xvEPA
United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/4-82-002
March 1982
Air
Comparison Of
Three Ozone Models:
Urban Airshed,
City-specific EKMA
And Proportional
Rollback
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EPA-450/4-82-002
Comparison Of Three Ozone Models:
Urban Airshed, City-specific EKMA
And Proportional Rollback
by
Gerald L Gipson
Air Management Technology Branch
Monitoring and Data Analysis Division
U.S. Environmental Protection Agency
Office of Air, Noise and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
March 1982
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This report is issued by the U.S. Environmental Protection
Agency to report technical data of interest to a limited
number of readers. Copies are available free of charge to
Federal employees, current contractors and grantees, and
nonprofit organizations - in limited quantities - from the
Library Services Office (MD-35), Research Triangle Park,
North Carolina 27711; or, for a fee, from the National
Technical Information Service, 5285 Port Royal Road,
Springfield, Virginia 22161.
This document has been reviewed by the Office of Air
Quality Planning and Standards, U.S. Environmental Protection
Agency, and approved for publication. Subject to clarification,
the contents reflect current Agency thinking.
Pub!ication= Number EPA-450/4-82/002
1i
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TABLE OF CONTENTS
List of Tables jv
List of Figures y
Preface vi
Executiye Summary vi i
1.0 Introduction 1
1.1 Background 2
1.2 Definition of Terms 5
2.0 Airshed Model Simulations 7
2.1 Base Case Simulations 9
2.2 Reductions in Hydrocarbon Emissions 16
2.3 Reductions in Hydrocarbons in an Area with a High HC/NO
Ratio 20
2.4 Control Strategy Predictions 20
3.0 Eval uation of Simpl e Model s 27
3.1 City-specific EKMA Application 27
3.2 Proportional Rollback Applications 31
3.3 Reductions in Hydrocarbon Emissions 31
3.3.1 Independent Model Tests 32
3.3.2 Common Basis Tests 36
3.4 Reductions in Hydrocarbons in an Area with a High HC/NO
Ratio 37
3.5 Control Strategy Predictions 40
4.0 Conclusions and Recommendations 47
5.0 References 51
iii
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LIST OF TABLES
2-1 Summary of Miscellaneous Measurements and Airshed Model Inputs.. 10
2-2 6-9 a.m. Urban Core Precursor Predictions Versus Observations... 15
3-1 Emission Reductions Needed tu Reduce Peak 03 to 120 ppm ......... 35
3-2 Emission Reductions Needed to Lower Peak 03 to 120 ppb .......... 39
3-3 Emission Reductions Needed to Lower Peak 03 to 120 ppb .......... 42
(High HC/NO Conditions)
A
iv
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LIST OF FIGURES
2-1 St. Louis Modeling Region ..................................... 8
2-2 Airshed Predicted and Observed Regional Peak Ozone ............ 12
2-3 Airshed Predictions Versus Observations of Peak Ozone ......... 13
2-4 Summary of Airshed Model Simulations .......................... 17
2-5 Airshed Model Sensitivity of Regional Peak Ozone to
Hydrocarbon Reducti ons ........................................ 18
2-6 Summary of Airshed Simulations for the High HC/NO Case ....... 21
yv
2-7 Comparison of Sensitivity to Hydrocarbon Reductions ........... 22
2-8 Summary of Airshed Control Strategy Simulations ............... 24
2-9 Control Strategy Effectiveness Compared to Uniform Emission
Reducti ons [[[ 26
3-1 Comparison of OZIPP Predicted Ozone Versus Observed Levels
and Airshed Model Predicted Levels 30
3-2 Comparison of Airshed, City-specific EKMA and Rollback to
Changes in Hydrocarbons (Independent Tests) 33
3-3 Comparison of Airshed, City-specific EKMA and Rollback to
Changes in Hydrocarbons (Common Basis Tests) 38
3-4 Comparison of Airshed, City-specific EKMA and Rollback to
Changes in Hydrocarbons (High HC/NO Base Case) 41
A
3-5 Comparison of Model Effectiveness Predictions for Control
Strategies (.Independent Tests) 44
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PREFACE
The Urban Airshed Model Simulations described in this report were
conducted by the Source Receptor Analysis Branch (SRAB) of the Office of Air
Quality Planning and Standards in EPA. The author gratefully acknowledges the
invaluable assistance of Conrad Newberry and Gerald Moss of SRAB in providing
the Airshed Modeling information needed to conduct this project. Special
thanks are extended to Dr. Edwin L. Meyer and Dr. Henry S. Cole for their
valuable comments and suggestions, and to Mrs. Carole Mask for typing and
editing.
vi
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EXECUTIVE SUMMARY
The purpose of this study has been to assess the validity of City-specific
EKMA and Rollback by comparing their predictions of control strategy effec-
tiveness with those of a more complex Eulerian Photochemical Air Quality
Simulation Model (PAQSM). In this type of analysis, the complex model is
assumed to provide the best estimates of control strategy effectiveness and is
used to simulate a number of potential control scenarios. The results are
then used to measure the performance of the simple models. An important
limitation of this study is that no absolute assurance exists that the PAQSM
provides accurate estimates of the effects of potential control programs.
Such a limitation cannot be overcome until the PAQSM is validated both in a
pre-control and a post-control situation. However, to minimize the possi-
bilities of errors in the model inputs, the PAQSM should reproduce existing
ozone patterns reasonably well (i.e., observations and model projections
should agree within some acceptable error bound).
In this particular study, a complex PAQSM, developed by Systems
Applications, Incorporated, the Urban Airshed Model, was applied to the
St. Louis Metropolitan Area using the 1976 Regional Air Pollution Study (RAPS)
data base. The model was used to simulate ozone patterns observed on specific
days in the RAPS, and then used to simulate changes in emission levels from
the base period. The effects were noted, then used to assess the ability of
the simple models to replicate these results. The major findings arising from
the study and their implications are listed below:
Finding: Neither Rollback nor City-specific EKMA reproduces Airshed
Model results under all conditions.
Implication: In particular circumstances, the use of simple models
can lead to results which are different from those found with
the Airshed Model. However, neither simple model was found to
be consistently overly or underly conservative in the predictions
of control program effectiveness.
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Finding: In estimating the level of control needed to reduce peak ozone
to 120 ppb, the difference in control levels obtained with
City-specific EKMA and those found with the Urban Airshed Model
was usually within about +10%. Most often, City-specific EKMA
control estimates were lower than the Airshed Model.
Implication: Several investigations have suggested that City-specific
EKMA is overly conservative in its predictions of emission
reductions necessary to achieve the ozor.e standard. Airshed
Model results suggest that this hypothesis is not correct.
Finding: Rollback predictions of controls needed to reduce peak ozone
levels to 120 ppb agreed with Airshed in some cases, but gave
substantially lower controls in others.
Implication: The rollback model may underpredict control requirements in
some cases. Airshed Model results imply that Rollback may
underpredict, in some cases, by as much as 20% (e.g, 40%
control versus 60%). The tendency to underpredict is most
prevalent when relatively high (e.g., 13:1) HC/NO ratios
/\
prevail.
Finding: Airshed Model predictions of peak ozone observed during the
RAPS were usually within +30% of observations. However, the
magnitude, location and timing of the regionwide predicted
peaks often differed from the observed peaks, sometimes
substantially.
Implication: While the above results are not surprising, they indicate
that procedures to account for these discrepancies in a regula-
tory application of a PAQSM need to be developed if PAQSMs are
to reach their potential for spatial and temporal resolution.
vn i
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Finding: The Airshed Model predictions of control strategy effectiveness
varied from day to day (i.e., strategies may be more effective
under some meteorological conditions than others). This same
finding applied to City-specific EKMA, and to a much lesser
extent, Rollback.
Implication: As a result of differing control effectivess predictions,
procedures for relating Airshed Model results to the statis-
tical form of the ozone standard are needed. Modeling a single
day to estimate the controls needed is not sufficient. At a
minimum, several days representing different meteorological
conditions corresponding to high ozone levels need to be
modeled.
Finding: When uniform emission reduction strategies were compared to
realistic control strategies (e.g. RACT, FMVECP), the dif-
ference in effectiveness of lowering peak ozone levels do not
appear to be substantial.
Implication: Temporal, spatial, and reactivity effects of control
strategies do not appear to substantially affect the regional
ozone peak. However, spatial and temporal patterns of ozone
throughout the region were not evaluated, so other differences
between uniform reductions and realistic control strategies may
be found.
ix
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1.0 INTRODUCTION
Detailed Photochemical Air Quality Simulation Models (PAQSMs) have been
identified as having the greatest potential for estimating the effectiveness
of control programs designed to reduce ambient levels of ozone.1 Compared to
other simpler models, the complex PAQSMs contain more detailed mathematical
respresentations of the chemical and physical phenomena leading to ozone
formation in the atmosphere. However, the complex nature of these models
generally require considerable amounts of input data and their operation is
often costly. These factors will likely preclude their use in many instances,
necessitating the use of simpler models in their place. Because PAQSMs are
generally perceived as providing the best estimate of the relationship between
ozone and its precursors, the limited number of cases in which they have been
applied can serve as one basis for evaluating the performance of simpler
models. In this study, control program effectiveness predictions of two
simple models - the City-specific Empirical Kinetic Modeling Approach (EKMA)
and Proportional Rollback - are compared to those obtained with a more complex
PAQSM.
Testing the performance of the simpler models by comparing their predictions
to those of the PAQSM as a measure of model validity has two major shortcomings.
First, when a PAQSM is applied to simulate existing (or base case) conditions,
the PAQSM model predictions may not always agree precisely with observations.
Such discrepancies could be due to errors in model inputs, observational
errors, or in the mathematical representations of the ozone forming phenomena.
More importantly, however, no guarantee exists that the more complex PAQSMs
will accurately simulate changes from base conditions, such as reduced precursor
emissions. A complete validation, as implied by this last limitation, cannot
be conducted until emission reductions are actually imposed and the observed
effects are compared with the PAQSM predictions. Unfortunately, the data base
necessary for such complete model validation does not exist. While the second
shortcoming cannot be circumvented, the first can be alleviated to some degree
by selecting for evaluation only those PAQSM results corresponding to cases in
which model predictions and observations agree reasonably well in the base
case. Even though no absolute validation is possible, the possibility of
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errors in the input data should be minimized because of the agreement between
PAQSM predictions and observations in the base case. As a result, greater
confidence is usually placed in the PAQSM predictions of control program
effectiveness for those days when the PASM simulates base case observations
accurately.
As indicated above, the primary focus of this study has been to compare
the predictions of control program effectiveness of two simple models with
those of a more complex PAQSM. The remainder of this chapter provides back-
ground information on the model applications and defines terms referred to
throughout the text. The succeeding chapters describe the details of the
model applications, the results of the model comparisons, and the conclusions
and recommendations arising from those comparisons.
1.1 Background
An indepth study has been undertaken by the U.S. Environmental Protection
Agency (EPA) to evaluate the performance of PAQSMs.2 One phase of that study
consists of applying a photochemical dispersion model developed by Systems
Applications, Incorporated - the Urban Airshed Model. In this application,
the detailed St. Louis Regional Air Pollution Study (RAPS) data base is used
to derive the inputs necessary to carry out model simulations. A number of
days have been simulated to evaluate the Airshed Model's performance in
characterizing ozone formation under conditions relevant to the data col-
lection period (1975 and 1976). In addition, a series of detailed sensitivity
tests have been conducted for three of these days in order to:
(1) identify those model inputs which most significantly affect
model predictions and subsequently require most care in data collection
efforts;
(2) isolate possible sources of error in model inputs or formulation;
(3) estimate the effects of changes in precursor emission levels on
predicted ambient ozone levels.
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The results of this last set of sensitivity tests provide the basis for
evaluating the two simpler models. Thus, the principal question addressed in
this study is whether or not the effects of emission changes predicted by the
simpler models are comparable to those predicted by the more complex Airshed
Model.
The preceding question would, at first, seem straightforward. However,
the manner in which these models are applied and the information provided by
each complicates the problem to some extent. For example, the Airshed Model
predicts pollutant concentrations, resolved both temporally and spatially
throughout the modeling region. The regional peak ozone concentration pre-
dicted by the Airshed Model may not agree with the observed peak simply
because an ozone monitor was not positioned near the location of the predicted
peak. The implications of such a disagreement with regard to regulatory
applications have not been fully resolved. Also, the Airshed Model prediction
at the site of the observed peak may be higher or lower than the measured
level. Like the aforementioned problem, procedures for incorporating these
potential discrepancies in a regulatory application have not been determined
to date. On the other hand, City-specific EKMA is by definition an empirical
approach, depending on measured levels of ozone. The effects of proposed
changes in precursor emissions (i.e., hydrocarbons and NO ) are evaluated
A
relative to the initial measured ozone level. The proportional rollback model
is essentially also an empirical type model. As with City-specific EKMA, the
base, or starting, point is determined by the measured ozone maximum. Thus,
the distinct possibility exists that the simple models and the complex PAQSM
could have significantly different base case conditions in terms of peak
ozone, since the simple models rely solely on observations while the PAQSM has
the capabilities of predicting ozone at locations for which monitoring data
are unavailable.
To circumvent this problem, two separate procedures were followed in
comparing the various models. The first entails using the Airshed Model
prediction of peak ozone as the base case, despite any discrepancies between
predicted and observed ozone peaks. On the other hand, the simple models are
used exactly as they would be applied in practice, relying entirely on the
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measured peak. The basis for comparison with these tests is the relative
change in ozone maxima from the base case. For example, if an Airshed Model
simulation was conducted in which hydrocarbon emissions were reduced by 50%,
the percent change in the predicted regional peaks is computed, and that
relative change is compared to the relative changes in peak ozone predicted by
the other models. To some degree, this puts the model predictions on a common
basis, even though they are starting from different bases. The second approach
consists of developing the inputs for the simple models on the basis of
Airshed Model simulations (i.e., the Airshed Model peak ozone would be used as
input to the City-specific EKMA and Proportional Rollback models). For
example, if the Airshed Model predicted a peak ozone level of 0.30 ppm, this
value would be used as the base, or starting, point for the siample models,
regardless of the observed peak ozone value. In effect, this procedure
involves "modeling a model," and also puts the model predictions on a common
basis.
Of the two procedures described in the preceding paragraph, the first is
the most rigorous test of the simple models' performance. For those cases in
which either of the simple models would be applied, the detailed data base
required for the Airshed Model would not normally be available, nor would the
PAQSM predictions. Thus, the results of the simple models would have to be
relied upon entirely. However, the second set of tests may help to explain
potential discrepancies between models. For example, consider the situation
in which the predicted Airshed Model peak is at a location with no nearby
ozone monitor. The sensitivity of this peak level to changes in precursor
emissions may be different from that associated with the measured peak. Thus,
even though a simple model may not agree with the PAQSM in the rigorous test,
agreement may exist if the conditions corresponding to the PAQSM predicted
peak are evaluated. Of course, a major limitation of the simpler models is
that they do not have the spatial and temporal predictive capabilities of the
PAQSMs (i.e., they rely entirely on observations). This is a limitation that
cannot be overcome.
One additional set of hypothetical tests was conducted. In the Airshed
Model sensitivity tests, the effects of increased hydrocarbon emissions were
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simulated. Because of the implementation of control programs, these tests
would not appear to be relevant for evaluating models which will be used to
establish emission reductions necessary to reduce peak ozone levels. However,
they afford the opportunity to examine the performance of the simpler models
under conditions which may be characteristic of cities with higher prevailing
HC/NO ratios than the ratios observed during the St. Louis RAPS. The pro-
X
cedure followed for these tests was to select, as the base case, the Airshed
Model simulation with the highest hydrocarbon emissions, i.e., a test in which
hydrocarbon emissions were deliberately increased above existing levels. The
two simpler models were then used to model the Airshed Model simulations, and
the sensitivity of peak ozone to changes in precursor emissions was evaluated
from this starting point rather than the observed, or true, base case. These
tests may be particularly illuminating when comparing City-specific EKMA with
Proportional Rollback. The higher emissions result in an increased HC/NO
/\
ratio, and afford an opportunity to compare the two simple models at that
higher ratio. The results of the tests have implications for the use of these
two models in areas which exhibit HC/NO ratios which are higher than those
A
found in the St. Louis area.
In viewing the model applications that follow, it must be emphasized that
this study is not intended to determine the emission reductions necessary for
the St. Louis area to achieve the ozone standard. Rather, it is intended to
compare three air quality models. It should be remembered that the RAPS data
base covers the 1975-1976 time period. A regulatory analysis for St. Louis
will require an updated data base.
1.2 Definition of Terms
Throughout the text, frequent reference is made to a number of specialized
terms and acronyms. For reference, several are defined below.
Hydrocarbons - Thir, term is somewhat of a misnomer, but is used throughout
the report for simplicity. It refers to emissions of
Volatile Organic Compounds (VOC) except methane. Ambient
concentrations of hydrocarbons refer to ambient levels of
Nonmethane Organic Compounds (NMOC).
NO - Nitrogen oxides refer to the sum of Nitric Oxide (NO)
and Nitrogen Dioxide (N02).
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Os - Ozone
RACT - This acronym is used to represent the Application of
Reasonably Available Control Technology to stationary
sources of hydrocarbon emissions.
FMVECP - This acronym represents the implementation of the
Federal Motor Vehicle Emission Control Program to
reduce emissions of vehicular sources.
PAQSM - Photochemical Air Quality Simulation Models refer to
the most detailed air quality models used to simulate
ozone formation in urban areas.
City-specific
EKMA - The term refers to a simple trajectory model used
according to the guidelines outlined in Reference 10.
Rollback - Rollback is a simple air quality model in which the
nonhackground portion of ozone is assumed to change
equally proportional with changes in hydrocarbon
emissions.
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2.0 AIRSHED MODEL SIMULATIONS
The Airshed Model is a complex PAQSM of the Eulerian (or grid) type.3 A
network of grid cells is overlaid on the region to be modeled and the physical
and chemical phenomena leading to ozone formation are mathematically simulated.
These processes include emissions of primary pollutants (i.e., hydrocarbons
and NO ) into each cell, the advection of pollutants from cell-to-cell,
X
chemical transformations of pollutants into intermediate and secondary species,
transport of pollutants into the modeling region from upwind areas, and entrain-
ment of pollutants from aloft due to growth in the mixed layer. The model
computes pollutant concentrations within each cell continuously and thus
attempts to reproduce pollutant-concentration time profiles measured at each
monitoring site within the modeling region.
In the St. Louis application,2 the area modeled is 60 km wide in the
East-West grid direction and 80 km in the North-South. The horizontal dimen-
sions of each cell are 4 km x 4 km. Each 4x4 km area is divided into four
individual cells in the vertical dimension: the bottom two cells making up
the mixed layer, and the two upper cells corresponding to the region above the
mixed layer. Pollutant concentrations initially within each cell are estimated
from available ambient measurements. Hourly emissions of primary pollutants
injected into each cell are input through the simulation period which begins
at 0500 Central Standard Time (CST) and ends at 1700 CST. The chemical trans-
formations are represented by a detailed chemical mechanism named Carbon
Bond II, which describes the NMOC-NO -03 photochemical interactions. The
/\
concentrations of pollutants transported into the modeling region are deter-
mined from measurements taken at locations outside, and upwind, of the modeling
•-egion. Finally, cell-to-cell advection is fixed by a wind field analysis
routine which resolves measured wind data into u (east-west) and v (north-
south) components within each cell.
Figure 2-1 illustrates the horizontal grid structure relative to the
St. Louis area and the monitoring network. Note that the modeling region
encompasses 21 RAPS monitoring sites from which much of the modeling inputs
are derived, as well as providing the air quality measurements necessary to
evaluate the model's performance. (Continuous measurements of wind speed,
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Figure 2-1„ St. Louis Modeling Region.
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wind direction, NMOC, NO, N02> and ozone, among others, were taken at each
site.) The outer four sites (j.e., Sites 122-125)1 are used to determine the
boundary concentrations which set pollutant transport into the modeling
region. For a more detailed discussion of the Airshed Model application, the
reader is referred to Reference 2.
2.1 Base Case Simulations
This section describes the results of applying the Airshed Model to
simulate ozone formation on three days in the St. Louis region. Recall from
Chapter 1 that the Airshed Model should reproduce observed ozone concentra-
tions reasonably well to serve as the basis for evaluating the performance of
simpler models. While a detailed evaluation of the Airshed Model's perform-
ance in the base case simulations is beyond the scope of this study, some
rudimentary analysis can provide insight into overall model performance.
The three selected days are June 7, 1976 (Julian Day 159), July 13, 1976
(Day 195) and October 1, 1976 (Day ?75). Each of these days is characterized
by different meteorological regimes, thus serving to provide a broad basis for
model evaluation and for investigating the effectiveness of control programs.
Day 159 is one of moderate transport characterized by a persistent, light
southeasterly flow. The peak one-hour average ozone concentration was
198 parts per billion (ppb), measured 3t Site 122, located well to the north
of the central urban area. Early morning concentrations of NMOC and NO
^
within the urban core were relatively high, and the estimated ozone concen-
tration above the early morning mixed layer was the highest of the three days.
On Day 195, winds were light to moderate out of the southeast. The measured
NMOC and NO concentrations in the urban core on this day were much lower than
.A
those of the other two days, while a moderate level of ozone aloft was estimated.
Nevertheless, the measured one-hour average peak ozone was the second highest
of the three days - 223 ppb at Site 114 slightly north of the urban core.
Day 275 had the highest measured ozone concentration of the three days -
244 ppb at Site 102 in the northern portion of the urban core. This day was
marked by extreme stagnation with high early morning precursor levels in the
urban core. The estimated level of ozone aloft was the lowest of the three
days. Some of the key measurements and Airshed Model inputs characterizing
the three days are summarized in Table 2-1.
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Table 2-1. Summary of Miscellaneous Measurements and Airshed Model Inputs.
Julian Day
159 195 275
Measured 03 peak, ppb 198 223 244
Site of measured 03 peak 122 114 102
Time of observed peak, CDT* 1600-1700 1600-1700 1500-1600
Measured 6-9 a.m. CDT urban 1.8 0.2 1.9
average** NMOC, ppmC
Measured 6-9 a.m. CDT urban .205 .048 .236
average** NO , ppm
^
Measured 6-9 a.m. CDT urban 7.6:1 7.0:1 8.1:1
average NMOC/NO ratio
^>
03 level aloft input to Airshed Model, ppb 114 78 63
0800-1500 CDT network average 1.0 2.3 0.6
wind speed, m/sec***
Maximum afternoon mixing height 1972 1853 529
input to Airshed Model, meters
* CDT, Central Daylight Time
** Urban averages computed from the 6-7, 7-8 and 8-9 a.m. average measured
at Sites 101, 102, 104, 105, 106, and 107. The reported NMOC/NO ratios
are the averages of the six site-ratios, and thus do not necessarily
equal the ratio of the urban average concentrations.
*** From Reference 3
10
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Of principal interest (although not necessarily the best indicator of
model performance) is the Airshed Model's prediction of regional peak ozone
concentration. Figure 2-2 was constructed to compare the model's predictions
with observations in terms of magnitude, location, and timing of the ozone
peaks. In this figure, the large rectangle indicates the modeling region while
the smaller one shows the relative location of the urban core. The location of
the Airshed's prediction of peak ozone is denoted by a "D" while the location
of the observed peak is marked by an "0". The magnitude and time of the cor-
responding peaks are shown to the right of each diagram, along with the percent
difference between observed and predicted peaks. The figure clearly indicates
significant differences between predicted and observed peaks in terms of magni-
tude and location. However, these differences are not necessarily indicative
of model accuracy. For example, the Airshed regional peak on Day 159 is at a
location with no nearby monitor, while the actual observed peak was outside the
modeling region. On the other hand, the predicted regional peak for Day 195 is
somewhat lower than the observed peak indicating a model underprediction. For
Day 275, the magnitude of the peaks is similar, although the locations are
somwhat different.
To provide a better indication of overall model performance, the Airshed
Model predictions of ozone at each of the 21 monitoring locations within the
modeling region were compared to the measured levels.* The most rigorous test
involves comparing the observed ozone peak at each site with the ozone level
predicted by the Airshed Model precisely at the time of the observed peak. The
results of this test are illustrated graphically in the left-hand portion of
Figure 2-3. When the timing criterion is relaxed, the Airshed peaks predicted
at each monitoring site are compared to the observations, regardless of the
time of the peak. These results are shown in the right hand portion of the
same figure. In each of the individual graphs in Figure 2-3, the predicted
Actually, the Airshed Model predicts cell-wide average concentrations, not
point concentrations necessary for direct comparison with monitored
pollutant concentrations. In the discussions that follow, the average
concentrations in the ground level cell corresponding to each monitor are
compared to the measured levels.
11
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DflY 159
D312 - (1700-1800 CDT)
o 198 - (1600-1700 CDT 3
PERCENT DIFFERENCE = 58X
DRY 195
CD
D
0174 - (1600-1700 COT)
0223 - 11600-nOO CDT)
PERCENT DIFFERENCE = -227.
DflY 275
0232 - (1400-1500 COT)
0244 - (1500-1600 COT)
PERCENT DIFFERENCE = -SX
ORIRSHEO MODEL PREDICTED PERK OZONE. PPB
oOBSERVED PERK OZONE. PPB
Figure 2-2. Airshed Predicted and Observed Regional Peak Ozone.
12
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(Ml KITH TINC CRITERION
(II MITMUT TIHE CRITERION
40 tO IN 100 MO 240 MO
OBSERVED OZONE. PPO
40 80 120 ISO tOO 240 ZM
OBSERVED OZONE. Pft
120 160 200 240 2BO
OBSERVED OZONE, rn
fen
40 80 120 ISO 200 Z40 280
OBSERVED OZONE. ff»
40 M 120 1M 200 140 ZOO
OMcrao OZONE, rfB
40 80 ICO 180 200 Z40 ZOO
OBSERVED OZONE, ff*
Figure 2-30 Airshed Predictions Versus Observations of Peak Ozone,
13
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ozone (y-axis) is plotted versus observed ozone (x-axis). for reference, the
45 degree line (solid.) indicates perfect agreement between predictions and
observations, while the other two dashed lines indicate over-and under predic-
tions of +30%. The results for Day 159 Indicate that most predictions are
within +30% of the observed level, regardless of the timing criterion. How-
ever, a tendency does exist for the model to overpredict ozone levels on this
day, especially at those sites with higher measured levels. On the other hand,
the Airshed Model tends to underpredict ozone levels on Day 195, even though
most predictions are within 30% of the observed levels. On Day 275, the most
rigorous test indicates a tendency to overpredict at the observed low ozone
levels, and underpredict at the higher levels. When the timing criterion is
relaxed, the agreement is improved somewhat, but underpredictions at locations
with high measured ozone are still prevelant.
A detailed evaluation of model performance should, to the extent possible,
address how well the model reproduces concentrations of precursor patterns as
well as ozone patterns. While such an analysis is beyond the scope of this
study, particular attention was focused on the Airshed Model's predictions of
early-morning, urban core concentrations of NMOC and NO because of the impli-
J\
cations for applying City-specific EKMA. (The latter will become evident in
the next chapter.) Airshed Model predictions of these precursors are compared
to observed levels in Table 2-2. General patterns of precursor levels were
reproduced by the Airshed Model (e.g., high precursor levels on Days 159 and
275, low levels on Day 195), although a tendency for underprediction was
found. (Note that in every case, NO is underpredicted, ranging from 14% to
J\
42%, while on two of the days NMOC levels were underestimated.) The urban
core, 6-9 a.m. NMOC/NO ratios predicted by the Airshed Model agree reasonably
well with the ratios derived from ambient measurements.
The elementary analyses just described were not intended to assess
conclusively the accuracy of the model, nor do they provide a complete picture
of model performance. However, they do give some insight into how well the
model is reproducing observed ozone levels. In most instances, predicted ozone
levels at the locations of monitoring sites are within +30% of the observations.
Except for Day 159, the model tends to underpredict the higher ozone levels.
14
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Table 2-2. 6-9 a.m. Urban Core Precursor Predictions Versus Observations*
Day 159
Predicted Observed
6-9 a.m. CDT urban average NMOC, ppm 1.2 1.8
6-9 a.m. CDT urban average NO , ppm .152 .205
J\
6-9 a.m. CDT urban average NMOC/NOV 8.1:1 7.6:1
A
Day 195
6-9 a.m. CDT urban average NMOC, ppm 0.2 0.2
6-9 a.m. CDT urban average NO , ppm .028 .048
/\
6-9 a.m. CDT urban average NMOC/NOV 8.9:1 7.0:1
/\
Day 275
6-9 a.m. CDT urban average NMOC, ppm 1.5 1.9
6-9 a.m. CDT urban average NO , ppm .203 .236
rt
6-9 a.m. CDT urban average NMOC/NO 7.7:1 8.1:1
y\
Urban average NMOC and NO levels are computed from the 6-7, 7-8, and
8-9 a.m. averages at Sites 101, 102, 104, 105, 106, and 107 and the cells
corresponding to those sites. The NMOC/NO ratios are the average of
6-9 a.m. ratios at the individual sites (cells), thus, they do not neces-
sarily equal the ratio of the urban average concentrations.
15
-------
On Day 159, the model is biased towards overprediction at most monitoring
sites. Finally, a tendency exists for the model to underpredict the early-
morning precursor levels within the urban core.
2.2 Reductions in Hydrocarbon Emissions
A number of sensitivity tests were conducted to assess how the Airshed
Model predictions of ozone respond to changes in hydrocarbons. These tests
were designed primarily to indicate the potential effectiveness of hydrocarbon
emission reduction programs. To establish this sensitivity over a wide range,
reductions of 17%, 42% and 75% (from base levels) were tested. These reduc-
tions were applied uniformly to area and point source hydrocarbon emissions, as
well as to initial NMOC concentrations.* Boundary conditions were held constant
in all simulations.
Figure 2-4 summarizes the sensitivity results with respect to changes in
the predicted peak ozone levels. The large boxes represent the modeling
region, with the inner ones indicating the urban core. One diagram is included
for each simulation. On each, the location, time and magnitude of the pre-
dicted peak ozone are shown. The first apparent result is that ozone levels
are lowered by reductions in hydrocarbons, although the relative changes are
different. For example, a 75% reduction in hydrocarbons gives ozone reductions
between 41 and 64%, depending on the particular day. Secondly, the results
indicate that, as hydrocarbons are reduced, the peak ozone level is likely to
occur slightly farther from the city (i.e., farther downwind). Although not
universally true, the peak often occurs later in the day under an emission
reduction scenario.
Figure 2-5 illustrates the simulation results in another way. First,
regional peak ozone concentrations are graphed against hydrocarbon reductions,
followed by a graph showing the relative change (i.e, percent change) in ozone
Clearly, a realistic control program would not produce uniform emission
reductions. However, the uniformity assumption makes the task manageable
and eliminates the need to make some arbitrary assumptions regarding point
versus area reductions. More realistic control strategies are discussed
in Section 2.4.
16
-------
DRY
a
a
312 PPB
17-18 COT
ftHCsOZ
o
D
299 PPt
17-18 COT
«HCc-m
onr
a
a
174 PPB
18-17 COT
AHCzOZ
a
a
169 PP8
17-18 COT
•HC=-I7Z
Dflr
D °
232 PPB
14-16 COT
*HC=OX
D a
209 PPB
10-17 COT
•MCs-ITZ
159
a
D
216 PFB
18-19 COT
«HCr-42X
195
a
D
139 rra
17-18 COT
•HCs-42X
275
a
D
149 PPB
18-17 COT
»MCx-4«
1
D
138 PPB
15-18 COT
•HC*-7SX
a
D
103 PPB
17-18 COT
•HC=-75X
O
D
83 PPB
18-17 COT
•MCs-TSt
O : LOCUTION OF PRE01CTED PERK OZONE
Figure 2-4. Summary of Airshed Model Simulations,
17
-------
I I I I I I 1 I I I
10 20 30 40 60 60 70 80 90 100
HTOROCMBON REDUCTION. X
OOflY 159 A DRY 195 O OHY 275
10 20 30 40 SO 60 70 BO 90 100
HrOftOCMMN KOUCTIOH. J
Figure 2-50 Airshed Model Sensitivity of Regional Peak Ozone to
Hydrocarbon Reductions.
18
-------
accompanying the hydrocarbon reductions. In this last figure, a line of unit
slope is included as reference to indicate equally proportional changes between
hydrocarbons and ozone. The figures clearly indicate that the 03 response to
changes in hydrocarbon is different for each day. The Airshed Model predicts
the largest incremental changes in 03 on Day 275, while Day 195 exhibits the
least response. In every case, the percent change in ozone predicted by the
model is less than the percent change in hydrocarbons input to the model.
These findings are not entirely surprising since boundary conditions remained
unchanged in all simulations, and one might expect the importance of boundary
conditions to vary from day to day.
In examining Figure 2-5, one may be tempted to estimate the emission
reduction necessary to achieve the National Ambient Air Quality Standard
(NAAQS) for ozone. Several caveats are in order. Recall that in the base case
simulations, the Airshed Model predictions of regional peak ozone did not agree
precisely with measured peaks. In fact, a large difference existed on Day 159.
Whether or not the Airshed Model's predicted peak would be accepted as the
basis for a regulatory application has not been resolved. Secondly, as described
above, boundary conditions were not changed in any simulation. In a regulatory
application, some estimate of future boundary conditions might be factored into
the model applications (e.g., the level of ozone aloft might be altered to
reflect the implementation of upwind control programs in the future).1 Finally,
the modeling results provide information on how the 03 levels change on any
particular day. To establish the control needed to meet the ozone standard,
this information must be related to the statistical form of standard. The
latter allows, on average, one maximum daily value to be greater than 0.12 ppm
at each site during a year.4 Procedures for relating the model output to the
form of the standard are being explored, but have not yet been prescribed.
Thus, the information presented in Figure 2-5 could not necessarily be used to
estimate reduction in hydrocarbon emissions needed to achieve the ambient ozone
standard.
19
-------
2.3 Reductions in Hydrocarbons in an Area With a High HC/NO.. Ratio
1 ' ------ -i - j^ i
Previous studies have suggested that the NMOC/NO ratio is an important
-XV
factor in ozone formation potential.5,6,7 In the base case simulations pre-
viously described, observed early-morning urban core NMOC/NO ratios were about
J\
8:1, while the molar ratio of hydrocarbon emissions to NO emissions is on the
x
order of 6:1. Evidence exists to suggest that higher NMOC/NO ratios may be
J\
prevalent in some urban areas.8 Consequently, a sensitivity analysis was
formulated to evaluate the effectiveness of hydrocarbon emission reductions
starting from a base point with higher NMOC/NO ratios. For each day, an
A
Airshed Model simulation was conducted in which hydrocarbon emissions and
initial concentrations were increased by 67%, giving an emission ratio of 10:1
and ambient ratios around 13:1. These simulations were in turn used as base
cases, and the previously described simulations were evaluated as if they
reflected reductions from this new, hypothetical base case.
The simulation results are summarized in Figure 2-6. Most findings are
comparable to results of the previous section (see Figure 2-4). As hydro-
carbons are reduced, ozone is reduced, although the reductions in ozone are
less than the hydrocarbon reductions input to the model. Peak ozone levels
predicted by the model tend to occur farther downwind and later in the day,
commensurate with higher reductions. Differences in responses of peak ozone
levels to changes in hydrocarbon emission reductions under the high HC/NO
A
ratio scenario versus the lower ratio conditions are highlighted in Figure 2-7.
Clearly, the model response or. two days is quite similar, while that on Day 159
is markedly different. In the latter case, the higher ratios imply that a
greater hydrocarbon reduction is required to achieve a comparable relative
change in ozone levels. Stated another way, the model is less sensitive to
changes in hydrocarbons than in the lower ratio case. Obviously, this is not
true for Days 195 and 275.
2.4 Control Strategy Predictions
The emission changes described in the previous two sections were hypothetical
in nature, i.e., changes were made uniformly throughout time and space. While
providing useful information, they are not truly indicative of realistic control
20
-------
DRY 159
°D
326 PPB
14-16 COT
•HC«OX
(3
211 PPB
18-17 COT
•HC=OX
D°
sat pfB
13-14 COT
o
D
312 PPB
17-18 COT
*HC«-40X
0
D
174 PPB
16-17 CDT
•HC=-40>
D °
232 PPB
14-16 COT
a
D
2BB PPB
17-18 COT
*HC*-60Z
DRY 195
a
D
169 PPB
17-18 COT
«HCi-60X
DRY 275
a o
209 PPB
18-17 COT
a
D
218 PPB
18-19 COT
•NCs-68X
a
D
139 PPB
17-1B CDT
*HCr-66X
a
D
149 PPB
16-17 COT
1
D
139 PPB
16-16 COT
*NCr-B6I
O
103 PPB
17-18 COT
•HC=-8SZ
a
D
83 PP8
18-17 CDT
•MC=OX
*HCr-4«
•HCS-60X
•MC=-66t
•MCr-86*
O s LOCATION OF PtCOlCTED PERK OtONE
Figure 2-6, Summary of Airshed Simulations for the High HC/NO Case,
n
21
-------
20 40 BO SO 100
HYDROCARBON REDUCTION. X
D TRUE BOSE CfiSE
HC/NOX BRSE CHSE
40 60 80 100
MTOROCMBON REDUCTION, 2
20 40 60 80 100
HVOROCIMBON REDUCTION. Z
Figure 2-7. Comparison of Sensitivity to Hydrocarbon Reductions,
22
-------
strategies, which inherently contain spatial and temporal nonuniformities. A
series of three strategies have been simulated for each day to assess the
effectiveness of typical control strategies.9 The first strategy consists of
applying Reasonably Available Control Technology (RACT) to stationary sources
of hydrocarbon emissions identified in EPA's Control Techniques Guidelines.16
A second control strategy was designed to simulate the effects of the Federal
Motor Vehicle Emission Control Program (FMVECP). In this strategy, the hydro-
carbon emissions from mobile sources alone were reduced to levels anticipated
in 1987, as a result of vehicular control. Note that in the FMVECP strategy,
only hydrocarbon emissions were changed (i.e., NO remained fixed). Finally, a
J\
combined RACT and FMYECP strategy was simulated.
Figure"2-8 contrasts the effectiveness of the control strategies in
reducing regional peak ozone levels with the results of the uniform reduction
scenario. Overall regional reductions in hydrocarbon emissions for the RACT,
FMVECP and combined strategies were 22%, 34% and 56%, respectively. Note,
however, that these reductions affect the spatial and temporal distribution of
emissions, as well as the reactive mix of emissions. For pxample, the FMVECP
strategy affects mobile source emissions only, and these emissions vary through-
out the day. Furthermore, in the FMVECP strategy, some of the more reactive
species are estimated to be removed to a greater extent than some of the less
reactive constituents.* Thus, one may not expect the realistic control strategy
to agree with a uniform emission reduction simulation in which hydrocarbons
were reduced by a comparable amount. Somewhat surprisingly, however, the
realistic control strategies tend to fall in line with the uniform reduction
scenarios in most instances. Some shifting in the location and timing of the
The Carbon Bond II Mechanism contains five classes of reactive hydrocarbons
- Paraffins, Ethylene, Olefins, Aromatics, and Carbonyls. The estimated
effects of the FMVECP give larger reductions in Ethylene and Olefins than
for any of the other constituents. Furthermore, it should be noted that
some approximations were made in determining changes in area source
emissions resulting from application of RACT and FMVECP. While these
approximations may tend to smear the spatial and temporal effects somewhat,
they would almost certainly be made in any application of the Airshed
Model.
23
-------
D
itt.
•HCxOZ
<
D
,1??,
kHCs-!7X
•ACT
o
0
ii™7
»HC>-22X
ur
1 1 1 B0
FHVECP
O
0
itfi
iMC*-34X
i
a
216
16-19
kHCr-42X
BftCT
FHVECP
i
0
167
14-16
>HC>-SBX
4
1
0
139
16-16
kHC=-75X
DflT 195 „„
.D
174
16-17
•NC=OX
D '
292
14-16
.
D
175*6
.NC=-17X
D .
209
16-17
RflCT
a
0
188
16-17
.HCX-22X
WCT
0 .
214
16-16
01
rnvECP
a
0
18-16
•
D
17?16
FHVECP
"
D
121
16-17
•
D
103
17-18
•HCs-34X »HC«-42X «HC=-68X *HC=-76X
IT 275
FHVECP
a
0
160
16-17
o
0
149
16-17
RflCT
FNVECP
0 "
120
15-16
a
a
63
16-17
«HCs-17Z *HCs-22X «HC»-S4X «HCr-42Z
«HC=-75X
a = LOCUTION OF PREDICTED PEAK OZONE
Figure 2-8. Summary of Airshed Control Strategy Simulations,
-------
peaks is noted, but these differences are usually small. (Note, however, this
conclusion may not apply to the temporal and spatial distribution of ozone
levels throught the region.) To examine how changes in the magnitude of the
peak compare to the uniform reduction scenarios, Figure 2-9 was prepared. In
these graphs, the Airshed Model sensitivies to uniform changes in hydrocarbons
(described in Section 2.3). are reproduced. Super-imposed on these graphs are
the RACT, FMVECP and combined strategy results (note that the three points are
not connected because they represent unique and distinct points). Again, a
line of unit slope is included as reference to show equally proportional
reductions. Although some differences do exist, the points generally follow
the same patterns as found with the uniform strategies. The results do suggest
the FMVECP strategy is usually more effective than the RACT strategy when
applied independently. This would suggest that control of area source emis-
sions would be of most benefit in reducing peak ozone levels. However, the
model indicated that control of point source emissions results in significant
additional reductions in ozone when implemented in conjunction with mobile
source control.
25
-------
• 20 40 60 M 100
HYOR0CRRMN REDUCTION. X
f I I I I I I
20 40 60 80 100
HYDROCARBON REDUCTION. X
DRBCT
A FMVECP
O RfiCT + FNVECP
(CURVE s UNIFORM EMISSION
REDUCTIONi SOLID LINE = 4S •
LINE)
20 40 60 M 100
HYOROCRRBON REDUCTION. X
Figure 2-9. Control Strategy Effectiveness Compared to Uniform
Emission Reductions»
26
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3.0 EVALUATION OF SIMPLE MODELS
The Airshed Model results described in the previous section provide the
ground work necessary for evaluating the two simpler models: City-specific
EKMA and Proportional Rollback. The Airshed Model was used to predict how
peak ozone levels respond to changes in hydrocarbons for a number of control
scenarios. The question to be addressed now is whether or not the simple
models predict similar effects. Before directly addressing this question,
however, a brief discussion of the two simpler models is needed in order to
clarify some of the comparisons to be made.
3.1 City-specific EKMA Applications
The City-specific EKMA technique has been described in numerous
publications, but will be briefly discussed here for background.1,10 This
model uses a LaGrangian, or trajectory, concept. A well mixed column of air
initially containing pollutants is envisioned to travel with the wind to the
site of peak ozone concentration. As the column moves with the wind, emissions
are injected into the column, the height of the column rises in accordance
with the diurnal variation in mixing height (resulting in pollutant dilution
within the column and entrainment of pollutants from aloft), and the HC-NO -03
/v
photochemical interactions are simulated by means of a chemical mechanism.*
By deriving the model inputs consistently with this concept, the model will
simulate the formation of ozone within the column, and calculate the resultant
peak one-hour average level occurring during the simulation period. Note that
the trajectory model does not have the capability of reproducing overall
spatial ozone patterns unless many trajectories are simulated separately.
The heart of the City-specific EKMA technique is the Ozone Isopleth
Plotting Package (OZIPP)11,12 computer model which incorporates, in mathemati-
cal form, the concepts described above. Inputs to the model can be divided
into six categories: trajectory, sunlight intensity, dilution, pollutant
This chemical mechanism is not the same one incorporated in the Airshed
Model.
27
-------
transport, post-0800 emissions, and reactivity. Procedures for estimating the
proper inputs have been described in Reference 10. and were .used in this
study. They are briefly summarized below:
1) Trajectory - the column of air originates in the center city and
begins moving at 0800 CDT towards the site of peak ozone at a uniform speed;*
2) Sunlight Intensity - light intensity is calculated internally in
the model from inputs on city location and simulation date;
3) Dilution - dilution is determined from mixing height measurements
(early .morning and maximum afternoon);
4) Pollutant Transport - level of ozone aloft was set to be the
same as that used in the Airshed Model simulations;
5) Post 0800 Emissions - emissions derived from countywide emission
inventories and the assumed trajectory;
6) Reactivity - all values recommended in Reference 10 were used
(i.e., no changes in reactivity were evaluated with EKMA).
By estimating the above inputs and specifying the pollutant concentrations
initially within the "column," the OZIPP model will compute the peak ozone
concentration occurring in the column. This level can be compared to the peak
measured at, or near, the end of the trajectory for a crude measure of the
OZIPP model's capability to simulate accurately the ozone formation processes.
For illustration, these absolute predictions have been made and three sets of
comparisons performed. In the first, all OZIPP inputs are derived in accordance
with the monitored peak on each day (i.e., the trajectory leads to the site of
the measured peak) and the OZIPP prediction is compared to the observed peak.
In the second set, all OZIPP inputs are derived according to Airshed Model
predictions of peak concentration (i.e., the trajectory leads to the location
This assumption corresponds to that used in the Level III EKMA delineated
in Reference 13.
28
-------
of the Airshed Model's predicted peak) and the OZIPP predicted peak ozone is
compared to the Airshed Model prediction. The last set of simulations is
similar to the second, except that the hypothetical, high HC/NOX, base case is
used for comparison. In the last two sets of simulations, care was taken to
replicate Airshed Model conditions as closely as possible in deriving OZIPP
inputs. Thus, in effect, OZIPP is being used "to model a model."
Figure 3-1 illustrates the results. The top graph compares OZIPP
predictions with observations on each of the three days. The bottom graph
shows the OZIPP/Airshed Model comparisons. In all cases, the OZIPP predictions
were within +30% of the observed levels or Airshed predictions, indicating
relatively close agreement. However, it must be emphasized that this type of
test is a very crude evaluation measure. For example, no assurance exists
that the early-morning, urban core precursor cloud is directly responsible for
the peak observation (i.e., the assumed trajectory may be incorrect). Thus,
the possibility exists that the relatively good agreement is simply fortuitous.
While predictions of peak ozone can be made with OZIPP, the major function
of the model is to generate ozone isopleth diagrams from which estimates of
required controls are made, or from which the effectiveness of proposed control
programs can be estimated. On such a diagram, peak concentrations of ozone
are plotted in the form of isopleths versus initial NMOC and NO concentrations.
A
These isopleths represent the model's predicted response of ozone peaks to
changes in levels of hydrocarbons and NO . Because of the simplifying assump-
A
tions in the OZIPP model and the likelihood that the model predictions may not
agree precisely with observations, the starting point on the diagram is estab-
lished from measured 03 and NMOC/NO values (i.e., this has the effect of
A
calibrating the model to existing conditions.) Control strategy effectiveness
is then evaluated relative to the starting point on the diagram (e.g., changing
HC X% and NOV Y% will change ozone by Z%). In the City-specific EKMA applica-
A
tions discussed in the next few sections, the ozone peak and the NMOC/NO
J\
ratio used to establish the starting point on the isopleth diagram are derived
from ambient measurements or from Airshed Model output, as appropriate.
29
-------
S3
ii
Is
40 80 120 160 200 240 290 320 980
OBSERVED OZOMC. PPB
S'
>-1
U I
O TRUE MSE CASE
« MIOH NC/NOX BRSE CRSE
0 40 SO 120 160 200 240 26O 520 380
AIRSHCO PKCOICTEO OIONE. PT8
Figure 3-1. Comparison of OZIPP Predicted Ozone Versus Observed Levels
and Airshed Model Predicted Levels.
30
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3.2 Proportional Rollback Applications
The Proportional Rollback model is based on the concept that ozone
concentrations change proportionally with hydrocarbon emissions. The model
makes predictions in a relative sense in that changes in emissions are related
to changes in air quality. Unlike OZIPP or the Airshed Model, Rollback cannot
be used to raake absolute predictions in a base case situation. The form of
the Rollback Model considered here is:
AHC
, t(fl3)E - B] - I(Q3)F - B]
(03)E - B -
where
AHC = fractional change in hydrocarbon emissions
COS)E = existing 03 level
(03)F = future 03 level
B = background 03 level
In the calculations described below, background ozone was always assumed to be
1/2 of the base case level of ozone aloft on the particular day in question.
Note that inclusion of a constant background ozone results in ozone changes
which are less than changes in hydrocarbons (i.e., a 1:1 relationship does not
exist). The factor of "1/2" was based on the work described in Reference 8.
As with the City-specific EKMA applications, the existing ozone concentrations
used correspond to either measured levels or Airshed Model peak predictions,
as appropriate.
3.3 Reductions in Hydrocarbon Emissions
Section 2.2 described the results of the Airshed Model simulations in
which hydrocarbons were reduced uniformly from base conditions. These simula-
tions have been replicated, to the extent possible, with the two simple models.
Two different sets of comparisons, along the lines described infection 3.1,
are made. The first involves comparing the models exactly as they would be
applied, without trying to compensate for known model differences. For example,
the base case ozone peaks used in City-specific EKMA and Proportional Rollback
are measured levels, while base case peaks used with the Airshed Model are the
31
-------
regional peaks predicted by Airshed. Other known differences in model inputs
exist between Airshed and EKMA also (e.g., NMOC/NO ratio, dilution rate,
J\
etc.). This is a very severe test and provides information on the model
predictions obtained under independent applications.
The second set of comparisons entails resolving known differences in
model inputs to eliminate this as a potential source of discrepancy between
models. In effect, the simple models were made to replicate, as closely as
possible, the conditions in the Airshed Model. Another way of looking at this
set of comparisons is that the simple models are being used to model the more
complex Airshed Model. Such tests may provide some information regarding
potential discrepancies found in the first set.
3.3.1 Independent Model Tests
Figure 3-2 shows the models' sensitivity to changes in hydrocarbons
for each day. The first apparent result is that relationships between model
responses differ according to the day being modeled. First, consider Day 159.
Rollback and Airshed agree reasonably well, while City-specific EKMA is more
conservative over most of the range of emission reductions (i.e., City-specific
EKMA predicts a smaller reduction in ozone for a given hydrocarbon reduction).
On Day 195, Airshed and City-specific EKMA agree reasonably well up to a 40%
reduction in emissions. In this range, both models are substantially more
conservative than rollback. At reductions greater than 40%, City-specific
EKMA becomes substantially more sensitive than Airshed to incremental changes
in hydrocarbons. On Day 275, Rollback and Airshed agree very well. While the
agreement between Airshed and City-specific EKMA is reasonably good up to a
hydrocarbon reduction of 40%. Again City-specific EKMA exhibits a greater
sensitivity to HC changes at the higher reductions. These findings demon-
strate that neither simple model reproduces the Airshed Model results over all
conditions. For two days, Proportional Rollback is in closer overall agree-
ment with the Airshed Model than is City-specific EKMA, but significant
differences between Airshed and Rollback occur for one day (Day 195).
While the overall model responses are an important consideration, a
major function of any of these models is to determine how much control is
32
-------
20 40 60 M 100
HTDROCMaON REDUCTION. Z
DRY 195
i i ' i i i i i
20 40 80 80 100
HYDROCARBON REDUCTION. X
•RIRSHED HOOEL
•CITY SPECIFIC EKHR
ROLLBACK
ZO 40 60 N 100
HTOROCIM80N REDUCTION. X
Figure 3-2, Comparison of Airshed, City-specific EKMA and Rollback to
Changes in Hydrocarbons (Independent Tests).
33
-------
needed to achieve the NAAQS for ozone. As briefly discussed in Chapter 2,
this question cannot be directly answered here. However, each of the .models
can be used to estimate the individual emission reductions necessary to lower
the peak ozone levels on each day to 120 ppb.* (Presumably, data such as
these would be used in fixing the overall degree of control needed to achieve
the ozone NAAQS.) Table 3-1 summarizes the results, assuming an independent
application of models. Two sets of Airshed Model predictions are presented in
the table. In the first, the Airshed Model results are used as is, regardless
of discrepancies between observed and projected peaks. Shown in parenthesis
is the second case which corresponds to a simplistic method for calibrating
the Airshed Model to base case measurements, here, the Airshed Model response
curve (Figure 3-1) is used to estimate the hydrocarbon reduction that will
give the same percent change in ozone necessary to lower the measured peak to
120 ppb. In the first case, City-specific EKMA predictions, when compared to
those of the Airshed Model, are lower, about the same, and lower on Days 159,
195 and 275, respectively. For those days in which City-specific yielded
lower controls than Airshed, the differences are on the order of 10%.** For
this same situation, applying Proportional Rollback results in controls that
are lower, about the same, and about the same as the Airshed Model. However,
the difference in predicted controls on Day 159 is on the order of 20%.**
For the simplistic calibration of the Airshed Model to base conditions,
the comparisons lead to different results. In this case, City-specific EKMA
estimates of control are higher, lower, and lower than those of the Airshed
Model, while Rollback gives estimates that are about the same, lower, and
* It should be emphasized that in all calculations of hydrocarbon reductions,
the boundary conditions were fixed at the base levels. In a true regu-
latory appion, some consideration may be given to altering boundary
conditions to reflect the impact of upwind control programs. This is
most important with respect to ozone aloft, especially with the simple
models.
** On Day 159, the control needed to reduce the Airshed predicted peak of
120 ppb is outside the range of tests, and thus the difference could be
soraewJiat greater.
34
-------
Table 3-1. Emission Reductions Needed to Reduce Peak 03 to 120 ppb.
i
City-specific
Day Airshed* EKMA Rollback
159 Base case peak 03, ppb 312 198 198
03 reduction necessary to 62% (39%) 39% 39%
lower peak to 120 ppb
HC reduction necessary to
_ lower peak to 120 ppb
| >75% (52%)
I
66%
56%
195 Base case peak 03, ppb
03 reduction necessary to
lower peak to 120 ppb
HC reduction necessary to
lower peak to 120 ppb '
^ |
275 Base case peak 03, ppb
03 reduction necessary to
lower peak to 120 ppb
HC reduction necessary to ,
lower peak to 120 ppb
174
31% (46%)
60% (>75%) : |
1
232
48% (51%)
57% (59%) '
i
1
223
46%
62%
244
51%
47%
223
46%
1 56%
1
1
244
51%
! 59%
1
The HC reductions in parenthesis refer to the control needed to lower
ozone by the same percent as the other two models.
35
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about the same. Once again, when City-specific EKMA over or underpredicts the
controls (as compared to Airshed), the differences are on the order of 10%.
The single rollback prediction that differs from the Airshed Model estimate is
lower by at least 20%.
The results just described are similar to the findings regarding the
comparisons of overall model response, i.e., the comparisons yield mixed
results. However, some important points should be made. When Rollback gave
results that were different from Airshed, the estimated controls were lower by
as much as 20% (i.e., the difference in controls was 20). On the other hand,
differences between City-specific EKMA and the Airshed Model were usually on
the order of £10%. City-specific EKMA yielded significantly higher controls
than Airshed in only one case (14% higher). Thus, based on these few compari-
sons, neither simple model would appear to consistently and significantly
overestimate the hydrocarbon emission reduction necessary to lower peak ozone
to the level of the standard.
3.3.2 Common Basis Tests
As described above, the comparisons of Section 3.3.1 were made on
the basis of independent applications of the three models. In this section,
comparisons of the models are made using the Airshed Model simulations as the
basis, i.e., the simple models are made to replicate the Airshed base case
situation as closely as possible. In this case, the Airshed Model predicted
peaks are input to Rollback and City-specific EKMA. In addition, the NMOC/NO
/\
ratio input to City-specific EKMA was altered to agree with 6-9 a.m. ratio
predicted by Airshed for the "urban core" (see Table 2-1). Additionally, the
OZIPP program was modified to reflect more precisely the growth in mixing
height embedded in the Airshed Model, and NMOC and NO concentrations aloft
-/\
corresponding to Airshed Model inputs were input to City-specific EKMA.*
Presumably, these modifications put the models on a more common basis and
allow for more direct comparison of model results, mitigating possible
discrepancies due to differences in model inputs.
The mixing height growth in Airshed is based on a piecewise linear fit to
measured mixing heights. NMOC and NO levels aloft were on the order of
.05 ppraC and .003 ppm, respectively.
36
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Figure 3-3 depicts the relative changes in peak ozone as a function
of changes in hydrocarbons. The results are not entirely different from those
of the preceding section. However, the overall agreement between Airshed and
City-specific EKMA is improved somewhat. Airshed and Rollback still agree
fairly well on Days. 159 and 275, although Rollback now exhibits greater
sensitivity to changes in hydrocarbons at the lower emission reductions on
Day 159. On this same day, City-specific EKMA's sensitivity is less than
Airshed's, up to a control level of about 65%.
Table 3-2 summarizes the individual emission reductions predicted by
each model which are necessary to lower the Airshed Model's peak ozone levels
to 120 ppb. City-specific EKMA gives lower controls than Airshed in all three
cases. The largest difference is 9%, although the possibility exists for a
larger difference on Day 159. Rollback agrees fairly well with Airshed except
for one case in which Rollback's control estimate was lower by 20% than Airshed
(i.e., 40% versus 60%). Once again, neither simple model consistently
overestimates controls needed to reduce peak ozone levels to 120 ppb.
These findings suggest that the model differences found in the
previous sections are not entirely due to discrepancies in model input. Even
when the models are put on a common, basis, differences do occur. Neither
simple model can reproduce the Airshed Model predictions over all conditions
considered, but neither simple model consistently overestimates the hydro-
carbon reductions necessary to reduce peak ozone levels to 120 ppb. While
Rollback performs adequately in many cases, the potential for significant
underprediction in some instances can be inferred. While City-specific EKMA
does not always agree with the Airshed Model, substantial over- or underpre-
dictions are not indicated with regard to the degree of control needed to
lower peak ozone to the level of the standard.
3.4 Reductions in Hydrocarbons in an Area With a High HC/NO Ratio
~~~~————— x
Recall from Section 2.3 that a special set of simulations was conducted
with the Airshed Model in which hydrocarbons were increased by 67%. These
simulations were in turn used as hypothetical base cases to evaluate control
program effectiveness at potentially high HC/NO conditions on each of the
37
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DRY 159
« 20 40 80 80 100
HTMOCMBON RCOUCTION.
•AIRSHED nOOEL
CITY SPECIFIC EKHfl
ROLLBACK
j» 40 80 00
HTMOCHRMII IKBUCTIOM*
100
1
Figure 3-3. Comparison of Airshed, City-specific EKMA and Rollback to
Changes in Hydrocarbons (Common Basis Tests).
38
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Table 3-2. Emission Reductions Needed to Lower Peak 03 to 120 ppb.
i
City-specific
Day
159 Base case peak 03, ppb
03 reduction necessary
to lower peak to 120 ppb
HC reduction necessary to
lower peak to 120 ppb
195 Base case peak 03, ppb
03 reduction necessary to
lower peak to 120 ppb
HC reduction necessary to
lower peak to 120 ppb
275 Base case peak 03, ppb
Reduction in 03 necessary
lower peak to 120 ppb
HC reduction necessary to
lower peak to 120 ppb
Airshed
312
62%
f >75%
1
174
31%
1 60%
I
232
to 48%
r 57%
1
EKMA
312
62%
! 72%
\
174
31%
! 51%
i
232
48%
1 50%
1
Rollback
312
62%
! 75%
i
174
31%
| 40%
1
232
48%
i 56%
1
39
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three days considered. These evaluations were reproduced with the simple
models to investigate comparability under these conditions. (All simple model
inputs were made comparable to the Airshed simulations.) Recall from the
previous discussions that little difference in Airshed sensitivity was found
except for Day 159-. Figure 3-4 compares the Airshed Model sensitivity to
those of the two simple models. Once again, neither of the simple models
adequately reproduces the Airshed Model results in all cases. City-specific
EKMA agrees fairly well on Day 159 but exhibits greater sensitivity on Day 195
and less on Day 275. Rollback's agreement with Airshed is relatively good on
Day 275, but exhibits greater sensitivity on the other two days.
Table 3-3 presents the HC controls necessary to reduce each peak to
120 ppb. In this hypothetical scenario, City-specific EKMA and Airshed agree
fairly well except for Day 195, in which the controls estimated with City-
specific EKMA are lower by about 10% (i.e., 63% versus 75%). On the other
hand, the estimates obtained with Rollback are all lower than those found with
Airshed, with differences ranging from 5% to 22%. Consequently, under the
high HC/NO ratio scenario, Rollback tends to give control requirements that
J\
are less than those of Airshed, while City-specific EKMA follows the Airshed
results more closely.
3.5 Control Strategy Predictions
In Section 2.4, three realistic control strategies were simulated: the
application of Reasonably Available Control Technology (RACT) to stationary
sources; the effect of the Federal Motor Vehicle Emission Control Program
(FMVECP); and the combination of the two. Regional emission reductions were
about 22%, 34% and 56%, respectively. In this section, the predictions of
strategy effectiveness obtained with the simple models are compared to those
found with the Airshed Model. Recall that only Airshed has the capability of
considering the spatial, temporal and reactivity effects found with these
types of control programs. It is useful, however, to examine how the predic-
tions of the simple models compare to those of Airshed for these more realistic
control strategies. In the comparisons, two bases are again used: (1) the
model applications are independent, and (2) the models are put on a common
basis (i.e., inputs to all models are made comparable to the extent possible).
40
-------
§
§
s
DRY 159
20 40 BO BO 100
HYDROCARBON REDUCTION. X
AIRSHED HOOEL
CITY SPECIFIC EKHfl
ROLLBACK
-------
Table 3-3. Emission Reductions Needed to Lower Peak 03 to 120 ppb.
(High HC/NO Conditions)
A
City-specific
Day
159 Base case 03, ppb
03 reduction necessary to
lower peak 03 to 120 ppb
HC reduction necessary to
lower peak 03 to 120 ppb
195 Base case 03, ppb
t
03 reduction necessary to
lower peak 03 to 120 ppb
HC reduction necessary to
lower peak 03 to 120 ppb
275 Base case 03, ppb
03 reduction necessary to
lower peak 03 to 120 ppb
HC reduction necessary to
lower peak 03 to 120 ppb
Airshed
325
63%
r
, >85%
f
211
43%
! 75%
i
L
331
64%
l
, 75%
1
EKMA
325
63%
1
.1 >85%
1
211
43%
[ 63%
i
1
331
64%
l
i 73%
1
Rollback
325
63%
I
, 76%
1
211
43%
! 53%
1
331
64%
1
, 70%
r
42
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Before describing the results, recall from Section 2.4 that control
strategy predictions obtained with the Airshed Model did not deviate substan-
tially from interpolated uniform emission reductions (see Figure 2-9).
Furthermore, comparison of the simple models with Airshed Model in simulating
the effectiveness of these more realistic strategies will necessarily focus on
predictions in the intermediate range of hydrocarbon reductions (i.e., 22% to
56%). Thus, the results can be anticipated to some extent from previous
discussions (primarily Section 3.3). Many of the same conclusions will hold,
although numerical differences may occur.
Comparisons of the three models are depicted graphically in Figures 3-5
and 3-6. In each figure, the change in 03 from the base case is plotted for
each day, with Airshed predictions on the abcissa and the simple model on the
ordinate. (City-specific EKMA is shown on the left and Rollback on the right.)
The diagonal line represents perfect agreement. Points that lie above the
line indicate that the simple model overestimates the effectiveness of a
control program (using Airshed as the standard) and vice versa. Stated
another way, points lying above the diagonal line would indicate that the
simple model results in too much "credit" being given to the control program.
In Figure 3-5, the independent model applications are shown. For the RACT
strategy, both models tend toward an overestimation of the effectiveness of
the program. For FMVECP, City-specific EKMA underestimates the effectiveness
in all cases, although only slightly in two. Rollback agrees very well in
two, and overestimates the effectiveness in another. The results are somewhat
similar for the combined strategy. Rollback agrees very well in two cases,
but overestimates the change in the third. City-specific EKMA agrees rela-
tively well in two cases, and underestimates effectiveness in the third.
Figure 3-6 presents the results when the models are put on a common basis.
The same general conclusions hold as for the independent tests.
43
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ID) CITY-SPECIFIC EKtlft
III ROLLBACK
uj •• TDL
0 10 20 M 40 60 60
0 10 20 30 40 60 60
0 10 20 30 40 SO 61
0 10
40 SO 60
0 10 M SO 40 60 60 0 10 20 30 40 60 60
RIRSHED MODEL PREDICTED CHRNGE IN OZONE. 7.
a Mr i8s * our IBS • DRY 276
Figure 3-5. Comparison of Model Effectiveness Predictions for
Control Strategies (Independent Tests).
44
-------
(Hi CITY-SPECIFIC CKM
IB I ROLLBACK
UJ " "0,
. KRCT
0 10 20 90 40 BO 80
0 10 20 90 40 SO 80
(3
_J 0 1
0 20 50 40 SO 6C
60
0 10 20 30 40 SO 60
8 10 20 90 40 SO 80 0 10 20 90 40 BO 60
BIRSHED MODEL PREDICTED CHPNGE IN OZONE, X
O OUT 168 * DRY 186 • DDT 276
Figure 3-6. Comparison of Model Effectiveness Predictions for Control
Strategies (Common Basis Tests).
45
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4.0 CONCLUSIONS AND RECOMMENDATIONS
As set forth in the introduction, the primary purpose of this study has
been to compare control effectiveness predictions of two simple models with
those of a more complex PAQSM. Presumably, the more complex model should
provide the best estimate because of its greater capacity for mathematically
depicting the phenomena of ozone formation. Nevertheless, no guarantee exists
that the PAQSM will provide accurate estimates of the effectiveness of reducing
hydrocarbon'emissions in lowering peak ozone levels. Though this problem
cannot be circumvented, the PAQSM should be able to reproduce existing ozone
patterns in order to mitigate to some extent the possibility of errors in the
input data. Provided this criterion is met, the PAQSM can serve as one means
of evaluating the simpler models ability to estimate the effectiveness of
control programs.
A rudimentary analysis of the Airshed Model's predictions of ozone
indicated that, in general, the predictions of peak ozone are within +30% of
observations. However, isolated differences outside this range were indicated.
Of the three days examinpd, the bias in the Airshed Model predictions tended
towards underprediction of high ozone levels, although individual exceptions
were noted. In one instance, the Airshed Model predicted the highest ozone
concentration in an area in which no ozone monitor was located. While this
prediction could obviously not be verified with observed data, it illustrates
the value of such a model in assessing the regional nature of an ozone problem,
and emphasizes the limitations of the simpler models.
The Airshed Model simulation of changes in hydrocarbons clearly
demonstrates the potential of this type model for evaluating relative changes
in ozone from base case levels. However, relating these results to typical
regulatory applications is not a straightforward procedure. First of all, the
Airshed Model simulations demonstrate that control programs may be more effec-
tive under certain meteorological conditions than for others, even though high
ozone levels are found under each. These findings imply some potential problems
in relating Airshed Model simulation results to the statistical form of the
ozone standard. Secondly, techniques for incorporating any Airshed Model bias
into regulatory analyses need further examination. Finally, in what may be a
47
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somewhat surprising finding, the limited number of Airshed Model simulations
considered in this study indicated that the spatial, temporal and reactivity
effects of particular control strategies may not be critical when compared to
the impact of across-the-board, uniform emission reductions. That is, the
ozone problem is more regional in nature, and does not appear to be significantly
affected by microscale phenomena.
Comparisons of the simple models with the Airshed Model were complicated
by differences in their mode of application and the form of the output typi-
cally available from each. Considering all of the tests, neither City-specific
EKMA nor Rollback adequately reproduced the predictions of the Airshed Model
over all conditions. Similarly, neither model was consistently overly or
underly conservative in the predictions of the effectiveness of control
programs in all situations. In other words, the findings did not indicate the
superiority of either model in reproducing all Airshed Model results.
When model estimates of the degree of control required to reduce peak
ozone levels to 120 ppb were made, the results again were mixed. However, in
most instances, the differences in control estimates predicted by the Airshed
Model and City-specific EKMA were about 10% or less, with City-specific EKMA
giving the lower control estimate in practically every case. Some investi-
gators have suggested that use of City-specific EKMA may result in overly
conservative control estimates (i.e., estimating control levels that would
result in overkill).14,15 This suggestion is not supported by the results of
this study. In fact, based on the Airshed Model results, City-specific EKMA
may underestimate slightly the level of control needed to reduce peak ozone to
120 ppb. On the other hand, Rollback agreed with Airshed very well in some
instances, but estimated much lower controls in other cases. In these cases,
the difference in controls could be as much as 20%. Thus, Rollback is not
overly conservative, but could lead to significant underpredictions of needed
controls in some cases, especially at higher HC/NO ratios.
A
The above findings clearly demonstrate that the use of simple models can
lead to different results than those obtained by using a PAQSM. Investiga-
tions are needed to unravel the reasons for these differences. While little
48
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can be done in examining the Rollback model, further studies of the differ-
ences between Airshed and City-specific EKMA can be performed. Specifically,
the following are recommended:
0 Airshed and City-specific EKMA contain two different chemical
mechanisms. Whether this, difference is contributing significantly to the
model discrepancies needs to be established.
0 The relative importance of boundary conditions in each model needs
closer scrutiny.
0 City-specific EKMA should be compared to a more complex trajectory
model which in turn should be compared to a PAQSM. Differences in the treat-
ment of specific physical phenomena should be identified and their effect
isolated to determine potential limitations with the City-specific EKMA
technique.
0 The comparisons conducted for the three days suggest that it will
be difficult to generalize about the relationships between the simple models
and the more complex PAQSMs. However, more days should be evaluated to
discern apparent trends.
° Tests such as the ones described in this report need to be conducted
for other cities to corroborate these findings.
0 Further investigations are needed to establish the importance of
NO in the Airshed Model versus City-specific EKMA.
49
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5.0 REFERENCES
1. Uses, Limitations and Technical Basis of Procedures for Quantifying
Relationships Between Photochemical Qxidants and Precursors,
EPA-450/2-77-021a, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina, November 1977.
2. K. L. Schere, "Evaluation of the Urban Airshed Model Using Data of the
Regional Air Pollution Study," presented at Symposium on RAPS Results,
St. Louis, Missouri, October 1980.
3. S. D. Reynolds and L. E. Reid, An Introduction to the SAI Airshed Model
and Its Usage, SAI Publication EF 78-53R, Systems Applications, Incorporated,
101 Lucas Valley Road, San Rafael, California, May 1978.
4. Code of Federal Regulations, "National Primary and Secondary Ambient Air
Quality Standards," Title 40, Part 50.9.
5. B. Dimitriades, "Oxidant Control Strategies, Part 1: An Urban Oxidant
Control Strategy Derived from Existing Smog Chamber Data." Environmental
Science and Technology. 11, 80 (1977).
6. B. Dimitriades, "An Alternative to the Appendix J Method for Calculating
Oxidant and N02 Related Control Requirements." EPA-600/3-77-001b,
International Conference on Photochemical Oxidant Pollution and Its
Control, Proceedings: Volume II, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, January 1977.
7. M. C. Dodge, "Combined Use of Modeling Techniques and Smog Chamber Data to
Derive Ozone Precursor Relationships." EPA-600/3-77-001b, ibid.
8. Procedures for Quantifying Relationships Between Photochemical Oxidants
and Precursors: Supporting Documentation, EPA-450/277-021b, U.S. Environ-
mental Protection Agency, Research Triangle Park, North Carolina, November
1978.
9. Personal communication between Gerald L. Gipson and Conrad Newberry,
Monitoring and Data Analysis Division, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, October 1981.
10. G. L. Gipson, et a!., Guidance for Use of City-specific EKMA in Preparing
Ozone SIPs, EPA-450/4-80-027, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, March 1981.
11. G. Z. Whitten and H. Hugo, User's Manual for Kinetics Model and Ozone
Isopleth Plotting Package. EPA-600/8-78-014a, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, July 1978.
51
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12. Ozone Isopleth Plotting Package (OZIPP). EPA-600/8-78-014b, U.S. Environ-
mental Protection Agency, Research Triangle Park, North Carolina, July
1978.
13. "Data Collection for 1982 Ozone Implementation Plan Submittals," Federal
Register, November 14, 1979, 44_, (221) 65669-65670.
14. H. E. Jeffries, et al., Effects of Chemistry and Meteorology on Ozone
Control Calculations Using Simple Trajectory Models and the EKMA Procedure,
EPA-450/4-81-034, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina, November 1981 (in press).
15. K. H. Jones, "Motor Vehicle Inspection/Maintenance Programs, A Critical
Review, Discussion Papers," Journal of Air Pollution Control Association,
Volume 31, Number 9, September 1981.
16. P. Peterson and R. Sakaida, Summary of Group I Control Technique Guideline
Documents for Control of Volatile Organic Emissions From Existing
Stationary Sources', EPA-450/3-78-120, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, October 1978.
52
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TECHNICAL REPORT DATA
(Please read Instructions a. , the reverse be fan- completing)
1. REPORT NO.
EPA-450/4-82-002
2.
4 TITLE AND SUBTITLE
A Comparison of Three Ozone Models: Urban Airshed,
City-specific EKMA, and Proportional Rollback
7 AUTHOR(S)
Gerald L. Gipson
9. PERFORMING ORGANIZATION NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
MDAD, AMTB, MD-14
Research Triangle Park, North Carolina 27711
12. .SPONSORING AGENCY NAME AND ADDRESS
Same
15. SUPPLEMENTARY NOTES
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE
March 1982
6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT NO.
10. PROGRAM ELEMENT NO.
1 1. CONTRACT/GRANT NO.
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
16. ABSTRACT
The results of using three different types of models to assess the potential
effectiveness of ozone control strategies are described. Data collected during the
RAPS program conducted in St. Louts were used to develop inputs for a complex
photochemical air quality simulation mode-. The model was used to simulate a number
of control strategies to establish their potential for lowering ozone levels. These
strategies were replicated with two simpler models - City-specific and Rollback. The
results obtained with the simpler models were then compared to those found with the
more complex model. Neither of the simpler models agreed with the complex under all
conditions. However, in estimating the degree of control necessary to reduce peak
ozone to the level of the national standard, the differences between the complex
model and City-specific EKMA were usually less than 10%. Differences between Rollback
and the complex model were usually less than 20%.
17.
a. DESCRIPTORS
Ozone
Control strategies
Photochemical pollutants
Photochemical models
EKMA
OZIPP
Rollback
:is. r;!?TPi3ui ic:-j S-A-,.*-.EPT
i
Unlimited
3
KEY WORDS AND DOCUMENT ANALYSIS
|b. IDENTIFIERS/OPEN ENDED TERMS C. COSATI F;ield/Group
i
!
i
i
i
i
19. SEC'^R. : Y CLASS ;Ttii<, tfeportl 2'- . NO. o= PA1ES
i Unlimited 62
'20. SEC - -:!TV CI.A3
j I
C • This pag,. • 22. PRICE
I
EPA For.-n 2273-1 (Rev. 4-77)
OBSOLETE
53
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