ENVIRONMENTAL HEALTH SERIES
ir Pollution
CALCULATING
FUTURE CARBON MON
EMISSIONS AND CONCENTRAT
• ^•LHKJHU^HS
niTmmB
FROM URBAN TRAFFIC DATA
NS
U.S. DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE
m m mmm m m m m m m m3
Public Health Service
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CALCULATING FUTURE CARBON MONOXIDE EMISSIONS
AND CONCENTRATIONS FROM URBAN TRAFFIC DATA
Wayne Ott, John F. Clarke, and Guntis Ozolins
U. S. DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE
Public Health Service
Bureau of Disease Prevention and Environmental Control
National Center for Air Pollution Control
Cincinnati, Ohio
June 1967
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The ENVIRONMENTAL HEALTH SERIES of reports was established to report
the results of scientific and engineering studies of man's environment: The com-
munity, whether urban, suburban, or rural, where he lives, works, and plays; the
air, -water, and earth he uses and re-uses; and the wastes he produces and must
dispose of in a •way that preserves these natural resources. This SERIES of re-
ports provides for professional users a central source of information on the intra-
mural research activities of the Centers in the Bureau of Disease Prevention and
Environmental Control, and on their cooperative activities with state and local
agencies, research institutions, and industrial organizations. The general subject
area of each report is indicated by the letters that appear in the publication num-
ber; the indicators are
AP -- Air Pollution
RH -- Radiological Health
UIH -- Urban and Industrial Health
Reports in the SERIES will be distributed to requesters, as supplies permit.
Requests should be directed to the Center identified on the title page.
Public Health Service Publication No. 999-AP-41
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CONTENTS
INTRODUCTION 1
THE METHOD 1
Preparing an Inventory of Emissions 1
Calculating Concentrations from Emissions: The Diffusion Model 4
Annual Concentrations 4
Hourly Concentrations 5
Discussion 5
APPLICATION OF THE METHOD: THE RESULTS 6
The Emissions Inventory for "Washington, D. C 6
Geographical Trends in Emissions Growth 8
Annual Concentrations 17
Hourly Concentrations 21
SUMMARY 24
IMPLICATIONS 26
ACKNOWLEDGEMENT 27
APPENDIX A 29
The Calculation of Mean Annual Concentrations 29
The Development of the Model 29
Application of the Model 33
APPENDIX B 39
REFERENCES 40
in
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FOREWORD
The basic goal of this study was to test the feasibility of an urban area approach
for calculating carbon monoxide emissions and concentrations from traffic data. That
the method provided reasonable results and that the bulk of this report is devoted to a
discussion of these results attests to the success of the approach. However, it should
not be assumed that the approach is now ready to be applied to a variety of other cities;
the authors caution that many of the steps in the approach and the underlying assump-
tions should best be further examined and refined. This report is intended not primar-
ily as an end in itself but as a starting point for future efforts.
ABSTRACT
An urban area approach was applied in Washington, D. C. , to calculate present
and future emissions and ambient concentrations of carbon monoxide. Calculations
•were based on 1964 and projected 1985 traffic volumes, emission factors, and a
meteorological diffusion model. The following findings were obtained: 1) With no
emission control, the total carbon monoxide emitted in Washington will approximate-
ly double in a non-uniform manner; the smallest increases will occur downtown and
the greatest increases at the outskirts. 2) The principal effect of such nonuniformity,
also apparent in Chicago, is to increase the area of the city exposed to high emission
densities, leading to consequent increases in the area over which concentrations oc-
cur. 3) The change in concentrations experienced at one site is influenced more by
the emission pattern of the entire urban area than by changes in the immediate vicin-
ity of the site. 4) Without control, the mean annual concentrations at four varied sites
in Washington would range from 2. 2 ppm to 11 ppm in 1985, representing increases
from 44 percent to 69 percent over 1964 values. 5) With 50 percent control, the 1985
concentration at the CAMP station will be 15 percent less than the 1964 value, and
values at the other sites will drop from 21 percent to 29 percent. 6) Concentrations
calculated for the •weekday peak-traffic period (6 to 8 AM) show two-hour average
concentrations in 1985 ranging from 1. 6 ppm to 25 ppm for various meteorological
conditions, representing increases from 40 percent to 120 percent over the 1964
values. 7) Even with 50 percent control, some peak-traffic-period concentrations
will increase 10 percent for certain meteorological conditions.
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CALCULATING FUTURE CARBON MONOXIDE EMISSIONS
AND CONCENTRATIONS FROM URBAN TRAFFIC DATA
INTRODUCTION
The endless proliferation of the automobile suggests to many observers that gas-
eous exhaust products may increase without limit, especially in ever-expanding urban
areas. Measurable carbon monoxide concentrations are usually traced to traffic flow,
since the primary source of carbon monoxide is vehicle exhaust. Because of the bio-
logically hazardous nature of carbon monoxide, 1 it is important to determine how
traffic patterns in the urban environment will affect carbon monoxide emissions and
concentrations.
A number of urban areas throughout the country have developed detailed projec-
tions of future traffic patterns for use in city planning. To evaluate the effect of
changing traffic patterns on carbon monoxide levels, it is necessary to develop a
method for calculating carbon monoxide emissions and concentrations from such traf-
fic data. Such an approach should be directed toward answering two questions:
1. How are the emissions distributed throughout the urban area -- both now and
in the future?
2. At various points, what average yearly and hourly concentrations -- both
present and future -- will result from these emissions?
This report describes an urban area approach by which traffic data provided by
traffic planning agencies can be used to derive estimates of carbon monoxide emis-
sions and their geographical distribution throughout the city; the estimates of emis-
sions then can be used in a meteorological diffusion model to calculate carbon monox-
ide concentrations at several selected points.
This approach has been applied to Washington, D. C. , where projected traffic
data for 1985 were available. Comparing the 1985 and 1964 results provides the
pattern of expected growth in emissions for different geographical regions and in-
dicates the resulting increase in concentrations at selected points within the city.
The pattern of emissions growth also has been briefly compared with that for
Chicago.
THE METHOD
Preparing an Inventory of Emissions
In developing a technique for estimating carbon monoxide (CO) emissions, one
must decide what kinds of emission data are needed. One approach, for example,
1
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Calculating Future Carbon Monoxide Emissions
applies several relatively simple steps to arrive at the average daily emissions for
the entire city. The average number of miles traveled by all vehicles on a single day
is obtained from traffic authorities, and this figure is multiplied by a factor repre-
senting the amount of CO released by a vehicle --on the average -- for each mile of
travel. Applied to the Washington, D. C. , metropolitan area, such calculations
show that 1, 150 tons of CO per day were released in 1964 and that -- based on pro-
jected traffic data -- emissions will climb to 2, 170 tons per day in 1985. This ap-
proach leads to dramatic results, an increase in emissions of 89 percent, but it
does not tell the whole story. During the 2 1-year span, the city will undergo a great
expansion. The 2, 170 tons emitted per day in 1985 "will be distributed over a much
•wider area, and consequently will be diffused in a greater volume of air. This
means that the increase in concentrations at particular points may bear no simple
relationship to the increase in total emissions.
To be of optimum use, the emission data should (1) take into account the changes
in area that will occur in the city, and (2) lend themselves to calculating the result-
ing concentrations with a meteorological diffusion model. Since the traffic varies
greatly from one part of the city to another, emissions can be more adequately de-
scribed if the urban area is divided into small segments, or zones, to allow com-
parisons among different geographical regions. The size and shape of the individ-
ual zones are not critical, although each zone should be small enough to show the
differences in emissions at various locations in the city and large enough to avoid
domination by unique or irregular features (lakes, parks, air terminals, etc.). For
simplicity and ease of understanding, the zonal configuration should form a regular
pattern, or grid, that can be superimposed over the map of the city.
The next step is to estimate the total emissions of CO from traffic for each zone
in the grid. Each zone will enclose a number of routes and segments of routes, each
of 'which will consist of many point sources of carbon monoxide -- vehicle tailpipes --
in continual movement. The amount of carbon monoxide released from each tailpipe
per mile is a function of the average speed of the vehicle. Thus, to estimate the total
daily emissions on a given route, one must know (1) the average velocity of the traf-
fic, and (2) the total number of miles traveled by all vehicles during a day.
Most municipal traffic agencies provide large street maps showing the number of
vehicles that travel each route or route segment on an average weekday, expressed as
the average daily travel (ADT) or traffic volume. The total number of miles traveled
on that route segment in an average weekday is usually expressed as daily vehicle -
miles of travel (DVMT). To convert the ADT into DVMT, we multiply the ADT (num-
ber of vehicles per day) by the length of the route segment.
A 1962 study of exhaust emissions in two large cities^ showed that carbon mon-
oxide emissions per vehicle-mile decrease with increased speed, as shown in Fig-
ure 1. Most traffic authorities can provide information on the average speeds on
various routes. Consequently, Figure 1 can be used to obtain a factor for convert-
ing vehicle-miles to emissions for each route. This emission factor can be used to
obtain the emissions on any route, and by adding the emissions for all routes •with-
in a zone the zonal emissions can be obtained.
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and Concentrations From Urban Traffic Data
0.35
0.30
0.25
x
o
o 0.20
Of.
<
0.15
10 15 20 25
AVERAGE SPEED OF MOTOR VEHICLE, mph
30
Figure 1. Emissions of carbon monoxide vs.
vehicle speed.
SOURCE: A.H. Rose, Reference I.
The preceding discussion outlines the development of a procedure for obtaining
an inventory of CO emissions by geographical zones. The overall approach consists
of five basic steps:
1. A uniform grid containing many small zones is superimposed on a street
map of the city.
2, The average velocities and the daily vehicle miles of travel for individual
routes are obtained from information provided by municipal traffic agencies.
3. Referring the average velocity to Figure 1 provides an emission factor, in
pounds of CO per vehicle-mile.
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Calculating Future Carbon Monoxide Emissions
4. Multiplying the daily vehicle-miles of travel by the emission factor gives
the total emissions for the route or route segment.
5. Adding the emissions from all routes within a zone yields the total emis-
sions for each zone.
Calculating Concentrations from Emissions: The Diffusion Model
The emissions inventory provides a summary of emissions throughout the city
by geographical zones. It is useful to present this information in graphical form and
to examine the trends in the growth of emissions by comparing the projected 1985
data with current data. However, the ultimate concern over pollution caused by motor
vehicles relates not to emissions, but to the concentrations that result from these emis
sions. The concentration measured at any point is a function not only of the geograph-
ical distribution of sources surrounding the point but of the meteorological conditions
responsible for diffusing the pollutant. To take into account source location (i. e. ,
source-to-receptor distance) and meteorological variables, a mathematical diffusion
model -was applied to the data in the emissions inventory.
Annual Concentrations. The emissions were derived from average daily traffic,
representative of traffic averaged over all weekdays of the year; and they are there-
fore daily average emissions. In applying the annual diffusion model, these emis-
sions were incorporated with wind data representative of an average year to arrive
at the mean annual carbon monoxide concentrations at several receptor sites. Since
the long-term average meteorological data, say over a 10-year period, will not
differ significantly from the average data for any other 10-year period, wind data
averaged from 1951 to I960 were used in the model as representative of an average
year. The pertinent wind data are the frequencies of wind speed and direction, which
are normally represented in a standard wind rose.
Pooler'* developed an urban diffusion model that he applied to Nashville to calcu-
late the mean monthly sulfur dioxide concentrations in considerable detail. To ob-
viate the need for an electronic computer, a model much simpler than Pooler's was
sought. Clarke^ developed an urban diffusion model that permits rapid calculation
of average hourly concentrations at a single receptor point. The latter model re-
quires an estimate of the emissions in each of many segments of a wheel-shaped pat-
tern, or overlay, centered on the receptor site. The diffusion model used in the
present study* consists of a combination of Clarke's method for rapidly calculating
concentrations with Pooler's method of estimating the vertical spread of the pollu-
tant with time and wind speed and his use of standard wind rose data. The resulting
model permits calculation of the mean annual concentration at a single point with-
out an electronic computer.
In practice, the overlay--a wheel-shaped pattern with three concentric rings
and eight radial sectors—is placed over a map of the city and centered on a recep-
tor site. Then the emissions in each of its 24 segments are calculated. These emis-
*For a full discussion of the derivation of the model used in the present study, and
its application, see Appendix A.
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and Concentrations From Urban Traffic Data
sion figures can be obtained from the summation of emissions from every route with-
in a segment. To simplify this computation, however, the data from the emissions
inventory (i. e. , the table listing emissions for each zone of the inventory grid) were
entered on a map containing the inventory grid, and the appropriate emission densi-
ties (emissions per unit area) were estimated by placing the overlay over the in-
ventory grid and considering the partial areas of the grid zones contained within each
overlay segment. The mean annual CO concentration at the receptor site (overlay
center) was then calculated from the emission densities by use of the diffusion equa-
tions, which incorporate the segment-to-receptor distances and data on the frequen-
cies of •wind speed and direction.
Hourly Concentrations. Calculation of the mean annual concentrations provides
only one data point for each year. More data points could be obtained if the calculated
concentrations represented values averaged over a shorter time period, perhaps an
hour. This would also provide more information on concentrations that are high in
magnitude but of short duration.
An examination of the variation of urban traffic with time reveals that although
the traffic volume varies considerably from hour to hour this diurnal variation is
much the same for all weekdays of the year. Consequently, this typical diurnal pat-
tern may be used to adjust average daily emissions to hourly values, and the result-
ing data can.be considered applicable to any weekday of the year. A diffusion model
may then combine wind data for a specific interval of time on a particular day with
emission densities for the same period to arrive at an average concentration for
those hours of that day. This permits the calculation of average hourly concentra-
tions for different hours of the day and for any number of days of the year.
The data assembled in the emissions inventory are average daily emissions, be-
cause they were derived from the traffic of an average weekday. To convert these
numbers to hourly values, the diurnal traffic pattern for the entire city can be as-
sumed to apply to each zone. Although traffic in each zone reaches its morning peak
at a slightly different time, the averaging interval was considered long enough that
these variations could be ignored without serious error. The diffusion model used
was the one developed by Clarke^ for rapid calculation of the average hourly concen-
tration at a single point. As mentioned previously, the annual diffusion model was
derived in part from this average hourly model, and consequently, there are rela-
tively few differences between the two: The hourly diffusion model uses wind data
averaged over as brief a period as a single hour rather than standard wind rose
data, and it uses a different method of estimating the vertical diffusion of the pollu-
tant with time of travel and meteorological conditions.
Discussion. It is desirable, where possible, to compare concentrations calcu-
lated by the diffusion model approach with those actually observed. Any monitoring
probe would have to be located at a specific point in the city--very likely a street--
and the measurements would be influenced by traffic in the immediate vicinity. Of
necessity, however, the diffusion model approach ignored this localized effect, be-
cause it was assumed that emissions are evenly distributed over the several-block
area of each zone rather than concentrated in narrow routes bounded by buildings and
other structures. Consequently, concentrations from a street-side sampling station
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Calculating Future Carbon Monoxide Emissions
could be expected to be greater than concentrations calculated for the same site using
the diffusion model. Although the traffic patterns for different streets are not exactly
identical, the measured-to-calculated ratio for one such site probably could be used
to convert the calculated concentrations for the other sites into data approximately
comparable to a street-side measurement. This method was employed as part of
the analysis of results.
Because of the above considerations and because some of the diffusion model
premises cannot be completely tested, the numerical values of the calculated concen-
trations will always be open to some question. These problems were minimized, how-
ever, by comparing the results calculated for 1964 with those developed for 1985. The
assumptions and constants are not likely to be different in 1985 than in 1964; hence, com-
parisons of the two sets of results developed from the same method are more valid than
the absolute values of each set of results taken by itself. Thus the most valid approach
in analyzing the results stresses the ratios between the two sets of numbers, emphasiz-
ing the changes and growth trends over the 21-year span.
APPLICATION OF THE METHOD: THE RESULTS
In 1965, the Bureau of Public Roads encouraged urban areas to begin developing
total transportation projections based on population, land use, economic factors, etc.
The Bureau also issued guidelines on the techniques to be used. Prior to this, how-
ever, many cities were already engaged in making such projections and they now have
considerable data available for use.
Washington, D. C. , is one of the cities that began its transportation study at an
early date. This section draws on traffic data developed in that study to illustrate the
application of the method by preparing an emissions inventory and calculating concen-
trations for the District of Columbia and the surrounding area.
The Emissions Inventory for Washington, D. C.
Authorities conducting the Washington Metropolitan Area Transportation Study
•were able to provide the traffic data necessary for preparing the emissions inven-
tory. They supplied large traffic flow maps of Washington, D. C. , and the surround-
ing area, showing future routes and projected average daily travel (ADT) for 1985.
Maps showing traffic flow for 1964 were also available from the District of Columbia
Department of Highways.
The zonal grid selected for the Washington emissions inventory is shown in Figure
2. It consists of five concentric rings, evenly spaced, and six radial sectors. A
circular pattern was selected because urban areas usually grow from the center out-
ward, and major routes often follow a concentric pattern. The square county boun-
dary line was incorporated as part of the grid, making the outer edges somewhat ir-
regular in pattern, but permitting the inclusion of Arlington county and a small por-
tion of Alexandria. In addition, five more zones were added in a 2-mile-wide strip
in Prince Georges and Montgomery Counties, Md. , making a total of 40 zones in the
grid.
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and Concentrations From Urban Traffic Data
WEST
MILES
Figure 2. Zonal grid for Washington, D.C.
Ideally, such a predominantly circular pattern should be placed with its center
at the center of the downtown area. In any given urban area, however, this point may
be difficult to locate exactly. In Washington, the center of the pattern was placed at
a point directly in front of The White House, on the northern part of The Ellipse, and
the six sectors were aligned so that one radial boundary ran east-west.
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Calculating Future Carbon Monoxide Emissions
Table 1 shows the completed emissions inventory, with listings for both 1964 and
1985. The zonal designations correspond to those in Figure 2. The emissions were
estimated in the manner outlined on page 3. The traffic data were obtained by super-
imposing the grid on the large traffic flow maps provided by the Washington traffic
agencies. Route distances within each zone were scaled by hand and multiplied by
the average daily travel for the route. The results were added to give the average
daily vehicle-miles traveled on all route segments within a zone. The process was
carried out with 1964 maps and repeated with 1985 maps.
The average velocities used are also shown in Table 1. The values chosen were
based on discussions with local traffic authorities and not on empirical data. Lacking
better information, it was necessary to assume the same velocities would apply in 1985
as in 1964. A later examination of isochronal maps* suggested that the chosen veloci-
ties were high, on the average. The inverse relationship between emissions and av-
erage speeds (Figure 1) guarantees, therefore, that the emissions are underestimates
of actual values.
Because the chief purpose of this report is to illustrate the methodology and its
application, the effects of emission control devices are not included in the 1985 emis-
sion figures. The effect of such control devices can be found by multiplying the 1985
emission values by the average percentage control anticipated for 1985.
All the traffic data were listed in a traffic inventory, T with much the same for-
mat as Table 1, and the average velocities were employed to arrive at the emissions
inventory of Table 1. The emission densities were obtained by dividing a zone's
emissions by the zone's area; thus the emissions per unit area of different zones may
be compared conveniently. The growth factors shown in the last column of Table 1
were computed by dividing the 1985 emission density by the 1964 figure. These fac-
tors provide an index of the growth in emissions for each zone. Since traffic data
for Alexandria were unavailable, emission data for zones Eg and P were not listed.
Geographical Trends in Emissions Growth
A pictorial description of the growth in CO emissions over the 21-year span is
provided by Figure 3. Smooth isolines have been drawn to enclose areas with emis-
sion densities that equal or exceed a specified value. It is clear that the area over
•which the highest emissions occur increases greatly by 1985, based on a no-control
situation. The mean emission density over the entire study area roughly doubles --
from about 16, 000 to 30, 000 pounds per day per square mile -- but the area over
which densities equal or exceed 100, 000 pounds per day per square mile increases
about 7 times. The 60,000-pound isoline increases its enclosed area about 3 times,
*These maps show the required driving times, for various directions, to travel from
the downtown area to the outskirts. They were supplied by the Traffic Survey and
Analysis Section of the District of Columbia Department of Highways.
•(•The traffic inventory appears in Appendix B.
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and Concentrations From Urban Traffic Data
TABLE 1
Emissions Inventory for Washington, D. C.
Zone
1st Ring (r=0.5)d
Al
cl
Dl
El
2nd Ring (r=l. 5)
A2
B2
Cl
Cl'
D2
E2
3rd Ring (r=2. 5)
A3
c3
C,1
D5
E5
F3
4th Ring (r=3.5)
A4
B4
°41
D4
E4
5th Ring (r=4. 5)
A5
B5
c5'
D5
E5
F5
Edges
L
M
N
O
P
Periphery
V
W
X
Y
Z
Total
Area,a
mi
0. 52
0.52
0.52
0.52
0. 52
0.52
1.58
1.58
1.23
0. 35
1.58
0. 92
1.58
2.62
2.62
2. 15
0.49
2.62
1.33
2.62
3.65
3.65
2.75
0.90
3.65
1.97
3.65
4.72
4.72
2.81
1.91
4.72
4.72
4.45
4.92
2.95
4. 35
2.00
10.60
14.60
9.40
9.40
14.60
147.46
Average
speed
mph
15.0
"
"
17. 5
"
"
n
,,
20. 0
„
"
n
"
22. 5
"
n
25.0
M
"
"
27.5
"
11
n
11
30. 0
n
"
11
"
--
Emission
f actor, b
Ib CO/mi
0.250
"
"
0.218
"
"
"
"
0. 196
,,
"
"
0. 180
"
,.
0. 165
M
"
"
0. 152
11
11
11
0. 141
"
11
"
"
--
Emission rate
Ib CO/day, 1000's
1964
63
47
38
53
28
45
94
68
65
14
49
42
91
80
75
56
25
62
28
49
75
58
27
23
68
21
83
66
72
29
20
68
53
54
66
20
35
31
59
49
68
95
135
2301
1985C
69
62
46
39
55
65
163
78
110
27
116
70
205
152
114
69
42
70
41
97
145
83
62
69
116
31
157
90
119
57
31
123
144
158
109
44
120
73
134
98
138
253
294
4346
Emission density
Ib CO/day-mi2, 1000's
1964
122
91
74
63
55
86
60
43
52
40
31
46
58
31
28
17
52
24
21
19
20
16
9.9
26
19
11
23
14
15
10
11
14
11
12
13
6.8
7.9
16
5.6
3.3
7.2
9.9
9.2
—
1985C
135
119
89
74
105
125
105
49
90
76
74
76
150
58
44
33
86
27
31
37
39
23
22
77
52
16
43
19
25
20
16
27
30
36
22
15
28
37
13
6.6
15
27
20
--
Growth
.factor
1985/1964
1.09
1. 31
1.21
1. 19
1.93
1.45
1.75
1. 14
1. 71
1.90
2. 39
1.67
2.24
1.90
1.56
1.94
1.66
1. 14
1.48
1.96
1.96
1.44
2.26
2.96
1.70
1.46
1.88
1.36
1.66
1.93
1.55
1.89
2.69
2.95
1.65
2.22
5.48
2.56
2.26
2.00
2.05
2.77
2. 17
--
A subtraction of area is made in zones containing a large portion of a river. The Potomac River divides
sector C into two parts, C and C'.
The emission factor was obtained from Figure 1.
cAssumes no control of emissions.
"The value for the radius r is the distance from the center of the grid to the midpoint of each of the five
concentric rings.
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10
Calculating Future Carbon Monoxide Emissions
and the next density category increases about 2-1/2 times. Thus the doubling of
the mean emission density comes about not so much because emis.sions in the high-
density zones grow greater but because so many zones with lower emissions be-
come higher density zones.
WEST
FAIRFAX COUNTY
0123
MILES
1964 \
Figure 3. Emission density isolines, 1964, left;
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and Concentrations From Urban Traffic Data
11
Figure 4 illustrates the growth trends in another way. The growth factors
from the last column of the emissions inventory (Table 1) are shown listed on the
zones of the inventory grid. Inspection shows that the growth in emission densities
forms a definite pattern. Little relative growth takes place in the center of the
city, whereas considerable growth occurs in zones far from the center of town.
WEST
FAIRFAX
MILES
1985, right (1,000 pounds CO per day per mi ).
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Calculating Future Carbon Monoxide Emissions
Figure 4. Emission density growth factors for Washington
Metropolitan Area-1964-1985.
For example, the emission zones Aj, Bj, and Cj, •which comprise most of the
downtown shopping area, have an average growth factor of 1. 20, or a growth in
emission density of 20 percent. By comparison, the outlying park and residential
area of zone 65 has a projected growth factor of 1.66, or an increase of 66 percent;
the growth factor in the far-out suburban zone Z is 2. 17, indicating an increase in
emission density of 117 percent. Thus, the greatest percentage growth for Wash-
ington, D. C. , occurs in the outskirts rather than in the downtown regions.
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and Concentrations From Urban Traffic Data
13
That this is a fairly general trend for Washington can be seen from Figure 5,
in which all the growth factors in each of the five concentric rings have been av-
eraged and plotted (dotted line). Since the rings are uniformly spaced, the ab-
scissa is also a measure of radial distance from the grid pattern's center. Clear-
ly, the mean growth factor tends to increase with distance, ranging from 1. 32 to
2. 46. The other two curves in Figure 5, which show the mean emission densities,
3.0
125
2.0
100
75
o
I—
<
>.
o
1.0 >-~ 50
25
0.0
MEAN GROWTH FACTOR*
(1985/1964)
1961
I
I
I
1.0 2.0 3.0 4.0
DISTANCE FROM CENTER OF GRID, miles"
5.0
Figure 5. Emission density profiles from Washington, D.C.
*The average of all growth factors within a ring
**Distance is measured from the center of the grid
to the midpoint of a ring.
-------
14 Calculating Future Carbon Monoxide Emissions
suggest an explanation for this trend. Percentage increases will be greater in the
outlying areas because the 1964 densities are relatively small in these regions, and
any increase means a large percentage growth. The absolute increase tends to de-
cline with distance, but not so much as the emission densities themselves. Conse-
quently, the absolute increase shows up as a much larger percentage growth in out-
lying regions -when compared with downtown regions. It is important to note that
although the emission densities are lower in the outer rings, these rings contain
more area than do the inner rings and thus account for a greater share of the total
CO emitted.
The fact that in Washington some emission densities in the distant areas may
more than double while downtown densities increase relatively little may be further
explained by returning to the traffic data from which these figures were derived.
The growth factor showing the growth in emission densities from 1964 to 1985 also
represents the growth in traffic densities -- vehicle-miles per unit area -- from
1964 to 1985.* Perhaps the fractional increase in traffic flow is less in the down-
town areas because the central regions of the city reach a kind of "saturation level"
beyond which further relative growth is limited. Most of the increase occurs in
the surrounding areas, where traffic is sparser and greater development can yet
occur.
If such a saturation density does exist, we would expect to find it in zones where
there is both extremely high traffic density and a unity growth factor. Figure 6
shows the traffic density profile for the sector of the Washington grid with the high-
est traffic densities, sector A. The growth factor in the innermost zone is almost
unity (1. 09). This indicates a saturation level between 500 and 550 thousand vehicle-
miles per day per square mile. However, the fact that there were many variations
from zone to zone suggests that more cases should be examined before generalizing
about the magnitudes. Note, for example, that the growth factor for sector A de-
clines at the 4. 5-mile mark, an exception to the general trend.
Figure 7 shows a map of traffic density growth factors for the Chicago metro-
politan area similar to the Washington growth factors shown in Figure 4. Data for
1956 and 1980 were supplied by the Chicago Area Transportation Study. In conduct-
ing the Chicago study, authorities divided the city into many small squares to form
a rectangular grid. When grouped properly, the squares form an almost concentric
pattern with 7 "sectors" and 8 "rings. " The overall growth pattern shows an im-
portant similarity to that of Washington: zones near the central business district
show little relative growth — or even an occasional decline--while zones in the out-
skirts show considerable increases. Since the study area in Washington is much
smaller than that in Chicago, the patterns for the two cities are not strictly com-
parable. In addition, the individual zones in Chicago are larger, and this may tend
to mask particular variations. In spite of the greater size of Chicago, the two cities
are similar in that the increase in vehicle-miles is about the same in both--92 per-
cent in Chicago and 94 percent in Washington.
*The growth factor for emission densities is the same as that for traffic densities
in Washington because the 1964 and 1985 average velocities were assumed to be the
same.
-------
and Concentrations From Urban Traffic Data
15
500
400
>.
a
3.0 ^,300
E
2.0 z 200
o
ce.
O
ac
1.0 100
1985
GROWTH FACTOR __———
0 1.0 2.0 3.0 4.0
DISTANCE FROM CENTER OF GRID, miles
Figure 6. Traffic density profiles for sector A only.
Figure 8 shows the mean traffic densities for Chicago. Although no growth
takes place within 5 miles of the center of town (unity growth factor), traffic
densities fall off greatly with distance in this region. This is contrary to the con-
cept of a single saturation level applying in such regions, and it implies the con-
cept may be oversimplified. A better hypothesis suggested by the Chicago data is
that an "optimum traffic density profile" is reached, after which traffic densities
do not change appreciably over time. This happens first at the center of the city
and then spreads. Again, this is speculation, for it is obviously necessary to look
at the pattern of growth in more cities to make a valid generalization.
-------
16
Calculating Future Carbon Monoxide Emissions
f
SCALE IN MILES:
02468
I I I I I
i .96 (CENTRAL BUSINESS DISTRICT)
(Data obtained from Chicago Area Transportation Study.)
Figure 7. Traffic density growth factors for Chicago
Metropolitan Area - 1956-1980.
-------
and Concentrations From Urban Traffic Data
17
5.0
4.0
300
2 3.0
<
O
0 2.0 £200
E
>-
1.0
100
u
LI-
LI-
<
0.0
MEAN GROWTH FACTOR
(1980/1956) \
25
5 10 15 20
DISTANCE FROM CENTER OF BUSINESS DISTRICT, miles
"Assumes 86% of the traffic occurs on tollways, expressways,
and arterial streets.
Figure 8. Traffic density profiles for Chicago.
Regardless of the exact pattern of growth, there is a definite tendency for traf-
fic densities (and hence emission densities) to grow much more in the outlying regions
than they do in the downtown areas in both Washington and Chicago. Since the out-
skirts of a city account for a much larger area than do the downtown regions, the
more significant increase in the city's total emissions takes place in the outskirts and
not in the downtown areas.
Annual Concentrations
To calculate the mean annual concentrations of carbon monoxide in Washington,
D. C. , four receptor sites were selected -- three in the downtown area and one
across the river in Arlington, as shown in Figure 9. One of the receptor sites
-------
18
Calculating Future Carbon Monoxide Emissions
Figure 9. Locations of the four receptor sites (overlay included).
(site 1) was chosen to coincide with the location of the Continuous Air Monitoring Pro-
gram (CAMP) station operated jointly in Washington by the National Center for Air
Pollution Control of the Public Health Service and the District of Columbia Depart-
ment of Public Health. The station, which provides continuous records of CO con-
centrations throughout the year, has been operating since 1962.
Figure 9 also shows the diffusion model overlay (circular pattern) centered on re
ceptor site 3. Although both the overlay and the emission inventory grid (Figure 2)
-------
and Concentrations From Urban Traffic Data
19
are circular, the two are not to be confused. The former is used as part of the dif-
fusion model to calculate the concentration at a particular point, # while the latter is
merely an aid in describing the city's emission patterns. In practice, the overlay
was placed over the emissions inventory grid, and the emissions in each of the 24
segments of the overlay were tabulated from the zonal emission densities and the
partial areas of the various emission zones enclosed within each segment.
Since emission densities decline rapidly with distance from the city's center
(Figure 5), the overlay diameter of 20 kilometers (12. 5 mi) is sufficient to cover
most of the urban area of Washington when centered on a downtown receptor site.
However, it was of interest to calculate the concentration for at least one site that
was not centrally located. To examine the appropriateness of using the model with
such a site, it is useful to examine the share of the concentration contributed by
the three rings of the overlay. Table 2 compares the proportion of the calculated
concentration contributed by each of the three rings. Because the third ring con-
tributes a relatively small share of the concentration to sites 1 and 2, we may as-
sume the region beyond may be ignored with little error. This is less true for site
3, and the contribution of the third ring nearly equals that of the other two rings for
site 4. This happens because the third ring passes through the heart of the down-
town area; it therefore suggests that the emissions immediately beyond the third
ring are not negligible. In addition, site 4 is located such that much of the area en-
closed lies outside the inventory grid, where no emission data were available; be-
cause emission densities were quite low at the outskirts of the city, they •were as-
sumed to be zero for this region. Although these assumptions were expected to
result in some error in the annual concentrations at sites 3 and 4, the error would
be such that the concentrations would be underestimates of the actual values --
especially for site 4.
TABLE 2
Percent Contribution to the Concentration by Overlay Ring
(1964 Data)
Receptor
site
1
2
3
4
Contribution within overlay ring number
1
48
60
32
32
2
43
33
52
39
3
9
7
16
29
Table 3 shows the street locations of the four receptor sites and the calculated
mean annual concentrations at each site. The mean CO concentration measured at
the Continuous Air Monitoring Program station for 1964 weekdays was 5. 9 ppm,
which is 2. 8 times larger than the calculated value. Table 4 lists the "street-side
concentrations" that have been multiplied by 2. 8 to adjust them to values comparable
to the CAMP measurement as discussed in the methodology section, page 5. It
should be noted that not all of the 2.8 factor is attributable to the street-side adjust-
ment; the fact that the velocities chosen for Washington were rather high has already
*For a full discussion of the annual diffusion model and its application, see Appendix A.
-------
20
Calculating Future Carbon Monoxide Emissions
led us to expect calculated values that are underestimates, as was discussed on page
8. The ambient street-side concentrations for 1985 range from 2.2 ppm to 10. 9
ppm; however, as mentioned previously, the increase of 1985 values over 1964 values
is thought to be more valid than the actual magnitudes of the values, because few of
the assumptions underlying the approach are likely to change between 1964 and 1985.
Thus Table 4 includes the ratio of the 1985 calculated concentration to the 1964 fig-
ure. Listed also is the emission growth factor -- the ratio of 1985 to 1964 emis-
sion density -- for the zone in which the receptor site is located. This permits a
comparison bet-ween the growth in concentrations and the growth in emissions.
TABLE 3
Calculated Concentrations
Receptor
site
1
2
3
4
Location
CAMP- -1st. and L St. , NW
Pennsylvania and 14th, NW
New Hampshire and 13th, NW
16th and Edison St. , Arlington
Mean annual concentration, ppm
1964
2.08
2.66
1.57
0.50
1985a
3.52
3.84
2. 38
0. 77
Assuming no emission control.
From Table 4 it is clear that the growth in concentrations is much less varied
from point to point than the growth in emissions. The growth in concentrations for
the four sites ranges from 44 percent to 69 percent, while the growth in emission
densities varies over the entire city from 9 percent to 248 percent (see Figure 4).
The concentration at site 4, for example, increases 54 percent, while the emission
density in that immediate area increases 70 percent. At the same time, the con-
centration at site 2 -- the most central site -- increases 44 percent, while the
emission density for that zone grows only 9 percent. Thus, the mean annual con-
centrations at all four receptor sites will increase about 50 percent, despite the
fact that the total emissions double and that the increase is quite disproportionate
throughout the city.
TABLE 4
Street-side Concentrationsa
Receptor
site
1
2
3
4
Mean annual concentration, ppm
1964
5. 9
7.6
4. 5
1.4
1985
10.0
10. 9
6.8
2.2
Ratio
1985/1964
1.69
1.44
1.51
1.54
Zone
A2
Al
B3
D4
Emission growth
factor in zone
1. 73
1. 09
1.56
1.70
aTable 3 concentrations adjusted to be comparable to CAMP data; no emis-
sion control assumed,,
In the downtown areas, -where emissions increase relatively little, the concen-
trations are affected by the increase in emissions in surrounding and more distant
zones as a result of transport of carbon monoxide from one zone to another. Thus,
the continued expansion in area of urban regions means that downtown concentrations
-------
and Concentrations From Urban Traffic Data
21
could go up indefinitely—even with no further increases in downtown traffic — simply
because there are greater quantities of carbon monoxide available in the general
area to add to the downtown levels.
Since the relationship between concentrations and emissions is linear, the ef-
fect of motor vehicle emission control on concentrations can be included simply
by multiplying the calculated concentrations by the average percentage control of
emissions expected in 1985. Table 5 shows the concentrations predicted if a 50 per-
cent average reduction in motor vehicle emissions is assumed for 1985. Despite
this large reduction, levels at the CAMP station decline only 15 percent, and con-
tinued growth in traffic beyond 1985 would soon wipe away this margin.
TABLE 5
Street-side Concentrations with Controla
Receptor
site
1
2
3
4
Mean annual concentration, ppm
1964
5.9
7.6
4.5
1.4
1985
5.0
5.4
3.4
1. 1
Change, %
-15
-29
-24
-21
aAverage reduction in 1985 emissions of 50 percent.
Hourly Concentrations
Although the mean annual concentration is a useful index of overall CO levels,
it tells very little about the higher peak values that may occur for short periods of
time. Consequently, the average hourly diffusion model developed by Clarke was
used to examine the peak-traffic-period concentrations and the diurnal variations.
Although this diffusion model can be used to calculate concentrations averaged over
as short a period as a single hour, a 2-hour period was selected as adequate.
The diurnal traffic pattern shown in Figure 10 represents factors derived from
the city-wide hourly traffic volumes (vehicles/hour) for Washington, D. C. To ob-
tain the emissions for a particular 2-hour period of the day, the emission densi-
ties for each zone were multiplied by the factor corresponding to that period.
Figure 11 shows calculated and observed CO concentrations for Sept. 24,
1964, at receptor site 1 (1st and L St., NW, the CAMP site) averaged over 2-
hour periods throughout the day. The observed concentrations are measurements
at the CAMP station; September 24th was selected as a day of fairly typical con-
centrations. The shapes of the two curves shown are very similar, although the
observed values are uniformly larger than the calculated values. As discussed
previously, this difference is expected because of the discrepancy between values
measured at a specific point alongside a street and values calculated from emis-
sions assumed uniformly distributed over a large area. The curves do not have the
same shape as the traffic factors of Figure 10 because the meteorological conditions
vary from hour to hour.
-------
22
Calculating Future Carbon Monoxide Emissions
2.0
1.0
0.0
6-8am = PEAK TRAFFIC PERIOD-
0 2
\
8 10 12
8 10 12
AM
PM
Each factor is the ratio of traffic volume averaged over a 2-hour period
to traffic volume averaged over 24 hours. Traffic volumes pertain to
weekdays only and are totals for the city as a whole, including traffic
into and out of the urban area.
SOURCE: District of Columbia Department of Highways.
Figure 10. Diurnal traffic factors.
Figure 12 compares calculated and observed concentrations for the 2-hour
peak traffic period (6:00 AM to 8:00 AM) on 17 different days scattered between
September 1964 and July 1965. For observed concentrations below 12 ppm, a
relatively consistent pattern emerges: observed values appear larger than cal-
culated values by an average factor of 2.0. This factor compares well with the ad-
justment factor of 2. 8 used to convert calculated mean annual concentrations to
street-side values. A simple factor does not explain the differences for the higher
concentrations, and this is probably due to a difficulty in specifying wind speeds
accurately. High concentrations usually result -when wind speeds are low and the
pollutant cannot diffuse rapidly, and at low wind speeds, the wind speed measure-
ments made at a single point (e. g. , the airport) are less representative of wind
speeds throughout the city than when speeds are high. Consequently, we can ex-
pect a better match between calculated and observed concentrations for the lower
concentrations than for the higher values.
-------
and Concentrations From Urban Traffic Data
23
14
12
10
8
6
4
2
0
E
o.
OBSERVED
CAMP DATA:
0
V
4 6
AM-
8 10 12
/\
246
PM-
8 10 12
Figure 11. Sept. 24, 1964, diurnal CO concentrations,
Washington, D.C.-site 1.
In Figure 13, the peak-traffic-period concentrations for the same 17 days were
calculated using both 1964 emission data and 1985 emission data with the 17 sets of
meteorological data. Again assuming no control, the 1985 concentrations (2-hour
averages) range from 1.6 ppm to 25. 4 ppm. Because the ratio of the two coordinates
of a point represents the relative growth of the concentration over the 21-year span,
the slope of a straight line connecting the point and the origin is actually a growth
factor. For these 17 selected cases, the increases range between 38 percent
and 120 percent, with an average value of 65 percent. This compares well with the
69 percent growth in mean annual concentration predicted for this receptor site by
the annual diffusion model. The fact that some short-term concentrations increase
120 percent without emission control means that these values would go up 10 per-
cent even with 50 percent control. As discussed previously, comparisons in the
growth of the calculated concentrations have greater validity than the individual
magnitudes of the concentrations, because few of the assumptions incorporated in
the model are likely to change over the 21-year span.
Perhaps the most interesting feature of the data in Figure 13 is the tendency for
nearly all concentrations to increase by at least 40 percent, regardless of their in-
dividual magnitudes. Although there is some tendency for the higher concentrations
to increase by a smaller proportion, it is not too pronounced. This suggests that
the annual average concentration goes up not only because the lower CO levels are
increased, but because the peak concentrations rise as well. Thus, the 69 percent
increase in the mean annual concentration comes about because concentrations of all
magnitudes shift upward in roughly the same proportion.
-------
24
Calculating Future Carbon Monoxide Emissions
a:
I—
Z
Z
o
22 -
20 -
18
16
14
12
10
8
6
4
/>"
1
1
1
1
1
6 8 10 12 14 16 18
OBSERVED CONCENTRATIONS, ppm
20 22 24
Figure 12. Calculated and observed concentrations
for the morning peak-traffic period on
17 days distributed between Sept., 1964,
and July, 1965.
SUMMARY
An urban area approach for calculating carbon monoxide emissions and concen-
trations, using urban traffic data and meteorological diffusion models, was applied
to Washington, D. C. , both to examine the feasibility of such techniques and to see
how changing traffic patterns would affect future levels.
Comparisons of city-wide emissions for 1985 and 1964 showed that, with no
emission control, the overall carbon monoxide emitted in the Washington metro-
politan area would approximately double in the 21-year span. The percentage in-
creases were found to vary widely throughout the city, however, with the smallest
percentage increase taking place in the central regions and the greatest relative
-------
and Concentrations From Urban Traffic Data
25
20
25
0 5 10 15
1964 CALCULATED CO, ppm
Figure 13. 1985 vs. 1964 peak-traffic period concentrations
calculated for site 1, Washington, D.C.
growth occurring in the outskirts of the city. Examination of the traffic patterns
of Chicago indicated that the same general pattern of emission growth applied,
suggesting that it may be common to most urban areas. It means that the regions
with the highest emission densities tend to enlarge in area, growing from the center
of the city outward.
Application of an annual diffusion model permitted calculation of mean annual
concentrations for 1964 and 1985 at four selected points within the Washington study
area. Comparison of calculated and observed concentrations at one site showed good
-------
Calculating Future Carbon Monoxide Emissions
agreement, considering the anticipated difference between overall average levels and
measurements influenced by a particular street. The increases in the calculated
concentrations, in the absence of emission control, ranged from 44 percent to 69 per-
cent over the 21-year period. According to the results, however, these changes
came about not because of traffic increases in the immediate area of the receptor
sites but because of changes in the overall traffic pattern throughout the city. With
50 percent average reduction in emissions in 1985, the concentrations for the four
sites, when compared with the 1964 values, showed declines ranging from 15 per-
cent to 29 percent.
Application of an average hourly diffusion model permitted the calculation of
concentrations applicable to shorter time periods, depicting the short-term varia-
tions in greater detail. Average concentrations for 2-hour periods throughout
a particular day were calculated for a single site. They revealed a distinct morn-
ing peak, and the shape of the diurnal pattern agreed well with that of observed levels.
To assess the possible increases in morning peaks, the concentrations during
the 2-hour morning peak traffic period were calculated both for 1964 and 1985, us-
ing meteorological data for 17 selected days. The 1964 results showed good corre-
spondence with observed values, differing by roughly the same expected factor en-
countered in the mean annual concentrations and attributable to the difference in the
circumstances for -which the values apply. In the absence of emission control, the
1985 morning peaks ranged from 1. 6 ppm to 25. 4 ppm. These values represented
increases ranging from 40 percent to 120 percent over 1964 calculated values, -with
an average increase of 65 percent. This suggests that the 69 percent increase in
the mean annual concentration calculated for this site with the annual diffusion model
resulted from increases in short-term concentrations of all magnitudes and not just
from increases in a certain range of magnitudes. Consequently, the peak concen-
trations do not appear to reach an upper-limit but increase along with the minimum
and average values. With 50 percent emission control, some peak values still in-
crease by as much as 10 percent for certain meteorological conditions.
IMPLICATIONS
This study has demonstrated the feasibility of an urban area approach for treat-
ing pollution from mobile sources, and has indicated the way in which traffic patterns
and meteorological conditions affect concentrations. It has raised several questions,
however, each of which opens a new area for possible future study.
The tendency for the high emission densities to spread — in the absence of possi-
ble control measures--implies that more people would spend more hours living and
working in regions subjected to maximum concentrations. This points to the need,
therefore, to examine not only the air quality levels but to look at the variation in
population densities in the urban area, considering the changes from year to year
as well as the population patterns for different hours of the day.
The fact that 50 percent control in 1985 brings only a 15 percent reduction over
1964 concentrations at the Washington CAMP station, with no reductions in some
-------
and Concentrations From Urban Traffic Data 27
short-term peak values, demonstrates the need to examine the entire urban area,
since concentrations are determined more by the total traffic pattern than by changes
at particular points. It would therefore be very interesting to look at a number of
cities to determine whether the same patterns that apply in Washington apply else-
where. If more cities are studied, it would be beneficial to adapt the approach to
the computer, incorporating changing traffic patterns, meteorology, and population
trends as part of a larger model for use in urban planning.
A final point of importance is the need to examine the various components that
make up a concentration measured in an urban area. Because concentrations are
very high in the immediate vicinity of vehicles traveling a given street, measure-
ments made at the edge of the sidewalk (i. e. , at the CAMP station) are certainly
not representative of the concentrations to which people in the vehicles are ex-
posed. Indeed, the concentrations that are most realistic from the standpoint of
commuter exposure may be several times greater than those measured by street-
side sampling probes. In addition, some share of the concentrations measured
in urban areas may be contributed from other cities by the long-distance transport
of the pollutant. Although this urban background component may be small at pres-
ent, the great urbanization likely to take place in areas such as the eastern coastal
region, for example, could result in very significant increases. Thus there is the
need to treat each component separately, examining in detail the circumstances
responsible for it.
In addition to refining and building upon the urban area approach, therefore,
future studies should look both at the smaller environments within the city and at
the larger picture involving many cities.
ACKNOWLEDGEMENT
Grateful appreciation is expressed for the guidance and consultation provided
by D. A. Lynn, Data Analyst, and Raymond Smith, Chief, Air Quality and Emis-
sion Data Branch, National Center for Air Pollution Control.
It should be noted that author John F. Clarke is with the Air Resources Field
Research Office, Environmental Science Services Administration, U. S. Depart-
ment of Commerce, Cincinnati, Ohio.
-------
APPENDIX A
The Calculation of Mean Annual Concentrations
The Development of the Model
The generalized Gaussian diffusion equation, equation (1), describes the con-
centration resulting from a point source as following normal distribution in both
the horizontal and vertical directions as measured from the plume centerline
(Figure A). It assumes the source is continuously emitting and that total reflection
from the ground takes place.
H2
exp | - —— j exp | -
x = 2Q ._
u
\/ZTT '
-------
30
Calculating Future Carbon Monoxide Emissions
Figure A.
/*-,
'*£>
0"*cr/i
o/\,
POINT SOURCES
OF EMISSIONS .*
THE SINGLE LINE SOURCE
Figure B.
-------
and Concentrations From Urban Traffic Data
31
X =
2QT
(2)
To apply equation (2),it is necessary to know the source strength Q^, the wind
speed u, and erz. For time periods ranging from 10 minutes to 1 hour,
-------
32
Calculating Future Carbon Monoxide Emissions
To convert this to a relationship applicable to area sources instead of line
sources, the area may be assumed to be composed of numerous closely packed
rows, as shown in Figure C. Considering each row, or line source, to have in-
finitesimal thickness, dr, summation is achieved by integrating equation (5) with
respect to r. The limits of integration, ri and r^i represent the distances from
the receptor point to the boundaries of the area source, and Q^ denotes the emis-
sion density for the area. Substituting Q-^ = Q^ dr, equation (5) is integrated di-
rectly:
X =
2.3
'A 0.55 0.75
u r
dr
1/4 1/4
X = 9.2 Q
A 0.55
u
(6)
SUMMATION OF
MULTIPLE POINT
SOURCES TO YIELD
AREA SOURCE J
Figure C.
When equation (6) is used to calculate concentrations for an urban area, it is
necessary to compensate for the "roughness" caused by buildings and other ir-
regular structures. These effects may be included by assuming that the pollutant
-------
and Concentrations From Urban Traffic Data
33
travels over a distance that is a fixed amount greater than the measured distance,
producing a "virtual" travel distance. The incremental travel distance takes into
account initial turbulence and dispersion of the pollutant caused by buildings, etc. *
An incremental travel distance of 100 meters -- previously found applicable in cal-
culating Washington, D. C. , oxides of nitrogen concentrations--was thus added to
all distances r^ and -c^.- This is shown in equation (7), which expresses the con-
centration X at a receptor site relative to the area source strength Q^ that lies
between distances r and r.
X
/Q,
9.2
0.55
u
100)
0.25
100)
0.25
(7)
Application of the Model
To calculate the concentration at a specific receptor site, it is necessary to
evaluate the contributions made by numerous area sources surrounding the recep-
tor site. This was carried out by adapting the simple receptor-oriented model
presented by Clarke. The latter employs a wheel-shaped overlay that can be
placed anywhere over a map of emission densities; the concentration at the center
of the wheel can then be calculated in a relatively straightforward manner. The
overlay used in the model of this study is shown in Figure D. It consists of 3 con-
centric rings divided into 8 sectors (for 8 wind directions) providing for a total of
24 segments or area sources.
ONE SEGMENT
IN6 NO. 3
SINGLE
SECTOR
OVERLAY PATTERN
Figure D. Diffusion model overlay.
*See, for example, Pooler, Reference 5.
-------
34
Calculating Future Carbon Monoxide Emissions
The overlay was applied by obtaining a value for the emission density in each
of the 24 segments when the overlay was centered on a particular receptor site.
The relative concentration X/Q^ contributed by a particular segment (bounded by
ri and T£) for a given wind speed u can be calculated from equation (7). To obtain
the mean concentration contributed by the segment (averaged over a long period of
time), the relative concentration can be evaluated for each wind speed, multiplied
by the emission density Q^ appropriate to the segment, and summed over all wind
speeds. The resulting mean concentration at the receptor site is then obtained by
summing overall segments. To reduce the work involved, five wind speeds were
selected for this study as representative of five wind speed classes as shown in
Table A:
TABLE A
Representative Wind Speeds Chosen for Each Class
Wind speed class, mph
Representative speed, mps
0-3
0.9
4-7
2.5
8-12
4. 5
13-18
6.9
> 19
9.6
The frequencies of occurrence of these speeds for various directions were ob-
tained from a U. S. Weather Bureau summary of hourly observations at Wash-
ington National Airport for the period 1951-1960. 9
In practice, the relative concentration for each wind speed class was multi-
plied by the frequency of occurrence of that class and the results added for the
five classes. Then the mean annual concentration at the receptor site "was ob-
tained by summing over all segments (over 3 rings and 8 radial directions). Com-
puting the relative concentration appropriate to each ring was simplified by eval-
uating equation (7) quite generally for the three rings:
Ring No. 1:
X
Q,
23.924
0.55
u
(8)
Ring No. 2:
X
Q
A
20.665
0. 55
u
(9)
Ring No. 3: =
18.637
0. 55
(10)
-------
and Concentrations From Urban Traffic Data
35
The entire process of calculating the mean annual concentration is shown
mathematically by equation (11), in which s denotes the number of the ring and
fu,0 is *ke fractional frequencies of winds of speed u and direction 6:
X
= £.
6, s,u
Q
s,0
A/s.u
1,6
(ii)
To obtain the mean annual concentration at another receptor site, the over-
lay was moved to that site and the process repeated.
Discussion
It is interesting to examine the effect of source distance on the concentration at
a particular receptor site as predicted by the model.
According to the diffusion equations, the concentration that results from a given
source decreases rapidly with increasing distance from the receptor. This is shown
in Figure E, in which the carbon monoxide concentrations are given as a function of
distance for a specified line source strength and a wind of 1 mps. The r.adii of the
three concentric rings of the overlay were originally selected so as to weigh equally
the relative concentration each contributed to the receptor site under a specified set
of meteorological conditions (Clarke, Reference 3). The effect of the greater dis-
tance between the receptor site and the outer ring was compensated for by the greater
area of the outer ring. However, the meteorological assumptions for which the over-
lay was originally designed differ from those used to calculate mean annual CO con-
centrations, and the relative concentrations for the three rings as evaluated from
equation (7) are not exactly equal. Table B shows the share of the relative concen-
tration that each ring would contribute if each ring enclosed the same emission den-
sity. With uniform emissions per unit area and no emissions beyond the third ring,
37. 7 percent of the calculated concentration would result from emissions within the
first 1, OOOmeters from the receptor site. Emissions from the next two rings would
account for 32. 7 percent and 29. 6 percent, respectively, of the calculated concen-
tration.
TABLE B
Percent Contribution of Each Ring With Uniform Emission Density
Overlay ring
1
2
3
Radii
0-1, 000 m
1,000-4,000 m
4,000-10, 000m
Relative concentrations
23.924
0.55
u
20,665
0.55
u
18.637
0.55
u
Percent
37.7
32.7
29.6
In actuality, the emission densities in an urban complex are not uniform. As
shown for Washington, the maximum emission densities occur near the center of the
city and decrease with distance. Table C shows the percent contribution of each of
-------
36
Calculating Future Carbon Monoxide Emissions
10.0
o
l_»
o
.01
10* I04
DISTANCE DOWNWIND, meters
10°
Figure E. CO concentration vs. source-to-receptor distance.
the three rings to the calculated concentration for the four receptor sites in Wash-
ington, D. C. For a location near the center of the city, at Pennsylvania and 14th NW,
the first ring is predominant. The trend between 1964 and 1985, however, is for the
more distant sources in the second and third rings to contribute an increasing share
to the resulting concentration, reflecting the greater increases in emission densities
in the areas enclosed by these rings. For a receptor site located away from the
center of the city, such as that at New Hampshire and 13th NW, the high emission
-------
and Concentrations From Urban Traffic Data
37
densities in the downtown areas (ring 2) contribute a greater share than the sources
nearby the receptor.
TABLE C
Percent Contribution of Each Ring for Washington, D. C.
Receptor site
1
2
3
4
Location
CAMP
Pennsylvania and 14th NW
New Hampshire and 13th NW
Arlington
Year
1964
1985
1964
1985
1964
1985
1964
1985
Contribution within
overlay ring number
48
50
59.6
52.7
31.9
32.8
32.2
37
43
40.5
33.0
38.6
52.2
47.2
39
37.4
9
9.5
7.4
8.7
15.9
20.0
28.8
25.6
It is interesting to speculate on the change in concentrations that would come
about at a downtown receptor site if emission densities at the outskirts increased to
the levels that now apply in the downtown areas, while no further increases oc-
curred downtown. For example, if the emission densities in the second and third
rings around the Pennsylvania and 14th NW, receptor site increased to the present
value of the first ring (possibly a saturation value), the concentration would increase
58 percent--even though no changes in emissions took place in the first ring. This
is a conservative figure, since emission densities of this magnitude in the third ring
would mean the area beyond the third ring (i. e. , beyond 10, 000 meters) could no
longer be ignored.
-------
APPENDIX B
Traffic Inventory for Washington, D. C.
Zone
1st Ring (r=0.5)c
A]
Bl
Cl
Dl
El
2nd Ring (r = 1.5)
A2
B2
c2|
D2
E2
F2
3rd Ring (r=2.5)
A3
B3
C3
C3'
D3
E3
4th Ring (r=3. 5)
A4
B4
C4
C4'
D4
E4
F4
5th Ring (r = 4. 5)
AS
B5
C5
CB'
D5
E5
Edges
L
M
N
0
P
Periphery
V
W
X
Y
Z
Total
Area,b
mi2
0.52
0.52
0.52
0.52
0.52
0.52
1.58
1.58
1.23
0.35
1.58
0. 92
1.58
2.62
2.62
2. 13
0.49
2.62
1.33
2.62
3.65
3.65
2.75
0.90
3.65
1.97
3.65
4.72
4.72
2.81
1.91
4.72
—
4.72
4.45
4.92
2.95
4.35
2.00
10.60
14.60
9.40
9.40
14.60
147.46
Average
speed,
mph
15.0
ii
ii
"
it
"
17.5
11
ii
M
11
"
It
20.0
M
"
M
M
"
II
22.5
"
11
"
"
11
"
25.0
"
"
"
11
—
25.0
27.5
"
"
"
11
30.0
it
"
"
"
—
Daily vehicle
miles of travel
(DVMT in thousands)
1964
254
189
153
130
114
179
432
314
296
64
223
192
420
409
373
182
129
315
143
252
406
320
151
130
378
119
463
399
434
176
122
411
--
323
353
435
132
' 227
204
422
347
483
661
960
12,819
1985
276
247
185
155
219
260
748
358
506
122
534
321
940
777
582
353
215
359
211
495
795
461
342
386
642
173
870
542
721
348
189
777
--
870
1,043
718
293
791
481
953
694
980
1,830
2,084
24, 846
Traffic density
(DVMT/mi2 in thousands)
1964
488
363
295
250
219
344
274
199
241
184
141
209
266
156
142
85
264
120
107
96
111
88
55
145
103
60
127
85
92
63
64
87
—
69
79
88
95
52
102
40
24
51
70
66
--
1985
532
476
357
298
422-
499
473
227
411
349
338
349
595
296
222
166
439
137
159
189
218
126
124
429
176
88
238
115
153
124
99
165
--
184
234
146
99
182
241
90
48
104
195
143
--
aFigure 2 shows the locations of each of the zones in the zonal grid.
bA subtraction of area is made in zones containing a large portion of a river. The
Potomac River divides sector C into two parts, C and C1.
dThe value of r is the distance from the center of the grid to the midpoint of each of
the five concentric rings.
39
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40 Calculating Future Carbon Monoxide Emissions
REFERENCES
1. Carbon Monoxide, A Bibliography with Abstracts, U.S. Department of Health,
Education, and Welfare; Public Health Service Publication No. 1503, Washing-
ton, D. C., 1966.
2. Rose, A. H. , et al. , "Comparison of Auto Exhaust Emissions From Two Major
Cities, " U.S. Public Health Service, Division of Air P6llution; paper presented
at the annual meeting of the Air Pollution Control Association, Houston, Texas;
June, 1964.
3. Clarke, J. F. , "A Simple Diffusion Model for Calculating Point Source Concen-
trations from Multiple Sources," Journal of the Air Pollution Control Association,
Vol. 14, No. 9, Sept., 1964, pp. 347-352.
4. Pooler, Francis, Jr. , "A Prediction Model of Mean Urban Pollution for Use
•with Standard Wind Roses, " International Journal of Air and Water Pollution,
Vol. 4, Nos. 3/4, Sept., I960, pp. 199-211.
5. Pooler, Francis, Jr., "A Tracer Study of Dispersion Over a City, " Journal of
the Air Pollution Control Association, Vol. 11, No. 12, Dec., 1966, pp. 677-
681.
6. Gifford, F. A. , "The Problem of Forecasting Dispersion in the Lower Atmo-
sphere, " Atomic Energy Commission, Division of Technical Information Ex-
tension, Oak Ridge, Tenn., 1961.
7. McCormick, R. A. , and C. Xintaras, "Variation of Carbon Monoxide Concen-
trations as Related to Sampling Interval, Traffic and Meteorological Factors, "
Journal of Applied Meteorology, Vol. 1, No. 2, June, 1962, pp. 237-243.
8. Miller, M.E., and G. C. Holzworth, "An Atmospheric Diffusion Model for
Metropolitan Areas, " Journal of the Air Pollution Control Association, Vol.
17, No. 1, Jan., 1967, pp. 46-50.
9. "Summary of Hourly Observations, Washington, D. C. , 1951-1960," Climato-
graphy of the United States No. 82-50, U.S. Weather Bureau, Washington, D. C. ,
1962.
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