S-EPA
United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/4-83-003d
January 1987
Air
Evaluation of
Rural Air Quality
Simulation Models
Addendum D:
Paradise 862
Data Base
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EPA-450/4-83-003d
January 1987
Evaluation of Rural Air Quality
Simulation Models
Addendum D:
Paradise SO2 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
and
Ellen W. Baldridge
Computer Sciences Corporation
Research Triangle Park, NC 27709
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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-450/4-83-003d
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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 Paradise Power Plant. The report serves as an addendum to a pre-
vious 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 supple-
mental information on model performance.
The Paradise S02 data base was collected by the Tennessee Valley Authority
and used previously by EPA for evaluating the impact of alternative modeling
techniques on the ability of MPTER to estimate peak S02 levels. This report
extends the previous EPA evaluation by including three additional models and by
presenting performance results in a standard format that facilitates perfor-
mance 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 0. Bonitata,
1982. Evaluation of Rural Air Quality Simulation Models. EPA Publication
No. EPA-450/4-83-003. U.S. Environmental Protection Agency, Research Trianqle
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 9
4. CONCLUSIONS 23
REFERENCES 24
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
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TABLES
Number Page
1A-C Difference of Observed and Predicted Averages of
the 25 Highest $03 Concentration Values (Unpaired
in Time or Location) 12-14
2A-C Average Difference Between Observed and
Predicted Concentration Values Event-By-Event
(Paired in Time) 15-19
3A-C Comparison of Maximum Observed and Maximum
Predicted Concentration Values 20-22
FIGURES
Number Page
1 Site Characteristics and Data Acquisition of the Paradise
S02 Monitors During 1976 7
2 Paradise Air Quality
Monitoring Network 8
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SECTION 1
INTRODUCTION
In October of 1982, EPA published the results of a comprehensive
evaluation of nine 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 adden-
dums to the Clifty Creek report summarizing the performance of 4 of the
original 9 models using data bases available for the Muskingum River Power
Plant-* and the Kincaid Power Plant.^ The purpose of this addendum is to
provide analogous statistical information on model performance based on S02
data collected by the Tennessee Valley Authority (TVA)-for the Paradise -
Power Plant.
The Paradise S02 data base has been used previously^ to evaluate the
impact of alternative modeling techniques on the ability of MPTER to estimate
ground level peak S02 concentrations. While the MPTER results presented
previously at Paradise are basically consistent with those presented here,
this addendum provides additional information, i.e., (1) it includes three
additional models, (2) it presents results in a standardized format 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 and Kincaid evaluations, only four of the original
9 models have been evaluated. The four models are the following: (1) CRSTER/
MPTER developed by EPA, (2) MPSDM developed by ERT, Inc., (3) TEM-8A developed
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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 dis-
persion curves as defined in the MPTER model, although their other features
may differ somewhat from each other and from MPTER. Because of the rela-
tively flat terrain (see Figures 1 and 2), no adjustments were made for
receptor height above plant grade. The principal technical features of
each of the four models used in this study are:
0 MPTER/CRSTER - UNAMAP5 (EPA)*
Full terrain subtraction
Briggs final plume rise
Uses stability classes A, B, C, D, E, F
Stability class restriction (G replaced with F)
Pasquill-Gifford ay and az
Mixing height (L) used for stability classes A-D
Uniform mixing beyond where az exceeds 1.6L
Full plume penetration. When H >L, then ground-level concentra-
tion (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.
*Although version 5 of MPTER was used in this evaluation, a new version
(UNAMAP6) is now available for regulatory use.
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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 °?)
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)
0 TEM-8A (Texas Air Control Board)
Flat terrain assumed
Horizontal dispersion for 60-minute averages (°y = Ci °yPG;
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.
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 input from the
model developers prior to this evaluation. This is due to the fact that
the environmental and meteorological features around the Paradise plant are
similar in complexity to those same features around Clifty Creek and devel-
oper input had already been received. The reader should refer to the Clifty
Creek report for a more detailed description of the models and options used.
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SECTION 2
AIR QUALITY DATA BASE AND STATISTICAL APPROACH
The Paradise Power plant, located in Kentucky, is operated by the Tennessee
Valley Authority (TVA). The plant is a coal-fired facility consisting of three
units. Emissions from two identical 704-MW units are vented through two sepa-
rate stacks, each stack having a height of 183 meters. Emissions from a third
unit (1150MW) are vented at 244 meters. The plant is relatively isolated in
flat terrain surrounded by rolling hills. The source, ambient concentration
and meteorological data used in the evaluation were collected on-site by TVA
in 1976 and supplemented with both surface and upper air meteorological data
from the NWS station at Nashville, TN.
The ambient-S02 monitoring network consists of 12 stations located at
distances ranging from 3 km to 17 km from the plant. Figure 1, reproduced
from reference 5, summarizes the azimuth and distance from the plant for each
monitor. Figure 2, also reproduced from reference 5, shows the location of
the 12 S02 monitoring stations relative to the plant and the on-site meteoro-
logical tower operated by TVA.
The on-site meteorological measurements were taken from a 110-meter tower
located 1.5 km west of the plant. Measurements included hourly averages of
wind speed, wind direction and ambient air temperature, all of which were
measured at the 110 meter level. A missing wind speed or wind direction was
flagged as missing data. A missing 110 meter temperature was replaced by the
10 meter temperature, the 1 meter temperature or the Nashville NWS surface
-------
temperature, in that sequence. Rural mixing heights and stability classes
were derived entirely from the Nashville NWS data using the standard CRSTER
preprocessor programs.
Stack data consisted of hourly averages of exit gas velocity, gas temper-
ature, and S02 emissions. Hourly average gas velocity and gas temperatures were
estimated by linear interpolation using hourly power generation estimates for
each unit. Hourly average emission rates were estimated using daily coal
consumption and periodic sulfur analysis.
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 various statistical measures of performance are enclosed
in parenthesis. The tables shown in the appendices to this report were gen-
erated with a software package that was specifically designed for model eval-
uation. The system, Model Evaluation Support System (MESS), was created to
aid in formatting and handling meteorologica-1, air quality and source char-
acteristic data.6 A statistical package incorporated into MESS calculates
and outputs the AMS performance statistics in the prescribed format.? No
adjustment was made for background since contributions to ground level
monitoring stations from sources other than the Paradise Plant are thought
to be minimal.
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Figure 1. SITE CHARACTERISTICS AND DATA ACQUISITION OF THE PARADISE S02
MONITORS DURING 1976
Receptor
number
Azimuth
(degrees)
Distance
(km)
Height
above plant
grade (m)
Percentage of
missing S02
data
(n=8784 hrs)
1
2
3
4
5
6
7
8
9
10
11
12
31
47
31
31
32
284
126
301
150
238
342
178.5
2.9
16.9
4.67
6.76
8.37
12.7
8.53
38
0
66
6.05
7.90
-1
24
21
36
15
12
-7
16
15
22
2
10
62.1
61.4
3.7
61.6
61.6
1.8
1.4
2.8
1.8
0.3
0.6
0.6
Note: This figure reproduced by permission of the author (see reference 5)
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/
V. PARADISE
fOWEB
PLANT
Figure 2. Paradise Air Quality Monitoring Network
Note: This figure reproduced by permission of
the author (see reference 5)
8
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SECTION 3
MODEL PERFORMANCE RESULTS
The performance statistics for the four rural models using 1-hour, 3-
hour and 24-hour averages of measured and predicted 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, Muskingum River, and Kincaid 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.using the averages of the 25
highest observed and predicted 1-hour concentrations. PPSP overpredicts the
average observed concentration at each of the 12 stations and for each of the
seven meteorological categories; occasionally by a factor of 3 or more.
MPSDM also overpredicts the average observed concentrations but not as
severely as PPSP. Both TEM-8A and MPTER are relatively unbiased in that
overprediction at some stations are offset by underpredictions at other
stations. Also, the 95 percent confidence limits for the all station/all
event category for both TEM-8A and MPTER encompass zero (0) indicating a
lack of significant bias in estimating highest observed 1-hour average
concentrations.
-------
Each model exhibits at least some degree of bias for one or more of the
wind speed and stability categories. Generally, the higher wind speed cate-
gories (>2.5m/s) and unstable conditions are associated with the largest
overpredictions while lower wind speeds and more stable conditions are
associated with the largest underpredictions. Table IB and Table 1C present
similar information for 3-hour and 24-hour averages respectively. The general
trend, which is most noticeable for MPSDM and PPSP, is for smaller overpre-
dictions as averaging period increases. Both TEM-8A and MPTER exhibit
noticeable underpredictions for 3-hour and 24-hour averages which were not
evident for 1-hour averages.
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. The number of events and average
observed concentration for the other three models differ slightly from those
for MPTER primarily due to the threshold imposed for computation of meaningful
performance statistics. For 1-hour averages (Table 2A), the general result
is for noticeable underpredictions by MPTER, MPSDM and TEM-8A and noticeable
overpredictions by PPSP. There are exceptions to this general tendency, e.g.,
MPSDM and TEM-8A overpredictions for very unstable conditions (A and B) and
PPSP underpredictions for very stable conditions (E and F). Similar results
were obtained for 3-hour averages (Table 2B) and 24-hour averages (Table 2C).
Note that observed 24-hour concentratons are relatively low compared with
those for shorter averaging periods and with the threshold concentration used
for 24-hour values (5yg/m3). This should be considered when interpreting
results presented in Table 2C.
10
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Table 3 compares the maximum observed and predicted concentration using
both the single highest values over all events and stations and the average
of maximum concentration for each station. The comparisons of average max-
imum values should be the more meaningful of the two since the 12 station
average of maximum values are considerably more robust than the single high-
est values for the entire network. Not surprisingly, the results are reason-
ably consistent with results presented in Table 1 for the average of the 25
highest observed and predicted values. For 1-hour averages, MPSDM and PPSP
clearly overpredict both the highest values and the average of maximum values
across the 12 station network. MPTER and TEM-8A are relatively unbiased,
underpredicting the highest value (almost 3,000yg/m3) by 37Qyg/m3 and
271yg/m3 respectively. Both MPTER and TEM-8A perform even better for the
12 station average of maximums (1604ug/m^) where slight overpredictions
of 77ug/m3 and 12yg/m3 occur.
For 3-hour and 24-hour averages, the margin of overprediction by PPSP
and MPSDM is smaller than for 1-hour averages while for MPTER and TEM-8A
the trend is toward underprediction. In fact, MPTER and TEM-8A each show
underpredictions which approach or exceed 10 percent for both 3-hour and
24-hour averages.
11
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22
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SECTION 4
CONCLUSIONS
Four rural models that had been previously evaluated using S02 measure-
ments at the Clifty Creek, Muskingum River and Kincaid power plants, were
evaluated using an additional data base collected at the Paradise power
plant located in Kentucky. The performance of the four models at Paradise
followed basic patterns that were similar to those observed for the other
three sites. PPSP clearly overpredicted concentrations for almost every
data category while MPSDM, MPTER and TEM-8A exhibited smaller biases that
were a function of averaging period and meteorological category.
For highest 1-hour concentrations, MPTER and TEM-8A were essentially
unbiased while MPSDM showed relatively large overpredictions. Following
trends noted for the other three data bases (Clifty Creek, Muskingum
River and Kincaid), overpredictions of highest 1-hour values by MPSDM
vanished for highest 24-hour averages while the lack of bias for MPTER
and TEM-8A shifted toward underpredictions of highest 3-hour and 24-hour
averages. Familiar trends were also apparent for meteorological categories;
higher wind speed categories and very unstable conditions are associated
with the largest overpredictions while lower wind speeds and more stable
conditions are associated with the largest underpredictions.
The contrast in performance between the highest concentration data
sets and the all data sets were also similar to that observed previously.
PPSP overpredicted while MPTER, MPSDM and TEM-8A tend to underpredict
significantly for almost every station, meteorological category and averaging
period.
23
-------
REFERENCES
1. Londergan, R. J., et al., "Evaluation of Rural Air Quality Simulation
Models." EPA-450/4-83-003, Environmental Protection Agency, Research
Triangle Park, N. C., July 1982.
2. Fox, D. G. "Judging Air Quality Model 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. Cox, W. M., et al., "Evaluation of Rural Air Quality Simulation Models,
Addendum C: Kincaid S02 Data Base." EPA-450/4-83-003C, Environmental
Protection Agency, Research Triangle Park, N. C., March 1986.
5. Pierce, T. E., "An Evaluation of Alternative Gaussian Plume Dispersion
Modeling Techniques In Estimating Short-Term Sulfur Dioxide Concentra-
tions," Environmental Sciences Research Laboratory, Office of Research
and Development, Environmental Protection Agency, Research Triangle Park,
N. C., June 1984.
6. Baldridge, K. W., "Model Evaluation Support System Users Manual." Pre-
' pared by Computer Science Corporation for EPA, Computer Science Corpora-
tion, Research Triangle Park, N. C., 1983.
7. "Statistical Evaluation Subsystem Users Manual." Environmental Protection
Agency, Research Triangle Park, N. C., 1985.
24
-------
APPENDIX A
Statistics For 25 Highest Values
-------
-------
TABLE A. COMPARISON OF Z5 HIGHEST OBSERVED AND PREDICTED
SOa CONCENTRATION VALUES (UNPAIRED IN TIME OR LOCATION)
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AVERAGE AVERAGE DIFFERENCE DIFFERENCE VARIANCE FREQUENCY
DATA SETS OBSERVED PREDICTED OF AVERAGES OF MEDIANS COMPARISON DISTRIBUTION
VALUE (Co) VALUE (Cp) (Co - Cp) (Co - Cp) ( So#* Z/Sp**2 ) COMPARISON
(US/M**3) (UG/M**3) (UG/M**3) (UG/M**3) (FOBS-FPRED)
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-450/4-83-003d
2.
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Evaluation of Rural Air Quality Simulation Models
Addendum D: Paradise S02 Data Base
5. REPORT DATE
January 1987
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
William M. Cox
Gerald K. Moss
8. PERFORMING ORGANIZATION REPORT NO.
Herschel
Ellen W.
W. Rorex
Baldridge
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Source Receptor Analysis Branch
Monitoring and Data Analysis Division
U. S. Environmental Protection Agency
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA-450/4-83-003d
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This addendum provides additional information regarding the performance of four
rural air quality simulation models using S02 air quality and a modelers data base
assembled for the Paradise Power Plant. The report contains numerous tabulations of
each model's performance in terms of statistical measures recommended by the AMS.
The four models evaluated included MPTER (EPA), PPSP (Martin Marietta Corp), MPSDM
(ERT) and TEM-8A (Texas Air Control Board). Results from this evaluation were similar
in many ways to those obtained-for Clifty Creek, Muskingum River and Kincaid. PPSP
clearly over-predicted concentrations in almost every data category while MPSDM,
MPTER, and TEM exhibited relatively small but detectable biases depending on averag-
ing period and data category.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
Air Pollution
Mathematical Modeling
Meteorology
Sulfur Dioxide
Statistical Measure
Performance Evaluation
Air Quality Impact
Assessment
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
20. SECURITY CLASS (This page!
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION is OBSOLETE
------- |