;
APTD-1470
AN AIR POLLUTION IMPACT
METHODOLOGY
FOR AIRPORTS - PHASE I
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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APTD-1470
AN AIR POLLUTION
IMPACT METHODOLOGY
FOR AIRPORTS - PHASE I
by
J. E. Norco, R. R. Cirillo,
T. E. Baldwin, and J. W. Gudenas
Argonne National Laboratory
Center for Environmental Studies
9700 S. Cass Avenue
Argonne, Illinois 60439
Contract No. IAG-0171(D)
EPA Project Officer: Donald Armstrong
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 1973
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The APTD (Air Pollution Technical Data) series of reports is issued by the
Office of Air Quality Planning and Standards, Office of Air and Water Pro-
grams, Environmental Protection Agency, to report technical data of interest
to a limited number of readers. Copies of APTD reports are available free of
charge to Federal employees, current contractors and grantees, and non-
profit organizations - as supplies permit - from the Air Pollution Technical
Information Center, Environmental Protection Agency, Research Triangle
Park, North Carolina 27711 or may be obtained, for a nominal cost, from the
National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22151.
This report was furnished to the Environmental Protection Agency by
Argonne National Laboratory, Argonne, Illinois 60439 in fulfillment of
Contract No. IAG-017KD) . The contents of this report are reproduced
herein as received from the contractor. The opinions, findings, and con-
clusions 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. APTD-1470
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TABLE OF CONTENTS
Page
ABSTRACT 13
1.0 INTRODUCTION 15
1.1 The Need for Improved Airport
Environmental Impact Analysis 15
1.2 Objectives of the Study 17
1.3 Land Use Implications 18
1.4 Review of the Methodology 21
2.0 REVIEW OF ENGINEERING STUDIES 24
2.1 History of the Project 24
2.2 Engineering Study Survey 28
2.3 Environmental Impact Statement Review 30
3.0 PREPARATION OF AIRPORT EMISSION FORECAST 34
3.1 Airport Activity Levels ; 34
3.2 Compilation of Emission Factors . 105
3.3 Computation of Emissions 131
4.0 ANALYSIS OF AIRPORT VICINITY LAND USE . . . 137
4.1 Methodology 137
4.2 Analysis of Land Use Data 140
4.3 Directional Analysis 150
4.4 Preparation of Land-Use-Based Emission Estimates . . . 157
4.5 Residential Emission Estimates .... ..... . 159
4.6 Commercial and Institutional Emission Estimates . . . . 161
4.7 Ground Transportation Emissions . ... 170
5.0 EMISSION DISPLAY 176
5.1 Airport Emissions 176
5.2 Airport Vicinity Land Use Emissions 217
6.0 METEOROLOGICAL AND AIR POLLUTION POTENTIAL ANALYSIS .... 225
6.1 Introduction 225
6.2j Transport Wind and Mixing High Climatalogy
for the St. Louis Metropolitan Area . 225
6.3 Existing Air Quality Data in the
Proposed Airport Vicinity ... 243
6.4 Dispersion Model Air Quality Estimates 246
7.0 AIR QUALITY MODEL ADAPTATIONS 252
7.1 FAA/Argonne Airport Air Pollution Model 252
7.2 Northern Research and Engineering Corp. Model .... 254
7.3 Systems, Science and Software Photochemical Model . . . 255
8.0 CONCLUSIONS AND OBSERVATIONS 258
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TABLE OF CONTENTS (Contd.)
Page
APPENDIX A: A Description of the Aerial Photographic
Technique for Determining Land Use 265
APPENDIX B: Detailed Land Use Data (Acreages) for
Addison, Elk Grove, Leyden and Maine Townships for
the Pour Study Years; 1960, 1964, 1966 and 1970 . . . 287
ACKNOWLEDGMENTS 307
REFERENCES 309
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List of Figures
No. Title
2.1 Columbia-Waterloo Site, Proposed New St. Louis Airport ..... 26
3.1 Schematic of Sources of Airport Air Pollutant Emissions .... 36
3.2 Hourly Access Traffic Volume, 1990 Speas Forecast ....... IQO
3.3 Aircraft Emission Calculation Procedure . . .......... 132
4.1 Land-Use-Based Emission Estimating Procedure .......... 138
4.2 Four Township Land Use Case Study Area Surrounding
O'Hare Airport ...................... 141
4.3 Residential Land Use ...................... 144
4.4 Commercial Land Use ...................... 145
4.5 Manufacturing and Warehousing ................. 146
4.6 Overall Land Use Trends in the O'Hare Study Area ........ 147
4.7 Total Operations - O'Hare Airport ............... 148
4.8 Zones Used for Land Use Directional Analysis .......... 151
4.9 Residential Land Use Patterns ............... . . 153
4.10 Commercial Land Use Patterns .................. 154
4.11 Manufacturing and Warehousing ..... , ...... ...... I55
4.12 Zones used for Land Use Distance Analysis ........... 156
4.13 Relationships of Land Use to Distance from O'Hare Airport . . . 158
4.14 The Relationship of Commercial Building Floor Space to
Commercial Land Use
4.15 Commercial/Institutional Building Size Distribution in Chicago . 166
4.16 Cook County Portion of Chicago Area Transportation Study Grid . 172
5.1 Annual Aircraft Emissions ...... . . . .......... 179
5.2 Aircraft Emissions by Mix, Speas Forecast ........... 182
5.3 Aircraft Emissions per LTO Cycle, Speas Forecast ........ 189
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List of Figures (Contd.)
No. Title
5.4 Aircraft Emissions per Enplaned Passenger, Speas Forecast . . . 190
5.5 Annual Ground Service Vehicle Emissions, Speas Forecast . . . 192
5.6 Annual Access Traffic Emissions, Speas Forecast 199
5.7a CO Emission Impact of Alternate Access Modes, Speas Forecast . 201
5.7b. HC Emission Impact of Alternate Access Modes, Speas Forecast . 202
5.7c. NO Emission Impact of Alternate Access Modes, Speas Forecast . 203
X
5.7d. Particulate Emission Impact of Alternate Access Modes,
Speas Forecast 204
5.8 Annual Total Emissions, St. Louis Airport 207
5.9 Diurnal Emission Pattern, St. Louis Airport 211
5.10 Carbon Monoxide Emission Densities from O'Hare Airport
and Surroundings 219
5.11 Hydrocarbon Emission Densities from O'Hare Airport
and Surroundings 220
5.12 Nitrogen Oxide Emission Densities from O'Hare Airport
and Surroundings 221
5.13 Particulate Emission Densities from O'Hare Airport
and Surroundings 222
5.14 Sulfur Dioxide Emission Densities from O'Hare Airport
and Surroundings 223
6.1 Monthly Frequency Distribution of Midday Stagnation -
St. Louis, Mo 233
6.2 Monthly Frequency Distribution of Midday Stagnation -
St. Louis, Mo. (Alternative Criteria) 233
6.3 Resultant Transport Wind Rose - St. Louis 237
6.4 Directional Distribution of Midday Stagnation 239
6.5 Percentage of each Direction Category with Midday Stagnation . 239
6.6 Resultant Seasonal Transport Wind Roses 240
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List of Figures (Contd.)
No. Title page
6.7 Air Quality Monitoring Stations in the Proposed Airport Region . 244
6.8 Dispersion Model Participate Estimates - 1968 247
6.9 Dispersion Model Particulate Estimates - 1975 249
6.10 Dispersion Model Sulfur Dioxide Estimates - 1968 250
6.11 Dispersion Model Sulfur Dioxide Estimates - 1975 251
A.I Four-Township Land Use Case Study Area
Surrounding O'Hare Airport 267
A.2 Sample Aerial Photograph from the O'Hare Study Area 268
A.3 Sample Interpretation of Aerial Photograph
Shown in Fig. A. 2 269
A. 4 Sample Dot Grid 279
A. 5 Sample Land Use Code Sheet 280
A.6 The Linear Regression Model,
Computation of y Values 284
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List of Tables;
_No. Title page
1.1 Phase I Program Output Summary . . i 19
3.1 Passenger Enplanement Growth Rates, St. Louis Airport 39
3.2 Annual Air Carrier Passenger Enplan^ments, St. Louis Airport . . 40
3.3 Air Cargo Forecast, St. Louis Airport 42
3.4 Aircraft Mix, Seat Capacity, and Ldad Factor Estimates
NADC Study 46
3.5 Calculation Procedure for Annual Aircraft Movements,
NADC Study ......... , ............... 48
3.6 Aircraft Seat Capacity and Load Factor Estimates,
McDonnell Study ....... i ............... 49
3.7 Calculation Procedure for Annual Aircraft Movements,
McDonnell Study ....... , . . . ............ 50
3.8 Aircraft Seat Capacity and Load Factor Estimates
Speas Study ......... i ............... 52
3.9 Calculation Procedure for Annual Alir craft Movements,
Speas Study ......... , ............... 53
3.10 Summary of Aircraft Activity and Hix Forecasts, St. Louis Airport 54
3.11 Diurnal Aircraft Activity, St. Louis Airport .......... 60
3.12 Service Times of Aircraft Ground Service Vehicles ....... 62
3.13 Total Daily Ground Service Vehicli Operating Time,
Speas Study Aircraft Activity ............... 64
3.14 Annual Aircraft Fuel Requirements, ............... 67
3.15 Annual Ground Service Vehicle FuejL Requirements ........ 67
3.16 Annual Aircraft Engine Maintenanc^ Tests ............ 70
3.17 Annual Fuel Requirements of Heating Plant ........... 70
3.18 Annual Air Carrier Originating Passengers ........... 74
3.19 Air-Passenger-Related-Visitor Ratjios .............. 76
3.20 Annual Passenger- Related Visitor^ ............... 77
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List of Tables (Contd.)
No. Title
3.21 Employment Forecasts, NADC Study 79
3.22 Employment Forecasts . 81
3.23 Airport Employee Diurnal Arrival and Departure Pattern .... 82
3.24 Annual Airport Casual Visitors 83
3.25 Airport Casual Visitor Diurnal Arrival and Departure Pattern . 84
3.26 Summary of Daily Person Trips To and From Airport 86
3.27 Modal Choice Summary 90
3.28 Vehicle Load Factors 90
3.29 Daily Truck Trips 93
3.30 Diurnal Distribution of Truck Trips and Vehicle Type
Distribution 94
3.31 Daily Vehicular Access Trips 101
3.32 Airport Vehicle Mileage and Operating Characteristics .... 104
3.33 Air Pollutant Emission Rates of Aircraft Engines 107
3.34 Aircraft Operating Modes and the Activities Included in Each . 108
3.35 Aircraft Times-in-Mode 110
3.36 Aircraft Gate Occupancy Time Ill
3.37 Ground Service Vehicle Fuel Consumption Rates 113
3.38a Ground Service Vehicle Uncontrolled Emission Factors 116
3.38b Ground Service Vehicle Controlled Emission Factors 117
3.39 Fuel Storage and Handling System Emission Factors 119
3.40 Aircraft Engine Maintenance and Test Cycle 121
3.41 Illinois Air Pollution Emission Regulations . 123
3.42 Airport Heating Plant Emission Factors 124
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List of Tables (Contd.)
No. Title
3.43 Ground Access Vehicle Emission Factors, Hot Operation ..... 129
3.44 Ground Access Vehicle Emission Factors, Cold Start ...... 130
4.1 Distribution of Land Use and Trends in the Study Area ..... 143
4.2 Emission Densities from Residential Land Use in the
Vicinity of O'Hare Airport ................ 162
4.3 Fuel Combustion Emission Densities for Various Two-Digit
Standard Industrial Classifications ........... 169
4.4 Emission Factors ....................... 171
4.5 Age Distribution of Vehicles Registered in Cook County, 111.. . 174
5.1 Aircraft Emissions by Mix, Chicago O'Hare International Airport 184
5.2 Aircraft Emissions by Mode of LTO Cycle . . .......... 186
5.3 Average Aircraft Emissions, O'Hare International Airport . . . 191
5.4 Annual Fuel Storage and Handling Emissions .......... 194
5.5 Annual Engine Testing and Maintenance Emissions ........ 195
5.6 Annual Airport Heating Plant Emissions ............ 197
5.7 Airport Emission Densities .................. 210
5.8 Peak Hour Emission Rates, St. Louis Airport .......... 213
5.9 St. Louis Airport Emissions, Speas Forecast .......... 214
5.10 1970 Emissions and Emission Densities from O'Hare Airport
and Surroundings ..... . . T r ~T". . .~. . .....
6.1 St. Louis Morning Soundings .................. 229
6.2 St. Louis Midday Soundings .................. 230
6.3 St. Louis Midday Soundings .................. 231
6.4 St. Louis Midday Soundings -
Frequency Direction Distribution .......... ... 236
6.5 St. Louis Surface Winds .................... 236
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List of Tables (Contd.)
No. Title Page
6.6 Low Wind Speed Persistence for Scott Air Force Base
and Lambert Field 241
6.7 Measured Air Quality in the Proposed Airport Vicinity .... 245
7.1 Rate Coefficients for Expanded Model of the Hydrocarbon/
Nitric Oxide Mechanism 256
A.I The Association between Measures of Land Use Averages .... 285
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AN AIR POLLUTION IMPACT METHODOLOGY FOR
AIRPORTS AND ATTENDANT LAND USE
January 1973
by
J. E. Norco, R. R. Cirillo,
T. E. Baldwin and J. W. Gudenas
ABSTRACT
It has been demonstrated that large commercial airports not only have
a significant direct impact on environmental quality as a result of activities
related to aircraft operations, but in many cases also induce a substantial
indirect impact by providing a focal point for urban development and industri-
alization. A comprehensive assessment of the environmental consequences of
siting a major airport facility must therefore take account of both the direct
and indirect impacts that can be expected to result.
Although large airports and the activities that tend to cluster about
them generally produce substantial emissions to all media as well as a variety
of the categorical pollutants, this report addresses only one aspect of the
impact assessment problem - the airport and its environs as an aggregate source
of air pollution. A methodology is presented for integrating the air pollution
impact of an airport and its associated ground support activities with that of
the induced urban development in its vicinity to provide a quantitative basis
for decisions related to airport site selection and for the development of the
land surrounding the site. Procedures for estimating airport-related air pol-
lutant emissions are defined. The latter can be transformed into air quality
estimates through the use of "rollback" analysis or atmospheric dispersion
models.
The impact assessment methodology is based on an approach which can be
adapted readily to other media and to various categorical pollutants. This
flexibility is achieved through a general protocol for identifying, isolating,
and quantifying an array of airport-related and urban activities which provide
environmental insults. The impact assessment methodology is intended to be
.general and is applicable to either existing or proposed airport facilities.
It was developed and field tested using data from the proposed St. Louis air-
port at Waterloo-Columbia, Illinois, from the Chicago O'Hare International
Airport, and from several other existing facilities.
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1.0 Introduction
This report presents a methodology for assessing the air pollution
impact of major commercial airports and the urban activities that surround
them. The ultimate goal of this work is to enable airport, transportation
and comprehensive planners to incorporate environmental considerations into
the site selection and design of airport facilities and into planning for
the development of the land in the airport environs. The first phase of the
EPA-sponsored program to develop this methodology is focused on the assess-
ment of air pollution emissions that are the direct and indirect result of
airport activities.
1.1 The Need for Improved Airport Environmental
Impact Analysis
Environmental Impact Statements
The growth of air travel and the resultant development and upgrading
of airport facilities have provided new challenges in planning for the needed
expansion while minimizing undesirable environmental effects. The mechanism
presently employed for the integration of environmental assessment into the
planning process is the Environmental Impact Statement which is required under
Section 102 of the National Environmental Policy Act (NEPA) of 1969. This
Act requires assessment of environmental effects of proposed airport and other
development actions involving federal funds. It is unfortunate, however, that
the requirements of the NEPA cannot be adequately met with current techniques
of quantitatively evaluating the impact of an airport site and its surround-
ing development. Typically, the Environmental Impact Statement will contain
an elaborate description of why the facility is needed and how it will be
constructed, but, generally, there are meager descriptions of the actual envi-
ronmental insults that will result from the operation of the facility.
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In many cases, nondescript terminology is used to explain in very qualita-
tive terms the impact of the proposed facility. For example, terms such as
"minimal, minor, not expected to be appreciable," etc., are used to describe
environmental effects such as the degradation of air and water quality, noise,
and so on. It is not therefore surprising that most of the airport Environ-
mental Impact Statements developed to date fail to provide a satisfactory
analysis of potential problem areas. This inadequacy reflects both a lack
of understanding of environmental problems and a lack of quantitative tools
to perform the necessary analyses. Furthermore, there is increasing evidence
that environmental consideration are being used by special interest groups as
a means of deterring airport development. The lack of adequate impact
methodologies compounds this problem, since airport developers are hard
pressed to confirm or refute contentions that environmental quality standards
will be violated in airport environs.
Growth of Air Travel
The growth of aeronautical activity and the need for new airports
9
and facility expansions is evident. The National Aviation System Plan
issued by the Federal Aviation Administration (FAA) provides for the orderly
acquisition of new facilities and equipment at a rate sufficiently great to
satisfy immediate requirements as well as to provide for future demands, but
in recent years, the facility construction has failed to keep pace with the
changes required by aircraft development and generated by increases in the
demand for air transport. The National Airport System identified by the FAA
is composed of approximately 3,240 airports included in the 12,070 existing
civil airports in the United States. A need for continuing expansion is fore-
seen throughout the next decade, since flight activity for 1982 is projected
to double that of 1971. The FAA recognizes the need through 1982 for 1,410
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additional airports, including 112 to accommodate both air carrier and
general aviation activity and 1,298 for the exclusive use of general avia-
tion. It is expected that an expenditure in excess of $4 billion will be
required to improve existing airports and to develop new facilities.
The Federal Aviation Administration recognizes that environmental
constraints may present serious potential problems for airport development
programs. According to the National Aviation System Plan, "Unless solutions
can be found, too many of the nations large airports may be precluded from
development to their full potential and other needed airports may not be
built. These problems include environmental factors such as aircraft noise,
compatible land use, airport access and ecology." Solutions to these problems
cannot be achieved unless the magnitude of their impacts can be adequately
described.
1.2 Objectives of the Study
The foregoing discussion has suggested deficiencies and needs in
current airport environmental impact assessment procedures. The studies
reported here were initiated in order to provide airport and land use plan-
ners with the tools necessary to perform comprehensive environmental evalua-
tions of airport sites and to prepare environmentally sensitive land use plans
for the surrounding area. This study focuses on the air pollution component
of the airport environmental impact evaluation process.
The methodologies described here are designed to yield airport and
related land use air pollutant emission estimates based on readily available
information. The methods employed are applicable to proposed or existing air-
ports. In the case of new facilities or expansions of existing installations,
the data necessary for an impact evaluation can be derived from engineering
studies, air transport forecasts, and regional land use and transportation
planning data. For existing facilities, actual observations, land use sur-
veys, or historical records are employed. The coupling of these emission
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estimates to an air quality model for eventual air quality impact and evalua-
tion of alternatives was not performed as part of this study; however, these
tasks are scheduled for the second phase of the program. The data system
design and the matching of data formats that are necessary to complete this
interface have been completed as a part of the effort reported here.
In addition to the generalized methodologies presented in this
report, specific results were obtained for the proposed St. Louis airport at
Waterloo-Columbia, Illinois, and for Chicago's O'Hare International Airport.
These two airports were used as demonstration sites for the development and
field testing of the methods, although information derived from other major
airports was utilized as well. A significant portion of the study was
devoted to a historical case study of land use in the vicinity of O'Hare.
This study provided (1) a basic understanding of airport area development
and (2) a foundation for the development of land-use-based air pollution
emission estimates.
A summary of the analytical methods and models employed is presented
in Table 1.1. It is expected that the complete package of procedures devel-
oped during the course of this program will be reduced to a fairly simpli-
fied workbook format that can easily be integrated into the routine airport
site selection and environmental impact analysis process.
1.3 Land Use Implications
The development of airport facilities clearly represents a substantial
public investment equivalent, for example, to the construction of a major high-
way system. The consequences are, in many respects, similar; i.e., the con-
struction of a new airport tends to have a significant effect on the use and
value of adjacent land. It tends to generate, divert, or eliminate travel that
would otherwise not have been affected; it creates employment opportunities; it
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Table 1.1
Phase I Program Output Summary
Output
1. Airport Activity
Model
2. Airport Rate of
Emissions of Air
Pollutants
3. Land Use Model
4. Land Use Rate of
Emissions of Air
Pollutants
5. Climatology Model-
Evaluation of Air
Pollution Potential
Parameters
Included
Air passenger movements
Aircraft movements
Aircraft mix
Access traffic volume
Aircraft
Ground service vehicles
Access traffic
Stationary sources
Projected land develop-
ment surrounding the
airport.
Spatially distributed
emission map
Meteorological data
Sources of
Information
Engineering studies
Existing airport data
Airport Activity Model
and emission factors
Census or aerial photo
data
Regional or Airport
Master Plan
Land Use Model and
land-use-based emission
factors
U.S. Weather Bureau
data tapes
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attracts certain industrial and commercial activities; and it may promote or
deter the development of communities in its vicinity. Moreover, just as a
highway system tends to induce a land use pattern that in turn creates a demand
for resources such as energy, water, and feeder transportation facilities,
so an airport creates both a direct demand for such resources and, through
its influence on community development and land use, an equivalent indirect
demand which must be satisfied through a reallocation of regional resources.
It follows that a major airport, like a major highway facility, is
likely to have a substantial direct and indirect effect on local environ-
mental quality which should be assessed as a routine part of any airport
site evaluation study. With the significant exception of aircraft noise,
however, detailed environmental impact evaluation has not normally been
undertaken prior to airport construction. This can be attributed partly to
the fact that aircraft noise is the only environmental effect associated
with airport operation that has hitherto aroused vigorous public response
and partly to the fact that no public agency has, until recently, possessed
the authority or responsibility to police aircraft operation from the environ-
mental standpoint. The FAA is primarily concerned with aircraft noise as
a phenomenon which can inhibit the realization of the National Airport System
Plan and compromise the very substantial federal investment in airport
facilities.
With the advent of the current national emphasis on environmental
protection, the federal government has addressed other aspects of the airport
environmental problem. For example, the EPA has recently proposed air
pollutant emission standards for aircraft engines. Although federal controls
of this kind are undoubtedly desirable, they fail to address the central re-
quirement that an assessment of the total environmental impact of building
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and operating a major airport and developing the surrounding land should
be an integral part of the site evaluation procedure as are aircraft safety
studies or terminal building design.
From the standpoint of environmental protection, an airport can be
viewed as a source of noise, air and water pollution, and solid waste. In
addition, an airport is responsible for the consumption of substantial
amounts of energy (electric and fuel) which results in the discharge of con-
siderable quantities of waste heat and air pollution. The major direct
sources of noise and air pollution are, of course, the aircraft themselves;
however, the fact that an airport is served by major access roads and incor-
porates parking and holding facilities for motor vehicles indicates that it
may also constitute a significant source of ground vehicle noise and air
pollution. Moreover, the facility serves as a focal point for the concentra-
tion of certain commercial and industrial activities that would not otherwise
be present.
For these reasons, the airport planner must now enlarge his view of
what constitutes significant considerations in airport sizing, location, and
operational characteristics. The need for coordination or regional growth
planning and total transportation system development with airport site plan-
ning is accentuated. Alternatives related to minimizing the adverse environ-
mental impacts of the urban/industrial development adjacent to an airport as
well as those of the airport itself must be considered. The "environmentally
sensitive" land use plan is likely to become a standard component of the over-
all airport planning process.
1.4 Review of the Methodology
The impact evaluation methodology presented in this report is based
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on the identification and isolation of air pollution producing activities,
both within the airport and in its environs. These activities are then quan-
tified and transformed into emission estimates, based on state-of-the-art
source emission factors. Airport-related activities have been functionally
separated into two broad categories; those which occur within the airport
boundaries, and those associated with the land use and ground transportation
activities which are external to the airport and are presumed to be, to some
extent, related to or induced by the presence of the airport facility. Each
of these activities is quantified in terms of an easily determined or measur-
able descriptor. Among the activities which are associated with air pollut-
ant emissions within an airport are included:
1. Aircraft
2. Ground service vehicles
3. Access traffic
4. Fuel distribution
5. Point sources within the airport
(i.e., heating plant)
In the case of a proposed airport, the independent or driving vari-
ables used to develop activity level estimates are the projected number of
enplaned passengers, the air cargo demand, and aircraft activity and mix esti-
mates. For existing facilities, the same driving variables are required;
however, historical observations are substituted for estimated activity
levels. The activity levels corresponding to the area surrounding the airport
are estimated on the basis of a land classification scheme (residential, com-
mercial, institutional, industrial, etc.). The level of activity or develop-
ment intensity associ.- ted with each of these land use categories is derived
from the comprehensive master plans for the proposed airport site area or
from projections derived from census data and aerial photographic techniques.
This information is utilized to convert activity level estimates based on
land use into air pollution emission estimates.
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Report Contents
Section 2 describes the use of standard engineering and planning
studies that normally form the basis for an analysis of a proposed airport
facility. The engineering studies that were conducted for the proposed
St. Louis airport at Waterloo-Columbia, Illinois, are summarized and re-
viewed .
Section 3 covers the derivation of airport-specific air pollution
emission estimating procedures.
In Section 4, the land use activity.and air pollution emission
estimation methodology is described. The results of a retrospective land
use case study at O'Hare Airport are also presented.
Section 5 presents results and compares the airport and land use
emission estimation methodologies.
Section 6 provides a case study of the meteorological/air pollution
potential of the proposed St. Louis airport site. The data reviewed and
analyzed includes wind speed and direction, persistence, mixing height,
actual air quality measurements and modeling results.
Section 7 outlines the current status of and required interfacing with
airport air quality models now available at Argonne. Three models are currently
available: (1) Argonne/FAA Airport Model; (2) Northern Research and Engineering
Corp. Model; and (3) Systems, Science, and Software Photochemical Model.
Section 8 is a summary, of conclusions and observations.
Appendix A contains a detailed description of an aerial photographic
technique that is appropriate for generating land use data.
Appendix B presents the historical land use trends in the 4 townships
adjacent to O'Hare International Airport.
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2.0 REVIEW OF ENGINEERING STUDIES
Airport engineering studies are those documents drawn up to satisfy
the technical needs of the decision makers charged with the responsibility of
providing air transportation to a community. With respect to the construction
of new airports, the three basic questions which these studies are designed
to answer are: (1) "Is it necessary?"; (2) " Where should it be built?"; and
(3) "What should it look like?" With the recent enactment of the National
Environmental Policy Act, one additional question was added to the list:
"What will be its effect on the environment." The document designed to
provide that answer is the Environmental Impact Statement.
The purpose of this section is to take the proposed new St. Louis
airport as an example and review the information that is generally available
in airport engineering studies. Since the ultimate goal of this study is
to develop a methodology to assess the air quality impact of a major air
carrier airport and its surrounding development, and since the Environmental
Impact Statement is commissioned to provide some of this information, it, too,
will be reviewed and comments will be offered as to its effectiveness in
meeting its charge. It was originally anticipated that at least part of the
master plan for the new St. Louis airport would also be available for review.
Unfortunately, the final decision on the airport site has not yet been made
and, as such, the master plan has not yet been drawn up.
2.1 History of the Project
Based upon conversations with those involved in the development of
the plans for a new St. Louis airport, the City of St. Louis was the first
to sense a need for an expansion of the commercial air transport facilities
in the region. The airport consultant firm of Landrum & Brown, Cincinnati,
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did the first study of the capacity of the existing airport serving St. Louis,
Lambert Field. In 1968, the city decided that Lambert would probably not be
able to handle the projected growth in air traffic and could not be expanded
to meet the need. The firm of Horner & Shifrin was retained to do a site
selection study for a new airport. The firm was commissioned to choose a site
without regard to its location with respect to the Illinois-Missouri boundary.
The study evaluated six sites in Illinois and six in Missouri. The conclusion
of that initial work which was published in August,1969, was that the best
site was near the towns of Columbia and Waterloo, Illinois, approximately 16
miles from the St. Louis central business district (see Fig. 2.1).
The firm of R. Dixon Speas was hired by the State of Illinois to do
2
an independent site selection study. Their report published in October 1970,
concluded the same as the original Horner & Shifrin work.
Following the Speas work, the Illinois legislature created the
St. Louis Metropolitan Area Airport Authority to be the officiating body for
all matters related to the airport. The Authority retained Speas to do a
Q
confirmatory study which again concluded that the Columbia-Waterloo site
was best suited for airport development.
With the State of Illinois taking most of the initiative up to this
point, the State of iMissouri commissioned its own study. This was carried out
by the Northro.p Airport Development Corporation (NADC) and published in
August, 1971 . It concluded that the Columbia-Waterloo site, along with
two sites in Missouri near Dardenne and near St. Charles, merited top consider-
ation for airport location.
The McDonnell-Douglas Aircraft Corp., which is a major employer in
St. Louis and which has one of its main plants located at Lambert Field,
4
undertook its own study in 1971. It concluded that expansion of Lambert,
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26
ST. LOUIS CO.
/
/
MISSOURI
ST. CLAIR CO.
MADISON CO.
ILLINOIS
RUNWAY-INITIAL
RUNWAY-FUTURE
Fig. 2.1 Columbia - Waterloo Site
Proposed New St. Louis Airport
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27
along with the introduction of STOL/VTOL aircraft into commercial service
would provide more than adequate facilities to serve the St. Louis area through
some future date.
would provide more than adequate facilities to serve the St. Louis area.
The Environmental Impact Statement for the St. Louis airport was
prepared by the St. Louis Metropolitan Area Airport Authority and submitted
to the East-West Gateway Coordinating Council for A-95 review. The draft
impact statement has also been submitted to the Federal Aviation Administration
in support of the Columbia-Waterloo site as the best choice for the new air-
port location.
At the time of this report, the final decision on the airport site
has not yet been made by the FAA. There are two groups trying to sway the
FAA decision and their polarization has increased with each suceeding study.
The central issue which divides them is the location of the airport, that is,
whether it is to be in Illinois or in Missouri. It is an indication of the
economic progress associated with the coming of an airport that both groups
are working furiously to have the airport located on their side of the
Mississippi River. The State of Missouri has recently formed its own Airport
Authority and has applied to the East-West Gateway Coordinating Council for
a study grant for the purpose of selecting an airport site in Missouri. This
request was denied by EWGCC as being redundant.
A public hearing held in St. Louis on August 1, 1972, by the FAA
was designed to air both sides of the controversy and provide an opportunity
to form a consensus of public opinion before the final site choice was made.
The hearing brought the governors of both Illinois and Missouri together to
argue for the site to be located in their respective states.
An airport referendum was placed on the November, 1972, ballot in
Missouri. The voters cast their ballots overwhelmingly to continue the use
of Lambert Field and to build a new airport, if and when needed, in Missouri.
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28
With all of the engineering studies, the political considerations,
and the public opinion differences before them, the FAA is faced with the
difficult task of choosing a site to serve St. Louis air transport needs.
The decision is expected shortly.
Despite all the uncertainty about the airport site, it is still
possible to develop an air quality analysis for the St. Louis airport,
independent of its final site. This is because the methodology developed
will be general and the activity levels used to make the calculations will
be valid for any site. There are likely to be some differences, however,
in the meteorological air pollution potential evaluations. For the purposes
of this report, it will be assumed that the airport is to be located at the
Columbia-Waterloo site whenever it is necessary to make some statements that
are site-specific. This will be true mainly in Section 6.0.
2.2 Engineering Study Survey
Horner & Shifrin Study
The original Horner & Shifrin Study is a brief survey of possible
sites for a new airport to serve St. Louis. The report does not attempt to
justify the need for a new airport but only evaluates alternative sites. The
twelve sites considered are judged on the basis of "environment, air space
clearance, accessibility, terrain, land cost." The environment evaluation is
not taken to mean impact on the natural environment, but rather means the
general location of the airport with respect to populated areas. Since this
study was intended to be an initial overview of the possible sites, it does
not contain any detailed information which could be extracted for use in a
thorough environmental impact analysis.
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29
Speas Study
2 8
The two studies done by Speas * were not available for first-hand
review. The Environmental Impact Statement did, however, utilize the Speas
work as the basis for its conclusions and presented a significant amount of
data from the Speas reports.
The studies present an extensive review of air passenger and air
traffic forecasts, and proceed to the justification of the need for a new
airport based on this data. Projections of aircraft activity and mix, and
diurnal patterns of activity are developed. This is the same data that is
necessary to begin an air quality analysis.
Extensive analyses of the aviation requirements (i.e., airspace
clearance, physical obstructions, etc.) are presented and evaluations of
alternative site development costs are discussed. This information is not
fundamentally necessary to conduct the environmental analysis.
Based on references in the Impact Statement, the Speas study attempts
to answer the first three questions of airport need, airport location, and
airport design in some detail. The environmental impact analysis is based
primarily on the noise effects and little, if any, analysis is done on air
quality impacts. There is however, a wealth of airport activity data which
which can be used to develop the air pollutant emission estimates. Since
the Impact Statement seems to use the Speas work as its baseline, it will
be used here also.
NADC Study
3
The Northrop report is an extensive study that is equal in scope
to the Speas studies. Airport need justification is based on projected air
traffic growth and regional economic development. The procedures used are
outlined in some detail and the data is easily transformed into required
format for environmental analysis.
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30
Additional evaluations of airspace, site engineering requirements,
ground access convenience, and airport economic benefits are included, and
a tradeoff analysis of alternative sites is performed.
An environmental evaluation of the alternative sites is included in
this study. The impacts on flora and fauna, air quality, noise levels, water
quality, and solid waste are discussed briefly, The air quality analysis is
based primarily on meteorological conditions prevelant in the area and the
existing air quality. No calculations of projected air pollutant emissions
from the airport are displayed and the only reference to the overall impact
is to the effect that federal air quality standards are not expected to be
exceeded.
McDonnell Study
4
The McDonnell Study is substantially briefer than either the: Speas
or NADC studies, and is concerned primarily with evaluating the ability of
Lambert Field to handle the projected air traffic increases. The report
focuses mainly on estimating aircraft activity for Lambert and displaying the
alternative solutions for matching capacity to demand. Even though the report
deals mainly with Lambert Field, the air traffic forecasts can still be used
in the environmental analysis.
No environmental analysis is undertaken in the McDonnell Study.
2.3 Environmental Impact Statement Review
In this section, in addition to reviewing the Impact Statement,
several suggestions for improving its ability to display environmental impact
will be offered.
The Environmental Impact Statement for the St. Louis airport contains
a brief discussion of the expected air quality impact of the proposed Columbia-
Waterloo site. The report singles out nitrogen oxides, carbon monoxide,
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31
hydrocarbons, particulates and sulfur oxides as the primary air pollutants
of concern. (It should be noted that the Statement seems to link jet fuel
odors with sulfur oxides. The general consensus, however, is that odors
i r)
are hydrocarbon-related.)
The_S_tatement itemizes the important emission sources to be aircraft,
engine overhaul test cells, fuel handling, jet fuel dumping, and ground
vehicles. To that list, it is felt that the airport heating plant should be
added since it can be a significant contributor, depending on the fuel used,
to the overall emission load. Fuel dumping is not expected to be significant
in the near future because even now airlines are implementing their own
regulations to eliminate this practice; this being done in anticipation of
government regulations.
A calculation of 1990 emissions for each of the sources is presented
in the Statement. Although the actual numbers are comparable to what is calcu-
lated in this study, the presentation of this one set of figures is not suf-
ficient to make any definitive statements about the air quality impact of the
airport. A display of how the emissions will vary through the forecast period,
where the emissions are coming from, diurnal emission patterns, and emission
trends is required for planners to assess actual impacts. Despite the number
of qualitative statements made which indicate that the air quality situation
is controllable, there is insufficient quantitative data to support the conclu-
sions. A substantial improvement in the ability of the Impact Statement to
reflect the air pollution situation could be had by increasing the scope and
detail of the calculations presented.
The Impact Statement summarizes the aniticipated air quality effects
by stating that the airport is not expected to result in a violation of
federal air quality standards and that improvements in aircraft and ground
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32
vehicle emission controls will further reduce the airport's impact on the
air environment. As before, however, there is not enough quantitative
demonstration of the validity of these statements.
There are several additional important items needed to complete the
analysis that have not been included in the Impact Statement. The first is
the effect on regional air quality of the surrounding land development that
will result from the improved transportation system provided by the airport
and its access roads. A later part of this study will show that airports
generate significant development of the land around them and that the emission
densities of this land use can equal and exceed the emissions from the airport
itself. The quality of the air in the airport vicinity will be as much or
more a function of the attendant land use as it is of the airport activity.
Because of this, the regional effects must be considered in developing a
strategy to meet air quality standards. The airport itself may not result in
violated standards but the airport, plus surrounding development, almost
certainly will if no controls are imposed.
A second important item which should have been considered is the
quality of the air on the airport grounds. While, as the Impact Statement notes,
airports may contribute on the order of only 1% of regional air pollutant emis-
sions, they can produce sufficient concentrations of pollutants on the site
itself to result in a local violation of air quality standards. Since the
standards include a one-hour measurement period, and since it is easily possible
for people to spend an hour or more at the airport, the local impacts cannot be
ignored.
A third item that needs mention is the effectiveness of various control
alternatives in keeping a check on the various emission sources. Some reference
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33
is made to controlling airport emissions by operational restraints placed
on aircraft. It is not clear how .much improvement could be realized based
upon the presented data, No statement is made about land use controls needed
to attain regional air quality goals,
The final point of emphasis is that emission data by itself is
not a measure of air quality. Emission rates must be translated into air
quality via dispersion modeling or some other technique in order to actually
provide an estimate of the impacts. Even the extensive emission calculations
carried out in this study do not transform directly into air quality projec-
tions and further work is necessary to make this forecast. This is scheduled
for Phase II of this program.
While several areas of improvement needed in the Environmental Impact
Statement air pollution analysis have been noted above, this is not an attempt
to criticize the St. Louis Metropolitan Area Airport Authority's efforts.
Many other impact statements carry the same deficiencies, and the entire
impact statement procedure has come under criticism. Complaints have been
voiced that the statements have become a weapon used by special interest groups
to achieve their own goals. Project leaders are accused of using them as
justification documents, while environmental groups have been accused of
using them as a legal weapon to stall much-needed programs, Clearly, some
revision of current procedures seems necessary, The first step should probably
be to make the statements what they were originally intended to be, that is,
an assessment of the impact on environmental quality of a project from an
objective and factual point of view, and a presentation of the strategies
that will be employed to maintain an acceptable environmental quality. The
methodology to be developed in this report is designed to provide the tools
to make such assessments and such presentations.
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34
3.0 PREPARATION OF AIRPORT EMISSION FORECASTS
The purpose of this section is to outline the procedure for estimat-
ing air pollutant emissions from the airport proper. A substantial amount of
information relevant to the particular airport under study is needed. This
information is divided into two categories: airport activity data and air
pollutant emission factors for airport activities. The application of the
emission factors to the activity levels results in an estimate of emissions
for the given airport.
3.1 Airport Activity Levels
The purpose of this section is to describe the data necessary to
represent airport activity and to outline methods of gathering the informa-
tion. The new St. Louis airport is used to illustrate the procedure.
The collection of sufficient data to represent the activity level of
an airport can be a large or a small task depending on one's definition of
"sufficient data." In general, the philosophy recommended in this report is
to collect as much detailed and airport-specific information as time and
resource constraints allow. Additional required information can then be extra-
polated from the wealth of data published for other airports. In some cases,
this may result in a detailed description of one phase of airport activity
and only a coarse approximation of another phase. This does not reduce the
credibility of the output so long as the extent of the approximations are
kept in mind.
For airports already in operation, the collection of first-hand data
means the setting up of a program of visits to the airport to make direct
observations. This is the most accurate as well as time- and resource-
consuming procedure. A detailed description of this procedure as applied
to Chicago's O'Hare International Airport has already been published.
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35
For airports only in the planning stage, reliance jnust be placed upon the
engineering studies which are drawn up as part of the airport need justifica-
tion. These studies will, in general, contain sufficient information to
enable a reasonable emission estimate to be made. The engineering studies
made for the proposed new St. Louis airport have been discussed in Section 2.0.
Figure 3.1 is a schematic description of the various activities of a
commercial air carrier airport which are likely to result in significant air
pollutant emissions. The actual emission sources are indicated by circular
figures on the diagram. Inspection of the figure indicates that each activity
can be classified as an independently variable activity or a dependently
variable activity. The primary independently variable activities (i.e., the
boxes having no input arrows) are the passenger demand and the air cargo
demand. All other activity levels can presumably be determined from this inform-
ation. In practice, however, the aircraft activity level is a semi-independently
variable activity. Though the activity is directly proportional to passenger
and cargo demand, it is also dependent on external influences such as aircraft
technology. The introduction of jumbo jets, wide-body jets, STOL aircraft, and
SST's can change the activity level and mix even though the passenger and
cargo demand remains constant.
The dependently variable activities are the ground service vehicle
activity and mix, the fuel storage and handling system, the engine test and
maintenance facility, the building heating and air conditioning plant, and
the access traffic activity and mix.
Each of these activities will be discussed in the following sections,
and methods will be outlined for estimating each activity level.
-------
Op
u>
H-
n
o
HI
O
l-h
1
5"
Passenger
§ Cargo
Terminal Bldg.
Requirements
Source of
emissions
jAir Cargo
' Demand
f~T
Passenger Demand
!
! Enplaned
| Deplaned
I Through
i Connecting
-e
!Employees
J Airport
I Visitors
Hangar
I
Requirements
Engine test
Maintenance
Facility
to
o>
Fuel Storage\
Handling
Ground
Service
Vehicle
Activity
and Mix
cess Traffic,
Activity § Mix)
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37
3.1.1 Passenger Demand
The air carrier passenger demand level is the main motivating factor
behind an air carrier airport. Without the desire for air travel, there would
be no need for an airport. The passenger demand level is measured by the
number of air travelers boarding aircraft at the airport under study. This
number is termed the enplaning passenger rate. The enplaning passenger rate
includes originating passengers and connecting passengers (persons arriving
at the airport aboard one aircraft arid departing aboard another). The deplaning
passenger rate which includes terminating and connecting passengers is approx-
imately equal to the enplaning rate. The through passenger rate includes
persons arriving and departing the airport aboard the same aircraft. The
enplaning passenger rate is a function of the region surrounding the airport.
The factors which most influence this demand for air travel are employment
in trade industries, regional population and family income, in that order.
For an airport already in operation, the enplaning passenger rate is
readily available from statistics kept by the airlines.
Several techniques are available for estimating the enplaning pas-
senger rate for a proposed airport. A sophisticated regional socio-economic
model can be designed which evaluates the above-mentioned factors along
with several secondary factors and determines the expected number of enplane-
ments. A simpler method is to forecast the enplanement growth rate based on
past information and trends. This technique requires only a simple growth
rate be applied to current enplanements. A combination of the techniques
would forecast a growth of the national air travel demand and prorate the
study airport's share of that growth based on the regional socio-economic
parameters.
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38
Of the studies done for the proposed new St, Louis airport, the NADC
study, the McDonnell study, and the ATA/TWA study, utilize the simpler
technique of applying growth factors to the current enplanement rate. The
5 2
FAA and Speas studies utilize the combination approach. The growth rates
utilized in the NADC and McDonnell studies and the resulting growth rates of
the Speas study are given on Table 3.1.
For the purposes of this report, it will be assumed that the passenger
enplanement rate is a given value. A detailed analysis of the above-mentioned
forecasting techniques is considered to be beyond the scope of this analysis.
The enplaning passenger rate for the proposed new St. Louis airport,
as determined in the above-mentioned studies and as summarized in the
Environmental Impact Statement is given in Table 3.2.
Passenger activity characteristics as they are related to the access
traffic activity are discussed in Section 3.1.8.
3.1.2 Air Cargo Demand
The air cargo demand for an operating airport is readily available
from airport statistics. Generally this data can be obtained from the same
sources as the air passenger data.
The air cargo forecast for a proposed airport is generally obtained
by applying a growth factor to the current air cargo activity. The air cargo
is divided among freight, express, and mail. As with the passenger forecasts,
distinction must be made between originating air cargo, transfer air cargo
(cargo unloaded off one airplane and loaded onto another), and terminating
air cargo.
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39
Table 3.1
Passenger Enplarieihent Growth Rates
1970
1971
1972
1973
1974
1975
1976-1980
1981-1985
1986-1990
1991-1995
1996-2000
Average Annual
NADC
_
1.0
9.5
10.0
10.5
10.5
10.0
9.2
8.5
7.6
6.4
St. Louis Airport
Change Over Previous Forecast Year
(Percent)
McDonnell
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
Speas
NA
NA
NA
NA
NA
NA
8.9 (1980)
NA
7.9 (1990)
NA
5.4 (2000)
NA - Not Available
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40
Table 3.2
Annual Air Carrier Passenger Enplariements
St. Louis
Source
ATA/TWA.
McDonnell
FM (Fiscal
Year)
NADC
Speas
(Millions
1970 1975
3.4 5.9
3.1 5.5
3.1 5.0
2.8 4.2
3.4 5.3
of
Airport
Passengers)
1980 1985 1990 1995
9.
8.
8.
6.
8.
7 ...
7 13.7 21.6
2 13.6
8 10.5 15.8 22.7
1 12.0 17.5 23.6
2000
-
-
-
31.0
30.7
From Ref. 1, Fig. 1-1
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41
For the proposed new St. Louis airport, the air cargo forecasts are
given in Table 3.3. Note should be taken that the NADC forecast is for
enplaned air cargo which includes transfer cargo. The Speas forecast is for
originating cargo only.
For the purposes of this study, it will be assumed that the air cargo
forecast is known along with the enplaning passenger rate.
3.1.3 Aircraft Activity and Mix
Aircraft activity and mix is perhaps the single most important piece
of accurate information needed to estimate air pollutant emissions from air-
ports. The activity level is measured by the number of aircraft movements
or operations, or by the number of aircraft landing-takeoff (LTO) cycles.
An aircraft movement is either a landing or a takeoff. An LTO cycle consists
of two movements and hence, has an activity level that is one-half the number
of movements. The mix is measured by the fraction that each aircraft type
makes up of the total number of movements.
For operational airports, the annual and peak hour aircraft activity
and mix can be obtained from the records which are required to be kept by the
Federal Aviation Administration. The published airline schedules are another
good source of data, although using schedules alone will cause the investi-
gator to miss the non-scheduled, charter, and general aviation activity which
can make up a significant fraction of the total activity at a commercial air
carrier airport.
For a proposed airport the annual aircraft activity can be derived
from the passenger activity. This calculation, however, requires the estima-
tion of two additional parameters; average aircraft seating capacity
and aircraft load factors. The estimation of these parameters, in turn,
requires the estimation of annual average aircraft mix at the study airport.
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42
Table 3.3
Air Cargo Forecast
1970
1971
1972
1973
1974
1975
1980
1985
1990
1995
2000
NADC Study
(Enplaning Tons)
58,000
67,850
79,950
93,250
108,400
211,350
409,450
746,950
1,223,500
2,008,500
St. Louis
Annual
Change
f o, *\
LW J
15
17
18
17
16
14
13
10
10
10
Airport
Speas Study
(Originating Tons)
43,547
83,014
159,836
281,687
496,427
799,500
1,287,604
Annual
Change
14
14
12
12
10
10
8
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43
As mentioned previously annual average aircraft mix is as much dependent
on the external influences of developing technology as on the character-
istics of the air passenger demand. The introduction of jumbo jets
and air buses (DC-10, L1011) drastically changes the available
seat capacity and hence offers the airline schedulers many alternatives
for meeting the passenger demand. If the diurnal pattern of demand for
air travel reaches extreme values at certain hours of the day then the
trend will be toward utilizing the high capacity airplanes during those
hours and substituting lower capacity aircraft during the slack hours .
If the pattern is more evenly distributed then the trend will be toward
medium capacity aircraft throughout the day. Likewise, the passenger trip
length will influence the choice of short-haul or lone-range equipment.
This interaction of the technological developments with the
characteristics of the passenger demand leads to an exceedingly
difficult task of estimating aircraft mix. There are a multitude of
alternative equipment combinations which will satisfy the demand. The
problem, in essence, does not have a unique solution. Estimates of
aircraft mix, therefore, should be made by those thoroughly familiar with
the characteristics of the passenger demand and with probable technological
advances . The ability to make a reasonably educated guess is also of great
value.
Once the mix estimates are made, the generation of annual aircraft
movements from air passenger enplanements then becorn.es a straight-
forward process. Additional perturbations in the scheme can be introduced
So account for specific situations relevant to the study airport.
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44
Estimates of peak hour aircraft activity are included as part of the
engineering studies of a proposed airport because they are strong
indicators of airport capacity. The peak hour activity is important to
the emission calculation in its relationship to the overall diurnal
pattern of weather in the airport vicinity. Should maximum activity
(maximum emissions) occur at a time when the ventilating characteristics
of the atmosphere are at a minimum the resulting air quality could be
substantially degraded. Experience shows that fortunately this is not
Generally the case but this will be discussed in more detail in Section 5.0.
The peak hour aircraft activity is generally taken as some fraction of
the annual activity. As with the annual average aircraft mix the peak hour
mix estimate is a function of the experience and predicative capability of
the forecaster.
The studies done for the proposed new St. Louis airport will be used
to illustrate the aircraft activity forecasting technique. The studies do
not all give complete results for the necessary parameters, annual and peak
hour activity and annual and peak hour mix, but enough information can be
extracted to perform the emission calculation if certain assumptions are
used to fill in the gaps.
HADC Study3
To make the annual activity forecast NADC first estinates,for the various
aircraft expected to be in service during the forecast period,the average
seating capacity per aircraft type, the load factor for each aircraft, and
the deplanement/enplanement factor for each aircraft (i.e. the fraction of
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45
available seats that are assigned to passengers destined to or from
St. Louis; this is a compensating factor for through passengers). This
enables a computation of the average number of passengers enplaning or
deplaning per aircraft movement for each aircraft type. A separate estimate
is made for the fraction of annual movements that will be made by each of the
aircraft types. This information is then used to weight the passengers per
aircraft movement for each aircraft type and arrive at an overall average
passengers per aircraft movement figure. The data used by KADC is given
on Table 3.4.
In proceeding to calculate annual air carrier movements NADC uses the
total enplaning plus deplaning passenger rate, referred to as the air passenger
movement rate. This is taken as simply twice the enplaning passenger rate
as described in Section 3.1.1. NADC reduces the air passenger movement
rate by 1$ to account for passengers moved by air taxi. The previously
calculated value of average passengers per aircraft movement is then applied
to this reduced value of passenger movements to get the number of scheduled
air carrier aircraft movements. This number is increased by 7.5% to account
for non-scheduled air carrier movements. (The value 7.5$ is typical of St.
Louis from historical data.) The scheduled plus non-scheduled movements
give the total air carrier movements for the year.
Air taxi movements are determined by dividing the number of air taxi
passengers (i.e. the 1% of passenger movements previously deducted by. an
average aircraft size varying from 20-30 passengers in the time span 1980-
2000 and by a 50$ load factor.
General aviation movements are assumed to be 5$ of the total aircraft
activity.
-------
Table 3.4 Aircraft Mix. Seat Capacity, and Load Factor Estimates
NADC Study - St. Louis Airport*
Aircraft Type
Jumbo Jets (B-747)
Air Buses (DC-10/
L-1011)
4-Engine Conventional
Jets (B-707/DC-8)
2 and 3 Engine Conven-
tional Jets (B-727/
DC-9)
STOL/VTOL
Small 2 -Engine Conven-
tional Jets (F-28)
Pctage. of Total Avg> N()< of Deplanement Passengers
Annual Scheduled Seats per Load Enplanement Per Aircraft
A/C Movements A/C Type Factor Factor Movement
1980 1990 2000 1980 1990 2000 1980 1990 2000 .980 1990 20' 1980 1'990 2000
5 10 20 375 450 525 .45 .55 .60 .80 .75 > 135 186 236
30 50 60 250 300 350 .48 .60 .60 .80 .75 .75 96 135 158
1° 5 -- 140 140 -- .50 .55 -- .80 .75 -- 56 58
-C-
52 -- -- HO -- -- .50 -- -- .80 -- -- 44
30 20 -- 110 110 -- .55 .65 -- .90 .90 -- 54 64
3 S -- 70 70 -- -.50 .50 -- .90 .90 -- 32 32
Avg. No. of Seats 167 23? 337 Avg> No> of Ts To7 155
per Aircraft 'Passengers per
Air Carrier Air-
craft Movement
*From Ref. 3, Table 4-8.
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47
The sum of the air carrier movements, the air taxi movements and
the general aviation movements yields the total number of annual aircraft
movements. The calculation procedure is shown on Table 3.5.
Annual average aircraft mix can be determined by adding the air taxi
and general aviation forecasts to the air carrier forecasts and recomputing
the percentages given on Table 3.4.
The peak hour activity is determined by taking appropriate fractions
of the annual activity. NADC assumes the peak month air carrier operations
to be 9% of the total annual operations, peak day to be 3.7% of the peak
month, and peak hour to be 7% of the peak day. Peak hour movements of air
taxi and general aviation aircraft were obtained by distributing the annual
activity equally over 365 days and over an assumed 12-hour operational day,
and adding these numbers to the air carrier activity.
The peak hour mix is not given in the NADC forecast.
A summary of the NADC forecasts will be presented in Table 3.10.
4
McDonnell Study
The McDonnell Study proceeds in essentially the same manner as the
NADC Study. That is, estimates of aircraft load factors and annual average
available seats are made and applied to the enplaning passenger figure to
arrive at annual aircraft movements. The aircraft mix used to derive the
annual average available seats is not given. No estimate is made for general
aviation activity, and the perturbations of non-scheduled air carrier
activity and air taxi activity are not included.
The data used by McDonnell for the annual activity calculations is
given on Table 3.6. The calculation procedure is illustrated on Table 3.7.
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48
(1)
Table 3.5
Calculation Procedure for Annual Aircraft Movements
NADC Study
Scheduled Air Carrier
Aircraft Movements
Total Air Passenger
Movements
-
Air Taxi
Passenger
Movements
[~Avg. No. of Passengers j
per A/C Aircraft Movement!
where
Air Taxi
Passenger
Movements
.01
Total Air
Passenger
Movements
(2)
(3)
Scheduled
Air Carrier
Movements
Air Taxi
Passenger
Movements
x .075
r ""
Non- Scheduled
A/C Aircraft
Movements
Air Taxi
i
EAvg. No. of Passengers perl
Mr Taxi Aircraft Movement!
Aircraft Movements I
(4)
(Scheduled Air
Carrier Movements
+
Non- Scheduled
A/C Movements
+
Air Taxi
Movements
s
19
(5)
Scheduled Air
Carrier Movements
,_
Non- Scheduled
A/C Movements
+
r ~]
Air Taxi
Movements
Total Annual
Aircraft
Movements
General Aviation
Aircraft Movements
t^ -
General Aviation
Movements
-------
49
Table 3.6
Aircraft Seat Capacity and Load Factor Estimates
McDonnell Study
Enplaning* Load Average Number of
Factor (%) Seats per Aircraft
92
117
134
166
211
1970
1975
1980
1985
1990
39.3
44
49
50
50
* For originating and connecting passengers only. Does not include
through passengers.
-------
50
Table 3.7
Calculation Procedure for Annual Aircraft Movements
McDonnell Study
(1)
Enplaned Air Carrier
Passengers
("Enplaning Load ~]
[_ Factor J
Required No. of
Departing Seats
(2)
Required No. of
Departing Seats
f~Avg. No. of Seats)
[_per Aircraft J
No. of Air Carrier
Aircraft Takeoffs
(3)
No. of Air Carrier
Aircraft Takeoffs
x 2
Total Annual Air
Carrier Movements
-------
51
The peak hour aircraft activity as forecast by McDonnell does not
maintain a percentage ratio to annual movements, The Environmental Impact
Statement makes special note of this factor in its summary of the different
studies.
Peak hour mix is given and will be presented on Table 3.10, along
with a summary of the McDonnell forecast.
2
Speas Study
The Speas Study was not available for review but the Environmental
Impact Statement contains sufficient information to enable a description of
the Speas procedure to be made. The procedure is based on a load factor
and a seat capacity estimate for the annual activity forecast, as with the
other studies. Annual aircraft mix is not available. The Speas data for
the annual calculations is given on Table 3.8 and a description of the calcu-
lation procedure is on Table 3.9.
Peak hour movements are taken as .0235% of the annual activity.
This is approximately the same as the NADC figure.
Peak hour mix estimates are made and will be summarized on
Table 3.10 with the rest of the Speas forecast.
Other Studies
The FAA5 and the ATA/TWA studies were not available, and insufficient
information was presented in the Impact Statement to enable an evaluation
of their procedures to be made. A summary of the forecast results, however,
will be presented on Table 3.10.
The results of the above forecasting techniques for St. Louis annual
and peak hour aircraft movements and annual and peak hour mix are given on
Table 3.10. The procedures described above used to make these estimates are
designed to be illustrative and not comprehensive.
-------
52
Table 3.3
Aircraft Seat Capacity arid Load Factor Estimates
Speas Study
Enplaning* Load Avg. No. of Seats
Factor (%) per Aircraft
1980 43 153
1990 50 202
2000 50 300
* For originating and connecting passengers only. Does not include
through passengers.
-------
53
Table 3.9
Calculation Procedure for Annual Aircraft Movements
Speas Study
|~~Avg. No. of Seats! ("Enplaning "1
Lper Aircraft J x [_ Load FactorJ
Enplaned Passengers per
A/C Aircraft Takeoff
(2)
Enplaned Air Carrier
Passengers
Enplaned Passengers per
A/C Aircraft Takeoff
" Scheduled "|
A/C Takeof f sj.
C3)
Scheduled
A/C Takeoffs
.075
r
Non-Scheduled"!
A/C Takeoffs J
(4)
Scheduled
A/C Takeoffs
Non-Scheduled
A/C Takeoffs
x' 0.15
I Air Cargo and j
1 General Aviation i
L.
Takeoffs
J
(5)
Scheduled
A/C Takeoffs
Non-Scheduled
A/C Takeoffs I
| Air Cargo and
General Aviation
Takeoffs
x 2
|j
Total Annual
Aircraft
Movements
-------
Table 3.10
Sunmary of Aircraft Activity and Mix Forecasts
St. -Louis Airport
' NADC
Annual Forecasts/
Peak Hour Forecasts
Total Aircraft Movements*
Aircraft Mix f%)**
Jumbo Jets (B-747)
Air Buses (DC-10/L1011)
4- Engine Conventional
Jets(B-707/DC-8)
2- § 3- Engine Conven'l
Jets (B-727/DC-9)
STOL/VTOL""' ' "-"
Small 2- Engine Conven'l
Jets (F-28)
Air Taxi
General Aviation
1970 1975 1980
: 1985
i
247000/58 303000/71
4.5
26.9
8.9
46.5
0.
;' 2.7
I i 5.5
" T ' H" '" " f. '
\ o.O
6.2
36.5
6.7
23.1
12.9
_.-JLJL_
6.1
5.0
1990
357000/83
8.8
43.9
4.4
0.
26*.T~~
4_.4_
I 7>1
5.0
1995
430000/100
12.2
48.9
2.2
0.
~~--£~g~--~
2.2
7.7
5.0
2000
492000/114
i 17.3
i 52.0
0.
0.
~~~
0.
1 8.4
;. 5.0
!
1
*Aircraft Movement forecasts include air carrier (scheduled and non- "scheduled.!, air taxi, and limited
general aviation activity.
**Aircraft Mix estimates are for annual operations only. Peak Hour Mix estimate is not available.
-------
Table 3.10 (Contd.)
Summary of Aircraft Activity and Mix Forecasts
St. Louis Airport
McDonnell
Annual Forecasts/
Peak Hour Forecasts
Total Aircraft Movements*
Aircraft Mix (%)**
Category 1 (747)
Category 2 (DC-10/
L1011/DC-8-63)
Category 3 (727/
707/DC-8)
Category 4 (DC-9/
FH227) ;
Category 5
(General Aviation)
1970
174000/46
0.
0.
71.7
13.0
15.3
1975
212000/50
2.0
18.0
48.0
16.0
16.0
1980
265000/59
5.1
30.5
40.6
8.5
15.3
1985
330000/78
9.0
41.0
30.8
5.1
14.1
1990
410000/102
15.7
45.1
23.5
2.0
12.8
1995
2000
*Annual Forecasts are for air carrier operations only. Peak Hour Forecasts are for air carrier, air
cargo and limited general aviation aircraft movements.
**Aircraft Mix estimates are for peak hour operations only. Annual mix estimate not available.
-------
Table 3.10 (Contd)
Summary of Aircraft Activity and Mix Forecasts
St. Louis Airport
Annual Forecasts/
Peak Hour Forecasts
Total Aircraft Movements*
Aircraft Mix (1)**
Category AA
(B-747/DC-10/L1011/DC-8-60)
Category A
(B- 707/DC- 8/B- 720/CV- 880)
Category B
(B- 727/DC- 9/B- 737/BAC/CV)
Category C
(F-27/Gulf stream/Lear Jet/
Falcon/King Air/DC- 3)
Category D § E
(Cessna 310, 320, 411,
150-210/Piper Apache,
Aztec, Cherokee, Comanchee/
Beech Queen Air)
1970
1975
272000/64
15.6
6.3
57.7
11.0
9.4
1980
304000/71
29.6
5.6
52.2
5.6
7.0
1985
361000/85
43.5
0.
43.5
7.1
5.9
1990
428000/101
51.5
0.
35.6
7.9
5.0
1995
475000/111
60.4
0.
27.0
8.1
4.5
2000
506000/119
67.2
0.
20.2
8.4
4.2
**
Aircraft Movement forecasts include air carrier (scheduled and non-scheduled), air cargo, and limited
general aviation activity.
Aircraft Mix estimates are for peak hour operatons only. Annual mix estimate not available.
-------
Table 3.10 (Conti)
Sunmary of Aircraft Activity and Mix Forecasts
St. Louis Airport
FM
Annual (FY) Forecasts
A/C Aircraft Movements
tExtrapolated
ATA/TWA
Annual Forecasts/
Peak Hour Forecasts
A/C Aircraft Movements
Total Aircraft
Movements*
1970
192000
183000/42
412000/81
1975
209000
242000/52
524000/107
1980
248000
319000/67
670000/147
1985
SllOOO1"
1990
1995
2000
*Total Aircraft Movement forecasts include air carrier, air cargo, and unlimited general aviation
and military activity.
Ul
-------
58
It will be noted that the air cargo forecast does not enter as a
determining factor. It is included only in a cursory way in the Speas
forecast (see Table 3.9). The reason for this is that based on the pro-
jections for air cargo given in Section 3»l-2 the aircraft which will
meet the passenger demand will also meet the cargo demand.
All of the studies mentioned above indicate that, barring the intro-
duction in the St. Louis area of a large bulk commodity requiring all-cargo
aircraft, the forecast .cargo tonnage can be handled by the cargo holds of
the passenger aircraft.
The information presented on Table 3.10 is the type of data on aircraft
activity and mix that is needed to estimate aircraft emissions. In the
cases where the data is not complete it is possible to utilize the given
information to calculate emissions. For example, the McDonnell and Speas
forecasts do not give an annual aircraft mix. The peak hour mix, although
probably different from the annual mix will be used instead. Since the
change in mix is much more dramatic from forecast year to forecast year
than it would be from peak hour to annual average in any given year this
approximation is probably not a bad one to make.
This type of filling in of data gaps is what is necessaiy to make an
emission estimate with a reasonable amount of time and resources expended.
Diurnal Activity
The diurnal pattern of aircraft activity at an airport is an important
piece of information for the reasons alluded to previously in the discussion
of peak hour forecasting. Should maximum activity occur at the same time
of day as minimum atmospheric ventilation the air quality could be
-------
59
seriously degraded. Conversely, should maximum activity occur during
maximum ventilation the air quality impact would be minimized.
The diurnal activity is measured by the total, number of aircraft
movements occuring during each hour of the day. The Environmental Impact
Statement presents a diurnal demand pattern for Lambert Field based upon the
Speas study. Speas is reported to have used historical air traffic data
from FAA Forms 7230-1, observations of traffic taken on field survey,
discussions with FAA personnel, and experience at other airports to generate
this pattern. The diurnal variation is given on Table 3.11.
The NADC report also presents a diurnal activity pattern but this is
for scheduled air carrier activity only. Since non-scheduled air carrier,
air taxi, and general aviation movements make up a significant part of the
activity at St. Louis the NADC pattern will not be used.
It will be assumed that the diurnal pattern will not change over the
forecast period to the year 2000.
3.1.1)- Ground Service Vehicle Activity and Mix
The ground service vehicles are those pieces of motorized equipment
which operate in the gate areas to load and unload aircraft and otherwise
prepare the airplane for its next departure. The activity level and mix
of equipment is dependent on the type of aircraft being serviced.
Different airlines also choose different pieces of equipment to service
the same type of airplane.
The primary variable which will be used to estimate emissions from
ground service vehicles is the amount of tine spent by each vehicle type
servicing a particular aircraft. Total service tiaes as a function or
-------
60
Table 3.11
Diurnal Aircraft Activity
St. Louis Airport
Percent of Daily Aircraft Movements
Hour Occurring During the Hour
1 0.97
2 0.97
3 0.45
4 0.45
5 0.52
6 0.52
7 2.60
8 2.60
9 6.24
10 6.24
11 6.76
12 6.76
13 6.91
14 6.91
15 6.46
16 6.46
17 7.43
18 7.43
19 5.87
20 5.87
21 4.08
22 4.08
23 1.71
24 1.71
100.00
-------
61
aircraft type are given in Table 3.12 for the various kinds of ground
support vehicles. These data were obtained from questionnaires completed
by airline representatives, personal interviews with airline employees and
12
observations of ramp activities at O'Hare International Airport in Chicago.
The data are averaged values of service times for the different airlines
participating in the survey at O'Hare.'. In addition-to the vehicles listed
in Table 3.12, there are a few others not listed which are not routinely
used to service aircraft in the ramp area and hence are not incorporated
into the activity estimate. These include deicers, glycol trucks, lift
trucks, and others. Operation time of these vehicles is generally small
compared to most of the others listed in the table.
The ground service vehicle operating times are computed on a diurnal
basis by multiplying one-half the total number of arrivals and departures
of each aircraft type in an hour "by the service time of each vehicle type.
This method accounts for the fact that in any given hour there may be more
arrivals than departures or vice-versa, but this discrepancy cancels out
on a daily basis.
Table 3.13 gives the projected time of ground service vehicle operations
based upon the Speas aircraft activity and mix forecasts of Table 3.10 and
the service times on Table 3.12.
-------
Table 3.12
Service Times of Aircraft Ground Service Vehicles
~ -^_. Aircraft
Time in Vehicle-Minutes
Vehicle ~'~"^_. « B-747 DC- 10 B-707 DC-8 B-727
1. Tractor 155 148 66
2. Belt Loader
3. Container Loader
4. Cabin Service
5. Lavatory Truck
6. Water Truck
7. Food Truck
8. Fuel Truck
9. Tow Tractor
10. Conditioner
' 11. Airstart
Transporting
Engine
Diesel
Power Unit
' 12. Ground
; Power Unit0
: Transporting
48 40 37
92 80 12
24 25 12 ;
24 18 15 ;
12 10 0
55 20 20
50 45 37
10 10 10
0
f-f
2
-
0
I Engine
: Gasoline i 0
0 30
0 ! 10
i
0 8
i
0 j 9
0 4
' Power Unit j j
Diesel 0
j Power Unit
; 13. Transporter
I
f 14. Auxiliary ,
I Power Unit
j
19
0 : 4
98 66
30 28
0 6
15 12
18 15
0 0
30 j 17.
40 20
5 10
30 0
5 0
4 0-
0 0
0 0
0 0
DC-9
48
15
0
0
15 j
10
17
15
5
B-737
85
30
0
15
C-880 I F-227 ' C-580
40 i 55 50
40 , 0 25
000
010 0
15 j 20
o
20
15
5
0^0
0
-
0
[_
0
0
0
0
0
0
o
0
0
20
20
15
0
10 10
10 10
10 10
10 20 |
5
0
15 0
11
35
0
5
o !^
i NJ
%
0
0
1
0 0
1
15 0 0
15
: - !
0 10
Yes Yes ! No
1
0 3
No Yes
o
Yes
0
Yes
j |
0
No
0 0
I
0
No
o
>
No
1
J
-------
63
Table 3.12 CContd.)
Service Times of Aircraft Ground Service Vehicles
Footnotes:
a. Normally used only in very hot or cold weather.
b. The airstart vehicle consists of a diesel generator mounted on the back of
a truck. It is assumed that the truck engine runs continuously for the
entire service time and the diesel generator runs for 75% of the time.
c. The ground power unit consists of a generator mounted on the back of a truck.
It is assumed that the truck engine and the generator run continuously.
About half of the generator units in service are diesel and the other half
are gasoline engine. Hence, the generator service time is divided equally
between a gasoline and diesel engine.
d. If an aircraft has an auxiliary power unit, it normally runs for the entire
gate occupancy time. Its emissions are included in the aircraft calcula-
tions .
Data from Ref. 12
-------
Table 3.13
Total Daily Ground Service Vehicle Operating Time
Speas Study Aircraft Activity - St. Louis Airport
i Vehicle \
I 1. Tractor
2. Belt
: Loader
3. Container
Loader
4. Cabin
; Service
5. Lavatory
: Truck
6. Water
; Truck
7. Food
Time in Vehicle-Hours
1975
422
135
101
51
1980
568
182
190
77
1985 j 1990
779 1010
236 299
320 447
i
110 146
t
i
i
88 108 I 135 167
35
116
Truck
; 8. Fuel Truck 131
9. Tow Tractor 44
10. Conditioner 11
11. Airstart
i ' [
I t
44
63 ! 80
I
153 I 202 259
180 239 ! 310
53
13
1
66 81
0 i 0
i
i
; Transporta- 5 I 7 | 6 i 8
tion Engine
i
t
Diesel 4 j 5 i 5 i 6
Power Unit j
, 12. Ground Power
Unit I i
Transporta- 2 j 2 0 0
tion Engine i
Gasoline 1 ! 1 0 0
Power Unit i
i j i
Diesel | 1 i 1 0 0
Power Unit
13 . Transporter
; ; i
19 28 40 54
\
I
1995
1210
352
574
179
191
96
2000
1365 ;'
393
676
205
208
106 I
i
j
306 \ 342 ]
;
372 ; 420
{
92 f 100
0
10
0
12
1
j
8 9
I
0 0
0 0
0 0
i j
68 ! 78 !
f
1
-------
65
3.1.5 Fuel Handling and Storage System
The operation of a major air carrier airport requires the storage
and handling of an extremely large quantity of fuel for both aircraft and
ground service vehicles. Fuel consumption at Chicago's O'Hare is on the
order of 75 million gallons of jet fuel and 300,000 gallons of gasoline per
month. The handling of such large volumes of fuel leads to the loss of
significant quantities through evaporation and spillage during transfer from
tank to tank. These losses appear as air pollutant emissions and should be
accounted for in the emission calculation. Since jet and ground service
vehicle fuels are hydrocarbon compounds, the emissions resulting from evapor-
ation and spillage are to be counted as part of the total airport hydrocarbon
emission.
In order to compute the emissions from fuel storage and handling,
an estimate must be made for the total amount of fuel that is to be handled
on the airport site. For aircraft, it is not a trivial matter to estimate
the fuel requirements. If one simply tabulates the fuel capacity of each
aircraft type and attempts to calculate fuel requirements based on the
number of aircraft operations, the resulting quantity will be a serious
overestimate. This is true since aircraft rarely must take on a full load
of fuel at any one destination. Fuel left in the tanks when the aircraft
lands can be a sizeable fraction of the capacity. This is especially true
for an airport such as St. Louis where a large number of flights are of
short to medium-range distance. Instead of this direct computation, an
indirect approach will be used. Based an O'Hare data , the average amount
of fuel pumped per aircraft LTO is approximately 2700 gallons. Data
from Los Angeles shows approximately 3200 gallons per LTO. This higher
-------
66
value can be attributed to the fact that Los Angeles serves a larger number
of long-range flights. Since St. Louis operations are mostly of short- to
medium-range, the lower fuel requirements of O'Hare will be used. Table 3.14
gives the proj ected annual aircraft fuel requirements for each of the three
forecasts. Even though this estimating procedure is crude, the resulting
projections are probably of the right order of magnitude.
For ground service vehicles, the amount of fuel required can be
estimated by utilizing the vehicle activity already presented in Section 3.1.4.
Additional information on fuel consumption rates of these vehicles will be
presented in Section 3.2.2, but the results of applying the activity estimates
to the consumption rates is presented on Table 3.15 as fuel required for
ground service vehicles.
This information on aircraft fuel requirements and ground service
vehicle fuel requirements will enable an estimate of fuel handling and evapor-
ative emissions to be made. The diurnal distribution of emission will be
taken to be the same as the aircraft diurnal distribution of Table 3.11.
3.1.6. Engine Test and Maintenance Facility
The operation of a major turbine engine maintenance and overhaul
facility at an airport involves the testing of aircraft engines to insure
proper performance and structural integrity. A test consists of running
the engine mounted either on a special test stand or on the airplane
through a set of typical flight conditions while measurements of performance
are made. If a substantial number of such tests are performed, the air
pollutant emissions can be significant and should be accounted for in the
emission calculation.
-------
67
Table 3.14
Annual Mfcraft Fuel Requirements*
Forecast
NADC
McDonnell
Speas
*Based on an
Forecast
NADC
Gasoline
Diesel Fuel
McDonnell
Gasoline
Diesel Fuel
Speas
Gasoline
Diesel Fuel
St. Louis Airport
Millions of G
1975 1980 1985
333 409
286 358 446
367 410 487
average of 2700 gallons of fuel per
Table 3.15
Annual Ground Service Vehicle Fiie
St. Louis Airport
Thousands of
1975 1980 1985
789 1085
13 14
737 1084 1507
40 45 46
697 964 1321
13 18 12
iallons
1990 1995
482 " 581
554
578 641
LTD.
I Requirements .
Gallons
1990 1995
1414 1832
14 14
2029
49
1723 2081
17 22
2000
664
-
683
2000
2228
13
-
-
2358
26
-------
68
The operation of a major maintenance facility at an airport is
strictly a function of the decision of the airlines as to its location. It
is an activity which can be.tied to aircraft activity only after an assump-
tion has been made as to the extent of the maintenance operations which are
to be located at the study airport. In general, it can be said that airlines
prefer to locate major maintenance facilities at airports which serve a large
number of originating and terminating flights. Airports which serve mainly
as connection points do not, in general, become the preferred location for
large-scale maintenance and overhaul operations. To illustrate, Los Angeles
14
International Airport is reported to have 42 turbine engine run-ups per day
in the test facility; San Trancisco International Airport is reported to
use 11,300 gallons of jet fuel per day in maintenance operations; in contrast,
Chicago's O'Hare International Airport has only one or two engine run-ups
per day . The reason for these differences is that San Francisco and
Los Angeles are both terminating/originating points, while Chicago is a con-
nection point.
In attempting to estimate the number of engine tests, the Los Angeles
14
data will be taken as representative of a major maintenance facility.
Los Angeles International has approximately 403,000 aircraft movements per
year. The 42 engine tests per day thus leads to a ratio of about 38 engine
run-ups per 1000 aircraft movements in a day. It should be emphasized that
these are engine run-ups and not aircraft run-ups; only one engine is
operating at a time.
In attempting to apply this ratio to the proposed new St. Louis
airport, it should be recognized that St. Louis is not likely to become a
major maintenance facility because much of its air traffic is connecting.
-------
69
On the other hand, it probably will not have as small a maintenance facility
operation as O'Hare because of the location of a major aircraft manufacturer,
McDonnell-Douglas, in the area. Hence it appears reasonable to assume that
something on the order of 20 engine run-ups per 1000 aircraft movements in a
day will be an adequate representation of the St. Louis operation. Table 3.16
gives the annual number of maintenance tests projected for each of the air-
craft activity forecasts based on this assumption.
The emissions are distributed evenly over the hours of the day shift
(9:00 AM to 4:00 M) for purposes of diurnal emission estimates.
3.1.7 Heating and Air Conditioning Plant
As shown in Fig. 3.1, the expected activity level of air passengers
and air cargo requires the sizing of terminal buildings to handle the volume
comfortably. Likewise, the aircraft require hangars for service and main-
tenance. These buildings are generally heated and air-conditioned from a
central plant located on the airport site. The operation of this plant can
be expected to produce some quantity of air pollutant emissions.
The Environmental Impact Statement estimates the building require-
ments to be 2.1 x 10 sq ft for the passenger terminal and 0.22 x 10 sq ft
for the cargo terminal. With these building sizes, it is possible to estimate
the amount of fuel required for space heating. Following standard procedures ,
it can be estimated that this building space in the St. Louis area will require,
on an annual average, 13,354 therms (10 Btu) per day to; heat. Assuming that
coal or oil will be the fuel most likely to be used because of their abundance
in the area, this heating requirement can be translated into fuel requirements
using standard methods. Table 3.17 summarizes the projected fuel require-
ments.
The heating plant emissions will be distributed equally over the
day for the purpose of estimating diurnal patterns.
-------
70
Table 3.16
Annual Aircraft Engine Maintenance Tests
St. Louis Airport
Number of Engine Run-ups per Year*
NADC McDonnell Speas
Year Forecast Forecast Forecast
1975 - 4380 5475
1980 5110 5475 6205
1985 6205 6570 7300
1990 7300 8030 8395
1995 8760 - 9490
2000 9855 - 10220
*Based on 20 run-ups per 1000 aircraft movements
in a day.
Table 3.17
Annual Fuel Requirements of Heating Plant
St. Louis Airport
Building Required Annual Fuel Required
Floor9Space* Energy** CoalOil
(ft ) (therms/day) (tons) (gallons)
2.3 x 106 13354 22.3 x 103 3.5 x 106
*From Ref. 1
**From Ref. 17
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71
3.1.8 Access Traffic
Airports are essentially transfer stations where people can switch
from ground transportation to air transportation or vice-versa. Since this
role as a modal transfer point is fundamental to airports, the planning for
adequate ground access facilities in as important as the planning for
aircraft facilities. Ground transportation is a significant source of
air pollutant emissions and hence must be included in the estinate of
total airport emissions.
Existing airports lend themselves well to ground transportation studies.
There are genei-ally a limited nuiiiber of roads leading into an airport and
most of these serve only as acess to some area of the airport proper such
as the terminal, the cargo area, hangar facility, and the like. This makes
for relatively easy monitoring of traffic flows which ars generated "by
airport activities. Surveys of airport users can be conducted in a simple
and accurate fashion; passengers can be interviewed while their flight is
in the air since they arc effectively a captive audience; employees can be
surveyed at their work stations; visitors can be interviewed in their
normal collection point in the terminal. The relative ease with which
airport-generated trips can be segregated from other urban travel has led to
a wealth of data on ground transportation collected at many airports.
For an airport in the planning stages the ground access is included as
a principle criteria in initial site selection. The cost and time-of-travel
are critical parameters which go into the evaluation of alternative sites.
Hence, there is likely to be a good deal of information on ground access
generated as part of the engineering studies. This combined with the
results of studies at existing airports provides a reasonably good data set
from which to estimate ground access vehicle activity and hence air pollutant
emissions.
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72
When attempting to estimate airport ground access air pollutant
emissions the Important items of information needed are the following:
. Number of vehicle trips
. Mix of vehicles
. Vehicle distance travelled
. Vehicle operational characteristics
The total volume of vehicle trips is the primary parameter. The
distribution of trips among the various types of vehicles (autos, taxis,
buses, rail, etc.) is a necessary resolution of the data since emissions
vary greatly with vehicle type. The travel distance is needed since air
pollutant emission factors arc given in the form of emissions per vehicle
mile of travel. The manner in which the vehicles are operated on the
airport grounds (speed, hot or cold start) substantially affects the emission
rate. The illustration of the generation of this information for the proposed
new St. Louis airport will make use of the data in the engineering studies
anr1 the information available from other airports.
As shown in Fig. 3.1, the driving functions behind the access traffic
activity are the number of person trips generated by the airport. Because
of their marked differences in ground travel patterns the people accessing
the airport are divided into three groups: passengers, visitors, and
employees. The visitor group is further subdivided into air passenger-related
visitors and casual visitors.
Passengers
The passenger group includes both originating and terminating passengers
as defined in Section 3.1.1. Both groups must make use of ground transport-
ation to arrive or depart the airport. Through and connecting passengers
-------
73
by definition do not leave the airport, and hence make no demands on the
ground transport system. There is a possibility, depending on the layout
of the terminal, that connecting passengers would need some form of trans-
port from one flight to another. This contingency will not be included in
this study because of its non-universal character.
The Environmental Impact Statement presents data from a passenger
survey taken at Lambert Field in September, 1969. This indicated that
during the survey period, 25.4% of the passengers were originating, 25.8%
were terminating, 30.0% were connecting, and 18.8% were through. Assuming
that over the forecast period, the originating and terminating passengers are
equal in number, it can then be said that 46% of the enplaning passengers are
originating in St. Louis. The remaining 54% are connecting, and hence make
no demand on the ground transport system.
Projections of passenger enplanements for the proposed new St. Louis
area have already been given on Table 3.2. The corresponding projections of
originating passengers is given on Table 3.18. The number of terminating
passengers is assumed to be equivalent. The diurnal variation of passenger
arrival is assumed to be equal to the diurnal variation of aircraft activity
for St. Louis, as given on Table 3.11. There is, perhaps, a slight difference
in phase between aircraft movements and passenger movements but this is most
probably less than an hour and will be ignored. Also, data is available
which shows that there is a slight difference in enplaning and deplaning
passenger diurnal patterns. The morning hours (7:00 AM - 10:00 AM) show a
larger number of enplaning passengers than deplaning, and the late night
hours (9:00 PM - 12:00 PM) show the opposite. It is not possible, however,
to include this effect, for to do so would require data that is consistent
with the aircraft activity pattern on Table 3.11. This information was not
-------
74
Table 3.18
Annual Air Carrier Originating Passengers*
St. Louis Airport
Source
NADC
McDonnell
Speas
2.5
2.4
Millions of Passengers
1975
1980 1985
3.1 4.8
1990
7.2
1995
10.5
2000
14.3
4.0
3.7
6.3
5.5
9.9
8.1
10.9
14.1
*0riginating passengers are approximately 463
of enplaning passengers.
-------
75
available for St. Louis, hence it is assumed that during any hour there are
as many enplaning as deplaning passengers. Except for the time periods
mentioned above, this is a good approximation.
Passenger-Related Visitors
To estimate the number of air passenger-related visitors, the
results of surveys at other airports were used. The ratios of visitors to
18
passengers are available for Cleveland's Hopkins Airport and Chicago's
1Q 9fi 91
O'Hare Airport , as well as for a number of other airports. ' Even
when all the figures are reduced to the common base of total visitors per
total (originating plus terminating) passengers, there is a significant
variation. The ratio varies from a low of 0.17 for Miami to a high of 1.93
for Denver. The data for Cleveland in Ref. 18 (Phase I, prior to completion
of the rapid transit link to the airport*) shows an average ratio of 0.64
visitors per passenger, while Ref.19 shows a ratio of 0.353 on a Wednesday
and 0.829 on a Saturday, leading to a weekly average of 0.444 for Chicago.
In selecting the Cleveland data for application to the St. Louis airport,
the following considerations were made: (a) only Refs. 18 and 19 contained
diurnal variations of visitor/passenger ratios; (b) the Chicago data was
collected over two days only,.as compared to seven days for the Cleveland
data; and (c) it was felt that the Cleveland airport would be closer in
character to the St. Louis airport than would Chicago's O'Hare which is
currently the busiest in the world. The visitor/passenger ratio as utilized
for the St. Louis calculations are given on Table 3.19. The annual number
of passenger-related visitors calculated by applying these ratios to the
forecasted originating passenger ratios are given on Table 3.20. The calcu-
lated numbers are doubled to account for visitors with terminating passengers.
-------
76
Table 3.19
Air- Pas seriger- Related-Visitor Ratios
Hour Visitors per Passenger*
1 0.587
2 0.587
3 0.587
4 0.587
5 0.587
6 0.587
7 0.587
8 0.402
9 0.432
10 0.533
11 0.541
12 0.670
13 0.780
14 0.618
15 0.581
16 0.549
17 0.687
18 0.721
19 0.654
20 0.820
21 0.686
22 0.640
23 0.576
24 0.747
*These ratios represent visitors coining with originating passengers per
originating passenger, or visitors with, terminating passengers per terminat-
ing passenger, or total visitors per originating plus terminating passenger.
Source: Ref. 18
-------
77
Table 3.20
Annual Passenger-Related Visitors*
Year
1975
1980
1985
1990
1995
2000
NADC
Forecast
_
3.9
6.1
9.1
13.1
17.9
St. Louis Airport
Millions of Visitors
McDonnell
Forecast
3.2
5.0
7.9
12.5
-
_
Speas
Forecast
3.1
4.7
6.9
10.1
13.6
17.7
*These figures account for visitors with originating and
terminating passengers.
-------
78
It might be noted parenthetically at this point that data from
O'Hare is utilized elsewhere in making the calculations for St. Louis. This
is not a contradiction in methodology but rather is done out of necessity.
As was stated in the opening discussion of this section, there are many informa-
tion gaps which must be filled in order to estimate air pollutant emissions
from an airport. Wherever possible, the data which is most applicable to
the study airport is used. In other cases, any data that is available is
used. Hence, we can reject the O'Hare visitor/passenger data because it is
felt the Cleveland data is more appropriate to St. Louis, and at the same
time, utilize O'Hare information at other points because it is the best avail-
able. An argument can be made for being consistent and using 0THare data
throughout the calculation. It is felt, however, that this will result in a
scaled-down picture of O'Hare and may not be as accurate a picture of
St. Louis as would be this "mixed set" of data.
Employees
The employee group for an airport includes airline personnel (both
passenger- and cargo-related), airport personnel (airport management, auto
parking attendants, etc.,), freight forwarders, concessionnaires, and indirect
support personnel (FAA, Public Health, Weather Bureau, etc.). There are two
estimates of employment available for the St. Louis airport.
The NADC forecast expands the 1971 employment level at Lambert Field
by quantifying the workload increase, labor productivity changes, service
level changes and economies of scale. Table 3.21, reproduced from the NADC
study, summarizes the figures used.
Reference 22 presents another set of employment forecasts based on
work done by Economics Research Associates. These are the forecasts utilized
in the Environmental Impact Statement.
-------
Table 3.21 Employment Forecasts, NADC Study
1.
Z.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Organization
or Function
Airport Management
Airlines
Air Cargo Handlers
Ground Transporta-
tion
Automobile Parking
Aircraft Fueling
Concessions
Skycaps
U.S. Post Office
Nat'l. Weather Serv.
Other U. S. Gov't.
Agencies
Air Taxi & Fixed
Base Operators
Hotel
Services
Provided
to
Passengers
Aircraft
Cargo
Passengers
Aircraft
Cargo
Cargo
Passengers
Passengers
Aircraft
Passengers
Passengers
Air Mail
Gen'l Public
Aircraft
Passengers
Passengers
1971*
Employ-
ment
116
51
3
272
1,453
91
181
266
90
72
341
35
155
50
192
75
0_
3,443
St. Louis Airport
Productivity Adjustment
Work Load Increase
1971-
1980
2.40
1.15
3.70
2.40
1.15
3.70
3.70
2.40
2.40
1.15
2.40
2.40
2.40
-
1.15
1880- 1885-
1985 1990
.54 1.49
.22 1.18
.97 1.81
.54
.22
.97
1.97
1.38
1.38
1.22
1.54
1.54
.49
.18
.81
.81
.36
.36
.18
.49
.49
1.54 1.47
-
1.22 1.18
_
_ ' _
1971-
1980
.91
.91
.91
1.00
.91
.84
.84
.91
.91
1.00
.91
1.00
.84
-
.91
^
Factor
1880-
1985
.90
.86
.82
.82
.91
.71
.78
.91
.91
1.00
.95
1.00
.91
-
.91
_
_
Employment
1885-
1990
.95
.95
.95
1.00
.95
.86
.91
.95
.95
1.00
.95
1.00
.91
-
.91
_
_
1980
254
54
10
652
1.527
282
561
583
197
83
748
88
311
50
202
125
100
Forecasts
1985
354
56
16
827 1,
1,684 1,
394
864 1.
727
246
101
1096
135
435
50
224
150
200
1990
501
63
28
229
886
615
416
942
319
119
1549
200
580
50
244
180
300
5,827 7,559 10,221
*Excludes headquarters personnel of Ozark Airlines and U.S. Military personnel (Army, Navy, Marines and Air National
Guard).
NOTE
Airline employment expanded by 300 jobs to com-
pensate for the reduction in normal manpower re-
quirements occasional by current economic decline.
-------
80
Since it is desired to estimate the airport emissiona up to the year
2000, it is necessary to extrapolate the employee forecasts. Because of the
rapid growth rate in the number of employees in the forecast period, it was
decided to extrapolate the employee/enplaned passenger ratio instead of the
i
employment itself, and then apply this value to the enplaned passenger,
forecast. This is a more desirable approach since the ratio tends to level
off in the later years of the forecast period. The results of this procedure
are given on Table 3.22 as employee forecasts for the entire period.
The diurnal pattern of employee arrivals and departures exhibits
three distinct peaks corresponding to the three work shifts. A diurnal distri-
bution of employee trips to and from work has been compiled from several
airports and Table 3.23 is drawn from this information. The table presents
the percentage of employees arriving and departing during a given hour.
For the purposes of calculating employee trips, it is assumed that
18
there is a 6% absentee rate. This figure is based on Cleveland data.
Casual Visitors
The casual visitor group includes those who come to the airport to
sightsee and tour, dine at the restaurants, secure trip information or tickets,
or conduct business. This represents a small but significant fraction of the
18
total airport population. Based on the Cleveland data , it can be assumed
that casual visitors amount to 9% of the enplaning passenger activity. This
is an extremely approximate figure, since the number of casual visitors
depends so heavily on the facilities available at the airport which will
attract this group. Table 3.24 presents the number of casual visitors for
the St. Louis airport as calculated from the enplaned passenger forecasts.
The diurnal variation of casual visitor arrivals and departures Is
given in Table 3.25 and is obtained from :a compilation of data on
several airports.
-------
81
Table 3.22
Employee Forecasts
Year
1971
1975
1980
1985
1990
1995
2000
St. Louis Airport
Number
NADC Study
3443
4420*
5827
7559
10221
13865*
18597*
of Employees
ERA Study
3443
5141"1"
6710
9360*
13521
18172*
23639*
* Extrapolated
+ Interpolated
-------
82
Table 3.23
Airport Employee Diurnal Arrival arid Departure Pattern
Hour
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Percentage of
Employees
Arriving
0.1 '
0.1
0.3
0.3
1.3
5.3
27.3
19.1
4.5
2.8
3.0
1.7
4.0
5.6
8.8
4.0
1.9
0.5
1.3
1.0
0.9
0.3
5.0
0.9
100.0%
Percentage of
Employees
Departing
3.0
0.5
0.7
0.3
0.2
0.4
1.0
3.5
1.3
2.0
2.2
3.5
1.2
3.0
6.1
25.0
14.9
6.0
, 3.6
3.0
3.0
2.8
5.0
7.8
100.01
-------
83
Table 3.24
Annual Airport Casual Visitors
Year
1975
1980
1985
1990
1995
2000
St.
NADC
Forecast*
--
608
943
1418
2046
2790
Louis Airport
Thousands of Visitors
McDonnell
Forecast*
493
786
1233
1944
-
-
Speas
Forecast*
477
729
1080
1575
2124
2763
*Casual visitor projections based on 9% of the forecasted enplaning
passenger rate.
-------
84
Table 3.25
Airport Casual Visitor Diurnal Arrival and Departure Pattern
Hour
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Percentage of
Casual Visitors
Arriving
0.2
0.2
1.5
0.0
0.6
0.1
1.5
3.2
0.0
9.7
2.0
2.6
4.7
3.0
2.6
5.5
3.5
6.1
15.4
20.4
7.5
4.0
2.7
3.0
100.0%
Percentage of
Casual Visitors
Departing
0.2
0.2
1.5
0.0
0.1
1.3
0.2
0.2
0.1
2.8
6.9
3.9
4.2
3.8
3.7
2.5
4.1
3.4
7.0
12.0
18.5
15.3
3.8
4.3
100.0%
-------
85
Summary of Person Trips
Table 3.26 summarizes the jjanner in which person trips to and from
the airport are itemized. The format is general, although the assumptions
which were made to fill in the data gaps lead to a considerable simplication
of the calculations.
The first assumption entails the equivalence of the originating and
terminating passenger rates and the diurnal pattern of each. The rates must
be almost equal over any appreciable averaging period unless there were some
unusual "source" or "sink" of passengers. As previously noted, the difference
in diurnal patterns between enplaning and deplaning passengers could not be
accounted for due to lack of data applicable to St. Louis and consistent
with the aircraft activity pattern.
The equivalence of the visitor groups with originating passengers
and meeting terminating passengers appears to be a reasonable assumption
18
since the visitor/passenger ratio was developed for both originating and
terminating passengers. The assumption that the departing visitors (both
leaving originating passengers and having met terminating passengers) have
the same diurnal pattern '.as the arriving visitors implies that visitors are
at the airport on the average for less than an hour. This is a good assump-
tion based on experience at other airports. ' The average time an
automobile used to transport air passengers is parked is on the order of
1.4 hours (excluding long-term parking in excess of 12 hours, which represents
air passengers who have driven to the airport themselves and parked for the
25
duration of their trip). If, in the averaging procedure, those autos which
were used to transport air passengers but which did not park (e.g., unloaded
the passengers and then departed) were included, this average would probably
be reduced to less than one hour. Hence, the equal diurnal distributions of
arriving and departing visitors is taken to be a good approximation.
-------
86
Table 3.26
Summary of Daily Person Trips To arid From Airport
St. Louis
Trip
Sourbe of Information
Activity Level Diurnal Pattern
Arriving
1. Originating
Passengers
2. Visitors with
Originating
Passengers
3. Employees
4. Casual Visitors
5. Visitors Meeting
Terminating
Passengers
NADC,3 McDonnell,4 Speas2 Studies
Application of Visitor/Passenger
Ratio of Ref. 18
6% Absentee Rate from Ref. 18.
NADC Study, Ref. 22
9% of Enplaning Passenger Rate
from Ref. 18
Equal to Visitors with
Originating Passengers
Aircraft Activity
Diurnal Pattern
Ref. 11
Ref. 11
Equal to Visitors
with Originating
Passengers
Departing
1. Terminating
Passengers
2. Visitors with
Terminating
Passengers
3. Employees
4. Casual Visitors
5. Visitors Leaving
Originating
Passengers
Equal to Originating Passenger
Level
Equal to Visitors Meeting
Terminating Passengers
(Group 5 - Arriving)
Equal to Employees Arriving
Equal to Casual Visitors
Arriving
Eqr.il to Visitors with Originat-
ing Passengers
(Group 2 - Arriving)
Equal to Originating
Passenger Pattern
Equal to Visitors
Meeting Terminating
Passengers
(Group 5 - Arriving)
Ref. 11
Ref. 11
Equal to Visitors with
Originating Passengers
(Group 2 - Arriving)
-------
87
Modal Choice
Once the pattern of person trips to and from the airport has been
established, the next step in proceeding to the vehicular activity pattern
is to determine a ground transportation mode choice or modal split for each
of the traveling groups previously defined.
For the passenger group, the mode choice is obtained from the
Impact Statement. This is information collected at Lambert Field in
St. Louis and is therefore specific to the St. Louis area. It is assumed
that there is no change in modal choice over the forecast period. There is
18
some indication that for the passenger group there is some variation in
mode choice during the day, but this is not very dramatic and will not be
incorporated here due to the lack of data relevant to St. Louis.
18
It should be pointed out that if the Cleveland data for diurnal
mode choice were to be used, the constraint of having to match the daily
average modal choice and the diurnal passenger pattern given for St. Louis
must be applied. The Cleveland data cannot, in fact, meet this constraint
and, in general, should not be expected to meet it. This is because the
Cleveland data has its own diurnal variation of person-trips built into it.
This illustrates one of the cautions which must be exercised in applying
information from other airports to the study airport. Once some information
is obtained from the study airport and is to be used in the activity projec-
tions, all other data must then be chosen to be compatible with this informa-
tion. The compatibility situation appears most often in applying diurnal
patterns and then constraining these to match some given daily average.
The mode choice situation above and the previously discussed differences in
the originating and terminating passenger diurnal patterns illustrate this
compatibility problem.
-------
88
area facing Chicago, manufacturing and warehousing Increased approximately
37Z from 1960 to 1970, while in the opposite quadrant, facing away from
Chicago, manufacturing and warehousing activity increased approximately
250% during the same period. The absolut^ magnitudes of the growth on
either side of the airport site were comparable.
The Relative Contribution of Land Use Activities to
Airport Area Air Pollutant Emissions
Manufacturing and warehousing emissions in the O'Hare Airport area
study are substantially greater (by approximately an order of magnitude)
than emissions from residential, commercial, or institutional sources;
however,motor vehicle emissions in the study area are the dominant source of
air pollutants in the airport vicinityparticularly, in the case of carbon
monoxide, hydrocarbons, and nitrogen oxides. Even after a 90% emission re-
duction is realized as a result of applying the Federal Emission Standards,
-------
89
For passenger-related visitors, the modal choice is gotten from the
18
Cleveland data . It is assumed that visitors with originating passengers
and visitors with terminating passengers have the same mode choices. No
information was available"on-the diurnal variation in mode,choices.
For employees, the modal choice is based on an average of ten airports.
Again, there was no information available on diurnal variation in mode choice.
For casual visitors, the modal choice is also gotten from a ten
airport average. No diurnal variation information was presented.
Table 3.27 summarizes the modal choice of each of the considered
groups and indicates the source of the information.
Vehicle Loading Factors
The next step in generating vehicle activity is to apply a vehicle
load factor (persons per vehicle) to the number of people using each mode.
The load factors are developed separately for each group since there is a
considerable variation.
The passengers and passenger-related visitors are treated together
since they, in general, travel together. The employees and casual visitors
are treated as separate groups. People from all groups who use mass transit
are included in a separate group because they all travel together. Table 3.28
summarizes the load factors used.
For autos, the load factors are based on an average of ten airports
surveyed. It can be seen that employees are heavily weighted toward the
lone-driver end of the spectrum while the casual visitors; gravitate toward the
drivepnplus-several passenger end.
The taxi and limosine load factors are based on observations made
13
at the terminal ramps of Chicago's O'Hare airport. Since this was the
only information available on taxi loadings, it was applied to both the
passenger and employee groups.
-------
90
Table 3.27
Modal Choice Summary
St. Louis Airport
% of Group Using Mode
Group ^"""""^^^
Passengers
Passenger-Relatec
Visitors
Employees
Casual Visitors
Private Rental Source of
Auto Auto Taxi Limbs ine Bus Information
65.0
95.4
91.6
97.5
11.0
1.2
0.
0.
13.0
1.1
1.1
0.1
2.0
1.7
0.
0.
9.0
0.6
7.3
2.4
Ref. 1
Ref. 18
Ref. 11
Ref. 11
Table 3.28
Vehicle Load Factors
St. Louis Airport
Persons per Vehicle Trip
~--->^tode
Group --^
Passengers
Passenger- Related
Visitors
Employees
Casual Visitors
All People
Using Public
Transit
Private
Auto
1.79
1.15
2.89
Rental
Auto
1.15
--
--
Taxi
1.65
1.65
1.65
Limosine
2.82
--
--
Bus
38
Commuter
Train-
Diesel
563
Rail
Transit-
Electric
300
-------
91
There was no Information available on rental auto load factors and
time did not permit the making of direct observations, It was decided to use
the auto load factor for employee trips since this probably came closest to
approximating the predominately business character of rental car useage.
The bus, commuter train and rail transit load factors were derived
from information in Ref. 26. (Although there are no current plans for a rail
link to the proposed new St. Louis airport, the rail factors are presented
for use in a trade-off study on alternative means of airport access. This
will be discussed later.) Mass transit tends to operate on a schedule,
rather than on a daily demand basis. In projecting the transit trips it
can be assumed, nevertheless, that the schedule will over the course of
time reflect the actual demand for travel. Therefore, to develop the vehicle
load factors it is assumed that in any hour in which there are enough people
seeking to use a mass transit mode to make up 75% of the capacity of a mode
trip, an additional trip will be scheduled. Buses.are assumed to have a
50-passenger capacity. Thus, whenever there are 38 people seeking to use
a bus, a trip will be scheduled in that hour; hence, the vehicle load factor
of 38. For a diesel commuter train, the capacity is 750 passengers (5 cars
at 150 passengers per car) and for an electric rail transit, it is 400
o/*
(5 cars at 80 passengers per car). The 75%-capacity trip generator produces
vehicle load factors of 563 and 300, respectively.
Truck Trips
Airports are not generators of large numbers of truck trips;
'surveys at Los Angeles airport and San Francisco airport show that trucks
account for only 2% and 5%, respectively, of total vehicle trips. For
O'Hare airport in Chicago trucks accounted for only 30% of the traffic in
1 ^
the cargo area. The cargo area itself accounts for less than 10% of the
-------
92
total traffic at 0'Hare;.-hence the San Francisco, Los Angeles and Chicago
data consistently point to the small fraction of truck trips. For the sake
of completeness, these trips will be included in the total ground vehicle
access accounting. i
A good estimate of truck trips can be made by applying a proportional
factor to the employee person-trips. -Based on "studies at 5 other airports,11
an average value of this factor is 0.128 truck trips per employee person-trip.
Table 3.29 gives the total number of daily truck trips for the NADC and the
ERA employee forecasts of Table 3.22 (assuming a 6% absentee rate) for the
St. Louis airport based upon this factor. This puts the truck trips for
St. Louis in the range of 2-3% of total trips which is consistent with the
aforementioned data.
Based on the information at other airports, a diurnal pattern for
truck trips can be determined. Also, a distribution of trips among light-
duty trucks or vans, heavy-duty gasoline-powered trucks, and heavy-duty
diesel-powered trucks can be made. This data is presented on Table. 3.30.
Summary of Vehicular Activity
With the above information it is possible to project the vehicular
activity and mix for the study airport. Starting from the generation of
the person-trips from the enplaning passenger forecasts, the calculation
proceeds as follows. We define the person-trips to be A.., the number of
people of group i arriving at the airport in hour j and D.., the number of
people of group i departing from the airport in hour j. The A^^ 's and D^'s
are calculated from the given information.
-------
93
Table 3.29
Daily Truck Trips
St. Louis Airport
From
NADC
Employee
Forecast*
--
701
909
1230
1668
2238
From
ERA
Bnployee
Forecast*
619
807
1126
1627
2186
2844
Year
1975
1980
1985
1990
1995
2000
*Based on 0.128 truck trips .per employee person-trip (including a 61
absentee rate).
-------
94
Table 3.30
Diurnal Distribution of Truck Trips
and Vehicle Type Distribution
St. Louis Airport
Hour
1
2
3
4
5
6
7
8
9.
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Percentage of
Trucks
Arriving*
Percentage of
Trucks
Departing*
. : 2,3 :
2.2
2.2
2.2
2.2
"2.2
2.5
3.2
.7.0
6.7
6.7.
6.0
5.0
5.9
5.9
5.2
5.5
4.3
4.2
5.0
100.0
Light Duty
Trucks
30.3
Percentage Distribution**
Heavy.Duty
Gasoline-Powered
Trucks
51.6
Heavy Duty
Diesel-Powered
Trucks
18.1
* From Ref. 11
** From Ref. 13
-------
95
i E jc F
U
A2j "
A4j - CV * F4j
A "A
5j A2j
where A.,. Is the number of originating passengers arriving at the airport in
hour j, E is the daily originating passenger rate from the study forecasts
(Table 3.18), and F.. is the fraction of originating passenger arriving in
hour j (Table 3.11); A., is the number of passenger-related visitors
arriving in hour j and R. is the visitor/passenger ratio (Table 3.19);
A-. is the number of employees arriving in hour j, W is the number of
employees from the projection (Table 3.22), a is the absentee rate (6%),
and F-. is the fraction of employees arriving during hour j (Table 3.23);
A^. is the number of casual yl^tors. arriving in hour j, CV is the daily
number of casual visitors (Table 3.24), and F,. is the fraction of casual
visitors arriving in hour j (Table 3.25);'A_. is the number of visitors
meeting terminating passengers in hour j.
For the departing trips, if we define D as terminating passengers,
D-. as visitors with terminating passengers, D». as employees, D, . as casual
visitors, and D_. as visitors having left originating passengers in hour j,
and if we make previously discussed assumptions, we have the following:
-------
96
where G^. and G,. are the fractions of employees and casual visitors leaving
in hour j (Tables 3.23 and 3.25).
To generate the vehicle trips, we apply the mode choice and vehicle
load factors from Tables 3.27 and 3.28, respectively. The result is the
following set of equations:
ijk
DB
ijk
ik
ik
j=l,24 k~l,7
(2)
ljk
' B2jk>/LFlk
B3jk/LF3k
V4jk
5jk
k=l,4
(3a)
k=l,4
k=5,7
k=l private auto
2 rental auto
3 taxi
4 limosine
5 bus
6 commuter train - diesel
7 rail transit - electric
(3b)
-------
97
V5jk
dV3jk ' ]
\ k=l,4 (4a)
dv4jk = DB4jk/LF4k[
dv5jk - vljk !
(4b)
DVjk =
where B. ., is the number of people of group i arriving in hour j using mode
13 K
k and DB . is the corresponding number for departing people j C., is the modal
choice of group i; LF , is the vehicle load factor for group i and mode k and
ilc
PLF, is the load factor for the mass transit modes; v. ., are the number of
vehicle trips of mode k made by people of group i in hour j ; and V., are the
total number of vehicle trips of mode k made in hour j . For departing vehicle
trips dv . and DV., are the corresponding values.
ijk JK
The truck trips are obtained by taking the proportioned ratio to
employee person-trips (Table 3.29) and applying the diurnal pattern and
vehicle type distribution (Table 3.30). This leads to the following equations:
Vt = W(l-a) x 0.128 (5)
-------
98
V.g Vt x T. x 0.303
Vj9"-Vtxlj.xp.516
VjlO - Vt x T. x C
k^S light-duty truck
k=9 heavy-duty,.gasoline-powered truck
k=10 heavy-duty, diesel-powered truck
DV.fl - V. x DT. x 0.303
Ja t J
DV.Q = V x DT. x 0.516 (6b)
J * t J
DV.10 - Vt x DT, x 0.181
where V is the total number of truck trips per day, T. is the fraction of
trucks arriving in hour j, and V., is the number of trucks of type k arriving
JK
in hour J. DT. and DV., have corresponding meanings for departing trucks.
3 «j '
The assumptions which were used to develop the person-trip projections
as given by Equation 1 have already been discussed. Several additional assump-
tions are implied by Equations 2,3, 4. They are the following:
. The mode choice, C,. , is the same, for arriving and depart-
ing people. (The Environmental Impact Statement shows that
actually there is a slight change in modal choice between
originating and terminating passengers.)
. Visitors are parked on the average less than an hour.
(dvljk " V5jk and dV5jk " Vljk)
. The load factors for employees and casual visitors are the
same for arriving and departing trips.
-------
99
. All mass transit vehicles remain at the airport for less
than an hour.
Figure 3.2 is a plot of the hourly vehicle trips to and from the airport
for the 1990 Speas forecast.
The early morning peak of arriving first-shift employees is clearly
discernable as is their late afternoon departure. It can be seen that the
late afternoon - early evening is the time of maximum access traffic activity.
This is a result of the departure of first-shift employees, the arrival of
second-shift employees, and the maximum air passenger activity. This pat-
tern is fairly consistent for all airports. Table 3.31 gives the daily
vehicular traffic for the forecast years of the NADC, McDonnell, and Speas
Studies. The traffic volumes projected- by this model are comparable to those
1 27
previously made for the St. Louis area. ' The previous projections estimate
between 70,000 and 108,000 one-way vehicle trips per day for an annual
enplaning passenger rate of 17.5 million in 1990. This model estimates
106,000 for the same passenger activity (Speas forecast).
Vehicle Mileage and Operational Characteristics
With the ground access vehicle activity and mix determined by the
previous analysis, the two additional pieces of information needed to
complete the data set for air pollutant emission calculations are the mileage
traveled by the vehicles in the airport and the operating characteristics of
the vehicles.
The Environmental Impact Statement suggests for St. Louis a value
of 3.5 miles as an average trip length from the freeway collection point
-------
100
"s
ARRIVING
n
DEPARTING
12
HOUR
24
Fig, 3.2 Hourly Access Traffic Volume, 1990 Speas Forecast
St. Louis Airport
-------
101
Table 3.31
Daily Vehicular Access Trips*
Year
1975
1980
1985
1990
1995
2000
NADC
42
62
92
131
178
St. Louis Airport
Thousands of Vehicle-Trips
McDonnell £
35
53
81
125
-
.
5p_eas
34
50
73
106
142
185
*Includes arriving plus departing trips.
-------
102
to some central destination at the airport. Although trip lengths vary
considerably with trip purpose , a justifiable distinction cannot be made
without at least a preliminary layout of the airport site. Since this will
not be available until the master plan fort the airport is drawn up, the
3.5 mile average was applied across the board to all one-way trips.
From an emission standpoint, the important characteristics of
vehicle operation are the average speed and the number of engine start-ups
that occur after the vehicle has been allowed to cool to ambient temperature
(cold start).
For an airport, there are several different speed ranges used by
vehicles. There are stretches of limited access roadway where the speed
limit is 50 mph or more; there are the terminal ramps where speed can go to
30 mph; and there are the-, low speed areas such as the parking lots and the
loading areas. For the purposes of this calculation, it will be assumed that
the average vehicle speed is 25 mph in an urban pattern as defined by the
federal automotive test cycle. This cycle includes stop-and-go portions and
should be adequate to represent airport travel.
As previously noted, it is assumed that all visitors arrive and
depart the airport within the same hour. Since the time it takes a vehicle
engine to cool to ambient temperature is on the order of 8-12 hours, these
vehicles are not subject to a cold start on the airport grounds. The
Environmental Impact Statement shows, however, that about 37% of the terminr
ating passengers who depart by private auto use a car that has been parked
at the airport. Since the air traveler's trip away from the airport is
generally at least 8 hours, it is assumed that 37% of the private auto trips
made by terminating passengers (i.e., 37% of dv-.- in Equation 4a).involve a
cold start*
-------
103
Employee autos are parked at the airport for a time which averages
close to 8 hours. Since the amount of time it takes a vehicle to fully reach
cold start conditions varies seasonally, the general practice is to assume
employee autos to have a 90% cold start when they are started up for the
departing trip. This practice will be followed here and the vehicle trips
represented by dv,.. in Equation (4a) will be assumed to have a 90% cold
start.
It is further assumed that all departing rental car trips (dv..?)
have a full cold start.
The taxi, limosine, bus and truck trips are assumed to have no
cold starts on the airport grounds.
Table 3.32 summarizes the vehicle mileage and operating characteris-
tics.
-------
Table 3.32
Airport Vehicle Mileage and Operating Characteristics
St. Louis Airport
Mode
Arriving
Vehicles
Speed (MPH)
Mileage (Mi)
fYild QtaTt"
\AJ J.U ij Ld-i. L*
Departing
Vehicles
Speed (MPH)
Mileage (Mi)
Cold Start
I
1
Private
Auto
25
3.5
25
3.5
a. 37% of
those
used
by pass-
engers
have
100%
cold
start
b. All em-
ployee
autos
have
Ono.
yu-6
cold
start
Rental
Auto
25
3.5
25
3.5
All
have
100%
cold
start
Taxi
25
3.5
XT
N o n 6
25
3.5
None
Limosine
25
3.5
25
3.5
None
Bus
25
3.5
25
3.5
None
Commuter
Train
Diesel
3.5
3.5
None
Rail
Transit
Electric
3.5
3.5
None
Light
Duty
Trucks
25
3.5
25
3.5
None
Heavy Duty
Gasoline
Powered
Trucks
25
3.5
25
3.5
None
Heavy Duty
Diesel
Powered
Trucks
25
3.5
25
3.5
None
-------
105
3.2 Compilation of Emission Factors
The purpose of this section is to compile the various emission fac-
tors which are necessary to convert the airport activity levels into rates of
air pollutant emissions. Emission factors are presented for aircraft, ground
service vehicles, the fuel storage and handling system, engine test and main-
tenance facilities, the heating and air conditioning plant, and access traf-
fic. Four pollutants are considered: carbon monoxide, total hydrocarbons,
nitrogen oxides, and particulates. Emission factors are presented in the most
frequently used units for ease of comparison. Units are adjusted in the final
computations.
3.2.1 Aircraft
A substantial amount of research has been done in recent years to
determine the rate of air pollutant emissions from aircraft engines. The emis-
sion factor is a function of two distinct pieces of information. The first
is the actual rate of emission of an aircraft engine as a function of its
power setting; the second is the amount of time the engine is operated at each
power setting during the course of a landing and takeoff cycle.
Three sets of aircraft emission factors were considered for use. The
28
first set was published by the Federal Environmental Protection Agency and
is given in the form of total emissions per LTO cycle. It was felt that this
format was not flexible enough to provide the resolution required to determine
which modes of aircraft operation were contributing most to the emission rate.
Also, the engine emission rates are based on dated information and more recent
and complete data is available.
The second set was based on work done by Northern Research and
2Q
Engineering Corporation. The data is published in a form which gives the en-
gine emission rates and the times of engine operation in each mode and can thus
-------
106
be used to segregate the emission contribution. The emission rates, however,
were based on only a limited number of engine tests since the more comprehen-
sive data was not yet available at the time of the NREC work.
The third set was based on work done by the Cornell Aeronautical
Laboratory and by Argonne. It is felt that the combination of the CAL
emission rates and the Argonne engine operating times represent the most up-
to-date and accurate information currently available.
The CAL engine emission rates are compiled from a substantial number
of measurements made on different families of aircraft engines. The data are
presented as a function of engine power setting. In tabulating the informa-
tion shown on Table 3.33, the aircraft operational mode rather than engine
power setting is used for ease in comparing different engine sizes.
The aircraft designations beneath each engine are designed to show
which airplanes use which engine family. Although the particular aircraft
may not use the specific engine shown, it uses one that is similar. For
lack of complete data for all engine types it is assumed that the aircraft
considered use one of the engines tabulated in Table 3.33. The operational
modes are defined on Table 3.34.
The engine emission rates compiled by CAL are used for the taxi, idle,
approach, takeoff, and climbout modes. For the landing mode, a simple algebraic
equation is used which reflects the effect of the utilization of thrust reversal.
The equation is written as
E . ,. - 0.6E ... + 0.16 E , + 0.24 E . , ,-
landing idle approach takeoff
where E. is the emission factor of mode i. The weighting factors were obtained
from aircraft operational experience.
Since the CAL data did not include information on particulate emission
29
rates, the NREC data was used. NREC data was also used to obtain all the
emission factors for the piston engine (ISO-520-A) since this also was not included
-------
107
Table 3.33 Air Pollutant Emission Rates
of Aircraft Engines
Aircraft
Class
Jumbo
Long
Range
Medium
Range
Short
Range
Engine Type
(Aircraft)
JT9D
B747 >.
< DC10 >
^ L1011 '
JT3D
,' B707 1
DCS /
«. J
JT8D
rB727-,
< DC9 >
^11737^
A-501-D13
rcsecTi
( F-27 )
^. J
Spey 511
fBAClli\
(P-2B j
Continental
10 -520 -A
Cessna 210
Piper 32-
300
Auxilary
Power
Unit
Mode
Taxi
Idle
Landing
Takeoff
Approach
Climbout
Taxi
Idle
Landing
Takeoff
Approach
Climbout
Taxi
Idle
Landing
Takeoff
Approacn
Climbout
Taxi
Idle
Landing
Takeoff
Approach
Climbout
Taxi
Idle
Landing
Takeoff
Approach
Climbout
Taxi
Idle
Landing
Takeoff
Approach
Climbout
In
Operation
Emission Rate (Ib/hr/eng)
CO
86
86
59
6
38
11
103
103
69
10
29
10
37
37
26
6
12
6
15
15
10
2
4
3
60
60
46
14
39
IS
30
30
35
62
12
62
2.8
HC
19
19
12
2
3
2
84
84
55
12
12
13
9
9
6
0.4
0.9
0.4
6
6
4
0.4
0.5
0.5
66
66
40
NA
4.2
0.2
2
2
2
1.5
2.5
1.5
0.11
MX
6
6
174
672
54
425
1
1
39
148
20 ,
94
2
2
36
133
20
87
2
2
8
23
8
21
1
1
42
153
30
115
0.2
0.2
0.2
0.4
0.05
0.4
1.24
PT
0.6
0.6
0.7
0.9
0.6
0.9
0.3
0.3
2
6
5
6
0.5
0.5
7
21
9
21
0.1
0.1
0.3
0.6
0.4
0.6
0.04
0.04
0.3
0.8
0.4
0.8
0.06
0.06
0.06
0.10
0.02
0.10
Not
Avail-
able
-------
108
Table 3.34
Aircraft Operating Modes and:
:tivities Included in Ear
Various
Mode
Taxi
Idle
Lending
takeoff
Approach
Climb-out
Engine .Operating
Times Included in Mode
Transit times between ramp and apron, apron
and runway and time required for turning and
alignment between taxiway and runway.
Push back from gate; waiting for signal to
begin taxiing; waiting at taxiway intersec-
tions; runway queing; gate queuing.
Touchdown to beginning of taxi on taxiway.
After alignment with runway to liftoff.
3000 ft altitude to touchdown
*.
Liftoff to 3000 ft altitude.
-------
109
in the CAL work. Data for the auxiliary power unit emissions was taken from
30
work done by Pratt and Whitney and compiled by CAL.
The amount of time an aircraft spends in each mode is a variable
dependent on the particular airport under study. At a given airport the times
will vary with the runway utilization, congestion, and changes in operational
procedures. For example, an aircraft landing on a distant runway will have
a longer distance to travel to the terminal, hence a longer time spent in
the taxi mode, hence a larger quantity of pollutants emitted than would the
same aircraft landing on a runway near the terminal. The procedure used to
generate the times given on Table 3.35 involves the averaging of many obser-
vations at O'Hare Airport and is discussed in detail in Ref. 10. While the
data for O'Hare may not be the same as for the proposed new St. Louis airport,
the lack of a master plan for the new airport dictates the use of the O'Hare
data.
In addition to the times shown on Table 3.35, a gate occupancy time
must also be included in the calculation procedure. This is necessary not
only to properly reflect the diurnal pattern of activity but also to account
for auxiliary power unit emissions. While an aircraft is at the gate the main
engines are shut down and, if the aircraft is equipped with an APU, it is in
operation for the entire gate time to provide electrical power. (If the air-
plane is not APU-equipped, a ground power unit must be used or a power line
from the service pit is hooked up). The diurnal average gate occupancy time
based on the O'Hare data is presented on Table 3.36.
3.2.2 Ground Service Vehicles
The collection of emission data for ground service vehicles presents
an unusual problem. To date no comprehensive study has been undertaken to
actually measure the emission rates from these vehicles even though their opera-
-------
Table 3.35
Aircraft Times-In-Mode
Aircraft Class
Jumbo
Long Range
Medium Range
Short Range
Time- In-Mode (Minutes)
Inbound
Taxi
7.2
4.2
3.6
3.6
Outbound
Taxi
7.2
4.9
4.4
4.4
Idle
4.2
2.4
2.0
2.0
Landing
0.6
0.6
0.6
0.6
Takeoff
0.5
0.5
0.5
0.5
Approach
3.0
3.6
3.0
4.5
Clinibout
1.9
2.2
1.9
3.6
-------
Ill
Table 3.36
Aircraft Gate Occupancy Time*
Gate Occupancy Time
Hour (Minutes)
1 60.8
2 56.5
3 61.8
4 65.8
5 62.4
6 87.0
7 46.1
8 43.9
9 48.5
10 56.3
11 57.9
12 47.6
13 56.8
14 47.4
15 52.0
16 55.7
17 49.1
18 45.1
19 49.1
20 43.2
21 44.0
22 33.2
23 24.2
24 54.3
*Based on data at O'Hare International Airport,
Chicago, Ref. 10
-------
112
tion and maintenance characteristics differ markedly from those of private
motor vehicles. The ground support equipment spends a large proportion of
its total operating time motionless but with thd engine at other than idle.
i
Very little data is available on emission rates from any vehicles in this
operating mode. Also, the maintenance on ground support equipment is
relatively poor. With the exception of the more expensive pieces of equip-
ment (such as the pushback tractors for the 747), the general airline feeling
is that it is more cost-effective to replace a deteriorated engine in a ser-
vice vehicle than it is to institute a regular maintenance program. These
factors combine to make one anticipate different emission patterns between
these vehicles and the normal urban traffic, even though the majority of the
ground service vehicles are just specially adapted trucks from a standard
production line.
12
As part of another airport modeling program , Argonne developed an
indirect approach to calculate ground service vehicle emissions. Based upon
a survey of ground support equipment at Chicago's O'Hare Airport, it was de-
31
cided that emission data published for heavy duty trucks would, if suitably
adapted, provide an adequate estimate of emissions from ground service vehicles.
As part of the survey personnel from the United Airlines facility at O'Hare were
asked to record fuel consumed and hours of operation for a number of vehicles
in their inventory. This permitted a calculation of fuel consumption rates for
these vehicles. Estimates of the fuel consumption rates of other service ve-
hicles were made based on similarities of engine type and service characteris-
tics. These data are given on Table 3.37 for the vehicles included in this study.
To utilize the gasoline engine emission factors which are given in gms/
mile and the above fuel consumption data, an estimate must be obtained for the
mileage rate of fuel consumption (miles/gallon). In addition, some form of speed
-------
113
Table 3.37
Ground Service Vehicles
Fuel Consumption Rates
Vehicle
1. Tractor
2. Belt Loader
3. Container Loader
4. Cabin Service
5. Lavatory Truck
6. Water Truck
7. Food Truck
8. Fuel Truck
9. Tow Tractor
10. Conditioner
11. Airstart
Transporting Engine
Diesel Power Unit
12. Ground Power Unit
Transporting Engine
Gasoline Power Unit
Diesel Power Unit
13. Transporter
Rate of Fuel
Consumption (gal/hr)
1.80
0.70
1.75
1.50*
1.50*
1.50*
2.00*
1.70*
2.35
1.75*
1.40
8.20
2.00
5.00
7.10
1.50
Data from Reference 12
*Estimated values
-------
114
correction must be Introduced Into the emission factors to compensate for the
low average speed operating characteristics of the service vehicles. The
fuel consumption data serves as a constraint to this system: the ratio of the
average speed (miles/hour) to the mileage fuel consumption rate (miles/gal)
must equal the fuel consumption rate. There are, of course, an Infinite num-
ber of combinations of average speed and mileage fuel rate which can match
the consumption rate. Values which are reasonable must be chosen. Based
upon an average fuel consumption rate of 1.67 gal/hr and an estimate of
average vehicle speed of 10 mph the mileage fuel consumption rate is computed
to be 6 miles/gal. This appears to be within expectations for this type of
vehicle. With these estimates, the emission factors can be adapted to the
service vehicles. This method need not be applied to diesel engines since
the emission factors are already in the form of gms/gal.
For this study two sets of basic emission data have been used to
develop ground service vehicle emission factors. The first set is referred
to as the "uncontrolled emissions" and represents emissions that are based
on the assumption that ground service vehicles will not be subject to the same
emission controls as are private motor vehicles. In fact, current legislation
does not apply to off-road vehicles and therefore the ground support equipment
is not subject to control. The second set Is referred to as the "controlled
emissions" and assumes that the 1975-76 Federal automotive emission controls
will apply to ground service vehicles also.
The basic emission data and a procedure for adapting the data to
varying conditions of vehicle age distribution, average vehicle speed, and
31
mode of operation has been published. For this study the ground service
vehicle age distribution is assumed to be the same as is reported for O'Hare,
the average vehicle speed is assumed to be 10 mph as discussed above, and the
-------
115
vehicles will be assumed to operate in the "hot start" mode.
The result of making the two separate assumptions regarding emission
controls results In two sets of emission factors. The uncontrolled emission
factors do not change during the forecast period while the controlled emis-
sion factors change as more new vehicles are brought into the population.
Table 3.38a gives the uncontrolled emission factors and Table 3.38b gives the
controlled emission factors.
It will be noticed that only one emission factor is presented for all
gasoline-powered ground service vehicles and only one for all diesel-powered
12
vehicles. The data from O'Hare led to the conclusion that the development
of separate emission factors for each piece of equipment was not justifiable
because of the engine similarities.
3.2.3 Fuel Storage and Handling System
Emissions from fuel handling and storage come from the evaporation
of liquid from storage tanks during the daily temperature fluctuations and
from the displacement of fuel vapors when tanks are filled. The first is
called the breathing loss and the second is called the working loss.
There is also the possibility of evaporation of fuel that is spilled
during aircraft and ground vehicle refueling operations. This is assumed to
be negligible in this study because significant quantities of spilled fuel are
generally washed away promptly by ground crews due to fire hazards. Thus the
spillage is more of a water pollution than an air pollution problem.
Breathing loss is a function of the type of storage tanks, the daily
temperature cycle, wind speed, fuel vapor pressure, and a number of other very
specific variables. It is possible, however, to assume that breathing losses
28
c in be controlled by vapor recovery systems installed in the tanks. This is
true for both fixed and floating roof tanks. It seems reasonable to expect
-------
116
Table 3.38a
Ground Service Vehicle Uncontrolled Emission Factors
Gasoline Engines
^^^ Pollutant
Year -^_
1975-2000
Emissic
CO
999.0
ns (grams/gal
HC
223.2
Ion)
57.0
Particulates
1.8
Diesel Engines
Pollutant Emissions (grams/gallon)
Year ^^-^^
1975-2000
CO
147.6
HC
29.5
NOX
154.4
Particulates
11.4
-------
117
Pollu-
\tant
Table 3.38b
Ground Service Vehicle Controlled Emission Factors
Gasoline Engines
Emissions (grams/gallonl
Year \
1975
1980
1985
1990
1995 -
2000
GO
385.20
171.72
35.94
21.42
HC
106.68
28.98
13.92
8.94
Same as 1990
NO
A
70.26
53.76
34.68
22.68
Participates
1.80
1.32
0.90
0.60
Diesel Engines
\ Pollu-
\tant
Year \^
1975
1980
1985
1990 -
2000
CO
128.10
109.17
97.50
Emissions f grams /gallon1)
HC
22.26
15.63
11.40
NO
184.38
211.80
229.20
Particulates
5.91
5.91
5.91
Same as 1985
-------
118
that a fuel system installed at a new airport will make use of this technology
and hence eliminate this emission source.
Even if it is assumed that the tank vapor recovery system can control
working losses as well as breathing losses, j:he aircraft and ground service
vehicle tanks are also subject to working losses. (The vapor emissions from
aircraft refueling operations can be seen as density waves appearing above
the wing filling ports.) Thus it will be assumed that the emissions from
fuel handling and storage will result solely from working losses associated
with aircraft and ground service vehicle refueling.
An empirical equation for estimating working losses has been developed
32
by the American Petroleum Institute. It is the following.
L is the loss rate in gallons per year, P is the true vapor pressure of the
bulk liquid in pounds per square inch, V is volume of liquid pumped in gallons
per year, and K-, is a constant which depends on V and tank capacity. A recom-
12
mended value for K_ is 0.25. For JP-5 jet fuel the true vapor pressure at
70°F is 1.09 psi; for gasoline used in ground service vehicles it is 7.25 psi.
The emission factors can then be written in terms of pounds of emission per
thousand gallons of fuel pumped using a fuel density of 6.67 Ibs/gallon. These
values are given on Table 3.39. Since the fuels are hydrocarbons these working
losses are tabulated under hydrocarbon emissions.
3.2.4 Engine Test and Maintenance Facility
The emission rates for aircraft engines have already been tabulated
in Section 3.2.1. In order to complete the set of emission factors for the
engine test and maintenance operation the testing cycle and the time spent
at each power setting must be specified. Data from the Los Angeles maintenance
-------
119
Table 3.39
Fuel Storage and Handling System Emission Factors
Working Loss
Hydrocarbon Emissions
(pounds/10 gallon of fuel pumped)
Aircraft
Fuel
0.55
Ground Service
Vehicle Fuel
3.63
-------
120
14
facility indicates that the averate test time for engines is 25 minutes.
The data also indicates that the engine is run at idle power for 75% of the
time, at cruise power for 25% of the time and very seldom is maximum power
used. The cruise power setting varies between 50-80% maximum power. To
simplify the computations by utilizing a specified power setting for which
emission factors have already been given, it was decided to utilize the ap-
proach power setting in place of the cruise setting. Approach corresponds to
about 40% maximum power. It is felt that in general maintenance operations
will tend to gravitate toward lower power settings during testing because of
the noise and safety problems associated with operating a stationary engine
at high power. Hence, the approach power approximation is probably fairly
good.
Table 3.40 summarizes the assumed test conditions.
3.2.5 Heating and Air Conditioning Plant
As stated in Section 3.1.7 the most probable fuels for use in the
St. Louis airport heating plant are coal and oil. The Impact Statement
declares that natural gas is in short supply in the area and hence it will
not be included here.
Emission factors for coal and oil combustion have been published
28
elsewhere . A modification is necessary, however, to reflect conformity of
the plant with appropriate state air pollution control legislation. The
proposed Columbia-Waterloo site for the new St. Louis airport would subject
33
the plant to Illinois regulations.
In order to determine the appropriate emission factors and the ap-
propriate state regulations to impose as constaints, an estimate of the heat
input of the plant must be made. Using the estimates of fuel required as
presented on Table 3.17 and a heating value for Illinois coal of 11,000 Btu/lb
-------
121
Table 3.40
Aircraft Engine Maintenance and Test Cycle
Power
Setting
Minutes
per Test
Idle
Approach
18._75
6.25
Total Test Time
25 minutes
-------
122
(144,000 Btu/gal for oil) the plant can be projected to have a heat input
on the order of 56 x 10 Btu/hr. This puts it into the intermediate size
range.
The State of Illinois regulations whiich apply to this size heating
plant are given on Table 3.41. The S02 regulation is given as a point of
information even though it is not used in this study. The CO regulation re-
quires an estimate of the working cycle of the plant boiler. Time did not
permit this to be made and hence this regulation was not used.
Application of the Illinois regulations to the published emission
2ft
factors gives the emission factors for the airport heating plant as shown
on Table 3.42.
3.2.6 Access Traffic
In this section the emission factors for the ten modes of ground
access to the airport are developed and tabulated.
Private Autos
Emission factors for private autos have been drawn from data published
28
by the Federal EPA and are based on test results for a standard urban driving
cycle which includes start-up, accelerations, cruise, and decelerations over
a 7.5 mile trip length. Reference 31 discusses these factors and also makes
reference to the criticisms that have been leveled against them. Although there
is not a universal acceptance of this data, it is felt that this is the best
information currently available and should be used until better data is avail-
able. (The Federal EPA is preparing to publish revised emission factors
shortly.)
A procedure has been developed to adapt the basic EPA emission test
data for CO, HC, and NO to representative local conditions of vehicle age mix,
X
-------
123
Table 3.41
Illinois Air Pollution Emission Regulations
Applicable to .New Euel Combustion Sources
lOxlO6 Btu/hr < Actual Heat Input <_ ^SOxlO6 Btu/hr
Pollutant
Emission Limitation
CO
200 ppm corrected to 50% excess air
HC
None
NO.,
None
Particulates
0.1 lbs/10 Btu heat input
SO,
1.8 Ibs (coal-fired)
1.0 Ibs (residual fuel
oil fired)
0.3 Ibs (distillate fuel
oil fired)
>per 10 Btu heat input
*From Ref 33
-------
124,
Table 3.42
Airport Heating Plant Mission Factors
Pollutant
CO
HC
NO
Particulates
St. Louis "Airport
t
Emissions
Coal- Fired
(Ibs/ton)
2.0
1.0
15.0
2.2
Oil- Fired
(lbs/103 gal)
0.2
3.0
40.0
14.4
-------
125
average trip speed and cold start contribution. This procedure will be fol-
lowed to estimate the CO, HC and NOV emission factors for vehicles in the
A,
St. Louis 'area. The particulate emission factors are taken directly from
Ref. 28 without modification.
The first adjustment to the basic data involves the vehicle age mix.
The national age mix has a greater proportion of older cars than does the
mix from urban areas. The age mix for Cook County, Illinois which includes
the City of Chicago is presented in Ref. 31 and will be used as the mix for
the St. Louis area.
The next adjustment is for average vehicle speed. An average urban
speed of 25 mph will be used as it is felt this is a representative value
for airport travel. This has already been discussed in Section 3.1.8.
The final adjustment involves the separation of the cold start con-
tribution to the total emissions. As pointed out in Section 3.1.8, the major-
ity of private autos travelling within the airport do not have a cold start
associated with them, since they are not at the airport long enough to ex-
perience the required cold soak of 8-12 hours for a full cold start. The
vehicles which do require a cold start as itemized in 3.1.8 will have this
contribution added on separately.
The projections of auto emissions through the forecast period to
the year 2000 include the effects of the 1975-76 emission standards. It is
assumed that 1975 model year vehicles will meet the standards for CO and HC
emissions and 1976 model year vehicles will meet the NO emission standards.
X
It will take until around 1985 before the pre-1975 uncontrolled vehicles are
phased out of the population based upon the Cook County vehicle age mix.
-------
126
Rental Cars
Emissions from rental cars differ from private automobiles by virtue
of the fact that rental autos are substantially hewer than private autos.
i
Rental cars are generally phased out after 6-8 months of service and new model
year cars are brought into the fleet almost as soon as they are available with
34
the transition being completed by January or February of the calendar year.
All rental cars will therefore be treated as less than a year old and the
above-mentioned adjustments to the EPA data will be made on this basis. The
effect of emission controls is included in the same manner as for private
autos. . ;
Taxis
Taxis, like rental cars, are generally newer than private autos.
In the City of Chicago, taxis are banned after reaching 5 years of age.
Three years seems to be a practical upper limit on taxi age; hence it seems
to be a reasonable approximation that 25% of the taxi fleet is made up of
new vehicles, 50% are a year old, and 25% are two years old. It is assumed
that taxis will meet the same emission standards as private autos.
Limousines
No data was available on limousine age distributions. These are
vehicles used by hotels and motels as courtesy cars as well as being fleet
vehicles used by private livery services. It is therefore assumed that
limousines have the same characteristics, and therefore the same emissions,
as taxis. ,
Buses
Buses are generally diesel-powered and hence the emission factors
31
selected correspond to the published diesel emission factors. Since no
standards have been set for diesel vehicles, the only distinction made in
-------
127
emissions is for pre-1970 and post-1970 .vehicles. The distinction is made
due to the introduction of a new needle valve injector which reduced the
amount of fuel that could be burned. The result was a decrease in CO and HC
emissions and an increase in NO emissions. The Cook County vehicle age
distribution is used to determine the mix of-pre- and post-1970 vehicles.
There is no speed dependence of diesel emissions but there is a
fuel usage dependence. A fuel consumption rate of 3 mi/gal is used since
this represents an average value for diesel engines.
The cold start emission is not a distinct characteristic of diesel
vehicles and hence is not separated from the overall emission.
Commuter Train-Diesel
28
Emission factors for diesel railroad engines have been published.
The emission rate is converted from guts/gal to gms/mile by applying an
26
average fuel consumption rate. A value of 3 gal/mi is a good estimate.
No standards have been set for diesel railroad engines and hence the
emission does not change through the forecast period.
Rail Transit-Electric
An electrically-powered rail rapid transit system produces no emis-
sions along its right-of-way; the generation of the electricity required to
power the system does in fact result in air pollutant emissions. A 10-car
train loaded with 80 passengers/car is estimated to consume 70 kw-hr/train-
26
mile. The average train length assumed in Section 3.1.8 was only 5 cars;
it is not, however, possible to make a simple proportional reduction of the
required power usage. Since the data used here is only an approximation,
it will be assumed that 70 kw-hr/train-mile is a conservative estimate.
-------
128
It will be assumed that the supplying power plant will be coal-fired
and that the plant can generate approximately 2260 kw-hr per ton of coal
burned based upon a coal heating value of 11,000 Btu/lb and a 35% efficiency.
28
The air pollutant emission factors for power plants are constrained to
33
meet Illinois regulations. The emission factors for the rail transit are
then computed as that part of the power plant emissions which are attributable
to the production of the electrical power necessary to run the system and are
tabulated in the form of grams per train-mile.
Light-Duty Trucks
The basic emission test data for light-duty trucks (<6000 Ibs GW)
are the same as for automobiles. The only difference in computing the cor-
rected emission factors is the rate at which emissions increase with vehicle
31
age. The application of appropriate deterioration factors makes this com-
pensation.
Heavy-Duty Trucks
The basic emission test data for heavy-duty gasoline-powered trucks
(> 6000 Ibs GVW) are tabulated in Ref. 31. Although there is some feeling
that truck emissions are vehicle-weight dependent, the current EPA position
is that there is not sufficient evidence to justify this distinction. Hence
only one set of emission factors is presented for vehicle weights over 6000 Ibs.
GVW.
The calculation procedure for corrected emission factors is the same
as for light-duty trucks.
Diesel Trucks
Diesel trucks are treated in exactly the same way as are buses and
hence have the same emission factors.
Tables 3.43 and 3.44 summarize the emission factors for all of the ten
modes of access to the airport.
-------
Table 3.43
Ground Access Vehicle Emission Factors
Hot Operation
(gms/ vehicle-mil e)
Commuter Rail Light- Heavy- Heavy-
Private Rental Train- Transit- Duty Duty Gas Duty Diesel
Auto Auto Taxi Limosine Bus Diesel Electric Truck Truck Truck
00
1975 HC
N0x
Particulates
CO
1980 HC
N0x
Particulates
CO
1985 HC
N0x
Particulates
1990-2000
18.51
3.98
5.44
0.10
3.36
0.82
1.67
0.10
0.65
0.33
0.60
0.10
0.52
0.32
3.40
0.10
0.45
0.30
0.50
0.10
0.45
0.30
0.50
0.10
5.66
1.15
3.40
0.10
0.49
0.30
0.50
0.10
0.49
0.30
0.50
0.10
5.66
1.15
3.40
0.10
0.49
0.30
0.50
0.10
0.49
0.30
0.50
0.10
40.43
6.66
64.57
2.00
35.42
4.84
72.04
2.00
32.50
3.78
76.40
2.00
95.30
68.00
102.10
34.00
95.30
68.00
102.10
34.00
95.30
68.00
102.10
34.00
14.06
4.21
216.54
30.94
14.06
4.21
216.54
30.94
14.06
4.21
216.54
30.94
37.31
8.16
6.46
0.10
14.20
3.06
3.49
0.10
2.69
0.69
1.43
0.10
54.72
15.72
11.35
0.10
25.42
7.45
7.85
0.10
7.58
2.75
5.34
0.10
40.43
6.66
64.57
2.00
35.42
4.84
72.04
2.00
32.50
3.78
76.40
2.00
SAME AS 1985
N9
VO
-------
Table 3.44
Ground Access Vehicle Bnission Factors
Cold Start
(gms)
Private Rental
Auto Auto
Taxi
Liraosine
Bus
Conrauter Rail
Train- Transit-
Diesel Electric
Light- Heavy- Heavy-
Duty Duty Gas Duty Diesel
Truck Truck Truck
CO
1975
HC
CO
1980
HC
CO
1985
HC
1990-
2000
147.97
11.88
58.50
5.03
29.00
2.59
27.26
2.74
23.78
2.19
23.78
2.19
136.37
12.46
25.75
2.35
25.75
2.35
136.37
12.46
25.75
2.35
25.75
2.35
0.
0.
0.
0.
0.
0.
SAME AS 1985
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
185.81
12.42
103.10
7.79
51.90
4.43
316.27
35.45
258.73
28.84
207.79
21.17
0.
0.
0.
0.
0.
0.
CJ
o
-------
131
3.3 Computation of Emissions
The purpose of this section is to describe the actual calculation
procedure used to apply the emission factors of Section 3.2 to the activity
levels of Section 3.1. In most cases the procedure is straightforward,
requiring only simple calculations. In some cases, however, there are some
subtleties which should be emphasized.
Computer programs have been written to handle the large volume of
data which must be manipulated. These programs, while relatively simple in
their logic, enable the investigator to explore many different sets of data
and to conduct "numerical experiments" to determine the effect of certain
variables and perform tradeoff analyses.
The computation procedure for each of the emission sources will be
discussed separately. The actual computations will be displayed in Section 5.
3.3.1 Aircraft
Figure 3.3 gives a schematic representation of how the calculation
of aircraft emissions is performed. The procedure is the same as that used
in the FAA/Argonne Airport Air Pollution Study.
The first step is to determine the number of arrivals of each aircraft
type for each hour of the day. The aircraft activity and mix data are com-
bined with the diurnal pattern to generate this information. The appropriate
emission factors for each airplane are chosen and the times-in-mode are
selected by assigning the aircraft to its range class as given in Table 3.33.
The emissions from the approach, landing and inbound taxi portions.
of the LTD cycle are then computed by applying the emission factors and the
time-in-mode. These emissions are tabulated as having occurred during the
arrival hour.
-------
132
Aircraft
Activity
and Mix
I Gate
Occupancy
Times
APU
Emission
Factors
Determine
No. of Arrivals
for Each Hour
Diurnal
Activity
Distribution
Determine
Range Class
of Each Aircraft
I Calculate
i Arrival
Emissions
Aircraft
Emission
Factors
Calculate
APU
Emissions
Characteristic
Times-in-Mode
for Each
Aircraft Type
Calculate 7
Departure
Emissions
If Hourly LTOs
>30 Add Runwayf
Queue
Distribute Emissions
by Hour
I
Sum Emissions
for Day
Fig. 3.3 Aircraft Emission Calculation Procedure
-------
133
The aircraft gate occupancy time as a function of the arrival hour
is then determined. Those aircraft that are equipped with auxiliary power
units (APUs) are assumed to have the unit in operation for the entire length
of the gate time.
The next step is to compute the emissions from the departing phase
of the LTO cycle. The emission factors and times-in-mode for the outbound
taxi, runway idle, takeoff, and climbout are used to generate this informa-
tion. At this point a check is made on the total number of LTOs occurring
during the hour under consideration. If the number is less than 30, then no
runway queuing time is included and aircraft are assumed to proceed immedi-
ately into takeoff position. If the number of LTOs is equal to or greater
than 30, then a simple linear relationship is used to account for delay time
waiting for takeoff clearance. This time is computed by the equation
T - (N-30)/10, where T is additional runway queuing time (in minutes) during
which the aircraft engines are assumed to be in the idle mode, and N is the
number of LTOs in the hour. This relationship was derived from experience
13
at Chicago's O'Hare Airport and assumes the utilization of two parallel
runways for airport operations.
It now remains to appropriately distribute the emissions to each
hour. It has already been stated above that the emissions during the arrival
segment of the LTO cycle are tabulated as having occurred during the arrival
hour. If the aircraft has an.APU operating during the gate occupancy period,
the emissions from the first 30 minutes of gate time are included in the
arrival hour. The remaining gate time APU emissions are included in the next
hour. The emissions for the departure segment of the LTO cycle are included
in the arrival hour if the gate occupancy time is 30 minutes or less. If it
is greater than 30 minutes, these emissions are tabulated in the next hour.
-------
134
The final step is to sum the total emissions for the day.
By making the calculation in this fashion, a detailed resolution of
the data can be had which will enable some definitive statements to be made
i
about the effectiveness of alternative techniques for reducing the aircraft
emission load. These will be discussed in Section 5.
3.3.2 Ground Service Vehicles
The computation of ground service vehicle'emissions starts with the
diurnal aircraft activity pattern that has been generated as described in
Section 3.3.1. Using the ground support requirements as given in Table 3.12,
the number of vehicle-hours of operation for each vehicle type in each hour
of the day can be computed. The results of this computation for the Speas
forecast are given in Table 3.13.
The fuel consumption rate for each vehicle type is next multiplied
by the operation time to generate the total amount of fuel used by all vehi-
cles in each hour. The emission factors are applied to this number to
generate the hourly ground service vehicle emissions. Both the uncontrolled
and controlled emission factors are applied to investigate the differences.
It can be argued at this point that additional emissions should be
13
added to account for intra-airport travel of service vehicles. There is data
to indicate that this travel can account for as much as 20% of the vehicle fuel
pumped at the airport. In order to estimate this, however, some indication of
airport layout must be had. For the St. Louis airport, this will not come
until the master plan is drawn up.
3.3.3 Fuel Storage and Handling System
Emissions from fuel storage and handling are computed by simply apply-
ing the emission factors of Table 3.39 to the fuel requirements of Tables 3.14
-------
135
and 3.15. This gives the dally emission rate. To obtain the hourly pattern,
the diurnal pattern of aircraft activity is applied to the daily figure.
3.3.4 Engine Test and Maintenance Facility
The engine testing cycle is defined in Table 3.40. The emissions
from a single test will depend on what engine type is being used. Since the
total number of engine tests is based on a proportional ratio to the total
number of aircraft LTOs, it seems reasonable to assume that the tests will
have a mix of engines that is the same as the airplane mix. Also, since the
maintenance facilities are set up primarily for gas turbine engines, it is
assumed that only the jet engines will be serviced.
For the St. Louis airport, only the JT3D, JT8D, and JT9D engine
types will be used to compute emissions. Their mix in the test facility is
taken to be the same as their mix in aircraft activity (Table 3.10).
The daily total emissions are computed first and are then distributed
equally over the time period of the daytime shift as discussed in Section
3.1.6
3.3.5 Heating and Air Conditioning Plant
The daily heating plant emissions are obtained by applying the emis-
sion factors of Table 3.42 to the fuel requirements of Table 3.17. The state
emission regulations have already been imposed on the emission factors, hence
there is no need to check for compliance. The daily emissions are distribu-
ted equally over the day.
3.3.6 Access Traffic
The procedure for calculating diurnal access traffic activity has
already been described in Section 3.1.8. To proceed from activity to emis-
sions involves first the application of the vehicle mileage traveled.
-------
136
As stated previously, the one-way trip length for all ground access vehicles
to the St. Louis airport was assumed to be 3.5 miles, based on information
in the Impact Statement. When this is multiplied by the number of
vehicle trips of each vehicle type, the total number of vehicle-miles traveled
by each access mode is obtained.
The hot operation emission factors for each access mode (Table 3.43)
are next applied to the vehicle mileage to obtain the emissions for the hot
start segment of vehicle travel.
The final step is to compute the cold start emissions. Section
3.1.8 enumerates those vehicles which are assumed to have a cold start. The
emission factors of Table 3.44 are used for the computations. It should be
noted that the cold start emissions are applied only to the appropriate
departing vehicles.
Since the vehicle activity is developed in a diurnal pattern, the
computed emissions are also diurnally distributed.
-------
137
4.0 Analysis of Airport Vicinity Land Use
4.1 Me tho dology
A complete environmental analysis of a major airport complex must
include the impact of the activities that are induced by airport develop-
ment. In an effort to develop a methodology for analyzing the development in
the vicinity of a major airport a case study in the vicinity of Chicago/0'Hare
International Airport was conducted. The land use analysis is quite similar
to the development of airport specific air pollutant emissions in that the
underlying approach is to (1) identify and isolate specific activities that
contribute to atmospheric emissions, (2) to quantify these activities and
(3) to transform the activities into emission rate estimates. The land use
study conducted in the vicinity of O'Hare focused on satisfying two basic
objectives:
1) To provide a basic understanding of airport area development by
means of a retrospective analysis.
2) To provide the testbed for developing and testing-land use-
based emission estimates. The case study proved extremely valuable in
achieving both of these objectives.
As mentioned above, the methodology for the land use analysis
consisted of isolating basic activities (conventional land use or zoning
classes). These activities are then quantified, emission factors applied
arid the spatial distribution of emissions results. This procedure is shown
schematically in Fig. 4.1. The land use information was derived from aerial
photographs using a unique method of photographic interpretation developed
by the Northeastern Illinois Planning Commission (NIPC). The NIPC land use
-------
Aerial Photos
Chicago Emission Inventory
Peoples Gas Company Data
Chicago Area Transportation
Study
Demand Model
Illinois Emission Inventory
Acreages of R, C,M, W
Dwelling Unit Densities
and
Commercial Institutional
Building Densities
Commercial, Residential
and Institutional Fuel
Quantity and Mix Requirements
Vehicle-Miles Per Sq. Mile
Average Speeds
Cold Starts
Industrial Emission Densities
itiesl
Application of Emission
Factors Gives Emissions
00
Display and Input to
Dispersion Model
Fig. 4.1. LAND USE BASED EMISSION ESTIMATING PROCEDURE
-------
139
interpretation scheme utilizes a. 24-category land use identification scheme.
For simplicity, these were aggregated to nine categories which, for purposes of
an air pollution analysis, are more than adequate. The nine categories
utilized in this study are:
1) Residential (Single and Multi-family)
2) Residential (Mobile Home Parks)
3) Commercial
4) Industrial
5) Warehousing
6) Institutional
7) Transportation, Communications, Rights of Way
8) Vacant, Agricultural
9) Recreation, Open Space, Water
It was further assumed that, for purposes of estimating air pollutant emissions,
only categories one through seven were considered to be activities having
significant impact.
Ground vehicle emissions in the airport surroundings were derived
from data based on the Chicago Area Transportation Study (CATS). The motor
vehicle emissions were computed using the output of the CATS Demand Model
which produces, on a one mile gridded basis, the vehicle miles and average
TO
speeds. State-of-the-art emission factors including the local age distribution,
deterioration factors and mileage estimates were applied. Ground vehicle
emissions were then merged with the emissions from the original six land use
categories described below to provide a complete picture of the land-use-
based emissions.
The aerial photographic interpretation technique, as described in
Appendix A, was applied to an area covering approximately 125 square miles
-------
140
covering four townships surrounding O'Hare Airport (Addison, Elk Grove,
Maine and Leyden). The study area is shown in Figure 4.2. This figure
illustrates the geographical relationship of the study area to the City of
Chicago and the Chicago Metropolitan Air Quality Control Region. Elk Grove,
Maine and Leyden townships are in Cook County and Addison township is in
OuPage County. Furthermore, portions of Leyden township, as well as the
airport property (approximately ten square miles), are within the corporate
limits of the City of Chicago. Figure 4.2 also shows an exploded view of
the study area covering the four townships, including the section numbers
within each township. The aerial photographic technique yields acreages
of each land use on a section-by-section basis (see Appendix B). Figure 4.2
also shows the main ground transportation routes in the airport vicinity.
These major highways, as well as the airport itself, add to the induced
development in the airport vicinity and are therefore very important in the
land use analysis.
Aerial photograph interpretations were conducted for four years
(1960, 1964, 1966, and 1970). This historical data base allowed a retro-
spective analysis including the trends in development of the various land use
categories which parallel the growth in air carrier operations at O'Hare.
Prior to 1960, O'Hare existed as a small military installation in a relatively
rural area. Major construction was completed in 1959 and the airport went
into large scale operations in 1960 and 1961. The land use case study of the
O'Hare vicinity, therefore, will reflect growth phenomena including the effects
of the airport and its associated ground transportation links.
4.2 Analysis of the Land Use Data
This section deals with the first of the earlier stated objectives
related to airport area land use development; namely, to provide a basic under-
standing of land use development in airport vicinities.
-------
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27
34
II
14
23
2(
35
2
.
II
14
23
2(
3S
12
13
24
25
36
<
12
13
24
25
K
ADDISON
LEYDEN
Fig. 4.2 Four-Township Land Use Case Study
Area Surrounding O'Hare Airport
-------
142
The development In the vicinity of O'Hare was analyzed using three
approaches:
i
1) A trend analysis of each of the land use categories for the
entire study area. This analysis produces an overview of the changes in
each of the categories.
2) A zonal analysis based on "pie"-shaped sectors surrounding the
airport. This analysis was done in an effort to indicate a directional
component to the land use activities.
3) A zonal analysis consisting of concentric square rings at
varying distances from the airport perimeter. This analysis was conducted
to establish the relationship between growth in each of the activity classes
as a function of distance from the airport.
Overall Land Use Trend Analysis
The basic seven land use classes deemed to be significant as sources
of air pollutant emissions were further aggregated, primarily to reduce the
analytical work load. The manufacturing and warehousing classes were
combined as were the commercial and institutional. These consolodations are
justified on the basis of similar fuel use demands and emission characteristics'.
Warehousing is considerably smaller in terms of total acreages than manufac-
turing, while institutional and commercial are generally comparable.
Table 4.1 and Figs. 4.3 through 4.6 show the overall land use
trends for the categories under consideration. The residential land use
increase (24.5% from 1960 to 1970) is considerable in view of the fact
that airport operations also experienced large growth which increased the
noise impacted areas. Figure 4.7 shows the growth in aircraft operations at
O'Hare Airport over the same time period (aircraft landings and takeoffs are
-------
Table 4.1 Distribution of Land Use and Trends in the Study Area
Residential
Single, Multiple and Mobile Home Parks
Commercial and
Institutional
Manufacturing and
Warehousing
1960
1964
1966
1970
Acres
18662
20380
21888
23280
% of
Study Area
23.2
25.3
27.2
28.9
% Change
From 1960
__
9.2
17.3
24.5
Acres
3121
3879
4255
5072
% of
Study Area
3.9
4.8
5.3
6.3
% Change
From 1960
24.3
36.3
62.5
Acres
2511
3337.
3981
5125
% of
Study Area
3.1
4.2
4.9
6.4
% Chanj
From 19(
32.9
58.5
104.1
Transportation, Communications
and Utilities
% of % Change
Acres Study Area From 1960
1960 14856
1964 15912
1966 16662
1970 18836
18.5
19.8 7.1
20.7 12.2
23.4 26.8
Vacant and Recreation, Open Space
Agricultural and Water
% of 1 Change % of % Change
Acres Study Area From 1960 Acres Study Area From 1960
30255 51.4 11088 13.8
25394 45.9 -16.1 11592 14.5 4.5
22990 41.9 -24.0 10770 13.4 -2.8
17024 35.1 -43.7 11199 13.9 1.0
to
-------
144
I960
1964
1966
1970
5-15%
15-35%
>35%
Fig. 4.3 RESIDENTIAL LAND USE
-------
145
I960
1964
1 TJ&
1966
1970
III 5-15%
15-35%
>35%
Fig. 4.4 COMMERCIAL LAND USE
-------
146
I960
(4
1966
1970
5-18%
18-36%
>38%
Fig. 4.5 MANUFACTURING AND WAREHOUSING LAND USE
-------
MANUFACTURING
AND
WAREHOUSING
60
74
Fig. 4.6 Overall Land Use Trends
in the O'Hare Study Area
-------
0.
o
,- 5
oo
60
62
64
66
68
70
YEAR
Fig. 4.7 TOTAL OPERATIONS - O'HARE AIRPORT
-------
149
separate operations). The overall Cook County population increase was
7.1 per cent; however, this figure is biased by the City of Chicago decline
of 5.2 per cent. It is interesting to note that the population growth was
considerable in the four townships constituting the study area. From the 1960
and 1970 census of population data, the following statistics were derived:
1970 1960 Change
Addison Township 491,882 313,459 + 56.9%
Elk Grove Township 79,642 27,876 +185.7%
Leyden Township 99,793 81,814 + 22 %
Maine Township 140,194 95,476 +46.8%
Total 391.909 246,974 + 59 %
The large difference between the residential land use increase and the
population increase for the four townships indicates that a significant portion
of the land development for that area was utilized for higher density housing.
The commercial and institutional land use increased 62 per cent in
the ten year period. This is comparable to the population increase as
expected, since the demand for services such as additional shopping, enter-
tainment and institutional facilities should increase proportional to the
population.
The manufacturing and warehousing class experienced the largest
percentage growth of the various land use categories; 104%. It is likely
'that a good part of this manufacturing and warehousing development was
spurred by the airport and ground transportation advantages of this area.
Most of the increase was due to the manufacturing while warehousing
contributed a small increase to the total acreage. A critical review of the
photographs indicated that most of the industry in the vicinity of the airport
-------
150
was medium or light industry with little of the large scale, heavy polluting
installations such as power plants, steel mills, refineries, etc.
Of course, a secondary effect of the large increase in manufacturing and
warehousing is its effect on the ground transportation required because of
|
an increased number of jobs and intermodal transportation.
The transportation, communications, and utilities category showed
i
increases similar to the residential land use. This category is largely
dominated by the acreage used for streets and rights of way of utilities
and is therefore closely related to development of residential property.
The category covering vacant and agricultural land experienced a
i
I
decline as expected since this represents the land source from which the
x
other land use categories derive their increases. The total drain on
!
vacant and agricultural property caused a 43.7 per cent decline in this
land use category in the ten year period.
The final'category; recreation, open space and water maintained a
fairly constant level over the ten-year period experiencing an overall gain
of 1 per cent. The total acreage of this category is approximately half
that of the residential land use in 1970 and approximately equal in acreage
.
to the commercial, institutional, manufacturing and warehousing combined
for 1970.
4.3 Directional Analysis
The directional analysis was conducted in an effort to identify the
directional componeit of land utilization in the airport area. In addition,
this analysis allowed the growth along major transportation links to be more
easily identified.
The study area was subdivided into eight pie-shaped zones of
approximately equal size as shown in Fig. 4.8. A trend analysis was
-------
151
8
~Sij-
^
"
.J
Fig. 4.8 ZONES USED FOR LAND USE/DIRECTIONAL ANALYSIS
-------
152
conducted for each zone and the land use per cent changes computed. The
results are shown as a "land use rose" for each class and year in Figs.
4.9 - 4.11. The residential land use is considerably greater towards the
east, particularly the northeast, whereas the greatest growth in terms of
per cent change as occurred in zone eight which increased from 24% to
40%. This is not particularly surprising since the areas to the west
of the airport are least developed.
The manufacturing and warehousing land use shows a high directional
relation to the City of Chicago and also the area along the Tri-State
Tollway south of the airport. However, the greatest growth in the ten-year
period has occurred in the northwest sectors. Zone eight, for example, has
increased almost threefold and zone seven has increased over fourfold.
An extrapolation of these growth rates for manufacturing and warehousing land
use in this area could lead to quite high manufacturing land use densities
in the not too distant future, if not already.
The commercial and institutional land use shows directional growth
similar to the residential land use. Zone eight shows the largest change
over the ten-year period as did the residential growth. This is probably
attributable to the transportation advantage offered by the Northwest Tollway.
which runs through Elk Grove Township in a southeast-northwest direction.
The Land Use/Distance Analysis
One of the basic objectives of this study is to compute and compare
airport emissions with those of the surrounding land use activities which are
assumed to be attendant with airport development. This portion of the analysis
was aimed at identifying changes in various land use activities as a function
of distance from the airport to provide a measure of local urbanization and
-------
153
29
31
32
44 13
33 26
33
33
35
40
37
32
46 13
35 27
37
34
1966
1970
RESIDENTIAL LAND USE PATTERNS IN VICINITY OF O'HARE AIRPORT FROM 1960 THROUGH 1970.
EACH LINE SEGMENT REPRESENTS THE PERCENT OF LAND USED IN THAT SECTOR.
Fig. 4.9
-------
154
3 3
V 4
1966
1970
COMMERCIAL LAND USE PATTERNS IN VICINITY OF O'HARE AIRPORT FROM 1960 THROUGH 1970.
EACH LINE SEGMENT REPRESENTS THE PERCENT OF LAND USED IN THAT SECTOR.
Fig. 4.10
-------
155
17
1960
1964
1966
1970
MANUFACTURING AND WAREHOUSING LAND USE PATTERNS IN THE VICINITY OF O'HARE AIRPORT FROM 1960
THROUGH 1970. EACH LI NE SEGMENT REPRESENTS THE PERCENT OF LAND USED IN THAT SECTOR.
Fig. 4.11
-------
156
ip
',2
s
rJ
Fig. 4.12 ZONES USED FOR LAND USE/DISTANCE ANALYSIS
-------
157
development in the immediate airport vicinity. The approach here was to
subdivide the study area into concentric square zones at varying distances
from the airport perimeter. The zones used in this analysis are shown in
Figure 4.12. Each zone is one section wide or approximately one mile. Since
the area of each zone is different, the per cent land use in each zone was
used as the measure of change. Figure 4J.3 shows the land use trends in each
zone. This figure indicates increased manufacturing and warehousing
development in the immediate vicinity of the airport perimeter (one-two miles).
Residential and commercial activity, on the other hand, increases with increased
distance from the airport.
4.4 Preparation of Land-Use-Based Fjnission Estimates
The evaluation of the air pollution impact of the airport and
surrounding land use is logically prepared in three basic steps:
1) Isolating and quantitifying the activity, 2) transforming the activity
into emission level and 3) preparing air quality estimates using a
dispersion model. This section describes the intermediate phase related to
converting activity levels of the land uses surrounding the airport into
emission estimates on an annual basis. As stated previously, the nine
categories of land use considered in this study were reviewed in terms of
their contribution to atmospheric emissions,and it was concluded that the
categories of significant impact were residential, commercial, institutional,
manufacturing, warehousing and ground transportation. All of these categories
except for a portion of the manufacturing air pollutant emissions are produced
through the combustion of fuels of various types. This reduces the complexity
to some degree for the estimating procedure. The problem then becomes one of
converting activity levels into a fuel mix and demand and applying standardized
-------
14
12
10
a
z
4
O
tM
U
O
I
MANUFACTURING
AND
WAREHOUSING
Ln
00
1234 5
ZONE
Fig. 4.13 RELATIONSHIPS OF LAND USE TO DISTANCE FROM THE AIRPORT
-------
159
emission factors to these parameters. In the case of manufacturing fuel.
combustion emissions, correlations were made directly with land use via
the Illinois Emission Inventory. This procedure will be described later.
The emission estimates presented in this section are derived from annual
fuel use estimates; however, shorter averaging periods could be utilized
using degree day proration of fuels. This procedure has been widely
utilized and produces effective correlations on a month by month basis.
4.5 Residential Emission Estimates
Residential land in the study area which is quite typical of
suburban housing is composed largely of single and small-to-medium multiple
dwelling buildings. The age distribution of buildings in this area is
considerably skewed towards newer buildings than in-close suburbs and therefore
are nearly all gas heated. For the analysis of residential emissions, it was
assumed that all the emissions are due to combustion of natural gas as the
space heating fuel.
The aerial survey technique which produces acreages of the various land
uses was utilized as the basic input data in addition to information derived
from The Peoples Gas, Light and Coke Company and the City of Chicago,
Department of Environmental Control.
The correlation of fuel use as a function of building size for
buildings in the Chicago Metropolitan area is compiled by the Peoples Gas
Company utilizing the entire City of Chicago as the data base. This information,
derived from actual fuel use information, has been verified through the use
of theoretical heating load estimates and design ambient temperatures. A
comparison of these two methods correlates extremely well. For the purposes
of this analysis, the actual Peoples Gas Company data was utilized. A summary
-------
160
of residential fuel use information is presented in the table below:
QUANTITIES OF GAS, OIL AND COAlj, USED YEARLY
PER DWELLING UNIT BY DIFFERENT SIZED RESIDENTIAL BUILDINGS
Number of Gas
Apartments/Building (Cubic Feet)
1 140,000 (.80)
2&3 126,733
4-7 109,840
8-19 95,000
20-59 80,000
60+ 72,000
(.80)
(.80)
(.80)
(.80)
(.80)
Oil
Gallons
1,068 I
965 I
837 i
726 i
610 i
549 i
[.75)
(.75)
(.75)
(.75)
(.75)
(.75)
Coal
(Tons)
6.48 (.64)
5.86
4.78
4.14
3.16
2.84
(.64)
(.68)
(.68)
(.75)
(.75)
Figures in parenthesis indicate burning efficiencies of the fuel
burning equipment.
The problem of estimating emissions is now reduced to estimating
the density of dwelling units (DU) and combining "this with the acreage
figures from the aerial survey and multiplying by the fuel use estimates given
above. Three categories of residential building densities were defined,
light, medium and heavy. These categories were established by reviewing the
aerial photos containing residential property in the study area and
partitioning this information into the three categories with approximately
equal acreages for each. Based on this sampling process, the following
categories were defined:
High density 6.2 DU per acre
Medium^density 3.7 DU per acre
Low density 1.6 DU per acre
These value^ represent the mean in each of the three categories. It is important
to note/£hat single dwelling unit buildings or individual houses represent the
overwhelming majority of the total building stock.
-------
161
The residential land use in each of the individual sections of the
entire study area were then appropriately categorized into one of the three
classes. This information, in combination with the densities, provides a
number of dwelling units per section for the study area. Using the fuel
demand figures provided above, emissions are then calculated (Table 4.2) for
the five primary pollutants (particulates, SO., CO, HC, NO ). A sample cal-
^ X
culation is shown below for particulate emissions from high density residen-
28
tial land use. Emission factors are from McGraw and Duprey.
Natural Gas Consumption =
6.2 -- x 1.4 x 105 ft3/DU/yr = .868 x 106 ft3/acre/yr
3C1T6
Particulate Emissions =
(19 lb/106 ft3) (.686 x 106 ft3/acre/yr) = 16.5 Ib/acre/yr
4.6 Commercial and Institutional Emission Estimates
The procedure for estimating emissions from commercial and institu-
tional land use is quite similar to that for residential. The problem is
basically one of transforming commercial acreage into emissions using ob-
served densities of commercial activity (square feet of building space per
acre) and fuel demand and mix information. The density of commercial and
institutional buildings was derived again by a sampling of sections in the
study area. This process involves isolating typical commercial land use plots
on the aerial photographs and measuring the ratio of building floor space to
acreage of land use. The measurements of floor space was accomplished by the
use of a planimeter and the observed number of stories of building height.
The land area was derived from the dot count or the use of a planimeter. An
error analysis comparing these two methods, which is given in Appendix A,
-------
162
TABLE 4.2 EMISSION DENSITIES FROM RESIDENTIAL LAND USE
IN THE VICINITY OF O'HARE AIRPORT
|
Dwelling
Participates
High
(6.2 DU/acre)
Medium
(3.7 DU/acre)
Low
(1.6 DU/acre)
16.5
9.8
4.3
Emissions
so2
.52
.31
.13
(Ib/acre/yr)
CO HC NO
17.4 6.9 43.4
10.4 4.1 -25.9
4.5 1.8 11.2
-------
163
shows that the two methods are quite comparable. An analysis of commercial
and institutional building density indicated that an essentially constant
ratio of building size to acreage was observed. This is shown by the scatter
diagram in Fig. 4.14. This figure indicates that the ratio of building space
to land area is fairly independent of acreage. This probably indicates that
cqmmercial property is fully utilized for buildings to the extent that
sufficient parking, landscaping, utility right of way and other miscellaneous
land requirements are also allotted. In addition, many communities require
minimum parking space as part of their zoning regulations. The spurious
points on the scatter diagram probably represent commercial property which
has not yet been developed to the full extent and the ratio would be much
lefss than average or, on the other hand, an intensive utilization of
ccjmmercial property possibly with tall buildings. The average value taken
frjom this analysis was 7,536 square feet per acre. This figure was then
applied to the commercial land use produced by the aerial photographic
technique and a total quantity .of commercial and institutional building
space results.
Computation of Energy Requirements for Commercial/Institutional
Land Use
The primary source of data for the analysis of fuel use required by
commercial and institutional buildings is an inventory compiled and updated
by the Chicago Department of Environmental Control (DEC) including historical
information on over 6,000 buildings in the City of Chicago. In addition, the
fuel mix was estimated from data prepared by the Peoples Gas, Light and
Coke Company. Their data indicated a breakdown of 85% natural gas and 15%
oil to satisfy heating demands. Coal use was assumed to be neglible for
the same reasons assumed in the residential analysis, that is, that the
-------
14
T
21
T
T
Fig- 4.14 The Relation of Commercial Building Floor Space
to Commercial Land Use
12
10
a:
o
c
>-"
LU
O
LU
O
2
CO
ce.
o
o
o
cc
30
O
o
o
o
o
o
4 6
COMMERCIAL LAND, ACRES
10
-------
165
buildings in the 0'Hare s tudy area are much newer than in Chicago and are
not likely to utilize coal as a space heating fuel.
The computerized inventory of commercial and institutional buildings
from the Chicago DEC was analyzed to prepare a frequency distribution of
building size. The results of this analysis are shown in Eig. 4.15. This
figure indicates that an extremely large proportion (approximately 87%)
of the commercial and institutional buildings- are in a size category less
than 50,000 square feet. The average energy demand in each of these
building size categories was also computed with this data base. These
averages were computed in terms of energy required (10 Btu per 1000
square feet). The distribution of buildings was divided into two
categories, less than 50,000 and greater, than 50,000 square feet. The
average heating requirement per square foot was computed for each of these
fi 2
two categories (.63 x 10 Btu/1000 ft. /day for buildings less than
9 f\ *)
50,000 ft. and .29 x 10 Btu/1000 ft. /day for others). These figures were
then combined to produce an expected value (E ) for the estimation of overall
commercial energy requirements. This is illustrated by the following
formulation:
Expected Energy Demand = f E + f« £
= [(.87) (.63 x 106) + (.13) (.29 x 106)] 365
= 208 x 106 Btu/1000 ft./year
E = energy demand f = fraction of buildings
Then:
(208 x 106 ^ 2) (7536 ft /acre) = 1.57 x 109 Btu/acre/yr.
-------
60
a
Q-
40
20
166
200
400 600 80
BUILDING FLOOR SPACE, 103ft2
1000
> 1000
Fig. 4.15 COMMERCIAL/INSTITUTIONAL BUILDING SIZE
DISTRIBUTION IN CHICAGO
-------
167
The combination of these two factors then provides, on a section-by-section
basis, the energy requirements for commercial and institutional land use.
Emission factors based on the previously mentioned fuel -mix can now be
applied to compute the total commercial/institutional emissions. These are
presented in the following table:
TSP S02 CO HC NOX
Emissions 26>7
Ib./acre/year
Estimation of Manufacturing and Warehousing Emissions
The land-use-based emission estimates for manufacturing and warehousing
are based on an analysis of the State of Illinois Emission Inventory which was
collected on a statewide basis in 1970. The design of the emission inventory
for Illinois was prepared by the Argonne Center for Environmental Studies
and the data included information relating to land use, acreages, and
employment. Fortunately, a large number of the industries surveyed submitted
this data. In addition, manufacturing type information according to standard
industrial classification (SIC) was acquired. This information was utilized
as the data base for estimating emission density factors on an SIC basis.
The inventory was initially reduced to cover only the Chicago Metropolitan
Area Quality Control Region to include sources typical of the Northeast
Illinois Area. The inventory was also partitioned according to fuel combusiori
and process emissions.
A detailed survey of the aerial photographs in the O'Hare vicinity
indicated that industries normally associated with large process emissions
were not present. The industry in the O'Hare vicinity was typically medium
sized with some heavy industries such as food and kindred products. Furthermore,
industries which would emit process emissions of HC, CO, NO (those related to
X
an airport area analysis) were also absent from the study area. Therefore,
-------
'168
the fuel combustion portion of the inventory was used as the analytical basis
for estimating density factors.
i
For each SIC class, the mean emission density was computed in
tons/acre/day from the Illinois inventory. The results of this analysis are
shown in Table 4.3. A more detailed statistical analysis of the relation
37
between emissions and land use is given in Kennedy, et al. For purposes of
estimating emissions and demonstrating the procedure, the overall mean
estimator for each pollutant was used to compute industrial emissions for
this study. These values were then multiplied by the manufacturing and
warehousing acreages in each of the sections to produce the manufacturing/
warehousing emissions.
-------
169
Table 4.3
Fuel Combustion Emission Densities for Various
Two Digit Standard Industrial Classifications
(Tons/Acre/Year)
c
V.
f
4-
CJ
r
CC
CO
-------
170
4.7 Ground Transportation Emissions
In cooperation with the Chicago Area Transportation Study (CATS),
Argonne has constructed a vehicular emissions computer simulation utilizing
output from the CATS Model and the latest federal EPA emissio*i factors (see
Table 4.A). This computerized system consist- -f two main segments. First,
an emission factor section which has the capability of producing emission
factors based upon variations of several parameters such as localized age
distribution, space-dependent,vehicle-mile, and speed estimates, and the
computation of the "cold start" emission phenomenon as described by General
38
Motors. This phenomenon is due to the relatively large emissions which
occur during the initial 2 to 3 minutes of operation of a cold automobile
engine.
The second segment of the system is an emissions computation seg-
ment, which utilizes the appropriate emission factors from the first segment
in combination with the output of the CATS, simulation model and produces
emissions on a gridded basis over the entire region that can be used in a
diffusion model or to produce emission density maps.
The traffic distribution data for private vehicles was supplied by
the CATS (Chicago Area Transportation Study) group traffic simulation model.
This model calculates the transportation activity in the 8-county North-
eastern Illinois region and distributes the traffic volumes over a highway
network.
The CATS planning activities utilize a traffic zone system. Over
1700 zones are used to simulate the transportation activities in the study
area. These zones vary in size from a quarter square mile in the central
business district to 36 square miles in the outlying counties (see Fig. 4J6),
-------
Table 4.4 Emission Factors* (Grams/Veh-mile)
1970
1975
1980
CO
Urban
56.5
23.6
6.80
Rural
29.7
12.6
3.89
Cold
Start
216.8
161.7
77.4
Crank-
case
1.22
.32
.06
HC
Evapor-
ation
2.93
1.26
.44
Exhaust
Urban
9.15
3.78
1.14
Rural
5.55
2.32
.73
Cold
Start
13.7
13.6
7.1
NO
X
Urban
9.49
7.31
3.88
Rural
9.72
7.48
3.94
*These are "weighted" factors based on 6 vehicle classes
using Cook County age distribution and federal EPA
deterioration and emission data.
-------
172
735
741
726 _j
729
7
727
7JO
32 7
736,,
742
33
737
743
728
731
7
34
714
723
696
699
708
715
724
697
7IT»
711
691
777
725
r
698
673
678
687
6
Hi
en
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747
744
7
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6*1
6*0
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745
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749
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101
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514
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616
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634
637
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624
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229
41
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96
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225
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226
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56
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371
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61
64
69
72
!
s
4
6
I
10
II
16
.2°.
*J
J2-1
"I
<
5
9
II
(7
28
29
30
Fig. 4.16 Cook County Portion of the
Chicago Area Transportation Study
Showing Grid Squares and 4-Township
Study Area.
-------
173
An intervening opportunity model is used by CATS to generate the transporta-
tion activity levels between zones in the regions. These activity levels
are generated in the form of trips between traffic zones in the region.
The intervening opportunity model determines the number of trips for dif-
ferent reasons in a zone and then determines the destination of these trips,
using probability distributions characteristic of the opportunities in the
zones.
After 'ie number of interzonal trips has been determined by the
intervening opp. mity trip generation model, modal split data (split
between publi ind 'ivate transit) is used to determine the number of
people using pri^ - transportation. Each interzonal trip can use several
paths and theref capacity constraint distribution model is used to
distribute the trips over possible roadways. The total vehicle miles in
each zone are summed from the trips and links occurring in that zone. The
average speed on each roadway is calculated from a volume/capacity rela-
tionship. The output from the CATS simulation which is used by the emission
inventory includes trip origin (to compute cold start emissions), roadway
speed, and roadway vehicle miles (all on a zone basis).
For the purposes of computing vehicle emissions in the airport
vicinity, the local registration data was utilized (Table 4.5). This data
was derived from the Illinois Secretary of State's Office of Vehicle Regis-
tration. It has been indicated that the age distribution for automobiles
in the Chicago area is significantly different from that on a national basis
28
for which emission factors presented in the latest federal EPA publication
indicate. The Argonne model utilizes the local age distribution for Cook
County in conjunction with a set of "primitive" emission factors supplied
by the federal EPA. These emission factors do not include the national age
-------
174
Table 4.5
Model Year
1970
1969
1968
1967
1966
1965
1964
1963
1962
1961
I960
1959
1958
1957
1956
1955
Pre 1955
ige Distribution of Vehicles Registered
Cook County Illinois as of July 1, 1970
i
Number of Vehicles
232345
288479
261092
224673
217727
209143
162912
133992
104548
59150
45933
20121
8453
9303
5577
4816
10012
Total 1,998,281
in
Percent of
Total
11.6
14.4
13.1
11.2
' 10.9
10.5
8.2
6.7
5.2
3.0
2.3
1.1
.42
.47
.28
.24
.50
100%
-------
175
weighting. The Argonne model also produces projected emissions through
1980.
The different vehicle types have been handled by creating 5
vehicle-size categories and producing an emission factor for each of the
5 categories, ranging from Class 1 which is light duty vehicles or cars to
Class 5 which is heavy duty trucks of the Diesel type. The distribution
of vehicle sizes was also derived from the Illinois Secretary of State's
Office of Registration data for 1970.
-------
176
5. EMISSION DISPLAY
Section 3 described the methodology used to estimate airport activity
and the various related emission factors which could be used for air pollutant
i
emission calculations. Section 4 provided the same information needed to
estimate land use emissions. There are a multitude of ways in which the emis-
sions from these sources can be presented; this section will discuss some
recommended procedures.
In choosing the appropriate forms of displaying the information, the
ultimate goal of incorporating these results into a regional air quality
management program must be kept in mind. Thus, while data on actual emission
rates are important, information on emission trends, relative order of magni-
tude of emission sources, emission densities, and effects of alternatives are,
perhaps, even more important. This type of presentation will enable planners
to glean the maximum amount of useful information with a minimum amount of
searching through the data. Since presentation of the data in this format
often requires little additional information other than what has already been
described in Section 3 and 4, the utility of this approach becomes even more
desirable.
Information presentation to improve the air pollution analysis of
the Environmental Impact Statement for the St. Louis airport will be discussed
as an example of the application of the emission display techniques.
5.1 Airport Emissions
As previously shown on Fig. 3.1, the air pollutant emissions for
the airport come from six major sources. They are the following:
. Airport
Ground Service Vehicles
Fuel Storage and Handling
-------
177
To make a comparison with an existing airport, the emissions per
aircraft LTO and emissions per enplaned passenger are calculated for O'Hare
airport and presented on Table 5.3.
5.1.2 Ground Service Vehicles
In evaluating the contribution of ground service vehicles to the
overall airport air pollutant emission load, two considerations must be dealt
with. The first is the order of magnitude of the ground support equipment
emissions relative to other sources; the second is the effectiveness of emis-
sion control in reducing this contribution.
Many studies of airport air pollution have treated the ground
support equipment as a negligible source of emissions and have not included
their contribution to the total emissions. This study, as well as that
12
undertaken for O'Hare Airport , indicates that this is not the case. Figure 5.5
displays the ground service vehicle emissions for the St. Louis airport using
the Speas forecast. The two sets of curves present the calculations for the
assumption that ground support equipment, as off-road vehicles, will not be
subject to emission controls, and for the assumption that these vehicles will
be subject to the 1975-76 federal automotive emission standards. These curves
are designated uncontrolled and controlled, respectively.
The figure indicates that for the case of St. Louis, the uncontrolled
emissions amount to about 1905 tons of CO, 425 tons of hydrocarbons, 111 tons
of NO , and 4 tons of particulates in 1990. This means that the CO and HC
X
emissions from ground service vehicles "approach 30% of the aircraft emissions
which is certainly not a negligible quantity. The N0x and particulate emis-
sions are on the order of 27, of the aircraft emissions. These results, along
with similar conclusions for O'Hare, point to the fact that ground service
-------
178
. Engine Test and Maintenance Facilities
. Heating and Air Conditioning Plant
, Access Traffic
Each will be discussed separately In the following sections and a summary
display of airport emissions will be presented in Section 5.1.7.
5.1.1 Aircraft Emissions
The primary concerns in aircraft emissions are the actual emission
rates, the source of emissions by aircraft mix and mode of operation, and
the trends in aircraft emissions. The data presentation format should reflect
these considerations.
Emission Rates
The rate of air pollutant emissions from aircraft is obtained by
using the calculation procedure recommended in Section 3.3 to apply the emis-
sion factors of 3.2 to the activity projections of 3.1. The result is dis-
played as a plot of emissions versus time for the forecast period. Figure 5.1.
shows this information as calculated for the three forecasts for the St. Louis
airport. This graph shows the actual rate of emissions from the projected
aircraft activity level and mix, and can be used as an input to a dispersion
model to estimate air quality.
As the figure shows for all forecasts, the carbon monoxide and
nitrogen oxide emissions are projected to increase sharply, the hydrocarbon
emissions are expected to increase at a slower rate and the partlculate emis-
sions are expected to remain essentially constant.
For the Speas forecast in the 1975-2000 period, the CO emissions
increase by 322Z, and the NO by 382X, the HC emissions increase by 195%, and
the particulate emissions increase by 7%. The aircraft activity level in this
period has increased by 86%.
-------
Fig. 5.1 Annual Aircraft Emissions
St. Louis Airport
-------
180
The deficiency in presenting emission rates alone can be seen by
trying to utilize the curves of Fig. 5;1 to estimate air quality impacts
\
of an airport and to plan for emission controls. The reason for the dif-
i
ferences in the growth rates of the four pollutant loads cannot be readily
discerned; neither can the major contributors to the emission be singled
out.
As discussed in Section 2, the Environmental Impact Statement for
the St. Louis airport does not include an extensive analysis of the air
pollution impact. The Statement should have included at least a curve
comparable to Fig. 5.1. The aircraft emissions are presented for 1990 only;
essentially only one point on Fig. 5.1 has been calculated. (By way of
comparison, the Impact Statement projects an aircraft emission rate in 1990
of 9,490 tons/year of CO, 4,015 tons/year of organics, 4,745 tons/year of
NO., and 2,920 tons/year of particulates. With the exception of particulates,
the rates are comparable to what is shown on Fig. 5.1. The particulate rate
as projected by the Impact Statement differs by an order of magnitude. It
is not possible to resolve this discrepancy since no information is presented
on the calculation procedure used.)
Emissions by Aircraft Mix
An additional piece of information needed to fully understand the
curves on Fig.5.1 is a breakdown of the emission contribution by aircraft
class.
A qualitative feel for the factors behind the differences in the
emission growth rate of each pollutant type can by had by looking at the
aircraft emission factors on Table 3.33 and by noting that all of the fore-
casts call for a gradual phasing out of the B-707/DC-8 and the B-727/DC-9
aircraft which utilize JT3D and JT8D engines respectively. These aircraft
-------
181
are to be replaced by B-747 and DC-10/L1011 aircraft which utilize the JT9D.
Table 3.33 shows that the JT9D has a CO emission rate that is comparable to
the higher-CO-emitting JT3D, an HC. emission rate comparable to the lower-
HC-emitting JT8D, an NO emission rate that is substantially greater than
either the JT3D or the JT8D, and a particulate emission rate that is substan-
tially lower than either the JT3D or the JT8D. This means that in the course
of the forecast period airplanes are being utilized which have on the average
higher CO and NO emissions, only slightly higher HC emissions and lower
X
particulate emissions. This can be illustrated graphically by plotting the
fraction of emissions being contributed by each aircraft class. Figure 5.2
shows the mix as forecast by Speas (from Table 3.10) and the relative contri-
butions oi each aircraft class to the emission rate. It can be seen that the
Class AA aircraft (747/DC10/L1011) contribute more to the CO and N0x than
the relative magnitude of their activity, somewhat more to the HC, and
substantially less to the particulates. Classes C, D and E (small business
aircraft and general aviation) are only small contributors to the emission
load even though their activity is about 13% of the total activity.
The use of Fig. 5.2 in tandem with Fig. 5.1 thus demonstrates several
facts not readily discernable from Fig. 5.1 alone. Over the course of the
forecast period (1975-2000), any attempt to reduce aircraft CO and NO emissions
for the St. Louis region should concentrate on the Class AA aircraft. These
airplanes contribute more to the overall CO and NO emission load than their
.X
numbers would warrant. Reductions in their CO and NO emissions would provide
X
more than a proportional reduction in total CO and NO emissions. For hydro-
carbons the same conclusion can be reached but the difference between Class AA
activity and contribution to HC emission load is not as pronounced. In addition,
-------
100
so
40
20
o
m A
in v
XLASS D8E ^CLASS C
CLASS B
CLASS AA
I I I
40 ,
20
1975 1985 1995
1975 1985 1995
YEAR
Fig. 5.2 Aircraft Emissions by Mix
St. Louis Airport, Speas Forecast
100
80
60
'"^ CLASS C ^ CLASS
CLASS B
20
100
80
60
40
20
CLASS AA
NOX
i I i
CLASS
CLASS C
-CLASS D8E'
PARTICULATES
CLASS AA
-CLASS A
I I i
1975 1985 1995
-------
183
the phasing out of Class A aircraft will result in an improved HC emission
rate, since these aircraft contribute more than their proportional activity
would indicate.
For particulates, Fig. 5.2 shows that even over the length of the
forecast period, Class B (727/DC9) aircraft will be contributing more than
their proportional activity. Their gradual phasing out of service helps to
keep the particulate emission rate constant. If it is deemed necessary,
however, to reduce particulate emissions from aircraft it will be necessary
to focus intial attention on this class of airplane.
In the forecast period, any attempt to impose emission controls on
Class C, D, and E aircraft will probably have a negligible effect on emissions.
This is true because their relative contribution to the emission load is
extremely small. If, however, the emissions from other aircraft sources are
controlled to bring them into line with their relative activity levels, then
the Class C, D, and E aircraft could be considered for controls. In essence,
they are a secondary emission source requiring control only after the other
primary sources are controlled.
It should be noted that the above conclusions apply to the St. Louis
airport aircraft mix. In referring to the application of controls to aircraft
emissions, it is important to realize that other airports may require the
primary controls be placed on different aircraft. Thus, when reference is
made to emission control by, aircraft type, it is intended to imply the type
of control that could be applied locally, such as limitations on engine idle
time. Emission controls on aircraft engines must be considered from the
national perspective. To illustrate the differences, Table 5.1 shows the
relative activity level and relative contribution to emissi6n rates from
-------
184
Table 5.1 .
Aircraft Emissions by Mix
Chicago O'Hare International Airport
1972
I
f
(Aircraft Class
! AA
A
B
C
D $ E
Activity
Level 1
3.5
-
27.5
51.2
6.1
11.7
Relative Emission Contribution (%)
CO
4.9
67.6
24.8
1.2
1.5
HC
1.7
87.8
8.9
0.7
0.9 j
x f Participates
16.5
32.7
45.7
2.1
3.0
0.8
29.9
68.3
0.3
0.7 1
-------
185
the aircraft mix at Chicago's Q'Hare airport. Here,the Class M aircraft
contribute only slightly more than their relative activity to the CO emissions
and less to the HC emissions. The NO and particulate rates are similar to
X
that for St. Louis. This information is, of course, valid for the one year
only.
Emissions by Mode
Another piece of data which adds to the utility of the overall analysis
is the distribution of air pollutant emissions among the various modes of air-
craft operations. It is known that in general the CO and HC emissions are
greatest during the taxi and idle portions of the LTO cycle and the NO emis-
X
sions are greatest during the high-thrust takeoff and climbout portions. A
quantitative presentation of this information is necessary to make any
decisions on the expected effectiveness of ~emission controls which call for
alterations in the operational cycle.
Table 5.2 gives the fraction of the total emission that is released
from each portion of the LTO cycle as calculated for 1990 using the Speas
forecast. For CO, HC, and NO , the other years of the forecast show only
X
slight changes from the fractions given on Table 5.2. A significant change
is observed in the distribution for the particulate emissions. This can be
explained by looking at the emission factors of Table 3.33 and the relative
particulate emission contribution of each aircraft class. The JT3D and JT8D
engines in use on Class A and Class B aircraft have very low particulate
emissions during the taxi and idle modes as compared to the other modes.
The JT9D shows a more even distribution among all modes. Hence, as the
activity levels of the Class A and B aircraft are reduced, the modal distri-
bution of particulate emissions approaches that of the Class AA aircraft.
-------
Table 5.2
Aircraft Emissions by Mode of LTD Cycle
St. Louis Airport
1990 - Speas Study
^ ~-^__^ Mode
'Pollutant ~^->^
CO
HC
MX
Particulates
(Range)*
Percent of Bnissions Coming from Made
Taxi ! Idle Approach
i
66.1 ! 23.5 6.4
70.1
5.6
17.9
7.8-26.8
24.1 2.4
2.4 . 11.3
6.1 ; 26.0
2.1-10.3 ; 31.2-22.0
Landing
2.1
2.1
6.7
4.1
4.5-3.7
Takeoff
0.3
0.3
21.4
9.4
11.1-7.6
Climbout
1.6
1.0
52.6
"36.5
43.3-29.6
*The contribution to particulate emissions by each mode varies over the forecast period
1975-2000. The 1990 value and the range of variation are given.
00
o\
-------
187
Table 5.2 illustrates that for the St. Louis airport, substantial
reductions in CO and HC emissions can be effected by the modification of
ground operations. The taxi and idle .modes account for 89.6 and 94.1% of the
CO and HC emissions, respectively. A reduction of 50% in taxi-idle time
could, therefore, decrease the total aircraft CO and HC emissions by about
45% and 47%, respectively. Techniques for reducing this taxi-idle time,
such as towing aircraft into position rather than having them move under
their own power, must, therefore, be given major consideration in the control
of emissions. (There are, unfortunately, other considerations which can
greatly change the viability of this technique. The safety aspects of such
a modification have not yet been fully evaluated. As with almost all engineer-
ing systems, benefit is not gained without paying a price for it).
Table 5.2 also shows that modification of ground operations will
do little to improve the NO emission picture. Only 8% of the total NO
X X
emitted comes from ground operations. It is highly unlikely that substantial
modifications for the sake of reducing NO emissions can be made in the
X
flight modes without adversely affecting the safety of the aircraft. Hence,
it appears that any required NO emission reduction will have to come from
controls placed on the aircraft engines themselves. NO control, therefore,
X
is out of the reduction ability of the local airport'operator.
Particulate emissions can be partially controlled by the changes in
ground operation but not nearly as dramatically as CO and HC. In 1990, a >
\
50% reduction in taxi-idle time would result in about a 13% reduction in
particulate emissions. By 2000, this could be as much as 20%.
Thus, the simple task of displaying the emissions by mode of air-
craft operation has provided several starting points from which to approach
the evaluation of alternative aircraft emission controls.
-------
188
Emission Trends
From the standpoint of regional air pollutant emission management,
an item of concern would be the projected,'trend in aircraft emissions. One
index of this trend is the average pollutant emission rate per aircraft LTD
at the study airport. This is obtained by dividing the annual amount of
pollutants emitted from all aircraft by the annual number of aircraft LTOs.
Figure 5.3 depicts this computation for the Speas forecast for the St. Louis
airport. It can be seen that the trend is toward higher aircraft emission
rates in CO and NO , somewhat higher rates for HC and lower rates for
particulates. The.implication is that on a per airplane basis, the emission
situation is worsening for CO, HC, and NO and improving for particulates.
This does not, however, complete the picture. Consideration of only
the above information might indicate that from an emission standpoint, it
would be more profitable to have passengers travel in many smaller, lower-
emitting aircraft rather than a few larger, higher-emitting ones. This
preliminary conclusion could have important implications if it were decided
to control airport air pollution by regulating the aircraft mix. The conclu-
sion does not stand up under further investigation. A more revealing index
of emission trend is the average pollutant emission rate per enplaned
passenger. Figure 5.4 is a plot of this rate for the Speas forecast. The
curves show a decrease in emissions per passenger for all pollutant species
with the exception of a slight increase in N0x for the 1975-1985 period. The
salient feature of this display is that the trend toward the wide body jets
is resulting in the movement of passengers at an improved air pollutant emis-
sion level. The air transportation system is improving its position in the
transportation/air pollution picture by moving people in and out of airports
with less emissions per person.
-------
100
189
80
60
40
20
HC
PARTIC
_.~ I
1975
1985
1995
2000
YEAR
Fig. 5.3 Aircraft Emissions per LTD Cycle
St. Louis Airport, Speas Forecast
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190
1.0
0.8
0.6
cc
UJ
ID
0.4
GO
3=
o
0.2
CO
HC
PARTI C
^MH
I
1975
1985 1995
YEAR
200-
Fig. 5.4 Aircraft Emissions per Enplaned Passenger
St. Louis Airport, Speas Forecast
-------
191
Table 5,3
Average Aircraft Emissions
O'Hare International Airport
1972
. !
i
Pollutant I
CO
HC j
i
N0x i
i
Particulates :
Average
Emission
Per Aircraft
LTD Clbs)
55.45
32.71
19.51
3.30
Average
Emission Per
Enplaned Passenger
(Ibs)
1.05
0.62
0.37
0.06
-------
192
2.8
2.4
2.0
1.6
1.2
0.4
g" 0
Q
£ 0.6
0.5
0.4
0.3
0.2
0.1
X
CO
HC
J I
X
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0
1975 1980 1985 1990 1995
0.003
0.002
2000 1975
YEAR
I I
NOX
UNCONTROLLED
CONTROLLED
1980 1985 1990 1995 2000
Fig. 5.5 Annual Ground Service Vehicle Emissions
St. Louis Airport, Speas Forecast
-------
193
vehicles represent a significant source of air pollutant emissions and are
likely candidates for some form of emission control. Such controls could
reduce overall airport emissions with little or no impact on safety.
It is reported that at the new Dallas-Ft, Worth airport, consider-
ation is being given to the adaptation of ground service vehicles to burn
propane gas in order to meet the 1975-76 federal automotive emission
standards. Figure 5.5 shows the effect these standards would have on
reducing ground service vehicle emissions at the St. Louis airport. In 1990,
the CO emissions could be cut by about 96%, the HC by 93%, the NO by 67%,
X
and the particulates by 64%. This, then, offers airport planners one safe
method of exercising local control over airport emissions.
5.1.3 Fuel Storage and Handling System
Since the emissions from fuel storage and handling are based on the
total airport fuel requirements which, in turn, are based on aircraft activity,
the emission growth pattern is the same as that for aircraft. Table 5.4
gives the total hydrocarbon emissions from fuel storage and handling, as
calculated for each of the three forecasts for the St. Louis airport.
The imposition of controls to eliminate this emission source would
require some sort of vapor recovery system to be installed on the fuel tanks
of all aircraft and ground service vehicles. If such a system could be
designed to be compatible with the requirements of weight, size, and safety,
the emissions could be effectively reduced to zero.
5.1.4 Engine Test and Maintenance Facility
The emissions from engine testing is also dependent on the aircraft
activity and therefore follows the same growth trend. Table 5.5 gives these
calculations for the St. Louis airport. The discussion on aircraft emission
rates (Section 5.1.1) explains the behaviour of these emission patterns.
-------
194
Table 5.4
Annual Fuel Storage and Handling Emissions
Year
1975
1980
1985
1990
1995
2000
St. Louis Airport
Hydrocarbon Emissions (tons/year!)
NADC McDonnell Speas
79 " 101
92 , 100 114
114 124 135
134 155 161
162 - 179
185 - 191
-------
Table 5.5
Annual Engine Testing and Maintenance Emissions
St. Louis Airport
Year
1975
1980
1985
1990
1995
2000
NADC
CO HC NO
Jv
Particu-
lates
_
56 17 11
73 20 16
93 23 21
116 26 27
135 27 32
2
2
2
2
2
Emissions (tons/year)
McDonnell
Particu-
GO HC NOY lates
J\.
66 39 8 1
85 44 13 1
103 45 18 2
126 48 24 2
-
-
Speas
CO HC W)v
Jv.
50 15 10
63 17 13
80 17 19
99 20 23
120 25 29
137 28 33
Particu-
lates
3
3
3
3
3
3
-------
196
Apart from controls placed on the aircraft engines themselves, it
Is not clear that these emissions can be controlled by the airport operator.
Conceivably, the test cell could be designed to control emissions with after-
i
burners, catalytic converters and the like. The costs and resulting benefits
of such an elaborate installation are probably not warranted, however.
Alternatively, some modification of the test cycle could be made.
A higher power setting could be used to minimize CO and HC emissions.while
sacrificing a little on the NO emissions. This would probably result in
X
other tradeoffs, however, such as increased noise and inability to perform
necessary tests on engine performance.
In unusual circumstances which require reduction of emissions from
every possible source, the number of tests could be curtailed. In summary,
it can be said that the reduction of air pollutant emissions from engine
testing is not within the feasible and practical control of the local airport
operator and must rely on engine emission regulations.
5.1.5 Heating and Air Conditioning Plant
The emissions from the airport heating and air conditioning plant
are a function of a number of variables which fall within the realm of control
of airport designers. Items such as choice of fuel, building size, building
thermal insulation, and others are decided upon by airport master-planners
prior to any construction. The actual decision, however, is usually made
upon considerations other than air pollution emission control. These consider-
ations, chiefly economic, are not easy to override in order to achieve
emission reductions. One variable, however, has been investigated for its
impact on St. Louis airport emissions and that is the choice of fuel. Table 5.6
-------
197
Table 5.6
Annual Airport Heating Plant Emissions
i
1
Pollutant
CO
HC
^x
Participates
St. Louis Airport
Emissions (tons /year")
Coal-Fired
Plant
22.3
11.2
167.3
24.5
!
Oil-Fired
Plant
0.4
5.3
70.0
24.5
-------
198
gives the emissions resulting from a coal-fired and an oil-fired]heating
plant. Both are subject to Illinois state regulations, as discussed in
Section 3.2.5. (
It is clear that an oil-fired plant will result in smaller emission
rates. The equivalence of the particulate emission rate is a result of the
application of the state regulations. To meet this standard, the coal plant
would require about 98% reduction of uncontrolled emissions, while the oil
plant would require only 26% reduction. The final decision on fuel choice
will require an analysis of the overall fuel costs, as compared to the cost
of equipment needed to meet state requirements.
Another alternative method of reducing emissions would be to decrease
the energy requirements of the buildings by use of new thermal insulation
materials. Some of these materials, such as highly reflective glass, have
already been installed on some of the newer office buildings. The resulting
decrease in energy requirements would proportionately reduce heating plant
emissions. Again, economic considerations-would tend to dominate the final
decision.
5.1.6 Access Traffic
Access traffic is the second largest source of airport emissions
and is exceeded only by the aircraft. Although automotive emissions do not
fall under the direct control of the airport planner, there are indirect
measures, such as reduction .of traffic congestion, which can indirectly
reduce the emission load.
Figure 5.6 is a display of the access traffic emissions as calculated
for the St. Luois airport, Speas forecast. The substantial dip in the curves
is a result of the imposition of the 1975-76 federal automotive emiss.ion
-------
199
1975
1980
1985 1990
YEAR
1995
2000
Fig. 5.6 Annual Access Traffic Emissions
St. Louis Airport, Speas Forecast
-------
200
standards. As more controlled vehicles are phased Into the population, the
total emission load decreases even though(the actual vehicle traffic is
increasing. By 1985, however, essentially all of the vehicles are controlled
and the increase in traffic volume begins to outweigh the advantages of the
emission controls.
It should be noted that these calculations are based on the assump-
tion that no additional automotive controls will be imposed beyond the
1975-76 standards and that the airport design will minimize traffic conges-
tion. Should congestion become a major problem, the emission rates could
conceivably double. (The correction factors for CO and HC are about 2.1 and
28
1.7, respectively, when average vehicle speed is reduced from 25 mph to
10 mph.) Minimization of this problem in the airport design will enable a
significant emission source to be held in check.
Alternate Access Modes
One of the airport design considerations now being emphasized is
the diversion of people who now access the airport by private automobile to
some other form of transportation. Because of the general nature of the
airport access model described in Section 3.1.8, it was possible to evaluate
the impact on air pollutant emissions of alternate access modes. A "numerical
experiment" was carried out by changing the access mode choice of the different
groups of people over the range of 100% private auto to 100% mass transit.
Figures 5,7a, b, c, d, show the results of diverting people from
autos to buses, diesel commuter trains, and electric rail rapid transit.
-------
CO
1990
BUS
DIESEL TRAIN
ELECTRIC TRAIN
2000
to
o
0 100
50
PERSON - TRIPS MADE BY PRIVATE AUTO,
0 100
Fig. 5.7a Emission Impact of Alternate Access Modes
St. Louis Airport, Speas Forecast
-------
HC
1990
BUS
DIESEL TRAIN
ELECTRIC TRAIN
2000
100
50
PERSON - TRIPS MADE BY PRIVATE AUTO.
0 100
50
Fig, 5.7b Emission Impact of Alternate Access Modes
St. Louis Airport, Speas Forecast .
-------
NO,
0.6
S 0.4
0.2
100
1975
50
to
o
PERSON - TRIPS MADE BY PRIVATE AUTO, %
Fig. 5.7c Bnission Impact of Alternate Access Modes
St. Louis Airport, Speas Forecast
-------
PARTICULATES
0.03
0.02
s
i
g
a
0.01
1975
1990
BUS
DIESEL TRAIN
ELECTRIC TRAIN
100
50
0 100 50
PERSON - TRIPS MADE BY PRIVATE AUTO,
0 100
Fig. 5.7d Emission Impact of Alternate Access Modes
St. Louis Airport, Speas Fbrecast
2000
tsj
o
SO
-------
205
The results indicate that in 1975, a significant reduction in CO,
HC, and NO emissions can be had by diverting people from autos to mass
transit. A slight increase is observed in particulate emissions resulting
from the electric rail rapid transit power plant. It can also be seen that,
with the exception of particulates, there is not much difference among the
three mass transit modes in emission improvement.
By 1990, the improvement in CO and HC emissions is not nearly as
dramatic as a result of the imposition of emission controls on autos. In
fact, the NO emissions increase markedly if diversion is made to buses,
and increase, somewhat, if diversion is made to electric rail transit.
The diesel train, because of its higher passenger capacity, is the only
mode which shows an NO emission improvement in a 1990 tradeoff.
X
By 2000, the tradeoff in CO and HC emissions has improved, although
not reaching the 1975 level. This is a result of the increase in auto
volume required to handle the increase in person-trips to the airport, over-
taking the improvements brought about by emission controls. The NO picture
A
is worse than in 1990 for the bus and electric rail transit; it is improved
for the diesel train. The particulate tradeoff has improved for all three
modes.
Based upon the above calculations for the St. Louis airport over
the period 1975-2000, it appears that the diesel commuter train, because of
its higher passenger capacity, is the best alternative to the automobile
from an emissions standpoint. The electric rail transit is next/ with the
bus last because of its high NO emissions. It should be noted that several
conditions could conceivably come together to change this picture. First,
-------
206
the Imposition of emission controls on diesel bus NO emissions could
substantially improve its position in a tradeoff. Second, any significant
traffic congestion could so deteriorate the CO and HC emissions as to make
i
the tradeoff to mass transit attractive throughout the forecast period. Third,
use of higher capacity electric trains and the imposition of stricter emis-
sion controls on power plants could put them in a better position with
respect to the diesel trains.
This type of tradeoff study, although hot comprehensive and
certainly not completely conclusive, is the type of information that can
and should be generated by airport planners in evaluating alternative emis-
sion control strategies.
5.1.7 Total Airport Emissions
The final segment of the airport emission display is the summation
of the individual contributions into the total emission rate. It is this
rate that will result in impacts on the regional air quality. The diurnal
patterns of this emission load are important in determining compliance with
federal ambient air quality standards, The relative magnitude of the various
emission sources needs to be displayed also to determine those sources which
should be subject to immediate controls and those which can be treated as
secondary contributors.
Annual Airport Emissions
Figure 5.8 gives the annual total airport emissions as calculated
for the St. Louis airport. In this display, the controlled ground service
vehicle and the oil-fired heating plant emissions are used. The presentation
of the computations for the three air traffic forecasts illustrates the range
of emissions that results from the range of forecasted activity. Emission
-------
to
o
HC
I
I
I
1975
1980 1985 1990 1995
2000
1975
YEAR
1 I T
PARTICUUTES
SPEAS
NADC
MCDONNELL
1980
1985 1990
Fig. 5.8 Annual Total Emissions
St. Louis Airport
1995 2000
-------
208
ranges can be presented as a function of other parameters but the important
ones have already been discussed in the sections on individual emission
sources* '
The figure shows that the NADC and the McDonnell forecasts act
essentially as upper and lower bounds on CO and NO emissions. The widest
variation is in 1990, where the CO emissions are between 6041 and 9734 tons/year
and the NO emissions are between 4699 and 6950 tons/year. The Speas forecast
seems to naturally fall as a mean; it is, therefore, suggested that the Speas
calculations be used as the emission estimate with the possible range of emis-
sions noted. For hydrocarbons, the Speas and NADC calculations almost coincide
with the McDonnell values as an upper bound. For particulates, all three fore-
casts are close together.
The Environmental Impact Statement suggests that the total airport
emissions based on the 1990 Speas forecast would be on the order of 10,400
tons CO, 4100 tons organics, 5000 tons NO-, 3000 tons particulates per year.
The CO, HC and NO estimates are comparable to the higher McDonnell projection,
2>
but the particulate estimate is an order of magnitude higher than all three
projections... It is not possible to do .a detailed evaluation of the differences
because insufficient information on the calculation assumptions used in the
Impact Statement is available.
Another method of presenting this data is in the form of emission
density. This would mean dividing' the total emission rate by the area of the
2
airport to get an emission density in units of tons/mile /day. A graphical
display of this data would require only a scale change on Fig. 5.8 . In
computing the emission density for the Columbia-Waterloo site, the land area
used must be carefully considered. The Impact Statement states that the total
airport boundary is expected to cover approximately 29.1 sq.mi. Of that area,
-------
209
only 7.4 sq ml is expected to be utilized for actual airport activity (runways
and taxiways, terminal buildings, airport facilities), with an additional
7.0 sq mi used for environmental buffer zones in the pre-2000 period. An
additional 10.4 sq mi is expected to be added for airport activity, and 4.3
sq mi for buffer in the post-2000 period. If the total 29.1 sq mi airport
area were to be used for emission density calculations, the Columbia-Waterloo
site would show a remarkable improvement over existing airports. O'Hare
Airport in Chicago has only about 10 sq mi. Table 5.7 shows the emission
densities for O'Hare, as compared to the Columbia-Waterloo site in 1990 when
the passenger activity will approximately equal O'Hare's. Both the 29.1 sq mi
total area and the 7.4 sq mi pre^2000 airport activity area are used for the
St. Louis airport.
It can be seen that on the basis of the total airport area, the
Columbia-Waterloo site will be well below current emission densities, but
on the basis of the active area, the NO emission density is actually higher
.X
than current values. This indicates that airports may be as significant an
air quality problem to themselves as they are to the surrounding region. The
air in the active area is subject to a much higher emission density loading
than the whole airport imposes on the region. This indicates the need to
consider local air quality impacts, as well as regional impacts when planning
for airport emission controls.
Diurnal Emission Patterns
Figure 5.9 gives the daily pattern of emissions as calculated for
the St. Louis airport using the 1990 Speas activity level. It can be seen
that the emission rate begins to climb at about 6:00 AM and continues until
a daytime average level is reached at about 11:00 AM. This level is main-
tained until about 7:00 PM, whereupon it begins to decline to a nighttime
-------
210
Table 5.7
Airport Emission Densities
Airport
O'Hare
Columbia-
Waterloo
(1990)
Area
9.9
29.1
7.4
Emission Density fTons/sq mi/yr)
CO
1828
253
993
HC
783
59
234
N0x ; Participates
456.
201
791
112
8
32
-------
30
25
20
S
12 10
CO
1 _
NOV
HOUR
I
PARTICULATES
Fig. 5.9 Diurnal Bnission Pattern
St. Louis Airport
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212
level which reaches Its low point at about 3:00 AM. Distinct peaks in the
emission rates can he seen in the 8:00-9:00 AM and 5:00-6:00 PM time periods
corresponding to peaks in automobile and aircraft activity.
This display can be used to make some statements about the expected
air quality, as compared to the national ambient air quality standards. The
CO standards are based on both 1-hour and 8-hour average concentrations.
Figure 5.9 shows that the high average daytime CO emission rate is sustained
for at least ten hours and that 1-hour peaks are observed above that level.
It is conceivable, therefore, that if the average emission rate is high
enough both the 1- and 8-hour standards could be violated on the airport
grounds.
The hydrocarbon standard is based on a 3-hour average during the
period of 6:00-9:00 AM (morning rush period). It is designed to limit the
midday oxidant formation. The figure shows that the emission rate is begin-
ning to increase during these hours and could, therefore, result in exces-
sive oxidant air quality levels if the daytime average emission rate is high.
On the other hand, if the daytime average rate is not too high, the 1-hour
photochemical oxidant standard could conceivably be met since the hydrocarbon
emission rate does not exhibit strong peaks.
The NO standard is based on an annual average and no specific
J\:
information can be gleaned from Fig. 5.9.
The particulate standard uses an annual and a 24-hour averaging
time. Thus, it is only the total daily emissions that would be used for
comparison to air quality standards.
One additional point needs to be made about the diurnal emission
pattern and that is the emission rate during the design peak hour. As
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213
Table 5.8
Peak Hour Emission Rate
St. Louis Airport
1990 Speas Forecast
Emission Rate (Ib/hrl
CO
HC
Particulates
Average peak hour
(from Fig. 5.9)
Design peak hour
% increase
3026
712
3552
17
825
16
2354
2695
14
91
104
14
-------
Table 5.9
St. Louis Airport Emissions
Speas Forecast
(Ton/Year)
Pollutant j
Source
Aircraft
CO
HC
^x
Part.
CSV*
Engine Test
Heating Plantt
Access Traffic
Total
Aircraft
CSV*
Fuel Handling
Engine Test
Heating Plant'*'
Access Traffic
Total
Aircraft
CSV*
Engine Test
Heating Plant"1"
Access Traffic
Total
Aircraft
CSV*
Engine Test
Heating Plant*
Access Traffic
Total
1975
2377
394
50
0.4
1140
4162
721
142
101
15
5
205
1190
1672
57
10
70
273
2082
165
2
3
25
6
199
1980
3576
567
63
0.4
440
4447
974
53
114
17
5
80
1243
2718
61
13
70
149
3011
177
2
3
25
8
213
Year
1985
4990
106
80
0.4
249
5425
1050
35
135
17
5
53
1296
4111
54
19
70
124
4377
180
1
3
25
12
221
1990
6805
83
99
0.4
361
7348
1437
30
161
20
5
78
1731
5531
48
23
70
180
5852
192
1
3
25
17
238
1995
8613
101
120
0.4
486
9320
1822
36
179
25
5
105
2171
6946
58
29
70
242
7344
187
2
3
25
23
238
2000
10040
114
137
0.4
632
10924
2125
40
191
28
5 c.
136 £
2525
8054
66
33
70
315
8537
177
2
3
25
30
235
*Ground Service Vehicles (Controlled)
fOil-Fired
Note: Numbers independently rounded may not add exactly.
-------
215
discussed in Section 3.1.3, one of the forecast parameters for the St.
Louis airport is the peak hour aircraft activity. This is somewhat higher
than the peak hour activity generated by applying the diurnal pattern of
Table 3.11 to the annual activity estimates of Table 3.10. This additional
peak hour load is a result of seasonal variations and represents the maxi-
mum activity that the airport is designed to handle. For the purposes of an
air quality analysis, the resulting increase in air pollutant emissions
during the design peak hour is of importance only for those pollutants with
short averaging time standards, namely, CO, hydrocarbons, and photochemical
oxidants. Table 5.8 shows the computed peak hour emission rate for the
1990 Speas forecast and compares the rate from Fig. 5.9 to the rate at the
design peak hour activity level. An increase of from 14-17% in emissions
is observed. If the CO and oxidant air quality is marginal to begin with, this
increased emission rate could result in a serious degradation of the air quality.
Relative Emission Strengths
In attempting to evaluate the possibilities of emission restrictions
placed on airport activities, it is necessary to know the relative strengths
of each of the emission sources. The final airport emission display is a
summary tabulation of all sources so that the primary contributors can be
isolated from the minor sources. Table 5.9 presents this tabulation for
the St. Louis airport. A similar compilation for O'Hare is presented later
on Table 5.10.
The tabulation shows that early in the forecast. Aircraft and ground
vehicles (service and access) contribute equally to the CO emission load.
Later, however, the imposition of emission controls on motor vehicles
essentially reduces their contribution to an order of magnitude less than the
-------
216
aircraft. Essentially the same can be said for the HC emission rates. For
NO and particulates, the aircraft start out as overwhelming contributors and
X
remain that way throughout the forecast:period.
i
The indication is that some form of control is required on aircraft
emissions. It was hoped that the recent federal EPA aircraft standards
could have been included in this study, but their late issuance precluded
this possibility.
For hydrocarbon emissions in the post-1975 period, the fuel handling
system would appear to be the prime target for control, other than aircraft.
The implications of controlling this source have already been discussed.
The airport heating plant is not significant in the CO and HC
picture but contributes substantially to the NO and particulate emissions.
Additional stationary source regulations would have to be.issued to control
this.
Engine testing is most significant in the CO and HC emission rate.
This is primarily because the idle power setting is used for most of the
test and this is the highest CO" and HC-emitting mode.
It should be pointed out that the above conclusions are based on
looking at the emission rates only. In order to determine if, in fact, any
emission controls are required, it is necessary to do an air quality calcula-
tion using these emission rates as input. This is planned for Phase II of
this program.
-------
217
5.2 Airport Vicinity Land Use Emissions
This section presents some of the results of the land use based
emission estimating methodology for the O'Hare airport study area. The
procedures described in Section 4 were applied to each of the air pol-
lutant producing land use classifications (residential, commercial, insti-
tutional, manufacturing, warehousing and motor vehicle emissions). Annualized
emission rates for each of the five primary pollutants were computed for
each individual section and then summed to provide a total emission rate
per section for each pollutant. For purposes of comparing emissions on a
density basis, the emission density of each section was also computed as
well as the emission density of the O'Hare airport facility. The density
for the airport was based on the total emissions for each pollutant divided
by the airport property (approximately 10 square miles). Total airport
emissions were developed using the procedures outlined in Section 3. The
calculations were based on annual average emissions for 1970. However, for
the airport emissions the levels are essentially equivalent to 1972 since
the aircraft activity was nearly the same for both years. It is important
to note here that the emissions calculated through this procedure are basically
uncontrolled, that is, neither stationary source emission controls required
by implementation plans nor Federal automotive emission limitations were
effecting significant reductions at the time of this calculation.
Figures 5.10 through 5.14 present the display of emission densities
for the individual sections of the study area in comparison with O'Hare
airport emission densities. Table 5.10 indicates the airport and surrounding
land use emissions broken down into the various activity components. While
airport emission densities of CO, HC and NO are considerable, it can be
X
shown that comparable densities are prevalent in the surrounding developed
areas, where ground transportation emissions are an extremely large contributor.
-------
218
It Is fairly easy to relate the emission density figures to the major arteries
and expressways in the airport vicinity. It should be noted that if the
airport CO and HC emission density were'computed based on the terminal areas
of the airport (90% of total emissions), then the densities would increase
by a factor of 5 to 10. Sulfur dioxide emission densities in the airport
vicinity are considerable due to the build-up of the manufacturing activities.
SO. emissions from the airport are composed largely of stationary heating
plants with a small contribution from aircraft.
-------
219
<500
tons
miz-yr
500-1000
tons
mi -yr
1000-2000
tons
mi -yr
>2000
tons
mi -yr
Fig. 5.10. Carbon Monoxide Emission
Densities from 0"Hare Airport
and the Surrounding Area
-------
220
<50
tons
mi -yr
50-200
tons
mi -yr
200-400
tons
mi -yr
>400
tons
mi -yr
Fig. 5.11. Hydrocarbon Emission
Densities from O'Hare Airport
and the Surrounding Area
-------
221
<50
tons
mi*-yr
50-150
tons
mi*-yr
150-300
tons
>300
tons
mi'-yr
Fig. 5.12. Nitrogen Oxides Emission
Densities from O'Hare Airport
and the Surrounding Area
-------
222
< 3
tons
mi'-yr
3-50
tons
mi*-yr
50-100
tons
mi*-yr
>ioo
tons
mi2 -yr
Fig. 5.13. Particulate Emission
Densities from O'Hare Airport
and the Surrounding Area
-------
223
< I
tons
mi*-yr
I-10
tons
mil-yr
10-100
tons
mi*-yr
>ioo
tons
mi* -yr
Fig. 5.14. Sulfur Dioxide Emission
Densities from O'Hare Airport
and the Surrounding Area
-------
Table 5.10
1970 Emissions Fran 4 Townships Surrounding O'Hare (Tons/Yr.)
so2
Part
CO
HC
^x
*based
Part
CO
HC
NO
Residential Commercial
189
142 75
150 36
59 38
370 321
on 115 sq. mi. excluding airport
O'Hare
Ground
Service
Aircraft Vehicles
542 23
9,108 4,245
5,377 942
3,205 440
Manufacturing
Warehousing
13,395
7,998
806
909
8,287
Motor
Vehicles Total
13,584
560 8,775
190,456 191,448
30,966 31,972
17,375 26,353
Density*
Tons/ 7
1 Mi -Yr.
118
76
1,657
276
225
- .. i
i
Airport Emissions (Tons/Yr.)
Access
Traffic
34
4,711
821
575
Heating Fuel
Plants Handling
511 -
32
32 582
291
Density**
Tons/ 7
Total ' Mi -Yr.
1,110 112
18,096 1,828
7,754 783
4,511 456
to
to
**based on 9.9 sq. mi. airport property
-------
225
6.0 METEOROLOGICAL AND AIK POLLUTION POTENTIAL ANALYSIS
6.1 Introduction
This section presents a description of the regional meteorology
and potential for high air pollutant buildup in the vicinity of the proposed
St. Louis airport. Much of the data used for this analysis was derived
from the National Weather Service at Lambert Field and through low level
soundings taken at the Arch in downtown St. Louis. Additional data were
derived from Scott Air Force Base, the State of Illinois Air Quality Annual
Report and modeling results of the St. Louis region conducted by Argonne
as part of the implementation plan for the State of Illinois. The
objectives of this section are to:
1) Present meteorological data which can be used to evaluate air
pollution potential.
2) Present existing measured air quality data in the airport vicinity.
3) Present existing modeling results which can be used to estimate
the background levels in the proposed airport area due to metropolitan
emissions.
6.2 Transport Wind and Mixing Height Climatology for the
St. Louis Metropolitan Area
Two of the most important parameters governing the dispersion of
air pollutants in the lower atmosphere are the transport wind and mixing
height. A review of the historical data for these two parameters allows the
assessment of the potential for air pollution buildup due to the emissions
related to the porposed airport and its surrounding development. In this
section the frequency of occurrence and persistence, of low wind speeds,
as well as expected mixing heights and frequencies of poor ventilation are
examined.
-------
226
The St. Louis urban sounding data was gathered under the National
Weather Service Environmental Support ijnit (EMSU). This data was gathered
39
from May, 1969 to April, 1971. These soundings were taken twice daily
near the center of the St. Louis Metropolitan Area at the Jefferson National
Expansion Memorial Park. This is the site of the Gateway Arch and the
immediate vicinity is bounded by the Mississippi River to the east and by a
built-up central urban complex south through west to north. Normally, unless
an air stagnation period extended into or began on a weekend, the soundings
were only taken on weekdays. The times of release are near sunrise and
near noon local standard time. In spite of these weekend discontinuities
and other less frequent data breaks due to problems such as instrument
failure, the available sample is large, and therefore, representative of the
total population in any given year.
Definitions and Procedures
The mixing height is an approximation to the height of the top of
the surface-based mixed layer through which (ideally) pollutants readily
mix at some distance downstream of the sources.
The morning mixing height (MNHT) is a calculated, not an observed,
mixing height. It is obtained in the following manner:
1) Add 3°C to the surface temperature of the sunrise sounding.
2) Extend a dry adiabat from this temperature to the sounding
curve.
3) The above-ground height of this intersection is the morning
mixing height.
-------
227
This method is employed because of the difficulties involved with
resolving shallow morning mixing depths with the existing instrumentation
and evaluation procedures. The calculated height is a relative index of
low-level stability and is normally representative of the urban mixing height
in the first few hours after sunrise.
The midday mixing height (MXHT) is the "observed" mixing height
obtained from the midday sounding as follows:
1) The height from the surface to the base of the lowest inversion
or isothermal layer will be the observed mixing depth.
2) In the absence of any isothermal or inversion layer, the point
at which the lapse rate becomes more stable than moist adiabatic will provide
the mixing depth lid.
3) If a lid as described in 1) and 2) doesn't exist below the
700 mb level, the mixing depth will be considered unlimited (3000 meters,
for this study).
The resultant transport wind (RTW) speed is obtained from the
urban soundings in the following manner:
1) Establish the mixing height (observed or calculated).
2) Find the horizontal distance out (HDO) from the release
point of the instrument package when it reached the mixing height.
3) Establish (from the radiosonde recorder) time elapsed (TE)
from the release to mixing height intersection.
4) HDO divided by TE equals the resultant transport wind speed
through the mixed layer.
Another parameter utilized in this climatology is the ventilation
factor. This parameter, commonly used during midday or maximum dispersion
-------
228
periods, is a product of the mixing height and the transport wind speed.
' F^>:
It Is a measure of the volume rate of
-------
229
Table 6.1 - ST. LOUIS MORNING SOUNDINGS (500 cases)
METERS
+ - Mean + -
Mean Stand. RTW Stand.
Month
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
March
April
May
N.
41
42
43
41
45
35
39
37
35
43
42
57
MNHT
427
332
297
383
438
596
547
568
533
699
473
420
Dev.
271
210
215
239
314
527
260
342
344
404
303
291
V
63
63
72
62
72
88
48
60
65
58
64
69
Speed
5.7
4.1
4.0
5.5
6.6
7.5
6.9
7.7
7.9
8.1
7.5
6.8
Dev.
2.8
2.4
2.3
3.0
3.1
4.0
3.0
3.2
3.2
4.7
3.4
3.6
V
49
59
58
55
47
53
43
42
41
58
45
53
MNHT « Morning Mixing Heights in Meters
RTW = Resultant Transport Wind Speed in Meters Per Second
V = Coefficient of Variation = Standard Deviation X 100
Mean
-------
230
Table 6.2 - ST. LOUIS MIDDAY SOUNDINGS (448 cases)
METERS
Month
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
N
30
43
41
37
39
32
38
37
33
37
43
38
Mean
MXHT
1407
1901
1484
1291
1207
935
722
715
749
1104
1346
1436
+ -
Stand.
Dev.
627
787
646
768
829
492
497
486
326
618
752
708 '
.V
45
41
44
59
69
53
69
68
44
56
56
49
Mean
RTW
Speed
5.5
4.5
4.4
4.7
6.5
6.1
6.0
6.6
7.5
7.4
7.3
7.1
+ -
Stand.
Dev.
2.4
2.3
1.6
2.5
3.3
3.3
2.8
3.8
3.6
3.9
3.5
3.4
V
44
51
36
53
51
54
47
58
48
53
48
48
MXHT - Midday Mixing Heights in Meters
RTW » Resultant Transport Wind Speed in Meters Per Second
V - Coefficient of Variation - Standard Deviation X 100
Mean
-------
231
Table 6.3
St. Louis Midday Soundings
(448 cases)
Mean
Month N Vent*.
June 30 8296 6445 78
juty 43 8944 6145 62
Aug. 41 6286 3438 55
Sept. 37 6475 6351 98
Oct. 39 9509 10752 113
Nov. 32 6289 5749 97
Dec'. 38 4857 5840 120
Jan. 37 5566 6037 103
Feb. 33 6161 5269 86
Mar. 37 8313 7600 91
Apr. 43 10521 '8665 82
May 38 10630 7504 71
(Moter2) , .
Vent. - Ventilation (Sec ) = MXHT (meters) X R1W Speed (meters per second)
V « Coefficient of Variation « Standard Deviation X 100
Mean
-------
232
RTW speeds occur In phase with the MNHT means. This is not surprising
since the highest stabilities are generally associated with the lowest
wind speeds.
This is in contrast to the midday parameters (Table 6.2), where
the annual maximum and minimum of RTW speed and MXHT (observed) means
are approximately 180° out of phase with each other. The annual varia-
tion of midday RTW speeds is about the same as the morning RTW speeds;
however, the maximum average MXHT values tend to occur in the late spring
and summer months with the peak mean value in July, the month where
unlimited MXHT values are most frequent. The monthly MXHT means drop to
a minimum in January. This trend is similar to and obviously related to
the annual temperature curve and the lagged solar radiation curve.
The midday ventilation means (Table 6.3) begin to give some
insight on air stagnation potential in spite of the high scatter. Two
important minima in the monthly means occur during the year. One is in
August, when MXHT values are decreasing from the July maximum faster than
the winds are increasing from the summer minimum. The second minimum is
more pronounced, with the lowest ventilation month of the year being
December. The first minimum (August) is most frequently a result of
low RTW speeds as opposed to the cold season minimum which is more fre-
quently associated with low MXHT values. (Note that the mean ventilation
values used in this study are the averages of the daily values of venti-
lation and not the product of the mean RTW and mean MXHT).
In Fig. 6.1 the data are stratified to exclude all data points
2
with ventilation >6000 m ps and transport wind speeds >4.0 mps. These
values coincide with the threshold levels for air stagnation in the
National Meteorological Center's forecast program. The ordinate of
-------
140-1
|30..
1
10-
233
M
M
0 N D
FIG.6,1- MONTHLY FREQUENCY DISTRIBUTION OF MIDDAY STAGNATION-ST. LOUIS, MO.
CRITERIA: RTW SPEED <4.0 MPS AND VENTILATION ^6000 M2PS...
ORDINATE IS % OF THE MONTHLY SAMPLE BASED ON 2 YEARS OF DATA
40-,
C/}
>>
30-
O
g
u
10.
JF MA MJJ AS 0 N D<
FIG. .6.2 - SAME AS FIG. 1 BUT. VENTILATION <40CO M2B5
-------
234
Fig. 6.1 is the percentage of the monthly data count (N in Table 6.3) for
2
which midday ventilation was .56000 m ps and the associated RTW speed was
i4.0 mps. The higher frequencies of thd,s condition are during the summer
and early fall months (June through September) with September having the
peak monthly frequency. There seems to be a weak secondary rise in Janu-
ary just before lowest monthly frequency in February, but this could
easily be due to the small sample size.
In Fig. 6.2 the same stratification was applied except that the
2
ventilation threshold was lowered to <4000 m ps. The basic pattern from
Fig. 6.1 to Fig. 6.2 does not change, but June is eliminated from the
clear summer maximum.
To date, since the beginning of air stagnation advisory (ASA)
services in the St. Louis Metropolitan Area in 1968, there have been five
advisories issued. Two of these have occurred in August (1969 and 1971)
and one each in January 1969, November 1969, and December 1971. Although
September is indicated as the peak frequency times for midday stagnation,
no advisories have yet been issued during that month. It should be
remembered that air stagnation advisories normally have a duration require-
ment (36 hours or more) as well as a minimum dispersion criteria (normally
early morning). Perhaps the reason for the higher frequency of advisories
in August may be related to the lesser scatter about the monthly means of
both ventilation and RTW speed. The standard deviation and coefficient
of variation (V) are at their minimum (Tables 6.2 and 6.3) in the month
of August, which would tend to support a higher probability that the
midday stagnation days would be consecutive, and therefore, meet the
ASA duration criterion. The sample of ASA occurrences is too small, how-
ever, to give any conclusive results. The very fact that midday stagnation
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235
frequency is at Its maximum in July through September, and the morning
calculated mixing heights OMNHT) and transport wind speed monthly aver-
ages are at their minimum for the same period, would isolate the July
through September period as the annual maximum for air stagnation
potential.
Directional-Frequency Distributions and Means
This analysis of the sounding data involves only the midday
soundings, and is a presentation of directional frequency. The total
data count is 448 which the 5 log N rule would allow, roughly, a maximum
of 13 directional frequency classes. As a result, 30° direction incre-
ments were chosen, allowing 12 classes beginning with 0°. Table 6.4
presents the annual distribution together with average RTW speeds, MXHT
and ventilation for each direction class;Figure 6.3 presents these data
in a frequency direction rose. The RTW direction frequency distribution
indicated in Table 6.4 and Fig. 6.3 agrees closely with the distribution
of hourly surface wind observations at Lambert Field from the period 1951
to 1960 (Table 6.5). In both cases, annual maxima are from the south and
northwest, with a minimum frequency from the east. The slight differ-
ences that do exist can most likely be assigned to the differences in
direction categories and the fact that RTW is a vector average through a
layer at midday, while the Lambert Field winds are surface winds from
hourly observations. The latter effect results in generally higher average
speeds from the RTW. The variation of average speeds with direction is
also similar to the surface wind distribution. In general, from Table 6.4
it is obvious that the RTW directions with easterly components have lower
average RTW speeds, MXHTs and ventilation factors. Only a couple of cases
of MXHTs and RTW speed averages depart from this generality.
-------
Table 6.4
N
% Total
!.'3an RTV7 Speed
K3an 1.KHT
J,!2an Vent.
360-29° 10-59° 60-89° 90-119° 12Q-U9" 150-179"
23 23 20 13 19 54
6.3 5.1 4.5 2.9
6.1 3.6 4.6 5.4
881 1529 1194 928
5509 4275 5812 4180
4.2 12.1
4.7 5.0
1013 968
4878 5066
180^909°" 210-239W 240-269° 270-299° 300-329° ;
62 47 44 59 44
13.8 10.5 9.8 13.2 9.8
7.6 6.1 6.9 7.1 6.7
1279 1383 2435 2340 1205
9565 8958 10829 9665 8652
330-359'
34
7.6
5.4
1173
7012
Total Data Count = 448
J.CCHT - Observed
RT.V = Resultant
Midday fixing Height (Meters)
Transport TJind Speed (rasters per second)
Ventilation = MXHT X RTtf Speed (SSiSES2)
( sec )
Table 6.S
to
Ul
OS
St. Louis surfaco «ir,ds (St. Louis-Lambert Intl. Aimort)
Annual Directional Distribution
Susraary of Hourly Observations (WBAN) 19^1 _ 1960
Dip. N
% Total U.5
Avg. 3.8
Speed
tu.p.s.
NK5 N3 ENE E ESE
3.2 3.9 3.2 3.9 5.2
3.6 3.5 3.8 U.I .3.9
SS S3S S
7.0 7.6 10.8
U.2 5.0 U.6
SSW SW TCSW W "WNW H-J KNVJ Calm
6.2 6.3 5.0 6.2 8.U 9.U 5.3 3.8
U.3 U.o U.U U.U U.9 U.7 U.3
-------
237
Fig. 6.3 Resultant Transport Wind Rose
Annual St. Louis Midday Urban
Soundings
Direction Class Size =30°
Based on a 2-Year Sample
with N = 448
(1 calm)
-------
238
Figure 6.4 presents the base data stratified as in Fig. 6.2, i.e.,
all data points (midday soundings) with RTW ^4.0 nips and ventilation >4000
2
m ps have been removed, leaving only capes of well-defined midday stagna-
tion. The difference is that Fig. 6.2 is categorized by months and Fig. 6.4
utilizes 30° directional classes. It is obvious from Fig. 6.4 that the
south-southeast class (150-179°) has the highest frequency of midday stag-
nation. This category is also the third highest direction frequency in the
total two-year data count (Table 6.4, Fig. 6.3). This is significant when
one considers the fact that the first and second highest frequency class
from Table 6.4, 180-209° and 270-299°, respectively, have a relatively low
frequency of midday stagnation on Fig. 6.4. To check on the extent to which
the location or magnitude of the primary maximum and minimum of Fig. 6.4
was dependent upon the arbitrary choice of the center of the interval, the
data were put into overlapping 30° intervals based on the 10° interval of
the basic data. There was no significant change from the indication of
Fig. 6.4.
A seasonal RTW direction-frequency distribution breakdown was
also made as the basis of the two-year sample of midday soundings. The
results of this breakdown are presented in the form of frequency-direction
distributions (wind roses) in Fig. 6.6. The seasonal breakdown is by
3-month intervals, i.e., Winter - December through February; Spring - March
through May, etc. The directional classes are the same as in Fig. 6.3, 6.4,
and 6.5, i.e., 30° intervals. However, one point stands out; namely, the
minimum generally associated with the easterly component directional
categories is not prominent in summer.
-------
239
l/l-IC.
0) J-J
O
A
-------
240
N
Winter
N=108
Fig. 6.6 - Resultant Transport Wind Roses
-------
241
TABLE 6.6
LOW WIND SPEED PERSISTENCE_TABLE FOR
SCOTT AIR FORCE BASE (1/1/59 - 12/31/68)
Stability
Class
1-3
1-3
4-5
4-5
Wind
Speed
(mph)
0-4
5-7
0-4
5-7
1 Hour Duration
Number of
Events
3635
4589
6254
10461
Percent of
Total Hours
5.3
66.6
8.9
15.1
2-12 Hour Duration
Number of Percent of
Events Total Hours
269 0.8
485 1.4
1335 4.6
2594 9.2
Total Time in Hours = 69417
LOW WIND SPEED PERSISTENCE TABLE FOR
LAMBERT FIELD (1/1/55 - 12/31/70)
Stability
Class
1-3
1-3
4-5
4-5
Wind
Speed
(mph)
0-4
5 - 7
0-4
5-7
1 Hour
Number of
Events
1586
4681
3505
16063
Duration
Percent of
Total Hours
1.5
4.4
3.3
15.2
2 - 12 Ho
Number of
Events
51
239
453
3102
ur Duration
Percent of
Total Hours
0.1
0.6
9.69.6
7.0
Total Time in Hours = 105430
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242
Low Wind Speed Persistence
A climatology computer program prepared by the Argonne Center for
Environmental Studies was used to scan weather data from Lambert Field
(1/1/55 to 12/31/70) and Scott Air Force Base (1/1/59 to 12/31/68) in order
to produce frequency distributions of low wind speed persistence. The
data was grouped by wind speed bands (0-4 m.p.h. and 5. - 7 m.p.h.),
atmospheric stability (stable air; class 4-5, unstable air; 1 - 3), and
wind speed persistence. This division by persistence produces the frequency
of long duration low wind speeds which are a strong contributing factor in
pollutant build-ups. The results are presented in Table 6.6.
The analysis indicated that no low wind speeds persisted greater
than twelve hours in the 0 - 4 m.p.h. band or the 5 - 7 m.p.h. band for either
Scott or Lambert Field data. Comparing the Scott and Lambert data (as they
are -on opposite sides of the Mississippi and are separated by approximately
thirty miles), low wind speed occurrences are generally similar in the St. Louis
area, although for stable air in the 0-4 m.p.h. band there is about twice
the probability of having a 2 - 12 hour persistence at Lambert Field.
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243
6.3 Existing Air Quality Data in the Proposed Airport Vicinity
An important component in the preparation of environmental impact
analyses for a major airport should be the establishment of baseline air
quality levels in the proposed airport vicinity. In the case of the proposed
St. Louis airport at Waterloo/Columbia, Illinois, air quality data has been
derived from the Annual Air Quality Report published by the State of Illinois.
Air quality data in the area southeast of the city of St. Louis is quite
limited with only one monitoring site in the immediate vicinity of the proposed
airport; this site being at Columbia, Illinois.
Illinois' annual air quality report indicates five sites in the area
southeast of St. Louis (see Fig. 6.5). The monitors at each of these sites
measure S09 and/or suspended particulates, many of these measure S02 through
the use of the lead candle sulfation technique which is, at best, a crude
indication of sulfur dioxide concentrations and trends. The existing data for
the region, going back several years in time, is shown in Table 6.7. At the
Columbia site, there exists a high volume particulate sampler which indicates
data from 1969, 1970 and 1971. The measurements show a relatively constant
value of particulates with a peak of 86 in 1970. The primary federal air
quality standards for particulate matter is 75 micrograms per cubic meter and
the secondary standard is 60 micrograms per cubic meter. Therefore, the air
quality in the airport vicinity based on this one sampler is approximately
equal to the primary standard at the current time. Additional particulates
may cause degradation to occur in excess of the primary standards. This monitor,
however, could be indicative of local sources in the Columbia area. The precise
location with respect to nearby particulate sources is unknown.
-------
244
ST. LOUIS CO
MISSOURI
ST. CLAIR CO.
MADISON CO.
ILLINOIS
FIG. 6.7 AIR QUALITY MTORiNG STATIONS IN THE PROPOSED AIRPORT REGION
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245
Table 6.7
Measured Air Quality in the Proposed
Airport Vicinity
Suspended Particulates - High volume data
(annual geometric mean)
Site
(1) Columbia
(3) Belleville
(5) E. St. Louis
1967
XX
XX
150
1968
XX
XX
140
1969
70
83
130
1970
86
90
112
1971
72
84
105
Lead Dioxide Candle Sulfation Data
Milligrams of S03 per 100 square centi-
meters per day (annual arithmetic average)
Numbers in parentheses are ppjn*
(1) Columbia
(2) Harrisonville
(3) Belleville
(4) Cahokia
(5) E. St. Louis
(5) E. St. Louis
1967
XX
XX
XX
XX
1968
XX
XX
XX
XX
1969
1.39 (.056) 1.45 (.058) 1.01 (.04)
S02 Bubbler Data (ppm)
XX
.04
.038
1970
.015
1971
.25 (.01)
.28 (.011)
.32 (.013)
i
.47 (.019)
.01 (.04)
.24 (.010)
.21 (.008)
.39 (.016)
.43 (.017)
.91 (.036)
.33 (.013)
.26 (.010)
.41 (.016)
.43 (.017)
.85 (.034)
.021
*Level candle^data has been convertedto ppm annual averages using the conversion:
mg per 100 cm per day x .04
Stern.* '
ppm.
k WN* flf -- fc^ ^ w
This is a rough approximation taken from
-------
246
Sulfation data from the Columbia station indicates a constant
level of sulfation from 1969 to 1970 with an in-cease in 1971 to .013 ppm.
These values assume a crude approximation for the conversion of sulfation
rates to parts per million. On this basis, the sulfur dioxide in Columbia
is less than both the primary standard (.03 ppm) and the secondary standard
(.02 ppm). It is not likely that the airport emissions will affect the
levels since little S02 is emitted unless there are stationary heating
plants using high sulfur coal or oil. However, the land use study in the
vicinity of O'Hare has indicated significant increases in SO- due to indus-
trial development in that vicinity. Therefore serious consideration should
be given to the sulfur dioxide air quality levels in the Columbia/Waterloo
site.
6.4 Dispersion Model Air Quality Estimates
As part of the implementation plan submitted by the State of Illi-
nois, Argonne prepared modeling analyses for sulfur dioxide and particulates
in the St. Louis metropolitan area. These analyses were based upon emission
inventories collected by both Missouri and Illinois agencies and, in addi-
tion, include the expected impact of the proposed 1975 regulated air quality
levels. Specific computer runs were performed to estimate levels of S0_ and
particulates in the proposed airport area due to the emissions from the
metropolitan area. The results of these analyses are shown in Figs. 6.8-6.11.
Figure 6.8 shows the predicted particulate air quality levels based
on the existing 1968 emission inventory. This figure shows that, aside from
local effects, the background levels due to sources in the St. Louis area
contribute approximately 5 to 10 micrograms per cubic meter in the airport vicin-
ity. This is based on the assumption that the natural background level is
-------
247
ST. LOUIS CO.
MADISON CO.
Fig. 6.8 Dispersion Model Particulate Estimates
Using 1968 Inventory
(Annual Geometric Msan pg/m3)
-------
248
40 ug/m^. This value was used consistently in the strategy analysis for
the State of Illinois. Figure 6.9 shows the 1975 projected impact if the
proposed Illinois and Missouri regulations are achieved. The 40 \ig/m3
isopleth has receded to an area midway between the City of St. Louis and
the airport site, indicating that the metropolitan area no longer contrib-
utes to particulate levels above the natural background in the proposed
airport site vicinity.
The calculated sulfur dioxide levels in the airport area, using the
1968 inventory, are approximately 10 micrograms per cubic meter (see Fig. 6.10);
considerably lower than the secondary standard (60 yg/m3). In 1975, it is
expected that these levels will decrease (Fig.6,11); however, as mentioned
above, industry in the vicinity of the airport may contribute significantly
to SO- levels, particularly since this is in a coal bearing region where the
use of this fuel is common. Even the use of 1% sulfur coal in large quanti-
ties by clustered industry may significantly deteriorate the air quality.
-------
249
ST. LOUIS CO.
MADISON CO.
Fig. 6.9 Dispersion Model Particulate Estimates
Based on 1975 Implementation Plan
Regulations (Annual Geometric Means
(Annual Geometric Means ^g/m3)
-------
250
ST. LOUIS CO.
MADISON CO.
MISSQU
Fig. 6.10-Dispersion Model SO, Estimates
Using 1968 Inventory
(Annual'Arithmetic Mean yg/m3)
-------
251
ST. LOUIS CO.
taADISON CO.
ILLINOIS
Fig. 6.11 -Dispersion Model SO- Estimates
Based on 1975 Implementation Plan
Regulations
(Annual Arithmetic Mean yg/m3)
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252
7.0 AIR QUALITY MODEL ADAPTATIONS
As alluded to In previous sections, the next step in developing an
air pollution analysis for airports is to transform the computed emission
i
rates into air quality forecasts via dispersion modeling or some other
technique, At present, there are three dispersion models including one photo-
chemical jnodel available at Argonne for use in computing airport air quality.
They are the FAA/Argonne Airport Air Pollution Model, the Northern Research
o
and Engineering Corp. (NREC) Model, and the Systems, Science and Software (S )
photochemical model. This section will outline the steps necessary to prepare
the already computed data for input into these models. A modified version
46
of the NREC model is being prepared by Geomet, Inc.; however, data on this
work was unavailable at the time of this report.
7.1 FAA/Argonne Airport Air Pollution Model
This model of airport air pollution has been developed by Argonne
for the Federal Aviation Administration and is made up of two sub-models.
The first deals with the simulation of airport activity and generates an emis-
sion inventory that is diurnally and spatially distributed. The second uses
the emission inventory as a data base for computing air quality, using a
modified steady-state gaussian plume algorithm. This particular algorithm
has been developed and extensively validated at Argonne.
Portions of the activity sub-model have been used in this study
to compute aircraft and ground service vehicle emission rates. Additional
portions were adapted to compute emissions from other sources. Adjustments
were necessary to reflect the fact that the model was initially developed
to operate from a data base of observed information at an existing airport.
The current application to the proposed new St. Louis airport meant that
the same resolutions of the output data could not be made, due to the lack
of information. The primary loss in this case is the spatial distribution
of emissions. This could not be determined because the airport master plan
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253
has not yet been drawn up. Once this is available, however, the full output
of the activity model can be utilized.
The activity sub-model further classifies the emissions by the type
of source producing them. Emissions which are generated over a large area
(e.g., ground service activity in the terminal area, automobile movement in
the parking lots, fuel evaporation from the filling of vehicle tanks, etc.)
are classified as area sources. Emissions generated in a long and narrow
strip (e.g., roadways, runways) are classified as line sources. Emissions
emanating from a very small area (e. g., heating plant, engine test cells)
are classified as point sources. While this classification can be made for
the St. Louis data, it is not possible to determine the dimensions and co-
ordinates of these source types because of the aforementioned lack of spatial
data.
It should be pointed out that even without the airport master plan, a
first order approximation of spatial distribution could be made and the air
quality calculations carried out on this basis.
The gaussian plume air quality sub-model utilizes the point, area, and
line source parameters calculated by the activity sub-model, and combines them
with meteorological information that is relevant to the area to compute air
quality. Since the input emission inventory is both spatially and diurnally
distributed, the resulting air quality is also spatially and diurnally
distributed.
The sub-model uses' a one-hour averaging time to compute pollutant
concentrations and makes use of time- and distance-dependent dispersion coef-
ficients. These give better results at the high and low ends of the wind
speed range than the coefficients that are distance-dependent only. In addi-
tion, a diurnal variation of important meteorological parameters, such as
wind speed, mixing depth, etc., is used instead of a seasonal average to
improve the model's accuracy.
-------
254
In making the computations the model extrapolates the point, area,
and line sources back to a virtual origin. This eliminates some of the wide
fluctuations in calculated concentrations Resulting from changes in the wind
direction-receptor location, referred to as the beacon effect.
To summarize, the projected emission rates can readily be translated
into air quality via the T?AA/Argonne Air Pollution Model. The data as
presented needs only to be modified to show spatial distribution in order to
serve as direct input into the air quality sub-model. Experience with this
model on Chicago's O'Hare Airport shows the calculated air quality to be
underestimated, as compared to observed air quality, particularly for hydrocarbon
concentrations.
7.2 Northern Research and Engineering Corp. Model
29
The NREC model was developed for the Environmental Protection Agency'
and is fundamentally similar to the FAA/Argonne model. An activity sub-model
and an air quality sub-model are used.
The basic differences between the Argonne and the NREC air quality
sub-models are the type of mathematical simplifications used to compute disper-
sion. The NREC model uses a one-hour averaging time and a gaussian plume
algorithm, as does the Argonne model. NREC, however, uses only point sources
that are located at ground level, instead of the virtual origin point, area,
and line sources. It therefore suffers from the large fluctuations in concentra-
tion resulting from the beacon effect. Also, the NREC model uses a dispersion
coefficients that are distance-dependent only.
As with the Argonne model, the emission calculations can be readily
adapted for input into the NREC model once some spatial resolution is available.
Experience with this model at Argonne has shown that it also underestimates
-------
255
air quality as compared to observations* In most cases, the discrepancies are
larger than that of the Argonne model.
7.3 Systems, Science and Software Photochemical Model
The photochemical modeling portion of the air quality computations
3 42
is based upon a package written by Systems, Science and Software (S ).
It consists of two computer codes; the first, SETUP, uses meteorological and
source data to create an input tape for the second, NEXUS/P (Numerical Exam-
ination of Urban Smog with Photochemistry). NEXUS/P then moves and diffuses
the pollutants, changes the pollutant concentrations as a result of photo-
chemical reactions, adds pollutants due to sources, and stores or retrieves
pollutants advected into or out of the borders of the computational grid.
The program was originally written to simulate the photochemical
reactions taking place in the Los Angeles smog. It is, therefore, necessary
to tailor SETUP to the conditions found around the airport under study.
The data read in at execution time includes wind measurements and
initial concentrations of hydrocarbons, NO, N02, and CO if desired. NEXUS/P
considers reactions occurring between and producing NO, NO,, HC, 0.,, and HNO .
HNO. and 0_ are the results of photochemical reactions, while CO is not
explicitly considered. Table 7.1 shows the reactions treated. Briefly, the S
model treats the transport of pollutants by assigning mathematical points to
given amounts of pollutant. The movements of each of these points are then
traced through time so that at any point in time and in any cell of the three-
dimensional grid system the concentration can be determined by simply counting
the number of points residing in that cell at that tine. The mechanism for
moving the points which takes into account both advection and diffusion is
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256
Table 7.1
Rate Coefficients for Expanded Model of the
Hydrocarbon/Nitric Oxide Mechanism
(Stoichiometry imbalances may occur because of lumped
parameter assumptions.)
Reaction Model Values from Validation
hu + NO, -> NO + 0 0.4 min"1
£
0 (+ 02) + M -> 03 + M 1.32 x 10"5 ppm'^in"1
03 + NO -> N02 (+ 02) 40 ppnf "Simi"1
0 + HC + 2RQ 6100
OH + HC » 2R02 80
R02 + NO -> N02 + 0.5 OH 1500 ppnf^min"1
R02 + N02 -» PAN 6 ppm'^in"1
OH + NO + HN02(:a) 10 '"
OH + N02 -> HN03 30
03 + HC -> R02 0.0125 ppm'^in"1
(H20 + 2) NO + N02 -» 2HN02^^ 0.01 ppm^min"1
hu + HN02 -> NO + OH 0.001 min"1
(a)
^ ^Rate constant lumps third body concentration.
^ %ater vapor lumped into rate coefficient.
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257
completely independent from the photochemical reactions. Consequently, it
is possible to employ several alternative photochemical reaction mechanisms,
if so desired. The dispersion and chemical reactions are treated in an
alternating step fashion. First, the pollutants are allowed to disperse
through the grid system for a time interval, then the dispersion is frozen
and a chemical reaction step occurs, etc. In the limit of very small time
steps, this alternating procedure approaches the simultaneous operation of
dispersion and chemical reactions,
The mathematical point method provides a very convenient means for
merging a distribution of airborne and ground-based sources into a cell or
grid type model which is essential for the treatment of photochemical reactions.
However, because of the limitations of computer core storage and run time,
the individual cells cannot be too small so that one tends to lose a certain
amount of spatial and temporal resolution. This is the sacrifice which must
be made if the photochemistry is to be treated without an intolerable consump-
tion of computer time.
This model is not yet operational at Argonne but is expected to be
so by approximately March, 1973, on the Chicago O'Hare data base. At the
present time, there are no unusual difficulties expected with adapting emissions
as calculated in this report to this model. The meteorological data appears to
be the segment requiring the most adjusting to the proper format.
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258
8.0 Conclusions and Observations
Future Significance of Airports as Air Pollution Sources
i
Based on an analysis of the proposed St. Louis Airport at Waterloo-
Columbia, Illinois, it is expected that uncontrolled air pollution emissions
from aircraft will approximately triple by the year 2000, despite the trend
towards higher capacity aircraft and lower per passenger emissions. The fore-
casted aircraft activity levels and mix used for this computation is quite
typical of future major commercial airports. These data indicate that air-
port complexes will become increasingly significant sources of air pollution,
unless relatively effective emission controls are applied. This impact may
be expected to become a major consideration in the development of environ-
mentally sensitive land use and transportation system plans.
Trends in Airport Access Traffic
If access traffic at most major airports retains the general composi-
tion and mix characteristics associated with the proposed St. Louis facility,
emissions from this source will decline as a result of the implementation of
Federal Automotive Emission Standards. However, increases in population of
vehicles will cause this trend to reverse by approximately 1985. If airports
similar to the St. Louis facility are served by mass transit systems, then in
the short term (3 to 5 years) the diversion of passengers from private auto-
mobiles to mass transit will result in significant emission reductions. By
1985, stricter emission controls for mass transit modes will be required in
order to preserve this relative benefit. (Mass transit modes considered are
diesel buses, diesel commuter trains, and electric rail transitthe latter
increases power plant emissions).
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259
Relative Importance of Aircraft vs Ground Vehicle Emissions
By 1975, the CO, HC, and NO emissions attributable to aircraft and
X
ground vehicles (ground service vehicles and access traffic) will be of the
same order of magnitude. By the year 2000, aircraft emissions will, in the
absence of engine exhaust controls, be an order of magnitude higher than what
is discharged by ground vehicles. This assumes that ground service vehicles
and access traffic both comply with federal automotive emission standards.
This indicates that there is likely to be a substantial increase in the need
for aircraft emission controls.
Contributions from Airport Ground Service Vehicles
Ground service vehicles are a major contributor to airport emissions
of carbon monoxide and hydrocarbons (approximately 12% for each pollutant).
Their contribution could increase to approximately 24% of the total by 1990
if they remain uncontrolled (there are no proposed or anticipated controls for
ground service vehicles at this time). This indicates that ground service
vehicles offer an attractive opportunity for reducing airport-related emissions,
particularly since they are generally fleet-operated and their control would
not involve significant safety implications.
Contribution of Aircraft Taxi/Idle Modes
A large fraction (approximately 90%) of aircraft carbon monoxide and
hydrocarbon emissions occurs during the taxi and idle modes. (This is based
on the application of the latest Cornell emission factors to the activity at
O'Hare International Airport and at the proposed St. Louis facility.) Opera-
tional controls, therefore, promise to be a relatively effective means of reduc-
ing airport-related emissions. It is necessary to note, however, that several
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260
additional factors, including noise abatement and safety implications, must be
considered in the evaluation of aircraft ground operational strategies.
i
Secondary Airport-Related Emissions
Evaporative hydrocarbon emissions from fuel handling and storage are
significant but are difficult to control because of the many small sources
involved (i.e., the aircraft and ground vehicle fuel tanks). On the other
hand, if vapor recovery systems or other such technique would prove economically
and technically feasible, the control of this source would be appropriate.
Emissions from airport heating plants are relatively small but should,
be considered for control (particularly NO ) as other airport sources are
2C
regulated.
Emissions derived from engine runup and testing at airports that are
not considered major service centers (for example, O'Hare) also represent a
relatively minor contribution to total airport emissions. At major service
terminals such as the Los Angeles International Airport, runup and testing
may be significant.
Relative Contributions of Airport and Surrounding Areas
to Emissions of Air Pollutants
While airports are major sources of air pollution, they are
generally no worse, on an emission density basis, than adjacent urbanized
areas. The case study conducted at O'Hare indicated that carbon monoxide,
hydrocarbons, nitrogen oxides, and particulate emission densities within the
10-square-mile O'Hare site. It must be noted, however, that the emission-^
densities associated with the terminal and taxiways where most of the airport
pollution is generated are considerably greater (possibly by a factor of 5 to 10)
than what is characteristic of urban land. Care should therefore be taken in
the use of emission-density-figures - particularly for new airports where the
trend is toward very large land acquisitions.
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261
An Increase in Residential Land Use in Airport Vicinities
Residential land development in the vicinity of O'Hare Airport
(A townships115 square miles) has not been significantly discouraged by
increased aircraft operations. Population increases since 1960 in the
immediate vicinity of the airport have been as large or larger than in
similar suburban areas. It would appear that once developers obtain appro-
priate zoning, or otherwise acquire the authority to construct residential
dwellings, there is always a market and a buyer for the land.
Induced Development in the Airport Vicinity
The land use case study in the O'Hare Airport environs indicated
that increased levels of activity predominate in the immediate vicinity of
the site (1 to 3 miles). The largest growth has been in manufacturing and
warehousing activities. Manufacturing activity in the 4-township study area
surrounding O'Hare has increased approximately 38% since 1964, while the
6-county northeast Illinois average increased 22%. A definite indication
of manufacturing and warehousing clustering in the immediate vicinity of
the airport was observed. Residential land use, on the other hand, increased
in density in proportion to the distance from the airport perimeter. Conven-
tional planning practices have promoted the concentration of industrial and
commercial activities in the immediate vicinity of the airport. Whatever the
locational advantages of this arrangement may be, this type of land use plan
tends to concentrate air pollution sources.
Directional Relation of Manufacturing Activity
Manufacturing land use in the O'Hare Airport study area indicated
that there were extremely large increases on the side of the airport site
that was away from the Chicago urban center. In the quadrant of the study
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262
area facing Chicago, manufacturing and warehousing increased approximately
37% from 1960 to 1970, while in the opposite quadrant, facing away from
Chicago, manufacturing and warehousing activity increased approximately 250%
during the same period. The absolute magnitudes of the growth on either side
of the airport site were comparable.
The Relative Contribution of Land Use Activities to
Airport Area Air Pollutant Emissions
Manufacturing and warehousing emissions in the O'Hare Airport area
study are substantially greater (by approximately an order of magnitude) than
emissions from residential, commercial or institutional sources; however, motor
vehicle emissions in the study area are the dominant source of air pollutants
in the airport vicinity - particularly, in the case of carbon monoxide, hydro-
carbons, and nitrogen oxides. Even after a 90% emission reduction is realized
as a result of applying the Federal Emission Standards, motor vehicles will
remain the largest contributors of carbon monoxide and hydrocarbons. Uncontrolled
manufacturing and warehousing emissions of nitrogen oxides, on the other hand,
will then exceed those due to transportation sources by as much as a factor of 2
to 5.
The Adequacy of Airport Environmental Impact Statements
On the basis of a detailed review of two environmental impact state-
ments for major airports (St. Louis and Dallas), wa conclude that current air-
port impact statements fail to present an adequate appraisal of air pollution
impacts. Often, little information is available to allow comparisons of
predicted air quality with national standards within or external to an airport.
Furthermore, virtually no information is available regarding the impact of
induced development on the airport environs. It is recommended that airport-
induced land development be established as a significant factor in airport
environmental impact evaluation.
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263
Air Pollution Potential for the Proposed St. Louis Airport
A meteorological and air pollution potential analysis for the pro-
posed St. Louis airport in Waterloo-Columbia, Illinois, indicates that the
southeast wind sector has displayed the highest frequency of stagnations,
based on a two-year record of mixing height soundings at the St. Louis
Gateway Arch. A southeast wind is favorable to the transport of air pollut-
ants from the proposed airport site to the St. Louis metropolitan area.
This wind category also has the third highest directional frequency. The dis-
tance from the airport to downtown St. Louis (approximately 19 miles) will,
however, attenuate the effects of a southeast wind. It is important to note
that the stationary and mobile source emission control regulations that are
effective in the St. Louis urban area were designed to be just sufficiently
stringent to achieve air quality standards. When St. Louis lies downwind of
the pollutant plume emitted by the Waterloo-Columbia airport, particularly
during stable atmospheric conditions that inhibit the lateral and vertical
dispersion of the plume, air quality standards in St. Louis may be violated.
This leads to the inference that there is a minimum distance between a major
airport and an adjacent urban concentration, below which the airport may
have an unacceptable impact on urban air quality. This distance is dependent
on the magnitude of the airport and urban concentration as air pollution
sources and the frequency with which the prevailing meteorological conditions
enable the airport pollutant plume to pass over the urban area. Air quality
models that are currently available can be employed to estimate this
minimum distance.
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265
APPENDIX A
A Description of the Aerial Photographic
Technique for Determining Land Use
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266
APPENDIX A
For purposes of estimating emissions based on land utilizations in
the vicinity of O'Hare airport, a historical land use analysis has been
performed. Data for this analysis were obtained from a land use survey of
approximately 125 square miles surrounding and including the airport. Aerial
photographs (at the scale 1"=400') of these 125 square miles were obtained for
four points in time during the 1960«70 decade (1970, 1966, 1964 and i960)*.
These photographs were first interpreted by identifying sites as belonging to
one of nine rather gross activity classes as described in this Appendix. The
nine basic activities were sampled in such a way that the most accurate data
could be obtained at minimum cost. This sampling technique will also be discussed
and'illustrated in this Appendix. Finally, the raw data were tabulated and
converted to acreage statistics at Argonne's computer facility.
Figure A.I shows a map of the area employed in the historical land
use survey. O'Hare Airport intersects the junction of four townships
(Elk Grove, Maine, Addison and Leyden) which are located in Cook and DuPage
Counties of Northeast Illinois. The boundaries of these four townships have
been used to define the study area. Portions of Norwood Township and of
the City of Chicago are also included within the study area.
These aerial photographs were obtained from two sources. The ones for
1970, 1966 and 1964 were borrowed from the Northeastern Illinois Planning
Commission. Comparable photographs for the i960 data were purchased from
Chicago Aerial Survey.
-------
ELK GROVE
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ADDISON
LEYDEN
Fig. A.I Four-Township Land Use Case Study
Area Surrounding O'Hare Airport
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268
Fig. A. 2 SAMPLE AERIAL PHOTOGRAPH FROM THE O'HARE STUDY "AREA
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269
^_-.^ ^
Fig. A.3 SAMPLE INTERPRETATION OF AERIAL PHOTOGRAPH
SHOWN IN FIG. A.2
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270
Laud.Use Survey; Data Collection
As previously mentioned aerial, photographs of the area were inter-
i
preted by identifying activities as belonging to one of nine activity classes.
These land use classes are defined as follows:
Category 1: Residential - Single and Multifamily
Includes all land areas occupied by residential
dwelling units. Structures comprised in this cate-
gory are single family houses, town houses, garden
apartments, four-plexes, low, medium-and high-rise
developments. In the case of multiple-family dwell-
ings adjacent parking facilities and land-scaped
grounds are also included. For single-family resi-
dences located in rural areas, all land within 100
feet of each residence or estate is also classified
as residential.
Category 2: Residential - Mobile Home Parks
These include all concentrations of three or more
mobile home units situated apart from any adjacent
sales or commercial display area. Isolated mobile
home units are included within the other residential
category.
Category 3: Commercial
Is representative of all display, sales, service
and merchandising enterprises. The following activi-
ties are included in this class:
Wholesale Trade - facilities not adjacent or directly-
related to the manufacture or processing of the
product being marketed (excludes on-site warehouses.)
Retail Trade - all marketing outlets situated inde-
pendently or in combination, the "predominant use"
in shopping centers and central business districts.
Shopping Centers - Related parking facilities and
landscaped areas.
Eating and Drinking places.
Nurseries and Orchards with directly-related marketing
facilities.
Finance, Insurance and Real Estate services and
supporting office facilities.
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271
Business services and related office facilities
(advertising, credit-adjustment collection,
mailing, steno and employment, building main-
tenance, reproduction and news).
Research and Development services.
Personal services (laundering, dry cleaning,
tailoring, photographic, beauty/barber, funeral,
shoe and apparel, vehicle rental services).
Professional services (medical, health (non-
hospital) office and lab services; private rest
homes, legal engineering, architectural,
accounting, auditing and urban planning (6594)
services).
Repair services (vehicle, electrical appliance,
jewelry, furniture repair, alterations and
maintenance services).
Contract construction and skilled trade service
(building, and building systems and finishing
services).
Welfare and charitable services (non-religious).
Business, professional, labor union and fraternal
associations and organizations office and meeting
facilities.
Animal husbandry services (veterinarian, animal
hospitals, hatcheries).
Hotels, motels, tourist courts and transient
lodgings.
Entertainment - indoor and outdoor: amphitheaters,
motion pictures, legitimate and playhouse theaters,
dancing pavilions.
Sports - stadia, arenas and field houses, racetracks,
etc.
Indoor tennis courts, bowling centers, ice-and roller
skating facilities.
Parking garages, ancillary structures, and land-
scaped property included.
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272
Category 4: Industrial (Except Warehousing)
These activities'also include all related
offices, parking and loading areas, storage yards,
and surrounding landscaped grounds located within
that parcel.
Manufacturing activities - All heavy, light and
general manufacturing activities.
Processing activities - industrial, agricultural,
natural resources products.
Category 5: Warehousing
Includes all warehousing and storage structures
not directly adjacent to a related primary activity
of a manufacturing activity.
Category 6: Institutional
Is representative of all of the following
institutional services:
Government Services
Executive, legislative, judicial facilities.
Protective (police, firefighting, civil defense)
service facilities.
Penal institutions - prisons, detention homes,
juvenile homes, etc., including all confinement
homes.
Hospital Services (public and private) and medical
and mental health and rehabilitation facilities,
sanitoria, rest homes and convalescent centers.
Educational Services: (including adjacent recreational
facilities and grounds serving the school)
Nursery, primary, secondary education.
University, college, junior college, graduate-
professional academies.
Special training and education (vocational,
business, skills, arts, etc.)
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273
Religious Services:
Churches, synagogues, temples
Convents, seminaries (non-secular)
Military Bases and Installations:
Military training bases and armorys
facilities (including barracks, exchange,
and recreation activities).
Military defense installations.
Military storage depots, transportation
centers, maintenance facilities.
Military administration, command and
communication centers.
Military air fields and dock facilities.
Public Buildings
Cultural centers: Libraries, museums,
art galleries (non-commercial), planetaria,
acquariums.
Public assembly, public auditoriums,
convention and exhibition halls.
Category 7: Transportation, Communications
and Utilities (T.C.U.)
Transportation Facilities
Railroad terminals (passenger and freight),
equipment and maintenance facilities.
Rapid rail equipment and maintenance
facilities.
Bus and taxi terminals (passenger), equipment
and maintenance facilities and garages.
Motor freight (trucking) terminals, vehicle
storage yeards, equipment and maintenance
facilities.
Marine craft and freight terminals, equipment
and maintenance facilities.
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274
Communications Facilities
Telephone exchange stations, relay towers,
switching and computerized operations
centers.
Radio broadcasting and transmitting facilities.
Television broadcasting and transmitting
facilities.
Utilities Functions
Electrical transmission rights-of-way
general plants, regulation and switching
sub-stations.
Petroleum and Natural Gas pipeline rights-of-
way, plants, storage and distribution
facilities.
Water pipeline rights-of-way, treatment plants,
storage and distribution facilities.
Sewage treatment plants, sludge drying beds,
disposal and control stations.
Solid waste disposal (refuse) incinerators,
garbage grinding and compositing plants.
Sanitary landfill areas, 'industrial disposal
sites, slag dumps, etc.
Railroad Rights-of-Way and Yard Facilities
Railroad rights-of-way, including switching
and marshalling yards.
Rapid transit rights-of-way, including
switching and storage yards, when separated
from a public thoroughfare.
Airports
Airport, heliport and flying field runway and
taxiing facilities, terminal (passenger and
freight), storage areas and all adjacent
property owned by the airport authority or
flying field.
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275
Highway and Street Right-of-Way
All public freeways, expressways, parkways,
arterials, collector and distributor streets
(not included within Private developments).
Freeways', expressways', parkways' and limited
Access arterials1 right-of-way - measured
between outside service drive curbing or
fencing.
Surface thoroughfare right-of-way - measured
from sidewalk to sidewalk where applicable.
Mines and Quarries
Includes all areas previously or currently
committed to excavation activity.
Adjacent property deeded to that same company
or individual, and held for future mining
operations should be included within the
vacant - under development/committed category
(8).
Category 8: Vacant. Agricultural
Vacant, Agricultural and Forest Areas
Agricultural activity areas: crops, live-
stock, fowl, housing-raising farms; orchards,
pasture lands, horticultural specialization.
Undeveloped and unused land area.
Forecasted areas (reserve or non-reserve)
within Non-Urban surroundings.
Vacant, Committed/Under Development
Includes areas or parcels (1) under owner-
ship by a corporation, organizations, insti-
tutions or non-agriculturally involved
individual with a currently-developed
activity directly adjacent or included
thereupon; (2) where construction or
excavation activity is currently in process.
Category 9: Recreation - Open Space - Water
Public and Quasi-Public Recreation/Open Space Area
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276
Recreational activities - golf courses,
tennis courts, skating areas, riding stables,
skiing, tobogganing areas, golf driving
ranges, minia'ture golf and go-cart tracks.
Playgrounds, athletic and swimming areas
(indoor and outdoor).
Marinas - boat laundering, rental and docking
facilities, yacht clubs.
Camping and picnicing areas, also organized
group camp areas.
Resort areas and facilities.
General park and recreation areas, including
leisure ornamental parks.
Botanical gardens and arboretums.
Zoological parks.
Fairgrounds and amusement .parks.
Historic and monument sites.
Cemeteries
Includes all burial ground, memorial
parks/gardens, mausoleums and inter-
ment activities.
Water
Includes all rivers, streams or creeks
exceeding 1/8 inch on a l":400 aerial
photo. All lakes and ponds are also
included in this class.
All of the land use sites in each aerial photograph were allocated
to one of these nine categories by placing a transparent overlay on the
photo. The various sites were then outlined on this overlay with different
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277
colored pencils.* An example of one of these aerial photographs is shown in
Fig. 2. The interpreted overlay for this photograph is illustrated in Fig. 3.
Quantities of land defined by the nine categories were then determined, using
47
a sampling method described by Berry and Baker. According to this method,
dots are assigned to cells of. a grid which is reproduced on a second trans-
parent overlay. After removing the first overlay from the interpreted aerial
photograph, it is laid over the dot grid and the dots falling in each land use
category are counted and recorded on a coding sheet.
This dot procedure is referred to as a "stratified systematic sample."
Berry and Baker describe and illustrate its construction'as follows:
First, point A is selected at random. The x
coordinate of A is then used with a new random
y coordinate to locate B, a second random y
coordinate to locate E, and so on across the
top row of strata. By a similar process, the
y coordinate of A is used in combination with
random x coordinates to locate point C and
all successive points in the first column of
strata. The random x coordinate of C and y
coordinate of B are then used to locate D, of E,
A STRATIFIED SYSTEMATIC
UNAUGNED SAMPLE
s
1
D
I
01
at
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-- -
F
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i
«B
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U;E
- - -
C
*
*
1
*
*
*
f
1
*
The persons employed for this task were inexperienced in aerial photo inter-
pretation. Mr. Walter Vissotski of the Northeastern Illinois Planning Commis-
sion (NIPC) was consulted to conduct a three-day course to the interpreters
and one supervisor. Where site interpretation was difficult or ambiguous,
Mr. Vissotski advised consulting similar overlays created by NIPC in generating
the 1970 land use inventory for the six-county region. These overlays were
loaned to the Argonne Center for Environmental Studies, along with the aerial
photos mentioned above.
-------
27ft
and F to locate G, and so on until all strata
have sample elements. The resulting sample
combines the advantages of randomization and
stratification with the useful aspects of
systematic samples, while avoiding possibil-
ities of bias because of the presence of
periodicities, (Berry and Baker, 1968,
p. 93). i
The sample used in this study was generated by the Northeastern Illinois
Planning Commission in conjunction with that agency's 1970 land use survey.
This sample contained 540 dots per square mile (i.e. normal 640 acre section)
and was created in the following manner. First a grid consisting of 576
cells was drawn and one dot was plotted in each cell according to the above
method. Next, boundary lines were drawn around the space which would
encompass the normal square mile area and 36 of the plotted points fell
outside these boundaries. These extra sampling points were left on die
dot grid to handle sections which appeared slightly larger than one square
mile, due to displacement inherent in aerial photography (Branch, 1971.
pp. 149-153)". Next, quarter-section boundaries were drawn on the square
mile dot grid. And finally, half of the lines defining the cells used in
plotting the dots were eliminated to reduce the coder's eye tension. This
procedure left four dots in each of 144 cells. The resulting dot grid is
illustrated in Fig. A. 4.
Amounts of land utilized by each class of land use activity were
then determined by counting the dots falling in the particular sites on the
interpreted sectional photographs. This was done by placing the clear
plastic overlays with the sites outlined and numbered in different colored
*
pencils over the dot sample. The number of dots falling into each of the
nine land use categories were then recorded by quarter-section subtotal on a
coding sheet. This coding sheet is illustrated in Fig. A.5. A different sheet
*The interpreted plastic overlays were systematically aligned with the dot
sample using a common boundary and one point. The western boundary of the
interpreted plastic overlay was always lined up with the western boundary
of the dot grid. Next, the northwest corner of the overlay was matched with
its corresponding point on the dot grid.
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279
Fig. A.4 Sample Dot Grid
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280
Fig. A.S Sample Land Use Code Sheet
Argoime National Laboratory
Center for Environmental Studies
Historical Survey - Airport Land-Use (1972)
OMtare Airport
Township
County
Section
Year
Date Coded
Coder
Col. Col.
'23 67 10 11 14 15 16 17
1 | X X | XX
X X
/Township / /Section / /County / / v /
Code / /lumber/ /lumber/ / Y"r /
LAND USE CATEGORIES
RESIDENTIAL-Single and Multi
Family
RESIDENTIAL-Mobile Home Parks
COM-ERCIAL
INDUSTRIAL (Except Warehousing)
WAREHOUSING
INSTITUTIONAL
T.C.U. (Rights of Kay)
VACANT, AGRICULTURAL
RECREATION'-Opcn Space-Water
1
2
3
4
5
6
7
8
9
TOTAL
QUARTER-SECTION'
1
NE
2
NW
3
SW
4
SE
Total
Col.
30
35
40
45
50
55
60
65
70
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281
was created for each of the four years for all sections in the study. Computer
cards used to analyze these data were then key punched directly from the
coding sheets. The numbers at the top and in the right-most column of the
sheet refer to columns in which data were left-justified in the punched card
format.
The total number of acres entered on each coding sheet was obtained
*
from the U.S. Geologic Survey. This total acreage was used to calibrate
the dot-per-acre ratio with the topographical displacement which varied from
photograph to photograph. In this dot-per-flcre (BAR) calibration, the
acreage in any given section (i) was divided by the total number of dots
falling in that section.
DAR. =
ID.
This ratio was then used to compute the acreage consumed by the various
land use categories in that section for each of the four years.
^DLU . (A. 2)
where:
A = Acreage consumed by land use category k in section i.
LUki
DAR. = Dot-per-acre ratio calibrated for a specific photo-
1 graph for section i.
D = Number of dots falling in sites of land use category
LUki k in section i.
*These results assume perfectly accurate interpretation of the aerial
photographs.
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282
Statistical Accuracy of the Stratified Systematic
Unaligned Sample
i
The statistical error inherent in the 540-dot sample has been calcu-
- 49
lated by the Northeastern Illinois Planning Commission. Their analysis
showed that the sample yielded results which were no more than ± 10 percent
in error 95.75 percent of the time, and no more than ± 20 percent in error
99.9 percent of the time. This analysis demonstrated that, whereas a larger number
of sample points would provide greater statistical accuracy, the number of sample
points would have to be greatly increased before even a slight reduction of error
could be realized. Therefore, the 540-dot sample was selected because it
optimized the statistical accuracy of the data with respect to the time and
effort required to collect it. An additional analysis of the reproducability
of results was performed for this study.
For this test of reproducability, land'use acreages derived from the
540-dot sample were correlated with planimeter measurements which provide a
nonsampled measure of land use acreages for any given section. Four photographs
(each containing approximately 640 acres) were selected for this comparative
analysis. In terms of complexity, these photos were medium to difficult to
interpret, and they contained 28 of a possible 36 land usages.
One photograph was selected from each of the four townships, and these were
varied among the years over which land use change was being surveyed in this
study. The photographs selected for this correlation of reproducability
were:
Addison Township Section 22 1960
Elk Grove Township Section 25 1964
Leyden Township Section 21 1966
Maine Township Section 32 1960
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283
The two measures of acreage consumed by the 28 section-specific land
use categories were taken from the transparent overlays used to interpret the
four photographs. First, the number of dots falling in the various land use
categories were counted and converted into acreage figures as described above.
Second, nonsampled measures of the land use consumption were taken. The colored
lines defining land use sites on the interpreted plastic overlays were traced
with the stilus of a planimeter, and the readings indicated. on the instrument
were tabulated and converted into acreage data. Next, -the acreage obtained
from the dot sample were regressed on acreages computed. from the planimeter
readings using a linear least squares model.
The slope of this model is b and the intercept is a.
where:
b = slope of the straight least-square line describing the
association between x and y. (Or: the ratio of the co-
variation in x and y to the sum of squares.)
x = land use acreage as measured by the planimeter readings.
y = land use acreage as measured by 540-dot stratified unal-
ligned sample.
and where :
a - II^I*
The resulting a and b coefficients were then used to compute new scores for
each y predicted from the association of the two independent measures of land
usage for all of 28 land use categories in the four-section test. These pre-
dicted (y ) acreages were computed using the relation:
P
yp = a + bx (A. 5)
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284
The principles of the linear least squares regression model and the computation
of predicted values for y are illustrated in Fig. A.6.
PUNJMETER MEASURE, i
Fig. A.6 Linear Regression Model
Finally, for each data point the predicted y value was subtracted from its
actual value and the difference taken as a proportion of the actual y value.
y =^2_ (A. 6)
y
This procedure yielded standardized error scores for land use acreage com-
puted from the 540-dot sample compared with the same acreages computed from
planimetar measures. Tha resulting values.ara interpreted as the proportion
of the dot-sample acreage for a section-specific land use category which is
unexplained by the association between the dot-sample and planimeter measures
over all of the 28 data points.
The results of these tabulations are illustrated in Table A.I.
Columns 1-4 define the section-specific land use categories for which the
relative errors have been calculated in this test. Columns 9 and 10 indicate
the value and rank of this error. This table demonstrates that the error
ranges from 50.93 percent to .23 percent. More importantly, an error greater
than 10 percent is obtained in only 8 of the 28 land use categories examined.
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285
Table A.I The Association between Measures of Land Use
Averages: 540 Dot Sanple vs Planemeter
Measurenent for 28 Land Use Categories from
Four Sections
(1)
Township
MA.
AD
EG
EG
AD
LY
I
EG
EG
LY
LY
LY
MA
LY
LY
MA
i MA
EG
LY
AD
EG
AD
EG
| AD
i
i LY
! EG
i MA
i
1
MA
MA
(2)
Section
32
22
25
25
22
21
25
25
21
21
21
32
21
21
32 '.
32
25
21
22 l
25 [
22 |
25 :
22
21
25
32
32
32
(3)
Year
60
66
64
64
66
66
64
64;
66
66
66
60
66
66
60
60
64
66
66
64
66
64
66
66
64
64
60
60
(4)
Land Use
Category
4
6
5
6
4
5
1
9
3
6
4
8
9
8
3
1
7
1
1
8
5 i
I
3
8
7
4
2
2
7
(5)
X
Planemeter
4.0
5.5
10.9
i 2.1
15.5
18.0
4.7
42.8
36.4
30.8
57.6
110.6
16.5
57.2
21.0
78.3
126.0
385.8
35.9
295.0
,.,
33.2
561. 7
39.1
58.2
66.7 i
5.6
420.4
(6)
i '
i
1 Dot
i
3.2
4.7
9.5
2.4
21.0
23.8
4.8
48.7
34. S
33.3
60.7
106.5
16.7
55.9
22.4
80.9
123.6
377.2
37.4
288.9
2.3
34. S
568.7
39.3
59. 4
67.8
6.4
420.6
(7)
Y.
Score
-1.63
-1.63
-2.21
- .54
4.71
5.02
- .73
5.21
-2.61
1.77
2.47
-4. S3
- .59
-1.93
.63
2.05
-2.77
-7.98
.79
-5.83 |
I
- .04 |
- .58;
8.29
- .50
.57
.50
- .03
.95
(8)
YP
Rank
20
21
23
17
4
3
19
2
24
7
5
26
18
22
10
6
25
28
9
27
15
11
1
16
12
13
14
8
(9)
Y-
. 1
Score
.5093
.3468
.2326
.2250
.2243
.2109
.1521
.1069
.0757
.0531
.0407
.0421
.0353
.0345
.0281
.0253
.0224
.0212
.0211
.0202
.0174
.0168
.0145
.0127
.0096
.0074
.0047
.0023
(10)
> .
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
2V
28
-------
286
As expected, the larger errors are found In the categories consuming relatively
few acres. Each of the 6 categories with an error greater than 20 percent
consume less than 25 acres using either of the two measures, and the 10 cate-
gories with an error greater than 5 percent consume less than 50 acres. Most
significantly, all 6 categories consuming more than 100 acres have less than
a 5 percent error; and only one of these errors is greater than 2.24 percent.
We conclude from the results of this test that the 540-dot sample
and the planimeter measure yield very similar land use acreages. This is
verified in an aggregate fashion using the product moment correlation (r)
which measures the extent to which data points are scattered (i.e., dispersed)
about the regression line describing the association between the two land
use measures. This r coefficient is .9997 where r is computed using the
formula:
(A.7)
-------
287
APPENDIX B
Detailed Land Use Data (Acreages) for Addison.
Elk Grove, Leyden and Maine Townships for
the four study years; 1960,
1964. 1966 and 1970
-------
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307
ACKNOWLEDGMENTS
The authors extend their appreciation to the following for
their assistance and cooperation:
Mr. Donald Armstrong, Land Use Planning Branch,
Environmental Protection Agency
Mr. Walter Vissotski of the Northeastern Illinois
Planning Commission
Dr. Donald Rote and other members of the
FAA Airport Program at Argonne National Laboratory
Mr. Allen Kennedy of the Argonne Center for
Environmental Studies
The St. Louis Metropolitan Area Airport Authority
East-West Gateway Coordinating Council.
We also extend thanks to our secretarial staff for their
usual fine effort and Ms Hope Rihel for her able assistance with
the graphic arts required to publish this report.
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309
References
1. St. Louis Metropolitan Area Airport Authority, "Draft Environmental
Impact Statement," submitted to DOT/FAA January 1972.
2. R. Dixon Speas Associates (Speas), "Site Selection StudySecond Air
Carrier Airport for the St. Louis Metropolitan Region for the State
of Illinois," October 1970.
3. Northrup Airport Development Corp. (NADC), "Site Survey for a New St.
Louis Regional Airport for the State of Missouri," August 1971.
A. McDonnel Douglas Aircraft Corp. (McDonnell), "Lambert St. Louis Metro-
politan Airport Study," October 1971.
5. Federal Aviation Administration, "Large and Medium Hub Aviation Fore-
cast 1966-1982 (FAA-71)," July 1971.
6. Trans World Airlines (ATA/FA'O, "ATA, Lambert St. Louis Municipal
Airport-Master Plan Report," December 1968.
7. Horner & Shifrin, Inc., "Major Airport Site Survey, St. Louis Metro-
politan Area," St. Louis, Missouri, August 1969.
8. R. Dixon Speas, "Confirmatory Evaluation of Three Southern Illinois
Sites for an Air Carrier Airport to Serve the St. Louis Metropolitan
Region," May 1971.
9. Federal Aviation Administration, "The National Aviation System Plan,
1973-1982," Dept. of Transp., Washington, D.C., March 1972.
10. D. M. Rote, Et al, "Monitoring and Modeling of Airport Air Pollution,"
paper presented at International Congress of Transportation Conferences,
Washington, D.C., June 1972.
11. Highway Research Board, National Academies of Science and Engineering,
"Urban Travel Patterns for Airports, Shopping Centers, and Industrial
Plants," National Cooperative Highway Research Program, Report No. 24,
1966.
12. D. M. Rote, et al, "Argonne Airport Vicinity Air Pollution Study,"
Report No. 8, Center for Environmental Studies, Argonne National
Laboratory, to be published.
13. Unpublished data from O'Hare International Airport, Center for Environ-
mental Studies, Argonne National Laboratory.
14. Los Angeles County Air Pollution Control District, "Study of Jet Air-
craft Emissions and Air Quality in the Vicinity of the Los Angeles
International Airport," NTIS No. PB 198 699, April 1971.
-------
310
References (Contd.)
15. Bay Area Air Pollution Control District, "Aviation Effect on Air Quality
in the Bay Region," San Francisco, California, February 1971.
16. Private communication, Boeing Aircraft Company representative at
O'Hare International Airport.
17. J. H. Callender (ed.), "Time-Saver Standards," McGraw-Hill, New York,
N. Y., 1972.
18. Regional Planning Commission, Cleveland, Ohio, "Survey Results,
Cleveland-Hopkins Airport Access Study," NTIS No. PB 195 045, June 1970.
19. City of Chicago, "1969 O'Hare Passenger Survey," Department of Public
Works, September 1970.
20. E. M. Whitlock and E. F. Cleary, "Planning Ground Transportation
Facilities for Airports," Highway Research Record No. 274, Highway
Research Board, National Research Council, 1969.
21. R. H. Horonjeff, "Survey of Ground-Access Problems at Airports,"
Transportation Engineering Journal of ASCE, Vol 95, No. TE1, February
1969.
22. Economics Research Associates, "Economic Impact of three Alternative
New Regional Airport Locations on the City of St. Louis, the City of
East St. Louis, and the Seven County Region," November 1971.
23. W. S. Hornburger, "Automobile Parking Requirements at Airports," paper
presented at 4th National Airport Conference of the Aero-Space
Transport Division, ASCE, San Francisco, April 1964.
24. A. M. Voorhees, "Airport Access, Circulation, and Parking," Journal
of the Aero-Space Transport Div., ASCE, Vol 92, No. ATI, January 1966.
25. L. E. Bender, "Planning Ground Transportation Facilities for New
Airports," Traff. Quart., pp 361-383, October 1954.
26. J. W. Scheel, "A Method for Estimating and Graphically Comparing the
Amounts of Air Pollution Attributable to Automobiles, Buses, Commuter
Trains, and Rail Transit," Paper 720166 presented at the SAE Automotive
Engineering Congress, Detroit, Michigan, January 1972.
27. A. M. Voorhees & Associates, "1990 Surface Transportation Analysis,
Lambert St. Louis International Airport," St. Louis, November 1971.
28. U.S. Environmental Protection Agency, "Compilation of Air Pollutant
Emission Factors," Research Triangle Park, N. C., February 1972.
-------
311
References (Contd.)
29. M. Platt et al., "The Potential Impact of Aircraft Emissions on Air
Quality," Northern Research and Engineering Corp. Report No. 1167-1,
Cambridge, Mass., December 1971.
30. L, Bogdan, H. T. McAdams, "Analysis of Aircraft Exhaust Emission
Measurements," Cornell Aeronautical Laboratory Report No. NA-5007-K-1,
Buffalo, N. Y., October 1971.
31. T. D. Wolsko, M. T. Matthies, R. E. Wendell, "Transportation Air
Pollutant Emissions Handbook," Argonne National Laboratory Report No.
ANL/ES-15, Argonne, Illinois, July 1972.
32. TI-3 Petroleum Committee, J. Air Poll. Cont. Assoc., 21(5), 260, 1971.
33. Illinois Pollution Control Board, Rules and Regulations, Chapter 2,
Air Pollution, 1972.
34. Private communication, Hertz Rent-a-Car representative at O'Hare
Airport, 1972.
35. Private communication, Bureau of Taxi Registration, City of Chicago,
1972.
36. Tippetts-Abbett-McCarthy-Stratton, "Draft Environmental Impact State-
ment, Dallas-Fort Worth Airport," Arlington, Texas, November 1971.
37. A. S. Kennedy et_ al., "A Methodology for Predicting Air Pollution Concen-
trations from Land Use," prepared for the U.S. EPA by Argonne National
Laboratory, Center for Environmental Studes, (to be published).
38. General Motors Corp., "Progress and Programs in Automotive Emission
Control," March 12, 1972.
39. D. E. Wuerch et al., "A Preliminary Transport Wind and Mixing Height
Climatology for St. Louis, Missouri," National Oceanographic and
Atmospheric Administration Technical Memorandum NWS CR49, June 1972.
40. A. C. Stern (ed.), "Air Pollution, Vol II," Academic Press, 1968.
41. L. J. Hoover et_ al., "Evaluation of Emission Control Strategies for
Sulfur Dioxide and Particulates in the St. Louis Metropolitan Air
Quality Control Region," Argonne National Laboratory IIPP-5,
October 1971.
42. A. J. Fabrick, J. E. Prager, and R. C. Sklarew, "Description of Systems,
Science and Software Air Pollution Computer Codes: SETUP and NEXUS/P,"
3SR-827, Systems, Science and Software, La Jolla, California, October
1971.
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312
References .(Contd.)
\
43. "Air Pollution Aspects of Odors," First Draft Copy, EPA, Feburary 1971.
44. "An Interim Report on Motor Vehicle Emission Estimation," David S.
Kircher and Donald P. Armstrong, EPA, January 12, 1973.
45. Brian J. L. Berry and Alan M. Baker, "Geographic Sampling," in Spatial
Analysis; A Reader in Statistical Geography, edited by Brian J. L. Berry
and Duane F. Marble, Englewood Cliffs: Prentice-Hall, 1963.
46. Melville C. Branch, City Planning and Aerial Information, Cambridge:
Harvard University, 1971.
47. Walter Vissotski and Peter Elliott, "1970 Land Use Survey," Northeastern
Illinois Planning Commission: Research Memo #11, 1972.
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BIBLIOGRAPHIC DATA
SHEET
1. Report No.
APTD-1470
3. Recipient's Accession No.
4. Title and Subtitle
An Air Pollution Impact Methodology for Airports
and Attendant Land Use - Phase I
5. Report Date
January 1973
6.
7. Author(s)
J. E. Norco, R. R. Cirillo, T. E. Baldwin. J. W. Gudenas
8. Performing Organization Rept.
No- ANL/ES-22
9. Performing Organization Name and Address
Argonne National Laboratory
Center for Environmental Studies
9700 South Cass Avenue
Argonne. Illinois 60439
10. Project/Task/Worlc Unit No.
11. Contract/Grant No.
EPA-IAG-0171 (D)
12. Sponsoring Organization Name and Address
EmRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
411 West Chapel Hill Street
Durham, North Carolina 27701
13. Type of Report & Period
Covered
Final (Phase I)
14.
15. Supplementary Notes
16. Abstracts
It has been demonstrated that large airports have a direct impact on environmental
quality as a result of aircraft operation, and an indirect impact by providing a focal
point for urban development and industrialization. This report addresses the air pollu-
tion impact of an airport and its environs. A methodology is presented for integrating
the air pollution impact of an airport and its associated ground-support activities with
that of the induced urban development in its vicinity, to provide a quantitative basis
for decisions related to airport site selection and for the development of land surrounc
ing the site. Procedures for estimating airport-related air pollutant emissions are de-
fined. The flexible impact methodology is achieved through a general protocol for iden-
tifying, isolating and quantifying an array of airport related and urban activities whi
which provide environmental insults. The procedures are general and applicable to either
existing or proposed airport facilities. It was developed and field tested using data
from the proposed St. Louis Airport at Waterloo/Columbia. Illinois, from the Chicago
O'Harp Tnfprnatinnnl " ^ ^ " -^ ---^ f---:-i-^-~
I TT)OT
ana from spvpral nthfvr
17. Key Words and Document Analysis. 17a. Descriptors
Airports
Air pollution
Environmental engineering
Land development
Land acquisition
Emission
Urban planning
Aircraft
17b. Identifiers /Open-Ended Terms
Airport-Air Pollution Impact
Airport Siting
Environmental Impact Methodology
17e. COSATI Field/Group 13B, IE
18. Availability Statement
Unlimited
(This
19. Security Clas
Report)
UNCLASSIFIED
20. Security Class (This
Paze
UNCLASSIFIED
21. No. of Pages
. 312
22. Price
FOBM NTIS-35 (REV. 3-721
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