EPA-450/3-77-004b
January 1977
POPULATION EXPOSURE
TO OXIDANTS
AND NITROGEN DIOXIDE
IN LOS ANGELES
VOLUME II:
WEEKDAY/WEEKEND
AND POPULATION
MOBILITY EFF1CTS
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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EPA-450/3-77-004b
POPULATION EXPOSURE
*
J- TO OXIDANTS AND NITROGEN DIOXIDE
IN LOS ANGELES
VOLUME II: WEEKDAY/WEEKEND
AND POPULATION MOBILITY EFFECTS
by
Yuji Horie, Anton S. Chaplin, and Eric D. Helfenbein
Technology Service Corporation
2811 Wilshire Boulevard
Santa Monica, California 90403
Contract No. 68-02-2318
Project No. DU-76-C190
Program Element No. 2AF643
EPA Project Officer: Neil H. Frank
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
January 1977
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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 (MD-35) , Research Triangle Park, North Carolina
27711; or, for a fee, from the National Technical Information Service,
5285 Port Royal Road, Springfield, Virginia 22161.
This report was furnished to the Environmental Protection Agency by
Technology Service Corporation, 2811 Wilshire Boulevard, Santa Monica,
California 90403, in fulfillment of Contract No. 68-02-2318, Project No.
DU-76-C190, Program Element No. 2AF643. The contents of this report
are reproduced herein as received from Technology Service Corporation.
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 endorse-
ment by the Environmental Protection Agency.
Publication No. EPA-450/3-77-004b
11
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TABLE OF CONTENTS
Section Page
LIST OF FIGURES 11i
LIST OF TABLES v
1. INTRODUCTION 1
1.1 RECEPTOR POINTS 3
1.2 WEEKDAY-WEEKEND DIFFERENCE 3
1.3 POPULATION MOBILITY 4
1.4 ISOPLETH MAPS 5
2. OVERVIEW OF POPULATION AND AIR QUALITY IN THE
LOS ANGELES BASIN 7
2.1 POPULATION PROFILE 10
2.2 AIR POLLUTION PROFILE 17
2.3 INTERFACING POPULATION AND AIR QUALITY DATA 21
3. WEEKDAY-WEEKEND DIFFERENCE IN AIR QUALITY AND POPULATION
EXPOSURE 27
3.1 WEEKDAY-WEEKEND DIFFERENCE IN Ox 28
3.2 WEEKDAY-WEEKEND DIFFERENCE IN N02 39
3.3 RELATING WEEKDAY-WEEKEND DIFFERENCE IN
EMISSIONS TO AIR QUALITY 46
4. EFFECTS OF DAILY POPULATION MOBILITY ON POPULATION
EXPOSURE 49
4.1 POPULATION-AT-RISK DISTRIBUTION FOR STATIC
AND MOBILE POPULATIONS 50
4.2 SIGNIFICANCE OF POPULATION MOBILITY IN POPULATION
EXPOSURE ESTIMATES 58
5. CONCLUDING REMARKS 65
REFERENCES 69
APPENDICES
A. DATA ON TOTAL POPULATION, WORKERS BY RESIDENCE, AND
WORKERS BY EMPLOYMENT LOCATION IN 1973 A-l
B. AIR QUALITY DATA FOR QX AND N02 IN THE LOS ANGELES AQCR. . . B-l
C. MONITORING STATIONS AND RECEPTOR POINTS C-l
D. METHODOLOGY TO CHARACTERIZE POPULATION EXPOSURE D-l
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LIST OF FIGURES
Figure No. Page
2.1 Topographical Features of the Los Angeles Basin . 8
2.2 Location of Monitoring Stations 9
2.3 Boundaries Showing 1973 Analysis Area, and
Los Angeles AQCR 11
2.4 Regional Statistical Areas Developed by Southern
California Association of Governments 12
2.5 Population Density in Persons per Square Mile
in 1970 13
2.6 Number of Persons Employed per Square Mile in
1970 15
2.7 Net Influx of Population (Workers) During Working
Time in Persons per Square Mile in 1970 .... 16
2.8 Diagram of Creating a Demographic Network for
Metropolitan Los Angeles AQCR 23
2.9 Locations of the 99 Receptor Points Assigned to
the Study Region 25
3.1 Isopleths of Percent of Days on Which the NAAQS
for Oxidant was Exceeded in 1973 29
3.2 Isopleths of Mean Duration (hours) on Days When
the NAAQS for Oxidant 31
3.3 The Difference in Percent of the Number of Days
on Which the NAAQS for Oxidant was Exceeded in
* 1973, Weekday Minus Weekend 32
« 3.4 Population Exposed to 0 Daily Maximum Hourly
Concentration Above tne NAAQS at Various
Frequencies 34
3.5 Population Exposed to 0 Hourly Concentration
Above the NAAQS at Various Frequencies .... 35
3.6 Isopleths of Percent of Days on Which the
California One Hour Standard for NO, was
Exceeded in 1973 L 38
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LIST OF FIGURES (Cont'd)
Figure No. Page
3.7 Isopleths of Mean Duration (Hours) on Days When
the California One Hour Standard for N0? was
Exceeded in 1973 . . . 40
3.8 The Difference in Percent of Days on Which the
California One Hour Standard for NO? was
Exceeded in 1973, Weekday minus Weekend ... 41
3.9 Population Exposed to N02 Daily Maximum Hourly
Concentration Above the California One Hour
Standard at Various Frequencies 43
3.10 Population Exposed to N02 Hourly Average
Concentration Above the California One Hour
Standard at Various Frequencies 44
4.1 Workers Exposed to Ox Hourly Concentration Above
the NAAQS During Working Time at Their
Residence and at Their Work Place 51
4.2 Exposure of all Workers to Oxidants Above the
NAAQS During Working Time (1), Non-Working
Time (2), Combination of (1) and (2) as (3)
and Total Time (--) 54
4.2a Probability Density Distribution of Workers
Exposed to Hourly 0> Above the NAAQS 55
4.3 Population Exposure to Oxidants Above the NAAQS. 57
IV
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LIST OF TABLES
Table No. Page
2.1 Percent of Days the NAAQS for Ox Was Exceeded
and the Mean Duration in Hours (x,x) in 1973 . 19
2.2 Percent of Days the California Standard for N02
was Exceeded and the Mean Duration in Hours
(x.x) in 1973 20
3.1 Regionwide Impact of Weekday-Weekend Phenomena on
Population Exposure to Photochemical Oxidants 36
3.2 Regionwide Impact of Weekday-Weekend Phenomena
on Population Exposure to Nitrogen Dioxide . . 45
4.1 Effect of the Consideration of Population Mobility
on the Estimates of Population Exposure to
0₯ in the 1973 Study Area 60
/\
4.2 Effect of Consideration of Population Mobility
on Estimates of Population Exposure to N0£
in the 1973 Study Area 60
A-l Total Population, Workers by Residence, and
Workers by Employment Location in 1973 .... A-2
B-l Corrected Ox Daily Maximum Hourly Average
Concentrations in 1973 B-2
B-2 Corrected Ox Hourly Average Concentrations in
1973 B-5
B-3 N02 Daily Maximum Hourly Average Concentrations
in 1973 B-9
B-4 N0£ Hourly Average Concentrations in 1973 ... B-l2
C-l Locations and Addresses of Air Monitoring
Stations C-2
C-2 Receptor Points Assigned to the Los Angeles
AQCR C-5
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1. INTRODUCTION
We have divided this report on the subject of population exposure
to photochemical pollutants in the Los Angeles Basin into three volumes.
Volume I is an executive summary which contains the highlights of
Volumes II and III. Volume III is entitled, "Population Exposure to
Oxidants and Nitrogen Dioxide in Los Angeles - Long Term Trends, 1965-1974."
In Volume III, trends in photochemical air pollution in the Los Angeles
Basin are discussed from two new aspects, characterization of air
quality relative to the standards and quantification of population ex-
posure to air pollution.
In this report, Volume II, two primary purposes of the study are
described. They are:
(1) analysis of the weekday-weekend effect on photochemical air
pollution and
(2) analysis of the effect of diurnal population mobility on
population exposure estimates in the Los Angeles Basin.
The analyses were performed by characterizing local air quality in
relation to the air quality standard and by quantifying exposure of the
population to air pollution. This was accomplished through the use of
Ox and N02 data for 1973. This year was selected because it provided the
most air quality monitoring sites producing data for the analysis. Most
of the past analyses of air quality data are expressed in concentration
o
units such as ppm (parts per million) and ug/m (micrograms per cubic
meter). It is not that these units are hard to understand, but rather
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this form of air quality presentation is inadequate because it does not
indicate adverse effects on public health explicitly or quantitatively.
The air quality standards have been set to protect the public health
(primary standards) or the public welfare (secondary standards). Quanti-
fication of the observed air quality in relation to the primary standard
should indicate explicit adverse impacts with respect to public health.
Therefore, hourly Ox air quality data are examined in relation to the
primary National Ambient Air Quality Standard (NAAQS, 160 yg/m or approxi-
mately 8 pphm for one hour average concentration). Because there is no
NAAQS for short-term NOp concentrations, hourly N0~ air quality data are
examined in relation to the California Ambient Air Quality Standard
3
(CAAQS, 470 yg/m or approximately 25 pphm for one hour average concentra-
tion). In this report, air quality is expressed in percentage of the time
the standard was exceeded and in mean duration of the excess air pollution
in hours per day.
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1.1 RECEPTOR POINTS
To determine population exposure to air pollution, air quality measure-
ments taken at widely separated monitoring stations are used to describe the
spatial distribution of air pollution levels. Using the statistics of popula-
tion and employment prepared by the Southern California Association of
Governments (SCAG), the spatially distributed population is approximated by
99 receptor points. Each receptor point represents the local population
size, the spatial position of the local population, and the area in which
the local population resides. The air quality at each receptor point is
estimated by spatially interpolating the air qualities observed at the
three nearest monitoring stations to that receptor point. In this manner,
the region's demographic data are merged with the air monitoring data to
estimate short-term air quality (hourly concentration and daily maximum
hourly concentration) experienced by the Los Angeles population.
1.2 WEEKDAY-WEEKEND DIFFERENCE
In order to investigate the weekday-weekend difference in air quality,
hourly concentration data were divided into weekdays and weekends and were
summarized in percentile concentration distributions. For each of given
percentiles (maximum, 1, 3, 5, 10, 25, 50, and 75%), the percentile con-
centration at each receptor point was estimated by spatially interpolating
the observed percentile concentrations at the nearest three monitoring
stations to that receptor point. Repeating this procedure for all the
percentiles, percentile statistics of interpolated concentrations at each
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receptor point were created for all time, weekdays, and weekends. The per-
centile indicating the percentage of the time (hours or days) the standard
was exceeded was determined to quantify air quality at each receptor point
in relation to the standard.
1.3 POPULATION MOBILITY
The population-at-risk distribution, which describes the percentages
of the population exposed to a concentration above the standard for a given
fraction of the time, is used to report the short-term exposure of the popu-
lation quantitatively. In determining population exposure to atmospheric
pollutants, a difficulty arises. Since people move around with time, the
air pollution concentration must be known as a function of both time and
the person's spatial position at that time. This difficulty associated with
population mobility is partially solved in this report by employing the
quasi-stationarity assumption that people stay near a given location during
a categorized time period.
The effect of diurnal population mobility between residence and work-
place on population exposure estimates was investigated in the following
manner. Hourly concentration data were divided into working time (weekday
7 A.M. to 6 P.M.) and non-working time. The hourly concentration data for
working time were merged with employment data to estimate exposure of the
workers population at their place of employment. The hourly concentration
data for nonworking time were merged with residential population data for
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workers to estimate the exposure of the workers population at their place
of residence. Exposure of the worker population during all time was com-
puted by combining the exposure during working time and non-working time.
The exposure of non-workers was based on their place of residence and the
concentration data for all time. Exposure of the total population during
all time was then computed by combining two subpopulations» the workers
population and the non-workers population.
1.4 ISOPLETH MAPS
The percentage of days on which the standard was exceeded was computed
by using the air monitoring data of daily maximum hourly concentrations while
the percentage of hours the standard was exceeded was computed from those of
hourly concentrations. Using the percentage of days exceeded and the percen-
tage of hours exceeded, the mean duration of excess air pollution in hours
per day was computed at each receptor point. The spatial variations of air
quality during all time, weekday, and weekend were then presented in isopleth
maps of the percentage of days the standard was exceeded and of the mean dura-
tion of excess air pollution in hours per day. The isopleth maps describing
the percentage of days the standard was exceeded during weekday and weekend
were used to examine the weekend-weekday difference in 0 and N09 air quality.
A <_
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2. OVERVIEW OF POPULATION AND AIR QUALITY IN THE LOS ANGELES BASIN
Among the nation's 247 Air Quality Control Regions (AQCR's), the
Los Angeles AQCR is special in that it is defined by its geographical
boundaries (mountains and ocean) whereas the great majority of AQCR's
are defined by their administrative boundaries (state and county lines).
Figure 2.1 depicts the topographical features of the Los Angeles Basin.
The AQCR (the area surrounded by solid lines) covers six different
counties: all of Orange and Ventura counties, and part of Santa Barbara,
Los Angeles, San Bernardino, and Riverside counties.
The difference between the AQCR boundaries and the county boundaries
makes it difficult to obtain demographic data specific to the AQCR. In the
analysis of population exposure to air pollution, the spatial distribution
of population as well as the population size must be known. However, a cen-
sus tract is too small for the spatial unit because there are less than 50
air monitoring stations in the region. During our search for the population
data to be used for the population exposure analysis, we found that the
Regional Statistical Areas (RSA's) developed by the Southern California
Association of Governments (SCA6) were a proper spatial unit for aggregating
the population data.
The year of 1973 was chosen for this study because in that year the
largest number of stations reported at least 50% of the possible obser-
vations. Figure 2.2 depicts the location of the 26 air monitoring stations
which were used for the detailed analysis made with the 1973 air quality
and population data. The vast majority of stations produced data that ex-
ceeded 80% completeness. The oxidant data at four stations, Point Mugu (3),
Chino (18), Upland (21), and Redlands (26), failed to meet our criterion
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Figure 2.1. TOPOGRAPHICAL FEATURES OF THE LOS ANGELES BASIN.
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V
\
~ i
i
Santa
Barbara
County
Los Angeles
0 5 in 15 20 25
Miles
County Boundary
AQCR Boundary
5
o
6
o
8
o
9
o
10
o
1. Ojai
2. Camarillo
3. Point Mugu
4. Newhall
5. Reseda
6. West L.A.
7. Lennox
8. Burbank
9. Los Angeles
10. Pasadena
11 . Long Beach
12. Azusa
13. Whittier
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
La Habra \
Anaheim
Costa Mesa
Pomona
Chino |
El Toro
Upland-ARB
Upland
Norco
Riverside
Riverside
San Bernardino
Redlands
1
1
1
i ;
^
County
/
12 /
0 /
17 ' o20
y / 2i«° _
X 18 i
i* ' V 22
^-oir rx./ °
/ 5 \
San Bernardino County
25
o 26
2-3-.-.^._r^'-
°24 '
n ^ \ i f^ \^f -I ^-i ^% ^ *-* 1 1 *^ +-\ /
\
Orange County
Stations for N0? only
0 Stations for N02 and Ox
Figure 2.2 Location of Monitoring Stations.
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10
for a valid station-year, i.e., more than 50% of possible observations.
Therefore, the remaining 22 stations were selected for the analysis of pop-
ulation exposure to 0Y air pollution, while all the 26 stations were used
A
for the N02 analysis. Considering the area coverage of these stations, the
study area for the 1973 analysis was selected as shown in Figure 2.3. It
can be seen that the 1973 Analysis Area approximately corresponds to the
Los Angeles AQCR minus a portion of Santa Barbara County whose population
data were not available foom the SCAG statistics.
2.1 POPULATION PROFILE
The Southern California Association of Governments (SCAG) provides
statistics of total population (at place of residence) and of total employ-
ment (at place of work). These SCAG statistics are aggregated into each of
the 55 Regional Statistical Areas (RSA's) which cover the six counties of
Ventura, Los Angeles, Orange, San Bernardino, Riverside, and Imperial (Fig. 2.4).
Because we also need to know the number of workers at their place of residence
for computing population exposure during non-working time, the aggregated sta-
tistics of workers by residence for each RSA were computed from the 1970 cen-
sus tract data by using the conversion table prepared by SCAG, which indicated
the number of census tracts belonging to each RSA (Appendix A, Table Al).
The spatial distribution of total population density is shown in Fig-
ure 2.5. A high population density area centers at the Los Angeles CBD and
extends to the southern half of Los Angeles County and portions of Orange
and San Bernardino Counties. The lowest population density is found in the
mountainous areas (Figs. 2.1 and 2.5).
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Ventura
County
Los Angeles County
/ San Bernardino County
0 5 10 15 20 25
Miles
Riverside
County
Orange
County
Santa
Barbara
County
j_ .
1973 Analysis
County Boundary
AQCR Boundary
Figure 2.3. Boundaries Showing 1973 Analysis Area, and Los Angeles AQCR.
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19 -SR37
REGIONAL STATISTICAL AREAS
Stut/iim Cahfamio tsstciotita of Cennaeiils
Figure 2.4. REGIONAL STATISTICAL AREAS DEVELOPED BY SOUTHERN CALIFORNIA
ASSOCIATION OF GOVERNMENTS
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14
Figure 2.6 depicts the spatial variation of workers population density
at their place of employment. The spatial distribution pattern is somewhat
similar to that of total population density. Because most workers com-
mute from their residence to their workplace, the number of workers at their
place of residence and that at their place of employment are quite different
for individual RSA's. Workers population density at their place of residence
was computed for each RSA and then was subtracted from that at their place of
employment. The difference indicates the influx of workers to that RSA during
working time. In this manner the daily population movement in the Los Angeles
Basin was determined as shown in Figure 2.7. The greatest daily migration
occurs at the Los Angeles CBD and the Southgate area. A moderate daily migra-
tion is seen at Long Beach, Inglewood, the central part of Orange county,
Pomona, the central San Fernando Valley, and Oxnard.
The study region (Fig. 2.3) includes 8,612 square miles (22,295 square
kilometers) and 9.9 million people. The population figure was arrived at by
interpolating SCAG population estimates for 1970 and 1975. The number of
workers in 1973 who worked inside of the study region were 4,083,358, while
those who 1ived inside of the study region were 4,110,024. This small dif-
ference in the number of workers is due to the diurnal migration of
workers from their place of residence to their place of employment.
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WORKERS PER SQUARE MILE
103 ^ 105
10 a, 103
ffila -10 % 10
i f\ O T f\
Figure 2.7. THE NET INFLUX OF POPULATION (WORKERS) DURING WORKING TIME IN
PERSONS PER SQUARE MILE IN 1970
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17
2.2 AIR POLLUTION PROFILE
A percentile concentration distribution is used in this study to char-
acterize annual short-term (one hour) exposures of the population to 0 and
/\
N02 air pollution. The short-term exposure of the population is character-
ized by two parameters: (1) the frequency of occurrence that an ambient
concentration exceeds the concentration threshold equal to the air quality
standard or a multiple of the standard, and (2) the mean duration of the
excess air pollution above the threshold in hours per day.
Using the California ARE data tape of hourly average concentration,
the percentile concentration statistics were developed for the 22 air
monitoring stations that were selected for the detailed analysis of
population exposure to 0 air pollution in 1973, and for the 26 air
A
monitoring stations selected for N02. In order to examine the "weekend
effect" on air quality and population exposure, the percentile
concentration statistics of hourly concentrations and daily maximum
hourly concentrations were computed for three time categories: all time,
weekday, and weekend. In order to incorporate daily population mobility
between residence and workplace into the population exposure analysis,
the percentile concentration statistics of hourly concentrations were
computed for the three additional time categories; working time (weekday
7 A.M. to 6 P.M.), non-working time, and weekday non-working time. The
percentile concentrations at each of the 22 air monitoring stations for
Ox and the 26 stations for N02 are all presented in Appendix B(Tables 81
through B4). In those tables, time categories 1, 2, 3, 4, 5, and 6
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18
refer, respectively, to all time, weekday, weekend, working time, non-
working time, and weekday non-working time.
The 0 air quality observed at each station during weekdays and that
/\
during weekends is summarized in Table 2.1 by the percent of days on which
the National Ambient Air Quality Standard (NAAQS) was violated and the mean
duration in hours of such violations. Our results confirm previous re-
ports. ' Some coastal stations (Anaheim, El Toro, West L.A., Lennox) have
a higher percentage of days exceeded over weekends than over weekdays, in-
dicating that the air at these stations tends to be more polluted during
weekends than weekdays. The majority of monitoring stations, however, have
a lower percentage of days exceeded during weekends than weekdays. It
should be noted that the mean durations of NAAQS violations at the four
coastal stations are all shorter over weekends than over weekdays. There-
fore, the air pollution dosage (time integral of concentration) at these
station sites may not necessarily be higher during weekends than weekdays.
Table 2.2 presents the summary of weekday-weekend difference 1n N0?
air quality at each monitoring station. It can be seen that the great
majority of stations have a lower percentage of days exceeded and a
shorter rcean duration of California one-hour standard violations
during weekends than weekdays. Therefore, the N02 air quality at these
station sites is better during weekends than weekdays. However, at the
three stations in Costa Mesa, Riverside-Magnolia, and Whittier, the oppo-
site is true. The N02 air quality at these three stations is worse during
weekends than weekdays.
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U.L (x'x)
UL
-------
21
Referring to Figure 2.2 which shows the location of each monitoring
station, we can get a rough picture of the spatial distribution of pollutant
levels. Oxidant air pollution exceeded the NAAQS more than 30% of days on
both weekdays and weekends at Noroco-Prado Park, Riverside (two stations),
San Bernardino, Upland, L.A. Downtown, Azusa, Burbank, Reseda, Pomona,
Newhall, and Pasadena. All of these stations are located in the Los Angeles
Downtown area or further inland. In contrast, stations such as
Costa Mesa, El Toro, Long Beach, and Lennox which exceeded the NAAQS less
than 20% of days over both weekdays and weekends are all located near the
coast.
For N02, the stations in commercial or industrial centers (L.A. down-
town, Burbank, West L.A., Long Beach, and Lennox) exceeded the CAAQS more
than 5% of days during weekdays but far less frequently during weekends.
Stations distant from the Los Angeles CBD (El Toro, Norco Prado Park, Riverside-
Rubidoux, San Bernardino, Redlands, Chino, Upland, Camarillo, and Newhall)
exceeded the CAAQS less than 1% of days over both weekdays and weekends.
2.3 INTERFACING POPULATION AND AIR QUALITY DATA
The task of interfacing the population data and the air quality data
starts with a search for a proper regional map on which the monitoring sta-
tions and the receptor points can be located. A receptor point is used to
aggregate the local populations in the areas in which they reside. For the
Los Angeles AQCR, a regional map showing the boundaries of the Regional
Statistical Areas (RSA's) was available (Fig. 2.4). A number of receptor
points were assigned to each RSA according to the size of the population
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22
and the land area. The criteria used for determining the number of recep-
tor points assigned to each RSA is as follows:
1. Regardless of the size of the population and/or the land area,
each RSA is represented by at least one receptor point.
2. An additional receptor point is assigned for each increment of
200 square miles or each increment of a resident population of
200,000.
For example, an RSA having a resident population of 500,000 and a land
area of 70 square miles is represented by three receptor points (one for
RSA and two for population of 400,000), while another RSA having a popula-
tion of 150,000 and an area of 300 square miles is represented by two recep-
tor points (one for RSA and one for land area of 200 square miles).
The number of people at each receptor point is computed in the following
manner: The total population or the total employment in each RSA is computed
by making a linear interpolation between the SCAG estimates for two time
points. For the study year 1973, the interpolation is made of 1970 and 1975
data. The number arrived at by interpolation is divided by the number of re-
ceptor points in that RSA and the result is assigned to each receptor point.
For subpopulations such as workers and non-workers population, the number of
people of a given subpopulation at each receptor point are given by the pro-
duct of (total population) x (percent of subpopulation) where the percentage
is computed from the 1970 census data for the RSA to which the receptor point
belongs.
A diagram showing how to create a demographic network is given in Fig-
ure 2. 8. First, the regional map of RSA's prepared by SCAG is copied by
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23
SEARCH FOR POPULATION DATA AND REGIONAL MAP
POPULATION DATA
AGGREGATED INTO
RSA's
ADDITIONAL DEMOGRAPHIC
DATA AGGREGATED INTO
RSA's THROUGH PRO-
CESSING THE CENSUS
DATA
CREATE THE POPULATION
DATA SET FOR RSA's
ASSIGN THE NUMBER OF
RECEPTORS TO EACH RSA
ACCORDING TO SOME RE-
CEPTOR PLACEMENT CRI-
TERIA
CREATE THE POPULATION
DATA SET FOR RECEPTORS
MAP OF REGIONAL
STATISTICAL
AREAS (RSA)
DIGITIZE THE
BOUNDARIES
OF RSA's
PLACE THE MONI-
TORING STATIONS
ON THE MAP
PLACE THE RECEPTOR
POINTS ON THE MAP
DETERMINE THE GEO-
GRAPHICAL BCUNDARY
OF THE STUDY AREA
PREPARE THE COMPUTER-READY DATA
SETS OF POPULATION, AIR QUALITY,
MONITOR LOCATION AND RECEPTOR
LOCATION
Figure 2.8. Diagram of Creating a Demographic Network
for Metropolitan Los Angeles AQCR.
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24
using a digitizer. Using the UTM coordinates given in SAROAD format or the
site addresses (Appendix C, Table Cl) the air monitoring stations are located
on the digitized map through a coordinate transformation (Fig. 2.2). In
order to determine a scale factor for the coordinate transformation, the
locations of the Los Angeles Downtown station and the Azusa station are
determined from their site addresses. The receptor points are located at
their proper places within the corresponding RSA. The receptor locations
are shown in Figure 2.9; their coordinates are found in Appendix C,
Table C2.
Next, we need to determine the exposure of a person to air pollution.
Thus, the spatial location of the person and the air quality of his location
must be known as a function of time. In the present study, however, we are
not interested in the actual exposures of an individual person to air pollu-
tion, but rather we are interested in the ensemble of potential exposures
of a large population, say 10,000 people. For this purpose, an appropriate
estimate of air quality at each receptor point should be sufficient to make
an estimate of population exposure at that particular locale, if the assump-
tion is made that the population size and sub-population composition will
be quasi-stationary over a year. This assumption should be good for the
analysis of exposure of part of the population such as elderly and school-
age populations because these populations tend to be locationally fixed,
i.e., most school-age children and elderly people stay close to their
resident locations most of the time.
However, the above assumption would not hold for the other portions
of the population, particularly the All Workers population because a large
-------
.o
CM
CM
IN;
figure 2.9. LOCATIONS OF THE 99 RECEPTOR POINTS ASSIGNED TO THE STUDY REGION
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26
percentage of that population spends a substantial part of their time at
their working places where the air environment may be quite different from
that of their residential locations. Therefore, a special analysis has
been performed for the 1973 air quality data and the population data. The
All Workers population data are aggregated into each RSA: (1) by their
residence locations and (2) by their working places. The air quality data
are classified by time categories; (1) non-working time and (2) working
time (weekday 7 A.M. to 6 P.M.).
As mentioned earlier, the spatially distributed population is aggregated
at each receptor point. The air quality at a receptor point was estimated by
interpolating the observed air quality at the three nearest neighboring moni-
4
toring stations to that point as
(2.1)
where C^ is the concentration estimated at j-th receptor point (x-,y.)»
J J J
C.j(i=l,2,3) are the concentrations observed at the three nearest neighboring
stations, i-th (i=l,2,3) air monitoring stations (x.) around the j-th recep-
tor point, and d. is the distance between the i-th monitoring station and the
j-th receptor point, i.e.,
(y. - y..)
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27
3. WEEKDAY-WEEKEND DIFFERENCE IN AIR QUALITY AND POPULATION EXPOSURE
It is very costly to test the effectiveness of various oxidant control
strategies on real-world photochemical air pollution by imposing additional
emission controls. One possible way of assessing the impact of emission
changes on levels of the two major photochemical pollutants, QX and N02
prior to the imposition of additional controls is to examine the weekday-
weekend differences in air quality of the two pollutants and relate them
to the weekday-weekend differences in the level of precursor pollutant emis-
sions. If enough weekdays and weekends are examined so that net meteorologi-
cal differences between weekday and weekend are minimized, it should be possible
to assess what impact the different levels of precursor emissions has had
on absolute levels and spatial patterns of ambient QX and N02.
In this report, the weekday-weekend differences in air quality are studied
by examining the frequency of violations of the air quality standards at each
of the 99 receptor points whose locations are shown in Figure 2.9. Using the
local population size assigned to each receptor point, the weekday-weekend
difference in population exposure to the two pollutants is thereby examined.
In order to determine how often the NAAQS for Ox and the CAAQS for N02 were vio-
lated at various parts of the Los Angeles Basin, the percentile concentration
distributions of hourly concentrations and daily maximum hourly concentrations
for 0 and N09 were computed from the original hourly concentration data fur-
X £
nished by the California Air Resources Board. These percentile concentration
statistics are presented in Appendix B (Tables Bl through B4), which show that
the percentile concentrations for all time, weekdays, and weekends are used
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28
for the analysis of weekday-weekend difference in air quality and popula-
tion exposure. The percentile concentrations for working time, non-working
time, and weekday non-working time were prepared for studying the effect of
diurnal population mobility.
Comparing the percentile concentration at each receptor point to the
air quality standard (NAAQS for DX and CAAQS for N02), the frequency of vio-
lations of the standard (hereafter termed "risk frequency") is determined by
the percentile concentration which equals the air quality standard. If
the standard falls between two percentile concentrations, the logarithm of the
percentile is determined by linear interpolation of the corresponding con-
centation values. From hourly concentrations, the percentage of hours
that the standard is violated (hereafter termed "hourly risk frequency")
is computed. Similarly, the percentage of days that the standard is
violated (hereafter termed "daily risk frequency") is computed from daily
maximum hourly concentrations. Using hourly risk frequency and daily risk
frequency, the average number of hours per day that the standard is violated
on days with standard violations (hereafter termed "mean duration") is also
computed at each receptor point. A more exact definition of each term
used in this report is given in Appendix D.
3.1 Weekday-Weekend Difference in CL
-^^^^^^^^^^^^^"j^^*^"^*^"^"^"^^^~""^"^"^~y^
The spatial variation of Ov air quality over the Los Angeles AQCR is
A
shown in Figure 3.1 in terms of the percent of days on which the NAAQS is
exceeded. It can be seen that in the coastal areas, the NAAQS was vio-
lated about 10% of the days or about 37 days per year while in the inland
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30
i
ro
10
Figure 3.1. ISOPLETHS of percent of days on which the NAAQS for oxidant was exceeded in 1973.
-------
30
areas it was violated as many as 50% of the days or 183 days per year.
Figure 3.2 shows isopleths of the average duration in hours per day on
those days with the NAAQS violations. In the coastal areas the average
duration was about three hours per day while in the inland areas it was
longer than five hours per day. From these two figures, we can compute
the approximate number of hours the NAAQS was exceeded in 1973 at various
locations. For example, in the coastal areas, the number of hours exceeded
should be approximately (37 days/year) x (3 hours/day) =111 hours per year
while in the inland areas it should be 183 x 5 - 915 hours per year.
The spatial distribution of Ov air quality during weekdays and that
A
during weekends were determined by computing the percent of days exceeded
during each period. Then, subtracting the percent of days exceeded during
weekends from that during weekdays, Figure 3.3 was obtained to show the
weekday-weekend difference in air quality in terms of the frequency of NAAQS
violations. It is seen that the coastal region has a negative value indi-
cating poorer air quality during weekends than weekdays, and that the inland
region has a positive value indicating a better air quality during weekends
than weekdays. A ridge on which there is no difference in air quality be-
tween weekdays and weekends divides the Los Angeles AQCR into the above two
regions. The ridge runs along the Santa Monica Mountains to the Los Angeles
CBD, and along the Santa Ana Mountains that separate Orange County and
Riverside County. These results are consistent with previous reports about
the weekend effect on Ov air pollution.5»6»7'8
-------
co
Figure 3.2 ISOPLETHS of mean duration (hours) on days when the NAAQS for oxidant
was exceeded in 1973.
-------
ro
Figure 3.3. The difference in percent of the number of days on which the NAAQS for oxidant
was exceeded in 1973, weekday minus weekend. (Dark line equals zero percent.)
-------
33
Noting that the National Ambient Air Quality Standards (NAAQS) have
been set to protect the public health, the percent of time (days or hours)
the NAAQS for 0 was violated would be indicative of the state of air quality
A
to which the public is exposed. The people in the Los Angeles AQCR, there-
fore, are stratified according to the frequency of the NAAQS violations.
Figure 3.4 shows the three distributions of the population stratified
according to the percent of days exceeded during all time, weekday, and
weekend. It can be seen from the figure that more people incur the most
frequent as well as the least frequent daily exposure above the NAAQS
during weekdays than weekends. This is because the frequency of Ox ex-
posure above the NAAQS is more uniform through the Basin on weekends.
On the average, however, the population in the Los Angeles Basin receives
1.5 percent less frequent daily exposure above the NAAQS during weekends
(Table 3.1).
Figure 3.5 shows the distribution of the population exposed at various
percents of hours the NAAQS was exceeded during all time, weekday, and week-
end. The relations of the three curves are essentially the same as those
seen in Figure 3.4. Also recall from Table 2.1 that the average duration
of 0 exposure is generally less on weekends including the coastal stations.
A
Therefore, it can be concluded that although poeple in the coastal areas
are subjected to a more frequent exposure during weekends than weekdays,
the population in the Los Angeles Basin on the whole are less frequently
exposed to a concentration above the NAAQS during weekends than weekdays.
These findings should be emphasized because the previous reports on the
c c -i o
weekend effect have not considered population exposure. f0>/'
-------
34
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20
40 60
Percent of days above the NAAQS
80
Figure 3.4 POPULATION EXPOSED TO DX DAILY MAXIMUM HOURLY CONCENTRATION
ABOVE THE NAAQS AT VARIOUS FREQUENCIES (1 FOR ALL TIME,
2 FOR WEEKDAY, 3 FOR WEEKEND).
-------
35
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Table 3.1 Regionwide Impact of Weekday-Weekend Phenomena on Population
Exposure to Photochemical Oxidants.
Time Period
All Time
Weekday
Weekend
Weekday/Weekend
Di f f erence
Percent of Days Exceeded
29.7 (29.4)
30.1 (29.7)
28.6 (28.6)
+1.5 (+1.1)
Percent of Hours Exceeded
6.16 (5.96)
6.31 (6.04)
5.77 (5.77)
+ 0.54 (+0.27)
( ): computed based on the mobile population assumption.
-------
37
The regionwide impact of the weekday-weekend phenomena on population
exposure to photochemical oxidants has been determined by computing the pop-
ulation weighted risk frequency for both hourly concentrations and daily
maximum hourly concentrations. The results are summarized in Table 3.1.
The regional averages of daily risk frequency and hourly risk frequency are,
respectively, 29.7 percent of the days and 6.16 percent of the hours. In
other words, an average person in the Los Angeles AQCR was exposed in 1973
to a concentration above the NAAQS 109 days per year or 540 hours per year.
The regional averages of daily risk frequency are 30.1 percent of the
days during weekdays and 28.6 percent of the days during weekends. The re-
gional averages of hourly risk frequency are 6.31 percent of the hours during
weekdays and 5.77 percent of the hours during weekends. Therefore, it can be
said that in 1973 an average person in the Los Angeles Basin received a less
frequent exposure above the NAAQS during weekends than weekdays by 1.5 per-
cent of the days or by 0.54 percent of the hours.
Table 3.1 also presents the regional averages of risk frequency,'Which
were computed by considering diurnal population movement between residence
and workplace. These refined estimates of regional average risk frequency
are close to but a little less than those based on the static population
assumption., i.e., people are locationally fixed to the place of their resi-
dence. According to the refined estimates, an average person in the Los Angeles
Basin received less frequent exposure above the NAAQS during weekends than
weekdays by 1.1 percent of the days or by 0.27 percent of the hours. The de-
tailed method of how to compute population exposure by considering diurnal
population mobility is described in Section 4.
-------
00
00
Figure 3.6 ISOPLETHS of percent of days on which the California one-hour standard
for NOp was exceeded in 1973.
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39
3.2 Weekday-Weekend Difference in NOp
The spatial variation of N02 air quality over the Los Angeles AQCR is
shown in Figure 3.6 in terms of the percentage of days on which the California
Ambient Air Quality Standard (CAAQS) was exceeded. It can be seen from the
figure that the percentage of days exceeded is the greatest (about 5% of
days) in the urban core areas consisting mainly of Los Angeles and Long Beach
Cities and decreases toward the surrounding areas where the CAAQS was ex-
ceeded only 1% of the days or less. The spatial distribution pattern of the
percentage of days exceeded is somewhat similar to that of population den-
sity (Fig. 2.5) and that of employment density (Fig. 2.6). This similarity
between NO^ air quality and human activity distribution pattern would be in-
dicative that H^2 air quality is more strongly affected by local emissions
than 0 air quality whose spatial distribution pattern does not show any
A
particular resemblance to either the population density pattern or the em-
ployment density pattern.
Figure 3.7 shows the spatial variation of the mean duration of stan-
dard violations in hours per day. The longest duration (3.5 hours per day)
occurred at the northern part of Orange County. It is interesting to note
that the spatial pattern of the mean duration shifts south-eastward from
that of the percentage of days exceeded.
Figure 3.8 was prepared to show the weekday-weekend difference in air
quality. The air quality difference is expressed in terms of the difference
in the percentage of days exceeded during weekdays and weekends. It can be
seen from the figure that most of Orange and Riverside counties have a nega-
tive value indicating a poorer air quality during weekends than weekdays, and
-------
Figure 3.7. ISOPLETHS OF MEAN DURATION (HOURS) ON DAYS WHEN THE CALIFORNIA ONE-HOUR STANDARD
FOR N02 WAS EXCEEDED IN 1973
-------
Figure 3.8. THE DIFFERENCE IN PERCENT OF DAYS ON WHICH THE CALIFORNIA ONE-HOUR STANDARD
FOR N02 WAS EX(
ZERO PERCENT.)
FOR N02 WAS EXCEEDED IN 1973, WEEKDAY MINUS WEEKEND. (DARK LINE EQUALS
-------
42
that the majority of Ventura, Los Angeles, and San Bernardino counties have a
positive value indicating a better air quality during weekends than weekdays.
Using the static population assumption, the distribution of the population
exposed at various frequencies of standard violations (population-at-risk dis-
tribution) has been determined for both N02 hourly average concentrations
and N02 daily maximum hourly concentrations. Figure 3.9 shows the distribu-
tions of the population exposed at various percentages of days exceeded
during three time periods; all time, weekdays, and weekends. It can be
seen that the entire population is exposed for a smaller percentage of days
during weekends than weekdays. An average person in the Los Angeles AQCR
is exposed to N02 air pollution above the CAAQS 4.4% of the days during
weekdays, and only 2.1% of the days during the weekends (Table 3.2).
The distribution of the population exposed at various percentages of
hours exceeded is shown in Figure 3.10. Again, the entire population is
exposed for a smaller percentage of hours above the CAAQS during weekends
than weekdays. Therefore, it can be concluded that people in the Los Angeles
AQCR are less frequently exposed to a concentration above the CAAQS during
weekends than weekdays because of the markedly better N02 air quality over
weekends.
The regionwide impacts of weekday-weekend phenomena on population ex-
posure to N02 are summarized in Table 3.2. The regional averages of daily
risk frequency and hourly risk frequency are, respectively, 3.7 percent of
the days and 0.46 percent of the hours. In other words, an average person
in the Los Angeles Basin was exposed in 1973 to a concentration above the
CAAQS 14 days per year or 40 hours per year. The regional averages of
-------
43
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Table 3.2 Regionwide Impact of Weekday-Weekend Phenomena on Population
Exposure to Nitrogen Dioxide.
Time Period
All Time
Weekday
Weekend
Weekday/Weekend
Di f fere nee
i
Percent of Days Exceeded
3.7 (3.8)
4.4 (4.5)
2.1 (2.1)
t2.3 (+2.4)
Percent of Hours Exceeded
0.46 (0.50)
0.57 (0.63)
0.18 (0.18)
+0.39 (+0.45)
( ): computed based on the mobile population assumption,
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46
daily risk frequency are 4.4 percent of the days during weekdays and 2.1 per-
cent of the days during weekends. The regional averages of hourly risk fre-
quency are 0.57 percent of the hours during weekdays and 0.18 percent of the
hours during weekends. Therefore, it can be said that in 1973 an average
person in the Los Angeles Basin received a less frequent exposure above the
CAAQS during weekends than weekdays by 2.3 percent of the days or by 0.39
percent of the hours.
Table 3.2 also presents the regional averages of risk frequency, which
were computed by considering diurnal population movement between residence
and workplace. The refined estimates of regional average risk frequency
are close to but a little greater than those based on the static population
assumption. According to the refined estimates, an average person in the
Los Angeles Basin received less frequent exposure above the CAAQS during
weekends than weekdays by 2.4 percent of the days or by 0.45 percent of the
hours. The detailed method for computing population exposure for a mobile
population is discussed in the next section.
3.3 RELATING WEEKDAY-WEEKEND DIFFERENCE IN EMISSIONS TO AIR QUALITY
According to a TRW study , auto use on weekends is less than on week-
days in the Los Angeles Basin. It is estimated that total auto trips de-
crease around 22% on weekends while total VMT (Vehicle Miles Traveled) de-
creases around 30%. Although a number of unknown factors such as stationary
source contributions and relationships between VMT and emissions are involved,
we can expect similar decreases in emissions of precursor pollutants (hydro-
carbons and oxides of nitrogen) on weekends.
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47
The reduced levels of precursor pollutant emissions on weekends should
be compared with decreases in population exposure to QX and N02 during week-
ends. From Table 3.1, population exposure to QX decreases on weekends by
(1.5/30.1) x 100 = 5.0% in daily risk frequency and (0.54/6.31) x 100 = 8.6%
in hourly risk frequency. From Table 3.2, population exposure to N02 de-
creases on weekends (2.3/4.4) x 100 = 52% in daily risk frequency and
(0.39/0.57) x 100 = 68% in hourly risk frequency. Therefore, the decrease
in population exposure to Ox is less than that in precursor pollutant emis-
sions while that in population exposure to N02 is greater than that in pre-
cursor pollutant emissions.
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49
4. EFFECTS OF DAILY POPULATION MOBILITY ON POPULATION EXPOSURE
In the preceding sections, the analysis of population exposure to
photochemical air pollution was made based on the static population as-
sumption which assumes that every person stays close to his resident
location. As mentioned in Section 2.3, the static population assumption
should be adequate for quasi-stationary segments of the population such
as elderly and school-age, but would not hold for the working population
because that population spends a large part of their time at their working
places where the air environment may be quite different from that of their
residential locations.
Therefore, in this section, Worker population is treated as follows:
Exposure of the population to 0Y air pollution above the NAAQS during working
Ai
time (weekdays from 7 A.M. to 6 P.M.) occurs at their place of employment
and exposure during non-working time (the rest of the time) occurs at their
place of residence. Distribution of people exposed at various risk fre-
quencies (hereafter termed "population-at-risk distributions") is computed
separately during working time and non-working time. Population-at-risk
distribution during all times is then computed from those during working
time and non-working time. Population-at-risk distribution for the non-
workers population is computed by using the static population assumption.
Finally, the population-at-risk distribution for the total population is
computed by combining those of Workers and Non-Workers.
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50
4.1 PQPULATIQN-AT-RISK DISTRIBUTION FOR STATIC AND MOBILE POPULATIONS
For the mobile population analysis, the exposure of workers during
working time is assumed to occur at their place of employment. For the
static population analysis, it is assumed to occur at their place of residence.
Therefore, the difference in the exposure of the mobile population and the
static population occurs during working time only. Figure 4.1 was prepared to
show the difference in 0 exposure of Workers during working time at their
/\
residence and at their work places. The Worker population at their work place
was computed from the employment statistics prepared by the Southern California
Association of Governments (SCAG)1 (Table Al). The Workers population at
2
their residence was computed from the 1970 census data and SCAG's estimates
of total population in 1970 and 1975 (Table Al). It is seen from the figure
that the population exposure at their work places is less than that at their
residences. Therefore, it can be said with respect to Ox air pollution in the
Los Angeles AQCR, that workers on the whole benefit by working at places with
a cleaner air environment than they would have if they stayed home during
working time.
In order to obtain the population-at-risk distribution for the mobile
workers during all time from those during working time and non-working time,
we have to go back to a risk frequency of an individual worker, and have to
compute the risk frequency of that worker during all time from those during
working time and non-working time (Appendix D). This is quite a contrast to
the static workers whose population-at-risk distribution during all time is
computed from a risk frequency during all time.
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A difficulty arises in computing the risk frequency of a mobile worker
during all time from that during working time at his work place and that during
non-working time at his residence. The difficulty is caused by the lack of
Information of an individual worker's mobility between residence and work place.
Although we have information about the worker population at their work place
and their residence, we do not have an origin-destination relationship that
informs us of each individual worker's residence and work place.
To alleviate the difficulty caused by the lack of origin-destination in-
formation, the hypothesis is employed that exposures of a worker during working
time and during non-working time are statistically independent. Under this
hypothesis, the probability density of a worker having a given risk frequency
during all time is given by the convolution of those during working time and
non-working time as:
j
Prob(R* = J) = ]T) Prob(R*w yi = k)Prob(R* T /T = j - k) (*-D
k=o n n
where R* is the risk frequency during all time T, R* that during working time
Tw and R* that during non-working time T . The following relationship exists
between R*, R*, and RJj,
R* " RW VT + R*n VT (4"2)
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53
The population-at-risk distribution for the mobile workers analysis
during all time was determined from those during working time and non-
working time by using Eqs. (4-1) and (4-2). The resulting population-
at-risk distribution during all time (curve 3) 1s compared in Fig. 4.2 with
that for the .static workers analysis during all time (dashed curve), that
at work places during working time (curve 1), and that at residence locations
during non-working time (curve 2). The results clearly show that irrespective
of the mobile or the static assumption, the greatest exposure of the Workers
population occurred during working time. This finding is consistent with our
understanding that oxidant air pollution is confined to daylight hours. At the
same time, this fact may support the importance of population mobility considera-
tion in a population exposure estimate. By comparing the population-at-risk
distribution for the mobile population (curve 3) to that for the static popu-
lation (dashed curve) which would have resulted from v/orkers always staying at
their residence locations, some differences are noted. To highlight the
differences between the two curves, their histograms are shown in Figure 4.2a.
It is seen that fewer members of the mobile workers population are annually sub-
jected to the most frequent, as well as the least frequent, exposure to 0
/\
above the NAAQS than those of the static workers population.
The influx of workers into the business districts during working time is
exhibited in Figure 2.7. A comparison of this population mobility map and the
isopleth map of oxidant air quality (Figure 3.6) shows that the daytime popula-
tion moves from residential areas of the highest 0 concentrations as well as
A
of the lowest 0 concentrations to the business districts where the oxidant
-------
54
o
c:
O)
3
cr
OJ
s_
O)
4->
-------
55
O)
J=.
!->
(U
O
JD
(O
X
o
20-
5
0)
3
(/)
O
CL
X
CU
00
S-
cu
_i^
s_
o
O)
>>
jQ
to
JZ
2
Q-
15-
5-J
I
Mobile Workers
1
Static Workers
I
6 10 14
Percent of hours above the NAAQS
18
Figure 4.2a PROBABILITY DENSITY DISTRIBUTION OF WORKERS
EXPOSED TO HOURLY Ox ABOVE THE NAAQS
-------
56
air quality is in between the two extremes. These patterns of workers' daily
mobility would explain the differences observed in the distribution of expo-
sures between the mobile workers analysis and static workers analysis.
Although the incorporation of population mobility consideration into
population exposure analysis has resulted in a lower estimate of population
exposure to 0 than the static population assumption, the same consideration
A
would result in a less conservative estimate of population exposure to NCL. The
reason for this is that the daytime population moves from residential areas of
lower NOg concentrations to the business districts where NO, concentrations are
higher. Therefore, the population mobility consideration in population
exposure analysis for Los Angeles would be more crucial for N02 and primary
pollutants such as TSP, CO, S02 and hydrocarbons produced in commercial
districts in identifying the population at an extreme risk than for Ox and
other secondary pollutants such as sulfate, nitrate and photochemically
produced aerosols which are subject to transport.
The 0 population-at-risk distribution for the mobile total population
A
during all times was obtained by linearly combining those for the mobile workers
population and the non-workers population. In Figure 4.3 the population-at-risk
distribution of the mobile total population (curve 3) is compared to that of
the static total population (dashed curve). The comparison shows that the static
population assumption overestimates the number of people of the Total population
who were exposed at the highest and the lowest risk frequency but underestimates
-------
57
0)
3
CT
0)
S-
IV
4J
CO
c
<0
C5 - 1 STD
1 r flLL WORKERS
2 = NON-WORKERS
3 - MOBILE POPL
DflSH - STflTIC P0PL
s_
o
a
a>
o
CL
X
c
o _
f- IT
+J O
ca
Q.
O
a.
o
c
o
u
£
o
o
5 10 15
Percent of hours above the NAAQS
20
25
Figure 4.3 POPULATION EXPOSURE TO OXIDANTS ABOVE THE NAAQS
(1 FOR WORKERS, 2 FOR NON-WORKERS, 3 FOR MOBILE
TOTAL POPULATION, and ~ FOR STATIC TOTAL POPULATION).
-------
58
the number of people who were exposed at a risk frequency 1n the middle range.
Figure 4.3 also shows that Workers were exposed at a smaller range of risk
frequency than Non- Workers.
4.2 Significance of Population Mobility in Population Exposure Estimates
It was shown in the preceding section that incorporation of daily popula-
tion mobility into the analysis improves our estimates of the distribution of
population subjected to different degrees of exposure to air pollution. However,
in reporting the state of air quality over a given region, it is more relevant
to know the change of some index from one year to another year than to know a
detailed population distribution for different degrees of exposure when the
latter is difficult to estimate correctly. Thus, we ask, 1s the static popu-
lation model adequate for estimating gross indices of population exposure such
as regional average of risk frequency R?
In computing the regional average of risk frequency for a mobile popula-
tion, we do not have to have the population-at-risk distribution which required
a complex computation involving the convolution given by Eqs. (4-1) and (4-2).
The regional average of risk frequency during all time T can be computed
directly from those during working time TW and non-working time Tn as
R(CS) - TW{RW(CS) + Tn Rn(Cs)>/T (4-3)
where "R(CS) is the average risk frequency during all time, ^y(Cs) that during
working time aiio T\ (o^/ c.n&c during iiou-workl ng time. This was clone for bocu
weekdays and all time.
-------
59
Using Eq. (4-3), the average risk frequency for the mobile workers popula-
tion during weekdays was computed from that for Workers at their work place
during working time and that for Workers at their residence during weekday
non-work time. The average risk frequency for the static worker's population
during weekdays was computed from that for Workers at their residence during
working time and that during weekday non-working time. Similarly, the average
risk frequency for the mobile workers population during all times was computed
from that for Workers at their work place during working time and that for
Workers at their residence during non-working time. And the average risk
frequency for the static workers during all times was computed from that for
Workers at their residence during working time and that during non-working
time.
The average exposure of the mobile workers population and that of the
static workers population to QX are given in Table 4.1, while those to N02
are given in Table 4.2. The average exposure of the mobile total population
and that of the static total population are also given in Tables 4.1 and 4.2.
The average exposure of the Total population Prt (either mobile or static)
Q - -..^. . HL ._..._L.
was computed from that of Workers population P., and that of Non-worker
R(CS) = {Pw *W(CS) + Pn Rn(Cs)}/PQ (4-4)
population Pn by
Table 4.1 shows that the static population model estimated the average
risk frequencies for Workers population during all times as 5.87 percent of
the total number of hours and 28.8 percent of the total number of days, while
-------
60
Table 4.1 Effect of the Consideration of Population Mobility on the
Estimates of Population Exposure to 0 in the 1973 Study Area.
Time Model
Static
Weekday Mobile
Amount of
Misestimate
Static
All Time Mobile
Amount of
Misestimate
Workers
5.
5.
+0.
5.
5.
+0.
961
53
43
87
57
46
(29
(28
(+1
(29
(28
(+0
8)2
.8)
.0)
.0")
.7)
.7)
Total
6.
6.
+0.
6.
5.
+0.
Population
22
04
18
09
96
13
(30.
(29;
(+0.
(29.
(29.
(-1-0.
1)
7)
4)
7)
4)
3)
1. Percent of hours above the NAAQS.
2. Percent of days above the NAAQS.
Table 4.2 Effect or the Consideration of Population Mobility on the Estimates
of Population Exposure to N02 in the 1973 Study Area.
Time Model
Static
Weekday Mobile
Amount of
Misestimate
Static
All Time Mobile
Amount of
Misestimate
Workers
0.5943(4.56)4
0.726 (4.92)
-0.132 (-0.36)
0.476 (3.86)
0.570 (4.12)
-0.094 (-0.26)
Total Population
0.572 (4.37)
0.626 (4.55)
-0.054 (-0.18)
0.460 (3.74)
0.499 (3.84)
-0.039 (-0.10)
3. Percent of hours above the California one-hour standard.
4. Percent of days above the California one-hour standard.
-------
61
the mobile population model as 5.57 percent of the hours and 27.4 percent of
the days. For the total population, the static population model estimated
the average risk frequencies during all times as 6.09 percent of the hours and
29.0 percent of the days, while the mobile population model as 5.96 percent
of the hours and 28.9 percent of the days. The relative misestimates of the
average risk frequency for Workers population by the static population model
are (0.46/5.57) x 100 = 8.3% in hourly risk frequency and (1.4/27.4) x 100 =
5.1% in daily risk frequency. These misestimates for Workers population
should be compared to those for the Total population which are (0.13/5.96) x
100 = 2.2% in hourly risk frequency and (0.1/28.9) x 100 = 0.3% in daily
risk frequency. Therefore it can be said that the relative misestimates for
0 by using the static population model are less than 9% for Workers
A
population and less than 3% for Total population.
From Table 4.2, the corresponding relative misestimates for N0£ by the
static population model are (0.094/0.570) x 100 = 16.5% in hourly risk
frequency and (0.26/4.12) x 100 = 6.3% in daily risk frequency for Workers
population, and (0.039/0.499) x 100 = 7.8% and (0.10/3.84) x 100 = 2.6% for
Total population. Therefore, it can be said that the relative misestimates
for N02 by using the static population model are less than 17% for Workers
population and 8% for Total population.
The above analysis shows that the magnitude of relative misestimates by
the static population model is greater for Workers population than Total
population, and for N02 than Ox. These findings are consistent with our
understanding that since Workers population constitutes only about 40% of
-------
62
Total population, the effects of diurnal population mobility are less pro-
nounced when considered for the Total population, and that since N02 concen-
trations are highest in business districts, while Ov concentrations are
A
moderate, the effects of population mobility are more pronounced for N02.
Analytical Errors
When the mobile population model is applied to estimate population ex-
posure, the air quality data as well as the population data have to be pre-
pared for a number of different time categories. The generation of the
percentile concentration statistics from each subset of the air quality
data introduce some error in the approximate population-at-risk distributions.
This resulted in small errors in the computations of the population exposure
parameters from each subset of the air quality and the population data. Let
us compare the numbers appearing in Tables 3.1 and 3.2 with those appearing
in Tables 4.1 and 4.2 which were prepared independently of Tables 3.1 and
3.2. For example, the average risk frequency of 0Y for the static total
«
population during weekday is 6.22 percent of hours in Table 4.1 while 6.31 per-
cent of hours in Table 3.1. The error [6.22-6.31| = 0.09 is caused by sub-
division of the air quality data iuring weekday into those during working time
and those during weekday non-work time. This error caused by the air quality
data subdivision should be compared with the error caused by the two different
models, |6.22-6.04] = 0.18. The magnitude of the former error reaches as much
as 50% of the latter.
From the facts described above, the mobile population model which de-
mands far greater amounts of data preparation, processing, and analysis than
does the static population model can be said to be of a limited value in
-------
63
computing the gross indices of population exposure. However, the population
mobility consideration is critical for correctly identifying the population-
at-risk, particularly for exposure of Workers population to primary pollutants
which are spatially correlated with employment locations.
-------
65
5. CONCLUDING REMARKS
Population exposure methodology was developed and applied to analyze the
weekday-weekend effect and the effect of diurnal population mobility on popula-
tion exposure estimates for two photochemical pollutants, 0₯ and NOo in the
A £
Los Angeles Basin. The following paragraphs summarize the findings and con-
clusions reached in this report.
Population Exposure Methodology
t Population exposure methodology was developed to
specify local and/or regional air quality relative to
the standards and to quantify population exposure to
air pollution.
e Two-new parameters, "risk- frequency," and "mean
duration" were introduced, and the method for
determining these parameters from air quality and
population data was developed.
For each of the two parameters a computer algorithm
was developed to obtain a distribution function and
an aggregated index.
The methodology and the computer algorithms for
determining the population exposure variables for a
mobile population were developed.
Computer software for drawing a digitized regional
map, isopleth map, and cumulative and density
distribution charts were developed.
-------
66
Weekday-Weekend Effects
Spatial analysis of 0 and N0? air quality over the
/\ £
Los Angeles Basin was performed by (1) computing
isopleths of daily risk frequency indicating a
percentage of days on which the standard was exceeded
and (2) computing isopleths of mean duration
indicating an average number of hours per day for
those days with violation of the standard.
For 0 , the coastal region where the standard was
J\
exceeded less than 20% of the days was more polluted
(by about 3% of the days) during weekends than
weekdays. The inland region where the standard was
exceeded more than 40% of the days was less polluted
(by about 7% of the days) during weekends than
weekdays.
On an annual basis the population on the whole is
exposed to Ox air pollution exceeding the NAAQS on a
smaller percentage of ooth hours and days during
weekends than weekdays. Therefore, it can be said
that although oxidant concentrations become higher
over weekends than weekdays at some coastal stations,
the average exposure of the basinwide population to Ox
is lower on weekends.
-------
67
For N02, the Los Angeles CBD and the surrounding area
where the California standard was exceeded more than
4% of the days were less polluted (by about 4% of
the days) during weekends than weekdays. Most of the
Orange county and the Riverside county portion where
the California standard was exceeded less than 3% of
the days were more polluted (by about 1% of the days)
during weekends than weekdays. This increase in N02
air pollution over weekends would probably be
attributed to the v/eekend pleasure drives toward these
areas.
The population on the whole is exposed to N02 air
pollution exceeding the California standard much less
during weekends than weekdays in both the percentage
of days and the percentage of hours.
Effects of Daily Population Mobility on Population Exposure
Because of the daily population migration from
residence areas of the worst Ox air pollution as well
as the least 0 air pollution to the business
A
districts of moderate 0₯ air pollution, fewer workers
A
are annually subjected to the most frequent as well as
-------
68
the least frequent exposures above the NAAQS than
there would be if they stayed home all the time.
Workers on the whole benefit by receiving less
frequent exposure above the NAAQS at their place of
employment.
Because of the daily population migration from
residence areas of moderate to low N02 air pollution
to the business districts of high N02 air pollution,
most workers receive more frequent exposure above the
California standard at their work places than they
would have if they stayed home all the time.
Population mobility considerations are important for
determining the population-at-risk accurately. This
is particularly true for the workers population.
However, the population mobility consideration is not
very critical in determining the aggregated indices of
population exposure.
-------
69
REFERENCES
1. "Suggested Revision of SCAG Growth Forecast Policy (June 1975), As
Modified (December 1975)," Southern California Association of
Governments, Los Angeles, California, December 1975.
2. "Census Tracts," Bureau of the Census, U.S. Department of Commerce,
Series PHC(l), May 1972.
3. "Directory of Air Quality Monitoring SitesActive in 1973," USEPA,
OAQPS, EPA-450-2-75-006, March 1975.
4. Horie, Y., and A. C. Stern, "Analysis of Population Exposure to Air
Pollution in New York-New Jersey-Connecticut Tri-State Region," USEPA,
OAQPS, EPA-450/3-76-027, March 1976.
5. California Air Resources Board, "Weekday vs. Weekend Oxidant Concen-
trations," California Air Quality Data, Vol. 6, No. 3, 1974.
6. Elkus, B., and K. R. Wilson, "Air Basin Pollution Response Function:
The Weekend Effect," Paper submitted to Science, University of California,
San Diego, undated.
7. Martinez, E. L. , and E. L. Meyer, Jr., "Urban-Nonurban Ozone Gradients
and Their Significance," Specialty Conference on "Ozone/Oxidants-
Interactions with the Total Environment," Edited by Air Pollution
Control Association, pp. 221-233, Dallas, Texas, March 10-12, 1976.
8. Cleveland, W. S., T. E. Graedel, B. Kleiner, and J. L. Warner, "Sunday
and Workday Variations in Photochemical Air Pollutants in New Jersey
and New York," Science. Vol. 186, pp. 1037-1038, December 13, 1974.
9. TRW, "Episode Contingency Plan Development for the Metropolitan Los
Angeles Air Quality Control Region," Final Report to USEPA Region IX,
December 1973.
10. Parzen, E., "Stochastic Processes," Chapter 1, Holden-Day, San
Francisco, 1967.
-------
A-l
APPENDIX A
DATA ON TOTAL POPULATION, WORKERS BY RESIDENCE, AND WORKERS
BY EMPLOYMENT LOCATION IN 1973
-------
A2
Table Al.
Total Population, Workers by Residence, and
Workers by Employment Location in 1973.
RSA Land Area
No. (Sq. Mile)
Ventura Co.
1
2
3
4
5
6
Los Angeles
Co.
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
San Bernardino
Co.
28
29
30
31
32
33
34
919.0
325.0
194.0
137.0
150.0
139.0
92.2
379.0
974.0
678.0
527.0
144.0
39.9
76.5
86.9
74.4
97.1
67.9
95.2
60.6
101.0
120.0
6.2
71.4
146.0
170.0
60.0
236.0
231.0
806.0
9484.0
3034.0
3452.0
2880.0
Total
Population
358
114378
147812
73477
64178
10639
24313
55252
54036
32696
1889
554377
260043
268710
13970
309625
918773
521836
422898
423399
796281
606388
86028
407910
660751
458794
150001
244144
304250
22365
7898
79582
26400
5893
Workers* by
Residence {%)
33.6
39.3
35.7
35.3
38.5
41.2
40.7
37.7
39.3
35.3
34.5
45.0
48.8
39.1
46.1
48.7
48.1
47.4
39.2
41.9
35.8
41.5
44.0
45.0
43.1
38.4
38.9
37.6
34.8
38.6
20.9
31.0
21.1
39.4
Workers by
Employment Location
98
39742
59934
4278
15370
3327
9480
7024
14936
14791
1371
202137
136963
58246
2881
137786
440289
256858
151449
194268
480957
163131
326976
140960
246729
121606
66249
75212
45054
6125
4122
29055
9717
2513
-------
A-3
Table AT. Total Population, Workers by Residence, and
Workers by Employment Location in 1973.
RSA
No.
Orange Co.
35
36
37
38
39
40
41
42
43
44
Riverside Co.
45
46
47
48
49
50
51
52
53
54
Imperial Co.
55
Land Area
(Sq. Mile)
28.8
45.6
49.8
62.4
100.0
71.1
101.0
52.2
205.0
90.4
61.1
354.0
289.0
129.0
504.0
238.0
709.0
478.0
347.0
4070.0
4241.0
Total
Population
167859
179490
317642
267211
184327
51876
46988
286413
30956
28439
38989
236657
26006
40472
13414
27540
3561
58977
40546
16476
79747
Workers* by
Residence (%}
38.9
42.7
42.7
38.4
42.5
36.2
37.1
41.4
36.5
19.3
34.6
36.3
22.4
28.3
30.0
31.1
34.6
39.1
39.9
36.8
33.9
Workers by
Employment Location
37579
92794
111836
53574
88399
12756
10338
119225
5680
24253
7587
82241
8009
10511
3082
6203
903
19117
15155
7049
2)937
-------
B-l
APPENDIX B
AIR QUALITY DATA FOR QX AND N02 IN
Table Bl Corrected Ox daily maximum hourly average concentrations in
1973 (1 for all times, 2 for weekdays, 3 for weekends).
Table B2. Corrected Ov hourly average concentrations in 1973 (1 for all
times, 2 for weekdays, 3 for weekends, 4 for working time, 5 for
non-working time, 6 for weekday non-working time).
Table B3 N09 daily maximum hourly average concentrations for 1973 (1 for
alf times, 2 for weekdays, 3 for weekends).
Table B4 NO? hourly average concentrations in 1973 (1 for all times, 2 for
weekdays, 3 for weekends, 4 for working time, 5 for non-working
time, 6 for weekday non-working time).
-------
B-2
Table Bl. Corrected Ox daily maximum hourly average concentrations in 1973
(1 for all times, 2 for weekdays, 3 for weekends). All
values in pphm.
PERCENTILE
HO. STATION DBS.
MAX
1
5
25
50
75
1 ANAHEIM
1
2
3
2 A 2 US A
1
2
3 .
361
259
102
365
261
104
26. B
26.2
23. B
46.0
46.0
35.0
20.0
20.0
23.0
35.8
38.4
31 .3
17.6
15.0
19.3
30.0
32.1
29.3
14.
13.
18.
28.
28.
28.
3
B
0
0
B
2
1B.B
10.0
12.2
23.9
>-? 7
£. _' . J
24.0
7.
7.
7.
16.
17.
15.
0
0
B
1
B
0
4.0
4 .0
5. a
3.0
3.9
7.4
2.0
2.0
3.0
3.0
3.0
4 .0
3 BURBAHK-PALJ1
i
2
3
365
261
104
29.0
29.0
21.0
23.3
25.6
20.4
20.0
20.3
19.0
13.
17.
18.
0
,3
.0
16.0
16.0
15.0
11.
11.
9.
0
1
4
6.0
6.0
6.0
3.0
3.0
3.0
4 CAMARILLO-PALM
1
2
3
5 COSTA MESA
1
2
3
6 EL TORO
1
2
3
7 LA HABRA
i
2
3
3 LENNOX
i
2
3
9 LONG BEACH
1
-. 2
3
357
255
102
343
252
91
357
256
101
362
260
102
365
261
104
364
261
26.0
26.0
17.0
21.0
21.0
19.0
19.0
19.0
18.0
30.0
30.0
24.0
24.0
24.0
14.0
20.0
20.0
11.0
17.8
21.4
15.8
17.9
16.0
19.0
15.3
13.8
17.4
24.0
23.3
24.0
12.5
11.0
13.4
13.8
16.4
9.7
14.0
14.8
13.3
14.1
14.0
15.7
11 .0
11 .0
12.5
22.0
21 .0
23.3
10.0
10.0
9.6
9.0
9.0
8.0
13
13
11
12
11
13
10
10
10
20
19
23
8
8
8
3
7
8
.0
.0
.3
.3
.8
.8
.0
.B
.3
.0
.0
.0
.1
.3
.2
.0
.0
.0
11.0
11.0
9.2
9.0
9.0
9.B
3.0
7.0
9.0
15.0
15.0
18.2
6.0
6.0
7.0
7.0
6.0
7.0
3.
9.
8.
7.
7.
7.
5.
5.
6.
9
9
3
5
5
6
5
5
6
1
0
0
0
0
0
1
0
0
.0
.0
.0
.0
.0
.0
.0
.0
.0
6.0
6.0
6.0
5.0
5. B
5.0
4.0
4.3
4.0
5.0
5.0
5.0
3.0
3.0
4.0
3.0
3.0
4.0
4.B
4.0
4.0
4.0
3.0
4.B
3.0
3.B
3.B
3.0
2.4
3.S
3.0
3.0
3.0
2.0
2.0
3.0
10 L.A. DOWNTOWN
i
2
3
365
261
52.0
52.0
25.0
30.3
31.0
23.7
19.4
19.6
17.3
17
17
17
.0
.0
.0
13.0
13.0
14.0
10
10
10
.0
.0
.0
6.0
6.0
6.0
3.2
3.2
«.E
-------
B-3
Table Bl (Continued)
HO. .STATION OBS.
MAX
PERCENTILE
3 5 IB
25
75
11 NEWHALL
1 363
2 260
3 103
12 NORCO-PRADO PRK
1 363
2 261
3 102
13 OJAI
1 202
2 143
3 59
14 PASADENA-WALNUT
1 364
2 260
3 104
15 POMONA
1 364
2 263
3 104
16 RESEDA
1 365
2 261
3 104
17 RIVERS I DE-MAGNOL
1 355
2 255
3 100
36
36
32
35
35
26
22
22
16
45
45
30
32
31
32
28
28
22
IA
36
36
27
.0
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29.0
29.0
27.6
25.8
25.8
24.3
18.4
19.0
15.6
35.8
37.8
28.1
29.8
28.6
32.0
24.0
24.8
21 .4
30.7
31 .0
27.0
27.0
27.6
22.6
23.0
22.6
23.3
16.0
16.2
14.0
23.0
29.2
27.0
24.8
24.0
25.7
22.0
23.0
20.0
25.0
25.8
23.4
25
26
21
20
20
20
14
15
14
26
25
26
24
23
24
20
20,
13.
23.
24.
23.
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0
6
0
22.0
23.0
18. 1
16.0
16.0
16.2
13.0
13.0
12.0
22.0
22.0
22. 0
20.0
20.0
21.0
17.0
17.0
16.0
20.9
21.0
20.0
16.0
17.0
13.0
11.0
12.0
9.0
9.0
9.0
8.0
16.0
16.0
14.0
14. 0
15.0
13.0
13.0
13.0
11.0
15.0
16.0
12.4
6
6
4
6
6
6
6
6
5
8
9
7
7
8
7
6
-j
1"
5
7
7
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3.0
3.0
3.0
3.0
3.0
3.0
4 .0
4.0
4.0
4.0
4 .B
5.0
3.0
3.0
4.0
3 .0
3.0
3. a
3 . 3
3 .8
4.0
- 18 RIVERS I DE-RUBIDOUX
1 365
2 261
3 104
19 SAN BERNADINO
1 353
2 255
3 98
20 UPLAND-ARE
1 303
2 215
3 38
31
31
26
34.
3S.
34.
51.
51.
36.
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0
0
0
0
0
0
27.3
29.6
26.0
28.0
27.8
31 .7
41.8
45.5
35.5
24.0
25.1
23.3
26.0
26.0
25.4
35.0
35.9
34.0
23.
24.
22.
23.
23.
20.
32.
33.
28.
0
0
0
0
6
5
5
9
0
21.0
21.0
19.0
19.0
20.9
17.0
29.0
29.0
26.6
14.0
15.0
12.4
15.0
16.0
11.9
22.0
23.0
19.0
-7
f
3
7
6
7
6
14
15
10
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3 .0
3 0
3 8
3 .0
3.0
3.0
5 .2
5 .0
6 . S
-------
B-4
Table Bl (Continued)
PERCENTILE
NO. STATION DBS. MAX 1 3 5 IB 25 50 75
21 WEST L.A.-WSTWOOD
i
2
3
22 UHITTIER
1
2
3
364
260
104
364
263
1B4
39.
39.
16.
23.
28.
27.
Q
a
0
a
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0
21
22
15
24
23
25
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14
15
14
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18
21
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13.
13.
17.
15.
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B
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4
2
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12
12.
16.
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B
B
B
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8.
8.
8.
8.
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0
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4
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4
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5
5
5
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5
5
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3
3
3
3
3
4
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.0
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B
.0
-------
B-5
HO. STATION
Table B2. Corrected Ox hourly average concentrations in 1973 (1 for all
times, 2 for weekdays, 3 for weekends, 4 for working time, 5
for non-working time, 6 for weekday non-working time). All
values in pphm. PERCENTILE
OBS. MhX 1 3 5
1Q
1 ANAHEIM
1 8173
2 5823
3 2353
4 2735
5 5438
6 3085
2 AZUSA
1 8162
2 5774
3 2388
4 2671
5 5491
6 3 1 33
3 BURBAHK-PALH
1 8315
2 5923
3 2392
4 2795
5 5520
6 3128
4 CANARILLO-PALH
1 7888
2 5627
3 2261
4 27B4
5 5184
6 2923
1. COSTA HESA
1 7439
2 5359
3 2B80
4 2557
5 4882
fa 2802
6 EL TORO
1 8B37
2 5725
3 2312
4 2720
5 5317
6 3005
26
26
23
26
23
"7
46
46
35
46
35
23
29
29
21
29
21 .
11
26.
26
1 7
26.
17.
14
21 .
21.
19.
21.
19.
14.
19.
19.
18.
19.
18.
9 .
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a
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B
0
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B
0
B
0
B
B
B
B
B
B
B
B
B
B
B
0
B
B
B
11
11
13
14
9
5
24
25
24
28
20
7
16
16
15
19
12
5
12
12
11
14
9
8
IB
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7
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8
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-------
B-6
Table B2 (Continued).
STATION OBS
PERCENTILE
5
IB
5 a
7 LA HABRA
1 8144
2 5793
3 2346
4 2737
5 5407
6 3361
8 LEHHOK
i 3316
2 5343
3 2373
4 2816
5 55BB
6 o i 27
9 LONG BEACH
i 82B1
2 5856
3 £345
4 2756
5 5445
b 3100
10 L.A. DOWNTOUH
i 8357
2 5933
3 2424
4 2733
5 5624
6 32BS
H NEWHttLL
1 3273
2 5929
3 2344
4 2824
5 5449
6 3105
12 NORCO-PRADO PRK
1 84B8
2 5999
3 24Q1
4 2723
5 5683
6 ?27?
30.
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-------
Table B2 (Continued).
B-7
MO. STATION
13 OJAI
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2.0
3.0
3. 0
4.0
7. B
2 0
1.0
4.0
4. 0
4.0
8. 0
3.0
2.0
4.0
4.3
4.0
9.S
2 0
2. 0
4.0
4. 0
4.0
9 a
2. B
1 B
53
3
2
7
4
2.
1
2
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cl
4
1
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C,
'
i
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i
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C
n
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1 0
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71.
m
,"i
ra -t. -j ...4 co ro
£> -yj '.ii co .:> ~--J
'..o ro LJI '-j / --.i
r- ro ro r.j ro ro
>". j co o co
ca nil ca t:-.! T:I ca
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f.:*i Lfn ca til ca
X:
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p.' oj en
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r.. /_ r . ro '.n -j ><
-j -.ii -j -- -_n CT-. 2:
ij-., H.^, jj-., r..r| i>J CO O
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ca i3'-. EI ca ra ca
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c^j -M i~* ca -^ 'si>
Da ca ca ca ca ca
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o rs is ca ca ra
ra ra ra ra CD P.'>
CD cj ca ca r.'ii a
i -.j -j -.j
i ra ca ra
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'",.n o-j c.n o"' '- *--
ra ca ra ca ra rra
..^ H-^ r. 3»-- ro ro
ca en oj co t-* ca
ta ca ta era ca ca
C33
i
i
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i
I O-J
i
i
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i en
i
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m
50
o
m
03
00
Co _p> ~-.j or-. c.n c.n
ra ca ca ca ca ca
.*-.. CH ---.I >5^ '>' '5"'i
csi KI ca ra ca ca
j-.v co > oj j rn jn- co ca ra
ca ca GJ ra ca CD
i cn
i-* ro .*- >j-J c>j o-j
ca ca EI ca ca ca
ca ca E! ca
r- 'i oj o.j i j-.. j
ta ca '-JD EI ei
t-- ro o c^j oj o-J
ca EI ca ca ca EI
t-.. i_* p.j, p.j, j.-* > ,.-... ,.. ro i'-.;i i-* !- i-* >-' '-'. P-J ro ro ca ca ij-i -- *- !-t
ca a en fa ca ca ro ca ra r.r;i a rra era ca ra ta a r.ui en EI r:a ca ra ca
cn ra C3 ca ca ca
rra era ca ca ca ra
la ta -- ta ca Ei
era CTJ i"a r:a ca ca
-------
B-9
Table B3. NO? daily maximum hourly average concentrations in 1973 (1 for
alt times, 2 for weekdays, 3 for weekends). All values in pphm.
NO.
STAilON OBS.
MAX
PERCENTILE
5 10
25
50
1 ANAHEIM
1
.-t
c.
3
: AZUSA
i
2
3
329
235
94
361
261
IBB
49
49
35
32
32
22
.0
.0
.0
.0
.0
.0
33
31
34
29
29
21
. 1
.8
.5
.0
.8
.4
25
23
33
25
27
18
.5
.3
.0
.0
.6
.4
22.
21 .
27
23 .
24
17
.0
.B
.8
.3
.0
.4
16.
16.
17.
19.
2B.
15.
5
0
0
0
0
4
11.
11 .
9.
14.
15.
ii .
B
B
9
B
B
B
8
8
6
10
11
3
.0
.0
.0
. 3
.0
a
6 .0
6.0
5 8
7.B
7 0
A .0
3 BURBAHK-PALM
1
2
3
364
261
103
38.
38.
29
.0
.0
.0
35
36
26
.5
.8
.5
31
32
20
.0
. 1
.9
27
33
28
.0
. 3
.0
23.
24.
17.
B
3
1
17.
18.
11.
B
B
6
1 1
13
8
.0
.0
.0
7 n
f . EJ
3 B
7 0
4 CAHARiLLO-PALfl
i
2
3
5 CHIHO
i
I
£
3
6 COSTA RESA
i
ii
7
7 EL TORO
i
~:
C.
3
8 LA HABRA
i
2
3
9 LENNOX
1
2
3
10 LONG BEACH
1
-*
i
3
356
254
102
340
247
93
353
259
94
355
255
IBB
356
257
99
363
261
1B2
361
26B
101
18
18.
IB
29
29
22
29
29
28
3H
3B
24
51
51.
35
39,
39.
25.
35.
35.
32.
.0
.0
.0
0
.0
.0
0
.0
.0
.8
.0
.0
.0
.0
. 0
0
0
0
0
0
0
14
15
10
22
22
17
26
25
26
23
22
23
33
32
33
31
33
25
32
33
30
.7
.0
.0
.0
.0
.8
.0
.8
.9
.0
.8
.4
.0
.8
.8
.0
.4
.0
. 8
.0
.8
10
11
9
18
18
12
20
17
22
20
17
21
26
26
27
29
30
22
29
30
25.
. 7
.8
.3
.0
.0
.6
.6
.6
.6
.0
.5
.4
.0
.0
.9
.5
.0
.0
.6
.0
.5
10.
IS
9
14
16
12
17
15
20
16
15
20
22
22
24
23
28.
19
26,
28.
24,
.0
.8
.B
.3
.0
.0
.0
.0
.4
.0
.0
.B
.B
.B
.0
.5
.3
.6
.3
.B
.B
8.
9
8.
12.
12.
11.
14.
14.
14.
12.
12.
11.
18.
18.
16.
18.
19.
14.
2B.
21.
19.
e
B
0
Q
0
B
B
B
0
.B
9
4
0
B
B
1
0
2
B
B
0
6.
6.
6.
Cj
_' .
IB.
8.
8,
8
8,
8
8
8.
13
13,
IB,
13.
15.
IB.
15.
15.
13.
0
9
B
B
B
.0
0
B
.B
B
B
. B
.B
.B
B
B
B
9
B
4
B
5
5
5
7
7
6
i-
rr
5
cr
J
5
C7
9
9
7
IB
10
8
IB
10
M
.0
.0
.0
.0
.0
.0
D
£_.
.G
.0
.B
.a
.a
.0
.0
.0
.0
.0
.0
.B
.0
A t~
y . ij
4 .0
3 .9
5 L
5 .E
4 0
4 .0
4.0
3.E
4 .S
4 G
4 i~
6.0
7.0
5 .0
f . L
8 0
7.0
)' £..
7 e
6 .0
-------
B-10
H 0
Table B3 (Continued).
STATION DBS
PERCENTILE
5 IS
25
11 L.A. DOyNTOWN
1 353
2 259
3 "54
12 HEWHftLL
i 368
2 263
j IBB
13 NORCO-PRADO PRK
1 352
2 258
3 94
14 OJAI
1 322
2 228
3 94
15 PASADEHA-yALHUT
1 364
2 260
3 184
16 PT. MUGU
1 342
2 244
3 98
17 POMONA
1 365
2 261
3 1E4
IS RESEDA
1 353
2 255
3 98
19 RJVERSIDE-HAGHOL
1 365
2 261
3 184
28 RIVERSIDE-RUBIDO
i 356
2 256
3 100
58.
58.
26.
2B.
20.
12.
17.
17.
13.
12.
12.
10.
33
33 .
20.
33,
33 .
14
36
36
24
19
19
15
!A
37
37
25
UK
33
27
33
B
e
B
B
e
B
0
e
B
e
B
B
0
a
0
. 0
. 0
. a
.0
.a
0
.0
.0
.0
.0
.0
.0
.0
0
0
32.
34 .
24.
17.
18.
12
13
14
12
IB
11
8
29
29
19
22
25
13
26
28
22
17
17
14
31
31
22
24
22
28
9
£ .
q
.8
.a
.a
.0
.6
.5
o
ii
.9
.3
g
.4
.0
.8
.4
.0
.4
~7
. ;
.B
.a
.4
.0
.0
.4
5
.0
2
27.
33 .
23.
14
16.
IE
12 .
13.
11 .
8.
9
7
24
24
18
15
17
10
24
24
21
16
16
13
24
25
2B
28
28
19
8
f:
e
B
3
B
0
B
2
.7
.0
.0
.3
.6
.3
.1
3
.9
.3
.B
.0
.8
.8
.0
.0
.6
.3
.8
0
.2
24 .
26.
21 .
13.
14.
18.
11 .
12.
IB.
7 .
8.
6.
23 .
23.
16.
14 .
14 .
13
22.
23.
21 .
14.
15.
11 .
22.
23.
18.
18.
19.
17 ,
0
E
4
B
B
B
B
B
B
B
B
7
B
B
B
B
2
B
B
B
B
B
B
5
B
B
B
, 6
0
,B
21.8
21 . B
18.3
11. B
12.8
9. B
1B.B
9.6
1B.B
6. B
6.B
6.0
2B.B
2B.B
13.0
ii.e
12.0
9.0
20.0
2B. 3
1 5 . B
12. B
12.0
13.8
18.0
19.3
16.0
1 6 . E
16.0
14. G
15.8
15. 6
10.9
9. 0
9.0
7.B
7.0
7. B
6.0
5.B
5.0
5.0
15. B
16. 8
IB. 4
8.B
9.B
8.8
15.0
16.0
12. B
9.8
1B.0
8.B
15.0
16.0
1,1.4
12.B
12.0
11. B
18.0
13. S
8.3
6.0
7.8
5.4
5.3
5.4
4.B
3.0
3.0
3.0
11 .0
12. B
9.0
6.8
6.3
5 3
10.3
11 .0
9.0
7.0
7.9
6.8
10.9
12.8
8.B
9 . 8
9.8
7.0
7.0
3 .0
6.8
4.0
4 .3
4.E
4.S
4 8
3.8
2.8
2 .8
2.8
8.8
8.9
7 .8
4 .0
4.B
3 .g
8 .8
7 8
5.0
5.8
5 B
7 8
8.8
6 4
6 8
7.8
6 8
-------
Table B3 (Continued).
B-ll
rlO STAlIOH
21 REDLANDS
^
£
3
21 8AH BERNADI
1
c.
3
23 UPLAND-ARB
i
c.
3
24 UPLAND-C1VI
1
i.
2
3
25 yEST L.A.-M
1
d
j
26 WHITHER
1
2
3
UBS
363
259
104
HO
356
258
98
3B1
215
86
C CTR
297
211
86
STWOOD
358
26B
98
357
258
99
PERCENTILE
n4v 1 3 5
18
18
18.
19.
19,
13,
24
24
18,
36 ,
36.
19.
47.
47.
31.
48.
48.
36
B
B
B
B
. B
.0
.0
B
B
B
B
.B
B
.0
B
. 0
.0
. B
16
16
16
16
16
12
23
23
17
23
24
16
36
39
30
31
29.
33
8
8
. 7
.B
.8
4
.a
.3
.1
.4
.B
2
8
.4
.4
. i'
.8
6
14
14
13
14
15
11
23
21
15
17
20
12
3B
"^2
25
26
25
28.
.a
6
3
. B
.a
a
.4
.a
.8
.a
.0
.B
*-t
8
.4
.0
.7
.4
13
13
13
13
14
ia
19
2B
15
15
16
12
28
28
23
23
23
24
.B
.B
.0
.B
.B
5
.B
.B
.B
<=:
.8
.0
.B
.4
.5
.5
,B
.4
i
11
12.
11 .
12
13.
q
17.
18.
13.
13
15.
IB.
22.
24.
19.
2B.
20.
17.
L0
B
.0
.0
B
. 0
.B
B
.0
B
0
B
.0
6
B
.0
.0
B
.0
25
8.
3.
j" .
IB.
IB.
8
13.
14.
11.
IB.
11.
8.
16.
17.
13.
14.
15.
11.
0
B
B
B
B
B
0
0
B
a
0
9
0
0
9
.0
B
B
5B "!
6
6
5
"7
t
8
5
10
12
9
7
7
6
1 1
12
9
i
ia
8
.B
0
. B
.8
.0
0
.0
.0
.B
.0
.0
.B
0
.0
.0
a
0
a
4
4
4
e
5
4
8
8
6
5
5
4
y
3
6
7
g
6
a
LI
F
a
. y
D
n
. 0
'e
£
.a
.0
n
2
. I'
.0
.a
.0
-------
B-12
Table B4. NC>2 hourly average concentrations in 1973 (1 for al.l times-, 2 for
weekdays,.3 for weekends, 4 for working time, 5 for non-working
time, 6 for weekday non-working time). All values in pphm.
PERCENTILE
.VfATIOK DBS
1
10
25
53
I riHAHEIi
.1
-i
-j
&
5
6
2 AZUSH
i
2
3
4
n
6
3 S URBAN
i
C-
3
?
*?
5
6
-4 C A MAR I
i
c.
4
c
,_{
6
5 CHIHO
1
"f
4
5
vj
6 COSTA
i
.i
c~
i
't
rr
^.
K
734B
5l8l
v - =;*
L' 4 4 1
4 8 99
274B
SB 96
p H 3 c
2264
2513
5533
3319
K-PALM
8462
6013
2449
2666
5796
3347
LLQ-PALM
7778
5533
2245
2691
5087
2 y 4 k1
7458
5332
2126
2286
5172
3B46
MESn
7634
5486
2148
2598
^~ H "? "
- tj _ _'
i H 8 3
49.
4*
7 ^
4C;
35.
24.
32
V* C .
22 .
32.
31.
31 .
38.
38.
29
38
3 6 .
36 .
18
18
IB.
it
18
H C'
i U
2 ^
2 Cl
22
23
2*
2C1
29
2?
28
V M
CL O
*" '.~
cl £
B
B
PI
0
S
0
E
B
B
0
0
ti
8
B
S
i
a
B
B
B
. a
. s
8
.0
u
. B
.a
.B
.a
s
a
i3
a
B
.E
0
21 .0
23 B
J tj 3
22. B
21 .B
17.8
20.0
21 .1
16 .0
23.0
19. a
23. a
24.8
26. a
19.3
2 8 . B
21 .0
22. B
8.0
9 B
P.B
9 .3
8.0
9 .a
14. B
is .a
11 .a
is. a
13. a
14 9
17.0
15.0
19.0
16.0
17. a
1 5 0
15 .0
15 .0
13 0
17 .B
14 0
1 ; £S
i c. . Ll
1 .-; p
i O . i.'
1? B
12 .0
19 0
15.0
16 .0
19 .0
20.0
,5.0
23 3
'! £ '"3
17 0
7.0
7 0
6 . id
7.0
7 .0
7.0
11 .0
11 0
10 .0
11 .0
10.0
11 .0
12. a
il 8
15.0
11 0
13 .0
12.3
12.
12.
12 .
14 .
11 .
10.
14.
15.
11 .
16.
13.
14 .
16 .
17.
12.
20 .
14.
14
6
6
6
6
6
.-
b
10
10
9
10
9
13
10
10
11
9
~ r?
13
0
B
B
0
0
B
B
B
B
B
B
0
B
0
B
.0
.B
.B
.B
.B
.3
.B
.B
.B
.B
.B
.B
.0
.B
.B
.0
.B
.B
.0
. 3
.B
9.0
9 0
8.0
11.0
8.0
8.0
12.0
12. D
9 B
13. B
11.0
12. a
13. B
14.0
1B.B
16.0
11.0
12. B
5.B
5. B
5.0
5.0
5. 0
5.0
8. 0
8 0
7 B
8.0
8.B
8 0
7. B
7.B
8. B
7.3
7. 0
7.0
6. 0
7.0
6. B
7.0
6.0
6. B
8.0
9. 0
6.3
1B.0
8.B
8.B
9.B
1B.0
7.0
12.0
S 0
8.B
4.0
4.0
3. 0
3.3
4.0
4.B
6.3
6. 0
5.3
6. B
5. a
6.3
4.3
5.3
4.0
4.3
4.3
5.0
4 0
5.8
4 0
5 0
4 .0
4 .0
5 0
6.0
4.3
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-------
Table B4 (Continued).
B-14
NO. STATION
OBS .
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1
PERCENTILE
3 5
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-------
Table B4 (Continued).
B-15
PERCENTILE
.TttilON 088
I"- H rt
10
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-------
B-16
Table B4 (Continued).
PERCENTILE
HO. STATION DBS. MAX 1 3 5 10 25 5B 75
25 WEST L.A,
i
2
3
4
5
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26 WHITTIER
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2
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-------
C-l
APPENDIX C
MONITORING STATIONS AND RECEPTOR POINTS
Table Cl. Locations and addresses of Air Monitoring Stations,
Table C2. Receptor points assigned to the Los Angeles AQCR.
-------
C-2
Table Cl. Locations and Addresses of Air Monitoring Stations
1. Anaheim #050230001101 (30176)
1010 S. Harbor Blvd., Anaheim, Orange County
2. Azusa #050500002101 (70060)
803 Loren Ave., Azusa, Los Angeles County
3. Burbank #050900002101 (70069)
228 W. Palm, Burbank, Los Angeles Bounty
4. Camarillo- #051030001101 (56408)
Palm
70 Palm Drive, Camarillo, Ventura County
5. Chino-River- #051300001101 (36173)
side Ave.
Central & Riverside, Chino, San Bernardino Cty.
6. Costa Mesa- #052390001101 (30186)
Harbor
2631 Harbor Blvd., Costa Mesa, Orange County
7. El Toro #052390001101 (30186)
3022 El Toro Rd., El Toro, Ora.ige County
8. La Habra #053620001101(30177)
621 W. Lambert, La Habra, Orange County
9. Lennox #053900001101 (70076)
11408 La Cienega Blvd., Lennox, LA County
10. Long Beach #054100002101 (70072)
3648 N. Long Beach Blvd., Long Beach, LA Cty.
11. L.A. Down- #054180001101 (70001)
town
434 S. San Pedro St., Los Angeles County
UTM
N = 3,742,467
E = 415,477
N = 3,777,371
E = 414,892
N = 3,782,904
E = 379,353
N = 3,787,765
E = 312,275
N = 3,760,145
E = 436,087
N = 3,721,444
E = 414,449
N = 3,716,916
E = 436,027
N = 3,753,372
E = 411,824
N = 3,755,070
E = 373,477
N = 3,743,190
E = 390,007
N = 3,767,650
E = 385,310
X-Y Coord.
Y = 1340
X = 1824
Y = 1634
X = 1819
Y = 1681
X = 1520
Y = 1722
X = 954
Y = 1489
X = 1998
Y = 1124
X = 1998
Y = 1124
X = 1998
Y = 1432
X = 1794
Y = 1446
X = 1470
Y = 1346
X = 1610
Y = 1552
X = 1570
-------
C-3
Table Cl (Continued).
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Newhall #055120001101 (70081)
24811 San Fernando Rd., Newhall , LA Cty.
Norco-Prado #055160001101 (33140)
Park
8850 Archibald Ave., Norco, Riverside Cty.
Ojai #055340001101 (56402)
401 Signal Hill St., Ojai, Ventura Cty.
Pasadena- #05570004101 (70083)
Walnut
1196 E. Walnut St., Pasadena, LA County
Point Mugu #056030001101 (56(409)
Naval Air Station, Ventura County
Pomona #056040001101 (70075)
924 N. Garey Ave., Pomona, LA County
Redlands #056200001101 (36165)
216 Brookside Ave., Redlands, San Bernardino
County
Reseda #054200001101 (70074)
18330 Gault St., Reseda, Los Angeles County
Riverside- #056400003F01 (33146)
Magnolia
9002 Magnolia Ave., Riverside, Riverside Cty
Riverside- #056535001101 (33144)
Rubidoux
5888 Mission Blvd., Rubidoux, Riverside Cty.
UTM
N = 3,805,831
E = 359,188
N - 3,756,446
E = 445,122
N = 3,813,704
E = 293,772
N = 3,779,120
E = 396,420
N = 3,767,844
E = 430,882
N = 3,768,069
E = 482,902
N « 3,785,129
E = 358,851
N = 3,751,835
E = 463,036
N = 3,757,641
E = 462,161
X-Y Coord.
Y = 1874
X - 1350
Y = 1458
X = 2074
Y = 1940
X = 798
Y = 1649
X - 1664
Y = 1630
X = 933
Y = 1554
X = 1900
Y = 1556
X = 2393
Y = 1699
X = 1347
Y = 1419
X = 2225
Y - 1468
X = 2218
-------
C-4
Table Cl (Continued).
22. San Bernardino #056680001101 (36151)
172 W. 3rd St., San Bernardino, S.B. Cty.
23. Upland-Civic #058440003101 (36174)
Center
155 D Street, Upland, San Bernardino Cty.
24. Upland-ARB #058440004F01 (36175)
1350 San Bernardino Rd., Upland, s.B. Cty.
25. West L. A. #054180002101(70071)
2351 Westwood Blvd., Los Angeles County
26. Whittier #058720001101 (70080)
14427 Leffingwell Rd., Whittier, LA Cty.
UTM
N « 3,773,634
E = 473,637
N = 3,768,863
E = 440,989
N = 3,769,410
E = 442,043
N = 3,767,403
E = 368,178
N = 3,754,019
E = 405,436
X-Y Coord.
Y = 1602
X = 2315
Y = 1562
X = 2039
Y = 1567
X * 2048
Y « 1550
X = 1426
Y = 1437
X = 1740
-------
C-5
Table C2. Receptor Points Assigned to the Los Angeles AQCR
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
County
Los Angeles
Los Angeles
ii
it
Los Angeles
n
Los Angeles
n
Los Angeles
Los Angeles
n
Los Angeles
n
n
n
n
Los Angeles
n
ii
Los Angeles
n
Los Angeles
Los Angeles
n
H
Los Angeles
n
n
n
n
RSA #
7
12
12
12
13
13
14
14
15
16
16
17
17
17
17
17
18
18
18
19
19
19
20
20
20
21
21
21
21
21
Code #
2071
2121
2122
2123
2131
2132
2141
2142
2151
2161
2162
2171
2172
2173
2174
2175
2181
2182
2183
2191
2192
2193
2201
2202
2203
2211
2212
2213
2214
2215
X-Coord.
1285
1361
1351
1400
1485
1521
1421
1510
1221
1380
1430
1521
1521
1521
1480
1480
1521
1475
1500
1505
1505
1545
1595
1650
1625
1565
1565
1565
1610
1610
Y-Coord.
1610
1670
1720
1630
1645
1650
1730
1710
1550
1570
1465
1510
1550
1590
1530
1580
1440
1460
1410
1320
1365
1350
1330
1320
1390
1420
1470
1520
1520
1470
-------
Table C2 (Continued).
C-6
No.
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
County
Los Angeles
ii
it
Los Angeles
Los Angeles
H
ii
I is Angeles
it
n
n
Los Angeles
Los Angeles
n
Los Angeles
Orange
Orange
Orange
n
Orange
n
Orange
Orange
n
San Bernardino
n
San Bernardino
n
Ventura
n
RSA =
22
22
22
23
24
24
24
25
25
25
25
26
26
26
27
35 .
36
37
37
38
Code #
2221
2222
2223
2231
2241
2242
2243
2251
2252
2253
2254
2261
2262
2263
2271
3351
3361
3371
3372
3381
38 I 3382
41
42
42
28
28
29
29
1
1
3411
3421
3422
4281
4282
4291
4292
1011
1012
X-Coord.
1660
1690
1725 .-
1555
1561
1561
1595
1641
1660
1710
1730
1765
1810
1840
"1900
1710
1800
1765
1785
1708
1750
1911
1825
1840
1960
2000
2190
2335
860
1125
Y-Coord.
1420
1480
1435 '
1545
1585
1640
1595
16?5
1560
1555
1620
1520
1595
1500
1580
1355
1410 '
1320
1355
1280
1250
. 1390'
1285
1335
1490
1590
1625 .
1555
1960
2050
-------
Table C2 (Continued),
C-7
No.
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
County
Ventura
ii
it
Ventura
ii
Ventura
Ventura
Vpntura
Ventura
Los Angeles
ii
Los Angeles
ii
ii
ii
Los Angeles
ii
ii
Orange
Orange
Orange
H
Orange
San Bernardino
H
H
M
H
Riverside
RSA =
1
1
1
2
2
3
4
5
6
8
8
10
10
10.
10
11
11
11
39
40
43
43
44
30
30
30
30
30
45
Code #
1013
1014
1015
1021
1022
1031
1041
1051
1061
2081
2082
2101
2102
2103
2104
2111
2112
2113
3391
3401
3431
3432
3441
4301
4302
4303
4304
4305
5451
X-Coord.
1040.
941
1185
940
940
1010
1235
1135
1185
1410
1348
1550
1641
1757
1880
1610
1732
1860 '
1855
1970
2028
2035
1915
2055
2260
2430
2473
2350
2070
Y-Coord.
2005
2115
1935
1745
1870
1675
1740
1645
1835
1935
1855
1878
1950
1895
1865
1750
1710 "
1728
1160
1052
1155
1240
1250
1725
1713
1740
' 1630
1650
. 1515
-------
Table C2 (Continued),
C-8
No.
90
91
92.
93
94
95
96
97
98
99
County
Riverside
n
Riverside
n
Riverside
Riverside
n
n
Riverside
ii
RSA $
46
46
47
47
48
49
49
49
50
50
Code t
5461
5462
5471
5472
5481
5491
5492
5493
.5501
5502
X-Coord.
2095
2170'
2280
2330
2455
2185
2273
2395
2430
2475
Y-Coord.
1380
1460
1360
1277
1310
1210
1110
1155
14«0
1430
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D-1
APPENDIX D
METHODOLOGY TO CHARACTERIZE
POPULATION EXPOSURE
-------
D-2
FORMULATION OF POPULATION EXPOSURE PARAMETERS
Suppose a person stays at a place where the air quality is continuously
monitored. Then, the pollution "dose" of that person over a time period T
u 1
can be given by
DOSE
= f C(t) dt (D_1)
where C(t) is the concentration reading at time t. A pollutant concentration
is usually measured at a constant time interval, say, every hour. Monitored
concentrations are often sorted in ascending order and summarized in percen-
tile concentration statistics. In this case, Eq. (D-l) reduces to
1
DOSE =jf C(f) df (D.2)
o
where C(f) is the concentration at the f percentile.
From the quantities in Eq. (D-2) we will derive the three exposure param-
eters; "dose rate," "risk frequency," and "mean duration." The dose rate is
the average concentration with respect to a subject person and is given, for
the above example, as
1
D = f C(f) df (D-3)
o
Namely, the dose rate is equal to the arithmetic mean concentration averaged
over the time period T, i.e., a year in this study. The risk frequency is the
-------
D-3
percentage of time that a subject person is exposed to a concentration above
2
a given concentration threshold C>
R(CS) = 1 - fs (D-4)
where f is the percentile given by a solution to C(f) = Cs- The mean dura-
tion can be determined when the percentile concentration statistics are available
for both hourly average concentrations and daily maximum hourly average concen-
trations. It is given by
T - 24 RhoUr'Rday
where Ru is the risk frequency for hourly average concentrations (hourly
risk frequency) and Rd the risk frequency for daily maximum hourly average
concentrations (daily risk frequency).
In the real world each individual moves around in space. Therefore, the
pollution dose of Eq. (D-l) should be rewritten as
T
DOSE = /C[r_(t),t] dt (D-6)
o
where jr(t) is the spatial position of the subject person at time t. Under
this situation, the conversion from Eq. (D-l) to Eq. (D-2) is not applicable
to Eq. (D-6). Therefore, there is no easy way to determine, for the subject
person, the three exposure parameters defined by Eqs. D-3) through (D-5).
In order to resolve the above problem, we propose to use the quasi-
stationarity assumption, i.e., each individual stays close to a receptor point,
-------
D-4
say, his office on weekdays from 8 A.M. to 5 P.M. Suppose that we divide the total
time period T into two non-overlapping time periods, working time TW and
non-working time T . Then, the three exposure parameters can be given by the
following equations:
D =
<(CS> = 24(Rhour/Rday)
where D is the dose rate during working time, D that during non-working
w n
time, R (CJ the risk frequency during working time, and Rn(C$) that during
W O
non-working time.
The above formulation is derived for a single person. The next step is
to extend the population exposure formulation for a single person into that
for a population of millions of parsons. Suppose that the spatial position of
the local population is approximated by a receptor point located approximately
at the center of their residence locations, and that the air quality at that
receptor point is estimated from the nearby monitoring stations by using the
interpolation equation (D-l). Then, the distribution function for each of
Q
the three population exposure parameters is given as:
-------
D-5
r I PT U(D1 - D*)/P (D-9)
- I PI U[R.(CS) - R*]/P {D_10.
i
S(T*) = I P. tj[T.(cs) _ T*]/PO (D.1V
where P. is the size of the local population at the i-th receptor point, P
the total number of people of the population, and U(x) the step function that
becomes unity when x is zero or positive and zero when x is negative. D*, R*,
and T* are, respectively, the threshold values of D, R(CS) and T(CS).
Once the distribution function is determined for a parameter D, R, or T,
the mean value of that parameter over the entire population is given by the
integral of the distribution function with respect to the threshold of that
parameter^ The average dose rate D", the average risk frequency ^(C^.) and the
O
average mean duration ^"(C) over the entire population are given as
D = / S(D*) dD* (0-12)
o
CO
=/ S(R*) dR* (°-13)
~(CJ = f S(T*) dx* (
o
The actual computation of U, R^(C^) and T(CS) was done by numerically integrating
the distribution functions S(D*), S(R*), and S(t*), respectively.
-------
D-6
Suppose that a distribution function is determined for two mutually exclu-
sive populations, working population and non-working population. Then, the
distribution function, S(R*) for the total population (sum of the two popu-
lations) can be computed from S (R*) of the working population and S_(R*) of
the non-working population as:
S(R*) - [Pw SW(R*) + Pn Sn(R*)]/Po (D-15)
where P is the size of total population that is given by the sum of the working
population PW and the non-working population Pn- The linear property of
Eq. (D-15) is also applicable to the other two distribution functions S(D*)
and S(T*).
-------
D-7
REFERENCES TO APPENDIX D
1. Craw, A. R., "A Contribution to the Problem of Placement of Air Pollution
Samplers," U.S. Dept. of Commerce, National Bureau of Standards, NBS
Report #10-284, May 1970.
2. Brasser, L. J., "A New Method for the Presentation of a Large Number of
Data Obtained from Air Pollution Survey Networks," Paper #SU-18B, Pro-
ceedings of the Second International Clean Air Congress, IUAPPA, Washington,
D.C., USA, December 6-11, 1970.
3. Csanady, G. T., "The Dosage-Area Problems in Turbulent Diffusion,"
Atmospheric Environment, Vol. 1, 1967, pp. 451-459.
4. Horie, Y., and A. C. Stern, "Analysis of Population Exposure to Air
Pollution in New York - New Jersey - Connecticut Tri-State Region," U.S.
EPA, OAQPS, EPA-450/3-76-027, March 1976.
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
REPORT NO.
EPA-450/3-77-004b
3. RECIPIENT'S ACCESSION NO.
TITLE AND SUBTITLE
Population Exposure to Oxidants and Nitrogen Dioxide
in Los Angeles Volume II: Weekday/Weekend and
Population Mobility Effects
5. REPORT DATE
January 1977
6. PERFORMING ORGANIZATION CODE
AUTHOR(S)
Yuji Horie, Anton S. Chaplin, and Eric D. Helfenbein
8. PERFORMING ORGANIZATION REPORT NO.
PERFORMING ORGANIZATION NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
10. PROGRAM ELEMENT NO.
2AF643
11. CONTRACT/GRANT NO.
68-02-2318
2. SPONSORING AGENCY NAME AND ADDRESS
Technology Service Corporation
2811 Wilshire Boulevard
Santa Monica, California 90403
13. TYPE OF REPORT AND PERIOD COVERED
contractor
14. SPONSORING AGENCY CODE
5. SUPPLEMENTARY NOTES
16. ABSTRACT
A new methodology was developed to characterize population exposure to air
pollution and was applied to analyses of photochemical air pollution and population
exposure to Ox and N02 in the Los Angeles Basin. The analysis was made on the 1973
air quality and population data to examine the weekend effect and the population mo-
bility effect on population exposure to the two pollutants.
N02 air quality was found to be better during weekends than weekdays throughout
the region except for Orange County. Ox air quality was found to be poorer in the
coastal region but better in the inland region during weekends than weekdays. Al-
though the daily maximum Ox concentration became slightly higher over weekends at
some stations in the coastal region, the majority of air monitoring stations in the
Los Angeles Basin showed a lower Ox concentration during weekends than weekdays. As
a result, the population on the whole received less exposure to Ox above the NAAQS
during weekends than weekdays.
Because of daily migration from their residence areas to the business districts,
workers receive less exposure to Ox and greater exposure to N02 than do non-workers
who stay near their residences all of tie time. The inclusion of population mobility
in the population exposure estimates proved to be important for determining a distri-
bution of the population-at-risk, but it turned out not to be crucial for determining
an aggregated index of population exposure. _
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Photochemical Air Pollution
Air Quality Monitoring
Population Exposure
Data Analysis
Weekday/Weekend Effect
Population Mobility
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (ThisReport}
Unclassified
21. NO. OF PAGES
Til;
Unlimited
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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