United States
Environmental Protection
Agency
Atmospheric Research and
Exposure Assessment Laboratory
Research Triangle Park, NC 2771 1
'V
Research and Development
EPA/600/S3-89/071 Sept. 1989
v°/EPA Project Summary
Air Quality Simulation Model
Performance for One-Hour
Averages
G. E. Moore, T. E. Stoeckenius, Steve Hanna, and Dave Strimitas
If a one-hour standard for sulfur
dioxide were promulgated, air quality
dispersion modeling in the vicinity of
major point sources would be an
important air quality management
tool. Would currently available
dispersion models be suitable for
use in demonstrating attainment of
such a standard in the vicinity of
large, elevated, buoyant point
sources such as utility power plants?
The results summarized in this report
suggest that using these models in
connection with an hourly average
standard does not present the
regulatory community with any
significant additional uncertainties
that are not already being dealt with
in connection with the current 3- and
24-hour standards. Predictions of
peak hourly average concentrations
unmatched in time or location are
slightly more conservative on
average than corresponding
predictions of 3- and 24-hour
averages. Although certain
conditions not specifically, treated by
the models, such as inversion break-
up fumigation, are known to occur
and to have the potential for
producing high ground-level impacts,
these conditions have been only
rarely observed in model evaluation
studies and do not seem to
contribute to the bulk of the 50
highest concentrations. In fact, nearly
all of the highest concentrations
observed in the evaluation studies
examined occurred under convective
conditions, which are treated by the
existing models. Peak model
predictions under such conditions
match reasonably well with the
highest observed concentrations,
although large prediction errors can
occur in any given hour.
Consideration of situations involving
impacts on elevated terrain or
building downwash phenomena have
been explicitly excluded. This review
of the results of model evaluation
studies was restricted to some fairly
simple analyses. Additional analyses
are recommended. Recom-
mendations to improve model
formulations are made regarding the
treatment of plume behavior in the
vicinity of the mixing height and the
simulation of plume dispersion within
the mixed layer during convective
conditions.
This Project Summary was
developed by EPA's Atmospheric
Research and Exposure Assessment
Laboratory, Research Triangle Park,
NC, to announce key findings of the
research project that is fully
documented in a separate report of
the same title (see Project Report
ordering information at back).
Introduction
An analysis of the results of previous
dispersion model performance evaluation
studies has been conducted to ascertain
the suitability of currently used models
for the prediction of hourly average
concentrations. This investigation was
motivated by the potential promulgation
of a statistical (expected exceedance) 1-
hour S02 NAAQS. Implementation of
such a standard would rely largely on the
use of dispersion models that are
capable of accurately predicting the
-------
whole upper tail of the distribution of
hourly average S02 concentrations, not
just the highest or second highest
concentrations.
Our analysis was confined to the study
of model performance for elevated,
buoyant point sources of the type
typically associated with fossil fuel-fired
power plants. Such sources represent
one of the major areas of model
application for the prediction of S02 air
quality impacts. We conducted analyses
designed to address the following issues:
How well do models predict the 1,
2 N highest concentrations
irrespective of time or location
matching?
How well do models predict the 1,
2, . . . , N highest observed
concentrations when observations
and predictions are matched in
time but not location?
How well do models predict
concentrations occurring under
specific meteorological conditions?
More specifically, what meteoro-
logical conditions produce the
highest observed concentrations?
What conditions are associated with
the largest discrepancies between
observed and predicted
concentrations?
To answer the above questions, we
reviewed the published results of
previous model performance studies and,
whenever possible, obtained the
aerometric and meteorological data from
those studies for further analysis.
Model Performance: Ability to
Predict Peak Concentrations
Short term (1- to 24-hour average)
ambient air quality standards are
designed to control the magnitude and
frequency of occurrence of peak ground-
level concentrations. Demonstrating that
the emissions from a major source do not
violate such standards often involves the
use of a dispersion model to predict the
highest concentrations occurring over a
one- to five-year period in the vicinity of
the source. A model's ability to
accurately and precisely predict peak
concentrations is therefore very
important. However, a model need not
necessarily predict the exact time and
location of the peak values as this
information is not usually of direct
relevance to the attainment
demonstration. On the other hand,
examining a model's ability to reproduce
the N highest observed concentrations
during the hours in which they occur (i.e.,
matching concentrations in time) provides
insight into the types of meteorological
conditions under which models do
especially well or poorly and the reasons
for such performance. This information, in
turn, can be used as a guide to improving
models and to the level of performance
that can be anticipated from a given
model under a given situation.
Concentrations Unmatched in
Time or Location
We examined the performance of
models in predicting the N highest
concentrations (typically around 25)
regardless of their time or location of
occurrence. In some cases, only
comparisons of the highest and second
highest observed and predicted
concentrations were available. We
caution the reader that it is difficult to
draw generalizations from such
comparisons because the highest and
second highest concentrations usually
have a large degree of variability.
With the exception of the Maryland
Power Plant Siting study (for which a
value of N = 50 was used), the CRSTER
and equivalent models tend to
overpredict slightly the peak
concentrations (N less than or equal to
25) or produce no significant bias. TEM,
on the other hand, underpredicts at some
sites and produces no significant bias at
others. The only other models for which
evaluation results are available from more
than one site are PPSP and MPSDM.
PPSP overpredicts consistently at all
sites, in some cases by more than a
factor of two. MPSDM also produced
significant overpredictions.
Model Performance for Hourly
Averages Compared with
Performance for 3- and 24-Hour
Averages
Would implementation of a 1-hour
average NAAQS for S02 result in
significantly greater modeling uncertainty
than currently exists in demonstrating
attainment of the 3- and 24-hour NAAQS?
To answer this question, we reviewed an
analysis of the variation in model
performance with averaging time by
Hayes. This study showed that the ratio
of the average of the 25 highest
predictions to the average of the 25
highest observations (unmatched in time
or location) decreases with increasing
averaging time for all models included in
the EPA Rural Model Evaluation Study,
the CEQM model at EPRI PMV Kincaid,
and for RAM (but not TEM) in the EPA
Urban Model Evaluation Study. Results
for ratios of the highest and second
highest concentrations were mixed. Thus,
models that overpredict peak 3- and 24-
hour averages tend to overpredict peak
hourly averages by a greater amount,
while models that underpredict peak 3-
and 24-hour averages may overpredict
peak hourly averages. This suggests that,
at least as far as bias goes, shifting
emphasis from 3- and 24-hour averages
to 1-hour averages will result in more
conservative predictions of peak
concentrations.
The study also examined the
correlation between observations and
predictions paired in time but not location
as a function of averaging time at the
EPRI PMV Kincaid site using the CEQM
model. The results indicate that
correlations for 3-hour averages are only
slightly better than those for hourly
averages, while the correlation for 24-
hour averages is not significantly different
from that of hourly averages. Thus, the
model's skill in predicting hourly
averages does not appear to be very
much worse than it is for 3- and 24-hour
averages.
Concentrations Matched in Time
A model's ability to predict peak
concentrations can also be examined by
comparing the N highest observed
concentrations with the highest
concentrations predicted during the same
hours (i.e., compare concentrations
matched in time but not location for the
hours with the N highest observed
concentrations). Such a comparison
evaluated the model's ability to
reproduce high observed concentrations
under the specific meteorological
conditions that cause them (exclusive of
wind direction).
As has been demonstrated by EPRI
and other studies, correlations between
time- matched observed and predicted
concentrations are generally very low or
not significantly different from zero.
Correlations for just the hours with the 50
highest observed concentrations are not
significantly different from zero except for
the PPSP and RTDM models. Also, the
number of predictions falling within a
factor of two of the observations is less
than 32 percent except for RTDM anc
PPSP.
On the basis of the above findings, we
conclude that the poor model skill foi
time-paired observations and predictions
is primarily the result of two factors:
1. Meteorological inputs to the
dispersion models do not reliably
-------
represent atmospheric conditions
occurring in a particular hour.
2. Model formulations are insufficient
to describe the actual plume
behaviors that lead to the highest
observed concentrations.
Model Performance Under
Specific Dispersion Conditions
One concern with using presently
available dispersion models to perform
attainment demonstrations for an hourly
average ambient standard is that these
models may not adequately reproduce all
of the various types of dispersion
phenomena that can lead to high hourly
average concentrations. We performed
several analyses in an attempt to identify
dispersion conditions and plume
behaviors that are associated with high
observed concentrations and to estimate
how well the models perform under such
conditions.
Plume Behaviors and Dispersion
Conditions Potentially Resulting
in High Concentrations
A great deal of meteorological and
aerometric data is needed to adequately
explain the specific dispersion conditions
and plume behaviors that lead to a high
observed hourly average concentration.
No model evaluation study carried out to
date has involved the collection of
enough data to allow one to
unequivocally group hours with high
observed concentrations into a handful of
well-defined categories of dispersion
conditions and plume behaviors. Enough
information from previous theoretical,
field, and laboratory studies is now
available to make it possible to describe
several general types of plume behaviors
and dispersion conditions that can, and in
some cases have actually been observed
to, lead to high ground-level concen-
trations or large discrepancies between
observed and predicted concentrations in
the vicinity of elevated, buoyant point
sources. Plume behaviors considered
are. convective fumigation, moderate
wind convection, bumping-loopmg,
limited rise, plume penetration, and
inversion break-up or shoreline
fumigation.
Applicability of Model
Formulations to Important
Plume Behaviors
No presently available point source
Dispersion model contains all of the
Algorithms needed to make accurate
predictions under all of the plume
behaviors. There are, however, sufficient
differences between the models included
in our review to suggest that some may
be better equipped to deal with these
phenomena than others. For example,
plume behaviors such as bumping-
looping and partial penetration in which
the plume centerline is near the mixing
height can prove to be difficult for models
to reproduce accurately unless some sort
of partial penetration algorithm is
included in the model. Neither the
CRSTER/MPTER/CEQM or SHORTZ
models have such an algorithm. These
models assume a zero ground-level
impact if the plume centerline is
predicted to be above the mixing height
and a perfect reflection of plume material
off of the inversion base otherwise. This
oversimplified approach can lead to large
prediction errors.
Accurate estimates of plume height
during convectively unstable conditions
are needed to produce accurate
predictions of ground-level impacts. The
plume breakup/touchdown algorithm in
the PPSP model represents an
improvement over the standard Briggs
formulas used in the other models that is
designed to meet this need.
Models that rely solely on the PGT
stability class to estimate the rate of
plume dispersion may have difficulty
differentiating between the effects of
various convective and mechanically
driven turbulepce phenomena and may
use inaccurate" bispersion coefficients for
some hours since the PGT classes are
based solely on wind speed, cloud cover,
and solar zenith angle. Some of the
models we reviewed (RTDM, SHORTZ
and PPSP) have the capability of using
more direct measures of turbulence
intensity in calculating dispersion
coefficients. If accurate estimates of
turbulence intensities are available, these
models should produce realistic
dispersion coefficients and concentration
predictions.
Dispersion Conditions
Associated with Highest
Observed Concentrations
The highest observed concentrations
frequently arise when a buoyant plume is
trapped under a strong inversion and
convective activity below the inversion
mixes relatively undiluted material to the
ground. High observed concentrations
can also occur when plume heights are
well below the mixing height and large-
scale eddies rapidly mix plume material
to the ground.
Dispersion Conditions
Associated with Over- and
Underpredictlons
During the hours with the 50m highest
observations the largest overpredictions
tended to occur under unstable
conditions whereas underpredictions
were the general rule during neutral and
stable conditions. The findings suggest
that the largest underpredictions for
models other than PPSP and RTDM are
primarily the result of incorrect plume
rise treatment during strong inversion
conditions indicative of bumping/looping
behavior. The analysis of the largest
over- predictions at selected sites shows
that overpredictions generally occur
under unstable light wind conditions
when the mixing height is relatively high,
that is, convective fumigation conditions.
Implications for Model
Applications in Connection with
a One-Hour Sulfur Dioxide
Standard
If an hourly average NAAQS for SO2
were to be promulgated, would currently
available dispersion models be suitable
for use in demonstrating attainment of the
standard in the vicinity of large, elevated,
buoyant point sources as utility power
plants? The results suggest that using
these models in connection with an
hourly average standard does not present
the regulatory community with any
significant additional uncertainties that
are not already being dealt with in
connection with the current 3- and 24-
hour NAAQS. Predictions of peak hourly
average concentrations unmatched in
time or location are slightly more
conservative on average than
corresponding predictions of 3- and 24-
hour averages, and model precision for
observations and predictions matched in
time is no worse for hourly averages than
it is for longer averaging times.
Although certain conditions not
specifically treated by the models, such
as inversion break-up and Seabreeze
fumigation, are known to occur and to
have the potential for producing high
ground-level impacts from elevated
sources, we found that these conditions
have been only rarely observed in model
evaluation studies and that they do not
seem to contribute to the bulk of the 50
highest concentrations. In fact, nearly all
of the highest concentrations observed in
the evaluation studies we examined
occurred under convective conditions,
which are treated by most of the models
-------
as Pasquill stability class A and B cases.
Peak predictions under such conditions
match reasonably well with the highest
observed concentrations, although large
prediction errors can occur in any given
hour. Thus, special dispersion situations
that might be of concern in modeling
peak hourly average concentrations do
not appear to occur frequently enough
around typical power plant sources to
invalidate the use of currently available
dispersion models. In reaching this
conclusion, we have explicitly excluded
consideration of situations involving
impacts on elevated terrain or building
downwash phenomena.
It should be noted that poor time-
matched model performance may be due
at least in part to the fact that model
predictions represent a mean value over
an ensemble of similar meteorological
conditions while the observed values
represent a single realization of an event
from the ensemble. It may be possible to
estimate the size of the contribution of
this effect to the observed lack of model
skill by comparing observed and
predicted concentrations unmatched in
time that belong to the same ensemble.
Ensembles could be defined in terms of
simple model inputs (e.g., all hours of A
stability, very light winds, and low mixing
heights).
Additionally, we recommend that
additional data analyses and possibly
field studies be carried out to identify
situations in which inversion break-up and
Seabreeze fumigation as well as
downwash phenomena occur and to
characterize model performance under
these conditions.
Recommendations for
Improvements in Model
Formulations
As pointed out above, one of the
principal causes of poor model
performance is the apparently unrealistic
treatment of plume behavior in the
vicinity of the mixing height. In particular,
the overly simplified treatment of the
vertical profiles of mean wind and
stability parameters used in most models
can lead to inaccurate estimates of plume
height, especially in the vicinity of
elevated inversions. This in turn can
result in inaccurate estimates of ground-
level concentrations. For example, the
CRSTER/MPTER/CEQM and SHORT,
models assume a zero ground-leve
impact whenever the predicted plum
height is greater than the mixing heigh
Given the inevitable errors present i
estimates of both of these quantities an
the fact that the plume rise is calculate
without regard to the limitation to ris
imposed by the elevated inversion, zer
ground-level concentrations are ofte
predicted when in actuality substanti<
impacts are occurring. Substantic
reduction of the number of cases c
underprediction could be achieve
through the use of more realisti
treatments of the interaction of plume an<
inversions.
Another area in which mode
improvements are needed is in th
simulation of plume dispersion within th
mixed layer during convective condition:
The commonly used Pasquill stabilit
classes and dispersion parameters do nc
always accurately describe the degree c
lateral and vertical dispersion as well a
formulations based on directly observe
stability parameters, such as u/w, or th
enhanced lateral dispersion of a plum
during bumping-looping as discussed b
Pierce.
G. £. Moore and T. E. Stoeckenius are with Systems Applicimhs, Inc., San
Rafael, CA 94903. Steve Hanna and Dave Strimitas are- with Sigma
Research Corp., Lexington, MA.
D. Bruce Turner is the EPA Project Officer (see below). JA.
The complete report, entitled "Air Quality Simulation Model Performance for
One-Hour Averages, " (Order No. PB 89-233 506/AS; Cost: $21.95. subject
to change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Ofgcer can be contacted at:
Atmosph&rfc*Research and Exposure Assessment Laboratory
-' ........ "~"°'~ - " ~~
Research Triangle Park, NC 27711
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
Official Business
Penalty for Private Use $300
EPA/600/S3-89/071
10" "BMCI
------- |