vvEPA
United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/5-79-005
May 1979
Air
The Impact of Future
Diesel Emissions on the Air
Quality of Large Cities
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EPA-450/5-79-005
The Impact of Future Diesel Emissions
on the Air Quality of Large Cities
by
Roy A. Paul
PEDCo Environmental, Inc.
505 S. Duke Street
Durham, North Carolina 27701
Contract No. 68-02-2585
EPA Project Officer: Richard Atherton
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air, Noise, and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
May 1979
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DISCLAIMER
The opinions, findings, and conclusions expressed in
this report are those of the author, and not necessarily
those of the U.S. Environmental Protection Agency.
ii
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ACKNOWLEDGEMENTS
Appreciation is extended to the EPA Project Officer,
Richard Atherton, Office of Air Quality Planning and Standards,
for his guidance in the conduct of this study. Other per-
sons from EPA who provided guidance include: George Kittredge,
Office of Mobile Source Air Pollution Control; Bill Peterson
and Frank Black, Environmental Science Research Laboratory;
and Justice Manning and Ray Morrison, Office of Air Quality
Planning and Standards. Debbie Barsky, Motor Emissions
Laboratory, provided timely data.
Mr. Carl Nelson, Manager of the Durham Branch, PEDCo
Environmental, Inc., also provided direction and review of
the project. Other persons from PEDCo Environmental who
made significant contributions include: Ted Johnson,
mathematical modeling; Jim Capel, computer programming;
Irene Griffin, computations; Herschal Slater, meteorology;
Lauren Smith, graphics; Kay Marr, editing; and Dian Dixon,
typing.
A number of persons in local agencies voluntarily
contributed information in a helpful manner that was greatly
appreciated. In New York this included Hugh Tipping and
Harold Nudelman, New York Department of Air Resources; Dick
Gibbs, New York State Department of Environmental Conser-
vation (Albany); Stanley Bogoff, New York City Traffic
Engineering Department; and Adolph Oppenheim, New York City
Planning Commission. In Chicago the following persons were
very helpful: Marie Bousfield, Chicago Department of Plan-
ning, and Roy Bell, Chicago Area Transportation Study. In
California, Mr. Robert Chung, Office of Planning and
Research (Sacramento) provided helpful information.
111
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TABLE OF CONTENTS
Paqe
EXECUTIVE SUMMARY 1
1. INTRODUCTION
1.1 Introduction 10
1.2 References 14
2. TRANSPORTATION SCENARIOS
2.1 Problems of Automobiles 15
2.2 Future Scenarios 21
2.3 Projections of Modal Choice 31
2.4 References 40
3. SCENARIOS OF DIESEL SALES
3.1 Diesel Automobiles 43
3.2 Diesel Trucks 47
3.3 Other Vehicles
3.4 References 57
4. EMISSION FACTORS
4.1 Emission Factors in Scenario E^ 5g
4.2 References 5]
5. METHODS OF ANALYSIS
5.1 Relating Emissions to Ambient Air Quality 63
5.2 Allocating Emissions to Receptor Sites 68
5.3 Monitoring Site Data 72
5.4 Estimates of Population Exposure 77
5.5 Projections of BAP 37
5.6 References gg
6. RESULTS OF ANALYSIS
6.1 The Impact of Future Diesel Emissions 94
6.2 Population Exposure 110
6.3 The Impact of Switching to Mass Transit 119
6.4 Caveats 128
6.5 References 130
APPENDIX A - PROJECTIONS OF MODAL CHOICE FOR
THREE TRANSPORTATION SCENARIOS 131
APPENDIX B - DERIVATION OF EQUATIONS FOR
a AND 8 PARAMETERS IN THE
CUMULATIVE LOGISTIC DISTRI-
BUTION FORMULA 150
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TABLE OF CONTENTS (continued)
Page
APPENDIX C - TABLE OF PROJECTED DIESEL SALES AND
VMT FRACTIONS 153
APPENDIX D - TABLE OF PROJECTED EMISSION
FACTORS 170
APPENDIX E - A ROUTINE FOR PROJECTING
MEAN ANNUAL TSP 178
APPENDIX F - TABLES OF PROJECTED MEAN
ANNUAL TSP AND BAP LEVELS 188
APPENDIX G - PROJECTED TSP LEVELS ASSOCIATED
WITH POPULATIONS AT RISK 205
APPENDIX H- PROJECTED TSP CONTRIBUTIONS FROM FOUR
MODES OF TRANSPORTATION 208
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EXECUTIVE SUMMARY
At the present time/ both the government and the automobile
industry are expressing considerable interest in diesel
engines. The diesel car consumes 25% less fuel than an
equivalent gasoline automobile. The diesel engine can meet
stringent emission standards for carbon monoxide and total
hydrocarbons, andf because of the low volatility of diesel fuels,
evaporative emissions are reduced.
On the other hand, the diesel engine emits particulate
matter in the form of soot. These particles are very small,
with 90% of them less than 1 micron in diameter. For this
reason, they are capable of passing into the pulmonary alveoli
without being impacted and removed in the upper respiratory
tract. Associated with this particulate emission is a wide
variety of organic materials. One group of hydrocarbons, the
polycyclic aromatic hydrocarbons, contain species that are
known to be carcinogenic in test animals. The degree of
hazard to humans from these and other organic materials is not
yet known precisely. This presents the problem of a pollutant
carrying certain amounts of a potential carcinogen into the
lungs of the general population, in addition to the negative
health implications of the particulate material itself.
This study examines the question of how the increasing use
of diesel engines will affect the air quality of the central
business districts (CBD's) in our three largest cities: Los
Angeles, Chicago, and New York. The pollutant analyzed was
total suspended particulates (TSP) in the ambient air. What
will be the effect on TSP of switching from automobiles to
greater usage of buses and taxis? What will be the effect of
the dieselization of the automobile population? Since future
events cannot be projected with any certainty, it is the
current practice to examine alternative possibilities. In this
study future transportation systems and future diesel sales
were analyzed in the context of alternative future scenarios.
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One set of alternative scenarios dealt with the future use
of transportation systems. (See Table 1.) The field cf.
transportation planning was reviewed in order to identify the
likely possibilities for using automobiles, buses, taxis, and
heavy duty trucks in the CBD of the three large cities. Of
special interest were the projected vehicle miles traveled
(VMT) for these four modes of transportation, since these are
the types of vehicles that generate large amounts of diesel
particulate emissions. In the first transportation scenario
(T-^) , automobile traffic, including light duty trucks, con-
tinues to increase in the future much like the trends of the
past two decades; bus systems experience growth; and heavy
duty truck traffic increases moderately. In the second
transportation scenario (T~) the use of the automobile is
moderated so that the vehicle miles travelled by cars do not
increase and other systems, such as buses, are used more
heavily. In the third transportation scenario (TO the use of
the automobile actually declines as a result of energy
shortages and recessions, or as a result of national policy.
Other modes experience growth, but overall transportation demand
is dampened.
A second set of scenarios dealt with the future sales and
usage of diesel engines. In the first diesel sales scenario
(D,) it was assumed that the diesel engine would remain an
insignificant fraction of the total automobile population. In
the second diesel sales scenario (D2) it was assumed that
diesel automobile sales would accelerate through a rapid growth
phase, reaching 25% of total car sales by 1985. This rate of
increase is described in a mathematical model which shows the
diesel engine reaching nearly complete dominance of the
automobile market in the early 1990's. Dieselization of the
heavy duty vehicle population was assumed to continue in a
similar pattern, following recent trends.
At the present time, the exact rate of particulate emissions
from future diesel automobiles is not known. Therefore, the
emission factors used in previous studies were used, but treated
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Table 1. SUMMARY OF SCENARIO COMPONENTS
Symbol
Tl
T2
T3
Dl
D2
El
Name
Description
Alternative Transportation Scenarios
Optimistic
Growth
Moderation
Automotive
Decline
Continued growth in automotive VMT
following recent historical patterns;
modest growth of bus, taxi, and HOT
modes in central cities.
Zero growth of automotive VMT coupled
with partial modal shift to bus and rail
transit; modest growth in taxi and HOT
usage except in Chicago.
Decline in automotive VMT due to
economy or government controls; modest
growth of bus usage; decline of taxi
and HDT usage.
Alternative Diesel Sales Scenarios
No Diesel
Full Scale
Dieselization
Diesel engine remains an insignificant
part of the automobile and taxi pop-
ulations; use of diesel in buses and
HDT's follows historical growth
pattern.
Diesel engines gain rapid acceptance/
becoming the predominant engine in
cars and taxis in the 1990 's; use of
diesel in buses and HDT's follows
historical growth pattern.
Alternative Emission Factor Scenarios
Emission factors from previous studies
were used.
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130
120 FH
110 z=3
100
C
H
PU
CO
c
(0
I
o
r-l
)-l
-P
I
(U
o
(0
3
O
(U
N
90
80
70
60
50
40
1978 80 82 84 86 83 90 92 94 96 98 2000
Figure 1. Projected Annual Mean TSP for N.Y. Monitoring Site at
170 E. 121 St.
4
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3.
C
DJ
W
id
I
-P
0)
o
0)
c
o
(1)
N
H
rH
Id
M
O
z
120
110
100
90
80
70
60
50
40
1978 80 82
84
86
88
90 92 94 96 98 2000
Figure. 2. Projected Annual Mean TSP for Chicago Monitoring Site at
X"4 / W. POj,K. .
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130
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110
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CO
EH
O
rH
4J
o
a>
a
a>
ta
o
90
80
70
60
50
40
1978 80
82
84
86 88
90
92
94
96 98 200O
Figure 3. Projected^
Mean TSP for California Monitoring Site at
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as a scenario option (E,). This allows for updating the study
as newer emission factors become available.
A relatively new method was used to project future ambient
levels of TSP. The relationship between transportation emissions
and ambient levels of TSP was determined by trace element
studies, rather than using an area-wide computer model* First,
16 air quality monitoring sites were selected from the central
business districts of the three cities where the impact of
diesel exhaust was expected to be most severe. Second, the
fractions of TSP emissions that are attributable to vehicles
and that were effective at given monitoring sites were measured
by trace element studies carried out by other investigators in
New York and Los Angeles. Third, air quality data for 1975
through 1977 were combined into a standard base year. This
procedure was patterned after the normalization of data for a
standard meteorolojical year. Fourth, all non-transportation
TSP emissions were held constant, so as to examine only the
effects of various transportation policies or dieselization
policies. Thus, the results of the study should be viewed as
an analysis of policy alternatives, and not as a forecast of
actual TSP levels. The results were expressed in terms of
population exposure as well as ambient TSP. Levels of
benzo(a)pyrene were also projected.
The projected TSP levels varied according to what
scenarios were analyzed, as shown in Figures 1, 2, and 3. The
largest effect was found to be the effect of different
scenarios of diesel automobile sales. In the non-diesel
scenarios (D,) the ambient TSP at all monitoring sites remained
at or below the present levels. In the rapid dieselization
scenarios the ambient TSP levels at all monitoring sites
increased dramatically after 1985. The amount of increase
ranged from 17 to 41 percent, depending on which site and which
transportation scenario were analyzed.
The effect of rapid dieselization of the automobile popu-
lation with.present emission factors would have a substantial
effect on the CBD residents in these three cities. Figure 4 shows
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(1)
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H
cn
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The impact of switching from heavy use of automobiles
to the increased use of bus and rail transit is a net improvement
in projected TSP levels. It was found that a large reduction
in the TSP contribution from the automotive mode of transportation
correlated with a very minor increase in the TSP contribution
from buses. This is primarily due to the large capacity of
buses, which can accommodate 40-100 commuters in scenarios
involving a modal shift. The VMT of automobiles can be reduced
by about 50 miles for each 1 mile increase in bus VMT experienced
in a modal shift. Despite the higher TSP emission rates for
buses compared to automobiles, their use for commuting
contributes to a significant reduction in TSP contribution from
the transportation sector in the central cities. In all
scenarios, contributions from buses represented a significant
portion of future ambient TSP levels.
Unlike buses, the impact of taxis closely parallels the
impact of automobiles. Dieselization of taxis is expected
to follow the same pattern as automobiles; the proportion of
diesel engines is assumed to be about the same in both taxi
and auto populations. A weakened economy is expected to
reduce the usage of taxis, but taxis are much less affected by
a modal shift than are automobiles. If taxis become dieselized,
then the contribution from the taxi mode will represent a
significant portion of the ambient TSP levels.
It is worth observing that the effect of dieselization
will not be felt until after 1985. All scenarios show a
continued improvement in TSP until this time, which reflects
the decreased use of leaded fuel.
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1. INTRODUCTION
1.1 INTRODUCTION
At the present time, both the government and the auto-
mobile industry are expressing considerable interest in diesel
engines. The Department of Energy (DOE) and the National
Highway Traffic Safety Administration (NHTSA) are interested in
the use of diesel automobiles because they consume approxi-
mately 25% less fuel by volume than equivalent gasoline-powered
engines. NHTSA has projected that the implementation of a
27.5-mpg fuel economy standard in 1985 could result in a
possible savings of 3 1/3 billion barrels of oil by the end of
the century, or about one-third of the Alaska North Slope oil
reserves. General Motors Corporation has intentions of
introducing diesel engines into its lines of automobiles. At
present, the Oldsmobile has a diesel option, and a decision to
convert more lines to diesel may be forthcoming in the near
2
future.
The diesel engine may offer some advantages for the
control of air pollution. First it can probably meet stringent
emission standards for carbon monoxide and for total hydro-
carbons. Second, because of the low volatility of diesel
fuels, evaporative emissions are reduced. Third, diesel engines
have been said to have superior durability. Over a 100,000
mile lifetime, emissions are claimed to increase only slightly.
For the presently regulated pollutants, the diesel may represent
a technical improvement in emission control.
On the other hand, the diesel engine represents a new
spectrum of pollutants with an unknown level of possible
hazards. During the combustion cycle, current diesel engines
create a wide range of hydrocarbons, more than 20,000
4
individual species. These compounds represent the combustion
products of fuel and lubricant, with hydrocarbon molecules
ranging in size from C, to about C.Q. The compounds of
greatest concern are those responsible for positive results on
10
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the Ames Test. This includes classic polyclyclic materials,
such as benzo(a)pyrene, but also includes other materials that
show positive Ames activity, which at the present time have not
been precisely identified. Thus, while the amount of
hydrocarbon emissions may be small, there is concern for the
release of potential carcinogens at any concentration.5
Another possible problem to overcome is that of par-
ticulate emissions associated with diesel exhaust. While
gasoline engines typically discharge little particulate matter
(if they burn unleaded fuel) the diesel engine combustion
process emits substantially more particulate matter. The
amount of particulate material currently emitted from diesel
cars and light trucks ranges from about 0.2 to 1.0 grams per
mile. (See Figure 1-1.)
DIESEL
CAR
A
B
C
D
E
ENGINE
DISPL
(L)
1.5
2.1
2.1
3.0
5.7
TEST
WT
(LB)
(
2250
3000
3000
4000
4500
PARTICULATE EMISSIONS
(g/mi)
3 0.2 0.4 0.6 0.8 1
Ml
i
1
B
BBBOUJ
.0
jSMSSSij
GASOLINE
PREREGULATION, LEADED FUEL
CURRENT PROD., NO LEAD & CONV-
0 0.2 0.4 0.6 0.8 1.0
Figure 1-1. Passenger Car Particulate Emission, 1975 Federal
Test Procedure.4
11
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Furthermore, it has been found that diesel particulates
are quite small in size, with 90% of the particulate mass less
than 1 micrometer in diameter. This is a particle size that is
generally considered respirable. They are capable of passing
into the pulmonary alveoli without being impacted and removed
7
in the upper respiratory tract. This presents the problem of
a pollutant (diesel particulates) carrying potential carcinogens
into the lungs of the general population.
The Environmental Protection Agency is actively in-
vestigating the potential problems of diesel engines. It has
begun an extensive research program to determine the
degree of hazard involved in diesel exhaust. The health
effects research involves exposing test cultures and organisms
to diesel exhaust residue from a variety of sources. Test
procedures are being developed to standardize the methods of
analyzing emissions. Other studies are examining the
variations in emission products with various engine con-
o
figurations and operating conditions.
The study presented in this report examines the question
of how the increasing use of diesel engines will affect ambient
air quality in our three largest cities: Los Angeles, Chicago,
and New York. Recognizing that some gaseous emissions may actually
be reduced, most of the report is devoted to analysis of total
suspended particulates (TSP) in the ambient air. Increased
diesel particulate and emissions of polycyclic organic material
(POM) can be expected from two possible sources. One is the
switching from gasoline to diesel engines in automobiles and
taxis. The second is the increased use of heavy duty diesel
trucks and buses. If there is a strong effort to switch from
automobiles to mass transit, then the emissions from buses and
taxis might be expected to increase. Such an increase must be
balanced against the reductions in automotive emissions.
The impact of diesel exhaust on the air quality of the
cities will depend upon certain unknown factors and future
events. Future events to consider include the type of
transportation system changes that are made, the availability
of fuels, the health of the economy, and the introduction of
12
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particulate standards for mobile sources. Some of the unknown
factors that may affect emission levels include the emission
factors that can be expected from future model years and their
rate of deterioration over the life of the vehicle.
Rather than projecting a future trend, it was considered
desirable to look at alternate future scenarios. Such
methodology has become the accepted practice, not only in
examining environmental impacts of governmental actions, but
9
also in planning desirable transportation systems. In order to
analyze the various factors that determine the impact of diesel
engines, future scenarios were developed as follows:
1. Transportation Scenarios (Chapter 2) - The recent
literature on transportation planning was reviewed and the
likely features of future transportation systems were
examined. The types of systems utilized in the future will
have a direct bearing on the vehicle miles traveled by
various vehicle types in central cities. These scenarios
were used to analyze the effects of modal split and the
intensity of transportation usage on the levels of TSP.
Of special interest is the increased use of diesel taxis
and buses in the central city.
2. Diesel Sales Scenarios (Chapter 3) - Whether or not
diesel engines are introduced into the automotive
population by American manufacturers will have consid-
erable impact. A mathematical model was developed to
describe a rapid diesel introduction scenario, and the
resulting diesel engine distribution in the automobile
population was described.
3. Emission Factors (Chapter 4) - The present production
line diesel engines have been tested and their emission
factors calculated. Deterioration factors for the aged
vehicle population have not been determined, and future
engines may have different characteristics than those
presently being tested. Emission factors from a previous
pilot study were utilized as one emission scenario for the
present study.
13
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Analyzing the impact of diesel in three large cities under
the limits.of the resources available in this study presented a
significant challenge. The cost, capabilities, and limitations
of atmospheric dispersion modeling were studied, and an
alternative method of analysis was developed in Chapter 5. The
methodology is based upon the use of trace element studies to
determine the proportion of total suspended particulate (TSP)
emissions at a monitoring site that are attributable to motor
vehicles. It is also based upon normalizing the base year
emissions data to a standard meteorological year; the
projection of future air quality levels assumes standard
meteorological conditions for each projection year. The
use of this methodology permitted the examination of the
effects of future diesel exhaust emissions on the particulate
(and related hydrocarbon) levels in the CBD of three large
cities. The results of this examination are presented in
Chapter 6.
1.2 REFERENCES
1. Strombotne, Richard L., Background Information from the
Edited Transcript and Proceedings of the Workshop on
Unregulated Diesel Emissions and Their Potential Health
Effects. DOT HS-803 527, Sponsored by Department of
Transportation, Department of Energy, and the Environ-
mental Protection Agency, April 27-28, 1978, pp. 1-8.
2. Stewart, Reginald, "G. M. Betting on Diesel Engine",
The New York Times, November 10, 1978.
3. Elder, Charles, "A Comparison of Diesel and Gasoline
Engines for Passenger Car Usage", General Motors Cor-
poration, from Proceedings, Ref. 1, pp. 160-181.
4. J. F. Chalmers, et. al., Review of the Research Status
on Diesel Exhaust Emissions, Their Health Effects, and
Emission Control Technologies, Aeorospace Report No.
ATR-78(7716)-3, Reissue A, Prepared by the Aerospace
Corporation for the U. S. Department of Energy, June
1978.
5. Santodonato, Joseph, Health Effects Associated with
Diesel Exhaust Emissions, Literature Review and Eval-
uation, EPA-600/1-78-063, Prepared by Syracuse Research
Corp. for the U. S. Environmental Protection Agency,
November 1978.
14
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6. Black, Frank, and Larry High, "Diesel Hydrocarbon
Emissions, Particulate and Gas Phase", presented at the
Symposium on Diesel Particulate Emissions Measurement
Characterization (unpublished) 1978.
7. Ref. 4, pp. 1-3.
8. Bureau of National Affairs, Inc., "EPA Initiates $9.5
million Program to Study Effects of Diesel Exhaust",
Environmental Affairs - Current Developments, November
10, 1978, pp. 1279-1280.
9. Bernard, Martin J. Ill, Applications of the New
"Alternative Futures" Planning Concept, Argonne Natonal
Laboratory, presentation to the Transportation Research
Board, Jaunaury 16-20, 1978.
15
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2. TRANSPORTATION SCENARIOS
2.1 PROBLEMS OF AUTOMOBILES
In the Clean Air Act of 1970, Congress anticipated that an
automobile emissions control program alone would not be
sufficient to achieve the air quality standards in all cities.
Where monitoring data showed that the control of stationary
sources and the control of automotive emissions were not
sufficient, additional steps would be required. Each State was
to submit a State Implementation Plan (SIP) which would show
how the National Ambient Air Quality Standards (NAAQS) were
to be met within each Air Quality Control Region (AQCR).
It was expected that the use of automobiles would have to be
reduced in some areas.
In response to the need for transportation planning, EPA
conducted a series of studies in thirteen cities to provide
assistance to the States in formulating strategies to reduce
the use of automobiles. Measures to reduce automobile usage
would be aimed at curtailing the vehicle miles traveled (VMT).
Such measures might include improvements in transit systems,
implementation of carpool programs, priority treatment for
buses on streets, restrictions on parking, or some "disincentive1
2
to the use of the automobile.
The use of automobiles in transportation has been growing
since automobiles first became available to the public.
Intracity mass transit, on the other hand, has been declining
for the last 50 years, except for a period of gasoline
rationing during World War II. In 1950, roughly 17% of urban
trips were by transit, whereas by 1965 it had declined to 5%.
In 1975 it dwindled to 2.5% (see Figure 2-1). The continued
population growth and prosperity during this century has
increased the demand for automobile travel and decreased the
necessity of using the less expensive modes of travel such as
bus and rail transit.
16
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150
Transit
Passengers
Urban Population
- 100
1925 1935
1945 1955 1965
Year
1975
C x-N
O 00
H C
4J 9
CO -H
Figure 2-1.
Trends in Transit Passengers and Urban Population,
1920-1975.
Leahy points out that the preference for the use of the
personal automobile has a rational basis in an expanding
economy. First of all, the door-to-door travel time for cars
is less than that for transit systems. Second, the service is
available whenever it is wanted. Third, the modern automobile
is very reliable and provides a high degree of personal
comfort, even luxury. Fourth, the automobile user feels more
secure from criminal attack than does the transit rider,
particularly in late evening hours.
Air pollution is not the only problem of the automotive
preference. Personal vehicles are presently consuming more
than 50% of the energy used in transportation and about 25% of
the petroleum being consumed in the United States. Leahy has
critically examined the current projections of automobile usage
and their implications for future energy demand. He and others
have pointed out that projected energy requirements would
exceed U. S. energy production by an increasing amount in the
future (see Figure 2-2). He showed the effects of various
projections of transportation energy usage, including an
extreme modal shift (30% use of electric car and 15% use of
diesel bus). He found that even this shift would be in-
sufficient to close the gap.4 It is apparent that there is a
need for reducing petroleum demand as a matter of national
interest.
17
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a
a
a
o.
36
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
|| | || IMPORTS
Ipg&j DOMESTIC PRODUCTION
Transportation Modal Split as
Projected by:
Baseline (Weiner)
Hirst
Frost & Sullivan
Interplan
-Leahv
Natural
Gas
Liquids
Alaska
Lower
48
States
1940
1950
1960
1970
Year
1980
1990
2000
Figure 2-2
Effects of Five Different Modal Splits on U. S.
Petroleum Required Supply.
18
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Holder has pointed out the energy efficiencies of various
modes of travel (see Figure 2-3). Energy consumption by any
one of these modes is a function of the energy efficiency of
the system and trip demand on the system. Energy efficiency
can be improved by increasing the density of use (persons per
vehicle per trip). The determinants of trip demand are
population and the number of trips per person. From a national
or regional perspective it is desirable to increase energy
efficiency by increasing the number of persons per vehicle-trip
and by shifting to the modes of transportation that have the
greater passenger capacities. Despite the obvious need, there
are wide differences of opinion on how much modal shift will
actually occur in the future.
Air
Auto
Train
Bus
C
P<
Hi*
) 80
H25
30 60 90 120
issenger Miles Per Gallon
of Fuel
Figure 2-3. Energy Efficiencies of Intercity Passenger
Transportation Modes^
The three cities examined in this study vary widely in the
types of urban transit systems presently being used. New York
City has perhaps the most extensive system of buses and subways
in the country. This has come about partly as a matter of
historical accident. New York was the first large city in the
United States, and the need for urban transportation was felt
in the 19th century before the personal automobile became the
19
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dominant mode of transportation (see Figure 2-4). Subway
systems were built early and carried over into the present era.
High capital costs and the slackened demand for such systems
make the further construction of them unlikely except where
they are highly subsidized. About 23% of the daily passenger
miles in the New York City Standard Metropolitan Statistical
Area is accommodated by rail and about 4% by bus transit. Like
New York, Chicago is also an older city and has a rail transit
system in place. About 23% of the daily passenger miles are by
rail and about 5% by bus. Los Angeles, by contract, has
developed almost entirely in the twentieth century and has
experienced much of its rapid browth after WW II, when the race
to automobiles was in full acceleration. As a result, the city
has no subway, system, and only about 2% of current passenger
trips are accommodated by bus.
d
n
s
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2.2 FUTURE SCENARIOS
A number of different projections of intracity modal split
have been proposed in recent years, based on alternative future
scenarios. Perhaps the most extensively documented scenarios
are those worked out by Stanford Research Institute (SRI).
Curry et al. detailed four possible futures for 1995 and 2025.
Their analysis was based upon energy reserves, economic
conditions, and society's values.
"The first scenario, "Success", is a story of competent
management, consensus, and technological ingenuity. The
United States continues to move toward rising affluence
so that by the year of 2025 the "post industrial society"
is a reality. It is a world of high technology, so-
phisticated communication, rapid mobility, and high levels
of consumption. 7It is the fulfillment of the traditional
American Dream."
The Success scenario depends upon a continued expansion of
energy supplies, not only oil but also alternative fuels as
they are needed. Projections of automobile usage derived from
this future are similar to the conventional projections of
highway planners. Growth in the next 20 years is treated as a
continuation of the growth rate experienced over the past
20 years. Changes in energy costs and personal values are not
usually taken into account.
"If, on the other hand, our resources and managerial
capabilities fall short of requirements, our technology is
not successful soon enough, and our opinions about
solutions are deeply divided, then the next 20 years are
likely to be a time of deepening recession, eventually
reaching a depression called Distress. The progress
downward will be in small increments, unlike the
collapse of 1929. By 1995 the resulting situationa
fragmented society struggling to get bycould not
persist."
"We have posited two alternative paths out of the
depression, so Distress becomes the first 20 years of two
full 50-year scenarios. Recognizing that managing a
divided and unruly society is impossible, people could in
their frustration come to accept the need for a tightly
Disciplined Society, whether by choice or by compulsion.
The result would be the sacrifice of many freedoms and
much diversity to the constraints imposed by an authori-
tarian technocracy- This bureaucratized leadership,
faceless and diffuse, would have at its disposal so-
21
-------
CO
0)
s
0)
tH
CJ
H
J3
0)
1
0.8
0.6
0.5
0.4
*« 0-3
3
| 0.2
0.1
iuccess
)iscipl .ned _
ociety
'oul We ither
1925 1945 1965 1985 2005 2225
Year
Figure 2-5. Automobile Vehicle Miles Traveled
in SRI Scenarios.3
Sr
o
(U
0)
CO
0)
1925 1945 1965 1985 2005 2225
Year
I'iyure 2-6. Per-Capita Automobile Ownership
in SRI Scenarios.3
aSource: Reference 7.
22
-------
phisticated psychotechnology and advanced social engi-
neering capability. Most people under this scenario would
still want the material rewards of economic success. They
would be willing to overhaul the institutional fabric of
society and to endure severe constraints on their
freedomincluding their freedom to travelto obtain that
abundance."
"If, however, in the face of that depression, the nation
had to face a unifying crisis as it did in World War II,
then it might not turn to authoritarianism. In this
scenario, Foul Weather, the climate takes a turn for the
colder and drierthe winter of 1976-1977 becomes typical.
Very large numbers of people are forced or attracted to
move to the South and Southwest to escape undesirable
climate and follow industrial relocations. Agriculture is
especially hard hit and the need to preserve farmland
pushes cities in the direction of increasing density.
However, the need for adaptation and reconstruction for
survival have brought about slowly rising affluence and an
unsurpassed degree of unity."
The disciplined society scenario brings to mind an
authoritarian regime that regulates life in the United States
in a manner that conflicts with our traditional values of
individual freedom and possibly with the Constitution. This
alternative was rejected for analysis in this study because it
was deemed unlikely that values would change in this direction.
This study is biased in the direction of examining the more
moderate possibilities rather than disaster planning.
The foul weather scenario presumes a change in the
continental climactic pattern in the short space of 50 years.
While speculation about trends in the weather is popular in
some circles, historical climatological data does not
necessarily require such a projection. The severity of recent
winters is within the normal range of fluctuation over the
period of weather recording. While the foul weather scenario
is a refreshing contrast to the conventional wisdom, it is a
future alternative that we have chosen not to analyze.
Another scenario of the SRI report was considered
-j
important to this study. Curry presents the case in this
manner:
-* ->
"Clearly the Distress scenario and its two successors are
undesirable. Given the present dominant set of values,
the Nation would opt for Success. We cannot, however,
23
-------
Table 2-1. FOUR SCENARIOS OF MODAL SPLIT BY SRI
City
New York
Los Angeles
Chicago
Mode
Auto
Bus
Rail
Total
Auto
Bus
Rail
Total
Auto
Bus
Rail
Total
Daily Passenger-Mile Estimates by Scenario (1,000's)
Year .2025
Present
128,311
5,227
29,042
162,580
110,648
2,683
0
113,331
96,642
4,620
22,120
123,382
Success
174,043
15,949
22,926
212,918
137,922
7,088
20,593
165,603
98,829
10,505
20,350
129,684
Foul
Weather
72,140
12,419
24,847
109,406
100,327
.15,346
24,318
139,991
45,927
11,220
26,544
83,691
Disciplined
Society
54,741
34,155
38,419
127,315
31,919
38,362
39,951
110,232
30,612
27,934
40,827
99,373
Transformation
85,671
14,547
25,534
125,752
68,382
7,516
18,258
94,156
49,141
11,781
21,500
82,422
ro
Source: Curry, David et al., Transportation in America's Future; Potentials for
the Next Half Century, Part I - Societal Context, DOT-TPI-77-21-1,
(PB 270467/4GI), June 1977.
-------
assume a frozen set of national values when they have not
remained so in the past and are not held unanimously even
now. If the present mix of values changes dramatically,
the kind of society that results will be significantly
different. In the Transformation scenario, coping with
the problems of the future is greatly eased by growth in
the popularity of a way of life that we call "voluntary
simplicity." It values frugality, simplicity, harmony
with nature, and inner spiritual development. As more and
more people adopt that way of life, some of the problems
become easier to solvethe need to provide economic
growth is not as great, energy demand does not grow as
quickly, and a preference for part-time employment reduces
the need for jobs. By the year 2025, the result is a
highly decentralized but bifurcated society with a slight
majority pursuing this new way of life and nearly as many
successfully striving to achieve the abundant life style
of the Success scenario."
The changes in values reflected in the Transformation
scenario are credible, especially since smaller changes of this
nature were evident in the 60's and 70's. Such an alternative
future, while less drastic than some other SRI views, means an
actual decline in energy use. It provides a scenario in which
the central city automotive VMT may be reduced by consensus
politics operating at the local level. It presents the
possibility of meeting air quality standards through a change
in life styles. The automotive decline projected in this
scenario could be brought about by federal or state laws requiring
a substantial shift to alternative modes of transportation.
While the political consensus to support such a forceful policy
may be lacking at the present time, it is useful for regulatory
officials to examine the results of the possible application of
such a policy. For this reason some of the SRI data for this
scenario has been used in this study.
The assumptions underlying these scenarios affect the use
of intracity mass transit in a way that has been analyzed in
detail by SRI, but will not be repeated here. However, the
results of their analysis are presented in Table 2-1. It will be
noted that each of the alternatives except one foretells a
considerable reduction in the use of automobiles. The scenario
"success" shows a continued expansion of vehicle use which is
similar to short-term projections used by highway planners.
25
-------
On the other hand, an energy-constrained future seems to
be the most popular view among professionals at the present
time. For example, a recent workshop was held in the Chicago
area to discuss alternative transportation futures. In this
conference there was a strong consensus that energy scarcity
was the appropriate scenario, while energy abundance was
considered unrealistic. In the energy "scarcity" scenario from
this conference, petroleum prices would double or triple in
terms of constant dollars by year 2000. Automobiles would
become more efficient and some would switch to non-petroleum
fuels. Despite technological breakthroughs, mobility would
drop by 15%, particularly as it is reflected in the VMT of
personal automobiles. There would be a move toward higher
density housing patterns and greater acceptance of public
o
transit.
By contrast, the short-term planning projections currently
being used in transportation planning show increases in the
Q
total VMT by automobiles. Koppelman has reviewed the
projections of various researchers and examined various
assumptions underlying the forecasts (see Figure 2-7).
01
rH
O
CO
0)
O
-P
2000
1600
1200
800
BNL ,
SRI
1970 1975 1980 1985 1900
Projection Year
1995
2000
Figure 2-7.
Alternative Projections of Annual Automobile
Miles.
26
-------
Apparently, the short-term for casts of automobile usage are
not as energy-constrained as F;
-------
quality continue to fail. Although air quality begins to
deteriorate after emission controls are fully effective (1985),
the populace is satisfied.
2.2.2 Moderation (Scenario T^)
In Scenario T2 the price of oil (and gasoline) increases
at an annual rate roughly equal to the rate of inflation,
following recent trends. Such a development could be caused by
the actions of OPEC Nations or it could result from a national
energy program. The price of oil begins to reflect declining
U.S. oil production and the increased cost of developing the
more remote reserves. The U.S. economy grows at a slower rate
than that experienced after World War II. Recessions occur
more frequently and the rate of real growth is less than r~l/2%.
As a consequence, the use of automobiles is projected to
decrease at a rate which offsets the VMT increase projected in
Scenario T, that is, the urban VMT of automobiles levels off
at the present rate. As the population continues to grow
moderately, adjustments in trip-making take place in the
following manner:
1. More persons join carpools aided by government
incentives.
2. More persons desire to ride the bus or subway
rather than put gas into the family car.
3. Fewer young families can afford a private home and
therefore live in apartments near work and shopping
facilities. Communities increase the stock of
apartments.
4. More families purchase new homes near work.
5. National and local governments continue to invest
in modest transit improvements.
6. More business firms move to suburban communities
near employee residences.
7. Research is conducted into the processing of
alternative fuels.
In Scenario T2 automobile VMT is projected at a zero
growth rate. The growth of heavy duty diesel trucks serving
the central city is slowed, but the growth rate does not reach
28
-------
zero because business activity continues to grow and trucks are
used to haul goods. As fuel prices increase/ the cost of
transporting goods is passed along. While families and
individuals reduce the number of non-essential trips they make,
business hauling is determined by the volume of economic
activity- Moreover, trucking does not compete directly with
rail in most of the central city- Therefore, in this scenario
truck VMT is projected at a reduced, but non-zero, rate of
growth. Scenario T2 is an artifact created in this study to
obtain an automobile VMT change that would be intermediate
between optimistic growth and decline. Its artifical nature is
apparent when the VMT growth rate is set as exactly zero. It
is rationalized that the effect of a reduced, but positive,
economic growth rate combined with rising costs of petroleum
products will keep up the trip demand but cause a certain
amount of modal shift. Therefore the bus VMT growth rate is
set one-third higher than the SRI rates in Scenario T,. This
allows for a modest growth in bus trip demand in addition to
population growth. Rail transit is assumed to make the same
number of trips, rather than to decline as indicated by SRI.
The moderation scenario has proven to be very useful.
2.2.3 Decline (Scenario T^)
In Scenario T3 the world demand for petroleum fuels
exceeds the supply sometime before the end of the century.
Some researchers expect this to occur in the 1980's. The
government adjusts to this event either by policy measures
implemented in advance or by crisis management as shortages
materialize.
If the energy shortfall becomes credible to a majority of
the people, then the government can take advance actions to
soften the impact. The import of oil can be restricted to a
certain level, thereby reducing the nation's dependence on
other countries. This creates a somewhat artificial shortage
in advance of real shortages, but it allows a long-term
adjustment'. Fuel rationing wbuld be required. Other ap-
proaches are to tax crude oil and gasoline or to encourage much
29
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Table 2-2.
AVERAGE ANNUAL VMT GROWTH RATES FOR FOUR TRANSPORTATION
MODES IN THREE TRANSPORTATION SCENARIOS
CO
o
Scenario
Optimistic
Growth (T, )
Moderation
(T2)
Automotive
Decline (13)
City
New York
Los Angeles
Chicago
New York
Los Angeles
Chicago
New York
Los Angeles
Chicago
Transportation Mode
Automobile
0.0071
0.0165b
0.0045
0
0
0
-0.0066
-0.0076
-0.0098
Bus
0.041
0.033
0.025
0.055
0.044
0.033
0.036
0.036
0.031
Raila
(-0.0042)
N/A
(-0.0016)
0
N/A
0
(-0.0024)
N/A
(-0.00056)
Taxi
0.0067
0.0043
0.0039
0.0033
0.0021
-0.0020
-0.0060
-0.0070
-0.0092
Heavy
Duty
Truck
0.0071
0.0049
0.0045
0.0033
0.0021
-0.0020
-0.0066
-0.0076
-0.0098
Rail growth rates adapted from SRI are shown in order to present a more complete picture
of modal choice/ but rail growth rates do not enter into the calculation of emissions
from area sources. This study considers only the next 22 years, a time period in which
it is considered unlikely that L. A. will complete a rail transit system. Thus the comment
"N/A" applies to each scenario.
This figure is derived from projections of the L. A. Air Resources Board.
-------
higher prices. Any effective action of this type will
contribute to recessions, thereby reducing the political
feasibility of such actions.
Because many people do not believe projections of energy
shortages, it is likely that a shortage will be manifested in
the form of a crisis. Perhaps one of the major oil producers
will suddenly stop its exports to the U.S., due to war or
political upheaval. A sudden 5% reduction in the supply is
sufficient to cause disruptions in daily living habits. During
a crisis prices may rise or else rationing and controls may be
implemented. A series of crises would have the long-term
effect of reducing the automotive VMT by the end of the
century. This is expressed as a negative increase in
automobile VMT with a major emphasis on alternative modes of
transportation. Automotive VMT data for this scenario are
adopted from the SRI scenario "transformation".
It may be noted that even in Scenario T~, the use of
vehicles decreases less than is shown by some of the more
pessimistic scenarios used by others. For example, Curry
et al. used one alternative scenario for Los Angeles that would
reduce automobile usage by 71% over the next 50 years (an
average of 1.4% per year).
One of the reasons for moderating some of the more gloomy
forecasts is that technology plays a large role in the way
society adapts to changing conditions. Alternative automotive
fuels are already known which are likely to be developed
quickly when they can compete in price with present fuels. For
example, liquid and gaseous fuels can be derived from coal a
plentiful resource. Hydrogen can be developed as a fuel once
certain safety problems are overcome. As the price of oil
increases these alternatives will become attractive. They
could become dominant sources of fuel following a crisis in
Scenario T3 or they could develop more slowly in a planned
changeover.
2.3 PROJECTIONS OF MODAL CHOICE
The changes in VMT that were projected for each scenario
were based primarily upon two scenarios from the SRI study
31
-------
supplemented by information from other sources (see Table 2-2).
The automobile, bus, and rail growth rates for the Optimistic
Growth scenario were derived from the "success" scenario of SRI
by assuming that vehicle miles are directly proportional to
passenger miles. A linear change rate was used to prorate the
growth over a 50-year period to each year between 1975 and
2000. SRI data were used because the rationale for these
scenarios has already been worked out in detail. However,
where data were available from governmental agencies, it was
substituted for auto VMT in Scenario 1. The automobile, bus,
and rail VMT rates were derived from Curry's "transition"
scenario in a like manner.
No data' were available in the SRI scenarios on the changes
in taxi or heavy duty truck travel. However, the trip demand
for both of these types of vehicles can be assumed to vary as a
function of economic growth rates. Taxi usage is related to
economic activity because it is used by the upper and middle
classes to conduct business within the central business
district (CBD). With less economic activity the trip demand
either drops or converts to other modes. Heavy duty trucks are
also related to economic activity because they are the means of
transporting goods in the CBD. Projections of taxi VMT changes
were made for the New York City CBD and were applied to other
cities and other scenarios by observing that taxis receive
somewhat more use than automobiles because they are the
recipients of passengers under the modal shift changes of
Scenarios T~ and T~. (The difference between automobile and
taxi rates of change were simply applied to the rest of the
column.) The rates of change for heavy duty trucks were
assumed to be the same as those for automobiles, except for
Scenario T3 where it assumed that a modal shift is accomplished
in a positive growth economy. Any modal changes not shown in
the table are considered to be either shifts to carpooling or
reductions in trip demand.
2.3.1 Los Angeles
One of the early EPA transportation studies was conducted
in the metropolitan Los Angeles area by TRW, Inc. Their
32
-------
analysis showed that the dominant use of the automobile in the
South Coast Air Basin was the principal source of air pol-
lutants causing violations of the oxidant standard. Despite
the need for changes in the transportation system, the
improvements that were being planned at that time would result
in only minor improvement in air quality. Incentive measures
for reducing the automobile miles travelled were judged
ineffective for inducing significant changes. Among the
measures considered, only gasoline rationing would bring about
a sufficient reduction in automobile use to meet air quality
standards. The use of personal vehicles could not be re-
strained in any practical way unless alternative means of
transportation were made available. Thus it was projected that
none of the proposed controls would allow the region to meet
the ambient standards for oxidants by 1977. This has proved to
be the case.
The rapid growth of the region to the level of 6 million
people occurred during an economic boom period in which the
U.S. enjoyed access to the majority of the world's fuel
resources. This optimism was reflected in the construction of
highways since World War II. For example, in a brief 20-year
period, 1950-70, the Los Angeles region increased its freeway mileage
by 1390%. In 1967 only 0.1% of all trips were made by
taxis. In total, the complete success of then current urban
transit projects would have resulted in only a 1.3% reduction
in VMT.10
Since the 1972 report a number of studies have been
conducted to analyze the possible modal split changes that
could occur in the Los Angeles area. ' ' For example, the
Rand Corporation studied measures that might be effective in
reducing the demand for automobile travel. Goeller applied
methodology developed in San Diego in a study for the Southern
California Association of Governments (SCAG). The SCAG had
adopted a goal of 20% VMT reduction to meet air quality
requirements in the region. The study looked at the tactics
thought to be feasible: bus system improvements, carpooling
incentives, and economic disincentives for auto use. It was
33
-------
found that improvement in the bus system could reduce VMT by
10% at best, and this was impractical in the short term.
Preferential treatment for carpool vehicles combined with
computer matching of riders could possibly achieve 20%
reduction. Gas price increases could also reduce VMT by 20%,
but the maximum reduction for combined strategies was 30%.
Beyond this, some passenger trips would have to be foregone or
switched to transit. According to Horowitz, a 30% reduction of
VMT would require a modal split of 39:63 (39% by bus),
depending on the transit schedule frequencies at peak hours.
Without a major crisis, even modest reductions in VMT seemed
unlikely.
The Optimistic Growth future (Scenario TI) for Los Angeles
was developed using an automotive VMT projection of the Los
18
Angeles Air Resources Control Board (LAARB). This projection
is based on a study of the trend in automobile registrations
and driver licenses. An annual growth rate of 1.65% is applied
to the 1975 automobile population (3.5 million vehicles) and
projected forward. It is assumed that the automotive VMT per
year remains constant at 7,897 miles per year per vehicle.
LAARB handles the growth of diesel truck mileage in a similar
manner. An annual growth rate of 3.75% is applied to the 1975
base year of diesel trucks (22,381 vehicles), assuming that the
annual mileage of these trucks will remain constant at 49,318
miles per year. However, it is the heavy-duty truck growth
rate that is needed for the definitions of scenarios.
Diesel trucks as defined by LAARB do not necessarily
include all the truck weights included by EPA in the definition
of "heavy duty trucks" (HDT). Furthermore, a certain amount of
diesel VMT growth is attributable to diesel sales penetration
into the HDT market. For these reasons, it was consiered more
appropriate to use the HDT growth rate derived from SRI data
rather than LAARB projections.
In this scenario bus VMT expands to maintain the present
level of service in the face of population growth and to serve
the handicapped and the elderly. It is affordable because of
the booming economy.
34
-------
Scenario T2/ Moderation, shows a zero rate of VMT growth
when averaged over the twenty year period to year 2000.
Assuming that the amount of business activity is still in-
creasing, the VMT of heavy duty trucks was assumed to
increase, but at a rate one-half as great as that of Scenario
T,. Bus service is projected to increase at a rate one-third
greater than Scenario T,, so that the increase in trip demand
due to population growth is accommodated by this mode
of transport. This means a significant net change in the
transportation habits of the region over a 20-year period.
In Scenario T- the economy stagnates, energy sources are
sharply curtailed, and the automobile VMT actually declines.
The rate of decline was adopted from the SRI "transformation"
scenario in which a change of values takes place. As noted
earlier, this projection is not as confining as the SRI
"disciplined society" scenario.
Since economic activity is curtailed, the growth of heavy
duty truck VMT is taken to be negative. On the other hand, bus
services expand to accommodate a modal shift, although economic
conditions cause total trip demand to drop. The bus VMT growth
factor is the same as for Scenario T_, although expansion takes
place for a different reason.
Projections of modal choice in Los Angeles according to
these scenarios are given in Appendix A, Tables A-l to A-3.
2.3.2 New York City
One of the 1972 transportation studies conducted by EPA was
19
conducted in New York City. At that time it was found that
the NAAQS for carbon monoxide (CO), oxidants (O ), and nitrogen
H
oxides (NO ) were violated, with the highest CO levels observed
J\,
in the Manhattan downtown and midtown districts. Maximum 8-hour
concentrations of 45 ppm CO had been recorded, which is 5 times
the 9-ppm standard. In general, a "rollback" of 72%-80% of the
19
1970 emissions level was required to meet standards.
The Federal automotive emission control program was expected to
reduce emissions by a substantial amount, but it was expected
that transportation controls would be required if the Federal
standard were to be met for CO.
35
-------
The 1972 report examined the necessity for VMT reductions
in the Manhattan central district and reviewed the 31 types of
control measures that were being considered by the New York
City Department of Air Resources. One strategy was the
development of a state-administered inspection and maintenance
program. Another approach was to reduce VMT in downtown areas
by limiting parking, by adopting vehicle-free zones, and by
improving traffic flow on other streets. A third approach was
the promotion of transit and carpool ridership. Other
strategies involved the delivery of goods during non-rush hours
and long-range plans affecting land use and transportation. It
was observed that some of the necessary measures would have ad-
verse economic effects, if not adverse political effects.
A recent study by the Tri-State Regional Planning
Commission (TRPC) reviewed the current air quality plans and
transportation plans for the region. While transportation
elements of the air quality plan could also be found in the
regional transporation plans, TRPC found that "... their study
and implementation have not always received the urgent
attention recommended in Air Quality Plans." This conclusion
was warranted in view of the fact that the 1972 recommendations
for transportation controls had not been carried out. The
commission staff projected that the present program, relying
heavily on Federal emission controls, was not projected to meet
air quality standards until 1989 (see Figure 2-8). Lacking the
public perception of any crisis, this city (like others in the
U.S.), will find it difficult to implement any program that
would seriously jeopardize the use of the personal automobile.
It was noted earlier that New York and Los Angeles have
vastly different transportation systems stemming from their
different development histories. Nevertheless, the changes
that can be made in the present systems and in the present
modal choices are highly conditioned by common national
policies and by the national economy. Therefore the growth
rates projected for the future under the three scenarios form
similar patterns.
36
-------
100
90
EMISSIONS
REDUCTION FROM
vehicle
turnover
other
hardware
controls
transportation
controls
<EMISSIONS
REMAINING
1970
1975 1980
1985
1990
1995
2000
Figure 2-8.
Emissions Rollback with Transportation Controls,
Carbon Monoxide, Manhattan CBD
37
-------
Like Los Angeles, the automotive VMT growth rates in New York
are positive under the Optimistic Growth Scenario (T-^) ; are
set at zero under the Moderation Scenario (T3) ; and arc* nega-
tive under the "decline" scenario. Both automobile and bus VMT
growth rates from Scenarios T, and T- were adopted from SRI
propositions. The growth of HDT and taxi VMT in Scenario T, was
adopted from projections used by TRW, Inc., and by local
government in the New York Air Quality Implementation Plan.
Projected VMT for automobiles, HDT's, taxis, and buses are
given in Appendix A., Tables A-l to A-3.
2.3.3 Chicago
Chicago is similar to New York in several ways. It is an
older city with an extensive rail transit system. Rail
transportation currently accounts for about 18% of intracity
passenger miles, which is comparable to the 23% carried by the
New York rail transit system. (Los Angeles, by contrast, has
no rail transit and none is expected in the near future.) The
use of buses in Chicago is also similar to the use of buses in
New York. For this reason, it is not surprising that the
projected VMT growth rates in Chicago are similar to those of
New York.
The data for automobile, bus, rail, and HDT in Scenarios
T, and T3 are taken from the scenarios developed by SRI (refer
to Table 2-2). Growth in taxi usage was estimated by assuming
that taxi growth rate would be similar to that of New York and
that the ratios between taxi and auto usage growth rates would
be the same.
The Moderation Scenario (T2) was developed using the same
estimations as the other cities. Growth of auto usage is zero,
growth of bus VMT is one-third greater than for Scenario T.^
and HDT and taxi VMT growth rates are one-half of Scenario T-^.
The base year data for VMT was approximated from 1970 data
provided by the staff of the Chicago Area Transportation Study
(CATS). It is their opinion that little change occurred from
1970 to 1976 in modal choice, so this should be a good
24
approximation. Since the base data did not give VMT
**.
38
-------
directly, it had to be approximated. The steps are displayed
in Table 2-3 where the number of trips taken by each type of
vehicle into or across the CBD is shown in the first column.
Some of the truck trips in the source reports were not
classified by truck type; but according to the report, it could
be assumed that they were of the same type as those that were
25
classified. Therefore, unclassified truck types were
proportionately distributed to the three truck categories.
In reviewing the average vehicle trip length, it was found
that the trips into or across the CBD exceeded the effective
length of the CBD (+5 mi). Therefore, the length of each
vehicle trip within the CBD could be taken to be 5 miles. For
convenience, the CBD trip lengths were normalized to 1 mile.
This could be done because the magnitude of VMT was unim-
portant; it is the relative VMT of different modes that is used
to calculate the relative emissions (see Chapter 5). Thus,
passenger trips could be divided by the assumed vehicle
occupancy to yield the normalized VMT in the CBD.
Projected VMT for automobiles, HDT's, taxis, and buses are
given in Appendix A, Tables A-4 to A-6.
Table 2-3.
DERIVATION OF VMT BASE YEAR DATA FOR THE
CHICAGO CENTRAL AREA21'22'24
Mode
Auto Driver
Auto Passenger
Suburban Rail
Rapid Transit
Bus
Taxi
Light Truck
Medium Truck
Heavy Truck
Weekday
Passenger.,
Trips x 10
310
110
105
194
153
74
23.7
2.7
17.5
Average
Trip b
Length
5.1
5.0
18.5
7.9
3.9
-
(5+)
(5+)
(5+)
Assumed
Vehicle
Occupancy
1
0
-
25
0.5
1
1
1
Normalized
VMT
310
-
-
6.1
148
23.7
2.7
17.5
Chicago Area Transportation Study and Northwestern Indiana
Regional Planning Commission, 1970 Travel Characteristics,
"Commodities and Commercial Vehicles," Table 34, and "Facility
Suppy and Usage", Table 1, 1975.
Reference a, "Trip Length," Table 1. The symbol () means
assumed datum.
39
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2.4 REFERENCES
1. Public Law 91-604, "Clean Air Amendments of 1970",
December 31, 1970.
2. Horowitz, Joel, and Steven Kuhrtz, Transportation Con-
trols to Reduce Automobile Use and Improve Air Quality
in Cities, EPA-400/11-74-002, U. S. Environmental Pro-
tection Agency report to Congress (November 1974),
p. 2.
3. J. D. Ward, et al., Toward 2000; Opportunities in
Transportation Evolution, DOT-TST-77-19, (PB266763/2GI),
U. S. Department of Transportation, March 1977, Chapter
II, "An Adaptive System Strategy."
4. Leahy, Michael Patrick, Future Scenarios for Urban
Transportation, UMTA-RDD-9-75-1, Urban Mass Trans-
portation Administration, August 1975.
5. Holder, Ron, et al., Fuel Conservation Measures:
The Transportation Sector - Volume II, Prepared by
Texas Transportation Institute for the (Texas) Go-
vernor's Energy Advisory Council, January 1975.
6. Curry, David, Transportation in America's Future:
Potentials for the Next Half Century, Part II -
Transportation Forecasts, DOT-TPI-20-77-21-2,
PB 270468/2GI), Prepared by Stanford Research Institute
for the Office of the Secretary of Transportation, June
1977.
7. Curry, David, et al., Transportation in America's
Future: Potentials for the Next Half Century, Part I -
Societal Context, DOT-TPI-77-21-1, (PB270467/4GI),
Prepared by Stanford Research Institute for the U. S.
Department of Transportation, June 1977.
8. Chicago Area Transportation Study, Proceedings - Year
2000 Alternative Transportation Futures Conference,
(PB277456/OGI), Northwestern Univ. Transportation
Center, March 10, 1978.
9. Koppelman, Frank, et al., Baseline Energy Consumption
Forecasts for Transportation; A Review and Evaluation,
(ANL-76-XX-6), Prepared by Northwestern Univ. Trans-
portation Center for Argonne National Laboratory, May
1976.
40
-------
REFERENCES (continued)
10. TRW Transportation and Environmental Operations,
Transportation Control Strategy Development for the
Metropolitan Los Angeles Region, APTD-1372, Prepared
for the U. S. Environmental Protection Agency, December
1972.
11. Branch, M. C., and E. Y. Leong, Research Investigation -
Air Pollution and City Planning, Univ. of California at
Los Angeles (1972), cited by TRW in Ref. 10.
12. Mikolowsky, W. T., et al., The Regional Impacts of
Near-Term Transportation Alternatives; A Case Study
of Los Angeles, Report No. R-1524-SCAG, (AD-A010980/1GT)
Prepared by RAND Corporation for the Southern Cali-
fornia Association of Governments, June 1974.
13. Horowitz, Joel, Transit Requirements for Achieving
Large Reductions in Los Angeles Area Automobile Travel,
EPA/400-11-76-001, U. S. Environmental Protection
Agency, November 1976.
14. Goeller, B. F., et al., Strategy Alternatives for
Oxidant Control in the L. A. AQCR, The Rand Corp.,
R-1368-EPA, December 1973.
15. Bigelow, J. H., et al., A Policy-Oriented Urban
Transportation Model: The San Diego Version, R-1366-SD,
The Rand Corp., December 1973.
16. Mikolowsky, W. T., et al., A Tradeoff Model for
Selecting and Evaluating Air Quality Control Stra-
tegies, The Rand Corp., R-1367-SD, December 1973.
17. Southern California Association of Governments Scenario
Plan for 20 Percent VMT Reduction, December 1973.
18. Los Angeles Resources Board, Personal Communication
with staff, January 1979.
19. TRW Transportation and Environmental Operations, Trans-
portation Control Strategy Development for New York
Metropolitan Area, (APTD-1371), Prepared for the U. S.
Environmental Protection Agency, December 1972.
20. Tri-State Regional Planning Commission, Air Quality
Plan Transportation Plan Consistency Assessment,
November 1976.
41
-------
REFERENCES (continued)
21. Chicago Area Transportation Study and Northwestern In-
diana Regional Planning Commission, 1970 Travel Char-
acteristicsFacility Supply and Usage, 1975, Table 1,
p. 6.
22. Chicago Area Transportation Study and Northwestern In-
diana Regional Planning Commission, 1970 Travel Cha-
racteristicsCommodities and Commercial Vehicles^
1975, Table 34, p. 113.
23. Bell, Roy, Chicago Area Transportation Study and North-
western Indiana Regional Planning Commission, Telephone
conversation, February 1979.
24. Chicago Area Transportation Study and Northwestern In-
diana Regional Planning Commission, 1970 Travel Cha-
racteristicsTrip JLength, 1975, Table 1, p. 9.
42
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3. SCENARIOS OF DIESEL SALES
3.1 DIESEL AUTOMOBILES
The automobile population, because of its large numbers,
may continue to contribute a significant part of the total air
pollution in coming years. Therefore, it is important to know
the distribution of diesel engines in the automobile population
for any given year in the future, so that the particulate
contribution from these vehicles may be taken into account.
When General Motors made known its intent to use diesel
engines, the New York Times reported "by 1985 the new engines
may power one car in every four that Detroit produces." The
idea that 25% of the new car line will be diesel in 1985 has
been repeated in governmental circles over the past year and
2
seems to have achieved the status of conventional wisdom.
Since the diesel represents about 1/2 of a percent of the
automotive sales at the present time, rapid diesel intro-
duction should be viewed as a drastic, forced change to meet
Federal requirements for fuel economy- However, this
particular solution to the problem is proposed by industry. If
the health risks of diesel are judged to be tolerable when
compared to other alternatives, then the government might accept
this change. Simultaneously, if industry can meet particulate
emission standards without sacrificing the fuel economy of
diesel, then all the major actors will be driving in the same
direction. Under this scenario the rapid introduction of the
diesel automobile seems very likely. In fact, there would be
no reason to stop at 25% in 1985; diesel sales should continue
until nearly all automobiles are diesel. A rapid changeover to
this type of engine would seem inevitable as all factions push
to solve the health and energy problems and as advertising is
brought to bear on the public. Conversely, if any one of the
major actors- finds a significant problem, then it is doubtful
that diesel would be used. Some other technology would be
selected. Thus, it seems to boil down to either a rapid
43
-------
introduction of diesel, or none. These alternatives can be
labeled no diesel introduction (Scenario D,), or rapid diesel
introduction (Scenario D2)
Let us consider Scenario D-. The concept of propelling
diesel sales from near zero to 25% within a few years implies
an accelerated introduction rate. Diesel sales would increase
at a logarithmic growth rate (rather than a linear rate) then
taper off as the market for diesel comes close to saturation.
This phenomenon can be approximated by a cumulative logistic
distribution curvean S-shaped growth curve which is familiar
to the engineering sciences. The formula for this curve is as
4
follows:
(1) F(x) =
where
F(x) = the fraction of new automobile sales which
are diesel,
x = the projection year of interest, 78, 79,
80,...etc., and
a and 3 = sealer quantities which determine the shape
and slope of the curve.
On this curve we may assume two points: (0.005, 78) and (0.25,
85). From these points we can derive the values of a and 3
for Scenario D0 using the step-by-step derivation of formulae
5
for a and 3 in terms of x and F(x) provided by Johnson (see
Appendix B). We find a = 86.83 and 3 = 1.669, and the diesel
introduction curve under Scenario D2 using Equation 1 above is
shown in Figure 3-1.
Note that the fraction of diesel engines in automobile
sales is not the same thing as the fraction of diesel in the
vehicle population. To estimate the number of diesels in a
population for any given year, one must first know the fraction
of the vehicle population represented by each model year. The
EPA methodology is to use the average frequency distribution
for vehicle age found in vehicle registration records. It is
44
-------
r i i i i i i i i i i
i i i i I i i i I
1978 80 85 90
Projection Year
Figure 3-1. Log Growth Phase for Diesel Engine Sales in New
Automobiles under Scenario 1^2-
then assumed that each year in the future will have this same
vehicle age distribution.
Research has also been conducted on the annual mileage
accumulation that is typical for various age classes of
vehicles. Older vehicles tend to be driven less. The travel
fraction is defined as the fraction of total annual VMT
attributable to a given age class of vehicles. The travel
fraction is obtained by multiplying the annual mileage
accumulation for an age class of vehicle by the fraction of the
vehicle population in that age class and then by dividing by
the sum of such products for all age groups. Under Scenario
D2 the travel fraction attributable to diesel automobiles is
obtained by multiplying the travel fraction for each age group
by the fraction of vehicles in that age group that were sold as
diesel when the vehicles were new, as determined by Equation 1.
A table of diesel travel fractions for Scenario D2 is listed in
45
-------
Appendix C (Table C-l) and the resulting curve for diesel VMT
as a fraction of the total automobile population VMT is shown
in Figure 3-2.
r \ i i i i i i i i i i i i i i
t I I i i i I I I I I I \ I I I I
1978 80
85 90
Projection Year
95
2000
Figure 3-2. Fraction of Total Automobile VMT Attributable to
Diesel Vehicles Under Scenario D.
46
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3.2 DIESEL TRUCKS
The diesel engine helps to solve some of the problems
faced by the trucking industry. For example, the use of diesel
engines can help control the fuel costs of trucking operations.
The longer lasting diesel engines can also help minimize the
capital cost of engine replacement. On the other hand, diesel
engines provide less torque and less power per engine kilogram.
Overall, the advantages of diesel have caused diesel truck
sales to increase rapidly in the last few years.
The fractions of heavy duty truck (HDT) sales that were
diesel from 1967 to 1977 are listed in Table 3-1 and are
displayed in Figure 3-3. One method of "projecting" the diesel
fraction is to obtain a straight line formula that provides the
best fit for all the available data. A linear regression
analysis performed on the diesel fractions listed in Table 3-1
yields the following equation:
(2) F(Y) = 0.0204 Y - 1.166
where
F(Y) = the fraction of HDT sales that are diesel, and
Y = the projection year of interest.
2
This equation fits the data with r =0.78, and could be
projected forward as shown in Figure 3-3. If this projection
were used, 100% diesel sales would be reached in year 2007.
However, it can be observed that the diesel sales fractions for
1975 to 1977 form a decidedly different pattern from the
pattern of diesel fractions for 1967 to 1974. In fact, the
linear rate of increase for the last three years is better
described by a different equation:
(3) F(Y) = 0.097 Y - 6.988.
If Equation 3 is used to project future diesel sales, we
-*
find that 100% dieselization is reached in 1983, only 4 years
47
-------
Table 3-1.
U.S. FACTORY SALES OF HEAVY DUTY TRUCKS FOR
DOMESTIC CONSUMPTIONa
Year
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
Net HOT
Production^
349,641
374,216
396,849
340,596
370,214
447,644
482,562
459,006
252,156
284,673
343,892
Factory
Diesel Sales
70,800
83,423
101,703
91,801
101,488
129,628
152,373
144,702
72,605
107,216
165,559
Diesel
Fraction
0.202
0.223
0.256
0.270
0.274
0.290
0.316
0.315
0.288
0.377
0.481
aSource: Automotive News, 1977 Marked Data Book Issue, p. 62,
and 1978 Market Data Book Issue, p. 49.
bNet heavy duty truck (HOT) production equals total U.S. domestic
truck production, minus the production of vehicles less than
6,000 Ibs GVW, and minus the production of vehicles 6-10,000 Ibs
GVW. EPA defines HDT's as greater than 8,500 Ibs, but the data
source did not use this definition. No significant discrepancy
is expected, since most HDT's are much heavier than 10,000 Ibs.
48
-------
1.00
.90
.80
S-70
i-H
EH
Q
ffi
c
o
60
50
-P
«J .40
CD . 30
0)
H
Q
.20
10
I I I I
x Observed sales
A 67-77 linear projection
75-77 linear projection
{Jj- logistic curve
. MTU "high" projection
I I II I I
1966 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 2000
Figure 3-3. Alternate Projections of Diesel Sales
in Heavy Duty Trucks.
49
-------
away. Intuitively, the first linear projection seems "too low"
while the second seems "too high". Also, both of these
equations reach 100% dieselizationa situation which would
seem to be an unreasonably complete domination of one type of
engine over another in a field where both have been applied
usefully for many years.
A reasonable middle ground can be reached if one assumes
that diesel engine sales have been suddenly propelled by a new
set of factors since 1973. These factors might include the
higher price of fuel as well as the need to control gaseous
engine emissions. If this is true, then we should expect
diesel sales to enter a period of logarithmic growth, like the
growth we have assumed for diesel automobiles in Scenario D~.
In the case of HDT's, we have some hard data which suggest that
we have already entered such a stage of growth.
In this study, we chose to assume that diesel sales for
HDT's entered a growth phase in 1974 that can be approximated
by a cumulative logistic distribution function. The form of
the equation was stated in subsection 3.1, and formulae for the
a and 6 parameters are derived in Appendix B. Given the points
on the curve (74, 0.315) and (77, 0.481) from Table 3-1, we
find a = 77.33 and $ = 4.281. The resulting projection is
shown'in Figure 3-3.
The use of the cumulative logistic distribution formula
provides a curve that approaches 1.00 as a limit. This means
that we do not expect one type of engine to completely
eradicate the other, even if it becomes the predominate choice.
It will also be noted that the logistic formula will not fit
the data for 1967-73. This is consistent with the assumption
that the forces affecting diesel sales during that period were
different.
It is interesting to compare this projection with an
earlier one based on data that were available in 1974. An
economic and emissions model was developed at Michigan
Technological University (MTU) to analyze the economic factors
50
-------
2
that affect diesel sales. Since economic conditions cannot
be predicted in advance, the researchers used a wide range of
projections. The high projection from that report was used in
a prior diesel study and is shown on Figure 3-3. It will be
noted that even these high estimates were unable to anticipate
the sudden jump in diesel sales that occurred in 1976 and 1977.
Nevertheless, the projected rate of increase in sales is very
rapid in the early 1980's, and intersects the logistic curve in
1989. The projection we have chosen is somewhat similar to the
MTU high projection, but is based on more recent information.
In this study we have chosen to limit the number of HOT
scenarios to one in order to keep the number of alternative
futures to a manageable level. The trucking industry already
uses a significant number of diesel engines (48% in 1974);
therefore there will be less change in the diesel fraction for
HDT's than for automobiles. Thus the projections of diesel
sales for HDT's do not affect the results as much as the
projections for automobiles. Furthermore, since the changes in
sales are less dramatic, one can expect a more narrow range of
possible error in the projection.
Based on the diesel sales scenario, the diesel fraction of
VMT by HDT's can be estimated for a given projection year.
Following EPA methods, the diesel fraction (fd) of HDT-VMT is
the sum of the travel fractions for each age class (f£n)°f
diesel HDT's, where a travel fraction is defined as the
fraction of the total VMT that is attributable to a model year
class of vehicle (f^ = £fdn^ ' For automob;i-les/ we multiplied
the diesel sales fraction for each model year by the travel
fraction for the age class and a given projection year to get
the diesel travel fractions. The travel fraction for a model
year changes over time and reflects the fact that older
vehicles pass out of the vehicle population and that those
remaining are driven fewer miles.
This estimating procedure assumes that the diesel and
gasoline vehicles have the same travel fractions for the same
51
-------
model years. However, recent research has shown that the
travel fractions for heavy duty gasoline (HDG) vehicles are
significantly different from the travel fractions for heavy
duty diesel (HDD's) of the same model year. (Travel fractions
may also differ for automobiles, but research on this has not
been done.) For example, 2-year old HDG vehicles will have
accumulated 19,000 miles and will have a travel fraction of
0.116. By contrast, 2-year old HDD's will have accumulated
73,600 miles and have a travel fraction of 0.178. In part
this reflects the usage patterns of vehicles equipped with diesel
engines, and in part it reflects the artificial nature of EPA's
definition of "heavy duty" which includes both 8,500-lb and
40,000-lb vehicles. It is the large tractor-trailers which
usually contain diesel engines and which are used for long
distance hauling.
The differences in HDG and HDD travel fractions neces-
sitate a different methodology for calculating diesel travel
fractions. For example, the HDD travel fractions for a model
year from Mobile Source Emission Factors are expressed as
fractions of the total HDD-VMT rather than fractions of the
total HDT-VMT.6 Yet it is the travel fractions of HDT-VMT that
are needed for the definition of this scenario.
The first step in calculating the diesel fraction of HDT-
VMT is to find the fraction of the total HOT population that is
diesel for each projection year. This is obtained by
multiplying the combined gasoline and diesel registration
fractions for each model year by the diesel sales fraction for
that year according to the HOT diesel sales scenario.
Mobile Source Emission Factors did not provide this data
explicitly; nevertheless, it is possible to estimate the
combined registration fractions from the data provided in the
tables of the referenced document. The estimating data are
displayed in Table 3-2. The gasoline registration fractions
and mileage accumulation rate (columns a and b) are taken
directly from Table III-5 in the referenced document, and the
diesel fractions and mileage are taken from Table IV-5. The
combined fraction is the sum of the other fractions weighted by
52
-------
Table 3-2. ESTIMATION OF THE
HEAVY DUTY TRUCK
VEHICLE AGE CLASS
COMBINED GASOLINE AND DIESEL
REGISTRATION FRACTIONS BY
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Gasoline
(a)
Fract. of
HDG Regis-
tration
0.037
0.070
0.078
0.086
0.075
0.075
0.075
0.068
0.059
0.053
0.044
0.032
0.038
0 .036
0.034
0.032
0 .030
0 .028
0 .026
0 .024
HDG3
(b)
Annual
Mileage
Accumula-
tion Rate
19,000
19,000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
Totals: 1.000
Diesel
(c)
Fract. of
HDG Regis-
tration
0.077
0.135
0.134
0.131
0.099
0.090
0.082
0.062
0.045
0-033
0.025
0 .015
0 .013
0 .011
0 .010
0 .008
0 .007
0 .006
0 .005
0 -004
HDDb
(d)
Annual
Mileage
Accumula-
tion Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
0 .992
(e)
Combined
(weighted)
Fract. of
HOT Regis-
tration*
0.069
0.122
0.123
0.122
0-094
0.087
0 .081
0 .063
0 .048
0 .037
0 .028
0 .018
0 .017
0 .015
0 .014
0 .012
0 .011
0 .010
0 .009
0 .008
e = 0 .998
n
HDG = heavy
HDD = heavy'
'(e) = (ab +
duty gasoline truck
duty diesel truck
cd) f (b + d)
53
-------
the mileage accumulation rate, as follows:
(4) e=(ab+cd)4(b+d)
n n n n n n n
where
e = the combined gas and diesel HOT registration
fractions for vehicle age class n,
a = the fraction of total gas HDT's of age class n,
b = the annual mileage accumulation rate for age class
n for gas HDT's,
c = the fraction of total diesel HDT's of age class
n,. and
d = the annual mileage accumulation rate for age class
n for diesel HDT's.
In other words, this estimation takes into account the
difference in mileage accumulation between diesel and gas
HDT's, and weighs the individual fractions accordingly. The
sum of all en is in error by only 1.2%.
The combined gas and diesel HOT registration fractions are
then used to calculate the diesel fractions of HDT's on the
road, as shown in Appendix C (Table C-2). Each entry in the
table represents the combined registration for each age group
multiplied by the diesel sales fraction that was applicable to
that age group when the vehicles were new. The fractions at
the top of the column apply to age class 1 for that projection
year, apply to age class 2 in the following year, and so on in
a stepwise fashion. The total fraction for each projection
year is charted in Figure 3-4. According to this projection,
gasoline engines remain in the HOT population through the end
of the century.
The first step in estimating the diesel fractions of HDT-
VMT was to calculate the diesel fraction of HOT's on the road
according to the HOT sales scenario. The second step is
somewhat more tedious. Tables C-3-78 through 3-4-2000
(Appendix C) show the calculation steps for each projection
year.
54
-------
ca
1.00
.90|
.80)
. 70|
.601
78 80 82 84 86 88 90 92 94 96 98 2000
Projection Year
»
Figure 3-4. Projected Diesel Fractions of HOT's on the
Road and HDT-VMT.
55
-------
In all EPA projections it is assumed that the model year
distribution of vehicles remains constant for any projection
year. Thus the diesel fractions of HDT on the road (column "a"
on the estimation tables) are taken directly from Table 3-3 for
the projection year of interest , while the gasoline fractions
of HDT on the road (column "c") are obtained by subtraction
from the combined gas and diesel registration fractions (Table
3-2). This relationship can be expressed as follows:
fgn ' fn ' fdn
where
f = gasoline fraction of HDT on the road of age class n,
f = total registration fraction for model year n for the
HDT projection scenario (from Table 3-2) , and
fa = diesel fraction of HDT on the road of age class n
n from Table 3-3.
The annual mileage accumulation rates (columns b and d in
Tables C-3-XX) were obtained from the referenced document and
are the same as in Table 3-2. The weighting factors for each
vehicle age class are obtained from the following formula:
wn = anbn + cndn
where
w = weighting factor for age class nf
a = the diesel fraction of HDT on the road for vehicle
age class n,
b = diesel mileage accumulation rate for vehicle age
class n,
c = gasoline fraction of HDT on the road for vehicle
age class n, and
d = gasoline mileage accumulation rate for vehicle age
n class n.
Following the EPA definition of travel fraction, the diesel
travel fractions may be defined as follows:
56
-------
(7) Diesel Travel = T
Fraction
dn
a b
20
w
n=l n
20
cndn
n=l
-1
Thus the diesel travel fractions in the estimation tables
(Appendix C) provide statements of the fraction of HDT-VMT
attributable to diesel for each vehicle age class and for each
projection year of interest.
3.4 REFERENCES
1. Stewart, Reginald, "G.M. Betting on Diesel Engine",
The New York Times, November 10, 1978.
2. Strombotne, Richard L., Background Information from the
Edited Transcript and Proceedings of the Workshop
on Unregulated Diesel Emissions and Their Potential
Health Effects. DOT HS-803 527, Sponsored by Depart-
ment of Transportation, Department of Energy, and the
Environmental Protection Agency, April 27-28, 1978, pp.
1-8.
3. Throgmorton, Jim, "Test City Methodology and Results"
in Air Quality Assessment of Particulate Emissions
from Diesel-Powered Vehicles, EPA-450/3-78-038, Pre-
pared by PEDCo Environmental, Inc., for U.S. Environ-
mental Protection Agency, March 1978, pp. 4-15.
4. Johnson, Norman L., and Fred C. Leone, Statistics and
Experimental Design in Engineering and the Physical
Sciences, Vol. 1, 2nd Edition, John Wiley & Sons, 1977,
pp. 144-5.
5. Johnson, Ted, Informal Notes on the Cumulative Logistic
Distribution Curve and Applications, December 21, 1978.
6. U.S. Environmental Protection Agency, Mobile Source
Emission Factors, EPA-400/9-78-005, Final document,
March 1978, p. 13.
7. Ref. 6., p. 16.
8. Automotive News, 1977 Market Data Book Issue, p. 62.
9. Automotive News, 1978 Market Data Book Issue, p. 49.
10. Ref. 6,.Tables III-5, III-5a, IV-5, IV-5a.
57
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4. EMISSION FACTORS
4.1 EMISSION FACTORS IN SCENARIO E±
The present methods of estimating the amount of par-
ticulate emissions from vehicles are relatively unsophisti-
cated. Most of the emissions research in recent years has been
directed towards gaseous pollutants, and the estimating
procedures for gases are more highly developed. The par-
ticulate emission factors for automobiles, whether gasoline or
diesel, are based almost entirely upon a factor assigned to a
model year class of vehicles, and they have not been corrected
for vehicle deterioration, maintenance conditions, types of
1 2
fuels, or types of operating cycles. ' The emission factors
that were available from a prior study of diesel particulates
were labeled E-^ and treated as a scenario alternative.
In past years, the particulates from gasoline automobiles
were composed predominately of lead compounds, usually about
two-thirds PbClBr and about one-third NH.Cl-PbClBr. Prior to
1975, automobiles were the noncatalyst type and more than 95%
of the gasoline consumed contained lead to boost the octane
4
rating of the fuel. Hoggan found that the lead concentration
in the atmosphere of Los Angeles had declined from 1970 to 1976
due to a reduction in the amount of lead in both regular and
premium gasolines. The average lead concentration in the
atmosphere could be calculated from the amount of leaded
gasoline consumed. Nevertheless, the methodology used in this
study utilizes the EPA emission rates for vehicles so that
particulate emissions can be estimated for projection years
when lead is not the primary particulate of concern. In any
case, comparable results are expected, since the amount of
unleaded gasoline consumed is related to the number of
catalytic vehicles in use.
58
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The current particulate emission factor used for pre-1975
automobiles is 0.25g/VMT, based upon the use of leaded fuel.
The pre-1975 emission factor for unleaded fuel use is
0.002g/VMT, but the fraction of pre-1975 vehicles using
unleaded fuel is considered insignificant for the purpose of
this calculation. For production model years 1975 and
thereafter, catalysts are the predominant method for meeting
the EPA emission control requirements. It is assumed that 70%
of the automobile population use catalysts with an emission
factor of 0.006g/VMT and that 30 percent use catalysts with
excess air which have an emissions factor of 0.015g/VMT.
(Catalyst vehicle emissions are due to sulfates rather than
lead.) Based on this premise, the gasoline vehicle model
classes manufactured in 1975 and beyond have been assigned a
weighted emission factor of 0.0087 g/VMT (calculated as
0.7 (.006) + 0.3 (.015). Obviously this value is not based on a
detailed analysis of the distribution of catalytic equipment in
production vehicles, but it is a degree of accuracy commensurate
with the available emission factors.
In any given projection year the combined emissions from
automobiles will depend upon the age class distribution and the
particulate emissions factor assigned to each age class of
vehicles. A table of travel-weighted particulate emission
factors has been prepared for Scenario D, and is presented in
Appendix D, Table D-l.
In Scenario D_, a certain fraction of each automobile age
class is diesel, and an emission factor of 0.5g/VMT is assigned
to this subclass. For each projection year, the travel-
weighted particulate emissions can be calculated using the
following convenient equation:
Etn = egn(tn ' tdn) + °'5tdn
59
-------
where
E. = the travel-weighted particxilate emissions factor
for vehicle age class n under Scenario D2,
e = the particulate emissions factor for gasoline vehicles
g applicable to vehicle age class n,
t = the travel fraction for vehicle age class n (from
n
EPA-400/9-78-006),
t, = the travel fraction for diesel vehicles under
Scenario D~ for age class n (from Table A-l), and
0.5 = the diesel particulate emission factor applicable
to all diesel automobiles under Scenario E,.
The emission factors for heavy duty trucks can be handled
in a manner similar to that used for automobiles. The emission
factors for pre-1975 HDT's is 0.90g/VMT based upon the use of
leaded fuel. The pre-1975 HDT emission factor for unleaded
fuel is 0.007g/VMT, but the fraction of pre-1975 vehicles
presently using unleaded fuel is considered insignificant for
the purpose of this calculation. Following the procedure of
the pilot study, 1977 truck and bus production data were
reviewed in order to roughly estimate the technological split
in post-1975 HDT's. About 60% would use standard catalysts,
and about 40% would use catalysts with excess air. Standard
catalyst HDT engines have a particulate emission factor of
0.02g/VMT, while HDT catalysts with excess air have an emission
factor of 0.05g/VMT. Thus the sales-weighted emission factor
would be estimated as 0.032g/VMT (0.6 x 0.02) + (0.4 x 0.05) =
0.032 . Such a factor is approximately correct if the present
technological split is assumed to remain the same in the
future.
Using the pre-1975 and the post-1975 emissions factors,
weighted emission factors can be calculated for gasoline HDT's
for each projection year (Appendix D, Table D-3). These
factors take into account .only the above emission factors and
the travel fraction represented by each model year according to
4
Mobile Source Emission Factors. The travel fraction applicable
to a particular model in a given projection year is simply
60
-------
multiplied by the emission rate to yield the entries in the
columns on Table D-3. Thus, the sum of each column represents
the weighted gasoline HOT emission factor for that projection
year.
The emissions from HDT's for a given projection year is
the sum of the emissions from HDT's and HDG's. The HDD
emissions are found by multiplying the applicable diesel travel
fraction by the diesel emission factor (0.9g/VMT) used for all
years. The calculation of total HDT emissions is shown in
Table D-4.
4.2 REFERENCES
1. U. S. Environmental Protection Agency, Compilation of
Air Pollutant Emission Factors, AP-42, 3rd ed., May 1978.
2. U. S. Environmental Protection Agency, Mobile Source
Emission Factors, EPA-400/9-78-005, Final Document, March
1978.
3. Briggs, Terrence, Jim Throgmorton and Mark Karaffa,
Air Quality Assessment of Particulate Emissions from
Diesel-Powered Vehicles, EPA-450/3-78-038. Prepared by
PEDCo Environmental, Inc. for U. S. Environmental Pro-
tection Agency, March 1978. Emission factors are treated
in Chapter 4.0, "Test City Methodology and Projections."
4. Hoggan, Margaret C., Arthur Davidson, Margaret F.
Brunelle, John S. Nevitt, and John D. Gins, "Motor Vehicle
Emissions and Atmospheric Lead Concentrations in the Los
Angeles Area," Journal of the Air Pollution Control
Association, Vol. 28, No. 12, December 1978, p. 1200-6.
5. Motor Vehicle Manufacturers Association, World Motor
Vehicle Data, 1978 ed., published by MVMA.
61
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5. METHOD OF ANALYSIS
The increased use of diesel engines in the future may
involve increased emissions of particulate matter. Increased
diesel usage could arise because of a shift in the choice of
transportation systems or because of the introduction of diesel
engines into the automobile population. The actual amount of
emissions will also depend on the amount of control that can be
built into diesel engines, as expressed through emission
factors. If large cities were to change their transportation
systems in order to meet air quality standards for gaseous
emissions or to reduce the energy costs of transporting people,
would they face new problems from increased levels of TSP or
polycyclic organic material (POM) associated with diesel
engines? The question is especially poignant for present
nonattainment areas for TSP and for areas marginally in
compliance with ambient air quality standards.
This question can be posed in an analytic framework:
1. Assume that the rate of emissions (Q) for a given
pollutant is known for a given city, and
2. Assume that the portion of these emissions that
actually affects a given ambient air quality monitoring
site (Q) is known, and
3,
3. Assume that the portion of Q that is emitted from the
transportation system (Qat) is known, and
4. Assume that the relationship between Q (which
includes Q .) and the resulting measured air quality level
(X) at the monitoring site is known. (This is the
relationship resulting from local meteorological con-
ditions, terrain, and the distances between emission
sources and the monitor.)
Then, one can observe the air quality levels resulting from emission!
for a given base period, calculate the change in Q caused by
emissions from a different set of circumstances as expressed in
alternative future scenarios (Qat2)/ and calculate the new air
quality level (x2) expected from this change. Another way of
62
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expressing the problem is to ask: If all factors in the base
period are kept constant except the transportation system
emissions, what new levels of ambient air quality can be
expected?
5.1 RELATING EMISSIONS TO AMBIENT AIR QUALITY
The method that is traditionally used for most types of
air quality assessments is atmospheric dispersion modeling.
Such a method is aimed at defining the mathematical rela-
tionship between pollutant emissions and air quality under a
wide variety of meteorological conditions, in fulfillment of
assumption 4 above. For example, modeling is recommended for
the analysis of the effects of a new point source of emissions
2
in an air quality maintenance area. It may also be used to
assess new sources which are subject to review under PSD
(prevention of significant deterioration) requirements.
Perhaps the best application of area-wide models is in the
development of a regional air pollution control plan, such as
the plan developed for the control of suspended particulates in
3
the Phoenix, Arizona area or for the analysis of particulates
4
in Federal Region VIII. The EPA makes available to the
professional public a series of computerized air dispersion
models on magnetic tape which may be ordered through the
National Technical Information Service.
For a number of years, one of the basic modeling tools has
been the Air Quality Display Model (AQDM). It was the first
model promoted by the EPA for air quality planning. It is
applied on a region-wide scale, using grid squares for regional
subdivisions. The amounts of emissions are estimated from an
emissions inventory by using emission factors to estimate
industrial point source emission rates. The dispersion of
pollutants is estimated by a Gaussian distribution formula,
which is used to allocate a portion of each emitter that is
effective at given receptor sites, usually air quality
monitoring sites or the centers of grid squares. Area sources
are handled by.pragmatically assigning the estimated emissions
to the center of a grid square and otherwise treating them like
point sources.
63
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The principles of estimation by means of Gaussian plume
g
formulae have been set forth by Turner. Pasquill stability
classes which underlie these estimates were originally de-
veloped from measurements in open rural areas, such as
grasslands, rather than in urban conditions with buildings,
8 9
street canyons, or other terrain. ' In general, the re-
presentativeness of data from models can be adversely affected
by complex terrain, by the presence of large bodies of water,
and by rural assumptions within city conditions. Updated
versions of the AQDM have ameliorated these problems. A more
recent regional model called RAM is available, but its validity
is still being tested. Regional modeling seems most
appropriate for interior cities of moderate size where the
underlying assumptions are better met.
In a recent pilot study of the effects of future diesel
fuel use on particulates, the AQDM was chosen as a vehicle for
analysis, and the test city chosen was Kansas City, Mo. As an
interior city of moderate size, it was a good candidate for
this approach. Even more important, diffusion modeling data
were already available and easily accessible to the investi-
gator, who had experience in its application in this particular
area.
By contrast, the present study focuses upon three very
large urban areas: New York, Los Angeles, and Chicagoeach
of which is located near a major water body. Regional modeling
may or may not be rigorously valid in these areas. More
important, it was clearly unfeasible to acquire dispersion
modeling data for such areas, to validate the models, and to
apply them to the present short-term study. It was desirable
to establish a method for addressing the questions posed in the
study without the involvement of a complex modeling procedure.
One of the alternate methods for analyzing air quality
problems is the "normalizing" of air quality data for a standard
set of meteorological conditions. This is most often applied in
the analysis of air quality trends. For example, suppose air
quality data from a monitoring site generally downwind from a
point source show an improvement in the annual average for a
64
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given pollutant over a 4-year period. Is this improvement due
to a reduction in source emissions or due to better meteorolog-
ical conditions in the later years? To answer this question/
one could follow the procedures recommended by Zeldin and
12
Meisel as follows:
1. Find the meteorological factors affecting a given
monitoring site by means of scatter diagrams and analysis
of variance. Use these factors to establish mete-
orological classeseach class having a different average
concentration associated with it.
2. Using meteorological records over a 5-year period,
establish an average or "standard" meteorological year,
with a typical frequency distribution for each of the
classes over a year.
3. Annual measures of pollutant levels are adjusted to
"what they would be if each year had the same meteor-
ogy". That is, since atypical meteorology would be
reflected in different frequency distributions for the
meteorological classes, the observed average for a year
within each class is weighted by that proportion of the
year which it would "normally" represent.
In the above example, after each year's monitoring data is
normalized for meteorology, the remaining variation can be
attributed to source emissions.
In this diesel study, one can normalize meteorology in a
similar manner, and the remaining variation can be attributed
to variations in emissions (Q). If at the same time, one keeps
point source emissions the samethat is, one assumes no
yearly fluctuations in point source emissions, then we must
deal only with the effects of a changing transportation system
on the air quality levels at selected monitoring sites. This is
precisely what we wish to analyze.
In a well-known equation of urban air pollution potential,
1.260
18
Holzworth used the following expression:
s 0-088UH-
- J.W-LJH . 2HU
where
65
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X = average ambient air concentration,
Q = average emission rate,
H = mixing height,
S = distance across a city, and
U = wind speed.
This formula shows that air pollution potential can be
expressed as a function of mixing height, wind speed, and city
size. Disregarding for the moment whether these are the
appropriate meteorological factors or whether the above
equation is the appropriate formula, consider the following:
if meteorology is normalized, then it may be considered a
constant for the purposes of this analysis. In other words,
for a standard meteorological year M:
Thus, if a procedure for normalizing meteorological data
is adopted, it becomes possible to estimate changes in x
directly from changes in Q. This analytic relationship was
suggested by Peterson among others. In form it is a rollback
14
equation, as previously described by de Never s and Morris.
Since this seemed to be a fruitful approach, three methods
of normalizing data were considered, with emphasis upon
appropriateness of assumptions and on the time and expense
of conducting the analysis:
1. Meteorological Classification. The procedures sug-
gested by Zeldin and Meisel were preceived as valid
for this project. However, because of the number of sites
(16) and the number of meteorological factors (6) to be
analyzed in each city, and becaise pf the several
observation posts in each city with combinations of
factors at each post, it appeared that a significant
expense could be committed to this step alone. Fur-
thermore, the retrieval of this quantity of meteorological
data and the structuring of data into computerized files
were significant. Thus, some alternative method was
desired.
2. Average Year Analysis. Johnson proposed that a
simplification of the standard year would allow a
considerable saving in analytical expense. For example,
if monitoring data were available for a 3-year period,
66
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all the air quality data from that period could be
averaged to obtain a standard meteorological year. Each
air quality measurement would represent a certain
combination of emissions and meteorological conditions,
to be averaged over the base period. If emissions could
be assumed to be constant over the base period, then the
variation in measured levels would be due to meteorological
variations. In other words, meteorological classes could
be established strictly on the basis of the levels of air
pollution. This also means accepting a 3-year normaliza-
tion period rather than 5 years.
3. Single Year Analysis. This procedure would be
essentially the same as #2, except that only one. year of
monitoring data would be employed rather than three. This
would be the simplest and least expensive method of
proceeding. However, it was ruled out for three reasons.
First, any one year that is selected could turn out to
represent a meteorological anomaly. Second, the problem
of a point source affecting a monitoring site is exac-
erbated if only one year is taken into account. Third, a
number of good sites had data missing for certain months;
therefore, data from only one year alone was insuffi-
ciently representative of the full range of meteorological
conditions .
The average year analysis (#2 above) was chosen for this
study. Non-vehicle emissions were assumed to be constant, so
that the effect of vehicle emissions could be isolated and
analyzed. Therefore, the "projections" used throughout this
report cannot be considered "predictions" because point sources
are not taken into account. Rather, the projections should be
considered as simply a tool for analyzing the effects of a
given set of scenario conditions and their relative impacts.
Some of the anomalies of this procedure should be noted.
An examination of the analytic equation (Equation 2) shows
that In x = In k + In Q. Therefore incremental changes in x
may be expressed as follows :
(3)
X
When a simple ratio formula such as Equation 2 is established,
an analysis of the changes in the numerator is completely
t
insensitive to the magnitude of the denominator. In other
words, a given percentage change in the denominator (regardless
of its magnitude) will yield the same percentage change in the
67
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numerator. Thus, many of the parameters in this study can be
expressed as fractional parts of Q, rather than as real units.
The magnitude of these parameters is irrelevant; only the
relationship between parts is important.
It may be noted that the formula used was essentially
the ATDL air dispersion model in the form:
(4) x = CQ/U
where
X = ground level air concentration,
C = dimensionless parameter,
Q = area source emissions, and
U = wind speed.
The only change is that under our meteorological normalization
procedure, U becomes a constant, and thus C/U = K, and the
above may be expressed as Equation 2. This model is considered
appropriate for the estimation of atmospheric pollutants from
area-wide sources. It is available on tape as a part of the User's
Network for Applied Modeling of Air Pollution (UNAMAP).
5.2 ALLOCATING EMISSIONS TO RECEPTOR SITES
The next step is to ascertain a method for determining the
factor (Q) representing the total emissions affecting a
receptor site. This procedure is usually carried out by an
allocation of each emission by means of dispersion formulae and
a statistical treatment. The analytic formulae (Equation 2) is
insensitive to the magnitude of Q . but it is very sensitive to
a
the proportion of o that is derived from transportation
a
elements (Q ,). It is very important to know the percentage
at
contribution of vehicle emissions to the overall emissions.
In the pilot study on diesel effects in Kansas City, it
was found that increased diesel use would increase the annual
average TSP level by less than 2yg/m . However, transportation
emissions represented only 2.9% of the Kansas City totals,
while they represented 37-66% in the cities of this study.
The fraction of total emissions that are from the transportation
68
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sector appears to be one of the key factors in the future
year TSP estimates.
Normally, estimating procedures are used to allocate
vehicle emissions to receptors. In recent years, however, it
has been increasingly feasible to replace estimates with actual
measurements. Trace element studies have been carried out in
two of the cities in this study which provide an improved level
of accuracy in calculating the contributions of various sources
to the ambient air quality. Using lead as a tracer for those
years in which leaded gasoline was dominant, researchers have
measured the automobile component of effective emissions, and
the results were used in this study.
19
Kleinman conducted an extensive study of trace elements
and TSP at three sites in New York City. Routine sampling and
analysis were conducted from 1969 to 1975. From this data
base, a mathematical model was developed by him to explain
levels in ambient TSP as a function of the levels of 13
elements: Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, and
Zn. Using various techniques, the concentration of these
elements in particulate matter give an indication of the source
of the particles. Some of the elements may be emitted from
different sources, but the source of airborne lead is un-
equivocably motor vehicles. According to Daisey et al., if
the ratio of lead to particulate matter is known for automobile
exhaust and if all the lead in a given aerosol originates from
automobile exhaust, then the aerosol concentration of lead can
be used to estimate the contribution of this source to TSP.
One of the results of Kleinman's study was that the automobile
contributed about 20-25% of the TSP in New York City in 1975,
or about 12yg/m annual average at the sites tested. Table 5-1
shows the variation in source contributions by year from this
study.
The automobile's contribution to TSP in New York City
(Table 5-1) is less than was shown on the NEDS emissions
inventory data. However, the average lead concentration
(12yg/m ) is much higher than the levels found in comparable
studies, especially in smaller cities.
69
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Table 5-1. CONTRIBUTIONS OF MAJOR AIR POLLUTION SOTTRCES
TO SUSPENDED PARTICULATE CONCENTRATIONS3
(Percent of Measured TSP)
SOURCE
Space Heating and
Power Generation
Automobiles
Incineration
Soil-Derived Particles
and Possibly Coal Fly
Asha
Sulfate related and
Sea Salt
Subtotals:
Industrial and Unident-
fied
Total:
1969 1972 1973 1974 1975
22 8 10 10 8
8 18 23 21 21
21 4 4 4 4
28 14 15 14 16
12 20 22 23 29
91 64 74 72 78
19 31 32 26 49
110% 95% 107% 98% 128%
dfor 1969 data.
Source: Kleinman, Michael T., The Apportionment of fi
Airborne Particulate Matter, New York University.
New York, June 1977, p. 138.
70
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21
For example, Solomon et al. found that the ambient
levels of lead at 19 sites in Champaign-Urbana, Illinois,
ranged from 0.2 to 2.8jag/m , with the highest levels found in
the central business district. This result is consistent with
the findings in a prior report by PEDCo Environmental, Inc.,
wherein the automobile contribution to TSP problems in Kansas
22
City was found to be a very small portion of the total (2.9%).
Apparently there is a substantial difference between the
severity of motor vehicle particulate effects in small cities
and the effects in large cities.
23
Hoggan et al. have studied the amount of lead in the
atmosphere in Los Angeles. This city experienced a 50% decline
in the amount of lead in the atmosphere between 1970 and 1976.
This reduction was almost wholly attributable to the decline in
the use of leaded gasoline combined with a decline in the
amount of lead in leaded gasoline. Even at the worst moni-
toring site in the study (station 76), the 1971 mean annual
lead concentration was only 8yg/m , which is less than the
12ug/m found by Kleinman in New York. A comparable figure for
3
the Los Angeles CBD in 1975 would be 3ug/m , or about one-
fourth the level found in New York.
Other studies conducted in the Los Angeles area tended to
confirm that the automotive contribution to TSP effective at
monitoring sites is less than Kleinman's findings in New York
24
City. For example, Hidy and Friedlander estimated the auto
exhaust contribution to the urban TSP aerosol in Los Angeles as
13%. Since this figure is consistent with findings of others,
it has been used in this study.
The reason for the lower automotive TSP contribution in
Los Angeles (compared to New York) is not known, but several
possibilities are apparent. The first is the tighter controls
on automobiles exercised by the California State Air Resources
Board, including regulations on the lead content in gasoline.
This influence would have its greatest effect in the early
1970's. The second reason is the fact that TSP effects on
monitoring sites can be highly localized. Central business
districts tend to have elevated TSP levels because of more
71
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intense use of local streets and because the canyon effects of
large buildings tend to reduce dispersion. In this regard, one
would expect greater local effects in Manhattan Island than
anywhere else.
In the course of work, no trace element studies were found
that would help determine the proportion of TSP attributable to
automobiles in Chicago. However, the land use and trans-
portation patterns in the central business district are more
similar to New York than they are to Los Angeles, and the local
effects on monitoring sites should be similar. On the other
hand, Chicago has a significant industrial TSP contribution
which would suggest that the proportion due to vehicles would
be less. The datum 17%, applied as an estimator for the city of
Chicago, represents a mean between the other two available
figures.
5.3 MONITORING SITE DATA
The selection of ambient air quality monitoring sites and
the obtaining of monitoring data of adequate quantity and
quality represented a significant problem in this project.
First, the monitoring sites in these three cities have
been established for objectives different from the objectives
of this study. Second, the use of the national system for the
Storage and Retrieval of Aerometric Data (SAROAD) entailed the
evaluation of data of questionable quality.
The aim of this study is the evaluation of vehicular
emissions under various conditions; therefore it was desirable
to select monitoring sites in the central business districts
(CBD's) of large cities where one would expect the worst
problems. The CBD represents the destination point for a great
deal of morning and afternoon commuting by automobile, bus, and
rail. The CBD contains the end loop of most bus routes and a
transfer point for others. Goods are transported into the CBD
by heavy duty trucks, and much business and social interchange
takes place using taxi transportation.
Ideally, all monitoring sites used in this type of study
should be labeled "center-city commercial" according to the
72
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SAROAD language codes. Each site should be located close to
ground level or "breathing zone" so as to represent the
exposure of people on the street. EPA has recommended that
ambient TSP monitors be located at a height of 2-15 meters and
5-25 meters from any roadway. Monitoring sites too close to a
street would sample the plume from street traffic and would not
29
represent the 24-hour exposure of the populace. Each site
should be monitored on a continuous basis, or at least frequent
enough so that a valid annual geometric mean can be calculated
for comparison with the National Ambient Air Quality Standards.
There should be a sufficient number of monitoring sites so that
the data will reflect the widely varying local conditions
affecting TSP levels. The selected sites did not entirely meet
the above criteria, but they represented the best choices
available from the SAROAD data network.
The five New York sites selected were all located in
Manhattan Island in order to represent the central city as much
as possible. These were the only active sites in Manhattan
25
which had data throughout the base period (see Table 5-2).
It is expected that these sites may be considered "center-city
commercial," although the SAROAD system did not list this
information for 3 of the 5 sites. All of the sites are near
large buildings, although two sites have large open spaces on
one side. (Pier 42 monitoring site is next to the Hudson River
and the Central Park Arsenal site is next to Cental Park.) The
air sampling at these sites is conducted far above street
level. For the 121st Street site air was sampled at 75 ft in
1975 and at 24 ft in 1976. The heights of other sites are not
correctly listed in SAROAD, but are known to be elevated above
J C
the street.
One of the problems encountered in New York was that the
SAROAD system did not contain all the data that had been
collected at the sites by the local agency. By comparing the
number of observations in the SAROAD report with the data
2fi ? 7
reports from local government, ' it was ascertained that
there were whole months with data missing. Of the five sites
selected, four of them had 3 months of data missing during 1976
73
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TABLE 5-2. CHARACTERISTICS OF MONITORING SITES USED IN ANALYSES
City /County
1. New York,
New York
2. New York,
New York
3. New York,
New York
4. New York,
New York
5. New York,
New York
6. Chicago,
Cook Co.
7. Chicago,
Cook Co.
Site Address
Steinman Hall
W. 141 St. and
Convent Ave.
170 E. 121 St.
Central Park
Arsenal, 5th Ave.
and 64th St.
240 2nd Ave.
Pier 42,
Morton St. and
Hudson River
3500 E. 114 St.
1947 W. Polk
Station Type
Center City -
Commercial
Central City -
Commercial
Central City -
Industrial
Suburban -
Commercial
SAROAD Code
334680057F01
334680014P01
334680014F01
334680014H01
334680005H01
334680010H01
334680010H01
141220022H01
141220033F01
Elevation
Above Ground
75 Feet
24 Feet
31 Feet
15 Feet
Years
Of Data
1975
1976
1977
1975
1976
1976
1977
1975
1976
1977
1975
1976
1977
1975
1976
1977
1975
1976
1977
1975
1976
1977
1975
1976
1977
Months
Missing
3
0
0
0
3
10
0
0
3
3
0
3
3
0
3
3
2
3
3
0
0
0
0
0
1
Number Of
Observations
40
42
49
25
21
5
60
106
62
64
120
78
100
90
75
69
62
53
40
98
115
113
59
60
53
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TABLE 5-2. (CONTINUED)
City /County
8. Chicago,
Cook Co.
9. Chicago,
Cook Co.
10. Chicago,
Cook Co.
1 1 . Tor ranee ,
Los Angeles
12. Los Angeles,
Los Angeles
13. Long Beach,
Los Angeles
Site Address
9800 South
Torrence Ave.
538 S.Clark St.
4015 North
Ashland Ave.
2300 Carson St.
434 S. San .
Pedro St.
2655 Pine Ave.
Station Type
Center City -
Industrial
Center City -
Commercial
Center City -
Commercial
Center City -
Residential
Center City -
Commercial
Center City -
Commercial
SAROAD Code
141220030H01
141220005H01
141220004H01
058260001P01
058260001F01
054180001101
054180001P01
054180001F01
054100001F01
054 10000 1A01
05410000 1P01
Elevation
Above Ground
16 Feet
133 Feet
64 Feet
4 Feet
4 Feet
72 Feet
100 Feet
100 Feet
25 Feet
25 Feet
25 Feet
Years
Of Data
1975
1976
1977
1975
1976
1977
1975
1976
1977
1975
1975
1976
1977
1975
1976
1977
1975
1975
1976
1977
1975
1976
1977
1977
1975
Months
Missing
1
0
0
0
0
0
0
0
0
9
3
0
1
0
0
3
10
3
0
0
3
0
1
11
9
Number Of
Observations
83
114
109
101
116
111
88
118
113
8
21
61
54
61
60
46
5
21
57
60
19
55
52
4
8
-------
TABLE 5-2. (CONTINUED)
City/County
14. Pasadena,
Los Angeles
15 . Pasadena,
Los Angeles
16. Lennox,
Los Angeles
Site Address
1196 East
Walnut St.
Keck
Laboratories ,
California
Institute of
Technology
11408
La Cienega Blvd.
Station Type
Center City -
Commercial
Center City -
Residential
Center City -
Commercial
SAROAD Code
055760004101
055760002P01
055760002F01
05390000101
Elevation
Above Ground
18 Feet
60 Feet
60 Feet
20 Feet
Years
Of Data
1975
1976
1977
1975
1975
1976
1977
1975
1976
1977
Months
Missing
0
0
3
9
3
0
0
0
0
3
Number Of
Observations
61
61
45
7
21
59
59
61
61
44
Source: EPA National Aerometric Data Bank, "Raw Data Listing-24 Hour", computer printout, run date
December 14, 1978.
-------
and 1977. No special pattern was observed in these "gaps"/ but
insufficient data was available during these years to calculate
a valid annual geometric mean. The problem was ameliorated for
the purpose of this study by using three years of data (1975-77)
to represent the base year 1976. This allowed the data gaps
for any given month to be represented by data from that month
in other years. Thus the standard normalized meteorological
year is slightly different for each monitoring site; but this
is unimportant as long as the definition of the normalized base
year is understood.
In Chicago, there were no special data problems in the
five monitoring sites that were selected. However, not all of
the sites are located in the commercial downtown district. Two
are located in an industrial area near the CBD, one is located
in a nearby residential district, and the other two are in the
CBD. The sampling height during 1975-77 ranged from 15 to
133 ft. All selected sites had good data records and were
located within Cook County, the central part of the Chicago
metropolitan area.
Only one suitable site was found in the downtown Los
Angeles CBD. The other five sites were "center-city" sites
within the metropolitan region that had good data records.
Like the other two cities, there were wide variations in
sampling heights, from 4 to 100 ft.
In summary, the five or six sites selected in each city
were centrally located and had the best data records that could
be found for each city. However, the sampling height and the
types of sites varied. The projected TSP levels for different
sites should be examined individually in order to look at the
effect of alternative scenarios at different locations within
the central city.
5.4 ESTIMATES OF POPULATION EXPOSURE
One of the desirable methods of expressing pollutant
levels is the population exposure analysis. According to Horie
and Stern the purpose of such an analysis is to describe the
state of air quality representative of the population at risk.
77
-------
For example, long term population exposure may be summarized in
(a) a population average air quality which indicates the
pollution level to which the population is exposed, (b) a
health index which indicates the percentage of the population
exposed to air pollution exceeding the primary National Ambient
Air Quality Standard (NAAQS), (c) a welfare index which
indicates the percentage of the population exposed to pollution
exceeding the national secondary standard, or (d) a population
dosage spectrum which indicates the distribution of the
population associated with various air pollution dose levels.
In this study, a population dosage spectrum was developed, as
in (d), to express the increase in people exposed to higher
levels of TSP in various future scenarios.
EPA has developed guidelines for population exposure
analyses. In this study the objective was to develop
exposure distribution curves as illustrated in the EPA
32
guidelines using data from the 1975 Trends Report.
The methods used to develop population exposure profiles
were straightforward, based on local conditions in the study
areas. The first step was to review the monitoring sites that
had been selected in each of the three cities for diesel impact
analysis in order to determine whether these same sites could
be used for a population exposure analysis. As described
earlier, monitoring sites were chosen to represent CBD's where
the greatest diesel impact was expected. The selection of
sites based on diesel impact conflicts with the objective of a
population analysis, wherein sites should be located on the
basis of where the population will be exposed. Therefore, the
population analyses were limited to those areas immediately
adjacent to CBD's where both a population at risk and a high
diesel impact were located.
One of the important criteria for the location of
monitoring sites in a population exposure assessment is the
density of the site network. The sites must be close enough
together so that they may be considered representative of the
ambient air breathed by the population at risk. The area
78
-------
represented by each monitoring site may be approximated by a
31
circular area around the site. The radius of this area
depends upon the pollutant being monitored; the representative
area for suspended particulate is said to be a 3-mile radius.
This must be viewed as a crude approximation since it does not
account for any meteorological factors. It is also a generous
estimate of the size of the representative area since it is
known that the TSP level in the ambient air can sometimes
change within a few hundred feet of a monitoring site.
Nevertheless, the EPA guideline is helpful in making a first
order judgment about the adequacy of monitoring for population
exposure to TSP.
In the metropolitan Los Angeles region/ only one moni-
toring site is actually located in downtown Los Angeles (see
Figure 5-1). Other monitors are located in adjacent communities
which are generally considered part of the same city. The
closest downtown site that was examined in this study was in
Pasadena, 9 miles away. Other sites were located in Torrance
(16 miles away) and Long Beach (18 miles away). The air
monitoring system in Los Angeles is spread thinly over a very
large area, which reflects the widely scattered population of
the region. This network has been judged adequate for the
study of oxidants, where the radius of representation is
33
considered to be 10 miles. In fact, Horie and Chaplin
completed an extensive population exposure study of nitrogen
dioxide and oxidants based on data from this network.
Nevertheless, no cluster of sites in central Los Angeles can
be chosen from this network such that the 3 mile areas around
the sites will overlap. In other words, there will be
substantial gaps between sites where the TSP levels to which
the population is exposed is not measured. Certainly the
handfull of sites selected for this study are not adequate for
the usual treatment of population exposure, and it was not
attempted f
79
-------
However, an examination of the Los Angeles CBD showed that
one statistical area was almost completely within the area of
representation around the monitoring station. This area is
designated "Regional Statistical Area No. 23" by the Southern
California Association of Governments (SCAG) and is shown in
Figure 5-1. In the 1970 census this area had a resident
population of 90,416 people. Of these, 10.9% were of school
age and 16.4% were over 65 years of age, two age groups that
are generally considered more susceptible to the harmful
effects of air pollution. Portions of large statistical areas
are also within the 3-mile radius, but data on the population
at risk were not available.
SCAG population projections show that the area is expected
to remain relatively stable, with population growth no more
than 6% over the next 20 years. (Recent trends actually show
a decline.) Thus the increased TSP levels projected for the
Los Angeles CBD under various scenarios may be interpreted as
the exposure levels of approximately 90,000 people in Regional
Statistical Area 23.
The monitoring sites in the central area of Chicago were
also analyzed. Three sites were close enough together so that
they represented a significant population at risk and could
represent, to some degree, the variability of exposure within
the area. As indicated in Figure 5-2, 12 statistical community
areas designated by the City of Chicago, Department of
Planning, City and Community Development, were found to lie
within the overlapping areas of representation around each TSP
monitor.
The EPA guidelines suggest a simple model for allocating
the real measurements taken at monitors to imaginary receptor
sites within designated population areas. Population areas are
simply districts in which population data is readily available.
The EPA model assumes a linear change in TSP levels between
stations, but does not take into account any effects of mete-
orology. An equally accurate method is to assign the popu-
80
-------
r^r ?: ,/;)^»^^|&eft^f 1 >-w
Statistical Area #23
:: ': I I! 'T }:z
i
Figure 5-1. Area Represented by a Downtown Los Angeles TSP Monitorincr
Station.
81
-------
lation areas to each monitoring site. Assignments should be
made on the basis of proximity to monitors, local meteorology,
buildings and topography, and historical patterns of TSP at
these sites. This method may be more subjective because it used
human judgment rather than a computer terminal, but it may also
be more valid because it accounts for more of the factors which
determine the representativeness of data from each monitoring
station. On the basis of these assignments, the ambient TSP
levels within community areas 3,4,5,6, and 7 were considered to
be monitored by the station at Lakeview High School, 4015 N.
Ashland. The ambient TSP levels within community areas 24, 27,
and 28 were considered to be monitored by the station at Cook
County Hospital, 1947 W. Polk St. The TSP levels in areas 8,32,
and 33 were considered to be montored by the station at the
G.S.A. Building, 538 S. Clark St. Thus, population areas were
assigned to each of the three monitoring stations where TSP
projections had been made.
Based on the above assignments, a dosage spectrum was
estimated for the people at risk in the central area of
Chicago. The following steps are presented in Appendix G,
Table G-l. First, the projection year mean annual TSP level at
each site was placed in order from the highest level to the
lowest. The highest projected TSP level could be said to
represent the exposure of the people in the population district
assigned to that monitor. In 1980 the people in this district
represented 12% of the total population of the three districts.
Thus 12% of the population in the total area analyzed were
projected to be exposed to an annual mean level of 81.1yg/m in
1988 for Scenario T-|D-,E, The second highest projected for
1980 in Scenario T-^D^-j^ was 78yg/m , and 37% of the total
population being studied was assigned to this monitor. Thus it
could be said that (12 +39) or 49% of the total population
under study was projected to be exposed to a level of 78ug/m
or higher. The lowest of the three mean annual levels for 1980
in Scenario TDEI was 66.8ug/m . Thus 100% of the population
82
-------
Chicago
O'Hare
International
Airport
3-Miles Radius
Location Of Monitoring Sites
1. 4015 N. Ashland
2. 1947 W. Polk
3. 538 S. Clark
Figure 5-2. Areas Represented by Three Downtown Chicago TSP
Monitoring Sites.
-------
was projected to be exposed to an annual mean level of
66.8ug/m3 or greater. These three data provide the minimum
number of points required for graphing a dosage distribution,
as presented in the next chapter.
Note that the number of data points could be increased by
establishing receptor sites within each community area and by
assigning a portion of the measured TSP levels to each receptor
site. This procedure would offer false security, because it
would not increase the number of real measurements. Further-
more, the method of allocation from real measurements to
receptor sites is based on assumptions that may or may not be
valid. Rather than increasing the number of possibly false
data points, the error margin inherent in graphically inter-
polating between too few data points was accepted.
In contrast to Los Angeles, Manhattan Island offered a
density of monitoring sites that was appropriate for the
analysis of population exposure to TSP. Five sites are located
such that the 3-mile radius around the sites overlap con-
siderably, as shown in Figure 5-3. The higher density of the
monitoring network in New York parallels the higher density
land use pattern. Not only does Manhattan Island represent one
of the world's largest business districts, but it is also the
home of over 1,400,000 resident New Yorkers. Thus the area
provides a good basis for an analysis of population exposure to
projected TSP levels.
The method used to establish the dbse distribution was the
same method as was used in central Chicago. Data on 289 census
tracts previously developed for a regional 208 water study were
used for the Manhattan analysis. Each census tract was
assigned to a monitoring station on the basis of proximity to
monitors, local meteorology, buildings, topography, and
historical patterns of TSP at the monitoring sites. These
assignments resulted in 5 population exposure districts on
Manhattan Island, as shown in Figure 5-4. Following the
procedure previously described, the projected TSP levels were
84
-------
Figure 5-3. Are^of Manhattan Island Represented by TSP Monitoring
85
-------
Figure 5-4. Population Districts Assigned to Each Monitorina
Site on Manhattan Island. y
86
-------
rearranged so as to reflect percentiles of the population
exposed. The population district associated with monitoring
site 2 contained 280,090 people, or 19.3% of the total
Manhattan population. The 1980 projected mean annual TSP
associated with this district for Scenario T-IDIEI was 72.8ug/m ,
the highest projected level. Therefore 19.3% of the population
are projected to be exposed to a level of 72.8yg/m in 1980 for
Scenario TIDIET- In a like manner, the population within the
population exposure district around each site was summed and
the cumulative fractions obtained were associated with various
projected TSP levels under alternative scenarios. The
breakdown of fractions of the population exposed to various
TSP levels is summarized in Appendix G, Table G-2. This table
provides the data points for tracing the population dosage
curves presented in the next chapter, "Results of Analyses."
Note that a constant population level is assumed for
future projection years. Population projections that were
carried out for the city of New York 208 water study indicate
that Manhattan Island is expected to increase by only 1.3% by
the year 2000. Even this modest increase would be a reversal
of recent trends tending to reduce the population. Thus, the
assumption of a stable population was considered reasonable.
5.5 PROJECTIONS OF BAP
One of the secondary objectives of this project was to
estimate the ambient levels of benza(a)pyrene (BAP) to be
expected under various scenario conditions. BAP is one of the
aromatic hydrocarbons found to be carcinogenic in test
animals. Research is underway which will help determine what
levels of exposure may be considered unhealthful for the human
population. While no health criteria have yet been established,
it was considered desirable to project BAP levels in the
present study. When health criteria are established, these
projections may be reexamined to determine whether or not they
are within tolerable limits of safety.
87
-------
The method used for the projection of BAP levels was based
upon the previous work of others. First of all, it is known
that some hydrocarbons tend to condense upon the surface
of microscopic soot particles as they are dischared from a
combustion source. BAP, an indicator species for the aromatic
group, is considered to be associated with soot particles. From
this observation alone one would expect to find definite ratios
between BAP levels and TSP levels, since the majority of TSP is
derived from various sources of combustion.
In a previous study, Briggs et al. developed BAP/TSP
ratios for the purpose of projecting BAP levels, given a
projected TSP level. Based on various BAP studies, the ratios
ranged from 2.15 X 10~ to 12.29 x 10~ . A mean ratio (7.21 X
10) was adopted in order to make a single order-of-magnitude
approximation under the alternative future scenarios. The use
of a conversion ratio means that the BAP emissions from the
transportation sector are estimated from the TSP emissions:
ETBAPY = rEy
where
ETBAPY = the BAP emissi°ns frc-m the transportation sector
in projection year Y,
E = the TSP emissions from the transportation sector
^ in projection year Y, and
r = EQ&PY/EY.- a dimensionless conversion factor when
both emissions are expressed in the same units.
If the ratio r were assumed to be applicable to all emissions
then we would have:
EBAPY = rQy
where
Enat>v = BAP emissions from all sources in projection
BAr* year Y and
Qv = the sum of all TSP emissions in projection year
x Y.
-------
If we substitute this expression into equation (2) and cross
multiply, we have:
(7) r*y = EBAPYkB
This shows that the mean annual TSP levels are directly
proportional to the BAP emissions. Conversely, the term rx
can be taken to represent the BAP level. Thus, the projected
BAP levels for each projection year and each scenario are
obtained by multiplying the TSP levels by r = 7.21 x 10 and
converting to ng/m . These projections are found in Appendix F,
Table F-2.
Note that there is no obvious reason why the ratio r,
which was developed for diesel emissions, should be applicable
to all sources in the base year. However, when the TSP values
found in New York and Los Angeles in the base year (from 62 to
3 3
104ug/m ) are multiplied by r and converted to ng/m . The BAP
levels 0.44 to 0.75 are obtained. This is within the range of
BAP levels (0.3 to 3.5ng/m ) found in cities by other in-
38
vestigators. No definitive explanation for this observation
is offered here; it will suffice to say that the projections
are expected to yield reasonable results.
5.6 REFERENCES
1. Briggs, Terrence, J. Throgmorton, and M. Karaffa,
Air Quality Assessment of Particulate Emissions from
Diesel-Powered Vehicles, EPA-450/3-78-038, Prepared for
U. S. Environmental Protection Agency, by PEDCo En-
vironmental, Inc., March 1978.
2. U. S. Environmental Protection Agency, Guideline
for Air Quality Maintenance Planning, Vol. 10 (re-
vised) , "Procedure for Evaluating Air Quality Impact of
New Stationary Sources", EPA-450/4-77-001, October
1977.
3. Richard, George, J. Avery, and L. Baboolal, An Im-
plementation Plan for Suspended Particulate Matter
in the Phoenix Area, Vol. Ill, "Model Simulation of
Total Suspended Particulate Levels", EPA-450/3-77-021c,
Prepared for U. S. Environmental Protection Agency, by
TRW Environmental Engineering Division, August 1977.
89
-------
4. Axetell, Kenneth, Characterization of Particulate
Sources Influencing Monitoring Sites in Region VIII
Non-Attainment Areas, for U. S. Environmental Pro-
tection Agency, Region 8 by PEDCo Environmental, Inc.,
June 1976.
5. Turner, Bruce D., Letter re: Information on Avail-
ability of UNAMAP (User's Network for Applied Modeling
of Air Pollution), Environmental Science Research Labo-
ratory, U. S. Environmental Protection Agency, August
31, 1978.
6. TRW Systems Group, "Air Quality Display Model", PB-189-
194, Prepared for Department of Health, Education, and
Welfare, Public Health Service, November 1969.
7. Martin, D. 0., and J. A. Tikvart, "A General Atmos-
pheric Dispersion Model for Estimating the Effects on
Air Quality of One or More Sources", APCA Paper, June
1968, pp. 64-148.
8. Turner, D. Bruce, Workbook of Atmospheric Dispersion
Estimates, Environmental Science Services Administra-
tion, by U. S. Environmental Protection Agency, (re-
vised) 1970.
9. Pasquill, F., "The Estimation of the Dispersion of
Windborne Material", The Meteorological Magazine, Vol.
90, No. 1,063, February 1961, pp. 33-48.
10. Gulberg, P- H., and W. K. Charles, "A Comparative
Validation of the RAM and PTMTP Models for Short-Term
SO2 Concentrations in Two Urban Areas", Journal of
the Air Pollution Control Association, Vol. 28, No. 9,
September 1978.
11. Busse, A. D., and J. R. Zimmerman, User's Guide for
the Climatological Dispersion Model, EPA-R4-73-024,
U.S. Environmental Protection Agency, December 1973.
12. Zeldin, M. D., and W. S. Meisel, Use of Meteorological
Data in Air Quality Trend Analysis, Prepared for U. S.
Environmental Protection Agency, by Technology Service
Corporation, November 1977.
13. Peterson, William B., Environmental Sciences Research
Laboratory, Meteorology Division, U. S. Environmental
Protection Agency, telephone conversation, November 28,
1978.
14. de Nevers, Noel, and J. Roger Morris, "Rollback Modeling:
Basic and Modified", Journal of the Air Pollution Control
Association, Vol. 25, No. 9, September 1975, p. 493-7.
90
-------
15. Johnson, Theodore, PEDCo Environmental, Inc., Durham
Branch, private conversation, December 15, 1978.
16. Miller, Charles W., "An Application of the ATDL Simple
Dispersion Model", Journal of the Air Pollution
Control Association, Vol. 23, No, 3, A'v/uifc 1978, pp.
798-800.
17. U. S. Environmental Protection Agency, Compilation
of Air Pollutant Emission Factors, 2nd Ed., AP-42,
February 1976. Chapter 3, "Internal Combustion Engine
Sources".
18. Holzworth, George C., Mixing Heights, Wind Speeds, and
Potential for Urban Air Pollution Throughout the Con-
tiguous United States, AP-101, U. S. Environmental
Protection Agency, Office of Air Programs, January
1972, p. 9.
19. Kleinman, Michael T., The Apportionment of Sources of
Airborne Particulate Matterr Doctoral Dissertation at
New York University, New York, N. Y., June 1977.
20. Daisey, J. M., M. A. Leyko, and T. J. Kneip, "Source
Identification and Allocation of PAH Compounds in the
New York Aerosol: Methods and Applications", Unpub-
lished paper, 1978, New York University Medical Center.
21. Solomon, R. L., J. W. Hartford, J. L. Hudson, D. Nead-
erhouser, and J. J. Stukel, "Spatial Variation of
Airborne Lead Concentration in an Urban Area", Jour-
nal of the Air Pollution Control Association, Vol. 27,
No. 11, November 1977., pp. 1095-99.
22. Reference 1, p. 4-7.
23. Hoggan, Margaret C., Arthur Davidson, Margaret F. Bru-
nelle, John S. Nevitt, and John D, Gins, "Motor Vehicle
Emissions and Atmospheric Lead Concentrations in the
Los Angeles Area", Journal of the Air Pollution Con-
trol Association, Vol. 28, No. 12, December 1978, pp.
1200-1206.
24. Hidy, G. M., and S. K. Friedlander, "The Nature of the
Los Angeles Aerosol", Proceedings of the Second Inter-
national Clean Air Congress, Englewood and Barry (edi-
tors) , Academic Press, N. Y., 1971, p. 391.
25. U. S. Environmental Protection Agency, "National Aero-
metric Data Bank Raw Data Listing-24 Hours", A standard
computer report of suspended particulate readings by
year/ month, and day. Run date December 14, 1978.
91
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26. Field investigation and interviews in New York City,
New York, conducted by the author, December 6-8, 1978.
27. "Data Report, Aerometric Network, Calendar Year 1976",
The City of New York Department of Air Resources, En-
vironmental Protection Administration, 1977-
28. "Data Report, Aerometric Network, Calendar Year 1977",
The City of New York Department of Environmental Pro-
tection, Bureau of Science and Technology, 1978.
29. U.S. Environmental Protection Agency, "Air Quality Sur-
veillance and Data ReportingProposed Regulatory Revisions",
Federal Register, Vol. 43, No. 152, Monday, August 7, 1978,
p. 34892.
30. Horie, Juji, and Arthur C. Stern, Analysis of Population
Exposure to Air Pollution in New York - New Jersey -
Connecticut Tri-State Region, EPA-450/3-76-027, Depart-
ment of Environmental Sciences and Engineering, University
of North Carolina, Chapel Hill, N.C. Prepared for
U.S. Environmental Protection Agency, March 1976.
31. U.S. Environmental Protection Agency, Guideline on Pro-
cedures for Constructing Air Pollution Isopleth Profiles
and Population Exposure Analysis, EPA-450/3-76-027,
Monitoring and Data Analysis Division, Office of Air
Quality Planning and Standards, Research Triangle Park,
N.C., October 1977. Seen esp. Figure 13, p. 53.
32. Environmental Protection Agency, National Air
Quality and Emissions Trends Report, 1975, EPA-450/1-76-002,
Monitoring and Data Analysis Division, Office of Air
Quality Planning and Standards, Research Triangle
Park, N.C., November 1976.
33. Horie, Yuji, and Anton S. Chaplin, Population Exposure
to Oxidants and Nitrogen Dioxide in Los Angeles
Volume I; Executive Summary, EPA-450/3-77-004a, pre-
pared by Technology Service Corporation for the U.S.
Environmental Protection Agency, January 1977.
34. Horie, Yuji, and Anton S. Chaplin, Population Exposure
to Oxidants and Nitrogen Dioxide in Los Angeles/
Volume I: Longterm Trends, EPA-450/3-77-004c, prepared
by Technology Service Corporation for the U.S. Environmental
Protection Agency, January 1977- See Table A2, p. A-4.
35. Southern California Association of Governments, Draft
SCAG-78 Growth Forecast Policy, Development Guide, Au-
gust 1978, p. 30.
92
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36. City of New York 208 Study, "1975 Population Estimates by
Census Tracts", A computer printout provided by Mr. Adolph
Oppenheim of the Department of Water Resources, City
Planning Commission, March 1979.
37. Reference 1, pp. 4-33 to 4-36.
38. Reference 20.
93
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6. RESULTS AND CONCLUSIONS
6.1 FUTURE TSP ESTIMATES
The previous chapters have discussed the design of the
study, the use of scenarios, and the methods of relating
emissions to air quality. The principal calculation steps used
in this study have been summarized in Appendix E, "A Routine
for Projecting Mean Annual TSP." After all the individual
parameters and factors were calculated and tables of values
established, the TSP projections for all the scenario com-
binations at all the monitoring sites were computed with the
aid of a simple computer program. The projected normalized
annual mean TSP levels are shown on Figures 6-1 to 6-16. The
data represented by these figures are listed in Appendix F.
The most striking result displayed in these projections is
the profound effect of the dieselization of the automobile
population. Consistently, the cumulative effect of rapid
dieselization (D2 scenarios) is a substantial increase in the
ambient level of particulate matter for all three cities and
for all monitoring sites. For example, at Los Angeles site #1
the year 1999 TSP levels for all D0 scenarios were estimated at
3
111 to 126yg/m depending on which transportation scenario was
used; this may be compared to a present normalized annual mean
of 95yg/m . At New York site #2 the projected 1999 levels for
D» scenarios ranged from 90 to 102ug/m , compared to a present
3
normalized annual mean of 72yg/m . At Chicago site #3 the D,
3
scenario projections were 97 to 109yg/m , compared to a present
level of 81yg/m . In general, the TSP levels increased between
17% and 41% from 1978 to 1999 in the D2 scenarios. Thus, it is
apparent that all cities where the automobile contributes a
significant portion of the ambient TSP will eventually
experience a significant increase in TSP levels after the
automobile population is switched to diesel.
94
-------
130
C
H
0.
W
EH
(U
2
fi
a
(D
N
120
110
100
90
70
60
50
40
1978 80
82
84
86 83 90 92 94 96 93 2000
Figure 6-1. Projected Annual Mean TSP for N.Y. Monitoring Site at
95
-------
04
s
o
H
4J
O
Q)
rH
US
C
73
01
N
i
80
70
60
50
40
1978 80 82 84 86 88 90 92 94
Figure 6-2. Projected Annual Mean TSP for N.Y. Monitoring
Pier 42, Morton St. and Hudson River.
98 2000
Site at
96
-------
130
c
H
PL4
w
EH
c
n>
2
u
H
H
-P
I
o
5-
(0
3
C
o
0)
N
120 ^=
110
100
1978 80 82 84 86 88 90 92 94 96 98 2000
Figure 6-3. Projected Annual Mean TSP for N.Y. Monitoring Site at
240 2nd Ave.
97
-------
130
3.
e
Dt
§
flj
M
-U
5
cd
a
c
o
0)
N
o
z
120 : -q-TT ^=P=
110
100
90
80
70
60
50
40
1978 80 82 84 86 88 90 92 94 96 98 2000
Figure 6-4. Projected Annual Mean TSP for N.Y. Monitoring Site at
Central Park Arsenal, 5th Ave. and 64th St.
98
-------
130
I
c
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110
100
90
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70
60
50
40
1978
2000
Figure 6-5. Projected Annual Mean TSP for N.Y. Monitoring Site at
Steinman Hall, W. 141 St. and Convent Ave.
99
-------
130
120
110
100
CP
3.
CO
(0
I
o
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Figure 6-7. Projected Annual Mean TSP for Chicago Monitoring Site at
y 1947 W. Polk.
100
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130
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120 .-r'^Tl-T^T^^fc
110 =
100
GO
50
40
1978 30 82 84 86 38 90 92 94 96 2000
Figure 6-11. Projected Annual Mean TSP for LOS Angeles Monitoring Site at
2300 Carson St., Torrance.
104
-------
130
s
c
H
eu
w
E-i
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120 =
110
100
90
80
70
60
50
40
Figure 6-12.
1978 80 82 84 86 88 90 92 94 96 98 2000
tonual Mean TSP for California Monitoring Site at
105
-------
130
3.
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0)
s
M
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0)
a
3
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120
110
'100
1978 80 82 84 86 88 90 92 94 96 98 2000
Figure 6-13. Projected Annual Mean TSP for California Monitoring Site at
2655 Pine Ave., Long Beach.
106
-------
D>
0.
W
(0
2
JJ
8
O
(0
c
0)
N
130 rn
120 H^
no
100 .
90
80
70
60
50
40
Figure 6-15.
1978 80 82 84 86 88 90 92 94 96 98 2000.
Projected Annual Mean TSP for LOS Angeles Monitoring Site at
Keck Laboratories, California Institute of Technology,
Pasedena.
107
-------
130
120 SSE
3.
C
0<
w
10
M
U
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§
rH
3
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M
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110
100
90
80
70
60
50
40
1978 80 82 84 86 83 90 92 94 96 2000
Figure 6-16. Projected Annual Mean TSP for Los-Angeles Monitoring Site at
11408 La Cienega Blvd., Lennox.
108
-------
One aspect of the projected ambient TSP levels is the
fraction that is respirable. We cannot know this with
certainty without studying the particle size distribution in
the base year. However, we can easily estimate the diesel
contribution from the transportation sector which is signifi-
cant because it is definitely known to be respirable. The
estimated diesel contribution to TSP in the 1999 D0 scenarios
3 3
were 15-21yg/m in New York, 14-20yg/m in Chicago, and
13-18yg/m in Los Angeles. The exact contribution depended
upon which monitoring site and which transportation scenario
was used.
Another interesting aspect of these projections is the
short-term decrease in TSP levels. Regardless of which
scenario combination was chosen, there was a net decrease in
ambient TSP levels from 1978 to 1984 due to a reduction in
particulate emissions from motor vehicles. This phenomena
should probably have been expected from the low particulate
emissions factors that are associated with current models of
vehicles. The table of travel-weighted emission factors in
Appendix D shows clearly how the automobile population
gradually becomes non-polluting in terms of particulate
emissions. In the non-diesel automotive (D,) scenarios the low
particulate emissions continue throughout this century. In the
diesel (D2) scenarios the travel-weighted particulate factors
increase substantially, and by 1995 they are over 50 times
greater than the factors for that year in the D-^ scenarios.
This study was carefully constructed to measure the impact
of alternate transportation systems on future TSP levels. It
is ironic, therefore, to observe how little difference the
transportation alternatives make in all D^ cases. These
projections show that changes in the modal choices will not
substantially alter the particulate contribution as long as the
TSP emission factors remain low. This result is consistent
with other estimates of the impact of transportation controls.
For example, in Chapter 2 it was shown that very little carbon
monoxide emissions reduction could be achieved in New York
109
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through a transportation program, compared to the profound
effects of a national automobile emissions control program (see
Figure 2-8). On the other hand, if the TSP emission factors
become higher, such as in the D2 scenarios, then the effects of
modal choice become important.
If scenario T2 (Moderation) were taken to represent the
effects of local programs to reduce automobile usage, then the
difference between scenarios T, and T2 might approximate the
results that could be expected from such programs. These
"reductions" would represent as much as 3-10yg/m by year 1999.
(It is difficult to call these differences "reductions" since
all the monitoring sites continue to show increased TSP levels
in the T2D2 scenario combinations.) If more pronounced changes
in modal choice were to take place, such as in the T,
scenarios, then an important part of the adverse effects of
diselization would be averted. The differences between the T,
3
and T3 scenarios would be about 9-15yg/m by the year 1999.
However, it must be recalled that substantial changes in modal
choice probably cannot be made without some adverse effects in
the present economy. Scenario T., might be the result of oil
crises, recessions, or restrictions on automotive choice. It
is well to remember that a man without a job to pay his bills
might not be too impressed with his reduced potential for
contracting emphysema.
6.2 POPULATION EXPOSURE
Another way to look at the projected effects of diesel on
air quality is through population exposure. Figures 6-17
through 6-25 show the changing distributions of the exposure to
higher TSP levels in the central areas of Chicago and New York,
the two cities where the variability of exposure could be
measured.
In Chicago, the population in the central area of the city
is presently exposed to a TSP that is close to the national
ambient air quality standard of TSyg/m . Figure 6-17 shows the
projected TSP exposures for the near term (1980) and for the
110
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0) -H
£ K
4J
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100
90
80
70
60
50
40
30
20
10
o
04
i960
-2000
0)
g
50
60
70
80
90
100
no
120
Population Exposed to the Given
Mean Annual TSP Level or Higher (%)
Figure 6-17. Projected Decrease in the Central Chicago
Population Exposed to TSP in Scenario T^D-E...
no
12C
Mean Annual TSP in yg/m
Figure 6-18. Projected Increase in the Central Chicago
Population Exposed to TSP in Scenario T1D9E1.
X £» X
111
-------
90
100
110
120
Mean Annual TSP in yg/m
Figure 6-19,
Projected Increase in the Central Chicago
Population Exposed to TSP in Scenario T2D2E,,
110
120
Mean Annual TSP in ug/m
Figure 6-20. Projected Increase in the Central Chicago
Population Exposed to TSP in Scenario T-D.-E, .
J £ JL
112
-------
000
80
90
100
110
120
Figure 6-21,
Mean Annual TSP in yg/m
Projected Increase in the Manhattan Island
Population Exposed to TSP in Scenario T,D,E,.
50
60
70 80 90 _ 100
Mean Annual TSP in yg/m3
110
120
Figure. 6-22. Projected Increase in the Manhattan Island
Population Exposed to TSP in Scenario T0DnI
113
-------
110
120
Mean Annual TSP in ug/m
Figure 6-23
Projected Increase in the Manhattan Island
Population Exposed to TSP in Scenario TD
no
120
Mean Annual TSP in ug/m
Figure 6-24. Projected Increase in the Manhattan Island
Population Exposed to TSP in Scenario TD
114
-------
Figure 6-25. Year 1980 Projected TSP Distribution on Manhattan Island
(All Scenarios).
-------
Figure 6-26. Year 1990 Projected TSP Distribution on Manhattan Island for
Scenario Combination T,D-E,.
-------
Figure 6-27.
-------
long term (2000) under scenario combination TIDIEI- This
combination means that automobile usage would continue to
increase, diesel would not be introduced, and the present low
particulate emissions would continue. Under these conditions
about 60% of the central Chicago citizens would be exposed to
TSP levels above the standard and about 40% below in 1980. In
year 2000 the situation would actually improve slightly, with
about 50% exposed to levels above the standard and about 50%
below.
Under other scenario conditions the exposure of the
central Chicago population would be much higher. Under
Scenario T,D2E, (increased auto usage combined with rapid
diesel introduction) 100% of the population would be exposed to
levels above standard by 1990 (see Figure 6-18). By year 2000
the population would be exposed to levels between 94 and
114ug/m . On the other hand, if automobile usage were to stop
growing (Scenario T2D2E,) or if auto usage were to decline
(Scenario T3D2E,), then the exposure would not be quite so
high, as shown in Figures 6-19 and 6-20. Nevertheless, almost
all the population will be exposed to levels above standard by
1990, and far above standard by year 2000.
In Manhattan Island the TSP population is presently
exposed to TSP levels below the national standard according to
data from the New York City monitoring sites. Under Scenario
T1D1E1' th^-s situation would remain essentially the same over
the next 22 years (Figure 6-21). Under Scenario T,D2E,
about 65% of the population would be exposed to levels above
the NAAQS by 1990. By year 2000 the population would be
exposed to TSP levels from 84 to 106yg/m . Alternately, if the
growth rate of automobile usage were reduced, as in Scenarios
T2D2E1 and T3D2E1'' then about 60% of the Manhattan population
would be exposed to levels above standard by 1990 and the
exposure in year 2000 would also be reduced (Figures 6-23 and
6-24).
These figures are another method for expressing the air
quality levels that can be expected under alternative scenario
combinations. They show, dramatically, the increased
118
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population exposure that can be expected if automobiles are
rapidly dieselized. They also show the beneficial effects of
switching from automobiles to buses and taxis, if dieselization
should continue.
6.3 THE IMPACT OF SWITCHING TO MASS TRANSIT
One of the objectives of this study was to determine the
impact of switching to mass transit, especially buses and taxis
in the central city. This impact is inherent in the trans-
portation scenarios and the different effects these scenarios
have upon the ambient TSP levels at various monitoring sites in
each of the three cities analyzed. For example, the effect of
switching from automobiles to greater use of buses (and rail
transport) is the difference between the projected TSP levels
in T, and T2 scenarios. This is not easy to visualize because
so many factors were taken into account in the construction of
these scenarios.
The effects of mass transit on TSP levels in each of these
scenarios can be seen more clearly if the TSP contribution from
each transportation mode is listed separately. To accomplish
this, the alternate equation 8a in Appendix E was used in a
separate computer run. The resulting tabulation is listed in
Appendix H. Based on this data, one of the most centrally
located monitoring sites from each of the three cities was
chosen for exhibition in Figures 6-28 to 6-30. These figures
present the TSP contribution from each of the four modes of
vehicular transportation to a given monitoring site for three
selected projection years. It should be noted that TSP
contribution does not mean vehicle particulate emissions;
rather, it means that portion of the ambient TSP level
attributable to a given mode of transportation. In other
words, if a mode of transportation were to magically vanish,
the ambient TSP level would theoretically drop by the amount of
TSP contribution from that mode of transportation.
Like the previous presentations, these figures show how
the dieselization scenarios (D, versus D2) dominate all other
119
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oo
PH
5°
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50 -
40
30
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20
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T1D2E1
o o
00 CT\
o
o
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T2D2E1
50
40
30
20
10
0
Figure 6-28. Projected TSP Contributions from Each Mode of
Transportation to the Monitoring Site at
240 2nd Avenue, New York City.
T3D2E1
120
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H CO
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40
30
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Figure 6-29.
Projected TSP Contribution From Each Mode of
Transportation to the Monitoring Site at
538 S. Clark St., Chicago
121
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CO
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4J O
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§ £
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Taxi
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00
40
30
20
40 -
30
20
50
40
430
t>0
520
T3°1E1
50
40
30
20
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Wl
T2D2E1
50 r- T3D2E1
40
30
20
10
Figure 6-30. Projected TSP Contributions From Each Mode of
Transportation to the Monitoring Site at
434 S. San Pedro St., Los Angeles.
122
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effects. In D.^ scenarios, where the diesel engine remains an
insignificant part of the automobile population, the automobile
TSP contribution becomes minute and the taxi contribution
essentially disappears. This is due to the continued
introduction of catalyst-equipped vehicles with very low
emission rates. In D~ scenarios, where the diesel is intro-
duced as the dominant automobile (and taxi) engine, the
automobile TSP contribution rises dramatically, followed by the
taxi contribution, which becomes very significant.
Careful scrutiny of these charts will reveal other, less
obvious, impacts. For example, the TSP contributions of buses
and heavy duty trucks increase in all scenario combinations.
It will be recalled that the dieselization scenarios affect
only automobiles and taxis, and that only a single projection
was made for heavy duty trucks. Based on information from New
York City, the diesel fraction of bus VMT was about the same
for buses as for HDT's; therefore the same projection was
applied to buses. Since the non-automobile vehicles are of
special interest, it is worth examining this result more
closely.
6.3.1 The Impact of Buses
In all scenarios, the TSP contribution from buses is
projected to increase from 1978 to the year 2000. Based on the
data from Appendix H, the bus contribution over this time
period is tracked in Figure 6-31 for a monitoring site in
downtown New York City. This steady increase has two causes.
First, the percent of buses that contain diesel engines is
expected to increase, and the number of bus miles that are
diesel will also increase. The higher particulate emission
rates from diesel buses compared to gasoline engines tend to
increase the particulate emissions from the bus mode of
transportation. Second, transportation planners expect bus VMT
in cities to increase, whether automobile usage continues to
increase (Scenario T,), whether auto usage levels off and a
modal shift occurs (Scenario TO, or whether a modal shift
occurs in combination with reduced transportation demand
123
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5.8
5.4
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4.6
4.2
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3.4
3.0
2.6
2.2
1.8
1.4
1.0
0
1L...I 1
Scenarios
T, Scenarios
T3 Scenarios
I I !
I I I
1978 80 82 84 86 88 1990 92 94 96 98 2000
Projection Year
Figure 6-31.
Projected TSP Contribution from the Bus Mode
of Transportation to the Monitoring Site at
240 2nd Ave., New York City.
124
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(Scenario T3). However, the reason for the increase is
different in all three cases.
In the 1^ scenario, bus VMT increases to meet the needs of
the growing population of the elderly and the poor. Public
agencies are able to increase their budgets for this purpose
because of a continuing prosperous economy. In the T2
scenario, bus VMT increases to meet the needs of a larger
portion of the commuting population during a modal shift, in
addition to meeting some of the increased needs of the elderly
and the poor. Increased revenues may be obtained from rider
fares as well as from public funds. The increased bus VMT
pushes the TSP contribution somewhat higher for scenario T2
than for scenario T, . In scenario T3, a less healthy economy
tends to reduce the total demand for transportation services,
including bus service. However, a modal shift from automobiles
to bus commuting causes bus VMT mileage to increase almost as
much in scenario T., as in scenario T,. (See Figure 6-31.)
One of the questions that can arise is how a large
reduction in the TSP contribution from automobiles can be
achieved (e.g. the difference between scenarios TiD2El an<*
T-D2E,), while at the same time there is only a modest increase
in the TSP contribution from buses. In other words, since
there is a decrease of 13.1 ug/m in the TSP contribution from
autos and taxies between scenarios T^ and T3 in the year 2000,
why is there not a comparable increase in the bus contribution?
(The comparable difference in the bus TSP contribution between
scenarios T, and T3 is only 0.3 ug/m .) The answer to this
.question lies in the nature of the bus vehicle and in the
present commitment to improved bus service.
First of all, the bus is a large capacity vehicle which
carries 40-50 persons at full load and many more people when
the aisles are utilized. In other words, if bus service were
expanded to accomodate a sudden change in the modal choice for
commuting, one bus could theoretically accomodate 40-50
commuters. If the bus route was short enough to allow two
trips during the rush hour, then perhaps 80-100 commuters could
125
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be accommodated. Therefore a very small increase in bus YMT
can accommodate a very large reduction in the VMT of auto-
mobiles used for commuting.
Second, the modal shifts implied in scenarios T2 and T3
also involve the commuter rail services, which have such minor
TSP emissions that they were deleted from this study. The
portion of automobile VMT accommodated by electrically-operated
subways will correlate with a zero increase in emissions in
this study. (Point sources, such as power plants, were not
included.) This will be true for New York and Chicago, but not
for Los Angeles where there is no rail service.
Third, it was previously noted that there is a commitment
to improve bus services in central cities for the poor and the
elderly. This means that the VMT differences between the
various scenarios are smaller for buses than they are for
automobiles, since bus service is expanding in all three cases.
Smaller VMT differences contribute to smaller differences
between the TSP contributions from the bus mode of trans-
portation in alternate scenarios.
The bus mode of transportation will contribute an
increasingly significant amount of the ambient TSP levels in
the future. In the T0 scenarios the bus contribution to
3
ambient TSP levels increases from about 1.8 yg/m in 1976 to
5.3 yg/m in the year 2000 at the monitoring site located at
240 2nd Avenue, New York City. At other monitoring sites in
New York, the comparable year 2000 bus contribution ranges from
4.3 to 5.5 yg/m . At centrally located monitoring sites in
downtown Chicago the comparable bus mode contribution to
ambient TSP is 2.5 to 5.3 yg/m . In the central area of Los
Angeles, the comparable TSP contribution rises from 2.1 yg/m
in 1976 to 5.5 yg/m in the year 2000. In the non-dieselized-
auto scenarios, the bus systems are second only to heavy duty
trucks in contributions to ambient levels of TSP from
transportation.
6.3.2 The Impact of Taxis
The impact of taxi transportation on the ambient TSP level
126
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ia
Scenarios
1978 80 82 84 86 88 1990 92 94 96 98 2000
Projection Year
Figure 6-32,
Projected TSP Contribution from the Taxi Mode
of Transportation to the Monitoring Site at
240 2nd Ave., New York City.
127
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is inherent in the results of scenario analysis. The
differences between D, and D2 scenarios includes the impact of
dieselized taxi engines. The differences between T.^ T2/ and
T3 scenarios include the impact of taxi emissions and how they
are modified by changes in the economy or by changes in modal
choice. Unlike the case of buses, the amount of TSP con-
tribution from taxis depends heavily upon which future scenario
combination is chosen for analysis.
Figure 6-32 illustrates the vivid contrasts between the
scenarios in the amount of TSP contributed from the taxi mode
of transportation. The impact of taxi TSP emissions follow the
same pattern as automobile emissions. If little or no
dieselization is assumed (D, scenarios), then the VMT-weighted
emissions become almost negligible by 1990, and the reduced
emission rate accounts for the reduced TSP contribution shown
in the figure. If full-scale dieselization is assumed (D2
scenarios), then the TSP contribution from taxis increases
dramatically. This increase is directly attributable to the
increased emission rates associated with the diesel engine.
A modest reduction in auto usage and a modest modal shift,
as reflected in the differences between scenarios T, and T2,
is not expected to cause a major change in the TSP contribution
from taxis. The use of taxis is more closely related to the
economy than to the modal choice of commuters. Therefore, when
the scenario includes conditions of economic recession
(scenario TO, the TSP contribution from taxis is reduced
substantially, as shown in Figure 6-32. Unless government
intervenes in a forceful manner, the principal factors
determining the TSP contribution from the taxi mode of
transportation will be the type of engine used followed by the
state of the economy.
6.4 CAVEATS
In this study, there were a number of conditions placed on
the results. These conditions have been mentioned elsewhere in
the report, but it suits the author to list these caveats as a
128
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part of the conclusions, as follows:
1. Projections of the future are highly conditional upon
human factors that are too complex to be predictable.
Therefore, one should realize that any one of the
scenarios could be right or all of them could be wrong.
Unfortunately, we will have to wait 10-20 years to
determine the accuracy of any one scenario.
2. This study was designed to isolate the effects of
diesel emissions on TSP under various scenario as-
sumptions. In order to perform this type of analysis, all
non-transportation emissions must remain constant. For
this reason the conditional projections in the report
should not be confused with "predictions" of actual TSP
levels to be expected. To make predictive forecasts, all
sources of particulate emissions should be taken into
account. The projections in this study should be used only
to examine the effects of diesel policy alternatives on
ambient TSP levels.
3. The relationship between emissions and air quality at
each monitoring site was based on trace element studies
calibrated at similar, but not the same, locations. More
accuracy would be possible if the local air monitoring
stations were identical to the research stations.
4. The trace element technique is generally an impr^ve-
ment over regional inventory methods. However, Pace
has developed a technique that allows TSP levels to be
statistically correlated with factors identified through
a microinventory of the area immediately surrounding a
given monitoring site. This statistical model has not
been calibrated for monitoring sites in these three
cities, but no doubt this new method will be very useful
in future studies of this type. Itis possible that an
inventory method would show somewhat higher diesel impacts
on TSP.
5. The present report is based on currently available
emission factors (E,) which have greatly affected the
outcome of the analysis. The method used in this report
can easily be applied to other emission factors as further
research shows them to be applicable.
6. Data from 1975 to 1977 were used to develop a standard
meteorological year, and all monitoring data were
normalized to this standard year. As meteorological
conditions vary unpredictably from these standard
conditions, so would the projections of TSP levels. This
is another reason for using these projections only for
analyzing policy alternatives and not for forecasting.
129
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7. This study was directed to the problem of the impact
of diesel participates. It should not be forgotten that
diesel engines are likely to benefit air quality by the
reduced emissions of gaseous pollutants, such as carbon
monoxide. The full range of costs and benefits are not
treated in this report.
In summary, the results of this study show that switching
from automobiles to alternative modes of transportation, such
as taxis or buses, will have only a minor impact on ambient TSP
levels as long as current types of emission controls are used.
However, if the automobile population is converted to diesel,
there will be substantial increases in the TSP levels ex-
perienced in the large cities after 1985. In this case, the
modal choice of the citizens will determine the extent of the
adverse impact.
6.5 REFERENCE
1. Pace, Thompson G., "An Empirical Approach for Relating
Particulate Microinventory Emissions Data, Monitoring
Siting Characteristics and Annual TSP Concentrations", Air
Management Technology Branch, Office of Air Quality
Planning and Standards, U. S. Environmental Protection
Agency, January 1979 (unpublished draft).
130
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APPENDIX A. PROJECTIONS OF MODAL CHOICE ACCORDING TO THREE
TRANSPORTATION SCENARIOS
131
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TABLE A-l. PROJECTION YEAR MODAL CHOCIES FOR T-,^ SCENARIOS IN NEW YORK CITY
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
1976 Base
n-VMTa
1.32
12.64
50.77
4.18
68.91
FRACTION
.019
.183
.737
.061
1.000
1977
n-VMT
1.374
12.725
51.130
4.210
69.439
FRACTION
.020
.185
.742
.061
1.008
1978
n-VVT
1.428
1 2 . 809
51.491
4.239
69.967
FRACTION
.09.1
.186
.747
.062
1.016
1979
n-VMT
1.482
12.894
51.851
4.269
70.496
FRACTION
.022
.187
.752
.062
1.023
1980
n-VMT
1.536
12.979
52.212
4.299
71.026
FRACTION
.022
.1KR
.758
.062
1.026
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
1981
n-VMT3
1.591
13.063
52.572
4.328
71.497
FRACTION
.023
.190
.763
.063
1.039
19R9
n-VMT
1.645
13.148
52.933
4.358
72.084
FRACTION
.024
.191
.768
.063
1.046
19f
n-VMT
1.699
13.233
53.293
4.388
72.613
1
FRACTION
.025
.192
.773
.064
1.054
1984
n-VMT
1.753
13.318
53.654
4.417
73.142
FRACTION
.025
.193
.779
.064
1.061
191
n-VMT
1.807
13.402
54.014
4.447
73.670
}S
FRACTION
.026
.194
.784
.065
1.069
CO
PO
MODE
BUS
TAXI
AUTO
y
HOT
TOT/I
.1986
n-VMT*
1 .861
13.487
54.375
4.77
74.200
FRACTION
.097
.196
.789
.065
1.077
1987
n-VMT
1 _Q1S
13.S79
54.735
4.506
74.728
FRACTION
T09R
,197
.794
.065
1.084
198
n-VMT
1.969
n.fiSfi
55.096
4.536
75.257
8
FRACTION
.029
.198
.800
.066
1.093
1989
n-VMT
2 094
13.741
55.456
4.566
75.787
FRACTION
.099
.199
.805
.066
1.099
1990
n-VMT
7.078
13.826
55.817
4.595
76.316
FRACTION
.010
.201
.810
.067
1.108 ,
-------
TABLE A-l. (CONTINUED)
fcjr\nc
BUS
TAXI
AUTO
HOT0
TOTAL
1991
n-VMT^
2.132
13.910
56.177
4.625
76.844
FRACTION
.031
.202
.815
.067
1.115
1992
n-VMT
2.186
13.995
56.537
4.655
77.373
FRACTION
.032
.203
.820
.068
1.123
1993
n-WT
2.240
14.080
56.898
4.685
77.903
FRACTION
.033
.204
.826
.068
1.131
1994
n-VMT
2.294
14.164
57.258
4.714
78.430
FRACTION
.033
.206
.831
.068
1.138
1995
n-VMT
2.348
14.249
57.619
4.744
78.960
FRACTION
.034
.207
.836
.069
1.146
CO
CO
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
1996
n-VMT c
2.402
14.334
57.979
4.774
79.489
FRACTION
.035
.208
.841
.069
1.153
1997
n-VMT
2.457
14.418
58.340
4.803
80.018
FRACTION
.036
.209
.847
.070
1.162
1998
n-VMT
2.511
14.503
58.700
4.833
80.547
FRACTION
.036
.210
.852
.070
1.168
1999
n-VMT
2.565
14.588
59.061
4.863
81.077
FRACTION
.037
.212
.857
.071
1.177
2000
n-VMT
2.619
14.673
59.421
4.892
81. 60S
FRACTION
.038
.213
.862
.071
1.184
n-VMT = normalized vehicle miles traveled.
HOT = heavy duty truck.
-------
TABLE A-2. PROJECTION YEAR MODAL CHOCIES FOR T2 SCENARIOS IN NEW YORK CITY
MODE
BUS
TAXI
AUTO
1_
HOT
TOTAL
1976 Base
n-VMTa
1.32
12.64
50.77
4.18
68.91
FRACTION
.019
.183
.737
.061
1.000
1977
n-VMT
1.393
12.682
50.77
4.194
69.039
FRACTION
.020
.184
.737
.061
1.002
1978
n-VVT
1.465
12.723
50.77
4.208
69.166
FRACTION
.021
.185
.737
.061
1.004
1979
n-VMT
1.538
12.765
50.77
4.221
69.294
FRACTION
.022
.185
.737
.061
1.005
198
n-VMT
1.610
12.807
50.77
4.235
69.422
0
FRACTION
.023
.186
.737
.061
1.007
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT3
1.683
12.849
50.77
4.249
69.551
FRACTION
.024
.186
.737
.062
1.009
1982
n-VMT
1.756
12.890
50.77
4.263
69.679
FRACTION
.025
.187
.737
.062
1.011
1983
n-VMT
1.82ft
12.932
50.77
4.277
69.807
FRACTION
.027
.188
.737
.062
1.014
1984
n-VMT
1.901
12.974
50.77
4.290
69.935
FRACTION
.028
.188
.737
.062
1.015
1985
n-VMT
1 .973
13.015
50.77
4.304
70.062
FRACTION
.n?q
.189
.737
.062
1.019
CO
MODE
BUS
TAXI
AUTO
HOT13
TOT/l
1986
n-VMT3
2.046
13.057
50.77
4.318
FRACTION
.030
.189
.737
.063
1987
n-VMT
2.119
13.099
50.77
4.332
FRACTION
.031
.190
.737
.063
19
n-VMT
2.191
13.141
50.77
4.346
38
FRACTION
.032
.191
.737
.063
19F
n-VMT
2.264
13.182
50.77
4.359
Q
FRACTION
.033
.192
.737
.063
1990
n-VMT
2.336
13.266
50.77
4.373
FRACTION
.034
.193
.737
.063
j
-------
TABLE A-2. (CONTINUED)
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
199
n-VMTa
2.409
13.266
50.77
4.387
70.832
1
FRACTION
.035
.193
.737
.064
1.029
19
n-VMT
2.482
13.307
50.77
4.401
70.960
92
FRACTION
.036
.193
.737
.064
1.030
199
n-VVT
2.554
13.349
50.77
4.414
71.087
3
FRACTION
.037
.194
.737
.064
1.032
199
n-VMT
2.627
13.391
50.77
4.428
71.216
'4
FRACTION
.038
.194
.737
.064
1.033
195
n-VMT
2.699
13.433
50.77
4.442
71.344
)5
FRACTION
.039
.195
.737
.064
1.036
CO
en
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
1996
n-VMT a
2.772
13.474
50.77
4.456
71.472
FRACTION
.040
.196
.737
.065
1.038
1997
n-VMT
2.845
13.516
50.77
4.470
71.601
FRACTION
.041
.196
.737
.065
1.039
1998
n-VMT
2.917
13.558
50.77
4.483
71.728
FRACTION
.042
.197
.737
.065
1.041
1999
n-VMT
2.990
13.599
50.77
4.497
71.856
FRACTION
.043
.197
.737
.065
1.042
2n
n-VMT
3.062
13.641
50.77
4.511
71.984
00
FRACTION
.044
.198
.737
.065
1.044
n-VMT = normalized vehicle miles traveled.
3HDT = heavy duty truck.
-------
TABLE A-3.
PROJECTION YEAR MODAL CHOICES FOR TS SCENARIOS IN NEW YORK CITY
MODE
BUS
TAXI
AUTO
HDT^
TOTAL
1976 Base
n-VMTa
1.32
12.64
50.77
4.18
68.91
FRACTION
.019
.183
.737
.061
1.000
1977
n-VMT
1.368
12.564
50.435
4.152
68.519
FRACTION
.020
.182
.732
.060
.994
1978
n-WT
1.415
12.488
50.100
4.125
68.128
FRACTION
.021
.181
.727
.060
.989
1979
n-VMT
'1.463
12.412
49.765
4.097
67.737
FRACTION
.021
.180
.722
.059
.982
1980
n-VMT
1.510
12.337
49.430
4.070
67.347
FRACTION
.022
.179
.717
.059
.977
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
1.558
12.261
49.095
4.042
66.956
FRACTION
.023
.178
.712
.059
.972
1982
n-VMT
1.605
12.185
48.760
4.014
66.564
FRACTION
.023
.177
.708
.058
.966
1983
n-VMT
1.653
12.109
48.424
3.987
66.173
FRACTION
.024
.176
.703
.05 a
.961
1984
n-VMT
1.700
12.033
48.089
3.959
65.781
FRACTION
.025
.175
.689
.057
.955
1985
n-VMT
1 . 748
11.957
47.754
3.
-------
TABLE A-3. (CONTINUED)
uAnp
BUS
TAXI
AUTO
HDT*>
TOTAL
1991
n-VMTa
2.033
11.502
45.744
3.766
63.045
FRACTION
.029
.167
.664
.055
.915
1992
n-VMT
2.080
11.427
45.409
3.739
62.655
FRACTION
.030
.166
.659
.054
.909
1993
n-VVT
2.128
11.351
45.074
3.711
62.264
FRACTION
.031
.165
.654
.054
.904
1994
n-VMT
2.175
11.275
44.739
3.683
61.872
FRACTION
.032
.164
.649
.053
.898
1995
n-VMT
2.223
11.199
44.403
3.656
61.481
FRACTION
.032
.163
.644
.053
.892
CO
uonir
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
2.270
11.123
44.068
3.628
61.089
FRACTION
.033
.161
.640
.053
.887
1997
n-VMT
2.318
11.047
43.733
3.601
60.699
FRACTION
.034
.160
.635
.052
.881
1998
n-VMT
2.365
10.972
43.398
3.573
60.308
FRACTION
.034
.159
.630
.052
.875
1999
n-VMT
2.413
10.896
43.063
3.545
59.917
FRACTION
.035
.158
.625
.051
.869
2000
n-VMT
2.460
10.820
42.728
3.518
59.526
FRACTION
.036
.157
.620
.051
.864
n-VMT = normalized vehicle miles traveled.
HOT = heavy duty truck.
-------
TABLE A-4. PROJECTION YEAR MODAL CHOICES FOR T SCENARIOS IN CHICAGO
MODE
BUS
TAXI
AUTO
HDTto
TOTAL
1976 Base
n-VMTa
6.1
148.0
334.0
17.5
505.6
FRACTION
.012
.293
.661
.034
1.000
1977
n-VMT
6.253
148.577
335.503
17.579
507.912
FRACTION
.012
.294
.664
.035
1.005
1978
n-VJ/T
6.405
149.154
337.006
17.658
510.223
FRACTION
.013
.295
.667
.035
1.010
1979
n-VMT
6.558
149.732
338.509
17.736
512.535
FRACTION
.013
.296
.670
.035
1.014
1980
n-VMT
6.710
150.309
340.012
17.815
514.846
FRACTION
.013
.297
.672
.035
1.017
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
6.863
150.886
341.515
17.894
517.158
FRACTION
.014
.298
.675
.036
1.022
1982
n-VMT
7.015
151.463
343.018
17.973
519.469
FRACTION
.014
.300
.678
.036
1.028
1983
n-VMT
7. .168
152.040
344.521
18.051
521.780
FRACTION
.014
.301
.681
.036
1.032
1984
n-VMT
7.320
152.618
346.024
18.130
524.092
FRACTION
.014
.302
.684
.036
1.036
1985
n-VMT
7.473
153.195
346.527
18.209
526.404
FRACTION
.015
.303
.687
.036
1.041
00
us>nc
Mv/UC
BUS
TAXI
AUTO
HOT
TOT* i
1<
n-VMT
7.625
153.772
349.030
18.288
528.715
386
FRACTION
.015
.304
.690
1 ,£36_-
1.045
19
n-VMT
7.778
154.349
350.533
18.366
531.026
87
FRACTION
.015
.305
.693
.036
1.049
19
n-VMT
7.930
154.926
352.036
18.445
533.337
88
FRACTION
.016
.306
.696
.036
1.054
19
n-VMT
8.083
155.504
353.539
1R.S74
535.650
89
FRACTION
.016
.308
.699
.m?
1.060
199(
n-VMT
8.235
156.081
355.042
ifi.fim
537.961
)
FRACTION
.016
.309
.702
.037
1.O64 ,
-------
TABLE A-4. (CONTINUED)
ur\nc
BUS
TAXI
AUTO
HDTb
TOTAL
1991
n-VMTa
8.388
156.658
356.545
18.681
540.272
FRACTION
.017
.310
.705
.037
1.069
1992
n-VMT
8.540
157.235
358.048
18.760
542.583
FRACTION
.017
.311
.708
.037
1.073
1993
n-VVT
8.693
157,812
359.551
18.839
544.895
FRACTION
.017
.312
.711
.037
1.077
1994
n-VMT
8.845
158.390
361.054
18.918
547.207
FRACTION
.017
.313
.714
.037
1.081
1995
n-VMT
8.998
158.967
362.557
18.996
549.518
FRACTION
.018
.314
.717
.038
1.087
CO
10
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
9.150
159.544
364.060
19.075
551.829
FRACTION
.018
.316
.720
.038
1.092
1Q97
n-VMT
9.303
160.121
365.563
19.154
554.141
FRACTION
.018
.317
.723
.038
1.096
1998
n-VMT
9.455
160.698
367.066
19.233
556.452
FRACTION
.019
.318
.726
.038
1.101
1C
n-VMT
9.608
161.276
368.569
19.311
558.764
99
FRACTION
.019
.319
.729
.038
1.105
?nnn
n-VMT
^9.760
161.853
370.072
19.390
561 .07S
FRACTION
.019
.320
.732
.03ft
1.1 OQ
n-VMT = normalized vehicle miles traveled.
bHDT = heavy duty truck.
-------
X
TABLE A-5. PROJECTION YEAR MODAL CHOICES FOR T2 SCENARIOS IN CHICAGO
MODE
BUS
TAXI
AUTO
HDTD
TOTAL
1976 Base
n-vMTa
6.1
148.0
334.0
17.5
505.6
FRACTION
.012
.293
.661
.034
1.000
1977
n-VMT
6.301
147.701
334.0
17.465
505.470
FRACTION
.012
.292
.661
.035
1.000
1978
n-VVT
6.503
147.408
334.0
17.430
505.341
FRACTION
.013
.292
.661
.034
1.000
1979
n-VMT
6.704
147.112
334.0
17.395
505.211
FRACTION
.013
.291
.661
.034
.999
1980
n-VMT
6.905
146.816
334.0
17.360
505.081
FRACTION
.014
.290
.661
.0.34
.999
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
i.ioi
146.520
334.0
17.325
504.952
FRACTION
.014
.290
.661
.034
.999
1982
n-VMT
7.308
146.224
334.0
17.290
504.822
FRACTION
.014
.289
.661
.034
.999
1983
n-VMT
7.509
145.928
334.0
17.255
504.692
FRACTION
.015
.289
.661
.034
.999
1984
n-VMT
7.710
145.632
334.0
17.220
504.562
FRACTION
.015
.288
.661
.034
.998
1985
n-VMT
7.912
145.336
334.0
17.185
504.433
FRACTION
.016
.287
.661
.034
.998
MODE
BUS
TAXI
AUTO
HOT
TOTf I
1986
n-VMT
8.113
145.040
334.0
17.150
504.303
FRACTION
.016
.287
.661
.034
.998
1987
n-VMT
8.314
144.744
334.0
17.115
504.173
FRACTION
.016
.286
.661
.034
.997
1988
n-VMT
8.516
144.448
334.0
17.080
504.044
FRACTION
.017
.286
.661
.034
.998
1989
n-VMT
8.717
144.152
334.0
17.045
503.914
FRACTION
.017
.285
.661
.034
.997
1990
n-VMT
8.918
143.856
334.0
17.010
503.784
FRACTION
.018
.285
.661
.034
.998
-------
TABLE A-5. (CONTINUED)
mae\r\c
M
-------
TABLE A-6. PROJECTION YEAR MODAL CHOICES FOR TS SCENARIOS IN CHICAGO
MODE
BUS
TAXI
AUTO
HDTb
TOTAL
1976 Base
n-VMTa
6.1
148.0
334.0
17.5
505.6
FRACTION
.012
.293
.661
.034
1.000
1977
n-VMT
6.289
146.638
330.727
17.329
500.983
FRACTION
.012
.290
.654
.034
.990
1978
n-WT
6.478
145.277
327.454
17.157
496.366
FRACTION
.013
.287
.648
.034
.982
1979
n-VMT
'6.667
143.915
324.130
16.986
491.748
FRACTION
.013
.285
.641
.034
.973
1980
n-VMT
6.856
142.554
320.907
16.814
487.131
FRACTION
.014
.282
.635
.033
.964
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
7.046
141.192
317.634
16.643
482.515
FRACTION
.014
.279
.628
.033
.954
1982
n-VMT
7.235
139.830
314.361
16.471
477.897
FRACTION
.014
.277
.622
.033
.946
1983
n-VMT
7.424
138.469
311.088
16.300
473.281
FRACTION
.015
.274
.615
.032
.936
1984
n-VMT
7.613
137.107
307.814
16.128
468.662
FRACTION
.015
.271
.609
.032
.927
1985
n-VMT
7.802
135.746
304.541
15.957
464.046
FRACTION
.015
.268
.602
.032
.917
ro
MODE
BUS
TAXI
AUTO
HOT
TCTAI
1986
n-VMT
7.991
134.384
301.268
15.785
459.428
FRACTION
.016
.266
.596
.031
.909
1987
n-VMT
8.180
133.022
297.995
15.614
454.811
FRACTION
.016
.263
.589
.031
.899
1988
n-VMT
8.369
131.661
294.722
15.442
45O. 1 QA
FRACTION
.017
.260
.583
.031
.891
1989
n-VMT
8.558
130.299
291.448
15.271
445.576
FRACTION
.017
.258
.576
.030
,881
1990
n-VMT
8.747
128.938
288.175
15.099
440. 9£9
FRACTION
.017
.255
.570
.030
.872
-------
TABLE A-6. (CONTINUED)
MUUt
BUS
TAXI
AUTO
HOT13
TOTAL
1991
n-VMTa
8.937
127.576
284.902
14.928
436.343
FRACTION
.018
.252
.563
.030
.863
1992
n-VMT
9.126
126.214
281.629
14.756
431.725
FRACTION
.018
.250
.557
.029
.854
1993
n-VVT
9.315
124.853
278.356
14.585
427.109
FRACTION
.018
.247
.551
.029
.845
1994
n-VMT
9.504
123.491
275.082
14.413
422.490
FRACTION
.019
.244
.544
.029
.836
1995
n-VMT
9.693
122.130
271. ROQ
14.242
417.874
FRACTION
.019
.242
.538
.028
.827
U)
MUUc
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
9.882
120.768
268.536
14.070
413.256
FRACTION
.020
.239
.531
.028
.818
1997
n-VMT
10.071
119.406
265.263
13.899
408.639
FRACTION
.020
.236
.525
.027
.808
1998
n-VMT
10.260
118.045
261.990
13.727
404.022
FRACTION
.020
.233
.518
.027
.798
1999
n-VMT
10.449
116.683
258.716
13.556
399.404
FRACTION
.021
.231
.512
.027
.791
2000
n-VMT
10.449
115.322
255.443
13.384
394.598
FRACTION
.021
.228
.505
.026
.780
n-VMT normalized vehicle miles traveled.
3HDT = heavy duty truck.
-------
TABLE A-7. PROJECTION YEAR MODAL CHOICES FOR T SCENARIOS IN LOS ANGELES
MODE
BUS
TAXI
AUTO
HOT13
TOTAL
1976 Base
n-VMT3
.78
.36
32.49
1.46
35.09
FRACTION
.022
.010
.926
.042
1.000
1977
n-VMT
.806
.362
33.026
1.467
35.661
FRACTION
.023
.010
.941
.042
1.016
1978
n-VVT
.831
.363
33.562
1.474
36.230
FRACTION
.024
.010
.956
.042
1.032
1979
n-VMT
.857
.365
34.098
1.481
36.801
FRACTION
.024
.010
.972
.042
1.048
19
n-VMT
.883
.366
34.634
1.4RQ
37.372
80
FRACTION
.025
.010
.987
.042
1.064
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
.909
.368
35.170
1.496
37.943
FRACTION
.026
.010
1.002
.043
1.018
1982
n-VMT
.934
.369
35.707
1.503
38.513
FRACTION
.027
.011
1.018
.043
1.099
1983
n-VMT
.960
.371
36.243
1.510
39.084
FRACTION
.027
.011
1.033
.043
1.114
1984
n-VMT
.986
.372
36.779
1.517
39.654
FRACTION
.028
.011
1.048
.043
1.130
1985
n-VMT
1.012
.374
37.315
1.524
40.225
FRACTION
.029
.011
1.063
.043
1.146
MODE
BUS
TAXI
AUTO
HOT
TOTfl
1986
n-VMT
1.037
.375
37.851
1.532
40.795
FRACTION
.030
.011
1.079
.044
i.ifiA
1987
n-VMT
1.063
.377
38.387
1.539
41.366
FRACTION
.030
.011
1.094
.044
1.179
1988
n-VMT
1.089
.379
38.923
1.546
4,1.937
FRACTION
.031
.011
1.109
.044
1.195
1989
n-VMT
1.115
.380
39.459
1.553
42.507
FRACTION
.032
.011
1.125
.044
1.217
1990
n-VMT
1.140
.382
39.995
1 . S60
43.077
FRACTION
.0"2
.011
1.140
1.277 1
-------
TABLE A-7. (CONTINUED)
ijr\nc
BUS
TAXI
AUTO
HDTb
TOTAL
1991
n-VMT a
1.166
.383
40.531
1.567
43.647
FRACTION
.033
.011
1.155
.045
1.244
1992
n-VMT
1.192
.385
41.067
1.574
44.218
FRACTION
.034
.011
1.170
.045
1.260
1993
n-VVT
1.218
.386
41.603
1.582
44.789
FRACTION
.035
.011
1.186
.045
1.277
1994
n-VMT
1.243
.388
42.140
1.589
45.360
FRACTION
.035
.011
1.201
.045
1.292
1995
n-VMT
1.269
.389
42.676
1.596
45.930
FRACTION
.036
.011
1.216
.045
1.308
en
uonc
Nlwllc
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
1.295
.391
43.212
1.603
46.501
FRACTION
.037
.011
1.231
.046
1.325
1997
n-VMT
1.321
.393
43.748
1.610
47.072
FRACTION
.038
L .011
1.247
.046
1.342
1998
n-VMT
1.346
.394
44.284
1.617
47.641
FRACTION
.038
.011
1.262
.046
1.357
1999
n-VMT
1.372
.396
44.820
1.625
48.213
FRACTION
.039
.011
1.277
.046
1.373
2000
n-VMT
1.398
.397
45.356
1.632
48.783
FRACTION
.040
.011
1.293
.041
1.391
an-VHT = normalized vehicle miles traveled.
bHDT = heavy duty truck.
-------
TABLE A-8. PROJECTION YEAR MODAL CHOICES FOR T2 SCENARIOS IN LOS ANGELES
MODE
BUS
TAXI
AUTO
HOT"
TOTAL
1976 Base
n-vMTa
.78
.36
32.49
.146
35.09
FRACTION
.022
.010
.926
.042
1.000
1977
n-VMT
.814
.361
32.49
1.463
35.128
FRACTION
.023
.010
.926
.042
1.001
1978
n-VI/T
.849
.362
32.49
1.466
35.167
FRACTION
.024
.010
.926
.042
1.002
1979
n-VMT
.883
.362
32.49
1.469
35.204
FRACTION
.025
.010
.926
.042
1.003
1980
n-VMT
.917
.363
32.49
1.472
35.242
FRACTION
.026
.010
.926
.042
1.004
AlAnc
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
.952
.364
32.49
1.475
35.281
FRACTION
.027
.010
.926
.042
1.005
1982
n-VMT
.986
.365
32.49
1.478
35.319
FRACTION
.028
.010
.926
.042
1.006
1983
n-VMT
1.020
.365
32.49
1.481
35.356
FRACTION
.029
.010
.926
.042
1.007
1984
n-VMT
1.055
.366
32.49
1.485
35.396
FRACTION
.030
.010
.926
.042
1.008
1985
n-VMT
1 .ORQ
.367
32.49
1.4R8
35.434
FRACTION
.031
.010
.926
.042
1 .009
MODE
BUS
TAXI
AUTO
HOT
TOTfl
1986
n-VMT
1.123
.368
32.49
1.491
35.472
FRACTION
.032
.010
.926
.042
1.010
1987
n-VMT
1.158
.368
32.49
1.494
35.510
FRACTION
.033
.010
.926
.043
1.012
1988
n-VMT
1.192
.369
32.49
1.497
35.548
FRACTION
.034
.011
.926
.043
1.014
1989
n-VMT
1.226
.370
32.49
1.500
35.586
FRACTION
.035
.011'
.926
.043
1.015
1990
n-VMT
1.260
.371
-32.49
1.503
35.624
FRACTION
.036
.011
.926
.043
*
1.016 j
-------
TABLE A-8. (CONTINUED)
MVJUC
BUS
TAXI
AUTO
HDTb
TOTAL
1991
n-VMT&
1.295
.371
32.49
1.506
35.662
FRACTION
.037
.011
.926
.043
1.017
1992
n-VMT
1.329
.372
32.49
1.509
35.700
FRACTION
.038
.011
.926
.043
1.018
1993
n-VWT
1.363
.373
32.49
1.512
35.738
FRACTION
.039
.011
.926
.043
1.019
1994
n-VMT
1.398
.374
32.49
1.515
35.777
FRACTION
.040
.011
.926
.043
1.020
1995
n-VMT
1.432
.374
32.49
1.518
35.814
FRACTION
.041
.011
.926
.043
1.021
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
1.466
.375
32.49
1.521
35.852
FRACTION
.042
.011
.926
.043
1.022
1997
n-VMT
1.501
.376
32.49
1.524
35.891
FRACTION
.043
.011
.926
.043
1.023
1998
n-VMT
1.535
.377
32.49
1.527
35.929
FRACTION
.044
.011
.926
.044
1.025
1999
n-VMT
1.569
.377
32.49
1.531
35.967
FRACTION
.045
.011
.926
.044
1.026
2000
n-VMT
1.604
.378
32.49
1.534
36.006
FRACTION
.046
.011
.926
.044
1.027
n-VMT = normalized vehicle miles traveled.
3HDT = heavy duty truck.
-------
TABLE A-9.
PROJECTION YEAR MODAL CHOICES FOR T3 SCENARIOS IN LOS ANGELES
MODE
BUS
TAXI
AUTO
HOT5
TOTAL
1976 Base
n-VMTa
.78
.36
32.49
1.46
35.09
FRACTION
.022
.010
.926
.042
1.000
1977
n-VMT
.808
.357
32.243
1.449
34.857
FRACTION
.023
.010
.919
.041
.993
1978
n-VVT
.836
.355
31.996
1.438
34.625
FRACTION
.024
.010
.912
.041
.987
1979
n-VMT
.864
.352
31.749
1.427
34.392
FRACTION
.025
.010
.905
.041
.981
1980
n VMT
.892
.350
31 .502
1.416
34.160
FRACTION
.025
.010
.898
.040
.967
CD
u one
BUS
TAXI
AUTO
HOT
TOTAL
1981
n-VMT
.920
.347
31.255
1.405
33.927
FRACTION
.026
.010
.891
.040
.967
1982
n-VMT
.948
.345
31.008
1.393
33.694
FRACTION
.027
.010
.884
.040
.961
1983
n-VMT
.977
.342
30.726
1.382
33.462
FRACTION
.028
.010
.877
.039
.954
1984
n-VMT
1.005
.340
30.515
1.371
33.231
FRACTION
.029
.010
.870
.039
.948
1985
n-VMT
1.033
.337
30.268
1.360
32.998
FRACTION
.029
.010
.863
.039
.941
BUS
TAXI
AUTO
HOT
TOT^I
1986
n-VMT
1.061
.335
30.021
1.349
32.766
FRACTION
.030
.010
.856
.038
.934
1987
n-VMT
1.089
.332
29.774
1.338
32.533
FRACTION
.031
.009
.848
.038
.926
1988
n-VMT
1.117
.330
29.527
1.327
32.301
FRACTION
.032
.009
.841
.038
.920
1989
n-VMT
1.145
.327
29.280
1.316
32.068
FRACTION
.033
.009
.834
.037
.913
1990
n-VMT
1.173
.325
29.033
1 . 305
31.836
FRACTIC j
.033
.009
.827
.037
.90*"
-------
TABLE A-9. (CONTINUED)
MUUt
BUS
TAXI
AUTO
HOT13
TOTAL
1991
n-VMT a
1.201
.322
28.786
1.294
31.603
FRACTION
.034
.009
.820
.037
.900
1992
n-VMT
1.229
.320
28.539
1.282
31.370
FRACTION
.035
.009
.813
.037
.894
1993
n-VVT
1.257
.317
28.292
1.271
31.137
FRACTION
.036
.009
.806
.036
.887
1994
n-VMT
1.285
.315
28.045
1.260
30.905
FRACTION
.037
.009
.799
.036
.881
1995
n-VMT
1.314
.312
27.798
1.249
30.673
FRACTION
.037
.009
.792
.036
.874
MODE
BUS
TAXI
AUTO
HOT
TOTAL
1996
n-VMT
1.342
.310
27.552
1.238
30.442
FRACTION
.038
.009
.785
.035
.867
1997
n-VMT
1.370
.307
27.305
1.227
30.209
FRACTION
.039
.009
.778
.035
.861
1998
n-VMT
1.398
.305
27.058
1.216
29.977
FRACTION
.040
.009
.771
.035
.855
1999
n-VMT
1.426
.302
26.811
1.205
29.744
FRACTION
.041
.009
.764
.034
.848
2000
n-VMT
1.454
.300
26.564
1.194
29.512
FRACTION
.041
.009
.757
.034
.841
an-VMT = nroraalizod vehicle miles traveled.
bHDT = heavy duty truck.
-------
APPENDIX B: DERIVATION OF EQUATIONS FOR a AND 3 PARAMETERS
IN THE CUMULATIVE LOGISTIC DISTRIBUTION FORMULA
150
-------
APPENDIX B
The following step-by-step derivation was contributed
by Ted Johnson, PEDCo Environmental, Inc., Durham, N.C. The
formula for the cumulative logistic distribution is as
follows (see Chapter 3) :
(1) F(x) = *
L 3
where
x = projection year of interest: 78, 79, 80 etc.
F(x) = fraction of new vehicle sales that are diesel,
a and 3 = sealer factors determining the shape of the
curve.
/ x - a\ 1
exp / ) =
V 3 / F(x)
(3)
(x)
(4) - KJ^J* = !/.
\F(x)
= -In/
6 \F(x
(5)
(x)
(6) x " a = -in f1 " F )
\ J. 1. \ **/ /
151
-------
This form of the equation is used to estimate the year
in which a given fraction of automotive sales will be diesel.
It also isolates a and $ in a convenient form.
Let us now assume that two points on the curve are
known:(x , F (x ) ) and (x2/ F(x2)). We then have:
(9)
(10)
(11)
(12)
x, = a + $ In
= a + B In
'r^t,)
^ MXiV
(F(x2) \
1 - F(X2) )
= 6 In
= (X]_ - x2)
F(x2)
- In
from Equation 8
from Equation 8
P(x2)
- In
- F(x2)
F(x2)
- " F(X2TJ
-1
Equation (12) can be used to determine 3, given two
points on the curve. Now let us derive an equation for a.
(13) x = a +
-x2)
In
In
F(x1) 1
- F(XI)
- In
r F(x2> i
[I - F(x2)J
-1
- F(x1)
(14) a =
(15) a =
In
In
In
F(XI)
This is from Equation 9,
substituting 3 from Equation
12.
In
F(x1)
-In
'(x2)
- F(x2)
F(XI)
- F(x1)
- F(x:
F(x )
- x. In
F(X2) 1
_1 - F(x2)J
- In
r F(x2}
[I - F(x2)
-1
Equation (15) can be used to determine a given two
points on the curve.
152
-------
APPENDIX C. TABLES OF PROJECTED DIESEL SALES AND DIESEL
TRAVEL FRACTIONS
153
-------
Table C-l. FRACTIONS OF AUTOMOBILE VMT ATTRIBUTED TO DIESEL VEHICLES UNDER SCENARIO D2
en
Model Year
Age of
Vehicles
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20+
TOTALS
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988
.001 .001 .002 .003 .006 .010 .016 .027 .040 .056 .071
.001 .001 .001 .002 .004 .007 .013 .022 .036 .054 .075
.001 .001 .001 .001 .002 .004 .007 .012 .021 .033 .050
.001 .001 .001 .001 .001 .002 .004 .006 .011 .019 .031
.001 .001 .001 .001 .001 .001 .002 .003 .006 .010 .017
.001 .003 .005 .008
.001 .002 .004
.001 .002
.001
.005 .005 .006 .008 .014 .024 .042 .071 .118 .180 .259
1989
.083
.095
.070
.046
.027
.014
.007
.003
.001
.001
.347
1990
.092
.112
.089
.065
.041
.023
.012
.006
.003
.001
.444
-------
TABLE C-l. (CONTINUED)
01
,cn
Model Year
Age of
Vehicles
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20+
TOTALS
1991
.098
.124
.105
.082
.057
.036
.019
.010
.005
.002
.001
.539
1992
.101
.131
.116
.097
.072
.048
.029
.016
.008
.003
.001
.622
1993
.103
.136
.123
.107
.085
.062
.040
.024
.013
.005
.002
.001
.701
1994
.105
.139
.127
.114
.094
.072
.052
.034
.019
.009
.004
.001
.001
.771
1995
.105
.140
.130
.118
.100
.080
.061
.043
.026
.013
.006
.002
.001
.825
1996
.106
.141
.131
.120
.103
.085
.067
.050
.033
.018
.009
.004
.002
.001
.870
1997
.106
.141
.132
.121
.105
.088
.071
.056
.040
.023
.012
.006
.003
.001
.905
1998
.106
.142
.132
.122
.107
.090
.074
.059
.044
.028
.016
.008
.004
.002
.001
.935
1999
.106
.142
.133
.123
.107
.091
.075
.061
.046
.030
.018
.011
.005
.003
.001
.952
2000
.106
.142
.133
.123
.108
.091
.076
.062
.048
.032
.020
.013
.007
.004
.002
.001
.968
-------
TABLE C-2. FRACTIONS OF HEAVY DUTY TRUCKS ON THE ROAD THAT ARE PROJECTED TO BE DIESEL
in
en
Vehicle
Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Combined
Gas and
Diesel HDT
Registration
Fractions
.069
.122
.123
.122
.094
.087
.081
.063
.048
.037
.028
.018
.017
.015
.014
.012
.011
.010
.009
.008
1973
.022
.037
.035
.032
.023
.019
.016
.011
.008
.005
.003
.002
.001
.001
.001
.216
1974
.022
.039
.037
.034
.025
.021
.018
.013
.009
.006
.004
.002
.002
.001
.001
.234
1975
.020
.038
.039
.037
.027
.023
.020
.014
.010
.007
.004
.003
.002
.001
.001
.001
.247
1976
.026
.035
.039
.039
.028
.025
.021
.015
.011
.007 .
.005
.003
.002
.002
.001
.001
.001
.261
1977
.033
.046
.035
.038
.030
.026
.023
.017
.012
.008
.006
.003
.003
.002
.002
.001
.001
.001
.287
1978
.037
.059
.046
.035
.030
.027
.025
.018
.013
.009
.006
.004
.003
.002
.002
.001
.001
.001
.001
.320
1979
.041
.066
.059
.046
.027
.027
.026
.019
.014
.010
.007
.004
.003
.003
.002
.002
.001
.001
.001
.359
1980
.045
.073
.066
.059
.035
.025
.026
.020
.015
.010
.007
.004
.004
.003
.003
.002
.002
.001
.001
.001
.402
1981
.048
.079
.073
.066
.045
.033
.023
.020
.015
.011
.008
.005
.004
.003
.003
.002
.002
.001
.001
.001
.443
1982
.052
.086
.080
.073
.051
.042
.031
.018
.015
.012
.008
.005
.004
.004
.003
.002
.002
.002
.001
.001
.492
1983
.055
.091
.086
.079
.056
.047
.039
.024
.014
.012
.009
.005
.005
.004
.003
.003
.002
.002
.001
.001
.538
1984
.057
.096
.092
.086
.061
.052
.044
.030
.018
.011
.009
.006
.005
.004
.004
.003
.002
.002
.002
.001
.588
1985
.059
.096
.097
.091
.066
.057
.048
.034
.023
.014
.008
.006
.005
.005
.004
.003
.003
.002
.002
.001
.624
1986
.061
.105
.102
.096
.070
.061
.053
.038
.026
.018
.011
.005
.005
.005
.004
.003
.003
.002
.002
.302
.677
-------
TABLE C-2. (CONTINUED)
Vehicle
Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Combined
Gas and
Diesel HOT
Registration
Fractions
.069
.122
.123
.122
.094
.087
.081
.063
.048
.037
.028
.018
.017
.015
.014
.012
.011
.010
.009
.008
1987
.062
.108
.105
.096
.074
.065
.057
.041
.029
.020
.013
.007
.005
.005
.004
.004
.003
.003
.002
.002
.705
1988
.064
.110
.109
.105
.078
.069
.061
.044
.031
.022
.015
.009
.006
.004
.004
.004
.003
.003
.002
.002
.745
1989
.065
.113
.111
.108
.081
.072
.064
.047
.034
.024
.017
.010
.008
.006
.004
.004
.003
.003
.003
.002
.779
1990
.066
.115
.114
.110
.083
.075
.064
.050
.036
.026
.018
.011
.009
.007
.005
.003
.003
.003
.003
.002
.803
1991
.066
.116
.115
.113
.085
.077
.069
.050
.038
.028
.020
.012
.010
.008
.007
.005
.003
.003
.003
.002
.830
1992
.067
.117
.117
.115
.087
.079
.072
.054
.038
.029
.021
.013
.011
.009
.008
.006
.004
.003
.003
.003
.856
1993
.067
.118
.118
.116
.088
.080
.073
.056
.041
.029
.022
.013
.012
.010
.008
.006
.005
.004
.003
.003
.872
1994
.068
.119
.119
.117
.089
.082
.075
.057
.042
.032
.022
.014
.013
.011
.009
.007
.006
.005
.003
.002
.892
1995
.068
.120
.120
.118
.090
.083
.076
.058
.043
.033
.024
.014
.013
.011
.010
.008
.007
.005
.004
.003
.908
1996
.068
.120
.121
.119
.091
.084
.077
.059
.044
.033
.025
.015
.013
.012
.010
.008
.007
.006
.005
.004
.921
1997
.068
.120
.121
.120
.092
.084
.078
.060
.045
.034
.025
.016
.015
.012
.011
.009
.008
.007
.005
.004
.934
1998
.068
.121
.121
.1.20
.092
.085
.078
.061
.046
.035
.026
.016
.015
.013
.011
.009
.008
.007
.006
.005
.943
1999
.069
.121
.122
.120
.092
.085
.079
.061
.046
.035
.026
.017
.015
.013
.012
.009
.009
.007
.006
.005
.949
2000
.069
.122
.122
.121
.093
.086
.079
.061
.047
.036
.027
.017
.016
.014
.012
.010
.009
.008
.007
.006
.962
-------
TABLE C-3-78.
ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
1 s
ij
16
< i w
17
1 A
JLO
19
A 7
20
Diesel
(a)
Fraction
of Total
HDT's
.037
.059
.046
.035
.030
.027
.025
.018
.013
.009
.006
.004
.003
.002
.002
.001
.001
.001
.001
'
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.032
.063
.077
.087
.064
.060
.056
.045
.035
.028
.022
.014
.014
.013
.012
-Oil
.010
.009
.008
.008
(d)
Mileage
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab + cd)
3331.2
5539.4
4593.7
3651.0
2658.0
2160.0
1812.0
1218.6
829.1
564.8
379.2
233.2
187.5
141.5
125.4
82.6
72.7
62.7
53.6
33.6
27,729.8
Diesel
Tr. el
Fractions
.098
.157
.116
.080
.061
.049
.041
.027
.018
.012
.007
.005
.004
.002
.002
.001
.001
.001
.001
.683
(Total)
TABLE C-3-79. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAI/ES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of . Total
HDT's
.041
.066
.059
.046
.027
.027
.026
:019
.014
.010 -
.007
.004
.003
.003
.002
.002
.001
.001
.001
*
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.028
.056
.064
.076
.067
.060
.055
.044
.034
.027
.021
.014
.014
.012
.012
.010
.010
.009
.008
.008
(d)
Mileage
Accumulation
Rate
19,000
19-,000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab+ cd)
3549.6
5921.6
5269.7
4165.8
2533.2
2160.0
1845.6
1249.2
857.8
592.2
406.0
233.2
187.5
168.0
125.4
106.4
72.7
62.7
53.6
0
29,560.2
Diesel
Travel
Fractions
.102
.164
.140
.099
.052
.046
.040
.026
.018"
.012
.008
.005
.003
.003
.002
.002
.001
.001
.001
.725
(Total)
158
-------
TABLE C-3-80. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SALF.S SCENARIO.
Vehicle
Age
1
2
3
J
4
5
J
e.
W
7
/
Q
7
1 fl
ill
1 *>
12.
1 1
Ij
i /
14
* c
1^
* f
16
17
18
19
20
Diesel
(a)
Fraction
of Total
HDT's
.045
.073
.066
.059
.035
.025
.026
.020
.015
.010
.007
.004
.004
.003
.003
.002
.002
.001
.001
.001
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25 , 700
21,300
18,400
15,400
(c)
Fraction
of Total
HDT's
.024
.049
.057
.063
.059
.062
.055
.043
.033
.027
.021
.014
.013
.012
.011
.010
.009
.009
.008
.007
Gasoline
(d)
Mileage
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab+ cd)
3768.0
6303.8
5633.7
4774.2
2866.0
2087.0
1845.6
1279.8
886.5
592.2
406.0
233.2
214.3
168.0
151.0
106.4
93.7
62.7
53.6
44.8
31,570.5
Diesel
Travel
Fractions
.105
.170
.146
.118
.063
.040
.038
.026
.018
.011"
.008
.004
.004
.003
.003
.002
.002
.001
.001
.764
(Total)
TABLE C-3-81. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of .Total
HDT's
.048
.079
.073
.066
.045
.033
.023
.020
.015
.011
.008
.005
.004
.003
.003
.002
.002
.001
.001
.001
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(c)
Fraction
of Total
HDT's
.021
.043
.050
.056
.049
.054
.058
.043
.033
.026
.020
.013
.013
.012
.011
.010
.009
.009
.008
.007
Gasoline
(d)
Mileage
Accumulation
Rate
19,000
19,000
17,900
16.500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab+ cd)
3931.8
6631.4
5997.7
5101.8
3282.0
2379.0
1744.8
1279.8
886J5
619.6
432.8
260.0
214.3
168.0
151.0
106.4
93.7
62.7
53.6
44.8
33,441.7
Diesel
Travel
Fractions
.106
.174
.153
.125
.076
.049
.031
.025
.017
.012
.008
.005
.004
.003
.003
.002
.002
.001
.001
.001
.797
(Total)
159
-------
TABLE C-3-82. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAL'ES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
3
9
10
n
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of Total
HDT's
.052
.086
.080
.073
.051
.042
.031
.018
.015
.012
.008
.005
.004
.004
.003
.002
.002
.002
.001
.001
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(0
Fraction
of Total
HDT's
.017
.036
.043
.049
.043
.045
.050
.045
.033
.025
.020
.013
.013
.011
.011
.010
.009
.008
.008
.007
(d)
Mileage
Accumulation
Rate
19,000
19', 000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab + cd)
4150.2
7013.6
6361.7
5429.4
3531.6
2707.5
2013.6
1218.6
886.5
647.0
432.8
260.0
214.3
194.5
151.0
106.4
93.7
79.4
53.6
44.8
35,590.2
'-'esel
Travel
Fractions
.108
.178
.157
.130
.081
.059
.040
.021
.01?
.012
.008
.005
.004
.004
.003
.002
.001
.001
.001
.831
(Total)
TABLE C-3-83. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of .Total
HDT's
.055
.091
.086
.079
.056
.047
.039
.024
.014
.012 .
.009
.005
.005
.004
.003
.003
.002
.002
.001
.001
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.014
.031
.037
.043
.038
.040
.042
.039
.034
.025
.019
.013
.012
.011
.011
.009
.009
.008
.008
.007
(d)
Mileage
Accumulation
Rate
19,000
13,000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab + cd)
4314.0
7286.6
6673.7
5710.2
3739.6
2890.0
2282.4
1402.2
857.8
647.0
459.6
260.0
241.1
194.5
151.0
130.2
93.7.
79.4
53.6
44.8
37,511.4
Diesel
Travel
Fractions
.108
.179
.160
.133
.084
.063
.047
.026
.015
.012
.008
.005
.004
.003
.002
.002
.001
.001
.
.852
(Total)
160
-------
TABLE C-3-84. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
9
J
10
1 ?
Lf,
13
i -/
I C
1 J
1 A
ID
1 Q
io
1 Q
17
20
Diesel
(a)
Fraction
of Total
HDT'«
.057
.096
.092
.086
.061
.052
.044
.030
.018
.011
.009
.006
.005
.004
.004
.003
.002
.002
.002
rooi-
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45 , 600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25 , 700
21,300
18,400
15,400
(c)
Fraction
of Total
HOT' s
.012
.026
.031
.036
.033
.035
.037
.033
.030
.026
.019
.012
.012
.011
.010
.009
.009
.008
.007
.007
Gasoline
(d)
Mileage
Accumulation
Rate
19,000
19-, 000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W
n
W
n
(ab-h cd)
4423.2
7559.6
6985.7
6037.8
3947.6
3072.5
2450.4
1585.8
972.6
619-6
459-6
286.8
241.1
194.5
176.6
130.2
93.7
79.4
67.9
44.8
39,429.1
Diesel
Travel
Fractions
.106
.179
.163
.138
.088
.066
.051
.031
.017"
.010
.008
.005
.004
.003
.003
.002
.001
.001
.001
__
.877
(Total)
TABLE C-3-S5. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SATES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
IS
19
20
Diesel
-------
TABLE C-3-86. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
1 A
i*4
15
±J
Ifi
ID
1 ft
JLO
1 O
19
20
Diesel
(a)
Fraction
of Total
HDT's
.061
.105
.097
.096
.070
.061
.053
.038
.026
.018 -
.011
.005
.005
.005
.OOA
.003
.003
.002
.002
.002
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.008
.017
.026
.026
.024
.026
.028
.025
.022
.019
.017
.013
.012
.010
.010
.009
.008
.008
.007
.006
(d)
Mileage
Accumulation
Rate
19,000
19-, 000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W
n
W
n
(ab+ cd)
4641.6
8051.0
7245.7
6505.8
4322.0
3401.0
2752.8
1830.6
1202.2
811.4
513.2
260.0
241.1
221.0
176.6
130.2
114.7
79.4
67.6
56.0
42,623.9
Diesel
Travel
Fractions
.105
.181
.159
.143
.093
.072
.057
.037
.023
.015
.009
.004
.004
.004
.003
.002
.002
.001
.001
.001
.916
(TOtal)
TABLE C-3-87. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SATES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of .Total
HDT's
.062
.108
.105
.096
.074
.065
.057
.041
.029
.020 '
.013
.007
.005
.005
.004
.004
.003
.003
.002
.002
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.007
.014
.018
.026
.020
.022
.024
.022
.019
.017
.015
.011
.012
.010
.010
.008
.008
.007
.007
.006
(d)
Mileage
Accumulation
Rate
19,000
19",000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab+ cd)
4696.2
8214.8
7661.7
6505.8
4488.4
3547.0
2887.2
1922.4
1288.3
866.2
566.8
313.6
241.1
221.0
176.6
154.0
114.7
96.1
67.6
56.0
44,085.5
Diesel
Travel
Fractions
.104
.180
.166
.138
.095
.074
.059
.038
.025
.016
.010
.005
.004
.004
.003
.003
.002
.001
.001
,001
.929
(Total)
162
-------
TABLE C-3-88. ESTIMATION 07 DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAL'ES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of Total
HDT's
.064
.110
.109
.105
.078
.069
.061
.044
.031
.022
.015
.009
.006
.004
.004
.004
.003
.003
.002
.002
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(0
Fraction
of Total
HDT's
.005
.012
.014
.017
.016
.018
.020
.019
.017
.015
.013
.009
.011
.011
.010
.008
.008
.007
.007
.006
Gasoline
(d)
Mileage
Accumulation
Rate
19,000
19', 000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab + cd)
4805.4
8324.0
7869.7
6927.0
4654.8
3693.0
3021.6
2014.2
1345. "7
921.0
620.4
367.2
267.9
194.5
' 176.6
154.0
114.7
96.1
67.6
56.0
45,691.4
Diesel
Travel
Fractions
.103
.177
.167
.145
.097
.076
.061
.040
.026
.017
.011
.007
.004
.003
.003
.003
.002
.001
.001
.001
-945
(Total)
TABLE C-3-39. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT's
.065
.113
.111
.108
.081
.072
.064
.047
.034
.024
.017
.010
.008
.006
.004
.004
.003
.003
.003
.002
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
»
Gasoline
(c)
Fraction
of Total
HDT's
.004
.009
.012
.014
.013
.015
.017
.016
.014
.013
.011
.008
.009
.009
.010
.008
.008
.007
.006
.006
(d)
Mileage
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab+ cd)
4955.0
8487.8
7973.7
7067. 4
4779.6
3802.5
3122.4
2106.0
1431.8
975.8
674.0
394.0
321.5
247.5
176.6
154.0
114.7
96.1
81.6
56.0
47,018.0
Diesel
Travel
Fractions
.102
.177
.165
.145
.098
.077
.062
.041
.028
.018
.013
.007
.006
.004
.003
.002
.002
.001
.001
.001
.953
(Total)
163
-------
TABLE C-3-90. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SALES SCENARIO.
Vehicle.
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT's
.066
.115
.114
.110
.083
.075
.064
.050
.036
.026
.018
.011
.009
.007
.005
.003
.003
.003
1 .003
.002
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
,
Gasoline
(c)
Fraction
of Total
HDT's
.003
.007
.009
.012
.011
.012
.017
.013
.012
.011
.010
.007
.008
.008
.009
.009
.008
.007
.006
.006
(d)
Mileage
Accumulation
Rate
19,000
19., 000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n."
W
n
(ab+ cd)
4914.6
8597.0
8129.7
7161.0
4862.8
3912.0
3122.4
2197.8
1489.2
1030.6
700.8
420.8
348.3
274.0
202.2
130.2
114-.7
96.1
81.6
56.0
47,841.8
Di' sel
Travel
Fractions
.102
.177
.167
.146
.098
.078
.061
.043
.029"
.020
.013
.008
.006
.005
.003
.002
.002
.001
.001
.001
.963
(Total)
TABLE C-3-91. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SAtES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of . Total
HDT's
.066
.116
.115
.113
.085
.077
.069
.050
.038
.028 '
.020
.012
.010
.008
.007
.005
.003
.003
.003
.002
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.003
.006
.008
.009
.009
.010
.012
.013
.010
.009
.008
.006
.007
.007
.007
.007
.008
.007
.006
.006
(d)
Mileage
Accumulation
Rate
19,000
19-.000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
TJ
Wn
(ab+ cd)
4914.6
8651.6
8181.7
7301.4
4946.0
3985.0
3290.4
2197.8
1546.6
1085.4
754.4
447.6
375.1
300.5
253.4
177.8
114.7
96.1
81.6
56.0
48,757.7
Diesel
Travel
Fractions
.100
.175
.165
.147
.099
.079
.065
.042
.030"
.021
.014
.008
.007
.005
.004
.003
.002
.001
.001
.001
.969
(Total)
164
-------
TABLE C-3-92. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT's
.067
.117
.117
.115
.087
.079
.072
.054
.038
.029
.021
.013
7011-
.009
.008
.006
.004
.003
.003
.003
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(c)
Gasoline
(d)
Fraction Mileage
of Total
HDT's
.002
.005
.006
.007
.007
.008
.009
.009
.010
.009
.007
.005
.006
.006
.006
.006
.006
.007
.006
.005
Accumulation
Rate
19,000
19", 000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab + cd)
4969.2
8706.2
8285.7
7395.0
5029.2
4058.0
3391.2
2320.2
1546.6
1121.4
781.2
474.4
401.9
327.0
279.0
201.6
131.0
96.1
81.6
67.2
49,663.7
Diesel
Travel
Fractions
.099
.173
.165
.147
.099
.080
.066
.045
.029"
.021
.015
.009
.007
.006
.005
.003
.002
.001
.001
.001
.974
(Total)
TABLE C-3-93. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR TOE HOT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT's
.067
.118
.118
.116
.088
.080
.073
.056
.041
.029 -
.022
.013
.012
.010
.008
.006
.005
.004
.003
.003
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(
Gasoline
(c)
Fraction
of Total
HDT's
.002
.004
.005
.006
.006
.007
.008
.007
.007
.008
.006
.005
.005
.005
.006
.006
.006
.006
.006
.005
-
(d)
Mileage
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab-f- cd)
4969.2
8760.8
8337.7
7441.8
5070.8
4094.5
3424.8
2381.4
1632.7
1112.8
808. Q
474.4
428.2
353.5
279.0
201.6
156.7
112.8
81.6
67.2
50,190.0
Diesel
Travel
Fractions
.098
.173
.164
.146
.099
.080
.066
.046
.031
.021
' .015
.009
.008
.006
.005
.003
.003
.002
.001
.001
.977
(Total)
165
-------
TABLE C-3-94. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR TH" unrr '"OJECTED SALT* SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
(b)
Fraction Mileage
of Total
HDT's
.068
.119
.119
.117
.089
.082
.075
.057
.042
.032
.022
.014
.013
.011
.009
.007
.006
.005
.003
.002
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.001
.003
.004
.005
.005
.005
.006
.006
.006
.005
.006
.004
.004
.004
.005
.005
.005
.005
.006
.006
(d)
Mileage
Accumulation
Rate
19,000
19-.000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W
n
n
(ab+ cd)
5023.8
8815.4
8389 . 7
7488.6
5112.4
4167.5
3492.0
2412.0
1661.4
1195.0
808.0
501.2
455.5
380.0
304.6
225.4
177.7
129.5
81.6
56.0
50,907.3
Diesel
Travel
Fractions
-098
.172
.163
.145
.099
.081
.067
.046
.032
.023
.015
.009
.008
.007
.005
.004
.003
.002
.001
.001
.981
(Total)
TABLE C-3-95. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HOT PROJECTED SAL-ES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of .Total
HDT's
.068
.120
.120
.118
.090
.083
.076
.058
.043
.033
.024
.014
.013
-Oil
.010
.008
.007
.005
.004
.003
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
.
Gasoline
(c)
Fraction
of Total
HDT's
.001
.002
.003
.004
.004
.004
.005
.005
.005
.004
.004
.004
.004
.004
.004
.004
.004
.005
.005
.005
(d)
Mileage
Accumulation
Rate
19,000
19-.000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W -
n
n
(ab+ cd)
5023.8
8870.0
8441.7
7535-4
5154.0
4204.0
3525.6
2442.6
1690.1
1222.4
861.6
501.2
455.5
380.0
330.2
249.2
198.7
129.5
95.6
67.2
51,378.3
Diesel
Travel
Fractions
.097
.172
.163
.145
.099
.081
.067
,047 '
.032"
.023
.016
.009
.008
.007
.006
.004
.004
.002
.001
.001
.984
(Total)
166
-------
TABLE C-3-96. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAEES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of Total
HDT's
.068
.120
.121
.119
.091
.084
.077
.059
.044
.033
.025
.015
.013
.012
.010
.008
.007
.006
.005
.004
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT's
.001
.002
.002
.003
.003
.003
.004
.004
.004
.004
.003
.003
.004
.003
.004
.004
.004
.004
.004
.004
(d)
Mileage
Accumulation
Rate
19,000
19", 000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
W
n
(ab+ cd)
5023.8
8870.0
8493.7
7582.2
5195.6
4240.5
3559.2
2473.2
1718.8
1222.4
888.4
528.0
455.5
406.5
330.2
249.2
198.7
146.2
109.6
78.4
51,770.1
Diesel
Travel
Fractions
.097
.171
.163
.146
.099
.081
.068
.047
.032
.023
.017
.010
.008
.008
.006
.004
.003
.002
.002
.001
.988
(Total)
TABLE C-3-97. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAiES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
of .Total
HDT's
.068
.120
.121
.120
.092
.084
.078
.060
.045
.034
.025
.016
.015
.012
.011
.009
.008
.007 -
.005
.004
(b)
Mileage
Accumulation
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25 , 700
21,300
18,400
15,400
.»
(c)
Gasoline
(d)
Fraction Mileage
of Total
HDT's
.001
.002
.002
.002
.002
.003
.003
.003
.003
.003
.003
.002
.002
.003
.003
.003
.003
.003"
.004
.004
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab+ cd)
5023.8
8870.0
8493.7
7629.0
5237.2
4240.5
3592.8
2503.8
1747.5
1249.8
888.4
554.8
509.1
406.5
355.8
273.0
219.7
162.9
109.6
78.4
52,146.3
Diesel
Travel
Fractions
.096
.169
.162
.146
.100
.081
.068
.047
.033
.023
.017
.010
.010
.007
.007
.005
.004
.003
.002
.001
.991
(Total) .
167
-------
TABLE C-3-98. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SAT£S SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT' s
.068
.121
.121
.120
.092
.085
.078
.061
.046
.035 '
.026
.016
.015
.013
.011
.009
.008
.007
.006
.005
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
Gasoline
(c)
Fraction
of Total
HDT 'a
.001
.001
.002
.002
.002
.002
.003
.002
.002
.002
.002
.002
.002
.002
.003
.003
.003
.003
.003
.003
(d)
Mileage
Accumulation
Rate
19,000
19-.000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4, .200
w =
n
n
(ab + cd)
5023.8
8924.6
8493.7
7629.0
5237.2
4277.0
3592.8
2534.4
177612
1277.2
915.2
554.8
509.1
433.0
355.8
273.0
219.7
162.9
123.6
89.6
52,402.6
DlMal
Ttavel
Fractions
.096
.170
.161
.145
.099
.081
.068
.048
.035
.024
.017
.010
.009
.008
.006
.005
.004
.003
.002
.001
.991
(Total!
TABLE C-3-99. ESTIMATION OF DIESEL TRAVEL FRACTIONS
FOR THE HDT PROJECTED SALES SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of. Total Accumulation
HDT's
.069
.121
.122
.120
.092
.085
.079
.061
.046
.035 -
.026
.017
.015
.013
.012
.009
.009
.007
.006
.005-
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200.
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(c)
Gasoline
(d)
Fraction Mileage
of Total
HDT's
__
.001
.001
.002
.002
.002
.002
.002
.002
.002
.002
.001
.002
.002
.002
.003
.002
.003
.003
.003
Accumulation
Rate
19,000
19,000
17,900
16,500
15,000 .
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab+ cd)
5078.4
8924.6
8545.7
7629.0
5237.2
4277.0
3626.4
2534.4
1776.2
1277.2
915.2
581.6
509.1
433.0
381.4
273.0
240.7
162.9
123.6
89.6
52,616.2
Diesel
Travel
Fraction;
.097
.169
.162
.144
.099
.081
.068
.048
.oar
.024
.017
.011
.009
.008
.007
.005
.004
.003
.002
.001
.992
(Total)
166
-------
TABLE -'G-3-2000.
ESTIMATION OF DIESEL TRAVEL FRACTIONS
F'lR THE HOT PROJECTED SACKS SCENARIO.
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Diesel
(a)
Fraction
(b)
Mileage
of Total Accumulation
HDT's
.069
.122
.122
.121
.093
-08§
.079
.061
.047
.036 '
.027
.017
.016
.014
.012
.010
.009
.008
.007
.006
Rate
73,600
73,600
69,900
63,300
56,600
50,000
45,600
41,200
38,200
36,000
34,600
33,800
33,100
32,400
30,900
28,700
25,700
21,300
18,400
15,400
(c)
Gasoline
(d)
Fraction Mileage
of Total
HDT's
.001
.001
.001
.001
.002
.002
.001
.001
.001
.001
.001
.001
.002
.002
.002
.002
.002
.002
Accumulation
Rate
19,000
17,000
17,900
16,500
15,000
13,500
12,000
10,600
9,500
8,600
7,800
7,000
6,300
5,900
5,300
4,900
4,700
4,600
4,400
4,200
W =
n
n
(ab+ cd)
5078.4
8979.2
8545.7
7675.8
5278.8
4313.5
3626.4
2534.4
1804.9
1304.6
942.0
581.6
535.9
459.5
381.4
296.8
240.7
179.6
137.6
100.8
52,997.6
Diesel
Travel
Fractions
.096
.169
.161
.145
.099
.OM
.068
.047
.034
.024
.018
.011 :
.010
.009
.007
.005
.004
.003
.002
.002
.995
(Total)
169
-------
TABLE D. TABLES OF PROJECTED EMISSION FACTORS
170
-------
TABLE D-l. TRAVEL WEIGHTED AUTOMOBILE PARTICULATE EMISSION FACTORS FOR
SCENARIO E,D2
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Age Class
Travel Fraction
.106
.142
.133
.123
.108
.092
.077
.064
.050
.035
.023
.016
.010
.007
.004
.003
.002
.002
.002
.002
1.001
1978
.001
.002
.002
.002
.027
.023
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.135
1979
.001
.002
.002
.002
.001
.023
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.109
1980
.002
.002
.002
.002
.001
.001
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.088
1981
.002
.002
.002
.002
.001
.001
.001
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.070
1982
.004
.003
.002
.002
.001
.001
.001
.001
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.058
1983
.006
.005
.003
.002
.001
.001
.001
.001
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.050
1984
.009
.008
.005
.003
.002
.001
.001
.001
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.051
1985
.014
.012
.007
.004
.002
.001
.001
.001
.004
.003
.002
.001
.001
.001
.001
.001
.001
.057
1986
.021
.019
.011
.006
.004
.002
.001
.001
.003
.002
.001
.001
.001
.001
.001
.001
.076
1987
.028
.028
.017
.010
.006
.003
.002
.001
.002
.001
.001
.001
.001
.001
.001
.103
1988
.036
.038
.026
.016
.009
.005
.003
.002
.001
.001
.001
.001
.001
.001
.001
.142
1989
.042
.048
.036
.024
.014
.008
.004
.002
.001
.001
.001
.001
.001
.001
.001
.185
1990
.046
.056
.045
.033
.021
.012
.007
.004
.002
.001
.001
.001
.001
.001
.231
-------
TABLE D-l. (CONTINUED)
t\s
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Age Class
Travel Fraction
.106
.142
.133
.123
.108
.092
.077
.064
.050
.035
.023
.016
.010
.007
.004
.003
.002
.002
.002
.002
1.001
1991
.049
.062
.053
.041
.029
.018
.010
.005
.003
.001
.001
.001
.001
.001
.275
1992
.051
.066
.058
.049
.036
.024
.015
.008
.004
.002
.001
.001
.001
.316
1993
.052
.068
.062
.054
.043
.031
.020
.012
.007
.003
.001
.001
.001
.355
1994
.053
.070
.064
.057
.047
.036
.026
.017
.010
.005
.002
.001
.001
.389
1995
.053
.070
.065
.059
.050
.040
.031
.022
.013
.007
.003
.001
.001
.415
1996
.053
.071
.066
.060
.052
.043
.034
.025
.017
.009
.005
.002
.001
.001
.439
1997
.053
.071
.062
.061
.053
.044
.036
.028
.020
.012
.006
.003
.002
.001
.452
1998
.053
.071
.067
.061
.054
.045
.037
.030
.022
.014
.008
.004
.002
.001
.001
.470
1999
.053
.071
.067
.062
.054
.046
.038
.031
.023
.015
.009
.006
.003
.002
.001
.481
2000
.053
.071
.067
.062
.054
.046
.038
.031
.024
.016
.010
.007
.004
.002
.001
.001
.487
-------
TABLE D-2.
TRAVEL WEIGHTED AUTOMOBILE PARTICULATE EMISSION FACTORS FOR
SCENARIO E
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Age Class
Travel Fraction
.106
.142
.133
.123
.108
.092
.077
.064
.050
.035
.023
.016
.010
.007
.004
.003
.002
.002
.002
.002
1978
.001
.001
.001
.001
.027
.023
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.132
1979
.001
.001
.001
.001
.001
.023
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.106
1980
.001
.001
.001
.001
.001
.001
.019
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.084
1981
.001
.001
.001
.001
.001
.001
.001
.016
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.066
1982
.001
.001
.001
.001
.001
.001
.001
.001
.013
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.051
1983
.001
.001
.001
.001
.001
.001
.001
.001
.009
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.038
1984
.001
.001
.001
.001
.001
.001
.001
.001
.006
.004
.003
.002
.001
.001
.001
.001
.001
.001
.029
1985
.001
.001
.001
.001
.001
.001
.001
.001
__.
.004
.003
.002
.001
.001
.001
.001
.001
.001
.023
1986
.001
.001
.001
.001
.001
.001
.001
.001
.003
.002
.001
.001
.001
.001
.001
.001
.019
1987
.001
.001
.001
.001
.001
.001
.001
.001
.002
.001
.001
.001
.001
.001
.001
.016
1988
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.014
1989
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.013
1990
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.012
-------
TABLE D-2. (CONTINUED)
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Age Class
Travel Fraction
.106
.142
.133
.123
.108
.092
.077
.064
.050
.035
.023
.016
.010
.007
.004
.003
.002
.002
.002
.002
1991
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.011
1992
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.010
1993
.001
.001
.001
.001
.001
.001
.001
.001
.001
.009
1994
.001
.001
.001
.001
.001
.001
.001
.001
.008
1995
.001
.001
.001
.001
.001
.001
.001
.001
.008
1996
.001
.001
.001
.001
.001
.001
.001
.001
.008
1997
.001
.001
.001
.001
.001
.001
.001
.001
.008
1998
.001
.001
.001
.001
.001
.001
.001
.001
.008
1999
.001
.001
.001
.001
.001
.001
.001
.001
.008
2000
.001
.001
.001
.001
.001
.001
.001
.001
.008
-------
TABLE D-3. TRAVEL WEIGHTED AND MODEL YEAR WEIGHTED GASOLINE HOT PARTICULATE EMISSION
FACTORS UNDER SCENARIO Ej BY VEHICLE AGE CLASS AND BY PROJECTION YEAR
ui
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Gasoline HOT
Travel Fractions
.061
.116
.122
.124
.098
.088
.079
.063
.049
.040
.030
.020
.021
.019
.016
.014
.012
.011
.010
.009
1978
.020
.037
.039
.040
.088
.079
.071
.057
.044
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.657
1979
.020
.037
.039
.040
.031
.079
.071
.057
.044
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.600
1980
.020
.037
.039
.040
.031
.028
.071
.057
.044
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.549
1981
.020
.037
.039
.040
.031
.028
.025
.057
.044
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.503
1982
.020
.037
.039
.040
.031
.028
.025
.020
.044
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.466
1983
.020
.037
.039
.040
.031
.028
.025
.020
.016
.036
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.438
1984
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.027
.018
.019
.017
.014
.013
.011
.010
.009
.008
.415
1985
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.018
.019
.017
.014
.013
.011
.010
.009
.008
.398
1986
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.019
.017
.014
.013
.011
.010
.009
.008
.386
1987
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.017
.014
.013
.011
.010
.009
.008
.374
1988
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.014
.013
.011
.010
.009
.008
.363
1989
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.013
.011
.010
.009
.008
.354
1990
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.011
.010
.009
.008
.345
-------
TABLE D-3. (CONTINUED)
Vehicle Age
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
TOTALS
Gasoline HDT
Travel Fractions
.061
.116
.122
.124
.098
.088
.079
.063
.049
.040
.030
.020
.021
.019
.016
.014
.012
.011
.010
.009
1991
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.010
.009
.008
.338
1992
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.009
.008
.332
1993
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.008
.326
1994
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
1995
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
1996
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
..321
1997
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
1998
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
1999
.020
.037
.309
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
2000
.020
.037
.039
.040
.031
.028
.025
.020
.016
.013
.010
.006
.007
.006
.005
.004
.004
.004
.003
.003
.321
-------
TABLE D-4.
PROJECTED PARTICULATE EMISSIONS FOR
HEAVY DUTY TRUCKSa
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Projected
Diesel Travel
Fractions
.683
.725
.764
.797
.831
.852
.877
.896
.916
.929
.945
.953
.963
.969
.974
.977
.981
.984
.988
.991
.991
.992
.995
Diesel
Emission
Factor
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Weighted
Gasoline
Emission
Factors
.657
.600
.549
.503
.466
.438
.415
.398
.386
.374
.363
.354
.345
.338
.332
.326
.321
.321
.32J
.321
.321
.321
.321
Projected
Emissions
g/n-VMT
1.57
1.62
1.66
1.70
1.74
1.77
1.81
1.83
1.86
1.88
1.91
1.92
1.94
1.95
1.96
1.96
1.97
1.97
1.98
1.98
1.98
1.99
1.99
aGas and diesel trucks combined.
177
-------
APPENDIX E. A ROUTINE FOR PROJECTING MEAN ANNUAL TSP
-------
APPENDIX E. A ROUTINE FOR PROJECTING
MEAN ANNUAL TSP
The chapters preceeding the appendixes have discussed the
procedures used to calculate the parameters that determine the
projected vehicular emissions and the projected total suspended
particulate (TSP) levels. While calculating these values
entails considerable work, it may be desirable to analyze
additional transportation scenarios/ additional diesel sales
scenarios, or other diesel emission factors. Since the number
of scenario combinations is the product of the number of
alternatives in each scenario component, the addition of any
one component multiplies the amount of additional computation.
Clearly, it is desirable to computerize the computational steps
so that additional calculations may be accomplished quickly and
easily.
In this appendix the computations used in this report are
derived as a sequence of computational steps which could be
thought of as subroutines for a computer program. These steps
could be expressed in a single holistic formula (a one or two-
page equation), but such an unwieldy expression is less useful
than a step-by-step sequence.
To be most useful, the formulae have been constructed so
that all inputs can be taken directly from one of the tables
in this report or from a scenario description parameter. For
example, transportation scenarios are expressed as presumed
linear growth rates for various modes of transportation;
therefore, these growth rates will be one of the inputs.
Diesel sales are taken from a logistic distribution function;
therefore, the presumed points on the logistic curve are inputs
for these computations. Emission rates are not presently known
with certainty, so their presumed values are scenario inputs
for some of the computational steps.
179
-------
In this study we have established the following rela-
tionship for the base year B 1976. For convenience, the base
year for emissions estimates is also considered a standard
meteorological year.
(1) ^B _ v
QB B
where
XR = the geometric mean of samples taken at a
monitoring site during 1975-1977 and nor
malized to a standard meteorological year.
Q_ = the total TSP emissions effective at a
given monitoring site in base year (B).
Since the standard meteorological year B is assumed to hold for
all projection years, the relationship between x an(i Q holds
for all projection years.
For projection year Y, the expression is as follows:
(2) ^1 - v
QY-kB
The TSP emissions Q that affect a given monitoring site are the
sum of the vehicle emissions and the non-vehicular emissions.
Since the vehicle emissions are the ones of concern, it is
desirable to express them in a separate term, as follows:
(3) *Y =
EY+EOY B
where
EY = vehicle particulate emission in year Y, and
E = other particulate emissions in year Y.
However, since the object of study is the vehicular emissions
(Ey) and their effects on ambient TSP levels, it is assumed
that all non-vehicular emissions remain constant. In other
words, EQY = Eg. Therefore, the following is also true:
(4) XY =
EY+EOB ~ B
180
-------
Note that Ey depends upon the conditions that are set out
in a particular scenario composition (T D E ) for the pro-
S S S
jection year Y, while E _. and k_ are fixed by the normalized
OB B
empirical relationship found in the base year. It is not
important to know the exact value of either E,, or E _, but it
x OB
is very important to know the relationship between Ey and E
in the base year (because E.. + E _ = Q_ fixes the value of k_)
x OB B -D
and the change in E for any projection year with respect to the
base year (because this change determines the value of x in
the projection year) .
Through trace element studies it was found that vehicle
emissions in the base year E_ represent a certain proportion p
B
of the total emissions. (See Chapter 5.) Thus, the ratio of
vehicle emissions to all other emissions can be expressed as
follows:
EB _ p
(5) -p> i _n
EOB X P
where
E_ = emissions from vehicles in the base year B,
B
E = emissions from sources other than vehicles
in the base year B,
p = the proportion of the particulate emissions
from motorized vehicles in the base year B, and
1-p = the proportion of the particulate emissions
from all other (non-vehicular) emissions in the
base year B.
By rearrangement of terms, E _ can be expressed as a
OB
function of p and E_ and then substituted into Equation 4 as
B
follows:
(6) *Y
However, from Equations 2, 3, and 6 we have the following
relationships:
/\T3 AlQ k AQ
(7) !, - B _ B B
181
-------
This expression for k, can be substituted into Equation b.
Then/ solving for XY:
*B (
"B \ E*
(8) XY = -^ x _ p
EY + EB( p J
Equation 8 is a convenient formula for the estimation of the
geometric mean TSP for any projection year Y. It is based upon
data from the base year and from the conditions of a certain
scenario composition.
By examination of Equation 8 it is found that the
dimensions of Ey are irrelevant. What is important is the size
of EY in respect to E_. For this reason some of the parameters
used in the calculations are either dimensionless or are
expressed as fractions of base year data.
In addition to estimating the total TSP level for a given
projection year (xy)/ it may also be desirable to examine the
ambient air TSP levels contributed by each mode of trans-
portataion. A modified version of equation 8 may be utilized
for this purpose. The term Ev is changed to the term E.-..,
1 JL ^\
where X represents one mode of transportation (bus, taxi/ HOT,
orautomobile). In other words/ equation 8 is used as if only
one mode of transportation were responsible for the total
emissions from the transportation sector. The equation must
then be adjusted to remove all non-transportation contri-
butions. The resulting equation is an expression for the
ambient TSP contribution from one mode of transportation as
follows:
(
XBEYX
<8a) XYX = -^-^ T - £ - (i - P)
-------
We now need to derive the term EY from base year data or
from scenario data. The basic equation for calculating the
normalized vehicle emissions for a given city in a given
projection year is as follows:
(9) Ey = E(a) + E(T) + E(b) + E(t)
where
EY = the total particulate emissions for a projection
year Y,
E(a) = the normalized particulate emission which is
a function of the characteristics of auto-
mobiles and light duty trucks in year Y,
E(T) = the normalized particulate emission which is a
function of the characteristics of heavy
duty trucks (HDT's) in year Y,
E(b) = the normalized particulate emission which is
a function of the characteristics of buses in
year Y, and
E(t) = the normalized particulate emission which is
a function of the characteristics of taxis in
year Y.
All emissions (E) are normalized to the base year 1976 and
expressed as fractions of the 1976 emission rate. They may be
considered as g/VMT (grams per vehicle-mile traveled) or as
fractional grams emitted for a standard 1-mile unit of travel.
Each E(x) function in Equation 9 may be conceived as the
product of a scenario-specific emission factor and a scenario-
specific mode-of-transportation fraction (modal fraction).
Thus, Equation 9 can be reexpressed as follows:
EY = faEa + fTET + ftEt + fbEb
183
-------
where each subscript refers to the modes of transportation
indicated by those letters in Equation 9, and
where
f = the fractions of total VMT in 1976 that are
projected to be traveled by transportation mode
(x) in projection year Y under a given trans-
portation scenario (T,, T2, ...etc.), and
E = a weighted emission factor which takes into
account the various adjustments that have to
be made under the diesel sales scenarios (D,
or D2).
The fractions f can be taken directly from tables in
X
Chapter 2. However, the normalized modal choice fractions (f )
J^
can also be expressed in a "simple interest rate" formula, as
follows:
(11) f = VD + r (Y~B)
x Bx xs
where
f = the modal choice fraction for transportation
mode x in projection year Y,
V = the fraction of total VMT in base year B that
were attributed to transportation mode x,
r = the annual rate of growth (expressed as a
fraction) for transportation mode x under
transportation scenario T (T,, T2,... etc.).
Y = the projection year of concern, and
B = base year 1976.
Each of the variables in Equation 11 is an input from a
f
scenario or from basic input data; therefore the equation is
in final form.
184
-------
Next, an expression is needed to calculate each of the
emission factors E in Equation 10. Each E is derived in a
X X
different manner. Let us first take E , which is a weighted
a
emission factor that depends upon automobile age class travel
fractions and the model-year emission factors for a given
projection year, as follows:
(12) E = V f E
a > an an
where
E = a weighted emissions factor which takes into
account the travel fractions and emission rates
attributable to each of 20 age classes of
vehicles on the road in projection year YT
f = the travel fraction attributable to automobile
age class n in projection year Y, and
= the emission rate applicable to all automobiles
of age class n in projection year Y.
However, E must take into account emissions from both diesel
all
and gasoline automobiles, and these can assume different values
under different scenarios. Instead of E we need to substi-
an
tute a term which weights the diesel emissions and gasoline
emissions according to fraction of that vehicle age class that
was diesel when the vehicles were new. After substitution/ we
have the following:
20
(13) E=VfFJEJ+ (1-F . )E
a y an adm adm adm agm
n=l
where E and f are defined as in Equation 12, and
3,
F = the fraction of automobile sales that were
adm diesel for model year m (m = Y - n) under a
given diesel sales scenario Dg,
E -, = the emission rate for all diesel automobiles of
adm model year m in projection year Y in a given
, emissions scenario E ,
S
185
-------
E = the emission rate for all gasoline automobiles of
agm model year m in projection year Y in a given
emissions scenario E .
s
The emission rates E , and E ^ can be taken from tables
adm agm
in Chapter 4. The diesel sales fraction F - can be taken from
tables in Chapter 3 or it can be derived from scenario input
assumptions. If it is to be calculated, the cumulative logis-
tic distribution formula applies/ as follows:
where
F £_(₯) = the diesel sales fraction/ a function of
the projection year Y under a given diesel
sales scenario D , and
S
a and $ = dimensionless parameters which determine
the shape of the described curve.
When two points (x,y) and (x2, y2) are given in a diesel
sales scenario D , then the a and 3 parameters can be
S
calculated/ using the following equations from Appendix A:
i r *-A~W,) i r tf-^HoM i~
(15) 3 = (x,-:
Equations 12 through 16 could be combined into one very large
expression and substituted into Equation 10, but the result
would be rather unwieldy. It is more effective to think of
these as a series of steps.
Now let us return to the term ET in Equation 10, which
represents the emissions from heavy duty trucks. ET must take
into consideration the diesel and gasoline truck sales, and the
186
-------
different travel fractions and emissions from these types of
vehicles, as well as the differences between vehicle age clas-
ses. The formula is as follows:
20 20
E =
T / , ""Tdn"Tdn / . v" "Tdn'~Tgn
n=l n=l
where
E = total emissions from HOT in projection year Y,
f_,, = the diesel travel fraction for a vehicle age
group n applicable to all projection years,
expressed as a fraction of total HDT-VMT,
E , = the weighted emission rate applicable to diesel
n HDT's for vehicle age class n in projection year
Y under emissions scenario E ,
S
1-fTdn = the 9as°line travel fraction, and
E = the weighted emission rate applicable to
gasoline HDT's for vehicle age class n in
projection year Y under emissions scenario E .
S
Since only one scenario is involved in HOT projected sales, the
term f , may be considered fixed. The values given in Table
D-3 may be substituted in the above equation. Values for E
depend only upon the model year of manufacture (m = y-n), and
may be input from the emission factor scenarios (Table D-3).
ETdn ~"~s taken to ke 2.0g/VMT for all model years in all
projection years, and therefore may be substituted directly
into Equation 17.
It was found that taxis have the same distribution of
diesel in the vehicle population as automobiles. Therefore,
E = E for any projection year, and the automobile emission
ci "C
factors can be used in solving Equation 10. Likewise, it was
found that buses have about the same distribution of diesel in
the vehicle population as HDT's. Therefore E, = E and this
value can be substituted into Equation 10.
187
-------
APPENDIX F - TABLES OF PROJECTED MEAN ANNUAL TSP AND BAP LEVELS
188
-------
TABLE F-l.
PROJECTED MEAN ANNUAL TSP LEVELS
(yg/m )
Projection
Year
1^76
1977
1978
1979
19EO-
19C1
.-19&2
1983
1SE4 ...
3985
19C6 ..
19C7
1968
1969
1S90 _
1V91
1992 ..
1993
._LSS4
1995
_1996_
1997
_is9a_.
1999
_20C3
Scenario
T1D1E1 T2D1E1
New York
62.9_71 _
63.105
- 63.315 - -
62.359
- 61.465
60.940
_.60.468
60.143
- 59.879 -
59.865
59.909
59.955
_. 60.238 . .
6G.Z10
_ 60.470
60.575
._ 60.786 _
60.640
60.8.42
61.055
61.219 _
61 .434
61.. 4 37
61.709
61.819
at 170
62.971 _
63.062
63. 153
62.176
01 .361
60.834
60.373- -
60.061
59.933
59.801
S9.91B
60. 001
60. 155
60.259
60.417
60.629
60.737
60.792
60.902
61 .008
61.275
61.381
61.488
61.652
61.759
Wi
Combination
T1D2E1
Wl
T3D2E1
E. 121 St.
62.971
62 .929
62 .970
fcl .803
6D.973
60 .353
59 .708
59 .304
59.D47
56.847
58 .886
58 .831
58 .869
58 .864
59 .008
59.005
59.002
59.060
59.059
59 .056
59.205
59 .203
59.200
59.243
59 .347
62. 971
63. 104
63. 313
62. 369
61.536
61.018
60. 705
60.641
60.899
61.514
62. 769
64. 406
66. 855
69.236
72.072
74. 669
77. 236
79.583
fil.635
63.103
85.027
fl6. 126
87.281
88. 345
88. 94/1
62.971
63.061
63.152
62 .185
61.429
60.907
60.598 --
60.535
60.868
61 .357
62.6TO
64.157
66.329 -.
68.59.3
71 .069
73.506
75.680
77.723
79.556
80.964
82.436
83.183
84 .201
84 .907
85.335
62.971
62.926
62.968
61 .813
61.039
60.425
59.925
59.757
59.961
60.307
61 .38}
62 .664
64.514
66.439
68.609
70.516
72.264
73.958
75.351
76.311
77.376
77.781
78.392
78.715 |
78.914
_1S76
1977
_4S7B_-
1 V79
_i5ea_
1981
19P2 _.
1983
J984
1985"
1986
1987
19P8_
1SP9
1990
.1 991
1992
-1993__
1994
_L5.?5_
1996
1997
1998
1999._
2000
New York
72.270
72.423
72.665
71.568
at Pier 42,
72.270-.
72.375
72.479
71.358
70.54J 70.423
69.939
_. 6.9.398 ..
69.02ft
. 68.721
66.736
6B.756
68.809
69.099
69.101
69.399
69.521
69.763
6S.824__..
69.827
j_ia.oj2
70.259
70.506
70.509
70.822
70.947
69.817
69.288
68.933
68.749
68.632
68.801.. ..
68.861
69.039
69.158
69.339
69.58E
69.706
69.770.
69.895
.70.. 017
70.324
70.446
70.568
70.756
70.879
72 .270
72.221
72 .269
70.929
69.978
69 .266
68.525
68.062
67 .767
67 .537
67.581
67.519
67.562
67.557
67.722
67.718
67.715
67.781
67 .7.80
67.777
67.948
67.945
67.942
67.992
68 .111
' '-'"
Morton
72.270
72.423
72.663
71.579
70.623
70. 029
69.670
69. 596
69.892
70.598
. .72.038
73.917
76.727
79.460
82.715
85.695
88.642
91. 336
93.690
95.719
97.583
98. P4ft
ICO. 170
101. 391
102.079
Gt. and
72.270
72.374
72.477
71 .368
7D.5DO
69.901
69.546
69.474
. 69.8S6
70.41.8
... 71.878.
73.631
76.1Z3
78.7Z2
81.564
84 .360
86.856
89.2OO
9 1 . 30 5
_ . 92.920 _
94.609
95.466
96.635
97.415
97.936
Hudson R.
72.270
72.221
-72.267
70.941
70.053
69.318
68.775
68.582
66.816
69.213
_70.414.
71.918
74.041
76.250
78.711
80.929 !
82.935 !
84 .879
86.481
67.614
88.803
89 .271
89.969
90.373
90.632'
"--
189
-------
TABLE F-l. (CONTINUED)
Projection
Year
1V76
1977
1978
1979__
1SFD
1 981
1982
1SF3_
19fi«
19B5
1956
1SS7
19F8
a 989
1V9D
1991
1S92
1993
1991
1995
1996
1997__.
1998
1999
2CCO
~1976
.1977_
1978
1979
1983
_19E1__
1982
_1983_
1981
3966
19E7
1968
.1969-
1990
_L95J_
3992
1993
. 1V91
_ 1995
4996-
1997
J999
_ZDCO_
Scenario Combination
T1D1E1 T2D1E1 T3D1E1 T1D2E1
T2D2E1
T3D2E1
New York at 240 2nd Ave.
71.626 7>l.626 74.626 71.626"
.71.781 71.731 71.576 71.781
75.031 71.812 71.625 75.032
_ 73.901 73.691 73.211 73.913
72.811 72.719 72.259 72.925
72.219 72.093 71.521 72.312
71.660 71.517 70.759 71.911
_71.271 71.178 70.281 71.861
70.962 70.993 69.976 72.171
70.916 70.870 69.739 72.899
70.997 71.0M1 69.781 71.386
71.052 71.106 69.720 76.327
71.352 71.289 69.761 79.229
71.353 71.113 69.759 62.050
71.662 71.599 69.929 85.112
T1.787.. 71.853 69.925 68.189
72.037 71.973 69.922 91.531
72.101 72. aih 69.991 91.313
72.1D3 72.171 69.990 96.711
72.356 72.30D 69.987 98.839
72.550 72.617 70.163 1 00. 76U
72.805 72.712 70.160 102.066
72.808 72.868 70.157 103.136
73.1.31 73.062 70.209 101.696
73.260 73.189 70.332 105.106
71 .626
71.733
71 .610
73.695
72.798
72.180
71.811
71.739
72.131
72.713
71.222
76.031
78.605
81.289
81.223
67.110
89.688
92.108
91 .281
95.919
97.693
98.578
99.786
100.622
101.129
71.626
71.575
71.623
73.253
72.337
. .71.608
71 .017
70.817
71.359
71.169
72.710
..71.262
76.155
78.736
81 .338 -
63.567
65.639
87.617
89.300
90.170
91.698
92.181
92.902
93.319
93.555
New York at Central Park Arsenal, 5th Ave.
and 64th St.
58.160 58.160 58.160 58.160*
58.283 58.211 58.121 58.283
58.178 58.328 58.159 58.176
5J.595 57.126 57.081. 57.60H
56.769 56.673 56.315 56.831
56.281.. 56.186 55.712 56.356
55.819 55.760 55.116 56.067
55.518 55.172 _. 51.773 56.008
55.301 55.326 51.536 56.216
5J5.2_9_2___55.233 51.351 .. .56.811
55.332 55.368 51.387 57.973
55.371 55.117. .. 51.337 59.186
55.608 55.560 51.371 61.717
55.610 55.656 51.367 63.916:
55.850 55.801 51.500 66.566
55..3JLL___S5.95.7__ 51.197 68.96B:
56.112 56.09S 51.191 71.335
56.192 56.118 ... 51.517 73.503
56.191 56.219 51.517 75.398
56.391 56.317 51.511 77.031
56.512 56.591 51.682 78.531
56.710 56.692 51.679 79.516
56.713- 56.790 ---51.677 80.613
56.995 "56.911 51.717 81.595
SZ-09* 57.01O 51.811 82.119
58.160
58.213
58.327
57.131..
56.735
56.2S1
55.968
55.910
56.218
56.669.
57.815
59.255
61.261
63.352
65.610
67.89.0
69.898
71.785
73.178
71.778
76.138
76.827
77.768
78.12.0
78.815
58.160
58.120
58.158
..57.093
56.376
55.808
55.317
55.192
55.380
55.700
56.690
57.877
59.585,
61.363
63.367
_ .65.129
66.743 ,
68.308
69.597
70.508
71.165
71.811
72.103
72.728
.. _ 72 .9 1 3
190
-------
TABLE F-l. (CONTIUED)
Projection
Year
1976 .
1977
1978
1979
3963..
1 961
1982 _
1963
a5&4
19E5
J986 .
1987
1988_
1 989
.19.?0_
1991
1 992 ...
1993
1994 .
1 995
1996
a 997
1998
1999
2DDO
Scenario Combination
Wi
New York
Convent
_._ 59.692
59.819
60.018.
59.112
. .58.264
57.767
57.320
57.011
S6.761
56.7MB
56.789.
56.833
__57.D73 _
57.074
5J7.321
57.421
5 7.621
57.672
57.671
57.876
56.031
58.235
58.238
58.1196
58.600
T2D1E1
T3D1E1
at Stienman
Ave .
59.692
59. 778
_ 59.865
58.939
58.166
57.666
57.229
56. 93<4
_ 56.784
56.697
56.627
56.876
57.023
57. 122
57.271
57.172
57.574
57.627
57.731
57.831
58.085
58.185
58.286
58.441
58.543
59 .692
C9.652
59 .691
58 .584
57.799
S7.211
56 .599
56.216
55.972
55.783
55.819
55.768
55.803
55.799
55 .935
55.932
55.929
55.984
55 .983
55.981
56 .122
56 .120
56 .1 17
56.159
56.257
T1D2E1
Hall, W.
59.69B
59. 818
60. 016
59. 121
58.331
57.841
57. 54«
57. 483
57. 728
58. 311
59.5DO
61. 053
63. 37h
65. 630
68. 319 _
70. 781
73. 214
75.439
77. 38B
79. 060
80. 599
81. 641
82.736
83. 744
84.313
T2D2E1
141 St.
59.692
59.778
59.863
58.947
58.230
57.73S
57.442
57.383
57.69.9
58.162
59.369
60.81 6
62.875
65.021
67.369 .
69.678
71.740
73.676
75.414
76.748
78.14 3
78.851
79.81.7
80.486
80.891
T3D2E1
and
59.692
59.651
59.689
58.594
57.861
57.278
56 .835
56.645
56 .839
57.167
58.184
59.401
61.155
62.980-
65.037
66 .844
68.501
70.107
71.430
72.366
73.347
73.734
74 .313
74 .644
74.833
1976
1.97.7 -
1978
1979
1980
1981
1962-
1983
-j.9fi.ij
1985
-aJ986-
1987
1968 -
1989
_L9-93__
1991
-1992-
1993
1 994
a V95
1 QCfc
1997
1-998-
1999
-2000-
Chicago
164.905
_165.202._
165.499
162.360
-159.660 -
157.742
156.237
1 54.672
1.53.J7.24D
153.297
-1.52.964-
152.695
- 152.908
153.120
at 3500
164.905
165.108
165.108
161.920
- 159. 445
157.296
-155.542
154.249
153. 313
152.. 901
-a 52. 577
152.314
- 152.. 528
152.465
E. 114
164 .905
164.718
164.772
1 6] .530
1 58.607
156.645
154 .928
53.420
52 .51 7
51 .871
51 .556
51 .301
51 .512
151.172
_153..134 152-75J 151.172- ..
153.349
153.287 ..
153.148
-153.184 ..
153.646
1 «51.7^1
153.735
154.020
154.109
154.1 10-
152.688
- 152.626 -
152.49-3
--152.43Z--
152.710
152. -73 4.
152.783
.-153.063
153.138
-J53.420
151 .382
151 .046
150.920
151 .141
15U.854
1.51 .189 _
150.899
150.887
151 .226
ISO .9 33
St.
164.905
165. 199
165.494
162. 401.
159.876
157.968
156. 916
156. 062
156. 506
157.757
160.637
164.579
170.566
177.045
183.782
190.485
196.540
202.281
2C7.424
211.929
2 15. 764
217.895
221. 041*
223.195
224. 142
164 .905
165.105
165.105
161 .962
159.655 -
157.536
156.2D4
155.5.92
1 55.99 0 -
157.168
159.890
163.584
169.21-1
174.935
- 181.41.7.
187.256
192.688
197.823
- 202.317
206.012
209.181 -
210.826
- 213.164
214.916
215.980
164.905
164.717
164 .766
161 .567
159.006
156.869
155.549
154 .672
-154 .995-
155.783
158.200
161 .433
166.352
170.975
. . 176.166
181.183
185.284
189.228
192.809
194.929
197.269
197.822
- 199.077
200.035
199. 703
191
-------
TABLE F-l. (CONTINUED)
Projection
Year
1976_
1977
1978 _
1979
198D
1 981
1982_
1V83
1984
_1985_.
1966
-19B7_
1968
J.969__
1990
.J991
1992
.1993_
1994
_19_95_
1996
_1957_
1998
1-999
23 CD
1 "?7£
1977
197B _
1979
19E3 _
1981
_L962_
1983
1984
19PS
.J.S86_
1987
_L9.8_B_
1989
1990.
1991
1992
1993
_1S5.4_
1995
1996
1997
1998 .
1999
_ZODD
Scenario Combination
T1D1E1
Chicago
80.529_.
80.674
...BO. 819
79.286
77.968
77.D31
._ 76.296
75.532
75.067
74.860__
74.698
7-4 .567. _
.74.670
74.774
74.781
74 .886
Wi
at 1947
.. 60.529
80.628
60.628
79.071
77.863
76.813
75 . 9 57
75.325
74.868
-_ 74.667 -
74.509
_ 74.380
74.485
74.454
74.593
74.563
74.856 74.533
74.788 74.468
74.756 74.438
75-.D3J 74 . 5.74 _..
75.072 74.610
75.075 74.609 _
75.214 74.746
75.257 .74.763 ..
75.257
Chicago
pn.i i?
88.271
88.429
86.752
85.310-
84.285
8.3.480
82.644
82.136
81.910
8J.732
81.588
8JL.7.02_
81 .815
81.823
81.937
81.904
81.830
8.L*Z96
82.096
82.141
82.144
82.296-
82.343
82.344
74.920
at 9800
- 88.112
P8.221
88.221
66.517
t5 . 1 95
84.046
63.109
82.418
81 .918
81.698
.. 81.. 525
81 .384
81.499
8 1 . 4 65
81.617
81.580
81.551
81.480
.81.4.47
81.596
81 .636
81 .635
- 81.785
81.825
SL. 97S_
T3D1E1
W. Polk
flO.529
80.438
80.464
78.881
77.551 .
76 .495
75.657
7H.921
74 .480
74 .164
74 .010
73.886
73.989
73.823
73.823
73.925
73.761
73.700
73.808
_. 73.667
73.831
73.689
73.684
73 .849
73.706
T1D2E1
80.529
80.673
80.816
79. 306
78.073 .
77. 151
76.627
76.210
76.427
77.039
78.445
80. 370
83.294
86.457
69.747
93. 021
95.978
98.781
101.293
103.493
105. 365
1C6.406
107.944
108.994
109.456
South Torrence
86.112
88.012
88 .041
86 .309
84 .854
83.698
82 .781
SI .975
81 .493
El .148
E0.980
80 .843
80.956
80.774
80.774
80.886
80.707
80.640
80.758
80.604
80.783
80.628
80.622
60.803
... eO.647
88."112
88.269
88.426
86.774
85.425
84.416
83.843
83.387
83.624
84.293
85.831
87.938
91.137
94.598
98. 198
101.780
105.015
108. 083
110.831
113.238
115.287
116.425
118.108
119.257
119.763.
T2D2E1
80.529
80.627
80.627
79.092
.77.96-5
76.930
76.280
75.9ai
76.176
76.751
78.080
79.884
82.632
85.427
88.592
91.444
94 .097
96.604
98.799
100.603
102.150
102.954
104.242
104.951
105.471
Ave.
88.11.2
88.219
88.219
86.540
85.307
84 .174
83.463
83.136
83.. 349
83.978
85.432
87.406
90.41 3
93.471
96.935
100.055
102.957
105.701
108.102
110.076
111.769
112.648
114.058
114.834
115.402
T3D2E1
. 80.529
80.437
80.461
78.899
77.649.
76.605
75.960
75.532
75.689;
76.074!
77.255;
78.834 :
81 .236
83.493
86.028
88.478
90.481
92.407
94.156
95.191.
96.334
. 96.603
97.216
_. 97.684-j
97.521.
88.112
ne.on
88.038
86.328
84.960
83.818
...83.113
82.645
82.817.
83.238
84.529
86.257
88.885
91 .355
94.129
96.809
99.001
101.108
103.022
104.154
105.405
105.700
106.371
106.883
106.704.
1.92,
-------
TABLE F-l. (CONTINUED)
Projection
Year
576
977
978
979
983
9E1
982
983
964
9P5
986
. J9£7_
3 9P8
1969
1990
1991
1992
1993._
1994
_199S
1996
1997
1998
._1999._
2T3D
Scenario Combination
T1D1E1
Chicago
83.719
83.870
8<4.020
62.427
81 .056
60.082
79.316
78. 52*4
78.011
77.826
77.657
__77.520
77.628
77.736
77.713
77.852_
77.821
_._77.753
77.718
?fi . n n i
78.016
78.018.
76.193
_. 78.238 _
78.239
Wi
at 538
R3.719
83.822
H3.822
82.2QU
60.948
79.856
78 .966
78.309
77.831
77.625
77.160
77.327
77.136
77. ID 3
77.513
77.517
77 .185
77.118
77.3S7
7Z.528
77.565
.77,565 _
77.707
77.715 .
77.888
Wi
S. Clark
83.719
83.621
P3.652
62 .006
80.623
79 .525
78 .651
77 .888
77 .130
77.102
76 .912
76.812
76 .919
76 .717
76.717
76 .851
76 .683
76 .619
76 .731
_76.585 .....
76 .756
76 .608
76 .602
76.775
76 .626
T1D2E1
St.
83.719
63.868
ei. OIB
82.118
PI. 166
80.207
79.663
79. 229
79.155
80. 090
81. 552
63. 55tt
86.593
89.882
93. 302
96. 705
99. 780
1D2. 69H
105. 305
107.592
109. 539
110.621
112.223
113. 311
113.792
T2D2E1
83.719
83.821
P3.821
82.225
81.051
79.978
79.302
78.991
79.193
79.791
81.173
83.019
85.905
P8.6U
92.102
95.086
97.621
100.131
102.713
.101. 5&8.
106.197
107.032
108.371
109.138
109.619
T3D2E1
83.719-
83.623
P3.649
62.321
60.721
79.639
78.969
78.521
78.688
79.088
60.315
81 .956
81 .151
86 .800
89.136
91.983
91 .065
96.067
97.886
_. 98.962.
ICO. 150
100.130
1C1 .067
101 .551
101 .381
Chicago at 4015 North Ashland Ave.
1976
_157J
1978
1979
1980
1981
1 982
1983
198$
1965
1 VP6
. 1987_
1968
J989-
1990
1991
1992
_1993_
1991
1995
1996
-li2J_
1998
1V99_
2GC3
69.01)
69.164
69.289
67.971
66.814
66.Q11
65.111
61.756
61.357
61.180
61.011
_63.928
64 .017
64.106
64.112
61.2D2
64.176
64.118
64.091
. 64 .326
64.362
64.364
64.483
_.. 64.520
64.520
69.040
69.125
69.125
_6_7..790___
66.755
65.851
65.123
64 .579
61.187
61.014
63.879
.63.769
63.858
63.83Z
63.951
63.925
63.899
63.813
63.818
63.935
63.965
63.965
64 .082
64 .114
64.232
69.040
66 .962
68 .964
67_.627._
66 .487
65.582.
64 .863
64 .232.
63.854
...63.583
63.151
63.344
63.433
63.291
63.290
63.378
63.238
63.185
63.278
63.157
63.298
63.176
63.171
63.313
63.191
69. 040
69. 163
69. 266
67.992
66.935
66. 144
65.695
65. 338
65. 524
66. 048
67.253
68.904
71.410
74. 122
76.943
79.749
82.285
84. 688
86.841
88.727
90.333
91. ?7K
92.543
93.444
93.840
69.040
69.124
69.124
67.808
66.842
. 65.955
65.39T7
65.141
65.3DB
65.601
66.940
68.487
70.843
73.239
75.953
.78.39:8
80.672
82.822
84 .703
86.250
87.577
88 .265
89.370
89.978
90.423
69.040
.68.961
68.982
67.642
66.570
65.676
65.123
..64.756
64 .891
65.221
66.233
. ._. 67.586
69.646
.._.71 .581
73.755
75.855.
77.572
. 79.223
80.723
81.610
82.590
fl?.8?l
83.347
83.748
83.608'
153.
-------
TABLE F-l. (CONTINUED)
Projection
Year
1976
1S77
1978-
1979
.-1SEJD
1VE1
1SE2
19f3
1S64-
1S85
4.St6
987
9E8-
SC9
9?D_
991
15 92
1993
.1994-
1995
.-1996...
1997
_JL9SB_
1999
2C30._
1976
1977_.
197B
.19.79
1980
1981 .
1962
1963-
19P4-
_J9E5_
19F6
19E7_
1988
19P9
-1993
1991
1 OC3-.
1993
l.SS4_
1995
1996-
1997
-1998
1999
20PQ
Scenario Combination
VlEl T2D1E1
Los. Angeles at
- 83.489 83.189
83.661 83.565
83.833 - - 63.6«4l
82.856 82.700
L_ R? -niji PI .913
81.554 81.311
81 .079 83 . 849
80.560 80.447
80.349 -- ED. 264 -
8C.215 P.O. 151
"0»307 «D . 165
80.232 F.Q.283
._.£0.33S 80.396 -
80.420 80.481
80. 4MB 60.6C5
80.626 80.692-
80.J09 80.781
80.754 80.830
80.7.40 ._ _ 80.920
80.842 81.016
£1.078_ 81.152
81.181 81.248
8 J . 1.8 6 81 . 4 40 . _
81.329 81.579
_.81.528 _ 81.675
Los Angeles at
104.285 104.285
.104.500 104.380
104.715 1C4.475
_l.Qi.«L9_7 1 0 3 . 3 03 _
102.526 102.317
_101.B68. . 101.565
101.274 100.987
-.1.00.626.- -100.485
100.363 100.257
LOQ..1-96 100.1 15
100.310 100.133
_1DO. 217.. -.100.280 -
100.345 ICO. 422
100.452 100.528
-- 100.486 100.683
100.708 100.792
- i nn A 1 1 i nn . 9OP --
100.868 100.964
100.852 101.076
100.978 101.195
101-.274 101.366
101.402 J01.486
--101.409- 101.726 -
101. 587 101. 899
104-836 -J 02.O2O
Wi
T1D2E1 T2D2E1
T3D2E1
2300 Carson St., Torrence
83.489
83.444
83.476
82 .514
81 .5^8
80.952
Pfl 4Q?
8C.014
- .79.835
79.637
79 .560
79.584
- 79.689
79.678
79.700
79.785
79 .870
79.828
79.916
79.913
79 .946
80.039
_ 80.132
80.166
80.163
434 S.
104.285
104.229
1C4.268
_ 103 .067.
101 .873
101 .116
1 CD .542
99.944
99 .720
99 .471
99.377
_ 99 .4 08
99.539
99.524
99.553
99.658
99.765
99.712
99.822
99 .8 1 8
- 99.859
99.975
100.092
100.134
-100.130
83.489 83.489
83.661 83.564
83.833 83.639
82.873 82.710
82. 155 81 .977
81.637 81.383
81.319 81 06 1
81.061 80.88S
81.368 81.155
81.869 81.580
83. 185 82 .62.5
84.738 84.085
87.099 86.038
89.673 88.09.5
92.412 90.3Z5
95.255 92.428
97.900 94.399
100.470 96.241
102. 73M 97.899
104.634 99.159
106.586 100.369
1C7.800 101.047
109.22J. 102.014
110.371 102.674
111.30U .103.018
San Pedro St.
104.285 104.285
104.500 1C4.3Z9
104.715 104.473
..-103.515 103.312.
102.619 102.397
101.972 . . 101.654
101.574 101.252
101.253 101.036
101.636 101.369
102.261. . 101.901
103.905 103.205
105.845 105.0Z9
108.794 107.470
112.009 110.028
115.430 132.823
118. 982 115.451
122.286 117.912
125.495 120.213
128.324- -122.284
130.697 123.85*8
133.136 125.370
134.651 126.217
136.426 127. 4&1
' 137.863 128.249
139.029 128. 7B4
83.489J
83.444
83.474
82.523
81 .621
81.022
80 .694
80.432
80.672
80.970
81 .836
83. 366
84 .808
86.528
88.373
90.168
91 .816
93.231
94.555
95.421
96.229
96.666
97.281
97.567
97.625
104.285
104.229
104.266
J03.079
101.952
101.203
100.794
100.466
100.766
101.139-
102.220
103.756
105.932
108.081!
110.386,
112.627;
im .686
116.453
-118.108
119.189
123.198
120.744
-121.513
122.869
-121.943
194
-------
TABLE F-l. (CONTINUED)
Projection
Vear
1976
IS 77,
1978
1979
I960
1VP1.
1 9C2
1VP3
.1 961
'l V85
1 9E6
1 ?87_
1 9P8
.1SE9
1V90
1991
1992
.1903
1 991
1995.
1S96
1997
1998
I 999
2 ODD
Scenario Combination
TiDiEi
T2D1E1
Los Angeles at
86.336
__.. 8 6 . 5 1 1
86.692
85.681
81 .880
, 81.335
83.813
__..83.307
83.089
82.951
83.015
82.968
83.071
83.163
83.1 91
83,375
83.161
83.507
83.191
83.598
83.813
83.919
83.955
_81 .1 03
81 .309
86.336
P.6.1 15
86.193
E5.521
81.736
.61 .081
83=606
83.190
83.301
JP2.881_
82 .899
83.021
83. 1 38
83.225
83.351
83 .111 .
8 3 . 5 35
83. 587
83.679
83.778
83 .919
8.1 . 0 1 9_.
81.217
81.361
81 .161
T3D1E1 T
2655 Pine
86.336
E6 .290
86.322
85-328
El .339
83.713
83.237
82 .712
82 .557
82 .353
82 .273
E2 .298
82 .107
82 .395
82 .1 18
P2 .505
82 .591
82 .550
82 .611
... 82.638
82 .672
82 .768
82 .865
82.899
82.897
1D2E1
Ave. ,
86. 336
86. 511
P6.692
85. 699
81. 956
81. 121
81. 092
83.825
61. 113
81.661
86. 021
67. 627
90. 069
92. 731
95.563
98. 503
101.239
1C3. 896
106.238
108. 202
10. 221
11.176
12. 915
11.135
15. 100
T2D2E1
T3D2E1
Long Beach
P6.33*-
86.111
86.191
85.531
81.773
81 .158
83.825
83.61 6
83.922
._81 .362
85.112
86.952
88.972
91 .099
93.105
95.580.
97.618
99.523
101.237
102.510
103.792
101 .193 _
105.523
106.175
106.552
86.336j
86.289
66 .320
85.337
81 .105
83.781
83.116
83.17JI
83.123
.83.732
81 .627
85.898
87.700
89.179
91 .387
93.212
91 .917
96 .1 10
97.779
98.675
99.510
99.963
100.599
100.891
100.951
976
977
978
979
veo
vei
982
9P3
964
985
9P6
1967
1988
1989
1990
1991
1992
1.993
1991
1995
1996
1997
1 998
1999
200D
Los Angeles at
87.177
87.657
87.837
86.816
86.302
85.150
81 .952
81 .108
81 .187
81 .017
81 .113
81 .061
81.172
81 .262
81 .290
81.1.77 .
81 .561
81.61 1
81.597
r 81.703
81.951
85.058
85.061
85.211
85.423
87.177
87.557
87.636
86.651
85.826
85.195
81.711
81.290
81 .098
83.979
83.991
.. 61.118
81.236
81.325
81.155
81.517
81.639
81 .691
P.I. 7 85
81.885
85.029
85.129
85.330
85. 176
C5.577
1196 East
P.7 .177
87.130
87.163
86 .156
85.151
61 .8 19
81 .337
P3 .836
83.616
83.111
83.360
83.386
83.196
83.1P.1
83.508
83.596
83.686
83.611
83.733
83.730
83.761
83.862
83.960
83.995
83.992 '
Walnut
87.177
87.657
87.837
86.831
86.079
85.537
85. 203
61.933
85.255
85.779
fl7. 158
86. 785
91.259
93.956
96.826
99.805
102.577
105. 269
107.612
109.632
1 11.676
112.919
111.138
115.613
1 16.621
St. ,
87.177
87.556
87.631
86.661
85.893
85.27C
81 .933
81.751
85.031
85.177
86.571
88.101
90.118
92.303
91.639
96.613
98.908
100.878
102.575
103.89.5
105.161
105.871
106.916
107.578
1Q7.960
Pasedena
87.177
87.130
87.161
86.165
85.520
81 .892
81 .519
81 .27 It
81 .525
81 .838
85.715
87.033
86.859
90.661
92.591
91 .175
96.202
97.681
99.072
99.979
100.826
101 .281
101.928
102 .227
102.289
195
-------
TABLE F-l. (CONTINUED)
Projection
Year
1976
1977
..1978_
1979
1S60._
1961
1982-
193
_15e«l_
1V65
1986
1987
.1988 _
1969
1993
19«1
1992 _
1993
1991
i-SS5
1996
1S9J
1998
.\999-
2000
Scenario Combination
T1D1E1
T2D1E1
Wl T1D2E1
T2D2E1
T3D2E1
Los Angeles at Keck Laboratories, California
Institute of Technology. Pasedena
_95.356 .
9S.S52
9.5..7.1 9
91.636
93. 718
93.116
92.603
92.011
91.77J
91.617
91.J22 .
91.636
._ 91.751
91.B51
9_1_.882_
92.086
__._92.181
92.232
92.217
92.332
92.603
92.720.
92.726
?2_.8.8.9_
93.117
. .95.356
95.113
95.530
91.155
93.556
92.868
_ 92.311
91.881
91.673
91.513
.91.560
91.6914
91.823
91.920
92.062
92.162
..92.263
92.319
92.422
_ 92.531..
92.687
92.797
93.016
93. 171
93.285
95.356 "95.356
95.305 95.552
95 .311 95. 719
91.213 91.652
93.150 93.832 ..
92.158 93.211
91.933 92.877
91.387 92.583
.91 .182 .. . 92.931 ....
90.956
90.869
90.896
91 .016
91 .003
..__ 'I -029 ....
91 .125
91 .223
91 .171
91 .275
.. 91.272
91 .309
- 91.115
91 .522
91 .560
91.557
93.506
95.008
96.782
99.179
102.119
105.517
108.791
111.816
111.753
117.337
1 19.507
121. 737
123. 122
121.715
126.056
127. 125
95.356
95.112
95.528
91.167
93.630
92.950
92.583- -
92.385
.. 92.690
93.176
91.369 _
96.036
98.26-8
100.617
103.163 .._
105.566
107. 81.6
109.920
111.81.1
113.253
111.636
- 115.110 ....
116.518
_ 117.268..
117.681
95.356
95.301
. 95.339.
91.253
93.223
92.538
92.161
91.866
_92.138
92.179
93.168
91.872
96.862
98.827
100.931 i
102.981
101.866
106.183
107.995 1
108.981 ;
109.937 '
110.106
111.109
111.135
111.502'
196
-------
TABLE F-2. PROJECTED LEVELS OF BAP
Projection
Year
1976
1977"
1978
197$
1 980
1 98 1"~ ~
1932
1-98-3
1 984
1 98 b
1986
193 7
938
' 9 ft '9"
090
' 791
992
994
1996
1997
1998
1999 "
1976
1977 '
1978
-1979
1980
~Y98T~
1982
1983
1984
1585 ' "
1986
1987
1998
19" ".SO
.50
.50
.50
.50""
.50
.50
.50
.50
.50
.51
.51
.51
.51
.51
.51
T2D1
at
. 45
"".45
. 46
-"."4-5
. 44
. 4^
. 44
.43
. 43
"". 4 3
. 43
"."43
.43
. 43
. 44
. 44
. 44
. 44
;-
-------
TABLE F-2. (CONTINUED)
Projection
vear
1?76 '
1977
1 778
1979
1 331
1*32
1989 '
1V91
1992 "
'1993
1991
1V95
1996
1997
1998
1999
2DOO
1976
1977
1 976
1979
19«>1
1-SBZ
1933
1981
1985
1986
19B7
1988
1989
1990
1991
1992
1993
1 99 S
1996
1997
1998
1999
2000
T1D1E1
New York
.51" " '
.51
.53
.52
.52 "
.51
.51
.51
.51
.51
."51
.51
.52
.52
~ .52 ""
.52
.52
.52
.52
.52
.52
.53
.53
New York
and 64th
.12 "
.12
.12
.11
.11
.10
.10
.10
.10
.10
.10
.10
.10
.10
.11
.11
".11
.11
.11
.11
.11
.11
r2Dl
at
. 51
. 51
~Y5i
. 53
. 52
. 52
. 52
. 51
" ."51
. 51
. c-l
. 51
.51
. 51
.52
. 52
'. 52
. 52
.52
.52
. 52
. 52
Scenario
El T3D1EJ
240 2nd
" .51
.51
-"-". 51' ~
.53
.52
.52
.51""
.51
.so
.50
.50
.50
.50
.50
.50
.50
"".50
.50
.50
.50
" .51
.51
.53 .51
.53 .51
.Si
at
St
."12
.12
.11
. 11
. 11
.10
.10
.10
. Id
. 1 J
.10
.10
.10"
.10
. 10
.10
.11
.11"
.11
.11
.11
.11
.11
.51
Central
.12
.12
" .12
.11
.10
.10
.39
.39
.39
.39
.39
39 "
.39
.39
.39
.39
.39
.39
.39
.39
.39
.39
.10
Combination
L T1D2E1
Ave.
.51
.51
... >5i) ... .
.53
.53
.52
.52
.52
.52
.53
.51
.55
.57
.59
"" .62
.61
.66
.68
.70
.71
.73
.71
"" ."75
.75
.76
T2D2
.51
.51
""".51"
.53
.52
.52
.52
.52
.52
.52
.51
.55
.57
.59
.61
.63
.65
.66
. 68
.69
.70
.71
"" .72"
.73
.73
Park Arsenal,
.12
.12
.12
.12
.1 1
.1 1
.10
.10
.11"
.11
.12
.13
."15
.16
.18
.50
.51
.53
.51 " ""
.56
.57
.57
.58
.59
.59
.12
.12
.12
.11
.11
.11
.10
.10
.11
.11
~"."l"2""
.13
.11
.16
.17
.19
.50
.52
.53
.51
.55
.55
.56
.57
.57
El T3D2E1
.51
.51
.51
.53
.52
.52
.51
.51
.51
.52
.52
.51
.55
.57
.59
.60
.62
.63
.61
.65
.66
.66
.67
.67
.67
5th Ave.
.12
.12
.12
.11
.11
.10
.10
.10
.10
.10
.11
.42
.13
.11
.16
.17
.16
.19
.50
.52
.52
.52
.52
.53
198
-------
TABLE F-2. (CONTINUED)
Projection
Year
1976
1977
1978
1 ?79
1980
19fll
1982
1 98~3
1 98 i«
1 93 5
1986
1587
1988
1789
19,90
1-591
1992
"1 99 3
1 994
"l^Vb
1996
1997
1 998
1'999
2300
Scenario
T1D1E1 T2D1E
New York
Convent
.i(9
.49
.49
~TV9"
.48
.47
.47
.₯7
.<47
V47 ~
.17
.47
.47
.17
.17
.47
.17
.17
."8
.08
.MB
.48
.48
.<48
at
Ave.
. 49
. 19
. 49
"Tie
. M8
. 17
. 47
-~; "?"
. 47
~V47"
. 47
. 47
. 17
".47"
. 47
."47
.47
' .47
."7
.47
. 48
.48
. 48
. 48
. 48
i Wi
Stienman
.49
".49
.49
748
.47
.47
.46
"."46
.46
.46" "
.46
.46
.46
.46
.46
.46
.46
.46
.46
.46
.46
- .46
.46
.46
.46
Combination
T1D2E1
Hall, W.
.49
.49
.49
.49" "
.48
.47
.47
" " .47 "
.47
~" .43
.49
.5U
.52
.54
.56
.58
.60
.62
.64
.65
.66
.67
.68
.69
.69
T2D2
141
. 49
.49
.49
>8
.46
.47
.47
.47
.47
.48
.49
. 5C
.52
.53
.55
. 57
.59
.60
.62
. 63
.64
.ft 5
.66
.66
.66
El T3D2E1
St. and
.49
.49
.49
."48"
.47
. 4 7
.47
."46 "
.47
.47"
.46
.49
.50
.52
.53
. 55
.56
. 58
.59
.59
.60
. 6 1
.61
.61
61
1976
-1977-
1978
1579
19BO
1 98 1 "
19R2
196 3
1984
T?8i>
1986
T987 -
1988
"IVfl1?
1990
I9V1
1992
1993
1994
1995'
1996
I 997
1998 '
1999
~2T)30
Chicago
.43
.43
.43
.43
.42
.42
.41
.IT"' '
.41
.41
.41
.41
.41
.41
.41
.41
.42
.42
.42
.42 '
.42
.42
.42
- .42
.42
at 3500
."3
.4"3
.43
.42
. 42
.42
.41
. 41
. 41
.41
.41
-.-41
.41
.41
.41
.41
.42
.42
.42
.42
.42
. 42
.42
' .H2" ~
.42
E. 114
.43
.43
.43
.42
.42
.41
.41
' .41
.40
.40
.40
.40
.40
.40 " "
.40
.40
.40
":4o"~
.40
.40"""
.40
.40
.40
" .40
.41
St.
.43
.43
.43
.43
.42
" .42
.41
.4 1
.42
' ."42
.43
.44
.46
.47
.49
.51
.53
" .54"
.56
.57
.58
.59
.60
.60
.61
.43
.43
.43
.43
.42
.42
.41
.41
.42
.42
.43
. 44
.45
. 47
.49
.50
.52
.53
.54
.55
.56
. 57
.58
.58
.58
.43
.43
.43
.42
.42
.41
.41
.41
.41
.41
.42
.43
.44
.45
.47
.48
.49
.51
.52
.52
.53
.53
. 54
.54
.54
199
-------
TABLE F-2. (CONTINUED)
Projection
Year
1976"
1977
1578
1979
i;sn
1S81
19? 2
1 *83
1981
1585
1 Vfl6
1 597
r?ae"
1989
1990
1991
1992
1793
199«T
1995
1-996"
1997
-1"398"
1999
?D03
1976
1977
1978
1979
1980
1981
1982
19«3
196 (4
1985
1986
1987
1988
1989
159Q
1991
1992"
1993
1991
1995
1996
1997
" 1 998'
1999
~2T>00"'
Scenario
T1D1E1
Chicago
' .50
.50
. 50
.19
.18
.48
r ."47
.17
.06
.46
.146
.46
.16
.16
.16
.16
.16
.16
.16
.16
.16
.16
.16
.17
.17
Chicago
T2D1E1 ..
at 1947
. 50
.50
.50
.19
. 18
. 17
.17
. "7
. it
. 16
. 16
. 16
. 16
. 16
. 16
. 16
. 16
.16
. 16
.16
.16
.. 16
. 16
.16
.16
at 9800
T3D1
W.
~.50
.53
.50
.19
.IS
.17
".17
.16
.16
.16
.16
.16
.16
.S6
.16
.16
.16
.16
.16
.16
.16
.16
.16
.16
.16
M
Combination
El T1D2E1 T
Polk
.50
.50
.50
.19
.18
.18
.17
.17
.17
.18
.18
.50
.51
.53
.55
.57
.59
.61
.63
.61
.65
.66
.67
.67
.68
.South Torrence
2D2E1
.50
.50
.50
.19
.18
. 18
.17
.17
.17
.17
.18
.19
.51
.53
.55
.57
.53
.6U
.61
.62
.63
.61
.61
.65
.65
Ave.
T3D2E1
.50
.50
.50
.19
.IS
.17
.17
.17
.17
.17
.18
.19
.50
.52
.53
.55
.56
.57
.5d
.59
.60
.60
.60
.60
.60
.60
.63
.61
.59
.58
.58
.57
.57
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
.56
. 60
.60
.60
.59
. 56
.58
.57
.56
.56
.56
. 56
.56
.56
.56
.56
.56
.56
. 56
.56
.56
.56
.56
.56
.56
.56
.60
.60
.60
.59
.58
.57
.57
.56
.56
.56
.55
.55
.55~
.55
.55
.55
.55
.55
.55
.55
.55
.55
.55
.55
.55
.60
.60
.61
.59
.59
.58
.57
.57
.57
.58
.59
.60
.62
.65
.67
.70
.72
.71
.76
.78
.79
.80
.81
.82
.82
. 60
.60
.60
.59
.58
.58
.57
.57
.57
.58
.59
.60
.62
.61
.66
.69
.71
.72
.71
.75
.77
.77
.78
.79
.79
.60 .
.60
.60
.59
.58
.57
.57
.57
.57
.57
.58
.59
.61
.63
.64
.66
.68
.69
.71
.71
.72
.72
.73
.73
.73
200
-------
TABLE F-2. (CONTINUED)
Projection
Year
r97~6
1V77
1976
1 979
1980
1 V8 1"
1982
19P3" "
1 98 <4
1585
1986
1?87"
1988
1989
1 990
"1 99'1
1992
1 99'3
1994
"1995
1996
1997
1998
1999 "
2000
Scenario
T1D1E1
Chicago
:i9 --
.19
.19
.17
.15
.14
1 .13
' 1.12
1 .11
1.11
1 . 10
~~r."i'iT"
1 .10
1 .10
1 .10
i.ii
1.11
1 .13
1 .10
1.11
1.11
1.11
1.11
1.11
1.11
T2D1E1
at 538
~i; 19
1. 19
1. 19
1.17
1 . 15
"""1. 13
1. 12
1.11
1. 11
1. U
1. 10
~i.ro
1. 10
"1. ill
1 . 10
1 . 1Q
1. 10
1. 10
1. 10
1. 10
1. 10
1.10
1. 10
1. 10
1. 11
T3D1E1
Combination .
T1D2E1
T2D2E1
T3D2E1
S. Clark St.
TV 19 ~
1.19
1 .19
.16
.15
.13
.12
.11
.10
.09
.09
"~~i ."09
1 .09
1 .~09
1 .09
1.09
1 .09
1 .09
1.09
1.09
1.09
1 .09
1.09
1.09
1 .09
1.19
1.19
1.19
1.17
1.15
1.14
1.13
1.13
1.13
i.m
1.16
"1.19
1.23
1.28
1.33
1.37
l.<42
1.16
1.50
1.53
1.56
1.57
1.59
1.6 1
1.62
1.19 '"
1.19
1.19
1.17
1.15
1. 1<4
1. 13
1. 12
1.12
1.13
1. 15
1.18 ""
1.22
"1.26
1.31
1.35
1.39
1.13
.46
.19
.51
. 52
.51
.55
.56
1. 19
1.19
1. 19
1.16
1.15
1.13
1.12'
1. 12
1.12
1. 12
1.11
1. 16
1.20
"~1 . 23
1.27
1.31
1.31
1. 36
1.39
1.11
1.12
1.13
1.14
1.14
1.44
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1986
1989
1990
1991
1992
1993
1994
"199!)
1996
1997
1998 .
"1999
2000
*
Chicago
.64
.64
.64
.63
.62
.61
.60
.60
.59
r~ .59
.59
.59
.59
.59
.59
. ..$.9 ...
.59
.59
.59
.59
.59
.59
.59
.59
.59
at 4015
.64
. 64
. 64
. 62
.61
.61
.60
. 59
. 59
.59
. 59
.59
.59
. 59
.59
.59
.59
.59
.59
. 59
. 59
.59
.59
. 59
. 59
North
.64
.63
.63
.62
.61
.60
.60
.59
.59
.59
.58
.58
.58
.58
.58
.58" "
.58
.58
.58
.56
.56
.58
'.58
.58
.56
Ashland
.64
.64
.64
.63
.62
.61
.63
.60
.60
.61
.62
.63
.66
.66
.71
.73
.76
.78
.80
.62
.83
".B4-
.as
.86
.66
Ave,
.64
.64
.64
.62
.62
.61
.6]
.60
.60
.61
.62
.63
.65
.67
.70
.72
.74
.76
.76
.79
.81
.61
.82
.83
.83
.64
.63 ;
.63J
.62
.61
.60
.60
.60 :
.60
.60
.61
.62
.64
.66
.68
.70
.71
.73
.74
.75
.76
.76
.77
.77
.77
201
-------
TABLE F-2. (CONTINUED)
Projection
Year
X976
1977
1978
1 J79
1980
1 98 1 "
HC2
1963
1 ?fl4
1
-------
TABLE F-2. (CONTINUED)
Projection
Year
1976
1 577
1978 ~
1975
' ISflO"
1 9P 1
1982
198 3
1984
1985
198fc
198 7
1986
1 789
1990
1991
I 99 2
1993
199<)
1995
1996
1997 "
1998
1999
2300
T1D1E1 T2D1E
Los Angeles
--.75" ' "
.75
" ".75
.75
".74
.73
.73
.73
.72
.72
.72
.72
.72
.72
.72
""".73
.73
.73
.73
.73
.73
."73
.73
73
.73
75
75
7S
74
74"
73
73"
72
12
72
72
72
72
72
73
73
73
73
73
73
73
73
73
73
74
Scenario
1 T3D1E1
Combination
T1D2E1
at 2655 Pine Ave .
.75
.75
.75
.74
.73
.73
.72
.72
.72
.72
.72
.72
.72
.72
.72
"" .72
.72
.72
.72
.72
.72
.72
.72
.72
.72
.75
.75
.75
.75
" '.74""
.74
.73
.73
.73
.7««
.75
.76
.78
.81
.83
.86
.88
.90
.93
.94
.96
.97
.96
.99
1.00
T2D2E1
f Long
.75"
.75
.75
.74
.74
. 73
.73
. 73
.73
. 73
.74
.76
.77
"" " .79
.81
.83
.85
".87
.88
.89
.90
.91
.92
.92
.93
T3D2E1
Beach
.75
.75
"'.75
.74
.7<«
.73
^73
.72
.73
.73
. 74
.75
.76
.78
.80
.81
.83
.84
.85
.86
.87
.87
.88
.88
.88
1976
1977
1978
1979
I960
1981
1982
1983
1981
198 5
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1 99 8
1999
2000
LOS
.60
.60
.60
.60
.59
.59
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.58
.59
.59
.59
.59
Angeles
.60
. 60
. 60
.60
. 59
. 59
. 58
.56
.58
. 58
.58
. 58
.58
.58
. 58
. 58
.58
. 56
. 58
.56
.59
.59
.59
. 59
.59
at 1196
.60
.60
.60
.59
.59
.58
.58
.58
.58
.57
.57
.57
.57
.57
.57
.58
.58
.58
.58
.58
.58
.58
.58
. 58
.58
East Walnut
.60
.60
.60
.60
.59
.59
.59
.58
.59
.59
.60
.61
.63
.65
.67
.69
.71
.72
.74
.75
.77
.78
.79
.80
.80
St.,
.60
.60
.60
.60
.59
.59
.58
.58
.59
.59
.60
.61
.62
.64
.65
.67
.68
.69
.71
.71
.72
.73
.74
7*
.74
Pasedena
.60
.60
.60
.59
.59
.58
.58
.58
.58
.58
.59
.60
.61
.62
.64
.65
.66
.67
.68
.69
.69
.70
.70
.70
.70
203
-------
TABLE F-2. (CONTINUED)
Projection
Year
1976
1977 "
1978
1979
198D
1981 "
1982
1982 '
1 -J8M
1985
1986
1S37
1988
1989
1990
1991
1992
1993
1995
1996
1997
1998
1999
2J°°
Scenario
TID
LE1 T2D1E
T
1 L3
Combination
D1E1 Tl
Vi V
Los Angeles at Keck Laboritories,
Institute of Technology, Pasedena
.69
.69
.66
.68
.67"
.67
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.67
.67
.67
.67
.67
.67
.69
169
.69
.67
.67
.67
. tb
. 66
. 66
. 66
. 66
. 66
.'66
. 66
. 66
.67
.67
.67 '
.67
.67
.67
.67
.67
.67
.69
.69
.69
.63
.67
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66
.66"
.66
.66
.66
.66
.66
.66
.69
.69
.69
.68
.68
.67
.67
.67
.67
.67
.69
"".70
.72
.76
.76
.81
.83
.85
.86
.88
.89
.90
.91
.92
.69
" " .69
.69
.66
.68
.67"
.67
.67
.67
.67
.68
.69
.71
.73
.7H
.76
.78
.79
.81
.82
.83
.83
.81
.85
.85
)2E1 T3D2El
California
.69
.6-9
.69
.66
,67
,67
,66
. 66
.67
.67
.70
.71
.73
.71
.76
.77
.76
.79
.79
.80
.80
.80
.80
1976
1977
1978
1979
I960
1981
1982
19P3
19«i»
1?85
1986
1987
1988
1389
1990
1991
1«2
1993
199
-------
APPENDIX G. PROJECTED TSP LEVELS ASSOCIATED WITH
POPULATIONS AT RISK
205
-------
TABLE G-l. PROJECTED TSP LEVESL ASSOCIATED WITH GIVEN
FRACTIONS OF THE^ CHICAGO CENTRAL AREA POPULATION
(yg/m3)
Projection
Year
Fraction of Population Exposed to
Given TSP Levels or Higher
0.12
0.49
Scenario T,D..E..
Scenario T.D.E.
Scenario
Scenario
1.00
1980
1985
1990
1995
2000
81.1
77.8
77.7
78.0
.78.2
78.0
74.9
74.8
75.0
75.3
66.8
64.2
64.1
64.3
64.5
1980
1985
1990
1995
2000
81.1
80.1
93.3
107.6
113.8
78.1
77.0
89.7
103.5
109.5
66.9
66.0
76.9
88.7
93.8
1980
1985
1990
1995
2000
81.1
79.8
92.1
104.6
109.6
78.0
76.8
88.6
100.6
105.5
66.8
65.8
75.9
86.3
90.4
1980
1985
1990
1995
2000
80.7
79.1
89.4
99.0
101.4
77.6
76.1
86.0
95.2
97.5
66.6
65.2
73.8
81.6
83.6
206
-------
TABLE G-2,
PROJECTED TSP LEVELS ASSOCIATED WITH GIVEN
FRACTIONS OF THE MANHATTAN RESIDENT POPULATION
(vig/m )
Projection
.193
.436
.750
.952
.999
1980
1985
1990
1995
2000
72.8
70.9
71.7
72, ,4
73.3
70.5
68.7
69.4
70.1
71.0
61.5
60.0
60.5
61.1
61.8
58.3
56.7
57.3
57.9
58.6
56.8
55.3
55.9
56.4
57.1
Fraction of Population Exposed to
Given TSP Levels or Higher
Scenario TID-IE-,
Scenario T-.D-E..
1980
1985
1990
1995
2000
1980
1985
1990
1995
2000
1980
1985
1990
1995
2000
72.9
72.9
85.4
98.8
105.4
70.6
70.6
82.7
95.7
102.1
61.4
61.5
72.1
83.4
88.9
58.3
58.3
68.3
79.1
84.3
56.8
56.8
66.6
77.0
82.1
Scenario T,.D0E..
221
72.8
72.7
84.2
95.9
101.1
70.5
70.4
81.6
92.9
97.9
61.4
61.4
71.1
81.0
85.3
58.2
58.2
67.4
76.7
80.9
56.7
56.7
65.6
74.8
78.8
Scenario T_DnEn
321
72.3
71.5
81.3
90.5
93.6
70.1
69.2
78.7
87.6
90.6
61.0
60.3
68.6
76.3
78.9
57.9
57.2
65.0
72.4
74.8
56.4
55.7
63.4
70.5
72.9
207
-------
APPENDIX H. PROJECTED TSP CONTRIBUTIONS FROM
FOUR MODES OF TRANSPORTATION
208
-------
Table H-l.
PROJECTED TSP CONTRIBUTIONS FROM FOUR
MODES OF TRANSPORTATION TO THE MONITORING
SITE AT 240 2ND AVE., NEW YORK CITY
Projection
Year
19?6
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
197~6
1977
197»
1979
1980
1951
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Bus
1.833
1.929
2.025
2.19C
t.244
2.402
2.565
2.718
2.7PO
2.923
3.085
3.234
3.403
3.421
3.575
3.714
3.853
3.974
3.994
4.11 5
4.257
4.379
4.379
4.523
4.646
Taxi
1.4F4
1.500
1.3 a
1.^18
.570
.770
.59E
.442
.3*4
.274
.229
.1^4
,17C
.159
*14£
.137
.1?5
.113
.1C1
.1C2
.1C2
.1C3
.1C3
.1C4
.105
Auto
Scenario T-D-E
5.977
6.017
6.058
4.897
3.912
3.094
2.406
1.305
1.388
1.108
.921
.730
.688
.643
.597
.551
.504
.457
.408
.411
.413
.416
.419
.421
.424
HOT
1
5.884
5.884
5.980
6.170
6.323
6.5«0
6.734
6.959
7.117
7.30&
7.427
7.507
7.744
7.785
7.985
B. 026
8.188
8.188
8.230
8.351
8.393
8.515
8.515
8.630
8.6PO
Totals
15.^77
15.330
15.571
14.475
13.448
12.846
12.305
11.931
11.628
11.613
11.662
11.715
12.006
12.007
12.306
12.427
12.670
12.731
12.733
12.978
13.166
13.413
13.416
13.729
13.854
Scenario T.D^E.
Z _L _L
1.833
1.929
2.025
2.19C
2.346
2.5C7
2.672
2.936
3.113
3.26C
3.42fc
3. sec
3.755
3.892
4.C52
4.193
4.335
4.455
'. '4.599
4.72C
1.484
1.492
1.5CD
1.2P5
.960
.754
.5P6
.439
3?5
.267
.2:1
. 1f7
.1^4
.153
.142
.130
.119
.1P7
.095
.09t>
5.
-------
Table H-l. (CONTINUED)
rojectioJ Bus
Year
1996
1997
, 1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
199Q
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Taxi
Auto
HDT
Totals
scenario T^D.E. (Continued)
4.866
4.987
5. 109
5.257
5.379
.096
.096
.C97
.097
.C97
.362
.362
.362
.362
.362
7.907
7.907
7.907
7.947
7.947
13.231
13.352
13.474
13.662
13.785
Scenario T^D-E^
1.832
1.929
2.025
2.090
2.244
2.402
2.459
2.610
2. 750
2.811
2.971
3.118
3.16fc
3,303
3.456
3.474
3.612
3.733
3.873
3.873
4.014
4.136
4.136
4.279
4.401
1.4F4
1.476
1 . 46fc
1.172
.524
.722
.555
.411
.312
.246
. £01
.168
.146
.135
.124
.113
.1C2
.091
.081
.C80
.079
.C79
.C78
.078
.077
5.977
5.936
5.895
4.702
3.700
2.P87
2.218
1.641
1.244
.979
.303
.671
.583
.538
.493
.449
.405
.362
.319
.317
.315
.312
.310
.307
.305
5.884
5.787
5.787
5.872
6.017
6.162
6.200
6.307
6.338
6.408
6.513
6.468
6.571
6.487
6.555
6.589
6.502
6.502
6.414
6.414
6.447
6.325
6.325
6.235
6.235
15.17*
15.128
15.176
13.836
12.884
12.173
11.431
10.969
10.673
10.444
10.488
10.426
10.469
10.463
10.628
10.624
10.621
10.688
10.687
10.684
10.855
10.8S2
10.849
10.899
11.018
Scenario T.D^E.
1.812
1.90E
2.003
2.165
2.219
2.376
2.537
2.688
2.749
2.891
3.051
3.198
1.5C1
1.517
1.526
1.236
1.005
.£08
.673
.5P3
.598
.672
.905
1.233
6.045
6.086
6.127
4.980
4.053
3.245
2.706
2.348
2.414
2.715
3.643
4.969
5.819
5.819
5.914
6.102
6.253
6.507
6.660
6.882
7.038
7.227
7.345
7.424
T5TY77
15.329
15.569
14.486
13.529
12.935
12.576
12.502
12.799
13.504
14.945
16.824
(Continued)
210
-------
Table H-l. (CONTINUED)
Projectio
Year
1988
1989
1990
1991
1992
1993
1994
1V95
1996
!997
1998
!9T9
?000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
?000
Bus
3.365
3.383
3.536
3.673
3.81 1
3.93C
3.950
4.069
4.210
4.331
4.«JL3_1
4.473
4.594
Taxi
scenario
1.708
2.Z37
2. £21
3.375
3.897
4. 4 CO
4.C69
5.219
5.548
5.739
5.997
6.195
6.3f 2
Auto
Wi "***
6.902
9.C48
11.368
13.617
15.743
17. £15
19.640
21.078
22.431
23.260
24,329
25.044
25.505
HOT
Lnued;
7.659
7.699
7.897
7.938
8.097
8.097
8.139
8.258
8.300
8.421
8.4?1
8.584
8.5R4
Totals
19.634
22.366
25.622
28.602
31.548
34.242
36.597
38.626
40.489
41.751
43.077
44.297
44.985
Scenario T~D~E.
1.812
1.9C c.
2.003
2.165
2.32C
2.479
2.643
2.903
3.079
3.224
3-^59C
3.541
3.713
3.849
4.0C7
4.14 7
4.287
4.4C6
4 . 5 4 £
4.66t
4.812
4.932
5.052
5.199
5.32G
1.5C1
1.3C9
1.517
1.225
.994
. 791
. t59
.571
.5F3
.655
.873
1.1F9
1.64&
2.147
2.695
3.2?5
3.7C5
4. 1P4
4. 5F5
4.917
5. Z28
5.3*2
5.625
5.757
5.658
^ z ±
6.045
6.P45
6.045
4.881
3.940
3.134
2.597
2.239
2.284
2.552
3.403
4.612
6.358
b.284
10.343
12.314
14.149
15.896
17.418
18.582
19.657
20.239
21.045
21.r38
21.806
5.819
5.819
5.819
6.004
6.152
6.4C4
6.554
6.667
6.818
6.893
7.119
7.196
7.311
7.349
7.426
7.582
7.6?1
7.621
7.660
7.660
7.819
7.819
7.819
7.659
7.859
15. 17'r
15.280
15.384
14.275
13.407
12.808
12.453
12.381
12.763
13.324
14.785
16.538
19.030
21.629
24.471
27.267
29.763
32.107
34.211
35.827
37.516
38.373
39.542
40.352
40.843
(Continued)
211
-------
Table H-l. (CONTINUED)
Projectior
Year
1 97 6
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Bus
Taxi
Auto
HOT
Totals
Scenario T^D.E.
1.812
1.90?
2.003
2.067
2.21V
2.376
2.431
2.5*1
£.749
2.780
2.938
3.084
3.133
3.266
3.41£
3.436
3.572
3.691
3.83C
3.870
3.97C
4.090
4.09C
4.232
4.353
1.5C1
1.493
1.4E5
1.1P2
.957
.757
,t?4
.535
.542
.603
.794
1.C7C
1.467
1.9PO
2.35£
2.790
3.187
3.555
3.876
4.110
4.294
4.3*4
4.540
4.617
4.645
6.P45
6. 004
5.963
4.781
3.8?3
3.028
2.495
2.136
2.163
2.400
3.177
4.274
5.849
7.576
9.389
11.C94
12.652
14.106
15.338
16.237
17.070
17.438
17-990
18.265
18.344
5.819
5.723
5.723
5.807
5.950
6.094
6.131
6.237
6.268
6.337
6.441
6.396
6.498
6.416
6.483
6.516
6.430
6.430
6.343
6.343
6.376
6.255
6.255
6.166
6.166
15.177
15.127
15.174
13.847
12.960
12.254;
11.681
11.488
11.722
12.119
13.350
14.824
16.947
19.157
21.648
23.836
25.842
27.786
29.388
30.521
31.709
32.177
32.875
33.280
33.508
L_212
-------
Table H-3.
PROJECTED TSP CONTRIBUTIONS FROM FOUR
MODES OF TRANSPORTATION TO THE MONITORING
SITE AT 434 S. SAN PEDRO St.,-LA
Projec
+ 1 nn
don
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
199C
1991
1992
1993
1 994
1995
1996
1997
1998
1999
?000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1903
1994
Bus
Taxi
Auto
HDT
Total
Scenario T^D^E^
2.091)"
2. 1ST
2.28C
2.353
2.511
'2.675
2.843
2.89Z
3.067
3.211
3.377
3.413
3.563
3.71fc
3.757
3.894
4.033
4.151
4.172
4.292
4.433
4.553
4.553
4.696
4.817
.080
.080
OFO
.064
.051
.040
.034
.G25
.019
.015
.C13
.011
.CC9
.LC9
.ere
.CC7
.CC7
.006
.COS
.COS
.CC5
.G05
.005
.ors
.CC.5
7.397
7.517
7.636
6.235
5.017
4.002
3.142
2.375
1.839
1.480
1.241
1.059
.940
.885
.828
.769
.708
.646
.581
.589
.596
.604
.611
.618
.626
3.990
3.990
3.990
4.117
4.219
4.424
4.528
4.606
4.710
4.762
4.952
5.006
5.036
5.112
3.165
5.310
5.337
5.337
5.365
5.365
5.512
5.512
5.512
5.539
5.660
13.557
13.772
13.987
12.769
11.798
11.140
10.546
9.898
9.635
9.468
9. 582
9.489
9.617
9.724
9.758
9.980
10.085
10.149
10.124
10.250
10.546
10.674
10.681
10.859
11.108
Scenario T^D.E.
2.C91C
2.185
2.28C
2.451
2.612
2.77&
2.94 t
3. 10 £
3.2P6
3.433
3.60L
3.754
3.930
4.067
4.226
4.366
4.507
4.626
-4.768
.080
.080
.080
.C6<,
.051
.040
.C71
.023
.C1E
.on
.on
.010
. rc9
.CT9
.CCP
.CT7
.C-P7
.C06
.ors
7.397
7.397
7.397
5.940
4.707
3.698
2.P58
2.129
1.625
1.289
1.065
.897
.785
.728
.672
.616
.560
.504
* .448
3 .990
3.990
3.990
4.117
4.219
4.321
4.422
4.499
4.600
4.651
4.727
4.892
4.97Q
4.996
5.048
5. Q74
5.100
5.100
5.126
13.5ffr
131 452
13. #47
12.572
11.589
10.837
10.259
9.757
9.529
9.387
9.405
9.552
9.694
9.600
9.955
10.064
10.174
10.236
10.34*
213
-------
Table H-2. (CONTINUED)
Projec-
tion
W iUJll
Year
1995
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Bus
4.88*
5.032
5.152
5.272
5.419
5.539
2.C90
i*18J
2.280
2.451
r 2.511
2.675
2.842
2.999
3.176
3.211
3.377
3.527
3.699
3.834
3.874
4.012
4.151
4.270
4.411
4.411
4.553
4.673
4.793
4.937
4.937
Taxi
.005
.COS
.005
.005
.CCS
.CCS
.CPO
.0?0
.080
.C£4
.C51
.04C
.C21
.023
.Dig
.014
.011
.CC9
.00*
.007
.CC'7
.CT6
.005
.CC5
GC4
CC4
.C04
.CC4
.CG4
OC4
.C04
Auto
Scenario T.DE"
.448
.448
.448
.448
.448
.448
Scenario T^D^E
7.397
7.341
7.285
5.?05
4.565
3.559
2.728
2.017
1.527
1.201
.984
.821
.712
.656
.601
.546
.A 92
.439
.387
.383
.380
.377
.373
.270
.366
HDT
L (Continued)
5.126
5.152
5.152
5.272
5.299
5.299
1
3.990
3.895
3.895
4.019
4.018
4.115
4.212
4.177
4.272
4.319
4.277
4.323
4.392
4.299
4.344
4.366
4.388
4.27Q
4.292
4.292
4.194
4.194
4.194
4.094
4.094
Total
* "V '
To. 447
10.638
10.758
10.998
11.171
11.292
~T3T557
13. $*»1
"__W^ ^If *
T3.5*
1 2. 33 9
11.145
1 0. 388
9.814
9.216
8.992
8.745
£.649
S.680
8.811
8.796
8.825
8.930
9.037
8.984
9.094
9.Q90
9.131
9.247
9.364
9.406
9.402
Scenario T3P2E1
2. '06 4
2.15£
2.252
2.324
2.481
2.642
2.808
2.856
3.029
3.172
3.335
3.371
Of 1
.GF1
.CM
.065
.053
.042
.C3fe
.033
.024
.037
.050
.068
7.471
7.592
7.713
6.232
5.191
4.192
3.529
3.087
2.194
3.621
4.901
6.734
3.941
3.941
3.941
4.066
4.167
4.369
4.472
4.549
4.651
4.7Q3
4.891
4.944
13.557
13.772
13.987
12.787
11.891
11.244
1 0. 846
10.525
1 0. 908
11.533
13.177
15.117
-------
Table H-2. (CONTINUED)
Projec-
v» _ _,
Tear
1986
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1 99£
1999
2000
1976
1977
1978
1979
1980
Bus
3.539
3.672
"3.71C
3.84t
3.983
4.10C
4.12 1
'4.239
4.37*
4.497
4.497
4.63c
4.75 7
2.064
2.15L
2.252
2.42C
2.579
2.743
2.912
3.06J-
3.245
3.39C
3.557
3.708
3.881
4.016
4.174
4.31 Z
4.451
4.56£
4.70V
4.827
4.97C
5. 08*
5.207
5.352
5.471
Taxi
. 093
.122
.152
.181
.2Cir
.273
.156
.27!
.2£9
.297
. 3T9
.316
.320
.DM
.081
. OE1
.065
.053
.C42
.C>75
.070
.07C
.034
.045
. 062
.093
.122
. 152
. 1P1
in
.273
.256
.273
.289
.2^7
.If, 9
.316
.32C
Auto
Scenario T^f^
V.412
12.439
15.738
18.983
22.096
25.163
27.921
30.160
32.297
33.686
35.449
36.710
37.633
Scenario T«D_
7.471
7.471
7.471
6.032
4.870
3. £74
3.210
2.767
2.? 22
3.155
4.206
5.700
7.859
10.238
12.784
15.219
17.488
19. £46
21.528
22.967
24.295
25.015
26.011
26.620
26.952
HDT
% (Continued)
5.023
5.0*9
5.102
5.244
5.271
5.271
5.298
5.296
5.443
5.443
5.443
5.471
5.590
Cl
3.941
3.941
3.941
4.066
4.167
4.267
4.368
4.443
4.543
4.594
4.669
4.831
4. 9Q 8
4.934
4.986
5.011
5.037
5.Q37
5.Q63
5.063
5. Q£ g
5.088
5.207
5.233
5.233
Tot«l
18.666
21.281
24.702
28.254
31.558
34.767
37.596
39.969
42.408
43.923
45.698
47.135
48.3C1
TJ.557
13.651
13.?45
12.5**
11.669
10. 926
10.524
1 0. 308
10.641
11.173
12.477
14.301
16.742
19.310
22.095
24.723
27.184
29.485
31.556
33.130
34.642
35.489
36.733
37.521
37.976
Scenario T-^E..
2.064
2.158
2.252
. .2.42C
2.480
.OTT
.081
.OF1
.065
.053
* * "fr 9 1
7.415
7.358
> 5.805
4.723
3.9*1
3.847
3.847
3.970
3.968
J» 3» 9
13.501
13.538
12.3S1
11.2*4
215
-------
Table H-2. (CONTINUED)
Projec-
tion
Tear
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Bus
2.642
2.808
2.962
3.137
3.172
3.335
3.483
3.653
3.787
3.826
3.962
4.100
4.217
4.356
4.356
4.497
4.615
4.733
4.876
4.876
Taxi
.042
.035
.030
.030
.034
.045
C*5
.076
.100
.124
. 14£
.170
.191
. 2f 9
.223
.276
. Z43
.Z53
.259
.262
Auto
scenario 13 D2£
3.728
3.064
2.621
2.652
2.940
3.888
5.220
7.137
9.221
11.417
13.477
15.354
17.101
18.576
19.643
20.596
21.017
21.657
21.963
22.033
HDT
1 (Continued)
4.064
4.160
4.126
4.219
4.265
4.224
4.270
4.338
4.246
4.290
4.312
4.334
4.217
4.239
4.239
4.142
4.142
4.142
4.Q44
4. Q44
Total
*
10.475
10.066
9.738
10.038
10.411
11.493
1 3. 028
15.204
17.353
19.658
21.899
23.958
25.725
27. $$0
28.461
29.470
30.017
30.785
31.141
31.215
216
-------
Table H-3.
PROJECTED TSP CONTRIBUTIONS FROM FOUR
MODES OF TRANSPORTATION TO THE MONITORING
SITE AT 538 S. CLARK ST., CHICAGO
Projec-
*? swi
uluu
Tear
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1 992
1993
1994
1995
1996
1997
1998
1999
zooo
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Bus
Taxi
Auto
HDT
Total
Scenario 1j. DI£I
U353
1.353
1.466
1.513
1.55C
1.709
1.750
1.78C
1.82C
1.97Z
2.004
2.025
2.195
2.207
2.22V
2.381
2.391
2.391
2.405
2.547
2.56C
2.56C
2.702
2.716
2.716
2.778
2.7P7
2.797
2.L54
1.792
1.413
1.099
.c?2
.629
.5C1
.415
-3r1
.3C£
.286
. 266
.245
.223
.202
.180
. iec
. 1£2
.182
.183
.1F3
.1P4
6.?67
6.295
6.324
5.101
4.054
3.200
2.484
1.859
1.425
1.135
.942
.796
.700
.653
.605
.557
.509
.460
.410
.412
.414
.415
.417
.421
.421
3.834
3.947
3.947
4.073
4.173
4.274
4.499
4.577
4. 68Q
4.732
4.8Q9
4.861
4.939
5.1C3
5.156
5.182
5.209
5.209
5.235
5.377
5.4Q4
5.404
5.404
5.432
5.432
14.ZI2
1 4. 383
14.534
12.940
11.569
1C. 596
9.831
9.037
8.554
8.339
8.170
8.034
8.141
8.249
8.257
6.365
8.334
8.263
8.331
8.516
8.559
8.562
&.706
&.751
8.752
Scenario T-D..E
1.353
1.353
1.466
1.513
1.669
1.709
1.75C
1.907
1.950
2.102
2.131
2.161
2.332
2.344
2.50*
2.521
2.534
2.675
* .2.688
2.83C
2.778
2.768
2.766
2.Z16
1.7CC
1.375
1.059
.789
.eCC
.474
.392
.329
.28!:,
. Z66
.246
.i:24
.203
.1F3
.162
.162
6.267
6.267
6.267
5.C33
, 3.988
3.133
2.421
1.804
1.377
1.C92
.902
.760
.665
.617
.570
.522
.475
.427
- .380
.380
3.834
3.947
3.834
3.956
4.054
4.152
4.249
4.323
4.420
4.469
4.542
4.591
4.664
4.6S9
4.733
4.762
4.786
4.646
4.669
4.669
14.232
14.336
14.336
12.717
11.461
10.369
9.479
8.822
8.347
tt.138
7.974
7.840
7.949
7.917
8.061
8.030
7.999
7.931
7.900
8.041
-------
Table H-3. (CONTINUED)
Projec-
Year
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
Bus
2.844
2.844
2.987
3.002
3.145
1.353
1.353
.466
.513
.669
.705
.75C
.907
.95C
.972
2.13£
2.161
2.332
2.344
2.369
2.521
2.534
2.534
2.688
2.685
2.844
2.844
2.844
3.002
3.002
1.334
1.334
1.445
1.491
1.526
1.6fc5
1.725
1.755
1.794
1.944
1.975
1.997
2.164
2.175
Taxi
I
.161
.161
.161
.160
.160
2.778
2.750
2.721
2.17C
1.701
1.323
1.C15
.748
.564
.443
.363
.2C2
.261
.241
.220
.199
.1FC
.160
.140
.139
.137
.176
. 134
.133
.171
2.*01
2.81G
2.E2C
2.2*4
1.851
1.477
1.212
1.066
1.091
1.223
1.636
2.224
3.C77
4.034
Auto
Scenario T2DiEi
.380
.380
.780
.380
.3 SO
Scenario T-U-K-
6.267
6.201
6.144
4.P80
3.831
2.977
2.278
1.679
1.269
.995
.P13
.677
.586
.538
.491
.445
.400
.356
.313
.309
.305
.302
.298
.294
.290
Scenario T.D.E.
6.318
6.347
6.376
5.171
4.187
3.345
2.784
2.411
2.^70
2.773
3.713
5.054
6.998
9.156
HDT
(Continued)
4.693 "
4.693
4.693
4.717
4.717
3.~8T4
3.834
3.834
3.956
3.935
4.029
4.124
4.068
4.160
4.206
4.142
4.186
4.253
4.137
4.180
4.202
4.083
4.Q? 3
4.103
3.962
3.982
3.84Q
3.840
3.859
3.716
3.779
3.891
3.891
4.015
4.114
4.213
4.435
4.512
4.614
4.665
4.741
4.792
4.868
5.030
Total
~" f. Of 9
8.078
8.220
8.259
8.402
14.232
14.137
14.165
12.519
11.137
10.039
9.167
8.402
7.943
7.615
7.455
7.326
7.433
7.260
7.260
7.367
7.196
7.132
7.245
7. 099
7.2*9
7.121
7.116
7.288
7. 15«
u.m
14.382
U.fSI
12.961
11.679
10.720
10.176
9.743
9.968
10.604
12.065
14.067
f7»10<6
20*395
218
-------
Table H-3. (CONTINUED)
Projec-
4 »«
tion
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
zcoo
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1 999
?000
1976
1977
1978
1979
1980
19?1
1982
Bus
2.19S
2.347
2.359
2.359
2.371
2.51 1
2.523
2.523
2.664
2.677
2.677
Taxi
5.054
6.C76
6.9S&
7.f 4Z
& . 6 2 1
9.2P6
9.822
1C. 145
10-. 5F2
10.*64
11.074
Auto
Scenario T,D0E,
121
11.482
13.727
15. £41
17. £71
19.665
21.068
22.380
23.138
24.16C
24.929
25.240
HDT
(Continued)
5.Q82
5.108
5.135
5.135
5.161
5.300
5.327
5.327
5.327
5.354
5.354
Total
-
23.81*
27.219
30.293
33.Z67
35.818
38.105
40.052
41.134
42.733
43.825
44.30*
Scenario T^E
1.334
1.334
1.443
1.491
1.645
1.6E 5
1.725
1.88C
1.92Z
2.073
2.107
2.131
2.299
2.311
2.472
2.485
2.49£
2.637
2.650
2.79C
2.804
2.804
2.944
2.959
- 3.1CC
2.801
2. 7^1
2.7C1
2.246
1.cC7
1.
-------
Table H-3. (CONTINUED)
Projec-
tion
Year
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
9? 3
984
V85
986
987
9£S
989
990
991
992
993
094
995
996
907
998
999
Bus
Taxi
Auto
Scenario TL 11 £
u
1.
1.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2.
2
2.
2.
8t
92
94
10
1T
29
31
33
4?
49
49
65
65
PC
RC
8C
95
95
G
2
<
7
L
9
1
5
C
i
*
C
r-
4 .
4
4
v
V
.
,
1.
1 .
1.
2.
3.
4.
4.
5.
6.
6.
7.
7.
7.
7.
7.
7-
97T
1.79
CF2
4T1
91 1
614
37t
171
9C7
5?:,
2f"£
7?C
111
<.29
553
754
£67
t(i
2.1 77
2. 1 °9
2.430
3.207
4.295
5.b 62
7.545
9.323
10.c-62
1 Z . 4 62
13.250
14.983
15.V08
1t. r^05
16.PC2
17.238
17.437
17.413
HDT
Total
(Continued)
4.
4.
4.
4.
4.
4.
4.
4.
4.
4.
4.
4.
?
3.
3.
3.
3.
3.
010
101
146
0*3
126
192
Q7ti
11:1
142
024
024
045
906
925
7P5
735
8Q4
663
9.
9.
9.
1C.
12.
14.
17.
19.
22.
24.
26.
2s.
29.
30.
30.
31.
32.
31.
037
201
601
828
470
967
314
949
496
578
580
399
475
663
943
581
067
897
3SRE
220
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-45Q/5-79-QQ5
2.
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
5. REPORT DATE
The Impact of Future Diesel Emissions on the Air
Quality of Large Cities
May 1979
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Roy A. Paul
9. PERFORMING ORGANIZATION NAME AND ADDRESS
PEDCo Environmental, Inc.
505 S. Duke Street
Durham, N.C. 27701
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-2585
Work Assignment #12
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. EPA, OAQPS, SASD
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
FTNAI
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
performed gt tne request of OMSAPC/OANR for an air quality
assessment of the particulate impact from diesel vehicles.
16. ABSTRACT
The impact of diesel-powered vehicles in the central business districts of
New York, Chicago and Los Angeles was studied. Annual particulate matter
emissions from automobiles, taxis, buses and light-duty trucks were projected
to the year 2000. The emissions were correlated with air monitoring data in
order to calculate the impact on air quality. Significant increases were
predicted in atmospheric particulate matter because of increased diesel
engine use.
KEY WORDS AND DOCUMENT ANALYSIS
a.
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COS AT I Field/Group
Air pollution
Air Quality Assessment
Total Suspended Particulate Matter (TSP)
Benzo (a) pyrene (BaP)
Diesel Emissions
Population exposed
Projected emissions
Diesel-powered vehicles
8. DISTRIBUTION STATEMENT
»
Unlimited
19. SECURITY CLASS (ThisReport)'
Unclassified
21. NO. OF PAGES
218
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
221
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