oEPA
United States
Environmental Protection
Agency
Office of Mobile Source Air Pollution Control
Emission Control Technology Division
2565 Plymouth Road
Ann Arbor. Michigan 48105
EPA 460/3-85-002
March 1985
Air
Improved Mobile Source
Exposure Estimation
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EPA 460/3-85-002
Improved Mobile Source Exposure Estimation
by
Melvin N. Ingalls
Southwest Research Institute
6220 Culebra Road
San Antonio, Texas 78284
Contract No. 68-03-3162
Work Assignment 6
EPA Project Officers: Robert J. Garbe
Craig A. Harvey
EPA Branch Technical Representative: Robert I. Bruetsch
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Mobile Source Air Pollution Control
Emission Control Technology Division
2565 Plymouth Road
Ann Arbor, Michigan 48105
March 1985
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This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are available
free of charge to Federal employees, current contractors and grantees, and
nonprofit organizations - in limited quantities - from the Library Services
Office, Environmental Protection Agency, 2365 Plymouth Road, Ann Arbor,
Michigan
This report was furnished to the Environmental Protection Agency by Southwest
Research Institute, 6220 Culebra Road, San Antonio, Texas, in fulfillment of
Work Assignment No. 6 of Contract No. 68-03-3162. The contents of this
report are reproduced herein as received from Southwest Research Institute.
The opinions, findings, and conclusions expressed are those of the author and
not necessarily those of the Environmental Protection Agency. Mention of
company or product names is not to be considered as an endorsement by the
Environmental Protection Agency.
Publication No. EPA 460/3-85-002
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FOREWORD
This project was conducted for the U.S. Environmental Protection Agency
by the Department of Emissions Research of Southwest Research Institute. The
project was begun in January 1984 and completed in June 1984. The project
was conducted under Work Assignment 6 of Contract 68-03-3162, and was
identified within Southwest Research Institute as Project 03-7338-006.
Mr. Robert J. Garbe of the Emission Control Technology Division, Office
of Mobile Source Air Pollution Control, Environmental Protection Agency, Ann
Arbor, Michigan, served as EPA Project Officer. Mr. Robert I. Bruetsch, of the
same EPA Division was the Branch Technical Representative. Mr. Charles T.
Hare, Manager, Advanced Technology, Department of Emissions Research,
Southwest Research Institute, served as the Project Manager. The project was
under the supervision of Melvin N. Ingalls, Senior Research Engineer, who
served as Project Leader and principal investigator. Ms. Pamela Nickoloff,
laboratory assistant, assisted with the programming and computer runs required
for the project.
Sections III and V of this report are modified versions of Sections III and
IV from "Mobile Source Exposure Estimation," EPA 460/3-84-004, the final
report to EPA by Southwest Research Institute for Work Assignment 6 of
Contract 68-03-3073. The information is repeated here to provide, in one
volume, a complete explanation of the mobile source exposure estimation data
base.
iii
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TABLE OF CONTENTS
Page
FOREWORD iii
LIST OF FIGURES vii
LIST OF TABLES ix
SUMMARY xi
I. INTRODUCTION 1
II. NEM N/M POLLUTANT CONCENTRATIONS 7
III. MOBILE SOURCE MICROENVIRONMENT POLLUTANT
CONCENTRATIONS 13
IV. NEM NEIGHBORHOOD/MICROENVIRONMENT
POPULATION 35
V. MOBILE SOURCE MICROENVIRONMENT
POPULATIONS 51
VI. NATIONAL NEIGHBORHOOD POPULATION 77
VII. RURAL EXPOSURE 89
VIII. PERSON HOUR EXPOSURE COMPUTER PROGRAM 91
IX. CONCLUSIONS AND RECOMMENDATIONS 101
REFERENCES 103
APPENDICES
A. SUPPORTING INFORMATION FOR CO CONCENTRATIONS
IN DIFFERENT NEIGHBORHOODS
B. SUPPORTING MATERIAL FOR CO CONCENTRATIONS
FROM MOBILE SOURCE MICROENVIRONMENTS
C. SUPPORTING MATERIAL FOR NATIONAL POPULATION
BY NEIGHBORHOOD TYPE
D. SUPPORTING INFORMATION FOR NATIONAL POPULATION
ESTIMATE BY N/M TYPE
E. LISTING OF MOBEX COMPUTER PROGRAM
v
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LIST OF FIGURES
Figure
III-l Parking Garage CO Concentration Distribution,
25 Percent Active Cars, Average Wind Speed 16
III-2 Wind Speed Distribution, Average of Seven
U.S. Cities 18
III-3 CO Concentration as a Function of Hourly Percent
ADT for the Sumner Tunnel (1961) 22
III-(f CO Concentration as a Function of Hourly Percent
ADT for an Average Roadway Tunnel 24
IV-1 Pattern of Neighborhood Types in a Portion of the
Philadelphia Study Area 36
IV-2 Chicago Study Area 37
IV-3 Philadelphia Study Area 38
IV-4 St. Louis Study Area 39
IV-5 Los Angeles Study Area ^°
V-l Hourly Average Cars in Motion for Weekdays in
Parking Garages ^
V-2 Hourly Average Cars in Motion for Saturdays in
Parking Garages 55
V-3 Hourly Average Cars in Motion for Sundays in
Parking Garages 56
V-4 Hourly Tunnel Traffic for Weekdays 59
V-5 Hourly Tunnel Traffic for Weekends 60
V-6 Number of Person Trips to CBD per Person in
Urban Areas 63
V-7 Hourly Traffic Distribution in the CBD for an
Average Weekday 66
Vll
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LIST OF FIGURES (CONT'D).
Figure*
V-8 Hourly Traffic Distribution in the CBD for
Saturday 69
V-9 Hourly Traffic Distribution in the CBD for
Sunday 70
V-10 Pedestrians for Individual Days of the Week as
a Percent of Total Weekly Pedestrians 71
V-ll Hourly Pedestrian Distribution in the CBD for
Weekdays 72
V-12 Hourly Pedestrian Distribution in the CBD for
Saturdays 73
VI-1 Pictorial Representation of the U.S. as One
Urban Area 78
VI-2 Percent of Urbanized Area Center City That Is
Urban Core 82
VIII-1 Block Diagram of Person Hour Exposure Computer
Program, MOBEX 93
viii
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LIST OF TABLES
Table
II-l Summary of CO Monitors Selected for Mobile
Source Exposure Estimate 8
II-2 1981 CO Emission Factors used for Each Neighborhood
or Microenvironment 9
II-3 Best Estimate of CO Scaling Factor for NEM
Micr ©environments 10
III-l CO Levels Found in Parking Garages 15
III-2 Probability Distribution Parameters for Parking
Garage Pollutant Distributions 19
III-3 Final Parking Garage Pollutant Concentration
Distribution 20
III-* CO Levels Found in Urban Commuter Tunnels 21
III-5 Pollutant Concentration Intervals for Roadway Tunnels 26
III-6 Distributions of Hourly Average CO Levels in
Roadway Tunnels 2^
III-7 SAROAD CO Monitors used in Street Canyon
Analysis 2«
III-8 Descriptive Statistics for Street Canyon
CO Readings 30
III-9 Distribution of Hourly Average CO Levels
in Street Canyons 31
III- 10 Conversion Table of Measured PPM CO to yg/m3
for 1 g/min Emission Factor 33
IV-1 Geographic Classification of NEM CO Study
Area Districts
IV-2 Assumptions Concerning Work NT's of A-O Groups
IV-3 Description and Apportionment of Activity
Pattern Subgroups
IV-* A-O Group Population by District Classification
in Four Cities
ix
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LIST OF TABLES (CONPD).
Table Page
IV-5 Collapsed Home-to-Work Trip Tables, Expressed as
Number of Trips and Fraction of Trips 47
V-l Parking Garage Construction in the U.S.,
1967-1982 52
V-2 Nationwide Person Hours of Exposure in Parking
Garages by Hour of the Day 57
V-3 Traffic Distribution by Day of the Week for
Several Situations 58
V-4 Nationwide Person Hours of Exposure in Tunnels
by Hour of the Day 62
V-5 Estimated CBD Daily Traffic as a Percent of
Weekly Traffic 64
V-6 Daily Person Trips and Vehicles in Street Canyons 65
V-7 Nationwide Person Hours of Exposure in Street
Canyons by Hour of the Day 75
VI-1 NEM CO Study Urban Area Classification 80
VI-2 Classification of Urban Areas by Percent of Residents
in Urban Core (Based on 1980 Census) 83
VI-3 National Population for Weekday 85
VI-4 National Population for Saturday 86
VI-5 National Population for Sunday 87
VI-6 Explanation of Neighborhood/Microenvironment (N/M)
Codes used in this Project 88
VII-1 Rural Exposure to Mobile Source Pollutants 90
VI1I-1 Exposure Computer Program Input 95
VIII-2 Example Input Format for Exposure Computer Program 99
VIII-3 Sample of Exposure Program Output 99
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SUMMARY
This project was conducted to provide an improved estimate of nationwide
annual person hours of exposure to non-reactive mobile source pollutants. It
extends the work done under EPA Contract 68-03-3073, Work Assignment 6. In
this project, the population activity files from the EPA NAAQS Exposure Model
(NEM) for CO, were used as the basis for national population estimates by hour
of the day in six neighborhood types (center city residential, center city
commercial, center city industrial, suburban residential, suburban commercial,
suburban industrial). In this report, the use of the term "neighborhood"
generally means these six areas. Occasionally, because material from other
studies is used, the term "neighborhood" may be used as the designation for all
urban or all suburban areas.
Populations in three mobile source microenvironments (street canyons,
tunnels and parking garages), developed under Work Assignment 6 of Contract
68-03-3073, were included in the nationwide population estimates. Since these
populations were calculated independently of the NEM neighborhoods, it was
necessary to subtract a number' of pedple equivalent to the mobile source
microenvironment populations from the NEM neighborhood microenvironment
populations. The corrected NEM neighborhood populations together with the
mobile source microenvironment populations constitute a complete hour-by-
hour assignment of the nationwide population to a set of location types.
Pollutant concentrations for the six NEM neighborhoods were developed
using 1981 CO monitor data from the EPA Storage and Retrieval of
Aerometrics Data (SAROAD) data base. Data from a total of 99 monitors were
used to develop CO concentration frequency distributions for each neighborhood
type. For each neighborhood, six different CO distributions were developed by
dividing the 24 hour day into 6 intervals for each of three day types (weekdays,
Saturdays, and Sundays). This process resulted in a total of 108 CO frequency
distributions to represent the nationwide mobile source ambient pollutant
concentrations in the neighborhoods. Within each neighborhood there were
several microenvironments. The pollutant concentrations in these
microenvironments were determined by multiplying the neighborhood
distribution values by a different factor for each microenvironment.
The CO concentrations in these distributions were divided by the 1981 CO
emission factors (in terms of grams per minute calculated from grams per mile
emission factors using the appropriate average speed) from the MOBILE3
computer program that were appropriate for each neighborhood type. The
resulting concentrations were equivalent to concentrations that would be
produced from a one gram per minute emission factor. Concentrations for any
pollutant, at any emission rate, can then be obtained by multiplying the
distribution concentrations by the appropriate emission factors for that
pollutant.
The pollutant concentrations in the three mobile source
microenvironments, developed under Work Assignment 6 of Contract 68-03-
3073, were used as the mobile source microenvironment concentrations for this
study. The concentrations for these microenvironments were also equivalent to
XI
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those resulting from a one gram per minute emission factor. Grams per minute
were used as the emission factor units to allow the use of an idle emission
factor in parking garages.
Person hours of exposure to any pollutant can be obtained by multiplying
the hourly population in each environment by each of the frequencies in the
pollutant frequency distribution for that hour. The total nationwide person
hours can be obtained by summing all hours and all environments. The pollutant
concentration levels corresponding to the various frequencies are obtained by
multiplying the concentration resulting from a one gram per minute emission
factor by the pollutant emission .factor. Since the emission .factor in every
environment is different, this calculation must be done for each environment.
A computer program was written, and provided to EPA, which performs
all the necessary calculations to produce a cumulative frequency distribution of
person hours of exposure at or above a list of specified pollutant
concentrations. The inputs to the computer program include urban and rural
populations for the year being studied, the study year emission factors for the
pollutant for each environment by day type, and the output distribution
pollutant concentration intervals, if desired. As an example, the nationwide
cumulative person hours of exposure to mobile source CO is shown below. The
program allows the exposure concentration intervals to be changed as desired,
so that the intervals shown are for example purposes and are not the only
intervals possible.
POLLUTANT: CO
EXPOSURE TO
CONCENTRATIONS
EXCEEDING
(MICROGRAM/CU.M)
0.000
3500.000
7000.000
10500.000
14000.000
24500.000
35000.000
45500.000
56000.000
66500.000
77000.000
87500.000
98000.000
108500.000
129500.000
150500.000
192500.000
248500.000
304500.000
360500.000
416500.000
486500.000
556500.000
626500.000
696500.000
PERSON
HOURS
EXPOSURE
(MlLLIONS)
1984405.260
82451.679
19394.141
6326.666
2362.171
452.174
166.296
89.024
54.733
34.776
22.702
14.858
9.499
6.952
4.041
2.929
1 .539
.823
.382
.129
.010
0.000
0.000
0.000
0.000
xii
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I. INTRODUCTION
Internal combustion engines used in motor vehicles produce exhaust gases
that contain a multitude of chemical compounds. Four of these pollutants are
regulated by standards (hydrocarbons, CO, NOX» and particulate matter). In
devising means to control these four regulated pollutants, undesirable chemical
compounds may inadvertently be produced or increased. Malfunction of the
engine and emission control systems can also change the concentrations of the
various chemical species in the exhaust. In addition, alternative fuels proposed
for use in motor vehicles can produce greatly different compounds and
concentrations of compounds than are currently produced by gasoline and diesel
fuels. The EPA has instituted a program to determine if any of these
unregulated mobile source emissions cause or contribute to a risk to public
health, welfare or safety. This study is a part of that program.
Previous Work
The work reported on here is the latest in a series of projects on the
subject of unregulated emissions conducted for the EPA by Southwest Research
Institute. Previous projects have developed methodologies for measurement of
unregulated pollutants in vehicle exhaust/!»2»3)* determined the magnitude of
many unregulated emissions in a variety of vehicles/1 f*-8) evaluated the
effects of engine and emission control system malfunctions/9-12' and measured
unregulated emissions using a variety of alternative fuels/13'*7' Thus, the
exhaust emission rates, in terms of mass per distance or mass per time, of many
unregulated pollutants are known.
To determine effects on health and welfare, emission rates of these
unregulated pollutants must be transformed into the ambient pollutant
concentrations to which people are exposed. Another of the previous projects
at SwRI examined localized situations heavily impacted by mobile sources in
which the dispersal of mobile source pollutants is hindered, causing higher than
usual concentrations/18) Several situations involving small areas, called
microenvironments, were identified. Mathematical dispersion models of these
situations were selected and validated to allow the prediction of ambient
concentrations in these microenvironments, based on knowledge of vehicle
exhaust emission rates. These models allowed identification of areas containing
highly concentrated mobile source pollution and permitted the evaluation of the
health and welfare effects of short term, high level exposure to these
unregulated emissions/19)
It was not possible from that study to ascertain long term effects due to
chronic exposure, such as cancer risk, for unregulated pollutants. Another
project was conducted to provide annual exposure information for mobile source
pollutants/20) jnjs information could be used to evaluate health effects of
long term exposure important in performing carcinogenic risk assessments.
That project used the EPA NAAQS Exposure Model (NEM) for CO, developed by
*Superscript numbers in parentheses refer to references at end of this report.
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the EPA Office of Air Quality Planning and Standards (OAQPS),(2D as the basis
for the exposure estimate. Throughout this report whenever the words NEM or
NEM computer program are used it means the actual computer code for the
models as developed by OAWPS. The terms NEM study or NEM CO study refer
to the study reported on in Reference 21. The NEM was not written for
nationwide estimation of mobile source unregulated emissions, and its structure
made it difficult to use for that purpose. Thus, while that project did provide a
method for estimation of nationwide person hours of exposure to mobile source
pollutants, a more refined methodology was needed. The work reported on here
provides that more refined methodology.
The exposures calculated in this project are expressed in terms of person
hours. Person hours of exposure as calculated in this project are simply the
number of people in a situation multiplied by exposure time in that situation in
hours. Thus, 1000 person hours of exposure can be one person exposed for 1000
hours, 1000 people exposed for one hour, or any other values of persons and
hours whose product is 1000.
Objective
The objective of this project was to develop an improved methodology to
obtain an estimate of nationwide, annual person hours of exposure to any mobile
source pollutant. It was then to incorporate that methodology into a computer
program which will calculate the person hours of exposure for any pollutant
when the emission factors for that pollutant are input.
Approach
To determine the annual exposure in person hours to any mobile source
pollutant, the number of persons exposed to the pollutant in various places must
be known as a function of time. The ambient concentration in these places
must also be known as a function of time. The exposure in person hours can
then be ascertained, expressed as the number of person hours in various
pollutant concentration intervals.
To determine how many people are exposed to mobile source pollutants, it
is first necessary to know something about where people are during their daily
activities. Obviously, all people in the United States do not follow the same
daily activity pattern. 3ust as obvious, however, is the fact that large groups of
the population do have similar activity patterns. In the previous mobile source
exposure study, the NEM computer program was used to determine exposure.
That program divides all the non-rural places where people can be into six
"neighborhoods," with six smaller areas called microenvironments in each
neighborhood. The neighborhoods were:
Center City residential
Center City commercial
Center City industrial
Suburban residential
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Suburban commercial
Suburban industrial
These neighborhoods were chosen to match the neighborhood descriptions used
in identifying EPA ambient monitor sites. The microenvironments used were:
indoors, work or school
indoors, home or other
transport vehicle
roadside
outdoors
kitchen
In the NEM CO study, every person in a city was assigned to a
neighborhood type, and to a microenvironment in that neighborhood, for each
hour of the day. Activities were simulated by changing neighborhoods and
microenvironments for various hours of the day. For the purpose of
neighborhood assignments, the people in each city were grouped together by age
and occupation. These groupings were called A-O groups. There were 12 A-O
groups, with a number of subgroups (up to six) within each main A-O group.
People within the same A-O group were called cohorts.
As another part of the previous project, three important
microenvironments where people are in close proximity to vehicles were
identified which were not included in the NEM. These microenvironments were
called mobile source microenvironments. Nationwide hourly populations for the
mobile source microenvironments were determined. These mobile source
microenvironments are:
street canyons
tunnels
parking garages
For this study, in place of the NEM, a new exposure program was written.
This new program does, however, utilize the activity patterns and A-O groups
from the NEM computer program. The new procedure starts by using the NEM
CO study activity patterns and cohort populations by neighborhood, to obtain
the percentage of each city's population in each
neighborhood/microenvironment (N/M) combination for each hour of the day.
These percentages are then multiplied by the number of people nationwide, that
are in cities similar to each of the four cities used. The four population
distributions are summed to yield a nationwide hourly population distribution in
each N/M combination. Once the nationwide population distributions are
obtained, the mobile source microenvironment hourly population distributions
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can be subtracted from the appropriate N/M hourly population distributions.
There is a different set of distributions for weekdays, Saturdays and Sundays.
Besides population in the various neighborhoods, pollutant concentration
must be known to obtain person hours of exposure to various pollution levels.
Pollutant concentrations vary with time in a given neighborhood. Thus,
neighborhood pollutant concentrations are best expressed as frequency of
occurrence distributions. However, the ambient concentrations of most
unregulated pollutants are not available from direct measurements. The
concentrations of these pollutants must be inferred from measurement of some
other pollutant.
Nationwide, mobile sources produce approximately 76 percent of the total
carbon monoxide emitted into the atmosphere.(22) For many of the microenvi-
ronments considered in this study, mobile sources are the only significant
source of CO. Pollutant concentrations are linearly proportional to the exhaust
emission rate ("emission factor"). More specifically, the ambient concentration
of a pollutant emitted from vehicles at the rate of 4 g/min will be twice as high
as the ambient concentration of a pollutant emitted at 2 g/min in the same
situation. Thus, CO concentration measurements can be used to determine the
concentration of any unregulated mobile source emission by multiplying the CO
concentration by the ratio of the unregulated emission factor to the CO
emission factor:
Pollutant Concentration = CO concentration [Pollutant emission rate1
V CO emission rate
In this project, measured or calculated CO concentrations in each neighborhood
and microenvironment were used as the indication of mobile source pollutant
concentration in that neighborhood. This approach assumed that the desired
pollutant and CO have equivalent dispersion and reaction characteristics in the
ambient air.
Any investigation of actual pollutant concentrations in any environment
reveals a rather wide range of pollutant levels. Thus, persons in these
environments at different times are exposed not just to one pollutant
concentration, but to a range of concentrations. The concentration level
depends mainly on the number of vehicles present and the amount of ventilation
(natural or artificial). The concentrations vary not only with time at a single
location, but also from location to location at a given time. By accounting for
the occurrence of pollutant concentration ranges for all locations of a
neighborhood or microenvironment for a year, pollutant concentration
frequency distributions can be developed which take into account the
concentration variation with time and location.
CO distributions were obtained from different sources depending on the
neighborhood or microenvironment under consideration. The CO concentration
for the NEM neighborhoods were obtained from CO ambient monitor data in the
EPA SAROAD data base. The street canyon mobile source microenvironment
also utilized monitor data from the SAROAD data base. The CO concentrations
in the tunnel and parking garage microenvironments were estimated from
various studies in the literature. Prior to use in the exposure computer
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program, the CO concentration values were divided by the appropriate CO
emission factors in grams/minute. This produced concentration distributions
equivalent to those from a 1.0 g/min emission factor.
To obtain pollutant concentrations for other pollutants, the computer
program multiplies the concentration distribution in the program by the new
pollutant emission rate. This is simply the equation shown above rearranged as
follows:
Pollutant concentration = / CO concentration | Pollutant emission rate
I CO emission factor/
The computer program then combines the hourly population values with
the pollutant frequency distributions to produce cumulative exposure
distributions for each N/M combination, including the mobile source microenvi-
ronments. Emission factors for the pollutant under study are required inputs
for the program. Provision was made for a different emission factor for each
N/M type.
Use of the Terms "Center City" and "Urban Core"
The EPA monitor site descriptions have two main classifications: center
city and suburban. It is from these classifications that the NEM CO study
neighborhoods were named. In this study, Census Bureau populations of
"urbanized areas," which consist of a center city and some surrounding areas,
are used to define the number of people in urban areas. It is most important to
understand that the EPA monitor site definition of center city (from which the
neighborhood type definition was taken) is not the same as the Census Bureau
definition of center city used in urban population figures. The EPA publication,
"AEROS Manual of Codes" defines center city as: "...core area of the city, not
its incorporated limits." Conversely, the Census Bureau, in its urban population
tables, defines the center city as incorporated limits. (See detailed explanation
in section IV). Because of the confusion arising from having two different
meanings for the term "center city," whenever possible in this report, the term
"urban core" is used in place of the EPA term "center city" for neighborhood
and CO monitor designations. The urban core for an urbanized area is not
necessarily contiguous. Cities other than the central city of the urbanized area
may have all or some of their land area devoted to "urban core" use, as may
unincorporated areas within the urbanized area. The exact meaning of "urban
core" is developed operationally in later sections. For the present, only a
conceptual understanding is needed.
Report Organization
In the report sections that follow, the information necessary to produce
both the pollutant distributions by N/M types, and the population distributions
by N/M type, is presented in detail. The pollutant concentration distribution
development is presented first because, in the case of the street canyon mobile
source microenvironment, the CO concentration is used to help define the
street canyon population distribution.
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For both the pollutant and population distributions, the information on the
general NEM type neighborhoods is presented first, then the information on the
three mobile source microenvironments. The next-to-last section of the report
explains the exposure estimation computer program and its use.
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II. NEM N/M POLLUTANT CONCENTRATIONS
The basic premise of this study is that measured (or calculated) CO
concentrations in various environments can be used to calculate the
concentrations of any mobile source-generated pollutant by knowing the
relative emission factor of CO and the pollutant of interest. The previous SwRI
project to estimate exposure to mobile source pollutants utilized the NEM CO
study. The NEM CO study was for four cities. There were six neighborhood
types used by the NEM, with one CO monitor representing each neighborhood
type in each city. Because of the difficulty in locating suitable CO monitors,
some monitors represented more than one neighborhood type. Thus, in the NEM
CO study, only 20 CO monitors were used to represent all four cities, and by
extrapolation, the entire country.
This project sought to increase the number of CO monitors used, by
selecting from the EPA SAROAD data base CO monitors by neighborhood type
from the whole country. The selection criteria for the CO monitors were:
Sufficient data (60% of possible hourly readings = 6100 hours)
Not a microscale monitor, such as a street canyon monitor
Representative of mobile source pollution (i.e., no large stationary
sources close by)
Cross section of range of CO concentrations encountered
Calendar year 1981 CO monitor data were chosen for use, to be compatible
with the 1981 street canyon CO concentration distribution generated in the
previous exposure study.
EPA provided a 1981 list of SAROAD station descriptions (on microfiche)
and a printed summary report of 1981 through 1983 CO concentration statistics
from CO monitors in the SAROAD data base. There were 346 CO monitors in
the 116 largest urban areas with 1981 CO readings. The site descriptions were
read from the microfiche to obtain the descriptions of these 346 monitors. The
site descriptions and statistics were combined and sorted by N/M type. Of the
346 CO monitors, 68 monitors had less than 6100 hours of data. Of the
remaining 278 monitors, no description was available for 7 monitors, and 12
monitors had no neighborhood type listed. From this list a total of 99 monitors
were selected for use in this study. The final monitor selection was made after
consultation with EPA.
A summary of the characteristics of the 99 monitors chosen is presented
in Table II-1. The individual monitors selected are listed by neighborhood type
in Appendix A. An attempt was made to have at least 20 monitors for each
neighborhood type. Unfortunately, for the industrial neighborhoods, both urban
core and suburban, only 8 monitors were available. Based on the four selection
criteria listed above, all of the data were screened as follows:
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. Sufficient data - All monitors chosen had 6100 or more hours of hourly CO
readings in 1981, except for two urban industrial and three suburban
industrial monitors which were included to provide larger industrial
neighborhood data bases. It should be noted that values were not assumed
for missing readings. The frequency distributions were developed from
the measured data.
Not a microscale monitor - Monitors were checked against a list of street
canyon monitors provided by EPA, OAQPS, in a previous project. Many of
the originally selected urban core-commercial monitors were street
canyon monitors, and so were removed from the list. Any monitor whose
description included the comment that it was a microscale monitor was
not considered.
Representative of mobile source pollution - National Air Monitoring Site
(NAMS) and State and Local Air Monitoring Site (SLAMS) monitors were
used as much as possible. According to EPA, OAQPS, the siting criteria
for NAM and SLAM monitors require that they not be influenced by
nearby point sources. This was the only check used for representativeness
of the monitors. No site visits were made.
Cross section of CO concentration - As can be seen from the listings and
the summary, there is a wide range of CO values, from a large number of
cities in different geographical areas. The monitors chosen include the
monitor that had the highest one hour reading in 1981 (67.5 in Denver).
TABLE n-1. SUMMARY OF CO MONITORS SELECTED FOR
MOBILE SOURCE EXPOSURE ESTIMATE
99 CO monitors were selected from 346 CO monitors with 1981 data.
. 42 monitors were National Air Monitoring sites, 31 monitors were State
and Local Air Monitoring sites.
56 of the 116 urban areas with population about 200,000 are represented.
29 of 41 states with urban areas over 200,000 population are represented.
The monitor with the highest one hour CO reading in 1981 is included
(67.5 mg/m^ in Denver).
Summary by neighborhood type in order of decreasing median value:
Monitor Maximum 1 hr CO. mg/m^ No. of
Max. Min. Median Monitors
Urban core-commercial 67.5 12.5 21.3 22
Urban core-residential 33.4 11.0 17.3 21
Suburban-commercial 33.4 8.1 17.0 20
Suburban-residential 32.2 9.2 16.5 20
Urban core-industrial 22.4 8.6 14.4 8
Suburban-industrial 28.8 7.0 11.1 8
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The hourly readings for the year 1981 from these monitors were requested
from the EPA National Air Data Branch (NADB). When received, the hourly CO
data from each of the 99 monitors were edited using a SwRI editing program
developed in a previous project and described in more detail in Section III, to
produce a set of data that could be used with the SPSS statistical computer
program. After the editing procedure was completed, the hourly CO data from
the different monitors were aggregated for each neighborhood type by the SPSS
program to produce summary statistics and hourly concentration frequency of
occurrence distributions for each neighborhood. The descriptive statistics are
shown in Appendix A. Different distributions were produced for weekdays,
Saturdays and Sundays.
To keep the distributions to a reasonable number, within each day and
neighborhood type; similar hourly distributions were combined to produce six
distributions for each day type, in each neighborhood. This procedure produced
a total of 108 distributions. Finally, the CO intervals for these distributions
were converted to values, in micrograms per cubic meter, representing a one
gram per minute emission factor. This change was accomplished by converting
the PPM values to yg/m^, then dividing by an appropriate 1981 CO emission
factor in grams/minute for each neighborhood and day type. Grams per minute
were chosen as the units for emission factors to allow the use of idle emission
factors where needed.
The 1981 CO emission factors in g/mile were provided by Rob Bruetsch,
the EPA Branch Technical Representative for this project, from a computer run
of the EPA MOBILE3 computer program. These factors were converted to
g/min by using the average cycle speed for use with this project. The
description of the emission factor used for each neighborhood and day type,
together with its numerical value in grams per mile and grams per minute, are
shown in Table II-2. The final frequency distributions are listed in Appendix A.
These NEM neighborhood pollutant concentration distributions, together with
the mobile source microenvironment pollutant concentrations presented in the
next section, constitute the pollutant concentration frequency distribution
inputs to the person hour exposure computer program.
TABLE H-2. 1981 CO EMISSION FACTORS USED FOR EACH
NEIGHBORHOOD
Neighborhood
Emission
Factor
Description
MOBILE 3
1981 CO
Value g/mile
Weekday
g/min
Saturday
& Sunday
g/mina
Urban Core Residential FTP 49.01 16.01 11.53
Urban Core Commercial 10 mph 90.31 15.05 10.84
Urban Core Industrial 10 mph 90.31 15.05 10.84
Suburban Residential FTP 49.01 16.01 11.53
Suburban Commercial FTP 49.01 16.01 11.53
Suburban Industrial FTP 49.01 16.01 11.53
aBecause actual measured Saturday and Sunday CO distributions were used for
the six NEM neighborhoods, a different weekend emission factor was used to
account for the different weekend traffic mix. This factor is 72 percent of
the weekday factor. See Section V.
9
-------
NEM Microenvironments
Within the NEM computer program, the measured neighborhood monitor
concentration values are modified to allow for different concentrations within
each microenvironment in a neighborhood. While the NEM CO study used six
microenvironments, these were combined to produce four microenvironments
for use in this study. They were:
indoors (includes work indoors, home indoors, and kitchen from
NEM CO study)
transport vehicle
roadside
outdoors
Note that the NEM microenvironments for work indoors, home indoors and
kitchens are combined as one indoor microenvironment for this study.
The NEM technique for modifying the neighborhood monitor concentration
to produce microenvironment concentrations was to develop a different scaling
factor (multiplier) for the hourly monitor concentration readings for each
microenvironment. The NEM CO study examined 75 reports of studies of CO
concentrations in the various microenvironments. As a result of this
examination, a range of ambient monitor scaling factors was developed for each
microenvironment. One value of the scaling factor was chosen as a best
estimate.^ 1) These best estimates, shown in Table II-3, were used in this
project's exposure computer program as the NEM microenvironment scaling
factors to be applied to the neighborhood CO concentrations. For a detailed
discussion of these microenvironments and how the scaling factors were
obtained, see Chapter 6 of Reference 21.
TABLE H-3. BEST ESTIMATE OF CO SCALING FACTOR
FOR NEM MICROENVIRONMENTS
Ambient CO
Microenvironment Scaling Factor
Indoors 0.85
Transport Vehicle 2.10
Roadside 1.20
Outdoors 0.95
10
-------
Of the microenvironments listed in Table II-3, only two transport vehicle
and roadside, are heavily impacted by mobile sources. Reference 21 indicates
that the "transport vehicle" CO data was intended to represent both cars and
trains, with rush hour traffic constituting much of the data examined. Several
studies have indicated that exposure concentrations from mobile sources do not
correlate well with fixed site monitors. This study was conducted to
specifically evaluate mobile source exposure. It was apparent that additional
microenvironments, specific to mobile sources, needed to be examined and
concentrations for these microenvironments be determined independent of fixed
site monitor values. Thus, it was necessary to define several new mobile source
microenvironments, independent of NEM neighborhoods. The emission
concentrations of these neighborhoods are defined in Report Section III and V.
11
-------
IIL MOBILE SOURCE MICROENVIRONMENT POLLUTANT CONCENTRATIONS
In the previous SwRI exposure study, three microenvironments were
identified that can have high pollutant concentrations as a result of mobile
sources. In this study these areas are called mobile source microenvironments.
The three mobile source microenvironments ares
Street canyons
Tunnels
Parking garages
These are not the only microenvironments where mobile sources have an
impact. However, within the effort allotted for this study, they were the
microenvironments for which information was most available. One important
mobile source micorenvironment that was not considered separately was the on-
expressway microenvironment. The exposure of a commuter on an expressway
during rush hour is a significnat mobile source exposure. While it is not covered
as a separate microenvironment, it is covered by the "transport vehicle"
microenvironment in the NEM. An examination of the NEM activity patterns
show that much of the exposure occurs during rush hour. The NEM "transport
vehicle" microenvironment scaling factor (Table II-3) was developed for
predominately congested expressway CO measurements. Thus, the on-
expressway mobile source microenvironments can be considered to be
accounted for in the NEM "transport vehicle" microenvironment.
Only for street canyons was a large amount of measured CO
concentration data available. For the other two mobile source microenviron-
ments, some method of estimating the frequency distribution of pollutant
concentration values was required. From an examination of a number of
individual studies of each microenvironment, an estimate of the average
concentration and range of concentrations within that microenvironment can be
determined. For any distribution, if an assumption is made about the shape of
the distribution, then a mathematical description of that distribution can be
obtained.
The Lognormal Distribution
The lognormal distribution has been used for about 15 years to describe
the distribution of ambient pollutant measurements both with time and
location.'23'25' The lognormal distribution gets its name from the fact that
the logarithms of the independent variable are normally distributed. Over the
years, a lively debate has been conducted in the literature on whether the
lognormal distribution was the best representation of the concentration distri-
bution. While other distributions, such as the Weibull distribution, have been
suggested/26' the lognormal appears to be the most widely used. For this
study, the lognormal distribution has the additional virtue of being definable
from available concentration values. Therefore, the lognormal distribution was
chosen as the mathematical form to use when there were insufficient data to
determine the distribution experimentally from measured values.
13
-------
The expression for the two-parameter lognormal distribution is:(27)
dF(x) =
where:
(Inx- y)2
dx
= dependent variable
= In (mediam)
a =-v/ln (mode) - y
The distribution can be completely defined if y and a are known. Appendix B
presents the various mathematical relationships between y and a and the
mean, median, mode, and range that are required to define lognormal
distributions when either the median and mode or the range and the mean are
known. Thus, for cases where there is insufficient data to define a
concentration distribution experimentally, a concentration distribution will be
defined using values for the median and mode or range and mean that are
estimated from available literature studies.
Parking Garage Pollutant Concentrations
The first microenvironment for which ambient air concentrations of
pollutants were developed was the parking garage. While CO concentrations in
parking garages have been a concern for years, few quantitative data are
available. The data available are often only a maximum CO or average CO
concentration. These values are not sufficient for this project. A distribution
of concentrations is needed, since using an average concentration would
eliminate any high-level exposures. However, there was not sufficient
information in the literature to determine this distribution. Table III-l is a list
of measured parking garage CO concentrations found in the literature. The
modeling study done under Contract 68-03-288^ also calculated a CO concen-
tration of 37 ppm in a "typical" (mode average) garage and 37^ ppm for a
"severe case" parking garage. These CO values all indicate that pollutant
concentrations are not normally distributed, but rather are skewed to the right,
with a long "tail" at the higher concentrations.
Since adequate data to define the distribution do not exist, it was decided
to choose a distribution equation, then use the CO values for the typical and
severe parking garage from Contract 68-03-2884 to define the distribution. The
concentration distribution is then in a mathematical form which could easily be
modified if measured data become available in the future. As explained earlier,
the lognormal distribution was chosen to represent the pollutant concentration
distributions where there were insufficient data to define the distribution
experimentally. As shown in Appendix B, a lognormal distribution can be
completely defined if the median and mode of the distribution are known.
14
-------
TABLE ffl-1. CO LEVELS FOUND IN PARKING GARAGES
Reference Study
_ CO Levels _ No. Study Location Date
20 ppm to over 100 ppm (off 28 Detroit 1961
scale 100). Several peaks appear as
though they would exceed 150 ppm
0-200 ppm Car Park A (Sat.) 29 England 1976
0-20 ppm Car Park A (Tues.)
0-200 ppm Car Park Bl
0-30 ppm Car Park B2
33 ppm (cl 200 car/hr Car Park B4
Max. 110-130 ppm Car Park Cl
50 ppm Car Park C2
105 ppm Car Park Dl
Max. 400-450 ppm Car Park D2
(mech. vent.)
30-87 ppm eight hr avg 30 Philadelphia 1977
peaks often above 200 ppm 31 Los Angeles 1975
max 365 ppm .
For this project, the typical garage CO value developed under a previous
project^8) was used as the mode. The median was adjusted so that the
frequency of occurrence in the "severe" concentration range was approximately
equal to one garage. Assuming 10,000 parking garages in the country, this is
equivalent to a frequency of occurrence of 0.01 percent.
A distribution in ppm CO, using the calculated typical and severe levels,
was first computed since ppm is a more familiar unit for CO, and for
comparison to the data in Table III-l. The mode was taken as 37 ppnr^1*' and
the median adjusted until the frequency of occurrence in the 300 to 400 ppm
interval was approximately 0.01 percent. This process resulted in a median
value of 48 ppm. The resulting distribution is shown in Figure III-l. Note that
the largest number of occurrences are in the range of the CO values shown in
Table III-l.
This study required ambient concentrations in yg/nv based on a 1.0
g/min emission rate. The typical concentrations obtained from the modeling
work done under Contract 68-03-2884 were actually calculated using a one
gram per minute emission factor. The typical garage has an ambient air level
of 3900 yg/m3 for 1.0 g/min pollutant emission rate; the severe garage, a level
of 46,000 yg/m^.dS) For both these situations,
the active cars represented 25
percent of parking capacity. For this distribution, the mode was taken as 3900
micrograms/m3 and the median 5500 micrograms/m3. Using this mean and
mode, and the calculations in Appendix B for lognormal distributions, a u of
8.6125 and ao of 0.58632 was obtained.
15
-------
24
o
r-l
X
$
B
o,
cu
0)
u
Q>
U
O
O
c
o
r4
-M
U
t,
20
16
12
8 '
4 -
50
100
150
200
CO, ppm
250
300
350
400
Figure lll-l. Parking garage CO concentration distribution,
25 percent active cars, average wind speed
-------
The model used for parking garage ambient concentrations in a previous
EPA studyU8' indicates that after a short period, generally less than 10
minutes, the concentrations are essentially proportional to the number of cars
divided by the effective ventilation rate. Thus, concentrations will vary with
number of active cars. The active cars per hour vary from 0 to 21.3 percent of
parking capacity (See Section V). However, except for the times a parking
garage is filling or emptying in connection with a specific event, the cars active
at any instant are generally less two percent. To account for the more active
times (such as before and after working hours for garages serving office
buildings) as well as the usual level of activity, it was decided to use two levels
of activity: Three percent and 19 percent active cars. The three percent
active cars would be used to represent normal activity. The 19 percent active
cars would be used to represent congestion associated with single events
(concerts, sporting events, parades, etc.). These distributions were developed
by scaling the 25 percent mode and median by the ratio of active cars to 25
percent active cars, since concentration varies linearly with number of cars.
As mentioned above, pollutant concentration varies not only with number
of active cars, but also with effective ventilation rate. The ug/3 pollutant
levels for the typical garage were calculated assuming a naturally ventilated
garage with an "average" value for wind speed. Ail underground parking
garages are mechanically ventilated, so the ninety percent of garages in the
country that are naturally ventilated'1*' are all above ground with ventilation
rate depending on wind speed. Since the person hour exposure for this study is
to be for an entire year, the ventilation rate can be expected to vary during the
year as wind speed varies. It has been shown in a number of studies that wind
speed at a given measuring station varies lognormally/32)
To obtain an estimate of how wind speed varies through the country, the
annual wind speed distributions for seven cities were averaged. The wind speed
distributions for each city were obtained from the NOAA publication "Airport
Climatological Summary."(33) The average of the seven distributions is shown
in Figure III-2. Note that approximately 65 percent of the time the average
wind speed is between 4 and 10 knots, 25.5 percent of the time it is between 11
and 27 knots, and 9.5 percent of the time it is 3 knots or less.
The pollutant distributions above were calculated using approximately a
seven knot wind speed for the typical (mode) garage. To account for the
concentration variation with wind speed, two other wind speeds (1.5 knots and
14 knots, the medians of the upper and lower wind speed intervals used) were
chosen to represent ventilation rates lower and higher than the mode. Pollutant
concentration distributions were calculated using three wind speeds by
multiplying the mean and mode of each of the three distributions calculated at
7 knots by the ratio 7.0/new wind speed, since pollutant concentration is
inversely proportional to ventilation rate.
These calculations resulted in six pollutant distributions; three
distributions depending on ventilation rate for each of two levels of active cars.
The mode, median, and of each of the distributions are shown in Table III-
2.
17
-------
0)
6
oo
50 r
40
30
o
0)
ro
£ 20
0)
u
10
9.5
0-3
29.8
4-6
35.2
21.2
3.2
7-10 11-16 17-21
Wind speed, knots
0.7
22-27
Figure II1-2. Wind speed distribution, average of seven U.S. cities
-------
Mode
14000
3000
1500
2200
470
235
Median
19500
4200
2100
3000
660
330
y
9.8782
8.3428
7,6496
8.0064
6.4922
5.7991
a
0.5756
0.5801
0.5801
0.5569
0.5827
0.5827
TABLE ffl-2. PROBABILITY DISTRIBUTION PARAMETERS FOR
PARKING GARAGE POLLUTANT DISTRIBUTIONS
Percent
Active Wind Speed, Probability Distribution Parameters,
Cars Knots
19 1.5
7
1.5
7
14
The continuous pollutant distributions generated must be converted into
discrete distributions to use in the person hour exposure computer program. A
computer program was written to evaluate the expression for the lognormal
distribution, producing frequencies of occurrence for each pollutant interval
using arbitrarily chosen pollutant intervals.
Note that the pollutant concentration frequency distributions developed
so far for parking garages are in terms of percent traffic and wind speed. The
computer program developed for this project to calculate person hours of
exposure evaluates exposure by hour of the day. Thus, the parking garage
pollutant frequency distributions must be converted into a format that can be
chosen by hour of the day. The computer program is also structured to allow
only one pollutant distribution to be used for each hour. The three distributions
for each traffic level, resulting from consideration of wind speed, were
combined into a single distribution for each traffic level by weighting the
frequencies of each wind Speed distribution by the percent occurrence of that
wind speed interval, then summing the weighted frequencies.
Parking garages serve a variety of activities. While some parking garages
are subjected to congestion regularly, others are never congested. In a previous
project it was estimated that for all parking garages, only about 25 percent are
regularly subjected to congested conditions, and these conditions occur only one
or two hours per day. Since the computer program allows for only one
frequency distribution for any hour, a composite frequency distribution was
developed to account for congested groups. This composite distribution was
calculated by weighting the frequencies for the three-percent-active-car distri-
bution by 75 percent and the 19-percent-active-car distribution by 25 percent,
then summing the weighted frequencies for each concentration interval.
19
-------
Since three percent active cars represents the normal activity, the three
percent distributions are used for all but two hours of the day on weekdays and
one hour on Saturdays. During the weekdays, congestion in parking garages will
occur most often at the morning and evening rush hours as people are going to
and leaving work. Thus, for weekdays, the composite concentration distribution
was used for the 7 to 8 A.M. and 5 to 6 P.M. hours. For Saturday, the
composite distribution was used for the 10 to 11 P.M. hour to account for
congestion from a specific event. The final two pollutant concentration
distributions used for parking garage exposure are listed in Table III-3.
TABLE IH-3. FINAL PARKING GARAGE POLLUTANT
CONCENTRATION DISTRIBUTION
yg/m3
Concentration3
0
360
163
618
773
1030
1288
1546
1804
2061
2319
2577
3000
4000
5000
6000
8000
10000
15000
20000
25000
30000
35000
40000
50000
3 Percent
0.2410
0.1180
0.1520
0.1150
0.1240
0.0710
0.0410
0.0260
0.0180
0.0130
0.0110
0.0130
0.0220
0.0120
0.0070
0.0070
0.0020
0.0010
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Composite
0.1810
0.0887
0.1149
0.0880
0.0982
0.0385
0.0405
0.0305
0.0252
0.0221
0.0203
0.0287
0.0553
0.0380
0.0259
0.0301
0.0142
0.0110
0.0064
0.0038
0.0021
0.0009
0.0000
0.0000
0.0000
afor a 1 g/minute emission factor
20
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Urban Commuter Tunnel Pollutant Concentrations
The literature on air pollution in urban commuter tunnels had been
extensively investigated under a previous EPA studydS). While CO is
monitored in almost all mechanically ventilated tunnels, there are few
published CO data. A list of the information found is presented in Table III-4.
The most complete study found was in Reference 34, which had detailed CO
levels for the Sumner Tunnel in Boston. From the information presented on the
Sumner Tunnel in Reference 34, plots of both average and maximum CO levels
as functions of percent of average daily traffic (ADT) per hour for weekdays
were developed. These plots are shown in Figure III-3.
TABLE m-4. CO LEVELS FOUND IN URBAN COMMUTER TUNNELS
CO Concentrations
110 ppm (7:30-8:00 A.M., No. Tube)
190 ppm (7:30-8:00 A.M., So. Tube)
140 ppm (5:30-5:45 P.M., West Tube)
75 ppm (avg over 3 days)
rarely exceeded 180 ppm
12-144 ppm mean
30-238 ppm peak
40-200 ppm (North Tube)
10-60 ppm (Center Tube)
10-100 ppm avg (West Tube)
250 max
40-250 ppm
42-122 ppm (Brooklyn Bound Tube)
Reference
No.
34
34
34
34
34
34
34
35
Study Location
Squirrel Hill Tunnel
Pittsburgh, PA
Liberty Tunnel
Pittsburgh, PA
Baltimore Harbor Tunnel
Baltimore, MD
Sumner Tunnel
Boston, MA
Lincoln Tunnel
New York, NY
Fort Pitt Tunnel
Pittsburgh, PA
Armstrong Tunnel
Pittsburgh, PA
Brooklyn Battery Tunnel
New York, NY
Study
Date
1969
1969
1971
1961
est. 1970
1971
1971
1971
All tunnels have ventilation systems designed for some maximum CO rate
at the maximum expected traffic levels. This means that for all tunnels, the
CO relationship with percent of average daily traffic (ADT) should be similar.
The maximum design CO level has generally been in the 200 to 250 ppm CO
range. Maximum hourly traffic is rarely over eight percent of ADT. Thus, the
Sumner Tunnel data can be used as the basis for development of pollutant
concentration distributions for roadway tunnels.
21
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220 -
200
Q Maximum
0 Mean
180
160
Max CO * a + b (% ADT)
_, ^ ^ |"M"V ^
s^^r -- KA ' ^ y
a - 52.896
b « 27.674
r2 * 0.7904
Q
Q
/
/'a
Q
Mean CO * a + b (% ADT)
a « 0.70840
b - 19.267
r2 - 0.9724
Figure III-3.
CO concentration as a function of hourly percent ADT
for the Sumner Tunnel (1961)
22
-------
A linear regression was performed on the average CO concentrations and
the percent ADT values shown in Figure III-3. The results of that analysis are
shown on the figure, together with the results of the regression analysis of the
maximum values. As might be expected, the maximum CO values did not
produce as good a linear fit as the mean CO values. The mean CO level is
obviously a function of the percent ADT.
If the pollutant concentration is assumed to be lognormally distributed for
a given percent ADT as a result of tunnel-to-tunnel variability, fleet
composition, weather and traffic flow variability; then with the mean concen-
tration taken as the distribution mean and the maximum concentration as an
indication of the range, the distributions for all tunnels could be defined from
the data on hand. However, from a summary of tunnel ventilation in Reference
18, the Sumner Tunnel appears to have a worse than average ventilation rate in
terms of cubic meters per meter of lane, while having higher than average
ADT. Thus, the Sumner Tunnel is likely to have higher CO levels than an
average tunnel.
The Sumner Tunnel ventilation rate is approximately 25 percent less than
the average rate. This factor can be taken into account by reducing the mean
CO value shown in Figure III-3 by 25 percent at a given percent ADT. The
maximum values will not be changed, since they are values that are found in
tunnels and must be included in the distribution. The ADT in the Sumner Tunnel
is approximately 1.6 times higher than the mean ADT for all tunnels.^8,19) TO
account for this difference, the percent ADT can be increased 1.6 times for a
given CO concentration. Again the maximum values are not adjusted. When
these two adjustments are made, the relationships between hourly percent ADT
and CO concentrations for an average tunnel are as shown in Figure III-4.
Assuming that the CO concentration in tunnels has a lognormal
distribution with respect to time, the CO distribution can be determined from
the mean and maximum CO values at any percent ADT. See Appendix B for the
method used to obtain these distributions. CO distributions were determined
for roadway tunnels at one percent ADT intervals starting with 0.5 percent
ADT and ending at 6.5 percent ADT. The equations obtained from the
calculations in Appendix B are continuous ppm CO distributions. This project
required discrete distributions in terms of yg/m3 for a 1.0 g/min emission
factor. However, it is also desirable to have the discrete distributions in terms
of ppm CO for comparison with measured data.
To convert from ppm CO to ug/m3 at 1.0 g/min, the emission factor for
vehicles in tunnels is required. The Sumner Tunnel data were taken in 1961.
Therefore, a 1961 CO emission factor should be used to convert the data in
Figure III-4 to a 1.0 g/min emission factor. A national average fleet emission
factor for 1961 is not available from published EPA emission factors. To obtain
the 1961 emission factor, Ms. Lois Platte at the EPA Mobile Source Laboratory
in Ann Arbor, Michigan was contacted. At her direction, the EPA emission
factor computer program MOBILE2 was run to generate a 1961 national fleet
CO emission at 35 mph. The resulting emission factor was 62.06 g/mile, which
converts to 36.2 g/min at 35 mph. The conversion factor for ppm CO at 36.2
g/min to vg/m3 at 1.0 g/min is: 1157/36.2 = 31.96.
23
-------
250
230
Figure III-4. CO concentration as a function of Hourly Percent ADT
for an average Roadway Tunnel
24
-------
The concentration intervals are shown in Table III-5. The discrete
pollutant distributions in terms of ppm CO are shown in Table III-6 for various
levels of hourly percent ADT. These distributions, with the yg/m3 interval
values replacing the ppm CO values, were used in the person hour exposure
computer program. The distributions were selected by hour of the day using the
"percent ADT by hour of the day" relationships developed in Section V.
Street Canyon Pollutant Concentration
The street canyon exposure calculated in this project is for people outside
on the sidewalk or in vehicles in the street canyons. It does not include people
in buildings adjacent to the street canyons. Unlike parking garages, where
there have been few measurements of vehicle pollutant levels, CO samples have
been taken in downtown street canyons by fixed monitors for a number of years.
The National Air Data Branch of the EPA was contacted regarding the
information that could be retrieved from the data bank of monitor readings
using the SAROAD system. From the information received, it appeared that
there was the potential for obtaining a great deal of information regarding CO
levels in street canyons. However, the data that were easily available were not
sufficient for the needs of the project. Since the raw data for CO is stored by
hour of the day, it is possible to get summaries of specific monitors by hour of
the day for each month of the year. These data could be used to obtain
frequency distributions for weekdays, Saturday and Sundays.
To use the stored monitor data, it was necessary to know which monitors
are in street canyons close to street level. There are currently over 4000 active
sites, which makes it a time-consuming task to locate CDB sites manually.
Discussions with personnel in EPA's Office of Air Quality Planning and
Standards led to the EPA group that is charged with inspection of all monitors
that are part of the National Air Monitoring (NAM) network. This group was
able to furnish a list of central city monitors which are known to be in street
canyons. The list is contained in Table III-7. From this list, data was obtained
for 23 street canyon monitors. These monitors had been specifically excluded
from the monitors used to develop CO concentrations in the NEM
neighborhoods. Thus, a total of 122 (99 plus 23) CO monitors were used to
develop the CO concentrations for this study.
The purpose in obtaining these data was to provide as large a measured
CO base as possible from which to generate a street canyon pollutant frequency
distribution. Data from all the cities were merged in one set of CO readings
regardless of city size or other variables. The time and effort allotted to this
project were not sufficient to weight the individual monitor values by city size,
street size, or traffic density, or to compare individual monitor distributions.
The data, as received, were formatted to have 12 hours of data from one
station on each line. This arrangement means that one day's data from one
station required two lines. If all 12 hours of data were missing, the whole line
was skipped. Thus, some days had two lines of data and some had only one line.
The statistical computer program used to analyze the information (Statistical
Package for the Social Sciences, SPSS) could not handle this type of variable
format. The data from the monitoring stations were also not all in the same
units. Some data were in ppm and some in milligrams per cubic meter. The
readings themselves consisted of four integers, with the decimal point location
25
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TABLE m-5. POLLUTANT CONCENTRATION INTERVALS
FOR ROADWAY TUNNELS
PPM CO Ug/mA at 1.0 g/mina
0
6.26
12.52
18.77
21.90
25.03
28.16
31.29
37.55
43.80
50.06
56.32
62.58
68.83
75.09
81.35
93.86
125.15
187.73
250.30
aBased on a
at 35 mph.
One ppm CO
0
200
400
600
700
800
900
1000
1200
1400
1600
1800
2000
2200
2400
2600
3000
4000
6000
8000
1961 CO emission factor of 62.06 g/mile
This is equivlaent to 36.20 g/min.
» 1157 yg/m3 CO.
26
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TABLE Efr-6. DISTRIBUTIONS OF HOURLY AVERAGE CO
LEVELS IN ROADWAY TUNNELS
PPM Frequency for Percent APT Shown
CO Interval
0-6.26
2.26-12.52
12.52-18.77
18.77-21.90
21.90-25.03
25.03-28.16
28.16-31.29
31.29-37.55
37.55-43.80
43.80-50.06
50.06-56.32
56.32-62.58
62.58-68.83
68.83-75.09
75.09-81.35
81.35-93.86
93.86-125.15
125.15-187.73
187.73-250.30
0.5
0.777
0.129
0.044
0.011
0.008
0.006
0.004
0.006
0.004
0.003
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.000
1.5
0.288
0.317
0.169
0.052
0.038
0.029
0.022
0.030
0.019
0.012
0.008
0.005
0.004
0.003
0.002
0.003
0.003
0.001
0.000
2.5
0.067
0.230
0.218
0.086
0.071
0.058
0.047
0.069
0.046
0.032
0.022
0.015
0.011
0.008
0.006
0.007
0.008
0.004
0.001
3.5
0.013
0.109
0.175
0.088
0.081
0.073
0.065
0.105
0.077
0.056
0.041
0.030
0.022
0.016
0.012
0.016
0.019
0.009
0.001
4.5
0.006
0.045
0.105
0.068
0.071
0.071
0.068
0.120
0.099
0.078
0.061
0.047
0.036
0.028
0.021
0.029
0.033
0.019
0.002
5.5
0.001
0.018
0.057
0.045
0.052
0.057
0.059
0.116
0.107
0.092
0.077
0.063
0.051
0.041
0.033
0.047
0.057
0.035
0.005
6.5
0.000
0.007
0.030
0.027
0.035
0.042
0.047
0.100
0.102
0.095
0.085
0.074
0.062
. 0.052
0.043
0.065
0.085
0.056
0.009
27
-------
TABLE m-7. SARD AD CO MONITORS USED IN
STREET CANYON ANALYSIS
SAROAD NO.
Hourly Readings
Available a
State
101 1960 085H01
10 2700 018G01
10 4360 056G01
11 0200 043F01
14 1220 040F01
15 2040 034F01
18 2380 026G01
19 2020 017P01
21 0120 034H02
22 0240 022P01
22 2160 007F01
23 1180 021G01
33 4680 058F01
33 4680 062F01
34 0700 029G01
36 1220 021G01
39 7140 045H01
39 7260 005G01
41 0300 009F01
44 2540 021G01
45 1310 053H01
4S 4570 046F01
49 1840 077F01
09* 0020 022112
26 4280 079H01
7848
8144
7782
6309
7616
7912
7782
5814
7768
6512
1084
8030
8090
8023
8002
7945
8336
6716
7948
8659
7899
2320
8529
N.A.b
N.A.
FL
FL
FL
GA
IL
IN
KY
LA
MD
MA
MA
MI
NY
NY
NC
OH
PA
PA
RI
TN
TX
TX
WA
DC
MO
City
Jacksonville
Miami
Tampa
Atlanta
Chicago
Indianapolis
Louisville
New Orleans
Baltimore
Boston
Springfield
Detroit
New York
New York
Charlotte
Cincinnati
Philadelphia
Pittsburgh
Providence
Nashville .
Dallas
San Antonio
Seattle
Washington
St. Louis
8760 hours in one year
Data from these monitors requested, but not available
28
-------
for the data indicated elsewhere on the line. In addition, it was desired to
separate out weekdays, Saturdays and Sundays. This need required checking the
data on each line and assigning the CO readings to the correct category. With
approximately 1*,000 lines of data, the editing process obviously had to be
computerized. A FORTRAN computer program was written to provide the
necessary editing. Appendix B contains a listing of the editing program.
Once edited, the data were processed using the SPSS program to obtain
frequency distributions, the mean, median, and range of the CO data in ppm for
each hour of the day for weekdays, Saturdays and Sundays. The minimum,
maximum, mean and median for each hour are presented in Table III-8.
Next, the CO distributions for each hour of the day were investigated.
CO distributions were generated using the SPSS computer program for each
hour of the day. The ppm CO concentrations were divided into 19 intervals. To
reduce the number of distributions to a more manageable level, the individual
hourly distributions were examined and similar distributions combined. This
process resulted in six distributions for each day type, as shown in Table III-9.
These values are presented in terms of ppm CO, because it is a familiar unit.
For use in the project, the ambient concentrations should be in yg/m3 for an
emission factor of 1.0 g/min.
An emission factor for a vehicle speed of 10 miles per hour was chosen for
use with street canyons. The EPA provided the 1981 CO emission factors at 10
miles per hour from the MOBILE3 computer program. This emission factor,
90.31 g/mile, was converted to 15.05 g/minute by multiplying by the vehicle
speed, 10 mph, in terms of miles/minute (10 mph = 0.167 miles/minute). For
weekdays, the CO ppm intervals were then converted to " y g/m3 of pollutant"
intervals by multiplying the ppm values by the conversion factor for CO ppm to
CO yg/m3 (1157) then dividing by the CO emission factor of 15.05 g/minute
(1157/15.05 = 76.88). This gives the CO concentration in yg/m3 for a 1 g/min
emissions rate. As is explained in Section V, the emission factor for weekends
is 28 percent less than for weekdays. For weekends then, a different set of
concentration values was defined using an emission factor of 10.8* g/minute,
and a conversion factor from CO ppm at 10.8* g/min to yg/m3 at 1 g/min of
106.73 (1157/10.8*). The new intervals are shown in Table 111-10. The
distributions shown in Table III-9, with the concentrations expressed in yg/m3
as shown in Table III-10, were used in the person hour exposure computer
program for street canyon exposure.
It should be noted that the CO monitor readings used in both the street
canyon microenvironment and the NEM neighborhood were at whatever ambient
temperature was occurring at the time of the reading. The emission factors by
which the CO concentration distributions are divided are for 75°F ambient
temperature. When the 1 g/min emission factor distributions are used for other
pollutants, the distributions will again be multiplied by 75°F emission factors
for the pollutant under investigation. This method will yield correct exposure
estimates only if the ambient temperature dependence of the pollutant under
investigation is the same as that of CO. If the pollutant under investigation has
a different temperature dependence the correctness of the resulting exposure
estimates will vary depending on the degree of similarity. An alternative
method would been to have divided each sites readings into temperature ranges
29
-------
TABLE m-8. DESCRIPTIVE STATISTICS FOR STREET CANYON
CO READINGS
Weekdays
Saturdays
Sundays
co
D
Hour
Ending
1 am
2
3
4
5
6
7
8
9
10
11
Noon
1 pm
2
3
4
5
6
7
8
9
10
11
Midnight
Concentration
Range
0-12
0-12.5
0-12.0
0-12.5
0-15.0
0-11.3
0-17.1
0-24.5
0-28.5
0-17.6
0-18.0
0-18.5
0-22.3
0-19.9
0-16.0
0-19 . 2
0-20.8
0-22.7
0-20.5
0-18.3
0-17.0
0-19.6
0-16.0
0-17.0
Mean
1.63
1.37
1.16
0.99
0.98
1.38
2.57
3.97
4.41
3.96
3.92
3.97
4.06
3.94
3.97
4.32
4.89
4.33
3.07
2.57
2.35
2.32
2.28
2.15
, ppnt
Median
1.09
0.99
0.83
0.75
0.79
1.02
2.04
3.47
3.81
3.36
3.37
3.45
3.49
3.41
3.51
3.96
4.42
3.65
2.46
2.02
1.98
1.98
1.96
1.73
Concentration
Range
0-11
0-13
0-12
0-9
0-9.5
0-7.3
0-8.8
0-8.5
0-10.2
0-19.0
0-9.5
0-11.0
0-17.0
0-17.0
0-17.5
0-13.0
0-13.3
0-14.0
0-14.5
0-11.5
0-11.6
0-17.0
0-16.4
0-11.5
Mean
2.26
2.10
1.85
1.42
1.20
1.21
1.49
1.78
1.99
2.12
2.28
2.40
2.45
2.53
2.62
2.62
2.57
2.48
2.30
2.35
2.34
2.45
2.59
2.50
, ppm
Median
1.95
1.49
1.18
1.00
0.96
0.98
1.06
1.49
1.70
1.87
1.99
2.01
2.01
2.02
2.03
2.04
2.02
1.99
1.94
1.97
1.96
1.99
2.04
2.00
Concentration
Range
0-19.4
0-16.0
0-15.3
0-14.3
0-13.6
0-.12..4
0-11.6
0-10.0
0-10.4
0-12.2
0-10.2
0-9.0
0-10.2
0-9.2
0-11.2
0-11.5
0-11.0
0-11.2
0-12.9
0-11.1
0-13.5
0-10.3
0-10.4
0-10.7
Mean
2.26
2.03
1.78
1.39
1.14
1.04
1.12
1.22
1.21
1.30
1.36
1.46
1.57
1.64
1.80
1.85
1.94
1.99
1.98
1.97
1.94
1.87
1.81
1.65
, ppm
Median
1.72
1.48
1.13
0.99
0.88
0.84
0.96
0.99
1.00
1.02
1.04
1.04
1.16
1.22
1.34
1.39
1.45
1.50
1.53
1.60
1.55
1.48
1.32
1.18
-------
TABLE ffl-9. DISTRIBUTION OF HOURLY AVERAGE CO LEVELS
IN STREET CANYONS
PPM CO
Interval
0.0-0.5
0.6-1.5
1.6-2.5
2.6-3.5
3.6-4.5
4.6-5.5
5.6-6.5
6.6-7.5
7.6-8.5
8.6-9.5
9.6-10.5
10.6-11.5
11.6-12.5
12.6-13.5
13.6-15.5
15.6-18.5
18.6-21.5
21.6-24.5
24.6-29.5
0.0-0.5
0.6-1.5
1.6-2.5
2.6-3.5
3.6-4.5
4.6-5.5
5.6-6.5
6.6-7.5
7.6-8.5
8.6-9.5
9.6-10.5
10.6-11.5
11.6-12.5
12.6-13.5
13.6-15.5
15.6-18.5
18.6-21.5
Percent Frequency for Time Interval Shown
l-6am
33.58
40.51
14.35
6.31
2.71
1.30
0.60
0.34
0.15
0.07
0.04
0.02
0.02
<0.01
l-3am
19.87
32.46
19.46
11.62
6.50
3.72
2.61
1.68
1.01
0.50
0.34
0.13
0.07
0.03
7am
6.21
28.58
25.68
. 17.50
10.00
5.41
3.00
1.39
0.98
0.57
0.35
0.10
0.12
0.04
0.04
0.02
4-6am
31.37
40.96
16.04
6.18
3.31
1.14
0.57
0.30
0.07
0.07
--
8-9am
1.91
11.37
18.62
18.18
15.05
10.49
8.11
6.03
3.26
2.39
1.63
1.04
0.66
0.35
0.47
0.30
0.07
0.03
0.02
7-8am
16.47
42.22
23.39
10.79
4.57
1.41
0.70
0.30
0.10
0.05
Weekdays
10 am- 3pm
2.23
12.92
19.88
18.00
14.27
10.39
7.57
5.49
3.50
2.36
1.42
0.76
0.51
0.32
0.29
0.09
0.01
<0.01
Saturday
9-noon
8.39
33.20
27.34
15.64
8.29
4.24
1.49
0.78
0.38
0.15
0.03
0.03
0.03
-
0.03
4 -6pm
1.82
10.05
16.16
16.73
14.21
11.33
9.34
6.75
4.70
3.24
2.16
1.28
0.84
0.53
0.50
0.24
0.09
0.01
l-6pm
6.13
29.98
26.22
15.69
9.47
5.48
3.25
1.67
0.98
0.56
0.27
0.10
0.08
0.05
0.02
0.05
^
7-Mid.
9.23
29.54
25.37
15.50
8.85
4.77
2.75
1.66
0.95
0.56
0.26
0.18
0.10
0.09
0.12
0.04
0.02
7-mid.
9.92
30.70
23.11
15.72
9.55
4.62
2.38
1.57
1.16
0.62
0.34
0.19
0.05
0.02
0.02
0.05
__
31
-------
TABLE m-9 (CONTD). DISTRIBUTION OF HOURLY AVERAGE CO LEVELS
IN STREET CANYONS
PPM CO
Interval
0.0-0.5
0.6-1.5
1.6-2.5
2.6-3.5
3.6-4.5
4.6-5.5
5.6-6.5
6.6-7.5
7.6-8.5
8.6-9.5
9.6-10.5
10.6-11.5
11.6-12.5
12.6-13.5
13.6-15.5
15.6-18.5
18.6-21.5
Percent
Frequency
for Time Interval Shown
Sunday
l-2am
18.51
31.90
20.31
11.49
7.64
3.08
3.38
1.44
1.08
0.41
o.is
0.21
0.21
0.05
0.10
0.05
3-4am
27.18
38.85
16.18
8.30
3.84
1.97
1.66
0.93
0.41
0.31
0.16
0.10
0.10
5-10am
28.73
48.47
15.09
4.89
1.43
0.91
0.17
0.12
0.05
0.03
0.03
0.02
0.05
0.02
llam-2pm
18.47
46.72
19.76
9.30
2.99
1.67
0.48
0.25
0.20
0.10
0.05
^»
3-llpm
14.84
39.46
21.86
11.60
5.74
2.96
1.54
0.89
0.43
0.28
0.26
0.11
0.01
0.02
-
Midnight
20.64
40.87
18.15
11.20
4.77
2.49
0.73
0.62
0.10
0.21
0.10
0.10
-
32
-------
TABLE ffl-10. CONVERSION TABLE OF MEASURED PPM CO TO
* FOR 1 g/min EMISSION FACTOR FOR
STREET CANYONS
Measured iig/m for 1.0 g/min Emission Factor
PPM CO
0
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
13.5
15.5
18.5
21.5
24.5
29.5
Weekdays
0
38
115
192
269
346
424
500
577
654
730
807
884
961
1038
1192
1422
1653
1884
2268
Weekends
0
53
161
267
375
481
587
695
801
909
1015
1123
1229
1336
1443
1657
1977
2298
2619
3153
33
-------
and develop frequency distributions for each temperature range. The one
gram/minute concentration distributions would then be obtained by dividing by
emission factors specific to those temperature ranges. While the method is
possible, the time and effort allotted for this project did not permit its use.
34
-------
IV. NEM NEIGHBORHOOD/MICROENVIRONMENT POPULATION
This section summarizes the details behind the NEM CO study activity
pattern population estimates. It is emphasized that the NEM CO study was a
previous study conducted for the purpose of regulatory analysis. The current
study used the NEM CO activity pattern populations because they appeared to
be the best available at the time. The approach and assumptions explained in
this section are those from the NEM CO study as understood by the author of
this report.
The NEM CO computer program uses data from one city at a time. In the
NEM CO computer study, four cities were investigated: Chicago, Philadelphia,
St. Louis, and Los Angeles. It was desired to have the population of each city
assigned (for each hour of the day) to neighborhood categories that would match
the EPA CO monitor location designation. This assignment was desired because
the monitor data in the EPA 5AROAD data base was used to determine the CO
concentration levels to which the population was exposed. The six
neighborhoods used to classify the CO monitor are:
Urban core residential
Urban core commercial
Urban core industrial
Suburban residential
Suburban commercial
Suburban industrial
In an actual city these neighborhoods are often intertwined, and do not
match areas such as census tracts for which population information is available.
Thus, population information is not available for these neighborhood
classifications. An example of this intertwining can be seen in the patterns of
neighborhood types in a portion of the Philadelphia area, shown in Figure IV-1.
NEM Districts
In order to simplify the situation and be able to use areas for which
population information was available, large portions of each of the cities,
representing contiguous census tracts, were designated by the NEM developers
as either urban core (called center-city in NEM reports) or suburban. These
areas of contiguous census tracts were designated "districts." The district
boundaries for each of the four cities in the NEM CO study are shown in Figures
IV-2 through IV-5. The urban/suburban designation for each of these districts
are listed in Table IV-I.
35
-------
IMORRISTOWN
4-3RIDGEPOR
RESIDENTIAL NEIGHBORHOOD
UCOMMERCIAL NEIGHBORHOOD
HINDUSTRIAL NEIGHBORHOOD
! I UNDEVELOPED
Figure IV-1. Pattern of neighborhood types in a
portion of the Philadelphia study area
36
-------
LAKE
MICHIGAN
Figure IV-2. Chicago study area
37
-------
(D
H
n
£ I-1-
CO M
D-
(-
P)
Ul
ft
&
*<
p)
n
(B
Conshohocken Jenklntow
4
I / Camden, NJ
Philadelphia
-------
WOOD RIVER
BELLAFONTAINE
NEIGHBORS
GRANITE CITY
LEMAY
Figure IV-4. st. Louis study area
39
-------
7
San Bernardino
Pacific
Ocean
Figure IV-5. Los Angeles study area
-------
TABLE W-l. GEOGRAPHIC CLASSIFICATION OF DISTRICTS
FROM NEM CO STUDY
City
Chicago
Philadelphia
St. Louis
Los Angeles
District
Number
1
2
3
4
5
6
7
8
1
2
3
k
5
6
1
2
3
*
5
6
7
1
2
3
4
5
6
7
Neighborhood
Designation
Urban Core
Urban Core
Urban Core
Urban Core
Urban Core
Suburban
Suburban
Suburban
Urban Core
Suburban
Suburban
Suburban
Suburban
Suburban
Urban Core
Urban Core
Urban Core
Urban Core
Suburban
Suburban
Suburban
Urban Core
Suburban
Suburban
Suburban
Suburban
Suburban
Suburban
37
38
39
Age-Occupation Group Populations
The population of each city was divided into 12 groups of common age
and/or occupation based on Bureau of Census groups. These groups are called
Age-Occupation (A-O) groups. Since the districts were based on census tracts
and the A-O groups based on census groups, the population of each A-O group in
each district could be determined for a city from census data. The A-O
populations were summed for all urban core districts and all suburban districts
separately for each city. The results of these calculations are shown in Table
IV-2.
41
-------
TABLE IV-2. NEM A-O GROUP POPULATION BY DISTRICT CLASSIFICATION IN FOUR CITIES
Age -occupation group
1. Students 18+
2. Professionals/Managers
3. Sales workers
4. Clerical and kindred
workers
; 5. Craftsmen, foremen,
kindred
6. Operatives and
laborers
7. Farm workers
8. Military, service
household
9. Married housewives
10. Unemployed or retired
11. Children under 5
12. Children 5-17
TOTALS
Chicago
Center-
City*
83,982
387,741
82,701
269,599
174,648
206,363
-
137.201
170,209
57,399
50,295
120,703
1,704,843
Suburban
25,639
119,336
25.482
66,877
60,795
55,626
-
30,803
92,439
16,385
38,455
92,292
624,115
Philadelphia
Center-
City*
18,429
43,002
14,398
64,792
39.052
98,768
862
57,695
93,110
96,979
75,481
209,444
812,009
Suburban
75.594
214,878
74,567
199.801
116.303
161.890
1,891
103,172
290,814
212,055
166,766
508,243
2,125,970
St. Louis
Center-
City*
16,319
35,848
11.729
50,088
24.115
63,188
685
46.636
65,402
87,668
49,973
147,659
599,320
Suburban
23.191
70,853
23.063
48,284
27.664
40.018
843
26.155
81.385
54.180
50,029
163,026
608,690
Los Anqeles
Center-
City*
75,047
155,876
46,364
151,100
66,589
134,679
978
71,972
166,345
202,601
115,731
267,644
1.454,924
Suburban
266,634
645,758
198,847
490,555
333,381
478,973
12,847
321.552
810,286
563,154
538,814
1.617,642
6,278,412
K>
*This chart is from a NEM CO report. Center-City is referred to as urban core in this report.
-------
Age-Occupation Subgroups
The A-O groups are further divided into subgroups to permit placement of
individuals within the group in different neighborhoods at different times of the
day. This allows for different commute times, different work shifts, indoor and
outdoor work, etc. To accomplish this division into subgroups, a number of
possible different group characteristics for that A-O group were envisioned,
then a percentage of the A-O groups having those characteristics was assigned.
The description of the characteristics of the subgroups and their percentages of
the main A-O categories are shown in Table IV-3.
Location Assumptions
In order to place people in the neighborhood type listed at the beginning
of this section it was necessary to make some assumptions about the physical
locations of the A-O groups. The first assumption was that all people are one
of only two places. They are either "at home" or "at work." (How commuting is
handled will be explained later). This assumption does not specify where "hone"
and "work" are. The next assumption, and a key one of the NEM CO study, is
that all persons are "at home" in a residential neighborhood. Persons can be "at
work" in any neighborhood. Each of the 12 A-O groups was then assigned one or
two Neighborhood Types (NT's) as work neighborhoods. These assignments are
shown in Table IV-^.
As can be seen from Tables IV-1 and IV-2, the A-O subgroups live in only
two neighborhoods (urban core residential or suburban residential). Thus, the
population of the district is the population in the residential neighborhood. For
some A-O subgroups, there can be two work neighborhoods different from the
neighborhoods in which they live. This gives a possibility of either two or four
different divisions of the A-O subgroups. In demographic studies, a group of
individuals having a statistical factor in common are designated a cohort. For
the NEM CO study, these divisions of the A-O subgroups are called cohorts. All
members of a particular cohort have the following in common:
1) are members of the same A-O groups,
2) are members of the same A-O subgroups with a specified daily
activity pattern,
3) live in the same neighborhood type (NT), and
4) work in the same NT.
Thus, the A-O subgroups can be divided into either two or four cohorts.
The populations of the "home" neighborhoods are known, the problem is how to
divide the A-O subgroups to obtain the cohort populations for those subgroups
that have two work neighborhoods. To solve this problem, the NEM developers
used data provided by regional transportation planning agencies to develop a
condensed trip table whose geographic elements were the urban core and
suburban district classifications. This table, showing number of trips and
fractions of trips for each city in the NEM CO study is presented in Table IV-5.
43
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TABLE IV-3. DESCRIPTION AND APPORTIONMENT OF
ACTIVITY PATTERN SUBGROUPS
Aae-occuoation grouo
Students 18 and over
Managers and professionals
Sales workers
Clerical and kindred workers
Craftsmen and kindred
workers
Operatives and laborers
SudQrouD
Code3
Oil
012
013
014
021
022
023
024
031
032
033
034
035
041
042-
043
044
045
046
051
052
053
054
055
056
061
062
063
064
065
066
Description
<30 min commute, 8 a.m. class
<30 min commute, 9 a.m. class
>30 min commute, 8 a.m. class
>30 min commute, 9 a.m. class
<30 min commute, single family
house
<30 min commute, others
>30 min commute, single family
house
>30 min commute, others
Indoor work, <30 min commute
Indoor work, >30 min commute
Outdoor work
Indoor and outdoor work
Traveling
Indoor work, 1st shift, <30 min
commute
Indoor work, 1st shift, >30 min
commute
Indoor work, 2nd shift, <30 min
commute
Indoor work, 2nd shift, >30 min
commute
Outdoor work
Indoor and outdoor work
Indoor work, 1st shift, <30 min
commute
Indoor work, 1st shift, >30 min
commute
Indoor work, 2nd. shift
Indoor work, 3rd shift
Outdoor work
Indoor and outdoor work
Indoor work, 1st shift, <30 min
commute
Indoor work, 1st shift, >30 min
commute
Indoor work, 2nd shift
Indoor work, 3rd shift
Outdoor work
Work in motor vehicle
Percent
23
45
11
21
47
21
22
10
43
21
5
9
22
56
26
9
4
1
4
50
24
10
2
4
10
39
18
6
3
18
16
44
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TABLE IV-3 (CONTD). DESCRIPTION AND APPORTIONMENT OF
ACTIVITY PATTERN SUBGROUPS
Age-occupation grouo
1
Service, military, and
private household workers
Housewives
Unemployed and retired
Children less than 5
Children 5 to 17
Subqrouo
Code3
081
082
083
084
085
086
091
092
093
101
102
103
104
105
106
111
112-
113
114
121
122
123
124
125
126
Description
Service, day time work, <30 min
commute
Service, day time work, >30 min
commute
Service, night time
Service, in motor vehicle
Military
Private household
No children at home
Some children <13
No children <13, some 13 to 18
Unemployed, job hunting
Unemployed, not job hunting
Disabled
Retired, full mobility
Retired, limited mobility
Retired, confined indoors
0 to 12 months
13 to 24 months
25 to 36 months
37 to 60 months
Elementary school, <30 min
commute
Elementary school, >30 min
commute, walk or bike
Elementary school, >30 min
commute, vehicle
High school, *30 min commute
High school,. >30 min commute,
walk or bike
High school, >30 min commute,
vehicle
Percent
36
17
22
3
14
3
42
49
9
20
24
20
30
4
2
21
20
20
39
56
4
7
26
2
5
aFirst'two digits indicate age-occupation group, third digit indicates
subgroup.
45
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TABLE IV-4. ASSUMPTIONS CONCERNING WORK NT'S
OF A-0 GROUPS
A-0 Grouo
Assumptions
1. Students 18+
2. Professional and administrative
3. Sales workers
4. Clerical workers
5. Craftsmen
6.' Operatives and laborers
7. Farmers
8. Service, military and household
9. Housewives
10. Unemployed and retired
11. Children under 5
12. Children 5 to 17
Work NT is same as home NT.
All work in a commercial neighbor-
hood; some work in suburban areas,
others in center-city.
Work neighborhood is suburban-
commercial or center-city commercial .
Work neighborhood is suburban-
commercial or center-city commercial .
Work neighborhood is suburban-
industrial or center-city industrial.
Work neighborhood is suburban-
industrial or center-city industrial.
There are no farmers in urbanized
area.
Work neighborhood is same as
home.
Work neighborhood is same as home.
Work neighborhood is same as
home.
Work neighborhood is same as home.
Work neighborhood is same as home.
*This table is taken from a NEM CO study report. In this report, Center-City
is referred to as urban core.
46
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It is this information that is used to divide the A-O subgroups by work
neighborhood.
TABLE IV-5. COLLAPSED HOME-TO-WORK TRIP TABLES, EXPRESSED
AS NUMBER OF TRIPS AND (FRACTION OF TRIPS)
To Work
Study Area From Home Center City Suburban
Chicago Center-city 3,182,820(0.935) 221,720(0.0650)
Suburban 524,260(0.8*3) 97,300(0.157)
Philadelphia Center-City 270,524(0.514) 256,210(0.486)
Suburban 256,210(0.256) 743,795(0.744)
St. Louis Center-city 290,476(0.725) 110,044(0.275)
Suburban 110,046(0.277) 287,154(0.723)
LosAngeJes Center-city 246,970(0.570) 1,851,400(0.430)
Suburban 581,480(0.133) 3,800,720(0.867)
The information in Tables IV-1 through IV-5 can be used to find the
population of each cohort. Calculation of cohort populations is best illustrated
by an example. One cohort may be defined as those persons residing in
suburban-residential NT in St. Louis who are managers or professionals (A-O
Group 2), and who are located in urban core commercial NT during working
hours who follow typical activity patterns of subgroup 22. This definition is
consistent with the list of assumptions in Tables IV-3 and IV-4. Table IV-2
shows that 70,853 people live in suburban residential NT's in St. Louis and
belong to A-O Group 2. From Table IV-4 we find that the fraction of A-O
Group 2 belonging to subgroup 22 is 21 percent. From the collapsed trip table,
Table IV-5, we find that 0.277 of the people from suburban residential NT's go
to a center-city NT for work. Thus, we may calculate the cohort population as
follows:
P5,2,22,2 = (70,853X0.21) (0.27) = 4.122 persons
Where P5,2,22,2 = the cohort population whose home NT is number 5
(suburban residential) A-O category is number 2
(professional/manager), A-O subgroup 22, and work NT
is number 2 (urban core commercial)
Populations of all cohorts were calculated similarly; a complete list of cohorts
and estimated cohort populations is provided in Appendix C.
Activity Patterns
The implicit assumption in the development of the cohorts is that they are
in the same place at the same time, i.e., they move from one neighborhood type
to another at the same time. To define this movement, it is necessary to
47
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develop activity patterns on an hour-by-hour basis for a twenty-four hour day.
In the NEM study, different activity patterns were created for weekdays,
Saturday and Sunday.
If the activity patterns are developed in terms of hourly times at "home"
or at "work" rather than by specific NT's, then the activity patterns can be
developed at the A-O subgroup level. The activity pattern can then be used by
each cohort in the A-O subgroups, with the only difference between cohorts
being that the "home" and "work" neighborhoods will be different.
In addition to being assigned to either at home or at work for each hour of
the day, as part of the activity pattern, subgroups were assigned to a
microenvironment for each hour of the day. The microenvironments used in the
NEM CO study were:
indoors at work
indoors - other
transport vehicle
roadside
outdoors
kitchen
Thus, a subgroup could be at home in a transport vehicle, or at work outdoors,
etc. Some combinations were not used. For instance, no subgroup is at home,
and indoors at work. No subgroup is at work in the kitchen.
The purpose of the different microenvironments, from an exposure
standpoint, is that each microenvironment provides a different adjustment to
the ambient air monitor concentration as explained in Section II of this report.
Assignments to these microenvironments were by the subgroups shown in Table
IV-4. That is, for any hour of the day, all the persons in an activity pattern
subgroup were in the microenvironment. The at home or at work and
microenvironment assignments by hour of the day were the same for all cities.
These hour-by-hour assignments are listed in Appendix C.
Neighborhood Populations
Because of the activity patterns, the population of any neighborhood type
changes from hour-to-hour. To obtain a neighborhood type population for any
hour it is necessary to sum the populations of the cohorts in that neighborhood
at the desired hour. This is done internally in the NEM computer program and
not available in tabular form. For this project, the districts (Figures IV-2 to IV-
5) and designations used by the NEM developers have been accepted at face
value for purposes of determining the neighborhood or residence for each A-O
subgroup, despite possible pockets of urban core land use in the districts
designated suburban and vise versa.
48
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For use in this project the neighborhood populations are required in terms
of percent of the total city population. To convert the NEM CO population
information into a form useful for this project, computer routines to convert
the NEM activity patterns and cohort populations to the percentage of each
city's population in each neighborhood/microenvironment (N/M) combination,
for each hour of the day, were written and run. This information is shown in
Appendix C for Chicago, Philadelphia, St. Louis, and Los Angeles. For each
city, there is a table of percent population for each day type (weekdays,
Saturdays, or Sundays). Thus, for each of the four cities, the percent of the
study area population in each of 2k different N/M types, is estimated for each
hour of the day. This information was then extrapolated to a national estimate
as explained in Section VI.
49
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V. MOBILE SOURCE MICROENVIRONMENT POPULATIONS
This study is concerned with the total, nationwide, annual person hours of
exposure. As such, it is not necessary to know which particular people are
exposed, or how each microenvironment is related to others. It is only
necessary to know how many people are in each microenvironment for each
hour of the day. To determine the mobile source microenvironment hourly
populations, information on the number of locations, size range, and daily usage
of each microenvironment was required. The published literature, as well as
special sources, were used to obtain the best possible estimates of these
parameters.
Number of Persons in Vehicles
To obtain the hourly population for each of the mobile source microenvi-
ronments, an estimate of the number of persons in each vehicle is required.
There are a variety of estimates given in the literature. Early in this project, it
was realized that obtaining an accurate estimate of person hour exposure would
require defining the microenvironment populations on Saturday and Sunday, as
well as during the work week (called "weekdays" in this report). Thus, any
differences in vehicle occupancy between weekdays and weekends should be
included in the calculations. The vehicle occupancies used in this study, taken
from References 36 and 37, are as follows:
Weekday Saturday Sunday
Cars l.ija 2.3* 2.3*
Buses 26b 23b 10.6b
aFrom Reference 36
bFrom Reference 37
Population of Parking Garages
From the literature search conducted under Contract 68-03-2884, Task
Specification 1, approximately 70 abstracts on parking garages were reviewed
for information on usage and pollutant levels. Of these abstracts, ten had some
useful information. This earlier study of parking garages had obtained
information from several different sources on the number of parking garages in
the country. The result was an estimate that varied from 5300 to 10,000
parking garages/1 *> This spread in the estimated number of garages was too
large to provide the estimate of the population in parking garages needed for
this project.
Therefore, a list of parking garage construction projects since 1967 was
obtained from Data Resources, Inc. This company provides construction
statistics based on the F.W. Dodge data bank of construction projects. F.W.
Dodge is widely recognized as the authoritative source for statistics on all
types of construction projects. The parking garage construction projects in the
51
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U.S. from 1967 to 1982 are listed in Table V-l. The total number of projects
from 1967 to 1980 is &W9."%' In Reference 18, construction data found in the
magazine, "Parking," indicated that there were an average of 330 parking
garages built per year between 1971 and 1980. Apparently, the construction
reported was primarily public parking garages. The F.W. Dodge data are for all
construction, and show approximately 592 garages constructed per year
between 1971 and 1980. The difference between the two construction
estimates is apparently a number of private garages included in the Dodge
statistics. If the same ratio of public to private garages has always existed,
then there would be almost twice as many parking garages in 1965 as the 1291
estimated garages. Assuming 2500 garages in 1965, and assuming 600 built in
1966, the number of parking garages in 1980 would be 11,600.
TABLE V-l. PARKING GARAGE CONSTRUCTION
IN THE U.S., 1967-1982
Number of
Year Projects
1967 682
1968 664
1969 646
1970 587
1971 482
1972 535
1973 642
1974 546
1975 503
1976 493
1977 544
1978 837
1979 717
1980 621
1981 633
1982 596
Source: F. W. Dodge/Data Resources, Inc.
The total number of parking spaces in these 11,600 parking garages was
calculated from the information in Reference 18 and from the information
supplied by Data Resources, Inc. The garages were divided into two groups, one
with 6200 large garages averaging 740 spaces per garage, and the other with
5400 smaller garages averaging 150 spaces per garage. The total number of
spaces is then 5,398,000.
Only three references could be found that had information on cars in
motion by time of day in a parking garage/28,31,38) Since there are over
10,000 parking garages in the country, this is an extremely small sample.
Nevertheless, this information will be used to represent the nationwide average,
since it is all that is available. From the three references, a composite of
52
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weekday vehicles in motion as a percentage of garage parking capacity was
obtained for each hour of the day. Similar information for Saturday was
derived from data in Reference 38. The percent of vehicles in motion by hour
for Sunday was estimated considering the values for weekdays and Saturday,
together with the fact that for most garages, Sunday would be a day of greatly
reduced activity. The average percentage of cars in motion by hour for each of
the three types of days in shown in Figures V-l, V-2, and V-3.
Two important points abouts the fraction of active cars need to be
emphasized. The curves presented are on a per hour basis. However, except
when all vehicles are trying to leave at once, the actual time any one vehicle is
in motion is much less than one hour, generally on the order of five minutes.
Thus, the vehicles in motion at any instant would equal the hourly fraction
divided by twelve. For the maximum fraction of active cars per hour ( 21.34
percent), this gave 1.78 percent active cars at any instant. This figure agrees
well with an Aerospace Corp. study(39) which found an average of 1.5 percent
active cars at all times in Los Angeles garages. However, after a concert or a
sporting event when the garage is full and all cars are trying to leave at once,
the time a car is in motion is more likely 15 or 20 minutes and the percentage
of vehicles approaches the maximum hourly fraction. The percentage of cars in
motion is important since it is used to select the proper pollutant concentration
distribution in the calculation of person hours of exposure. The second point is
that the people in the cars are, in general, not exposed to the garage pollution
levels for a full hour. A fifteen minute exposure has been used in this study.
The total nationwide person hours of exposure for any hour of the day was
obtained by multiplying the hourly fraction of cars in motion by the total
number of garage parking spaces available nationwide, then multiplying by 1.4
persons per car for weekdays or 2.3 persons per car for weekends and dividing
by four (for the assumed quarter-hour exposure). An example may help in
visualizing the number of people involved in the parking garage
microenvironment. For the weekday hour ending at 11 A.M., the active cars
are 0.2134 of the total spaces, thus the number of people in the parking garage
microenvironment during that hour in 1980 was:
0.2134 (5.398 x 106) x 1.4 = 1,612,707 persons
Then: 1,612,707/4 = 403,177 person hours of exposure
The complete listing of parking garage person hours of exposure is shown in
Table V-2.
Persons in Urban Roadway Tunnels
From previous studies at SwRI, it is known that the total number of urban
tunnels is 59, and that the average daily traffic for all days of the week is
52,000.(18' If the weekly traffic distribution is known, then the weekday and
Saturday and Sunday average tunnel ADT can be calculated. Since tunnels
occur on all types of roadways, expressways, and arterials it was decided to
average the weekly traffic distribution for several different road types to
obtain a weekly distribution for urban tunnels. These distributions and the
average are shown in Table V-3. It was not possible to weight them by
53
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range of data
mean
Ul
CP
c
(0
&
rfl
+J
O
4J
C
O
O
at
V4
in
a
u
a>
^
o
-.-i
x:
0)
0)
-H
+J
O
0.40
0.30
0.20
0.10
f
I
I
I
M
8 10 N 2
Hour of the Day
10
M
Figure V-l. Hourly average cars in motion for weekdays in Parking Garages
-------
o.30 r
Ui
C >i
O -P
H -H
4-> O
u m
* a
M (0
M-l U
0.20
C
W -H
o
H 4-1
4J
U
M
8 10 N 2
Hour of the Day
8
10
M
Figure V-2. Hourly average cars in motion for Saturdays in Parking Garages
-------
(0
4J
O
4J
C ^i
O -P
H -H
4-> U
U (0
ITJ P[
^ (0
«W U
nj cr>
w -M
0 ^
(0 (0
V^ Oi
nt
O
0)
-H
0.30 i-
0.20
0.10
M
J_
10
N
8
10
M
Hour of the Day
Figure V-3. Hourly average cars in motion for Sundays in Parking Garages
-------
TABLE V-2. NATIONWIDE PERSON HOURS OF EXPOSURE IN PARKING GARAGES BY HOUR OF THE DAY
PARKING GARAGE
HOUR
NATIONAL POPULATION FOR WEEKDAY
4567
10
11
12
AM
PM
HOUR
AM
PM
HOUR
Ln AM
^J PM
0. 0. 0.
326660. 343664. 317025.
0. 0. 0.
415916. 499720. 558693.
0. 0. 0.
155193. 155193. 155193.
0.
359345.
0.
370303.
0.
346875.
NATIONAL POPULATION FOR
456
0.
599043.
0.
614562.
0.
620770.
NATIONAL POPULATION FOR
456
0.
155193.
0.
1 16394.
0.
38798.
18704.
195165.
SATURDAY
7
12415.
620770.
SUNDAY
7
0.
0.
130928.
156623.
8
18623.
614562.
8
38798.
0.
242586.
136596.
9
105531.
543174.
.9
1 16394.
0.
367091.
77272.
10
186231.
372462.
10
155193.
0.
403177.
84830.
11
254516.
167608.
11
155193.
0.
330628.
47421.
12
329008.
0.
12
155193.
0.
-------
population, traffic, or road type since these values were not available in the
references.
TABLE V-3. TRAFFIC DISTRIBUTION BY DAY OF THE WEEK
FOR SEVERAL SITUATIONS
Daily Traffic as a Percent of Weekly Traffic
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Urbana
13.5
14.5
13.7
13.7
14.0
15.0
16.2
CBD*
5.8
15.5
15.3
15.0
14.8
15.5
17.8
Nashville
Urbanb
10.5
15.2
14.7
14.7
14.7
15.9
14.8
San Antonio
Expresswayc
10.5
15.0
15.0
15.0
15.0
16.0
13.5
Unweighted
Average
10.1
15.1
14.7
14.6
14.6
15.6
15.6
aReference 40
bReference 41
cReference 42
From the seven day average ADT of 52,000 and the information in Table
V-3, the total traffic for all 59 urban commuter tunnels can be calculated.
Using 1.4 persons per car on weekdays and 2.3 persons per car on weekends, the
person exposures for each day type can also be calculated. The results of these
calculations are shown below.
Day Type Total Vehicles/Day Person Exposure/Day
Weekday
Saturday
Sunday
3,199,924
3,350,256
2,169,076
4,479,894
7,705,589
4,988,875
The hourly traffic distribution in two tunnels for weekdays and weekends
was found in the literature.^*^) one tunnel was identified as the Sumner
Tunnel in Boston, the other tunnel simply as an "urban tunnel." The hourly
traffic distributions for these two tunnels are shown in Figure V-4 for
weekdays, and Figure V-5 for weekends. For each day type, the distributions
from the two tunnels look very similar. The data were taken from different
studies conducted during different years, but there is a possibility that the
tunnel identified in Reference 44 as an "urban tunnel," is, in fact, the Sumner
Tunnel. It is used nevertheless, since there is so little information in the
literature.
58
-------
Sumner Tunnel, ref. 39
Urban Tunnel, ref. 56
U.Ub
i-H
-3 -H 0.06
o
-------
Sumner Tunnel, ref. 39
"Urban Tunnel, ref. 56
U.UB
r-H
TO O
±> * 0.06
O H-l
FH U-l
m
O E-i
C to u*u
0 3
H O
p rn
u
g ^| 0.02
PM
0
nl 1
.
.... -.
r ~w---" j "*~ _ _"3
^rr" L~n"l
. _ .
~, n
-i n
n i i i i i i i i i i i i
M246810N246810M
AM PM
Time of Day
Figure V-5. Hourly tunnel traffic for weekends
-------
No data were found on the individual period of exposure. Considering that
a large number of the exposures occur during peak traffic periods when traffic
is moving at its slowest rate, a five minute period of exposure was chosen for
tunnels. Thus, the total person hours of exposure for any hour of the day is the
person exposure per day given above, times the appropriate hourly fraction
given in Table V-4 or V-5, divided by 12 (for the assumed one-twelfth hour
exposure). As an example, consider the hour ending 8 A.M. on weekdays:
(4,479,894 x 0.0611)/12 = 22810 person hours. The complete listing of tunnel
person hours of exposure by hour of the day is contained in Table V-4.
Number of People in Street Canyons
The street canyon exposure in this project is for pedestrians and motorists
within the canyonj it does not include persons in buildings adjacent to the street
canyon. Therefore, the number of people and exposure times are those outside,
in the canyon. Because of the sparsity of information about street canyon
populations on hand, a computerized literature search was run to assist in
locating additional information. Only four references were found that had
information useful in determining the total number of people per day traveling
to, from, or within the central business district (CBD) in vehicles.
It was clear from the information obtained that the street canyon
population would have to be obtained in two parts: Vehicular and pedestrian.
After examining the data found in the references, it appeared that the best way
to arrive at CBD population in vehicles was to use CBD cordon counts, which
were available for the peak traffic hour for a number of cities in Reference 44.
Table 10-40 in Reference 44 listed the peak-hour person cordon count for the
top 20 urbanized areas in the U.S. Based on these counts, there were 2,245,000
people passing the CBD cordon limits during the peak traffic hour in the 20
most populous urban areas.
Peak hour traffic generally averages about eight percent of total daily
traffic/36^1) Using this value, there would be a total of 28,062,500 person
trips per day having their origin or destination within the CBD. Approximately
2.5 percent of all trips in an urban area are within the CBD, and 27.5 percent of
all trips (urban and rural) either originate or end in the CBD, but not both.0'
Thus, total trips in the CBD are approximately nine percent higher (2.5/27.5 =
0.09) than measured at the cordon. To account for the intra-CBD trips, which
are not measured at the cordon, the cordon counts were multiplied by 1.09 to
give 30,616,188 person trips per day into, out of, or within these CBDs. The
total population of these urban areas was given as 64,920,646. Using these
figures, an average of 0.472 CBD trips/person per day was obtained.
Two other studies of individual cities (Rochester, NY, and San Antonio,
TX) yielded 0.578 and 0.737 CBD trips per person.^5t*2) From Reference 44,
the urban population averages 2.43 trips per person per day for all purposes.
Reference 40 indicates that 24.5 percent of all urban trips are into, out of, or
within the CBD. These two figures give 0.60 trips per person per day for the
CBD.
The available data on CBD person trips per day is plotted as a function of
urban population in Figure V-6. Some studies have indicated that CBD trips per
61
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TABLE V-4. NATIONWIDE PERSON HOURS OF EXPOSURE IN TUNNELS BY HOUR OF THE DAY
TUNNEL
HOUR
NATIONAL POPULATION FOR WEEKDAY
4567
10
11
12
AM
PM
7466.
18442.
4779.
18592.
2987.
20384.
1904. 2016.
23519. 25685.
5227. 14821.
23669. 22362.
22810.
21503.
22250.
17210.
19712.
15568.
NATIONAL POPULATION FOR SATURDAY
HOUR
AM
PM
1
21190.
36794.
2
14512.
37179.
3
9825.
36345.
4
5779
41546
5
5522.
39812.
6 7
4688. 12522.
35959. 37436.
8
19842.
40775.
9
23245.
36088.
NATIONAL POPULATION FOR SUNDAY
HOUR
AM
PM
1
13719.
23822.
2
9396.
24071.
3
6361.
23531.
4
3742
26898
5
3575.
25776.
6 7
3035. 8107.
23281. 24238.
8
12846.
26399.
9
15050.
23365.
10
26520.
31914.
10
17170.
20662.
18853.
14000.
1 1
30437.
29281.
1 1
19706.
18958.
18704,
1 1162,
12
38078
27226
12
24653
17627
-------
O Most populous 20 urban areas, Ref. 45
O Providence, R.I., Ref. 45
D San Antonio, Texas, Ref. 49
A Rochester, N.Y., Ref. 48
o
Ul
0)
cm
c
o
M
01
Oi
Q
m
0.8
0.6
0.4
0.2
0
O
0
O
o
O
0
©
o
o
_L
_L
JL
JL
6 8 10
Urban Area Population in Millions
12
14
16
Figure V-6. Number of person trips to CBD per person in urban areas
-------
person increase as the size of the urban area decreases. In Figure V-6, this
trend appears very weak. In any case, the 0.60 CBD trips per person computed
above appears high. A more reasonable average, obtained from population-
weighting the data in Figure V-6, is 0.473 CBD trips per day per person.
Using the 1980 urban population of 132,023,885 and 0.473 CBD trips per
person, there are approximately 62,500,000 CBD trips per day in the U.S. In
earlier studies, it was assumed that street canyons constituted 60 percent of
the CBD streets.^ 8) if CBD trips are considered equally distributed over the
CBD, then there are 37,500,000 (62,500,000 x 0.600) person exposures in
vehicles to the street canyon environment each day on weekdays.
For estimates of number of persons exposed on Saturday and Sunday,
three weekly traffic distributions from two different references were
used.(40,41) The average weekly urban traffic distribution obtained from these
references is shown in Table V-5.
TABLE V-5. ESTIMATED CBD DAILY TRAFFIC AS A PERCENT
OF WEEKLY TRAFFIC
Urban* CBD*
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
13.2
14.5
13.7
13.7
14.0
15.0
16.2
5.8
15.5
15.3
15.0
14.8
15.8
17.8
Urban
(Nashville)b
10.5
15.2
14.7
14.7
14.7
15.9
14.8
Average Used
9.9
15.1
14.6
14.5
14.5
15.5
16.3
10
15
15
15
15
15
15
aRef erence 40
^Reference 41
In Table V-5, the values in the column headed "Used," are the daily traffic
percentages used in this study for street canyons. The weekday values were
adjusted to give the same percentage for all weekdays, the Sunday value
rounded off to the nearest whole percent, and the Saturday value adjusted so
that the total was 100 percent.
The total number of person trips and vehicles in street canyons for each
day of the week was calculated starting with the base figure of 37,500,000
persons trips in vehicles on weekdays. Of all the person trips to and from the
CBD, 47.4 percent are by car and 52.6 percent by transit/*'" Of the transit
trips, 74 percent are bus passengers and 26 percent are rail passengers.^6)
These facts can be used to determine the total person trips by all means of
transportation into the CBD as follows:
37,500,000 = 0.474y + 0.74 (0.526y)
Where y = total number of person trips into the CBD
64
-------
Solving this equation gives 43,441,000 person-trips in the CBD each
weekday, with 20,590,000 (0.474 times 43,441,000) person trips by auto and
16,903,000 (0.74 times 0.526 times 43,411,000) person trips by bus. Using these
values and the values of 1.4 persons per car and 26 persons per bus on weekdays
as shown earlier, the weekday vehicle trips in all CBDs can be calculated as
shown below:
Weekday vehicle trips = 20.590.000 + 16.903.000 = 15,358,000
1.4 26
The daily vehicle trips for Saturday and Sunday can be calculated from
the weekday vehicle trips and the daily vehicle trip relationship in Table V-5.
The Saturday and Sunday vehicle trips are 15,358,000 and 10,240,000,
respectively. Using these vehicle trip values, the persons per vehicle shown
earlier, and the weekday ratio between car person trips and bus person trips
(20,590,000/16,903,000 = 1.22), the Saturday and Sunday person trips can be
calculated using the following equation:
person trips = (1.22 + 1) BV
1.22 k + 1
c
where: B = persons per bus
C = persons per car
V = vehicle trips
The average persons per vehicle can then be obtained by dividing the person
trips by the vehicle trips. The results of these calculations are presented in
Table V-6.
TABLE V-6. DAILY PERSON TRIPS AND VEHICLES IN
STREET CANYONS
Weekdays Saturday Sunday
Daily person trips 37,500,000 59,410,000 36,380,000
Daily vehicle trips 15,358,000 15,358,000 10,240,000
Persons per vehicle 2.44 3.87 3.55
The hourly traffic flow as a fraction of daily traffic for weekdays was
obtained from Reference 36. A population-weighted average for the data from
various city sizes was used. The hourly traffic distribution is shown in Figure
V-7.
65
-------
Data from Reference
c
Computed from SAROAD CO data
0.09
0.08
H °-07
^ O.uo
^1
H
id
Q 0.05
*
o
** 0.04
o
0 0.03
-p
o
(0
ti n 02
0.01
.. - »
f"
...
*
*-
M W
_ _.
^--
M
8 10
8 10 M
Hoxir of the Day
Figure V-7. Hourly traffic distribution in the CBD for an average weekday
-------
Saturday and Sunday traffic distributions were not found in the literature
for the CBD. However, if the assumption that the CO is almost entirely from
mobile sources is correct, then the hourly traffic distribution should be able to
be deduced from the measured hourly CO distribution for street canyons given
in Section III. To check the assumption that CO level is related to traffic, the
weekday traffic distribution presented in Figure V-7 was paired with the
SAROAD concentration data in Section III by hour of the day. A linear
regression was performed using the mean hourly CO level and the hourly
percent ADT. The regression analysis produced a coefficient of determination,
r2, of 0.9598. The equation and its coefficients are:
p = a + bx
Where: p = hourly percent ADT
x = hourly mean ppm CO
a = -M972
b= 1.9276
To see the physical meaning of this equation, the linear form can be
rearranged as follows:
P = b (x - c)
In this form, "c" can be thought of as the background concentration (assumed
constant for the entire day), and "b" as the reciprocal of the contribution of one
percent ADT to the ambient CO PPM.
Since: P = b (x - c) = bx - be
Then: a = -be
and: c = - §
Thus, from the regression analysis (for weekdays):
background concentration = -(-1.4972/1.9276) = 0.777 ppm, and
"b"(reciprocal of ADT "Emission factor") = 1.9276 (percent ADT/PPM).
The percent ADT for each hour of the day for weekdays was calculated
using the above equation. These values are also shown in Figure V-7 for
comparison with traffic count data. As would be expected from the high
coefficient of determination, the two values of ADT agree closely,
demonstrating that the hourly CO pattern can be used to develop an hourly
traffic pattern.
Implicit in the equation is some actual value for the weekday ADT. To
apply this equation to Saturday and Sunday, the "b" coefficient must be adjusted
67
-------
for the traffic count difference between weekdays and Saturdays and Sundays.
The "b" coefficient was changed by the ratio of weekday vehicles to Saturday
and Sunday vehicles. The ratio was 1.0 for Saturday and 1.5 for Sunday.
However, when the weekday equation, adjusted for traffic differences, was
applied to the Saturday and Sunday CO distribution, the calculated hourly
percent ADT values did not total 100 percent.
After some thought, it was realized that the weekend on-the-road fleet
had a smaller percentage of commercial vehicles (mostly trucks) than the
weekday fleet. Since truck CO emissions can be several times car emissions,
this change would reduce the fleet CO emission factor on weekends. With
lower mobile source CO emissions and reduced industrial activity, the weekend
background would also probably be lower.
If the emission factor and background are assumed to be equal on
Saturday and Sunday, but the Sunday traffic is two-thirds of the Saturday
traffic, then two equations can be written for the percent ADT as a function of
PPM with only two unknowns; the background and the "b' coefficient. When
these two equations are solved, the resulting background level is 0.589 PPM.
The "b" coefficient for Saturday, which had the same total traffic as weekdays,
is 2.650. This is equivalent to a 28 percent reduction from the weekday fleet
emissions factor. The "b1 coefficient for Sunday is 1.5 times the Saturday
coefficient (or 3.975) to account for the reduced Sunday traffic. The "percent
of ADT" distributions which result from applying these equations to the hourly
PPM readings (in Table III-8 of Section III) are shown in Figures V-8 and V-9 for
Saturday and Sunday, respectively.
Three references for pedestrian street canyon population were all that
could be found in the literature.l*7»*8'^9> The total number of pedestrian trips
was calculated from Reference 49, which indicated that there were 5.8
weekday pedestrian trips for each weekday vehicle trip into the Chicago CBD.
Since no other information could be found, this number was used for all CBD's
in the country. At 5.8 pedestrian trips per trip in a vehicle in a CBD and
37,500,000 person trips in vehicles in all CBD's (Table V-6), there are 89,076,000
pedestrian trips in U.S. CBD's each weekday.
From a study of Seattle pedestrians/*8^ the daily pedestrians as a percent
of total weekly pedestrians were calculated. This distribution by day of the
week is shown in Figure V-10. The average weekday pedestrian trips are 16.54
percent of the total weekly trips. The total weekly trips are then 538,552,000.
Saturday trips are 13.0 percent of the weekly trips, giving 70,012,000 pedestrian
trips on Saturday. Sunday pedestrian trips are 4.3 percent of the total weekly
trips, or 23,158,000 Sunday pedestrian trips.
From the three references, hourly distributions of pedestrians in street
canyons for weekdays and Saturdays were developed. These distributions are
shown in Figures V-ll and V-12 as percent of total daily pedestrians. There
were no distribution data available for Sundays. Therefore, the Saturday
distribution in percentage terms was used for Sunday also. There were no data
in the literature as to how long people spend for each pedestrian trip.
Therefore, the exposure period was assumed to be 15 minutes.
68
-------
o
H
£ 0.06
(0
M
Cj
x. n nc
(0
T>
^
3
<8 0.04
w
i-t
(0
o 0.03
in
O
g 0.02
H
4J
O
n)
£ 0.01
Onn
M 2 4
6 8 10 N 2 4 6 8 10 I-
A.M. P..M.
hour of the day
Figure V-8. Hourly traffic distribution in the CBD for Saturday
-------
0.07
0 0.06
H
<*-!
n)
E< 0.05
-------
20
18
3 16
Ifl
H
iJ 14
(0
0)
D
-------
N>
nS
H
10
-------
0, '^
c
2 -12
4
I
) 8 10 W
P.M.
Time of Day
Figure V-12. Hourly pedestrian distribution in the CBD for Saturdays
-------
To calculate the person hours of exposure in street canyons for any hour
of the day, the total street canyon vehicle trips are multiplied by hourly
fraction of vehicle trips, then divided by 4. The total pedestrian trips are
multiplied by the hourly fraction of pedestrian trips, then divided by four. The
vehicle person hours and pedestrian person hours are then added together to
obtain total street canyon person hours of exposure for that hour of the day.
For example, between 5 and 6 P.M. on a weekday:
(37,500,000 x 0.0675)/4 = 632,813
(89,076,000 x 00900)/4 = 2.004,210
Total street canyon
person hours = 2,637,023
The complete listing of street canyon person hours is shown in Table V-7.
-------
TABLE V-7. NATIONWIDE PERSON HOURS OF EXPOSURE IN STREET CANYONS BY HOUR OF THE DAY
STREET CANYON
HOUR
NATIONAL POPULATION FOR WEEKDAY
4567
10
1 1
12
Ln
AM
PM
HOUR
AM
PM
HOUR
AM
PM
198764.
3349644.
1
739965.
2708582.
1
629630.
1008919.
1 18601.
3104685.
2
632872.
3002317.
2
533550.
1 122137.
69144.
2567648.
3
517077.
3160219.
3
436231.
1218473.
69144
2302050
4
329726
2817160
4
290131
1 123189
85082.
. 2680365.
214932. 541902.
2637023. 1422336.
NATIONAL POPULATION FOR SATURDAY
567
239125.
. 2595082.
266070. 393746.
1913576. 1321649.
NATIONAL POPULATION FOR SUNDAY
567
199181.
. 1088442.
169748. 205220.
892090. 717496.
1385258.
969932.
8
548368.
1075442.
8
253097.
627015.
1923246.
717717.
9
873348.
101 1476.
9
331752.
596417.
1732214.
608954.
10
1 169844.
996258.
10
442570.
549778.
1826915.
453315.
1 1
1733340.
1049728.
11
631136.
527041.
2230800
356277
12
2346215
912564
12
854517,
437072,
-------
VI. NATIONAL NEIGHBORHOOD POPULATION
To calculate mobile source exposure estimates based on the number of
persons in different physical locations and the pollutant concentrations in those
locations, it is helpful to think of the nation not as a collection of urban areas
spread here and there, with suburban and rural areas in between, but rather as
one large urban area, surrounded by a suburban area and a rural area, as
depicted in Figure Vl-1. For this project, the urban and suburban population are
combined as the 1980 "urban area" populations from all 366 urban areas defined
by the Census Bureau. The rural population is all the remaining population.
Figure VI-1 shows three neighborhoods in the urban and suburban areas.
These neighborhoods were used in the NEM CO study. The percent of a city's
population in each of these neighborhoods was determined as explained in
Section IV of this report for each of the four cities used in the NEM CO study.
In the NEM CO study, the same set of activity patterns and neighborhood-to-
neighborhood movements was used for each city for a given classification of
person (A-O subgroups). Only the number of people in each A-O subgroup
changed for each city (see Section IV).
The NEM CO study produced neighborhood populations on an hour-by-hour
basis for four cities. Conceivably it would be possible to use the NEM CO study
methodology for each of the 366 urbanized areas in the country. Then sum each
neighborhoods population to obtain nationwide population estimates for each
neighborhood. In practice, this technique would have required a great more
time and effort than were allotted for this project. However, for national
exposure estimates, the neighborhood population for the four cities can be
scaled up to national population estimates by determining the population of the
other U.S. cities that are similar (ultimately in terms of hour-by-hour
locations; indirectly in terms of A-O distribution, residence and work locations,
etc.) to each of the four cities used in the NEM study. The procedure was to
multiply the population percentages from each city by the number of people in
all U.S. cities that are similar (in terms of the criteria explained below) to that
city, then sum these four population tables to produce a table of nationwide
population in each N/M combination during each hour of the day, for each day
type.
To obtain National CO exposure the NEM CO study used a similar scaling
technique on the person hours of exposure to CO levels. The lists of cities that
were chosen as similar to each of the four cities investigated in the NEM CO
study were obtained from Mr. William Biller, EPA consultant. From the
information provided by Mr. Biller, the list of cities shown in Table VI-1 was
compiled. The NEM CO study used four cities: Chicago, St. Louis,
Philadelphia, and Los Angeles. The cohort populations for each of these cities
produced a different percentage of people in urban core neighborhoods. These
percentages are shown below:
77
-------
1980 Population
Urban Area (Urban Core + Suburban) Population = 139,170,613
Rural Population = 87,334,142
Figure VI-1. Pictorial representation of U.S. as one urban area
78
-------
NEM CO Study Estimate of
City Percent of People in Urban Core
Chicago 75
St. Louis 50
Philadelphia 30
Los Angeles 20
It should be pointed out that the geographical area covered by the NEM
study for any city was not the same as any of the Census Bureau-defined
geographical areas for that city. More specifically, the areas used for the four
cities in the NEM CO study were not the same as the Census Bureau's
geographical definition of "urbanized area" for those four cities. Nevertheless,
the Census Bureau populations of urbanized areas were used in the NEM CO
study to extrapolate the four cities to nationwide results. For this study also,
the Census Bureau populations of urbanized areas were used to extrapolate the
neighborhood populations in the four cities to national neighborhood
populations. As explained in the Introduction section of this report, the Census
Bureau uses the phrase "center city" to mean all of the area within the city
limits of an urbanized area's central city. Thus, the census information on
percent of people in the center city cannot be used by itself to determine the
percent of people in the "urban core" neighborhood type though it is possible
that in some cases urban core may equal center city.
When the NEM CO Study list of cities in Table VI-1 was examined, it
became apparent that the NEM assignment of cities would not be suitable for
this project and that different criteria for determining similar cities were
necessary. As one example, Jacksonville, Florida is listed in Table VI-1 as a
Chicago-like city (i.e., one with 75 percent of the population in the urban core).
It is true that the census figures show 89 percent of the people in the
Jacksonville urbanized area living in the center city limits. What is not
evident is that the Jacksonville city limits extend far beyond most of the urban
core area. Driving through the city reveals that most of the land within the
Jacksonville city limits could be classified as suburban, and much of it as rural.
Thus, the problem of the size of the incorporated area of a city compared
to the size of its urban core area must be addressed if the census data on
urbanized areas are to be used. The population density in persons per square
mile, of both the portion of the urbanized areas outside the center city and the
portion inside the center city, was examined for the 116 urban areas above
200,000 population. The population density outside the center cities varied
from 385 to 4685 persons per square mile. Of the 116 urban areas, only 8 had
population densities outside the center city below 1000. Nine urban areas had
population densities above 3000 outside the center city. The population density
within the center cities varied from 1343 to 22,491 persons per square mile.
The urbanized area population outside the center city limits, for most urban
areas will be primarily suburban. Thus, suburban population densities will be
under 4685. For purposes of this study, after comparing center-city population
79
-------
TABLE VI-1. NEM CO STUDY URBAN AREA CLASSIFICATION
Chicago
(75% Center City
Residents)
New York
Chicago
San Francisco/
Oakland
Pittsburgh
Cincinnati
Buffalo
Indianapolis
Jacksonville
Bridgeport, Conn
Tulsa
Grand Rapids
Flint
Southbend
Davenport/
Moline/
Rock Island
Des Moines
Canton
Elgin/Aurora
Lansing
Ft. Wayne
Rockford
Madison
Detroit
St. Louis
(50% Center
City Residents)
Cleveland
St. Louis
Milwaukee
Atlanta
Kansas City
Louisville
Dayton
Ft. Lauderdale/
Hollywood
Birmingham
Akron
St. Petersburg
Omaha
Nashville
Honolulu
Richmond
Youngston/Warren
Tampa
Orlando
Wichita
Albuquerque
W. Palm Beach
Charlotte
Baton Rouge
Peoria
Columbia
Shreveport
Chattanooga
Little Rock
Corpus Christi
Columbus
Colorado Springs
Austin
Philadelphia
(30% Center
City Residents)
Philadelphia
Boston
Washington, D.C.
Minneapolis/
St. Paul
Baltimore
Providence/
Powtucket/
Warwick
Columbus
Norfolk/
Portsmouth
Rochester
Springfield/
Chicopee/
Holyoke
Toledo
Albany/
Schenectady/
Troy
Hartford
Syracuse
Wilmington
Al lento wn/
Easton/
Bethehem
New Haven
Trenton
Newport News/
Hampton
Worchester
Harrisburg
Charleston
Wilkes-Barre
Scranton
Lawrence/
Haverhill
Los Angeles
(20% Center
City Residents)
Los Angeles/
Long Beach
Houston
Dallas
Seattle/
Everett
Miami
San Diego
Denver
San Jose
New Orleans
Phoenix
Portland
San Antonio
Memphis
Sacramento
San Bernardino/
Riverside
Oklahoma City
Salt Lake City
El Paso
Tacoma
Tucson
Spokane
Fresno
Mobile
Thousand Oaks/
Oxnard/
Ventura
Las Vegas
SO
-------
density with known urban core characteristics for several cities, a population
density of 3000 was assigned the value of 10 percent "urbanized core" (in terms
of land use and population density) in character, while a population density of
10,000 or more was assigned a value of 100 percent urban core in character.
The basic assumption is that urban core characteristics are proportioned to
population density. A linear relationship was assumed between these two points
as shown in Figure VI-2.
Figure VI-2 was used to adjust the census figures for the percentage of
urban area people living inside the center city to percentage of people living in
the area that is characterized as urban core. The calculations were performed
as follows. The 1980 center city population density for each urban area was
first obtained from census data. From Figure VI-2, the percentage of center
city that is urban core was obtained using the population density. The
percentage urban core (divided by 100) was multiplied by the center city
population, also from census data, to obtain the number of persons in the urban
core. The number of persons in the urban core was divided by the population of
the whole urban area (from census data) to obtain the percentage of the urban
area population in the urban core.
For example, St. Louis had a 1980 urban area population of 1,848,598, and
a population within the center city limits of 453,085. The population density
within the city limits was 7428 persons per square mile. Entering Figure VI-2
with 7428, it can be seen that approximately 65 percent of the center city
population lives in neighborhoods that could be classed as urban core. Thus, the
St. Louis urban core population is 294,505 (0.65 times 453,085). The percentage
of the urban area population that lives in the urban core is approximately:
((294,505/1,848,598) x 100) = 16 percent.
When these calculations were done for the 116 urban areas over 200,000
population, a much different list of similar cities, based on residence in
neighborhood type, was obtained, as shown in Table VI-2. St. Louis is classed in
the NEW CO study as having 50 percent of the population within the urban core,
and Philadelphia is classed as having 30 percent of the population within the
urban core, yet neither city is in its respective NEM CO Study classification in
Table VI-2. Thus, the city designations have been changed to type A, B, C, and
D, for urban areas with 75 percent, 50 percent, 30 percent, and 20 percent of
the population within the urban core, respectively. Note that there are far
fewer cities in the A, B, and C categories in Table VI-2 than in Table VI-1.
From the list in Table VI-2, it may appear that the NEM CO study N/M
population percentages for Philadelphia were switched from 30% urban core
residents to 50% urban core residents, and that there was no NEM CO study
city for the Type C area (30% urban core residents). This is not the case. The
NEM CO study N/M population percentages from the four cities in the CO study
were used to represent the percent urban core residents that the NEM CO study
area produced. That is, the NEM CO study N/M population percentages from
Chicago study was used to represent Type A urban areas, the percentages from
the St. Louis study area were used to represent Type B urban area, the
percentages from the Philadelphia study are were used to represent Type C
urban areas, and the percentages from the NEM CO study Los Angeles study
area were used to represent Type D urban areas. Because the NEM CO study
81
-------
00
N>
100 |-
90
J 80
CO
H
I
+J
4J
H
U
4J
c
a)
u
0)
70
60
50
40
-p
§ 30
20
10
3000
I
I
4000 5000 6000 7000 8000
Urbanized Area Center City Population Density
9000
10000
Figure VI-2. Percent of urbanized area center city that is urban core
-------
area boundaries were not the same as the Census Bureau boundaries of the
city's urbanized area, for extension to nationwide N/M populations, the vaiue
for the Philadelphia urbanized population is included in those cities that use the
N/M population percentages for a Type B urban area, while the value for the St.
Louis urbanized area population is included in those cities that use the N/M
population percentages for a Type D urban area.
TABLE VI-2. RECLASSOTCATION OF URBAN AREAS BY PERCENT OF
RESIDENTS IN URBAN CORE (BASED ON 1980 CENSUS)
Type A Urban
Areas (7596 Urban
Core Residents
New York
Chicago
Type B Urban
Areas (50% Urban
Core Residents
Philadelphia
San Francisco
Baltimore
Type C Urban
Areas (30% Urban
Core Residents
Detroit
Washington DC
Miami
Milwaukee
New Orleans
Buffalo
Bridgeport
Syracuse
Trenton
Type D Urban
Areas (20% Urban
Core Residents
Remainder of
urban areas
For each urban area type (A, B, C, or D) the total population of all the
urban areas of that type was multiplied by the percent of people in each N/M
classification for that urban area type, from Section IV. The populations of the
four urban area types were summed by N/M classification to produce
nationwide estimates of the population in each N/M classification by hour of
the day. These population distributions are included in Appendix D of this
report.
The numbers of people in the Mobile Source microenvironments (street
canyon, tunnel and parking garage) were determined on a national basis, as
explained in Section V of this report. These mobile source populations must be
subtracted from appropriate NEM N/M types for each hour of the day, since
they were arrived at independently of the NEM procedure. The process of
choosing the N/M types from which to subtract the various mobile source
microenvironment populations highlighted one of the limitations of the NEM
program. This limitation was that not every N/M type has a nonzero population
for each hour of the day.
As an example, it would be most logical to subtract the hourly street
canyon population from the urban core-commercial/vehicle and urban core-
commercial/roadside NEM N/M types. However, some hours of the day have
zero people in these N/M types. Even when there are people assigned to these
N/M types, the total number is often less than the street canyon population.
For most of the nighttime hours, especially on Saturday and Sunday, only two
NEM N/M types have nonzero populations: urban core-residential/inside and
83
-------
suburban-residential/inside. These results seem to indicate that the constraints
of the NEM computer program may cause certain hour populations of some N/M
types to be underestimated.
The numbers of people subtracted from each N/M type, by hour of the
day, for the mobile source microenvironments are shown in the tables in
Appendix D of this report. The final national urban populations by N/M type
are shown in Tables VI-3, VM, and VI-5. The explanation of the N/M codes in
these tables is presented in Table VI-6. Note that the tables show "Mobile
Source" as a neighborhood type, and street canyons, tunnels and parking garages
as microenvironments; so that the tables present the hourly populations of all
places that will be used in the calculation of exposure. As such, these tables
will be the population input to the exposure computer program.
84
-------
HOUR
TABLE VI-3. NATIONAL POPULATION FOR WEEKDAY
456789
10
11
12
N/M
AH
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
c» PM
^ AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
49165015.
29421948.
0.
0.
0.
965465.
0.
192069.
777719.
14022989.
0.
0.
0.
0.
0.
0.
1205327.
9581951.
0.
1 161110.
0.
0.
0.
0.
86130233.
55914265.
0.
0.
0.
1646203.
0.
423024.
777021.
13897516.
0.
0.
0.
0.
0.
0.
918661.
7379715.
0.
863087.
0.
0.
0.
0.
198764.
3349644.
7466.
18442.
0.
326660.
49842171.
25524062.
0.
253786.
0.
1483189.
0.
3314698.
858956.
14257687.
0.
0.
0.
0.
0.
0.
300598.
7804537.
0.
1 161065.
0.
0.
0.
1777414.
87040796.
48813775.
0.
489617.
0.
2435232.
0.
6244020.
777290.
12858398.
0.
916236.
0.
108557.
0.
0.
230634.
6039253.
0.
863057.
0.
0.
0.
1339557.
1 18601.
3104685.
4779.
18592.
0.
343664.
50323418.
21177257.
0.
4369018.
0.
2409960.
0.
2632114.
0.
14597277.
230627.
0.
0.
0.
0.
212713.
301136.
10085488.
0.
0.
0.
0.
0.
656991.
87776520.
41006425.
0.
8048922.
0.
4124405.
0.
4800733.
0.
13285185.
239001.
0.
0.
409054.
0.
195005.
230993.
7742569.
0.
0.
0.
0.
0.
499844.
69144.
2567648.
2987.
20384.
0.
317025.
50469392.
24519075.
0.
1774254.
0.
393660.
0.
3895133.
0.
14932097.
0.
0.
0.
0.
0.
1 16844.
301461.
8082861.
0.
1159587.
0.
0.
0.
1499090.
88100716.
46661325.
0.
3806766.
0.
835274.
0.
6673752.
0.
13722272.
0.
0.
0.
59039.
0.
108557.
231209.
6249720.
0.
862071.
0.
0.
0.
1119222.
69144.
2302050.
1904.
23519.
0.
359345.
50453410.
21088961.
0.
3126098.
0.
192069.
0.
9103583.
0.
15340358.
0.
0.
0.
0.
0.
0.
301427.
1370629.
0.
3595580.
0.
0.
0.
1499090.
88100704.
35052502.
0.
6725359.
0.
423024.
0.
19296863.
0.
14319249.
0.
0.
0.
108557.
0.
0.
231187.
1042014.
0.
2699788.
0.
0.
0.
1119222.
85082.
2680365.
2016.
25685.
0.
370303.
50322275.
41284854.
0.
2095759.
0.
1076578.
0.
2298948.
0.
979469.
0.
993437.
0.
0.
0.
0.
300464.
902829.
0.
0.
0.
0.
0.
0.
88100383.
72797977.
0.
3163320.
0.
121 1980.
0.
4809671.
0.
777768.
0.
3097123.
0.
0.
0.
0.
230545.
682578.
0.
0.
0.
0.
0.
0.
214932.
2637023.
5227.
23669.
0.
346875.
47727538.
47953301.
1293562.
0.
0.
447143.
0.
0.
0.
379313.
0.
0.
0.
0.
0.
0.
302032.
903221.
1162197.
0.
0.
0.
0.
0.
84596185.
83932771.
2410976.
672102.
0.
1815203.
0.
0.
0.
746257.
0.
0.
0.
0.
0.
0.
231590.
682840.
863811.
0.
0.
0.
0.
0.
541902.
1422336.
14821.
22362.
18704.
195165.
38489899.
46249738.
3294132.
0.
589217.
2457433.
0.
0.
0.
553583.
0.
0.
0.
0.
0.
0.
7619925.
903479.
1 159800.
0.
0.
0.
0.
0.
70521658.
81933019.
7953206.
0.
1256062.
4496270.
0.
0.
0.
754060.
0.
0.
0.
0.
0.
0.
5895017.
683011.
862213.
0.
0.
0.
0.
0.
1385258.
969932.
22810.
21503.
130928.
156623.
2B359526.
49002430.
2738201.
0.
0.
0.
0.
0.
14680521.
531769.
0.
0.
0.
0.
0.
0.
9399576.
904767.
1 159968.
0.
0.
0.
182375.
0.
53444637.
86420767.
5607554.
0.
0.
0.
0.
0.
13060313.
755558.
0.
0.
108557.
0.
0.
0.
7232337.
683870.
862325.
0.
0.
0.
146473.
0.
1923246.
717717.
22250.
17210.
242586.
136596.
24634137.
49068674.
1610992.
0.
1155657.
0.
3180132.
0.
15615653.
622400.
0.
0.
0.
0.
0.
0.
8082861.
905260.
1160729.
0.
0.
0.
1499090.
0.
46014177.
86423733.
3014452.
0.
1977865.
0.
6972740.
0.
13531034.
764620.
182265.
0.
168502.
0.
0.
0.
6249720.
684198.
862833.
0.
0.
0.
1119222.
0.
1732214.
608954.
19712.
15568.
367091.
77272.
19977536.
49371641.
2757266.
0.
0.
0.
7828125.
0.
15380564.
469656.
0.
0.
0.
0.
1 16844.
0.
7614269.
905730.
0.
0.
2664482.
0.
474616.
0.
37688767.
86423355.
4487373.
0.
0.
0.
15810465.
0.
12858398.
763643.
606785.
0.
299246.
0.
108557.
0.
5891246.
684512.
0.
0.
1985997.
0.
354703.
0.
1826915.
453315.
18853.
14000.
403177.
84830.
23179919.
49155531.
645534.
0.
111651.
0.
6635101.
0.
14925382.
590274.
0.
0.
0.
0.
212713.
0.
9243893.
1204218.
0.
0.
0.
0.
1499090.
0.
42429594.
86127862.
1372310.
0.
132075.
0.
14057587.
0.
13532136.
769539.
0.
0.
168502.
0.
195005.
0.
7122276.
917922.
0.
0.
0.
0.
II 19222.
0.
2230800.
356277.
18704.
11162.
330628.
47421.
I/I
1/1
1/3
1/3
1/4
1/4
1/5
1/5
2/1
2/1
2/3
2/3
2/4
2/4
2/5
2/5
3/1
3/1
3/3
3/3
3/4
3/4
3/5
3/5
5/1
5/1
5/3
5/3
5/4
5/4
5/5
5/5
6/1
6/1
6/3
6/3
6/4
6/4
6/5
6/5
7/1
7/1
7/3
7/3
7/4
7/4
7/5
7/5
8/7
8/7
8/8
8/8
8/9
8/9
-------
TABLE Vl-4. NATIONAL POPULATION FOR SATURDAY
HOUR
8
10
II
12
N/M
AH
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
00 PM
°" AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
. PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
50016569.
47085276.
0.
1919905.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88329959.
83334542.
0.
3477994.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
739965.
2708582.
21190.
36794.
0.
415916.
50125665.
36875936.
0.
5831057.
0.
3060970.
0.
2917852.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
b.
0.
0.
0.
0.
0.
0.
88397634.
64331774.
0.
10888320.
0.
6039525.
0.
5292312.
0.
0.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
632872.
3002317.
14512.
37179.
0.
499720.
50242866.
1465B594.
0.
9177135.
0.
7698840.
0.
17154931.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88400914.
25814180.
0.
13559402.
0.
14205879.
0.
32933401.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
517077.
3160219.
9825.
36345.
0.
558693.
50431431. 50522109. 50495415. 50361663. 50202983. 50201501. 32005147. 25437801.
26289070. 43914811. 47189124. 49273980. 39215619. 44492013. 49559709. 49583749.
0.
1 339600.
0.
5083375.
0.
16179148.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88403747.
47694445.
0.
2186378.
0.
7086390.
0.
29656115.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
329726.
2817160.
5779.
41546.
0.
599043.
0.
2340517.
0.
771736.
0.
1980306.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88403927.
78635548.
0.
3964874.
0.
1282304.
0.
3022939.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
239125.
2595082.
5522.
39812.
0.
614562.
0.
322519.
0.
1914699.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88404510.
84315769.
0.
506800.
0.
2360084.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
266070.
1913576.
4688.
35959.
0.
620770.
0.
50471.
0.
322519.
0.
123233.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88390336.
86546516.
0.
83810.
0.
506800.
0.
283500.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
393746.
1321649.
12522.
37436.
12415.
620770.
0.
7392125.
0.
3312374.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88380867.
67029268.
0.
14239118.
0.
6241877.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
548368.
1075442.
19842.
40775.
18623.
614562.
0.
4331413.
0.
1155657.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
87769894.
77675979.
0.
7937841.
0.
1977865.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
873348.
1011476.
23245.
36088.
105531.
543174.
8945485.
492730.
965465.
0.
8093168.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
57631260.
86677196.
13246805.
1052174.
1646203.
0.
15058724.
0.
0.
0.
193005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1 169844.
996258.
26520.
31914.
186231.
372462.
6550541.
498855.
8200614.
0.
8966303.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
45285644.
86754511.
13503738.
1086045.
13540123.
0.
14656834.
0.
0.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1733340.
1049728.
30437.
29281.
254516.
167608.
42484440.
49842159.
919937.
0.
111651.
0.
5730261.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
73757718.
88388734.
1754421.
0.
132075.
0.
1 1358942.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
2346215.
912564.
38078.
27226.
329008.
0.
1/1
1/1
1/3
1/3
1/4
1/4
1/5
1/5
2/1
2/1
2/3
2/3
2/4
2/4
2/5
2/5
3/1
3/1
3/3
3/3
3/4
3/4
3/5
3/5
5/1
5/1
5/3
5/3
5/4
5/4
5/5
5/5
6/1
6/1
6/3
6/3
6/4
6/4
6/5
6/5
7/1
7/1
7/3
7/3
7/7
7/4
7/7
7/7
8/7
8/7
8/8
8/8
8/9
8/9
-------
TABLE VI-5. NATIONAL POPULATION FOR SUNDAY
HOUR
8
to
II
12
N/M
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
00 PM
^ AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
50129145.
48869424.
0.
1233505.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88398189.
85761072.
0.
2108320.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
629630.
1008919.
13719.
23822.
0.
155193.
49514862.
42329511.
0.
18771 19.
0.
909768.
711660.
4927702.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
o.
0.
87015518.
75510368.
0.
3402922.
0.
1636635.
1388841.
7274005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
533550.
1 122137.
9396.
24071.
0.
155193.
50324752.
27344922.
0.
558274.
0.
1948858.
0.
20129626.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88403339.
49219818.
0.
1 187820.
0.
3015682.
0.
34173561.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
436231.
1218473.
6361.
23531.
0.
155193.
4966141 1.
22326152.
71 1660.
127454.
0.
2211556.
99897.
25244146.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
86847312.
39253396.
1388841.
201394.
0.
3819036.
160404.
44273732.
0.
0.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
290131.
1123189.
3742.
26898.
0.
155193.
50562637.
33251843.
0.
2665200.
0.
1667540.
0.
12357123.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88405289.
56054105.
0.
5035675.
0.
2044025.
0.
24448306.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
199181.
1088442.
3575.
25776.
0.
1 16394.
50592232.
47094618.
0.
0.
0.
2905880.
0.
215880.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88405667.
83163474.
0.
0.
0.
4507881.
0.
324386.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
169748.
892090.
3035.
23281.
0.
38798.
50555239.
47399435.
0.
344736.
0.
2592709.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88402117.
83258265.
0.
681860.
0.
4 164608.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
205220.
717496.
8107.
24238.
0.
0.
47958373.
49539816.
2544544.
848468.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
82923945.
87034752.
5440332.
1 104662.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
253097.
627015.
12846.
26399.
38798.
0.
45770803.
48509745.
4545994.
1896556.
0.
0.
215880.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
79499741.
85992004.
8349431.
2160866.
0.
0.
324386.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
331752.
596417.
15050.
23365.
116394.
0.
37314179.
50426825.
11960969.
0.
0.
0.
1 179108.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
63914465.
88173418.
22215534.
0.
0.
0.
1980111.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
442570.
549778.
17170.
20662.
155193.
0.
38730925.
50440979.
9596530.
0.
1318579.
0.
684799.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
70791703.
88183705.
13594792.
0.
2183882.
0.
1463439.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
631136.
527041.
19706.
18958.
155193.
0.
42184270.
50495360.
6003910.
0.
750850.
0.
1266719.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
73206787.
88220624.
11051635.
0.
1623694.
0.
2060135.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
854517.
437072.
24653.
17627.
155193.
0.
I/I
1/1
1/3
1/3
1/4
1/4
1/5
1/5
2/1
2/1
2/3
2/3
2/4
2/4
2/5
2/5
3/1
3/1
3/3
3/3
3/4
3/4
3/5
3/5
5/1
5/1
5/3
5/3
5/4
5/4
5/5
5/5
6/1
6/1
6/3
6/3
6/4
6/4
6/5
6/5
7/1
7/1
7/3
7/3
7/7
7/4
7/5
7/5
8/7
8/7
8/8
8/8
8/9
8/9
-------
TABLE VI-*. EXPLANATION OF NEIGHBORHOOD/MICROENVIRONMENT
(N/M) CODES USED IN THIS PROJECT
Neighborhood No.**
Urban core residential 1
Urban core commercial 2
Urban core industrial 3
Suburban residential 5
Suburban commercial 6
Suburban industrial 7
Mobile source microenvironments
(collectively)* 8
Microenvironments No.**
Indoor 1
Vehicle 3
Roadside 4
Outdoor 5
Street Canyon 7
Tunnel 8
Parking Garage 9
Example: N/M = 5/1 = suburban residential/indoor
*Not a NEM CO Study neighborhood but rather the collective
designation for Mobile Source microenvironments.
**Numbers missing were for neighborhood and microenvironments
defined in the NEM CO study, but not used in this study.
88
-------
VIL RURAL EXPOSURE
The NEM neighborhoods and mobile source microenvironments all occur in
what the Census Bureau defines as urban areas. Yet the ultimate goal of this
project is to define exposure levels for all persons in the U.S., not just those in
urban areas. Obviously, some estimates of rural population and pollutant
concentrations are required to complete the person hour exposure estimate for
the entire country. This brief report section will cover the rural population
distribution and pollutant concentrations included as inputs to the person hour
exposure computer program in the project.
The Census Bureau lists 366 areas of the country with populations over
50,000 as urban areas. The total population of these 366 areas in 1980 was
139,170,613. For this project, the remaining 87,334,142 people of the total
226,504,755 people in the U.S. were assigned to the rural "neighborhood." Since
no detailed study of the rural population was undertaken, there were no further
subdivisions of the rural neighborhood into different neighborhood classes or
microenvironments. Since the entire rural population was in one N/M type, the
hour-by-hour distribution of population in the rural neighborhood consisted of
the total rural population every hour of the day.
In general, persons living in rural areas do not experience high
concentrations of mobile source pollutants. To estimate the magnitude of total
exposure, CO was again used as the indication of mobile source emissions.
Background levels of CO range from 0.03 to 0.22 ppm/22) However, air masses
that have recently traversed urban areas show levels as high as 1.0 ppm in rural
areas/22)
A detailed examination of rural exposure to mobile source pollutants was
not part of the scope of this study. However, from the data presented above, it
is estimated that all rural exposure to CO is below 2 ppm. Using the 1980
mobile source 35 mph emission factor for CO of 15.51 g/min, this 2 ppm CO
converts to a mobile source exposure upper limit of 149 yg/nV at 1.0 g/min
emission factor. For purposes of this study, it was assumed that 50 percent of
the person hour exposure was between 0 and 37 yg/m3, 30 percent of the
person hour expsure was between 37 and 75 yg/m3, 15 percent between 75 and
112 yg/m3 and 5 percent between 112 and 149 yg/m3. For the 87.3 million
people in rural areas, the person hour exposure distribution is then as shown in
Table VII-1.
89
-------
TABLE Vn-1. RURAL EXPOSURE TO MOBILE SOURCE POLLUTANTS
Concentration
Exceeded
g/m3 (a)
0
37
75
112
149
Person Hours
(Millions)
765,048
382,524
153,009
38,252
0
a 1 g/min emission factor
90
-------
Vffl. PERSON HOUR EXPOSURE COMPUTER PROGRAM
The purpose of this project was to provide a means to estimate national
exposure to any mobile source pollutant in terms of a cumulative distribution of
person hours of exposure to a range of pollutant concentrations. The technique
chosen to calculate the person hours of exposure was based on pollutant
concentrations resulting from a one gram per minute emission factor. The
person hours of exposure for a pollutant with other than a one gram per minute
emission factor can then be obtained by multiplying the pollutant
concentrations by the emission factor for that pollutant. This is possible
because the emission factor in the mathematical models of pollutant dispersion
is simply a multiplier to the complex dispersion equation. Thus, pollutant
concentration is directly proportional to emission factor.
Since vehicles can have different emission factors in each of the N/M
types for which populations have been estimated and concentration distributions
defined, it is necessary to calculate person hours of exposure for each
microenvironment separately. Then, using common values of pollutant
concentrations for all N/M types, the person hours of exposure in each
microenvironment are summed to obtain the total nationwide cumulative person
hour exposure distribution. Because of the extensive number of calculations, a
computer program was obviously required.
Microenvironment Exposure Model
To calculate only person hours of exposure in a specific
microenvironment, no information about the the movement of people from
place to place is required. All that is required is the population and
concentration distributions, nationwide, for each microenvironment. This
approach to calculating person hours of exposure is referred to as "place
specific" as contrasted to other models which are considered "people specific"
since they follow a group of people through the day.
For a place specific model, the number of people in the microenvironment
multiplied by the annual frequency of each pollutant concentration gives the
person hours of exposure in the microenvironment for that concentration
interval. In this project, hourly population values were used with pollutant
concentration ranges appropriate to each microenvironment. Since hourly
populations and pollutant concentration levels are different for weekdays,
Saturdays and Sundays, three sets of hourly distributions are required. The
person hours of exposure are summed for all hours of the day and all day types
for the entire year to give the annual person hours of exposure in each
microenvironment. The mathematical expression of the model for a single
microenvironment and single day type is:
Pkj= Z ajTijFjjk
Where:
pkj = person hours exposure to concentration interval "k", for
day type "j"
Tij = population during hour "i" in the microenvironment on
day type "j"
91
-------
^ijk = Frequency of occurrence of concentration in interval "k"
during hour "i" of day type "j"
as = number of type "j" days in a year. The number of
weekdays per year was taken as (52x5) + 1 = 261. From
this number was subtracted 13 holidays, for a total of
2*8 weekdays. The holidays were further divided into 10
"Saturday type" holidays and 3 "Sunday type." Thus,
there were 52 + 10 = 62 "Saturdays," and 52 + 3 = 55
"Sundays."
After calculating the person hours within a given pollutant interval, the
person hours in each interval are summed to yield the cumulative frequency
distributions for each day type. Next, a common set of pollutant concentration
values is chosen for all microenvironments. Each microenvironment cumulative
distribution is interpolated to obtain the cumulative person hours of exposure at
the common pollutant concentration values. The cumulative person hours at
each concentration value are then summed for all microenvironments and day
types.
The Computer Program
The computer program written to process the exposure model uses the
pollutant concentration frequency distributions developed in Sections II and III
of this report and the national microenvironment populations from Section VI.
The program allows the determination of person hours of exposure for years
other than 1980 by inputing national population values for urban and rural
population. Emission factors, in terms of grams per minute, must be input for
each NEM neighborhood and each mobile source microenvironment, together
with a rural emission factor. Pollution concentration intervals for the output
cumulative distribution may be input or, if not input, determined by the
computer program. The output of the program is a single cumulative
distribution with 25 concentration values in one column and the corresponding
number of person hours of exposure to concentrations exceeding those values in
a second column. The lowest concentration used by the computer program in
the cumulative distributions is zero.
A block diagram of the computer program is shown in Figure VIII-1. The
program consists of a main program, two subroutines, one function and five
block data routines. The main program, called MOBEX, calculates the
multiplier for population other than 1980, calculates the person hours of
exposure in each concentration interval for each microenvironment using the
algorithm shown above, calculates the cumulative distribution for each
microenvironment, sums the person hours for all microenvironments and days,
then prints the output. Subroutine CHOPOL chooses the correct concentration
frequency distribution depending on the day type and hour of the day.
Subroutine CALCIN determined common concentration intervals for all
microenvironment cumulative person hour distributions, if the desired
concentration intervals are not input. Function FLAGR is the interpolation
routine to obtain the person hours exposure in each microenvironment at the
common concentration intervals. The block data routines contain the various
population and concentration values as shown below:
92
-------
IRead
Input
>+
Calculate urban popu
for years other than
'
Start neig
loop
lation multiplier
1980
'
hborhood |
r"*
Does
neighborhood
» rural?
Yes
INO
1
»
U-
No
No
No
No
No
-
Start day type loop
,
Start hour
loop | *
Start pollu
concentrate
loop
\
Last concen
interval?
Selec
frequ
(Subr
"Calcu
of ex
conce
for h
micro
tration
Sum p
obtai
distr
Replace urban population
multiplier with rural
population multiplier
ency distribution
outine CHOPOL)
1
date person hours
posure in pollutant
ntration interval,
our, day and
environment
1
erson hours to
n cumulative
ibution
| Yes
Last hour?
Last day?
,
Last micro-
.
Last neight
M Determine emission
factor for N/M type
|*
, Yes
.Yes
Yes
Calcu
of ea
conce
lower
Pollutant frequency
distribution files
(Block Data NEBCON,
MOBCON, and RURCON)
Population file
(Block Data NATPOP)
Microenvironment
multiplier
J
late actual value
ch pollutant
ntration interval
Bound
Pollutant concentration
lower bound file
(Block Data POLCON)
Determine a set of common pollutant intervals
for all N/M types. If not input, use subroutine
CALCIN.
*
No
T.
*l
Start co
concentr
Last com
NO p
L
^j Start day loop [«
j Start N/M type loop [
Last common concentration value?
_L
\ Last N/M type?I
>
Interpolate each N/M type cumulative person hour
distribution to get person hours at common
concentration value. (Function FLAGR)
For each concentration lower bound sum the
cumulative person hours for all days and all
N/M types
Print final cumulative person hour
distribution
Figure Vlii-l. Block diagram of person hour exposure
computer program, MOBEX
93
-------
Biock Data Name Contents
NATPOP nationwide hour by hour populations
for each N/M type
POLCON The lower bound values of each
pollutant concentration interval
for each pollutant frequency
distribution in each neighborhood
or microenvironment type
NEMCON The frequency values for each
pollutant concentration frequency
distribution in each NEM neighborhood
type
MOBCON The frequency values for each
concentration frequency distribution
in each mobile source microenvironment
RURCON The frequency values for the
concentration frequency distribution
for the rural environment.
A complete listing of the computer program is contained in Appendix E.
Computer Program Input
The input to the computer program consists of three or four parts,
depending on whether or not the output pollutant distribution interval values
are input. The first part is a title line. Up to
-------
TABLE Vm-1. EXPOSURE COMPUTER PROGRAM INPUT
Line
I
1
2
Columns
Input Parameter
Remarks
0-40 Title
Pollutant name
Urban population
Rural population
Pollutant Concentration
Weekday urban residential
neighborhood emission
factor in g/min
Weekday urban commercial
neighborhood emission
factor in g/min
Weekday urban industrial
neighorhood emission
factor in g/min
Weekday suburban residential
neighborhood emission
factor in g/min
Weekday suburban commercial
neighborhood emission
factor in g/min
Weekday suburban industrial
neighborhood emission
factor in g/min
Weekday street canyon
emission factor in g/min
any desired title up to 40
characters long
up to 16 characters
population of all urban areas
for year under consideration.
If year is 1980, may input 0.
Total U.S. population minus
the urban population as
defined above. If year is
1980, may input 0.
0 = program calculates
pollutant concentrations
1 = pollutant concentrations
are input
assumed to be FTP emission
factor
assumed to be factor at
10 mph
assumed to be factor at
10 mph
assumed to be FTP emission
factor
assumed to be FTP emission
factor
assumed to be FTP emission
factor
assumed to be factor at
10 mph
-------
TABLE Vffl-1 CONPD). EXPOSURE COMPUTER PROGRAM INPUT
Line Columns Input Parameter Remarks
3 ~ Weekday tunnel emission assumed to be factor at
factor in g/min 35 mph
3 -- Weekday parking garage assumed to be 0.5 times idle
emission factor in g/min factor plus 0.5 times 10 mph
factor, (LDGV only)
3 ~ Dummy mobile source use tunnel factor as dummy
emission factor (to allow
for future program expansion)
3 Weekday rural emission assumed to be factor at
factor in g/min 35 mph
* Saturday urban residential assumed to be 0.72 times
emission factor in g/min weekday FTP factor
^ Saturday urban commercial assumed to be 0.72 times
emission factor in g/min 10 mph factor
4 Saturday urban industrial assumed to be 0.72 times
emission factor in g/min 10 mph factor
4 Saturday suburban residential assumed to be 0.72 times
emission factor in g/min FTP factor
4 Saturday suburban commercial assumed to be 0.72 times
emission factor in g/min FTP factor
4 Saturday suburban industrial assumed to be 0.72 times
emission factor in g/min FTP factor
4 Saturday street canyon assumed to be 0.72 times
emission factor in g/min 10 mph factor
* Saturday tunnel emission assumed to be factor at
factor in g/min 35 mph
* Saturday parking garage assumed to be 0.5 times idle
emission factor in g/min factor plus 0.5 times 10 mph
factor (LDGV only)
* Dummy mobile source use tunnel factor as dummy
emission factor (to allow
for future program expansion)
96
-------
TABLE Vin-1 CONPD). EXPOSURE COMPUTER PROGRAM INPUT
Line Columns
Input Parameter
6&7
Saturday rural emission
factor in g/min
line 5 contains Sunday
emission factors, in the
same order as the weekday
and Saturday factors
Desired pollutant concen-
tration lower bound values
for output pollutant concen-
tration list, 25 values must
be input in micrograms/m3.
Remarks
assumed to be 0.72 times
35 mph factor
in current version of program
Sunday emission factors are
equal to Saturday factors
This is an optional input. If
input, third number in line 2
must equal 1.
97
-------
lowest concentrations. When the computer program calculates the intervals,
the final increment is 20 times the first increment.
An example input is shown in Table VIII-2. This example is for CO. Since
the population is for 1980, zero was input for urban and rural populations. The
emission factors are the MOBILE3 factors used in Sections II and III of this
report to convert the ambient CO distribution. In this example, the output
concentration intervals are input.
Computer Program Output
The output of the person hours exposure program is a single page with two
columns, each having 25 lines. The left hand column contains the lower bounds
of the pollutant concentration intervals. The right hand column contains the
cumulative nationwide person hours of exposure at or above each concentration
value. A sample output page is shown in Table VIII-3. The person hours of
exposure to concentrations of zero and above will be the total nationwide
person hours of exposure for a year. For the 1980 population, this value is
1.98<*xl012 person hours (226,504,825 people x 2* hours per day x 365
days/year).
Example Problem
As an example of how to use the computer program, the nationwide
exposure to mobile source CO for 1981 was examined. The input values are
those shown previously in Table VIII-2. The 1981 CO emission factors are from
a computer run of the EPA emission factor program, MOBILE3. The 1981 U.S.
population was not available, so the 1980 population was used. For this
example, the computer-generated output concentration intervals were used,
rather than the concentration intervals shown in Table VIII-2.
The program output is that shown previously in Table VIII-3. Since CO is
the pollutant, the concentration in micrograms per cubic meter can be
converted to ppm by dividing by 1157, if the more familiar units for CO are
desired. The output shows that only about t.2 percent of the nationwide annual
person hours of exposure are at concentrations at or above 350 yg/m3
(approximately 3 ppm). Less that one hundredth of one percent of the person
hours of exposure occur at concentrations of 350 yg/m^ (30 ppm) or above (the
one hour NAAQS is 35 ppm). Comparing the computer output with the CO
concentration distributions in Sections II and III of this report reveals that any
exposure above 77,000 yg/m^ could only be from two mobile source
microenvironments, tunnels and parking garages. Exposure above 150,000
y g/m^ could only be from parking garages.
Comparison of This Study's CO Exposure with the OAQPS NEM CO Study
The CO exposure resulting from the use of the mobile source
microenvironments, as was done in this project, cannot be compared directly
with CO exposure from the NEM CO analysis done by the EPA OAQPS as
presented in Reference 21, since total national exposure was not included in
that report. The previous SwRI exposure study did use the NEM computer
program to obtain a national exposure estimate. That estimate, as presented in
98
-------
TABLE VHl-2. EXAMPLE INPUT FOR EXPOSURE
COMPUTER PROGRAM
TEST RUN NO. OF EXPOSURE PROGRAM CO
0,0,0
16.01,15.05,15.05,16.01,16.01,16.01,15.05,15.51,12.15,15.51,15.51
11.53,10.84,10.84,11.53,11.53,11.53,10.84,15.51,12.15,15.51,11.17
11.53,10.84,10.84,11.53,11.53,11.53,10.84,15.51,12.15,15.51,11.17
0,500,1500,3000,4000,5000,6500,7500,9000,11000,12000,13500,14500,
16000,18000,21500,25000,30000,35000,40000,42000,44000,46000,48000,50000
TABLE Vm-3. SAMPLE OF EXPOSURE PROGRAM OUTPUT
TEST RUN NO.5 OF EXPOSURE PROGRAM
POLLUTANT: CO
EXPOSURE TO
CONCENTRATIONS
EXCEEDING
(MICROGRAM/CU.M)
0.000
3500.000
7000.000
10500.000
14000.000
24500.000
35000.000
45500.000
56000.000
66500.000
77000.000
87500.000
98000.000
108500.000
129500.000
150500.000
192500.000
248500.000
304500.000
360500.000
416500.000
486500.000
556500.000
626500.000
696500.000
PERSON
HOURS
EXPOSURE
(MILLIONS)
1984405.260
82451.679
19394.141
6326.666
2362.171
452.174
166.296
89.024
54.733
34.776
22.702
14.858
9.499
6.952
4.041
2.929
1.539
.823
.382
.129
.010
0.000
0.000
0.000
0.000
99
-------
Reference 20, shows the highest exposure level to be 60 ppm. There were
0.32x10 ^ person hours of exposure at or above 60 ppm. In the current study,
with the mobile source microenvironments included, there were approximately
30x10*> person hours of exposure at or above 60 ppm.
This difference does not imply that the OAQPS study was not correct for
the purpose for which it was conducted. The OAQPS study was conducted for
an entirely different purpose; namely to investigate exposure at different levels
of ambient CO standards. Ambient standards compliance is determined by
ambient air monitors, which are specifically sited so as not to be influenced by
microscale situations. One of the contentions of the current project is that, in
general, mobile source microenvironment concentrations are not directly
related to ambient monitor concentration, and thus have little bearing on what
the NAAQS for CO should be. The purpose of the present project is to estimate
the level of exposure to mobile source pollutants, wherever they are found, to
determine if mobile source emissions standards are necessary for any mobile
source pollutant in order to protect public health and welfare. Thus, it was
proper for the OAQPS study not to include mobile source microenvironments,
while it was a necessity that this study include them.
100
-------
IX. CONCLUSIONS AND RECOMMENDATIONS
This project sought to provide an improved methodology for estimates of
annual nationwide exposure to mobile source pollutants.
Improvements Over Past Studies
Improvements to the methodology were made in the following areas.
A more accurate estimate of hour by hour nationwide population in the
various NEM neighborhoods was obtained by using a new technique to
extend the four city populations to a national estimate.
The mobile source microenvironment populations were also subtracted
from appropriate neighborhood microenvironment populations.
The number of CO monitors used to determine the nationwide
neighborhood CO concentration distributions was increased by a factor of
five.
Finally, a computer program was written to provide a single nationwide
person hour exposure estimate for any mobile source pollutant, when
emission factors for that pollutant in each neighborhood type are input.
Limitations and Futher Improvements Needed
As with all estimates, the final values are only as good as the assumptions
and data that went into the calculations. This methodology is an improvement
over the exposure estimate provided under Contract 68-03-3073, Work
Assignment 6. Yet there are still areas in which more data would result in a
more accurate estimate of mobile source pollutant exposure.
One area that could be refined further is the population estimate for the
various N/M types. Despite its detail on the city level, the NEM activity
pattern procedure does not result in nonzero populations for all N/M types
during all hours of the day. A different approach to N/M type population
estimates, which would consider nationwide data from new studies of
population activity patterns, could provide better hour by hour N/M type
population estimates on a national scale.
A commuter on a crowded expressway was identified in a previous
study'18) as an exposure to high mobile source pollutant concentrations.
This situation was not included in the mobile source microenvironments,
since there is currently insufficient information available to provide the
necessary population and pollutant concentration data. The "vehicle"
microenvironment in each neighborhood type provides some estimate of
the expressway microenvironment, but a better estimate could be
obtained by not using the vehicle microenvironment from the six NEM
neighborhood types, but instead adding an on-expressway commuting
microenvironment to the mobile source microenvironments.
101
-------
As could be seen from the example problem in the previous report section,
all of the exposure to the highest concentrations was in parking garages.
Yet, parking garages have the least-documented estimate of mobile
source pollutant concentrations. A project to measure parking garage CO
concentrations in a number of locations would do much toward providing a
more accurate estimate of all parking garage pollutant concentrations.
Future Work
It is not expected that the improvements outlined above would radically
change the overall picture of exposure provided by the methodology presented
here. However, if significant health and welfare effects were found for any
pollutant at the higher concentrations, then a more accurate estimate would be
required for the relatively small number of person hours of exposure that occurs
at these higher concentrations. Should more accurate estimates of person hours
of exposure to mobile sources be desired, the following additional work is
recommended:
1. Develop a new estimate of nationwide population by hour of the day
(activity patterns) in the six neighborhood types so that all N/M's
have some people in them for all hours of the day.
2. Remove or modify the "vehicle" microenvironment from the six
neighborhoods and add an on-expressway mobile source
microenvironment.
3. Recalculate the hourly nationwide population in the six
neighborhoods by subtracting the new hourly populations in the
mobile source rnicroenvironments from the most appropriate N/M's
in the six neighborhoods.
it. Conduct a program to measure CO in a variety of parking garages
and incorporate these measured values into the exposure computer
program.
5. To improve the representativeness of the CO monitor information
used, some weighting techniques for the CO readings should be
developed to account for monitor city size, altitude, and ambient
temperature.
6. Incorporate the findings of the recent EPA studies in Washington
and Denver to obtain better adjustments to the fixed CO monitor
values for some of the microenvironments.
7. Develop the methodology so that cohort populations could be
followed through the day to yield number of persons exposed to
given CO levels for different time spans, rather than merely
obtaining person hours of exposure.
102
-------
REFERENCES
1. Springer, K.3., and Ingalls, M.N., "Measurement of Sulfate and Sulfur
Dioxide in Automobile Exhaust." Prepared for the Environmental
Protection Agency under Contract No. 68-03-2118, EPA 460/3-76-015,
August 1976.
2. Dietzmann, H.E., Smith, L.R., Parness, M.A., and Fanick, E.R.,
"Analytical Procedures for Characterizing Unregulated Pollutant
Emissions from Motor Vehicles." Interim Report under Contract No. 68-
02-2497 to the Environmental Protection Agency, Publication No. EPA
600/2-79-017, February 1979.
3. Smith, L.R., Parness, M.A., Fanick, E.R., and Dietzmann, H.E.,
"Analytical Procedures for Characterizing Unregulated Emissions from
Vehicles Using Middle-Distillate Fuels." Interim Report to the
Environmental Protection Agency under Contract No. 68-02-2497, Report
No. EPA 600/2-80-068, April 1980.
4. Springer, K.3., "Investigation of Diesel-Powered Vehicle Emissions VII."
Final Report, to the Environmental Protection Agency under Contract 68-
03-2116, EPA 460/3-76-034, February 1977.
5. Springer, K.3., "Characterization of Sulfates, Odor, Smoke, POM, and
Participates from Light and Heavy Duty Engines - Part IV." Final Report
to the Environmental Protection Agency under Contract No. 68-03-2417,
EPA 460/3-79-007, June 1979.
6. Hare, C.T., "Characterization of Gaseous and Particulate Emissions from
Light-Duty Diesels Operated on Various Fuels." Final Report to the
Environmental Protection Agency under Contract 68-03-2440, EPA 460/3-
79-008, July 1979.
7. Smith, L.R., "Characterization of Exhaust Emissions from High Mileage
Catalyst-Equipped Automobiles." Final Report to Environmental
Protection Agency under Contract 68-03-2884, Task Specifications 7 and
10, EPA 460/3-81-024, September 1981.
8. Smith, L.R., "Unregulated Emissions for Vehicles Operated under Low
Speed Conditions." Final Report to the Environmental Protection Agency
under Contract 68-03-3073, Work Assignment 4, EPA 460/3-83-006, May
1983.
9. Urban, C.M., "Regulated and Unregulated Exhaust Emissions from
Malfunctioning Non-Catalyst and Oxidation Catalyst Gasoline
Automobiles." Final Report to the Environmental Protection Agency
under Contract No. 86-03-2499, Report No. EPA 460/3-80-003, January
1980.
103
-------
10. Urban, C.M., "Regulated and Unregulated Exhaust Emissions from
Malfunctioning Three-Way Catalyst Gasoline Automobiles." Final Report
to the Environmental Protection Agency under Contract 68-03-2588,
Report No. EPA 460/3-80-004, January 1980.
11. Urban, C.M., "Regulated and Unregulated Exhaust Emissions from a
Malfunctioning Three-Way Catalyst Gasoline Automobile." Final Report
to the Environmental Protection Agency under Contract 68-03-2692.
Report No. EPA 460/3-80-005, January 1980.
12. Urban, C.M., "Unregulated Exhaust Emissions from Non-Catalyst Baseline
Cars Under Malfunction Conditions." Final Report to Environmental
Protection Agency under Contract 68-03-2884, Task Specifications 4 and
5, Report No. EPA 460/3-81-020, May 1981.
13. Hare, C.T., "Characterization of Gaseous and Participate Emissions from
Light-Duty Diesels Operated on Various Fuels." Final Report to the
Environmental Protection Agency under Contract 68-03-2440, EPA 460/3-
79-008, July 1979.
14. Ullman, T.L., and Hare, C.T., "Emissions Characterization of an
Alcohol/Diesel-Pilot Fueled Compression-Ignition Engine and Its Heavy-
Duty Diesel Counterpart." Final Report to Environmental Protection
Agency under Contract 68-03-2884, Task Specification 6, Report No.
EPA 460/3-81-023, August 1981.
15. Bykowski, B.B., "Characterization of Diesel Emissions from Operation of
a Light-Duty Diesel Vehicle on Alternate Source Diesel Fuels," Final
Report to the Environmental Protection Agency under Contract 68-03-
2884, Task Specification 3, Report No. EPA 460/3-82-002, November
1981.
16. Ullman, T.L., and Hare, C.T., "Emissions Characterization of a Spark-
Ignited, Heavy-Duty, Direct-Injected Methanol Engine." Final Report the
Environmental Protection Agency under Contract 68-03-3073, Work
Assignment 2, Report No. EPA 460/3-83-003, November 1982.
17. Smith, L.R., Urban, C.R., and Baines, T.M., "Characterization of Exhaust
Emissions from Methanol- and Gasoline-Fueled Automobiles." Final
Report to the Environmental Protection Agency under Contracts 68-03-
2884 and 68-03-3073, Task Specifications 11 and 12, and Work
Assignments 1 and 3, Report EPA 460/3-82-004, August 1982.
18. Ingalls, M.N., "Estimating Mobile Source Pollutants in Micorscale
Exposure Situation." Final Report to the Environmental Protection
Agency under Contract 68-03-2884, Task Specification 1. Report No.
EPA 460/3-81-021, July 1981.
19. Harvey, C.A., et al, "A Study of the Potential Impact of Some
Unregulated Motor Vehicle Emissions." SAE Paper 830937 presented at
the 1983 SAE Passenger Car Meeting, Dearborn, Michigan, June 1983.
104
-------
20. Ingalls, M.N., "Mobile Source Exposure Estimation." Final Report to the
Environmental Protection Agency under Contract 68-03-3073, Work
Assignment 6. Report No. EPA 460/3-84-004, March 1984.
21. Johnson, T., and Paul, R.A., "The NAAQS Exposure Model (NEM) Applied
to Carbon Monoxide." Draft Final Report by PEDCo Environmental, Inc.
for EPA, Contract 68-02-3390, Work Assignments 13 and 16, April 12,
1982, revised June 1982, and December 1982.
22. "Carbon Monoxide," National Research Council, Committee on Medical
and Biologic Effect of Environmental Pollutants, National Academy of
Sciences, Washington, D.C., 1977.
23. Larsen, R.I., "A New Mathematical Model of Air Pollutant Concentration
Averaging Time and Frequency." Journal of the Air Pollution Control
Association, Vol. 19, No. 1, January 1969.
2k. Larsen, R.I., "A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards," AP-89, Environmental
Protection Agency, Research Triangle Park, N.C., November 1971.
25. Mage, D.T., and Ott, W.E., "Refinements of the Lognormal Probability
Model for Analysis of Aeormetric Data," APCA Journal Vol. 28, No. 8,
August 1978.
26. Johnson, T., "A Comparison of the Two-Parameter Weibull and Lognormal
Distributions Fitted to Ambient Ozone Data. Proc. of Specialty
Conference on Quality Assurance in Air Pollution Measurement, Air
Pollution Control Association, 1979.
27. Aitchison, J., and Brown, J.A.C., The Lognormal Distribution. Cambridge
University Press, New York, 1957.
28. Hama, G.M., et al, "Air Flow Requirements for Underground Parking
Garages," American Industrial Hygiene Journal, December 1961, pp 462-
470.
29. Barker, I.W., and Fox, M.F., "Vehicular Pollution in Car Parks," Royal
Society of Health Journal, Vol. 96.
30. Glazer, N., "The Regulation and Control of Carbon Monoxide in Enclosed
Parking Garages," APCA Paper 78-4.6, presented at the 71st Annual
Meeting of APCA, June 1978.
31. Ayres and Hayakawa, Consulting Engineers, "Project Program Plan and
Statement of Probable Costs. Music Center and Mall Garages. Phase I
and III." Submitted to County and Los Angeles for Capitol Projects No.
7085.09, 7065.21 and 7065.22, May 1975.
32. Mage, D.T., "Frequency Distributions of Hourly Wind Speed
Measurement," Atmospheric Environment, Vol. 14, pp 367-374, 1980.
105
-------
33. "Airport Climatological Summary." Climatography of United States No.
90, for various airports. National Climatic Center, National Oceanic and
Atmospheric Administration, Asheville, N.C.
34. Rodgers, S.J., et al, "Tunnel Ventilation and Air Pollution Treatment,"
Prepared for FHWA, Office of Research by Mine Safety Appliance
Research Corp. Report FHWA-RD-72-15, NTIS No. PB210-360, June 30,
1970.
35. "Study of Air Pollution Aspects of Various Roadway Configurations," Final
Report to the New York City Department of Air Resources under
Contract 20962* by the General Electric Company, 3198 Chestnut Street,
Philadelphia, PA., dated September 1, 1971.
36. Sosslau, A.B., and Hassom, A.B., "Quick-Response Urban Travel
Estimation Techniques and Transfer rable Parameters," National
Cooperative Highway Research Program Report 187. Transportation
Research Board, National Research Council, Washington, D.C., 1978.
37. "Short-Range Urban Transit Study, San Antonio, Texas," prepared for the
San Antonio Transit System and City of San Antonio by Wilbur Smith and
Associates, Houston, Texas, 1972.
38. Thayer, S.D., "Vehicle Behavior In and Around Complex Sources and
Related Complex Sources Characteristics, Vol IVParking Facilities,"
Final Report prepared by Goemet, Inc., Rockville, MD, under Contract
No. 68-02-1094 Task Orders to the U.S. Environmental Protection
Agency, Research Triangle Park, N.C., Publication No. EPA 460/3-74-
003d, October 1973.
39. Forrest, L., "Assessment of Environmental Impacts of Light Duty Vehicle
Dieselization," Aerospace Report No. ATR-79(7740)-1. Draft Final
Report by Aerospace Corporation under Contract DOT-TSC-1530, March
1979.
40. Matson, T.M., Smith, W.S., and Hurd, F.W., Traffic EnEineering. McGraw-
Hill Book Company, Inc., New York, N.Y., 1955.
41. "Highway Capacity Manual," Highway Research Board Special Report 87,
Highway Research Board, National Research Council, Washington, D.C.
1965.
42. "San Antonio-Bexar County Urban Transportation Study Report 6A and 6B
- Origin-Destination Survey 1969," Texas Highway Department, Austin,
Texas.
43. Schlaug, R.N., and Carlin, T.J., "Aerodynamics and Air Quality
Management of Highway Tunnels." Final Report for Contract DOT-FH-
11-8538 to Science Applications, Inc. Report FHWA-RD-78-185, January
1980.
44. Homberger, W.S., editor, Transportation and Traffic Engineering
Handbook, second edition, Prentice Hall, Inc. Englewood Cliffs, N.3.
106
-------
45. Curtin, J.F., "Traffic-Transit-Parking in Downtown Rochester: Now to
1975." Contained in Highway Research Board Bulletin 293, Highway
Research Board, National Research Council, 1961.
46. Meyer, J.R., Kain, 3.F., and Whol, M., The Urban Transportation Problem.
Harvard University Press, Cambridge, MA., 1965.
47. Pushkarev, B., and Zupan, 3. "Pedestrian Travel Demand," Highway
Research Record No. 355, Highway Research Board, National Academy of
Sciences, Washington, D.C., 1971.
48. Cameron, R.M., "Mechanical Measurements of Pedestrian Volumes."
Transportation Research Record No. 498. Transportation Research Board,
National Academy of Sciences, Washington, D.C., 1974.
49. Rutherford, G.S., and Schofer, J.L., "Analysis of Some Characteristics of
Pedestrian Travel," Transportation Research Record No. 605.
Transportation Research Board, National Academy of Sciences,
Washington, D.C, 1976.
107
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APPENDIX A
SUPPORTING INFORMATION FOR CO CONCENTRATIONS
IN DIFFERENT NEIGHBORHOODS
-------
TABLE A-l. SAROAD CO MONITORS USED FOR THE
URBAN CORE - RESIDENTIAL NEIGHBORHOOD
Monitor No.
262180007F01
060380006F01
334680004F01
141220051G01
231180014G01
173740003F01
512200045F01
056980004101
010380027G01
442340021G01
104360060G01
381460080F01
210120018F01
142120010F01
056400003F01
372200047F01
192020012F01
334680010F01
220240021F01
482140010F02
103540019F01
City,
Monitor
Height,
ft.
Independence, MO (Kansas
Colorado Springs
New York (Queens)
Chicago
Detroit
Wichita
Milwaukee
San Jose
Birmingham
Memphis
Tampa
Portland
Baltimore
East St. Louis
Riverside (L.A. area)
Oklahoma City
New Orleans
New York
Boston
Norfolk
Pensacola
City)
21
12
95
12
15
15
28
13
9
10
20
15
13
32
10
10
75
14
10
12
Max.
1 hr.
mg/m^
33.4
29.9
27.6
25.9
25.7
22.0
20.7
19.6
19.4
17.3
17.3
16.4
15.6
15.1
15.0
15.0
14.8
14.7
11.5
11.5
11.0
Median 17.3
Remarks
25
89
1
3
5
81
23
22
42
36
48
27
14
11
2
40
26
1
8
37
106
NAMS
SLAMS
SLAMS
NAMS
NAMS
NAMS
SLAMS
NAMS
SLAMS
NAMS
NAMS
NAMS*
NAMS
NAMS
SLAMS
SLAMS
NAMS
SLAMS
Fairly good geographical cross section of urban areas: 7 in NE, 6 in SE,
6 in NW and 2 in SW. Six of too 10 urban areas represented; 18 of top 50.
Over half are NAM sites (11 of 21). Seventeen states represented
(41% of those with urban areas over 200,000).
*Used in NEM
A-2
-------
TABLE A-2. SARQAD CO MONITORS USED FOR THE
URBAN CORE - COMMERCIAL NEIGHBORHOOD
Monitor No.
060580002F01
053900001101
361300014H01
4918400S1F01
152040030F01
030860014G01
334680062F01
381460002F01
442340024G01
397260005G01
220240002AO 5
090020017101
334680005F01
242260047F01
052460002101
482120006F02
512200080F01
151180011H01
361460014F01
055300005101
310720007F01
231180016F01
City
Denver
Lennox (L.A.)
Cleveland
Seattle
Indianapolis
Tucson
New York City
Portland
Memphis
Pittsburgh
Boston
Washington, DC
New York City
Minneapolis
Escondido (San Diego)
Newport News, VA
Milwaukee
East Chicago, IN
Colunibus, OH
Oakland
Camden, NJ (Phily)
Detroit
Monitor
Height,
ft.
9
20
10
15
16
12
10
18
9
15
10
34
45
12
13
16
12
20
12
12
12
12
Median
Max.
1 hr.
mg/m
67.5
31.1
28.8
26.5
25.6
24.2
24.0
23.8
23.6
22.3
21.9
20.7
18.9
18.4
17.3
16.8
-16.2
16.1
15.0
13.8
13.3
12.5
21.3
City
Rank
««
21
2
15
20
31
59
1
27
36
12
8
7
1
13
16
78
23
3
32
6
4
5
Remarks
NAMS
NAMS*
NAMS
SLAMS
NAMS
NAMS
NAMS
NAMS
NAMS
SLAMS
NAMS
SLAMS
SLAMS
NAMS
SLAMS
NAMS
SLAMS
SLAMS
in NE, 2 in SE,
represented (41%
5 in NW,
of those
Fair geographical distribution of cities: 12
3 in SW. Half are NAM sites. Seventeen states
with urban areas over 200,000). Eight of top 10 urban areas represented,
20 of top 50.
*Used in NEM
A-3
-------
Monitor No.
366600032H01
054180103101
360060018H01
104760001G01
397260026G01
512200040F01
056860004101
104360035G02
TABLE A-3. SAROAD CO MONITORS USED FOR THE
URBAN CORE - INDUSTRIAL NEIGHBORHOOD
City
Monitor
Height,
ft.
Toledo
Los Angeles
Akron
West Palm Beach
Pittsburgh
Milwaukee
San Francisco
Tampa
Eight sites. Fair distribution:
Six states represented. Two of top
areas, represented. No other monitors available.
25 22.4 57
36 20.7 2
12 18.5 50
12 15.3 56
25 13.5 12
12.1 23
20 9.2 6
13 8.6 48
Median 14.4
4 in NE, 2 in SE, 1 in NW, 1 in SW.
10 urban areas, 8 of top 50 urban
Remarks
SLAMS
NAMS
SLAMS
NAMS
A-4
-------
Monitor No.
102700019G01
060580009F01
030600004G01
054180105101
234000001F01
340700034G01
332900005F01
491840078F01
222100003F01
454570036F01
481850001G01
051600002101
397140004H01
101960083H01
330130002F01
140780002G01
451880003F01
452330026F01
483240007F01
051360001101
TABLE A-4. SAROAD CO MONITORS USED FOR THE
SUBURBAN - RESIDENTIAL NEIGHBORHOOD
City
Miami
Denver
Phoenix
Los Angeles
Oak Park (Detroit)
Charlotte, NC
Hempstead (sub. NYC)
Seattle
Sommerville (Boston)
San Antonio
McLean (D.C. area)
Concord, CA (S.F. area)
Philadelphia
Jacksonville, FL
Amherst (Buffalo, NY)
Calumet City, IL (Chicago)
Fort Worth
Harris Co. (Houston)
Virginia Beach, VA (Norfolk)
Chula Vista, CA (San Diego)
Monitor
Height,
ft.
10
15
13
30
12
14
15
11
14
15
18
24
17
12
12
15
15
14
12
12
Median
Max.
1 hr.
mq/icr
32.2
31.4
23.0
21.9
21.9
20.9
19.2
17.3
17.3
16.8
16.1
16.1
16.1
13.2
12.9
11.4
10.9
10.6
10.1
9.2
16.5
City
Rank
18
21
19
2
5
74
1
20
8
30
7
6
4
44
29
3
9
10
37
16
Remarks
NAMS
NAMS
SLAMS
SLAMS
SLAMS
NAMS
NAMS
NAMS
NAMS
SLAMS
NAMS
__
NAMS
SLAMS
NAMS
SLAMS
NAMS
NAMS
Good geographical distribution: 7 in NE, 4 in SE, 3 in NW, 6 in SW.
Over 50% NAM sites (11 of 20). Fourteen states represented. All
urban areas in top 10, and 19 of top 50 represented.
A-5
-------
TABLE A-5. SAORAD CO MONITORS USED FOR THE
SUBURBAN - COMMERCIAL NEIGHBORHOOD
Monitor No.
030600016G01
054200001101
491840075F01
373000126F02
512200044F01
320040015H02
056580007101
101960082H01
482660012F02
182380019G01
231820020F01
145680001G01
056240001101
222160014F01
030860011G01
361460004F01
364340004H01
261040001G01
io2270001G01
056800006101
City
Phoenix
Los Angeles
Seattle
Tulsa
Milwaukee
Albuquerque
Sacramento
Jacksonville , FL
Richmond, VA
Louisville
Grand Rapids
No. Riverside (Chicago)
Redwood City (S.F. Bay)
Springfield, MA
Tucson
Columbus, OH
Middle town, OH (Cinci)
Clayton (St. Louis)
Lauderdale Lakes, FL
San Diego
Monitor
Height,
ft.
10
24
11
4
4
9
15
12
15
. 20
15
14
12
12
17
12
14
10
20
Max.
1 hr.
ag/m^
33.4
31.1
25.3
21.6
21.2
20.7
19.6
19.0
17.8
17.6
16.4
15.3
13.8
13.8
12.9
11.5
11.5
10.0
9.8
8.1
City
Rank
19
2
20
60
23
62
34
44
54
38
72
3
6
53
59
32
24
11
28
16
Remarks
NAMS
SLAMS
SLAMS
NAMS
SLAMS
NAMS
SLAMS
SLAMS
(used
SLAMS
NAMS
NAMS
NAMS
(used
NAMS
SLAMS
in NEM)
in NEM)
Median 17.0
Excellent geographical distribution: 6 in NE, 4 in SE, 5 in MW, 5 in SW.
Seven of 20 are NAM sites. Fourteen states represented. Three of the top
10 and 14 of the top 50 areas represented.
A-6
-------
Monitor No.
050900002101
Oil300003GO2
147160005G01
336620014F01
311300004F01
452560035H01
391620002F01
335680001F01
TABLE A-6. SAROAD CO MONITORS USED FOR THE
SUBURBAN - INDUSTRIAL NEIGHBORHOOD
City
Burbank (L.A.)
Fairfield, AL (Birmingham)
Skokie (Chicago)
Syracuse, NY
Elizabeth, NJ (near NY)
Houston, TX
Chester, PA (Philadelphia)
Rensselear, NY
Monitor
Height,
ft.
15
15
39
15
13
4
7
14
City
Rank
28.8
16.3
13.7
12.0
9.8
8.2
7.5
7.0
2
42
3
70
1
10
4
55
Remarks
SLAMS*
(used in NEM)
SLAMS
Median 11.1
Eight sites. Poor geographic distribution: 5 in NE, 1 in SE, none in NW,
2 in SW. No NAM sites. Seven states represented. Five in top 10 and 6
in top 50 urban areas. No other monitors available.
*Used in NEM
A-7
-------
TABLE A-7. DESCRIPTIVE STATISTICS FOR URBAN RESIDENTIAL NEIGHBORHOOD HOURLY CO READINGS
WEEKDAYS
CONCENTRATION,P PM
oo
HOUR
ENDING
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
SATURDAYS
CONCENTRATION,PPM
SUNDAYS
CONCENTRATION,PPM
MEAN
1.05
0.98
0.87
0.81
0.83
1.16
1.81
2.15
1.75
1.30
1.04
0.98
0.95
0.94
1.00
1.14
1.35
1.54
1.47
1.47
1.44
1.44
1.40
1.30
MEDIAN
0.84
0.80
0.69
0.63
0.70
0.93
1.14
1.43
1.19
0.99
0.92
0.89
0.87
0.88
0.92
0.97
.1.01
1.03
1.02
1.01
1.00
1.00
0.99
0.96
MAXIMUM
19.1
18.3
13.0
22.0
21.0
13.0
21.5
26.0
22.3
13.9
11.3
24.0
22.6
11.1
12.2
10.9
20.0
22.5
22.0
22.5
15.7
14.8
16.5
17.0
MEAN
1.29
1.32
1.17
1.04
0.96
1.04
1.16
1.20
1.14
1.02
0.93
0.90
0.87
0.84
0.86
0.91
0.99
1.14
1.31
1.43
1.49
1.54
1.53
1.51
MEDIAN
0.95
0.96
0.92
0.85
0.78
0.87
0.94
0.95
0.94
0.89
0.86
0.82
0.82
0.81
0.83
0.85
0.89
0.95
0.97
1.00
1.01
1.02
1.02
1.01
MAXIMUM
13.1
13.0
12.3
10.4
8.5
10.0
10.0
10.0
10.1
10.0
10.0
10.0
6.0
5.0
20.0
27.0
28. 0
29.0
22.0
22.0
16.0
16.0
14.3
12.8
MEAN
1.27
1.30
1.15
1.04
0.94
0.94
0.97
0.98
0.89
0.84
0.78
0.76
0.74
0.76
0.76
0.79
0.86
1.02
1.13
1.24
1.30
1.29
1.24
1.12
MEDIAN
0.96
0.96
0.90
0.83
0.75
0.74
0.80
0.85
0.81
0.80
0.76
0.73
0.73
0.75
0.74
0.77
0.82
0.88
0.94
0.96
0.98
0.97
0.97
0.92
MAXIMU
13.7
11.3
12.9
10.9
17.0
16.0
13.0
10.0
8.0
11.0
5.0
4.0
4.0
4.0
3.5
7.0
9.5
10.5
8.5
13.0
10.7
11.4
12.3
11.9
-------
TABLE A-8. DESCRIPTIVE STATISTICS FOR URBAN COMMERICAL NEIGHBORHOOD HOURLY CO-READINGS
WEEKDAYS
CONCENTRATION,PPM
SATURDAYS
CONCENTRATION,PPM
SUNDAYS
CONCENTRATION,PPM
noun.
ENDING
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
MEAN
1.67
1.36
1.12
1.00
1.07
1.64
2.75
3.66
3.43
2.78
2.41
2.34
2.29
2.25
2.42
2.72
3.07
3.02
2.43
2.26
2.22
2.25
2-20
2.09
MEDIAN
1.04
0.99
0.91
0.78
0.85
1.03
1.97
2.66
2.47
2.02
1.89
1.77
1.65
1.67
1.95
2.00
2.04
2.01
1.80
1.70
1.66
1.64
1.54
1.48
MAXIMUM
22.0
20.0
16.3
16.3
14.0
17.0
24.0
27.0
39.9
18.1
19.0
16.1
19.7
16.5
13.9
17.2
39.2
58.7
55.0
41.9
27.5
25.2
25.7
23.0
MEAN
2.29
1.97
1.61
1.34
1.19
1.29
1.57
1.78
1.80
1.72
1.72
1.74
1.70
1.70
1.72
1.77
1.80
1.87
1.96
2.15
2.23
2.38
2.48
2.45
MEDIAN
1.50
1.16
1.02
0.97
0.93
0.97
1.02
1.14
1.26
1.24
1.27
1.20
1.10
1.14
1.11
1.18
1.25
1.30
1.48
1.54
1.65
1.76
1.85
1.85
MAXIMUM
23.0
22.0
20.0
17.0
15.0
16.0
18.0
15.0
13.0
14.5
15.7
20.9
17.3
13.0
11.1
11.8
13.3
24.3
17.3
23.7
16.0
17.5
20.0
22.3
MEAN
2.22
1.94
1.64
1.38
1.20
1.17
1.23
1.27
1.24
1.23
1.25
1.27
1.34
1.38
1.41
1.50
1.56
1.67
1.76
1.85
1.91
1.97
1.88
1.81
MEDIAN
1.48
1.21
1.03
0.98
0.91
0.92
0.96
0.97
0.98
0.99
0.99
0.99
1.00
1.01
1.01
1.02
1.02
1.04
1.15
1.29
1.36
1.33
1.22
1.15
MAXIMUM
22.0
18.1
19.7
19.9
17.6
18.0
13.4
10.0
11.5
8.2
8.0
8.4
9.0
8.6
11.2
11.5
10.4
11.2
16.4
18.4
16.5
12.9
13.9
13.0
-------
TABLE A-9. DESCRIPTIVE STATISTICS FOR URBAN INDUSTRIAL NEIGHBORHOOD HOURLY CO READINGS
WEEKDAYS
SATURDAYS
SUNDAYS
HOUR
ENDING
1
2
3
4
5
6
7
8
9
10
> 11
S 12
13
14
15
16
17
18
19
20
21
22
23
24
IsUHU
MEAN
1.14
1.07
0.81
0.87
0.88
1.08
1.60
2.00
1.91
1.59
1.38
1.27
1.21
1.16
1.20
1.37
1.53
1.51
1.40
1.40
1.41
1.42
1.40
1.36
E.W1KAT1UH ,
MEDIAN
0.83
0.73
0.55
0.55
0.55
0.71
1.03
1.37
1.42
1.13
1.03
1.02
1.01
1.00
1.00
1.03
1.05
1.04
1.01
1.01
1.01
1.01
0.99
0.98
,FPM
MAXIMUM
11.0
10.0
7.0
9.0
10.0
10.0
11.2
19.5
14.0
15.0
16.0
13.0
9.0
9.0
14.0
9.5
12.0
13.0
16.0
18.0
18.0
17.0
16.0
13.0
CON<
MEAN
1.44
1.46
1.04
1.13
1.01
0.98
1.14
1.21
1.23
1.17
1.13
1.06
1.04
1.00
0.98
1.02
1.04
1.09
1.20
1.33
1.36
1.44
1.49
1.46
SENTPvATIOI
MEDIAN
0.98
0.96
0.82
0.76
0.65
0.62
0.82
0.96
0.96
0.99
0.97
0.96
0.97
0.97
0.98
0.98
C.98
0.99
1.00
1.00
1.01
1.00
1.01
1.01
I, PPM
MAXIMUM
11.0
12.0
7.5
12.0
10.0
9.0
9.0
9.0
9.0
8.0
10.0
7.0
6.0
7.0
4.0
4.0
4.3
7.0
8.0
10.0
10.3
14.3
16.1
10.0
CONC
MEAN
1.40
1.32
1.10
1.15
1.03
0.99
1.03
1.04
1.02
0.99
0.95
0.91
0.88
0.86
0.34
0.84
0.87
0.95
1.06
1.15
1.23
1.23
1.23
1.18
ENTRATIOfl
MEDIAN
0.99
0.96
0.80
0.72
0.61
0.60
0.65
0.77
0.84
0.94
0.95
0.95
0.91
0.95
0.93
0.94
0.95
0.96
0.97
0.98
0.99
0.99
0.98
0.95
f.PPM
MAXIMUM
10.0
10.0
13.3
8.2
7.0
8.5
8.0
9.0
8.0
7.0
9.0
10.0
8.0
7.0
5.0
4.0
4.2
6.0
8.0
8.0
7.0
8.0
8.0
9.0
-------
TABLE A-10. DESCRIPTIVE STATISTICS FOR SUBURBAN RESIDENTIAL NEIGHBORHOOD HOURLY CO READINGS
WEEKDAYS
CONCENTRATION,PPM
SATURDAYS
CONCENTRATION,PPM
SUNDAYS
CONCENTRATION,PPM
I-IUUK
ENDING
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
MEAN
1.02
0.94
0.85
0.77
0.76
1.03
1.67
2.17
1.94
1.38
1.07
0.92
0.85
0.81
0.83
0.89
1.03
1.22
1.38
1.48
0.00
1.46
1.43
1.29
MEDIAN
0.67
0.55
0.52
0.50
0.51
0.95
1.03
1.45
1.04
0.99
0.95
0.82
0.74
0.69
0.77
0.86
0.96
0.98
0.99
1.00
0.00
1.00
0.99
0.97
MAXIMUM
26.5
23.5
21.0
16.0
12.0
13.0
14.0
22.0
28.0
21.0
16.0
13.0
13.0
10.0
7.0
7.0
13.0
18.8
24.3
27.3
00.0
24.6
24.6
23.4
MEM
1.29
1.24
1.13
1.00
0.90
0.91
1.08
1.21
1.15
1.00
0.89
0.83
0.77
0.74
0.71
0.71
0.75
0.89
1.14
1.33
1.47
1.51
1.52
1.45
MEDIAN
0.96
0.89
0.75
0.57
0.54
0.60
0.93
0.97
0.96
0.81
0.76
0.73
0.60
0.57
0.58
0.57
0.63
0.81
0.97
0.99
1.00
1.00
1.00
0.99
MAXIMUM
15.3
17.9
13.0
11.0
10.0
10.0
10.0
11.5
13.0
12.2
14.0
11.1
10.4
8.6
7.3
6.7
5.7
6.3
10.0
11.0
13.0
14.0
13.9
14.6
MEAN
1.25
1.23
1.15
1.02
0.90
0.86
0.88
0.93
0.85
0.77
0.72
0.69
0.69
0.67
0.66
0.65
0.68
0.83
1.03
1.21
1.34
1.32
1.29
1.19
MEDIAN
0.97
0.96
0.81
0.68
0.55
0.55
0.62
0.68
0.68
0.61
0.54
0.53
0.54
0.53
0.53
0.53
0.53
0.68
0.90
0.97
0.98
0.98
0.97
0.93
MAXIMUM
11.6
13.3
12.9
11.3
9.8
8.0
8.0
8.0
9.0
9.0
6.0
5.0
5.0
5.0
5.0
5.0
5.0
11.5
13.0
10.0
11.0
11.0
11.0
11.0
-------
TABLE A-31 . DESCRIPTIVE STATISTICS FOR SUBURBAN COMMERCIAL NEIGHBORHOOD HOURLY CO READINGS
WEEKDAYS
CONCENTRATION,PPM
SATURDAYS
CONCENTRATION,PPM
SUNDAYS
CONCENTRATION,PPM
ENDING
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
MEAN
1.48
1.16
1.02
0.92
0.91
1.23
2.07
2.85
2.50
1.83
1.55
1.48
1.43
1.40
1.46
1.66
1.91
2.06
2.07
2.12
2.11
2.11
1.95
1.77
MEDIAN
0.99
0.95
0.73
0.58
0.66
0.98
1.47
2.00
1.75
1.07
1.03
1.03
1.02
1.02
1.03
1.05
1.43
1.49
1.43
1.46
1.34
1.20
1.04
1.02
MAXIMUM
22.0
18.0
19.0
18.4
17.0
16.0
16.0
26.0
29.0
17.0
14.0
13.0
14.0
13.9
15.3
15.0
17.0
19.0
22.0
22.0
26.0
26.0
27.0
25.0
MEAN
2.01
1.67
1.51
1.27
1.10
1.15
1.35
1.48
1.49
1.42
1.37
1.39
1.39
1.37
1.39
1.38
1.43
1.60
1.80
1.98
2.08
2.13
2.13
2.02
MEDIAN
1.04
1.01
0.99
0.96
0.79
0.96
1.00
1.02
1.02
1.02
1.02
1.03
1.02
1.02
1.02
1.02
1.02
1.03
1.04
1.15
1.31
1.30
1.24
1.05
MAXIMUM
21.0
20.0
20.0
16.0
16.0
14.0
13.0
15.0
12.0
11.0
8.0
8.0
10.0
13.0
11.0
11.5
15.0
12.0
16.0
14.0
18.0
18.0
20.0
21.0
MEAN
1.89
1.63
1.49
1.29
1.07
1.02
1.09
1.09
1.02
1.02
1.00
1.00
1.09
1.04
1.01
1.02
1.10
1.29
1.45
1.62
1.69
1.76
1.73
1.55
MEDIAN
1.03
1.01
0.99
0.97
0.80
0.85
0.96
0.97
0.97
0.98
0.97
0.98
0.99
0.98
0.98
0.98
0.98
1.00
1.02
1.03
1.03
1.04
1.02
1.00
MAXIMU1
17.0
19.0
17.0
16.0
13.0
11.0
10.5
10.0
7.5
7.0
8.0
7.0
8.0
8.0
8.0
11.0
11.0
9.0
10.0
15.0
17.0
19.0
19.0
18.0
-------
TABLE A-12. DESCRIPTIVE STATISTICS FOR SUBURBAN INDUSTRIAL NEIGHBORHOOD HOURLY CO READINGS
I
M
U)
WEEKDAYS
CONCENTRATION,PPM
SATURDAYS
CONCENTRATION,PPM
SUNDAYS
CONCENTRATION,PPM
nuur,.
ENDING
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
_ __ _
MEAN
1.35
1.03
1.17
1.14
1.13
1.34
1.86
2.15
1.96
1.58
1.31
1.19
1.05
1.00
0.94
0.99
1.11
1.26
1.38
1.48
1.56
1.60
1.62
1.56
MEDIAN
0.70
0.58
0.60
0.57
0.57
0.71
1.00
1.22
1.15
0.97
0.82
0.74
0.69
0.67
0.62
0.65
0.70
0.81
0.81
0.81
0.85
0.86
0.86
0.81
MAXIMUM
17.0
16.0
16.0
15.0
14.0
13.0
15.0
19.0
17.0
14.0
12.0
12.0
11.0
11.0
12.0
12.0
13.0
18.0
20.0
24.0
23.0
25.0
22.0
21.0
MEAN
1.76
1.30
1.57
1.48
1.40
1.41
1.49
1.47
1.40
1.20
1.07
0.99
0.94
0.86
0.84
0.80
0.88
1.03
1.19
1.41
1.56
1.71
1.81
1.88
MEDIAN
0.92
0.76
0.75
0.68
0.63
0.66
0.73
0.83
0.85
0.78
0.74
0.65
0.62
0.58
0.60
0.58
0.62
0.67
0.81
0.94
0.90
0.94
0.95
0.06
MAXIMUM
20.0
13.0
18.0
16.0
15.0
13.0
13.0
12.0
12.0
10.0
10.0
10.0
10.0
10.0
10.0
4.0
5.0
8.0
9.0
11.0
12.0
14.0
16.0
15.0
MEAN
1.76
1.34
1.56
1.44
1.33
1.27
1.29
1.27
1.15
0.96
0.83
0.77
0.79
0.77
0.73
0.70
0.75
0.89
1.07
1.24
1.30
1.35
1.39
1.37
MEDIAN
0.80
0.72
0.73
0.65
0.62
0.62
0.63
0.68
0.68
0.66
0.58
0.56
0.55
0.52
0.50
0.50
0.54
0.59
0.69
0.81
0.79
0.81
0.78
0.73
MAXIMUM
16.0
11.0
14.0
14.0
14.0
12.0
11.0
11.0
9.0
9.0
7.0
6.0
14.2
14.1
12.3
6.0
6.0
7.0
10.0
12.0
12.0
12.0
13.0
14.0
-------
TABLE A-13. FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR WEEKDAYS
URBAN RESIDENTIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
36
109
180
253
325
398
470
541
614
686
759
831
904
975
1120
1337
1554
1770
2132
2150
2175
2200
2225
2250
1-5AM
6AM
7-10AM 11AM-3PM 4-6PM 7PM-MID
.4280
.4330
.0830
.0310
.0120
.0060
.0030
.0010
.0010
.0003
.0004
.0003
.0001
.0002
.0000
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3140
.4540
.1330
.0610
.0220
.0090
.0050
.0020
.0010
.0002
.0004
.0000
.0000
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1770
.4410
.1930
.0820
.0390
.0250
.0150
.0090
.0050
.0040
.0040
.0020
.0010
.0010
.0010
.0010
.0002
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3280
.4810
.1390
.0370
.0080
.0030
.0010
.0010
.0002
.0001
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2300
.4310
.1970
.0740
.0270
.0140
.0060
.0030
.0030
.0010
.0010
.0010
.0010
.0004
.0010
.0010
.0004
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2480
.4550
.1660
.0620
.0280
.0160
.0090
.0050
.0040
.0020
.0010
.0010
.0010
.0004
.0004
.0002
.0000
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKD
WKD
WKD
WKD
WKO
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
38
116
192
270
346
422
500
576
654
730
808
884
961
1038
1192
1422
1653
1884
2268
2300
2325
2350
2375
2400
URBAN COMMERCIAL
1-5AM
6AM
7-10AM 1 1AM-2PM
3-6PX 7PM-MID
.3440
.4220
.1290
.0520
.0220
.0120
.0060
.0040
.0020
.0020
.0020
.0010
.0010
.0003
.0010
.0003
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2500
.4080
.1470
.0860
.0490
.0250
.0120
.0090
.0050
.0030
.0030
.0004
.0010
.0010
.0002
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0810
.2820
.1980
.1270
.0900
.0620
.0450
.0340
.0260
.0150
.0130
.0080
.0060
.0040
.0050
.0040
.0010
.0003
.0002
.0001
.0000
.0000
.0000
.0000
.0000
.1070
.3700
.2020
.1160
.0810
.0500
.0320
.0190
.0110
.0060
.0040
.0020
.0010
.0004
.0010
.0002
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0760
.3250
.2130
.1260
.0820
.0550
.0400
.0280
.0190
.0110
.0080
.0050
.0040
.0030
.0030
.0010
.0010
.0004
.0001
.0004
.0000
.0000
.0000
.0000
.0000
.1190
.3700
.2210
.1210
.0670
.0370
.0230
.0140
.0090
.0050
.0040
.0030
.0020
.0010
.0020
.0010
.0010
.0003
.0002
.0002
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKO
WKD
WKD
WKD
WKD
WKD
WKO
WKD
WKD
A-14
-------
TABLE A-13 (CONT'D). FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR WEEKDAYS
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
38
116
192
270
346
422
500
576
654
730
808
884
961
1038
1192
1422
1653
1884
2268
2300
2325
2350
2375
2400
URBAN INDUSTRIAL
1-6AM
7AM
8-9AM
10AM-4PM
5-6PM 7PM-MID
.4550
.3690
.0920
.0420
.0200
.0110
.0070
.0020
.0010
.0020
.0010
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2570
.3970
.1760
.0670
.0370
.0220
.0140
.0160
.0060
.0040
.0020
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1870
.3550
.2140
.1 120
.0530
.0290
.0150
.0130
.0060
.0060
.0020
.0030
.0020
.0020
.0010
.0003
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2830
.3900
.2180
.0710
.0190
.0090
.0040
.0030
.0010
.0010
.0010
.0001
.0001
.0001
.0002
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2510
.3790
.2110
.0840
.0370
.0200
.0100
.0030
.0020
.0010
.0010
.0010
.0003
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2670
.4580
.1560
.0540
.0240
.0120
.0080
.0070
.0040
.0030
.0020
.0020
.0010
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKO
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
36
109
180
253
325
398
470
541
614
686
759
831
904
975
1120
1337
1554
1770
2132
2150
2175
2200
2225
2250
1-5AM
SUBURBAN RESIDENTIAL
6AM 7-10AM 1 1AM-3PM
4-6PM 7PM-MID
.5280
.3320
.0780
.0290
.0120
.0090
.0040
.0030
.0020
.0010
.0010
.0004
.0004
.0003
.0003
.0001
.0001
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.3980
.4150
.1120
.0440
.0170
.0080
.0030
.0020
.0010
.0004
.0004
.0000
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2240
.4080
.1730
.0770
.0420
.0240
.0150
.0120
.0080
.0050
.0040
.0030
.0020
.0020
.0020
.0010
.0003
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.4300
.4260
.0990
.0290
.0080
.0040
.0010
.0010
.0010
.0001
.0002
.0001
.0001
.0002
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3510
.4290
.1320
.0500
.0200
.0090
.0030
.0020
.0010
.0010
.0002
.0003
.0004
.0003
.0000
.0001
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3120
.4 140
.1370
.0600
.0280
.0170
.0100
.0070
.0040
.0030
.0020
.0020
.0020
.0010
.0010
.0010
.0001
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
A-15
-------
TABLE A-13 (CONT'D). FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR WEEKDAYS
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
36
109
180
253
325
398
470
541
614
686
759
831
904
975
1120
1337
1554
1770
2132
2150
2175
2200
2225
2250
1AM
SUBURBAN COMMERCIAL
2-SAM
6AM
7-10AM 11AM-3PM 4PM-MIO
.3330
.3950
.1290
.0520
.0280
.0140
.0120
.0080
.0060
.0040
.0040
.0040
.0030
.0030
.0010
.0020
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4610
.3670
.0920
.0320
.0160
.0090
.0080
.0040
.0040
.0030
.0010
.0010
.0010
.0004
.0010
.0004
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3230
.4380
.1220
.0550
.0260
.0160
.0070
.0050
.0030
.0020
.0005
.0005
.0010
.0000
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1540
.3440
.1990
.1150
.0650
.0390
.0240
.0160
.0100
.0090
.0070
.0040
.0030
.0020
.0030
.0020
.0010
.0004
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.2270
.4160
.2110
.0800
.0340
.0160
.0080
.0030
.0020
.0010
.0004
.0004
.0002
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1900
.3740
.2030
.0960
.0510
.0280
.0180
.0120
.0080
.0050
.0040
.0030
.0030
.0020
.0030
.0020
.0010
.0003
.0001
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
SUBURBAN INDUSTRIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
36
109
180
253
325
398
470
541
614
686
759
831
904
975
1120
1337
1554
1770
2132
2150
2175
2200
2225
2250
I AM
2-5AM
7AM
8-10AM 11AM-4PM 5PM-MID
.3880
.3740
.1 100
.0460
.0290
.0110
.0080
.0070
.0080
.0050
.0060
.0020
.0000
.0030
.0030
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4550
.3550
.0780
.0430
.0220
.0120
.0120
.0060
.0050
.0040
.0040
.0010
.0010
.0004
.0010
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2310
.4300
.1370
.0530
.0480
.0220
.0210
.0160
.0140
.0090
.0110
.0030
.0020
.0020
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2090
.4200
.1720
.0730
.0350
.0250
.0120
.0100
.0110
.0060
.0090
.0030
.0040
.0030
.0040
.0020
.0004
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3940
.4010
.1150
.0510
.0190
.0090
.0040
.0020
.0010
.0010
.0020
.0003
.0004
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3280
.3980
.1360
.0600
.0280
.0130
.0090
.0080
.0040
.0030
.0030
.0020
.0020
.0020
.0010
.0020
.0010
.0002
.0001
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
A-16
-------
TABLE A-14. FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SATURDAYS
CONC. INTERVAL
LOWER BOUND
HICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-2AM
.2940
.4570
.1340
.0540
.0240
.0110
.0070
.0050
.0030
.0030
.0040
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
URBAN RESIDENTIAL
3-6AM 7-9AM
.3640
.4600
.0980
.0390
.0200
.0070
.0050
.0030
.0020
.0010
.0010
.0003
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2740
.5120
.1270
.0480
.0170
.0100
.0050
.0020
.0030
.0003
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
,0000
.0000
.0000
.0000
.0000
.0000
10 AM -NOON
.3390
.5140
.1 100
.0200
.0090
.0040
.0010
.0010
.0010
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
1-6PM
.3530
.5130
.1000
.0200
.0070
.0030
.0010
.0010
.0000
.0003
.0002
.0003
.0002
.0000
.0002
.0000
.0002
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
7PM-MID
.2940
.4570
.1340
.0540
.0240
.01 10
.0070
.0050
.0030
.0030
.0040
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
URBAN COMMERCIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
53
161
267
375
481
587
695
801
909
1015
1123
1229
1336
1443
1657
1977
2298
2619
3153
3197
3232
3267
3301
3336
1AM
2-6AM
7AM
8AM-2PM
3-6PM
7PM-MID
.1440
.3710
.1750
.1150
.0700
.0460
.0330
.0130
.0120
.0010
.0020
.0060
.0020
.0040
.0020
.0010
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2900
.4180
.1390
.0740
.0310
.0150
.0130
.0050
.0060
.0020
.0020
.0010
.0010
.0010
.0010
.0010
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2430
.4400
.1500
.0820
.0330
.0210
.0090
.0090
.0070
.0000
.0000
.0010
.0020
.0010
.0000
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1700
.4370
.2030
.0860
.0450
.0250
.0140
.0090
.0040
.0020
.0010
.0010
.0004
.0010
.0010
.0003
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1600
.4390
.2040
.0810
.0550
.0240
.0120
.0090
.0070
.0040
.0020
.0010
.0010
.0010
.0000
.0000
.0000
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1240
.3590
.2260
.1120
.0690
.0390
.0210
,0190
.0090
,0060
.0060
.0030
.0020
.0010
.0010
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
A-17
-------
TABLE A-14 (CONT'D). FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SATURDAYS
CONC. INTERVAL
LOWER BOUND
MICR06RAM/CU.M.
0
53
161
267
375
481
587
695
801
909
1015
1123
1229
1336
1443
1657
1977
2298
2619
3153
3197
3232
3267
3301
3336
1AM
.3330
.3830
.1390
.0460
.0370
.0280
.0090
.0090
.0060
.0030
.0030
.0030
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
*0000
.0000
.0000
.0000
URBAN INDUSTRIAL
2-4AM 5-6AM
.3850
.4020
.0960
.0530
.0210
.0150
.0100
.0060
.0050
.0010
.0020
.0010
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4640
.3480
.0920
.0490
.0290
.0060
.0000
.0050
.0030
.0020
.0030
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
7AM-2PM
.3320
.4520
.1310
.0510
.0180
.0050
.0040
.0020
.0020
.0020
.0004
.0000
.0000
.0000
.0000
.0000
.0000
, .0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
3- 5PM
.2980
.5260
.1300
.0340
.0120
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
6PM-MIO
.2580
.4870
.1470
.0530
.0180
.0130
.0090
.0050
.0040
.0020
.0030
.0000
.0000
.0000
.0004
.0004
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT
SAT
SAT.
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SUBURBAN RESIDENTIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
«53
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-2AM
3-6AM
7-NOON
1-5PM
6PM
7PM-MID
.3910
.3760
.1130
.0450
.0300
.0170
.0090
.0050
.0030
.0010
.0030
.0020
.0010
.0010
.0030
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4860
.3380
.0940
.0400
.0130
.0110
.0050
.0060
.0030
.0020
.0020
.0010
.0003
.0003
.0000
.0000
.0000
.0000
.0000
.0000 .
.0000
.0000
.0000
.0000
.0000
.4040
.4260
.1030
.0310
.0130
.0070
.0060
.0040
.0020
.0010
.0010
.0010
.0010
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4880
.4130
.0790
.0150
.0020
.0010
.0002
.0004
.0000
.0002
.0004
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4140
.4380
.1030
.0300
.0070
.0050
.0030
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2960
.4300
.1420
.0580
.0270
.0160
.0100
.0060
.0050
.0040
.0020
.0010
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT"
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
A-18
-------
TABLE A-14 (CONT'D). FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SATURDAYS
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
SUBURBAN COMMERCIAL
1-3AM
4-6AM
7AM
8AM-2PM
3-6PM
7PM-MID
.2810
.3770
.1490
.0750
.0470
.0210
.0130
.0100
.0060
.0020
.0020
.0020
.0040
.0020
.0020
.0050
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4230
.3660
.0970
.0470
.0270
.0110
.0070
.0040
.0020
.0050
.0020
.0020
.0010
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3120
.4090
.1530
.0510
.0290
.0180
.0060
.0040
.0060
.0050
.0050
.0000
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2370
.4290
.2110
.0650
.0240
.0130
.0070
.0050
.0030
.0020
.0020
.0010
.0002
.0003
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2250
.4510
.1980
.0580
.0260
.0130
.0120
.0070
.0050
.0020
.0000
.0020
.0010
.0000
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1910
.3720
.1940
.0960
.0520
.0290
.0180
.0130
.0130
.0060
.0050
.0030
.0020
.0010
.0030
.0020
.0004
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SUBURBAN INDUSTRIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-4AM
5-6AM
7-1 1AM NOON-1PM 2-6PM
7PM-MID
.3630
.3540
.1150
.0660
.0390
.0210
.0130
.0090
.0030
.0020
.0020
.0000
.0030
.0010
.0030
.0050
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4440
.2960
.0990
.0640
.0220
.0370
.0170
.0050
.0020
.0020
.0000
.0020
.0030
.0050
.0030
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3240
.4320
.1300
.0410
.0200
.0190
.0160
.0040
.0020
.0010
.0040
.0010
.0040
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4240
.4190
.1030
.0280
.0130
.0030
.0020
.0000
.0000
.0000
.0080
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4450
.4170
.0900
.0250
.0120
.0060
.0030
.0000
.0010
.0000
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2990
.3910
.1450
.0600
.0300
.0200
.0190
.0060
.0090
.0060
.0050
.0020
.0020
.0020
.0020
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
-------
TABLE A-15. FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SUNDAYS
CONC. INTERVAL
LOWER ROUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-2 AM
.2820
.4870
.1220
.0490
.0240
.0120
.0080
.0060
.0040
.0050
.0010
.0020
.0000
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
URBAN RESIDENTIAL
3-4AM 5-6AM
.3650
.4570
.0880
.0400
.0210
.0100
.0080
.0050
.0010
.0020
.0020
.0010
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4300
.4370
.0730
.0270
.0140
.0090
.0040
.0040
.0010
.0020
.0010
.0000
.0000
.0000
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
7-9 AM
.3660
.4910
.0870
.0310
.0100
.0090
.0030
.0020
.0003
.0000
.0000
.0000
.0000
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
10AM-5PM
.4010
.5210
.0580
.0160
.0030
.0002
.0002
.0002
.0000
.0001
.0000
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
6PM-MID
.3010
.4680
.1350
.0510
.0220
.0110
.0050
.0030
.0010
.0010
.0004
.0003
.0003
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
URBAN COMMERCIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
53
161
267
375
481
587
695
801
909
1015
1123
1229
1336
1443
1657
1977
2298
2619
3153
3197
3232
3267
3301
3336
1-2AM
3-7 AM
8AM-NOON 1-5PM
6-10PM 11PM-MID
.1950
.3600
.1840
.0940
.0560 '
.0400
.0300
.0150
.0110
.0080
.0030
.0010
.0010
.0010
.0010
.0010
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3330
.4160
.1330
.0500
.0260
.0130
.0110
.0060
.0060
.0010
.0010
.0004
.0004
.0010
.0004
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2880
.4710
.1370
.0530
.0260
.0110
.0060
.0020
_ .0040
.0000
.0010
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2270
.4950
.1490
.0540
.0310
.0200
.0080
.0060
.0060
.0020
.0020
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1790
.4180
.2000
.0840
.0450
.0280
.0190
.0070
.0050
.0050
.0030
.0020
.0010
.0010
.0002
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2050
.3880
.1870
.0940
.0500
.0290
.0160
.0100
.0070
.0050
.0040
.0030
.0020
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
A-20
-------
TABLE A-15 '(CONT'D). FREQUENCY DISTRIBUTION OP AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SUNDAYS
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
53
161
267
375
481
587
695
801
909
1015
1123
1229
1336
1443
1657
1977
2298
26 J9
3153
3197
3232
3267
3301
3336
URBAN INDUSTRIAL
1-3AM
4-8AM
9-NOON
1-6PM
7-8PM
9PM-MID
.3290
.4290
.1220
.0560
.0150
.0190
.0170
.0050
.0050
.0010
.0020
.0000
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4330
.3530
.1230
.0450
.0160
.0110
.0110
.0060
.0030
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3660
.4680
.1160
.0270
.0070
.0090
.0040
.0020
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3520
.5320
.0960
.0110
.0060
.0020
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3050
.4900
.1350
.0400
.0140
.0090
.0030
.0000
.0030
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3240
.4330
.1430
.0420
.0250
.0120
.0120
.0080
.0020
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1856
2160
2460
2963
2989
3023
3058
3093
3128
SUBURBAN RESIDENTIAL
1-2 AM
3-4AM
5-9AM
10AM-5PM
6PM
7PM-MID
.3800
.3900
.1 110
.0450
.0280
.0210
.0110
.0050
.0030
.0030
.0020
.0010
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4500
.3570
.0970
.0340
.0240
.0160
.0090
.0040
.0040
.0030
.0020
.0010
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4790
.3810
.0770
.0300
.0150
.0090
.0060
.0030
.0010
.0002
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.5170
.4040
.0600
.0130
.0030
.0020
.0004
.0000
.0000
.0001
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4580
.4210
.0770
.0300
.0080
.0040
.0000
.0000
.0000
.0000
.0000
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3600
.4070
.1180
.0530
.0240
.0160
.0100
.0040
.0040
.0010
.0020
.0010
.0000
.0002
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
A-21
-------
TABLE A-15 (CONT'D). FREQUENCY DISTRIBUTION OF AMBIENT CO CONCENTRATIONS
IN SIX NEIGHBORHOODS FOR SUNDAYS
SUBURBAN COMMERCIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-4AM
5-6AM
7AM
8AM-NOON 1-5PM
6PM-MIO
.3240
.3640
.1480
.0630
.0310
.0180
.0150
.0100
.0050
.0070
.0050
.0050
.0020
.0010
.0010
.0020
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4420
.3780
.0880
.0400
.0160
.0130
.0070
.0040
.0050
.0020
.0010
.0020
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3860
.4150
.1 110
.0400
.0120
.0170
.0090
.0010
.0020
.0040
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3540
.4440
.1380
.0360
.0140
.0070
.0030
.0020
.0010
.0010
.0005
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3410
.4480
.1430
.0370
.0190
.0060
.0020
.0010
.0010
.0002
.0000
.0005
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2740
.3760
.1770
.0790
.0360
.0200
.0120
.0080
.0050
.0040
.0030
.0020
.0010
.0020
.0010
.0003
.0003
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUBURBAN INDUSTRIAL
CONC. INTERVAL
LOWER BOUND
MICROGRAM/CU.M.
0
50
152
250
352
452
553
653
752
853
954
1055
1151
1257
1355
1557
1858
2160
2460
2963
2989
3023
3058
3093
3128
1-2AM
3-7 AM
8AM-NOON
1-5PM
6-7PM
8PM-MID
.3440
.3840
.1070
.0550
.0310
.0160
.0090
.0220
.0150
.0000
.0070
.0050
.0040
.0000
.0000
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4120
.3440
.0860
.0540
.0360
.0250
.0120
.0100
.0060
.0030
.0030
.0030
.0030
.0010
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4310
.4080
.0880
.0260
.0210
.0070
.0070
.0030
.0040
.0020
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.5210
.3900
.0620
.0150
.0030
.0050
.0030
.0000
.0000
.0000
.0000
.0000
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.4400
.3880
.1000
.0400
.0130
.0070
.0050
.0030
.0020
.0000
.0020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3460
.4070
.1330
.0490
.0180
.0070
.0090
.0090
.0050
.0070
.0040
.0010
.0030
.0010
.0010
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
DAY
TYPE
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
A-22
-------
APPENDIX B
SUPPORTING MATERIAL FOR CO CONCENTRATIONS
FROM MOBILE SOURCE MICROENVIRONMENTS
-------
DEVELOPMENT OF LOGNORMAL POLLUTANT DISTRIBUTIONS FOR
PARKING GARAGES AND ROADWAY TUNNELS
The lognormal distribution has historically been used to describe
ambient air concentration distributions. For areas .where the pollutant
concentration distribution is not completely defined by measured- value?,
but values such as the mean, median, mode or range are known, the properties
of the lognormal distribution can be used to define the complete pollutant
distribution.
The lognormal distribution is defined as:
(x) - _ /-rd exp
(in X - y)
dx
Obviously, if a and y are known, the distribution can be defined for any
^raliiA f\f V
value of X.
Additionally for a lognormal distribution:
Mean, X « e (jj + 1/2 a2)
Median = e^
Mode = ey"a
The coefficient of variation n, defined as the standard deviation divided
by the mean is:
S
^^^B
n »
For the parking garage, the concentration distributions were developed
by using a CO concentration value known to be typical of parking garages
as the distribution mode, then adjusting the median so that the frequency
of occurence in the 300 to 400 ppm CO range was 0.01 percent. If the median
and mode are known, then y and a are solved for as follows:
In (median)= In
y » In (median)
In (mode) - In (e^
u - a2 m In (mode)
o « Vln (mode) - y
with y and 0 defined, the entire lognormal distirbution can be defined.
For roadway tunnels a slightly different approach was used. The
measured data on concentrations in roadway tunnels produced a mean value
and a maximum value of CO concentration as a function of average daily
traffic (ADT).
B-2
-------
Using the proper CO emission factor, these CO levels were converted to
pollutant concentrations for a 1.0 g/minute emission factor (see text).
The CO concentrations and pollutant concentrations at 1.0 g/minute for
several ADT values are shown in the table below.
Max.
ADT
0.5
1.5
2.5
3.5
4.5
5.5
6.5
Mean
5.2
14.3
23.4
32.5
41.6
50.7
59.9
66.7
94.4
122.1
149.8
177.4
205.1
232.8
Pollutant Concentration
Uq/m^ at 1.0 g/min(a)
Mean
166.2
457.0
747.9
1038.7
1329.6
1620.4
1914.5
Max.
2131.8
3017.2
3902.5
4787.8
5669.9
6555.3
7440.6
(a)
based on a CO emission factor of 62.06 g/mile at 35 mph
Assuming that the pollutant concentration is lognormally distributed,
the problem is to define the distribution for each value of ADT, given only
the mean and maximum concentrations. Note that if the minimum concentration
is assumed to be 0 then the range (maximum - minimum) is also known. The
expression for the mean of a lognormal distribution is shown above. It can
also be shown that the standard deviation, S, can be approximated by the
following relationship, assuming that the range available is the result of
a large number of observations (2)
S =
Thus the standard deviation for the tunnel distribution can be estimated
from the range. If the standard deviation and mean are known the coeffi-
cient of variation is known and a can be obtained as follows:
n
a
Vln
(n
- 1
In (n2 + 1)
1)
B-3
-------
Once a is known, y can be obtained from the equation for the
mean:
7 (y + 1/2 o*)
A = S
y + 1/2 a2 - In X
M - in x - 1/2 a2
With both M and a known the distribution is fully defined. The
table below shows the y and a for the tunnel pollutant concentration dis
tributions at seven different values of ADT.
APT
0.5
1.5
2.5
3.5
4.5
5.5
6.5
range
(max-min)
2131.8
2017.2
3902.5
4787.8
5660.0
6555.3
7440.6
327.97
464.18
600.38
736.58
872.29
1008.51
1144.71
mean
- x~
166.2
457.0
747.0
1038.7
1329.6
1620.4
1914.5
" j_ ,
x"
1.97
1.02
0.80
0.71
0.66
0.62
0.60
a
1.26
0.84
0.71
0.64
0.60
0.57
0.55
y
4.32
5.77
6.37
6.74
7.01
7.23
7.40
References:
1. Aitchison, J. and Brown, J.A.C., The Lognormal Distribution.
Cambridge University Press, New York, 1957.
2. Sokal, R. and Rohof, T.J., Biometry, W. H. Freeman and Co., 1981,
B-4
-------
LISTING OF EDITING PROGRAM FOR SAROAD DATA TAPES
PROGRAM EDTEPA( INPUT, OUTPUT, EDDAT,TAPE2=EDDAT ,TAPE60=I NPUT)
DIMENSION IMON(2),IDAY(2),ILINE(2),ITYPE(2), RDG( 12,2) , IUNTC2)
DIMENSION NARK2).NAR2(2),NAR3<2),NAR4(2),ISTA<2),DUM<12),IDEC(2)
ISTA(2) « 1960085
l»l
J-1
K=1
50 READ 1000,NARKK),ISTA(K),NAR2(K),NAR3(K),IMON(K),IDAY(K),
tlLINE(K), NAR4(K),ITYPE(K),IUNT(K>,IDEC(K),(ROG(M,K),M»1>12)
IF(EOF(60))999,55
55 CONTINUE
IF(IDECm.NE.I) GO TO 57
DO 56 L-1,12
56 RDG(L,K)- RDG(L,K)/10.
57 IF(IUNT(K).NE.05) GO TO 59
DO 58 L-1.12
58 RDG(L,K>* RDG(L,K)/ 1 .15
59 IF(ISTA(2).EQ.ISTA(1)) GO TO 60
IF(K.E0.2) GO TO 220
60 CONTINUE
IF(K.NE.I) GO TO 100
IF(IMONd).NE.I) GO TO 150
65 IF(IDAYd).NE.J) GO TO 170
*f 70 IF(ILINEd).NE.O)GO TO 180
en CALL DAYWEMIMON
-------
CALL DAYWEKMMONCK),IDAY(IO,ITYPE(K))
WRITE(2,3000) NAR1(K), I STACK),NAR2«IDAY<2)
)»ITYPEC2)
-I STAC 2)
-IUNTC2)
=IDEC(2)
)=ILINE(2)
00 225 M=1,12
225 RDG(M,1)=-ROGCM,2)
I»IMON(K)
J=IDAY(K)
60 TO 70
1000 FORMATCI3,I7,A3,I3,I2,I2,I1,I 6,I 2,12,1 1,12F4.0)
2000 FORMAT(IX,FOR STATION ",17," SOMETHING IS WRONG AT ",l2,"/n,l2,
1 "LINE 2")
2010 FORMATdX.FOR STATION ",17," DATA IS MISSING FOR MONTH ",12,
1 ". NEXT MONTH WITH DATA IS MONTH ",12)
2020 FORMAT(1X,FOR STATION ",17," DATA IS MISSING FOR MONTH ",12,
1 " DAY ",12,". NEXT DAY WITH DATA IS DAY » 12)
2030 FORMATdX.FOR STATION ",17," THERE IS NO FIRST LINE OF DATA FOR "
1 ,I2,»/",I2)
2040 FORMATdX.FOR STATION ",17," SOMETHING IS WRONG AT ", 12,"/", 12,
1 "LINE 1")
2050 FORMATdX.FOR STATION ",17," THERE IS NO SECOND LINE OF DATA FOR"
1 1X,I2,"/"I2,". NEXT DATA IS ",12,"/",I 2," STATION B,17)
3000 FORMAT(I3,I7,A3,I3,I2,I2,I1,I 6,1 2,I 2,11,I2F4.1)
999 STOP
END
SUBROUTINE DAYWEMIMON,I DAY,I TYPE)
COMMON/SATDA /ISAT(52)/SUNDA /ISUN(52)
IDATE =(IMON *100) « I DAY
I TYPE = 1
DO 100 L - 1,52
IFdDATE.EQ.ISAT(L)) ITYPE =2
IF( IDATE.EO.ISUN(D) ITYPE =3
100 CONTINUE
RETURN
END
SUBROUTINE DUMREC( DUM,IDUML,IDUM4.K)
-------
00
I
-o
0
042101
1
242101
DIMENSION DUMU2)
DO 200 1= 1,12
DUM(I) = 99.9
200 CONTINUE
IF(K.EO.I) IOUML
IF(K.EO.I) IOUM4
IF(K.E0.2) IOUML
IF(K.E0.2) IDUM4
RETURN
END
BLOCK DATA SATDATE
COMMON/S ATDA/IS AT(52)
DATA ( ISATd ),l=1 ,52)7
1 0103,0110,0117,0124,0131,0207,0214
0221,0228,0307,0314,0321,0328,0404
0411,0418,0425,0502,0509,0516,0523,
0530,0606,0613,0620,0627,0704,0711
0718,0725,0801,0808,0815,0822,0829
0905,0912,0919,0926,1003,1010,1017
1024,1031,1107,1114,1121,1128,1205
1212,1219,12267
2
3
4
5
6
7
8
END
BLOCK
tNL>
BLOCK DATA SUNDATE
COMMON/SUNDA/ISUN(52)
DATA ( ISUNd ), 1=1,52)7
1 0104,0111,0118,0125,0201,0208,0215
0222,0301,0308,0315,0322,0329,0405
0412,0419,0426,0503,0510,0517,0524
0531,0607,0614,0621,0628,0705,0712
0719,0726,0802,0809,0816,0823,0830
0906,0913,0920,0927,1004,1011,1018,
1025,1 101,1108,1115,1 122, 1129,1206
1213,1220,12277
2
3
4
5
6
7
8
END
-------
APPENDIX C
SUPPORTING MATERIAL FOR NATIONAL POPULATION
BY NEIGHBORHOOD TYPE
-------
TABLE C-l. ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR WEEKDAYS
O
to
HOUR OF DAY
10 " 12 13 14 15 16 17 18 19 20 21 22 23 24 AO DAY
82
82
82
82
«2
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
91
91
82
82
82
82
91
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
93
82
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
91
82
82
82
83
82
91
82
82
83
82
82
82
83
83
82
83
82
82
82
82
83
82
82
81
82
91
91
82
82
82
82
91
91
82
82
ai
82
91
83
91
91
91
91
91
91
94
82
83
91
91
82
82
83
91
91
91
82
82
95
91
91
91
82
82
81
81
91
91
91
91
91
91
91
91
94
91
91
91
91
82
82
94
93
91
91
82
82
95
91
91
91
82
82
85
81
91
91
91
91
91
91
91
91
95
93
93
91
91
82
82
94
94
91
91
82
82
94
95
91
91
82
82
81
81
91
91
91
91
91
91
91
91
94
95
91
91
91
82
82
94
91
91
91
82
82
95
91
91
91
82
82
82
82
92
92
92
91
92
91
92
92
92
92
92
91
92
82
82
92
92
91
91
82
82
92
91
91
91
82
82
81
85
91
95
91
91
91
91
91
91
94
93
93
91
91
82
84
93
93
91
91
83
82
91
95
91
91
82
82
81
81
95
95
91
91
91
91
91
91
94
95
91
91
91
82
82
94
94
91
91
82
82
95
95
91
91
83
83
81
81
91
91
91
91
91
91
91
91
95
91
91
91
91
82
82
94
91
91
91
82
83
95
91
91
91
82
82
85
81
93
91
91
91
91
91
91
91
94
91
91
91
91
92
83
91
91
82
93
91
82
95
91
82
93
91
82
82
85
82
91
82
82
83
93
82
93
82
82
93
82
93
91
91
82
82
82
82
91
82
82
82
82
84
91
82
82
84
84
93
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
84
82
91
82
82
82
82
82
91
82
82
81
82
82
82
82
82
82
82
82
82
B2
82
82
82
91
91
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
91
82
82
82
82
82
91
82
82
82
82
81
82
82
82
82
82
82
82
82
82
82
82
91
91
82
82
82
82
91
91
82
82
82
82
91
91
11
12
13
14
21
22
23
24
31
32
33
34
35
41
42
43
44
45
46
51
52
53
54
55
56
61
62
63
64
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
82 82 82 82 82 82 82 82 91 95 94 95 92 95 91 95 95 82 82 82 82 82 82 82 65 WKD
82 82 82 62 R2 82 93 93 93 93 94 92 93 93 92 93 93 82 82 82 82 82 82 82 66 WKD
EXPLANATION OF CODE:
1. SEE TEST TABLE IV-3 FOR EXPLANATION OF AO GROUP CODES
2. FIRST DIGIT OF TWO DIGIT ACTIVITY CODE IS EITHER 8 OR 9.
8 = HOME NEIGHBORHOOD
9 = WORK NEIGHBORHOOD
3. SECOND DIGIT OF TWO DIGIT ACTIVITY CODE IS MICROENVIRONMENT AS FOLLOWS:
1 = INDOORS AT WORK 3 = VEHICLE 5 = OUTDOORS
2 = INDOORS, OTHER 4 = ROADSIDE
4. EXAMPLE: ACTIVITY CODE 94 IS WORK NEIGHBORHOOD, ROADSIDE MICROENVIRONMFNT
-------
TABLE C-l (CONT'D). ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR WEEKDAYS
O
I
u»
HOUR OF DAY
1
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
3
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
4
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
5
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
6
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
7
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
84
82
82
84
83
9
91
91
82
82
81
82
82
82
82
82
82
82
82
82
82
82
82
82
83
81
81
83
81
81
81
10
91
-------
TABLE C-2. ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR SATURDAYS
O
HOUR OF DAY
1
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
2
82
82
82
82
R2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
3
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
4
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
5
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
6
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
7
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
9
82
82
82
82
82
82
82
82
82
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
10
82
82
82
82
82
82
85
82
82
85
83
93
82
83
82
82
82
82
85
82
83
82
82
82
83
82
83
1 1
85
82
82
82
85
84
82
85
82
85
82
95
85
84
85
82
82
85
85
82
84
82
82
84
84
82
82
12
83
85
82
83
82
82
82
82
85
82
82
91
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
13
82
83
82
82
82
83
82
82
82
82
82
92
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
14
82
82
82
82
82
82
83
82
82
82
82
95
82
82
82
85
83
82
82
82
82
85
85
82
82
82
82
15
85
82
85
85
83
82
82
83
82
82
82
93
83
82
82
82
84
82
82
84
82
82
82
85
85
83
85
16
82
82
85
85
92
85
82
82
82
82
82
91
82
85
82
82
82
82
83
85
85
82
82
82
82
84
82
17
82
82
82
82
82
82
82
84
82
82
82
82
82
82
82
83
82
82
84
82
82
82
82
82
82
82
82
18
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
84
82
82
82
82
82
83
82
82
82
82
82
19
82
82
82
82
82
82
82
82
82
82
82
85
82
82
82
82
82
83
82
82
82
84
82
82
82
82
82
20
83
82
82
82
82
82
82
82
83
83
82
82
82
82
82
82
82
84
82
83
82
82
83
83
82
82
82
21
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
22
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
23
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
24
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
AO
1 1
12
13
14
21
22
23
24
31
32
33
34
35
41
42
43
44
45
46
51
52
53
54
55
56
61
62
DAY
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
82 82 82 B2 82 82 82 82 82 82 82 82 82 82 85 82 83 84 82 82 82 82 82 82 63 SAT
82 82 82 fl2 82 82 82 82 82 83 84 82 82 85 83 82 82 82 82 82 82 82 82 82 64 SAT
82 82 82 82 82 82 82 82 82 83 82 82 82 82 82 82 85 84 82 82 82 82 82 82 65 SAT
82 82 82 82 82 82 82 82 82 82 82 82 82 82 85 82 82 82 82 84 82 82 82 82 66 SAT
EXPLANATION OF CORE:
1. SEE TEST TABLE I V-3 FOR EXPLANATION OF AO GROUP CODES
2. FIRST DIGIT OF TWO DIGIT ACTIVITY CODE IS EITHER 8 OR 9.
8 = HOME NEIGHBORHOOD
9 = WORK NEIGHBORHOOD
3. SECOND DIGIT OF TWO DIGIT ACTIVITY CODE IS MICROENVIRONHENT AS FOLLOWS-
1 = INDOORS AT WORK 3 = VEHICLE 5 = OUTDOORS
2-= INDOORS, OTHER 4 = ROADSIDE
4. EXAMPLE: ACTIVITY COPE 94 IS WORK NEIGHBORHOOD, ROADSIDE MICROENVIRONMENT
-------
o
I
01
TABLE C-2 (CONT'D). ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR SATURDAYS
HOUR OF DAY
1
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
3
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
4
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
5
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
6
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
7
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8
82
82
82
82
82
82
82
R2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
9
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
10
85
82
82
81
83
82
82
85
83
84
82
82
83
82
82
82
85
82
85
82
82
82
82
85
82
11
85
85
82
83
82
84
82
82
82
82
84
83
82
82
82
85
82
82
82
83
85
84
82
82
84
12
82
85
82
84
82
82
82
82
82
82
82
82
82
83
85
82
82
85
82
82
85
82
85
82
82
13
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
14
82
82
83
83
84
82
82
83
82
82
82
82
82
82
82
83
82
82
85
82
82
82
84
82
85
15
83
82
84
84
82
82
85
84
82
83
85
84
85
82
82
82
83
82
82
85
84
84
85
84
85
16
82
82
82
83
85
83
82
82
85
82
84
85
84
85
82
85
82
83
82
85
82
85
82
82
82
17
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
85
83
82
82
82
82
85
82
18
82
82
82
81
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
19
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
S2
82
82
82
82
82
82
82
20
82
82
82
82
82
82
84
82
82
82
83
82
82
82
82
82
82
82
82
82
83
82
83
83
82
21
82
83
82
82
82
82
83
82
82
82
84
82
82
82
82
82
82
82
82
82
82
82
82
82
82
22
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
23
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
24
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
AO
81
82
83
84
85
86
91
92
93
101
102
103
104
105
106
111
112
1 13
1 14
121
122
123
124
125
126
DAY
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
EXPLANATION OF CODE:
1. SEE TEST TABLE IV-3 FOR EXPLANATION OF AO GROUP CODES
2. FIRST DIGIT OF TWO DIGIT ACTIVITY CODE IS EITHER 8 OR 9.
8 = HOME NEIGHBORHOOD
9 = WORK NEIGHBORHOOD
3. SECOND DIGIT OF TWO DIGIT ACTIVITY CODE IS MICROENVIRONMENT AS FOLLOWS-
1 = INDOORS AT WORK 3 = VEHICLE 5 = OUTDOORS
2 = INDOORS, OTHER 4 = ROADSIDE
4. EXAMPLE: ACTIVITY CODE 94 IS WORK NEIGHBORHOOD, ROADSIDE M ICROENV IRONMENT
-------
TABLE C-3. ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR SUNDAYS
?
HOUR OF DAY
1
82
82
82
82
82
82
82
82
82
62
82
82
82
82
82
«2
82
82
82
«2
82
82
82
82
82
82
82
82
82
82
82
2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
3
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
4
82
82
«2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
5
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
6
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
7
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
9
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
85
82
83
82
82
82
82
82
83
82
82
82
82
10
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
85
82
82
82
82
82
82
82
82
83
82
82
82
11
82
82
82
82
83
83
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
83
83
82
82
84
82
83
12
82
85
82
82
82
82
82
82
82
82
84
82
82
83
82
82
R2
82
82
82
82
82
82
82
82
82
82
82
82
82
82
13
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8?
82
82
82
82
82
82
82
14
82
83
85
84
82
82
82
84
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
85
82
82
85
82
82
15
85
82
82
82
82
82
82
82
85
85
82
93
85
85
82
85
85
82
82
84
82
84
83
85
82
85
82
85
85
85
82
16
85
85
85
85
85
84
85
82
82
82
84
95
85
82
82
85
82
82
82
82
85
82
85
83
85
82
85
82
82
85
82
17
82
85
82
85
82
82
82
83
82
82
82
91
82
82
85
82
85
85
82
82
82
82
82
82
84
82
82
82
82
82
85
18
82
82
82
82
82
82
82
82
82
82
82
82
82
82
84
82
82
82
85
82
82
82
82
82
82
82
82
84
82
82
82
19
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
84
82
82
82
82
82
82
82
82
82
82
82
20
82
81
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
21
81
81
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
22
81
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
23
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
24
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
AO
1 1
12
13
14
21
22
23
24
31
32
33
34
35
41
42
43
44
45
46
51
52
53
54
55
56
61
62
63
64
65
66
DAY
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
EXPLANATION OF CODE:
1. SEE TEST TABLE IV-3 FOR EXPLANATION OF AO GROUP CODES
2. FIRST DIGIT OF TWO DIGIT ACTIVITY CODE IS EITHFR 8 OR 9.
8 = HOME NEIGHBORHOOD
9 = WORK NEIGHBORHOOD
3. SECOND DIGIT OF TWO DIGIT ACTIVITY CODE IS MICROENVIRONMENT AS FOLLOWS:
1 = INDOORS AT WORK 3 = VEHICLE 5 = OUTDOORS
2 = INDOORS, OTHFR 4 = ROADSIDE
4. EXAMPLE: ACTIVITY CODE 94 IS WORK NEIGHBORHOOD, ROADSIDE MICROENVIRONMENT
-------
o
I
TABLE C-3 (CONT1D). ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUPS FOR SUNDAYS
HOUR OF DAY
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 AO OAY
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
8?
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
85
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
85
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
83
82
82
82
85
82
83
83
83
82
82
82
82
83
83
82
82
82
82
83
82
83
82
82
82
82
82
82
82
82
84
82
82
82
82
82
82
82
82
82
82
85
82
82
82
82
82
82
83
85
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
83
84
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
85
85
82
82
82
82
82
82
82
82
85
85
85
82
82
85
83
85
82
85
82
85
82
85
82
82
82
82
85
82
85
85
85
85
85
85
82
85
85
82
82
85
82
85
84
85
82
82
82
82
82
82
85
82
85
85
85
82
84
82
82
82
82
82
85
82
82
82
82
82
82
82
82
82
82
83
83
85
85
82
82
82
85
82
82
82
82
82
82
82
82
82
82
84
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
84
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
83
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
81
82
83
84
85
86
91
92
93
101
102
103
104
105
106
111
112
113
114
121
122
123
124
125
126
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
EXPLANATION OF CODE:
1. SEE TEST TABLE IV-3 FOR EXPLANATION OF AO GROUP CODES
2 FIRST DIGIT OF TWO DIGIT ACTIVITY CODE IS EITHER 8 OR 9.
8 = HOME NEIGHBORHOOD
9 = WORK NEIGHBORHOOD
3 SECOND DIGIT OF TWO DIGIT ACTIVITY CODE IS MICROENVIRONMENT AS FOLLOWS:
1 = INDOORS AT WORK 3 = VEHICLE 5 = OUTDOORS
2 = INDOORS, OTHER 4 = ROADSIDE
4. EXAMPLE: ACTIVITY CODE 94 IS WORK NEIGHBORHOOD, ROADSIDE M ICROENVIRONMENT
-------
TABLE C-4. COHORT POPULATIONS BY STUDY AREA
Cohort description
A-0 qrouo
Students 18+
01
Professionals
02
Sales workers
03
/ . * t \
Home
NTa
CR
1
SR
5
Work
NTa
CR
1
SR
5
i
!
CR
1
CR
1
SR
5
i
SR
5
CR
1
CC
2
SC
6
CC
2
SC
6
CC
2
i
Sub-
grouD
1
2
Cohort population
Chicaqo
19,316
37,792
3 j 9,238
4 : 17,636
1 ! 5,897
2 i 11,538
3 2,820
4 5,384
1 i 170,393
2 j 76,133
3
4
1
2
3
4
1
79,758
36,254
11,845
5,293
5,545
2,520
47,282
2 ! 21,126
3
4
1
2
3
4
Phila-
delphia
4,239
8,293
2,027
3,870
St. Louis
3,753
7,344
1,794
3,427
17,387 j 5,334
34,017 | 10,436
8,315 i 2,551
15,875 ! 4,870
i
14,501
6,479
6,788
3,085
5,710
2,551
2,673
' 1,215
35,661
15,933
22,132 16,692
10,060 j 7,587
13,364
5,971
6,256
2,834
3,489
1,559
1,633
742
14,490
6,474
6,782
3,083
i
8,806
65,332 ! 18,812
3,934 29,191
4,122 | 30,581
1,874 [
1 I 33,250
13,901
8,405 1
8,805
4,002
4,442 ! 4,000 !
Los
Anqel as
17,261
33,771
8,255
15,760
61,326
119,985
29,330
55,993
41,759
18,658
19,547
8,885
31,503
14,076
14,746
6,702
40,366
18,036
18,895
8,589
263,140
117,573
123,172
55,987
11,364
2 ; 16,238 ! 2,169 1 1,953 | 5,550
3 i 3,866 | 517 j 465 | i,32l
4 6,959 i 930 ! 837 ! 2,378
j 5 ; 17,011 i 2,273 ' 2,046 ! 5,814
CR
i ,
1
i
t : | i i
sc 1 1 2,312 i 1,749 i 1,044
6
z !
I i
4
1,129
269
484
1,183
854 ! 510
203 | 121
366 219
895
534
8,573
4,187
997
«* t
1,794
7 * W
4,38S
(continued)
C-8
-------
TABLE C-4 (CONT'D).. COHORT POPULATIONS BY STUDY AREA
Cohort description
1
A-0 qroup
Sales workers
03 (cont.)
!
i
1
Clerical
Home
NTa
SR
5
SR
5
CR
workers 04 1 1
CR
1
SR
IB
i
i
i
i
SR
5
1
i
i
Work
NT*
CC
2
SC
6
CC
2
SC
6
Sub-
qrouD
1
2
3
4
5
1
2
3
4
5
i
2 1
3
4
5
6
1
2
! 3
CC
2
4
5
6
1
2
3
4
5
6
! sc i i
6
2
3
4
5
6
Cohort population
Chicaqo
9,244
4,514
1,073
1,932
4,722
1,719
839
200
Phila-
delphia
I
St. Louis
1
11,322
5,529
1,317
2,370
5,792 i
20,742
10,130
2,412
360 I 4,341
879 j 10,612
,
141,163
65,540
22,687
10,083
2,520
10,083
9,813
4,556
1,577
701
175
-701
31,571
14,658
5,074
2,255
564
2,255
5,880
2,730
945
420
105
420
26,033
12,087
4,184
1,860
465
1,860
10,250
4,759
1,647
732
183
732
39,508
18,343
6,349
1 2,822
706
2,822
4,315
2,107
502
903
2,208
1
5,602
Los
Angeles
11,372
5,554
1,322
2,380
5,818
74,133
2,736 1 36,204
651 1 8,620
1,173 i 15,516
2,866 ; 37,928
22,242
48,231
10,327 i 22.393
3,575
1,589
397
1,589
5,806
2,696
933
7,751
3,445
861
3,445
36,385
16,893
5,848
415 i 2,599
104 i 650
415 ; 2,599
t
11,765
5,462
1,891
840
210
840
72,380 ! 15,274
33,605 \ 7,092
36,537
16,963
5,872
2,609
652
2,609
; 238,175
110,175
11,633 j 2,455 i 38,278
5,170 1,091 17,01?
1,293 273
5,170 1,091
4,253
17,013
(continued)
C-9
-------
TABLE C-4 (CONT'D). COHORT POPULATIONS BY STUDY AREA
Cohort description
A-0 group
Craftsmen 05
Laborers 06
Home
NTa
CR
1
CR
1
SR
5
Work
NTa
CI
3
si
7
Sub-
qroup
1
2
3
4
5
6
1
2
3
4
i 5
CI
3
SR
w* »
5
w
CR
1
CR
1
SI
7
CI
3
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
i
SI
7
1
2
3
4
5
6
Chicago i
81,648
39,191
16,330
3,266
6,532
16,329
5,676
2,725
1,135
Cohort population
Phila-
delphia
14,010
6,725
2,802
560
1,121
2,802
5,516
2,648
1,103
St. Louis
9,562
4,590
1,912
382
765
1,912
2,496
1,198
499
227 221 i 100
454
1,135
25,626
12,300
5,125
1,025
2,050
441 200
1,103
LOS
Angeles
18,978
9,109
3,796
759
1,518
3,796
14,317
6,872
2,863
573
1,145
499 | 2,863
i
20,533 i 6,018 j 22,170
9,856
4,107
821
1,643
5,125 : 4,107
1
i
4,772 ! 37,618
2,291
954
191
382
954
75,251
34,731
11,577
5,789
18,056
7,524
1,505
3,009
7,524
I 27,638
! 12,756
4,252
2,126
34,731 : 12,756
30,872 j 11,339
2,889
1,204
241
481
1,204
10,641
4,434
887-
1,774
4,434
i
7,813
3,750
1,563
313
625
1,563
19,542
9,020
3,007
150
9,020
8,017
5,231 j 10,882
2,414 5,022
805
1,674
402 i 837
2,414 5,022
2,146
4,464
19,542
2,354
144,520
69,370
28,904
5,781
11,562
28,904
29,939
13,818
4,606
2,303
13,818
12,283
22,586
10,424
785 ! 3,475
392
2,354
2,093
i 1,737
10,424
9,266
(continued)
c-io
-------
TABLE C-4 (CONT'D). COHORT POPULATIONS BY STUDY AREA
Cohort description
A-0 qroup
1
Laborers 06 |
(cont.)
1
I
Home
NTa
SR
5
SR
5
i
i
. j
Service ' CR
workers 08
Housewives
09
Retired 10
1
SR
5
CR
1
SR
5
i
CR
Work
NTa
CI
3
SI
7
i
Sub-
group
1
2
3
4
5
6
1
2
3
4
i 5
! 6
1
CR
1
SR
5
1
2
3
4
5
6
1
2
3
4
5
6
CR
1
SR
5
CR
1
2
3
1
2
3
Cohort population
1
Chicaqo
18,289
8,441
2,813
1,406
8,441
7,503
3,406
1,572
524
/s ** f\
Phila-
delphia
22,294
10,289
3,430
1,715
10,289
9,146
40,843
18,851
6,284
262 3,142
1,572 I
IM /» «
,397
49,393
18,851
16,756
20,770
23,324 | 9,808
30,184
4,116
19,208
10,976
11,090
5,236
6,777
924
St. Louis
6,791
3,134
1.045
522
3,134
2,786
8,817
4,069
1,356
Los
Angeles
24,843
11,466
3,822
1,911
11,466
10,192
161,945
74,744
24,914
678 i 12,457
4,069 i 74,744
3,617 I 66,439
16,789
7,928
12,693 i 10,260
1,731
8,077
4,616
37,142
17,539
22,698
3,095
4,312 j 14,444
2,464 1 8,254
1,399
6,529
3,731
9,416
4,446
25,910
12,235
15,834
2,159
10,076
5,758
115,759
54,664
5,754 j 70,741
785 9,647
3,662 i 45,017
2,092
25,724
I
i '
71,488
83,402
15,319
38,824
45,295
8,320
I
! 1 ! 11,480
1 | 1 i 2
i
3
4
5
6
13,776
11,480
17,219
2,296
1,148
39,106 j 27,469 69,865
45,624 , 32,047 81,509
8,380 : 5,886 | 14,971
I i
122,142 1 34,182 340,320
142,499 ! 39,879 i 397,040
26,173 l 7,325 ! 72,926
i
19,396
23,275
19,396
29,093
3,879
1,940
17,534 I 40,520
21,040 i 48,624
17,534 40,520
26,300
3,507
1,753
60,781
8,104
4,052
(continued)
c-n
-------
TABLE C-4 (CONT'D). COHORT POPULATIONS BY STUDY AREA
Cohort descn'otion
-D flPOUD
-------
TABLE C-5. FRACTION OF CHICAGO POPULATION IN EACH N/M TYPE FOR WEEKDAYS
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
.. AM
PM
AM
PM
AM
n PM
I AM
fl PM
WAM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
N « 1
2
3
CITY IS CHICAGO
1
.7046
.2877
0.0000
0.0000
0.0000
.0049
0.0000
.0010
.0170
.3511
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0200
.1559
0.0000
.0162
0.0000
0.0000
0.0000
0.0000
.2550
. 1319
0.0000
0. 0000
0.0000
.0014
0.0000
.0008
.0015
.0329
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0019
.0148
0.0000
.0015
0.0000
0.0000
0.0000
0.0000
R CITY
R C ITY
R CITY
2
.7172
.2393
0.0000
.0074
0.0000
.0121
0.0000
.0348
.0170
.3295
0.0000
.0195
0.0000
.0021
0.0000
0.0000
.0049
.1286
0.0000
.0162
0.0000
0.0000
0.0000
.0273
.2590
. 1162
0.0000
.0026
0.0000
.0032
0.0000
.0121
.0015
.0308
0.0000
.0018
0.0000
.0002
0.0000
0.0000
.0005
.0122
0.0000
.0015
0.0000
0.0000
0.0000
.0026
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
3
.7274
.2060
0.0000
.0528
0.0000
.0121
0.0000
.0226
0.0000
.3387
.0052
0.0000
0.0000
.0086
0.0000
.0038
.0049
.1594
0.0000
0.0000
0.0000
0.0000
0.0000
.0127
.2616
.0996
0.0000
.0221
0.0000
.0035
0.0000
.0088
0.0000
.0317
.0005
0.0000
0.0000
.0008
0.0000
.0004
.0005
.0151
0.0000
0.0000
0.0000
0.0000
0.0000
.0012
N = 5 =
6 =
7 =
4
.7320
.2456
0.0000
.0158
0.0000
.0020
0.0000
.0302
0.0000
.3477
0.0000
0.0000
0.0000
.0013
0.0000
.0021
.0049
.1340
0.0000
.0162
0.0000
0.0000
0.0000
.0219
.2627
. 1095
0.0000
.0091
0.0000
.0016
0.0000
.0138
0.0000
.0325
0.0000
0.0000
0.0000
.0001
0.0000
.0002
.0005
.0128
0.0000
.0015
0. 0000
0.0000
0.0000
.0020
SUBURBAN
SUBURBAN
SUBURBAN
5
.7320
.2409
0.0000
.0455
0.0000
.0010
0. 0000
.0543
0.0000
.3607
0.0000
0.0000
0.0000
.0021
0. 0000
0.0000
.0049
.0242
0.0000
.0563
0.0000
0.0000
0.0000
.0219
.2627
.0892
0.0000
.0219
0.0000
.0008
0.0000
.0377
0.0000
.0337
0. 0000
0.0000
0. 0000
.0002
0.0000
0.0000
.0005
.0023
0.0000
.0053
0.0000
0.0000
0. 0000
.0020
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
6
.7320
.5572
0.0000
.0506
0.0000
.0157
0.0000
.0241
0.0000
.0170
0.0000
.0715
0.0000
0.0000
0.0000
0.0000
.0049
.0152
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2627
.2095
0.0000
.0144
0.0000
.0042
0.0000
.0111
0.0000
.0015
0.0000
.0066
0.0000
0.0000
0.0000
0.0000
.0005
.0014
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
M *
DAY IS MKO
7
.6846
.6813
.0334
.0075
0.0000
.0199
0.0000
0.0000
0.0000
.0170
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0049
.0152
.0162
0.0000
0.0000
0.0000
0.0000
0.0000
.2485
.2479
.0104
.0023
0.0000
.0061
0. 0000
0.0000
0.0000
.0015
0. 0000
0.0000
0.0000
0.0000
0.0000
0. 0000
.0005
.0014
.0015
0.0000
0. 0000
0.0000
0.0000
0.0000
1 « INDOORS
3 » VEHICLE
4 - ROADSIDE
POPULATION IS
8
.5089
.6659
.1058
0.0000
.0031
.0427
0.0000
0.0000
0.0000
.0170
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
. 1249
.0152
.0162
0.0000
0.0000
0.0000
0.0000
0.0000
.1942
.2417
.0311
0.0000
.0023
.0145
0.0000
0. 0000
0.0000
.0015
0. 0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
.0119
.0014
.0015
0.0000
0.0000
0.0000
0.0000
0.0000
M =
9
.2774
.7087
.0282
0.0000
0.0000
0.0000
0.0000
0.0000
.3347
.0170
0.0000
0.0000
.0021
0.0000
0.0000
0.0000
. 1523
.0152
.0162
0.0000
0.0000
0.0000
.0036
0.0000
.1237
.2562
.0140
0.0000
0.0000
0.0000
0.0000
0.0000
.0313
.0015
0.0000
0.0000
.0002
0.0000
0.0000
0.0000
.0144
.0014
.0015
0. 0000
0. 0000
0. 0000
.0004
0.0000
2364970.
10
.2409
.7087
.0091
0.0000
.0058
0.0000
.0378
0.0000
.3425
.0170
.0052
0.0000
.0034
0.0000
0.0000
0.0000
. 1340
.0152
.0162
0.0000
0.0000
0.0000
.0219
0.0000
.1066
.2562
.0046
0.0000
.0017
0.0000
.0211
0.0000
.0321
.0015
.0005
0.0000
.0003
0.0000
0.0000
0.0000
.0128
.0014
.0015
0.0000
0.0000
0.0000
.0020
0.0000
11
.2126
.7087
.0248
0.0000
0.0000
0.0000
.0562
0.0000
.3295
.0170
.0129
0.0000
.0065
0.0000
.0021
0.0000
.1249
.0152
0.0000
0.0000
.0381
0.0000
.0091
0.0000
.0946
.2562
.0083
0.0000
0.0000
0.0000
.0311
0.0000
.0308
.0015
.0012
0.0000
.0006
0.0000
.0002
0.0000
.0119
.0014
0.0000
0.0000
.0035
0.0000
.0009
0.0000
12 N/M
.2338 1.1.
.7046 1.1.
.0043 .3.
0. 0000 . 3.
.0017 .4.
0.0000 .4.
. 0538 . 5.
0.0000 .5.
.3439 2.1.
.0170 2.1.
0.0000 2.3.
0.0000 2.3.
.0034 2.4.
0.0000 2.4,
.0038 2.5.
0.0000 2.5.
.1502 3.1.
. 0200 3. 1 .
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
.0219 3.5.
0.0000 3.5.
.0959 5.1.
.2550 5.1.
.0033 5.3.
0.0000 5.3.
.0004 5.4.
0.0000 5.4.
.0345 5.5.
0.0000 5.5.
.0322 6.1.
.0015 6. 1.
0.0000 6.3.
0.0000 6.3.
.0003 6.4.
0.0000 6.4.
.0004 6.3.
0.0000 6.5.
.0143 7.1.
.0019 7.1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
.0020 7.5.
0.0000 7.5.
5 = OUTDOORS
-------
TABLE C-6. FRACTION OF CHICAGO POPULATION IN EACH N/M TYPE FOR SATURDAYS
n
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PH
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
.7361
.6825
0.0000
.0504
0.0000
0.0000
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2475
0.0000
.0155
0.0000
0.0000
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
CITY RES
2
.7361
.5841
0.0000
.0949
0.0000
.0214
0.0000
.0326
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.1983
0.0000
.0381
0.0000
.0120
0.0000
.0146
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
IDENTIAL
CITY COMMERCIAL
CITY INDUSTRIAL
3
.7361
.3099
0.0000
.1678
0.0000
.1027
0.0000
.1525
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.0967
0.0000
.0503
0.0000
.0429
0.0000
.0731
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
N = 5 «
6 =
7 '
CITY IS CHICAGO
4
.7361
.4493
0.0000
.0152
0.0000
.0471
0.0000
.2213
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
. 1603
0.0000
.0058
0.0000
.0129
0.0000
.0839
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
SUBURBAN
SUBURBAN
SUBURBAN
5
.7361
.6686
0.0000
.0255
0.0000
.0210
0.0000
.0210
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2388
0.0000
.0107
0.0000
.0062
0.0000
.0083
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
6
.7361
.6975
0.0000
.0074
0.0000
.0312
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2531
0.0000
.0026
0.0000
.0082
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
M = 1
3
4
D*Y IS SAT POPULATION IS
7
.7361
.7244
0.0000
.001 1
0.0000
.0074
0.0000
.0031
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2601
0.0000
.0003
0.0000
.0026
0.0000
.0010
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
= INDOORS
- VEHICLE
= ROADSIDE
8
.7361
.5967
0.0000
.0941
0.0000
.0453
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2055
0.0000
.0379
0.0000
.0205
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
M *
9
.7329
.6605
0.0000
.0697
0.0000
.0058
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2629
.2363
0.0000
.0260
0.0000
.0017
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
5 = OUTDOORS
2364970.
10
.4638
.7335
. 1466
.0026
.0049
0.0000
.1177
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.1711
.2620
.0417
.0020
.0014
0.0000
.0487
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
It
.3630
.7296
.0352
.0065
. 1455
0.0000
. 1S93
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.1385
.2604
.0236
.0035
.0443
0.0000
.0565
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.6537 .1.
.7361 .1.
.0166 .3.
0.0000 .3.
.0017 4
W W If ~
0.0000 .4.
.0609 .5.
0.0000 .5.
. 0038 2. 1 .
0.0000 2. 1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.5.
0.0000 2.5.
0.0000 3. 1.
0.0000 3. 1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.2307 5.1.
.2639 5. 1.
.0050 5.3.
0.0000 5.3.
.0004 5.4.
0.0000 5.4.
.0268 5.5.
0.0000 5.5.
.0004 6. 1.
0.0000 6.1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7. 1.
0.0000 7. 1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
-------
TABLE C-7. FRACTION OF CHICAGO POPULATION IN EACH N/M TYPE FOR SUNDAYS
O
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
-AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
CITY IS CHICAGO
1
.7361
.7000
0.0000
.0361
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2528
0.0000
.0111
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
PER CITY
'ER CITY
rER CITY
2
.7318
.6200
0.0000
.0391
0.0000
.0239
.0043
.0531
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2607
.2246
0.0000
.0113
0.0000
.0073
.0033
.0207
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
3
.7361
.4113
0.0000
.0080
0.0000
.0443
0.0000
.2694
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.1505
0.0000
.0040
0.0000
.0154
0.0000
.0930
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
N = 5
6
7
4
.7314
.3421
.0043
.0030
0.0000
.0410
.0005
.3469
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2605
.1257
.0033
.0010
0.0000
.0125
.0001
.1237
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0004
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
= SUBURBAN
= SUBURBAN
= SUBURBAN
5
.7361
.5396
0.0000
.0289
0.0000
.0283
0.0000
.1361
0.0000
.0038
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
.2639
.1796
0.0000
.0146
0.0000
.0073
0.0000
.0615
0.0000
.0004
0.0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
RES 1 DENT 1
6
.7361
.6908
0.0000
0.0000
0.0000
.0407
0.0000
.0046
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2639
.2523
0.0000
0.0000
0.0000
.0104
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
AL M =
COMMERCIAL
INDUSTRIAL
DAY IS SUN POPULATION IS
7 8
.7361 .7228
. 6869 . 7206
0.0000 .0133
.0075 .0155
0.0000 0.0000
.0418 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0. 0000 0. 0000
0.0000 0.0000
0.0000 0.0000
.2639 .2538
.2474 .2595
0.0000 .0101
.0023 .0044
0.0000 0.0000
.0142 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0. 0000 0. 0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
1 INDOORS M
3 - VEHICLE
4 - ROADSIDE
9
.6628
.7076
.0687
.0285
0.0000
0.0000
.0046
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
,2332
.2568
.0296
.0071
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2364970.
10
.6434
.7361
.0833
0.0000
0.0000
0.0000
.0094
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2204
.2639
.0410
0.0000
0.0000
0.0000
.0025
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
11
.5177
.7361
.2064
0.0000
.0084
0.0000
.0036
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.1994
.2639
.0594
0.0000
.0024
0.0000
.0027
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.6078 1.1.
.7361 .1.
. 0989 . 3.
0.0000 .3.
.0053 .4.
0.0000 .4.
.0241 .5.
0.0000 .5.
0.0000 2.1.
0.0000 2.1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.3.
0.0000 2.5.
0.0000 3.1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.2189 5.1.
.2639 5.1.
.0350 5.3.
0.0000 5.3.
.0033 5.4.
0.0000 5.4.
.0067 5.5.
0.0000 5.5.
0.0000 6.1.
0.0000 6.1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.3.
0.0000 6.5.
0.0000 7.1.
0.0000 7.1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
f 5 = OUTDOORS
-------
TABLE C-8. FRACTION OF PHILADELPHIA POPULATION IN EACH N/M TYPE FOR WEEKDAYS
o
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
CITY IS PHILADELPH
1
.2689
. 1872
0.0000
0.0000
0.0000
.0066
0.0000
.0014
.0052
.0835
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0068
.0534
0.0000
.0070
0.0000
0.0000
0.0000
0.0000
.7051
.4628
0.0000
0.0000
0.0000
.0144
0.0000
.0035
.0065
. 1 131
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0076
.0598
0.0000
.0072
0.0000
0.0000
0.0000
0.0000
ER CITY
ER CITY
IK CITY
2
.2722
. 1648
0.0000
.0013
0.0000
.0094
0.0000
.0197
.0052
.0770
0.0000
.0059
0.0000
.0006
0.0000
0.0000
.0018
.0432
0.0000
.0070
0.0000
0.0000
0.0000
.0102
.7124
.4034
0.0000
.0040
0.0000
.021 1
0.0000
.0522
.0065
.1041
0.0000
.0080
0.0000
.0009
0.0000
0.0000
.0019
.0487
0.0000
.0072
0.0000
0.0000
0.0000
.0111
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
3
.2742
.1358
0.0000
.0263
0.0000
.0165
0.0000
.0167
0.0000
.0798
.0016
0.0000
0.0000
.0026
0.0000
.0011
.0018
.0571
0.0000
0.0000
0.0000
0.0000
0.0000
.0033
.7185
.3372
0.0000
.0670
0.0000
.0361
0.0000
.0404
0.0000
. 1080
.0020
0.0000
0.0000
.0034
0.0000
.0016
.0019
.0629
0.0000
0.0000
0.0000
0.0000
0.0000
.0041
N = 5
6
7
4
.2751
. 1540
0.0000
.0123
0.0000
.0029
0.0000
.0261
0. 0000
.0825
0.0000
0.0000
0.0000
.0004
0.0000
.0006
.0018
.0446
0.0000
.0070
0.0000
0.0000
0.0000
.0088
.7212
.3866
0.0000
.0315
0.0000
.0069
0.0000
.0557
0.0000
.1117
0.0000
0.0000
0.0000
.0005
0.0000
.0009
.0019
.0504
0.0000
.0072
0.0000
0.0000
0.0000
.0093
= SUBURBAN
= SUBURBAN
= SUBURBAN
5
.2751
. 1255
0.0000
.0177
0.0000
.0014
0.0000
.0651
0.0000
.0865
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
.0018
.0073
0.0000
.0205
0.0000
0.0000
0.0000
.0088
.7212
.2908
0. 0000
.0552
0.0000
.0035
0.0000
.1593
0.0000
. 1167
0.0000
0.0000
0.0000
.0009
0.0000
0.0000
.0019
.0086
0.0000
.0224
0.0000
0.0000
0.0000
.0093
RESIDENT!
6
.2751
.2324
0.0000
.0081
0.0000
.0061
0.0000
.0142
0. 0000
.0052
0.0000
.0194
0.0000
0.0000
0.0000
0.0000
.0018
.0050
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7212
.5975
0.0000
.0249
0.0000
.0099
0.0000
.0393
0.0000
.0065
0.0000
.0259
0. 0000
0.0000
0.0000
0.0000
.0019
.0057
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
AL M =
COMMERCIAL
INDUSTRIAL
DAY IS KKD
7
.2604
.2653
.0093
.0013
0.0000
.0035
0.0000
0.0000
0.0000
.0052
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0018
.0050
.0070
0.0000
0.0000
0.0000
0.0000
0.0000
.6929
.6877
.0194
.0054
0.0000
.0144
0.0000
0.0000
0.0000
.0065
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0019
.0057
.0072
0. 0000
0.0000
0.0000
0.0000
0.0000
1 = INDOORS
POPULATION IS
8
.2186
.2555
.0191
0.0000
.0043
.0146
0.0000
0.0000
0.0000
.0052
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0423
.0050
.0070
0.0000
0.0000
0.0000
0.0000
0.0000
.5798
.6704
.0639
0.0000
.0104
.0372
0.0000
0.0000
0.0000
.0065
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0475
.0057
.0072
0.0000
0.0000
0.0000
0. 0000
0.0000
M =
9
.1794
.2701
.0176
0.0000
0.0000
0.0000
0.0000
0.0000
.0786
.0052
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
.0525
.0050
.0070
0.0000
0.0000
0.0000
.0009
0.0000
.4434
.7075
.0460
0.0000
0.0000
0.0000
0.0000
0.0000
. 1061
.0065
0.0000
0.0000
.0009
0.0000
0.0000
0.0000
.0586
.0057
.0072
0.0000
0.0000
0.0000
.0012
0.0000
2935233.
10
.1565
.2701
.0118
0.0000
.0079
0.0000
.0191
0.0000
.0809
.0052
.0016
0.0000
.0010
0.0000
0.0000
0.0000
.0446
.0050
.0070
0.0000
0.0000
0. 0000
.0088
0.0000
.3804
.7075
.0258
0.0000
.0173
0.0000
.0572
0.0000
. 1096
.0065
.0020
0.0000
.0014
0.0000
0.0000
0.0000
.0504
.0057
.0072
0. 0000
0.0000
0. 0000
.0093
0.0000
1 1
.1234
.2701
.0182
0.0000
0.0000
0.0000
.0536
0.0000
.0770
.0052
.0039
0.0000
.0020
0.0000
.0006
0.0000
.0423
.0050
0.0000
0.0000
.0158
0.0000
.0024
0.0000
.3115
.7075
.0381
0.0000
0.0000
0.0000
. 1312
0.0000
. 1041
.0065
.0055
0.0000
.0025
0.0000
.0009
0.0000
.0475
.0057
0.0000
0.0000
.0165
0.0000
.0029
0.0000
12 N/M
.1448 . 1.
.2689 .1.
.0051 .3.
0. 0000 . 3.
.0006 .4.
0.0000 .4.
.0448 .5.
0.0000 .5.
.0814 2. 1.
.0052 2.1.
0.0000 2.3.
0.0000 2.3.
.0010 2.4.
0.0000 2.4.
.0011 2.5.
0.0000 2.5.
.0516 3.1.
.0068 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
.0088 3.5.
0.0000 3.5.
.3526 5.1.
.7051 5.1.
.0114 5.3.
0.0000 5.3.
.0011 5.4.
0.0000 5.4.
.1157 5.5.
0.0000 5.5.
.1101 6.1.
.0065 6.1.
0.0000 6.3.
0.0000 6.3.
.0014 6.4.
0.0000 6.4.
.0016 6.5.
0.0000 6.5.
.0577 7.1.
.0076 7. 1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
.0093 7.5.
0.0000 7.5.
5 OUTDOORS
3 = VEHICLE
4 ortAnc i nc
KUnuo 1 Ut
-------
TABLE C-9. FRACTION OF PHILADELPHIA POPULATION IN EACH N/M TYPE FOR SATURDAYS
CITY IS PHILADELPH
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
.2763
.2700
0.0000
.0059
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.6944
0.0000
.0270
0.0000
0.0000
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2
.2763
.2065
0.0000
.0300
0.0000
.0213
0.0000
.0182
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.00*11
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.5400
0.0000
.0881
0.0000
.0499
0.0000
.0434
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
3
.2763
.0776
0.0000
.0424
0.0000
.0439
0.0000
. 1120
0.0000
0.0000
0.0000
.001 1
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.2225
0.0000
. 1089
0.0000
. 1 168
0.0000
.2730
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
4
.2763
.1456
0.0000
.0082
0.0000
.0310
0.0000
.0912
0.0000
.0011
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.3996
0.0000
.0180
0.0000
.0605
0.0000
.2432
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
5
.2763
.2468
0.0000
.0146
0.0000
.0023
0.0000
.0126
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0. 0000
.7237
.6562
0.0000
.0326
0.0000
.0100
0.0000
.0248
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
6
.2763
.2650
0.0000
.0013
0.0000
.0101
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.7003
0.0000
.0040
0. 0000
.0194
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
DAY IS SAT POPULATION IS
7
.2763
.2744
0.0000
.0002
0.0000
.0013
0.0000
.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.7167
0.0000
.0007
0.0000
.0040
0.0000
.0023
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
8
.2763
.2146
0.0000
.0428
0.0000
.0189
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
.7237
.5554
0.0000
. 1 171
0.0000
.0511
0. 0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
9
.2759
.2460
0.0000
.0224
0.0000
.0079
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7214
.6410
0.0000
.0653
0.0000
.0173
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2935233.
10
.1792
.2728
.0458
.0036
.0066
0.0000
.0444
0.0000
0.0000
0. 0000
.0011
0. 0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
.4747
.7150
. 1099
.0087
.0144
0.0000
.1223
0.0000
0.0000
0.0000
.0016
0.0000
0. 0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0. 0000
0.0000
11
.1518
.2735
.0472
.0029
.0396
0.0000
.0374
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.3806
.7147
.1125
.0089
.1111
0.0000
.1172
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.2357 1.1.
.2763 .1.
.0041 .3.
0.0000 .3.
.0006 .4.
0.0000 .4.
.0355 .5.
0. 0000 . 5.
.0011 2.1.
0.0000 2.1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.5.
0.0000 2.5.
0.0000 3. 1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.6128 5.1.
.7237 5.1.
.0142 5.3.
0.0000 5.3.
.0011 5.4.
0.0000 5.4.
.0932 5.5.
0.0000 5.5.
.0016 6.1.
0.0000 6.1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7.1.
0.0000 7.1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4..
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
N = 1 - CENTER CITY RESIDENTIAL
2 « CENTER CITY COMMERCIAL
3 « CENTER CITY INDUSTRIAL
5 = SUBURBAN RESIDENTIAL
6 = SUBURBAN COMMERCIAL
7 = SUBURBAN INDUSTRIAL
INDOORS
VEHICLE
ROADSIDE
OUTDOORS
-------
TABLE C-10. FRACTION OF PHILAEDLPHIA POPULATION IN EACH N/M TYPE FOR SUNDAYS
n
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
.2763
.2731
0.0000
.0032
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.7075
0.0000
.0161
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
TER CITY
TF.R CITY
PER CITY
2
.2712
.2359
0.0000
.0077
0.0000
.0028
.0051
.0300
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7123
.6240
0.0000
.0274
0.0000
.0127
.0114
.0595
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
3
.2763
. 1516
0.0000
.0031
0.0000
.0080
0.0000
. 1132
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.4051
0.0000
.0097
0.0000
.0238
0.0000
.2828
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
N =
till
4
.2705
.1244
.0051
.0005
0.0000
.0099
.0007
. 1411
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7108
.3270
.0114
.0016
0.0000
.0311
.0014
.3617
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
5 = SUBURBAN
6 » SUBURBAN
7 * SUBURBAN
r is PHIL
5
j
.2763
. 1748
0.0000
.0166
0.0000
.0084
0.0000
.0761
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.4628
0.0000
.0408
0.0000
.0166
0. 0000
.2011
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
ADELPH
.2763
.2598
0.0000
0.0000
0.0000
.0157
0.0000
.0009
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.6826
0.0000
0.0000
0.0000
.0383
0.0000
.0027
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL M
COMMERCIAL
INDUSTRIAL
DAY IS SUN POPULATION IS
7
.2763
.2618
0.0000
.0013
0.0000
.0133
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7237
.6840
0.0000
.0054
0.0000
.0343
0.0000
0.0000
. 0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
8
. 2578
.2723
.0186
.0041
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.6786
.7148
.0450
.0089
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
= 1 = INDOORS M
9
.2507
.2660
.0248
.0104
0.0000
0.0000
.0009
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.6529
.7060
.0680
.0177
0.0000
0.0000
.0027
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2935233.
to
. 1868
.2763
.0821
0.0000
0.0000
0.0000
.0075
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
.5209
.7237
. 1856
0.0000
0.0000
0.0000
.0172
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
11
.2235
.2763
.0389
0.0000
.0089
0.0000
.0050
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.5848
.7237
.1078
0.0000
.0190
0.0000
.0121
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
«
12 N/M
.2350 .1.
.2763 .1.
.0306 .3.
0.0000 .3.
.0052 .4.
0.0000 .4.
.0056 .5.
0.0000 .5.
0.0000 2.1.
0.0000 2.1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.5.
0.0000 2.5.
0.0000 3.1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
. 6026 5. 1 .
.7237 5.1.
.0912 5.3.
0.0000 5.3.
.0134 5.4.
0.0000 5.4.
.0165 5.5.
0.0000 5.5.
0.0000 6. 1.
0.0000 6. 1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7.1.
0.0000 7. 1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
= 5 = OUTDOORS
3 = VEHICLE
4 = ROADSIDE
-------
TABLE C-1L. FRACTION OF ST. LOUIS POPULATION IN EACH N/M TYPE FOR WEEKDAYS
o
CITY IS ST.
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
.4903
.3337
0.0000
0.0000
0.0000
.0144
0.0000
.0024
.0065
. 1078
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0069
.0640
0.0000
.0089
0.0000
0.0000
0.0000
0.0000
.4876
.3256
0.0000
0.0000
0.0000
.0089
0.0000
.0027
.0040
.0784
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0047
.0487
0.0000
.0047
0.0000
0.0000
0.0000
0.0000
2
.4954
.2881
0.0000
.0020
0.0000
.0200
0.0000
.0403
.0065
.0996
0.0000
.0074
0.0000
.0008
0.0000
0.0000
.0011
.0515
0.0000
.0089
0.0000
0.0000
0.0000
.0125
.4918
.2859
0.0000
.0023
0.0000
.0129
0.0000
.0361
.0040
.0723
0.0000
.0055
0.0000
.0006
0.0000
0.0000
.0012
.0417
0.0000
.0047
0.0000
0.0000
0.0000
.0070
3
.4991
.2345
0.0000
.0470
0.0000
.0359
0.0000
.0330
0.0000
.1031
.0020
0.0000
0.0000
.0033
0.0000
.0014
.0011
.0693
0.0000
0. 0000
0.0000
0. 0000
0.0000
.0036
.4954
.2443
0.0000
.0435
0.0000
.0222
0.0000
.0271
0.0000
.0751
.0012
0.0000
0.0000
.0022
0.0000
.001 1
.0012
.0510
0.0000
0.0000
0.0000
0.0000
0.0000
.0024
4
.5007
.2777
0.0000
.0209
0.0000
.0048
0.0000
.0470
0.0000
. 1065
0.0000
0.0000
0.0000
.0005
0.0000
.0008
.001 1
.0530
0.0000
.0089
0.0000
0.0000
0. 0000
.0110
.4970
.2708
0.0000
.0231
0.0000
.0053
0.0000
.0378
0.0000
.0775
0.0000
0.0000
0.0000
.0003
0.0000
.0006
.0012
.0427
0.0000
.0047
0.0000
0.0000
0.0000
.0059
5
.5007
.2401
0.0000
.0305
0.0000
.0024
0.0000
. 1 106
0.0000
.1115
0.0000
0.0000
0.0000
.0008
0.0000
0. 0000
.0011
.0084
0.0000
.0250
0.0000
0.0000
0.0000
.0110
.4970
.1916
0.0000
.0370
0.0000
.0027
0.0000
.1222
0.0000
.0806
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
.0012
.0051
0.0000
.0140
0.0000
0.0000
0.0000
.0059
LOUIS
6
.5007
.4227
0.0000
.0160
0.0000
.0093
0.0000
.0254
0.0000
.0065
0.0000
.0246
0.0000
0.0000
0.0000
0.0000
.0011
.0059
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4970
.4108
0.0000
.0181
0. 0000
.0059
0.0000
.0299
0.0000
.0040
0.0000
.0174
0.0000
0.0000
0.0000
0.0000
.0012
.0034
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
DAY IS WKD POPULATION IS
7
.4784
.4809
.0141
.0028
0.0000
.0075
0.0000
0.0000
0.0000
.0065
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
.0059
.0089
0.0000
0.0000
0. 0000
0. 0000
0.0000
.4804
.4744
.0113
.0040
0.0000
.0106
0.0000
0.0000
0.0000
.0040
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
.0012
.0034
.0047
0.0000
0.0000
0.0000
0.0000
0.0000
8
.3939
.4640
.0361
0.0000
.0073
.0271
0.0000
0.0000
0.0000
.0065
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0505
.0059
.0089
0.0000
0.0000
0.0000
0. 0000
0.0000
.4044
.4670
.0453
0.0000
.0080
.0220
0. 0000
0.0000
0. 0000
.0040
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0410
.0034
.0047
0.0000
0.0000
0.0000
0.0000
0.0000
9
.3241
.4912
.0298
0.0000
0.0000
0.0000
0.0000
0.0000
. 1016
.0065
0.0000
0.0000
.0008
0.0000
0.0000
0.0000
.0630
.0059
.0089
0.0000
0.0000
0.0000
.0010
0.0000
.3095
.4890
.0339
0.0000
0.0000
0.0000
0.0000
0.0000
.0736
.0040
0. 0000
0.0000
.0006
0.0000
0.0000
0.0000
.0480
.0034
.0047
0.0000
0.0000
0.0000
.0007
0.0000
1219561.
10
.2783
.4912
.0226
0.0000
.0173
0.0000
.0323
0.0000
.1045
.0065
.0020
0.0000
.0013
0.0000
0.0000
0.0000
.0530
.0059
.0089
0.0000
0. 0000
0.0000
.0110
0.0000
.2700
.4890
.0171
0.0000
.0107
0.0000
.0394
0.0000
.0763
.0040
.0012
0.0000
.0009
0.0000
0. 0000
0.0000
.0427
.0034
.0047
0.0000
0.0000
0.0000
.0059
0.0000
11
.2196
.4912
.0354
0.0000
0.0000
0.0000
.0954
0.0000
.0996
.0065
.0049
0.0000
.0025
0.0000
.0008
0.0000
.0505
.0059
0.0000
0.0000
.0198
0.0000
.0026
0.0000
.2154
.4890
.0246
0.0000
0.0000
0.0000
.0971
0.0000
.0723
.0040
.0039
0.0000
.0015
0.0000
.0006
0.0000
.0410
.0034
0.0000
0.0000
.0106
0.0000
.0017
0.0000
12 N/M
.2663 .1.
.4903 .1.
. 0082 . 3.
0.0000 .3.
.0011 .4.
0.0000 .4.
.0748 .5.
0.0000 .5.
.1050 2.1.
.0065 2.1.
0.0000 2.3.
0.0000 2.3.
.0013 2.4.
0.0000 2.4.
.0014 2.5.
0.0000 2.5.
.0619 3. 1.
.0069 3. 1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
.0110 3.5.
0.0000 3.5.
.2442 5.1.
.4876 5.1.
.0082 5.3.
0.0000 5.3.
.0006 5.4.
0.0000 5.4.
.0841 5.5.
0.0000 5.5.
.0763 6.1.
.0040 6. 1.
0.0000 6.3.
0.0000 6.3.
.0009 6.4.
0.0000 6.4.
.0011 6.5.
0.0000 6.5.
.0474 7.1.
.0047 7.1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
. JO. 0000 7.4.
.0059 7.5.
0.0000 7.5.
1 * CENTFR
2 - CENTER
3 « CENTER
C ITY
C ITY
CITY
RESIDENTIAL
COMMERCIAL
INDIISTRI AL
= SUBURBAN
= SUBURBAN
= SUBURBAN
RESIDENTIAL
COMMERCIAL
INDIISTRI AL
INOOORS
VEHICLE
ROADSIDE
M = 5 = OUTDOORS
-------
TABLE C-12. FRACTION OF ST. LOUIS POPULATION IN EACH N/M TYPE FOR SATURDAYS
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
O AM
K> PM
O AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
1
.5016
.4885
0.0000
.0122
0. 0000
0.0000
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4760
0.0000
.0208
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
ER CITY
ER CITY
ER CITY
.5016
.3828
0.0000
.0526
0.0000
.0368
0.0000
.0285
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.3680
0.0000
.0610
0.0000
.0378
0.0000
.0300
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL
COMMERCIAL '
INDUSTRIAL
3
.5016
.1380
0.0000
.0877
0.0000
.0775
0.0000
.1975
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.1485
0.0000
.0759
0.0000
.0772
0.0000
. 1951
0.0000
0.0000
0.0000
.001 1
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
N = 5
6
7
CITY IS ST. LOUIS
4
.5016
.2554
0.0000
.0140
0.0000
.0709
0.0000
.1604
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.2780
0.0000
.0121
0.0000
.0368
0.0000
. 1697
0.0000
.001 1
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
= SUBURBAN
= SUBURBAN
- SUBURBAN
5
.5016
.4531
0.0000
.0239
0.0000
.0046
0.0000
.0199
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4521
0.0000
.0222
0.0000
.0074
0.0000
.0168
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
6
.5016
.4835
0.0000
.0020
0.0000
.0161
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4847
0.0000
.0025
0.0000
.0114
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL M =
COMMERCIAL
INDUSTRIAL
DAY IS SAT
7
.5016
.4983
0.0000
.0004
0.0000
.0020
0.0000
.0009
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4940
0.0000
.0004
0.0000
.0023
0.0000
.0017
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1 = INDOORS
3 = VEHICLE
POPULATION IS
8
.5016
.3941
0.0000
.0763
0.0000
.0312
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.3821
0.0000
.0826
0.0000
.0337
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
M =
9
.5007
.4446
0.0000
.0397
0.0000
.0173
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4967
.4458
0.0000
.0420
0.0000
.0107
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1219561.
10
.3285
.4955
.0810
.0061
.0144
0.0000
.0768
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.3362
.4917
.0660
.0067
.0089
0.0000
.0856
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0.0000
0.0000
0. 0000
11
.2750
.4968
.0833
.0048
.0720
0.0000
.0704
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.2567
.4924
.0844
.0060
.0724
0.0000
.0832
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.4282 .1.
.5016 .1.
. 0088 . 3.
0. 0000 . 3.
.0011 .4.
0.0000 .4.
. 0626 . 5.
0. 0000 . 5.
.0014 2.1.
0.0000 2.1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.5.
0.0000 2.5.
0.0000 3.1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.4164 5.1.
.4984 5.1.
.0101 5.3.
0.0000 5.3.
.0006 5.4.
0.0000 5.4.
.0695 5.5.
0.0000 5.5.
.0011 6.1.
0.0000 6. 1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7. 1.
0.0000 7. 1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
5 = OUTDOORS
4 - ROADSIDE
-------
TABLE 013. FRACTION OF ST. LOUIS POPULATION IN EACH N/M TYPE FOR SUNDAYS
HOUR
AH
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
N = 1 - CENTER
2 « CENTER
3 - CENTER
CITY IS ST. LOUIS
1
.5016
.4951
0.0000
.0065
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4856
0.0000
.0128
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0,0000
0,0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
2
.4934
.4222
0.0000
.0155
0.0000
.0057
.0082
.0581
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4902
.4288
0.0000
.0197
0.0000
.0098
.0082
.0401
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
CITY RESIDENTIAL
CITY COMMERCIAL
^ITY INDUSTRIAL
3
.5016
.2720
0.0000
.0052
0.0000
.0119
0.0000
.2116
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.2874 *
0.0000
.0065
0.0000
.0136
0.0000
. 1892
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
N = 5 =
6 =
7 =
4
.4920
.2227
.0082
.0008
0.0000
.0210
.0014
.2562
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4893
.2145
.0082
.0009
0.0000
.0220
.0009
.2593
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
SUBURBAN
SUBURBAN
SUBURBAN
5
. 5016
.3268
0.0000
.0271
0.0000
.0157
0.0000
.1311
0.0000
.0014
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
.4984
.3117
0.0000
.0300
0.0000
.0100
0.0000
.1450
0.0000
.0011
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
6
.5016
.4689
0.0000
0.0000
0. 0000
.0310
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4739
0.0000
0.0000
0.0000
.0229
0. 0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
M = 1
3
4
DAY IS SUN POPULATION IS
7
.5016
.4745
0.0000
.0028
0.0000
.0243
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4984
.4742
0.0000
.0040
0.0000
.0202
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
= INHOORS
= VEHICLE
= ROADS mF
a
.4701
.4943
.0315
.0073
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4637
.4931
.0348
.0053
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
M = 5
9
.4596
.4838
.0403
.0177
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4528
.4878
.0440
.0106
0.0000
0.0000
.0016
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
= OUTDOORS
1219561.
10
.3370
.5016
.I486
0.0000
0.0000
0.0000
.0160
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.3550
.4984
.1329
0.0000
0.0000
0.0000
.0105
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0. 0000
11
.3959
.5016
.0795
0.0000
.0177
0.0000
.0085
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4036
.4984
.0738
0.0000
.0116
0.0000
.0094
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.4273 1.1.
.5016 1.1.
.0539 .3.
0. 0000 . 3.
.0090 .4.
0.0000 .4.
.0114 .5.
0. 0000 . 5.
0.0000 2. 1.
0.0000 2. 1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0. 0000 2. 5.
0.0000 2.5.
0.0000 3. 1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.4177 5.1.
.4984 5.1.
.0589 5.3.
0.0000 5.3.
.0103 5.4.
0.0000 5.4.
.0116 5.5.
0.0000 5.5.
0.0000 6.1.
0.0000 6.1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7.1.
0.0000 7.1.
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
-------
TABLE C-14. FRACTION OF LOS ANGELES POPULATION IN EACH N/M TYPE FOR WEEKDAYS
HOUR
AH
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
.1832
.1157
0.0000
0.0000
0.0000
.0052
0.0000
.0007
.0025
.0465
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0029
.0230
0.0000
.0029
0.0000
0.0000
0.0000
0.0000
.7926
.5337
0.0000
0.0000
0.0000
.0146
0.0000
.0042
.0083
.1613
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0105
.0822
0.0000
.0098
0.0000
0.0000
0.0000
0.0000
CITY IS LOS ANGELE
5 6
IS HKD
8
POPULATION IS
9
.1851
.0962
0.0000
.0009
0.0000
.0074
0.0000
.0172
.0025
.0431
0.0000
.0031
0.0000
.0003
0.0000
0.0000
.0008
.0187
0.0000
.0029
0.0000
0.0000
0.0000
.0043
.8007
.4661
0.0000
.0043
0.0000
.0213
0.0000
.0608
.0083
.1492
0.0000
.0109
0.0000
.0012
0.0000
0.0000
.0027
.0670
0.0000
.0098
0.0000
0.0000
0.0000
.0151
.1869
.0775
0.0000
.0175
0.0000
.0131
0.0000
.0136
0.0000
.0446
.0008
0.0000
0.0000
.0013
0.0000
.0006
.0008
.0244
0.0000
0.0000
0.0000
0.0000
0.0000
.0015
.8064
.3986
0.0000
.0713
0.0000
.0365
0.0000
.0461
0.0000
. 1547
.0025
0.0000
0.0000
.0044
0.0000
.0022
.0027
.0862
0.0000
0.0000
0.0000
0.0000
0.0000
.0058
.1877
.0976
0.0000
.0066
0.0000
.0014
0.0000
.0161
0.0000
.0460
0.0000
0.0000
0.0000
.0002
0.0000
.0003
.0008
.0193
0.0000
.0029
0.0000
0.0000
0.0000
.0037
.8089
.4425
0.0000
.0367
0.0000
.0084
0.0000
.0649
0.0000
.1595
0.0000
0.0000
0.0000
.0006
0.0000
.0012
.0027
.0695
0.0000
.0098
0.0000
0.0000
0.0000
.0127
. 1877
.0821
0.0000
.0127
0.0000
.0007
0.0000
.0337
0.0000
.0480
0.0000
0.0000
0.0000
.0003
0.0000
0.0000
.0008
.0032
0.0000
.0087
0.0000
0.0000
0.0000
.0037
.8089
.3276
0.0000
.0590
0.0000
.0042
0.0000
.1937
0.0000
. 1658
0.0000
0.0000
0.0000
.0012
0.0000
0.0000
.0027
.0119
0.0000
.0307
0.0000
0.0000
0.0000
.0127
. 1877
.1544
0.0000
.0068
0.0000
.0031
0.0000
.0099
0.0000
.0025
0.0000
.0103
0.0000
0.0000
0.0000
0.0000
.0008
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8089
.6704
0.0000
.0288
0.0000
.0112
0.0000
.0491
0.0000
.0083
0.0000
.0353
0.0000
0.0000
0.0000
0.0000
.0027
.0078
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.1797
.1764
.0052
.0020
0.0000
.0054
0.0000
0.0000
0.0000
.0025
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0008
.0022
.0029
0.0000
0.0000
0.0000
0.0000
0.0000
.7775
.7687
.0215
.0073
0.0000
.0193
0.0000
0.0000
0.0000
.0083
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0027
.0078
.0098
0.0000
0.0000
0.0000
0.0000
0.0000
. 1500
. 1733
.0172
0.0000
.0021
.0106
0.0000
0.0000
0.0000
.0025
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0183
.0022
.0029
0.0000
0.0000
0.0000
0.0000
0.0000
.6518
.7562
.0700
0.0000
.0126
.0391
0.0000
0.0000
0.0000
.0083
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0654
.0078
.0098
0.0000
0.0000
0.0000
0.0000
0.0000
.1119
. 1839
.0118
0.0000
0.0000
0.0000
0.0000
0.0000
.0439
.0025
0.0000
0.0000
.0003
0.0000
0.0000
0.0000
.0226
.0022
.0029
0.0000
0.0000
0.0000
.0004
0.0000
.5057
.7953
.0554
0.0000
0.0000
0.0000
0.0000
0.0000
.1517
.0083
0.0000
0.0000
.0012
0.0000
0.0000
0.0000
.0805
.0078
.0098
0.0000
0.0000
0.0000
.0016
0.0000
7719108.
10
.0948
. 1839
.0082
0.0000
.0063
0.0000
.0123
0.0000
.0452
.0025
.0008
0. 0000
.0005
0.0000
0.0000
0.0000
.0193
.0022
.0029
0.0000
0.0000
0.0000
.0037
0.0000
.4445
.7953
.0286
0.0000
.0175
0.0000
.0619
0.0000
.1569
.0083
.0025
0.0000
.0019
0.0000
0.0000
0.0000
.0695
.0078
.0098
0.0000
0.0000
0.0000
.0127
0.0000
11
.0797
. 1839
.0118
0.0000
0.0000
0.0000
.0301
0.0000
.0431
.0025
.0021
0.0000
.0010
0.0000
.0003
0.0000
.0183
.0022
0.0000
0.0000
.0066
0.0000
.0011
0.0000
.3548
.7953
.0426
0.0000
0.0000
0.0000
.1552
0.0000
.1492
.0083
.0077
0.0000
.0032
0.0000
.0012
0.0000
.0654
.0078
0.0000
0.0000
.0225
0.0000
.0041
0.0000
12
.0931
.1832
.0030
0.0000
.0003
0.0000
.0253
0.0000
.0454
.0025
0.0000
0.0000
.0005
0.0000
.0006
0.0000
.0222
.0029
0.0000
0.0000
0.0000
0.0000
.0037
0.0000
.4031
.7926
.0140
0.0000
.0012
0.0000
. 1342
0.0000
.1572
.0083
0.0000
0.0000
.0019
0.0000
.0022
0.0000
.0793
.0105
0.0000
0.0000
0.0000
0.0000
.0127
0.0000
N/M
.1.
.1.
.3.
.3.
.4.
.4.
.5.
.5.
2.1.
2.1.
2.3.
2.3.
2.4.
2.4.
2.5.
2.5.
3.1.
3.1.
3.3.
3.3.
3.4.
3.4.
3.5.
3.5.
5.1.
5.1.
5.3.
5.3.
5.4.
5.4.
5.5.
5.5.
6.1.
6.1.
6.3.
6.3.
6.4.
6.4.
6.5.
6.5.
7.1.
7.1.
7.3.
7.3.
7.4.
7.4.
7.5.
7.5.
N = 1 - CENTER CITY RESIDENTIAL
2 = CENTER CITY COMMERCIAL
3 = CENTFR CITY INDUSTRIAL
N = 5 = SUBURBAN RESIDENTIAL
6 = SUBURBAN COMMERCIAL
7 = SUBURBAN INDUSTRIAL
1 = INDOORS
3 * VEHICLE
4 = ROADSIDE
M = 5 = OUTDOORS
-------
TABLE C-15. FRACTION OF LOS ANGELES POPULATION IN EACH N/M TYPE FOR SATURDAYS
CITY IS LOS ANGELE
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
O
I AM
^ PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
n PI
PM
AM
PM
AM
PM
1
.1884
.1792
0.0000
.0086
0.0000
0.0000
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.7762
0.0000
.0331
0.0000
0.0000
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2
. 1884
. 1453
0.0000
.0213
0.0000
.0103
0.0000
.0109
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.6011
0.0000
.0975
0.0000
.0603
0.0000
.0505
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
3
. 1884
.0614
0.0000
.0318
0.0000
.0277
0.0000
.0669
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
.8116
.2429
0.0000
. 1230
0.0000
. 1278
0.0000
.3157
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
4
. 1884
.1028
0.0000
.0048
0.0000
.0210
0.0000
.0592
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.4490
0.0000
.021 1
0.0000
.0636
0.0000
.2756
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
5
. 1884
. 1698
0.0000
.0089
0.0000
.0028
0. 0000
.0068
0. 0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0. 0000
.8116
.7335
0.0000
.0379
0.0000
.0109
0.0000
.0293
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
6
. 1884
.1815
0. 0000
.0009
0.0000
.0059
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
.8116
.7867
0.0000
.0043
0.0000
.0206
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
DAY IS SAT POPULATION IS
7
. 1884
. 1868
0.0000
.0002
0.0000
.0009
0.0000
.0005
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.8044
0.0000
.0006
0.0000
.0043
0.0000
.0023
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
8
. 1884
. 1480
0.0000
.0283
0. 0000
.0120
0. 0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0. 0000
0.0000
0. 0000
0. 0000
0. 0000
0. 0000
0.0000
0.0000
0.0000
0.0000
.8116
.6238
0.0000
. 1332
0.0000
.0547
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
9
. 1878
. 1663
0.0000
.0157
0.0000
.0063
0.0000
0.0000
.0006
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8093
.7265
0.0000
.0676
0.0000
.0175
0.0000
0.0000
.0022
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
7719108.
10
.1205
. 1866
.0321
.0017
.0052
0. 0000
.0299
0. 0000
0. 0000
0. 0000
.0006
0. 0000
0. 0000
0. 0000
0.0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0.0000
0. 0000
0. 0000
0.0000
.5436
.8012
.1129
.0105
.0146
0.0000
.1382
0. 0000
0. 0000
0. 0000
.0022
0. 0000
0.0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
0. 0000
11
. 1008
. 1864
.0249
.0019
.0302
0.0000
.0319
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.4272
.8022
.1332
.0094
. 1174
0.0000
.1315
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
. 1597 1. 1.
.1884 1.1.
.0053 .3.
0. 0000 . 3.
.0003 .4.
0.0000 .4.
.0225 .5.
0. 0000 . 5.
. 0006 2. 1 .
0.0000 2. 1.
0.0000 2.3.
0.0000 2.3.
0.0000 2.4.
0.0000 2.4.
0.0000 2.5.
0. 0000 2. 5.
0.0000 3. 1.
0.0000 3.1.
0.0000 3.3.
0.0000 3.3.
0.0000 3.4.
0.0000 3.4.
0.0000 3.5.
0.0000 3.5.
.6780 5.1.
.8116 5.1.
.0181 5.3.
0.0000 5.3.
.0012 5.4.
0.0000 5.4.
.1120 5.5.
0.0000 5.5.
.0022 6.1.
0.0000 6.1.
0.0000 6.3.
0.0000 6.3.
0.0000 6.4.
0.0000 6.4.
0.0000 6.5.
0.0000 6.5.
0.0000 7.1.
0. 0000 7. 1 .
0.0000 7.3.
0.0000 7.3.
0.0000 7.4.
0.0000 7.4.
0.0000 7.5.
0.0000 7.5.
N = 1 = CENTER CITY RESIDENTIAL
2 - CENTER CITY COMMERCIAL
3 = CENTER CITY INDUSTRIAL
5 = SUBURBAN RESIDENTIAL M = 1 = INDOORS
6 = SUBURBAN COMMERCIAL 3 = VEHICLE
7 = SUBURBAN INDUSTRIAL 4 = ROADSIDE
M = 5 = OUTDOORS
-------
TABLE C-16. FRACTION OF LOP ANGELES POPULATION IN EACH N/M TYPE FOR SUNDAYS
HOUR
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
O PM
K> AM
* PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
1
. 1884
. 1639
0.0000
.0044
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
. 7932
0.0000
.0184
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
ER CITY
ER CITY
ER CITY
.1654
.1588
0.0000
.0083
0.0000
.0041
.0030
.0172
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7977
.6925
0.0000
.0325
0.0000
.0156
.0140
.0710
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL
COMMERC 1 AL
INDUSTRIAL
3
.1884
.1060
0.0000
.0021
0.0000
.0052
0.0000
.0745
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.4645
0.0000
.0103
0.0000
.0259
0.0000
.3086
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
N = 5
6
7
CIT
4
.1848
.0848
.0030
.0003
0.0000
.0098
.0005
.0928
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7962
.3527
.0140
.0017
0.0000
.0334
.0013
.4215
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
= SUBURBAN
- SUBURBAN
* SUBURBAN
Y IS LOS ANGELE
5
.1884
.1259
0.0000
.0109
0.0000
.0042
0.0000
.0469
0.0000
.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.5078
0.0000
.0495
0.0000
.0193
0.0000
.2327
0.0000
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
6
. 1684
.1751
0.0000
0.0000
0.0000
.0124
0.0000
.0008
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.7714
0.0000
0.0000
0.0000
.0377
0.0000
.0025
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
RESIDENTIAL M »
COMMERCIAL
INDUSTRIAL
DAY IS SUN POPULATION IS
7
. 1864
. 1768
0.0000
.0020
0.0000
.0096
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.8116
.7682
0.0000
.0073
0.0000
.0362
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
O.ffOOO
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1 « 1 NDOORS
3 - VEHICLE
8
.1793
. 1662
.0090
.0022
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7572
.8015
.0545
.0102
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
M
9
. 1718
. 1632
.0158
.0052
0.0000
0.0000
.0008
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.7361
.7913
.0730
.0203
0.0000
0.0000
.0025
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
7719108.
10
.1360
. 1884
.0463
0.0000
0.0000
0.0000
.0060
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.5780
.8116
.2165
0.0000
0.0000
0.0000
.0171
0.0000
0.0000
0.0000
0.0000
0. 0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
11
. 1482
. 1864
.0309
0.0000
.0068
0.0000
.0024
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
.6587
.81 16
.1189
0.0000
.0194
0.0000
.0147
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
12 N/M
.1576 1.1
.1664 1.1
.0224 1.3
0.0000 1.3
.0027 1.4
0.0000 1.4
.0057 1.5
0.0000 1.5
0.0000 2.1
0.0000 2.1
0.0000 2.3
0.0000 2.3
0.0000 2.4
0.0000 2.4
0.0000 2.5
0.0000 2.5
0. 0000 3. 1
0.0000 3.1
0.0000 3.3
0.0000 3.3
0.0000 3.4
0. 0000 . 4
0. 0000 . 5
0. 0000 . 5
. 6608 . 1
.8116 .1
. 0935 . 3
0. 0000 . 3
.0160 .4
0. 0000 . 4
.0214 .5
0. 0000 . 5
0. 0000 . 1
0. 0000 6. 1
0.0000 6.3
0.0000 6.3
0.0000 6.4
0.0000 6.4
0. 0000 6. 5
0. 0000 6. 5
0. 0000 7. 1
0. 0000 7. 1
0.0000 7.3
0. 0000 7. 3
0.0000 7.4
0.0000 7.4
o. oooo 7. ;
0.0000 7.5
5 = OUTDOORS
* ROADSIDE
-------
APPENDIX D
SUPPORTING INFORMATION FOR NATIONAL POPULATION
ESTIMATE BY N/M TYPE
-------
TABLE D-l. NATIONAL POPULATION IN EACH N/M TYPE BY HOUR FOR WEEKDAYS
HOUR
NATIONAL POPULATION FOR WEEKDAY
5 6 7
10
It
12
N/M
O
I
CO
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
CENTER
CENTER
OFNTFP
49165015.
29487280.
0.
0.
0.
965465.
0.
192069.
979469.
17576006.
0.
0.
0.
0.
0.
0.
1207567.
9581951.
0.
1 166643.
0.
0.
0.
0.
86150233.
55930598.
0.
0.
0.
1646203.
0.
423024.
777768.
13948359.
0.
0.
0.
0.
0.
0.
920 1 54 .
7379715.
0.
866775.
0.
0.
0.
0.
49R42171.
25524062.
0.
322519.
0.
1483189.
0.
3314698.
979469.
16340410.
0.
1118753.
0.
116844.
0.
0.
302032.
7804537.
0.
1 166643.
0.
0.
0.
1777414.
87040796.
48813775.
0.
506800.
0.
2435232.
0.
6244020.
777768.
1285839R.
0.
969645.
0.
108557.
0.
0.
231590.
6039253.
0.
866775.
0.
0.
0.
1339557.
CITY RESIDENTIAL
CITY COMMERCIAL
CITY INDUSTRIAL
50323418. 50539298. 50539298.
21177257. 24519075. 21088961.
0. 0. 0.
4432423. 1846123. 3200159.
0. 0. 0.
2409960. 393660. 192069.
0. 0. 0.
2632114. 3895133. 9103583.
0. 0. 0.
16870243. 17383921. 18136335.
300966. 0. 0.
0. 0. 0.
0. 0. 0.
493051. 75241. 116844.
0. 0. 0.
212713. 116844. 0.
302032. 302032. 302032.
10091603. 8082861. 1370629.
0. 0. 0.
0. 1 166643. 3603286.
0. 0. 0.
0. 0. 0.
0. 0. 0.
656991. 1499090. 1499090.
87776520. 88100906. 88100906.
41006425. 46661325. 35052502.
0. 0. 0.
8064773. 3824733. 6743874.
0. 0. 0.
4124405. 835274. 423024.
0. 0. 0.
4800733. 6673752. 19296863.
0. 0. 0.
13334777. 13778526. 14377363.
239300. 0. 0.
0. 0. 0.
0. 0. 0.
409054. 59039. 108557.
0. 0. 0.
195005. 108557. 0.
231590. 231590. 231590.
7746646. 6249720. 10420T4.
0. 0. 0.
0. 866775. 2704925.
0. 0. 0.
0. 0. 0.
0. 0. 0.
499844. 1119222. 1119222.
N = 5 = SUBURBAN RESIDENTIAL
6 « SUP-URBAN COMMERCIAL
7 - SUBURBAN INDUSTRIAL
50539298.
41284854.
0.
2165134.
0.
1076578.
0.
2298948.
0.
979469.
0.
3848053.
0.
0.
0.
0.
302032.
909930.
0.
0.
0.
0.
0.
0.
88100906.
72797977.
0.
3180664.
0.
121 1980.
0.
4809671.
0.
777768.
0.
3151521.
0.
0.
0.
0.
231590.
687312.
0.
0.
0.
0.
0.
0.
M = 1 -
3 =
4 >
47727538.
48121710.
1856355.
344736.
0.
921255.
0.
0.
0.
979469.
0.
0.
0.
0.
0.
0.
302032.
909930.
1 166643.
0.
0.
0.
0.
0.
84596185.
83932771.
2416199.
681860.
0.
1815203.
0.
0.
0.
777768.
0.
0.
0.
0.
0.
0.
231590.
687312.
866775.
0.
0.
0.
0.
0.
INDOORS
VEHICLE
ROADSIDE
38489899.
46604374.
4793257.
0.
589217.
2780744.
0.
0.
0.
979469.
0.
0.
0.
0.
0.
0.
7619925.
909930.
1 166643.
0.
0.
0.
0.
0.
70521658.
81940850.
7981672.
0.
1256062.
4496270.
0.
0.
0.
777768.
0.
0.
0.
0.
0.
0.
5895017.
687312.
866775.
0.
0.
0.
0.
0.
M = 5
28359526.
49388607.
2786718.
0.
0.
0.
0.
0.
16641375.
979469.
0.
0.
116844.
0.
0.
0.
9399576.
909930.
1166643.
0.
0.
0.
182375.
0.
53444637.
86427597.
5619683.
0.
0.
0.
0.
0.
13098951.
777768.
0.
0.
108557.
0.
0.
0.
7232337.
687312.
866775.
0.
0.
0.
146473.
0.
» OUTDOORS
24634137.
49388607.
1684410.
0.
1155657.
0.
3180132.
0.
17082956.
979469.
300966.
0.
192085.
0.
0.
0.
8082861.
909930.
1166643.
0.
0.
0.
1499090.
0.
46014177.
86427597.
3032807.
0.
1977865.
0.
6972740.
0.
13531034.
777768.
239300.
0.
168502.
0.
0.
0.
6249720.
687312.
866775.
0.
0.
0.
1119222.
0.
19977536.
49388607.
2837901 .
0.
0.
0.
7828125.
0.
16340410.
979469.
740309.
0.
376207.
0.
116844.
0.
7619925.
909930.
0.
0.
2664482.
0.
474616.
0.
37688767.
86427597.
4507532.
0.
0.
0.
15810465.
0.
12858398.
777768.
669147.
0.
299246.
0.
108557.
0.
5895017.
687312.
0.
0.
1985997.
0.
354703.
0.
23179919.
49165015.
711660.
0.
1 11651.
0.
6635101.
0.
17169956.
979469.
0.
0.
192085.
0.
212713.
0.
9249504.
1207567.
0.
0.
0.
0.
1499090.
0.
42429594.
86130233.
1388841.
0.
132075.
0.
14057587.
0.
13583600.
777768.
0.
0.
168502.
0.
195005.
0.
7126017.
920154.
0.
0.
0.
0.
1119222.
0.
.1.
.1.
.3.
.3.
.«.
.4.
.5.
.5.
2.1.
2.1.
2.3.
2.3.
2.4
2.4
2.5
2.5
3.1
3.1
3.3.
3.3.
3.4.
3.4.
3.5.
3.5.
5.1.
5.1.
5.3.
5.3.
5.4.
5.4.
5.5.
5.5.
6.1.
6.1.
6.3.
6.3.
6.4.
6.4.
6.5.
6.5.
7.1.
7.1.
7.3.
7.3.
7.4.
7.4.
7.5.
7.5.
-------
TABLE D-2. NATIONAL POPULATION IN EACH N/M TYPE BY HOUR FOR SATURDAYS
HOUR
NATIONAL POPULATION FOR SATURDAY
567
N =
10
11
12
N/M
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
D AM
UJ PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
50762891.
48716610.
0.
1919905.
0.
0.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88407792.
84649787.
0.
3477994.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
50762891.
38838396.
0.
5831057.
0.
3060970.
0.
2917852.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
o.
0.
0.
0.
0.
0.
88407792.
65908530.
0.
10888320.
0.
6039525.
0.
5292312.
0.
0.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
:L',:L.. CITY RESIDENTIAL N
CENTER
CENTER
CITY COMMERCIAL
CITY INDUSTRIAL
50762891.
16605610.
0.
9177135.
0.
7698840.
0.
17154931 .
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88407792.
27409709.
0.
13559402.
0.
14205879.
0.
32933401.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
50762801 .
28043915.
0.
1339600.
0.
5083375.
0.
16179148.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88407792.
49184636.
0.
2186378.-
0.
7086390.
0.
296561 15.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
50762891.
45668173.
0.
2340517.
0.
771736.
0.
1980306.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88407792.
80131642.
0.
3964874.
0.
1282304.
0.
3022939.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
« 5 * SUBURBAN RESIDENTIAL
6 = SUBURBAN COMMERCIAL
7 = SUBURBAN INDUSTRIAL
50762891. 50762891.
48534289. 50264431.
0. 0.
322519. 50471.
0. 0.
1914699. 322519.
0. 0.
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0. 0.
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88407792. 88407792.
85540909. 87535920.
0. 0.
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0. 0.
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0. 0.
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0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
M » 1 * INDOORS
3 = VEHICLE
4 * ROADSIDE
50762891.
40057486.
0.
7392125.
0.
3312374.
0.
0.
0.
0.
0.
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0.
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0.
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0.
0.
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0.
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0.
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M = 5 =
50636515.
45272677.
0.
4331413.
0.
1 155657.
0.
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0.
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88124292.
78486053.
0.
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0.
1977865.
0.
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r>tiTn<~>nps
32643251.
50278777.
8945485.
492730.
965465.
0.
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0.
0.
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212713.
0.
0.
0.
0.
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0.
0.
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0.
0.
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58163038.
87358762.
13246805.
1052174.
1646203.
0.
15058724.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
26931016.
50272652.
6550341 .
498855.
8200614.
0.
8966303.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
46423597.
87312225.
13503738.
1086045.
13540123.
0.
14656834.
0.
0.
0.
0.
0.
0.
0.
195005.
0.
0.
0.
0.
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0.
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0.
0.
43874666. '
50762891. .'
919937. '
0. .'.
111651. .*.
0. .4.
5730261. .5.
0. .5.
212713. 2.1.
0. 2.1.
0. 2.3.
0. 2.3.
0. 2.4.
0. 2.4.
0. 2.5.
0. 2.5.
0. 3.1.
0. 3.1.
0. 3.3.
0. 3.3.
0. 3.4.
0. 3.4.
0. 3.5.
0. 3.5.
74868080. 5.1.
88407792. 5.1.
1754421. 5.3.
0. 5.3.
132075. 5.4.
0. 5.4.
11358942. 5.5.
0. 5.5.
195005. 6.1.
0. 6.1. .
0. 6.3.
0. 6.3.
0. 6.4.
0. 6.4.
0. 6.5.
0. 6.5.
0. 7.1.
0. 7.1.
0. 7.3.
0. 7.3.
0. 7.4.
0. 7.4.
0. 7.5.
0. 7.5.
-------
HOUR
TABLE D-3. NATIONAL POPULATION IN EACH N/M TYPE BY HOUR FOR SUNDAYS
NATIONAL POPULATION FOR SUNflAY
2 3 4 5 6 7 ,
10
11
12
N/M
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
50762BQI.
49528480.
0.
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0.
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0.
0.
0.
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0.
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0.
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88407792.
86289950.
0.
2108320.
0.
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0.
0.
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0.
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0.
0.
0.
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0.
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o.
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50051231.
43056572.
0.
18771 19.
0.
909768.
711660.
4927702.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
87022095.
76084708.
0.
3402922.
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1636635.
1388841 .
7274005.
0.
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0.
0.
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0.
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0.
0.
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0.
0.
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0.
50762891.
28001995.
0.
558274.
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1948858.
0.
20129626.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
88407792.
49747229.
0.
1 187820.
0.
3015682.
0.
34173561.
0.
0.
0.
195005.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
49952665.
23054690.
711660.
127454.
0.
221 1556.
99897.
25244146.
0.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
0.
0.
0.
0.
0.
0.
86849931.
39830130.
1388841.
201394.
0.
3819036.
160404.
44273732.
0.
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0.
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0.
0.
195005.
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0.
0.
0.
50762891.
33947559.
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2665200.
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12357123.
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212713.
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0.
0.
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0.
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0.
88407792.
56589001.
0.
5035675.
0.
2044025.
0.
24448306.
0.
195005.
0.
0.
0.
0.
0.
0.
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50762891 .
4764B495.
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88407792.
83563766.
0.
0.
0.
4507881.
0.
324386.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
50762891 .
47B37204.
0.
344736.
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0.
0.
0.
0.
0.
0.
0.
0.
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83562230.
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4164608.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
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0.
0.
0.
0.
48226963.
49923945.
2544544.
848468.
0.
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0.
0.
0.
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0.
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0.
0.
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0.
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0.
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0.
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0.
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82960096.
87504037.
5440332.
1 104662.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
46009287.
46R74605.
4545994.
1896556.
0.
0.
215880.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
79724453.
86246927.
8349431.
2160866.
0.
0.
324386.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
37631430.
50762891.
11960969.
0.
0.
0.
1179108.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
o.
64212147.
88407792.
22215534.
0.
0.
0.
1980111.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
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39162077.
50762891.
9596530.
0.
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71166586.
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13594792.
0.
2183882.
0.
1463439.
0.
0.
0.
0.
0.
0.
0.
0.
0.
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0.
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42750934. .1.
50762891. . 1.
6003910. .3.
0. .3.
750850. .4.
0. .4.
1266719. ' .5.
0. .5.
0. 2.1.
0. 2.1.
0. 2.3.
0. 2.3.
0. 2.4.
0. 2.4.
0. 2.5.
0. 2.5.
0. 3.1.
0. 3.1.
0. 3.3.
0. 3.3.
0. 3.4.
0. 3.4.
0. 3.5.
0. 3.5.
73674486. 5.1.
88407792. 5.1.
11051635. 5.3.
0. 5.3.
1623694. 5.4.
0. 5.4.
2060135. 5.5.
0. 5.5.
0. 6.1.
0. 6.1.
0. 6.3.
0. 6.3.
0. 6.4.
0. 6.4.
0. 6.5.
0. 6.5.
0. 7.1.
0. 7.1.
0. 7.3.
0. 7.3.
0. 7.4.
0. 7.4.
0. 7.5.
0. 7.5.
1 = CENTER CITY RESIDENTIAL
2 = CENTER CITY COMMERCIAL
3 * CENTER CITY INDUSTRIAL
> - SUBURBAN RESIDENTIAL
6 = SUBURBAN COMMERCIAL
7 = SUBURBAN INDUSTRIAL
M = 1
3 = VEHICLE
4 * ROAOSIDF
OUTDOORS
-------
TABLE D-4. MOBILE SOURCE MICROENVIRONMENT PERSON HOURS
SUBTRACTED FROM NEM N/M TYPES
Weekdays
Urban Core
Hour
Ending
1 AM
2 AM
3 AM
4 AM
SAM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
Noon
1 PM
2PM
3 PM
4PM
5PM
6 PM
7 PM
8 PM
9 PM
10 PM
11 PM
Midnight
Indoor
69,906
85,888
217,023
65,332
168,409
354,636
386,177
319,933
16,966
9,484
Residential
Vehicle
562,793
1,499,125
48,517
73,418
80,635
66,126
68,733
63,405
71,869
74,061
69,375
344,736
Commercial
Roadside Indoor Vehicle
201,750
120,513
70,339
154,452 1,806,402
1,467,303 300,966
959,846 740,309
2,244,574
3,553,017
2,082,723 1,118,753
2,272,966
2,451,824
2,795,977
2,854,616
474,112 600,156
323,311 425,886
477,700
357,069
509,813
389,195
Industrial
Roadside
116,844
192,085
376,207
192,085
116,844
493,051
75,241
116,844
Indoor
2,240
1,434
896
571
605
1,568
5,656
5,611
6,115
7,101
6,709
6,451
5,163
4,670
4,200
3,349
Vehicle
4,446
6,843
6,675
5,914
. 5,533
5,578
7,056
7,706
-------
TABLE D-4 (Cont'd). MOBILE SOURCE MICROENVIRONMENT PERSON HOURS
SUBTRACTED FROM NEM N/M TYPES
Weekdays
o
Hour
Ending
1 AM
2AM
3 AM
4AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
Noon
1 PM
2PM
3 PM
4PM
5 PM
6PM
7 PM
8 PM
9 PM
10PM
11 PM
Midnight
Suburban
Residential
Indoor
190
202
523
16,333
7,831
6,830
3,864
4,242
2,371
Vehicle
5,223
28,466
12,129
18,355
20,159
16,531
17,183
15,851
17,967
18,515
17,344
9,758
Commercial
Indoor Vehicle
747
478
299
38,638
55,064 1,971
60,477 1,885
51,464
50,843
53,409
49,592
56,254
58,114
54,398 52,031
31,511
25,708
22,210
13,148
14,125
8,229
Industrial
Indoor
1,493
956
597
381
403
1,045
3,771
3,741
4,077
4,734
4,472
4,301
3,442
3,114
2,800
2,232
Vehicle
2,964
4,562
4,450
3,942
3,688
3,718
4,704
5,137
-------
TABLE D-5. MOBILE SOURCE MICROENVIRONMENT PERSON HOURS
SUBTRACTED FROM NEM N/M TYPES
Saturday
Urban Core
Suburban
Hour
Ending
1 AM
2 AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
Noon
1 PM
2 PM
3 PM
4 PM
5 PM
6 PM
7 PM
8 PM
9 PM
10 PM
11 PM
Midnight
Residential
Indoor Vehicle Roadside
746,322
637,226
520,025
331,460
240,782
267,476
401,228
559,908
435,014
638,104
1,493,215
1,390,229
1,633,334
1,962,460
1,947,016
1,754,845
1,753,362
1,345,165
990,451
841,867
780,664
719,068
688,903
920,732
Commercial Residential
Indoor Vehicle Roadside Indoor Vehicle
14,833
10,158
6,878
4,045
3,865
3,282
17,456
26,925
212,713 354,398
212,713 531,778
1,137,953
212,713 2,248,315
212,713 1,315,245
1,576,756
212,713 1,595,529
212,713 1,490,191
1,496,094
1,225,140
989,404
888,913
810,074
681,566
557,714
19,058
-------
TABLE D-6 MOBILE SOURCE MICROENVIRONMENT PERSON HOURS
SUBTRACTED FROM NEM N/M TYPES
Sunday
Urban Core
Suburban
Hour
Ending
1 AM
Z AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
Noon
1 PM
ZPM
3 PM
4PM
5PM
6PM
7 PM
8PM
9 PM
10PM
11 PM
Midnight
Residential
Indoor Vehicle Roadside
633,746
536,369
438,139
Z91,Z54
200,254
170,659
Z07,65Z
268,590
238,484
317, Z51
431,152
566,664
659,056
7Z7,061
657,073
728,538
695,716
553,877
437,769
384,129
364,860
336,066
3Z1,91Z
Z67,531
Commercial Residential
Indoor Vehicle Roadside Indoor Vehicle
9,603
6,577
4,453
Z.619
2,503
2,125
5,675
36,151
224,712
297,682
374,883
467,699
528,878
212,713 574,340
5Z7,411
576,734
534,896
400, 29 Z
303,965
Z69,285
254,923
234,374
224,087
187,168
-------
APPENDIX E
LISTING OF MOBEX COMPUTER PROGRAM
-------
PROGRAM MOBEX 73/74 OPT=1 PMDMP pTN 4.8+552 84/07/09. 16.17.07 PAGE
1 PROGRAM MOBEXtINPUT,OUTPUT )
DIMENSION XNDY(3),ITITLE(4) .XMULTU) ,SUM( 25.32,3) ,FRAC(25).
1 IEMISS(2),XNEF(8,3).XMSEMF(4,3),CONEXC(25),6RSUM(25),
2 X(25),Y(25),TEMP<26)
5 COMMON /TOTPOP/ P<32,24,3) /CONC/CONVAL(25,32,3)
DATA XMULT/ O.B5.2.10,1,20,0.95/,XNDY/ 248.,62.,55./
1000 FORMATC4A10, A10.A6)
2000 FORMATdHI, /////, 2IX,4A10,/,24X,"POLLUTANT: », A IO.A6,///,
1 17X,"EXPOSURE TO*,15X,»PERSON*,/,
10 2 17X,"CONCENTRATIONS*,12X,«HOURS«,/,
3 17X,"EXCEEDING*, 17X,"EXPOSURE*,/,
4 17X,*(MICROGRAM/CU.M)*11X,"(MILLIONS) *,/,
5 17X,* *11X,* *)
2010 FORMATdH ,22X,F10.3,11X.F1 1.3)
15 C
C***"* READ INPUT VALUES
C
READ 1000, (TITLE, IEMISS
READ *, YURPOP, YRURPO, CONINV
20 DO 50 JDAY-1,3
50 READ «, (XNEF(N,JDAY),N=1,6),(XMSEMF(M,JDAY),M=1,4),XNEF(8,JDAY)
IF(CONINV.LT.I.O) GO TO 60
READ *,(CONEXC(I),I=1,25)
60 CONTINUE
25 C
c***** CALCULATE POPULATION MULTIPLIER FOR YEAR OTHER THAN 1980
C
IFCYURPOP.EO.O.) YURPOP= 139 170683.
POPMLT = YURPOP/139170683.
30 IF(YRURPO.EO.O.) YRURPO = 87334142
C
C***** BEGIN CALCULATION LOOP
C
INM= 0
35 DO 500 N= 1,8
IF(N.EO.S) POPMLT = YRURPO/87334142
DO 500 M* 1,4
INM= INM+1
DO 500 JDAY= 1,3
40 DO 200 KCON=1,25
SUMCKCON,INM,JDAY)=0
200 CONTINUE
00 300 KHR = 1,24
CALL CHOPOL(N,M,JDAY,KHR,FPAC)
45 DO 250 KCON=1,26
TEMP(KCON)=0
250 CONTINUE
DO 300 KCON =1,25
TEMP(26-KCON)= TEMP(27-KCON)
50 1 +(FRAC(26-KCON)*POPMLT*(P(INM.KHR,JDAY)/1,OE6)* XNDY(JDAY))
SUM{(26-KCON),INM,JDAY)*TEMP(26-KCON) + SUM((26-KCON),INM,JOAY)
300 CONTINUE
C
C*«"»* DETERMINE CONCENTRATION INTERVALS FOR EACH N/M ,EACH DAY
55 C
IF(N.NE.7) EMSFAOXNEF(N,JDAY)
IF(N.NE.7) XM «XMULT(M)
-------
PROGRAM MOBEX 73/74 OPT=1 PMDMP FTN 4.8+552 84/07/09. 16.17.07 PAGE
IF(N.E0.7) EMSFAC * XMSEMF(M,JOAY)
IF(N.E0.7) XM=1.0
60 LLL=N*4
IF(N.E0.7)LLL=INM
DO 500 1=1,25
CONVALd, INM,JDAY) * CONVAL(I,LLL,JDAY) * XM * EMSFAC
500 CONTINUE
65 C
C*»*«* DETERMINE SET OF COMMON INTERVALS
C IF INTERVALS NOT INPUT,THEY ARE DETERMINED IN CALCIN
C
IFCCONINV.EO.O ) CALL CALCIN(CONVAL,CONEXC)
70 C
c*»««* INTERPOLATE TO GET NEW CUMCULATIVE PERSON HOURS AT
C THE COMMON INTERVAL BOUNDRIES
C
DO 600 JDAY=1,3
75 DO 600 INM= 1,32
DO 550 J=1,25
X(J)=CONVAL(J,INM,JDAY)
Y(J)» SUM(J,INM,JDAY)
550 CONTINUE
80 DO 600 1= 1,25
IFCCONEXCU ).GT.X(25)> GO TO 575
SUM(I,INM,JDAY) = FLAGR(X,Y,CONEXC(I),2,1,25)
W IF(SUM(I,INM,JDAY).LT.O) SUMCI,INM,JDAY)= 0.
1 GO TO 600
85 575 SUMd, INM, JDAY)- 0.0
600 CONTINUE
CALL PMDARRY(25,4,1)
CALL PMDLOAD
C
90 C****« PRINT TITLE
C
PRINT 2000, ITITLE, IEMISS
C
c*»»«« SUM CUM. PERSON HOURS AT EACH INTERVAL BOUND FOR ALL
95 C N/M AND DAY TYPES
C
DO 750 KCON= 1,25
GRSUM (KCON) = SU M (KCON, 1 ,1 ) +SUM (KCON, 1,2) -t-SUM (KCON ,1,3)
DO 700 INM= 2,32
100 GRSUM
-------
SUBROUTINE CHOPOL 73/74 OPT=1 PMDMP FTN 4.8+552 84/07/09. 16.17.07 PAGE
1 SUBROUTINE CHOPOL(N,M,JDAY,KHR,FRAC)
DIMENSION FRAC(25)
COMMON/PLFRC1/ POLFR1 (25,6,3) ,POLFR2(25,6,3) ,POLFR3 < 25 6,3)
1 POLFR4(25,6,3),POLFR5(25,6,3),POLFR6(25,6.3)/
5 2 PLFRC2/ POLFRS(25,6,3),POLFRT(25,7,3),POLFRP(25 2 3)/
3 PLFRC3/ POLFR8(25,6,3)
60 TOdIO,120,130,140,150,160,500,170) N
J10 GO TO {lit,1 12,t t3) JDAY
111 CONTINUE
10 60 TO (201,201,201,201,201,202,203,203,203,203,204,204,
1 204,204,204,205,205,205,206,206,206,206,206,206) KHR
112 CONTINUE
GO TO (201,201,202,202,202,202,203,203,203,204,204,204,
1 205,205,205,205,205,205,206,206,206,206,206,206) KHR
15 113 CONTINUE
GO TO (201,201,202,202,203,203,204,204,204,205,205,205,
1 205,205,205,205,205,206,206,206,206,206,206,206) KHR
120 GO TO (121,122,123) JDAY
121 CONTINUE
20 GO TO (201,201,201,201,201,202,203,203,203,203,204,204,
1 204,204,205,205,205,205,206,206,206,206,206,206) KHR
122 CONTINUE
GO TO (201,202,202,202,202,202,203,204,204,204,204,204,
1 204,204,205,205,205,205,206,206,206,206,206,206) KHR
25 123 CONTINUE
M GO TO (201,201,202,202,202,202,202,203,203,203,203,203,
i. 1 204,204,204,204,204,205,205,205,205,205,206,206) KHR
130 GO TO (131,132,133) JOAY
131 CONTINUE
30 GOTO (201,201,201,201,201,201,202,203,203,204,204,204,
1 204,204,204,204,205,205,206,206,206,206,206,206) KHR
132 CONTINUE
GO TO (201,202,202,202,203,203,204,204,204,204,204,204,
1 204,204,205,205,205,206,206,206,206,206,206,206) KHR
35 133 CONTINUE
GO TO (201,201,201,202,202,202,202,202,203,203,203,203,
1 204,204,204,204,204,204,205,205,206,206,206,206) KHR
140 GO TO (141,142.143) JDAY
141 CONTINUE
40 GOTO (201,201,201,201,201,202,203,203,203,203,204,204,
1 204,204,204,204,205,205,206,206,206,206,206,206) KHR
142 CONTINUE
GO TO (201,201,202,202,202,202,203,203,203,203,203,203,
1 204,204,204,204,204,205,206,206,206,206,206,206) KHR
45 143 CONTINUE
GO TO (201,201,202,202,203,203,203,203,203,204,204,204,
1 204,204,204,204,204,205,206,206,206,206,206,206) KHR
150 GO TO (151,152,153) JDAY
151 CONTINUE
50
GO TO (201,202,202,202,202,203,204,204,204,204,205,205,
1 205,205,205,206,206,206,206,206,206,206,206,206) KHR
152 CONTINUE
GO TO (201,201,201,202,202,202,203,204,204,204,204,204,
55 1 204,204,205,205,205,205,206,206,206,206,206,206) KHR
153 CONTINUE
GO TO (201,201,201,201,202,202,203,204,204,204,204,204,
-------
SUBROUTINE CHOPOL
73/74 OPT»1 PMDMP
FTN 4.8*552
84/07/09. 16.17.07
PAGE
W
60
65
70
75
80
85
90
95
100
105
1 10
1 205,205,205,205,205,206,206,206,206,206,206,206) KHR
160 GO TO (161,162,163) JDAY
161 CONTINUE
GO TO (201,202,202,202,202,202,203,204,204,204,205,205,
1 205,205,205,205,206,206,206,206,206,206,206,206) KHR
162 CONTINUE
GO TO (201,201,201,201,202,202,203,203,203,203,203,204,
1 204,205,205,205,205,205,206,206,206,206,206,206) KHR
163 CONTINUE
GO TO (201,201,202,202,202,202,202,203,203,203,203,203,
1 204,204,204,204,204,205,205,206,206,206,206,206) KHR
170 GO TO (171,172,173) JOAY
171 CONTINUE
GO TO (201,201,201,201,201,201,201,201,201,201,201,201,
1 201,201,201,201,201,201,201,201,201,201,201,201) KHR
172 CONTINUE
GO TO (201,201,201,201,201,201,201,201,201,201,201,201,
1 201,201,201,201,201,201,201,201,201,201,201,201) KHR
173 CONTINUE
GO TO (201,201,201,201,201,201,201,201,201,201,201,201,
1 201,201,201,201,201,201,201,201,201,201,201,201) KHR
201 L=1
GO TO 210
202 -2
GO TO 210
203 L=3
GO TO 210
204 L=4
GO TO 210
205 L-5
GO TO 210
206 L»6
GO TO 210
210 CONTINUE
DO 250 KCON=1,25
IF(N.FO.I) FRAC(KCON)
IF(N.E0.2) FRAC(KCON)
IF(N.E0.3) FRAC(KCON)
IF(N.E0.4) FRAC(KCON)
IF(N.E0.5) FRAC(KCON)
IF(N.E0.6) FRAC(KCON)
IF(N.EO.B) FRAC(KCON)
250 CONTINUE
GO TO 900
500 CONTINUE
GO TO (510,520,530,540) M
510 GO TO (511,512,513) JDAY
511 CONTINUE
GO TO (601,601,601,601,601,601,602,603,603,604,604,604,
1 604,604,604,605,605,605,606,606,606,606,606,606) KHR
512 CONTINUE
GO TO (601,601,601,602,602,602,603,603,604,604,604,604,
1 605,605,605,605,605,605,606,606,606,606,606,606) KHR
513 CONTINUE
GO TO (601,601,602,602,603,603,603,603,603,603,604,604,
1 604,604,605,605,605,605,605,605,605,605,605,606) KHR
520 GO TO (521,522,523) JDAY
POLFR1(KCON,L,JDAY)
POLFR2(KCON,L,JDAY)
POUFR3(KCON,L,JDAY)
POLFR4(KCON,L,jnAY)
POI_FR5(KCON,L,JDAY)
POLFR6(KCON,L,JOAY)
POLFR8(KCON,L,JDAY)
-------
SUBROUTINE CHOPOL 73/74 OPT-1 PMDMP FTN 4.8+552 84/07/09. 16.17.07 PAGE
H5 521 CONTINUE
GO TO (603,602,601,601,602,604,607,606,606,606,606,606,
1 605,605,606,607,607,607,606,606,605,605,604,603) KHR
522 CONTINUE
GO TO (604,603,602,601,601,601,602,604,604,605,605,606,
120 1 606,606",606,607,607,606,606,607,606,605,605,605) KHR
523 CONTINUE
GO TO (604,603,602,601,601,601,602,604,604,605,605,606,
1 606,606,606,607,607,606,606,607,606,605,605,605) KHR
530 GO TO (531,532,533) JDAY
125 531 CONTINUE
GO TO (601,601,601,601,601,601,601,602,601,601,601,601,
1 601,601,601,601,601,602,601,601,601,601,601,601) KHR
532 CONTINUE
GO TO (601,601,601,601,601,601,601,601,601,601,601,601,
130 1 601,601,601,601,601,601,601,601,601,601,602,601) KHR
533 CONTINUE
GO TO (601,601,601,601,601,601,601,601,601,601,601,601,
1 601,601,601,601,601,601,601,601,601,601,601,601) KHR
540 CONTINUE
135 GO TO 601
601 1=1
GO TO 610
602 L-2
GO TO 610
140 603 L-3
GO TO 610
604 L=4
GO TO 610
605 L=5
145 GO TO 610
606 L=6
GO TO 610
607 L«7
GO TO 610
150 610 CONTINUE
DO 650 KCON=1,25
IF(M.f0.1)FRAC(KCON)»POLFRS(KCON,L,JDAY)
IF(M.EQ.2)FRAC(KCON)=POLFRT(KCON,L,JDAY)
I F(M .EO.3)FRAC(KCON)=POLFRP(KCON,L,JDAY)
155 IF(M,E 0.4)FRAC(KCON)=POL FRS(KCON,L,JDAY)
650 CONTINUE
900 RETURN
END
-------
SUBROUTINE CMC IN
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.t7.07
PAGE
w
I
10
15
20
25
30
35
SUBROUTINE CALCINC CONVAL, CONEXC)
DIMENSION CONVAL(25,32,3), CONEXC(25)
MAX = 0.0
DO 100 JDAY=1,3
DO 100 INM = 1,32
100 IF(CONVAL(25,INM,JDAY).GT.MAX) MAX = CONVAL(25,INM,JDAY)
IF(MAX.GT.I) GO TO 200
IF(MAX.GT.0.0001) ll=-4
IF(MAX.GT.O.OOI) Il=-3
IF(MAX.GT.O.OI) Il=-2
IF(MAX.GT.O.I) 11 1
GO TO 500
200 CONTINUE
I 1=0
IF(MAX.GT.IO) 11=1
IF(MAX.GT.IOO) 11=2
IF(MAX.GT.IOOO) 11=3
IF(MAX.GT.IOOOO) 11=4
IF(MAX.GT.100000) 11=5
500 (MAX = MAX/(10»*lI)
IF(IMAX.E0.10) GO TO 510 .
IMAX = IMAX+1
510 IMAX= I MAX » (10**I I )
SCALE = IMAX/200
CONEXC(1)=0
CONEXCC2) = SCALE
DO 600 J=3,25
XMULT" 1.0
IFU.GT.5) XMULT" 3.0
IFU.GT.14) XMULT= 6.0
IF(J.GT.16) XMULT=12.0
IFU.GT.17) XMULT=16.0
IF(J.GT.21) XMULT-20.0
CONEXC(J) = CONEXC(J-I) + (XMULT » SCALE)
600 CONTINUE
RETURN
END
-------
FUNCTION FLAGR 73/74 OPT=1 PMDMP FTN 4.8+552 84/07/09. 16.17.07 PAGE
1 FUNCTION FLAGR ( X,Y,XARG,IDEG,MIN,N )
C
C FLAGR USES THE LAGRANGE FORMULA TO EVALUATE THE INTERPOLATING
C POLYNOMIAL OF DEGREE IDEG FOR ARGUMENT XARG USING THE DATA
5 C VALUES X(MIN)...X(MAX) AND Y(MIN)...Y(MAX) WHERE
C MAX = MIN + IDEG. X(l) IS ASSUMED TO BE IN ASCENDING
C ORDER, AND SUBSCRIPT CHECKING IS PERFORMED. TERM IS
C A VARIABLE WHICH CONTAINS SUCCESSIVELY EACH TERM OF THE
C LAGRANGE FORMULA. THE FINAL VALUE OF YEST IS THE INTERPOLATED
10 C VALUE. SEE CARNAHAN ET AL.APPL ,NUM.METH.,W I LEY, 1969,P.29.
C
DIMENSION X( 1), Yd)
C
C LOCATE AN X-VALUE NEAR XARG
15 DO 20 1=1,N
IF (X(l) ,LT. XARG) 60 TO 20
MIN » I - IDEG/2
MAX « MIN + IDEG
GO TO 30
20 20 CONTINUE
C
C CHECK SUBSCRIPT BOUNDS
30 CONTINUE
IF (MIN ,GT. 0) GO TO 40
25 MIN = 1
w MAX = MIN + IDEG
' GO TO 50
00 40 CONTINUE
IF (MAX .LF. N) GO TO 50
30 MAX « N
MIN » MAX - IDEG
50 CONTINUE
C
C COMPUTE VALUE OF FACTOR
35 FACTOR = 1.0
DO 60 J=MIN,MAX
IF (XARG ,NE. X(J» GO TO 60
FLAGR = Y(J)
RETURN
40 60 FACTOR = FACTOR*(XARG - X(J))
C
C ..... EVALUATE INTERPOLATING POLYNOMIAL
YEST = 0.0
DO 80 I =MIN,MAX
45 TERM = Y(I)*FACTOR/(XARG - X(l)>
DO 70 J=MIN,MAX
IF (I ,NE. J) TERM = TERM/(X(I) - X(J))
70 CONTINUE
YEST = YEST + TERM
50 80 CONTINUE
FLAGR = YEST
RETURN
END
-------
BLOCK DATA MOBCON
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
a
10
15
20
25
30
35
40
45
50
55
BLOCK DATA MOBCON
COMMON/PLFRC2/POLFRS(25,6,3),POLFRT(25,7,3),POLFRP(25,2,
C*»* ORDER OF DATA IS WEEKDAYS BY HOUR GROUP THEN SAT. AND S
DATA ( (POL FRS(I,J
A
B
C
D
E
F
G
H
1
J
K
L
H
DATA
N
0
P
0
R
S
T
U
V
w
X
Y
DATA(
A
B
C
D
f
f
G
H
1
J
K
L
M
DATA(
N
0
P
0
R
S
T
u
V
W
X
.3358,
.4051,
.1435,
.0631,
.0271,
.0130,
.0060,
.0034,
.0015,
.0007,
.0004,
.0002,
.0002,
((POLFRSd,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
(POLFRSU.J
.1987,
.3246,
.1946,
.1162,
.0650,
.0372,
.0261,
.0166,
.0101.
.0050,
.0034,
.0013,
.0007,
(POLFRSd, J
.0003,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
,n,j=t.
.0621,
.2858,
.2568,
.1750,
.1000,
.0541,
.0300,
.0139,
.0098,
.0057,
.0035,
.0010,
.0012,
J,1),J=1
.0004,
.0004,
.0002,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
,2),J=1,
.3137,
.4096,
.1604,
.0618,
.0331,
.0114,
.0057,
.0030,
.0007,
.0007,
.0000,
.0000,
.0000,
,2),J=1,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
6), 1 = 1,1
.0191,
.1137,
.1862,
.1818,
.1505,
.1049,
.0811,
.0603,
.0326,
.0239,
.0163,
.0104,
.0066,
,6), 1=14
.0035,
.0045,
.0030,
.0007,
.0003,
.0002,
.0001 ,
.0000,
.0000,
.0000,
.0000,
.0000,
6),l«1 ,
.1647,
.4222,
.2339,
.1079,
.0457,
.0141,
.0070,
.0030,
.0010,
.0005,
.0000,
.0000,
.0000,
6),l»14,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
3)/
.0223,
.1292,
.1988,
.1800,
.1427,
.1039,
.0757,
.0549,
.0350,
.0236,
.0142,
.0076,
.0051,
,25)/
.0032,
.0029,
.0009,
.0001 ,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
13)/
.0839,
.3320,
.2734,
.1564,
.0829,
.0424,
.0149,
.0078,
.0038,
.0015,
.0003,
.0003,
.0003,
25)/
.0000,
.0000,
.0000,
.0003,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0182,
.1005,
.1616,
.1673,
.1421,
.1133,
.0934,
.0675,
.0470,
.0324,
.0216,
.0128,
.0084,
.0053,
.0050,
.0024,
.0009,
.0001,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0613,
.2998,
.2622,
.1569,
.0947,
.0548,
.0325,
.0167,
.0098,
.0056,
.0027,
.0010,
.0008,
.0005,
.0002,
.0005,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0923,
.2954,
.2537,
.1550,
.0885,
.0477,
.0275,
.0166,
.0095,
.0056,
.0026,
.0018,
.0010/
.0009,
.0012,
.0004,
.0002,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.oooo/
.0992,
.3070,
.2311,
.1572,
.0955,
.0462,
.0238,
.0157,
.0116,
.0062,
.0034,
.0019,
.0005/
.0002,
.0002,
.0005,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
.0000,
WKO
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKO
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
-------
BLOCK DATA MQBCON
75/74 OPT=1 PMOMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
I
I-1
o
60
65
70
75
80
85
90
95
100
105
t 10
Y .0000, .0000, .0000, .0000,
DATAf(POLFRS(I,J,3),J=1,6),I=1,13)/
.0000, .OOOO/
A
B
C
D
E
F
G
H
1
J
K
L
M
.1851, .2718, .2873, .1847, .1484, .2064,
.3190, .3885, .4847, .4672, .3946, .4087,
.2031, .1618, .1509, .1976, .2186, .1815,
.1149, .0830, .04B9, .0930, .1160, .1120,
.0764, .0384, .0143, .0299, .0574, .0477,
.0308, .0197, .0091, .0167, .0296, .0249,
.0338, .0166, .0017, .0048, .0154, .0073,
.0144, .0093, .0012, .0025, .0089, .0062,
.0108, .0041, .0005, .0020, .0043, .0010,
.0041, .0031, .0003, .0010, .0028, .0021,
.0015, .0000, .0003, .0005, .0026, .0010,
.0021, .0016, .0002, .0000, .0011, .0010,
.0021, .0000, .0005, .0000, .0001, .OOOO/
DATA((POLFRS(I,J,3),J=1,6),I*14,25)/
N
0
P
0
R
S
T
U
V
w
X
Y
.0000, .0010, .0000, .0000, .0002, .0000,
.0005, .0010, .0002, .0000, .0000, .0000,
.0010, .0000, .0000, .0000, .0000, .0000,
.0005, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .0000,
.0000, .0000, .0000, .0000, .0000, .OOOO/
C»*» ORDER OF DATA IS WEEKDAYS BY ADT FROM 0.5 PCT TO 6.5 PCT
DATA((POLFRT( I,J,1),J=1,7),I=1,13)/
A
B
C
D
E
F
G
H
1
J
K
L
M
.777 .288 .067 .013 .006 .001 .000
.129
.044
.011
.008
.006
.004
.006
.004
.003
.002
.001
.001
DATA( (POL
N
0
P
0
R
S
T
U
V
W
X
Y
.001
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.317
.169
.052
.038
.029
.022
.030
.019
.012
.008
.005
.004
RT(I,J,1
.003
.002
.003
.003
.001
.000
.000
.000
.000
.000
.000
.000
.230
.2.18
.086
.071
.058
.047
.069
.046
.032
.022
.015
.011
,J=1,7),
.008
.006
.007
.oon
.004
.001
.000
.000
.000
.000
.000
.000
.109
.175
.088
.081
.073
.065
.105
.077
.056
.041
.030
.022
=14, 25>/
.016
.012
.016
.019
.009
.001
.000
.000
.000
.000
.000
.000
.045
.105
.068
.071
.071
.068
.120
.099
.078
.061
.047
.036
.028
.021
.029
.033
.019
.002
.000
.000
.000
.000
.000
.000
.018
.057
.045
.052
.057
.059
.116
.107
.092
.077
.063
.051
.041
.033
.047
.057
.035
.005
.000
.000
.000
.000
.000
.000
.007
.030
.027
.035
.042
.047
.100
.102
.095
.085
.074
.062
.052
.043
.065
.085
.056
.009
.000
.000
.000
.000
.000
.000
DATA«POLFRT(1,J,2),J=1,7),I=1,13)/
SAT
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SAT
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
WKO
WKD
WKD
WKD
WKD
WKO
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKO
WKD
WKD
WKD
WKD
WKD
WKD
SAT
-------
BLOCK DATA MOBCON
73/74 OPT=1 PMDMP
FTN 4.8+552
B4/07/09. 16.17.07
PAGE
115
120
125
130
135
140
145
150
155
160
165
170
A .777
B .129
C .044
D .011
E .008
F .006
G .004
H .006
I .004
J .003
K .002
L .001
M .001
, .288
, .317
, .169
, .052
, .038
. .029
, .022
, .030
, .019
, .012
, .008
, .005
, .004
, .067
, .230
, .218
, .086
, .071
, .058
, .047
, .069
, .046
, .032
, .022
, .015
, .011
DATA ( (POL FRT ( 1 , J , 2 ) , J = 1 , 7 )
N .001
0 .001
P .001
0 .001
R .001
S .000
T .000
U .000
V .000
W .000
X .000
Y .000
, .003
, .002
, .003
, .003
, .001
, .000
, .000
, .000
, .000
, .000
, .000
, .000
DATAC (POLFRTC 1 , J,
A .777
B .129
C .044
D .011
E .008
F .006
G .004
H .006
1 .004
J .003
K .002
L .001
M .001
, .288
, .317
, .169
, .052
, .038
. .029
, .022
, .030
, .019
, .012
, .008
, .005
, .004
DATA( (POL FRT ( I,J,
N .001
0 .001
P .001
0 .001
R .001
S .000
T .000
U .000
V .000
W .000
X .000
Y .000
, .003
.002
.003
.003
.001
.000
.000
.000
.000
.000
.000
.000
DATA ( (POLFRP( 1 , J,
A 0.2410,
B 0.1180,
C 0.1520,
0.1810 ,
0,0887 ,
0.1149 ,
, .008
, .006
, .007
, .008
, .004
, .001
, .000
, .000
, .000
, .000
, .000
, .000
3),J=1,7)
, .067
, .230
, .218
, .086
, .071
, .058
, .047
, .069
, .046
, .032
, .022
, .015
, .011
f
f
*
9
t
9
f
9
9
9
t
9
9
9
9
9
9
9
9
t
9
9
9
9
,1
9
9
9
9
9
9
9
9
9
9
9
9
9
3), J=1,7), |.
, .008
, .006
, .007
, .008
, .004
, .001
, .000
, .000
, .000
, .000
, .000
, .000
1),J«1,2)
9
9
9
9
9
9
9
9
9
9
»
9
,\
.013 ,
.109 ,
.175 ,
.088 ,
.081 ,
.073 ,
.065 ,
.105 ,
.077 ,
.056 ,
.041 ,
.030 ,
.022 ,
=14, 25)/
.016 ,
.012 ,
.016 ,
.019 ,
.009 ,
.001 ,
.000 ,
.000 ,
.000 ,
.000 ,
.000 ,
.000 ,
=»1 , 13)7
.013 ,
.109 ,
.175 ,
.088 ,
.081 ,
.073 ,
.065 ,
.105 ,
.077 ,
.056 ,
.041 ,
.030 ,
.022 ,
'14,25)7
.016 ,
.012 ,
.016 ,
.019 ,
.009 ,
.001 ,
.000 ,
.000 ,
.000 ,
.000 ,
.000 ,
.000 ,
*1 , 13) /
.006
.045
.105
.068
.071
.071
.068
.120
.099
.078
.061
.047
.036
.028
.021
.029
.033
.019
.002
.000
.000
.000
.000
.000
.000
.006
.045
. 105
.068
.071
.071
.068
.120
.099
.078
.061
.047
.036
.028
.021
.029
.033
.019
.002
.000
.000
.000
.000
.000
.000
, .001 ,
, .018 ,
, .057 ,
, .045 ,
, .052 ,
, .057 ,
, .059 ,
, .116 ,
, .107 ,
, .092 ,
, .077 ,
, .063 ,
, .051 ,
, .041 ,
, .033 ,
, .047 ,
, .057 ,
, .035 ,
, .005 ,
, .000 ,
, .000 ,
, .000 ,
, .000 ,
, .000 ,
, .000 ,
, .001 ,
, .018 ,
, .057 ,
, .045 ,
, .052 ,
, .057 ,
, .059 ,
, .116 ,
, .107 ,
, .092 ,
, .077 ,
, .063 ,
, .051 ,
, .041 ,
, .033 ,
, .047 ,
, .057 ,
. .035 ,
, .005 ,
, .000 ,
. .000 ,
, .000 ,
. .000 ,
, .000 ,
, .000 ,
.000
.007
.030
.027
.035
.042
.047
.100
.102
.095
.085
.074
.062
.052
.043
.065
.085
.056
.009
.000
.000
.000
.000
.000
.000
.000
.007
.030
.027
.035
.042
.047
.100
.102
.095
.085
.074
.062
.052
.043
.065
.085
.056
.009
.000
.000
.000
.000
.000
.000
f
t-
f
f
f
f
f
9
9
/
f
f
9
f
9
9
9
9
9
9
9
/
9
9
9
9
9
f
9
p
f
f
/
f
f
f
t
f
f
f
p
/
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
WKD
WKD
WKD
WKO
-------
BLOCK DATA MOBCON
73/74 OPT=1 PMDHP
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84/07/09. 16.17.07
PAGE
M
175
180
185
190
195
200
205
210
215
220
225
D 0.1150, 0.0880 ,
E 0.1240, 0.0982 ,
f 0.0710, 0.0385 ,
6 0.0410, 0.0405 ,
H 0.0260, 0.0305 ,
I 0.0180, 0.0252 ,
J 0.0130, 0.0221 ,
K 0.0110, 0.0203 ,
L 0.0130, 0.0287 ,
M 0.0220, 0.0553 /
DATA ((POLFRP(I,J,1),J=1,2),I=14,25) /
N 0.0120, 0.0380 ,
0 0.0070, 0.0259 ,
P 0.0070, 0.0301 ,
0 0.0020, 0.0142 ,
R 0.0010, 0.0110 ,
S 0.0000, 0.0064 ,
T 0.0000, 0.0038 ,
U 0.0000, 0.0021 ,
V 0.0000, 0.0009 ,
W 0.0000, 0.0000 ,
X 0.0000, 0.0000 ,
Y 0.0000, 0.0000 /
DATA ((POLFRPCI,J,2),J=1,2),I=1,13) /
A 0.2410, 0.1810 ,
0.1180, 0.0887 ,
0.1520, 0.1149 ,
0.1 150, 0.0880 ,
0.1240, 0.0982 ,
0.0710, 0.0385 ,
0.0410, 0.0405 ,
0.0260, 0.0305 ,
0.0180, 0.0252 ,
0.0130, 0.0221
0.0110, 0.0203
0.0130, 0.0287
B
C
D
E
F
G
H
I
J
K
L
M 0.0220, 0.0553 /
DATA ((POLFRP(I,J,2),J=1,2), 1=14,25) /
N 0.0120, 0.0380 ,
0 0.0070, 0.0259 ,
P 0.0070, 0.0301 ,
0 0.0020, 0.0142 ,
R 0.0010, 0.0110 ,
S 0.0000, 0.0064 ,
T 0.0000, 0.0038 ,
U 0.0000, 0.0021 ,
V 0.0000, 0.0009 ,
W 0.0000, 0.0000 ,
X 0.0000, 0.0000 ,
y o.oooo, o.oooo /
DATA «POLFRP(I,J,3),J=1,2),1=1,13) /
A 0.2410, 0.1810 ,
P 0.1180, 0.0887 ,
C 0.1520, 0.1149 ,
D 0.1150, 0.0880 ,
E 0.1240, 0.0982 ,
F 0.0710, 0.0385 ,
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
WKD
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SAT
SUN
SUN
SUN
SUN
SUN
SUN
SUN
-------
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PAGE
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235
240
245
G
H
I
J
0.
0.
0.
0.
0410, 0.0405
0260, 0.0305
0180, 0.0252
0130, 0.0221
.0110, 0.0203
.0130, 0.0287
K 0.
L 0.
M 0.0220, 0.0553
DATA ((POLFRP(1,J
N 0.
0
0.
P 0.
0 0.
R
0.
0120, 0.0380
0070, 0.0259
0070, 0.0301
0020, 0.0142
0010, 0.0110
0.0000, 0.0064
T 0.0000, 0.0038
U 0.0000, 0.0021
V 0.0000, 0.0009
W 0.0000, 0.0000
X 0.0000, 0.0000
Y 0.0000, 0.0000 /
END
3),J=1,2),I=14,25) /
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
SUN
n
(-
u>
-------
BLOCK DATA NATPOP
73/74 OPT
PMOMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
ra
10
15
20
25
30
35
40
45
50
55
BLOCK DATA NATPOP
COMMON/TOTPOP/P( 32, 24, 3 )
DATA <(P(I , J,1),J=1,24), 1=1, 4)/
A49165015., 49842171., 50323418.,
B47727538., 38489899., 28359526.,
C29421948., 25524062., 21177257.,
D47953301., 46249738., 49002430.,
E 0., 0., 0.,
F 1293562., 3294132., 2738201.,
G 0., 253786., 4369018.,
H 0., 0., 0.,
1 0., 0., 0.,
J 0.
K 965465.
L 447143.
M 0.
N 0.
0 192069.
P 0.
DATA «P(
A 777719.
B 0.
C14022989.
0 379313.
E 0.
F 0.
G 0.
H 0.
1 0.
J 0.
K 0.
L 0.
M 0.
N 0.
0 0.
P 0.
DATA ((P(
A 1205327.
B 302032.
C 9581951.
D 903221.
E 0.
F 1 162197.
G 11611 10.
H 0.
1 0.
J 0.
K 0.
L 0.
M 0.
N 0.
0 0.
P 0.
DATA ((P(
A86130233.
P84596185.
C55914265.
9
9
t
9
9
9
9
1
f
f
9
9
t
9
9
t
9
t
t
p
9
1
f
f
f
f
9
9
f
f
9
f
f
t
9
\
p
f
9
589217
1483189
2457433
0
0
3314698
0
,J,1>,J*1
858956
0
14257687
553583
0
0
0
0
0
0
0
0
0
0
0
0
,J,1), J=1
300598
7619925
7804537
903479
0
1 159800
1161065
0
0
0
0
0
0
0
1777414
0
,J,1),J=1
87040796
70521658
48813775
9
9
9
*
y
f
9
9
9
9
9
0.,
2409960.,
0.,
0.,
0.,
50469392.
24634137.
24519075.
49068674.
0.
1610992.
1774254.
0.
0.
1 155657.
393660.
0.
0.
3180132.
2632114., 3895133.
0., 0.
9
9
9
9
9
9
9
9
9
9
9
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50453410.
19977536.
21088961.
49371641.
0.
2757266.
3126098.
0.
0.
0.
192069.
0.
0.
7828125.
9103583.
0.
9
9
9
9
9
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9
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24), 1=5, 8)/
9
f
9
9
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9
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0., 0.
14680521., 15615653.
14597277., 14932097.
531769., 622400.
230627., 0.
0., 0.
0., 0.
0., 0.
0., 0.
0., 0.
0.
0.
0.
0.
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0.
24),l=9,12)/
9
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0.
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0.
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8082861 .
8082B61.
905260.
0.
1 160729.
1159587.
0.
0., 0.
0., 0.
0., 0.
0., 0.
0.. 0.
182375., 1499090.
656991., 1499090.
0., 0.
9
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9
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9
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9
9
9
9
9
9
9
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0.
15380564.
15340358.
469656.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1 16844.
0.
0.
301427.
7614269.
1370629.
905730.
0.
0.
3595580,
0.
0.
2664482.
0.
0.
0.
474616.
1499090.
0.
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
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9
9
9
9
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50322275.
23179919.
41284854.
49155531.
0.
645534.
2095759.
0.
0.
1 11651.
1076578.
0.
0.
6635101.
2298948.
0.
0.
14925382.
979469.
590274.
0.
0.
993437.
0.
0.
0.
0.
0.
0.
212713.
0.
0.
300464.
9243893.
902829.
1204218.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1499090.
0.
0.
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
1
9
9
9
9
9
9
9
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9
9
9
1
9
9
f
9
9
9
9
9
9
9
9
9
9
9
9
1
24). 1=13, 16)/
9
f
9
87776520., 88100716.
53444637., 46014177.
41006425.,
46661325.
9
9
f
88100704.
37688767.
35052502.
9
9
9
88100383.
42429594.
72797977.
9
9
f
-------
BLOCK DATA NATPOP
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
I
H-
cn
60
65
70
75
80
85
90
95
too
105
no
083932771.,
E 0.,
F 2410976.,
G 0.,
H 672102.,
I 0.,
J 0.,
K 1646203.,
L 1815203.,
M 0.,
N 0.,
0 423024.,
P 0.,
DATA ( CPU,
A 777021.,
B 0.,
C13897516.,
D 746257.,
E 0.,
F 0.,
G 0.,
H 0.,
1 0.,
J 0.,
K 0.,
L 0.,
M 0.,
N 0.,
0 0.,
P 0.,
DATA ((P(l,
A 918661.,
B 231590.,
C 7379715.,
D 682840.,
E 0.,
F 863811.,
G 863087.,
H 0.,
1 0.
J 0.
K 0.
L 0.
M 0.
N 0.,
0 0.,
P 0.,
DATA ((P(l,
A 198764.,
B 541902.
C 3349644.
D 1422336.
E 7466.
F 14821.
G 18442.
H 22362.
1 0.
81933019., 86420767., 86423733.,
0.,
7953206.,
489617.,
0.,
0.,
1256062.,
2435232.,
4496270.,
0.,
0.,
6244020.,
0.,
J,1 ),J=1,24)
777290.,
0., 1
0.,
5607554.,
8048922.,
0.,
0.,
0.,
4124405.,
0.,
0.,
0.,
4800733.,
0.,
,l=17,20>/
0.,
3060313.,
12858398., 13285185.,
754060.,
0.,
0.,
916236.,
0.,
0.,
0.,
108557.,
0.,
0.,
o.,
0.,
0.,
J,1), J-1,24)
230634.,
5895017.,
6039253.,
683011.,
0.,
862213.,
863057.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
1339557.,
0.,
J,1),J=1,24>
1 18601 .,
1385258.,
3104685.,
969932.,
4779.,
22810.,
18592.,
21503.,
0.,
755558.,
239001.,
0.,
0.,
0.,
0.,
108557.,
409054.,
0.,
0.,
0.,
195005.,
0.,
,l=21,24)/
230993.,
7232337.,
7742569.,
683870.,
0.,
862325.,
0.,
0.,
0.,
0.,
0.,
o.,
o.,
146473.,
499844.,
o.,
,l=25,28)/
69144.,
1923246.,
2567648.,
717717.,
2987.,
22250.,
20384.,
17210.,
0.,
0.,
3014452.,
3806766.,
0.,
0.,
1977865.,
835274.,
0.,
0.,
6972740.,
6673752.,
0.,
0.,
13531034.,
13722272.,
764620.,
0.,
182265.,
0.,
0.,
0.,
168502.,
59039.,
0.,
0.,
0.,
108557.,
0.,
231209.,
6249720.,
6249720.,
684198.,
0.,
862833.,
862071.,
0.,
0.,
0.,
0.,
0.,
0.,
1119222.,
1119222.,
0.,
69144.,
1732214.,
2302050.,
608954.,
1904.,
19712.,
23519.,
15568.,
o..
86423355.,
0.,
4487373.,
6725359.,
0.,
0.,
0.,
423024.,
0.,
0.,
15810465.,
19296863.,
0.,
0.,
12858398.,
14319249.,
763643.,
0.,
606785.,
0.,
0.,
0.,
299246.,
108557.,
0.,
0.,
108557.,
0.,
0.,
231187.,
5891246.,
1042014.,
684512.,
0.,
0.,
2699788.,
o..
0.,
1985997.,
0.,
0.,
o.,
354703.,
1119222.,
o..
85082.,
1«26915.,
2680365.,
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-------
BLOCK DMA NATPOP
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
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DATA UP (I
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84/07/09. 16.17.07
PAGE
-------
BLOCK DATA NATPOP
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PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
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BLOCK OAT* NMPOP
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FTN 4.8+552
84/07/09. 16.17.07
PAGE
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BLOCK DATA NATPOP
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A
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84/07/09. 16.17.07
PAGE
-------
BLOCK DMA NATPOP 73/74 OPT=1 PMDMP
OPT 1 PMDMP FTN 4.8+552 84/07/09.16.17.07 PAGE
405
.,
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END
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-------
BLOCK DATA POLCON
73/74 OPT=1 PMOMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
N)
to
10
15
20
25
30
35
40
45
50
55
BLOCK DATA
BLOCK DATA POLCON
COMMON/CONC/CONVAL(25,32,3)
DATA ((CONVAL(
A 0., 36.,
B 614., 686.,
C 1554., 1770.,
D 0., 38.,
E 654., 730.,
F 1653., 1884.,
G 0., 38.,
H 654., 730.,
1 1653., 1884.,
J 0., 36.,
K 614., 686.,
L 1554., 1770.,
M 0., 36.,
N 614., 686.,
0 1554., 1770.,
P 0., 36.,
0 614., 686.,
R 1554., 1770.,
DATA ( (CONVAH
A 0., 38.,
B 654., 730.,
C 1653., 1884.,
n o., 200.,
E 1400., 1600.,
F 4000., 6000.,
G 0., 360.,
H 2061., 2319.,
1 10000. ,15000.,
J 0., 38.,
K 654., 730.,
L 1653 884.,
DATA (CONVALU
M 0., 37.,
N 325., 350.,
0 525., 550.,
DATA ((CONVAL(
A 0., 50.,
B 853., 954.,
C 2160., 2460.,
D 0., 53.,
E 909., 1015.,
F 2298., 2619.,
G 0., 53.,
H 909., 1015.,
1 2298., 2619.,
J 0., 50.,
K 853., 954.,
L 2160., 2460.,
M 0., 50.,
N 853., 954.,
0 2160., 2460.,
P 0., 50.,
0 853., 954.,
R 2160., 2460.,
POLCON 73/74
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DPT=1 PMDMP
-------
BLOCK DATA POLCON
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
to
Ul
60
65
70
75
80
85
90
95
100
105
1 10
DATA ( (CONVAU I , J,2)
A 0., 53., 161.
B 909., 1015., 1123.
C 2298., 2619., 3153.
D 0., 200., 400.
E 1400., 1600., 1800.
F 4000., 6000., 8000.
6 0., 360., 463.
H 2061., 2319., 2577.
I 10000.,15000.,20000.
J 0., 53., 161.
K 909., 1015., 1123.
L 2298., 2619., 3153.
DATA (CONVALC1,32,2)
M 0., 51., 104.
N 452., 487., 521.
0 730., 765., 799.
DATA ( (CONVAU I, J,3)
A 0., 50., 152.
B 853., 954., 1055.
C 2160., 2460., 2963.
D 0., 53., 161.
E 909., 1015., 1123.
F 2298., 2619., 3153.
6 0., 53., 161.
H 909., 1015., 1123.
I 2298., 2619., 3153.
0., 50.,
152.
J
K 853., 954., 1055.
L 2160., 2460., 2963.
M 0., 50., 152.
N 853., 954., 1055.
O 2160., 2460., 2963.
P 0., 50., 152.
0 853., 954., 1055.
R 2160., 2460., 2963.
DATA ( (CONVAU I, J,3)
A 0., 53., 161.
B 909., 1015., 1123.
C 2298., 2619., 3153.
P 0., 200., 400.
E 1400., 1600., 1800.
F 4000., 6000., 8000.
G 0., 360., 463.
H 2061., 2319., 2577.
I 10000.,15000.,20000.
J 0., 53., 161.
K 909., 1015., 11?3.
L 2298., 2619., 3153.
DATA (CONVAU I,32,3)
M 0., 51., 104.
N 452., 487., 521.
0 730., 765., 799.
END
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-------
BLOCK DATA NEPCON
73/74 OPT=1 PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
BLOCK DATA NERCON
COMMON/PLFRCI/ POLFR H 25,6, 3) ,POLFR2 (25,6, 3 ) .POLFR3 (25 6 3)
1 POLFR4(25,6,3),POLFR5(25,6,3),POLFR6<25,6,3>
DATA ((POLFR1,J=1,6),I=1,13)/
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.0000,
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-------
BLOCK DATA NEBCON
73/74 OPT=1 PMDMP
FTN 4.8+552
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PAGE
K)
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65
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105
1 10
DATA
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SUN
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-------
BLOCK DATA NEBCON
73/74 OPT=t PMDMP
FTN 4.8+552
84/07/09. 16.17.07
PAGE
ra
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1 15
120
125
130
135
140
145
150
155
160
165
170
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6
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SAT
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WKO
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WKD
WKD
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-------
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PAGE
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175
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205
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F
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DATA
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-------
BLOCK DATA NEBCON
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-------
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-------
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COMMON/PLFRC3/ POLFR8(25,6,3)
DATA «POLFR8(I,J,1),J=1,6),I=1,13>/
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0 .0000, .0000, .0000, .0000,
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J .0000, .0000, .0000, .0000,
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0
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0
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U
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W
X
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.0000, .0000, .0000,
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WKD
WKD
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.5000,
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84/07/09. 16.17.07
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA 460/3-85-002
2.
3. RECIPIENT'S ACCESSIOWNO.
4. TITLE AND SUBTITLE
IMPROVED MOBILE SOURCE EXPOSURE ESTIMATION
5. REPORT DATE
March 1985
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Melvin N. Ingalls
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Southwest Research Institute
6220 Culebra Road
San Antonio, Texas 78284
10. PROGRAM ELEMENT NO.
Work Assignment 6
11. CONTRACT/GRANT NO.
68-03-3162
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Protection Agency
2565 Plymouth Road
Ann Arbor, Michigan 48105
13. TYPE OF REPORT AND PERIOD COVERED
Final Report (1/84 to 6/84)
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16, ABSTRACT
This project was conducted to provide an improved estimate of nationwide annual
person hours of exposure to non-reactive mobile source pollutants. National
population estimates by hour of the day were developed using the population
activity files for six neighborhood types from the EPA NAAQS Exposure Model (NEM)
for CO, with the addition of three mobile source microenvironments (street canyons,
tunnels and parking garages). The NEM neighborhood populations together with the
mobile source microenvironment populations constitute a complete hour-by-hour
assignment of the nationwide population to a set of location types. Mobile source
pollutant concentrations for the six NEM neighborhoods were derived from 1981 CO
monitor data contained in the EPA SAROAD data base. Mobile source pollutant concen-
trations for the three mobile source microenvironments were derived for data in the
literature. A computer program was written to combine the population and pollutant
concentration distributions by hour of the day to produce an annual person hours
of exposure distribution of mobile source pollutants. If the mobile source emission
rate of a pollutant is known, the information in this report can be used to deter-
mine the person hours of exposure to various concentrations of that pollutant.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Air Pollution
Exhaust Emissions
Motor Vehicles
Exposure Estimates
Parking Garages
Tunnels
Street Canyons
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
214
20. SECURITY CLASS (Thispage}
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
------- |