EPA-450/4-83-003c
Evaluation Of Rural Air Quality
Simulation Models
Addendum C: Kincaid S02 Data Base
By
William M Cox
Herschel W Rorex
Gerald K. Moss
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
March 1986
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This report has been reviewed by the Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
EPA-450/4-83-003c
11
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PREFACE
This report summarizes performance statistics for several rural point
source models. The performance of the models is based on S02 data collected
near the Kincaid Power Plant. The report serves as an addendum to a previous
publication* on model performance which was based on data from the Clifty
Creek Power Plant. Other addenda to the Clifty Creek publication are also
planned for additional data bases and for presentation of supplemental infor-
mation on model performance.
The Kincaid S02 data base was collected by the Electric Power Research
Institute (EPRI) for an extensive evaluation of selected models, two of which
(TEM-8A and CRSTER/MPTER) are included in this evaluation study. This report
extends the EPRI evaluation by including two additional models and by pre-
senting performance results in a standard format that facilitates performance
comparison among models and among other data bases, for which results are now
available.
*Londergan, R. J., D. H. Minott, D. J. Wackter, T. Kincaid and D. Bonitata,
1982. Evaluation of Rural Air Quality Simulation Models. EPA Publication
No. EPA-450/4-83-003. U.S. Environmental Protection Agency, Research Triangle
Park, N.C. (NTIS No. PB 83-182758).
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TABLE OF CONTENTS
Page
PREFACE iii
TABLES vi
FIGURES vi
1. INTRODUCTION 1
2. AIR QUALITY DATA BASE AND STATISTICAL APPROACH 5
3. MODEL PERFORMANCE RESULTS 11
4. CONCLUSIONS 29
REFERENCES 31
APPENDICES
A. Statistics For 25 Highest Values
B. Statistics For All Events
C. Statistics For Highest Concentrations At Each Station
D. Comparisons Of Highest Values For Various Pairings
E. Comparison Of Model Performance Obtained By EPA
And By EPRI For The MPTER Model
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TABLES
Number Page
1A-C Difference of Observed and Predicted Averages of
the 25 Highest $02 Concentration Values (Unpaired
in Time or Location) 13-17
2A-C Average Difference Between Observed and
Predicted Concentration Values Event-By-Event
(Paired in Time) 18-23
3A-C Comparison of Maximum Observed and Maximum
Predicted Concentration Values 24-26
FIGURES
Number Page
1 EPRI PMV&D Project And CECo Air Quality
Monitoring Networks 5
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SECTION 1
INTRODUCTION
In October of 1982, EPA published the results of a comprehensive
evaluation of eight rural air quality simulation models using ambient S02,
meteorological, and source data for the Clifty Creek Power Plant.1 The
evaluation was based on recommendations by the American Meteorological
Society (AMS) regarding the use of statistical methods for comparing ob-
served air quality with model predictions.2 EPA later published an adden-
dum to the Clifty Creek report summarizing the performance of 4 of the
original 9 models using a similar data base available for the Muskingum
River Power Plant.3 The purpose of this addendum is to provide analogous
statistical information on model performance based on S02 data collected
by the Electric Power Research Institute (EPRI) for the Kincaid Generating
Station.
The Kincaid S02 data base has been used previously by EPRI '^ to
conduct a comprehensive operational and diagnostic evaluation of selected
rural models, two of which (TEM-8A and CRSTER/MPTER) are also evaluated by
EPA. Uhile the EPRI evaluation also included results using a tracer gas,
this addendum focuses only on 502- The results presented in this addendum
supplement those obtained by EPRI in the following ways (1) it includes two
additional models (see paragraph below), (2) It presents results in a stand-
ardized format recommemded by the AMS and used in previous evaluations, and
(3) it establishes a framework for including results for new models as they
are developed and evaluated.
As in the Muskingum evaluation, only four of the original 9 models have
been evaluated. The four models are the following: (1) CRSTER/MPTER developed
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by EPA, (2) MPSDM developed by ERT, Inc., (3) TEM-8A developed by the
Texas Air Control Board, and (4) PPSP developed by the Martin Marietta
Corporation. This evaluation is confined to these four models since they
essentially span the range of technology represented by the rural models as
a group. The five models not applied here all use the Pasquil1-Gifford
dispersion curves as defined in the MPTER model, although their other features
may differ somewhat from each other and from MPTER. The principal technical
features of the MPTER/CRSTER model and technical differences between it and
the other three models as run for this study are:
0 MPTER/CRSTER (EPA)
Full terrain subtraction
Briggs final plume rise
Uses stability classes A, B, C, D, E, F
Stability class restriction (G replaced with F)
Pasquil 1-Gi fford oy and L, then ground-level concentration
(X) = 0
Wind profile power-law coefficients of 0.10, 0.15, 0.20, 0.25, 0.30
and 0.30 for stability classes A-F.
0 MPSDM (ERT)
ASME (1979) dispersion coefficients
Terrain treatment (1/2 height, unstable and neutral; full terrain
subtraction, stable)
Transitional plume rise
5 stability classes (F used for all stable)
Stack-tip downwash
Buoyancy enhanced dispersion (°y and °z)
Mixing height not used for stable conditions
Uniform mixing not assumed
Briggs partial plume penetration
Wind profile power law coefficients (.09, .11, .12, .14, .20)
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TEM-8A (Texas Air Control Board)
Flat terrain assumed
Horizontal dispersion for 60-minute averages (ay = C-j ayPG;
where C = 3.35, 2.70, 2.14, 1.71, 1.37, 1.37 for stabilities 1-6)
Transitional plume rise
Mixing height not used for stable conditions
Uniform mixing at 2 times the distance beyond where az = 0.47L
Full plume penetration when H >2L; then x = 0-
PPSP (Martin Marietta)
Briggs dispersion coefficients
Stability class selection based on (1) convective theory of
scaling for daytime and (2) the Turner algorithm for nighttime
Partial penetration of buoyant plume into the capping inversion
Briggs plume rise formulas, including the "breakup" and
"touchdown" models
No terrain adjustments
It should be noted that EPA did not directly solicit jnput from the
model developers prior to this evaluation. This is due to the fact that
the environmental and meteorological features around the Kincaid plant are
less complex than those for Clifty Creek and Muskingum River, for which
developer input had already been received. The reader should refer to the
Clifty Creek and Muskingum reports for a more detailed description of the
models and options used.
Since two of the models, CRSTER/MPTER and TEM-8A, were evaluated as
part of EPRI's validation effort, the reader should be aware of minor
differences between this study and the EPRI study that leads to small but
perceptible differences in performance results. These study differences,
which are discussed in Section 2, are primarily related to how the model
input data was processed and minor differences in treatment of S02 back-
ground concentrations.
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SECTION 2
AIR QUALITY DATA BASE AND STATISTICAL APPROACH
The Kincaid Generating station, located in central Illinois, is operated
by the Commonwealth Edison Company (CECo). The plant is a coal-fired base
load facility with two 660-MW generators; emissions are vented through
a single stack having a height of 187 meters. The plant is relatively
isolated from other major sources and is surrounded by flat terrain. The
data base was assembled by EPRI during 1980-1981 as part of a major study
to evaluate the operational performance of selected rural models. The
source, ambient and meteorological data were collected on-site and supple-
mented with additional meteorological information for nearby NWS stations.
Descriptions of the data base, monitoring network design, quality assurance
procedure and model performance results are documented in several EPRI
publications.4»5,6
The ambient S02 data base was aquired at a total of 30 stations, 20
which were deployed specifically for the EPRI Plume Model Validation study
and 10 which are routinely operated by CECo. The network of stations
(Figure 1) was fixed during the study period except for brief periods during
which portable S02 monitors at sites 3 and 12 were moved during "special"
events. The ambient data used in this evaluation consisted only of periods
during which monitors were fixed as indicated in Figure 1.
For purposes of this evaluation, the meteorological data used was
"Level Ib" as defined in the EPRI Report.4 It consists primarily of
on-site measurements taken to correspond with meteorological data commonly
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o EPRI STATION
A CECO STATION
FIGURE 1. EPRI PMV&D Project And CECo Air Quality Monitoring Networks.
(Adapted From Figure 3-3 in Reference 4)
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available for model applications. The on-site measurements include wind
speed, wind direction and air temperature recorded at a height of 10 meters.
Missing values were filled with values from the 50 meter level on-site.
Stability class was determined using the Turner method from on-site winds as
above, and cloud cover observations from the Springfield National Weather
Service (NWS) station, which is located approximately 25 miles northwest of
the plant. If an NWS cloud cover observation was missing, stability was
determined from on-site data. Mixing heights were determined from on-site
t-sonde data when possible using the Holzworth method. When t-sounde data
were missing, mixing heights were determined from NWS upper air stations at
Peoria.
Stack information consisted of hourly averages of S02 emission rate, exit
gas velocity and gas temperature. Although emissions and temperature data
were measured continuously, engineering estimates based on plant operating
information were substituted during the EPRI study when stack monitoring
equipment was not providing accurate data.
The performance statistics resulting from the evaluation are documented
in Appendices A through D to this report. Upper and lower 95 percent confid-
ence limits for the difference between cne averages of the 25 highest observed
and predicted values are enclosed in parenthesis. The tables shown in the
appendices to this report were generated with a software package that was
specifically designed for model evaluation. The system, Model Evaluation
Support System (MESS), was created to aid in formatting and handling meteo-
rological, air quality and source characteristic data.'' A statistical package
incorporated into MESS calculates and outputs the AMS performance statistics
in the prescribed format.8 Hourly background values were computed using the'
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same approach described in the Clifty Creek and Muskingum reports, i.e., a
background concentration for each hour was calculated as the average of
values at monitor locations outside the 90° sector downwind from the Kincaid
Plant.
As indicated in the INTRODUCTION, performance results for TEM-8A and
MPTER differ slightly from results presented by EPRI. Several factors
involving data input to the models and the treatment of background concen-
trate contribute to the differences. A summary of these factors is listed
below:
0 Background Adjustments For this study, the EPA method was used and
this resulted in slightly lower air quality estimates. Differences in
highest measured concentrations are usually less than 1-2 percent. Also
direct comparison of concentrations resulting from this evaluation and
those reported by EPRI are difficult since EPRI computed concentrations
in units of parts per billion (ppb) while for this study, concentrations
a're reported in micrograms per cubic meter (ug/m^).
0 Additional Ambient Monitoring Stations For this study, data taken
at the two "portable" SO-,? stations (stations 3 and 12) were included.
This results in additional data values included in the nign 25 statis-
tical summaries (unpaired in space and time) which would cause a
slight upward shift in average values.
0 Wind Directions For this study, the model preprocessor was adapted
to use on-site wind-directions measured to the nearest degree. The
EPRI study rounded wind direction to the nearest ten degrees which were
then randomized to the nearest degree as used in routine applications
-------
of CRSTER/MPTER. This difference could affect highest hourly predicted
values, unpaired in space or time, by 3-5 percent.
Source Data For this study, stack data (emission rate, exit velocity
and temperature) were input for each hour. For EPRI's study weekly
average stack parameters were input for MPTER/CRSTER. Also the
formula used to convert stack units from ppb to ug/m^ differed
slightly in that standard pressure was used in this study *niie
EPRI adjusted by hourly atmospheric pressure. The resulting
impact on individual hourly concentrations should be less than a
few percent.
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SECTION 3
MODEL PERFORMANCE RESULTS
The performance statistics for the four rural models using the combined
1980 and 1981 data are presented in detail in the Appendices. Because the
material is too voluminous to discuss easily, a series of summary tables
analogous to those presented in the Clifty Creek and Muskingum River reports
are shown. Table 1, which is derived from the information contained in
Appendix A, compares the bias of each model for the 25 highest observed
and predicted concentrations. Table 2, which was derived from Appendix B,
compares the bias of each model using all concentration data. Table 3,
which was derived from Appendix C and Appendix D, compares the ability
of each model to predict the highest concentrations.
Table 1A compares the bias of each model for the 25 highest 1-hour
averages. PPSP overpredicts for each data category, frequently by a factor
of 3 or more. MPSDM also tends tc overpredict the highest 25 observed
1-hour values but not as frequently nor by the magnitude of overprediction
by PPSP. Both TEM and MPTER tend to be relatively unbiased; TEM slightly
underpredicts the all station/all event category but exhibits an equal
number of stations with over and underpredictions. MPTER also slightly
underpredicts the all station/all event category but tends to consistently
underpredict the high 25 concentrations (26 of 30 stations underpredicted).
Results within wind speed and stability categories vary from model to model;
however, the middle wind speed categories (2.5 m/s to 5.0 m/s) and unstable
conditions (Class C) are associated with overprediction for all four models.
11
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Both TEM and MPTER are relatively unbiased (slight underpredictions) for
very unstable conditions (stability categories A and B). The models
(except PPSP) tend to underpredict for very stable conditions (class E
and F). Table IB and Table 1C present similar information for 3-hour and 24-
hour averages respectively. The general trend is for less overprediction
(MPSPM and PPSP) as averaging period increases. For MPTER and TEM, under-
predictions noted for 1 hour averages do not appreciably change for 3-
hour and 24-hour averaging periods.
The statistics presented in Table 1 for PPSP clearly indicate overpre-
diction; however, comparisons between PPSP and the other three models should
be made with the knowledge that a number of hours of "missing" PPSP concen-
trations occurred because solar insolation data required by PPSP were not
available. The probable effect is for understatement of PPSP overpredic-
tions since increasing the number of (non-missing) valid predictions can
only result in a higher value of the average of the highest 25 PPSP estimates,
and not a lower value. The corresponding average of the 25 highest observed
concentrations would be unaffected since all valid observed values were
used in the calculation including events when PPSP estimates were not
possible.
Table 2A presents results for the all data categories, i.e., all valid
data values within a data category paired in time. The number of events and
average observed values are for MPTER; differences in these two values occur
among the models due to the threshold imposed for computation of meaningful
performance measures. For 1-hour averages (Table 2A), MPSDM and TEM appear
to have little overall bias. The number of stations being overpredicted or
underpredicted is about equal for both models. PPSP tends to overpredict
12
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the average 1-hour observed values by a considerable amount at most stations;
however, it slightly underpredicts at 2 stations. MPTER tends to under-
predict the average 1-hour observed value for each of the data categories
shown. Similar results were obtained for 3-hour averages (Table 2B) and
24-hour averages (Table 2C). Note that average observed 24-hour concen-
trations are very low and frequently near the threshold concentration
(5 yg/m2) which should be considered when interpreting results in Table 2C.
Table 3 compares the maximum observed and predicted concentration using
both the single highest values over all events and locations and the average
of maximum values for each station. The comparisons of average maximum
values should be more meaningful of the two since maximums from 30 stations
are involved. Basically the results are consistent with those presented in
Table 1. For 1-hour averages, PPSP and MPSDM clearly overpredict peak
values. MPTER and TEM tend to be relatively unbiased, underpredicting the
average of maximum observed values (597 Ug/m2) by only 22 and 89 Wg/m3
respectively. For 3-hour averages ana 24-hour averages, the margin of
overprediction by PPSP and MPSDM is smaller than for 1-hour averages; MPTER
and MPSDM shift toward greater underprediction of the observed peaks.
From a regulatory point of view, the ability of the models to estimate
the highest 3-hour and 24-hour average concentrations is of particular con-
cern. From Table 38 and 3C, both MPTER and TEM underestimate the highest
observed value by approximately a factor of two for both the 3-hour and
24-hour averaging periods. While MPSDM overpredicts the highest 3-hour and
24-hour observed values, the magnitude of the overpredictions is small (i.e.
13
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3 percent for 3-hour averages and 22 percent for 24-hour averages). PPSP
overpredicts the highest 3-hour and 24-hour averages by over a factor of
five.
A more robust measure of the ability of each model to estimate the
highest concentrations is obtained by comparing the highest (or second
highest) observed and predicted values at each of the stations in the net-
work( See Appendix C). For example, the ratio, R, of the root mean square
error of the difference between the second highest estimated and observed
values is a useful guage of how well each model duplicates the value usually
used in determining NAAQS attainment. The value of R for 3-hour value
ranges from 40 percent for TEM-8A to over 300 percent for PPSP while for
24-hour average, R ranges from 54 percent for TEM-8A to over 900 percent
for PPSP. Overall these results indicate that the better performing models
are capable of estimating the highest 3-hour and 24-hour values well within
a factor of two and the second highest value at each station within approxi-
mately 50 percent.
14
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SECTION'IV
CONCLUSIONS
The S02 data base collected at the Kincaid Power Plant during
1980/1981 was used to evaluate the performance or 4 rural models which
had been previously evaluated from data collected in 1975/1976 at
Clifty Creek and Muskingum River. Overall results we^e similar in many
ways to results reported earlier. PPSP clearly overpredicted concentra-
tions in almost every data category while MPSDM, MPTER and TEM exhibited
relatively small but detectable biases depending on averaging period and
data category.
Among the latter three models, MPSDM tended to overpredict the 25
highest observed concentrations while TEM and MPTER tended to underpredict
the 25 hignest observed values. For MPSDM, overpredictions tended to
decrease noticeably with increasing averaging period with 1-hour values
being overpredicted by the greatest extent. For MPTER and TEM, there
was no obvious trend toward greater underpradiction with averaging
period increases. The most noticeable contrast with results from previous
studies occured for MPTER; overprsdictions of 1-hour values ^ere obvious
for Clifty Creek and Muskingum River which were not apparent here, espe-
cially for the more unstable conditions.
Results presented previously by EPRI and those presented in this
addendum are quite close and only differ quantitatively due to slight dif-
ferences in the way the data were processed. The reader is referred to
Appendix E which compares the performance of CRSTER/MPTER reported by EPRI
with results obtained from this study for selected data categories.
29
-------
EPA plans to continue the evaluation of rural models using the tracer
data base collected at Kincaid and other high quality data bases as they
become available. EPA is also in the process of evaluating the results
from various rural studies and plans in the future to issue additional
reports summarizing the performance of rural models across data bases.
Hopefully a format can be developed that is amenable for quantitatively
distinquishing differences in performance levels among the models and among
data bases.
From a regulatory point of view, the ability of the models to
estimate the highest 3-hour and 24-hour average concentrations is
of particular interest. MPTER, MPSDM and TEM-8A were all successful
in estimating the network-wide highest observed 3-hour and 24-hour
observed values within approximately a factor of two. A more robust com-
parison using the second highest 3-hour and second highest 24-hour value
from each of the 30 stations indicated that the better performing models
were able to estimate the second highest values at individual stations
within approximately 50 percent.
30
-------
REFERENCES
1. Londergan, R. J., et al., "Evaluation of Rural Air Quality
Simulation Models."" E"PA~-450/4-83-003, Environmental Protection
Agency, Research Triangle Park, N. C., July 1982.
2. Fox, D. G. "Judging Air Quality Mode: Performance," (A Summary
of the AMS Workshop on Dispersion Model Performance, Woods, Hole,
MA, Sept. 1980). Bulletin of the Amer. Met. Soc., Volume 62,
No. 5, May 1981, pp. 599-609.
3. Cox, W. M. and G. K. Moss, "Evaluation of Rural Air Quality
Simulation Models, Addendum A: Muskingum River Data Base."
EPA-450/4-83-003a, Environmental Protection Agency, Research
Triangle Park, N. C. June 1985.
4. Reynolds, D. S. et al ., "Operational Validation of Gaussian Plume
Models at a Plains Site." EPRI EPA-3076, Project 1616-9, Electric
Power Research Institute, Palo Alto, California, 1984.
5. Bowne, N. E., et al . "Overview, Results and Conclusions for the EPRI
Plume Model Val idation and Development Project: Plains Site." EPRI
EA-3074, Project 1616-1, Electric Power Research Institute, Palo
Alto, California, 1983.
6. Cher, M., et al ., "Plume Model Validation and Development Field
Measurements - Plains Site," EPRI EA-3064, Project 1616-8. Electric
Power Reserach Institute, Palo Alto, California 1984.
7. Baldridge, K. W., "Model Evaluation Support System Users Manual."
Prepared by Computer Science Corporation for EPA, Computer Science
Corporation, Research Triangle Park, N. C. 1983.
8. Statistical Evaluation Subsystem Users Manual." Environmental
Protection Agency, Research Triangle Park, N. C., 1985.
31
-------
APPENDIX A
Statistics For 25 Highest Values
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