient retrieval
and selection of data for construction of model input files.
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In some data reference systems, the system automatically maintains
the pointers on the basis of the data, linkages specified during data
entry. If the system does not provide internal pointers to associated
data elements, then explicit addresses (such as record number) aiuo* be
written in the file, and the user must maintain the necessary address
directory.
Figure 4 illustrates a sample point source item.
SOURCE IDENTIFIERS
ITEM NO., CATEG.,
NAME
PLANT NO.,
POINT NO.,
ADDRESS,
CITY, CO., STATE
AQCR NO.
STACK
1
POINTERS
CONSUMPTION
COAL
SCC CODE
POINTER
OIL
SCC CODE
POINTER
1
UTM COORD. [ HEIGHT (m)
EXIT AREA (SQ. m)
EXIT VEL (m/s)
TEM (DEC K)
[ UNIT NAME | UNIT FACTOR [ COAL CONSUM. DATA | METHOD j CONF. LEV.
UNIT NAME
UNIT FACTOR | OIL CONSUM. DATA [ METHOD | CONF. LEV.
UNIT NAME
UNIT NAME
SO2
POINTE
UNIT
POLLUTANTS
HC
R POINTER
CO
POINTER
FACTOR
1 UNIT
FACTOR
SO2
EMISSION DATA
METHOD | CONF. LEV.
HYDROCARBON EMISS. DATA
UNIT NAME
| UNIT
FACTOR
CO EMISS. DATA
METHOD
METHOD
CONF. LEV. |« 1
CONF. LEV. | COMMENT
SA-2579-9
FIGURE 4 SAMPLE POINT SOURCE ITEM
The Stack component can describe any number of stacks at one point
source. The element will contain the number of stacks, and a pointer to
a data element for each stack containing a standard sequence of
parameters: UTM coordinates, height(meters); exit area(square meters);
exit velocity(meters/second); gas temperature(deg K).
If each of the second level data elements, as shown in the example,
consists of a fixed sequence of data components, with only the last one
(comments) optional, then the separate components do not require system
identifiers.
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b. Area Sources
Because the format of an area or a line source will have to provide
for an indefinite number of UTM coordinates, these are placed in a
separate data element. The area and line sources are defined in terms
of a single principal pollutant; thus only that pollutant is identified,
instead of a list of them as in a point source item. The emission of
that pollutant will usually have to be computed from consumption data by
means of standard emission factors, so the consumption data should also
be included in each area source description in the inventory.
Figure 5 shows a sample area source item.
SOURCE IDENTIFIERS
ITEM NO., AREA NO.
CATEGORY
ETC.
UTM COORD.
POINTERS
, H2/V2,
[H1/V1, H2/V2, ]«•-
DESCR. COMMENT
LOCATION
APPROX AREA
POLLUTANT
NAME
POINTER
UNIT NAME } UNIT FACTOR [ EMISSION DATA [ METHOD CONF. LEV. |
STACK
HT.
FLOW
TEMP.
SA-2579-10
FIGURE 5 SAMPLE AREA SOURCE ITEM
This example shows how two separate areas can be included in one
area source item. An area source item can describe a set of
non-contiguous areas to avoid duplication of the complete description of
each of a set of area sources having identical emission characteristics.
The number in the UTM Coordinate component specifies the number of
different areas described in the item. For each area, there will be a
separate data element containing its coordinates.
The Stack data in an area source item refers to an equivalent stack
for purposes of dispersion modeling. The stack area is assumed to be
the whole source area; volumetric flow rate is estimated from fuel or
other consumption data. The consumption data component is not shown in
this sample; it is structured the same as the consumption component
shown in the point source sample.
Only a very slight change in format is needed to use a single area
source item to describe the emission of more than one pollutant, as in
point sources. Therefore, although we are here suggesting a single
pollutant per area source item, that choice can be made by the emission
inventory manager at the time that the areas are defined.
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c. Line Sources
An important type of line source is automobile traffic. The format
of line source items will differ from area sources primarily in the
significance of the UTM coordinate list that will be the endpoints of
straight-line segments approximating the road route. The category name
"Road" can identify this type of source. The activity name can be
"Automobile", and additional elements should be appended to the item
under the names:
* Traffic (average vehicle flow, average speed, vehicle mix)
* Way (type, dimensions).
3. Other Data
File data describing the composition of fuels or other consumed
materials may be useful in the inventory, if they are variable for
different sources. A data element for composition can be attached to
any entry in the consumption list by a second pointer. The pointer
identification is: Pointer 1 to the consumption data element, Pointer 2
to the composition data element (if any).
The composition element can be a paired list of impurity element
symbol or compound formula and weight fraction for as many constituents
as are signifacant. The element or formula name can be terminated by a
blank, and each pair can be separated by a slash. A sample is shown
below.
S .02 / As .005
Another category of data that may be needed in some source items is
certain special emission factors, such as those for nitrogen oxides, for
sources where there are actual measurements of the factors. The
measured emission factors can be added to the data structure by a third
pointer under the appropriate consumable. The emission factor data
element will have to identify each pollutant calculated by each factor.
The data can be entered in the same format as that for composition,
above, except that the identifiers will be pollutants and the numerical
values will be emission factors.
Other source data may be added to any item by use of a labeled
pointer to an extension record. Any number of such extension records
may be attached to any item, but no additional storage space need be
allocated to items that do not include such data.
4. Data Element Formats
Most of the data elements will be numeric or text constants and
will require very little formal formatting. Real numbers must be
written in standard notations such as decimal or exponential (ie. 94.27,
1.03 E-4). Text elements can be written free-form. It is often
convenient to have one printing character such as "#" or "$" defined as
a text element terminator. A continuation character can also be defined
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to enable the entry of multiline text elements. On some systems, the
linefeed key is most effective for this purpose, because it initiates a
new line at the terminal.
The emission and consumption data elements, on the other hand, can
have a large variation in amount of data and should allow for different
levels of time resolution. These therefore require format rules to
provide exact identification of descriptors and data.
Since the data elements for consumption data may deal with a large
variety of consumable products, a single unit of measure (e.g.,
grams/second) may not always be appropriate. Therefore, the first
component of the data element is the name of the unit of measure and the
second is the conversion factor to convert that unit to the standard
inventory system unit.
For those sources for which a series of time-sequence measurements
are available, it will be useful to describe the emission or consumption
by those measurements. On the other hand, there will be many sources,
especially area sources, for which only approximate estimates can be
made, usually on the basis of some repetitive patterns over time. The
format rules described below can be used to describe either a
consecutive time sequence or repetitive time patterns or any convenient
combinations of both. This component of the data element may be very
long (up to 75,000 characters), but it will still be one logical record.
Each emission or consumption data element also includes a component
for Method, for Confidence Level, and an optional component for any
further comments that may be considered useful to add for documentation
of the data. Method will generally be described by means of a concise
standard descriptor or abbreviation of the name of the method used to
obtain the data. For example, "EF" can identify emission values
calculated from consumption data by application of the standard Emission
Factor table. Confidence level should be an estimate of the probable
error of the values given, based on a knowledge of the errors of the
method of measurement, or the reliability of the source data.
The emission element format is the following:
Data Element = Unit name + Unit factor + Data Part + Method +
Conf. Level + Comment(optional)
Data Part = Time Part/Data Part or Value
Time Part = Time Code:Time Range or (Time Part, Time Part, ...)
Time Range = Time Identifier or Time Identifier-Time Identifier
Time Code = A, M, W, D, or H
This definition is hierarchically recursive, so that nested data sets
can be constructed by combination. For example,
M:ll-2/H: means Monthly groups, Nov. to Feb. range, hourly data.
Because there are no time units specified between months and hours, it
also means that the hourly pattern repeats daily during that range of
months.
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Operator characters are:
: means a 'range/data' sequence follows;
/ means a datum or a data set follows;
, is a separator between components of a simple data set;
; is a separator between data sets of a multiple data set;
- is a connector between first and last time identifiers
(inclusive) of a time range;
( ) enclose a multiple group of time pattern descriptions.
Some examples are:
M: 1/1.4,2/1.7,3/1.9, 12/1.3; is a set of values for each of
the 12 months.
H: 1/9.7,2/10.3,—--24/8.2; is a set of 24 hourly values, for some
"average" day.
(M:ll-3/D:7-l/H:21-7, M:6-7/D:l/H:22-6)/1.4; specifies a repeating
pattern of 8 pm to 7 am on Saturdays and Sundays during November
through March, and 9 pm to 6 am on Sundays during June and July,
with an emission value of 1.4 units during each of these
intervals.
This looks complicated, but the notation is self-descriptive once
the user becomes familiar with the meaning of the punctuation symbols.
It satisfies two important criteria; it can concisely describe a large
variety of possible time-dependent data, and it can be read by machine.
There appears to be no way to design a preprinted, "fill in the
blanks", form for data to be entered in this format. The reason is that
the format is designed to provide a concise method of describing any
sequence or repetitive pattern of time-dependence using several
different time units, so that the syntax must be dynamic as in a
language, rather than a static syntax that can be preconstructed .
For each time interval code, the total period covered, and the
notation is shown in Table 9.
Table 9
Time Interval Codes and Ranges
Time Interval Code Period
Annual A: range in calendar years to which the data
applies (e.g., 69-73)
Monthly M: 1 year 1-12 values
Weekly W: 1 year 1-53 values
Daily D: 1 week 1-7 values; 1=SUN, 2=MON, 3=TUE,
4=WED, 5=THU, 6-FRI, 7=SAT
Hourly H: 1 day 1-24 values
All the time units refer to inclusive intervals (i.e., M:l-2 means
January and February). Counted hours, not clock time, is used (i.e.,
H:7-8 means from 6 am to 8 am). Zero (0) is avoided because it leads to
confusion in data entry and because it is not common usage. Since the
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week is defined to be Sunday through Saturday, a year will generally
begin and end with a partial week, so that there will always be 53
identifiable weeks in every year.
The emission data should correspond to the average emission during
the specified interval, for a current example of the period.
Multi-nested intervals can be used where sufficient data exist and the
emission changes considerably over the larger time intervals. For
example, hourly emission data for an average day during each of several
months may be useful, if the emission rates change considerably through
the year.
The large choice of time intervals permits the entry of detailed
time-dependence of emission if the information is available and less
detailed averages when that is all that is known. If data are available
only for portions of a data set, only those elements of the set need be
identified and entered in the inventory because the format includes a
numeric identifier for every time interval for which data are entered.
Since M: and W: interval sets both cover a pericd cf one year,
they are mutually exclusive. The D: and H: intervals can be used with
any one of the first two in a multiple set, and D: and H: can also be
combined. By the use of these formats, each emission data element can
contain as few as one value, averaged over a typical year, or a value
for every hour of a year.
Although it uses a concise notation, this format is sufficiently
redundant to permit recognition of many entry errors. Manual
proofreading will detect many common typing errors, and the use of the
computer for checking the internal consistency of data and the syntactic
correctness of the data elements will also be feasible.
5. Summary Listing
The following listing summarizes the form and arrangement of
identifiers in a source item. Generally, most of the entries will be
data relative to higher level identifiers and an identifier for lower
level data. They will only be entered into an item if they represent
actual information so that only a small fraction of the entries in this
table would occur in any single inventory source item. Mary of the
entries in the table (e.g., names of pollutants) represent only a few
examples of an unlimited set of possibilities and are included to
display their arrangement and notational style.
100
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Point
Identifiers
NO. Item number. For file maintenance.
Category Type of source
Name Ordinary name of facility
Street address
City, County, State, AQCR No.
Pollutants
S02 Chemical formula or abbreviated name
Unit Emission rate data Method C.L. Comment
HC
Unit Emission rate data Method C.L.
Consumption
Coal SCC code
Unit Consumption rate data Method C.L.
Activities
Trucks
Count/day
Helicopters
Count/day Comment
Stacks Count
UTM coord. Height Exit area Exit velocity Temp.
Area
Identifiers
NO.
Category
Area Number Assigned for map reference
City, County, State, AQCR No.
UTM Coord Area count
Coord list
Description of area
Pollutant Heat
Unit Emission rate data Method C.L.
Consumption Fuel Gas SCC code
Unit Consumption rate data Method C.L.
Activity
Autos
Flow Speed Veh. mix
Population
Persons Housing units Hous. mix
Line
(See Area—elements are essentially identical)
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E. Format Extensions for Batch Data Entry
To prepare the data items off-line for batch entry, it is necessary
to provide additional format conventions to indicate the logical
boundaries of data components and the hierarchic level structure of each
data item. If an existing data management system is obtained, it will
have its own formats for this purpose.
If a data management system is expected to be used to maintain the
RAPS Emission Inventory, but the scheduling of data processing requires
that data be prepared for machine entry before the entry formats for
that system are available, then this format can be used to enter the
data onto machine-readable media, and the formats converted later to
those required by the system. The special-purpose conversion program
will be a temporary program used only once to process the data
previously entered. After that, all new data will be prepared directly
in the system format.
This extended format can also be used for the purpose of making
sequential files for temporary use without the use or support of a
hierarchic data management system. For this latter purpose, it is also
useful to include a code to show whether a given data item is a current
one in the file or has been replaced by an updated version, because the
actual deletion of items and compaction of the file will require
processing by a file maintenance program.
The characters chosen as delimiters for this extended format have
not been used for the internal data formatting. These data structures,
together with all format defining characters, can be implemented within
the conventional 64-character ASCII subset that is available on most
keyboard data entry devices and printers:
[n Left bracket, followed by an integer, indicates
opening of a hierarchic data level, n = data level
n] Close data level n, and all inner levels.
< > Delimiters of data element names
# Pound sign - data element separator.
Each new data item will begin with [1. To indicate the status of
the item, the integer 1 can be changed. [1 means that the item is a
current one in the file; [0 means that it has been superseded. A
partial example of a data item shows the manner of use of these format
controls:
[1 [2 # 234# MFG# UNIVERSAL HORSE-COLLARS* 999 WHINNY ST.,
ST. LOUIS, M0.# 70# 2] [2 # S02 [3# G/S # M:l/1.4,
2/1.7, 3/1.9 12/1.3# 3] # HC [3# KG/HR # A:69-72/19.5# 2] [2
# 1 C3# 321.0,963.1# 22# 0.83# 35# 300# 13
The denotes this as a point source (not needed unless all types of
sources are included on one file). The denotation is the
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abbreviated name for Source Identifiers, a level 2 data structure.
The denotation is the name of the next level 2 structure,
but it contains two level 3 data sets. The terms S02 and HC are not
names of data structures, they are data, but a level 3 data structure
depends from each of them. is the name of another level 2 data
component, containing the parameters of each stack.
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F. Implementation
The next steps in the implementation of this emission inventory are
the following:
* Specify the set of point sources, area sources, and line sources
in the St. Louis metropolitan area that will be used as the
initial basis for the RAPS emission inventory.
* Assemble the data applicable to these sources from other
inventories and from other data sources.
* If a general-purpose data management system is not available,
then write a system of programs for file definition,
data entry, and updating of the emission inventory.
* Specify data entry procedures based jointly on the formats
described in this report and the capabilities of the system.
Any conflicts must be resolved by changing the format structures
or the choice of punctuation characters, as necessary.
* Enter a small set of data to verify the correctness of the
structure and the formats of the data base.
* Enter and proof-check the entire initial data set.
* Write and test the programs for
- Computing emission values from consumption data;
- Accessing data items on the basis of specified ranges
of time, emission values, or source locations;
- Constructing input data files for atmospheric models.
* Produce input data files for models using either data system
procedures or separate computer programs.
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G. Specifications for Computer Programs
1. Data Management System
These systems usually consist of a group of interconnected
programs. This specification concerns only the external features
related to the operation of the RAPS Emission Inventory.
* Data bases of 20 million character size, containing up to 2000
items, each including 10 to 100 data elements, in up to i
hierarchic levels.
* Batch data entry from punch cards, magnetic tapes, or disk
files.
* Capability for defining hierarchic data structures, including
list components.
* Symbolic naming of data components.
* Unrestricted lengths of lists and records.
* Retrieval of items by content or partial content of any data
component, including logical combinations of retrieval
specifications.
* Editing and updating individual items or groups of items.
* Interface for external programs to access data items and data
components in the files.
* (Optional) Capability of calling special subroutines within the
data system for operations requiring recognition of specialized
formats within a data component.
* (Optional) Construction of data files in specified formats for
use by external programs.
* (Optional) Sorting and indexing selected subfiles by content.
2. Input and Output Subroutines for Emission Model Programs
These are intended to be standardized subroutines, callable by any
emission model program to provide access to the emission inventory file.
The input subroutine must:
- Access source item via file address;
- Read consumption data component from source item;
- Extract SCC code, unit, time ranges, and consumption
values;
- Read emission factor for SCC code from Emission Factor
File;
- Convert values to the units required by the model.
The output subroutine must:
- Generate calculated emission data element for the pollutant
and for the time ranges defined by the model, in the format
required by the inventory;
- Append the emission element to the same source item from
which the consumption data were read.
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3. Verification Program
This can operate in three different ways. It can be part of a data
entry program; it can be a separate program to correct preliminary data
files prior to merging into the Master Inventory; or it can operate
within the data management system on data sets retrieved by the system.
It should perform these tasks:
* Verification of syntax of formatted data components for
logically correct usage of operator and delimiter characters.
* Comparison of material names with those in a thesaurus file to
verify that every name is one of an allowed set.
* Printing listings of erroneous items with bad elements marked.
4. Data File Generator Program
This capability may be included in a complete data management
system. It should do the following operations as a minimum, although
more extensive formatting capabilities may be useful.
* Accept user or file input of specifications for the exact
sequence of data values and data representations required by an
external program.
* Take its input from a subset of the emission inventory file
selected by the data system and extract data fros data
components (the input subroutine for the emission model can be
used for this purpose).
* Convert coordinate locations to the grid identification code
used by the model.
* Interpolate emission values, if necessary, to the time intervals
required by the model.
* Write the output file on the disk, magnetic tape, or punch
cards, as specified.
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VI TASK D: SURVEY AND EVALUATION OF EXISTING EMISSION INVENTORY DATA
A. Introduction
Air pollution studies have been conducted in the Saint Louis area
for many years, and several emission inventories have been developed.
To avoid any unnecessary duplication of effort, it was important to
review the existing emission inventories, to compare their content with
the information needed for RAPS, and, if they appeared to contain useful
information, to transfer this information to the RAPS inventory.
The City of Saint Louis has been involved in air pollution control
since its first antismoke ordinance was passed in 1893. Smoke and fumes
were, of course, the first targets of pollution control efforts; even so,
50 or 60 years passed before effective pollution control measures were
applied to the largest pollution sources of the Saint Louis area, the
power plants and the steel mills. In 1964, an "interstate Air Pollution
Study, Saint Louis-East Saint Louis Metropolitan Areas" was undertaken
by the U.S. Public Health Service. Questionnaires were sent out to de-
termine fuel use and combustible waste disposal practices in the area as
well as manufacturing activities. A revised emission inventory, still
based on 1963 data, was published in December 1966 as Phase II of the
interstate study.
After the Metropolitan Saint Louis Interstate Air Quality Control
Region had been established, the first comprehensive inventory v.as taken
in 1968, to serve as a basis for the Implementation Planning Program (IPP)
This inventory included the jurisdictions of Jefferson, Saint Charles,
and Saint Louis Counties and Saint Louis City in Missouri; and Madison,
Monroe, and Saint Clair Counties in Illinois. Franklin County was added
in 1969. Four additional Illinois counties, Bond, Clinton, Randolph,
and Washington, were added to the air quality control region in 1971.
Only sulfur dioxide (SOg) and particulates were included in the initial
IPP inventory. The more recent inventories contain estimates of carbon
monoxide (CO), hydrocarbons, and oxides of nitrogen (NO ) as well. The
X
following inventories are described in some detail in Section II :
• IPP Emission Inventory-1968
• IBM Emission Inventory-1970
• DAQED Emission Inventory-1971
107
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• NATO Emission Inventory-1971
• NEDS Emission Inventory-1973
In addition, the following traffic and transportation inventories
are discussed :
• Streets and highways
• Railways and vessels
The emission inventories in current use by the Missouri and Illinois
regulatory agencies were recently (Summer 1973) acquired and transferred
to the NEDS files under another contract; therefore, they are not con-
sidered separately.
Contact was also established with research groups, such as the
Metromex consortium, active in the Saint Louis area. However, these
groups do not have any emission inventory data. Nor were any data avail-
able from industrial sources, such as the Waste Control Council.
The data available from the various air pollution control organiza-
tions deal with pollution arising from industrial, commercial, residential
and traffic activities. There appear to be no direct emission data on
such transient sources as agricultural spraying or fugitive dust.
B. Existing Emission Inventories for the Metropolitan Saint Louis
Interstate Air Quality Control Region*
1. IPP Inventory-1968
The Implementation Planning Program (IPP) emission inventory
covering sulfur dioxide and particulate matter in the six counties and
the City of Saint Louis originally making up the Air Quality Control
Region was assembled by the EPA Region VII office in 1968. This inven-
tory was used by Argonne National Laboratory as a basis for diffusion
modeling in the development of the IPP for Illinois and Missouri.
The 1968 inventory lists yearly emissions of SO and particulates
from 116 major spot sources and 316 area sources, on the basis of ques-
tionnaires and letters of intent filled out by local agencies. Typical
printout pages are shown in Figures A-l and A-2 in Appendix A.
*
The pioneering interstate study by U.S. P.H.S. is mentioned above on
page 107.
108
-------
2. IBM Inventory-1970
In 1970 the International Business Machine Company (IBM) compiled
an emission inventory for the Office of Air Programs, This inventory
included only three Missouri counties (Jefferson, Saint Charles, and
Saint Louis) and Saint Louis City; therefore, it is not directly compar-
able with other inventories in the area.
Though it covers a smaller area, the IBM inventory is consider-
ably more detailed than the IPP inventory. It contains data on 105 point
sources, giving their location; stack parameters; fuel consumption data;
and calculated S00, particulate, CO, hydrocarbon, and NO emissions in
£ A.
tons per year (Figure A-3, Appendix A). From these data, emission
inventories were calculated for each of the political jurisdictions.
In the IBM inventory, area sources were treated by counties.
Each county was divided into source areas, varying in size from 4 to
700 square kilometers. For each area, emissions of particulates, SOp,
CO, hydrocarbons, and oxides of nitrogen were calculated for domestic,
commercial, and industrial sources, as well as for incinerators and motor
vehicles. A summary of the data is shown in Figure A-4, Appendix A.
3. DAQED Emission Inventory-1971
The former Division of Air Quality and Emission Data (DAQED)
acquired an emission inventory for the Saint Louis area by using the
rapid survey technique described by Ozolins and Smith.13 This technique
is based on information available in most urban areas; it does not entail
extensive surveys or sampling procedures. The results are, however,
considerably more accurate than those derived by gross approximations
based on published production or consumption data.
The inventory included S02, particulate matter, CO, hydrocarbons,
and oxides of nitrogen. The data are given in metric units. Summary
information on population, area, some background information, and air
quality data, is shown in Tables 15 and 16.
4. NATO Emission Inventory-1971
The North Atlantic Treaty Organization (NATO) emission inventory
for the Metropolitan Saint Louis Interstate Air Quality Region was pre-
pared in 1969 to serve as a comparison and model for the development of
emission inventories in Frankfurt, Germany, and Ankara, Turkey. The so-
called NATO Emission Inventory covered the following pollutants:
109
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• Sulfur dioxide
• Particulates
• Carbon monoxide
• Hydrocarbons
• Oxides of nitrogen
Essentially, this inventory was an updating from the IPP and DAQED emis-
sion inventories of all area and major point source emissions. In June,
1971, the NATO inventory was further updated by using new emission factors.
The NATO inventory is the most versatile of the inventories
discussed so far. The data are grouped in the following 23 sections:
(1) Summary if Air Pollutant Emissions, Study Area.
(2) Summary of Air Pollutant Emissions, Saint Louis City,
Missouri.
(3) Summary of Air Pollutant Emissions, Saint Louis County,
Missouri.
(4) Summary of Air Pollutant Emissions, Jefferson County,
Missouri.
(5) Summary of Air Pollutant Emissions, Saint Charles County,
Missouri.
(6) Summary of Air Pollutant Emissions, Franklin County,
Missouri.
(7) Summary of Air Pollutant Emissions, Madison County,
Illinois.
(8) Summary of Air Pollutant Emissions, Monroe County, Illinois,
(9) Summary of Air Pollutant Emissions, Saint Clair County,
Illinois.
(10) Summary of Air Pollutants from Point Sources.
(11) Summary of Air Pollutants from Area Sources.
(12) Combustion of Fuels, Stationary Sources.
(13) Transportation Sources.
(14) Process Losses.
(15) Emissions from Evaporative Losses.
(16) Emissions from Solid Waste Disposal.
112
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(17) Point Source Emissions.
(18) Area Source Emissions (272 grids).
(19) Annual Fuel Consumption (by counties).
(20) Housing Units (by jurisdictions).
(21) Population, Housing Units (by grids).
2
(22) Emission Densities (tons/km ).
(23) Flights per year (various airports).
Samples from the inventory are shown in Figures A-5 through A-10,
Appendix A.
5. NEDS Inventory-1973
The inventories discussed so far show the gradual evolvement
of the techniques for collecting and processing inventory data. The
earliest inventory, the IPP inventory, contained information on only two
pollutants, sulfur dioxide and particulates, while subsequent inventories
include also data on carbon monoxide, hydrocarbons, and oxides of nitro-
gen. The data collection methods have also become more reliable. For
the IPP inventory, information on area source emissions was obtained
from gross estimates on fuel delivery and consumption for the entire
area under study. The use of the rapid survey technique for the DAQED
inventory, which breaks down the information into geographic areas of
the community and makes use of formulas using degree-days (a unit repre-
senting the deviation from the mean ambient temperature—65°F—for one
day), the number and size of dwellings, and other refinements, provided
considerably more accurate estimates of emissions from area sources.
Similar techniques were used for all subsequent inventories.
A comparison of the presentations of data shows an increasing
degree of sophistication and usefulness indicated by the various ways
in which the data are broken down and presented, for example, in the
NATO inventory.
The actual emissions of pollutants shown in the tabulations
have been continuously updated to reflect current practices as new sources
came into being or to reflect the installation and effect of control
equipment. For this reason, the inventories currently maintained by the
local and state regulatory agencies, such as the Saint Louis City or
County Air Pollution Control Office, are the only ones which can be used
for the purposes of RAPS. The data in these inventories, some of which
113
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are in the form of computer printouts (others are on Inspectors' Report
Sheets), have recently (Summer 1973) been acquired and entered into the
National Emission Data Systems (NEDS) inventory. The characteristics of
this inventory system, presented in APTD-1135, "A Guide for Compiling a
Comprehensive Emission Inventory,"14 are discussed below.
The NEDS was created by the EPA Office of Air Programs to pro-
vide a uniform format for source inventory data on a national scale. It
consists of a number of separate files suitable for computerized process-
ing and has provisions for storing data for both point and area systems
as well as separate files containing various input/output, maintenance,
and editing programs. The concept is shown in Figure 6.
The system distinguishes between point sources (emitting at
least 100 tons per year) and area sources for the primary pollutants:
particulates, S02, CO, hydrocarbons, and NO . The decision to designate
sources emitting more than 100 tons per year of primary pollutants as
point sources is based on the estimate that a 100-tons-per-year source
would produce, under average meteorological conditions, an ambient con-
centration of about 5 to 8 micrograms of pollutant per cubic meter.
This is about 10 percent of the secondary annual federal air quality
standard for SO or particulates. (Page 10-2 of APTD-1135.14)
In addition, about 70 compounds or categories of industrial
sources are designated as point sources regardless of quantities of
emissions.
A point source is defined, therefore, as any emitting point or
plant or facility whose summation of emitting points totals 100 tons or
more per year of any one of the five primary pollutants: CO, SO , NO ,
tL X
particulates, or hydrocarbons; or as any of the pollutant sources listed
below, regardless of quantity of emissions.
• Chemical process industries: adipic acid, ammonia, ammonium
nitrate, carbon black, charcoal, chlorine, detergent and
soap, explosives (TNT and nitrocellulose), hydrofluoric acid,
nitric acid, paint and varnish manufacturing, phosphoric
acid, phthalic anhydride, plastics manufacturing, printing
ink manufacturing, sodium carbonate, sulfuric acid, synthetic
fibers, synthetic rubber, terephthalic acid.
• Food and agricultural industries: alfalfa dehydrating,
ammonium nitrate, coffee roasting, cotton ginning, feed and
grain, fermentation processes, fertilizers, fish meal proces-
sing, meat smoke houses, starch manufacturing, sugar cane
processing.
114
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EMISSION INVENTORY
DATA SYSTEM
POINT SOURCE FILE
EMISSION CALCULATION
PROGRAMS
AREA SOURCE FILE
INPUT, OUTPUT
PROGRAMS
HAZARDOUS POLLUTANTS
SOURCE FILE
AREAWIDE INVENTORY
PROGRAMS
EMISSION FACTOR FILE
IPP MODELING CONVERSION
PROGRAM
GEOGRAPHICAL ID FILE
AREA SOURCE GRIDDING
PROGRAM
CONTROL EQUIPMENT
ID FILE
TREND PROJECTION
ANALYSIS PROGRAMS
IPP PROCESS ID FILE
POPULATION DATA FILE
SOURCE: "Guide for Compiling a Comprehensive Emission Inventory,"
Environmental Protection Agency (1972).
SA-2579-11
FIGURE 6 EMISSION INVENTORY SYSTEM CONCEPT
115
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• Metallurgical industries: aluminun ore reduction,
smelters, ferroalloy production, iron and steel ri
smelters, metallurgical coke manufacturing. ri:;c i
metals industries); aluminum operation?, i>rass -nc '". •.':::'<:
smelting, ferroalloys, gray iron foundries ler.c f!--Itinc,
magnesium smelting, steel foundries, zinc proce- = .--_ .-
(secondary metal industries).
• Mineral products industries: asphal" rcx-ii;^, ;.?C::P:TIC
concrete batching, bricks and related cia^- rer rac tor i e= .
calcium carbide, castable refractories, Cerent, ceramic
and clay processes, clay and fly ash ~in:erinc, coc.1 '.lean-
ing, concrete batching, fiberglass manui scturi ns, :nt manu-
facturing, glass manufacturing, gypsum '"animf actr.rir.g, li~e
manufacturing, mineral wool manufacturing, paperboarc rani;-
facturing, perlite manufacturing, ohnsph.-. t e rock nre::arat ion,
rock, gravel, and sand quarrying snc :?r'-ce?5ir.g
• All petroleum refining and petrochemica:
• All wood processing operations.
• Petroleum storage (storage tanks and '--'j'.
• Miscellaneous: fossil fuel stea- elect
municipal or equivalent incinerators, o
• Hazardous pollutant sources.
Another category of point sources,
emissions, are pollutants designated as hazard-"j?,
still in preparation.
The location of all point source^ i? ->.=
Transverse Mercator (LTM) coordinates. This -v=-e
Armyc provides projections of square grid zor.ts
ing units. The system has the advantage of contir.
the country, and it is rapidly becoming the acerptr
coordinates for a body of technical information.
The NED System contains other files taesi'
These are
• The Population Data File, \\hich cor.t^
as number of household units in an ar-
facturing employees, number of re*a:.
ments. This information is uccr - <-, c
116
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where no direct data are available, as for example, emissions
resulting from solid wastes, fuel consumption for space
heating, and others.
• The Emission Factor File, which contains conversion factors
that provide an estimate of the production of a given pol-
lutant by a process or activity. For example, it has been
estimated that two pounds of solvent per person per year are
emitted from dry cleaning plants in moderate climates. In
conjunction with appropriate population data contained in the
Population Data File, the total amount of hydrocarbons per
unit area can thus be calculated. The most complete collec-
tion of factors has been published by EPA in the Compilation
of Air Pollutant Emission Factors (AP-42).
• The Control Equipment File, which contains information on
types and efficiencies of various control devices.
As indicated in Figure 6, the NED System contains a number of
programs; these include not only "housekeeping" programs, such as those
for input and output, editing, and emission calculation, programs per-
mitting outputs by area, source classification, and pollutant, as well
as conversion programs selecting data for implementation planning pro-
grams and trend analysis.
The system is designed to provide data on annual emissions of
pollutants; estimates of shorter emission rates—weekly, daily, or hourly—
can be calculated automatically by applying appropriately resolved pro-
duction throughput, or other control data to the base emission figure.
Such an approach can provide the time resolution necessary for RAPS where
no actual hourly measurements are available. Typical printouts are shown
in Figures A-ll and A-12 in Appendix A.
Table 17 shows a comparison of the various emission inventories.
Only limited conclusions can be drawn from a comparison of these data.
The data in the IBM inventory, covering a much more limited area, are not
comparable at all; nor do the other inventories cover exactly the same
territory. The data shown for the NEDS inventory have been adjusted to
include only the counties included in the two NATO inventories; thus,
these three inventories are directly comparable. Good agreement is shown
for sulfur oxides (if a correction of about 100,000 tons per year,
corresponding to the SO emissions of Franklin County, is added to the
IPP and DAQED figures). The figures for the total emission of partic-
ulates and carbon monoxide are much higher in the NEDS inventory than in
the older inventories; the emission values for hydrocarbons and nitrogen
oxides again show quite good agreement (the contribution from Franklin
County is fairly small).
117
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The inventories are too close in time to allow any clear trends
to show up. The differences are probably better viewed as examples of
changes produced by better inventory techniques. Even so, closer examina-
tion of NEDS data shows examples of values that suggest the desirability
of rechecking the data; for example, a CO emission of 1,340,000 tons from
a single blast furnace or evaporation of 14,000 tons from a single tank
suggests a check. The former amounts to almost 35 percent of the total
estimated emission of carbon monoxide for the air quality control region;
if correct, it will certainly affect the view of carbon monoxide as a
pollutant whose origin is primarily related to traffic sources.
As the NEDS inventory represents the current understanding of
emissions in the Saint Louis area, it could and should be used for plan-
ning purposes. For example, it can serve as a basis for the determination
of the number of point sources that should be monitored for the RAPS
emission inventory, since only the most important point sources can be
monitored. A review of the point sources listed in the NEDS inventory
shows the breakdown of point sources by size in Table 18 and Figure 7.
Table 18
NUMBER OF POINT SOURCES
Pollutant
Particulates
SO
X
NO
X
HC
CO
Emission Category
(tons/year)
>10*
271
345
352
269
93
>io2t
99
184
111
99
31
>103t
28
67
24
23
11
>104t
6
13
7
1
6
Data from NEDS printout of 19 December 1973.
some sources not in earlier printouts.
T>ata from NEDS printout of 28 August 1973.
Includes
Both of these figures are erroneous (Personal Communication, Illinois
Environmental Protection Agency, December 1973).
119
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120
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This information is even more relevant if it is arranged to indicate what
percentage of total emissions can be evaluated by monitoring a given num-
ber of sources. By referring to Figures 8 through 12, we see that we can
monitor, say, 90 percent of all emissions from point sources greater than
1 ton/year by observing about 40 sources for SOg, about 5 for CO, 50 for
particulates, and 100 for hydrocarbons; oxides of nitrogen, however, would
require the monitoring of 330 sources to achieve the 90 percent level.
Alternatively, we could decide to use an emission of 100 tons per year
as a cutoff point. In that case, 184 sources for S02, 31 sources for CO,
99 sources for particulates and hydrocarbons, and 111 sources for NO
should be monitored. Table 19 summarizes.
As mentioned above (page 114) the 100 ton-per-year break point
is consistent with a 10 percent sensitivity for modeling or measuring con-
centrations near the levels of the national secondary air quality standards.
While this is probably equally suitable for the modeling verification
studies of RAPS, the sensitivity of such multisource models to this break
point should be investigated by specific studies as recommended above on
pages 48-49.
Table 19
NUMBER OF SOURCES MONITORED
Pollutant
S°2
Particulates
CO
Hydrocarbons
NO
X
Type of Inventory
90 Percent of
Emissions from
Point Sources
40
50
5
99
330
100 Tons
per Year
Inventory
184
99
31
99
111
The above data can also be used to evaluate the relative im-
portance, and thus the appropriate treatment, of area sources.
For example, if we assume that all sources emitting more than
100 tons per year of any pollutant will be monitored directly, the remain-
ing point sources (that is, those emitting between 1 and 100 tons per year)
121
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f < 10,000 TON/YEARS
70
60 -
50 r< 100,000 TON/YEARS
40
30
20
10
OL
TOTAL AQCR
ALL POINT SOURCES
P.S. > 100 T/YR
P.S. > 1000 T/YR
P.S. > 5000 T/YR
P.S. > 10,000 T/YR
P.S. > 100,000 T/YR
TOTAL AREA SOURCES
NUMBER
SOURCES
358
184
67
26
13
4
TONS/YEAR
1 ,233,805
1 ,220,897
1,182,909
1,144,906
1,060,480
990,500
608,000
12,908
PERCENT OF
POINT SOURCES
100.0
96.9
93.8
86.8
81.1
49.8
PERCENT
OF TOTAL
100.0
98.9
95.9
92.8
85.9
80.3
49.3
1.1
0 50 100
SOURCE: NEDS Inventory (1973).
150 200 250
NUMBER OF POINT SOURCES
300
350
400
SA-2579-14
FIGURE 9 S02 EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL REGION
123
-------
100
< 10,000 TONS/YEAR
30
20
10
ALL POINTS
TOTAL AQCR
ALL POINT SOURCES
P.S. > 100 T/YR
P.S. > 1000 T/YR
P.S. > 10,000 T/YR
AREA SOURCES
NUMBER
SOURCES
480
111
24
7
TONS/YEAR
432,790
311,577
231,745
205,770
162,100
121,213
PERCENT OF
POINT SOURCES
100.0
74.3
66.0
52.0
PERCENT
OF TOTAL
100.0
72.0
53.5
47.5
37.5
28.0
100
200 300 400 500
NUMBER OF POINT SOURCES
600 700
SA-2579-15
FIGURE 10 N0x EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL REGION
124
-------
100
LU
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60 —
50
40
30
20
10
TOTAL AQCR
ALL POINT SOURCES
P.S. > 100 TONS/YR
P.S. > 1000 TONS/YR
P.S. > 10,000 TONS/YR
ALL AREA SOURCES
NUMBER
SOURCES
470
99
23
1
TONS/YEAR
294,908
78,295
71,051
45,960
14,100
216,613
PERCENT OF
POINT SOURCES
100.0
90.7
58.7
18.0
PERCENT
OF TOTAL
100.0
26.5
24.1
15.6
4.8
73.4
> 10,000 TONS/YEAR
oL
100
200 300 400 500
NUMBER OF POINT SOURCES
600
700
SA-2579-16
FIGURE 11 HYDROCARBON EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL
REGION
125
-------
100
I
< 100 TONS/YEAR
< 1000 TONS/YEAR
< 10,000 TONS/YEAR
[ OF POINT SOURCES
80 fr < 100,000 TONS/YEAR
70
60
O 50
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30
20
10
0
1000 TONS/YEAR
100 TONS/YEAR
OF ALL SOURCES
< 10,000 TONS/YEAR
< 100,000 TONS/YEAR
TOTAL AQCR
ALL POINT SOURCES
P.S. > 100 T/YR
P.S. > 1000 T/YR
P.S. > 10,000 T/YR
P.S. > 100,000 T/YR
TOTAL AREA SOURCES
NUMBER
SOURCES
216
31
11
6
1
TONS/YEAR
3,852,627
1 ,684,792
1 ,680,920
1,674,300
1,661,300
1 ,340,000
2.167,835
PERCENT OF
POINT SOURCES
100.0
99.8
99.4
98.6
79.5
PERCENT
OF TOTAL
1000
43.7
43.6
43.5
43.1
34.8
56.3
0 50 100 150 200 250 300 350
NUMBER OF POINT SOURCES
SA-2579-17
FIGURE 12 CO EMISSIONS FOR THE SAINT LOUIS AIR QUALITY CONTROL REGION
126
-------
will be assigned to area sources. For purposes other than those of the
emission inventory itself, it may be desirable to monitor directly a few
point sources emitting less than 100 tons per year. Thus, a source lo-
cated near an air quality monitoring station may be monitored directly
to aid in evaluating the measurements from the station, and would, there-
fore, be treated as a point source rather than included as an area source.
In addition to these minor point sources, the area sources also contain
emissions of totally diffuse origin (mainly traffic, but also domestic
heating). The hourly distribution patterns of these two types of sources
are different. It thus becomes necessary to establish the distribution
patterns of minor point sources if they amount to a significant fraction
of the total area emissions.
Table 20 shows the breakdown of minor point sources in relation
to diffuse area sources. The data indicate, for example, that minor point
sources are an important component of area S0_ emissions (75 percent) and
£
small fractions of CO, hydrocarbon, and particulate emissions. On the
other hand, only in the case of NO do minor point sources constitute an
X
appreciable fraction (18.4 percent) of the total emissions of the Saint
Louis area.
Thus, a realistic decision can be reached in the framework of
actual emissions in the Saint Louis area.
C. Traffic and Transportation Inventories
1. Streets and Highways
A comprehensive study of surface transportation in the Saint
Louis area was initiated in 1965 by the East-West Gateway Coordinating
Council in cooperation with the State Highway Commission of Missouri,
the Illinois Department of Transportation, and related federal and local
agencies. The purpose of the study was to develop a balanced, multimodal
transportation plan for the period extending to 1990 based on forecasts
of socioeconomic and land use data.
As part of this program, traffic volume data were gathered and
appropriate traffic maps were prepared. Average daily traffic counts were
obtained for all major streets and highways in the City of Saint Louis,
Saint Louis County, and portions of Saint Charles, Franklin, and Jefferson
Counties in Missouri and Madison, Monroe, and Saint Clair Counties in
Illinois. The following functional definitions were used for classifica-
tion purposes:
127
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Table 20
BREAKDOWN OF AREA SOURCES
S°2
Remaining point sources
> 1 ton/year
Area sources
Total
CO
Point sources > 1 ton/year
Area sources
Total
NO
X
Point sources > 1 ton/year
Area sources
Total
EC
Point sources > 1 ton/year
Area sources
Total
Particulates
Point source > 1 ton/year
Area sources > 1 ton/year
Total
Number
174
185
369
371
460
Emissions
(tons/year)
37,988
12,908
50,876
3,872
2,167,835
2,171,707
79,832
121,213
201,245
7,244
216,613
223,857
10,150
104,161
114,311
Percent
of Point
Sources
3.1%
0.2%
25. 6r:
9.2"
4.0%
Percent
of All
Sources
3.1%
1.1
4.1%
0.1%
56.3
56.4%
18.4%
28.0
46.5%
2.5%
73.4
75.9%
2.9%
29.3
32.2%
Remaining
Point Sources
as Percent of
Area Sources
75 %
0.2%
39.6%
3.3%
9 . 0%
128
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• Freeways and expressways are high-volume, high-speed high-
ways designed to carry about 12,000 vehicles per lane per
day at speeds of 35 to 50 miles per hour. All cross traffic
is carried on separate levels, and there are no traffic
signals.
• Principal arterials carry large volumes of traffic between
different parts of the urban system and connect to the free-
way system. Typically, they carry 6,000 vehicles per lane
per day at average speeds of 25 to 35 miles per hour. Minor
arterials carry traffic between the principal arterial net-
work; they also serve as local streets. They frequently
carry up to 6,000 vehicles per lane per day at speeds of 20
to 30 miles per hour.
• Local streets provide access to all areas. They vary in
design capacity.
Data were gathered for all freeways and major and minor arterials.
Street names were alphabetized, and segments were designated by intersec-
tions (not by UTM coordinates). The resulting data are shown graphically
on a traffic map in Figure 13, and they are updated yearly by the Missouri
and Illinois traffic authorities.
2. Railways and Vessels
The U.S. Department of Transportation is planning three trans-
portation studies in the Saint Louis area, as a part of the Saint Louis
Air Pollution Studies. The goal of these studies is to ascertain the air
pollution attributable to railways and vessels in addition to quantifying
the emissions from specific transportation systems and facilities . The
studies are being coordinated with EPA's Regional Air Pollution Study.
The first of these studies deals with waterborne vessels, which
constitute a portion of the mobile pollution sources of the Saint Louis
area. A comprehensive listing of sources, such as tugboats, tankers,
recreational vessels, and so forth, will be organized by engine horsepower.
The frequency of usage will be determined, and the pollutant emission cal-
culated using appropriate emission factors. If the data appear to warrant
additional investigation, increasingly accurate inventories are planned
according to the scheme shown in Table 21.
A similar study is planned for rail operations in the Saint Louis
area. Rail activity in the region has been studied for many years. The
Transportation Systems Center, DoT, recently estimated annual surface
129
-------
MEPAffCO IY THE
MISSOURI STATE HIGHWAY DEPARTMENT
OFFICE OF PLANNING
IN COOPERATION WITH THE
U S DEPARTMENT OF TRANSPORTATION
FEDERAL HIGHWAY ADMINISTRATION
BUREAU OF PUBLIC ROADS
1969
TRAFFIC MAP
OF
ST. LOUIS METROPOLITAN AREA
INTERSTATE AND FREEWAY SYSTEM
SA-2579-18
FIGURE 13 1969 TRAFFIC MAP OF SAINT LOUIS METROPOLITAN AREA INTERSTATE
AND FREEWAY SYSTEM
130
-------
Table 21
CLASSIFICATION OF POSSIBLE EMISSION INVENTORIES
Type Inventory
Based on
published data
Rapid survey
Comprehensive
Extensive
field survey
Source
Description
Exhaust
averages
from
literature
Exhaust
averages
calculated
from opera-
tors duty
cycles
Exhaust and
evaporative
averages
from tests
Individual
exhaust and
evaporative
tests
Position
Resolution
Region
Terminals
Mainlines
Fine grid
Fine grid
Time
Resolution
Yearly average
Weekly/daily
average
Weekly average
and possible
daily maximums
Weekly
averages and
possible
hourly
maximums
Source: "Air Pollution Guidelines, NATO Committee on
Challenges of Modern Societies (1971).
131
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Table 22
1967 EMISSIONS FROM RAIL OPERATIONS
WITHIN 100 MILES OF SAINT LOUIS
Pollutant
Particulates
S°2
CO
HC
NO
X
Aldehydes
Organic acids
Total
103 Ib
97
253
273
195
292
16
27
1,153
Source: J. C. Sturm, 'Railroads and
Air Pollution: A Perspective,'
Report No. FRA-RT-73-33, U.S.
D.O.T. Federal Railroad Admin-
istration, Washington, B.C.
(May 1973).
freight transportation exhaust emissions within 100 miles of Saint Louis.
These estimated rail exhaust emissions for 1967 are shown in Table 22.
The rail source emission inventory will describe
• The quantity of emissions by source type
• The location of the sources
• The time pattern.
As in the case of the vessel inventory, a series of inventory
methodologies will be used (similar to the ones shown in Table 21) start-
ing with a review of published data on rail operations and standard
132
-------
emission factors. Subsequent inventories with increasing refinements
in location, emission, and time factors are planned for those pollutants
whose emissions appear to be of sufficient magnitude to warrant further
investigation.
A separate study of emissions from Saint Louis railway terminals
is also planned. This study will concern itself not only with emissions
from locomotives, but also with emissions from stationary sources at
terminals, such as power plants, fuel storage, incinerators, and with
operational factors, such as freight handling by trucks or cargo spillage.
D. Summary
The requirements of accuracy and spatial and temporal resolution
that the RAPS emission inventory should exhibit are not met by any of
the existing inventories. The first four inventories—the IPP, IBM,
DAQED, and NATO emission inventories—are essentially of only historical
interest.
The NEDS inventory contains the best current estimates, and it con-
stitutes a useful data base for those emission sources that, for prac-
tical reasons, cannot be individually measured or closely simulated by
emission modeling. It can serve as a base for the determination of the
scope of the task, since it details the source size and the descriptions
of location, normal operating schedule, source classification code number,
and stack parameters.
It certainly could meet the requirement for long-term data on a '.vide
range of pollutants. For model verification purposes for a limited range
of pollutants, the content of the NEDS inventory is, however, inadequate
since it only gives annual emission data and does not provide sufficiently
detailed information for the determination of hourly emissions of suf-
ficient precision even with the application of appropriate models.
Data on traffic and transportation are in part contained in the area
source emission data of NEDS. For freeways and major arteries, \vhich
will be treated as line sources, these data will have to be supplemented
by information on the traffic mix, average speed, type of roadway, and
other factors.
133
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VII TASK E: EMISSIONS MODELING
A. Introduction
The specification of the rate and distribution of emissions is basic
to any air quality simulation model. However, emissions per se have
rarely been measured in modeling studies conducted prior to RAPS; usually,
emissions are parameterized on the basis of available emissions-related
data. The so-called emissions model is thus a methodology that relates
available data to the required mass flux density of pollutant emissions.
Depending on the type of the emissions source and the requirements
of the various air quality simulation models, emission models are con-
veniently classified according to the mobility and configuration and type
of the source. Figure 14 is a three-dimensional matrix illustrating some
64 possible combinations of emission features that describe various
emission model types. These are only broad classifications, and the
number of real possibilities is considerably greater. Referring to the
discussion of Task B, for example, we note that there are at least four
major categories of gaseous pollutants to be considered: carbon monoxide
(CO), oxides of nitrogen (NOX), hydrocarbons (HC), and sulphur oxides
(SOX) . Also, particulates may need to be classed as large (> 2^.m) or
small (<, 2(am), while mobile surface emissions can be subdivided similarly
as gasoline and diesel powered roadway vehicles, trains, on-ground aircraft,
and so forth. The saving factors are that not all combinations exist in
the real world, some are of no major concern, and others can be grouped
and treated in a single approach.
Other constraints also limit the task of specifying the input require-
ments and the structure of emission models likely to be required in the
RAPS. Many such constraining factors have already been discussed in
other task reports. Emission model requirements are determined by the
specific requirements of simulation models to be evaluated in RAPS. For
example, the minimum emission-time resolution for most model applications
is specified as one hour. (We do consider, later in this section, sub-
hourly specifications for vehicular emissions.) Spatial resolution has
also been addressed in Task B (Section IV), but an objective methodology
for evaluating the required geographical resolution of major point sources
is presented in this task report.
135
-------
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B. Modeling
1. Review of Existing Models
An extensive and in-depth review of the available literature
on emissions modeling resulted in the identification of 28 papers and
reports deemed significant in the context of this RAPS study. Appendix B
summarizes these models by listing (1) the author's abstract, (2) our
summary of the significant features of the models, and (3) the form and
availability of documentation. Table 23 lists the models reviewed and
summarizes the models according to emissions type, source mobility and
configuration, and the degree of experimental verification. The last
aspect is particularly important to RAPS where appropriate short-term
specification of emissions is essential to the overall evaluation of
air quality simulation model performance. In this regard, it is sig-
nificant that only three of the 28 models reviewed have undergone in-
dependent experimental evaluation of the emission estimates.
While Table 23 provides an overview of the emission models,
Table 24 lists details of the input requirements and outputs of selected
emission models. Generally, these models are the more comprehensive
ones reviewed; as such, they reflect the state of the art and outline
the facility and process parameters needed to simulate emissions from
the smaller sources (discussed under Task B). In Table 24, the input
requirements of the various models surveyed are subdivided into model
inputs and model parameters, two categories with subtle distinctions.
The attempt was to list as model parameters those data that are essentially
internal to the emission model in that they are usually specified only
initially. Of course, they may need to be revised periodically as updated
>, or more representative values become available. Model inputs on the other
hand encompass operational inputs required to actually run the model and
generate emission rates. The classification system should not be viewed
as rigid or formal, but rather as a convenience.
The discussion in Section IV points out many of the inadequacies
of emission modeling techniques for use in a research program focused on
the development and evaluation of air quality simulation models. In the
past, emission data have been derived primarily from long-term average
emission estimates—especially for the stationary sources. In turn, most
of the long-term data have been derived not from direct measurement, but,
by applying emission factors to survey information (e.g., fuel consumption).
Hourly emission estimates (and finer spatial resolution) have then usually
been derived from simulation techniques surveyed in this review. The
resulting aggregation of emission factors and correlation and algorithms
137
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for the simulation of high resolution spatial and temporal emissions is.
thus, defined as the emission model. Because of the uncertainties end
potential for error in these methods, an emission inventory sy=tem for
RAPS that relies heavily on measurement techniques has Veon propose:-..
However, two areas have been identified where the measurc-m.;:: t ^.ppioach
is not feasible and where the use of simulation te^hniq^es i? recommended.
As discussed in Section IV, small stationary sources i\ 11 111 to this cate-
gory. And it will be 'shown here that the magnitude o: the mobile emission
(i.e., roadway emission) network requires the use of er.ission simulation
techniques together with some special measurements.
In spite of the limitations of many of the emission mocels. we
have sought to identify those models that are appropriate to the treatment
of small stationary sources and mobile sources. V, e have also included
models that have heretofore been used to simulate emissions iro; the
larger point sources; without the special, direct measurement s recommended
in Section IV, these models would provide the best (though acmittedly
coarse) emission estimates for these sources. Having thus placec in
perspective the role of emission models in RAPS. \\e discuss belov a
cross section of models and the needs they appear to serve.
• The Argonne Model of Roberts'1* for the simulation 01
hourly SC>2 emissions from distributed residential.
commercial,, and institutional sources, ana for ho^rlv
SC>2 and thermal emissions from manor point sources.
• The emissions model developed at Systems Ar;piic a ticr.s.
Inc. (SAI). by Roberts1 • 2 for nourly stationary ~. r.ir.t
and area source emissions of XO^. and hydrocarbon.-. .
This model is also recommended for the
vehicular NOX, CO, and hydrocarbon
ens si.
to one reservation. The SAI aggregate— re: ic~la:
emissions into two square mile are;. - -lur^ • -.---_
feature that may be too gross for direct :. npl i ca t ion
to RAPS. Therefore, we suggest that tn<_- --. I r>o^l
be modified to provide link-by-lini: ;r.i - sior.- i i
the major links in the manner cf the St^r.- .r- ." e--" ar
Institute (SRI) model of Ludvig4 '..r. :lc- rc-tair.ir.^ th,
spatial averaging (on the smaller scale oi :.n^ -c,
kilometer) method for the secondarv link?.
*
Model reports are reviewed in Appendix B.
145
-------
• The Geomet model5 for diurnal emissions from river
vessels and railroads.
• The Platt's model for Northern Research and Engineering,6*
for aircraft emissions.
Several categories of emissions are notable by their absence
from the above listing. To the best of our knowledge, there are no
models available that treat heat emissions from vehicular or distributed
area sources although the inputs to such models already seem to be re-
quired in the SAl, SRI, Geomet, and Argonne models. However, appropriate
thermal emission factors are required. Another shortcoming of the available
models is the treatment of particulate emissions. While four of the 28
models reviewed treat particulates, none provides output on size dis-
tribution.
Furthermore, very little has been done to develop a regionally
applicable model of man-induced water vapor emissions. To be sure, the
knowledge and expertise are available to develop such a model, but a
rigorous and comprehensive methodology has not been compiled. Perhaps
the closest facsimile to what is required is the work at the University
of Alaska by Benson20 for Fairbanks, Alaska, where water is considered
a primary atmospheric pollutant in winter. Thus, we recommend the use
of an aqueous emissions model, similar to Benson's, but expanded as dis-
cussed later.
Lastly, the emissions modeling work of the Ontario Department
of the Environment for Toronto is perhaps the singularly most comprehensive,
available methodology; it is not, however, generally available. We suggest
that official requests be submitted to the Ontario Department of the
Environment since many of their methods and experiences might be of
benefit in the development of the RAPS emissions inventory.
2. Specification of Emissions
The basic requirement of the RAPS emissions inventory program
is to provide hour-by-hour emission rates for the various pollutants and
heat and water vapor. The pollutant emissions are of primary importance
*
This model is currently being modified by Geomet under Contract 68-02-
0665 with the Meteorology Laboratory, Environmental Protection Agency,
Research Triangle Park, North Carolina.
146
-------
(they specify the basic emission inventory requirements), while heat and
water vapor can be determined from data acquired in the course of listing
the pollutants (see Task B).
For stationary sources, there is a three-stage sequence in
which emissions should be specified in RAPS.
(1) Direct measurement of emissions.
(2) Measurement of inputs to the pollutant-generating
process with subsequent parameterization of the
input-output (i.e., emissions) relationship.
(3) Parameterization of both the inputs and input-
output relationship.
An example of the first would be the use of in-stack monitors at major
power-generating facilities. The second method would employ measurement
of process parameters directly related to emissions (e.g. fuel consumption
rate), while in the third case these parameters might be determined from
a statistical base or as a function of a known indirect indicator (e.g.,
degree-hours). Prior to RAPS, regional studies employed the third method
almost exclusively. In RAPS, however, greater emphasis must be placed
on the first and second methods.
a. Stationary Sources
Direct monitoring (input or output) of all stationary
sources is not practicable in view of financial constraints, nor is it
required; hence, there is a need for emission models. The available
inventories for Saint Louis have been reviewed in discussions of Tasks B
and D to identify the number and size of the major point sources of SOX
and NOX (CO and HC are emitted primarily by distributed sources). A
major point source may be considered as one that receives individual
treatment in an air quality dispersion model, not one that is aggregated
into an area source. About 13 point sources (e.g., power-generating
plants) have been identified as "principal" major emitters with emission
rates far in excess of "secondary" major point sources. As a first step,
we have proposed (Task B) routine emissions monitoring for all principal
sources and routine input monitoring for secondary point sources. Routine-
input or emissions monitoring is not required for the remaining individual
stationary point and area sources. The best available emissions models
will serve adequately.
147
-------
The types of input or emissions measurements recommended
above for various stationary sources are those necessary to provide the
accurate and current emissions inventory required as input to the oper-
ation or evaluation of mesoscale air quality simulation models. Where
the intent is to investigate microscale phenomena, the basic or routine
inventory will need to be supplemented with special emission inputs.
Depending on model receptor locations (or the location of air quality
monitoring stations),, it may be necessary to provide higher resolution
emissions data for some of the lesser sources. Thus, for example, minor
point sources near a particular receptor may either need to be monitored
continuously or, at least extensively, during selected periods. More
specific requirements cannot be set until actual monitoring sites are
determined or specific research studies defined. This is especially
true for emissions from mobile sources; further considerations along
these lines are presented later in this section.
b. Mobile Sources
For routine use, the mobile emission inventory should be
estimated for link or line source geometry using an average route speed
model formulation such as that used in APRAC-1..21 Links are straight
segments of major arterial streets and freeways that serve as the trans-
portation network for the area. In most urban complexes, this kind of
network has about a one-half to one-mile span in suburban areas and a
one-block span in downtown areas. The link geometry is recommended
because it is conveniently related to the parameters needed to estimate
emission, links can conveniently be aggregated into area sources (but
not the reverse), and control strategies to be investigated later will
deal with modifications of the locations or conditions on the links (not
the areas).
The mobile emission inventory is thus envisaged to comprise
three distinct elements: (1) the basic roadway network, (2) traffic
characteristics, and (3) an emissions model. The first element is a
listing of the characteristic features of each link in the roadway net-
work, such as
• Link location (geographical location of the
endpoints).
• Facility type (e.g., suburban freeway, downtown
arterial).
• Roadway characteristics (e.g., number of lanes,
grade, elevation, foundation).
148
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Traffic characteristics are those parameters that describe the vehicle
population and traffic flow. In this category, the inventory must con-
tain data on
• Vehicle mix by age and type (e.g., numbers and
ages of light duty vehicles and heavy duty diesel
and gasoline-powered vehicles) .
• Vehicle speed (by facility type and location).
• Distribution of origin-destination (0-D)
locations.
Because vehicle emissions cannot be measured directly in situ, they need
to be determined with the aid of an emissions model using traffic and
roadway data in the manner of Roberts' model8 or Ludwig's model.4 (A
number of other sources discuss the requirements and availability of
roadway and traffic data. )23; 23; E4.> S5,26 As indicated above, \ve recommend
the use of the average-speed emissions model on a link-by-link basis as
the primary mobile emissions format; in this manner, area-averaged emissions
can be obtained appropriate to the dispersion model structure or individual-
link data could be retained for the larger roadways for line-source models.
We further suggest that 0-D data be used to identify cold-start emission
contributions. Lastly, a multimodal emission model should be used for
individual microscale studies. But the collection of the appropriate
data and the operation of the model to generate emissions data should
not be a part of the basic inventory; rather it may be considered as a
separate task of the individual miscroscale study. Lastly, we note that
the use of multimodal models19 is still in a preliminary stage pending
the availability of emissions test data.
The average route speed model requires inputs of traffic
volume on the links and link speed. Diurnal traffic factors and emission
factors for the vehicle mix on the link are also parameters of the model.
There is enough similarity of utilization (volume) on most
urban streets that estimates based on periodic samples, supplemented by
data from fixed sensors on high-volume freeways and on selected arterial
streets will serve the purpose. These periodic samples are routinely
taken by the traffic department as part of monitoring the adequacy of
the street system. The volume data from these surveys are expressed in
average daily traffic and require a diurnal pattern to convert the daily
into hourly traffic. For this reason, monitors capable of hourly or
finer resolution should be installed on typical streets representative
of different usages: arterials at various distances from the center of
149
-------
town, arterials with various functions (downtown distributor; suburban
collector; shopping center, university, or airport service). These
different functions may imply different diurnal variation patterns and,
hence, should be monitored. In addition, fixed monitors in such places
will help to determine the variation by day, week, or season; from these
data, factors for similar facilities may be developed and used for the
emission models.
As in the hourly variation patterns, speed (and hence
emissions) vary with volume of traffic, but speed distribution will be
similar from day to day. Speed data are needed for the same kinds of
facilities as those described above, and both fixed and portable monitors
capable of providing speed information should be used.
Special consideration should be given to the inclusion of
cold-start emission effects. Martinez, Nordsieck and Eschenroeder27 have
evaluated the comparative impacts of cold- and hot-start emissions
(vehicular HC, NO, and CO) for Los Angeles in the time period from 0600
to 0900. Their findings are shown in Table 25 expressed as the ratio of
emissions with cold start to emissions without cold start.
Table 25
RATIO OF COLD-START EMISSIONS TO
HOT-START EMISSIONS
Species
NO
CO
Reactive
hydrocarbons
Year
1968
1.15
1.48
1.08
1971
1.16
1.57
1.10
1974
1.16
1.53
1.11
1980
1.01
2.30
1.11
We see that the neglect of the cold-start emissions con-
tribution would have a moderate impact on the estimation of reactive
hydrocarbons and a severe impact on CO by 1980. The emission inventory
must therefore provide cold-start emission factors. Martinez27 provides
values that may be used for light-duty vehicles through 1976; these are
summarized in Table 26.
150
-------
Table 26
COLD-START EMISSIONS FROM
LIGHT-DUTY VEHICLES
< 1968
1969
1970
1971
1972
1973-74
> 1975
Cold-Start Emissions
(gm/start)
NOY (as NO,,)
A £t
5.66
6.56
5.89
5.36
4.02
3.78
-1.04*
HC
10.73
9.54
10.88
6.18
4.25
4.25
2.68
CO
254.1
143.6
143.3
92.53
76.74
76.74
43.06
The negative factor is, of course, used in con-
junction with the running emission factor, thereby
effectively reducing net NOX emission.
Under special conditions, as when forecast values of air
quality are compared with measured values, continuous and detailed mon-
itoring of vehicle behavior in the immediate environment of the measure-
ments may be required with emissions being forecast with a multimodal
emission model such as Ludwig's (reviewed in Appendix B). Such monitoring
would not be done area-wide or continuously, because it is not appropriate
to mesoscale studies. The multimodal model contains emission factors for
the standard vehicle maneuvers: steady driving, slowing and speeding,
stopping, and accelerating from a stop. Hence, the resolution of the
model in space can be adjusted to the location and needs of the experiment.
That is, a downtown block might be divided into several links, each a
few tens of meters long. Vehicle modes such as acceleration, steady
driving, congested driving, or stopping, would be assigned to each link.
Volume in each link would be measured on a time scale comparable with
the needs of the experiment.
151
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Special microscale studies of vehicle-generated emissions
may require subhourly inputs of vehicle parameters. This is especially
important when the distribution of inert pollutants in heavily traveled
areas is evaluated (e.g., CO in the central business district or in the
corridor of major freeways). For such situations, 15-minute resolution
may be necessary. In the earlier discussion on special measurements of
stationary emissions, specific details of special mobile emissions re-
quirements must await formalization of the plans for individual research
studies.
c. Water Vapor Emissions
A special aspect of the overall emissions modeling program
(both for stationary and mobile sources) is the treatment of aqueous
emissions. As noted earlier, a comprehensive regional model for water
has not been developed although emission factors are available at various
levels of refinement. For example, Benson20 presents emission factors
for water (and carbon dioxide) emissions from the combustion of gasoline,
fuel oil, and coal. For gasoline and fuel oil, emissions of water are
readily specified on the basis of spatial and temporal distributions of
fuel consumption. These will generally be available directly from the
basic inventory of the other pollutants (e.g., S02, CO). Two exceptions
may be surface vehicles and major point sources. For the former, the
link inventory will provide facility type and vehicle volume, mix, and
speed information; additional factors (or submodels) will therefore be
required to obtain gasoline consumption. Direct monitoring of water
emissions from large point sources may not be the optimum method. Rather,
grab samples as discussed in Section IV may be more appropriate both be-
cause of economies of the method and because of relative invariability
of the exhaust water content. These would then be related to the more
available information on fuel and process features.
The man-induced emissions of water from air conditioning
(ac) units, small cooling ponds, sewage treatment plants, and so forth
should be aggregated into gross composite emissions for specified areas
of the order of one to two kilometers on a side. For ac units, statis-
tical relationships based on an inventory of ac capacity and type and
the ambient conditions of wind, humidity, and temperature would be
adequate to estimate the corresponding emission rates. Similar procedures
could be used for cooling ponds and the like; for sources of this type,
the inventory should contain information on the area and volume of the
ponds, input water temperature, and degree of mechanical mixing. The
emission rate is then determined for the appropriate time and location
using an analytical or statistical model of potential evapotranspiration.
152
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However, rather than calculate the emissions of water from the measured
or prescribed inputs, we recommend that such "meteorologically-controlled"
aqueous emissions from man-induced sources be determined outside the
RAPS emissions inventory. We view these sources as those in which man's
activities provide the potential (i.e., a water supply) for aqueous
emissions to the atmosphere but in which meteorological processes regulate
the magnitude of the vapor flux. In summary, aqueous sources in this
category include the following:
• Cooling ponds
• Sewage treatment ponds
• Irrigation •
• Space cooling units.
Lastly, anomalous sources of aqueous emissions, in par-
ticular, wet cooling towers with their enormous outputs of water vapor
and their significant impact on visibility, gaseous reactions, and such,
must be considered. For example, moisture discharge rates for very
large power plants (e.g., 2,000-MW fossil steam plants) are of the order
of 1.5 x 105 Ib/min—a rate equivalent to the daily deposition of a
1-cm column of water on a 10-km2 area.
To simulate these emissions accurately, the inventory
should contain the design performance standards and power generation
rates for each source. The environmental parameters required in the
inventory are the ambient wet bulb temperature and relative humidity.
Figures 15 and 16 illustrate performance curves for a natural-draft
cooling tower that dissipates 8 x 109 Btu/h; note that the performance
curves are also used to estimate the sensible heat output of the tower
as required for the thermal emissions inventory.
3. Emissions Model Verification - Mobile Sources
In view of uncertainties in determining mobile emissions, an
experimental program of emissions model verification is advocated. The
uncertainties arise from imprecisions both in the specification of process
input parameters and in the accuracy of the postulated input-output
relationships.
Recalling previous discussions on the nature of emissions models,
we may conveniently define two principal types. In one, the input (I) is
known as a function of time (t), and its relationship to the emissions is
153
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110
u. 105
UJ
•§ 100
tr
LU
0.
LU
95
LU 90
85
45 50 55 60 65
WET BULB TEMPERATURE
SOURCE: Bowman and Biggs, 1972.
70
75
SA-2579-6
FIGURE 15 PERFORMANCE CURVES FOR COOLING TOWERS
GIVING THE EXIT TEMPERATURE AS A
FUNCTION OF THE AMBIENT WET BULB
TEMPERATURES FOR VARIOUS EXIT RELATIVE
HUMIDITIES
154
-------
160
CO
o
150
D 140
5 130
UJ
oc
I
o
120
110
45 50 55 60 65
WET BULB TEMPERATURE
SOURCE: Bowman and Biggs, 1972.
70
75
— °F
SA-2579-7
FIGURE 16 PERFORMANCE CURVES FOR COOLING TOWERS
GIVING THE MOISTURE DISCHARGE AS A
FUNCTION OF THE AMBIENT WET BULB
TEMPERATURE FOR VARIOUS EXIT RELATIVE
HUMIDITIES
155
-------
specified by an input-dependent emission factor (EF); the functional form
for the specification of the time-dependent emission rate E(t) is
E(t) = I(t) • EF(I)
In the second type,, the input rate is also unknown and, hence,, a time-
dependent input factor (lF(t)) is also required. The emission rate for
this case is then given as
E(t) = I • IF(t) • EF(I)
Implicit in these formulations is a knowledge of the spatial distribution
of the inputs. Mobile sources (and certain stationary area sources) are
especially complex in that the time-dependence of the input factor must
be expressed as a function of location.
Thus the so-called emissions model may actually be composed of
three components: (1) the apportionment of inputs on a temporal basis,
(2) the apportionment of inputs in space, and (3) the relation of emissions
to inputs. Similarly, the field (i.e., in situ) verification program
also must serve to evaluate the emissions model performance in these
three areas.
Parameterization of the inputs is particularly relevant to the
generation of the mobile emissions. The recommended procedure is to use
a statistical database (e.g., ADT) together with continuous measurements
of vehicle volume, speed, and mix on selected key links to generate a
dynamic link-by-link mobile inventory. Evaluation of the adequacy and
representativeness of this procedure should be undertaken. Two methods
appear feasible for this purpose: (1) aerial photography, and (2) side-
looking aerial radar. The first method could easily provide spatial
inventories of vehicle volume and mix by facility type, location, and
time of day; less easily obtainable, although possible, is the deter-
mination of vehicle speeds. The second method can provide volume and
speed distributions, but not vehicle mix. Again, these evaluations are
recommended for selected hours, days, and seasons; we do not advocate
continuous usage of either method throughout RAPS.
A similar but less diificult problem is the parameterization
of temporal and spatial variations of fuel consumption by distributed
area sources.
156
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For mobile sources, it is advisable to evaluate parameteriza. ioi
of the inputs and outputs separately; we have already discussed treatment
of the former for vehicular sources. For the latter, it remains to verify
the representation of time-averaged emissions on a per-link basis. Both
average speed and multimodal emission factors can be used, and both are
based on emissions during the federal driving cycle (FDC). What is not
known are the impacts of deterioration, mix, road grade, drag, and so
forth. Model evaluation studies should be undertaken to evaluate the
simulation of emissions on a variety of link types (such as freeways,
expressways, major arterials, and local feeders) in congested areas.
Accurate link measurements of vehicle inputs—volume, mix, speed, and
acceleration—will be necessary. Emissions should be determined as
averages for 15-minute intervals for all vehicles on the link. A mass
budget approach seems most appropriate for this purpose.29;30 The con-
cept of the mass budget approach is simple, yet its implementation in
the field can be difficult—especially when accuracies of 10 to 20 per-
cent are required (see Task B). Simply stated, the fluxes of effluvia
into and out of a control volume are measured, and the net flux is equal
to the pollutant source or sink within the volume. For the horizontally
homogeneous, height-dependent wind field u(z), the mass flux density
Q(gm m~l sec"1) through a control volume having zero vertical flux through
the top (H) is given by
H H
Q = f x (z)u(z)dz - J x (z)u(z)dz
o o
Here, x(z) is the height-dependent pollutant concentration; the subscripts
1 and 2 refer to the upwind location and the downwind location. Thus,
determination of the mass flux density (i.e., emission rate) requires
the following:
• Value of the height H.
• Up- and downwind vertical profiles to H of the
horizontal wind vector.
• Up- and downwind vertical profiles to H of the
pollutant concentration.
For individual roadways, conventional air quality sensors
mounted on fixed masts are used to obtain concentration profiles. Remote
sensors may be more desirable conceptually because of their path-averaging
capabilities, but the state-of-the-art is generally not yet sufficiently
157
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advanced for this purpose. One possible exception is the correlation
spectrometer which may have application in the evaluation of emission
rates from elevated point sources. In this case (i.e., tall stack),
the pollutants are emitted into an atmospheric regime where the wind
shear is small. Hence, the mass flux equation may be rewritten, where
Q 5. u (Tv (z)dydz - ffy (z)dydz)
J v1 *1 «J J *2 (
The correlation spectrometer provides the vertical mass loading; cross-
wind (y) traverses up- and downwind of the plume can thus be used together
with the mean transport wind to determine Q (gm sec"1).
In conclusion, we stress the need to evaluate the parameteriza-
tion of both inputs and subsequent output emissions by models, thus
ascertaining the representativeness of the parameters that are input
to the inventory and evaluating the precision with which the emissions
are subsequently modeled.
4. Resolution of Source Location
A major practical concern in developing an emissions inventory
is the degree of resolution required in specifying the location of the
point sources. Normally, point sources are resolved to 100 meters in
operational inventories; however, subsequent errors introduced in predicting
concentrations from such sources may be intolerable in a research study.
A 10-meter resolution in source location has been proposed. Before
adopting such a specification, it seems desirable to objectively evaluate
errors in the predicted concentration field resulting from a mislocation
of the source.
Perhaps the most difficult task in the evaluation process is
the definition of a meaningful measure of the error (i.e., the difference
in predictions with and without an error in the source location). We
recognize that this error can be a function of many parameters: source
strength, stack height, lateral and longitudinal receptor position, and
atmospheric stability. For this evaluation, a worst-case meteorological
situation should be used in order to determine the most stringent source-
location resolution required. However, a single stability category
cannot be used to satisfy this criterion as the worst-case stability for
a low stack can differ from that for a tall stack. We have chosen, there-
fore, two stability regimes—slightly stable (Class E) and slightly
unstable (Class C). Source strength and stack height will be seen to be
158
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inputs in the assessment methodology, and thus the individual source
features are retained. For the impact on receptor location, two alter-
natives exist: (1) the user is concerned \\ith UK error associated with
a particular source at one or more specified receptor location (e.g.,
air quality monitoring site), or (2) the user \\ishes to evaluate the
impact of a particular source throughout the study region. Therefore,
we define, as the measure of error, the integral value of the error over
both the lateral (cross-wind) and longitudinal (downwind) directions; the
longitudinal distance may be taken as the distance either to the receptor
or to the downwind edge of the study region.
The error in concentration resulting from mislocation of a
source is equivalent to that which would result if a receptor were
similarly mislocated. We shall take this source location error to be
the absolute value of the difference between the concentration
at the
correct location and the concentration XE a"t a point some crosswind dis-
tance away. A basic Gaussian plume formulation for atmospheric dispersion
is assumed
X
TTCJ
Q
_ CAP
TCF a u
y z
1
2
/y + ,\y
\ cy
where
Q = source strength (gm sec"-'-) .
^y = source location error (m).
y = crosswind direction (m).
H = source height (m).
u~ = effective transport wind speed (m sec~l) .
, a - horizontal and vertical standard deviation
z
of the pollutant distribution, respectively
(m).
159
-------
b
o = ax .
a, b, c, d = experimentally determined constants
dependent on stability conditions.
x = distance along the mean wind direction from
the source (m) .
We define the integrated measure of error, IE, as
(IE) = - dydx . (1)
Since Q and u are constant over the integration, it is possible to define
an integrated normalized concentration error. IXE. which is independent
of these quantities.
(IE) = — (INE) . (2)
TTU
To determine the allowable source location error, Ay, for known
values of Q, H, u, stability and downwind distance, Equation (2) is solved
for INE, which is then used to obtain Ay from Figures 17 through 24. How-
ever, Equation (2) contains two unknowns, IE and INE. In order to solve
Equation (2), a value for IE that represents a tolerable level of concen-
tration error must be defined as a reference. The magnitude of this ref-
erence value may be arbitrarily chosen; the error limit presented here
is specified on the basis of (1) a reference source strength, Qreference'
and (2) an allowable source location error for that strength (from which
(INE)reference is found using Figures 17 through 24). Thus, IE in Equa-
tion (2) becomes IE*, the reference integrated error at downwind distance
x for a specific source height and stability.
Q
* reference , .„.
(IE) = - = - (INE) . (3)
TTU reference
Combination of Equations (2) and (3), where IE now equals IE*, yields
an equation for INE:
reference
(INE) = (INE) - . (4)
reference Q
160
-------
The tolerable resolution Ay is then determined (from Figure:
17 through 24) on the basis of INK together with atmospheric stability,
source height, and the selected downwind distance. Figures? 17 through
20 apply to stable atmospheric conditions, \\hile Figures 21 through 24
apply to unstable ones. Source heights of 0, 5, 30, and 100 meters are
used in the figures, while Ay values are 5, 15, 30, GO, and 100 meters;
interpolation must be used lor intermediate values. A step-by-step pro-
cedural outline and example follow. Note that a minimum _y of 10 meters
is recommended because of the impact of stack diameter and initial mix-
ing in limiting the occurrence of smaller values.
The determination comprises the following steps:
Step Task
1. Select stability category (stable or unstable), stack
height (H), downwind distance (x) , Q , and
reference
ily reference.
2. Using stability, H, x, and Ay reference, determine
INE from Figures 17 and 24.
reference
3. Select Q, and using it, INEref^^ and Qreference,
solve Equation (4) for INE.
4. Using stability, H, x, and INE, determine Ay from
Figures 17 through 24; let Ay = 10 meters when analysis
indicates a lesser value.
For example, we shall use a reference source strength,
^reference' of 10° tons Per vear> i«e., the value given by the Code of
Federal Regulations to delineate between treatment of a source as a
point or area source. The source location error generally accepted at
the present in the most emission inventories is 100 meters, so we shall
take Ay as 100 meters. For the specific case of say an 85,000 tons-per-
year source strength, a stack height of 30 meters (typical of a power
plant stack in Saint Louis), in stable atmospheric conditions, at a re-
ceptor point of 10 km downwind of the stack, INEreference is found to be
409 from the appropriate graph—Figure 19. Use of Equation (4) then
gives
100 tons/year
(INE) = (409) i = 0.48 .
85,000 tons/year
161
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500
1000
1500
2000 2500
INE
3000 3500
4000 4500
SA-2579-2
FIGURE 17 VARIATION OF INTEGRATED NORMALIZED ERROR
WITH LONGITUDINAL DISTANCE FROM THE SOURCE
WHEN SOURCE HEIGHT IS AT THE SURFACE AND
ATMOSPHERIC CONDITIONS ARE STABLE
162
-------
10"
10H
E —
500 1000 1500 2000 2500 3000 3500
SA-2579-3
FIGURE 18 VARIATION OF INTEGRATED NORMALIZED ERROR
WITH LONGITUDINAL DISTANCE FROM THE SOURCE
WHEN THE SOURCE HEIGHT IS FIVE METERS AND
ATMOSPHERIC CONDITIONS ARE STABLE
163
-------
FIGURE 19 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN SOURCE
HEIGHT IS 30 METERS AND ATMOSPHERIC CONDITIONS
ARE STABLE
164
-------
10=
10"
H = 100 m
S = STABLE
0 25 50 75 100 125 150 175 200 225 250 275 300 325
INE
SA-2579-20
FIGURE 20 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN
SOURCE HEIGHT IS 100 METERS AND ATMOSPHERIC
CONDITIONS ARE STABLE
165
-------
H = 0
S = SLIGHTLY UNSTABLE
FIGURE 21 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN
THE SOURCE HEIGHT IS AT THE SURFACE AND
ATMOSPHERIC CONDITIONS ARE SLIGHTLY UNSTABLE
166
-------
10°
10"
E ~
10°
lO'
H = 5 m
S = SLIGHTLY UNSTABLE
FIGURE 22 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN
SOURCE HEIGHT IS FIVE METERS AND ATMOSPHERIC
CONDITIONS ARE SLIGHTLY UNSTABLE
167
-------
H = 30
S = SLIGHTLY UNSTABLE
0 10 20 30 40 50 60 70 80 90 100 110
FIGURE 23 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN
SOURCE HEIGHT If 30 METERS AND ATMOSPHERIC
CONDITIONS ARE SLIGHTLY UNSTABLE
168
-------
10=
10"
10°
10"
H = 100 m
S - SLIGHTLY UNSTABLE
FIGURE 24 VARIATION OF INTEGRATED NORMALIZED ERROR WITH
LONGITUDINAL DISTANCE FROM THE SOURCE WHEN
SOURCE HEIGHT IS 100 METERS AND ATMOSPHERIC
CONDITIONS ARE SLIGHTLY UNSTABLE
169
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The resulting Ay for this INE, obtained from Figure 19, is very small,
suggesting that such a large source must be precisely located, i.e.,
within 10 meters. With a source strength of 1,000 tons per year and
other variables as above, ^y is approximately 18 meters.
A significant feature of this method is its flexibility: The
error measure can be taken to most any selected downwind distance or
distance interval. In addition, rather than specifying a single resolu-
tion required of all stacks, the method yields a recommended spatial
resolution for each source based on the stack height and emission rate
of the individual source.
C. Consideration of Natural Emissions
Pollutant emissions from natural phenomena require evaluation in
the development of an accurate inventory for the region. The natural
emissions or production of CO; NO, N02, S02, nonmethane hydrocarbons,
and particulate material in the Saint Louis area are generally small
compared with other pollution emissions, but some are large enough to
produce a measurable background. There is enough known about the pro-
duction of the primary pollutants in the biosphere or atmosphere to
estimate the production rates or source strengths within a selected area
and volume.
For the purposes of RAPS, the Saint Louis source area can be defined
as a circle of 35 kilometers radius centered in the business district of
Saint Louis. Urban and suburban areas include East Saint Louis, Granite
City, Alton, and Belleville (all in Illinois). The area of this circle
is about 4000 km , the estimated vegetated source area is half of this,
or 2000 km2.
The source strengths of each of the five emission components will
be estimated according to its potential production mechanism or mechanisms
active in this particular area. Calculated atmospheric concentrations
in the Saint Louis area resulting from these emissions must also include
background concentrations of the air transported into the Saint Louis
air space if a realistic value is to be obtained.
Carbon monoxide is produced naturally in the atmosphere by the
oxidation of methane.31 Atmospheric methane is largely naturally produced
from decay of vegetation. The best estimated global production rate is
about 2x10^ tons per year. All of this methane is oxidized in the atmos-
phere to produce more than 3x10^ tons of CO annually. The CO, in turn,
is oxidized to CO2. The 4000 km2 area chosen is 8xlO~6 of the global
170
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area; therefore, the average daily CO production rate is estimated at
70 tons per day.
There are no known, natural sources of NC>2 but, NO is generated by
vegetation.3S The global NO natural production rate is 500 x 10^ tons
per year, from an approximate 1x10^ km2 vegetation covered area. If we
use the 2000 km^ area for trees and shrubery, NO production in the region
is about 30 tons per day.
All of the SOg produced naturally in the area would result from
ozone oxidation of H0S, again generated by the decay of vegetation. The
total annual production of H9S from the 1x10^ km2 of land areas of the
earth is about TOxlO6 tons.33 Using the same area ratio as for NO gives
an estimated production of 4 tons of HgS per day. If we consider the
4 tons HgS to be oxidized in situ, it produces 8 tons of S02 per day.
The nonmethane hydrocarbons emitted naturally are all terpenes or
isoprene. Pipperton estimates worldwide natural emissions of these
substances to be 109 tons per year.34 Hence, 50 tons per day of non-
methane hydrocarbons are produced in the area. The diurnal variation
in terpene vapor emission rates is extreme; nearly all of these vapors
are released from plants during the hours of daylight. There is also
a seasonal variation with a summer maximum and a winter minimum. All of
the vapors are released through the foliage. Through the year, the
hourly rate of emission will vary from essentially zero (winter night)
to about 10 tons per hour (summer daylight maximum).
Natural particulates in the atmosphere of the Saint Louis region
are mostly the product of chemical reaction. The oxidation of S02 to
sulfate and NOX to nitrate and the formation of polymeric organic par-
ticles from terpenes produce most of the background. The only direct
natural particulate in the Saint Louis area is dust. The dust loading
will vary widely with wind speed. The average dust loading is 5 _.g/m3.
The secondary or condensed aerosol from natural sources adds about 15 to
20 |ag/m to the background.
The yearly global average for dust generated, 200 x 10° tons,25 is
reasonable to apply to the Saint Louis area; thus, the average dust pro-
duction in the Saint Louis region is about 25 tons per day.
Table 27 summarizes the estimated emissions of CO, NO , SO2, H2S>
nonmethane HC, and particulates from natural sources alone and all
sources in the Saint Louis region. Carbon monoxide, S02 and NOX pro-
duction from natural sources is only a small part of the total regional
production and can be expected to have little impact on the RAPS studies.
171
-------
Particulates from natural sources are of marginal significance. Nonmethane
HC, on the other hand, may be significant during summer daylight periods.
Therefore,, we recommend that a procedure be developed for estimating the
contribution of natural HC for incorporation in the RAPS Emission Inventory.
Table 27
POLLUTANT EMISSIONS IN THE
SAINT LOUIS AREA*
(Tons/Day)
Type
CO
NOV
X
S0x
H2S
Nonmethane hydrocarbons
Particulate (dust)
Natural
70
30 +
0
4
50*
25
Total
5100
810
2040
—
790
520
The area is 4000 km2 in size.
Expressed as NO.
Maximum rate = 10 tons/hour.
D. Summary of Conclusions and Recommendations
The specific conclusions and recommendations that result from the
studies carried out under this task are summarized below:
No existing models adequately meet the requirements of RAPS; to
provide emissions data with the necessary high resolution in space and
tine, it is recommended that
• Direct information be acquired of emissions or of factors
determining emissions to the extent possible.
• Where not possible (i.e., for small point sources, and
area sources), the most suitable of the existing models
should be used in adapted and improved form. Specifically,
for stationary sources,
172
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- Direct data from emissions monitoring should be used
\\herever available (probably only for the largest
sources, such as power stations).
- For other large sources, information on hourly fuel
consumption rates, process operations, and such, should
be obtained and used to derive emissions.
- For small point sources and area sources, modeling
procedures based upon the models noted below (but. with
refinement of the input data wherever possible) should
be used.
Recommended models are
- The Argonne model2 for hourly S02 from distributed
residential^ commercial,, and institutional sources and
hourly SOg and heat from major point sources.
The Systems Applications, Inc. (SAI) model-;3 for hourly
NOX and HC from stationary point and area sources.
For automotive mobile sources, we recommend that an average
route speed model be used (with a link or line source
geometry) supplemented with measured data of traffic flow
from fixed sensors on high volume freeways and on selected
arterials. The following models are recommended:
- The SAI model1;3 for hourly CO, XOX and HC with modified
inputs derived from Stanford Research Institute's model4
for spatial and temporal distribution of vehicle number
and speed on a link basis for primary traffic and area
basis for secondary traffic.
- Recommended for other mobile sources, are the Geomet
model5 for diurnal emissions from river vessles and
railroads and the Northern Research and Engineering
model,5 as revised by Geomet (in preparation), for
aircraft emissions.
- For both stationary and mobile sources, the Ontario
Department of the Environment (1971) model for Toronto
should be referred to for overall guidance and planning
methodology.
For microscale studies under the RAPS program the basic inventory
need to be supplemented with special emission inputs. These should
be obtained on an experimental or project basis. Detailed data can be
acquired from small stationary sources or, for mobile sources, from con-
tinuous and detailed monitoring of vehicle behavior in a given area with
the application of a multimodal emission model to forecast emissions.
173
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In view of the limitations of existing mobile emission source models
and the impossibility of resorting to direct information (as is done in
the case with major stationary sources), it is recommended that special
steps be taken to improve and verify such mobile emission models. Spe-
cifically,
• An experimental evaluation should be made of mobile
emissions models to refine the specification of process
input parameters and the accuracy of postulated input-
output relationships.
• Parameterization of inputs for mobile emissions (statistical
database plus continuous measurements of vehicle volume.
speed, and mix on key links to generate a dynamic liiik-by-
link inventory) should be verified on a selective basis via
aerial photography or side-looking aerial radar or both.
• Temporal and spatial variations of fuel consumption by
distributed area sources should be spot metered in fine
scale.
• Parameterization of outputs from mobile sources should be
evaluated in various models to verify representation of
time-averaged emissions on a per-link basis taking into
account types of link and the impacts of deterioration,,
mix, road grade, drag, and so forth.
• Of the pollutant emissions (CO, NOX, S02, HgS, nonmethane
HC, and particulates) from natural phenomena in the Saint
Louis area, nonmethane HC may contribute a significant
background during summer daylight hours and should be
included in the RAPS emission inventory. Suitable pro-
cedures to estimate these should be developed early in
the RAPS program.
• In the context of the RAPS program, the accuracy of point
source locations is best considered in terms of the effect
of errors throughout the study area. Accordingly, we
propose that a measure of error in source location be
defined as the integral value of the error over both the
lateral (cross wind) and longitudinal (downwind)
directions.
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REFERENCES
1. P. J. Roberts, P. M. Roth, and C. L. Nelson, "Contaminant Emissions
in the Los Angeles Basin—Their Sources. Rates, and Distribution."
Report No. 71, SAI-6. System Applications. Inc. (March 1971).
2. P. J. Roberts et al., "Chicago Air Pollution Systems Analysis
Program: A Multiple-Source Urban Atmospheric Dispersion Model,
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-------
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178
-------
Appendix A
SAMPLE EMISSION INVE.\Tuf;Y Phl.NTOUTi
This appendix consists of 12 li^urc- 1 is: eel
on the following page.
A-l
-------
CONTEXTS
A-l Typical Source Printout from IPP Inventory . , A-5
A-2 Typical Area Source Printout from IPP Inventory A-6
A-3 Typical Point Source Printout from IBM Inventory .... A-7
A-4 Typical Emission Summary by Counties
from IBM Inventory A-ll
A-5 Summary of Air Pollutant Emissions for the Study Area
from the NATO Inventory A-12
A-6 Summary of Air Pollutant Emissions, Saint Louis City,
Missouri, from the NATO Inventory A-13
A-7 Combustion of Fuels by Stationary Sources
from the NATO Inventory A-14
A-8 Annual Fuel Consumption by Counties from the
NATO Inventory A-15
A-9 Population and Housing Units by Grids
from the NATO Inventory A-16
A-10 Emission Densities from the NATO Inventory A-17
A-ll National Emission Data System Point Source Listing . . . A-18
A-12 National Emission Data System Condensed Point Source
Listing for Particulate for All Values Greater Than
or Equal to Zero A-19
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A-19
-------
Appendix B
SUMMARY OF EMISSION MODEL REPORTS
B-l
-------
CONTEXTS
LIST OF ILLUSTRATIONS B-7
INTRODUCTION B-9
I Argonne--Chicago Air Pollution Systems Analysis Program:
A Multiple-Source Urban Atmospheric Dispersion Model . . B-ll
II Argonne—Transportation Air Pollutant Emissions
Handbook B-19
III ESSA—The Diurnal and Day-to-Day Variations of
Fuel Usage for Space-Heating in Saint Louis,
Missouri B-27
IV FHA—APRACM0D, Modification of APRAC-1A B-31
V Free University of Berlin—Numerical Simulation of
Temporal and Spatial Distributions of Urban Air
Pollution Concentration B-35
VI GE—Study of Air Pollution Aspects of Various
Roadway Configurations B-39
VII GRC—Evaluation of a Diffusion Model for Photochemical
Smog Simulation B-47
VIII Geomet—Validation and Sensitivity Analysis of the
Gaussian Plume Multiple-Source Urban Diffusion Model . . B-49
IX IBM—The IBM Air Quality Diffusion Model with an
Application to New York City B-61
X IBM—Source Emissions and the Vertically Integrated
Mass Flux of Sulfur Dioxide Across the New York
City Area B-67
XI Metropolitan Washington Council oi Governments--
Estimating Auto Emissions of Alternative Transpor-
tation Systems B-71
B-3
-------
XII National Air Pollution Control Administration—
Saint Louis SC>2 Dispersion Model Study, Basic Data
B-77
XIII National Air Pollution Control Administration—
Emission Factors B-81
XIV NR&E—The Potential Impact of Aircraft Emissions
Upon Air Quality B-87
XV Ontario Department of the Environment—
Information System Descriptive Manual B-93
XVI Pacific Environmental Services—Controlled Evaluation
of the Reactive Environmental Simulation Model B-97
XVII Rutgers University—Comparison of Air Pollution
from Aircraft and Automobiles B-103
XVIII Sacramento Regional Area Planning Commission—
General Inventory of Air Pollution Sources
and Emissions B-107
XIX SRI—A Practical, Multipurpose Urban Diffusion Model
for Carbon Monoxide B-109
XX SRI—Procedures for Estimating Highway User Costs,
Air Pollution, and Noise Effects B-117
XXI SRI—A Preliminary Study of Modeling the Air Pollution
Effects from Traffic Engineering Alternatives B-125
XXII SAI—(1) Contaminant Emissions in the Los Angeles
Basin—Their Sources, Rates, and Distribution.
(2) Extensions and Modifications of a Contaminant
Emissions Model and Inventory for Los Angeles B-131
XXIII SDC—Development of a Simulation Model for Estimating
Ground Level Concentrations of Photochemical
Pollutants B-145
XXIV TRC—Sensitivities of Air Quality Prediction to
Input Errors and Uncertainties B-149
XXV TRW—Prediction of the Effects of Transportation
Controls on Air Quality in Major Metropolitan Areas
B-4
. B-153
-------
XXVI University of Alaska—Ice !'og: Low Temperature
Air Pollution B-lfc-
XXVII University of California 'Lid vis;—I In. J,-i-.,t;
of Highwavs on Air Quality
XXVIII USWB—A Simple Diffusion Model for Calculating
Point Concentration from Multiple Source? B-167
B-5
-------
ILLUSTRATIONS
B-l Flow Diagram for Calculation of Pollution Levels .... B-18
B-2 Speed Adjustment Factors for Hydrocarbon Exhaust
Emission Factors and Carbon Monoxide Emission Factors . . B-23
B-3 Profile of Carbon Monoxide Versus Traffic Flow Rate
at FDR Drive B-40
B-4 Relationship Between Vehicular Pollution Factors
Taken at Exhaust Plane and Traffic Speed B-42
B-5 Flow Chart of Auto Emissions Model B-73
B-6 Exhaust Emission Flow Diagram B-84
B-7 Flow of Source Subroutine B-101
B-8 Automobile Hydrocarbon and Carbon Monoxide Emissions
Added per 1,000 Stops B-120
B-9 Automobile Hydrocarbon and Carbon Monoxide Emissions
per 1,000 Miles of Driving at Uniform Speed B-120
B-10 Automobile Emissions Added from Speed Changes
per 1,000 Vehicle-Miles B-121
B-ll Factor to Convert Reference Year Emissions
to Emissions in Year Y B-122
B-12 Relationship Between Carbon Monoxide Emissions
and Steady-Speed Driving B-127
B-7
-------
INTRODUCTION
In this Appendix we have reviewed and summarized twenty-seven
reports on the treatment and parameterization of man-made emissions.
These summaries are intended to provide the potential user with a con-
cise overview of the scope and content of each of the models. Each
summary identifies the author and his or her affiliation, includes the
author's abstract, and provides a synopsis of the emission model method-
ology together with a statement of the model's format and availability.
For convenience, the reports are listed alphabetically by affilia-
tion. The depth and merit of the various models are reflected in the
summaries. While our review is extensive, it should not be interpreted
as all inclusive. Many reports were reviewed initially and later deleted
from this final listing either because they were of no direct use to
RAPS or because the methods they contained were more appropriately
documented or developed elsewhere.
B-9
-------
I ARGONNE NATIONAL LABORATORY
Chicago Air Pollution Systems Analysis Program: A Multiple-
Source Urban Atmospheric Dispersion Model. J. J. Roberts,
E. J. Croke, A. S. Kennedy, J. E. Norco, and L. A. Conley,
Argonne Natl. Lab. Rept. No. ES-CC-007, prepared for Chicago
Dept. of Environmental Control and National Air Pollution
Control Administration, May, 1970.
A. Author's Abstract
This report is a comprehensive documentation of the dispersion-
model development phase of the Chicago Air Pollution Systems Analysis
Program. A multiple-source, urban atmospheric dispersion model has been
developed which described transients such as morning transitions in
atmospheric stability and mixing layer height. The dispersion model
has been validated by comparison with over 10,000 hourly averages of
sulfur dioxide monitored by the Department of Environmental Control of
the City of Chicago. For example, the model accounts for 50% of the
variance in 6-hr averages of observed data and 70% of the variance in
24-hr averages. Of particular significance in the capability of the
model to describe "area sources" as volumetric clouds of pollutant and
thus to evaluate the effect of these sources on dose points within, as
well as external to^ the area. v ,
i. 1 -)'!,,
The atmospheric transport kernel in the model describes the
instantaneous release (delta function) of pollutant, advection according
to piecewise constant hourly wind vectors, and Gaussian diffusion about
the centroid. Continuous plumes are simulated by integration in time of
this point-source Green's function.
This report details the transport theory and all other computerized
algorithms that influence the dispersion problem and presents statistical
results of extensive validation studies.
B-ll
-------
Bo Summary
An inventory of average hourly emissions of sulfur dioxide was
developed for power plants and industrial, residential, commercial, and
institutional sources in the Chicago area. Hourly SC>2 emissions from
power plants were derived from actual power output logs; hourly emissions
from other sources were obtained through reduction of annual fuel-use data
according to seasonal and diurnal patterns. Major emitters were con-
sidered individually as point sources, with the remaining emissions
grouped into area sources.
1. Treatment of Area Sources
The area source overlay grid is input to the model. It is
specified by five parameters: the x, y coordinates of the origin, the
number of grid squares in the x and y directions, and the length of the
side of each grid square. The grid used in the validation computation
consisted of l.Oxl.0-mile squares at the center of the array, with
surrounding 2.0x2.0-mile, then 4.0x4.0-mile grid squares.
Representative pollutant emission rates and stack heights are
assigned to each grid square for each source: (1) low-rise space
heaters; (2) high-rise space heaters; and (3) industry.
a. Low-rise Residential and Commercial Emissions
Hourly emissions from low-rise residential and commercial
sources are assumed to be proportional to the number of heating degree
days, with a fixed fraction of annual emissions uniformly prorated to
hot water heating. The heating cycle is assumed to occur between the
hours TON and TOFF, with emissions equal to the hot water rate at all
other times. A "janitor function" is synthesized to account for the
greater than usual fuel demand during the first two hours of the
heating day. Hourly emissions are given by
B-12
-------
Q (1-PCTHW) (65-TEMP) TE TEMP <, 65°F and
annual
Q = -—— — TON --" time of day
heating DDAVG (TONOFF) _„_,
— lUrr
Q . (PCTHW)
Q annual
hot water =
8760
Q = Q +Q
total heating hot water
where DDAVG = average number of degree days/year
PCTHW = fraction of annual fuel used for hot water
TEMP = hourly average temperature
TONOFF = TOFF - TON + 1
1.5 TON < time of day < TON + 1
TE = janitor function =
1.0 TON + < time of day < TOFF
All moderate-sized residences were assigned a. stack height of 50 feet.
b. High-rise Residential, Commercial and Institutional
Emissions
These emissions are estimated using the above method, but
with TON =1 and TOFF = 24. Physical stack height was assumed to be
200 feet.
Industrial Emissions. Industrial area source emissions
are distributed uniformly over the calendar year:
Q
Q = annual
hourly
2. Treatment of Point Sources
a. Power Plant Emissions
Actual hourly power output (MW) from Chicago power
plants was used in the validation study of the model. The following
power plant emission algorithms are proposed as the production version
of the program.
B-13
-------
For each season of the year, a graph is made of peak
daily load versus average daily temperature. From these graphs the
peak load is estimated as a linear function of temperature. Several
daily charts of system load versus hour of the day, within a given
season, are superimposed. Weekday patterns for summer and winter result,
with spring and fall having two weekday patterns each that are distin-
guished by average daily temperature. Since little weekend data are
usually available, a typical weekend pattern is used for all seasons.
The patterns are stored as data statements containing 24 numbers, one
for each hour of the day. Given the average daily temperature, the day
of the week, and the season of the year, the hourly system load is
calculated by subtracting the data statement of the appropriate hour
from the peak load obtained from the graph of peak load versus temperature.
If individual unit load is plotted against total system
load, functional relationships of the form
b
U
l
= b, + fb -b \\ ~ai a < S <
a
I \ 2 1} —— 1 2
b La2~aiJ S > a
2 2
where U = individual unit load
S = system load
a , a - b , b = coefficients derived from the graph
1* 2* 1 2 H
can be formulated for each unit, making it possible to obtain individual
unit load from the previously calculated system load.
Graphs of utility company data give the variation of unit
load with thermal input, and coefficients describing this variation are
derived. Thermal input is give i by
T = AL + B
B-14
-------
where T = thermal input
L = unit load
A,B = derived coefficients
Hence, emission rates are determined:
[T(therms/hr)] [105 BTU/therm] (avg. annual % coal used]
" C°al ~ [12,000 BTU/lb] [2000 Ib/ton]
[38.0] [% sulfur of coal]
[12,000 BTU/lb] [2000 Ib/ton]
[T(therms/hr] [10s BTU/therm] [avg. annual % oil used]
[18,000 BTU/lb] [8000 Ib/kgal]
(kgal/hr)
[157] [% sulfur of oil]
[18,000 BTU/lb] [8000 Ib/kgal]
The thermal emission rate is given by
thermal emission rate = 0.12 T
To divide total thermal and S00 emission rates among the
£
stacks, the percent of effluent handled by each stack is applied to
the total rate.
b. Industrial Emissions
The fifty largest industrial source emitters are treated
as point sources, with the remaining emissions aggregated into uniformly
distributed square mile area sources. Plants in this category are
assumed to have a stack height of 150 feet.
A detailed analysis of the diurnal, weekly, and seasonal
operating patterns for each plant was performed, yielding factors by
which the maximum process load is multiplied to obtain the process load
at a given time.
B-15
-------
p /
L = /shift weighting)! monthly weighting I L
I / \ / MAX
\ factor / v factor /
P
where L = process load
P
L = maximum process load
MAX
Shift factors are tabulated for weekdays, Saturdays, and Sundays or
special holidays for each of the three shifts, 0000-0800, 0800-1600,
and 1600-0000 local time. Monthly factors account for seasonal variations.
The space-heating thermal load is assumed to be linearly
related to temperature.
s s [55-T]
L =LMAX IT -IO^T.55
s
where L = space-heating thermal load
s
L = maximum space-heating thermal load
T = temperature
The total thermal load is
s P c o
L = L + L = L +L
c o
where L equals total thermal load and L and L are the portions of the
thermal load due to coal and oil, respectively.
c [LC(therms/hr)] [10s BTU/therm] [36.8] [% sulfur of coal]
S 2 r) [12,000 BTU/lb] [2000 Ib/ton]
o [L°(therms/hr)] [105 BTU/therm] [157] [% sulfur of oil]
S 2 lkgal/hr) - [18,000 BTU/lb] [8000 Ib/kgal]
When a dual-fuel interruptible plant is receiving natural gas, the SO
-------
Heat and sulfur dioxide emissions from a plant are
apportioned to its stacks by weighting the total emission rates by the
percentage of effluent handled by each stack.
c. Additional Sources
Annual emission information from several additional
point sources was stored in a subroutine of the dispersion model and
prorated by appropriate program statements. These emissions were dis-
tributed using one of three source patterns: (1) uniform proration of
annual output; (2) 24-hour degree day response, with 20% of fuel use
prorated uniformly for hot water; and (3) a special diurnal pumping
station pattern. The pumping station pattern allowed two levels of
output, a high level between the hours 0600 and 2200, and a low level,
about one-third of the daytime emission rate, at all other times.
C. Model Availability
A program listing of the Argonne emissions model is included in
the report.
B-17
-------
"ITRAN"
ENTRY "TRAN"
(CONCENTRATION FROM A POINT SOURCE)
I. CALCULATE HEIGHT OF VIRTUAL
SOURCE ABOVE LID.
2. CHECK ON SOURCE SIGNIFICANCE
BY PLUME CALCULATION
YES \ NO
f RETURN
3. EVALUATE CONCENTRATION AT
HOUR M FROM ONE-HOUR EMISSION
START IKB AT HOUR M-N
2. CONCENTRATIONS DUE TO POINT SOURCES
3. CALCULATE HOURLY EMISSIONS FROM AREA
SOURCES
«. CALCULATE CONCENTRATIONS FROM REFERENCE
AREA SOURCE GRID.—
PSEUDO"
VIRTUAL UPWIND
SOURCE CALC.
5. TRANSLATE REFERENCE AREA SOURCE GRID TO
EACH RECEPTOR POINT AND CALCULATE
CONCENTRATIONS.
SEPARATE COMPUTER PR06RAMS
S A-2579-25
FIGURE B-1 FLOW DIAGRAM FOR CALCULATION OF POLLUTION LEVELS
B-18
-------
II ARGONNE NATIONAL LABORATORY
Transportation Air Pollutant Emissions Handbook. T. D. Wolsko,
M.T. Matthies, and R. E. Wendell, Argonne National Laboratory,
Argonne, Illinois, July, 1972.
A. Author's Abstract
The objective of this report is to describe a procedure that can
be used to quantify the air pollutant emissions (carbon monoxide, hydro-
carbons, nitrogen oxides) from transportation sources. The report is
designed to enable someone not familiar with air pollution problems from
transportation systems to quantify such emissions with the aid of the
background information, emission data, computer program descriptions,
and manual calculation procedures contained herein. Although emissions
from transportation vehicles are not completely understood, the state-
of-the-art of measuring air pollutant emissions from transportation
sources is presented. This report will be revised if and when a more
complete understanding of emissions from transportation sources evolves.
Section 1 provides a brief background on the sources of air pollu-
tion from transportation vehicles. The basic emission data used to
calculate transportation emissions are described in Section 2. Two
computer programs that can be used to calculate emissions are described
in Section 3. The first program, TREFACT, calculates emission factors
for widely varying vehicle characteristics and transportation system
operation. The second program, TREMISS, uses regional transportation
simulation data to calculate emissions for large transportation networks.
Complete source listings of both programs are given in Appendix A.
Manual emission calculation procedures are illustrated by means of
sample problems in Section 4. These procedures are designed to provide
B-19
-------
a simple method for calculating air pollutant emissions from roadways.
A fairly complete set of emission factors is tabulated in Appendix B,
so that transportation emissions can be calculated without using either
of the computer programs.
B. Summary
Two computer programs were written to relieve the environmental
impact analyst of the emission calculation burden. The first program,
TREFACT, calculates the emission rates for vehicles or mixes of vehicles
based on vehicle usage (speed, vehicle miles, type of trip). The second
program, TREMISS, was developed to calculate emissions for the complex
traffic simulation grid used by urban regional transportation planners.
The outputs from a typical regional traffic simulation model (origins,
vehicle miles, speed) and TREFACT outputs are used to calculate grid-
dependent emissions for the entire urban region. A detailed description
of each of these programs is provided, along with input descriptions and
sample problems. Complete listings of both programs are contained in an
appendix.
The computer programs are used to calculate grid-dependent emissions
for large urban transportation networks. A method for manually calculating
air pollutant emissions from transportation sources is described in another
section of the report. This method is designed to calculate emissions from
individual transportation links (e.g., highways).
The emission factors calculated by TREFACT are for the standardized
urban and rural speeds. TREMISS has speed adjustment data to modify the
emission factors for the speed-dependent transportation network. The
speed adjustment curves from the figures accompanying this summary have
been transformed into a set of linear equations that will be used to
adjust the emission calculations for individual roadways. Urban and rural
speed adjustment factors should be used with urban and rural emission
B-20
-------
factors, respectively. It should be remembered that this speed adjust-
ment data are representative of typical traffic and does not represent
free-flowing traffic at that average speed.
TRUFACT calculates emission factors for transportation systems with
widely varying vehicle types and operating conditions. Emission factor
can be calculated for any year between 1960-1990 for six classes of
vehicles. A vehicle-class-weighted emission factor is also calculated.
Options are available to include the effects of cold-start operation,
retrofitting control devices on pre-1968 vehicles (Class 1 only), and
consideration of diesel-powered vehicles.
Urban regional transportation planning involves an enormous computer
traffic simulation system. The typical Chicago Area Transportation Study
(CATS) traffic simulation network has approximately 1,800 transportation
activity grids which cover the eight-county Chicago metropolitan area.
A computer program was written to calculate grid-dependent air pollutant
emissions (CO, HC, NO ) from transportation sources for the CATS planning
x
area. This program, TREMISS, uses output data files from the CATS traffic
simulation model, but data from any traffic simulation program can be used,
Regional traffic simulations are quite costly and there may not
always be data available for the study year. Therefore, TREMISS has the
additional capability of linearly interpolating between simulation output
data for two study years. This is extremely helpful when traffic data is
not available for a particular year. TREMISS is structured to interpolate
between 1965 and 1985 simulations for any intervening year.
Emissions can be calculated for each grid using either one set of
"weighted" emission factors or six sets of emission factors, one for each
class of vehicles. (Diesels [Class 6] may or may not be used in the
calculation.) Additionally, the complete set of grids may be grouped into
B-21
-------
any number of subsets. Each such subset has its own" set of emission
factors. However, the mixing of "weighted" and "class" emission factors
is not permitted.
Emission summaries for each grid and the totals for all grids are
the major outputs. If class-dependent emission factors are used,
summaries by class are also given. Emission summaries for any specified
set of grids representing a county, municipality, or problem area (e.g.,
Cook County, Chicago, Chicago Business District) can also be calculated.
B-22
-------
2.0
tr
o
o
1.0
o
o
2.0
DC
O
O
<
5
W
3 1.0
o
CJ
URBAN TRAVEL
1.2
1.1
DC
O
u
u. 1.0
h-
z
UJ
S
§ 0.9
\
O
O
0.8
0.7
RURAL TRAVEL
10 15 20 25 30 35 40 45 50 55 60 65
SPEED — mph
URBAN TRAVEL
CC
O
O
ui
5
(-
§
o
1.2
1.1
1.0
0.9
\
0.8
0.7
RURAL TRAVEL
10 15 20 25 30 35 40
SPEED — mph
45 50 55 60 65
SA-2579-26
FIGURE B-2 SPEED ADJUSTMENT FACTORS FOR HYDROCARBON EXHAUST EMISSION
FACTORS AND CARBON MONOXIDE EMISSION FACTORS
B-23
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Within
Program
x
x
x
x
User
Supplies
x
X
X
X
From
AP-42
x
X
X
X
Proxies/Comments
Factors available in
report for use as
desired
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Spatial Distribution
-Links
-Grinds
-Area
x
x
NP
NP
x
x
NP
NP
NP
x
NP
NP
X
NP
NP
x
X
NP
NP
NP
x
NP
NP
NP
NP
NP
NP
For heavy duty truck
calculations
NP = No provision
B-24
-------
Output Available
Pollutants
-CO Yes
-HC Yes
-NOX Yes
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x
Language FORTRAN ; PL/1
Equipment IBM
Validation/calibration; Chicago area and EPA data
Time resolution; Hourly (peak/off-peak); daily
Spatial resolution; Subareas; grids
B-25
-------
Ill AIR RESOURCES FIELD RESEARCH OFFICE, ESSA
The Diurnal and Day—to-Day Variations of Fuel Usage for
Space-Heating in St. Louis, Missouri. D. B. Turner,
Atmospheric Environment, 1968.
A. Author's Abstract
Data on the wintertime emissions of SO from residential and
£t
commercial space-heating sources by 2-hr periods were needed for use in
a diagnostic dispersion model. Analyses were made of hourly steam-
output data from a centralized heating plant and hourly gas~sendout
data for December 1964 at St. Louis, Mo., to determine dependence upon
temperature and other factors. Methods were then developed to determine
the rate of fuel use from residential and commercial space-heating
sources for each hour of the day from values for the hourly temperature,
the hour of the day, and the day of the week. Relations developed from
December 1964 data were tested on data for January and February 1965.
B. Summary
1. Description of Algorithm
A mathematical representation of the diurnal and day-to-day
variations of fuel consumption resulting from residential and commer-
cial space heating was developed for St. Louis, Missouri. Available
data included hourly temperatures and hourly gas and steam output rates.
Steam output rates are taken as representative of commercial sources,
and gas send-out rates are taken as representative of residential sources,
To obtain equations relating daily output and daily temperature,
graphs were constructed to give the least-squares linear regressions for
weekdays and weekends. For weekdays, 20 data pairs were used to give
B-27
-------
DS = 8.40 - 0.1078 DT
DG = 30.00 - 0.3498 DT
where DS = daily steam output rate
DG = daily gas send-out rate
DT = daily temperature
The effect of short-term temperature variation on the diurnal cycle
of space heating was investigated by determining the deviation of the
hourly temperature from the daily value for both gas and steam, and then
averaging these deviations for each hour for all weekday data. Plots of
both gas and steam deviations versus temperature deviation were made, and
the finite difference forms of the daily send-out equations were applied
to give output deviations with zero temperature deviation. These hourly
values, calculated for weekdays, Saturdays (only four data days were used)
and Sundays (only five data days were used), represent diurnal variation
due to all causes other than temperature.
To make these variations applicable to other space heating sources,
the values were divided by the slope of the regression line, thus con-
verting to units of temperature. Saturday and Sunday values were
adjusted so that the same total daily heat is required for a day with the
same daily temperature, regardless of whether it is a Saturday, Sunday, or
weekday.
Hourly steam output or gas-send-out is given by
A = a+b(T+H)
where A = hourly output
a,b = regression constants in daily output equations
T = hourly temperature
H = diurnal value (calculated above) for appropriate
hour of day, day of week, gas or steam
B-28
-------
It is assumed that no lag tir.ie exists between a change in temperature
and its effect on space heating requirements.
2. Validation
The above equation was derived from data recorded in St. Louis
during the month of December, 1964, and was used to calculate hourly
output for January and February, 1965, for comparison with actual data.
Error was measured in degrees Farenheit. For steam, 71% of the cal-
culated values were less than 6 F in error, and 95% were less than 12°F
in error. For an hour with a temperature of 30°F, an error of 12°F
results in about 25% error in steam output.
Overall, 80% of the calculated data were within ± 7°F of the
actual values. The author states that this degree of accuracy is
commensurate with the accuracy of the emission inventory. It is assumed
that derived diurnal factors for gas will be nearly the same for oil and
coal, and that steam factors will reflect patterns of commercial space-
heating with any fuel.
C. Model Availability
A computerized treatment for calculation of hourly emission estimates
is not referenced in the paper.
B-29
-------
IV FEDERAL HIGHWAY ADMINISTRATION
APRACM0D, Modification of APRAC-1A. P. W. Blow, FHWA.
A. Author's Abstract
None given.
B. Summary
APRACM0D is a modified version of the APRAC-1A urban diffusion model
reported on in A Practical, Multipurpose Urban Diffusion Model for Carbon
Monoxide. The purpose of the model is to estimate carbon monoxide con-
centrations at arbitrary points in an urban region resulting from pro-
jected vehicular travel.
The APRAC-1A urban diffusion model was modified to accept a standard
FHWA Urban Transportation Planning Battery BUILDHR data set with carbon
monoxide emission words added on each link record as input rather than
the link card inputs for APRAC-1A. The modification further allows more
links to be processed than the original program.
The program is composed of 17 separate decks and is written in
FORTRAN.
B-31
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Within User From
Program Supplies AP-42
x
x
X
X
X
X
X
X
Proxies/Comments
Average CO of vehicle
mix for year of study
interest
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Spatial Distribution
-Links
-Grinds
-Area
NP
NP
x
NP
NP
NP
x
NP
NP
x
x
NP
NP
NP
NP
NP
NP
NP
Speed and Distance
NP = No provision
B-32
-------
Output Available
Pollutants
-CO Yes
-HC No
-NOX No
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x
Language FORTRAN
Equipment IBM 360
Validation/calibration; Based on APRAC-1A
Time resolution; i hour
Spatial resolution; Radial segments-22.50 out to 16 km
from receptor point
B-33
-------
V FREE UNIVERSITY OF BERLIN
Numerical Simulation of Temporal and Spatial Distributions of
Urban Air Pollution Concentration. H. D. Fortak, Proc. of Symposium
on Multiple-Source Urban Diffusion Models, Chapel Hill, N.C., APCO
Pub. No. AP-86, 1970.
A. Author's Abstract
A multiple-source diffusion model for the simulation and prediction
of long-term (climatological) ground-level sulfur dioxide concentrations
in urban areas is described. The computer input consists of data from an
emission source inventory together with statistics on relevant diffusion
parameters.
Because of the capacity of available computers, only a limited number
of the largest emission sources (approximately 150) can be treated indivi-
dually. Smaller industrial emission sources are treated as residential
sources. These are represented by a large number of stacks (about 150)
of the same dimensions, distributed over areas of 1 square kilometer, for
which the mean area emissions have been estimated.
The meteorological input consists of data on wind direction, wind-
speed, and Pasquill-Turner stability classes. These parameters are
assumed to be spatially homogeneous throughout the metropolitan area.
Low-level emissions (residential) are correlated with low-level windspeeds
and Pasquill-Gifford diffusion parameters, whereas high-level emissions
(industrial) are correlated with extrapolated windspeeds and Brookhaven
diffusion parameters. The program also uses corresponding statistics for
urban boundary layer depths and values for parameters affecting absorption
at the earth's surface.
The diffusion model used is basically Gaussian. It is modified,
however, such that turbulent diffusion is restricted exclusively to the
B-35
-------
depth of the urban boundary layer. This is true for all sources having
effective emission heights less than the height of the upper limit of The
boundary layer. The rate of decay of sulfur dioxide is taken into account,
as well as the experimentally determined absorption at the earth's surface.
The model calculates fields of steady-state ground-level concentra-
tions that correspond to a given spatial distribution of emission sources
and to any possible combination of relevant meteorological diffusion para-
meters. Knowledge of frequency distributions of these meteorological
diffusion parameters permits the derivation of frequency distributions of
ground-level concentrations for any location within or outside of the
metropolitan area. The computerized experiments simulate frequency dis-
tributions of ground-level concentrations for a great number of regularly
arranged grid points (up to 2500 with a mesh size of 500 by 500 meters)
and for a variety of time periods (months, heating period, seasons, year,
etc.). The frequency distributions are characterized by a limited number
of parameters (mean, percentiles, etc.). Each parameter is plotted as a
system of isograms on a map of the metropolitan area.
Experiments to validate the model were conducted during the heating
period in 1967-68 at four continuously monitoring stations that had been
installed at special locations within the limits of the metropolitan
area of Bremen. During the sampling period, the assumption of a suffi-
ciently homogeneous wind field was validated by wind measurements at the
same locations. The calculated frequency distributions of half-hourly
mean values of concentrations generally agreed fairly well with those
derived from observed values. Comparison, however, shows that the model
does not simulate ground-level concentration fields in the vicinity of
industrialized areas very well, because uncontrollable low-level emissions
from industrial plants could not oe taken into account in the diffusion
model.
B-36
-------
B. Summary
Sources in the city of Bremen, Germany, are classified in three
groups. First are individual stacks with emission rates greater than
one kilogram of SO per hour, which are treated as point sources. Records
^
for these sources contain information as to output by volume, maximum
emissions, and mean winter and summer emissions. Whenever possible,
daily emission variation data and emissions for holidays were obtained.
The second source category includes individual stacks from small
industries that contribute less than 0.02% each to the total emission
rate. Since these sources contribute such a. small amount to the total
emissions, they are treated in the same manner as emissions from space
heating.
Emissions from space heating are apportioned as area sources in a
500 X 500-meter grid. From the sulfur content of the fuels, the total
fuel consumption and the total number of dwelling units, a mean emission
rate of 8 g SO /hr per dwelling unit is obtained. This rate assumed the
£1
daily mean temperature remains constant throughout the heating period.
To make emissions from space heating a function of time, a relationship
between daily mean temperature and daily emissions is used in some cases.
Multiplying 8 g/hr (or the corresponding temperature corrected value)
by the number of dwelling units in each area gives the mean emission rate
for that area. This is then divided by the number of individual stacks
representing the area source.
C. Model Availability
A computer code for the emission inventory methodology is not pre-
sented.
B-37
-------
VI GENERAL ELECTRIC COMPANY
Study of Air Pollution Aspects of Various Roadway
Configurations; APTD-1146; New York City Dept. of
Air Resources, EPA, FHWA, New York Division Office;
Prepared by General Electric Company, 3198 Chestnut
Street, Philadelphia, Pa., 1971.
A. Author's Abstract
This experimental study was undertaken to (1) ascertain the current
air quality in the immediate vicinity of various urban roadway con-
figurations; (2) determine how the selected urban roadway configurations
aid or hinder the diffusion of the pollutants emitted by urban traffic;
and (3) develop mathematical relationships between traffic, traffic
speed, pollutant concentration, meteorological parameters and roadway
configuration. It is possible to assess the impact of the 1970 Clean
Air Act by future monitoring programs which determine the extent to which
the clean air goals are achieved. It is possible to identify those areas
in and around a given configuration that do not meet the National Air
Quality Standards under certain meteorological and traffic conditions.
It is possible for urban and transportation planners to compute in
advance the pollution anticipated for a proposed roadway design in order
to insure that dwellings adjacent to the proposed design will not be
exposed to pollutant concentrations in excess of the National Air Quality
Standards.
B. Summary
One section of the report presents nomographs which provide highway
designers with gross estimates of the CO concentration to be expected
for various roadway configurations. Figure A presents representative
relationships of CO concentration versus traffic flow rate for one of
B-39
-------
90
80
70
60
50
8 40
30
20
10
I
I
2000 4000 6000
TRAFFIC FLOW RATE — N vehicles per hour
8000
SA-2579-27
FIGURE B-3 PROFILE OF CARBON MONOXIDE VERSUS TRAFFIC FLOW RATE AT
FDR DRIVE
B-40
-------
the ten sites monitored during this study. The CO values shown in the
curves are those of the vertical plane of probes reflecting the maximum
values. The traffic values shown are those of the total traffic involved.
Regression and correlation analyses were performed to determine
whether the relationship between environmental variables and carbon
monoxide and hydrocarbon concentrations could be predicted mathematically.
The resultant simple correlation coefficients and multiple correlation
coefficient, R, show large variations. It must be concluded, therefore,
that the postulated regression equation does not adequately describe the
effects of the wind parameters on the carbon monoxide and hydrocarbon
concentrations .
It is assumed that any given roadway can be divided into independent
line sources and each probe located at the roadway measures the concen-
tration from its immediate line source plus the concentration emanating
from the other (x-1) line sources. Furthermore, it is assumed that the
concentration decreases exponentially with distance from each probe
location.
The model was further generalized by defining the vehicular pollution
factor, , by dividing the unperturbed concentration, CC>2, by the traffic
flow rate in the immediate vicinity of the probe location.
The vehicular pollution factor, ,
CO
* = ~ (1)
N
For eight of the configurations evaluated, it was found that d>
shows a strong linear relation with traffic speed (correlation of 0.81)
as shown by Figure B. The viaduct site exhibited pronounced meteorological
effects which resulted in the two lower points to the extreme left in the
figure. The low velocity points in the lower right hand is data collected
B-41
-------
8 4
oo a
o
<
LL
O
t-
O
a.
O
I
UJ
S
in o in
IN
TJ- to !">
MdLU — d33dS OlddVdi
CO
CC
O
u
O
a.
O LLI
z: LU
I Q.
LU 00
>0
LU
LU
CO LLJ
CO
51
LU X
CC UJ
<•
CO
LU
OC
o
LL
B-42
-------
in a city street. Data from the tunnel sites was not included in
Figure B.
The linear relation between <£ and traffic speed was found to be :
4> = [-0.51 (T ) + 26.9] = 10~3 (2)
i i
for T: 15 mph =£ T ^ 49 mph
(PPM-Hr)
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Within User
Program Supplies
From
AP-42 Proxies/Comments
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
NP
NP
NP
NP
Experimentally
measured at roadside
for CO
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
NP
NP
NP
NP
NP
{Average speed at point of
fmeasurement
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
NP
NP
NP
NP
NP
NP
NP
Hourly traffic count
at point of measurement.
Spatial Distribution
-Links
-Grinds
-Area
NP
NP
NP
{.Determined for area
I bounded by roadway
/configuration
NP = No provision
B-44
-------
Output Available
Pollutants
-CO Yes
-HC No (Could not cotair. statistically 'good'
results)
-NOX No
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x
Language NP
Equipment NP
Validation/calibration; Calibrated/validated for 10 different roadway confi<
urations in NY Metropolitan area
Time resolution; 1 hour
Spatial resolution; Area occupied by roadway configuration
B-45
-------
VII GENERAL RESEARCH CORPORATION
Evaluation of a Diffusion Model for Photochemical Smog Simulation.
A, Q. Eschenroeder, J. R. Martinez, and R. A. Nordsieck, Final
Report, General Research Corporation, prepared for Environmental
Protection Agency, Contract No. 68-02-0336, October, 1972.
A. Author's Abstract
Extensive improvements have characterized this evaluation of the
GRC Photochemical/Diffusion model. Despite the limitations of smog
chamber experimental data, they have served an essential purpose toward
updating the kinetics portion of the model. Consistency of rates and
reactivities is now achievable using recently measured coefficients for
a wide variety of systems. Model methodology revisions have enhanced the
realism of the advective and diffusive descriptions. Previous assumptions
regarding transverse (cross-streamline) horizontal diffusion have been
confirmed by an exhaustive series of parametric tests. Photochemical/
diffusion validations were successful for trajectories occurring during
four days of the 1969 smog season in Los Angeles. Our measure of success
is concentration-history fidelity with a minimum of adjustments of
diffusion parameters. (Chemical coefficients were scaled from the smog
chamber studies and held fixed for the simulations carried out to date.)
Future directions for air pollution model development are discussed in
detail in an appendix as information supporting the experimental recom-
mendations.
B. Summary
General Research Corporation's diffusion model uses hourly emissions
of carbon monoxide, ozone, hydrocarbons, nitrogen dioxide and nitric oxide
B-47
-------
from both fixed and moving sources on a 2-mile interval grid to predict
pollutant concentrations. The emissions inventory used is that compiled
by Systems Applications, Inc., (see Review No. XXII).
B-48
-------
VIII GEOMET
Validation and Sensitivity Analysis of the Gaussian Plume
Multiple-Source Urban Diffusion Model. R, C. Koch and S.
D. Thayer, Geomet, Inc., Final Report, Contract No. CPA
70-94, prepared for Environmental Protection Agency,
November, 1971.
A. Author's Abstract
This report, submitted by GEOMET to the National Environmental
Research Center, presents the analysis and results of a program of
validation and sensitivity analysis of the steady-state Gaussian plume-
type of urban diffusion model.
The report develops a careful definition of the fundamental short-
term steady-state model and its various modes of implementation, in
terms of emission and environmental input parameters, and of calculational
modes. A set of computer programs developed especially for validation
and sensitivity study purposes is described.
The validation study consists of a variety of comparisons of short-
term and long-term concentration predictions from the model, with com-
parable measured 862 concentrations covering three months of 2-hour
values at ten locations in St. Louis, and one month of 1-hour values at
eight locations in Chicago. The predictions use hourly estimates of
meteorological and emission parameters. The atmospheric stability is
estimated from hourly weather observations from an adjacent airport
using the McElroy-Pooler diffusion parameters based on Turner's
definitions of stability categories. The mixing layer ceiling is
estimated from radiosonde observations taken twice daily from remote
locations (100 to 200 miles away). The wind speed and direction are
B-49
-------
hourly averages of several continuous records. The emission rates of
the largest sources are identified and located individually. For other
sources a mean emission rate per unit area is estimated for a square
gridwork of points with a one-mile spacing between adjacent points.
Each emission rate is related to hourly estimates of space heating and
other operating requirements. No "calibration" or other adjustment of
the inputs or output concentrations is employed anywhere in the analysis.
Individual short-term model results (1- to 2-hour periods) show
large deviations from the observed concentrations, but the frequency
distributions of calculated short-term concentrations over a month or a
season compare quite well with the comparable frequency distributions
of observed concentrations. No single factor could be found which
accounts for a significant fraction of the individual deviations.
Predicted long-term concentrations show consistently good agreement with
observations, as contrasted with the significant overestimation usually
found in other model implementations.
A technique is proposed for calculating the long-term estimates
which obviates the need to calculate every short-term concentration in
the period. A sampling process is used in which a statistically selected
set of as few as five to ten percent of the short-term periods is employed,
and the representativeness of the distribution is maintained.
In the sensitivity analysis, the insensitivity of the model con-
centrations to the parameters of wind speed profile parameter value, and
the distribution of area source emission heights, is demonstrated.
Quantitative description is given of the sensitivity of the model to the
following parameters: changes in spatial variability in emissions,
vertical diffusion parameter, pollutant half-life, wind speed, mixing
ceiling, wind direction, and downwind variation in emission rates.
B-50
-------
Finally, recommendations are made on implementation and use of
the model described herein, and for further study in special problem
areas highlighted by the study.
B, Summary
The GEOMET multiple-source diffusion model designates individual
sources with emission rates significantly higher than the average rate
as point sources, e.g., power plants, large industrial plants, and
large commercial and municipal emitters. For the remaining emitters
(area sources such as residential homes, high-rise apartments, and var-
ious smaller commercial and industrial establishments), it is assumed
that an average emission rate is representative of a small region.
Hourly estimates of SO point and area source emission rates are required.
£t
Two methods of parameterizing temporal and spatial variations of these
rates have been used in this study, one for the St. Louis emissions data
and one for the Chicago data.
1. Treatment of St. Louis Emission Data
The following algorithms for estimating S02 emissions as a
function of time of day, day of week, location, and temperature were
adapted from methods developed by D. B. Turner.*
a. Area Sources
Area source emissions were divided into 5000 X 5000-foot
grid squares, and included data from residential, commercial, river
vessel, automobile, railroad, backyard burning, and industrial sources.
Turner suggests the following algorithm for area sources:
Personal communication to GEOMET, Inc.
B-51
-------
Q(t) = Q + q D (t) + Q F (t) + q D (t) + Q + Q F (f> + Q + Q
rrr cc c c v a a wb
+q D (t) + Q F (t)F (t)
x c p d h
D (t) = 65-T(t) - A (t)
r r
D (t) = 65-T(t) - A (t)
c c
where Q(t) = SC>2 emission rate
Q = base residential S00 emission rate
r ^
q = residential heating S00 emission rate per degree
r ^
T(t) = ambient air temperature
A (t) = residential temperature correction factor
r
A (t) = commercial temperature correction factor
c
Q = base commercial SO emission rate
c 2
F (t) = commercial diurnal variation factor
c
q = commercial heating SO emission rate per degree
c 2
Q = river vessel SO emission rate
v 2
Q = base automotive SO emission rate
a 2
F (t) = automotive diurnal variation factor
a
Q = railroad SO emission rate
w 2
Q = backyard burning SO emission rate
D £
q = industrial heating SO emission rate per degree
x 2
Q = base industrial process emission rate
P
F (t) = industrial day of week variation factor
d
F (t) = industrial diurnal variation factor
h
B-52
-------
Introducing subscript notation, let i,j appear on
parameters that vary from one area source to another, let k denote fuel
type, a denote annual or national average, and & refer to the hourly
period. Then
q, _ !fuu v^ Vv ij
lr) ij D R JL*I E H
k k
where H = average annual U.S. household space heating energy requirement
a
D = average annual U.S. degree days
a
R = average number of rooms per U.S. household
a
R. . = average number of rooms per dwelling unit in grid square (±t j)
S = SO emitted per unit fuel k
E = heating efficiency of fuel k
k
H = heat content of fuel k
k
( N } = number of residential dwelling units using fuel k in grid
\ k/iJ
square (i,j)
(Qr)ld ' IT" ("rjij "
w
s • • -
K = number of fuels
where F = summer day fuel consumption as fraction of average winter day
s
D = average winter degree day
w c
K
S k=l
where At = duration of season
s
C . = number of commercial sources in grid square (i,j)
W = annual quantity of fuel k used by JLth source
B-53
-------
w
k!
S
fraction of annual quantity of fuel k used by 2th source
in summer season
SO emitted per unit of fuel k
2 _ c
K "
k
k=l
W
-Q
where D = winter season degree days
w
w
kl
fraction of annual quantity of fuel k used by 2th source
in winter season
i y^
~Dw ^
w
.w
where I = number of industrial sources in grid square (i,j)
Substituting, the original algorithm becomes
Q±J(t> =
+
+
™ _ 4
F D
-^ + 65-T(t)
1-F
s
H R. . •^ S /N j. .
r D R ^~^ E H
a a k=l k k
, r C ..
F (t) 1 K ij
c 65-T(t) - A (t) -r~> y-^ / v
CiC
s
65-T(t) - A- Ct)
c
D
w
r\ m*^*^f •*^ *H^^V * ' -T*"^
w _ L k=l 1=1
TK CU 1
V^ s V^ (^\ w
k=l 1=1
+ Q + Q F (t) + Q
v a a w
65-T(t) - A (t)
c
rK
D
ij
w
k=l
B-54
-------
I
ij
+ F (t)F (t) V"1 /Q
d h \ p/1
_
Effective emission heights for area sources are
determined primarily on the basis of prevalent building heights in the
area.
b . Point Sources
Industrial Sources. Sulfur dioxide emissions from
industrial sources are comprised of emissions from process and space
heating fuel consumption. Process fuel emissions are estimated by the
product of the peak process emission rate and factors related to the
temporal variation of emissions. Space heating emissions are determined
in terms of degree day response. The SO emission rate, Q(t), is
2i
expressed by
Q(t) = Q F (t)F(t) + q D (t)
p d h x c
where Q = peak process SO emission rate
p 2
F (t) = fraction of peak for day of week
d
F (t) = fraction of peak for hour of day
h
q = heating fuel SO emission rate per degree
x 2
D (t) = 65 - [T(t) + A (t)]
c c
T(t) = ambient air temperature
A (t) = commercial temperature correction factor
c
The quantities Q , F (t) and F (t) are obtained from
p d h
questionnaires containing plant operation statistics. According to
season, q is
x
B-55
-------
n
r—i
q = — } W S
x D t-J J j
a j=l
or
n
q = — ) F . W S
X °w j=l
where W . = annual quantity of fuel j usecl
J
S = SO emitted per unit fuel j
j 2
D = annual number of degree days
a
D = winter season degree days
w
= fraction of annual quantity of fuel j used in winter season
w) J
The SO emission rate equation then becomes
£i
n
Q(t) = Q F (t)F(t) + [65 - T(t) - A (t)] ^- ) W S
p d h c D 4—< j j
a j=l
In addition to emission rate parameters, data inputs
include location coordinates of the source, physical stack height and
the plume rise, wind-speed product.
Power Plant Sources. According to the available data,
one of two methods of estimating power plant emission is used.
For some plants hourly power outputs were available. The
information included graphs of fuel weight flow rate, stack temperature,
and stack exit gas volume flow rite as functions of power output. A
linear relationship was found between fuel flow rates and power output:
F = A + AL
B-56
-------
where F = fuel weight flow rate
L = power output
A ,A = empirical parameters
Emission rates are
S
Q = FE
100
where Q = SO emission rate
£t
F = fuel flow rate
E = SO emission factor
2
S = percent sulfur content of fuel
Stack temperature and volume flow rate are also linearly
related to output, but it was found necessary to divide the range of
power outputs from zero to a peak value into three equal parts and to
use a linear approximation over each part or class of the range. This
gives
(L-L )
Ts =
P3
I
P/3
where T = stack temperature
s
L = power output
L = lower limit of power output class
J6
L = peak power output
P
T = stack temperature for power load of L«
Ms
T = stack temperature for power load of L „ + 1/3 L
u * I
and
B-57
-------
v = v + 7— v - v
s i ^rr u ,
where V = stack gas volume flow rate
s
V = stack gas volume flow rate for power load of L,
J6 /o
V = stack gas volume flow rate lor power load of L + 1/3 L
u I
For another plant, estimates were made for each two-hour
period of the day of average emission rates, wind speed and plume rise
product. These estimates were derived by government personnel based
on discussions with the plant operator, and are assumed representative
of all days in the data period.
2. Treatment of Chicago Emission Data
The algorithms employed by GEOMET to obtain hourly SO emission
^
rates from the Chicago emission data were those developed by Argonne
National Laboratory and described previously in Section II of this
appendix. The area source grid consists of one mile squares. Additions
to the algorithms include Turner's temperature correction factor in the
residential and commercial area source treatments and the following
equation for describing heat emission rates for the three classes of
additional point sources:
240 Q
H = 0.15
6 -— 3680 N
100
where H = heat emission rate
e
Q = SO emission rate
£
S = percent sulfur content of fuel
N = number of stacks
B-58
-------
3. Model Sensitivity
Sensitivity to the vertical distribution of area source
emissions was investigated. It was concluded, for the cases tested,
that in general the differences resulting from the use of various
vertical distributions were negligible.
To study model sensitivity to diurnal variation in emission
rates, the factors(represented by algorithms) which affect emission rate
estimations were analyzed. The inputs to the algorithms are temperature,
electric power load at generating stations, and hour of the day, but
errors associated with the measurement of these inputs are small. There-
fore, the assumptions made in formulation of these algorithms were
investigated.
In the emissions model, annual emission rates act as scaling
factors for diurnal variations; an error in one of these estimates
creates systematic error in model predictions. However, the authors
feel that if concentrations are considered at a number of widely
dispersed locations, the errors tend to cancel each other.
Sulfur dioxide emissions are characterized as arising from
three classes of operations: (1) electric power generation, (2)
industrial processing and (3) space heating. Hourly power plant
emissions are estimated by means of linear relationships with hourly
electric power load of individual generating units. Error in this
regard is small, but uncertainty would be increased for proposed power
plants where hourly loads would need to be predicted.
Diurnal emissions from industrial processing are allocated on
the basis of utilization factors concerning day of the week, hour of the
day, etc. Variations between actual and estimated emissions may be
considerable, since industrial operations must respond to fluctuations
in demand and breakdowns in equipment. The authors state that errors
B-59
-------
in emission rate estimated in the immediate vicinity of individual
sources result, but a random distribution of these over an urban area
tend to balance.
Algorithms for emissions from space heating operations contain
correction factors such as Turner's temperature correction factor and
Argonne's "janitor function." It was felt that random errors associated
with individual sources of these types are probably small, and a more
serious type of error is associated with the assumputions in the algorithms
of no time lag between outside temperature change and fuel consumption.
This type of error is critical to short-term model sensitivity, sometimes
resulting in an emission error of a factor of 2; it is not important
in calculating long-term concentrations.
Systematic errors due to unseasonable temperatures can result
when thermostats are controlled only partially by temperature, since the
algorithm will immediately respond to the temperature change. In these
cases, the emission rate error will persist for the duration of the
unseasonable weather. The resulting concentration error will be minimal
over the long-term, but short-term concentration error will be directly
proportional to the emission error. A careful study of the magnitude
and duration of heating emissions is required to determine the nature of
heating system response to rapid and extreme temperature fluctuations.
C. Model Availability
Computer program listings of the emissions treatments for both
Chicago and St. Louis data appear in the GEOMET report.
B-60
-------
IX IBM
The IBM Air Quality Diffusion Model with an Application to New
York City. L. J. Shieh, P. K. Halpern, B. A. Clemens, H. H.
Wang, and F. F. Abraham, IBM Corp., Palo Alto Scientific Center,
Report No. G320-3290, June, 1971.
A. Author's Abstract
An experimental multisource air pollution diffusion model based on
the Gaussian plume formulation is described. The model is capable of
incorporating point and area sources, time and space dependence of
source strengths, and time and space dependence of meteorological
variables. Numerical experiments sumulating the SO concentration
£
distribution for New York City are presented for January 11, 1971. The
numerical results agree favorably with experimental measurements.
B. Summary
The IBM diffusion model requires 2-hourly SO emissions data in
£i
order to predict the average ground level concentrations for a 2-hour
interval. The emissions treatment recognizes individual point sources,
which include power generation, municipal incinerators, manufacturing
plants, industrial combustion, chemical and oil refinery and mineral
smelting, large hospitals, etc; and area sources, which account for
commercial and domestic space and hot water heating.
1. Treatment of Area Sources
The grid square size for area sources is variable, allowing for
specification of different grids for urban, sub-urban, and rural areas.
These specifications are based on receptor locations relative to the
sources and the distribution of source strengths. Computations were
B-61
-------
made using a 1.0 x 1.0-mile square grid in the urban area, 2.0 * 2.C in
the sub-urban area, and 4.0 X 4.0 in the rural area. Necessary inputs
include the number of different area source sizes usec and the number of
area source grids in each area source size; the model groups source grids
(for any grid size) in a rectangular region, thus also requiring as input
the number of rectangular regions for the entire source grid; and, for
each region, the x,y coordinates den.oting the limits of the region and
the size of the source grid for that region.
A source emission height for each area source size must be
input to the model, and it is taken as the average building height within
the area source grid.
Total annual SO emissions are first broken down into daily,
£t
then 2-hourly emissions. One method used to generate daily patterns was
to relate daily emissions to the average daily temperature, where
Q Q
annual annual
Q = v + V (DD)
daily Yl 365 Y2 TDD
and Q = daily total SO output
daily 2
Q = annual toxal SO output
annual 2
DD = degree days = 65 - daily mean temperature in °F if the
latter is 65°F or less; otherwise, it is zero
TDD = annual total degree days
y = fraction of sources due to hot water heating
y = fraction of sources due to space heating
£t
and YI + Y2 = 1
An alternate approach divides the calendar year into three
seasons and allocates fractions of the annual source emissions to each
season. Let &/_> a and a represent these fractions. The seasonal
1 2 3
B-62
-------
emissions may be written as
Q . = a Q
season 1 1 annual
where ca + a + a =1
123
Daily emissions are found by dividing by the number of days in the season
in question. Estimates of daily emissions using this method are not as
precise as those calculated by the former method. To reduce daily SO
2t
output to 2-hourly source emissions, statistically derived coefficients
representing diurnal variations of emissions as a function of ambient
temperature are introduced. It is assumed that for each 10°F interval
of daily mean temperature a typical diurnal emission pattern exists,
from which these coefficients can be derived. Two-hour source emissions
are calculated with the expression:
where |3 . (1=1, 12) equals the fraction of daily output at a particular
2-hour interval. Values of p are given in the table.
Small point sources are included with the area source emissions
if certain criteria are satisfied: (1) the total annual emissions are
less than one-tenth of the total annual emissions of the associated area
source; (2) the stack height of the point source is within 20 m of the
associated area source emission height; and (3) the point source has no
appreciable plume rise. All other point sources are considered individually.
2. Treatment of Point Sources
The model requires bi-hourly emissions data from point sources,
and if measured data are not available, the following simulation technique
B-63
-------
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B-64
-------
(i.e., emission model) is suggested by the authors. Point sources are
classified according to industry, and operational modes for each industry
are established. Patterns for daily output will vary according to mode
of operation; that is, operation on weekdays versus weekends, seasonal
trends, shift factors, etc. Several patterns for a particular industry
are distinquished, and diurnal emissions are obtained through the
application of the various factors derived from the patterns appropriate
to the mode of operation under consideration.
C. Model Availability
The computer program for the IBM model is considered experimental
and is not available outside IBM.
B-65
-------
IBM
Source Emissions and the Vertically Integrated Mass Flux
of Sulfur Dioxide Across the New York City Area.
P. Halpern, C. Simon, and L. Randall, Journal of Applied
Meteorology, 1971.
A. Author's Abstract
Sulfur dioxide concentrations (in ambient air) , obtained from
helicopter soundings and traverses, together with wind data from
pibals, were used in a kinematic box model to determine hourly average
three-dimensional fluxes of SO for the New York City area. Continuous
£
dry-bulb temperature and pressure height records were obtained con-
currently and utilized in the analysis and interpretation of the flux
data.
The SO fluxes were compared to degree-day-dependent emissions
&t
from residential, industrial and utility sources. Results indicate that
the vertical structure of the fluxes is related to diurnal variations of
the temperature lapse rates. Furthermore, the emission rates determined
from the measurement of flux in 13 tests agree with those derived from
the New York City sulfur dioxide emission inventory within limits which
have been considered acceptable for usein air pollution modeling.
B. Summary
1. Treatment of Emissions
The emissions treatment described by Halpern et al., considers
hourly SO emissions from residential, utility, and industrial sources.
^
Hourly values of power plant emissions were obtained from the utility
companies. Hourly manufacturing and industrial emissions are estimated
by uniformly distributing annual emissions data over an 8-hour day,
B-67
-------
5-day week, and 50-week year. Daily residential emissions are obtained
in the manner described in the IBM model summary previously given.
Diurnal variations in residential rates are estimated using data on
duration of hourly operation times for apartment house boilers using
fuel oil. Total boiler operating time for each day is correlated with
degree day values:
t = 17.2TT + 192.0
where t equals the total operati on time of the boiler in minutes and TT
represents the degree days. Since space-heating boilers receive fuel
oil at a constant rate when operating, knowledge of t and the fuel sulfur
content is sufficient to describe the hourly SO emission rate.
£*
2. Model Validation
Sulfur dioxide fluxes are calculated from concentration measure-
ments taken by a helicopter and using wind measurements obtained from
pibal and radiosonde soundings. A kinematic box model based on conser-
vation of mass is used to verify the hourly emission rates and to
demonstrate a mass balance of SO .
£t
For thirteen test cases, the vertically integrated SO flux was
£t
greater than the flux estimated from the emissions inventory. The
average value of the ratio of source emissions flux to the vertically
integrated flux was 0.74 for tests during May and 0.83 for tests in
November. The authors state that this indicates that the source emission
estimates were more responsive to the degree-days dependence in the colder
November tests than in the warmer May tests. The average value for the
ratio for all tests was 0.78.
The mass balance of SO has been verified by comparing calculated
^
SO flux to source emission data.
£t
B-68
-------
C. Model Availability
A computer program for the emission inventory methodology presented
is not included in the paper.
B-69
-------
XI METROPOLITAN WASHINGTON COUNCIL OF GOVERNMENTS
Estimating Auto Emissions of Alternative Transportation Systems;
Office of the Assistant Secretary for Environment and Urban
Systems, Washington, D. C. 20590; Prepared by S. D. Berweger
and G. V. Wickstrom, Dept. of Transportation Planning, Metro-
politan Washington Council of Governments, 1972.
A. Author's Abstract
This report discusses the development and application of a model
which can estimate the relative magnitudes of carbon monoxide, hydro-
carbons, and oxides of nitrogen automobile emissions for alternative
regional transportation systems.
The computation of auto emissions is accomplished by means of a
computer program which accepts travel and facility data, together with
assumed emission rates, and calculates speed and vehicle-miles of travel
by type of facility by sub-area; applies the rates (for peak and off-
peak travel speeds and volumes) and calculates the amount of emission
by sub-area. Emission rates are a function of the speed of travel and
the age distribution of vehicles in the year under study. A portion of
the program logic is based on a model developed by the Tri-State
Transportation Commission for estimating highway facility requirements,
modified for the purpose of calculating auto emissions.
These findings are based on the emission factors supplied by E.P.A.
If these factors are revised as a result of further research, it is quite
possible that some of the results obtained would change. Revisions to
the emission factors can be readily accommodated by the model developed
in this study.
B-71
-------
B. S ummary
The methodology makes use of vehicle trip forecasts, along with
highway network information, to estimate future travel, the speeds at
which this travel will occur, and the emission levels produced. The
methodology does not require trip distribution and traffic assignment
model procedures. It is aimed at providing a regional overview of the
relative magnitude of the air pollution problem in an area. The method-
ology is not intended to provide air quality forecasts as no diffusion
models have been used.
For each system, estimates of average daily and peak-hour emissions
of carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO )
X
were made and compared. Computer programs have been developed to do much
of the work and are available.
The three-stage Auto Emissions Model developed in this study is
shown in the figure. For each system tested, a trip generation sub-model
is used to determine automobile vehicle trip origins, a travel description
sub-model is utilized to convert those trips into travel characteristics
and, finally, an emission sub-model is used to convert these travel
parameters into estimates of pollutant emissions.
The emission sub-model utilizes average speeds, to determine the CO,
HC, and NOX emission rates, in pounds per vehicle mile, for each facility
type. This rate is then multiplied by the VMT on each facility type to
determine the total pollutants emitted for each of the sub-areas. This
process is carried out for both peak-hour and daily conditions.
The emission factors used in this study were supplied by the E.P.A.
CO and HC emissions per vehicle-nile of travel decrease with increasing
speeds while emissions of nitrogen oxides are assumed to be constant
for all speeds.
B-72
-------
DATA REQUIREMENTS
SOCIO-Er-";3W!C FORECASTS
HOUSEHOLD INCCVE,
DISTRIBUTION OF POPULATION
AND EVPLOWENT
I PHASE 1
(TRIP GENERATION MODEL
IPHASE 2 IPHASE 3
iTRAVEL DESCRIPTION MODEL iPOLLUTANT EM ISSIONS MODEL
TRANSIT SYSTEM
ALTERNATIVE
HIGHWAY SYSTEM
ALTERNATIVE
ENVIRONMENTAL PROTECTION
AGENCY EMISSIONS FACTORS
TRAVEL AND
SPEED BY
FACILITY TYPE
EMISSIONS
OF
OXIDES OF
NITROGEN
SA-2579-29
FIGURE B-5 FLOW CHART OF AUTO EMISSIONS MODEL
There was concern over combining trip specific speed-emission factors
with facility specific speeds. At the time the study was initiated,
however, there were no data available to resolve this inconsistency.
The most important relationship used in the travel description
sub-model is between vehicle-miles of travel (VMT) density, vehicle trip
origin density, and expressway supply. In the Washington, D.C. area, the
relationship was found to be the same as that found in the New York region,
that is:
where :
VMT = vehicle-miles of travel per square mile
B-73
-------
VTO = vehicle trip origins per square mile
FE/FO = proportion of total roadway surface area made up by
expressways
It was found that the expressway speed estimates calculated for the
New York region were too high for use in the Washington area; therefore,
the source deck was recompiled using a speed equation specifically
calibrated for the Washington region.
To obtain an estimate of peak-hour speeds, the 24 hour VMT was
factored to peak hour volumes per lane by facility type, using observed
peak-to-daily and directional flow factors. The Technical Advisory
Committee eventually decided that the EPA emission factors published in
the EPA document, "Air Pollution Emission Factors" were the best available
for current use. These factors were modified somewhat by EPA to account
for 1976 conditions and the fact that the age distribution of automobiles
in the Metropolitan region was different (younger) than the national
sfcjfc
average. This resulted in a slightly lower base emission rate for
the region, but the proportional changes in rates for speed variations
remained identical to that published.
Preliminary data from a current five-car study in California indicates
that the impact of control devices might well be to flatten out the CO and
HC curves, that is, emission rates may not decrease as rapidly for a given
speed increase as they did under the old curves.
M. J. McGraw, and R. L. Duprey, "Compilation of Air Pollutant
Emission Factors" (Preliminary Document), Environmental Protection
Agency, Research Triange Park, N. C., April, 1971.
"Motor Vehicle Emission Factors for Metropolitan Washington, D.C.,"
Environmental Protection Agency, Office of Air Programs Memo, November
4, 1971,
B-74
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Within User
Program Supplies
From
AP-42 Proxies/Comments
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Spatial Distribution
-Links
-Grinds
-Area
x
NP
NP
NP
NP
NP
NP
x
NP
x
NP
NP
NP
Average speeds for
each mode based on
total VMT by mode
NP
x
NP
NP
NP
NP
NP
NP
NP = No provision
B-75
-------
Output Available
Pollutants
-CO Yes
-HC Yes
-NOX Yes
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x
Language Basic FORTRAN iv
Equipment IBM 360/30
Validation/calibration; Calibrated/validated on emissions for Metropolitan
Washington, B.C.
Time resolution; Peak/off-peak
Spatial resolution; Area specified by zonal configuration
B-76
-------
XII NATIONAL AIR POLLUTION CONTROL ADMINISTRATION
St. Louis SO;? Dispersion Model Study—Basic Data.
D. B. Turner and N. G. Edmisten, Unpublished report,
November, 1968.
A. Author's Abstract
This report contains descriptions of basic data for a three month
period used with a research study formulating a dispersion model for
tlie St. Louis Metropolitan Area. This includes emission information
for sources of sulfur dioxide as well as methods of estimating emissions
for each 2-hours for the three-month period, hourly meteorological data
from routine and special instrumentation, and 24-hour sulfur dioxide
concentrations for 40 locations and 2-hour concentrations for 10
locations.
B. Summary
This unpublished report outlines methods of computing the number of
dwelling units per grid square and the population per grid square, from
basic census data.
1. Estimation of the Number of Dwelling Units per Grid Square
When the census tracts are larger than a grid square, the pro-
cedure is to first calculate the total number of grid squares per census
tract by dividing the number of acres in the tract by the number of acres
in a grid square. The number of inhabited grid squares in the tract is
estimated, deleting rivers, lakes, parks, etc. Weights are then assigned
individually to the squares to adjust their dwelling unit density to that
of the total census tract. The number of each type of heating unit (coal,
gas, or oil) is estimated for an average grid square from census data.
B-77
-------
Finally, the number of each type of heating unit per average grid square
is multiplied by the assigned weights, giving the number of each type of
unit in the grid square in question.
When the census tract is smaller than a grid square, the
fraction of the total census tract in each grid square of which it is a
part is estimated, and weights are assigned to each portion of the tract
to compensate for nonresidential areas. The total number of each type of
unit in the census tract is multiplied, individually, by the fraction of
area and by the assigned weight. These products are summed for each of
the census tracts within the grid square, yielding the number of each type
of unit in the square.
2. Estimation of the Population per Grid Square
When the census tract is larger than a grid square, the total
number of grid squares per census tract is calculated, and the number of
inhabited grid squares in the tract is estimated. Weights are individually
assigned to the squares to adjust their population density to that of the
total census tract. By dividing the population of the total census tract
by the number of grid squares per tract, the number of people per grid
square is found. Finally, multiplying the average grid square population
by the assigned weight gives the population of the grid square in question.
If the census tract is smaller than a square, the fraction of the
total census tract in each grid square of which it is a part is estimated,
and a weight is assigned to each portion of the tract to compensate for
nonresidential areas. The total population of the tract is multiplied,
individually, by both the fraction of area and the assigned weight, and
the sum of these products, for each census tract within the grid square,
equals the population of the square.
B-78
-------
C. Model Availability
A computer code for the methodology presented is not included in
the report.
B-79
-------
XIII NATIONAL AIR POLLUTION CONTROL ADMINISTRATION
Emission Factors. A. H. Rose, Jr., W. D. Krostek, National Air
Pollution Control Administration, June, 1969.
A. Author's Abstract
A method has been developed for the prediction of gaseous pollutants
emitted into the national environment from on-the-road gasoline-powered
vehicles. The method involved two quantities, the emissions emitted per
mile traveled, and the total mileage traveled. Vehicular emissions were
characterized by engine type and load under actual operating conditions.
Five vehicle types and two use classes were used to define characteristic
vehicular emissions. Vehicle types were automobile (auto), light duty
truck (LOT), and three weight classes of heavy duty trucks (HDT). The
two use classes were urban and rural.
The method allows comparison among the possible degrees of control,
methods of control, and implementation dates. The method could be utilized
on a regional rather than national basis, and could include particulates
as well as gaseous pollutants.
B. Summary
1. Inputs
The method requires knowledge of: (1) the mean annual road
emissions of a characteristic vehicle in mass per distance traveled, and
(2) the total distance traveled during the calendar year by that portion
of the total vehicular population characterized by the vehicle. Classi-
fication of vehicles was by use: urban or rural driving; and by general
type: auto, IDT [GVW less than 6,000 Ibs], HDT(II) [GVW of 6,000-10,000
Ibs], HDT(III) [GVW of 10,000-19,500 Ibs], HDT(IV) [GVW over 19,500 Ibs].
Vehicles in a class were categorized by the model year (the model year is
the calendar year the vehicle entered the population). This is basically
B-81
-------
characterization of vehicular emissions by engine type, load, and age.
Reference characteristic emissions were obtained for each of the vehicular
types and use classifications. The deterioration factor used to account
for vehicle age was considered to be solely a function of accumulated road
mileage, thus accommodating changes among vehicles in driving usage. It
was assumed that vehicular deterioration will not change and that the
mileage distribution with age will also remain constant. HDT vehicles
were assumed to deteriorate as automobiles, assuming that the increase
in mileage traveled is offset by more durable construction.
Three gaseous pollutants were considered, hydrocarbons (based on a
hydrogen/carbon ratio of 1.85 to 1.00), carbon monoxide, and nitrogen
oxides (as NO ). Emissions from evaporative sources and crankcase blowby
£
were presumed to consist entirely of hydrocarbons.
Rural driving was characterized by hot start 45 mph (average route
speed) operating conditions; urban driving, by cold start 25 mph (average)
operating conditions. The relationships among the various operating
conditions are themselves independent of mileage.
REFERENCE AUTOMOTIVE EMISSIONS
N0o
CO
HC
Type
- exhaust
- exhaust
- exhaust
Value
gm/mile
ppm
gm/mile
%
gm/mile
ppm
1962
5.757
1500
87.234
3.7
5.678
456
1963-
1967
5.757
1500
87.234
3.7
5,678
456
1968-
1969
5.757
1500
35.103
1.5
3.428
275
1970
5.757
1500
23.000
1.0
2.200
176
1971-
1974
5.757
1500
23.000
1.0
2.200
176
1975-
1990
0.960
250
11.500
0.5
0.611
49
HC - evapora- gm/mile
tive
HC - crank-
case
gm/mile
2.767
3.151
2.767
2.767
2.767
0.490
Concentration values are based on exhaust volume of 70.68 standard cubic feet/mile
and pollutant densities of 16.33 gm/scf for hydrocarbon, 33.11 gm/sfc for carbon
monoxide, and 54.30 gm/scf for nitrogen dioxide.
Emissions are for federal composite cycle.
B-82
-------
2. Out put s
National or regional gaseous pollutants emitted from on-the-ro:;-'
gasoline powered vehicles.
3. Resolution
Annual national or regional.
40 Validation
None.
B-83
-------
Composite Cycle
HC
CO
0.9101
0.8217
NO2 : 0.8743
Hot Cycle
HC
CO
NO,,
0.911
0679
0.8742
Hot 25 mph Road
HC
CO
NO
2 '
1.266
1.392
1.000
Cold 25 mph Road
HC
CO
1.120
1.085
N02 : 1.260
Composite Cycle to Hot Cycle
Hot Cycle to Hot 25 Road
Temperature
HC
CO
NO
2 '
0.679
0.612
1.000
Speed
Hot 45 mph Road
HC
CO
NO,
1.000
0.920
1.295
Seasonal (Summer to Annual)
Additional (Regional)
Mean Annual Mean Annual
Exhaust From Exhaust From
Urban Vehicle Rural Vehicle
FIGURE B-6 EXHAUST EMISSION FLOW DIAGRAM
SA-2579-30
B-84
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Within User From
Program Supplies AP-42
Proxies/Comments
Vehicle Emission Factors
-By model year x
-By size class x
-By power plant x
-Deterioration factor x
x
x
X
X
Uses vehicle
mix for year of
interest in study
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
x
NP
NP
NP
NP
Average rural and
urban routes
NP
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
x
NP
NP
x
NP
NP
NP
NP
Spatial Distribution
-Links
-Grinds
-Area
NP
NP
NP
NP
NP
NP = No provision
B-85
-------
Output Available
Pollutants
-CO
-HC
-P articulates
Yes
Yes
Yes
No
Availability of Program Documentation
Included in report
Referenced in report
Language
Equipment
Validation/calibration;
Time resolution;
Spatial resolution;
Yes
NP
NP
No
None
Annual
National or regional
B-86
-------
XIV NORTHERN RESEARCH AND ENGINEERING
The Potential Impact of Aircraft Emissions Upon Air Quality. .
M, Platt, R. C. Baker, E. K. Bastress, et al., Northern
Research and Engineering Corp., Cambridge, Mass., 02139,
Rept. APTD-1085, December 29, 1971.
A. Author's Abstract
Three categories of emissions were presented: aircraft operations,
non-aircraft airport operations, and airport surroundings. In general,
each of the sources in these categories can be accurately represented
as either point, line, or area sources of time-dependent strength (i.e.,
emissions per unit time). However, to reduce the complexity of analysis
in this study, each emission source is represented by one or more con-
tinuous point sources of constant strength over the time period being
considered. Each point source is a specific location within the airport
or its environs. The strength Q of a point source was evaluated as the
total emissions E associated with the point during the time period,
divided by the length T of the period.
B. Summary
1. Aircraft Operation
The modes of operation of a typical aircraft which were con-
sidered in this study have been established as: (1) approach, (2) landing,
(3) taxi [after landing and before take-off], (4) idle and shutdown,
(5) maintenance, (6) start-up and idle, (7) idle at runway, (8) take-off,
(9) climb-out, and (10) fuel venting.
B-87
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Operational modes
(4) idle and shutdown
(6) start-up and idle
(5) maintenance
(7) idle at runway
are characterized by pounds of pollutants per aircraft class. The
total is calculated by using characteristic times periods for each
mode for each aircraft class.
Operational modes
(3) taxi
(1) approach
(9) climb-out
(2) landing
(8) take-off
are characterized by pounds of pollutants per aircraft class. Dis-
tribution of traffic among runways, average taxi times, characteristics
of the airport under study, and the times and positions along represen-
tative flight paths are used to calculate the amount and distribution of
the pollutants.
Operational mode
(10) fuel venting
is characterized by total pounds of hydrocarbon per aircraft class and
is associated with a point source at 600-meter altitude in take-off.
2. Automobile Travel
Automobile travel within the airport is characterized by a
constant emission of pollutant per automobile per unit automobile travel.
The total emissions due to automobile operation for a time period were
calculated using:
B-88
-------
a. Average travel per automobile within the airport
b. Fraction of automobiles entering the airport per
passenger
c. Average number of passengers per aircraft
d. Aircraft activity data.
The first two items are considered characteristic of the airport, while
the third item is considered characteristic of an aircraft class.
The total emissions were distributed along the airport roadway
system, which in addition to highways, included roads within the parking
areas. The distribution is accomplished by locating point sources at
stations which divide each roadway into a number of equal segments. The
emissions of each segment were equally divided between the two bounding
stations. This resulted in the same type of distribution as would be
obtained for trapezoidal integration of a uniform line source of emissions.
Service vehicle emissions were calculated in an approximate manner
as 50% of automobile emissions. They are then included in the distribution
of the automobile emissions.
3. Model Sensitivity
The mathematical model of airport emissions and the dispersion
of those emissions used to predict pollutant concentrations involves two
categories of physical parameters:
a. Meteorological parameters, which consist of atmospheric
stability, wind speed, wind direction, mixing height,
and for short-term calculations, wind persistence.
b. Source parameters, which consist of the strength and
location of emission sources with respect to a receptor.
Parameters in the first category were the subject of a sensitivity analysis,
with parameters in the second category investigated indirectly in the analysis
of control techniques.
B-89
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4. Accuracy of the Model
Airport emissions are modeled by a large number of continuous
point sources of constant strength distributed throughout the airport.
This results in two limitations on the validity of the model. First,
the model is limited to time periods that are much larger than character-
istic times of individual aircraft activity. Second, the model is
limited to receptors that are not in close proximity to the emission
sources. Since an airport has many emission sources which would be best
represented as lines or areas, the assumption of point sources results in
an important limitation of the model.
In providing a check of the results obtained using the model,
a comparison was made with emissions and concentrations reported for Los
Angeles International Airport. While the emissions for carbon monoxide
are in excellent agreement, agreement for the other pollutants is poor.
The aircraft emissions of particulates, nitrogen dioxide, and total hydro-
carbons for Los Angeles International Airport are greater than those
obtained using the model by factors ranging up to 6.7 for particulates.
Since the activity data were similar for the two cases, the disagreement
can be traced directly to the emission factors.
Concerning the other pollutants, it is significant that agree-
ment is so good for carbon monoxide which can be measured most accurately
in engine emission tests and so poor for the other pollutants where
different measurement techniques can give very different results.
The measured concentration of carbon monoxide is a factor of
2.8 larger than the analytical value. It is felt that the imprecision
with which the surroundings were modeled is the likely cause for this
discrepancy (for instance, emissions of automobiles on roadways adjacent
to the airport not directly included in the model). Therefore, concen-
trations due to aircraft emissions alone should be predicted more
accurately than concentrations due to all emissions.
B-90
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EMISSION MODELS CHECKLIST
Input Data Requirements
Within User
Program Supplies
t ro:.i
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
x
x
X
NP
X
X
NP
NP
NP
NP
x
x
X
NP
-ies/Comments
NP
NP
NP
XP
Spatial Distribution
-Links
-Grinds
-Area
x
x
x
NP
NP
NP
NP = No provision
B-91
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Output Available
Pollutants
-CO Yes
-HC Yes
-NOX Yes
-Particulates Yes
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x*
Language NP
Equipment NP
Validation/calibration; Los Angeles Airport - 1970 data
Time resolution; 1 hour
Spatial resolution; Airport boundary
* M. Platt, K. M. Chug, and R. D. Siegel, "Computer Program for the
Air Quality Analysis of Airports" (NREC Report No. 1167-2), Northern
Research and Engineering Corporation, Cambridge, August 1971.
B-92
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XV ONTARIO DEPARTMENT OF THE ENVIRONMENT
Information System Descriptive Manual, Air Management
Branch, Department of the Environment, Province of
Ontario, Canada, January, 1971.
A. Author's Abstract
None given.
B. Summary
The Toronto emissions-inventory methodology considers three source
categories: (1) major intense and discontinuous sources (point and line
sources); (2) numerous minor sources (area sources); and (3) sources beyond
the grid system boundary.
If emissions are greater than 100 Ibs per day, the source is con-
sidered a point source; otherwise, its emissions are included in area
source calculations. The 100 Ibs per day criterion may be modified. The
Toronto area is divided into 1 kilometer grid squares for area sources,
with point sources located on a 5 X 5-kilometer grid to a tenth of a
kilometer.
Area sources include emissions from motor vehicles, shipping,
railroads, aircraft, and gasoline storage, from space heating and waste
disposal of apartments, schools and universities, industrial and commer-
cial sources, and public buildings, and from space heating of residential
dwellings. All minor sources within one grid square are summed, and the
total is distributed evenly over that square. Line sources are treated
as special cases of area sources.
Sources beyond the grid boundary are treated as providing a flux
through the border, with assigned source strength and initial vertical
B-93
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distributiono These parameters maintain constant values along the length
of the border.
The five pollutants considered are sulfur dioxide, nitrogen oxides,
particulates, carbon monoxide, and hydrocarbons. Provision is made for
sources which, under certain conditions, must switch from their regularly
used fuel to another type, thus changing their emission patterns. The
information system is designed to accept new data and can modify old data.
The capability exists to modify classes of emissions by a fixed percentage.
1. Automotive Emissions
Roadway emissions are divided into five classes: (1) super-
highway (line source), (2) other highway, (3) major artery, (4) residential,
and (5) business street. Information is collected such as the vehicle count
per day for all roads in each grid square (for residential streets a count
of 500 is assumed, slightly higher for residential streets with large
apartment buildings), and road lengths in each grid square. Yearly emission
quantities are found by multiplying the vehicle count per day by road length
and by 365, and then applying the emission factor appropriate to the
emission class. The hourly or daily emissions are computed using the
appropriate diurnal, daily and seasonal factors that have been tabulated.
2. Point and Other Area Source Emissions
Patterns for point source operations, process and nonprocess,
were developed based on days of the week in operation and the number of
shifts per day. Seasonal and daily space heating variations for both
point and area sources were developed using degree day fluctuations, with
hourly variations derived from natural gas demand records over 24-hour
periods during different seasons. Other area source emission variations
were determined in a similar fashion, based upon usage or frequency
variation data.
B-94
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To obtain hourly emissions from the total annual amount of
pollutant, the procedure outlined below is applied. The annual total is:
a. Adjusted by a seasonal factor. Depending on the
season, one of four seasonal percents (which add
to 100) is mulitplied by the annual pollutant
total and then divided by 25,
b. Adjusted to a daily rate, by dividing by 365,
c. Adjusted for day of week, using one of the seven
percentages associated with a particular day, and
d. Adjusted for weather if a heating emission. For
area sources, the factors depend on dwelling type
and fuel type. For point sources, fuel combustion
emission are adjusted by a space heating percent.
This is done by multiplying by the amount of the
degree day total and dividing by the degree year
total.
e. Only area sources also adjusted by hour of the day.
A table of diurnal codes, each containing 24 numbers
which add to 100, are applied so that emissions are
spread over the hours of the day, based on the
components of the code.
C. Model Availability
As stated earlier, the Toronto emissions inventory methodology was
developed by the Department of the Environment, Ontario Province, Canada.
The emission model is in the form of a computer code, but it is not
generally available for public dissemination. Accordingly, the model can
only be obtained through a formal request to the Department.
B-95
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XVI PACIFIC ENVIRONMENTAL SERVICES
Controlled Evaluation of the Reactive Environmental
Simulation Model. L. G. Wayne, A, Kokin, and
M. I. Weisburd, Final Report, Pacific Environmental
Services, Inc., Contract No. 68-02-0345, prepared
for Environmental Protection Agency, EPA R4-73-013a,
February, 1973.
A. Author's Abstr a ct
The development and validation of an operational version of the
Reactive Environmental Simulation Model (REM) were completed. REM was
specifically designed to handle large chemical mechanisms to assess the
impact on air quality of air pollution control devices, fuels, propul-
sion systems, stationary sources, and transportation systems where
thorough evaluation of emissions, emission constituents and reaction
rates are required. The version delivered to the U.S. EPA under this
contract contains a mechanism involving 32 reactions, 12 accumulating
species, and 12 nonaccumulating species. Larger or smaller mechanisms,
also, can be readily inserted into REM for specified purposes.
REM is based on a Lagrangian moving coordinate system which enables
the numerical simulation of the chemical reactions that take place in a
parcel or column of air moving along a dynamic wind trajectory. The tra-
jectory approach gives REM considerable flexibility and is adaptable for
use in a number of practical operational situations. These include short-
term air quality forecasting; analysis of the impact of sources of air
pollution at various designated locations on the air quality at specified
receptor points; interpolation of contaminant concentrations and dosages
at locations not covered by air monitoring stations, and control strategy
evaluations.
B-97
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REM contains such user features as reverse and forward trajectory
routines; automatic and objective interpolation from input emission
inventory, meteorological and air quality data bases; a chemical dynamics
routine capable of accommodating mechanisms based on elementary chemical
reactions; and automatic estimation of mixing depth and solar irradiance
based on input of local weather and sun angle data.
REM has attained a running time which makes it cost-effective for
practical use. On an IBM System 370/155, the real-time-to-simulation
ratio for the 31-step mechanism is 150:1; for carbon monoxide alone it
is 3000:1; for some of the shorter mechanisms available it should run
more than 300:1. The program, also, is user-oriented in that it provides
simple input procedures, user documentation, receptor point and time-of-
day selectivity, flexibility in treating specific problems, and ability
to conveniently select any of an infinite number of trajectories on any
number of days of interest. The modular construction of REM, also, makes
it easy to add, replace, or delete individual modules. REM can be used
as an unlimited receptor point model and to chronicle emission inputs
contributing to the air quality at any receptor point.
The validation record of REM over a large number of runs may be
summarized as follows:
• CO, less than a factor of 2 in more than 80% of the
comparisons; 40% agreement within one part per million.
• O , within a factor of 2, 75% of the comparisons.
O
• NO, within a factor of 2, 75% of the comparisons;
90% agreement within 0.02 ppm.
• NO2, within a factor of 2, 60% of the comparisons.
The shapes of predicted time profiles of contaminant concentrations are
generally similar to those observed at air monitoring stations.
B-98
-------
The ease and economy at which REM'can be utilized under a variety
of data availability situations make potential improvements in accuracy
and application possible on a cost-effective basis.
B. Summary
1. Treatment of Source Emissions
A subroutine (module) of the REM program computes emissions of
NO, CO, and reactive and less reactive hydrocarbons from freeway and
street traffic. Emissions of NO and reactive and less reactive hydro-
carbons are calculated for stationary area sources. All urban grid
squares are chosen as four square-mile blocks, although the kilometer may
also be used as the standatd. Emissions within these squares are viewed
as essentially uniform and continuous.
Inputs to the module include time, position of the pollutant
column, the distribution of daily traffic on streets and freeways,
emission factors, and diurnal usage curves. Traffic and grid system
distributions and diurnal curves for the Los Angeles Basin are taken from
the emissions treatment developed by Systems Applications, Inc., a review
of which appears in this appendix.
Traffic emissions are computed as a function of time by applying
the emission factors for the respective pollutants to the traffic levels
at the air parcel location, qualified by the daily traffic usage curve.
Area source emissions as a function of time are found by applying a con-
stant diurnal usage curve to grid system distributions of average hourly
pollutant emissions.
2. Module Method of Operation
Following is the procedure by which pollutants, are injected
into an air parcel.
B-99
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1. Initialization (background concentration).
2. Traffic distribution curves are evaluated.
3. The grid in which the trajectory point lies is
found.
4. The three closest adjacent grids are found (those
with the closest midpoints).
5. The distance from the trajectory point to each
grid midpoint is calculated.
6. Is the trajectory point on the midpoint of the grid
square in which it lies? If yes, go to step 9; if
no continue.
7. The contribution to the parcel from each traffic grid
for each pollutant is calculated (the contribution is
inversely proportional to the square of the distance
from the trajectory point to the grid midpoint).
8. If area sources are to be added, the area source con-
tribution from each grid square for each pollutant is
calculated (inversely proportional to the distance
squared). Skip to step 11.
9. The traffic contributions of the trajectory grid only
are calculated.
10. If area sources are to be added, the area source con-
tributions of the trajectory grid only are calculated.
11. The traffic and area contributions are then modified by
the appropriate diurnal usage curves and added.
C. Model Availability
A computer listing of the emission inventory is included in the
supplemental User's Guide of the report.
B-100
-------
DAYLIGHT
SAVINGS
TIME
SET TIME
TO DAYLIGHT
TIME
FIND THREE
CLOSEST
ADJACENT
GRIDS
SET
MIDPOINTS OF
THE FOUR
GRIDS
FIND DISTANCES
OF TRAJECTORY
POINT TO
GRID MIDPOINTS
EVALUATE
TRAFFIC
DISTRIBUTION
CURVES
CALCULATE
CONTRIBUTIONS
OF TRAFFIC GRIDS
(1/DIST2)
FIND GRID
IN WHICH
TRAJECTORY
POINT LIES
SA-2579-31a
FIGURE B-7 FLOW OF SOURCE SUBROUTINE
B-101
-------
ADD TRAFFIC
CONTRIBUTIONS
OF TRAJECTORY
GRID ONLY
CALCULATE
CONTRIBUTIONS OF
SOURCE GRIDS
(1/DIST2)
ADD AREA SOURCE
CONTRIBUTIONS
OF TRAJECTORY
GRID ONLY
DAYLIGHT
SAVINGS
TIME
RESET TIME
TO STANDARD
TIME
SA-2579-31b
FIGURE B-7 FLOW OF SOURCE SUBROUTINE (concluded)
B-102
-------
XVI1 RUTGERS UNIVERSITY
Comparison of Air Pollution from Aircraft and Automobiles
(Project Eagle) . C. Bright et al., Rutgers University,
New Brunswick, New Jersey, AD713913, September, 1970.
A. Author's Abstract
None given.
Li. Summary
Emissions from either aquadrome or roadway areas are considered to
constitute a surface of randomly distributed multiple sources. This
surface is assumed to be a uniform area source with respect to a
receptor located a variable distance downwind from the edge of the
aquadrome or roadway segment.
Emissions are calculated by assuming aircraft passenger load factors
and the number of passengers per commuting automobile. The number of
passengers and vehicle road or air miles from each source to central
locations are used to determine the total number of auto and aircraft
miles. This mileage is used to calculate emissions using the following
emission factors:
B-103
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Hydrocarbons
Carbon monoxide
Nitrogen oxides
Particulates
MOTOR VEHICLE EMISSIONS
(gm/passenger mi)
1970*
Emissions
8.81
38.95
10.35
0.40
1970^
Actual
12.10
72.35
6.84
0.47
1975*
Goals
0.40
8.87
0.73
0.08
1975"1"
Actual
8.51
45.24
7.84
0.38
1980
Goals
0.20
3.79
0.32
0.02
1980^
Actual
3.11
18.87
3.52
0.17
TOTAL AIRCRAFT VEHICLE EMISSIONS: APPROACH, CRUISE,
TAKE-OFF AT 100% LOAD FACTOR
(gm/passenger mi)
1970
1975
1980
Hydrocarbons
Particulates
Carbon monoxide
Nitrogen oxides
0.3696
1.0107
1.4717
0.8151
0.3696
1.0107
1.4717
0.8151
0.3696
1.0107
I.4717
0.8151
*Based on HEW estimates of emission-controlled vehicles that will be in
production at that date.
In converting the grams per vehicle mile to grams per passenger mile,
an average automobile passenger occupancy of 1.24 persons is used.
For aircraft, occupancy load factors of both 100% and 50% were con-
sidered. The 100% load factor was demonstrated to be feasible in the
Project Eagle Study. As shown in the tabulation above, the values for
aircraft in grams per passenger mile is computed allowing separate
emission rates for approach, cruise, and take-off. In converting the
grams per vehicle mile to grams per passenger mile, a trip length of
20.8 miles is used for the 53-passenger aircraft. This 20.8-mile trip
length represents the average air passenger distance traveled in an urban
air transportation system during 7 a.m. to 9 a.m. and 4:30 p.m. to 6:30 p.m.
between Manhattan and the 11 transportation centers in Connecticut
(Bridgeport, New Haven, and Strmiord), New Jersey (Linden/Rahway, New
Brunswick, Paterson and Newark), and New York (Farmingdale, Hempstead,
Mt. Vernon, and White Plains).
'Based on a methodology considering automobile age and vehicle usage
corresponding to automobile age. This procedure is currently being used
by the National Air Pollution Control Administration, Durham, North
Carolina.
B-104
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EMISSION MODELS CHECKLIST
Input Data Requirements
Within User
Program Supplies
From
AP-42
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
x
x
X
X
X
X
X
X
X
NP
X
NP
NP
NP
NP
x
NP
P roxi es/C omment s
By mix on road for
year of study
interest
NP
NP
NP
NP
Spatial Distribution
-Links
-Grinds
-Area
x
NP
NP
x
NP
NP
NP
NP = No provision
B-105
-------
Output Available
Pollutants
-CO
-HC
-NOX
-Particulates
Yes
Yes
Yes
Yes
Availability of Program Documentation
Yes
No
Included in report
Referenced in report x
Language NP
Equipment NP
Validation/calibration: None
Time resolution: 4 hours
Area; Tri-State (Connecticut, New Jersey, and New York)
Aircraft emission factors
based on 1968 NRC work -
no program given.
Auto emission factors
based on NAPCA data.
B-106
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XVIII SACRAMENTO REGIONAL AREA PLANNING COMMISSION
General Inventory of Air Pollution Sources and Emissions.
The Air Pollution Threat, Sacramento Regional Area Planning
Commission, NTIS No. PB191382, October, 1969.
A. Author's Abstract
The Sacramento Region has three major sources of pollution:
transportation, agriculture waste burning, and other stationary sources.
The report was directed at estimating the amounts of organic gases,
carbon monoxide, nitrogen oxide, sulfur dioxide and aerosols that are
emitted from their sources. The predominant emission is carbon monoxide
with an estimated 1,215 tons being produced daily. Carbon monoxide is
produced mainly from gasoline powered motor vehicles. The other
emissions that are produced in the region are organic gases (267 tons/day),
nitrogen oxide (87 tons/day), aerosols (46 tons/day), and sulfur dioxide
(9 tons/day).
B. Summary
An emission inventory of the Sacramento, California region was
compiled for organic gases, carbon monoxide, nitrogen oxides, sulfur
dioxide, and aerosol emissions. Sources were classified as transportation,
agricultural waste burning, and stationary sources.
The transportation category is divided into gasoline and diesel
powered motor vehicles, railroads, and aircraft. Pollutant emission is
based on estimates of the different fuels used. Gasoline usage is based
on an estimate of 1.1 gallons per day per person. Emission factors for
gasoline and diesel powered vehicles take into account existing smog
control devices.
B-107
-------
Agricultural emissions arise from agricultural waste burning and
crop processing, the latter being included with stationary sources. In
the Sacramento region 15% of total agricultural waste is burned. Emission
figures represent tons of emissions produced on a straight daily average.
Seventy-five percent of the burning is done from October to February, with
emission figures higher during this season.
Stationary sources include agricultural product processing, where
1% of the total amount of crops processed is estimated as the amount of
particulate lost due to processing. Annual figures are divided by the
number of days per year to obtain daily values. Emissions from petroleum
handling result when tank trucks, service station tanks, and automobile
tanks are filled. A daily figure for gallons handled is found by
multiplying the population of a region by the estimate of gallons per
day per person used. Emissions estimates for solvent use, metal, and
mineral processing, and chemical manufacturing are obtained from regional
agencies. To estimate emissions from the incineration of solid waste, the
total amount of refuse burned daily is found by assuming an average figure
of 2 Ibs per person per day and multiplying by the population. This amount
is divided between municipal dump burning (75%) and backyard burning (25%).
Backyard burning is further divided into incinerator, open, and landscape
burning. Heating of homes and industrial fuel use are the main sources of
emissions from fuel combustion. Natural gas figures are obtained from the
utility company, and bottled gas is accounted for either directly or by
increasing the yearly utility company figure by 10%. Total daily gas is
divided between domestic (75%) and industrial (25%) sources.
C. Model Availability
A computer program for the emissions model is not included.
B-108
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XIX STANFORD RESEARCH INSTITUTE
A Practical, Multipurpose Urban Diffusion Model for Carbon
Monoxide. F. L. Ludwig, W. B. Johnson, A. E. Moon, and
R. L. Mancuso, Stanford Research Institute, Menlo Park,
California, September, 1970.
A. Author's Abstract
This report describes the development and current status of a
resceptor-oriented diffusion model that can be applied to urban areas to
give the following outputs: (1) carbon monoxide (CO) concentration
isopleths for a given set of meteorological conditions and times of
day, (2) sequences of hourly CO concentrations at specific locations
for given sequences of meteorological conditions, and (3) climatological
summaries of CO concentration for specific locations if a historical
record of meteorological data is available. The model can be used to
obtain the frequency distributions of concentrations averaged over
various time intervals for specific hours of the day or days of the week.
B-109
-------
B. Summary
Because nearly all urban carbon monoxide emissions are from
internal combustion vehicles, the model assumes emissions to be at
ground level. Furthermore, the emissions within each segment are
assumed to be uniformly distributed. The inventory of vehicular
emissions has two components: (1) primary network link emissions
from vehicles traveling on the network of major arterial streets and
freeways, and (2) secondary background emissions from vehicles traveling
over the less densely traveled local and feeder streets.
The primary network links for which the emissions are computed
are the sections of major arterial streets and freeways between inter-
sections with other major arterials and freeways. The average length
of the links on the primary network is approximately one mile, although
the links may be much shorter in densely traveled downtown areas, and
more widely spaced in outlying areas. Traffic volumes on links vary
hour-by-hour over a day and are different for weekend days than for
weekdays. Further, there are smaller seasonal variations. Highway
engineers and planners average these variations into a quantity that
they call average daily traffic (ADT). For use in the synoptic model,
the emissions must be expressed as hourly averages, so the average daily
traffic is expressed as a function of time. The amount taken to occur
within any given hour is based on the hourly distribution of trips
compiled by the traffic study agencies of many areas.
To calculate the contribution of the emissions from traffic links
within the five closest segments, the links are first identified with
the segments through which they pass. Then the length of the link that
lies within a given segment is determined. The emissions within the
four segments farthest from the receptor are calculated by a different
technique than that used for the closer segments.
B-110
-------
Before other computations begin, The city is divided into a grid
of one-mile (1.6 km) squares. The traffic link data are used to deter-
mine the average daily CO emission in each of these squares. This is
accomplished by dividing each link into small increments, less than 0.08-km
long. The average daily emission from each of these small pieces is
determined by assuming emissions are spread uniformly along the link.
Then, the emissions from all the small pieces of link within a square
are added together to give total daily emissions for that square. This
total is paired in the computer memory with the coordinates of the point
at the center of the square. These calculations are done only once for
the city.
A trapezoidal grid system is used in the four outer emission
segments. As the location of the receptor changes or the wind direction
changes, these trapezoidal grids are superimposed on different parts of
the fixed city emission grid described in the preceding paragraph. The
average emissions at each point on the trapezoidal grid are determined
by interpolation between the values at the points on the fixed grid. The
average of the emission values determined for the points on the trapezoidal
grid defines the emission rate for the segment.
Historical link-volume data are obtained from traffic departments
in the cities, towns, and counties in the region being studied. Because
traffic varies according to seasonal, weekly, and daily cycles, an
observation of volume for one day must be adjusted for the weekly and
seasonal fluctuations. The resultant corrected value is recorded as the
average daily traffic (ADT) for that location.
The number of vehicle-miles traveled on streets not represented by
the primary network is computed from an estimate of the total vehicle-
miles traveled in the area and the total vehicle-miles on the links of the
primary network. The local street mileage is taken to be the difference
between the two.
B-lll
-------
Link speeds for use in the emission rate calculation are determined
from averages of peak and off-peak travel hours on various kinds of route
facilities. Speed data were obtained from the traffic survey.
For most locations, the peak traffic hour speeds were taken as
equal to 80% of the off-peak speeds. Peak-hour speeds were generally
assigned to the four heaviest traffic hours of the day.
The emission rate, e (g/vehicle-mile), is determined from the
equation
3
e = c S
where S is the average speed over the link, in mi/hr, and c and |3 are
constants. For vehicles in use before exhaust control systems, c = 1121
and (3 = -0.849, as determined by Rose et al. (1969) from observations on
a number of vehicles in several locations. For autos reflecting the level
of emission controls mandated for 1968-70, one hundred thirty-nine emission
values were calculated from emission data presented by Beckman et al.
(1967)2 and from actual observations of speeds, speed changes, and stops
for a variety of road types and traffic congestion conditions. Regression
analysis was used to examine the relation between emissions and average
speed, both with and without controls. The following power function
proved to be the best fit for 1969 model year automobiles:
-0.48
e = 245 S
1 A. H. Rose and W. D. Krostek, "Emission Factors," U.S. Dept. of Health,
Education and Welfare, National Air Pollution Control Administration,
p. 5, 1969.
2 E. W. Beckman, W. S. Fagley, and 0. Sarto, "Exhaust Emission Control by
Chrysler—The Cleaner Air Package," Air Pollution—1967 (Automotive Air
Pollution), Hearings before the Subcommittee on Air and Water Pollution
of the Committee on Public Works, United States Senate, Ninetieth
Congress, First Session, Part 1, pp. 411-424, 1967.
B-112
-------
Consideration of emission control progress yielded the emission
model for 1980 and later model year automobiles:
-0.48
e = 34 S
C. Model Availability
The APRAC model is fully documented in a recent report by Mancuso
and Ludwig3. The program is available in two versions: one for the SRI
CDC S400 computer and another for the EPA IBM 360/50.
3 R. L. Mancuso and F. L. Ludwig, "User's Manual for the APRAC-1A
Urban Diffusion Model Computer Program," CRC/EPA Contract No.
CAPA-3-68(1-69),SRI Project 8563, Stanford Research Institute,
Menlo Park, California, 1972.
B-113
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EMISSION MODELS CHECKLIST
Input Data Requirements
Within
Program
User
Supplies
From
AP-42
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Spatial Distribution
-Links
-Grinds
-Area
x
X
X
X
X
X
X
X
NP
NP
X
NP
NP
NP
x
NP
NP
x
x
Proxies/Comments
Average CO of vehicle
mix for year of study
interest
Speed and distance
NP
NP
NP
NP
NP
NP
NP
NP = No provision
B-114
-------
Output Available
Pollutants
-CO Yes
-HC No
-NOX No
-Particulates No
Availability of Program Documentation
Yes No
Included in report
Referenced in report
Language
Equipment
Validation/calibration: Calculated data were low compared with
CAMP-observed data in St. Louis
Time resolution; 1 hour
Spatial resolution: Radial segments-22.50 out to 16 km
from receptor point
* See Section C, Model Availability
B-115
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XX STANFORD RESEARCH INSTITUTE
Procedures for Estimating Highway User Costs, Air Pollution, and
Noise Effects. D. A. Curry and D. G. Andersen, NCHRP Report 133,
prepared by Stanford Research Institute, 1972.
A. Author's Abstract: None given
B. Summary
Because the researchers were unable to locate data in the proper form,
the approach to the estimation of emissions is to compute emissions for a
vehicle of a particular type and characterize the emissions from that
vehicle as "typical" of the vehicles that will be built during the next
few years. The assumption is that the control technology will not change
the patterns.
The emission estimating procedure assumes that during the next few
years vehicles will achieve carbon monoxide and hydrocarbon control through
use of the engine mondification package and a catalytic afterburner, and
will achieve nitrogen oxides control through exhaust gas recirculation.
Emission characteristics of a vehicle using the assumed type of engine
modification package were obtained for a vehicle manufactured by the
Chrysler Corporation. Because most manufacturers are using this type of
control, it is assumed that the vehicle is typical in that regard.
The emissions for stopping and changing speeds were computed under
the assumption that slowing down and speeding up would occur under con-
stant acceleration conditions. The emissions added due to speed changes
were computed by applying the emission characteristics of the test vehicle
to the pattern of speed changes analyzed elsewhere in the report and by
characterizing the results by the best straight line that could be drawn
B-117
-------
through the emission values plotted against volume to capacity (v/c)
ratio for each type of facility.
The reference emissions provide an estimate of the pounds of each
pollutant generated by travel over the analysis segments if all of the
traffic consisted of automobiles meeting the exhaust emission control
requirements for the 1969 model year. The table below gives the emission
limits in grams per mile and their relative ratios for various model
years as used in the conversion procedure (the ratios do not always track
the emission limits exactly, owing to changes in test procedures). These
relative values were combined with the fraction of the vehicle population
represented by each model year as predicted by averaging the fractions for
the years 1966-1970.
AUTOMOBILE EMISSION LIMITS FOR VARIOUS MODEL YEARS
Actual or Permissible
Emission (gm/mi)
Model Year
1967 and earlier
1968 and 1969
1970
1971-72
1973-74
1975
1976-79
HC
13.6
5.1
3.4
3.4
3.4
0.41
0.41
CO
117
59
39
39
39
3.9
3.9
NO*
4.0
4.0
4.0
4.0
3.0
3.0
0.4
Emission
Factor Used
(1970=1.0)
HC
and
CO
3.5
1.5
1.0
1.0
1.0
0.1
0.1
NOx
1.0
1.0
1.0
1.0
0.75
0.75
0.1
1980 and later 0.20 2.0 0.2 0.05 0.05
Use of the conversion factor assumes that the future controls will
reduce emissions proportionally for all operating conditions. Use of a
catalytic afterburner for control of carbon monoxide and hydrocarbons
will result in emissions that approximate this assumption.
B-118
-------
The emissions for single-unit gasoline trucks were estimated to be
2.5 times those of the reference automobile under all conditions.
The last step was to incorporate a degradation factor for the pre-
dicted increase in vehicle emissions due to wear, derived from data of
Rose and Krostek. The degradation factor increases at a decreasing rate
with average mileage drive, and hence with age:
Vehicle Degradation
Age (yr) Factor
0 1.00
1 1.05
2 1.12
3 1.18
4 1.20
5 1.22
6 1.23
7 1.24
:> 8 1.25
A. H. Rose, Jr. and W. D. Krostek, "Emission Factors," Department of
Health, Education, and Welfare, June, 1969.
B-119
-------
100
op
000
pou
CARBON MONOXIDE
§
g
/
i CO
HC
20 40 60 80
SPEED STOPPED FROM — mph
1.0
0.8
0.6
0.4
0.2
Q.
o
o
o
o
•a
c
O
CD
OC
<
O
tr.
0
>
I
100
SA-2579-32
FIGURE B-8 AUTOMOBILE HYDROCARBON AND CARBON MONOXIDE
EMISSIONS ADDED PER 1,000 STOPS
8
| 80
pound
CARBON MONOXIDE
S
S
8
0
HC-
1.0
0.8
0.6 C
o
o
a
CD
SL
O
O
0.2 g
>
100
20 40 60 80
UNIFORM SPEED — mph
SA-2579-33
FIGURE B-9 AUTOMOBILE HYDROCARBON AND CARBON MONOXIDE
EMISSIONS PER 1,000 MILES OF DRIVING AT UNIFORM SPEED
B-120
-------
cs
co
CO
(N
o
CN
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ui
O
X
o
o
i
o
CO
o
I
LLJ
tr
LU
a.
CO
LU
CD
I
o
a
LU
LU
85
o
tr
LL
Q
LU
Q
Q
CO
z
g
co
CO
LU
LU
-J
E
O
O
I-
CO
LU
cc.
D
CJ
o
to
S
§ 8 8
oooi/spunod — aaixoNow Noaavo
B-121
-------
in
CN
>
tr
LU
cc
LU
z
CO
co
CO
LU
o
co
g
CO
CO
5
LU
cc
<
LU
LU
o
2
LU
cc
LU
H-
oc
LU
>
I
cc
o
CD
LU
CC
u
LL
bO-LOVd
B-122
-------
EMISSION MODELS CHECKLIST
Input Data Requirements
Within User From
Program Supplies AP-42
Proxies/Comments
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mod e-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Spatial Distribution
-Links
-Grinds
-Area
x
X
NP
NP
NP
NP
x
X
NP
x
NP
x
NP
NP
Based on 1969
reference auto,
conversions supplied
for other years
NP
NP
NP
NP
NP
NP
NP
NP = No provision
B-123
-------
Output Available
Pollutants
-CO Yes
-HC Yes
-NOX No
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
Referenced in report x
Language NP
Equipment NP
Validation/calibration; None
Time resolution; 1 hour
Spatial resolution; Link
B-124
-------
XXI STANFORD RESEARCH INSTITUTE
A Preliminary Study of Modeling The Air Pollution Effects From
Traffic Engineering Alternatives. F. L. Ludwig, R. C. Sandys, and
A. E. Moon, Stanford Research Institute, Menlo Park, California,
Air Pollution Control Association Journal, Vol. 23, No. 6, pp. 499-504,
June, 1973.
A. Author's Abstract
Three separate mathematical models were combined to calculate the
changes in carbon monoxide (CO) concentrations that might result from
traffic engineering changes. The three models used were: (1) The Dynamic
Highway Transportation Model (DHTM) which relates traffic flow patterns to
physical parameters and traffic signal characteristics of a network; (2) an
emission model that predicts CO emissions from traffic flow parameters
such as number of stops, idling time, etc.; and (3) the APRAC-1A urban
diffusion model which calculates CO concentrations from source distribu-
tions and meteorological factors. The composite model was applied to
traffic in downtown Chicago for a specific set of meteorological conditions.
Results are compared for two traffic signal control schemes. In those
blocks where concentrations were highest, the model indicates a 20% reduc-
tion is possible through improved traffic signal controls. The model
should be useful for testing other traffic control measures.
B. Summary
Moon has presented a methodology for calculating the amount of CO
generated by a "reference" automobile under four conditions: idling,
1 D. A. Curry and D. G. Andersen, "Procedures for Estimating Highway User
Costs and Air and Noise Pollution Effects," Final Report, Highway Re-
search Board Project 708, Stanford Research Institute, Menlo Park,
California, 1971.
B-125
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steady cruising, stopping, and starting. The third and fourth conditions
can be combined, with the results expressed as the amount of CO emitted
per vehicle stop. Because of continual changes in the pollution control
devices required for cars, and the changing mix of vehicle-model years,
it is necessary to convert emissions for "reference" cars to averages for
a particular year. The appropriate conversion factors have been provided
in Reference 1.
According to Moon, 0.15 gram per second (gm/s) of CO are released by
an idling vehicle. The amount of CO released by a vehicle traveling at a
steady speed is a function of that speed. Figure 1 shows an approximation
of the function; the curve has been fitted with exponential functions to
simplify the calculations. At the low speeds, below about 7 or 8 mph, the
curve shown is based on the assumption that a slowly moving vehicle emits
CO at the same rate as an idling vehicle; thus, the emissions per vehicle-
mile will be inversely proportional to the speed. The emissions for each
vehicular stop (and subsequent start) are an exponential function of the
steady driving speed from which the stop was made.
A factor is used to convert emissions of CO from a vehicle made in
the reference year (1968) to emissions for years after 1971. The factor
accounts for the mixture of vehicles manufactured in different years,
based on national averages of vehicle life expectancy. It is based on
existing legislation for the 1970 to 1975 era and on expected further
limitation on emissions through 1980, and assumes no retrofit requirements.
Moon's emissions model can calculate the emissions of CO on a given
street segment, given:
1. Number of vehicle-miles of steady-speed driving on the link
2. The speed achieved during steady-speed operation
Si The number of stops and starts during the hour
4. The number of vehicle-seconds spent stopped and idling.
B-126
-------
700
500 I-
300
200
> 100
8 70
E 50
P 30
<
tr
2
° 20
(/>
C/3
2
UJ
uT
10
I I I I T
i i i i i
E = 2350 S
-1.72
E = 86 S
-0.55
E = 9.1
ill i
5 7 10
S, SPEED — mph
20 30
50 70 100
SA-2579-41
FIGURE B-12 RELATIONSHIP BETWEEN CARBON MONOXIDE EMISSIONS AND
STEADY-SPEED DRIVING
B-12 7
-------
The Dynamic Highway Transportation Model can provide the above items of
information. For this study, an intermediate computer program was written
to accept the magnetic tape output of the DHTM and create a punch card
deck for input to the APRAC-1A Diffusion Model; this is not the most effi-
cient method of treating the problem.
This model is offered for central business district applications.
For the outlying streets, the method using the formula developed by Rose
2
and Krostek to relate emissions to average speeds and traffic volumes is
considered adequate.
o
A. H. Rose, Jr. and W. D. Krostek, "Emission Factors," Department of
Health, Education, and Welfare, June 1969.
B-128
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EMISSION MODELS CHECKLIST
Input Data Requirements
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
Within User Prom
Program Supplies AP-42
x
NP
NP
NP
NP
NP
NP
x > NP
NP
x NP
x NP
NP NP
Proxies/Comments
1968 Reference;
factored to later
years
Starts; stops;
idling; steady-state
speed; vehicle miles/
link
Spatial Distribution
-Links
-Grinds
-Area
x
NP
NP
NP
NP
NP
NP = No provision
B-129
-------
Output Available
Pollutants
-CO
-HC
-NOX
-Particulates
Yes
No
No
No
Availability of Program Documentation
Included in report
Referenced in report
Language
Equipment
Validation/calibration:
Time resolution:
Spatial resolution;
Yes
No
x
NP
NP
None
1 hour
Intersection
B-130
-------
XXII SYSTEMS APPLICATIONS, INC.
(1) Contaminant Emissions in the Los Angeles Basin—Their Sources,
Rates, and Distribution. P. J. Roberts, P. M. Roth, and C. L.
Nelson, Appendix A., "Development of a Simulation Model for
Estimating Ground Level Concentrations of Photochemical Pol-
lutants," Systems Applications, Inc. Report No. 71 SAI-6,
Contract No. CPA 70-148, prepared for the Environmental
Protection Agency, March 1971.
(2) Extensions and Modifications of a Contaminant Emissions Model
and Inventory for Los Angeles. P. J. Roberts, M. Liu, S. D.
Reynolds, and P. M. Roth, Appendix A, "Further Development
and Validation of a Simulation Model for Estimating Ground
Level Concentrations of Photochemical Pollutants," Systems
Applications, Inc., Report No. R73-15, Contract No. 68-02-
0339, prepared for the Environmental Protection Agency,
January, 1973.
A. Author's Abstract
(1) Perhaps the most tedious and mundane aspect in the development
and validation of a simulation model of reaction and dispersion processes
in the atmosphere is the compilation of a complete contaminant emissions
inventory. Yet, such an inventory is a sine qua non in model validations
and, if done properly, the emissions estimates probably constitute the
most precise segment of requisite input data. Contrast, for example,
the relatively low magnitudes 01 errors in emissions estimates with the
imprecision of wind speed and direction estimates, both at the surface
and aloft, as well as with the uncertainties in estimates of the variation
in mixing depth with location and time. Furthermore, as emissions inventory
need be carried out but once to serve as an adequate representation of
a region, whereas meteorological data must be collected for each validation
day, and for the purposes of this modeling venture, represented through
hourly variation in wind field and, mixing depth. It thus seemed wise
B-131
-------
to put a considerable effort into the establishment of an accurate
emissions inventory for the Los Angeles Basin.
Particular emphasis was placed on developing a detailed representation
of the spatial and temporal traffic distribution in the Basin, as vehic-
ular emissions account for approximately 97% of CO; 85% of reactive hydro-
carbon, and 62% of NOX emissions. Attention was also given to those
sources which, while responsible for only a small proportion of emissions
on an area-wide basis, contribute heavily to pollutant concentration
levels in their own locale—airports, power plants, and refineries.
In this Appendix, we present in detail the emissions inventories developed
for the major moving and fixed sources of pollution in the Los Angeles
Basin.
(2) During the last months of 1970, we prepared a pollutant emissions
inventory for the Los Angeles Basin for use in the modeling of the
transport, diffusion, and reaction of atmospheric contamination. Pollutant
sources were grouped into five categories—automobiles (and other motor
vehicles), aircraft, power plants, refineries and distributed fixed
sources. Emissions rates for a 2x2-mile grid system covering the Basin
were compiled for nitrogen oxides, carbon monoxide, and hydrocarbons.
Temporal variations in emissions rates were also determined. The complete
inventory is reported in "Contaminant Emissions in the Los Angeles Basin—
Their Sources, Rates, and Distribution," by P.J.W. Roberts, P. M. Roth,
and C. L. Nelson (1971).
Early in 1972, we had the opportunity to make a number of modifica-
tions and extensions for the emissions inventory. The changes which
affected all segments of the original inventory, were motivated by a
variety of factors, but most heavily by a desire to improve the accuracy
or the resolution of the inventory, or to correct errors. It is the
purpose of this report to document all modifications and extensions that
B-132
-------
were implemented. In general, this report is segmented similarly to its
predecessor, the exceptions being that (1) changes applicable to all
portions of the inventory are included in an introductory general section
and (2) the one modification to the refinery inventory as a matter of
convenience, is included in the section dealing with distributed fixed
sources. Finally, we wish to point out that only changes are reported
here; we have not attempted to present a final version of the inventory,
either in summary or in detail, in this document. One must read both
this report and the original to construct the complete inventory.
B. Summary
The emissions model developed by SAI considers both fixed and
moving sources of air pollution in the Los Angeles Basin. Fixed sources
are characterized as area sources, with the exception of point source
power plant emissions, which are treated as volume sources. Both auto-
motive and aircraft emissions are distributed over an array of grid
squares. Concentrations of carbon monixide, hydrocarbons and nitrogen
oxides are predicted, using hourly emissions estimates.
1. Treatment of Automotive Emissions
Automotive emissions are estimated for 2.0x2.0-mile grid squares
and are assumed to be attributable to exhaust, crankcase leakage (blow-by)
and evaporation. The Federal Driving Cycle (FDC) is adopted.
a. Exhaust Emissions
Surface Streets. Average emission rates for surface
streets are estimated by calculating
Q.a(t) = y(t)Q.C + [l-y(t)]Q h
where y(t) equals the fraction of cars started at time t that are "cold-
started" (superscript c); the relation between cold-start and hot-start
B-133
-------
(superscript h) emissions is:
h
zP + (l-z)T
h c
Q. = Q.
i i
zP. + (l-z)T.
where z = fraction of vehicle registration that are automobiles
h c
P. ,P. = hot-running and cold-start auto emission rates
T. = emission rates from trucks and buses
c h
Both Q. and Q. are based on the FDC, which is defined
to simulate a trip having an average speed of about 19.6 mph.
A variation exists in the emissions rate due to the non-
uniform distribution of cold vehicle starts during the day. To account
for this, a correction factor, g.(t), is formulated.
An integral representing the average emissions rate for
each vehicle is derived, relating the emissions from a vehicle during
c
a time interval after a cold-start, e. , to those after a hot-start,
h c n
e. . Functional forrr.s for e. an., e, are establishoc., givinc a
c
linear decrease in e. during cue xirst 7-1/2 minutes of operation and
H
a constant emissions rate thereafter, equal to e. . The integral is
approximated by an expression for the average emissions rate at the
midpoint of a 15-minute time interval that assumes that the trip
start time is uniform during the time interval. The correction
factor is defined as this approximation divided by the average
emissions rate.
This treatment considers the effect of vehicle start
variation only for the period 6 a.m. to 9:23 a.m. The "midday bump"
and evening rush hour variations are not considered because the model
was not validated for that time period. Also, it should be noted that
large quantities of data are needed to properly evaluate (3 (t). Pertinent
data include driving patterns in an area, trip length, average speed,
time between trips, emissions data as functions of time for both hot-
B-134
-------
and cold-starts, etc. Since large quantities of this data are not
generally available, the authors have made simplifying assumptions.
The correction factor is
E.S(t)
B (t) = —
Q.S(t)
s
where Q. (t) is the average emissions rate as defined previously, and
c
E
—— he [/ \ h - 1 Cnf(l-y ] h ~ ]
s/ \ 30 e. + n-l (1-y )e. +y , e. + —\{ n/e. + y e
± (O = * L\ n-l/ i n-l ij 2 t ' i n ij
c c
n-2 n
30 + Cn-l + 2
where n = a fifteen-minute time interval
s / \
E /1 \ - average emissions rate of species i evaluated at the mid-
point of time interval n
c = total number of trips started in a time interval
n
y = ratio of cold-starts to total starts during a time interval
n
e. (t) = emission from a vehicle during a time interval after a hot-start
e. = average emissions rate of species i from an automobile over
the time period between 7-1/2 and 22-1/2 minutes after a
cold start
e. = average emission rate of species i from an automobile over the
time period between zero and 7-1/2 minutes after a. cold-start
h c
The e and e values are derived on the basis of the FDC,
_ i i '
and e and e follow from these. Values of c are based on an analysis
i i n
of data by Kearin et al. (1971) , where the weekday was segmented into
D. H. Kearin, R. L. Lamoureux, B. C. Goodwin, "A Survey of Average Driving
Patterns in Six Urban Areas of the United States: Summary Report",
Report 1M-(L)-4119 Vol. 7, System Development Corp., Santa Monica
(January 1971).
B-135
-------
eight time periods, each with an assigned constant value of y.
The total emissions from surface streets for a particular
grid square are
EiS(t) = ^ QiS(t)d/(t)MS3i(t)
g
where d (t) = fraction of daily nonfreeway traffic count assignable to
an hourly period
s
M = nonfreeway vehicle mileage per day for the grid square
in question
Values of d (t) are based on an average of 52 randomly selected streets
Jl/
(the sample being stratified according to the magnitude of daily traffic
flow) , the counts on individual streets being weighted in proportion to
the magnitude of traffic flow on the street. Traffic counts were taken
from state and city data.
Freeways. In calculating freeway emissions, all vehicles
are assumed to be hot-running. It has been found that average emissions
rates for freeways in Los Angeles correlate well with average route
speed alone, although many other factors influence these rates.
Freeway emissions rates for species i for a particular
grid square are given by
a = a (v) i
where v ,v = average speed in the fast and slow directions, respectively
f s
n ,n = number of vehicle miles driven per hour in the fast and slow
directions, respectively
B-136
-------
a = hot-running emissions rate of species i
i
a ,b = empirically determined constants
i i
"Fast" and "slow" refer to an assignment of names to the two opposing
directions of flow on a freeway.
f
nf = I^
"A
n =
1+x
f f f
N = d M
a a
where d = fraction of daily freeway traffice counts assignable to an
hourly period
M = freeway vehicle mileage per day for a grid square
x = n/n
The average vehicle flow as a function of time for both
directions on all freeways in the Los Angeles Basin is calculated from
15-minute count data for a 24-hour period at 31 locations. Values of x
are computed using these data. Average freeway speed as a function of
time for both freeway directions was obtained from state division of
highways data.
To determine the constants a and b , average emissions
i i
rates for hot-running conditions at a known average speed are necessary.
These rates were estimated in the same manner as those for surface
streets. Also required are the slopes of emissions rate/average speed
curve. Data for these are very scarce, and the best such data available
are somewhat out of date (less than 50% of the vehicles on the road in
B-137
-------
September, 1969, are represented by the test group). Correlations
based on the data give estimates of average emissions rates at high
speeds under hot-running conditions that appear to be rather low. The
authors decided to modify the values, basing their modifications on the
premise that automobile manufacturers design their systems with the
expectation that they will be tested at low average speeds. Thus, it
may be expected that average emissions rates at low speeds have decreased
more rapidly from year to year than rates at high average speeds. Since
there is no data on which to base estimates of revised slopes b., they
are estimated using two points, the hot-running emissions rates at 19.6
mph and the rates at 60 mph, the latter computed from
Q. (60) + 1/3
h hi
I (19.6) - Q± (60)
J
where Q. (19.6) are the FDC values, modified for hot-running conditions,
Vi
and Q. (60) are computed from the data described above. The factor
one- third is purely a guess and could range from 0.1 to 0.5.
Total Exhaust Emissions. Total vehicle emissions for a
particular grid square are given by
Ei(t) =
b. Crankcase Emissions
It is estimated that 15% of all automobiles are not
equipped with PCV devices. This study uses a figure of 0.7 gin/mile for
the average vehicle in the Los Angeles Basin.
c. Evaporative Losses
Evaporative losses are the sum of fuel tank losses and
carburetor losses, and involve only hydrocarbons. The authors assume
B-138
-------
that these losses occur mainly during the hours 7 a.m. to 7 p.m. PDT,
and that they are distributed evenly over this period. Evaporative
emissions in each square are assumed to be proportional to the number of
nonfreeway vehicle miles in that square.
/ number of \ / \
/ , , ., \/evaporative \
automobiles I
/ evaporative \ \ / \ losses /
/ \ \ registered / \ /
emissions for = m
\ / ii /total nonfreeway \
\ square i,j / (,.••,
\ vehicle mileage /
where m. . equals thousands of nonfreeway vehicle miles per day driven in
!J
square ij.
2. Treatment of Aircraft Emissions
Hourly emissions from airport operations are classified as
arising from ground and airborne operations. For each class of aircraft
it is assumed that pollutants are emitted at a uniform rate during each
of five operating modes; the amount of contaminants injected into a cell
is thus proportional to the length of the flight path in that cell. Air-
craft emissions are treated as volume sources, well-mixed in their cells,
and are assumed to occur at a uniform rate over each 1-hour time period.
Emissions indices measured from one aircraft are assumed representative
of all aircraft of that class.
a. Ground Operations Emissions
Ground operations are assigned three modes of operation:
taxi, landing, and take-off. The taxi mode includes taxi between runway
and satellite upon landing, between satellite and end of runway and
awaiting clearance for take-off, and idle at satellite. The landing
mode includes emission from touchdown on the runway to turn-off from
the runway, with the take-off mode being the reverse.
B-139
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Ground operation emissions rates into ground cell ijl for
an hourly period are
-3 7 3
10 d a
.LI/ U Ct • - -m ^ii i —
Qk LM Y n M y k
ijm,je,m=l K ^—' u u ^—' gu gu
U— 1 S^l
where i,j are indices for the x,y coordinates
m is an index for the z coordinates ( = 1 for jrrcund cells)
H is an index for the hourly period
k refers to the type of pollutant (CO, HC, NO, and NO.,)
£
g refers to ground operation mode (taxi, landing, and take-off)
u refers to aircraft class (long-range jet transport, medium-range
jet transport, business jet, turboprop transport, piston engine
transport, piston engine utility, and turbine engine helicopter
j^
Q ijmji = emissions rate
d = fraction of total daily flights in an hourly time period
IL
A. . = fraction of airport area belonging to a ground cell
K = 60 (min/hr)
n = number of flights per day of aircraft class u
M = average number of engines per aircraft class u
u
jj
f = pounds of pollutant k emitted per 100 Ibs of fuel consumed by
gu
aircraft class u operating in mode g
C = pounds of fuel consumed per engine of aircraft class u operating
gu
in mode g
b. Flight Operations Emissions
The modes of operation for airborne operations are an
approach mode and a climb-out mode. It is assumed that arrival and
B-140
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departure rates within each time period are equal; that aircraft follow
straight-line flight paths while in approach and climb-out modes; and that
ascent and descent angles for all aircraft at a particular airport are
equal to those of the aircraft class having the highest fraction of total
operations at that facility. Other assumptions are that flight paths
originate and terminate at the most frequently used runway at each air-
port; that a fixed proportion of aircraft of a given class arrive and
depart from each airport; and that an unknown temporal distribution of
flight operations at an airport may be represented by the temporal
distribution from another airport having a similar mix of aircraft by
class.
Emissions rates for airborne flight operations are given by
-3 7
k 10 d P V\ k t
Q. . „ = I ijm X n M f C u
" K / ' u u u up?
u=l u
where t = time spent in descent from inversion height to touch-down by
aircraft of class u
t' = time spent in descent from 3000 feet above ground elevation
u
to touchdown by aircraft of class u
k
f = pounds of pollutant k emitted by aircraft of class u per 1000
Ibs fuel consumed during descent
C = pounds of fuel consumed per engine of aircraft of class u
during descent from 3000 feet
P = fraction of the length of the flight path assignable to
ijm ,,
cell ijm
The above equation also describes the emissions rate during climb-out,
jj
with the appropriate changes in the definitions of t , f . t' and C .
u u u u
B-141
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3. Treatment of Fixed Source Emissions
a. Power Plants
Emissions from point source power plants are distributed
evenly as volume sources in the column of cells up to the inversion base,
under the assumption that the plume is well mixed in these cells. Since
diurnal variations in NO emissions were substantial, hourly emissions
x
averages for the two seasons in some plants and for one day in others
are used as source strengths and apportioned as described above.
b. Oil Refineries
Refinery emissions are treated as area sources and are
assumed to be well mixed in the cell into which they are injected. Total
daily emissions for nitrogen oxides and low and high reactivity organic
gases and the individual refinery crude capacities were available from
the Los Angeles County Pollution Control District. The total emissions
are distributed uniformly over 24 hours and in proportion to the crude
capacity of each refinery.
c. Distributed Sources
Additional sources are treated uniformly as area sources.
Since some of the emissions from these sources are spread over highly
populated areas, the modeling area was divided into regions of high and
low population density.
Petroleum Marketing, Domestic, Ship and Railroad Emissions-
Nitrogen Oxides. It is assumed that half of the total daily domestic
emissions and the total daily emissions attributable to petroleum
marketing operations occur between 6 a.m. and 6 p.m. PST. Half of the
daily ship and railroad emissions are assumed to occur at the Port of
Los Angeles. The emissions cited above are distributed uniformly over
the high-population grid square. All emission rates are assumed to be
uniform between 6 a.m. and 6 p.m. PST.
B-J.42 ,..--.
-------
Incineration—Nitrogen Oxides. The authors assume that
the total daily emissions occur between 6 a.m. and 6 p.m. PST, and that
emission rates are constant. In Los Angeles County, emissions assignable
to a grid square are proportioned to the number of permit units issued
for the 5-mile square area containing that grid square. Due to lack of
information on the spatial distribution of industrial plants in Orange
County, emissions are assumed to be released in the highly populated
squares.
Mineral Processing Plants and Metallurgical Plants—
Nitrogen Oxides. These emissions are apportioned for each county in the
same manner as incineration emissions.
Petroleum Production and Other Industries—Nitrogen Oxides.
Petroleum production emissions are also apportioned in the same manner as
incineration emissions, but are distributed uniformly over 24 hours. Other
industries contribute emissions which are distributed uniformly between
6 a.m. and 6 p.m. PST, mainly over the south central portion of Los
Angeles County, with smaller amounts in other areas.
All Fixed Sources—Organic Gases. Organic emissions from
petroleum marketing, dry cleaning, degreasing, and other organic solvent
users are assumed to be uniformly distributed over the highly populated
squares between the hours of 6 a.m. aid 6 p.m. PST. Surface painting
and coating operations emissions are distributed uniformly between 6 a.m.
and 6 p.m. PST in proportion to the number of paint bake oven permits in
each square. Petroleum production organic emissions are spread uniformly
over 24 hours and in proportion to the number of petroleum processing
equipment permit units in each square. Incineration, mineral processing
plant, power plant and other industrial organic emissions are not
considered significant enough to be included.
B-143
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All Fixed Sources—Carbon Monoxide. The total fixed
source carbon monoxide emissions in the modeling area are considered
negligible.
C. Model Availability
Although the emission inventory methodology has been thoroughly
discussed and the appropriate equations listed, no operational computer
code is included in the report.
B-144
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XXIII SYSTEM DEVELOPMENT CORPORATION
Development of a Simulation Model for Estimating Ground Level
Concentrations of Photochemical Pollutants. M. Weisburd,
L. G. Wayne, R. Danchick and A. Kokin, Final Report. System
Development Corporation, prepared for Environmental Protection
Agency, Contract No. CPA 70-151, January, 1971.
A. Author's Abstract
Modeling photochemical air pollution for simulation on the computer
must, at the present time, be based on the limited operational and experi-
mental data available. Computer simulation dictates that the gaps and
uncertainties which occur in our present understanding of photochemical
pollution be dealt with in order to simulate the photochemical system as
a functional whole. All modeling, therefore, must involve the making of
assumptions and the application of theoretical considerations. Modeling
strategies largely differ in the type and amount of theory applied.
The approach taken by the System Development Corporation (SDC) stays
relatively close to the state-of-the-art in that (1) while the conveniences
of theoretical mechanisms are necessary in some instances, the modeling is
largely based on the operational and experimental data available, (2) the
first iteration of the concept of the model is based on present understand-
ing, and (3) the basic model is flexibly constructed so that it can be
modified, expanded, and refined as new information becomes available.
SDC, accordingly, has taken an approach which emphasizes those
mechanisms and functions which through experience and observation, in
themselves, and in combination, appear to have the greatest potential for
predictive accuracy. These include, in their order of assumed importance:
(1) chemical mechanisms; (2) the trajectory (and history) of moving air
parcels; and (3) the effect of vertical stability (mixing depth).
B-145
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The basic assumption taken is that the chemical mechanism largely
governs, both quantitatively and qualitatively, the ground level concen-
trations of primary and secondary contaminants at particular times and lo-
cations. This accuracy is dependent upon, and is maximized by the ability
to follow in a detailed manner the cumulative build-up of pollution in
samples of air as they move across the metropolitan area. The 33-step,
25-species mechanism, and the calculation of trajectories based on a dense
network of meteorological stations, contained in the current version of
the model, reflect the degree of confidence placed in this approach. The
calculation of mixing depth is of similar importance as it affects the
concentrations of primary pollutants.
B. Summary
The SDC model is an early version of the KEM transformation model,
the emissions treatment of which has been previously discussed. It should
be noted that an earlier version of the model contained a subroutine to
include pollutants from large point sources. However, initial runs of the
model with this routine indicated no significant difference in air quality
from that obtained using area source emissions alone, although an insuf-
ficient number of trajectories were run to conclusively establish the impact
of the routine.
1. Sensitivity to Changes in Emission Factors
One sensitivity run consisted of doubling the emission factors
for all contaminants. The results were an approximate 20% increase in
ozone, no appreciable effect on NO , nearly an 11% increase in CO, and
&
a 25% reduction in nitric oxide concentrations. The effect of doubling
emission factors must be taken as the doubling of emissions in the imme-
diate vicinity of the trajectory only, as changes in emission factors do
not affect the value of background concentrations.
B-146
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Reduction of the emission factors by one-half decreased ozone
concentration at the trajectory terminus by only 1 pphm, CO by approximately
5%, and both oxides of nitrogen by less than half the control values.
The emission factor for nitric oxide alone was increased by 25%,
with a negligible effect on concentration.
2. Sensitivity to Changes in Background Contamination
The effect of reducing background contamination was explored in
a series of runs using different trajectories. Results confirm that the
relative effect differs "substantially" from one location to another.
3. Sensitivity to Changes in the Diurnal Traffic Curve
The diurnal traffic density curve used in the model assumes a
peak at 8 a.m. PDT and a constant lower value between 1 p.m. and 6 p.m.
PDT. Two runs were made with different trajectories, where the assumed
traffic density curve was altered. For one run the curve was shifted so
that the morning peak occurred at 7 a.m. PDT, resulting in minimal changes
in concentrations from those of the control run. The other case tested
utilized a trajectory leading to downtown Los Angeles that was much more
directly affected by the morning traffic peak. The curve was shifted by
2 hours, resulting in a 50% reduction in ozone, a one-third reduction in
CO, little change in NO , and approximately a 100% increase in NO. The
t*
effect of change in the curve is highly dependent on trajectory location
and time of day.
B-147
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XXIV TRAVELERS RESEARCH CORPORATION
Sensitivities of Air Quality Prediction to Input Errors and
Uncertainties. G. R. Hilst, Proc. of Symp. on Multiple-Source
Urban Diffusion Models, Chapel Hill, N.C., APCO Pub. No. AP-86,
1970.
A. Author's Abstract
With the advent of computer-oriented simulation models of the physical
and chemical system that produces varying levels of air quality, it is both
possible and desirable to assess the model's sensitivities to errors and
uncertainties in the input variables. From such analyses, it is possible
to derive more explicitly the levels of accuracy that must be observed in
the specification of the inputs for source strengths and distributions,
wind velocity, horizontal and vertical diffusion rates, and pollutant
chemical reactions or physical decay and loss rates, if the air quality
predictions derived from these inputs are to remain within useful limits of
accuracy and uncertainty.
For the present report, a single case study is utilized to test the
sensitivity of the Travelers Research Corporation Regional Model to random
and systematic errors in the source-strength input and to systematic errors
in the remaining input variables. It has been found that random errors in
the source strengths do not produce comparable errors in the air quality
prediction. Beyond this, and in order of decreasing sensitivity, the
TRC model has been found to be highly sensitive to systematic regional wind-
direction errors and moderately sensitive to systematic errors in source-
strength estimates, decay or loss rates, and vertical diffusion rates.
The model is insensitive to errors in lateral diffusion rates, at least
for the multiple-source distribution encountered in Connecticut. These
Sensitivities are quantified for the case studied.
B-149
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The case used for the sensitivity analyses also provided an oppor-
tunity to determine the effect of changes in the input variables on the
verification of the model, since observations of SO were available from
^2
the verification program. Significant improvements in the TRC model
verification, over that obtained from the original, independently chosen,
input variables, were found when either the source strengths or the decay
or loss rates were adjusted appropriately. This result suggests the need
for much better understanding of the decay and loss of airborne SO .
£
B. Summary
Using the Travelers Research Center Gaussian plume model, an assess-
ment was made of the model's sensitivity to errors in various input param-
eters, including misestimates of source strength and positional errors.
1. Source Strength Error
The model was found to be relatively insensitive to large random
errors in the specification of source strength. Evidently, among multiple
sources random errors tend effectively to cancel each other. Assuming a
Gaussian distribution of fractional error in concentration
X - X
T E
X
T
where X = true value of concentration
T
X = erroneous value of concentration
the range of the fractional error increases by only a factor of 2, while
the RMS value of the error in source strength, Q, increases by a factor
of 8.
B-150
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While no systematic error in concentration is introduced by
random errors in Q, the standard deviation of concetration error is
broadened as random errors in Q increase.
The model was much more sensitive to systematic errors in Q.
Since it is prescribed that the concentration, X, is directly proportional
to Q, there must be a one-to-one correspondence between systematic errors
in Q and errors in X. Thus,
=[(QT-QE)/QT]
The standard deviation for systematic error is zero.
3. Positional Error
Mislocation of sources, receptors, etc., automatically invokes
errors in all of the input parameters. Therefore, a systematic error in
the direction of the general wind field was utilized for the assessment of
sensitivity to the compounded errors of position. Noting that the refer-
ence values for all input parameters were arbitrarily chosen, results show
that mean error is less than or equal to 50% and individual error is
greater than 200%. The mean fractional concentration errors are not
extreme, but the spread of these errors is.
B-151
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XXV TRW
Prediction of The Effects of Transportation Controls on Air Quality
in Major Metropolitan Areas. Prepared by TRW, Inc., McLean, Virginia,
APTD-1363, EPA, Office of Air Quality Planning and Standards, Research
Triangle Park, North Carolina, 27711.
A. Author's Abstract
Transportation data in the form of vehicle miles traveled (VMT) by
geographic areas have been used as the basis for calculating emission
rates and air quality. The methodology involved:
Step 1. Assignment of VMT and speed to elements of a grid network.
This required superimposing a rectangular grid network consisting
of one mile squares or one kilometer squares over a base map of
the metropolitan area and summation of VMT from each of the indi-
vidual roadways which may fall within one of the small grids to
obtain the total VMT for each element or small grid in the grid
network. The speed for each grid of the grid network is obtained
by averaging the speeds from each element or roadway within the grid
for each element of roadway as a proportioning factor. In this
manner the many vehicular sources moving within an individual grid
can be represented by a single stationary source, which produces the
same amount of emissions, equal to the size of the individual grid.
This equivalent source is called an area source because the emis-
sions from the grid or area source are now considered as evenly
distributed or evenly produced over the entire area of the individual
grid.
Step 2. Use of vehicle emission factors to calculate emissions on
a per-grid basis.
B-153
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Step 3. Conversion of emission rates to pollutant concentration
1
on a per-grid basis by the method of Gifford and Hanna.
Step 4. Application of transportation control strategies to the
data base to obtain predicted concentration patterns for each con-
trol strategy for the year 1977.
The resulting concentrations are presented graphically as isopleths
over a map of the metropolitan area. Pollutants considered were carbon
monoxide, hydrocarbons, and oxides of nitrogen, with carbon monoxide being
of primary interest.
B. Summary
Transportation data in the form of vehicle miles traveled (VMT) by
geographic areas have been used as the basis for calculating emission rates
and air quality. The methodology involved:
Step 1. Assignment of VMT and speed to elements of a grid network.
This required superimposing a rectangular grid network consisting of
one mile squares or one kilometer squares over a base map of the
metropolitan area and summation of VMT from each of the individual
roadways which may fall within one of the small grids to obtain the
total VMT for each element or small grid in the grid network. The
speed for each grid of the grid network is obtained by averaging
the speeds from each element or roadway within the grid for each
time period required, using the VMT along each element of roadway
S. R. Hanna and F. A. Gifford, Jr., "Urban Air Pollution Modelling,"
Presented at 1970 International Air Pollution Conference of the
International Union of Air Pollution Prevention Associations, ATDL
Contribution No. 37.
B-154
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as a proportioning factor. In this manner the many vehicular
sources moving within an individual grid can be represented by a
single stationary source, which produces the same amount of emis-
sions equal to the size of the individual grid. This equivalent
source is called an area source because the emissions from the grid
or area source are now considered as evenly distributed or evenly
produced over the entire area of the individual grid.
Step 2. Use of vehicle emission factors to calculate emissions on
a per-grid basis.
1. Specific Approach
From urban transportation demand models (the traffic assignment
portions), VMT and speed data were obtained. Wherever possible, VMT and
speed data were obtained by zone. If no zonal information was available,
zonal estimates were made. However, the emission levels are actually
used as average zonal values and are rough approximations rather than
point measures. In addition, the factors used to convert VMT and speed
data into emission levels themselves introduced errors which are of at
least equal magnitude to those introduced by allocation of link VMT into
zones. Most of the VMT and speed information supplied was for a 24-hour
average weekday (in some cases peak-hour data were available) and, there-
fore, had to be adjusted to estimate the eight-hour and one-hour maximum
requirements of the project. These estimates were made utilizing daily
hourly traffic counts made available by the traffic departments and trans-
portation planning agencies in each of the cities.
Most of the traffic assignment data provided had base years in
1968 and 1969. These same models generally provided projected values for
VMT (and very often speeds) for the year 1980 and beyond. Interpolations,
therefore, had to be made for the year 1977. In making these interpolations
B-155
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employment, residential, and population data were used whenever available.
When these data or growth models were not available, interpolations were
generally based on straight line assumptions.
In a number of cities speed information had to be estimated for
each of the zones. When this was done, the speeds were based upon link-
node data using weighted averages weighting the respective speeds using
link volumes as weights. The program input and output are oriented to a
rectangular grid network which overlays the city of interest. The results
are presented in tabular form as the average concentrations for each
element of the grid network. Punched card output was used to produce
concentration isopleths or concentration density maps which were overlaid
on a map of the metropolitan area to give a visual representation of the
spatial distribution of the pollutant of interest.
2, Program Input
Three groups of input data are required: traffic data, meteoro-
logical data, and emission factors for each pollutant.
Traffic Flow Data. The resulting grid network of VMT and speed
from above provide the basic traffic flow data required for emission
calculations.
To correspond with the time periods specified by the National
Ambient Air Quality Standards, peak 1-hour and peak 8-hour VMT are required.
For comparison with the ambient air quality standards for hydrocarbons and
nitrogen dioxide, a Larsen transformation was used to convert estimates
to 3-hour and annual averages, respectively. When these data were not
available, they were estimated as percentages of the 24-hour VMT by using
traffic count information at various stations within the area. The pro-
gram applied this percentage whenever necessary to obtain the appropriate
data format. Particular streets may vary significantly from the mean
B-156
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values obtained by this procedure; however, the allocation of VMT to
grids as described above tends to minimize the effect of individual streets
on the predicted concentrations values for the individual grid.
Traffic speed data on a per-grid basis are required as an adjust-
ment to the average vehicle emission factors. Carbon monoxide and hydro-
>
carbon emissions decrease with increasing average vehicle speed; therefore,
the emission factors must be adjusted to be more realistic. EM has pub-
lished emission factors curves as a function of average vehicle speed for
carbon monoxide and hydrocarbons. These curves are incorporated in the
program.
The modal split of VMT must be considered, i.e., what is the
proportion of cars, buses, and trucks comprising the total VMT. Data on
the modal split are not typically available on a detailed basis; therefore,
this factor is most readily accounted for in the formulation of area
emission factors as discussed below.
Emission Factors. EPA has developed a computer program to pro-
duce emission factors for the entire vehicle population for a given
metropolitan area for any calendar year from 1960 to 1990 for which
vehicle age-type distribution data are available. Emission factors are
subject to revision as required by changes in vehicles, control devices,
"representative" driving cycles, and measurement techniques.
3. Outputs
1. Annual emissions (tons/year) for each grid and total
emissions for a grid network. -
2
2. Emission rates (mg/m /sec) for each grid.
3. Total 8-hour and 1-hour emissions for each grid.
Br-157
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4. Resolutions
Generally, the data available for each city did not permit
classification into fine-grained geographic areas (e.g., the eight rings
used for Washington, D.C.). Since the impact of flow controls was based
on general experience and Judgment, for the purposes of pro.ject analysis,
the respective cities (except Washington, D.C.) were divided into three
broad geographic areas: (1) the CBD or core, (2) an area surrounding the
core but still within the political boundaries of the city, and (3) the
remaining area outside the boundaries of the city. This three-way division
allowed relatively easy division of zones while still permitting our
analysis to be somewhat more fine grained than would otherwise be possible.
(.
B-158
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EMISSION MODELS CHECKLIST
Input Data Requirements
Within User
Program Supplies
From
AP-42
Vehicle Emission Factors
-By model year
-By size class
-By power plant
-Deterioration factor
Vehicle Operating Modes
-Hot/cold start
-Route average
-Link average
-Mode-by-mode
Traffic Features
-Vehicle mix
-Engine operating time
-Vehicle miles traveled
-Fuel consumed
x
X
X
X
X
X
X
X
NP
X
NP
NP
NP
NP
x
NP
Proxies/Comments
Uses vehicle mix
for year of
interest in
study
Speed and distance
NP
NP
NP
NP
Spatial Distribution
-Links
-Grinds
-Area
NP
x
X
NP
NP
NP
NP = No provision
B-159
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Output Available
Pollutants
-CO Yes
-HC Yes
-NOX Yes
-Particulates No
Availability of Program Documentation
Yes No
Included in report x
*
Referenced in report x
Language NP
Equipment NP
Validation/calibration; Compared with Washington, D.C., camp data; CO
CO data show best agreement
Time resolution; 1 hour; 8 hour
Spatial resolution; 1 mile or 1 kilometer square
*
Uses EPA computer program for emission factors for entire vehicle
population for a given metropolitan area for years 1960 to 1990.
B-160
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XXVI UNIVERSITY OF ALASKA
Ice Fog: Low Temperature Air Pollution. Prepared by Carl S.
Benson, Geophysical Institute, University of Alaska,, UAG R-
173, November, 1965.
A. Author's Abstract
Stable pressure systems over interior Alaska sometimes produce
prolonged extreme (below -40°C) cold spells at the surface. The rate
of radiative cooling of the air is enhanced by suspended ice crystals,,
which are themselves a result of the initial cooling.
Radiation fogs formed during the onset of cold spells are generally
of short duration because the air soon becomes desiccated. These fogs
consist of supercooled water droplets until the air temperature goes
below the "spontaneous freezing point" for water droplets (about -40°C);
the fog then becomes an ice crystal fog, or simply "ice fog." During the
cooling cycle, water is gradually condensed out of the air until the
droplets freeze. At this point there is a sharp, discontinuous decrease
in the saturation vapor pressure of the air because it must be reckoned
over ice rather than over water. The polluted air over Fairbanks allows
droplets to begin freezing at the relatively high temperature of -35°C.
Between -35 and -40° C the amount of water vapor condensed by freezing of
supercooled water droplets is three to five times greater than the amount
condensed by 1°C of cooling at these temperatures. This results in rapid
and widespread formation of ice fog, which persists in the Fairbanks area
as long as the cold spell lasts. The persistence of Fairbanks ice fog
depends on a continual source of moisture (4.1 x 10 Kg H?0 per day)
from human activities within the fog.
B-161
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Ice fog crystals are an order of magnitude smaller than diamond
dust, or cirrus cloud crystals, which in turn are an order of magnitude
smaller than common snow crystals (0.01, 0.1 and 1 to 5 mm respectively).
The differences in size are shown to result from differences in cooling
rates over five orders of magnitude. Most of the ice fog crystals have
settling rates which are slower than the upward velocity of air over the
city center. The upward air movement is caused by convection cells driven
by the 6°C heat island over Fairbanks. This causes a reduced precipitation
rate, which permits the density of ice fog in the city center to be three
times greater than that in the outlying areas.
The inversions that occur over Fairbanks during cold spells begin
at ground level and are among the strongest and most persistent in the
world. They are three times stronger than those in the inversion layer
over Los Angeles. Thus, the low-lying air over Fairbanks stagnates and
becomes effectively decoupled from the atmosphere above, permitting high
concentrations of all pollutants. The combustion of fuel oil, gasoline,
and coal provides daily inputs of 4.1 x 106 kg C02; 8.6 x 103 kg SO ;
and 60, 46 and 20 kg of Pb, Br and Cl respectively, into a lens-like
layer of air resting on the surface with a total volume less than
Q O
3 x 10 m . The air pollution over Fairbanks during cold spells couldn't
be worse, because the mechanisms for cleaning the air are virtually
eliminated while all activities that pollute the air are increased.
B. Summary
Aqueous emissions are an important pollutant in Fairbanks, Alaska,
in cold weather, when the capability of the air to hold water in solution
is reduced. The Geophysical Institute, University of Alaska, estimates
aqueous and COg emissions from combustion, power plant cooling ponds,
and miscellaneous sources, such as sewage treatment plants, mine tunnels,
people and animals breathing.
B-162
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1. Combustion Emissions
Complete combustion of the representative molecular species is
assumed, with average molecular weight ratios of 1.38 (H 0/fuel) and
3.10 (COQ/fuel) for gasoline and 1.33 (H 0/fuel) and 3.13 (C09/fuel) for
^J £ £
fuel oil. For coal, the calculation of these ratios is more complex
because the water contained in coal is exhausted directly to the atmosphere
during combustion. It is assumed that 20 percent by weight of the coal
is water. Total molecular weight ratios for coal are 0.68 (H9O/fuel)
and 2.37 (C02/fuel).
The above ratios are applied to the quantities of each fuel
type burned to give monthly and daily emissions of H 0 and CO .
2. Power Plant Cooling Emissions
Given the water flow rate to and from the cooling pond and the
net temperature change of water, the required heat loss may be calculated.
This heat loss is accomplished by three processes: (1) evaporation to
the atmosphere; (2) radiation to the sky; and (3) conduction through the
bottom of the pond.
Assuming an extreme winter vertical temperature gradient in
the pond, no convection in the water, and the condition of all heat con-
ducted downward being conducted away through the soil below, the calculated
vertical heat flux from conduction for a large plant at Fort Wainwright,
Alaska, is less than 0.1 percent of the total required flux. Therefore,
conduction can be neglected.
Radiative cooling is maximized using
Q = 0.105 + 0.0018 T
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and multiplying Q by the area of the pond. A temperature T over the open
water surface must be assumed. The value of maximum radiative heat flux
at the plant cited above is slightly over 5 percent of the total flux
and is negligible when ice fog forms over the pond. Therefore, during
ice fog conditions, the evaporative heat loss approaches the value of
the total heat loss.
To estimate the amount of water introduced into the atmosphere
from power plant cooling waters, each plant must be analyzed separately
in a manner similar to that described for the Fort Wainwright plant.
3. Miscellaneous Emissions
Aqueous emissions from miscellaneous sources such as sewage
treatment plants are treated individually by calculating heat loss from
the warm water discharge flow rate, the amount of open water area, the
amount of heat dissipated by mixing with river water, and so forth. For
tunnels, the moisture flow rate through the fans (and therefore the
emission rate from tunnels) are estimated using the air flow rate past
the fans and the relative humidity and temperature of the air. Another
important source of aqueous emissions in very cold climates is the breathing
of people and animals. Saturated air is exhaled at 35°C. This exhalation
process together with respiration rates and the populations of each sex
and species gives the amount of water vapor released to the environment.
It has been shown that the loss of water vapor through perspiration exceeds
the loss from respiration; therefore, the moisture output from breathing
can be doubled to give a combined perspiration and respiration loss.
Other miscellaneous sources include moist air discharge from
laundries and air leaks from buildings and houses.
B-164
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XXVII UNIVERSITY OF CALIFORNIA AT DAVIS
The Impact of Highways on Air Quality. L. 0. Myrup, unpublished
report, August, 1972.
A. Author's Abstract
None given.
B. Summary
Carbon monoxide emissions for Sacramento, California, input to the
pollution transport and diffusion model developed at the University of
2
California at Davis are distributed into 1.7 mi areas. Total emissions
for each area are formulated by first measuring the total miles of resi-
dential streets, arteries, and highways in each area. The average traffic
volumes for each of the three types of streets are obtained, and car-mile
factors computed. Vehicle emission factors at appropriate vehicle speeds
are then applied, yielding the total amount of pollutant for each kind of
street, and after summation, the total emissions for the grid square in
question.
C. Model Availability
No computer program for the emission inventory methodology presented
is available.
B-165
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XXVIII U.S. WEATHER BUREAU
A Simple Diffusion Model for Calculating Point Concentrations from
Multiple Sources. J. F. Clarke, Journal of the Air Pollution
Control Association, September, 1964.
A. Author's Abstract
None given.
B. Summary
The application of a method designed by Hagan, et al. (1964) resulted
in the source inventory for the city of Cincinnati used in Clarke's calcu-
lations. Two source categories for sulfur dioxide and nitrogen oxides
are presumed: (1) combustion sources, which include space and process
heating, power generation, transportation, and waste incineration sources;
and (2) industrial process losses, which include only the losses from
processes, not from combustion. Emissions are allocated on the basis of
unit-area emission rates for each of 19 areas suggested by population
density maps and modified subjectively to more clearly define commercial
and industrial areas.
Among the assumptions made in apportioning emissions are that the
amount of fuel used for space heating is directly proportional to the
degree day value, and that this fuel is used only during days registering
a degree day value. It is assumed that industrial and commercial sources
that use fuel for power and waste incineration and process heat operate
at a uniform daily rate. Fuel used for transportation is taken as con-
stant for days without a degree day value and inversely proportional
J. E. Hagan, III and G. A. Jutze, "A Rapid Technique for an Air Pollution
Emissions Survey," Public Health Service Manuscript (1964).
B-167
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to that value when one is registered. This assumption is based on seasonal
variations of gasoline sales.
The industrial process losses used are engineering estimates derived
from knowledge of the characteristics of the individual processes, as
sufficient quantitative data was not available.
One power plant was thought significant enough to be included as a
point source. The plant does not operate at a uniform daily rate, and
fuel usage is not a simple function of season or time of day, since the
output of the plant is used to both heat and cool, according to season.
Since operational data from the plant were not readily available and the
precision of the general inventory did not seem to warrant the effort
required to calculate otherwise, emissions from this plant were taken as
invariant in time.
C. Model Availability
A computer code for the emissions treatment used for Cincinnati is
not referenced in the paper.
B-168
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO. 2.
EPA-450/3-74-n30
4. TITLE AND SUBTITLE
A Regional Air Pollution Study Preliminary Emissi
Inventory
7. AUTHOR(S)
Fred E. Littman Konrad T. Semrau
Sylvan Rubin Walter F. Dabberdt
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Stanford Research Institute
Menlo Park, California 94025
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Protection Agency
Research Triangle Park, North Carolina 27711
15. SUPPLEMENTARY NOTES
3. RECIPIENT'S ACCESSIOIyNO.
5. REPORT DATE
January 1 974
J" 6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPCC~ \C
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO
68-02-1026
13. TYPE OF REPORT AND PERIOD COVERED
Final Rpport
14. SPONSORING AGENCY CODE
16. ABSTRACT
This report presents an operational plan for providing emissions data for the
Saint Louis Regional Air Pollution Study (RAPS). It also describes existing
emission inventories for the Saint Louis area and reviews in detail emission models
that have in the past been used to provide emissions data.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS b.lDENTIFI
Regional Air Pollution Study (RAPS)
Inventory
Emissions UTM Coordinates
Pollutants Fortran
Emission Modeling
Area Sources
Point Sources
13. DISTRIBUTION STATEMENT 19. SECURI
Dalaaca Mini -irn-l 1 ^
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INSTRUCTIONS
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To be published in, Supersedes, Supplements, etc.
16. ABSTRACT
Include a brief (200 words or less) factual summary of the most significant information contained in the report. If the report contains a
significant bibliography or literature survey, mention it here.
17. KEY WORDS AND DOCUMENT ANALYSIS
(a) DESCRIPTORS - Select from the Thesaurus of Engineering and Scientific Terms the proper authorized terms that identify the major
concept of the research and are sufficiently specific and precise to be used as index entries for cataloging.
(b) IDENTIFIERS AND OPEN-ENDED TERMS - Use identifiers for project names, code names, equipment designators, el£. Use open-
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(c) COSATI FIELD GROUP - Field and group assignments are to be taken from the 1965 COSATI Subject Category List. Since the ma-
jority of documents are multidisciplinary in nature, the Primary Field/Group assignment(s) will be specific discipline, area of human
endeavor, or type of physical object. The application(s) will be cross-referenced with secondary Field/Group assignments that will follow
the primary posting(s).
18. DISTRIBUTION STATEMENT
Denote releasability to the public or limitation for reasons other than security for example "Release Unlimited." Cite any availability to
the public, with address and price.
19. &20. SECURITY CLASSIFICATION
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B-l 70
EPA Form 2220-1 (9-73) (Reverse) U
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2. RAPS Participants
Since the National Air Data Branch, Monitoring and Analysis
Division, Office of Air Quality Planning and Standards, Office of
Air and Waste Management, is the organization within the Environ-
mental Protection Agency charged with maintenance of emission
inventories as well as emission factors, NADB was assigned lead
responsibility for the emission inventory in the St. Louis AQCR.
The National Air Data Branch maintains the NEDS (National
Emission Data Systems) inventory, which provides uniform, nation-
wide computerized coverage of emission data, obtained orginally by
local air pollution control agencies. In the St. Louis area, this
comprises the following agencies:
Missouri Air Conservation Commission
Air Pollution Control Div., St. Louis County Health Dept.
Air Pollution Control, City of St. Louis
Illinois Environmental Protection Agency
This inventory is based on annual figures; since the requirements
of RAPS dictated a more detailed resolution, NADB contracted with a
number of companies to provide the necessary inputs. The following is
an alphabetized list of the contractors and their principal contributions:
Environmental Science and Engineering - Area Sources Methodology
GCA/Technology - Hydrocarbon Emissions Methodology
- Airport Emission Methodology
Rockwell International
Science Center
Air Mo;:'*to*-ing Center - Point Source Methodology
- Special Inventories for Field Studies
- Emission Inventory Data Handling System
- Heat Emissions
- Non-Criteria Pollutant Inventory
- Particle Size Distribution Inventory
Stanford Research Institute - RAPS Preliminary Emission Inventory
Washington University - Automotive Line and Area Source
Methodology
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In addition the Department of Transportation has contributed
resources and manpower into helping the National Air Data Branch
develop the RAPS Emission Inventory.
Department of Transportation
Transportation Systems Center
Federal Highway Administration
River Vessel Methodology and Inventory
Rail Methodology and Inventory
Automotive Line and Area Source
Inventory
The National Air Data Branch is using several analyses developed
for use on the National Emission Data System. These analyses will be
applied to the RAPS emission inventory. They are:
IBM-Federal Systems Division
PEDCo-Environmental
Research Triangle Institute
- Weighted Sensitivity Analysis
- Source Inventory Percision Analysis
- Computer Assisted Area Source Emission
Gridding Procedure
- Presentation of NEDS Emission Data
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