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

-------
                                    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

-------
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

-------
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

-------
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

-------
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


-------
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

-------
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.
                                        vii

-------
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

-------
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

-------
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

-------
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.

-------
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

-------
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

-------
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).

-------
         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.

-------
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,

-------
Figure 2-1„  St. Louis Modeling Region.

-------
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.

-------
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

-------
     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

-------
                             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

-------
                    (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

-------
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

-------
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

-------
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

-------
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

-------
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

-------
          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

-------
                            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

-------
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

-------
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

-------
     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

-------
                       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

-------
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

-------
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

-------
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

-------
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

-------
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

-------
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

-------