EPA-450/3-75-069-B
April 1975
PHOTOCHEMICAL
OXIDANT MODELING
Volume II -
Detailed Technical Report
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-450/3-75-069-b
PHOTOCHEMICAL
OXIDANT MODELING
Volume II -
Detailed Technical Report
by
F. Record, R. M. Patterson, M. T. Mills,
E. P. V. Ward, D. A. Bryant, and R. C. Galkiewicz
GCA Technology Division
Bedford, Massachusetts 01730
Contract No. 68-02-1376, Task Order 14
EPA Project Officer: Thomas McCurdy
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, N. C. 27711
April 1975
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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 - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, 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 GCA
Technology Division, Bedford, Massachusetts 01730, in fulfillment of
Contract No. 68-02-1376. The contents of this report are reproduced
herein as received from GCA Technology Division. The opinions, findings,
and conclusions expressed are those of the author and not necessarily
those of the Environmental Protection Agency. Mention of company or
product names is not to be considered as an endorsement by the Environmental
Protection Agency.
Publication No. EPA-450/3-75-069~b
11
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ABSTRACT
This report describes review and analysis activities which have been
undertaken to support the EPA goal of developing technical and policy
guidelines for assessing the oxidant air quality impact of highway
development under the 3-C planning process. Separate sections discuss
somewhat diverse topics, although they are all directed towards oxidant
impact assessment. These sections include (1) a review of the techniques
and computer models available for estimating mobile source emissions;
(2) a brief summary of oxidant formation processes; (3) a discussion of
the oxidant modeling activities of this project using the DIFKIN and
Gifford-Hanna photochemical models, and (A) a "test run" of the 109(j)
and indirect source review guidelines.
iii
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CONTENTS
Page
Abstract ±±±
List of Figures v
List of Tables vii
Acknowledgments viii
Sections
I Introduction and Summary 1
II Emissions Models 3
III Summary of Knowledge and Theory of Oxidant and NO
Formation 13
IV Oxidant Modeling Activities 31
V Case Study of Air Quality Review Requirements 84
Appendices
A Block Data for DIFKIN Run 97
B Input for DIFKIN Run
C Output From DIFKIN Run
IV
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FIGURES
No. Page
1 Average Daily 1-Hour Concentrations of Selected Pollu-
tants in Los Angeles, California, July 19, 1965 . 16
2 Weekday and Weekend 1-Hour Average NO Levels in Chicago,
1962 Through 1964 18
3 Diurnal Variation of CL at Welfare Island for Monday
Through Friday, Saturday and Sunday 19
4 Monthly Mean NO Concentrations at Four Urban Sites 20
5 Monthly Mean N0~ Concentrations at Four Urban Sites 21
6 Monthly Variation of Mean Hourly Oxidant Concentrations
for Three Selected Cities 22
7 Maximum Daily 1-Hour Average Oxidant Concentrations as a
Function of 6- to 9-a.m. Averages of Total Hydrocarbon
Concentrations at CAMP Stations, June Through September,
1966 Through 1968 and in Los Angeles, May Through
October 1967 25
8 Maximum Daily 1-Hour Average Oxidants as a Function of
6- to 9-a.m. Averages of Nonmethane Hydrocarbons at
CAMP Stations, June Through September, 1966 Through
1968, Los Angeles, May Through October 1967 26
9 Required Hydrocarbon Emission Control as a Function of
Photochemical Oxidant Concentration 27
10 Pollutant Relationships at Los Angeles, Data Period:
July to September 1969 to 1972 28
11 Map of the Denver Metropolitan Area Showing Air Moni-
toring Stations (Lettered) and Wind Stations (Numbered) 48
12 Variation of Diffus^vity With Height for Different Sta-
bility Conditions 55
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FIGURES (continued)
No. Pa;
13 First DIFKIN Evaluation Run 58
14 Second DIFKIN Evaluation Run 59
15 Third DIFKIN Evaluation Run 60
16 Fourth DIFKIN Evaluation Run 61
17 Fifth DIFKIN Evaluation Run 62
18 Scheme for Combining Rectilinear Source-Grid Squares With
Radial Wind Directions 68
19 Emission Density Pattern for Steady State Gifford-Hanna
Model Validation for Denver (6 a.m. to 12 noon Average) 70
20 Concentration (ppm) Profiles for Steady State Gifford-
Hanna Model Validation for Denver (August 13, 1973 -
12 noon) 72
vi
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TABLES
No. Page
1 Data Sets for Appendix J Discussion 33
2 Variation of Diffusion Parameters With Stability
Condition 40
3a Daily Freeway VMT (k mi) for Denver (1973) 49
3b Daily Surface Street VMT (k mi) for Denver (1973) 50
4 Stationary Source Emissions for NO and HC for the
Denver Metropolitan Area 51
5 Summary of the Most Important External Inputs for
DIFKIN 53
6 Summary of Air Quality and Meteorological Data for
the Validation Day 57
7 Comparison of Measured and Calculated Concentrations
at 1400 Hours for August 13, 1973 64
8a Hourly CO Validation Using Simple Gifford-Hanna
Photochemical Model 75
8b Hourly RH Validation Using Simple Gifford-Hanna
Photochemical Model 77
8c Hourly 03 Validation Using Simple Gifford-Hanna
Photochemical Model 79
vii
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ACKNOWLEDGMENTS
We wish to thank our project officer, Mr. Thomas McCurdy, for his help-
ful comments during the conduct of this study.
We also wish to acknowledge Mr. Charles Pratt of Wilbur Smith and Asso-
ciates for his assistance in supplying information on the transportation
planning process in Denver„
viii
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SECTION I
INTRODUCTION AND SUMMARY
This document reports on a number of somewhat diverse topics, all cen-
tered around assessing the oxidant air quality impact of highway project
or system modifications arising from the 3-C planning process. The
work described here is in support of EPA's goal of providing technical
and policy guidance for 109(j) and indirect source reviews of 3-C plans,
and for developing air quality maintenance plans for oxidant air quality
maintenance areas.
The first section deals with techniques and computer models which are
available to calculate mobile source emissions. Comparisons are made
of the emission densities estimated by different techniques, and of the
various features of the computer models.
Next, a brief summary of a literature survey of the knowledge and theory
of oxidant and NO- formation is presented. This review is not meant to
be exhaustive, but rather to provide enough background for the reader
to have a better appreciation of later sections of this report, especially
the section which describes the oxidant modeling activities which were
undertaken during this project.
In the third section, the oxidant modeling activities which were under-
taken for this project are described. This involved a test of Appendix
J and a review of three large computer codes — REM (Pacific Environ-
mental Services), DIFKIN (General Research Corporation), and AREAWIDE
(Systems Applications Incorporation) — as well r.s the much simpler
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Gifford-Hanna photochemical model. The purpose of this review was to
determine which of these models might be used for highway impact evalua-
tion. It was decided to apply the DIFKIN and the Gifford-Hanna models
to Denver, based on ease of application, severity of input data require-
ments, and plausibility of the treatment of atmospheric transport and
diffusion and the photochemical reaction processes. The remainder of
this section describes the application of these two models and discusses
the results.
Completing the report is a "test run" review of 109(j) and indirect
source guidelines for Denver, Colorado. During this review it was found
that more formal arrangements need to be made to ensure that air quality
is considered in transportation planning. It was also found that pre-
sent guidance does not provide the procedures or methods necessary for
deciding whether entire highway systems or component projects are con-
sistent with the air quality goals of state implementation plans. It
was determined that the indirect source review procedures do not provide
adequate guidance for assessing the oxidant impact of highways.
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SECTION II
EMISSIONS MODELS
1—8
There are numerous methods for estimating emissions from motor vehicles.
The basic algorithm is essentially the same for the various methods. By
the method given in EPA publication AP-42 , emission strengths for
exhaust hydrocarbons and oxides of nitrogen are calculated by:
n+1
e = C. d. m. S^ (1)
np * -* ip ipn in ip
i-n-12
where e = emissions (grams per vehicle mile) for calendar year n
and pollutant p,
c. = emissions (grams per vehicle mile) for pollutant p for
P it" model year at low mileage at an average speed of
19.6 miles per hour.
d. = emission deterioration factor for the it model year,
P calendar year n, and pollutant p for vehicles with
emission controls,
m. = weighted annual travel of the ifc model year during
calendar year n,
and S. = weighted speed adjustment factor for exhaust emissions
of pollutant p for the itn model year.
The weighted speed correction factor, S. , is in turn computed by the
relation:
k
" v. (2)
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with S, = weighted speed adjustment factor for exhaust emissions of
m pollutant p for the i^h model year during calendar year m,
f. = the fraction of the total annual vehicle miles traveled at
m speed j during calendar year m,
v. = the average speed correction factor for average speed j and
P pollutant p,
and k = total number of different average speeds.
Evaporative and crankcase hydrocarbon emissions are calculated by:
n + 1
h. m. <3>
i in
i = n - 12
where f = combined evaporative and crankcase hydrocarbon emissions
for calendar year n,
h. = combined evaporative and crankcase emission rate (grams per
vehicle mile) for the i*-*1 model year,
and m. was defined previously.
To calculate actual emissions, e and f must be multiplied by the
np n
number of vehicle miles traveled (VMT) for the region of interest during
a given time period.
As mentioned earlier, the real differences among vehicle emission models
do not arise from the computational methods, but rather from the extent
and definition of the input parameters. It can be stated that, in
general, newer methods will provide better results because of improved
input data, and perhaps because experience provides for improved
2-5
specification of input parameters. Additional methods have been
given for computing motor vehicle emissions; almost all use the same
basic technique described for AP-42 and all require the same basic data.
A recent technique distinguishes between "hot starts" and "cold starts"
in calculating emissions. Cold start data are obtained from the
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Federal Test data (AP-42) by assuming a fraction of the test results of
total emissions to have occurred during the first 2 minutes or cold
start portion of the cycle. This fraction varies with year and is based
on General Motors tests results. me grams per mile emissions during
the "hot" portion of the test are calculated from the remaining portion
of the test sample. The hot emissions are subtracted from the cold
emissions to determine the excess emissions attributable exclusively to
cold start. The result is a new set of emissions data (grams per mile)
for use with VMT data, and a set of "cold start" emissions to be
applied where these conditions obtain.
The method has been carried further to define a range of "fractions of
a cold start" which increase with an increase in soak time. Starts
after a 12-hour soak are considered to be fully cold.
Q
EPA has recently published a supplement revising the internal combus-
tion engine sources portion of AP-42. The new methodology for estimating
emissions is quite different from that previously employed and
described above due mainly to three factors:
• The new method does not include deterioration
factors,
• "Cold start" and "hot start" emissions are included,
• Emissions projections are given in an appendix and not
and not listed in the main section dealing with
internal combustion engine sources,
The emission factors which are presented in this supplement are based on
measurements taken during EPA's annual surveillance programs and cover
calendar years 1971 and 1972. Deterioration is accounted for implicitly
in the emission factors for the year of measurement. For example,
emission factors are presented as X grams per kilometer of exhaust
hydrocarbons for a 1969 model year vehicle in calendar year 1972.
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Other changes in the method of estimating motor vehicle emissions can
best be described by examining the new algorithm used to compute emis-
sions. For light duty vehicles (automobiles) and light duty trucks
this is:
n
e = 7 c. m. v zs r. (4)
npstw / j ipn in ips ipt iptw
i=n-12
where e = Composite emission factor in g/km (g/mi) for
calendar year n, pollutant p, average speed s,
ambient temperature t, and percent cold opera-
tion w.
= The FTP (1975 Federal Test Procedure) mean emis-
sion factor for the i1-'1 model year light duty
vehicles during calendar year n and for pollutant
P,
m. = The fraction of annual travel by the i model year
light duty vehicles during calendar year n,
t~V\
v. = The speed correction factor for the i model year
1^s light duty vehicles for pollutant p and average
speed s,
+~Vi
z. = The temperature correction factor for the i model
year light duty vehicles for pollutant p and ambient
temperature t,
r. = The hot/cold vehicle operation correction factor for
P the ±th model year light duty vehicles for pollutant
p, ambient temperature, t, and present cold operation
w.
The discussion of these variables applies to automobiles and light
trucks except where noted.
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FTP EMISSION FACTOR (c. )
ipn
These data are divided by geographic area into: low altitude (non-
California), high altitude, and California only. The tabulated values
are applicable to calendar years 1971 and 1972 (only 1972 for trucks).
California emission factors are presented separately since California
vehicles have been, in the case of several model years, subject to
emission standards which differ from those standards applicable to
vehicles under the Federal emission control program. For those model
year California vehicles which did not have separate emission standards,
the national emission factors are assumed to apply in California as
well. Emissions at high altitude are differentiated from those.at low
altitude to account for the effect that altitude has on air-fuel ratios
and concomitant emissions. The tabulated values are applicable to
calendar years 1971 and 1972 (only 1972 for trucks) for each model year.
FRACTION OF ANNUAL TRAVEL BY MODEL YEAR (nu)
No significant change has occurred from previous emission estimation
methods.
SPEED CORRECTION FACTORS (v. )
^ ips
Speed correction factors enable the "adjustment" of FTP emission factors
to account for differences in average route speed. Since the implicit
average route speed of the FTP is 19.6 miles per hour (31.6 kph), esti-
mates of emissions at higher or lower average speeds require this
correction.
It is important to note the difference between "average route speed" and
"steady speed." Average route speed is trip-related. It is based on a
composite of the driving modes (idle, cruise, acceleration, deceleration)
encountered in, for example, a typical home-to-work trip. Steady speed
7
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is highway facility-oriented. For instance, a group of vehicles traveling
over an uncongested freeway link (volume/capacity of, say, 0.1) might be
traveling at a steady speed of about 55 mph (89 kph) . Note, however, that
steady speeds, even at the link level, are unlikely to occur where re-
sistance to flow occurs (unsynchronized traffic signaling, congested flow,
etc.)
Previously, the limited data available for correcting for average speed
were presented graphically. Recent research has resulted in revised speed
relationships by model year. To facilitate the presentation, the data
are given as equations of the form
ips =
V = 6Xp (A + Bs + Cs (5)
where s is the speed in miles per hour.
The values of the coefficients A, B, and C apply only for the range of
the data, from 24 to 72 kilometers per hour (15 to 45 miles per hour).
Since there is a need, in some situations, to estimate emissions at very
low average speeds, correction factors have been developed for this pur-
pose for 8 and 16 kph (5 and 10 mph) .
TEMPERATURE CORRECTION FACTOR (z± fc)
The 1975 FTP requires that emissions measurements be made within the limits
of a relatively narrow temperature band (68 to 86 F) . Such a band facili-
tates uniform testing in laboratories without requiring extreme ranges of
temperature control. Present emission factors for motor vehicles are
based on data from the standard Federal test (assumed' to be at 75 F) .
The correction factors are expressed in equational form and can be ap-
plied between 20 F and 80 F. For temperatures outside this range, the
appropriate endpoint correction factor is applied.
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HOT/COLD VEHICLE OPERATION CORRECTION FACTOR (r, . )
iptw
The 1975 FTP measures emissions over three types of driving: a cold
transient phase (representative of vehicle start-up after a long engine
off period), a hot transient phase (representative of vehicle start-up
after a short engine-off period), and a stabilized phase (representative
of warmed-up vehicle operation). The weighting factors used in the 1975
FTP are 20 percent, 27 percent, and 53 percent of total miles (time) in
each of the three phases respectively. Thus, when the 1975 FTP emission
factors are applied to a given region for the purpose of accessing air
quality, this can be viewed as if 20 percent of the light duty vehicles
in the area of interest are operating in a cold condition, 27 percent are
operating in a hot start-up condition, and 53 percent are operating in a
hot stabilized condition. For noncatalyst vehicles (all pre 1975 model
year vehicles), emissions in the two hot phases are essentially equivalent
on a gram/kilometer (grams/mile) basis. Therefore, the 1975 FTP emis-
sion factor represents 20 percent cold operation and 80 percent hot
operation.
There are many situations where these particular weighting factors may
be inappropriate. For example, light duty vehicle operation in the center
city may have a much higher percentage of cold operation during the after-
noon peak when work-to-home trips are at a maximum and vehicles have been
soaking for 8 hours. The hot/cold vehicle operation correction factor
allows the cold operation phase to range from 0 percent to 100 percent
of total light duty vehicle operations. This correction factor is a
function of the percent of cold operation, w, and the ambient tempera-
ture, t. The correction factor is:
w + (IQO-w)f(t)
r. —
lptw 20 + 80 f(t)
where f(t) is a function of temperature presented in AP-42, Supplement
No. 5.
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The new methodology also allows for calculating evaporative emissions of
hydrocarbons and idle emissions of hydrocarbons, nitrogen oxides, and
carbon monoxide.
Emissions from light duty, diesel-powered vehicles are calculated as be-
fore, except that the emission factors are given for pre-1973 model years,
Projection to future years is given as an appendix.
Emissions from heavy-duty gasoline vehicles are calculated by
n
e = / c. m. v. (7)
nps *-^ ipn in ips
i = n - 12
where the factors were defined previously. For heavy-duty diesel-powered
vehicles the model year distribution is omitted:
n
e = /. c- v,
nps *-^ ipn ips
i = n - 12
Values for c. are based on tests of vehicles on-the-road over the San
ipn
Antonio Road Route (SARR). The SARR, located in San Antonio, Texas, is
7.24 miles long and includes freeway, arterial and local/collector high-
way segments. Since the SARR is an actual road route, the average speed
varies depending on traffic conditions at the time of the test. However,
the average speed tends to be around 29 kph (18 mph) with about 20 percent
of the time spent at idle. The test procedure emission factor is composed
entirely of warmed-up vehicle operation. Based on a preliminary analysis
of vehicle operation data, HDV operation is primarily (about 95 percent)
warmed up.
10
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Of course, it is quite necessary to estimate motor vehicle emissions be-
yond calendar year 1972, and this capability is provided in an appendix
to the revised AP-42. This is done purposely to separate the analytical
results of EPA's surveillance testing program from what are acknowledged
to be "best guesses" of future emission factors. There are several rea-
sons for this separation. First, current legislation allows for limited
time extensions for achieving the statutory motor vehicle emission stan-
dards. Secondly, Congressional action changing the time table for achiev-
ing these standards and/or changing their levels is likely in the future.
Thirdly, new data on catalyst-equipped (1975 automobiles) are becoming
available. The methods presented in the appendix for estimating emissions
are similar to those described above.
There is a final source of data which might prove useful, especially for
estimating emissions from higher speed, highway traffic. This is the
9
Modal Analysis Model developed for EPA. This model predicts emissions
from a single vehicle or an ensemble of vehicles for a user-specified age
distribution over any desired driving sequence within the range of the
applicability of the model. The key point for highway traffic is that
the model can predict emissions which reflect a constant cruise speed,
not an "average route speed" which might include stops and starts. With
proper resources, the model could even be used to estimate average emis-
sions for a given urban area based on the typical driving cycle for that
area instead of on the Federal Test Procedure.
11
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REFERENCES
1. Compilation of Air Pollutant Emission Factors. EPA Report AP-42
(Second Edition). April 1973.
2. Special Area Analysis (SAPOLLUT Model), U.S. DOT. August 1973.
3. Kircher, David S. and Donald P. Armstrong. An Interim Report on
Motor Vehicle Emission Estimation. EPA-450/2-73-003. October 1973.
4. Wolsko, T. D., M. T. Matthies, and R. E. Wendell. Transportation Air
Pollution Emissions Handbook. Argonne National Laboratory Report
ANL/ES-15. July 1972.
5. Sauter, G. C. and W. R. Ott. A Computer Program for Projections of
Vehicular Pollutant Emissions in Urban Areas, JAPCA, 24:54. No. 1
January 1974.
6. Wendell, R. E., J. E. Norco, and K. G. Groke. Emission Prediction
and Control Strategy: Evaluation of Pollution from Transportation
Systems, JAPCA, 23:91. No. 2. February 1973.
7. Cirillo, R. R., J. E. Norco, and T. D. Wolsko. The Effect of Cold
Start on Motor Vehicle Emissions and Resultant Air Quality. Paper
74-127, 67th Annual Meeting, Air Pollution Control Association.
Denver. 1974.
8. Supplement No. 5 for Compilation of Air Pollutant Emission Factors.
EPA Report AP-42 (Second Edition). Unedited copy. April 1975.
9. Automobile Exhaust Emission Modal Analysis Model. EPA Report
EPA-460/3-74-005. January 1974.
12
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SECTION III
SUMMARY OF KNOWLEDGE AND THEORY OF OXIDANT AND N02 FORMATION
OXIDANT AND OXIDES OF NITROGEN FORMATION
Fossil fuels are composed of hydrocarbons, combustion of which produces
carbon dioxide and water. But since combustion is usually less than
100 percent efficient, the exhaust gases contain unburned fuel which
enters the atmosphere. Oxides of nitrogen - in particular, nitric oxide
(NO) and nitrogen dioxide (N09) - are formed under high-temperature
conditions from the combination of atmospheric nitrogen and oxygen.
From these inputs of hydrocarbons and oxides of nitrogen to the atmos-
phere, a complex system of reactions occurs resulting in the formation
1 2
of oxidants and other products. '
The series of reactions by which oxidants are formed have been studied
1 2
to determine the relationship between the precursors and the products. '
Nitric oxide is the primary product formed under high temperature
conditions, with only a small amount of NO- being formed. NO combines
with oxygen to form NO :
2 NO + 02 -> 2 N02 (1)
Nitrogen dioxide absorbs ultraviolet light from sunlight (photolysis)
and the following reactions result:
uv
N02 -> NO + 0 (2)
00 + 0 -»• 00 (3)
13
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There are several possible results of the above. The ozone (0_) may re-
act with nitric oxide, thus reversing the above reactions and completing
the nitrogen dioxide photolytic cycle:
+ NO •* N02 + 02 (4)
Alternatively, the oxygen atom or the ozone may react with a hydrocarbon
molecule, forming an oxidized hydrocarbon.
The oxidant found in the largest quantity in polluted atmospheres is ozone
Nitric oxide acts as an ozone regulator by reacting with it to form nitro-
gen dioxide and oxygen (Equation (4)). Ozone does not begin to
accumulate in the atmosphere and result in the high ozone concentrations
frequently seen until nitric oxide has virtually disappeared. Hydro-
carbons interact with the nitrogen dioxide photolytic cycle, leading to
the disappearance of nitric oxide and to the accumulation of ozone and
2
hydrocarbon oxidation products. The varying reactivity of different
hydrocarbons affects the rate of formation and the amounts of oxidant.
In general, the saturated hydrocarbons are the least reactive.
Meteorological variables affect the nitrogen dioxide photolytic cycle
1 2
and the formation of oxidants. ' The intensity and wavelength of light
are important in the photolysis of NO,,. Photolysis is most likely
to occur when the light wavelength is between 3000 and 3700A. Light of
strong intensity increases the rate of photolysis, so the formation
of oxidants is affected when light intensity is affected by such factors
as altitude, season or a hazy atmosphere. Temperature also influences
the rate of reaction - higher temperatures increase the probability that
an endothermic reaction will occur. Therefore, factors which affect temp-
erature (season, time of day) will affect the formation of oxidant. The
concentration of precursors and products and thus the rate of formation
and the amount of oxidants are influenced by meteorological conditions -
wind speed and direction,.pressure systems, inversions, and mixing depth.
Other variables which affect oxidants formation are topography, the
number and distribution of sources, and rates of emission of the
precursors.
14
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Oxidants are also formed naturally in any area where the atmosphere
contains NO and reactive hydrocarbons. Ozone is also formed in the
atmosphere by electrical discharge or at high altitudes by solar
radiation.
TEMPORAL AND SPATIAL CHARACTERISTICS
Because the nitrogen dioxide photolytic cycle and the formation of oxi-
dants are influenced by meteorological variables, the concentrations
of the precursors and products show certain temporal and spatial patterns.
In urban areas, a weekday diurnal pattern of NO, N0_, and 0 has been
noticed - Figure 1 shows a typical pattern for a day in Los Angeles.
Levels of all three pollutants are low during the night. Nitric oxide
and hydrocarbons are emitted in large quantities by morning rush hour
traffic. Nitric oxide is oxidized to nitrogen dioxide (Equation (2))
and since hydrocarbons are present, the reaction tends not to be reversed
(Equations (3 and 4)). During the late morning, as the intensity of sun-
light increases, nitrogen dioxide reacts with hydrocarbons, and ozone and
other oxidants begin to accumulate. Peak ozone levels occur usually from
11:00 a.m. to 2:00 p.m. and are determined by the rate of destruction of
4
ozone. Atmospheric instability, decreased traffic, and the formation of
oxidants all help to decrease precursor concentrations during the middle
of the day. Afternoon rush hour traffic contributes more NO and hydro-
carbons, thus increasing NO concentrations. But since the intensity of
X
sunlight has decreased, little ozone is formed and, in fact, the accumu-
lated ozone tends to be destroyed by nitric oxide, returning to low night-
time levels. Figure 1 shows the time relationships of the pollutant peaks.
The early afternoon ozone peak is noticeably lower on cloudy days and on
sunny days when the wind speed increases. The nitric oxide and nitrogen
dioxide peaks are also noticeably lower on windy days. The concentra-
tions of all the pollutants are higher when the mixing depth is less.
15
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0.50
0300 0600
0900 1200 1500
TIME OF DAY
1800 2)00 2400
Figure 1. Average daily 1-hour concentrations of selected pol-
lutants in Los Angeles, California, July 19, 1965^
16
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The diurnal patterns of NO, NO , and 0 on weekends appear to be differ-
r\ o C fi ** -^
ent from weekdays, ' ' ' Figure 2 shows, using Chicago as a typical
example, how the average emission input levels of NO drop considerably
due to decreased traffic. But Figure 3 shows that ozone may be even higher
on the weekends, as it was in New York City, though the emissions of pre-
cursors have been reduced. It has been hypothesized that this is due to
less NO available to regulate ozone levels, to a different N0?/N0 ratio,
3
or to persistence of partially reacted hydrocarbons. This suggests that
a reduction in traffic volume with a corresponding decrease in NO and
hydrocarbon emissions may actually increase oxidant concentrations. '
124
The concentrations of NO, NO and 0~ also show seasonal patterns. ' '
Nitric oxide mean concentrations are higher during late fall and winter
when there is less atmospheric mixing and less ultra-violet energy for
forming secondary products, Figure 4. The nitrogen dioxide pattern is
less distinct with less variation from month to month. Mean concentra-
tions are higher in winter months when the rate of photolysis is lower,
Figure 5. The highest monthly mean concentrations of oxidants occur
during the period from late spring to early fall when the rate of photo-
chemical reactions is highest, Figure 6.
For a long time it was believed that high oxidant concentrations reg-
ularly occurred only in urban areas. Recent studies of oxidants in
rural areas have shown surprisingly high concentrations. In studies of
ozone at selected sites in New York State, high ozone values occurred
simultaneously in rural and urban areas. Rural ozone levels did not
tend to decrease to near zero at night. It is possible that high rural
ozone levels are due to the transport of ozone and ozone precursors
from urban areas, to reactions of naturally occurring precursors, or to
7 8
transport of ozone from the stratosphere to the troposphere. ' In
another study of high levels of ozone in rural areas it was suggested
that ozone clouds as much as 50 or 60 miles long could form downwind
9
from electric power plants. , Monitoring at CAMP stations for 1964 - 1973
has shown that a large downward trend in oxidant concentration has been
17
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MONDAY-FRIDAY
-SATURDAY
-SUNDAY
till
vi
10
10
A.M
v
P.M
HOUR OF THE DAY
Figure 3. Diurnal variation of 0 at Welfare Island for Monday
through Friday, Saturday and Sunday^
19
-------
0.24
0.20
0.16
0.12
O
z
0.08
0.04
0.0
I I I
I I I I I I
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FEB. APR.
IUN AUG
MONTH
OCT
DEC
Figure 4. Monthly mean NO concentrations at four urban sites
20
-------
0.20
0.16
I 0.12
a.
0.08
0.04
-LOS ANGELES,1967
, 1966
I I I I I I i 1 I I
FEB
APR
JUNE
MONTH
AU6
OCT
DEC
Figure 5. Monthly mean NO concentrations at four urban sites'
21
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22
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occurring but the factors responsible for this trend in the central busi-
ness districts may not be causing similar reductions at suburban or down-
4
wind sites.
A study of the vertical distribution of oxidants showed that concentrations
may increase by factors of from 2 to 10 at higher altitudes within the
mixing layer. Below 10,000 feet, this appears to be the result of urban
air pollutant emissions and subsequent photochemical reactions. This
suggests that ozone buildup in advected air masses is primarily the re-
sult of a continuous photochemical aging of the air mass. The smaller
ground level ozone measurements probably result from physical quenching
on surfaces and/or chemical quenching.
It appears, then, that problems with high oxidant concentrations are not
confined to urban areas - natural sources and long-range transport, both
horizontally and vertically, are factors which seem to be making oxidants
a regionwide, and probably nationwide, problem.
QUANTITATIVE RELATIONSHIPS OF OXIDANTS AND PRECURSORS
The appearance of oxidants in the urban atmosphere is dependent upon
chemical reactions. These chemical reactions in turn are dependent on
variables such as sunlight, wind and temperature as well as atmospheric
dilution and dispersion. Because of the numerous variables involved,
the relationship between precursor emissions and atmospheric oxidant
concentrations is indirect and difficult to quantify. Nonlinearity
characterizes the photochemical system.
A study was performed to attempt to define the relationship of hydro-
carbons to oxidants in ambient atmospheres of several cities. The
only assumption made was that there exists a relationship between early
morning average hydrocarbon concentrations and subsequent maximum
hourly average oxidant concentrations. In order to define the hydro-
carbons which were likely to become involved in oxidant forming re-
actions, the average concentrations of nonmethane hydrocarbons from
23
-------
6 to 9 a.m. were determined. A measure of nonmethane hydrocarbons is
a better measure of the reactive hydrocarbons than total hydrocarbons
is, even though not all nonmethane hydrocarbons are considered reactive.
It was found that at any given hydrocarbons level, there exists a limit
on the amount of oxidant which can be generated, Figures 7 and 8. The
atmospheric conditions that lead to maximum oxidant potential occur on
about 1 percent of all days so the maximum oxidant concentration for
a given hydrocarbons concentration is reached only infrequently. The
average 6 to 9 a.m. concentration of 0.3 ppm C nonmethane hydrocarbons
can produce a maximum hourly average oxidant concentration of up to
0.1 ppm. Greater nonmethane hydrocarbons concentrations may also pro-
duce the same oxidant concentration but under extreme atmospheric con-
ditions, a minimum concentration of 0.3 ppm C is sufficient.
Appendix J of 40 CFR 51 is a graph of the reduction in hydrocarbon emis-
sions required to achieve the national standard for photochemical oxidants
concentration, Figure 9. The graph is based on the results of the
study of the relationship between total hydrocarbons and resultant oxi-
dant levels discussed above. Appendix J does not use the nonmethane
hydrocarbons relationship with oxidant which might, perhaps, be more
accurate. Also, it assumes that there is no hydrocarbon or oxidant back-
ground - considering the recent studies which were discussed, this appears
to be an invalid assumption.
Oxidant concentration has also been studied as a function of both 6 to
12
9 a.m. hydrocarbons and oxides of nitrogen concentrations. Figure 10 shows
the pollutant relationships for Los Angeles. It suggests that there is
an optimum hydrocarbon/oxides of nitrogen ratio with respect to maximum
attainable oxidant concentration. A reduction in hydrocarbons with no
change in oxides of nitrogen results in a reduction in oxidant. But
a reduction only in oxides of nitrogen may sometimes result in an increase
in oxidant.
24
-------
0.30
0.25
0 DENVER
® CINCINNATI
A LOS ANGELES
O PHILADELPHIA
A WASHINGTON
0.20
E
ex
a
X
O
0.15
0.10
/
A
A ABA
B A
B B
A
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O A
O H ©OAOOH ABO AQOB «O A
OOA ©3 A « OB A A •• A
O AOAO9 ROOOAAOOOAa AOAO AB A
Q B AA BAA A 8 A AOA* BOdA«B A«A
A B AHA AOA ABA ABA99AO A A®® A • A—
BH AJjfciJSA A ABAJBAA ABA«BO AOA B «®AB
B B AOAAABABABA AA *BAO9Aa«B *«A A
0.05
2 3
TOTAL HYDROCARBONS, ppm C
Figure 7. Maximum daily 1-hour average oxidant concentrations as a
function of 6- to 9-a.m. averages of total hydrocarbon
concentrations at CAMP stations, June through September,
1966 through 1968 and in Los Angeles, May through
October 1967!0
25
-------
0.30
0.25
0.20
E
a.
a
"
z 0.15
X
o
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T
' LOS ANGELES
APPROXIMATE UPPER-LIMIT
OBSERVED OXIDANT
PHILADELPHIA,,
PHILADELPHIA
WASHINGTON A
WASHINGTON
WASHINGTON
LOS ANGELES A
A DENVER
' LOS ANGELES
A A PHILADELPHIA
LOS ANGELES
A
WASHINGTON AAA A > A A AAA
A A * A A
A VA*A A A
WASHINGTON ^^A^A AA A AA
— * A±A. A A A 4^ A
_ _ AM A AAA A
A^AAj^A^A^AAA £A AA
0.5 1.0 1.5
NONMETHANE HC, ppm C
2.0
2.5
Figure 8. Maximum daily 1-hour average oxidants as a function of
6- to 9-a.m. averages of nonmethane hydrocarbons at
CAMP stations, June through September, 1966 through
1968, Los Angeles, May through October 1967^
26
-------
cfl
M
4J
C
0)
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SNOISSIW3 NOayVDOHQAH Nl NOIiODQSH
o
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27
-------
6-9 a.m.
Total and (Non-Methane)
Hydrocarbon Cone., ppn C
20 30 40 50 60
6-9 a.m. Oxides of Nitrogen Concentration, pphm
70
Figure 10. Pollutant relationships at Los Angeles, data period:
July to September 1969 to 197211
28
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These graphs are among the simpler models attempting to define the
relationship of oxidants with their precursors - hydrocarbons and
nitrogen oxides. Complex air pollution models have been developed
with varying degrees of reliability and include proportional rollback,
rollback along a precursor curve, and dispersion modeling. These are
discussed in a companion document, "Photochemical Oxidant Modeling
Techniques Applicable to Highway System Evaluation," GCA/Technology
Division, June 1975.
29
-------
REFERENCES
1. Air Quality Criteria for Photochemical Oxidants. PHS, EHS, NAPCA,
Publication Number AP-63, March 1970.
2. Air Quality Criteria for Nitrogen Oxides. PHS, EHS, NAPCA,
Publication Number AP-84, January 1971.
3, Elkus and Wilson. Air Basin Pollution Response Function: The
Weekend Effect. Submitted to Science.
4. Altschuller. Evaluation of Oxidant Results at CAMP sites in the
United States. JAPCA, 25 (1): 19, January 1975.
5. Bell Laboratories. Statistical Analysis and Phenomenological
Interpretation of the Atmosphere in the New York - New Jersey
Metropolitan Region.
6. Cleveland, et al, Sunday and Workday Variations in Photochemical
Air Polutants in New Jersey and New York. Science, 186: 1037,
DC 1974.
7. Stasiuk and Coffey. Rural and Urban Ozone Relationships in
New York State. JAPCA, 24 (6): 565, June 1974.
8. Coffey and Stasiuk. Evidence of Atmospheric Transport of Ozone
into Urban Areas. ES and T, 9 (1): 59, January 1975.
9. Power Plant Gases Source of Puzzling Rural Ozone Clouds. Science
News 106:260.
10. Gloria, et al. Airborne Survey of Major on Basins in California.
JAPCA, 24 (7): 645, July 1974.
11. Schuck, et al. Relationship of Hydrocarbons to Oxidants in
Ambient Atmospheres. JAPCA, 20 (5): 297, May 1970.
12. Paskind and Kinosian. Hydrocarbon, Oxides of Nitrogen and
Oxidant Pollutant Relationships in the Atmosphere Over California
Cities. Presented at APCA.
30
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SECTION IV
OXIDANT MODELING ACTIVITIES
In our initial review of techniques for the prediction of NOX and oxidant
levels, four models were investigated, three of which can be classified
as complex computer models requiring a wide range of input parameters.
One of the models studied represented a rather simplified approach to the
calculation of pollutant levels. A brief description of each of these air
quality models is given below. It is beyond the scope of this report to
give a detailed exposition of the internal workings of each of these
models, particularly the more complex ones. References should be con-
sulted if more information concerning a particular technical point is
desired. Details of the model operations will be treated in some depth,
however, if a thorough understanding of a given point is needed for suc-
cessful application of the model by a potential user.
In addition to these four models, an assessment was made of the Appendix
J technique for estimating oxidant/hydrocarbon reduction relationships
based on data from Denver. This section begins with a discussion of the
Appendix J results. Following this, the four models are discussed.
The remainder of this section describes the application of two of these
models in the Denver area.
APPENDIX J RESULTS
To assess how well the Appendix J nonlinear rollback method would work
for Denver, it would have been desirous to construct an upper-limit curve
for Denver, based on maximum daily 1-hour average oxidant concentrations
31
-------
as a function of 6:00 a.m. to 9:00 a.m. averages of total hydrocarbon con-
centrations, and then to convert this to an Appendix J type curve show-
ing the reduction in hydrocarbon emissions required to meet the oxidant
standard as a function of the maximum measured 1-hour oxidant concentra-
tion. Unfortunately, there were not sufficient data to define an unam-
biguous upper-limit curve.
As an alternative, sets of two or more consecutive days were chosen for
which the oxidant standard was exceeded on the first day but not vio-
lated on the last day. These sets are listed in Table 1, along with the
6:00 a.m. to 9:00 a.m. average hydrocarbon concentrations, the percent
reduction in hydrocarbon concentration on successive days (a surrogate for
emissions also assumed in Appendix J), and the approximate reduction in
hydrocarbons necessary to meet the oxidant standard as predicted by Ap-
pendix J. In most instances, oxidant levels fall below the standard with
less percent reduction in hydrocarbons than predicted by Appendix J.
This is not of great consequence, however, since these oxidant-hydrocarbon
pairs fall below the upper-limit curve on which Appendix J is based. Of
greater interest is sets 2 and 4. During the first 2 days in each of
these sets, plotted oxidant-hydrocarbon pairs would lie above the Ap-
pendix J basis upper-limit curve. This would imply that a lower con-
centration of hydrocarbons would be associated with a given concentra-
tion of oxidant than is given by the upper-limit curve on which Appendix
J is based. If the upper-limit curve were to include these hydrocarbon-
oxidant pairs, a smaller percent reduction in hydrocarbons to meet the
oxidant standard would be reflected by a shift downward in the Appendix
J curve.
GENERAL MODEL DESCRIPTIONS AND INPUT REQUIREMENTS
1 2
Urban Air Shed Photochemical Simulation Model, * (SAI Model)
Of all the models studied, this model, which was developed by Systems Ap-
plications Inc. (SAI) for the EPA Meteorology Laboratory, provides the
32
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Table 1. DATA SETS FOR APPENDIX J DISCUSSION
Set
1
2
3
4
5
6
7
8
9
10
11
12
13
n a
UX
412
78
549
549
78
196
59
529
529
59
196
78
196
39
176
98
216
59
176
98
274
59
196
127
196
143
225
294
225
176
88
HCb
4079
3724
2061
1507
1352
1795
2305
1374
2106
1795
1552
1729
2327
2261
2017
1419
2372
1795
2372
1574
1685
1751
1818
1773
2172
1286
1862
1662
1485
1951
1906
% HCC
reduction
8.7
26.9
10.3
-28.4
-53.3
14.8
-11.4
2.8
29.7
24.3
33.6
-3.9
2.5
40.8
10.7
10.7
-31.4
2.3
Appendix J<*
70
90
90
20
85
85
20
20
10
22
10
40
20
20
30
50
30
10
measured as ozone by chemiluminescence.
i
yg/m-J total hydrocarbons.
Percent reduction from previous day's concentration.
Approximate percent reduction required to meet the standard.
33
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most detailed treatment of the physical and chemical processes important
in the formation of photochemical smog. Concentrations of reactive
species are determined by means of a finite difference scheme for an
array of grid cells. Meteorological inputs to the program consist of
wind speed and direction for a number of stations in the area. These
values are then interpolated to give a flow vector for each ground
level grid. Each grid cell is also assigned an hourly mixing depth
which depends upon the terrain elevation and the measured mixing depth
at one or more meteorological stations. The input of initial concen-
trations and hourly meteorological variables for specified measurement
locations is handled by means of data preparation programs. These pro-
grams also determine correlations among corresponding input parameters
at various stations, so that measured values may be extended over the
entire grid system.
The SAI model also employs a rather sophisticated emission inventory
routine for mobile and stationary sources. In addition to standard inputs
such as vehicle-miles traveled (VMT) for different road types, the emis-
sions submodel requires such detailed information as airport emissions for
different aircraft types, mole fraction of NO in auto NO emissions, and
X
correction factors for each pollutant species to account for the non-
uniform distribution of vehicle starts.
The most important advantage of the SAI model over the other two complex
computer models we investigated is its ability to predict concentrations
for all grid squares at a particular time. This high degree of spatial
resolution is obtained at the expense of long running times (1 hour simula-
tion = 10 hours real time). Another disadvantage in applying the model to
any given city is the fact that some aspects of the program are quite
specific to Los Angeles. In particular, for the correlation of wind speeds,
wind directions, and mixing depths, the Los Angeles basin is subdivided
within the program itself into a number of topographically similar regions.
Another drawback to the application of the SAI model from the viewpoint of
34
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a potential user is the inability to specify different values of hori-
zontal and vertical diffusion coefficients without making modifications
to the program itself. This situation seems curious in light of the
significant role played by these variables in the determination of ozone
and NO concentrations and the extensive amount of computer time re-
quired for the simulation of these diffusion effects.
In spite of the fact that the SAI model is an excellent research tool, it
was felt that the long running times and extensive data requirements would
make it difficult for a 3-C planning agency to use the model for analysis
of different transportation strategies without a considerable investment
in time and manpower.
3 4
Reactive Environmental Simulation Model (REM) '
The second computer model which we examined was developed for EPA Meteo-
rology Laboratory by Pacific Environmental Services, Inc. Although the
model utilizes a complex chemical reaction module, its approach toward
the problem of atmospheric transport and dispersion is much simpler than
for the SAI model. Rather than trying to predict hourly pollutant con-
centrations for every grid point, the REM employs a coordinate system
which moves along a particular wind trajectory. Unlike the SAI model, the
REM does not provide for either vertical or lateral transport by turbulent
diffusion. The reaction volume may be considered to be a cylinder of unit
cross sectional area bounded on top by the mixing height and on the bottom
by the ground surface. The pollutants entering the bottom of the cylinder
are assumed to be instantaneously uniformly mixed throughout the cylinder
volume, which assumes the role of a reaction chamber. The wind speed and
direction data are handed in much the same manner as in the SAI model but
the emissions inventory is much less detailed. For example, the emission
factor used for both freeways and surface streets does not appear to be
corrected for the different speeds on the two different road types.
35
-------
One of the most attractive features of this trajectory approach is the
short computing time (1 hour simulation time = 150 hours real time for
the IBM 370/155). The main drawback in the REM is the neglect of turbu-
lent diffusion in the vertical direction. With this in mind we turned our
attention to another trajectory approach which does consider this process.
Diffusion/Kinetics Code5'6 (DIFKIN)
As in the case of the REM, DIFKIN calculates the trajectory of an air
parcel across an emission grid network and determines time dependent con-
centrations of reactive pollutants. The equation for turbulent dif-
fusion in the vertical direction is solved to obtain concentrations and
fluxes for as many as five mesh points from the ground surface to the top
of the mixing layer. DIFKIN requires that reaction rates and stoichio-
metric coefficients be read in as data for each model run, thereby facili-
tating any updates which may be required for the chemical system. The
model also allows for trajectories to be run backward in time, a feature
which is necessary to determine trajectory starting points so that the
air parcel under study will pass over a measurement station.
Since DIFKIN utilizes its own emission calculation routines it was not
necessary to input emission rates externally, although the program does
provide for that option. Provision is also made for the input of station-
ary source emissions of both NO and HC.
x
Due to the reasonable running time (~2 minutes IBM 370/158 time for each
trajectory) and the relative ease of program modification compared to the
SAI model, we selected the DIFKIN model as a tool for the study of NO and
X
0,. impact due to planned projects under the 3-C process. This decision
was not meant to imply that the SAI model was technically deficient, but
that its current level of complexity would make its routine use by a 3-C
planning agency quite difficult. This problem might be alleviated in the
future with the development of better data preparation programs. While
36
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the DIFKIN model is less specific to Los Angeles than the SAI model, a
minor amount of reprogramming was necessary before the model was ready to
run for the Denver area. This activity will be discussed more fully in a
later section.
A Simple Dispersion Model for the Analysis of Chemically Reactive
Pollutants?
For the past several years F. A. Gifford and S. R. Hanna of the Atmo-
spheric Turbulence and Diffusion Laboratory (ADTL) at Oak Ridge, Tennessee
have been active in the development and validation of simple methods for
estimating pollutant concentrations in urban areas. Much of their effort
had been restricted to the analysis of chemically inert pollutants, but
they have recently generalized their model to treat pollutants undergoing
photochemical reactions.
The Gifford-Hanna model is basically a "box model" in which average pol-
lutant concentrations within the box are taken to be proportional to the
ratio of the average area source strength to the wind speed. The pro-
portionality constant is set equal to the average width of the region
divided by the average depth of the pollutant cloud over the area. This
depth is allowed to vary as a function of atmospheric stability and may
be calculated by an integration of the Gaussian plume formula over the
extent of the area source.
For purposes of chemical kinetics calculations the pollutant concentrations
are assumed to be uniform within the volume defined by the area of the
region and the depth of the pollutant cloud. By nondimensionalizing the
concentrations which appear in the equations governing chemical reactions,
it is possible to gain some insight into the importance of a particular
reaction upon the concentration of a particular species. For instance, if
the steady state nondimensionalized concentration of a particular sub-
stance is close to unity, it is possible to neglect chemical transforma-
tion for the associated meteorological conditions and emission rates.
37
-------
The reaction scheme utilized in this model was originally proposed by
Friendlander an<
four equations.
Q
Friendlander and Seinfeld and may be written in terms of the following
9t
[N02][lffl] (a [NO] -X[N02]) (D
[NO] = - a [N02 ] [NO] [RH ] (2)
1 -2 -1
where a = -rr ppm sec
1 -i
y = -" sec
2.4 x 10
1 -1 -1
0 = "-r ppm sec
3 x 10
^ 1 -2 -1
A = r- ppm sec
3 x 10
(3)
3 = 0.02 ppm
NO = nitric oxide concentration (ppm)
NO™ = nitrogen dioxide concentration (ppm)
RH = reactive hydrocarbon concentration (ppm)
0~ = ozone concentration (ppm)
The concentrations in equations (1) through (4) are indicated in brackets.
No functional dependence with solar radiation intensity is specified for
38
-------
these reaction rates. Nondimensionalized concentrations C and times
* L J
t may be found from the following transformations:
<* • <«
where Ax = A Z
U = wind speed (m/sec)
Q = emission density (cm^ m~2 s~^)
A and Z in turn depend upon the stability of the atmosphere for the par-
ticular day in question. The variation of these two parameters with
stability is shown in Table 2. Equations (1) through (3) can then be
written in terms of nondimensionalized concentrations and times.
]*H* C1
[N02]* = [NO ]* - 1 +0°]* [KH]* C2 - [N02]* [RH]* C3 (8)
-/[N02]*/[No] *\
c4-N0/No C5 (9)
Ax3
where Cl - aQ QRH -—^ (a) (10)
£. \J Zt
02 = aQNO QRH 77
c3 = X
39
-------
A x
"* • 8 "NO, ZZ
(d)
2 U Z
c5 =
3 -2 -1
Q. = emission rate of substance i (cm m s )
Table 2. VARIATION OF DIFFUSION PARAMETERS WITH
STABILITY CONDITION
Parameter
A
Z
Stability condition
Unstable
50
2000m
Neutral
200
150m
Stable
600
40m
Two approaches are possible for utilizing equations (7) through (9) in the
prediction of ozone and NOX levels. The first option is to assume that
the process has reached a steady state so that the time derivative on the
left side of the equations can be set to zero. The equations may then be
solved algebraically for the nondimensionalized concentrations. These may
then be redimensionalized and compared with measured values. Since the
steady state approximation may not strictly be applied due to the time
variation of emission density and solar radiation intensity, it is open to
some question which hour should be chosen for the comparison of measured
and "steady state" values. In his Los Angeles validation study Hanna
picked the noon hour for a comparison.
The second way in which the model may be applied is actually to solve the
chemical kinetics equation numerically by use of a Runge-Kutta technique
to obtain values for nondimensionalized concentrations as a function of
time. If an hour such as 6:00 a.m. is chosen as the time when all non-
dimensionalized concentrations are equal to 1, then the time variation of
40
-------
the wind speed, emission rate, and nondimensionalized concentrations may
be used to project actual concentrations from their 6:00 a.m. values.
The assumptions made concerning the emissions estimates are no less severe
than those dealing with chemical reaction rates. The ratio of N02 to NO
emissions is set equal to the I N02 / I NO I concentration ratio at
6:00 a.m. The following relationships are also assumed to hold between RH
and NOX emissions:
QNO =°'3QRH
In spite of the rather drastic approximations made in the development of
this model, it was felt that it should be applied along with the more
detailed complex approaches due to the extensive data requirements
and computing time required for their application.
SELECTION OF TEST MODELS
In the previous section we briefly touched upon our reasons for the
selection of the DIFKIN model over the SAI and REM models in our test of
the application of complex photochemical models to the 3-C planning
process. The following four selection criteria were employed during the
screening process.
• Technical accuracy
• Program modifications required for application to a
new location
• Input requirements
• Computer time
41
-------
The REM model was ruled out early in the study due to the assumption in-
herent in the calculation that the pollutants within a moving column of
air are always uniformly mixed. Another technical difficulty with the
model is failure to include the effect of speed in vehicular emissions.
These problems outweigh any advantages due to simplified model operation
and the relatively small amount of computer time required for photo-
chemical simulation.
While the SAI model is the technically superior of the three complex
models, it falls short in the other three categories. To run the SAI
model for an area different from the Los Angeles basin would require ex-
tensive modifications in the data preparation programs to include the
topographic effects of the new area. Also, the quality of emissions and
meteorological data required for the program would require a data gathering
effort out of proportion with that required for other 3-C planning ac-
tivities unless much of this information had been generated from previous
studies. And finally, the treatment of both horizontal and vertical dif-
fusion effects for a large number of grid cells requires excessive amounts
of computer time.
Although the SAI and DIFKIN models require similar input data bases, the
number of necessary program modifications and the amount of computer time
required for program operation are less prohibitive for the latter model.
These two factors were considered more important than the lack of a
capability to predict hourly concentrations for each grid cell.
A common failing with all three complex models is the lack of any clear
indication as to which variables the model is most sensitive. It is not
obvious to the potential user how his time may be most profitably spent
in the collection of data. For example, the user is uncertain whether M
more resources should be devoted to a more accurate specification of dif-
ferent highway speeds or a better determination of the diffusion profile.
While several paragraphs of a users manual may be devoted to a discussion
42
-------
of a plotting routine, there is often little guidance concerning more im-
portant inputs such as initial reactive hydrocarbon concentrations. In
general we feel that before any of these more complex models can be more
widely applied, a niore user-oriented operations manual will be required.
EVALUATION OF THE DIFKIN MODEL
We shall now describe the steps used to evaluate DIFKIN as a tool for
the analysis of NO and oxidant impact of actions taken as a part of the
X
3-C planning process. It is important to remember that this evaluation
is based upon our actual experience in applying the model to the Denver
area, so that the associated difficulties and necessary program modifica-
tions would be similiar to those encountered in a modeling effort carried
out by a planning agency. In this connection, the first part of our
discussion will be devoted primarily to the data preparation and opera-
tional aspects of DIFKIN, as opposed to the details of the theoretical
formulation. Since, in the final analysis, the model must be judged on
the basis of its predictive capability, we shall report on an actual
model evaluation effort using hourly air quality and meteorological data
for a particular day.
Model Application to Denver
One fact to bear in mind when applying a photochemical model to a given
urban area is that most models were designed and validated based upon
measurements taken in the Los Angeles area. This is understandable in
light of the extensive monitoring network, the well studied local inversion
phenomenon, and the absence of significant emission sources outside the
Los Angeles basin. There is no real guarantee that the DIFKIN model, once
validated using the Los Angeles data base, will accurately account for 0^
and NOX levels in other major urban centers where the presence of higher
levels of SO and suspended particulates may alter the reaction scheme.
An even more important consideration is the selection of initial pollu-
tant concentrations for the air parcel used in DIFKIN.
43
-------
Aside from any potential technical difficulties which may arise in the
application of a photochemical simulation model to a new area, there
remains the task of data preparation and even program modification neces-
sary for successful operation of the code. The reprogramming effort is a
particularly bothersome task since it involves the removal or replacement
of "actual program statements and block data entries specific to the Los
Angeles basin validation site. We shall now treat in somewhat greater
detail the modifications required so that the DIFKIN model could be run
for Denver.
Required Program Modifications
The DIFKIN program and Los Angeles test data set were obtained from
Dr. Kenneth Demerjian of the EPA Meteorology Laboratory, Research
Triangle Park, North Carolina. The code, which was originally written by
General Research Corporation (GRC) to run on an IBM 370 computer, had
been translated by EPA personnel to operate on a UNIVAC 1108 system.
The program modifications required so that DIFKIN could again be run on
an IBM 370 included the following:
• The appropriate commands had to be inserted in the dummy
DIFKIN subroutine SECOND to enable it to call IBM system
routines capable of obtaining the internal machine time
for the purpose of timing the DIFKIN computations.
• The UNIVAC machine language subroutine MCHAR, used for
moving a string of individual characters from one storage
location to another, had to be replaced by an equivalent
IBM machine language subroutine.
• The backspace commands contained in SETPLT, the printer
plot subroutine, were removed in order to minimize the
number of disk actuations used in the running of the
program. This may not be necessary when using other
facilities where disk actuations are cost-effective.
• The control cards which govern the execution of the program
had to be written to coordinate peripheral devices and the
unit numbers representing them in DIFKIN and the IBM
computer facilities. Fourteen disk files had to be properly
defined for DIFKIN use.
44
-------
The following DIFKIN program modifications were required for the incorpora-
tion of a new data base:
• The appropriate wind station names and coordinates,
compass point azimuths, and distance criteria were
inserted in SETIN, a DIFKIN subroutine called upon
to initialize the aforementioned variables.
• Subroutine BARIER, a routine which checks whether or
not two given points are on the same side of a straight
line barrier, had to be altered to remove the effect
of Los Angeles topographical features such as the San
Gabriel mountains, Santa Monica mountains, and Palos
Verdes hills. Interpolation of wind speed and direction
measurements for trajectory calculations is not
permitted between those stations located on opposite
sides of a barrier. No barrier was utilized for
Denver since the mountains west of the city do not
pass between wind measurement stations.
• BLOCK DATA programs ONE through FIVE were changed so
that mobile and stationary source emission parameters
for Denver could be entered.
Application Results
Input Data Base - The input requirements of the DIFKIN program may be
clearly separated into emissions, meteorological, and photochemical.
Of these three categories the emissions input constitutes the largest
volume of data necessary for the operation of the code. Most of it
is actually contained within the program itself in the form of data
statements within Block Data Subprograms listed in Appendix A.
The first block data subprogram contains data statements for the follow-
ing arrays:
1. FFWY - This two dimensional array consists of average
daily freeway VMT (k mi) for each of 625 grid cells
superimposed upon the Denver meteorpolitan area.
2. FSRF - This array is identical to FFWY except that it
contains surface street VMT data.
45
-------
3. FXN02, FXHCR - These two arrays are filled with 625
values for distributed N02 and reactive hydrocarbon
emissions, respectively.
4. FPPN02, FORN02, FORHCR - These three arrays contain
coordinates and emission strengths for power plant
N02 emissions, oil refinery N02 emissions, and oil
refinery reactive hydrocarbon emissions. Space is
provided for up to 30 power plants and oil refineries.
This input option was not utilized in DIFKIN run for
Denver (i.e., zeros were entered for the coordinate
values and emission strengths). All NC^ and reactive
hydrocarbon emissions were assigned to the arrays
FXN02 and FXHCR. The model treats power plant and
oil refinery emissions no differently from other
emissions except that separate adjustment factors
are provided.
As mentioned previously, the block data format required for the emissions
input makes even slight changes in the data base quite cumbersome. For
example, the two dimensional array FSRF is equivalenced to four one
dimensional arrays due to a restriction to the number of lines in the
data statement.
The freeway and surface street VMT data for Denver were obtained from the
Colorado Division of Highways for the base year of 1972 and projected
travel for the year 2000. Projected values for 1975 were found by simple
interpolation. The original breakdown by road type was according to the
following classifications:
Road classification Average speed (mi/hr)
Freeway 40
Expressway 34
Primary arterial 26
Minor arterial 23
Collector 21
Centroid connector 20
Ramps 20
Major arterial 23
46
-------
Both freeways and expressways were assigned to the DIFKIN freeway
category and the remaining links were treated as surface streets. VMT
data for each link were allocated to an appropriate 2 mi x 2 mi grid square
by means of a program developed by the Colorado Division of Highways.
The grid used in this procedure (see Figure 11) was a system of 16 rows
and 15 columns previously developed as a coordinate system for the
9
APRAC-la carbon monoxide simulation program. Listings of VMT data for
freeways and surface streets based upon this grid system are given in
Tables 3(a) and (b). This grid was modified by adding four rows on the
top, five rows on the bottom, and five columns on each side so that it
would conform to the dimensions required by the DIFKIN program. Emis-
sions for these added grid cells were set equal to zero.
Stationary source emissions of NO and reactive hydrocarbons were obtained
for the Denver area through the EPA National Emissions Data System (NEDS)
from a point source listing. The emission rates of NO and reactive
hydrocarbons in kg/hr, assigned to the appropriate grid squares are dis-
played in Table 4. Unfortunately there is no distinction made in NEDS
between reactive and nonreactive hydrocarbons emissions or the fraction
of NO and N02 in the oxides of nitrogen emission estimate. The values
reported in Table 4 are total hydrocarbon and NO emissions. DIFKIN
X
actually assumes that all NO emissions are really NO even if they are
X
reported as NO since the factor 30/46 (the NO to NO™ molecular weight)
is applied to all NO emissions both mobile and stationary.
X •
The next two block data programs contain the arrays FSLOW6, FSL.OW7,
FSLOW8, FSLOW9, FFAST6, FFAST7, FFAST8, and FFAST9, which represent the
peak average traffic speeds in the slow and fast direction for each hour
between 6:00 a.m. and 9:00 a.m. for all 625 grid squares. These data
were based primarily upon field observations in the Denver area by GCA
and its subcontractor Wilbur Smith and Associates.
47
-------
^n J_ • V •
T— --X
t
I^j—*
'9 '1C "11 *12 '13
DENVER METROPOLITAN AREA
STATE HIGHWAY DEVELOPMENT PLAN
Figure 11. Map of the Denver Metropolitan Area showing air
monitoring stations (lettered) and wind stations
(numbered)
48
-------
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51
-------
The peak traffic (slow/fast) volume ratio on freeways for each hour be-
tween 6:00 a.m. and 10:00 a.m. is given in the fourth block data program.
The corresponding array names are FEAT6, FRAT7, FRAT8, and FRAT9. These
data were obtained in the same manner as the data in the second and
third block data programs.
The speed correction factors (B(I), C(I), 1=1,3) for NO , HC, and CO
X
mobile source emissions, were changed to correspond to the AP-42 cor-
rection factors. The new input values are shown in Appendix A.
The second emissions related variable which was changed from the Los
Angeles value was VBAR, the off-peak average speed on freeways. The
value of 44 mi/hr was estimated with the aid of a Colorado Division of
Highways publication which deals with a calibration study of the
present highway network. Input parameters for the arrays YFR (fraction
of vehicle starts which are cold starts) and CSF (correction factors
to account for emissions following cold starts) were left with the
values used in the Los Angeles study. These last two variables played
no role in our DIFKIN runs for Denver since hot running and cold start
emission factors were set equal to one another. Average speeds for
the surface streets are set equal to 19.6 mi/hr in the program to cor-
respond to the 1972 Federal Driving Cycle.
The two remaining emission related arrays which must be specified within
the program itself are TFWY (fraction of total daily freeway VMT for
each hour) and TSRF (fraction of total daily surface street VMT for
each hour). Data statements for TFWY and TSRF are located in the
DIFKIN subroutine FLXDAT.
The remainder of the emission inputs and all of the meteorological and
photochemical input variables are read into the DIFKIN code as "problem
control inputs." A complete list of these variables is given in the
DIFKIN users manual and many of the entries may be generated with little
difficulty. Table 5 presents a list of selected problem control
52
-------
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concentrations were set equal to
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53
-------
inputs which are most significant for a potential user of the program
along with an account for the manner in which each variable was assigned
a value for the Denver test case. A full list of actual inputs used in
the DIFKIN calculation is given in Appendix B.
The basic meteorological input to the program consists of hourly wind
speed and direction measurements at a number of stations so that an
air parcel trajectory can be determined. The locations of two of the
three stations used in the Denver study are shown in Figure 11. The
third station is located to the north of the smaller 15 x 16 grid.
Hourly wind direction and speed values were obtained from the Colorado
Department of Health, Air Pollution Control Division for the validation
day of August 13, 1973.
Additional meteorological data for Denver Stapleton Airport were obtained
from the National Climatic Center, Asheville, North Carolina for the
year 1973. From these data we were able to extract information as to
wind speed and cloud cover which could be used for atmospheric stability
determinations, which could in turn be of some guidance in the specifica-
tion of turbulent diffusivity profiles. The variation of diffusivity
with height for different stability conditions is shown in Figure 12,
taken from the document describing the DIFKIN model evaluation.
The rate constants and stoichiometric coefficients for the DIFKIN chem-
ical kinetics calculation were chosen to be those used in the sample
calculation for Los Angeles with the exception of the hourly NCL photo-
dissociation rate constant k-. Hourly values for this reaction rate
were developed for the validation day by use of a computer program similar
to the one described in Appendix G of the DIFKIN users manual.
Concentration Measurements - Air quality data for 1973 were obtained from
EPA Region VIII for the six monitoring sites in the Denver AQCR. The
oxidant data (measured as ozone by chemiluminescence) were reviewed for
days on which the 1-hour maximum concentrations exceeded the standard,
54
-------
100 -
UJ
_i
00
CO
a:
UJ
>
50
UJ
_l
m
UJ
m
UJ
2XI04 4x I04 6xl04 8xl04
VERTICAL DIFFUSIVITY, cm2/s
10'
2xlOs
Figure 12. Variation of diffusivity with height for
different stability conditions
55
-------
since interest focused on how DIFKIN would perform on such days. Monday,
August 13 was chosen for modeling since all five stations which were
operating reported values exceeding the standard, allowing the largest
number of spatial points at which to check DIFKIN calculated values. No
other day had this many stations reporting a violation. Unfortunately,
the station which was not operating this day was the only one measuring
N09, and it ceased operation at the end of July. However, three of the
remaining five stations commenced operation only in August, so August
13th was picked for its oxidant data at the expense of N09 data to com-
pare with DIFKIN calculations. August 13, 1973 hourly concentrations
for the Denver measurement stations are given in Table 6.
Test of Model Predictions - For the validation day, trajectory starting
points were chosen so that the trajectory would end at a measurement sta-
tion at 2 p.m. The actual procedure was to run the trajectories back-
ward in time from a particular measurement location so that calculated
and observed concentrations could be compared. In all cases the cal-
culated starting point of the trajectories lay outside the city itself.
We then had to face the problem of specifying for the air parcel initial
ground level air concentrations and concentration profiles of CO, RH,
NO, N0?, 0~, and HN09. Data from the measurement stations was no help
in this task since they were far removed from the starting point of
the trajectory. Another difficulty was the absence of reactive hydro-
carbon (RH) measurements for the day in question. The decision was
finally made to use the following background initial concentration values
for the air parcel: |COJ = 2 ppm, |RH| = 1 pphm, [NO] = 1 pphm,
[NO 1 = 1 pphm, and fHN09~| = 1 pphm. The initial concentration of 0_
I ^J L ^J -*
was set equal to the ratio k,/k2, where k.. is the NO 2 photodissociation
rate at the startup time of the trajectory and k? is the rate constant
for the reaction: NO + 0 —* N09 + 0,. All concentrations were assumed
to be uniform with height above the ground. The concentrations and emis-
sion fluxes along five trajectories are displayed in Figures 13 through 17.
56
-------
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57
-------
a.
a.
cc
\-
o
o
o
DIFKIN COORDINATES OF TRAJECTORY
END POINT (20,32)
W 57th AND GARRISON
MEASURED OZONE CONCENTRATION AT TRAJECTORY
END POINT =25.5 pphm
140 210
TIME FROM START OF TRAJECTORY, min
Figure 13. First DIFKIN evaluation run
58
-------
a.
Q.
o
14
z
III
o
o
o
3
DIFKIN COORDINATES OF TRAJECTORY
END POINT (24,28)
2095 JEFFERSON
MEASURED OZONE CONCENTRATION AT TRAJECTORY
END POINT = 26.5 pphm
OZONE
0
1.5
1.0 x
e
I
0.5 x"
3
70 140 210 280
TIME FROM START OF TRAJECTORY, min
350
Figure 14. Second DIFKIN evaluation run
59
-------
d
ex
z"
O
o
o
DIFKIN COORDINATES OF TRAJECTORY
END POINT (28, 28)
EAST COLFAX AND COLORADO
MEASURED OZONE CONCENTRATION AT TRAJECTORY
END POINT = 13.3 pphm
, HC FLUX
NO FLUX
NO
70 140 - 210 280
TIME FROM START OF TRAJECTORY, min
350
Figure 15. Third DIFKIN evaluation run
60
-------
z
o
O
O
O
DIFKIN COORDINATES OF TRAJECTORY
END POINT (28,34)
E78 »h AND STEELE
MEASURED OZONE CONCENTRATION AT TRAJECTORY
END POINT = 16.5 pphm
OZONE
1.5
o
X
0.55
70 140 - 210 280
TIME FROM START OF TRAJECTORY,min
350
Figure 16. Fourth DIFKIN evaluation run
61
-------
CL
D.
o
<
Ill
o
z
o
o
DIFKIN COORDINATES OF TRAJECTORY
END POINT (26,24)
2005 S. HURON
MEASURED OZONE CONCENTRATION AT TRAJECTORY
END POINT =15.0 pphm
- 2.5
- 2.0
O
x
c
6
•>»
E
X
TIME FROM START OF TRAJECTORY, min
Figure 17. Fifth DIFKIN evaluation run
62
-------
It is interesting that the increase of ozone with time is quite similar
in the first three figures even though the pollutant fluxes along the
three trajectories are quite different. A complete output listing for
the first trajectory run is given in Appendix C. The first leg of the
trajectory is not shown on the computer plot in Appendix C due to format
restrictions. These trajectories have different durations due to the
restriction that each trajectory has to originate within the DIFKIN grid.
A comparison of measured and calculated CO, RH, and 0~ concentrations is
shown in Table 7 for trajectories ending at 2 p.m. at each of the five
measurement stations. Concentrations for RH and 0 are consistently
underpredicted. Correlation coefficients for measured and predicted con-
centrations are given below.
Pollutant _r
CO 0.3
HC -0.1
03 -0.1
These poor correlations may be due in part to the uncertainty regarding
initial pollutant concentrations and the small number of wind stations
used in the trajectory determination.
EVALUATION OF THE GIFFORD-HANNA MODEL
In our discussion of the evaluation procedure for the DIFKIN model,
most of the emphasis was placed on data preparation and model operating
characteristics. Major modifications to the actual model structure were
beyond the scope of this particular effort. For the case of the Gifford-
Hanna model, however, we are actually dealing with a technique for estim-
ating reactive pollutant concentrations, rather than a specialized pho-
tochemical model with formalized input scheme. In his description of
the calculation technique, Hanna uses a set of photochemical reactions
only as an example of this technique for the analysis of chemically reac-
tive pollutants. In this case we felt that no clear distincetion could
63
-------
Table 7. COMPARISON OF MEASURED AND CALCULATED CONCENTRATIONS
AT 1400 HOURS FOR AUGUST 13, 1973
Station
A
C
D
E
F
[co]
calculated
ppm
2.16
3.37
2.94
2.12
4.48
Hd
measured
ppm
4.0
3.0
4.0
—
4.0
H
calculated
pphm
2.05
3.41
2.48
0.91
5.42
w
measured
pphm
—
28.3
16.7
11.7
8.3
h]
calculated
pphm
6.16
5.41
4.93
3.42
6.26
M
measured
pphm
25.5
26.5
13.3
16.5
15.0
The followinp transformation was used to convert total hydrocarbon
measurements |HC| to reactive hydrocarbon concentrations:
pphm
carbon
665(ug/m carbon)/(ppm carbon)
- 1.5 ppm carbon
x 100
where: 665 = conversion from yg/m carbon to ppm carbon
1.5 ppm carbon = approximate methane background
6 = average number of carbon atoms per reactive
hydrocarbon molecule
64
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be made between minor modifications to the model or more important struc-
tural changes. Under these circumstances we felt that it was proper to
present two separate evaluations of the Gifford-Hannd model. In the
first procedure we modify the steady state version of the model to allow
pollutant transport from adjacent grid cells. The second test involves
a time-dependent model analysis of 0_, RH, and NO concentrations, closely
•J X
following the procedure used by Hanna in his 29 September 1969 Los Angeles
model validation study.
Application to Denver
In practice, the application of the Gifford-Hanna model to an area other
than Los Angeles can be carried out with comparative ease due to the
many simplifying assumptions made in the development of the model. It
is these assumptions, however, which make the model less sensitive to
the particular characteristics of a new area. For example, the Gifford-
Hanna photochemical model provides no mechanism to account for variations
in solar radiation intensity with latitude and elevation. The assump-
tion that the N0« and NO emission rates are in the ratio of 3 to 2 is
actually based upon Los Angeles concentration measurements. Further-
more, assumptions regarding the lack of cross wind variations in pollu-
tant concentration may be more appropriate for Los Angeles than Denver.
Also, as was the case for the DIFKIN model, there is no guarantee that
the reaction scheme utilized in the Gifford-Hanna model validation for
Los Angeles will apply in another area with a different mixture of other
pollutant such as SO- and particulates.
Required Program Modifications - In the development of the Gifford-Hanna
photochemical model the assumption was made that the atmosphere over a
city could be described in terms of a single reactor volume. While such
an approximation may be convenient for treating the emission, transport,
and chemical transformation processes with simple expressions, it cer-
tainly restricts its application to the evaluation of those strategies
which result in an overall increase of emissions over an urban area.
65
-------
Spatially selective changes in emission patterns resulting from the ap-
plication of transportation controls could not be studied adequately with
a single box model. Hanna makes the statement in his paper that his
model can be easily generalized to use the advention scheme outlined in
12
an earlier paper by Gifford and Hanna. In this paper they present a
method for calculating a surface concentration x (ug/ffl ) of a nonreactive
substance due to area source emissions upwind of a receptor point by
13
integration of the Gaussian plume formula.
2
where Q. = source strength (yg/m -sec)
D = distance to the edge of the area source (m)
u = wind speed (m/sec)
a = vertical dispersion parameter (m)
x = distance from source (m)
The vertical dispersion may be parameterized according to the following
power law:
b
a = ax
z
where the parameters a = 0.15 and b = 0.75 could be applied to average
yearly conditions. If the receptor square (0,0) is surrounded by an
array of other grid squares, then the total concentration contribution
may be written as:
66
-------
1-b
Ur-l/'l
V°'0) + 2- 2^ QA
i=-4 i=-4
2
where Q.(i,j) = emissions from square (i,j) (yg/m - s)
6x = width of a grid cell (m)
r = number of grid blocks square (i,j) is from
the central square (distance)
f(i,j) = parameter which depends upon the wind direction
frequency distribution.
A method for mapping a radial wind direction distribution upon an array
of square grid cells is illustrated in Figure 18.
For chemically reactive substances the actual concentration may be ob-
tained by multiplying the concentration x by the nondimensionalized value
T "I *
C I . In the modified version of the Gifford-Hanna model used in this
r ~i
analysis, the steady state values of C were obtained using the fol-
lowing expressions for A, Z, Ax, u and Q (variables discussed earlier).
U
where Ax = distance from receptor square to square (i,j) (m)
Q = Q (i,j), the emission rate from square (i,j)
X (i,j) = concentration contribution due to emissions from
square (i,j)
67
-------
SOURCE
GRID
RECEPTOR
POINT
Figure 18. Scheme for combining rectilinear source-grid squares with
radial wind directions
68
-------
APPLICATION RESULTS
Input Data Base
The required input for the Gifford-Hanna model is a subset of the data
base used for DIFKIN with the exception that the Gifford-Hanna model
does not calculate its own emissions from VMT data. To generate NO
X
and total hydrocarbon emissions from mobile sources the methodology of
14
AP-42 was employed to calculate HC and NO emissions for grid squares
X
8 mi on a side, following the procedure used in the Los Angeles valida-
tion. This emission network was constructed from the Denver APRAC-la
grid (with one column of 2 mi by 2 mi dummy squares added on the east
side), which was discussed in connection with the DIFKIN model input.
This gridj shown in Figure 19, was utilized in our modified steady
state version of the Gifford-Hanna model. These emission rates repre-
sent an average over the period from 6 a.m. to 12 noon. For the time
dependent validation an emission rate for each hour was obtained from a
spatial average of emission rates over a subset of six 8 mi by 8 mi
squares ((1,2), (1,3), (2,2), (2,3), (3,2), (3,3)). Stationary source
emissions were not input to the Gifford-Hanna model. Since the emission
3 2
rates required by the model are in units of cm /m /sec the g/sec emis-
sions of HC were converted with the assumption that the molecular weight
is equal to 42g. This is really an overestimate of reactive hydrocarbon
emissions since it implies that the mobile source hydrocarbon emissions
are 100 percent propylene. In the DIFKIN model the calculated mobile
source hydrocarbon emissions were scaled by the factor 0.7 to obtain the
reactive fraction. Another important difference between DIFKIN and
Gifford-Hanna emission schemes is that the former model assumes that
all NO emissions are actually NO, while the latter uses an NO/NO- emis-
X £*
sion ratio of 1.5.
The meteorological input for the steady state version of the Gifford-Hanna
model consists of an average wind speed and direction for three meteoro-
logical stations over the period of 6 a.m. to 12 noon for the validation
69
-------
SOURCF -STRENGTHS FOR NO IN CM**3/M**2/ SEC
0.00003 0.00022 0.00007 0.00001
O. 00010 0.00090 0.00047 0.0000"*
0.00006 0.00070 0.00042 0.00001
0.00000 O.OOO07 0.00006 0.00001
SOURCE STRENGTHS FOR N02 IN CM**? /«!*** /SEC
0.00002 0.00014 0.00004 0.00001
0.00007 0.00059 0.00031 0.00002
0.00004 0.00045 0.00027 0.00000
0.00000 0.00004 0.00004 0.00001
STRENGTHS FUR RH IN CM**3/M**2/St!C
0.00006 0.00043 0.00015 0.00002
0.00024 0.00209 0.00113 O.OO006
0.00013 0.00167 0.00099 0 .OOO02
0.00000 0.0001.7 0.00012 0.00002
Figure 19. Emission density pattern for steady state Gifford-Hanna
model validation for Denver (6 a.m. to 12 noon average)
70
-------
day. In the validation of the time dependent model hourly wind speeds
from the nearest meteorological station were utilized for each hour
for purposes of scaling the nondimensionalized concentrations.
This combined Gifford-Hanna multiple area source and photochemical model
allows for emissions to be input from more than one grid square. In
our validation studies, which will be described later, both this modified
steady state and the original time-dependent version of the Gifford-Hanna
model will be evaluated.
Concentration Measurements
The Gifford-Hanna model validation exercise utilized the same August 13,
1973 hourly concentration measurements that were employed in the DIFKIN
model evaluation. For the steady state model validation, noon con-
centration measurements were compared with predicted values. In the eva-
luation of the time-dependent model, 6 a.m. concentrations are projected
in time by 1 hour increments using wind speeds, emission rates, and non-
dimensionalized concentrations. These projected concentrations are then
compared with actual measurements at the respective locations.
The first validation test of the Gifford-Hanna model was performed using
our modified version of their approach discussed eaerlier in this section.
Using this technique we generated "steady state" concentrations of NO,
NCL, HC, and ozone for an array of 16 grid squares each 8 mi on a side.
An average wind speed of 1.74 m/sec from a distribution of wind directions
and a neutral stability condition were assumed for this test. The re-
sults of calculations given in Figure 20 show that as in the case of
DIFKIN, ozone concentrations, based upon a noon measurement, are under-
predicted. Even the highest calculated ozone concentration (2.0 pphm)
among the 16 squares was considerably below the average measured noon
value (15.9 pphm) among all of the stations.
71
-------
CONCENTRATIONS OF NO FROM GROUND SOURCES
0.7E-02 0.3E-01 0.1E-01 0.2E-02
0.2E-01 0.1E 00 0.6E-01 0.6E-02
0.2E-01 0.8E-01 0.5E-01 0.5E-02
0.2E-02 0.1E-01 O.lE-01 0.4E-02
CONCENTRATIONS OF N02 FROM GROUND SOURCES
0.5E-02 0.2E-01 0.9E-02 0.1E-02
.0.1E-01 0.9E-01 0.4E-01 O.AE-02
0.2E-02 0.9E-02 0.8E-02 0.3E-02
CONCENTRATIONS OF RH FROM GROUND SOURCES
0.2E-01 0.7E-01 0.3E-01 0.5E-02
0.5E-01 6.3E 00 0.1E 00 O.lE-01
0.4E-01 0.2E 00 0.1E 00 O.lE-01
0.6E-02 0.3E-01 0.3E-01 0.9E-02
"cfONCENTRATIONS OF 03'
O.lE-01 O.lE-01 O.lE-01 O.lE-01
O.lE-01 0.2E-01 O.lE-01 O.lE-01
Q,1E-OI 0.2E-PL 0,1EH?1.0,1E-01 ..
O.lE-01 0.1E-01 O.lE-01 O.lE-01
Figure 20. Concentration (ppm) profiles for steady state
Gifford-Hanna model validation for Denver
(August 13, 1973 - 12 noon)
72
-------
The simple Gifford-Hanna photochemical model was then applied to analyze
hourly concentrations from 6 a.m. to noon. In this method, almost
identical to the one used by Hanna, values for A and Z were determined
from Table 2 assuming neutral stability conditions. For purposes of
calculation of the nondimensionalized concentrations, an average wind
speed of 1.74 m/sec was used to represent the average winds during the
6 a.m. to noon period. An average emission density was obtained by
averaging emissions from six adjacent grid cells with the highest emis-
sions. These input parameters were then used to obtain hourly nondimen-
sionalized concentrations for HC, NO, and N09 by solving Equations (1)
through (3) using a Runge-Kutta technique and assuming all nondimen-
sionalized concentrations are equal to 1.0 at 6 a.m. With the exception
of ozone, concentrations for the period after 6 a.m. were calculated
according to the following expression:
['] • r §: W*
"6
where U = wind speed (m/sec)
U, = wind speed at 6 a.m. (m/sec)
32
Q = source strength (cm /m /sec)
3 2
Q, = source strength at 6 a.m. (cm /m /sec)
• l*
C = nondimensionalized concentration
|C]/• = concentration (ppm)
Ozone concentrations were determined from Equation 4 with the assumption
that NO- and NO emissions are in the ratio of 2 to 3:
73
-------
The value of the wind speed used in the determination of nondimension-
alized concentrations was an average for all three meteorological sta-
tions over the period from 6 a.m. to noon. In the concentration pro-
jection described by Equation (16), the hourly wind speed measurement
was taken from the meteorological site closest to the concentration
measurement station. The ratio Q/Qg was based solely upon the hourly
travel distribution used in the DIFKIN evaluation. The results of these
calculations are presented in Tables 8(a) through (c), where we have
compared the projected and measured values for CO, RH, and ozone. The
correlations between predicted and measured concentrations are given
below.
Pollutant £
CO 0.23
RH 0.29
03 0.78
Although there appears to be a reasonable correlation between measured
and calculated ozone concentrations, the magnitude of the calculated
values is much too small for the later hours of the morning. The most
interesting result of this study is that the nondimensionalized concen-
trations of NO, N00, and HC do not depart significantly from unity during
the simulation period. The conclusion which may be drawn for the Gifford-
Hanna calculations is that the impact of the Denver emission densities
upon photochemical simulation is negligible when compared with the Los
Angeles sample run.
IMPLICATIONS OF MODEL RESULTS
Although the results of our model evaluation studies were less than
encouraging, we feel that there still exists a role for photochemical
modeling in the 3-C planning process. In spite of the difficulties
encountered in trying to achieve a reasonable agreement between measured
74
-------
Table 8a. HOURLY CO VALIDATION USING SIMPLE GIFFORD-HANNA
PHOTOCHEMICAL MODEL
Hour
t*
u6/u
Q/Q6
[co]*
[co] cal
[ Co] mea
Station A
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
2.0 ppm
18.0
5.4
3.5
4.8
3.1
3.9
2 . 0 ppm
1.0
1.0
3.0
3.0
3.0
3.0
Station C
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
2 . 0 ppm
18.0
5.4
3.5
4.8
3.1
3.9
2 . 0 ppm
3.0
2.0
2.0
2.0
3.0
4.0
Station D
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
3.5 ppm
31.5
9.5
6.0
8.4
5.5
6.8
3 . 5 ppm
5.0
5.0
5.0
6.0
6.0
5.0
75
-------
Table 8a (Continued)
HOURLY CO VALIDATION USING SIMPLE GIFFORD-
HANNA PHOTOCHEMICAL MODEL
Hour
t*
U6/u
Q/Q6
[<*>]*
[co] cal
[co] mea
Station F
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
2.0
3.0
3.0
2.0
1.5
2.0
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
3 . 0 ppm
18.0
24.3
20.7
14.4
11.7
15.6
3.0 ppm
3.0
4.0
5.0
5.0
5.0
4.0
76
-------
Table 8b. HOURLY RH VALIDATION USING SIMPLE GIFFORD-HANNA
PHOTOCHEMICAL MODEL
Hour
t*
U6/u
Q/Q6
[«]*
|~RH] cai
[RH] mea
Station C
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
0.99
0.98
0.97
0.96
0.96
0.95
35.0 pphm
311.9
92.6
58.6
80.6
52.4
64.8
35.0 pphm
21.7
18.3
25.0
28.3
28.3
48.5
Station D
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
0.99
0.98
0.97
0.96
0.96
0.95
21.7 pphm
193.3
57.4
36.3
50.0
32.5
40.2
21.7 pphm
31.7
51.7
25.0
21.7
'20.0
18.3
Station E
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
0.99
0.98
0.97
0.96
0.96
0.95
5 . 0 pphm
44.6
13.2
8.4
11.5
7.5
9.3
5.0 pphm
5.0
3.3
3.3
3.3
5.0
11.7
77
-------
Table 8b (Continued). HOURLY RC VALIDATION USING SIMPLE
GIFFORD-HANNA PHOTOCHEMICAL MODEL
Hour
t*
u6/u
Q/Q6
M*
[RH] cai
f" RH J mea
Station F
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.0
3.0
1.0
0.75
1.0
0.6
0.75
1.0
3.0
2.7
2.3
2.4
2.6
2.6
1.0
0.99
0.98
0.97
0.96
0.96
0.95
15.0 pphm
133.7
39.7
25.1
34.6
22.5
27.8
15.0 pphm
11.7
18.3
18.3
21.7
18.3
11.7
78
-------
Table 8c. HOURLY 03 VALIDATION USING SIMPLE GIFFORD-
HANNA PHOTOCHEMICAL MODEL
Hour
t*
03! cal
"°3~
mea
Station A
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.4 pphm
1.4
1.4
1.5
1.5
1.5
1.6
1 pphm
3
4
-
9
14
19
Station C
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.4 pphm
1.4
1.4
1.5
1.5
1.5
1.6
2 pphm
3
5
6
11
16
21
Station D
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.4 pphm
1.4
1.4
1.5
1.5
1.5
1.6
1 pphm
3
9
6
-
12
16
79
-------
Table 8c (Continued). HOURLY 03 VALIDATION USING
SIMPLE GIFFORD-HANNA
PHOTOCHEMICAL MODEL
Hour
t*
[O3]cal
To3 J mea
Station E
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.4 pphm
1.4
1.4
1.5
1.5
1.5
1.6
3 pphm
-
4
5
6
8
12
Station F
6
7
8
9
10
11
12
0
0.21
0.42
0.63
0.83
1.04
1.25
1.4 pphm
1.4
1.4
1.5
1.5
1.5
1.6
_
3 pphm
3
6
13
16
11
80
-------
and calculated concentrations, the two models used in this study can
provide some degree of insight into the relationship between ozone and
NO concentrations and emissions and meteorological parameters. It is
X
hoped, however, that before further model applications are carried out,
a much greater effort be expended in applying and validating these
models for other urban areas besides Los Angeles. In light of the
problems we encountered in the application of these models to just a
single city, we feel that an effort of this type would certainly be
worthwhile. In this connection we recommend that more attention be de-
voted to the selection of reasonable initial concentrations for the
air parcels and that a greater number of meteorological stations be used
for the determination of trajectories, if possible.
It must be emphasized that during this modeling exercise we did not set
out to conduct an extensive theoretical evaluation of the technical
aspects of each model. Sucn activities would have been beyond the scope
of this particular effort. Rather, we have assumed the role of a 3-C
planning agency in our approach to these models so that our findings
relate as much to the general feasibility of model application as well
as to the accuracy of the calculation technique. This is why we have
devoted a considerable section of this report to the treatment of the
more logistical aspects of model application such as data collection
and validation procedures. Where appropriate, we have called out what
we felt to be significant technical flaws in the models. An example
is the problem we found in selecting initial pollutant concentrations
for the DIFKIN model. It is hoped that the experiences we have des-
cribed will be of value to other photochemical modeling applications.
81
-------
REFERENCES
1. Reynolds, S. D., Mei-Kao Lui, T. A. Hecht, P. M. Roth, and J. H.
Seinfield. Urban Air Shed Photochemical Simulation Model Study.
Volume I - Development and Evaluation. Prepared by Systems
Applications, Inc., Beverly Hills, California for the Office of
Research and Development. U. S. Environmental Protection Agency.
Washington, D. C. EPA-R4-73-030a. July 1973.
2. Reynolds, S. D. Urban Air Shed Photochemical Simulation Model
Study. Volume II - User's Guide and Description of Computer
Programs. Prepared by Systems Applications, Inc., Beverly Hills,
California for the Office of Research and Development. U. S.
Environmental Protection Agency. Washington, D. C.
EPA-R4-73-030f. July 1973.
3. Wayne, L. G., A. Kokin, and M. I. Weisburd. Controlled Evalua-
tion of the Reactive Environmental Simulation Model (REM).
Volume I: Final Report. Prepared by Pacific Environmental
Services, Inc. Santa Monica, California for Office of Research
and Monitoring Environmental Protection Agency. Washington, D. C.
EPA R4-73-013a. February 1973.
4. Kokin, A., L. G. Wayne, and M. Weisburd. Controlled Evaluation
of the Reactive Environmental Simulation Model (REM). Volume II:
User's Guide. Prepared by Pacific Environmental Services, Inc.
Santa, Monica, California for the Office of Research and Moni-
toring. Environmental Protection Agency. Washington, D. C.
EPA R4-73-013b. February 1973.
5. Eschenroeder, A. Q., J. R. Martinez, and R. A. Nordsieck. Evalua-
tion of a Diffusion Model for Photochemical Smog Simulation.
Prepared by General Research Corporation. Santa Barbara, Califor-
nia for the Environmental Protection Agency. EPA-R4-73-012,
Volume a. October 1972.
6. Martinez, J. R., R. A. Nordsieck, and M. A. Hirschberg. User's
Guide to Diffusion/Kinetics (DIFKIN) Code. Prepared by General
Research Corporation. Santa Barbara, California for the Environ-
mental Protection Agency. EPA-R4-73-012, Volume b. December 1973.
7. Hanna, S. R. A Simple Dispersion Model for the Analysis of Chem-
ically Reactive Pollutants. Atmospheric Environment. Pergamon
Press. 7:803-817. 1973.
8. Friedlander, S. K. and J. H. Seinfeld. A Dynamic Model of Photo-
chemical Smog. Environmental Science and Technology. 3:1175-1181.
1969.
82
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9. Stanford Research Institute- User's Manual for the APRAC-1A
Urban Diffusion Model Computer Program. Prepared for the Coor-
dinating Research Council and the United States Environmental
Protection Agency, Division of Meteorology. September 1972.
10. Refinement and Modification of Planning Models and Techniques.
Calibration of the Existing Highway Network for the Denver
Metropolitan Area. Colorado Division of Highways. Planning
and Research Division. Joint Regional Planning Program Tech-
nical Memorandum 4-2/001. October 1973.
11. Martinez, J. R., R. A. Nordsieck, and A. Q. Eschenroeder. Morning
Vehicle-Start Effects on Photochemical Smog. Environmental
Science and Technology.
12. Gifford, F. A., Jr. and S. R. Hanna. Modeling Urban Air Pollution.
Atmospheric Environment 7:131-136. 1973.
13. Gifford, F. A., Jr. An Outline of Theories of Diffusion in the
Lower Layers of the Atmosphere. Chapter 3. Meteorology and
Atomic Energy 1968 (D. Slade, editor,) USAEC-TID-24190. 1968.
14. Compilation of Air Pollutant Emission Factors, Second Edition.
U. S. Environmental Protection Agency. Office of Air and Water
Programs. Office of Air Quality Planning and Standards.
Research Triangle Park, North Carolina. April 1973.
83
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SECTION V
CASE STUDY OF AIR QUALITY REVIEW REQUIREMENTS
GENERAL
This Section will present a case study of some of the present requirements
for reviewing the air quality impacts of highway systems. Two specific
examples will be examined:
1. Review of urban transportation plans and programs for
consistency with State Implementation Plans ("109(j)
review").
2. Evaluation of major highways for oxidants impact under
EPA indirect source review regulations.
Both examples are drawn from the Denver transportation planning process.
In both cases, oxidants are the concern, not other pollutants. Review
requirements studied here were those in effect when this project began and
many have subsequently been revised.
The following discussion will begin with the consistency review, followed
by the indirect source review, and then by some concluding remarks.
CONSISTENCY REVIEW
Introduction
Requirements - Section 109(j), Title 23 USC (added by Section 136(b) of
the Federal-Aid Highway Act of 1970, P.L. 91-605) states that:
84
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"the Secretary, after consultation with the Administrator of
the Environmental Protection Agency, shall develop and promulgate
guidelines to assure that highways constructed pursuant to this
title are consistent with any approved plan for the implementation
of any ambient air quality standard for any air quality control
region designation pursuant to the Clean Air Act, as amended."
The Federal Highway Administration (FHWA) has promulgated guidelines as
required by the law quoted above. Interim guidelines were issued in
1973, followed by revised, final guidelines in late 1974.3 An Environ-
mental Impact Statement was prepared on the final (1974) guidelines.
The present analysis will use the 1973 guidelines which were in effect at
the start of this project.
Under the 1973 FHWA guidelines, the highway agency is required to:
"establish a continuing review procedure with the cognizant
air pollution control agency to:
"(i) Assess the consistency of the transportation plan and
program with the approved State Implementation Plan;
"(ii) Annually solicit comments from the cognizant air pol-
lution control agency including its assessment of the
consistency of the plan and program with the approved State
Implementation Plan prior to plan approval by the policy
board.
"(iii) Identify and resolve differences with the cognizant
air pollution control agency."
An annual determination of consistency between the transportation plan
and the State Implementation Plan (SIP), often referred to as the "109(j)
review," must be documented and endorsed by the 3-C agency policy board.
The guidelines require that both the highway plans and the planning process
be reviewed for consistency, and that the FHWA Regional Administrator must
*"3-C" refers to the continuing, comprehensive, cooperative transporta-
tion planning process required by 23 USC, Section 134.
85
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consult with the EPA Regional Administrator in these reviews. This review
for consistency is one of many items that FHWA required be done to obtain
the annual certification of each agency's 3-C planning process.
The guidelines also specify that a particular highway project cannot be
approved unless the FHWA determines that it is consistent with the SIP.
This project determination is to be included in the environmental impact
statement for the highway project.
Example of Consistency Review - In the discussion to follow, an example
will be presented of how the 109(j) consistency review would proceed,
using the Denver 3-C planning as a case study. In this example, what will
be examined is how EPA would participate, not how FHWA would perform its
review. Hence, the intent is to perform a trial run of the existing re-
quirements and guidelines.
Criteria for EPA Review
The FHWA guidelines cited above require that the FHWA and EPA Regional
Administrators consult with one another at certain times and that the EPA
identify deficiencies to the FHWA. However, the FHWA guidelines (including
the later 1974 revision) provide no criteria nor identify any technical
methods for the EPA functions.
EPA had not (as of the start of this project) formally established any pro-
cedures or criteria for its role. EPA is expected, however, to issue
*
guidance on consistency review shortly. In the interim, certain ten-
tative procedures or guidelines have been recommended to the EPA Regional
Administrators. These instructions, referred to as the Strelow-Myers
Memo, will be used here as the basis for the example consistency review.
•f
This has been issued as "Guidelines for Analysis of Consistency Between
Transportation and Air Quality Plans and Programs," prepared jointly by
EPA and FHWA, April, 1975'.
86
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Another possible source of guidance for EPA review of 109(j) consistency is
4 18
suggested by EPA to be a report prepared by a consultant as part of the
guidelines for air quality maintenance planning. Because the report was
not final before the present project- was begun, it will only be used as a
reference and not as if it were a formal basis for review.
The Denver Situation
Status of the 3-C Process - The 3-C transportation planning process in the
Denver metropolitan area is carried out jointly by the Colorado Division
of Highways (CDH), the Regional Transportation District (RTD), and the
Denver Regional Council of Governments (DRCOG). This is referred to as
the Joint Regional Planning Program (JRPP). The JRPP has just produced
its official long-range transportation plan for the Denver region, and
it is presently developing a program for implementing the plan. The plan
is for the year 2000. No schedules have yet been adopted for construction
of either the highway element or the public transportation element. In the
lexicon of 3-C planning, the JRPP is now in Phase IV, implementation and
continuing planning.
Plans - For this trial evaluation, the JRPP will be evaluated primarily
on the basis of the following of its products:
• The year 2000 transportation plan, as described in the
report of the adopted plan-* and the technical analysis.
• The draft short-term plan'' available when this project
began.
• The current operations plan** for the JRPP.
This review has been based upon information available in the fall of 1974
and may not accurately reflect the current status of the JRPP. The actual
109(j) review and certification of the JRPP is still underway; FHWA, EPA,
and the other agencies involved have not yet reached agreement on whether
109(j) requirements are being met and certification has not yet been
granted.
87
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Colorado State Implementation Plan - In order to assess the 3-C plan and
program it is necessary to compare it with pertinent elements of the State
Implementation Plan (SIP).
GCA reviewed the SIP, and its Transportation Control Plan (TCP) component,
in a previous report.9 There it was found that the promulgated TCP in-
cludes several elements:
1. Program for inspection and maintenance of motor vehicles,
using the idle test mode, to be fully implemented by
1 December 1975.
2. Program for equipping of pre-1968 model year vehicles with
engine air bleed devices, to be implemented by 1 July 1976.
3. Program for modification of 1968-1975 model year vehicles
for high altitude, to be implemented by 1 July 1976.
4. Program for control of hydrocarbon emissions from stationary
transportation, manufacturing, and processing facilities.
5. Creation of bus-carpool lanes on existing roadways.
6. Limitation on construction of parking facilities, to be
implemented on 1 January 1975.
7. Removal of on-street parking in the Denver Central Business
District (CBD).
8. Mass transit improvements.
9. Limitation on gasoline sales.
EPA Region VIII now expects the TCP to be amended to include slightly
different measures for inspection and maintenance and for modification of
existing automobile engines.
In response to the EPA-promulgated TCP, the Air Pollution Control Com-
mission (APCC) recently promulgated Regulation number 9 which deals
explicitly with measures 5 through 8 mentioned above. Elements of
Regulation number 9 are:
.88
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1. Exclusive Bus-Carpool Lanes, to be implemented by
1 January 1976.
2. Creation of Park-N-Ride Facilities with Express
buses to the CBD; with plan submission by 1 October
1974.
3. Carpooling programs, to be implemented by large em-
ployers (>250 employees) by 1 April 1975, and by
other employers (50 to 250 employees) by 1 October
1975. In addition, employers must submit a descrip-
tion of incentives which encourage employees to make
use of mass transit facilities. Large employers
must submit their plan by 1 February 1975, with sub-
mittal by other employers by 1 August 1975.
In addition, the regulation requires that DRCOG submit recommendations
to the APCC by 1 March 1975 on parking requirements that may stimulate
9
the use of public transportation and decrease single passenger VMT.
The RTD is specifically charged with planning the details of the bus-
carpool lanes and park-and-ride facilities.
These SIP elements will be the basis for consistency evaluation.
Consistency Review of 3-C Plans
An urban transportation planning process is complex, involving numerous
activities and people. Consequently it is difficult to assess except by
examining its products. Nonetheless there are some other indicators that
can be examined. One is the operations plan, which is intended to be a
description of the long range planning activities of the JRPP. Review of
Q
the current operations plan0 shows that there are repeated references to
air quality, but only as one of many nontransportation considerations in
the 3-C process. In its description of the JRPP organization, no specific
provisions are made for formally incorporating air quality planning. As
an example, the Colorado Air Pollution Control Commission is not identified
as one of the official participants in plan preparation or review. Neither
89
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EPA guidelines nor FHWA guidelines2 for 109(j) review define how to en-
sure that air quality is considered in the planning process, nor does the
approved SIP mandate any specific role in transportation planning for the
air agencies. Nonetheless, this would appear essential for a formal
arrangement for coordination and cooperation between the air pollution
control agency and JRPP. This apparent deficiency has also been cited by
the Colorado Air Pollution Control Commission.
Although evidence of such formal coordination is not apparent from the
present review, the JRPP does claim to have established coordination.
In its own presentation relative to the 109(j) review, the JRPP Agency
Directors stated:
"The Colorado Department of Highways has established a con-
tinuing review procedure with the Air Pollution Control Di-
vision of the Colorado Department of Health to assess the
consistency of each project with the State Implementation
Plan for air quality.
"The Colorado Department of Highways is presently developing
an acceptable modeling technique for analyzing air quality
impacts on a regional basis. This model will be used by the
Joint Regional Planning Program to assess its long range and
short range transportation plans and programs."^
In conclusion, it is not clear from this review whether there is adequate
coordination between the JRPP and the air agency. An appropriate deter-
mination would require more formal arrangements for incorporation of air
quality considerations in transportation planning before certifying
109(j) compliance.
Consistency Review of Long-Range Plans
EPA's guidance on 109(j) review calls for concentrating on short-term
plans, with the idea of examining long-range projects after air quality
maintenance planning is underway. Denver's long-range plans will none-
theless be reviewed briefly here, because a demonstration air quality
90
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maintenance plan has been done for Denver,' and the short-range plan^ is
not yet well defined.
First, the long-range plan^ does not explicitly provide for all the specific
SIP measures listed earlier (e.g., bus-carpool lanes). It is not intended
to be a detailed design of any facility, so this might not be judged to be
a deficiency. Second, the long-range plan does explicitly include a major
increase in the public transportation system. GCA earlier analyzed the
JRPP plan and showed that in 1985, at the end of the air quality mainte-
nance forecasting period, the JRPP plan would reduce total vehicle-miles
of travel by an estimated 6 percent compared to an alternative highway-
intensive plan. No specific computation was made of emissions for the
two cases; automotive exhaust emissions would presumably be lower with
the JRPP plan. Furthermore, it was estimated (on the basis of linear
rollback) that in 1985 the regional ambient concentrations of carbon
9
monoxide and oxidants would meet national standards.
While there are many issues beneath these simple statements, it is rea-
sonable to conclude that the analysis done to date does not indicate that
the long-range JRPP plan would be inconsistent with the SIP in the long
run. No analysis has been done to demonstrate the oxidants impact of the
proposed plan relative to any alternative plan, however.
Consistency Review of Short-Term Plans
At present the JRPP short-range plan' deals only with a 5-year highway
plan. Public transportation elements are being planned in conjunction
with JRPP by one of its members, the Regional Transportation District, but
separate short-range plans are being prepared. As was noted earlier, RTD
is preparing plans for complying with the transit-related elements of the
SIP. The short-range highway plan could nevertheless be faulted for not
mentioning either the exclusive bus-carpool lanes or the park-and-ride
facilities. Furthermore, the draft short-term plan explicitly states that
91
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selection of projects has been on the basis of transportation need,
without apparent explicit regard for air quality.
To assess the air quality implications of the short-range plan would
require an air quality analysis of each major project proposed for
the 5-year period. The only such analysis reviewed for the present
9
study is the macroscale analysis in the maintenance study. It is
not possible from that analysis to state whether one or more of the
proposed projects would either delay attainment or cause a violation
of the air quality standards. Furthermore, no such analysis can be
made without a comparison of the traffic flows with and without the
proposed projects. No traffic projections have been done yet for the
short-term plan; the projections have been done only for the year 2000,
with no intermediate forecasts. The intermediate year forecasts, in
turn, cannot be done until intermediate year constructions programs
are tentatively agreed upon, which was just being done as the present
review was begun. Without such traffic analyses it is not possible
quantitatively to state the impact on regional air quality of the 5-year
highway program or any element thereof.
The conclusion, then, is that the short-range highway plan requires further
development and air quality analysis before consistency can be assessed.
It might thus be argued that certification should be granted on the basis
that no inconsistencies are apparent and that the planning work cannot
continue without certification.
INDIRECT SOURCE REVIEW
Introduction
An example indirect source review was performed in order to determine the
ability of the present requirements and procedures to assess the impact
of major highway projects'on oxidant concentrations.
92
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Requirements
At the time this project began, EPA had published regulations12 for the
review of the air quality impact of highways and other "indirect sources"
of air pollution.* The regulations focused on carbon monoxide, but
specified that oxidants review would be required for highways with more
13
than 50,000 vehicles per day within 10 years of construction. Detailed
guidelines 14 have been provided for the analysis required by the regula-
tions. The guidelines, like the regulations, deal chiefly with carbon
monoxide but include a brief procedure for the analysis of oxidant impacts.
The review reported here used the oxidants procedure^ from the guidelines.
Case Study
Three highways in the Denver transportation system plan^ are expected to
carry a volume of 50,000 vehicles per day or more and thus would be re-
quired to be reviewed for oxidants under current guidelines. (One of these
three, 1-470, is a major new highway in an underdeveloped corridor in the
southwest Denver area and was previously studied for this project in con-
nection with the DIFKIN model described in Section IV of this report). An
analysis was performed of a highway of the general type to be built in all
three cases: divided highways with an Average Annual Daily Traffic of
50,000. It was determined from telephone conversations with officials of
the Colorado Division of Highways that enough design information would be
available for any of the three highways to perform the requisite analysis.
Calculations were performed to determine the rate of emissions of both
hydrocarbons and nitrogen oxides from the generalized highway. Performance
of the calculations was straightforward.+ At the conclusion of the
*The implementation of these regulations was later delayed but technical
requirements have not been altered.
"^Certain steps could be clarified in the procedure.15 in particular the
procedures do not clearly identify how to treat traffic flows in the two
directions.
93
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calculations, the procedure calls for comparison of the emissions from
the highway with those that would occur without the highway. However,
no guidance is given for establishing a reference geographic area, so that
the comparison will be quite different depending upon what size of area is
chosen.
An alternative guideline document is one used for review of environmental
impact statements.!' Again, this document is a nonbinding guideline for
internal EPA use. This guideline includes a brief procedure for a
mesoscale analysis for HC and NO , but again gives little guidance on how
X
to select the appropriate geographic area for the analysis.
In conclusion, existing EPA guidelines do not provide sufficient guidance
to allow a quantitative assessment of the oxidants impact of an indirect
source (in this case, a highway).
SUMMARY AND CONCLUSIONS
The example review of the Denver area 3-C planning process and plan has
shown that:
1. More formal arrangements must be made to assure considera-
tion of air quality in JRPP transportation planning.
2. The long-range JRPP plan has been analyzed by linear roll-
back techniques and appears to be acceptable for maintaining
air quality, but no quantitative air quality comparison has
been made between the proposed plan and any alternative.
3. The short-range plan, a part of the long-range plan, is
not adequately documented but shows no qualitative in-
consistencies with the SIP. On the other hand, no air quality
analysis has been performed to show whether some or all of
the short-range projects would delay attainment of the ambient
air quality standards. Hence, it is not possible to assess
its consistency with the intent of the SIP to attain the
standards, regardless of whether the system would allow
meeting standards when completed. It might be argued, how-
ever, that the planning process should be certified so that
the necessary planning and analysis can proceed.
94
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With respect to indirect source review, it was found that present EPA
mesoscale evaluation procedures do not provide adequate guidance for
evaluation of the oxidants impacts of highways, one reason being the dif-
ficulty of defining the geographic area to be used for evaluations.
In summary, present guidance does not provide the procedures or methods
necessary for deciding whether either entire highway systems or component
projects are consistent with air quality goals. In particular, present
processes for consistency review of transportation system plans would
require the addition of a qualitative air quality review to allow deter-
mination of consistency with the SIP.
REFERENCES
1. Final Environmental Impact Statement. Air Quality Guidelines. U.S.
Department of Transportation, Federal Highway Administration,
Washington, D.C. Report Number FHWA-EIS-73-01-F. September 1974.
p. 1, 17-21.
2. Air Quality Guidelines. U.S. Department of Transportation, Federal
Highway Administration, Washington, D.C. Federal-Aid Highway
Program Manual. Volume 7, Chapter 7, Section 9. (Also published in
Fed Register. Vol. 38, November 16, 1973. pp. 31677-31679).
3. Air Quality Guidelines. U.S. Department of Transportation, Federal
Highway Administration, Washington, D.C. Federal-Aid Highway
Program Manual. "Volume 7, Chapter 7, Section 9. Transmittal 105,
November 26, 1974. Total pages: 11. (Also published in Fed Regist,
Vol. 39, December 24, 1974. pp. 44441-44443.)
4. Acting Assistant Administrator for Air and Waste Management, and
Director, Office of Federal Activities, U.S. EPA. Coordinated Pro-
cedures for Review of Highway Air Quality Impacts. Memorandum of All
Regional Administrators. U.S. Environmental Protection Agency.
Washington, D.C. PG-E-74-1. April 26, 1974. 8 pages (plus
attachments).
5. Denver Regional Council of Governments. Regional Land Use, Highway
and Public Transportation Plans/Denver Region. Summary Report.
Draft. October 17, 1973.
6. Denver Regional Council of Governments. Transportation System Report.
December 1973.
95
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7. Denver Regional Council of Governments. Transportation Systems
Planning. A Five Year Capitol Improvement Program. June 30, 1974.
Draft.
8. Joint Regional Planning Program. Joint Regional Planning Program
Operations Plan. Revised, October 1974.
9. GCA/Technology Division. Development of an Example 10-Year Air
Quality Maintenance Plan for Denver AQMSA. Final Report. Prepared
for U.S. Environmental Protection Agency, Research Triangle Park,
N. C. 27711. September 1974.
10. Colorado Department of Health, Colorado Air Pollution Control Com-
mission. Letter to Mr. David Howell, Denver Regional Council of
Governments. April 1, 1974. Enclosure: Consistency Assessment.
11. Joint Regional Planning Program Assessment Statement. Enclosure to:
Colorado Department of Highways. Letter to Mr. A. J. Siccandi,
Division Engineer, Colorado Division, Federal Highway Administration.
May 7, 1974.
12. U.S. Environmental Protection Agency. Review of Indirect Sources.
40 CFR, Part 52.22(b). In: 39 FR 25292ff. July 9, 1974.
13. Ibid, Part 52.22(b)(6)(111).
14. Guidelines for the Review of the Impact of Indirect Sources on
Ambient Air Quality. U.S. Environmental Protection Agency, Research
Triangle Park, N. C. 27711. Draft. January 1975.
15. Ibid. Appendix A, Methods for Estimating Emissions From Highways.
16. Ibid. p. A-3.
17. Guidelines for Review of Environmental Impact Statements. Volume 1.
Highway Projects. U.S. Environmental Protection Agency, Office of
Federal Activities. September 1973. pp. 27, 28.
18. Guidelines for the Review of Urban Transportation Plans and Programs
Pursuant to 23 CFR 770.204. Draft Report. Alan M. Voorhees and
Associates, Inc. EPA Contract 68-02-1388. April 1974.
96
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APPENDIX A
BLOCK DATA FOR DIFKIN RUN
97
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0001
0002
0003
0004
0005
0006
c
c
c
c
c
0007
BLOCK DATA
BLOCK DATA PROGRAM NUMBER ONE
LOAD TRAFFIC AND STATIONARY SOURCE DATA INTO COMMON /DATFIL/
COMMON /DATFL1/ FFWY<25,25), FSRF(25,25), FXN02(25,25),
1 FXHCR125.25), FPPN02(30,3). FORN02C30,3),
2 FORHCR(30,3)
DIMENSION FFWYAI385), FFWY8<240), FSRFA1264), FSRFBU631,
1 FSRFCI154), FSRFDJ44), FXN02A(291), FXN02BU74),
2 FXN02CI160), FXHCRA(290), FXHCRBU72), FXHCRC(163)
EQUIVALENCE (FFWYAJ1) ,FF«IY« 1,1 ) ) , CFFWYB11),FFWY(11,16))»
1 (FSRFA(1),FSRF(1,1)), (FSRFB{1),FSRF(15,11)>,
2 (FSRFC(1),FSRF(3,18)), { FSRFDU ) ,FSRF(7,24) ) ,
3 (FXN02A(1),FXN02(1,D), ( FXN02B {1) ,FXN02 { 17,12) ),
4 (FXN02C11J,FXN02(16,19)), (FXHCRA(1),FXHCRI 1,1)),
5 (FXHCRBC1),FXHCR(16,12)), iFXHCRCC1),FXHCRJ13»19))
DIMENSION IPPN02(30)t JPPN02(30), XPPN02C30J,
1 IORN02(30), JORN02(30), XORN02OO),
2 IORHCROO), JORHCR130), XORHCR » 30 )
EQUIVALENCE (IPPN02U ,FPPN02(1,1) , ( JPPN02 ( 1 ) ,FPPN02( 1 ,2)
1 (XPPN02U ,FPPN02(1,3> , ( IORN02 (1 ) ,FORN02( 1, 1)
2 (JORN02«1 ,FORN02(1,2) , (XORN02< 1 ) ,FORN02( 1 ,3)
3 (lORHCRU ,FORHCR(1,1) , < JORHCR CU ,FORHCR( 1 ,2 )
4 (XORHCRd ,FORHCR(1,3)
C
C
DATA FFWYA/
£ 13*000., 13*000., 13*000., 13*000., 13*000., 13*000., 13*000.,
£ 13*000. ,13*000. ,13*000. ,13*000. ,13*000.,
£ 9*000.,
£ 0.,
£ 0.,
£ 0.,
£ 0.,
£ 0.,
£ 0.,
£ 47.,
£ 0.,
£ 0.,
£ 0.,
£169.,
£ 0.,
£ 0.,
£ 0.,
£ 97.,
£ 0.,
0.,
o.,
o.,
0.,
o..
o..
66.,
0.,
76.,
0.,
0.,
0.,
0.,
0.,
69.,
0.,
26. ,16* 000
0.,
0.,
0.,
0.,
0.,
0.,
36.,
0.,
143.,
0.,
0.,
0.,
o.,
0.,
54.,
0.,
O.i
o.,
0.,
0.,
0.,
0.,
54.,
0.,
28.,
0.,
0.,
0.,
22.,
0.,
23. ,
0.,
• »
0.,
0.,
0.,
0.,
o.,
0.,
106.,
0.,
0.,
o..
21.,
0.,
o.,
0.,
9.,
0.,
o.,
0.,
0.,
0.,
87.,
0.,
20.,
0.,
24.,
0.,
0.,
l.t
7.,
24.,
0.,
0.,
0.,
0.,
0.,
0.,
7.,
0.,
15.,
0.,
o.,
0.,
0.,
25.,
12.,
28.,
0.,
0.,
0.,
0.,
50.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
17.,
0.,
72.,
0.,
0.,
35.,
0.,
0.,
0.,
o.,
0.,
0.,
0.,
0.,
66.,
0.,
9.,
0.,
66.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0.,
0., 0.
O.t 0.
0., 0.
0., 4.
0., 0.
0., 7.
0., 0.
0., 0.
0., 0.
63. ,100. ,141.
0.,
0.,
0.,
0., 0.
0..174.
O.t 0.
67. ,169. ,357.
0.,
9./
0.,
».
),
),
>t
t 0.,
t 0.,
f 0.,
, 7.,
t 0.,
, 28.,
» 0.,
t 0.,
, O.t
,176.,
, o..
, 0.,
t 0.,
,130.,
98
-------
0008
O009
0010
DATA FFWYB/
C 43., D.,208., 94., 44., 0.,
0.,
O.i
0., 0.
O.i
0.,
G 0., 0.,
G 0., 0., 0.,
G 83., 37., 0.,
G 0., 0., 0.,
G 54., 0., 0.,
G 0., 0., 0.,
G 45., 6., 0.,
G 0., 0., 0.,
G 22., 9., 0.,
G 0., 0., 0.,
G 10., 5., 0.,
0
0
0
0
0
0
0
0
0
0
G 13*000. ,!3*000.,
G 0., 0. , 0.,
C/
DATA FSRFA/
0
G 13*000. ,13*000.,
G 0., 0., 0.,
C 0., 0., 0.,
G 0., 0., 0.,
C 0., 0., 0.,
G 0., 0., 0.,
G 0., 0., 0.,
G 0., 0., 0.,
G 0., 0., 10.,
G 0., 0., C.,
G 0., 20., 23.,
G 0., 0., 0.,
C 27., 18. ,153.,
G 0., 0., 0.,
G 0., 0., 0.,
G128.
G/
DATA FSRFB/
G 87., 54., 15.,
G 0. , 0. , 0. ,
C262..144., 63.,
G 0. , 0. , 0.,
G146., 99., 64.,
G 0., 0., O.,
G206.,117., 29.,
C 0., 0. , 0. ,
G 51., 10., 2.,
C 0., 0., 5.,
G 3., 1., 0.,
6 0., 7., 7.,
0
0
0
0
0
24
0
31
0
50
0
139
0
1
0
10
0
14
2
10
22
0
13
0
6
., 0., 0., 0., 0., 0., 0., 0., 59. ,248.,
., 0., 0., 0., 0., 0., 0.» 0., 0., 0.,
., 0., 0., 0., 0., 8., 53., 14. ,108., 0.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., O.t 0., 0., 21., 61., 0., 78., 0., 7.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 0., 0., 81., 0., 0., 48., 0., 0., 16.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 45., 2., 0., 0., 43., 0., 0., 0., 28.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
1 3*000 . , 1 3*000 . , 1 3*000 . ,1 3*000 . , 1 3*000. ,
.
13*000. , 13*000. , 13*000. ,1 3*000. ,13*000. ,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 8., 0., 3., 0., 0., 0., 0., 0., 0.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 4., 9., 0., 0., 0., 0., 0., 0., 6.,
., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
., 25., 15., 14., 4., 1., 0., 8., 2., 0.,
., 0., G., 0., 0., 0., 0., 0., 0., 0.,
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END
102
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OOO9
END
104
-------
0001
0002
0003
0004
0005
0006
C
C
C
C
BLOCK DATA
BLOCK DATA PROGRAM NUMBER THREE
COMMON /DATFL3/ FFAST(25,25,4)
.DIMENSION FFAST61625), FFAST7(625), FFAST8(625), FFAST9(625J
EQUIVALENCE (FFAST6U S .f^ASTI i ,1 ,1)) » (FFAST7(1J,FFAST(1,1,2)),
1 (FFAST8(1),FFAST(1,1,3)), (FFAST9U),FFAST(1 ,1,4»)
DATA FFAST6/
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G95*0./
0007
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DATA FFAST8/
C130*0.,
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C 0., 0., 0., O.,45.,50., 0.,
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DATA FFAST9/
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END
106
-------
0001
0002
0003
0004
0005
C
C
C
C
0006
BLOCK DATA
BLOCK DATA PROGRAM NUMBER FOUR
COMMON! /DATFL4/ FRATIO ( 25,25,4)
DIMENSION FRAT61625), FRAT71625), FRAT8(625), FRAT9<625)
EQUIVALENCE tFRAT6< 1 i tFRATIOU ,111J ) t (FRAT7 (1) ,FRATIO( 1 , 1,2 ) ) ,
1 (FRAT8U) ,FRATIOU,1,31), (FRAT9 (1 ) , FRATI01 1, 1,4 ) )
DATA
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.0,0.0,0,
.2,2.2,0,
.2,2.2,2,
.0,2.2,2,
,0,0.0,0
,0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
,0,0.0,0
0,0.0,0
,0,0.0,0
,0,2.2,2
3,2.3,0
0,0.0,0
0,0.0,0
,0,0.0,0
0,0.0,0
2,0.0,0
,2,2.2,0
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.2,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
0,0.0,0
0,0.0,0
0,0.0,0
.0,0.0,0
0,0.0,0
9,1.9,1
0,0.0,0
5,1.5,1
0,0.0,1
3,3.0,3
5,1.5,0
0,0.0,3
0,3.0,3
0,3.0,0
0,0.0,0
5,0.0,0
.0,0.0,0.
.0,0.0,0,
.0,0.0,0.
.0,0.0,0,
.0,0.0,0.
.5,1.0,1,
.0,1.9,1.
.5,1.9,0.
.2,0.0,0,
.0,1.0,1,
0,1.9,1.
.0,3.0,3.
.0,1.9,0.
0,1.9,0.
.0*1.9,0,
.0,1.9,0,
0,0.0,0,
0,0.0,1,
0,0.0,1,
0,0.0,1
0,1.5,1,
5,1.7,1,
7,1.5,0,
0,0.0,0,
0,0.0,0,
5,1.5,1,
0,1 .0,0,
0,3.0,0,
0,1.9,0,
0,1.9,1,
0,0.0,1,
0,0.0,0,
.0,0.0,0.
,5,0.0,0,
,5,0.0,0.
,5,0.0,0.
.5,0.0,0.
.0,1.0,0,
,0,1.0,0,
.0,1.5,0.
,0,1.9,0.
.9,1.9,2.
,0,0.0,0.
,0,0.0,0.
.0,0.0,0,
.9,1.9,0,
,9,1.9,1,
.0*1.9,1.
0,0.0,0
0,0.0,0
0 ,0.0 ,0
0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
0,1.9,1
0,2.0,0
0,0.0,0
0,0.0,0
0,0.0,0
,0,0.0,0
9,0.0,0
9,1.9,0
0,10*0.
.0,10*0.
.0,10*0.
,0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.9,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
.0,10*0.
,0,10*0.
,0,10*0.
107
-------
695*0./
0007.
0008
0009
DATA
£130*0
CO.0,0
CO.0,0
£0.0,0
£0.0,0
£0.0,0
CO.0,0
£0.0,0
£0.0,1
£1.9,1
£0.0,1
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£95*0.
DATA
£130*0
£0.0,0
£0.0,0
£0.0,0
£0.0,0
CO.0,0
£0.0,0
£0.0,0
£0.0,1
£1.5,1
£0.0,1
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£0.0,0
£95*0.-
END
FRAT8/
Io,o,
.0,0,
.0,0.
.0,0,
.0,1.
.0,0.
.0,0,
.5,1.
.9,1.
.3,1.
.0,0.
.0,0.
.0,0.
.0,0.
.0,0.
.0,0,
0,0.0,0,
0,0.0,0,
0,0.0,0,
0,0.0,0,
9,1.9,0,
0,1.9,1,
0,0.0,0,
9,1.9,1,
5,1.5,0,
7,1.9,2,
0,0.0,1,
0,0.0,0,
0,0.0,3.
0,0.0,3,
0,0.0,3,
0,3.0,1,
FRAT9/
.0,0,
.0,0,
.0,0.
.0,0,
.0,1,
.0,0,
.0,0.
.3,1.
.5,1.
.2,1.
.0,0.
.0,0.
.0,0.
.0,0,
.0,0.
.0,0.
0,0,
0,0.
0,0.
0,0,
5,1.
0,1.
0,0.
5,1.
3,1.
3,1.
0,0.
0,0.
0,0,
0,0.
0,0.
0,2.
0,0,
0,0,
0,0,
0,0,
5,0.
5,1.
0,0,
5,1.
3,0,
5,2,
0,1,
0,0,
0,2,
0,2,
0,2,
5,1,
.0,0.
,0,0,
0,0.
.0,0,
,0,0.
.9,1.
.0,0,
,5,1,
.0,0,
3,3.
5,1.
0,0,
,0,3,
0,3.
0,0.
,5,0.
0,0,
0,0.
0,0.
0,0,
0,0.
5,1.
0,0.
3,1.
0,0,
0,2,
3,1.
0,0.
5,2.
5,2.
5,0,
3,0,
,0,0
,0,0
0,0
0,0
0,0,
9,1
0,0
5,1
,0,1
0,3,
5,0
0,3
0,3
0,0
0,0
0,0
0,0.
0,0.
0,0,
0,0.
0,0.
5,1.
0,0,
3,1.
0,1,
,5,2,
3,0,
0,2,
5,2.
5,0.
0,0.
0,0,
.0,0,
.0,0,
.0,0,
.0,0,
.0,0,
.5,1.
.0,1.
.5,1.
.2,0.
.0,1.
.0,1.
,0,3.
.0,1.
.0,1.
.0,1.
,0,1,
0,0,
0,0,
0,0.
0,0,
0,0.
3,1.
0,1,
3,1.
1 ,0,
5,1,
0,1,
5,2,
5,1,
0,1.
0,1,
0,1,
,0,0.0,0,
0,0.0,0,
0,0.0,0,
0,0.0,0,
0,0.0,1,
0,1.5,1.
9,1.7,1,
9,0.0,0,
0,0.0,0.
0,1.5,1.
9,1.0,1.
0,3.0,3.
9,0.0,1.
9,0.0,1.
9,0.0,0,
9,0.0,0,
0,0,
0,0.
0,0.
0,0.
0,0.
0,1.
5,1.
5,0,
0,0.
0,1.
5,1.
5,2.
5,0.
5,0.
5,0,
5,0,
0,0
0,0
,0,0
0,0
0,1
3,1
3,1
0,0
0,0
3,1
0,1
5,2
0,1
0,1
0,0
0,0
0,0.0,0,
.0, 1.5,0,
0,1.5,0.
0,1.5,0.
5,1.5,0.
7,1.0,1.
5,0.0,1.
0,0.0,1.
0,0.0,1.
5,1.9,1.
0,0.0,0.
0,0.0,0.
9,0.0,0,
9,1.9,1.
0,1.9,1.
.0,0 .0,1.
.0,0.0,0,
.0,1.3,0,
.0,1.3,0,
.0,1.3,0.
.3,1.3,0,
.3,1.0,1,
.3,0.0,1,
,0,0.0, 1,
.0,0.0,1,
.3,1.5,1,
.0,0.0,0,
.5,0.0,0,
.5,0.0,0,
.5,1.5,1.
.0,1.5,1.
.0,0.0,1,
,0,0.0,0
,0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
0,0.0,0
5,0.0,0
9,0.0,1
9,2.0,2
0,0.0,0
0,0.0,0
0,0.0,0
9,0.0,0
9,1.9,0
,9,1.9,1
.0,0.0,10*0.
,0,0.0,10*0.
,0,0.0,10*0.
,0,0.0,10*0.
,0,0.0,10*0.
,0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.9,1.9,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.9,0.0,10*0.
0,0.0,0
0,0.0,0
0,0.0,0
,0,0.0,0
0,0.0,0
0,0.0,0
,0,0.0,0
3,0.0,0
5,0.0,1
5,1.7,1
0,0.0,0
0,0.0,0
,0,0.0,0
5,0.0,0
,5,1.5,0
5,1.5,1
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.5,1.5,10*0.
.7,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.0,0.0,10*0.
.5,0.0,10*0.
108
-------
0001 BLOCK DATA
C
C BLOCK DATA PROGRAM NUMBER FIVE
C
0002 COMMON /COLDST/ NBETA, TCS(20), CSF(60)
0003 COMMON /GRID/ XI, X2, Ylt Y2, NX, NY, DELXt DELY, DELT
0004 COMMON /TABLES/ THOU*". :<.•») • T2(8), YFR(8)
0005 COMMON /SPDFCT/ A(3), B{3), C(31, VBAR
C
0006 DATA NBETA/20/, TCS/360., 365., 370., 375., 3BO., 385., 390.,
1 395., 400., 410., 420., 425., 430., 435., 440., 500., 520.,
2 535., 550., 560./, CSF/20*1.0, 1.0, 1.057, 1.087, 1.074, 1.054,
3 1.038, 1.029, 1.023, 1.019, 1.014, 1.011, 1.007, 1.002, 0.998,
4 0.996, 0.994, 0.992, 0.989, 0.998, 1.0, 1.0, 1.186, 1.287,
5 1.243, 1.179, 1.125, 1.094, 1.075, 1.063, 1.047, 1.038, 1.025,
6 1.007, 0.995, 0.988, 0.979, 0.972, 0.965, 0.993, 1.0 /
0007 DATA X1,X2,Y1,Y2/0.,50.,0.,50./, NX,NY/25,25/, DELX.DELY/2.,2./
0008 DATA THOUR/0.,60.,120.,180.,240.,300.,360.,420.,480.,540.,600.,
1 660.,720.,780.,840.,900.,960.,1020.,1080.,1140.,1200.,1260.,
2 1320.,1380./
0009 DATA TZ/0., 360., 540., 690., 810., 990., 1110., 1260./
0010 DATA YFR/0.90, 0.85, 0.25, 0.30, 0.20, 0.50, 0.15, 0.20/
0011 DATA B/0.,-0.662,-0.842/,C/0.0295,0.,0./,VBAR/44./
£
0012
109
-------
APPENDIX B
INPUT FOR DIFKIN RUN
110
-------
DIFKIH SABPLE RON - COMPOTE TRAJECTORY, ?LUXES, *MD COHCEHTRATIOMS
IS TPAJFCTOBY INPUT EXTERNALLY NO
ARE FLUXES INPUT EXTERNALLY NO
DO WE COMPUTE SPECIES CONCENTRATIONS YES NO
PRINT STATION COOEDS, WIND DATA YES YES
730813 1CWELSCENTENNIAL WELL36PT212121 33336332727 3 6212«»2736 3363636331821212H
730813 1CWELSCENTSNNIAL WELLMPH 332 /563223U333tt5568tt34tttt
730813 2DWW DENVER SEWEE 36PT2929313131313U313136 6 « 2363U363U3U31 236181618
DENVER SEWER
BRIGHTON
BRIGHTON
HPH 7121513
36PT3036 33636
HPH 3 5 911
730813 2DWW
730813 3BRIN
730813 3BBIN
999999
WTK'J SPEED MULTIPLIER
DO WE WAN? rXTRS THAJECTORY OUTPUT
START DATE AND LOCAL TIME(2UHP CLOCK)
IS LOCAL TIPE STANCAPD OR DAYLIGHT
START LOCATION (STATION ID OR COORDS)
TRAJECTORY DURATION, HOURS
TRAJECTORY SEGMENT LENGTH, HOURS
STARTING AZIMUTH AND VELOCITY «OPTIONAL<
DIRECTION FLAG (POS=FRWRD, NEG=BKWRD)
WEIGHTING FLAG *01*1/H, 02#1/P**2<
NUMBER O*1 CLOSE STATIONS TO USE
GRID BOu.tDAPT*? «ST.MI.< 0.
HOT-STAPT EMISSION FACTORS, GM/MI
COLD-START EMISSION FACTORS, GM/MI
1969/1969 GROWTH FACTORS AUTO, STAT
FREEWAY VEHICLE ADJUST. FACTORS
SURFACE ST. VEHICLE ADJUST.FACTORS
POWER PLANT ADJUST. FACTORS
OIL REFINERY ADJUST. FACTORS
AREA STATIONARY ADJUST. FACTORS
INITIAL TIME STEP
UPPER LIMIT FRACTIONAL CHANGE
LOWER LIMIT FRACTIONAL CHANGE
PRINT INTERVAL
UPPER I.iriT ON DELT
LOWER LIMIT ON D3LT
VERTICAL MESH INTERVAL *METERS<
TIME INTERVAL FOR UPDATING K1
DO WE WANT A FIXED STEP SIZE
SHALL WE OUTPUT THE TIHE STEPS
SHALL WE OUTPUT THE EXECUTION TIBES
SHALL WE OUTPUT EVERY CYCLE
DO WE WANT PUNCHED OUTPUT
ZS K1 VARIABLE
IS INVERSION HEIGHT VARIABLE
DO WE HAVE ONLY AN INERT SPECIES
NUMBER OF INTEGRATION STEPS
NUMBER OF REACTIONS
NUMBER OF SPECIES
NUMBER OF SPECIES IN STEADY STATE
NUMBER OF TRACER SPECIES XO OR 1<
HOW MANY SPECIES HAVE A FLUX
"UMBER OF VFPTICAL STATIONS
SPECIES NAME AND MOLE WEIGHT NO
5 7 3 1 3 U
3 5
536 3 6 91215151
I 7 U 5 5 5
1.0
NO
730813
STANDARD
1.0
1.0
-1
01
03
50.
2. 3<»
2.3<»
1.0
1.0
1.0
1.0
1.0
1.0
.01
.03
0=01
30.
0.5
0.002
115.
10.
NO
NO
YES
NO
NO
YES
YES
NO
25000
16
10
U
1
3
5
7 5
1UOO
20.00
8.00
7.7
7.7
1.0
1.0
1.0
1.0
1.0
U53BU77962788
1818 33633 6 918212U21
7755556833U67
32.00
41.33333
95.4
95.«
1.0
1.0
30.01
111
-------
HC
H02
OZON
HN02
N03
N205
OH
K02
CO
72.20
«6.01
ue.
U7.00
62.
108.
17.
68.1
28.00
POSITION OF FIRST SPECIES WITH FLUX
POSITION OF SECOND SPECIES WITH FtUX
POSITION OF THIRD SPECIES WITH FLUX
NO PPHM T.OO
HC PPHM 1.00
N02 PPHM 1.00
03 PPHM 1.558
HN02 PPHH 1.00
N03 PPHH - COMPUTED
N205 PPHM- COMPUTED
OH PPHM- COMPUTED
R02 PPHH- COMPUTED
CO PPHM 200.6
RATE CONSTANTS -
1.00
1.00
1.00
1.558
1.00
PHOTON C N02
NO
0
OH
R02
R02
OH
OH
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
EEACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
REACTION NO.
6 03
200.0
1
2
S 03
& HC
& HC
& NO
C N02
6 NO
& N02
6 HC
PHOTON & HONO# OH 6 NO
N02 C 03 * N03 6 02
N03 6 N02 # N205
# N03
03
NO
N02 6 02
9S02
8P02
N02 & .125*OH
PAN
HN02
HN03
R02
N205
N205
N02
5 H20 f 2HN03
NO 6 N02 6 H20 f 2HN02
N02 & PARTICLES * PRODUCTS
0. 1.
1. 0
1
2
10
00
00
00
558
00
00
00
00
558
00
XKK
3
4
5
6
7
8
9
10
11
12
13
1«
15
16
BEGIN NO
200.0
^
\267
1.000E-6
100.
1.0E+3
2.0
15.
30.
ft.OOE-5
200.0
1.00
1.00
1.00
1.558
1.0
200,0
BY CODE
BY CODE
BY CODE
BY CODE
OBTAINED FHOH -UPBATE-
OBTAINED FBOM -UPR1TE-
5.0E-5
14.
60.5
(
.001
0.0
STOICHIOMETBIC COEFFICIESTS
112
-------
0 0
0 0
1. 0
0 0
1. 0
0 0
0 0
0 1.
0. 0.
0. 0.
0. 0.
0. 0.
1. 0.
0. 0. END NO
0. 0. BEGIN HYDBOCARBOH
0 0
1. 0.
1. 0
0 0
0 0
0 0
0 0
1. 0
0 0
0 0
0. 0.
0. 0.
0. 0.
0. 0.
0. 0. END HYDROCARBON
1. 0. BEGIN N02
0 1.
0 0
0 0
0 1.
1. 0
0 0
1. 0
0 0
0 0
1. 0
1. 0.
0. 1.
0 0
1. 0.
1. 0. END N02
0. 1. BEGIN OZONE
1. 0
0 0
0 0
0 0
0 0
0 0
0 0
1. 0
0 0
113
-------
1. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0. END OZONE
0. 0. BEGIN HN02
0 0
0 0
0 0
0 0
0 0
0 1.
0 0
0 0
1. 0
0 0
0. 0.
0. 0.
0. 0.
0. 2.
0. 0. END HN02
0. 0. BEGIN N03
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 1.
1. 0
0. 1.
0. 0.
0. 0.
0. 0. END N03
0. 0. BEGIN N205
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 0.
0. 1.
1. 0.
1. 0.
0. 0.
0. 0. END N205
0. 0. BEGIN OH
0 0
114
-------
0
1.
0.
0
1.
1.
0
0
0
0.
0.
0.
0.
0.
0.
0
0.
0.
1.
1.
0
0
0.
0
0
0.
0.
0.
0.
0.
1000
1010
1020
1030
10UO
1050
1100
1110
1120
1130
11UO
1150
1200
1210
1220
1230
1240
1250
1300
1310
1320
1330
13UO
1350
1400
iaio
0
0
0.125
0
0
0
0
1.
0
0.
0.
0.
0.
0.
0.
0
8.
8.
0
0
0
0
1.
0
0
0.
0.
0.
0.
0.
K1
END OH
BEGIN S02
END R02
O.U1609B 00
0.42082E 00
O.U2536E 00
O.U2967E 00
0.H3373E 00
O.H3750E 00
0.i»«09UE 00
O.UUU01E 00
O.U4665E 00
0.«"883E 00
0.45051E 00
O.U51652 00
0.35222E 00
O.U5222E 00
0.a516«S 00
O.U5050E 00
0.«t»882E 00
0.««66UE 00
O.W399E 00
O.UU093E 00
0. U37i;9S 00
0.13371E 00
O.U2965E 00
O.U253«E 00
0.U2080E 00
O.H1607E 00
END STOICHIOHETHT
115
-------
1IJ20
1430
1UUO
1«50
1500
1510
1520
1530
1550
1600
1610
1620
1630
16«0
1650
1700
LAST CAPD K1
DIFF. UPDATE
DIFF. UPDATE
DIFF. UPDATE
LAST CARD
2ND LAST CABD
BSD
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0,
0.
0.
U11175
U0553E
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116
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APPENDIX C
OUTPUT FROM DIFKIN RUN
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TECHNICAL REPORT DATA
(Please read Instructions on ttte reverse before completing)
1. REPORT NO.
EPA-450/3-75-069-6
3. RECIPIENT'S ACC£SSION>NO.
4. TITLE AND SUBTITLE
Photochemical Oxidant Modeling:
Technical Report
Volume II - Detailed
5. REPORT DATE
April 1975
6. PERFORMING ORGANIZATION CODE
7'ftolerFM. Patterson, Michael T. Mills, Ela^o P.V.Ward,
David A. Bryant, Rebecca C. Galkiewicz, Frank Record
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
GCA Technology Divjsion
Burlingtor Ruud
Bedford, Massachusetts '0-1730
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-1376
.,''O' (SORING AGENCY NAME AND ADDRESS
Environmental Protection Agency
| Office of Air and Waste Management
I Office of Air Quality Planning and Standards
I Rpg^arrh Trianle Park North Carolina ?7711
'
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
15 SUi'PLFMEN PAPY NOTES
This repcrt describes review and analysis activities which have been undertaken
to support the EPA goal of developing technical and policy guidelines for
assessing the oxidant air quality impact of highway development under the 3-C
pio'v'mq process. Separate sections discuss somewhat diverse topics, although they
ci*>: ^i] directed towards oxidant impact assessment. These sections include
of the techniques and computer models available for estimating mobile
;v!ons; (2) a brief summary of oxidant formation processes; (3) a dis-
• of the oxidant modeling activities of this project using the DIFKIN and
J-rianna photochemical models, and (4) a "test run" of the 109(j) and indirect
review guide!ines.
i' ?'
p-
l~
5
Highways
KEY WOROS AND DOCUMENT ANALYSIS
DESCRIPTORS
Automotive Emissions
Ox i riant
Oxidant
Precursors
Air Pollution Forecasting
13 DISTRIBUTION STATEMENT
Unlimited
b.lDENTIFIERS/OPEN ENDED TERMS
Photochemical Oxidant
Models
Appendix J Relationships
Linear & Non-linear
Rollback
Proportional Models
19. SECURITY CLAP* (This Report)
Unclassified
20. SECURITY CLASS (This page)
Unclassified
c. COSATI Field/Group
_ .
21 NO. OF PAGES
148
22. PRICE
EPA Form 2J20-1 (9-73)
141
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