905R80013
30-53.7
The Location and Magnitude of Maximum Short-Term
Ground Level Impacts of Effluents from Tall Stacks
Dennis A. Trout
U.S. Environmental Protection Agency, Region V
Chicago, Illinois
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80-53.7
Introduction
The 3-nour national ambient air quality standard for S&2 reauires that at
any point, the SO? concentration not exceed 0.5 ppm (1300 ug/nr) more than
once per year, mis standard poses the need to adequately assess such
maximum short-term impacts. This paper focuses on assessment of the short-
term impacts of S0£ sources with tall stacks (stack heights greater than
1UU m).
The original intent of building tall stacks for sources of large S02 emis-
sions potential was to reduce the maximum ground-level ambient air quality
impacts of the stack's effluent, the theory being that the effluent's ambient
concentration (mass/unit volume) would be reduced by spreading the same amount
of pollutant mass through a greater atmospheric volume. However, there have
been conflicting opinions on how successful tall stacks have actually been In
reducing maximum ground-level impacts. These differences in opinion center
about the expected location and magnitude of maximum short-term ground level
impacts of effluents from tall stacks.
The purpose of this paper is to review the data relevant to the assessment
of the location and magnitude of maximum short-term impacts of effluents from
tall stacks. The questions this investigation seeks to answer are: 1) At what
distance from a tall stack is its maximum short-term impact observed? 2) What
atmospheric conditions are associated with the occurrence of maximum short-
term impacts? and 3) Which, of various dispersion coefficients when used in
standardly available models, best estimate the locations and magnitudes of
expected maximum short-term impacts of effluents from tall stacks?
Procedure
Because theory is born out by observation, answers derived from the pertinent
observations are preferred to those based purely on theory. In order to avoid
observational oias, sufficient and necessary observations must be evaluated;
that is, the temporal and spatial characteristics of the observations must be
considered. It must also be determined that the observations are not biased
by the manner in which they are made.
In order to adequately assess the distance from the stacks to short-term
maximum impacts, it would be desirable to obtain quality assured data from a
network consisting of continuous monitors at numerous distances along each of
numerous radials extending in different directions from the stack. The moni-
tors should be located so that they adequately cover both near (less than
2 km) and far field distances from the stack with a spatial density sufficient
to determine the distance to maximum impact. Ideally the source(s) to be
analyzed would be isolated such that background contributions to the pollutant
concentrations being monitored are minimized. Furthermore, it would be desir-
able that the period of record of observations extend over several years so that
year to year variations in meteorological extremes may also be accounted for.
The longer the period of record and the greater the monitoring spatial density,
the greater the probability that occurrences of worst case meteorological
conditions and the constraining emission characteristics of the source will
coincide so that concentrations representative of maximum short-term impacts
of effluents from tall stacks can be observed by a monitor.
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80-53.7
Obviously if the effluent characteristics of the stack are dependent on or
controlled as a function of expected meteorological conditions (i.e., by Sup-
plementary Control Systems (SCS), sometimes referred to as dynamic emission con-
trol systems, or emissions limit control systems, etc.), the probability of
ODserving short-term maximum impacts may be greatly reduced. Therefore, a
monitoring network might not observe the maximum possible short-term concen-
tration if tall stack effluents are controlled by a correctly designed and
properly operating SCS application or similar meteorology dependent emission
control technique.
Upon determining the periods of observed maximum short-term impact, the
meteorological conditions associated with those periods can also be determined.
Upon determining the location, magnitude, and meteorological conditions associ-
ated with maximum observed short-term impacts, it is then possible to compare
model calculated locations ana magnitudes of maximum short-term impacts with
observations. Such comparisons may be used to assess which model results best
compare with observations, but cannot necessarily be used to conclude that the
transport and dispersion of the effluent mass is accurately modeled with
respect to the actual atmospheric cause-effect relationships. In order to
evaluate and compare the adequacy of a model with respect to accurately
accounting for plume rise, transport, and dispersion of the pollutant mass,
detailed and comprehensive observations of such factors would be required.
In order to determine if a model is satisfactorily performing from a regula-
tory perspective, comparisons with observations are made with respect to the
upper ends of the frequency distributions of predictions and observations. This
comparison is called for, particularly in light of the requirements of the short-
term ambient air quality standard which is based on the upper end of the frequency
distribution of observations. That is, violations of the ambient air quality
standards (e.g., the 3-hour SC^ standard) are determined by the observation of
more tnan one occurrence per year of concentrations exceeding a set maximum.
Observations
The first observation to be made is that there is no monitoring network avail-
able which ideally provides all the observational desires previously set forth.
However, of the limited data available, there appears to be a set of observations
from a monitoring network in the vicinity of the tall stacks of the Muskingum
River Power Plant operated by the Ohio Power Company of the American Electric
Power System, which comes closest to satisfying observational requirements. The
data from the Muskingum River Power Plant's SC^ monitoring network is relied
upon for the determination of the distance from the stacks to maximum short-term
(3-hour) S02 impacts of effluents from tall stacks. Once the distance to
maximum 3-hour SOo impacts is determined, data from monitors which are located
at comparable distances to other tall stack sources can also be evaluated with
respect to the magnitude of maximum 3-hour SC^ impacts.
The Muskingum River Power Plant is located in Washington County in south-
eastern Ohio in the valley formed by the Muskingum River. The terrain in the
vicinity of the plant is hilly with elevations as high as 350 m above mean
sea level (150 m above the base of the plant stacks). The total generation
capacity of the Muskingum River Power Plant facility is approximately 1,500
megawatts. The current sulfur content of the coal fired averages about
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80-53.7
9 to 10 Ib SOg/lO Btu. No flue gas desul furlzatlon system is employed and
the effluent is emitted through two separate 252 m stacks. The combined SC^
emission rate from the two stacks when the plant is operating at full load is
approximately 15,500 g/s or 62 tons/hr (assuming 9 Ib S02/10° Btu coal fired).
Distance to Maximum Short-Term Impacts
Since January 1978, four continuous S02 monitors have been in location along
a radial extending in a northeasterly direction from the Muskingum River Power
Plant. Table I identifies the Muskingum River Power Plant S02 monitoring sites
and their locations. It can be seen that the closest monitor, the Center Bend
monitor, is 1.7 km from the centroid of the two stacks.
Taole I. Muskingum River Power Plant $62 monitors.
Distance Azimuth Height above
from Stacks3 from Stacks3 Stack Base
Name Monitor No. (km) (°) (m) _
Center Bend 5 1.7 37 74
Hackney 2 4.6 41 74
Rich Valley 3 8.5 36 • 105
Cal dwell 4 20.7 33 132
Beverly 1 5.2 138 56
Mount 01 i vet 6 6.1 240 108
a Stacks 1 and 2 are located 645 m apart along a 240° azimuth. Distances and
azimuths indicated are from the centroid of the two stacks.
Because four of these monitors are located within 4° of a straight line (37°)
raaial from the source of emissions, it may be assumed that all four monitors
on occasion will be simultaneously impacted by emissions from the plant (i.e.,
when the mean transport winds are from the SW (217°)). Because these four
monitors are located in the same downwind direction from the plant, it was
assumed that data from this array of monitors were sufficient to determine
tne distance to highest observed impacts.
Table II lists the three highest observed 3-hour concentrations at each
monitor since January 1978 and the dates of those occurrences at each monitor.
It can be seen that the highest 3-hour concentration ever observed at any of
these four monitors occurred at the monitor closest to the plant on May 5, 1979.
It can also be seen that the highest 3-hour concentration observed at the
monitor next closest to the plant also occurred on May 5, 1979.
Meteorology Associated with Maximum Short-Term Impacts
Table II also presents the meteorological conditions associated with the
monitored concentrations. It can be seen that the highest 3-hour S02 impact
occurred in the afternoon under conditions associated with very unstable
atmospheric conditions; that is, very light wind speeds (less than 1.5 m/s),
clear skies, and a well developed mixed layer (inversion base 1300 m above
ground level). Thus, the maximum 3-hour S02 concentration measured by the
Muskingum River Power Plant monitoring network was measured at the monitor
closest to the stacks, and occurred under very unstable atmospheric conditions.
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80-53.7
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80-53.7
The maximum impacts ooserved at monitors located at greater distances from
the power plant occurred during periods of higher wind speeds and/or more
stable atmospheric conditions. It should also be noted that these more
distant monitors, monitors 3 and 4, are higher in elevation than monitors
5 and 2, and therefore closer to plume height. If the monitors were located
at lower elevations, they would be expected to observe lower concentrations
than those measured at the higher elevations.
Because it is observed that maximum short-term concentrations occur close
to a source with tall stacks, other sources with quality assured monitors
close to their tall stacks also warrant review. In addition to the Muskingum
River Power Plant, there are three Tennessee Valley Authority (TVA) power
plants which were found to have quality assured continuous SC^ monitors close
to their tall stacks. Table III lists these four power plants with quality
assured continuous SC^ monitors within 2 km of their tall stacks. Table IV
lists the stack design and full load operating parameters and emission rates
for the power plants identified in Table III.
Although there are no other continuous monitors along the same radial from
the stack for the TVA monitors, and thus no means of further assessing the
distance to maximum impact at these plants, the meteorological conditions
associated with the occurrences of maximum short-term impacts at these monitors
can be further evaluated. It is observed upon review of the meteorological
data during periods of measured maximum short-term SO? concentrations, that
conditions generally consisted of those associated with very unstable
atmospheric conditions; i.e., very light and variable winds, clear skies,
and well developed mixing layers, which occurred during the late morning and
afternoon perioas.
Thus, the modeling of maximum 3-hour SO? concentrations resulting from tall
stack effluents should be approximated by~the transport and dispersion process-
es associated with very unstable atmospheric conditions. Although such trans-
port and dispersion processes have not necessarily been adequately observed
and understood, Gaussian air quality models which account for the occurrence
of very unstable atmospheric conditions by use of stability dependent dispersion
coefficients have traditionally been used to estimate air quality impacts.
Pasquill-Gifford class A stability dispersion coefficients1 are one of the sets
of coefficients that have been used by regulatory agencies and others in model-
ing tall stack effluent impacts under very unstable atmospheric conditions.
Because a controversy has existed regarding the use of the Pasquill-Gifford
(P-G) curves for class A (very unstable) atmospheric conditions in estimating
ground level pollutant impacts from tall stacks, the sets of dispersion
coefficient curves to be considered herein will include the P-G class A curves
and the curves derived from field studies for which tracers were released at
heights of 100 m or higher. The P-G class B curves are considered because
several investigators have suggested that replacing the P-G A curves with the
P-G class B curves would yield a better comparison of maximum calculated with
maximum measured impacts. The contention being that the P-G class A curves
when used with standardly available models (such as the U.S. EPA CRSTER or
MPTER models) result in overestimates of ground level impacts and that these
impacts are calculated to occur too close to the stack for sources having tall
stacks.
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80-53.7
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30-53.7
Three independent sets of field experiments have been found in the literature
which investigate the dispersion of tracers released at heights of 1QO m and
higher under very unstable atmospheric conditions. Singer and Smith,^ Vogt, et
al.,4 and Thomas, et al., have derived or and err curves based on such field
investigations. Table V identifies the curves of dispersion coefficients
considered herein. This table indicates the investigator, experiment site,
tracer release height, and surface roughness length (2Q) for each set of curves
considered.
Figures 1 and 2 graphically present the curves of o~ and a^, respectively,
for each of the tracer studies identified in Table V.
Analyses of Periods of High Short-Term
""Impacts'at'Monitors Near~Ta11 Stacks
Table VI indicates the highest 3-hour SOo concentrations observed under very
unstable atmospheric conditions at each of the monitors located within 2 km
of the power plants identified in Table IV. Table VI also compares the observed
values with the concentrations calculated using the alternative sets of
dispersion coefficient curves. Calculated values are based on the use of:
on-site meteorological data, assumed full load operating conditions (for each
stack in operation at the time), Briggs plume rise, and steady state Gaussian
dispersion assuming co-located stacks and an unlimited mixing height (because
no on-site measurements of mixing layer depth or actual plume height were avail-
aole). The calculated 3-hour concentrations are the average of three 1-hour
calculations. It should be noted that experience shows higher impacts are
calculated at full load under unstable atmospheric conditions than for reduced
load operating conditions. Furthermore, background concentrations were found
to be negligible during these periods based on upwind observations.
It can be seen from Table VI that all the calculated concentrations result-
ing from the use of each of the alternative sets of dispersion coefficients
underpredict the observed 3-hour average values at each monitoring site.
Calculations based on the use of the P-G A and Karlsruhe A curves, however,
appear to best approximate observations. It should be noted, although not
directly shown, that even if the mixing layer was assumed to be equal to
plume height, the 3-hour average concentration would still be underpredicted
by use of the P-G B, BNL 82 and Jiilich A curves. Furthermore, analyses have
also shown that even if it is assumed that the mean wind blew continually in
the direction of the monitor, the concentrations calculated by use of the
P-G B, bNL t$2, and Julich A curves still underpredict observations.8
Obviously the comparisons made in Table VI do not necessarily consider the
upper ends of the frequency distributions of predicted and observed concen-
trations (to be addressed later herein) as recommended in the "Guideline on
Air Quality Models."y However, Table VI does provide an example of the com-
parative ranking of the impacts calculated at select monitoring sites using
the alternative sets of dispersion coefficient curves.
The relative calculated impacts using the alternative sets of dispersion
coefficient curves can also be demonstrated graphically and are depicted by
Figures 3 and 4.
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80-53.7
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80-53.7
Figure 3 indicates the maximum normalized concentration as a function of
plume height for the alternative sets of dispersion coefficient curves
assuming an unlimited mixing height. Maximum concentrations would be a
factor of two larger for the worst case assumption of plume trapping, where
the plume height is restricted to a height equal to the height of the mixing
layer (that is the plume is reflected by the base of an inversion just above
the plume center!ine). It can also be seen from Figure 3 that, for plume
heights greater than 200 m, lower maximum normalized concentrations are
calculated using the P-G B, BNL 82, and Jiilich A curves compared to the P-G A
curves. The Karlsruhe A values are slightly higher (about 20% higher) than
the P-G A values.
Figure 4 indicates the distance from the source to the point of maximum
impact as a function of plume height for the alternative sets of dispersion
coefficient curves. As can be seen from Figure 4, use of the P-G B, BNL 62,
and Jiilich A curves calculate maximum impacts for plume heights greater than
200 m to occur at greater distances from the source than calculated by use
of the P-G A curves. Maximum impacts are calculated to occur closer to the
source by use of the Karlsruhe A curves than by use of the P-G A curves.
Analyses of the Upper Ends of the Frequency
Distribution of Predictions and Observations-
Sulfur Dioxide Emission Limitation (SDEL) Systems (where plant load and
emission rates are reduced when high ground level impacts are predicted) are
employed at both the TVA Widows Creek and Cumberland power plants. Because
the emission rates may be reduced at both Widows Creek and Cumberland during
periods of predicted high impacts, comparison of the upper ends of the
frequency distribution (based on assumed maximum emission rates) would be
inappropriate. Therefore (lacking readily available hourly operations and
emission data), comparisons of the upper ends of the frequency distributions
can only be appropriately made for the Muskingum River and John Sevier power
plants. In order to compare the upper end of the frequency distribution of
predicted concentrations to maximum observed concentrations, the highest
and second highest concentrations predicted to occur at the Muskingum River
and John Sevier monitoring sites for each year of meteorological data avail-
able are compared in Table VII with the highest and second highest concen-
trations observed during each year of monitoring data available. The cal-
culated values indicated in Table VII assume full load operating conditions
as indicated in Table IV. Calculations were made using the U.S. EPA MPTER
model, a steady state Gaussian model (similar to the U.S. EPA CRSTER model
but able to account for multiple sources and separated stacks and locate
receptors using Cartesian coordinates) using the P-G dispersion coefficients
for class A stability conditions. The MPTER model was otherwise run with
the same modeling assumptions used in the CRSTER model. Hourly sequential
surface meterological data used in the MPTER model were from Huntington, WV,
for Muskingum River and from Bristol, TN, for John Sevier; mixing height
data in both cases were from Huntington, WV.
It can De seen from Table VII that maximum impacts calculated using P-G A
dispersion coefficients compare well with observations. The MPTER modeling
results for the Muskingum River monitors also consistently indicated that the
maximum 3-hour impacts were calculated to occur at the monitor closest to the
stacks.
-------
80-53.7
Table VII. Highest and second highest 3-hour
calculated at the closest monitor
John Sevier power plants (ppm).
S02
site
concentrations observed and
to the Muskingum River and
Observed
Year
Highest
Second
Year
Calculated
Hiqhest
Second
Muskingum River
1978
1979
John Sevier
1973
1974
1975
1976
1977
1978
.39
.63
.17
.23
.45
.40
.43
.34
.20
.41
.16
.22
.39
.39
.31
.30
1964
1971
1972
1973
1974
1975
1977
1964
1970
1971
1972
1973
1974
.43
.33
.40
.40
.36
.55
.41
.32
.26
.24
.29
.23
.21
.29
.25
.34
.34
.30
.43
.34
.32
.18
.21
.25
.16
.17
Review of the meteorological conditions for the periods of predicted maxi-
mum impacts indicates that very unstable atmospheric conditions are associated
with the maximum calculated values. These conditions include light and
variable wind velocities, clear skies, and mixing depths of 1000 m or more
which compare well with the meteorological conditions associated with the
maximum observed impacts.
The model calculation methodology used in deriving the concentration esti-
mates shown in Table VII assumes that if the calculated height of the plume
at final rise by methodology of Briggs' exceeds the height of the mixing
layer that the plume will be completely and permanently contained in the
stable layer aloft and therefore that no ground level impacts will result.
This modeling methodology assumes that plume rise will not be influenced
by the height of the mixed layer or by the temperature structure of the
atmosphere under unstable conditions as classified by Turner.11 It might
be suggested, therefore, that these modeling assumptions are not realistic
and that if high concentrations are observed close to tall stacks that such
concentrations result due to the plume being trapped within a mixing layer
having a height much less than the height to which the plume would rise if
an unlimited mixing layer existed. Such a condition would cause higher
concentrations to be calculated with each of the sets of dispersion coeffi-
cients than if an unlimited mixing height was assumed. That is, the cal-
culated maximum concentration would be greater and occur closer to the plant
for lower plume heights, see Figures 3 and 4.
-------
30-53.7
Analyses of the Muskingum River data assuming the effluent plume is trapped
within the mixing layer observed during the period of maximum measured
impact, demonstrate that use of the P-G 3, BNL B2, and Jiilich A dispersion
coefficient curves result in underpredictions, as well as the predicted
location of maximum impact to occur further downwind from the source than that
observed. Further analyses of the Huskingum River data demonstrate that if the
effluent plume is actually trapped within a mixing layer sufficiently shallow
such that a higher concentration is calculated to occur at the Center Bend
monitor than at the next closest monitor, that use of the P-G B or BNL B2
curves would result in a higner calculated maximum concentration than the use
of P-G A curves with the plume trapped within the actual observed mixing
1ayer.
Without actual observations of plume height, wind and temperature profiles,
trajectory of tne elevated plume, and the three dimensional distribution of
the pollutant mass with time, caution is warranted with respect to revision
of tne plume rise, transport and dispersion, and mixing layer trapping
assumptions and associated model algorithms.
Summary ana Recommendations
Based on existing measurements of maximum 3-hour impacts of tall stack
effluents, it is observed that:
t The highest measured 3-hour impacts occur at the monitor closest to
stacks (for cases where the directions and elevations of the different
monitors relative to the source are comparable).
> Maximum impacts measured at monitors within 2 km of sources with tall
stacks occur in the afternoon with light and variable wind velocities,
mixing depths of 800 m or more, and very unstable atmospheric condi-
tions.
• Highest impacts measured at monitors located more distant from the
source tend to occur during conditions of higher wind speeds and/or
greater atmospheric stability.
Based on calculations of maximum impacts for monitoring sites in the vicinity
of tall stacks, it is observed that:
t Calculations using on-site meteorological measurements and P-G B, BNL 82,
or Julich A dispersion coefficient curves underpredict observations
during the periods of maximum impact observed at monitors within 2 km
of sources with tall stacks.
t Comparisons of calculations and observations for the Center Bend monitor
assuming the effluent plume is trapped within the mixing layer observed
during the period of maximum measured impact demonstrate that the use
of the P-G B, BNL B2, and Julich A dispersion coefficient curves result
in unaerpredictions, as well as the predicted location of maximum impact
to occur further downwind from the source than that observed.
-------
80-53.7
a Comparisons between calculations assuming the effluent plume is trapped
within a mixing layer sufficiently shallow such that a higher impact is
calculated to occur at Center Bend than at the next closest monitor,
demonstrate that use of either the P-G B or BNL B^ curves would result
in higher calculated maximum concentrations than the use of the P-G A
curves with the plume trapped within the actual observed mixing layer.
» The highest and second highest concentrations observed during
each year of available measurements at monitors within 2 km of
sources with tall stacks compare well with the highest and second
highest values calculated by the U.S. EPA MPTER model for those
monitoring sites (considering each year of meteorological input
data available and assuming full load operating conditions and
the use of the P-G A dispersion coefficient curves for very unstable
atmospheric conditions).
• Use of P-G A and Karlsruhe A curves in models such as CRSTER and MPTER
result in calculated impacts that better agree with the location and
magnitude of observed maxima than use of the P-G B, BNL 82, or Jiilich A
curves.
What has been observed and reported herein is that the magnitudes and distances
to maximum 3-hour concentrations as predicted using model algorithms and assump-
tions as contained in the U.S. EPA CRSTER and MPTER models compare well (perhaps
fortuitously) with observations in the vicinity of tall stacks. Note, it has
not been suggested, and should not be inferred, that the P-G A dispersion
coefficients (or any other set of dispersion coefficients) do or do not accu-
rately describe the distribution of pollutant mass of an elevated plume. Nor
have any claims been made or inferred herein as to the accuracy of any other
model algorithms or modeling assumptions.
Due to lack of on-site measurements of plume height, wind and temperature
profiles, and trajectory of the elevated plume, caution is warranted. The
potential effects of factors such as increased initial plume growth during
plume rise, enhanced plume rise due to multiple stacks, transitional plume
rise effects, changes in height of the plume center!ine, and non-Gaussian
conditions influencing transport and dispersion have not been applied in
the calculations or comparisons made herein.
In order to better assess the location and magnitude of maximum short-term
impacts of tall stack effluents under unstable atmospheric conditions, more
monitors are required to be sited near such sources than has been standardly
practiced to date. Measurement of plume height, wind and temperature profiles,
and distribution of the effluent mass as a function of downwind distance is
also recommended in order to better ascertain the transport and dispersion of
tall stack effluents during unstable atmospheric conditons. Investigations
including such measurements are warranted and necessary for the improvement of
present modeling methodology.
-------
80-53.7
References
1. F.A. Gifford, Jr., "Use of routine meteorological observations for estimating
atmospheric dispersion." Nuclear Safety, _2(4): 47 (1961).
2. MNL, Report to the U.S. EPA of the Specialists' Conference on the EPA Modeling
Guideline. Argonne National Laboratory, Chicago, IL, 1977, 322 pp.
3. I.A. Singer and M.E. Smith, "Atmospheric dispersion at Brookhaven National
Laboratory." Int. J. of Air & Water Pollution, JjD: 125 (1966).
4. K.J. Vogt, et al., "New sets of diffusion parameters resulting from tracer
experiments in 50 and 100 meter release height." Proceedings of the NATO/
CCMS 9th International Technical Meeting on Air Pollution Modeling ana its
Application, Toronto, August 28-31, 197J-S, pp. 221-239.
5. H. Kiefer and W. Koelzer, Janrebericht 1978 der Abteilung Sicherheit.
Kernforschungszentrum Karlsruhe, 1979, pp. 191-195.
6. M.E. Smith (ed), Recommended Guide for the Prediction of the Dispersion of
Airborne Effluents. 2nd ed., Am. Soc. of Mech. Engineers, 1973, 85 pp.
7. G.A. Briggs Plume Rise. U.S. Atomic Energy Commission, Division of
Technical Information, 1969, 81 pp.
8. D.A. Trout, "A review of horizontal and vertical dispersion coefficients
applied in the calculation of ground level pollutant concentrations resulting
from emissions from tall stacks under very unstable atmospheric conditions."
Proceedings of the NATQ/CCMS 10th International Technical Meeting on Air
Pollution Modeling and its Application, Rome, October 23-26, 197§, 10 pp.
9. U.S. EPA, Guideline on Air Quality Models. U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, EPA-450/2-78-027, 1970.
10. J.M. Leavitt, et al., "Sulfur dioxide emission limitation (SDEL) program at
TVA power plants," JAPCA, 26(12): 1133 (1976).
11. D.B. Turner, Workbook of Atmospheric Dispersion Estimates. USDHEW, PHS Pub.
No. 995-AP-26, 1970, 84 pp.
12. D.A. Trout, Comparisons of predictions with observations and analyses of
observations of high short-term pollutant concentrations monitored in the
vicinity of sources with tall stacks." Proceedings of the AMS/APCA Second
Joint Conference on Application of Air Pollution Meteorology, New Orleans,
March 24-27, 1980, 12 pp.
If,
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